European Economic and Political Issues, Volume 9

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EUROPEAN ECONOMIC AND POLITICAL ISSUES VOLUME 9 No part of this digital document may be reproduced, stored in a retrieval system or transmitted in any form or by any means. The publisher has taken reasonable care in the preparation of this digital document, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained herein. This digital document is sold with the clear understanding that the publisher is not engaged in rendering legal, medical or any other professional services.

EUROPEAN ECONOMIC AND POLITICAL ISSUES VOLUME 9

WILLIAM B. STICKLE EDITOR

Nova Science Publishers, Inc. New York

Copyright © 2009 by Nova Science Publishers, Inc. All rights reserved. No part of this book may be reproduced, stored in a retrieval system or transmitted in any form or by any means: electronic, electrostatic, magnetic, tape, mechanical photocopying, recording or otherwise without the written permission of the Publisher. For permission to use material from this book please contact us: Telephone 631-231-7269; Fax 631-231-8175 Web Site: http://www.novapublishers.com NOTICE TO THE READER The Publisher has taken reasonable care in the preparation of this book, but makes no expressed or implied warranty of any kind and assumes no responsibility for any errors or omissions. No liability is assumed for incidental or consequential damages in connection with or arising out of information contained in this book. The Publisher shall not be liable for any special, consequential, or exemplary damages resulting, in whole or in part, from the readers’ use of, or reliance upon, this material. Any parts of this book based on government reports are so indicated and copyright is claimed for those parts to the extent applicable to compilations of such works. Independent verification should be sought for any data, advice or recommendations contained in this book. In addition, no responsibility is assumed by the publisher for any injury and/or damage to persons or property arising from any methods, products, instructions, ideas or otherwise contained in this publication. This publication is designed to provide accurate and authoritative information with regard to the subject matter covered herein. It is sold with the clear understanding that the Publisher is not engaged in rendering legal or any other professional services. If legal or any other expert assistance is required, the services of a competent person should be sought. FROM A DECLARATION OF PARTICIPANTS JOINTLY ADOPTED BY A COMMITTEE OF THE AMERICAN BAR ASSOCIATION AND A COMMITTEE OF PUBLISHERS. LIBRARY OF CONGRESS CATALOGING-IN-PUBLICATION DATA Available upon request.

ISBN 978-1-60876-558-4 (E-Book)

Published by Nova Science Publishers, Inc.

New York

CONTENTS Preface

vii

Chapter 1

The Non-market Sector in Europe and in the United States: Underground Activities and Home Production Francesco Busato and Bruno Chiarini

1

Chapter 2

How European Financial Institutions Affect Their R&D and Economic Development Jian Tong and Chenggang Xu

25

Chapter 3

An Almost Ideal Demand System (AIDS) of Canadian Tourism to Europe Panagiotis Panagopoulos and Thanasis Stengos

43

Chapter 4

Single Mothers in Europe: A Decomposition Approach Libertad González

83

Chapter 5

Overreaction in Venture Capital and Private Equity Markets: Evidence from European Markets before the Burst of the Bubble Marina Balboa-Ramón and José Martí-Pellón

101

Chapter 6

The Role of Public Benefits: A Case Study for Seven European Countries Hans Hansen

121

Chapter 7

Secondary Market Behavior of European Securitization Andreas A. Jobst

153

Chapter 8

An Attempt to Measure the Influence of the Maastricht Criteria and the Stability Pact on Growth and Unemployment in the European Union Vítor Castro and Elias Soukiazis

227

vi

Contents

Chapter 9

The Orientation of the Financial System: Alternative Approaches and the Case of the European Monetary Union Bert Scholtens

253

Chapter 10

Comparison of Cultural Profiles of Young Managers from the Countries of South-Eastern Europe and Russia Janez Prašnikar, Marko Pahor and Hugo Zagoršek

271

Index

287

PREFACE This new book focuses on economic and political issues is Europe. The economy of Europe comprises more than 710 million people in 48 different states. Like other continents, the wealth of Europe's states varies, although the poorest are well above the poorest states of other continents in terms of GDP and living standards. The difference in wealth across Europe can be seen in a rough East-West divide. While Western European states all have high GDPs and living standards, many of Eastern Europe's economies are still emerging from the collapse of the USSR and former Yugoslavia. Throughout this article "Europe" and derivatives of the word are taken to include selected states that are geographically in Asia, bordering Europe - such as Azerbaijan and Cyprus. As a continent, Europe has the third largest economy in the world. Europe's largest national economy is that of Germany, which ranks third globally in nominal GDP, and fifth in purchasing power parity (PPP) GDP; and its second is that of the United Kingdom, which ranks fifth globally in nominal GDP and sixth in PPP GDP. The European Union is the world's largest (as determined by the IMF and the World Bank - 2005) or second largest economy if counted as a single unit Chapter 1 suggests that the “home production” and the “underground” sectors are two crucial phenomena for properly understanding the European and the United States business cycles. These sectors spell out the labor reallocation mechanism between market and nonmarket sectors, and rely upon two important and distinguishing aspects: a different degree of family institutionalization and the incentive for individuals and firms to seek tax-free income. The analysis is fruitfully carried out by reviewing two broad classes of multi-sector dynamic general equilibrium model incorporating different informal sectors. It is surprising, but the literature on the role of informal sectors in macromodels is not large, although their implications are extremely relevant. In Chapter 2 we attempt to address these questions with a focus on financial institutions, and our theory is motivated and is going to be tested by observations from European economies. First we develop an endogenous growth model, where financing mechanisms, development levels, R&D activities, and economic growth are endogenized jointly. Financial development is regarded as an evolution of the financing regimes, together with the economy’s development level. In our model, R&D is broadly defined to consist of all activities that improve knowledge about technology, including imitation, innovation, and invention.

viii

William B. Stickle

As tourism is the one of the largest growing industries in the world, estimating its demand is very important for policy reasons of the destination countries involved. Disaggregating consumers into groups and estimating their demand function has been posed several times in the tourism context but data unavailability has made it hard to deal within a system approach as the Almost Ideal Demand System (AIDS) model, which has theoretical advantages over its single equation counterparts. In Chapter 3, the demand for tourism in Europe from Canadians is estimated, both for aggregated consumers and disaggregated on the purpose of travel. The findings indicate a noticeable difference in elasticity estimates for each group. In particular, the own price elasticities for leisure demand is larger for tourism in all cases but Italy, while the opposite stands for the business tourism. Moreover, higher elasticity values are estimated, indicating that Canadians’ response to prices and income today is increased. Overall, it is clear that more research is needed on AIDS modeling of tourism for purpose of travel to generate accurate forecasts and estimates for policy reasons. Chapter 4 explores the sources of the large cross-country variation in the incidence of single mother families in Europe. I describe the prevalence of these families in each country over the period 1994-2001, using data from the European Community Household Panel. Then I analyze the contribution to the overall cross-country variation of its different demographic components: marriage rates, cohabitation, divorce, co-residence, marital and non-marital fertility, re-partnership, etc. The analysis suggests that the variation in the prevalence of single mothers across countries can be attributed to differences in mainly three demographic rates: non-marital fertility, divorce rates, and co-residence. Other factors that were potentially relevant a priori turn out not to be significant, such as marriage rates, cohabitation patterns, marital fertility, or the number of widows. The venture capital and private equity market is made up of a supply curve and a demand curve. According to Gompers and Lerner (2002), the supply curve is given by investors’ wishes to commit funds to this market, whereas the demand curve is given by the number of firms looking for this type of financing. The venture capital and private equity process is basically made up of three stages: fundraising, investment and divestment. Given the permanent need to use the market as a source of funds for new investments, the venture capital and private equity one is a cyclical process activated every two or three years. The inherent cyclical nature of this process may determine the appearance of continual imbalances between supply and demand in venture capital and private equity markets. Given the importance of this market in the launching and development of new industries (Bygrave and Timmons, 1992); or the positive impact it exerts on the economy (Barry, 1994; Fried and Hisrich, 1994), on innovation (Kortum and Lerner, 2000) or job creation (Birch, 1979; Storey, Watson and Wynarczyk, 1989), this work aims to offer an explanation of these imbalances. Except for the work by Gompers and Lerner (2002), who studied this problem for the American case, there is no previous work on this topic. The aim of Chapter 5 is to explain the imbalances between supply and demand in private equity markets. We seek to assess whether an overreaction effect is produced in new fundraising when there are changes in demand and in liquidity. The main cause presented for this effect is the inelastic behaviour of the short term supply. The hypotheses are tested on panel data referring to aggregate figures of a group of 20 European countries. The overreaction hypothesis is fully confirmed in the case of demand as regressed coefficients are significantly greater than one. The same is partially confirmed in the case of liquidity.

Preface

ix

The implications of these results are indeed important since the European Venture Capital and Private Equity market is experiencing a sound growth since the mid of the first decade of the twenty first century, fueled by large levered acquititions that could lead to a shock similar to that experienced by internet companies at the beginning of the century. As explained in Chapter 6, non-voluntary unemployment is a risk outside the control of the individual. By selecting certain occupations the individual knows that he or she may be exposed to a higher risk of unemployment than in other occupations, e.g. workers in the construction and building industry are exposed to unemployment due to the weather to a higher degree than employees in the IT industry. Such varying risks of unemployment may be reflected in the pay agreements of different industries, but exactly who may be unemployed and for how long, is still beyond the control of the individual. Unemployment insurance benefits are designed to help coping with the financial situation as unemployed, they secure the financial basis for existence in this situation, and enables the unemployed to have time to look for a new relevant job. The primary aim of unemployment insurance benefits is to provide security, the main hypothesis being that human beings perform better when they feel financially secure. Future ageing problems and early retirement from the labour market is a bad mixture. Early retirement is very popular in many countries and there are several roads leading to this situation, in some countries also via public schemes, which almost guide to early retirement. Early retirement may be interpreted as a kind of voluntary unemployment. The incentives to join and to stay in such schemes will be studied. The schemes for sickness and disability are different from those for unemployment and early retirement because they are health related. Compensation for sickness often results in high replacement rates, which could be an invitation to short term leaves. Asset-backed securitization (ABS) is a highly flexible yet complex refinancing technique that involves the issuance of contingent claims with varying seniority and maturity on the cash flow performance of a designated pool of asset exposures. Efficient risk management of ABS obligations requires both investors and issuers to thoroughly understand the inherent spread dynamics in this growing segment of fixed income markets. In Chapter 7 we model the secondary market spread dynamics of different types of European ABS transactions (synthetic and traditional CDO, MBS and Pfandbrief) in a multi-factor GARCH process, which conditions the mean specification of stationary spreads on their heteroskedasticity for valuation and forecasting purposes. We find that historical spread changes are mostly difference stationary and follow a steady positive trend with asymmetric mean reversion depending on the direction and the lag structure of past spread changes. Model estimates indicate that spread stationarity is statistically and economically more sensitive to past spread levels at one lag if they immediately follow a spread decline rather than a spread increase. However, spread dynamics are mostly stochastic in response to past spread levels beyond one lag, which relegates stationarity to a first order effect. Hence, stochastic inference methods based on autoregressive effects of past spread levels fall short of fully capturing short-term spread dynamics. Most of the conditional spread volatility is informed by asymmetric ARCH effects from negative errors of forecasted past variance, which contributes most of the economic significance to generally positive GARCH effects. Given significant volatilityinduced future spread movements, a GARCH approach permits a more accurate estimation of time-varying confidence intervals for the parametric specification of spread movements. Amid persistently widening spreads, negative shocks associated with unexpected spread

x

William B. Stickle

declines testify to a significant departure from the historical pattern and induce higher spread volatility, which tends to be propagated over time by further negative past innovations. In light of notoriously weak information transparency and valuation uncertainty in large parts of primary and secondary ABS market segments, the recognition of time-varying volatility is imperative to forecasting ABS spreads especially at times when a sudden contraction of spreads at time of greater investment risk entails a higher risk premium and asset volatility. In Chapter 8 we attempt to measure the direct effects of the Maastricht criteria and the Stability and Growth Pact (SGP) on growth and unemployment in the European Union (EU) member countries, covering the period of 1980-2001. The evidence from a empirical analysis panel suggests that growth in the EU has been negatively influenced by the imposition of the Maastricht criteria and by the SGP restrictions. Estimating the pre and post-Maastricht periods separately, it was found that the higher fiscal discipline after Maastricht did not benefit the growth of real output as expected. In that sense, we find evidence that the effort that has been made by all EU countries to reduce the budget deficit has been negatively affecting the growth of real output. Exchange rate stability also seems to have a negative effect on growth. On the contrary, the efforts to reduce the inflation have been beneficial to the output growth, giving support to the price stability requirement of the SGP. Nevertheless, the growth objective of the SGP is far from being achieved, as our results demonstrate. Finally, the unemployment analysis through growth confirms the idea that unemployment follows the economic cycle and that the fall in unemployment due to growth of real GDP was smaller in the post-Maastricht period. Our main conclusion is that the Maastricht criteria and the SGP have been somehow unfavourable to growth and unemployment in Europe, so we argue that a more flexible SGP is needed to stimulate aggregate demand and productive investment in order to achieve full employment and a higher growth in Europe. The financial system is generally held responsible for the smooth operation of the real economy. Monetary and financial authorities aim at securing this smooth operation and use the financial system as a transmission vehicle for their policies. Chapter 9 tries to find out about the orientation of the financial system within the European Monetary Union. To this extent, we construct a conglomerate index of the financial system. It is compared with two previously constructed measures of cross-country differences in financial systems. We find that there is substantial heterogeneity as to the structure of the financial system, it seems wise to take account of the fact that the – generic – ESCB policies will affect the financial institutions and markets of the participating countries differently. In Chapter 10 we present the results of a comparative study of cultural profiles of young managers from certain countries on the territory of the former Yugoslavia (Slovenia, Croatia, Bosnia and Herzegovina, Serbia and Montenegro) and Russia. These are managers who have recently completed their education or are still included in post graduate studies of business sciences and therefore have little or even no work experience. Additionally, they come from transitional countries and perceive new social circumstances similarly, thus forming similar values, beliefs and points of view. On the other hand, they belong to different national cultures. Our research confirms the influence of both types of factors on the formation of managers’ cultural profiles. Its significance is also in that it shows the usefulness of the methodology employed in such research.

Preface

xi

In the next part we first present the results of the existing studies in selected countries. In the third part we present the methodology. We follow with the presentation of the results and the comparison of cultural profiles of different groups of managers. The concluding part contains the main observations.

In: European Economic and Political Issues Editor: William B. Stickle, pp. 1-24

ISBN 978-1-60456-267-5 c 2009 Nova Science Publishers, Inc.

Chapter 1

T HE N ON -M ARKET S ECTOR IN E UROPE AND IN THE U NITED S TATES : U NDERGROUND ACTIVITIES AND H OME P RODUCTION∗ Francesco Busato1 and Bruno Chiarini2† 1 Columbia University 2 University of Napoli “Parthenope”, Studi Economici, Via Medina 40, 80133 Napoli, Italy

Abstract This paper suggests that the “home production” and the “underground” sectors are two crucial phenomena for properly understanding the European and the United States business cycles. These sectors spell out the labor reallocation mechanism between market and non-market sectors, and rely upon two important and distinguishing aspects: a different degree of family institutionalization and the incentive for individuals and firms to seek tax-free income. The analysis is fruitfully carried out by reviewing two broad classes of multi-sector dynamic general equilibrium model incorporating different informal sectors. It is surprising, but the literature on the role of informal sectors in macromodels is not large, although their implications are extremely relevant.

Journal of Economic Literature Classification Numbers: E320, E260, J22, H200. Keywords: Two-sector Dynamic General Equilibrium Model, Underground Economy, Propagation of Shocks, Taxation.

1.

Introduction

In the industrialized countries, along with the official (market) activity sector, other informal activities often flourish. As a matter of fact, in the United States (US hereafter) ∗

We have benefited from the comments and suggestions of John Donaldson, Edmund Phelps, Elisabetta Marzano and Guido Rey, and we thank the participants in the seminars at various Universities. † E-mail address: [email protected]

2

Francesco Busato and Bruno Chiarini

economy the household sector is sizeable both in terms of input used in this sector and in terms of home-production output. The evidence for many European economies indicates large and increasing black markets for both labor forces and output. Despite these evidence, informal sectors have only recently been incorporated into macroeconomic models, and we are not aware of contributions drawing comparative implications.1 This is somewhat surprising, since the different structure of the European and the US non market sectors may represent one of the most important distinctive characteristics of their business cycles, and provide a more careful explanation of the unemployment experiences. In fact, several features characterizing the informal sector offers an additional explanation for the European and US labor market differentials (unemployment, wages, rigidities etc.).2 The relation between market and underground activities deserve some attention, as well. This paper compares the household production with the underground sector as two cases in point of non-market sectors. These two informal sectors provide useful insights concerning the inputs’ allocation between the corresponding labor markets (the official and the unofficial). There are many candidate factors capable to enhance agents’ willingness to shift resources out of market activity. The excess of tax burden and social security contributions is at the heart of both informal sectors. The tax policy and enforcement policies measure the opportunity cost of engaging in nonmarket activities and may generate a further distinction between United States and European economies in terms of labor market and business cycle features. An informal sector is an important flexibility-enhancing instrument available in economies with high distortionary taxation. The paper reviews the literature comparing the characteristics and the performance of two classes of dynamic general equilibrium models of the US economy and European Economy (EE hereafter), where an explicit formalization of a nonmarket sector is included. The paper highlights the common features and the differences between underground activities and home production, and underlines the corresponding economic mechanisms by relying on numerical simulations. Both the US and the EE are characterized by a remarkable burden of taxation and a large and increasing share of the informal economy. In this context agents reallocate resources from the market to either the home production sector or to the underground sector. The choice of the destination sector depends on the social structure and several institutional factors. The first aspect is related to the stability of family as an institution, while the laws’ enforceability accounts for the second distinguishing element.3 The family model is 1 Here we are not discussing whether informal sectors are a burden to society or, on the contrary, are a source of economic strength, nether we make effort to measure the social or welfare costs of these informal sectors. Moreover, we are concerned with the size of the underground economy as encompassing those activities which are otherwise legal but go unreported or unrecorded. 2 Among the labor market features used by an extensive literature with different theoretical and empirical frameworks, to investigate on the differences between the US and European economies, we quote the role stressed for employment protection systems, benefit replacement rates and benefit duration, active labor market policies, union density and coverage; payroll tax rate, participation rates of marred women, the the hours worked and the employment rates of prime-age men. It is surprising that in general schemes as well as in the Europe versus North America analysis, which use the above arguments, the underground economy and home production are not involved in these frameworks. 3 The enforceability may be defined as a probability be forced to effectively pay the tax rates and a severe surcharge tax, after being discovered violating the law.

The Non-Market Sector in Europe and in the United States...

3

crucial since it characterizes the domestic allocation of labor between genders and, within the family, the willingness of its components to accept any kind of informal and temporal job. Informal activities are different whether the family context is highly institutionalized or not. A key feature common to both the EE and US economies is that the non market sectors enhance the ability and willingness of agents to substitute into and out of market activity. In particular, there are more opportunities for for allocating labor services, for undertaking risk sharing (on the household side), and for smoothing production and profits (on the firms’ side). Allocating their time between market and nonmarket activities, rather than simply between labor and leisure as in the canonical model, agents produce significant phenomena and business cycle issues that traditional schemes often time neglect. Differences between these two classes of models concern the tradeability of the produced commodity, the source of the resources used for financing investments, and the cyclicality of labor services allocated to the two non market sectors. More precisely, there are five main differences: the response to economic policies, the commodities’ number and their substitutability, the financing of capital investment, the insurance opportunities offered by the second sector, and the different cyclical properties between home production and underground activities. The remainder of this paper is organized as follows. Section 2 reviews a set of stylized facts on the role and the features of the burden of taxation in US and European countries, and the characteristics of the informal sectors in the two economies. Section 3 reviews two dynamic general equilibrium models with, respectively, an underground sector and a home production sector. Section 4 reviews differences and similarities of the two model’s solutions. In Section 5, using taxation experiments, we show the reallocation mechanism generated by the models. Section 6 concludes the papers.

2.

Stylized Facts

2.1.

Burden of Taxation and Informal Sector’s Size

The literature on the informal sector argues that the increase in taxation and social security contributions is responsible for the increase of the size of the informal sectors observed in US and European economies.4 Here we review characteristics and sizes of the burden of taxation and document the size of the informal sectors in the two economies. Although there may exist some methodological problem in comparing tax systems,5 the following figures show a high level of tax burden as common characteristic between the two economies. They indicate, however, remarkable differences. As tax-to-GDP ratio, the burden of taxation in EE stood at almost 41% in 1999. About 11 percentage points higher than in the United States. If taxes are defined as income taxes and the social contributions 4

See Schneider and Enste (2000) and the papers quoted therein and McGrattan, Rogerson and Wright (1997). Of course, the informal sector may be explained also by institutional mechanisms such as labor market regulations, but the burden of taxation remain the main reason to push firms and workers to develop an informal sector. 5 The sources are OECD (1999; 2001) and Joumard (2002).

4


over the labor cost, this percentage reaches 46.4% in Italy, 51.3% in Germany, 48.1 in France and 30.3% in United Kingdom. Between 1985 and 2002, the general government current tax receipts (which excludes capital receipts) for the EU ranges from 41.8 to 43.9 per cent of the nominal GDP. In the US in the same period, this percentage is between 28.7 to 30.8. The tax mix is also very different. Most EU countries rely heavily on social security contributions. In 1999 these are about 32% of total tax revenue whereas US social contribution reaches 24% of the total revenue. The average effective tax rate on labor in Europe is about 15 percentage points higher than in the US. The tax wedge on labor is one of the distinctive features. Labor income is heavily taxed in many European countries such as Italy, France, Belgium Austria. Moreover, as stressed recently from an Oecd study, both the average and marginal tax wedges on labor are high. In 2000, the EU area reported an average effective tax wedge on labor (personal income tax plus employee’s social security contributions plus employer’s social security contribution) of 40%. This figure in the United States is about 30%. An analogous picture is valid for the marginal tax wedge on labor. These features, in particular the high marginal tax wedges on labor, affect the participation rate and the working hours and, as stressed by several studies,6 in systems where taxes are levied on households, discourage the other family members from taking on a job. This creates an incentive for many family members to stay outside the official labor market. This kind of behavior characterizes pensioners and older workers, unemployed young adults and spouses of low-income earners. Table 1 reports the marginal effective tax rate on additional income for different family types. This is the amount of earnings which are taxed away via income taxes or mean testing procedures and cancellation of benefits. Table 1. Marginal Effective Tax Rates on Additional Income Principal E. Secondary E. France Germany Italy Spain EU US

Full-time Full-time Part-time 28 38 51 50 33 25 23 19 35 19

NE 76 80 63 78

31 11

77 68

Unemployed Full-time Part-time* 29 30 31 19 37 19 23 19 38 20

38 0

Part-time NE 69 115 84 77 107 102

All numbers in the Table are in percentages; “Principal E”: principal earner of wage income; “Secondary E”: secondary earner of wage income; “NE”: non-employed; (*) Employed without benefit entitlements. Source: Joumard (2002) and OECD (1999).

High marginal effective tax rates over a range of earnings imply a low incentive to earn in that range. This could reduce hour of work and earnings without affecting the net income. The effect on the official labor market of tax and benefit systems may be relevant, reducing the work incentives, preventing formal part-time work and thereby encouraging nonmarket 6

See Joumard (2002) and the works quoted therein.


5

labor supply and labor demand. Underground and home production phenomena may be an explanatory factor when the structure of social security contributions and income taxation produce an high marginal effective tax rate on the other family members earning. This is particularly true when taxation is levied on couples rather than individual basis, though the family structure is much more complex in many of the EU countries. It is not a case that both many of the EU countries and the US have the joint as the unit of taxation (France; Germany; US; Spain; Portugal; Switzerland) and others (Italy; Netherlands; Denmark; Canada) with individual basis have full transfer of basic relief (tax paid by one partner is dependent on the income of the other).7 These aspects of tax structure and benefits are important in a policy area that has been completely neglected such as the nonmarket sector consequences.8 Finally, statutory rates on corporate profits are somewhat high and broadly the same in the major OECD countries. Effective corporate tax rate ranges from 37% of Italy to about 40% in Germany. A bit lower figure is imposed in the US. Of course the corporate tax follows alternative complicated approaches. The tax structure applied to net income may be graduated on income or may be a unique flat rate tax applied to net capital income. The important point here is that high rates encourage individuals and firms to seek tax-free income.

2.2. 2.2.1.

The Informal Sector in US and EE The United States and the Size of Home Production

The US non market sector is characterized by a large home production, while underground activities account for a small share of GDP. In particular, Greenwood, Rogerson, and Wright (1995) document that investment in household capital is larger than that in market capital by about 15 percent. More importantly, household production generates a sizeable portion of aggregate output. For example, Eisner (1988) reports that home production constitutes between 20 and 50 percent of the value of gross national product, and Bonke (1992) estimates that the value of household production is around 40 to 50 percent of GNP in most western countries. In addition, several studies indicate that a typical North-American married couple allocates a large size of its discretionary time to work in household production activities.9 This quantity is close to the time that it does working for paid compensation.10 A large body of literature introduce a household production sector into a stochastic growth model, mainly for the US economy. Benhabib, Rogerson, and Wright (1991), Greenwood and Hercowitz (1991), and Greenwood, Rogerson, and Wright (1995) find that introducing a household production sector into an otherwise standard closed economy business cycle model, improves the ability of the model to explain the volatility of output, the relative volatilities of output, consumption, investment, and hours, the correlation between 7

See OECD (1997). On the contrary, the importance between marginal tax rates and poverty trap, in-work benefit, and family taxation have been extensively investigated. See, OECD (1997). 9 In particular, Juster and Stafford (1991) find that a typical married US couple spends 25 percent of their time working at home while allocating 33 percent of their time on market activities. 10 Benhabib, Rogerson and Wright (1991) indicate that the output of the household sector may be as much as half that of the market sector. 8

6


hours and productivity, and the correlation between the investments in home and market capital. Einarsson and Marquis (1997)resolve some drawbacks of the canonical home production model. They demonstrate that casting the home production mechanism into an endogenous growth model, it is possible to generate comovement between employment and output across the market and the home sectors, even with a single technology shock to market production. McGrattan, Rogerson, and Wright (1997) estimate a model with a household production sector to study the impact of different types of fiscal policies. Their results suggest that the model has considerably different implications for fiscal policies than those of the standard model. Rupert, Rogerson and Wright (2000) include home production into the standard life cycle model, and show that there exist a downward bias in the estimates of the intertemporal substitution elasticities obtained with the standard model and Baxter and Jermann (1999) use home production to solve the excess sensitivity of consumption puzzle. Gomme, Kydland and Rupert (2001) introduce home production in a time-to-build technology, improving the ability of the model in matching the comovement of sectoral investments. Perli (1998) presents a business cycle model with the home production, showing that this provides an indeterminate equilibrium. In this case, cycles may be driven by self-fulfilling expectations. Canova and Uribe (1997) construct an international business cycle model with a household production sector and show that the model can generate some important features of the data as household production provides important channels for transmitting business cycles between countries. The main focus was to examine the ability of dynamic equilibrium models to account for the business cycle behavior of the labor market, while augmented with an household production sector.11 In this context, a non-market sector appears to be a relevant shock absorber, for it is able to influence the ability and willingness of individuals to substitute into and out of market activity. In the US the shadow economy constituted between only 3.6 and 4.2 percent of GNP in 1970. In 1990 this share reached about 7 percent and, in 1998 it was estimated to about 8 percent. Although it may appear relevant, these estimates represent the smallest hidden economy size in the OECD countries. 12 2.2.2.

The European Economies and the Size of Underground Economy

In the EE, the picture is different. Household production sector appears relatively smaller in size (in terms of hours allocated to household activities) compared to the US. This appearance is due to a different degree of institutionalization of the family in US and EE. The EE home production is as sizeable as the US, but its cost for the family is lower than US. In the United States, there exists a high degree of de-institutionalization of the traditional family model: this means that the family model is characterized by high divorce rates, high proportion of live births outside marriage, while unemployed young adults do not remain close to their families. In the majority of the couples both spouses have jobs. On the contrary, in many European countries, in particular in Southern Europe (Greece, 11

Quoting Gronau (1986), “the greatest contribution of the theory of home production in the past decade was in its service to the better understanding of consumption behavior and changes in labor supply over the business cycle”. 12 See, Schneider and Schneider and Enste (2000).


7

Italy, Spain and France) the family model is more traditional, with a domestic division of labor between genders and within the family. Adult children (who often continue to live with their parents) and older parents are taken care of within the same home. These countries share in common a lower rate both of divorce and birth of births outside marriage. In more than half of all couples only the husband has a job while pensioners, women and young adults have a high willingness to accept any kind of temporary and informal job. One of the main difference between these two household (society) models seems to be the work/job sharing arrangement between spouses. Both members have a regular job in the North-American society, while only one (usually the husband) has a regular job in more than half of southern European couples. Hence young pensioners, women and young unemployed have a high willingness to accept any kind of informal and temporary job. Underground or informal activities are more relevant, in this context, than home production activities.13 We may draw two implications. First, home production is quite costly in the US where both components of a couple work in the majority of marriages. In this case, under certain conditions, it may pay to substitute market with nonmarket jobs. Conversely, in EE where a large share of families only the husband works, the home production is much cheaper. The second implication from the different family structure is the high degree of EE family institutionalization, which tends to tie together several members, along with higher average and marginal effective tax rates, make many of them prone to look for black jobs. In the EE countries, there exist a pernicious effect, because under the current tax system, the probability to be detected is negligible for a firm. This means that firms have a strong incentive to shift a part of their labor demand on the shadow economy. European nonmarket sector is determined by underground activities, which represent a large part of GNP. Schneider and Enste (2000), show that the size of underground economic activity is quite large and increasing in many countries. In particular, several Southern European countries have underground economies almost one-third as large as officially measured GNP. For instance, Greece has a share of the hidden economy of official GNP of about 27-30 percent. Estimations for Italy range from 20 to 27 percent while for Spain this range is 16-23 percent and Belgium of 21-23 percent of GNP. The importance of the hidden activities is shown also for several northern countries such as Norway, Sweden and Denmark (about 17-18 percent), but also France and Germany report relevant estimation (close to 15 percent). The literature demonstrates that there can be interesting interactions between underground and market activity and that these interactions cannot be neglected in analyzing the European countries. Loayza (1996) and Sarte (2000) model the connection between the informal sector and the formal one, drawing interesting macroeconomic implications using AK growth models. Ihrig and Moe (2001) develop a dynamic model of a representative agent’s decision to accumulate capital and to work in both the sectors with the aim of studying the role of government taxation policies on the underground sector. These authors, simulating their simple dynamic model, show that lowering tax rates as opposed to increasing the enforcement of tax policies, play a larger role in determining a country’s standard of living. The results support the view of the informal sector as an important source of subsistence and as a relatively easy way to expand employment during recessions. These works 13

See, Jurado Guerrero and Naldini (1997) and Gallie and Paugam (2000).

8


refer to developing countries but they are interesting also for many developed countries with a sizeable underground sector. Busato and Chiarini (2004) incorporating an underground sector in a dynamic general equilibrium model, improve the fit of the model to the Italian data, especially along several important labor market dimensions. They show that underground activities offer risk sharing opportunities by allowing households to smooth income through a proper labor allocation between the two sectors. Conesa, Diaz-Moreno and Galdon-Sanchez (2001) analyze the role of underground activities as an explanation of differences in registered aggregate fluctuations. The quantitative implications of the model show a substantial improvement in capturing the cyclical behavior of the economy.

3. 3.1.

Non-Market Sector in Dynamic Equilibrium Models The Underground Economy

In order to describe the working of the reallocation mechanism, this section reviews an underground economy model set up by Busato and Chiarini (2004). In this model there exist three agents: the firm, the consumer-worker-investor, and the government.14 In addition there are two sectors: the market and the underground sector, and there is a homogenous consumption good. In this context, agents are subject to distortionary taxation, but they can use the underground sector to evade taxes, while optimally allocating labor across sectors. In addition, the consumer-worker-investor is consistent with the traditional family model suitable to represent many European countries cases. Each firm i ∈ [0, 1] produces final output by using two different technologies, one i , and the other with the non-market sector, y i .15 associated with the market, ymt ut i i ymt = λm t kt

α

θti nit

1−α

i and yut = λut (1 − θti )nit ,

(1)

i , is the result of capital, k i , and market labor, ni where the market output, ymt t mt ≡ i i i i θt nt , applied to a Cobb-Douglas technology. Next, θt (0 < θt < 1) denotes the share of i , is labor demand allocated by i − th firm to the market sector. The non-market output, yut i i produced with a production function which uses only non-market labor, nut ≡ (1−θt )nit .16 u Finally, λm t and λt denote sectoral stochastic productivity shocks. Following Prescott and Mehra (1980), we assume that each firm solves a myopic profit maximization problem, on a period-by-period basis, subject to a technological constraint, and to the possibility that it may be discovered producing in the unofficial economy, convicted of tax evasion and subject to a penalty surcharge. 14

The model presented here resembles that in Busato and Chiarini (2004). This technology specification is equivalent to a more general set-up where both production functions use α i 1−α β i 1−β i i i capital and labor, for example ymt = Mt kti nmt and yut = Zt kut nut . From Uzawa (1965) and Lucas (1988) if β < α we can set the smaller elasticity to zero without loss of any generality. Since underground activities are labor intensive, we can simplify the model, and preserving the main economic intuition, by assuming that underground sector produces using only labor. We anticipate that in a Rational Expectations Equilibrium (REE) firms use both technologies (for a formal proof see Busato and Chiarini (2004)) 16 We could imagine that the same firm produces in the market economy in the day, while in the underground economy by night. 15


9

Firms are subject to distortionary taxation, which is partially evaded by allocating resource to the underground (and untaxed by definition) sector. Market-produced revenues, i , are taxed at the stochastic corporate rate t , where q m denotes the price of qtm (1 − tt )ymt t t i , market-produced good. Firms do not pay taxes on non-market produced revenues, qtu yut u where qt is the price of non-market-produced commodity. Firms, however, may be discovered evading, with probability p ∈ (0, 1), and forced to pay the stochastic tax rate, tt , increased by a surcharge factor, s > 1, applied to the standard tax rate. Note that since the market-produced and the non-market produced goods are identical, in a REE they must have the same price. Since qt = 1 holds in the equilibrium, we can impose it along the solution. In the first i , are: case (firm is discovered,with probability p), revenues, denoted as yD,t i i i yD,t = (1 − tt )ymt + (1 − stt )yut

In the second case (firm is not discovered, with probability 1 − p), revenues equal: i i i yN D,t = (1 − tt )ymt + yut

To compute total expected revenues, we apply linear projection, and we have i i i expectation operator conditional E yt |It = pyD,t + (1 − p) yN D,t , where E denotes an i + (1 − pst )y i , on information set It . Simplifying, we rewrite E yti |It = (1 − tt )ymt t ut where (1 − pstt ) > 0 ensures that a firm cannot go bankrupt. The cost of renting capital equals its marginal productivity rt , net of capital depreciation, δ. The cost of market labor is represented by the wage paid for hours worked, augmented by social security stochastic tax rate, tt , which, for simplicity, is assumed equal to social security tax rate. We denote the former as wtm = (1 + tt )wt , where wt is pre-tax wage, while the cost of non-market labor equals the pre-tax wage, i.e. wtu = wt . To introduce a traditional family model, with a domestic division of labor between genders and within the family, we suppose that the economy is populated by a continuum of consumers, uniformly distributed over the unit interval. Each consumer works in only one of the two sectors. They receive incomes that are functions of the sectoral, idiosyncratic, shocks. Within the economy there exist extended families, exogenously determined and of fixed size. We assume that family members have perfect information concerning each other’s idiosyncratic shocks to each sector. For simplicity suppose there exists one family, which is composed by two working individuals, Mr. κ and Miss. l17 . Without loss of generality, we assume that Mr. κ works in market sector, while Miss. l works in the nonmarket sector. Since Mr. κ and Miss. l belong to the same family, it is sensible to assume that their preferences do not differ significantly. We assume therefore, that they have the same utility function for consumption. The heterogeneity, however, concerns their labor supply, which is consistent with the fact they work in different sectors. This theoretical family structure is a reasonable approximation of a traditional family with a high degree of institutionalization. To model their preferences for consumption and labor, we generalize the structure presented 17

We choose to restrict the analysis to one family to keep notation simple. The size and the number of the extended family can easily be enlarged.

10


by Busato and Chiarini (2004), which derives from Cho and Rogerson’s (1988) extended family labor supply model. Precisely, we specify instantaneous utility function as follows: U (cκt , clt , ltκ , ltl ) = ϕu(cκt ) + (1 − ϕ)u(clt ) − v (ltκ ) ltl − µ(ltl )

(2)

where u(cκt ) and u(clt ) represent utility from Mr. κ and Miss l consumption, and v(ltκ )ltl describes the disutility of working in both sectors. We interpret the last term, µ(ltl ), as reflecting the idiosyncratic cost of working in the non-market sector. This cost may be associated in particular with the lack of any social and health insurance in the non-market sector. Finally, ϕ and (1−ϕ) denote the relative weights of Mr. κ and Miss l utility function. An aspect of primary interest in our labor market is workers’ labor supply in the two sectors of the economy. Mr. κ, which works in the market sector, supplies ltκ , and receive a wage wtκ = wt (1 − τ ), where τ is the tax rate on wage income. Miss l, who works in the other sector, offers ltl , and earns a wage wtl = wt . The family budget constraint is wt (1 − τ )ltκ + wt ltl + Rt Kttot = Cttot + Xttot

(3)

where Cttot = cκt + clt and Xttot represents total consumption and total investment by the family, respectively. Eventually they pool their savings together, and rent the grand total, Xttot , to the firms, which capital stock evolves according to a standard capital accumulation tot = (1 − δ)K tot + X tot , where δ denotes the exogenous and constant constraint, Kt+1 t t depreciation rate. In this context we introduce a Risk Sharing Contract, defined as follows. Definition 1 (Risk Sharing Contract ) The contract has three features: 1. ltκ = θt Lt and ltl = (1 − θt ) Lt . Mr. κ and Miss. l pool together their labor supplies, Lt , then they allocate a share θt to market sector, and the remaining 1 − θt to non-market sector. 2. The extended family chooses total consumption Cttot .Then Mr. κ and Miss. l consumption will be cκt = ωCttot and clt = (1 − ω)Cttot .18 3. We assume that agents accept the contract, that it holds for each period in time, and that it is incentive compatible and perfectly enforceable19 .

Readers unfamiliar with Contract Theory would call it a “marriage” contract. Since we are not interested in studying consumption reallocation, we assume that family member undertake a Perfect Risk Sharing scheme that allows each consumer to have the same consumption profile. 18

In this way individual consumption is disentangled from individual income. It may be interesting to note that this is the argument behind the risk sharing and consumption literature (see Deaton, 1992 for a survey). In that context, optimal risk sharing is induces by financial market completeness. In our model, the insurance comes from the real sector. 19 By definition, an implicit contract will need to be sustained as an equilibrium in the interaction between the parties (Salanie’, 1997). The contract we present in this model has the very simple goal to provide insurance against production idiosyncratic risk. For this reasons we assume that agents accept the contract.


11

Definition 2 (Perfect Risk Sharing ) After entering the contract, consumers agree on a perfect risk sharing scheme, in the sense that they set ratio between marginal utilities equal to a constant, ′

i.e.

uκ (Cκ,t ) ′ ul (Cl,t )

=

φκ φl .Since

′

′

uκ (cκt ) = ul (cκt ) = u′ (Ct ), we have cκt =

φκ l φl ct .

Assuming, that both

consumers have the same weight within the family, we can set φκ = φl , and therefore cκt = clt . The PRS is defined in the sense of the two consumers enjoying the same consumption profile, smoothed on period by period basis. In terms of total consumption, we have cκt = clt = 12 Ctj , where Ctj represents consumption chosen by j-th household at time t.

The contract has the simple goal to pool together labor supply, and income insuring the family against idiosyncratic shocks.20 To complete the description of extended family behavior, we specify the functional forms for (2), consistent with the Risk Sharing Contract and the Perfect Risk Sharing scheme. In particular, preferences of j-th consumer or family, are described by the following function, where total labor supply is normalized to unity (nt = 1): Uj =

∞ X

β t uj (cjt , njmt , njut ).

t=0

In particular, the instantaneous utility function (separable between consumption and labor) is specified as follows: u

j

(cjt , njmt , njut )

(θtj )1+γ (1 − θtj )1−η (cjt )1−q − 1 j −h (1 − θt ) − f , ≡ 1−q 1+γ 1−η

(4)

where cjt denotes consumption profile of consumer j, θtj her market labor supply, and 1 − θtj her non-market labor supply.21 The second term, h

(θtj )1+γ 1+γ (1

− θtj ), represents the

(1−θj )1−η

t overall disutility of working, while the last term, f 1−η , reflects the idiosyncratic cost of working in the underground sector. In particular, this cost may be associated with the lack of any social and health insurance in the underground sector. To have a well behaved utility function, we assume that h, f ≥ 0, γ, η > −1, that all the parts of the momentary utility function are well behaved22 . The representative household, next, faces the following budget constraint:

wt (1 − τt )θtj + wt (1 − θtj ) + Rt ktj = cjt + xjt , 20

Note that in this paper we do not consider strategic interaction among agents. It is clear, however, that this would be a natural development of the structure presented here. 21 To represent consumer behavior in this environment, we refer to Cho and Cooley (1994) family labor supply model. They distinguish labor supply with regard to an intensive (the hours worked), and an extensive margin (the employment margin). In our model we reinterpret these two dimensions as representing worker’s labor supply in the regular and in the underground sectors 22 Restriction on the utility function to make the inter-temporal optimization problem well defined are derived in Busato and Chiarini (2004).

12


where xt denotes investment at time t. Notice that in this model capital stock is not taxed. If it were, it should be necessary to allow for the possibility of deducing depreciated capital from taxable income, since this in one of the reasons behind the existence of an underground sector. Finally, investment increases the capital stock according to a standard state equation: kt+1 = (1 − δ)kt + xt .

3.2.

The Household Production Economy

Enforcement of tax policies plays a large role in determining resources reallocation. The enforceability rules in the EE are weak. This has led us to introduce a tax-evasion model into the general equilibrium economy described above. Enforceability in US economy is stronger than in the European economy and we stylize this fact, assuming that the probability to be detected in US is equal to one. In this case, the expected revenues i (1 − p) yN D,t = 0. In the following model for the US economy we, therefore, do not specify an underground sector. As well as underground activities, household production is a large part of the economic activity.23 More importantly, the addition of household production influences the ability and willingness of individuals to substitute into and out of market activities.24 In this sense home production is similar to underground activities, even though movitations for shifting resources to one or the other sector are different, and are detailed below. To carry out a consistent comparison between these two nonmarket activities, it is necessary to present a home production model augmented with distortionary taxation. This section reviews a home production model such as that of McGrattan, Rogerson and Wrigth (1997). Consider first the corporate sector. The i-th firm, i ∈ [0, 1], is characterized by production technologies for the market and the non-market sectors that display constant returns to scale, and which are specified as follows: i i ymt = λm t kmt

α

nimt

1−α

i i and yht = λht kht

β

niht

1−β

,

(5)

i produce market where employment supplied to the market nim and the capital stock km i , whereas employment supplied in the home sector ni and home capital sector output ym h khi produce home output yhi . Maximization implies factor prices equal marginal product because of the constant returns to scale. Next, assume that consumers are infinitely lived and homogenous, and total population is normalized to unity. The j ∈ [0, 1] household has preferences over stochastic processes for aggregate consumption flow, cjt , and leisure, ℓjt , described by the following utility function: 23

Home production has been part of standard labor paradigm. Fundamental references include Becker (1965), Pollak and Watcher (1975), and Gronau (1986)). Only recently has been introduced into macro models. However, the literature is quite large: see Benhabib, Rogerson and Wright (1991) for a survey, or among the many Rios Rull (1993), McGrattan, Rogerson and Wright (1992), Fisher (1992), Fung (1992), Perli (1998), Gomme, Kydland and Rupert (2001). 24

As reported in Greenwood, Rogerson and Wrigth (1993), a typical family spends almost as much time in production activities such as cooking, cleaning, and so on, as it does working for salary.


Uj =

∞ X

13

β t uj (cjt , ℓt ),

t=0

where the instantaneous utility is assumed to be a constant relative risk aversion transformation of a Cobb-Douglas function,

uj (cjt , ℓjt ) ≡

cqt ℓ1−q t

1−ρ

1−ρ

−1 ,

where leisure in this context is an aggregate of total available time normalized to unity, market hours nmt , and non-market hours nht : ℓjt = 1 − njmt − njht . Consumption, next, is an aggregate of private consumption cjpt , and government consumption cjgt : n o1 j e1 j e1 e1 , ct = φ1 (cpt ) + (1 − φ1 )(cgt )

(6)

where φ1 ∈ (0, 1), and the parameter e1 ≦ 1 is the household willingness to substitute between the two types of consumption goods. Private consumption itself is an aggregate of market consumption cjmt , and non-market consumption cjht : n o1 e2 cpt = φ2 (cjmt )e2 + (1 − φ2 )(cjht )e2 ,

(7)

where notation is analogous to that of (6). Next, feasibility in the market sector is ensured by the following equation: cmt + xt = (1 − τht ) wt hmt + (1 − τkt ) rt kmt + δτkt kmt + Tt ,

(8)

where τht and τkt are the stochastic taxes on capital and labor, wt and rt are the marginal prices for capital and labor, and Tt is a lump-sum transfer. Following McGrattan, Rogerson and Wright (1997) we assume that the depreciated capital is tax deducible, for which reason it is added up to the income side of previous equation. The fiscal authority faces a budget constraint: cgt = hmt τht wt + τkt rt kmt − δτkt kmt − Tt . Notice that the transfer Tt ensures that the government balances its budget in each point in time, given realization of stochastic tax rates (τht and τkt ) and of νt (defined below). In addition, notice that the home capital stock is not subject to taxation, at least in the basic formulation of the model.25 Finally, it is assumed that government consumption is a stochastic process given by: cgt = νt yt , where νt is a random variable and yt is the aggregate output. Finally, let aggregate capital stock, kt , evolving according the following: 25

In a policy experiment presented below the possibility of taxing home capital stock is taken into account.

14

Francesco Busato and Bruno Chiarini kt+1 = (1 − δ)kt + xt ,

where kt = kmt + kht .

4.

Two Mechanisms for Risk Hedging and Optimal Labor Allocation

First notice that, technically speaking, both the home production model and the underground economy model, are characterized by three agents: a large number of myopic firms, a large number of identical infinitely-lived and forward looking households, and the government. In addition there are two sectors: the market and the nonmarket sector. Differences between these two classes of models concern, however, the tradeability of the produced commodity, the origin of resources used for financing investments, and the cyclicality of labor services allocated to the two non market sectors. We outline five issues: the reaction to policy distortions, the commodities’ number and their substitutability, the financing of capital investment, the insurance opportunities offered by the second sector, and the different cyclical properties between home production and underground activities.

4.1.

Risk Sharing and Labor Flexibility

In both the models, agents are more willing to shift resources out of market activity in response to policy distortions. Thus, in home production and underground economy models, policies do not affect only total hours worked but also how hours are allocated between the market and the nonmarket sectors.26 In these models there exist a high degree of flexibility of the labor inputs. However, whereas in the home production labor flexibility involves the labor supply, in the underground economy it is a key feature of both firms’ labor demand and households’ labor supply.

4.2.

Consumption Goods and their Substitutability

In the home production class of models there exist two goods, denoted as market and non-market commodities, each of which is produced with a sector specific technology. In addition, the preference specification allows for different degrees of substitutability between market and non-market goods.27 In the model with underground sector there exists only one homogenous good, which is produced using two different technologies: one associated with market sector, and the other with underground sector. In this environment it is natural to focus on the case of perfect substitutability between market-produced final output and underground-produced one. This 26

This aspect has also important development implications. In fact as agents change their allocation of time spent in market and nonmarket work, differences in output per person will be due to both differences in capital and in market hours per worker. See Parente, Rogerson and Wright (1999). 27

It is customary, in this literature, to consider the version with perfect substitutability as the benchmark simulated economy.


15

latter issue, however, can be generalized, developing underground models with two goods and relative prices.

4.3.

Investments Financing

The home production model shows that only market-produced goods can be consumed and invested, either into market capital or into non-market capital. There are no uses for home production output other than consumption - it cannot be sold or transformed into capital, for example, the way that market-produced output can. In the underground economy model, however, there exists only one capital stock (invested in the market sector), but market and non-market-produced output can be transformed into market capital and, in our simple version, without any adjustment cost. The underground sector offers an additional channel for financing capital stock accumulation, and an additional dimension along which firms can employ the available labor supply.28 While home production model is a legitimate two sector model, the underground economy model could be more appropriately defined as a two technology model, since the same good is produced using two different technologies. Notice that, when households shift working time in the home sector, in general they decrease the marginal product of capital in the market sector, thereby causing a change in the desired allocation of capital across the two sectors: agents will invest more in the home sector. In the model of underground economy presented above, when agents draw working time out of market sector to the underground economy the product of capital falls but there is not change in the capital allocation across sectors.

4.4.

Production and Consumption Smoothing

Notice that an underground sector offers profit smoothing opportunities for firms, and insurance opportunities for consumers. More precisely, firms can smooth their profits by a proper allocation of labor demand between the two sectors, on a period by period base. In addition, consumers can smooth not only consumption, by substituting over time consumption and investments, but they can also smooth income, by allocating their labor supply across sectors, on a period by period base. In the model with underground sector consumers have two sources of income, which, being countercyclical, offer insurance against bad times. This mechanism is absent in models with home production.

4.5.

Cyclical Properties of Labor Services

Finally, Ingram, Kocherlakota and Savin (1997) find that hours spent in home production are acyclical whereas other studies find that home hours are countercyclical.29 It is important to notice that this implies that during recessions home production models predict that workers may adjust by switching into leisure, whereas a model with underground activities predicts a switch into underground activities. Difference is that in our class of model, non-market income increases during recessions, mitigating slumps, by offering insurance 28

Technically speaking, the specification of consumer intertemporal feasibility constraint, equation (3), incorporates this feature. 29 Benhabib, Rogerson and Wright (1991), Greenwood, Rogerson, Wright (1995), Canova and Ubide (1997), Blankenau and Ayhan Kose (2002).

16

Francesco Busato and Bruno Chiarini Table 2. Underground Activity Model q 1.0 s 1.30

η 0.62 p .03

h 6.0 β 0.98

f 1.0 t, τ .275

γ 2.0 θ¯ .735

α .36 ρm , ρz .95

δ .025 σ m , σz .712

According to the Italian Tax Law (Legislative Decree 471/97, Section 13, paragraph 1) the surcharge

s equals 30 or 200 percent of the statutory tax rate. We present results just for the first value. The standard deviations of innovation, σm , σz , are defined as percentages. opportunities to household. Again this mechanism is not present in home production models.

5.

The Reallocation Mechanism: A Fiscal Policy Experiment

RBC models with fiscal policy do a good job in matching some observed comovements in the data. In the set up considered by this model taxes affect labor and consumption allocations, and stimulate production and labor demand in the informal sector. Because it seems that government taxation plays a relevant role in the allocation of output and labor input between these sectors, our interest in this analysis is motivated by the desire to assess its empirical implications in term of resource reallocation in economies with an informal sector. In particular, we investigate how changes in corporate and personal income taxes affects production and labor allocation between the market and the non-market sector.

5.1.

Calibration

The underground-activity model is calibrated for the Italian economy though the analysis can be generalized to a large number of European countries which present a sizeable underground sector.30 The calibration is based on the seasonally adjusted ISTAT series from 1970:1 to 1996:4, expressed in constant 1995 prices, and on a set of underground output estimations provided by Bovi (1999). More details are presented in Busato and Chiarini (2004). For convenience, calibrated parameters are presented in Table 2. The home production model is, instead, calibrated for the US economy. The parameters’ estimated are taken from McGrattan, Rogerson and Wright (1997), with use procedure presented in McGrattan (1994). Parameters are included in Table 3.31

5.2.

Taxation and Household Production

To have an idea of the dimension of the taxation impact on the relationship between the household production and the market sector, we may imagine to eliminate distortionary 30

Countries like Belgium, Denmark, Greece , Portugal and Spain have a large share of the underground sector. See, Schneider and Enste (2000). 31 There is an important difference between the two calibrations. Busato and Chiarini (2004) calibrate tax rate relying on the statutory tax rates, while McGrattan Rogerson and Wright (1997) use effective tax rates.


17

Table 3. Home Production Model q 5.27 a1 1.00

e1 0.62 a2 .485

e2 6.0 a3 0.21

b .448 a4 .234

b1 0.00 τk 0.57

b2 .385 τn 0.23

b3 .020 δ 0.22

b4 .525

Source: McGrattan, Rogerson and Wright (1997).

taxation in the US market sector, setting in the home production model of Section 6, τht = τkt = 0. A further experiment is accomplished introducing taxation over non-market activities. According to McGrattan, Rogerson and Wright estimates, the effect of eliminating distortionary taxation in the market sector is quite remarkable: output increases by 43 percent, market consumption increases by 47 percent, market investment increases by 87 percent, market hours increase by 22 percent, and the stock market capital more than double. In the home sector, however, the picture is reversed for all variables but capital, which increases by 34 percent. In other words, there is a shift in labor from the home sector to the market sector, while capital stock increases in both sectors.32 The second experiment concerns the introduction of a tax on the home production capital. In order to do this, the feasibility constraint (6) should be rewritten as follows: cmt + xt = (1 − τht ) wt hmt + (1 − τkt ) rt kmt + δτkt kmt + Tt − τp kn ,

(9)

where τp can be interpreted as a residential “property tax”. When τp is set different from zero, all variables are lower with the exception of market consumption. With respect to the base case (τp = 0),the latter rise ranges from 3 to 7 per cent, whereas home production and (since home capital is produced in the market) market production fall from 1.2 to 2 per cent. Of course home capital stock, being the taxed factor, falls. However, since a property tax does not affect labor/leisure choice, market capital/labor ratio does not change. Moreover, the reduction of capital stock is associated to a large reduction in investment, and, by this end, there is an increase in market consumption. The labor input reduces slightly in home production sector while in the market sector the fall ranges from 1.24 to 2 per cent. The simulations show that in this economy there may be frequent and relevant opportunities from substituting between market and home goods.

5.3.

Taxation and Underground Activities

In Figure 1 (Tax Cuts on the Income and the Corporate Tax Rates) the square line represents market output, the line with circles denotes total output, the line with triangles represents non-market output, and the dotted line represents the tax rate profile. 32

Notice that a model that ignores the home production sector has different production. See for example the contribution of McGRattan (1994), where market sector fluctuations are much larger that in a model augmented with an household production sector.

18

Francesco Busato and Bruno Chiarini Response to a 1 Std Deviation Income Tax Rate Cut 0.8

Percentage deviation from Certainty Steady State

0.6

0.4

0.2

0

−0.2

−0.4

−0.6

−0.8

−1

0

5

10

15

20 25 Horizon (quarters)

30

35

40

45

40

45

Response to a 1 Std Deviation Corporate Tax Rate Cut

10

Percentage deviation from Certainty Steady State

8

6

4

2

0

−2

−4

−6

−8

0

5

10

15

20 25 Horizon (quarters)

30

35

Figure 1.

Here we give a brief insight of the allocation mechanism in the underground model of Section 5 performing an impulse-response analysis cutting income and corporate tax rates. A cut in the corporate tax rates, remarkably increases production and labor input in m the market sector ( ∆y ym = +8%), while reduces labor and production in the underground sector. In particular, production activity in the underground economy falls by more than u six percent ( ∆y yu = −6.5%). Notice, however, that the fall in the unreported activities thwarts to some degree the expansion effects of the tax cut. The positive impact on output


19

and income taxation induces firms and households to work less in the underground sector highlighting a strong reallocation effect between the two economies. The reaction of the economic system is diminished when the model is subject to a ∆yu m cut to personal income taxes. In particular, we have that ∆y ym = −0.5% and yu = +0.6%. Both impact responses are smaller than those of standard RBC models without the underground sector. That is because the consumers can reallocate consumption and labor intra-temporally within the two sectors, reducing the loss generated by the fiscal policy. More precisely, they shift resources from the underground to the market sector. While taxes causes a distortion in the formal sector in both the US and European economies, driving a remarkable reallocations of inputs and outputs between sectors, the existence of different informal sectors have an equally important effects on the labor market and the economy. These effects, possibly, create different cyclical and welfare implications. These models can be extended in different directions but, if one wishes to study the labor market structure and the cyclical properties of these economies and perform comparative analysis, the informal sectors cannot be neglected.

6.

Conclusions

This paper suggests that home production and underground sectors are two crucial phenomena for properly understanding the European and United States economies. These sectors spell out the mechanism of reallocation of the labor input and production between market and nonmarket sector and rely upon two important and distinguishing aspects: a different degree of family institutionalization and the incentive for individuals and firms to seek tax-free income. This is fruitfully done reviewing two dynamic general equilibrium models incorporating different informal sectors and attributing their differences to the EE and US economies tax enforceability rules and family features. It is surprising, but the literature on the role of informal sectors in macromodels is not large, although their implications are extremely relevant. The review of these models provide important policy implications. First, our analysis support the long-held view that the rise of the tax and social security burdens is the most important cause of the increase of informal activity. Experiments carried out in McGrattan, Rogerson and Wright (1997) and Busato and Chiarini (2004) provide empirical support to this analysis. Taxes distort production and labor choices stimulating production and labor supply in the untaxed sector of the economy. Second, the effects of these reallocation mechanisms may hamper, to some degree, the effectiveness of a fiscal contraction policy. This happens because the underground and the home production sectors offer to the agents a channel through which they may reallocate their resources, avoiding (at least partially) the fiscal policy effects.Third, since the size of unrecorded activity is relevant, it may distort our understanding of the business cycle, raising difficulties for policy analysis. Fourth, the informal sectors are features of the labor markets that may help to understand many of their dynamic phenomena.

20


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Gronau, R, 1986, Home Production-A Survey, Handbook of labor economics, 1, 27330 Ihrig J. and Moe K.S. 2001, Lurking in the Shadows: the Informal Sector and Government Policy, Board of Governors of the Federal Reserve System, June. Ingram B.F., Kocherlakota N.R. and Savin N.E. (1997), Using Theory for Measurement: an Analysis of the Cyclical Behavior of Home Production, Journal of Monetary Economics, 40, 435-456. Juster F.T. and Stafford F.P. 1991, The Allocation of Time: Empirical Findings, Behavioral Models, and Problems of Measurement, Journal of Economic Literature, 29, 471-522. Joumard I. 2002, Tax Systems in European Union Countries, Oecd Economic Studies, 34, 91-151. King R.G., C.I. Plosser and S.T. Rebelo, 1988a, Production, Growth and Business Cycles: I. The Basic Neoclassical Model, Journal of Monetary Economics, 21, 195-232. King R.G., C.I. Plosser and S.T. Rebelo, 1988b, Production, Growth and Business cycles: II New directions, Journal of Monetary Economics 21, 309-342 King R.G. and S.T. Rebelo, 1999, Resuscitating Real Business Cycles, in Taylor J.B. and Woodford M. (eds.), Handbook of Macroeconomics Vol.1 (Elsevier Science, Amsterdam). Hansen G.D., 1985, Indivisible Labour and Supply and the Business Cycle, Journal of Monetary Economics 16, 309-327. Hodrick. R. J and E. Prescott, 1997, Postwar U.S. Business Cycles: An Empirical Investigation, Journal of Money, Credit, and Banking, 29, 1, 1-16 Ingram B.F., N.R. Kocherlakota and N.E. Savin, 1994, Explaining Business Cycles. A Multiple-Shock Approach, Journal of Monetary Economics 34, 415-428. Loayza N. 1996, The Economics of the Informal Sector: a Simple Model and some Empirical Evidence from Latin America, Carnegie-Rochester Conference Series on Public Policy, 45, 129-162 Lucas R.E.Jr., 1988, On the Mechanics of Development Planning, Journal of Monetary Economics 22, 3-42. Mare’ M., 1996, L’evasione in Italia e nei paesi OECD: Evidenze Empiriche, Determinanti ed Effetti Economici, Moneta e Credito 49, 393-443. McGrattan E.R., 1994, The Macroeconomic Effects of Distortionary Taxation, Journal of Monetary Economics 33, 559-71.


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McGrattan E.R., R. Rogerson and R. Wright, 1997, An Equilibrium Model of the Business Cycle with Household Production and Fiscal Policy, International Economic Review 38, 267-90. Olson M., 1982, Environmental Indivisibilities and Information Costs: Fanaticism, Agnosticism, and Intellectual Progress, The American Economic Review, Papers and Proceedings of the Ninety-Fourth Annual Meeting of the American Economic Association, 72, 2, 262-266. OECD, 1997 Job Strategy: Making Work to Pay. Taxation, benefits, Employment and Unemployment, Paris OECD 1999 Benefit System and Work Incentives, Paris. OECD, 2001 Revenue Statistics 1965-2000, Paris. OECD, 2001 Taxing Wages 1999-2000, Paris. Parente S.L., R. Rogerson and R. Wright, 1999, Household production and development, Fed Bank of Cleveland, Economic Review, 35, 21-35. Perli R., 1998, Increasing Returns, Home Production and Persistence of Business Cycles, Journal of Economic Dynamics and Control, vol. 22 (April 1998), pp. 519-43. Pollak R.A, M.L. Watcher, 1975, The Relevance of the Household Production and its Implications for the Allocation of Time, Journal of Political Economy, 83, 255-77 Prescott E. C. and R. Mehra, 1980, Recursive Competitive Equilibrium: the Case of Homogenous Household, Econometrica, 1365-79 Ramey V.A. and M.D. Shapiro, 1997, the Effects of Government Spending, http://www.nber.org/papers/w6283

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Spiro P. 1994, The Underground Economy: Toward a More Balanced View of Alternative Methodologies Canadian Business Economics, 2, 4, 18-21. Stokey N. and R.E. Lucas with E.C. Prescott, 1989, Recursive Methods in Economics Dynamics, (Harvard University Press; Cambridge MA). Tanzi V., 1983, The Underground Economy in the United States: Annual Estimates, 1930-80, International Monetary Fund Staff Papers, 30, 2, 283-305. Tanzi V., 1980, Inflationary Expectations, Economic Activity, Taxes, and Interest Rates, The American Economic Review, 70, 1, 12-21. Thomas J.J., 1992, Informal Economic Activity in London School of Economics, Handbooks in Economics, (Harvester Wheatsheaf; London) Thomas J.J., 1999, Quantifying the Black Economy: ’Measurement Without Theory’ Yet Again?, Economic Journal 109, 381-89. Uhlig H., 1999, A Toolkit for Analyzing Nonlinear Dynamic Stochastic Models Easily, in R. Marimon and A. Scott (eds) Computational Methods for the Study of Dynamic Economies, (Oxford University Press; Oxford). Uzawa H., 1965, Optimal Technical Change in an Aggregative Model of Economic Growth, International Economic Review 6, 18-31.


ISBN: 978-1-60456-267-5 © 2009 Nova Science Publishers, Inc.

Chapter 2

HOW EUROPEAN FINANCIAL INSTITUTIONS AFFECT THEIR R&D AND ECONOMIC DEVELOPMENT1 Jian Tong1 and Chenggang Xu2 1 2

University of Southampton, UK London School of Economics, UK

Observations from East West Comparison Almost all successful development in history has involved intertwined institutional and technological changes. Moreover, such development has always been associated with an economy catching up to the more developed economies in terms of wealth and technology. European economies are no exception. Gerschenkron and Cameron, in particular, have independently observed that the banking systems in continental Europe played an essential role in its catching up in the nineteenth century (Gerschenkron, 1962; Cameron, 1967). There is a growing literature that has made great progress in exploring and testing the relationship between institutions and economic development. However, substantial gaps still remain and many important questions are still being debated. Related to European economies, the question of what are the institutional mechanisms that help or hinder technological change and economic development is still interesting. Here we attempt to address these questions with a focus on financial institutions, and our theory is motivated and is going to be tested by observations from European economies. First we develop an endogenous growth model, where financing mechanisms, development levels, R&D activities, and economic growth are endogenized jointly. Financial development is regarded as an evolution of the financing regimes, together with the economy’s development level. In our model, R&D is broadly defined to consist of all activities that improve knowledge about technology, including imitation, innovation, and invention. When studying European economies, we must face the fact that a little more than a decade ago a substantial part of European economies was centralized, such as economies in 1

This paper summarizes theoretical and empirical results from Tong and Xu (2004a, 2004b) in an intuitive way for general readers.

26

Jian Tong and Chenggang Xu

Central and Eastern Europe (CEE) and formal USSR (FUSSR) economies. It is well recognized that a centralized financial system, where all national financial resources are concentrated in state banks, creates the so-called “soft-budget constraint” (SBC) syndrome that is one of the most serious problems in centralized economies (Kornai, 1979; Dewatripont and Maskin, 1995; for recent surveys, see Maskin and Xu, 2001; Kornai, Maskin, and Roland, 2003). However, the centralized financing regimes appeared to catch up quickly at earlier development stages, although the SBC is inefficient. Later these economies experienced a fast decline pattern. Given that the negative experience was associated with heavy investments in R&D in those economies (both in monetary and in human capital terms), from a standard growth theory point of view, the fall of the centralized financing regimes seems puzzling. Understanding these puzzles is important for the transition of these economies, since no economy can be completely separated from its past — or there is a path dependency. Moreover, understanding these puzzles is also important for general understanding of the market economies and long run development policies in general. For both purposes, we need to study closely the regularities of the CEE-FUSSR economies as compared with those of the Western economies.

Figure 3.1. Growth Pattern of West European Countries, 1950-2000.

How European Financial Institutions Affect Their R&D and Economic Development 27 Tong and Xu (2004a) compare the development paths in the last half century between Western economies (mostly Western European economies) — market economies — and Central and Eastern European and formal USSR economies — formally centralized economies prior to the 1990s. Figure 3.1 shows time series of development levels of each Western economy relative to that of the world frontiers (hereafter referred to as the development level) measured by the ratio of the per capita GDP of a particular economy to that of the U.S. The horizontal line in each diagram in Figure 3.1 is the steady state level of the corresponding economy. From the figure, we can see that in general those economies are stable after achieving their steady state levels. To further investigate their development patterns, we use Figure 3.2 to show the development paths of four Western European economies (Austria, Belgium, France, and Italy) for the period from 1950 to 2000 based on a five-year average (data source: Madison, 2003).2 In that figure, we present the growth rate differences with those of the world frontiers on the vertical axis. A regular catching-up pattern seems to emerge, where all these economies are catching up before the 1980s; thereafter, their catching-up ended with small growth cycles when their development levels were close to the frontiers (from 70% to 81% of the U.S. level).

Figure 3.2. Catching-up Patterns of France, Italy, Belgium, and Austria.

2

The development path of each economy is plotted in a development level growth rate space so that the development level and growth rate of each economy can be compared with those of the world frontiers, which are proxied by those of the U.S., given that the data come from the latter half of the twentieth century. The development level relative to that of the world frontiers is measured by the ratio of the per capita GDP of this economy to that of the U.S. It is presented by the horizontal axis.

28


Figure 3.3 is a CEE-FUSSR counterpart of Figure 3.1 for the West. In comparison, a common regularity that can be observed is that all the economies “overshot” their steady state levels, together with the rapid adoption of new technologies,3 and declined afterwards. Figure 3.4 illustrates the catching-up patterns of some CEE economies (Hungary, Poland, Romania, and the USSR). All CEE economies underwent fairly rapid catching-up in their earlier stages (before 1975). However, all this ended with large growth cycles when their development levels reached the level that was above their estimated steady state levels.4

Figure 3.3. Growth Pattern of Central and Eastern European and formal USSR economies, 1950-2000. 3

4

For example, except in synthetic fibers, the USSR adopted major new technologies that were introduced in the 1940s and 1950s (oxygen steel making, continuous steel casting, synthetic fibers, polyolefins, HVAC [300 kv and over], nuclear power, NC machine tools) around the same time as the UK, FRG, and Japan (Bergson, 1989). The data reflecting the collapse of the centralized economies over the last decade are the last two points on the curves. The basic development pattern of these economies would not change qualitatively if these data were excluded. The only reason for including the data for the period from 1990 to 2000 is to present the data in the same way as those for the West European economies.

How European Financial Institutions Affect Their R&D and Economic Development 29

Figure 3.4. Catching-up Pattern of the USSR, Hungary, Poland, and Romania.

Table 3.1. Total R&D Expenditure, 1967-85 (Percentage of GNP) 1967 1975 1982 1985

US

Japan

WE

USSR

Czech

Hungary

3.07 2.38 2.69 2.68(b)

1.58 2.01 2.47 2.80(b)

1.78 1.81 2.04

3.0 3.5 3.6 5.0(c)

2.9(a) 3.1(a)

1.8(a) 2.8(a)

Note: All the data in the table include both military and civilian sectors. Source: Hanson and Pavitt (1987). (a) Poznanski (1985). (b) National Science Board (1989). (c) Linz and Thorton (1988).

A very interesting associated fact is that when the catching-up reversal occurred in the CEE-FUSSR economies, the R&D (both in the civilian and military sectors) in these economies was among the highest in the world (Tables 3.1-3.2). For example, beginning in 1975 R&D intensity in the USSR was the highest in the world both in monetary terms and in terms of human capital inputs; compared with the U.S., the R&D intensity and the number of scientists employed in R&D in the USSR in 1975 was about 47% and 63% higher,

30


respectively (Table 3.1 and 3.2).5 Obviously “standard” growth theories are not able to reconcile these facts. Table 3.2. Scientists and Engineers in R&D, USSR vs. US

1950 1960 1970 1975

USSR million

US million

USSR per 10,000 workers

US per 10,000 workers

1.25 2.73 6.62 8.74

1.59 3.81 5.47 5.35

14.7 27.5 54.2 66.0

26.2 55.8 66.8 61.5

Note: Scientists and engineers in both countries exclude humanities specialists, social scientists, and psychologists. Source: Bergson (1989).

Endogenous Growth Model with Financing Regime and R&D The theory by Tong and Xu (2004a) emphasizes the impacts of project selection mechanisms associated with different financial institutions on development. It also explores how the development level determines the choice of financial institutions. Project selection mechanisms are related to the incentives provided by the financial institutions to entrepreneurs for R&D. These incentives include “carrots” to reward entrepreneurs and “sticks” to prevent cheating. Our model focuses on the latter because, in our view, these are particularly important in dealing with the following important features of R&D: a) the uncertainty of R&D projects for innovation/invention can be very high, so that essential knowledge for a project is only obtained ex post; whereas the uncertainty for imitation is low, since reasonably accurate information can be collected ex ante; b) individuals with R&D ideas usually do not have the resources to finance projects, so they need outside investment; c) entrepreneurs have informational advantages over projects that they work with, and with these advantages they may benefit by cheating on the value of the projects. Cheating can be deterred if it is punished whenever it is revealed. Moreover, an effective deterrence is a better R&D selection mechanism for innovation. However, such punishments can be enforced only when they are consistent with the financiers’ ex post calculations. But the commitment to ex post punishments depends on the financing mechanisms. Two types of financial institutions are studied: regime s with more centralized decision-making,6 reflecting the conglomerates’ internal financing; “relation-based financing”7 or a centralized financing system in the real world; and regime m with more decentralized decision-making, reflecting venture capital or syndicated financing in the real world. The institutions of regime s have no

5

6 7

For all countries under comparison, the R&D includes both the civilian and military sectors. Great care has been taken to rely on the most prominent experts in the field for the source of our data: we use Madison for the GDP/GNP data; and Bergson and Hanson for the R&D data. When financing decisions are concentrated in the hands of a single financier, we regard the corresponding financial institution as a single-financier financing regime, i.e., regime s. The term is borrowed from Rajan and Zingales (2003).

How European Financial Institutions Affect Their R&D and Economic Development 31 commitment for ex post punishment, i.e., they are associated with soft budget constraints,8 while regime m is committed to punishing cheating ex post, i.e., it is associated with hard budget constraints.9 An alternative approach to deal with the R&D-related incentive problem in our model is to select R&D projects ex ante. Associated with the abovementioned R&D feature a), the effectiveness of pre-screening R&D depends on the information available ex ante. The more novel the project, the less information is available to make ex ante judgments, whereas it is much easier to evaluate projects that have marginal novelty, such as those involving technology imitation. Thus a relatively backward economy will benefit from imitation which reduces the problems of cheating in R&D when projects are selected ex ante. We model the degree of backwardness of an economy as the distance from the world frontiers. Regime m institutions are more efficient in innovation, while under certain conditions regime s institutions can be more efficient in technology imitation, although they are less efficient in innovation. We predict a conditional convergence such that in equilibrium, economies with stronger legal institutions chose a regime m that leads to higher steady-state development levels, whereas those with weaker legal institutions chose a regime s. Since ex ante R&D selection is less effective in solving incentive problems when the development level is higher and imitation opportunities diminish, this leads to lower steady-state development levels for regime s. We also analyze the catching-up dynamics by decomposing the development dynamics into a “convergence effect” and a “growth inertia effect”. The magnitude and the direction of the two effects govern the development dynamics of an economy. The key factor that determines the magnitude of each effect is what we discover from the model: the “inertia factor” of the economy. The “inertia factor”, a measure of the ability to preserve the momentum of growth performance, is determined by institutions. Moreover, it is empirically observable as the autocorrelation coefficient of the growth rate. At a catching-up stage, the convergence effect and the growth inertia of an economy point in the same direction. Thus, a backward economy with a higher “inertia factor” will catch up faster. However, when an economy’s development level is close to its steady state, a higher “growth inertia” may make the economy prone to growth cycles. Among other factors, the “inertia factor” of an economy is affected by how much ex ante R&D selection is used in the economy, which in turn is determined by the financing regime. In general, the “inertia factor” under regime m is smaller than that under regime s. Together with the results of how financing regimes determine their steady state, our theory predicts that the institutions of regime s lead to a fast catching up when an economy is at an earlier development stage, however, it is likely to fall into growth cycles around a relatively low steady-state development level. In contrast, although the institutions of regime m may have higher steady-state development levels associated with more stable catching-up patterns, their catching-up speed may vary depending on the legal institutions. 8

9

An observable indication of the existence of a substantial SBC problem in an economy is a large amount of non performing loans (NPL), such as those in transition economies and in Japan during the last decade. The NPL/GDP ratio in Japan was 15.3% in 2001, far higher than any other developed economy. For the contractual foundations of the commitment problems associated with centralized and decentralized financing regimes, see Dewatripont and Maskin (1995); for the contractual foundations of the commitment problems associated with different financing regimes in market economies, see Huang and Xu (1998, 2003).

32


These predictions shed light on why economies associated with some financial institutions, such as centralized financing or “relationship financing”, catch up quickly at earlier stages but encounter serious problems later even when investments in R&D are high. Our theory is consistent with some observed development patterns, including the rise and fall of centralized economies. Our model is based on a discrete-time version of the Romer model. In our model, we have infinitely lived consumers. In each period some of them become entrepreneurs (E) randomly. In the infinite periods, each period t has the following stages: Stage 0: selecting financing regime (m, s) and pre-screening projects; signing of contract between E and financiers for financing projects Stage 1: for each project invest I1; private information on the project is revealed to E. − Regime m commits to liquidate bad projects at stage 2, so E stops bad project at this stage. − Regime s does not commit to liquidate bad projects at stage 2, so E cheats. Stage 2: invest I2; − Good projects are finished; bad projects need more time/investment: information on the quality of projects become public knowledge. − Decision on bad projects to be made: liquidate or refinance. Stage 3: invest I3 to refinance a bad project and bad projects finish. R&D projects in our model are uncertain. With a probability of q a project is a good one and needs 2 stages to finish. With a probability of 1−q a project is bad and needs 3 stages to finish. Moreover, the first two stages’ investments, I1 + I2, are sunk. We make the following basic assumptions: A1: Refinancing bad projects is ex post efficient (cheaper than a new good project at stage 3). A2: Refinancing bad projects is ex ante unprofitable (at stage 2). Moreover, we assume that there are agency problems that an entrepreneur has private information at stage 2, and private benefit to continue a bad project. We model two financial institutional choices, ζ ∈ {s, m}. Here, regime s stands for single-financier-financing regime; whereas regime m stands for multi-financier-financing. A regime m incurs extra transaction costs σF which is determined by enforceability of contracts. Here, F denotes renegotiation cost. This renegotiation cost has a strategic effect that it can help regime m to commit to terminate bad projects ex post (for detailed explanations see Tong and Xu, 2004, and Huang and Xu, 1999). Therefore, we have the following result.

Result 1 Under regime s all bad projects will not be stopped, i.e., there is a pooling equilibrium. Under regime m all bad projects will be liquidated at stage 1, i.e., there is a separating equilibrium. We may interpret liquidation of bad projects as an ex post screening mechanism for uncertain R&D projects. In addition to this mechanism, financial institutions also have an option to select projects ex ante, i.e. to use a pre-screening approach. A key determinant of prescreening is the cost of information on a proposed R&D project. We suppose that the cost is proportional to the distance of an economy to the world frontier. That is, there is an

How European Financial Institutions Affect Their R&D and Economic Development 33 ”advantage of backwardness” that the more backward economies have better chances to imitate more advanced economies at a lower cost. This idea is captured in the model by assuming that the cost is λ (at) ψ (θ), where θ is the precision of prescreening and at is relative At /Aft and At and Aft are the knowledge stocks of the follower development level, at economy and the world frontier respectively. A financial institution (ζ, θ) is chosen to maximize expected net present value of profits in competitive capital markets. Specifically, in our model, for every period t at stage 0, after receiving R&D proposals from E, financiers choose a financing regime and a pre-screening precision {ζ, θζ} to maximize the expected NPV of the projects.

R&D and Growth The institutional factor in our model boils down to equilibrium number of completed projects in each period given there is free entry in R&D sector and capital market. Combining standard Romer model results for profits of intermediate firms, for growth of knowledge stock, together with equilibrium number of completed projects in each period, we derive two growth equations with initial condition (a0, g0):

at +1 = at

g t +1

⎛ ⎛ 1 + gt = C * ⎜ λ ⎜ (at ⎜ ⎜ αω ⎝ ⎝ 1+ g f 1+ ρ

1 + gt 1+ g f ⎞ ⎞ (1 + ρ ) 2 ⎟⎟ − C * (λ (at )) + δL. ⎟⎟ αω ⎠⎠

Figure 3.5. Steady States for Different Financing Regimes.

34


The equilibrium financial institutions and their steady states described by the growth equations are illustrated in Figure 3.5. Together with the steady states, the figure also illustrates another major result of our model — how an optimal financing regime is determined corresponding to different parameter ranges. The curve σc draws the boundary between the two regimes. When the laws in an economy are bad and the development level is low, SBC regime may have advantages for economic development. When the laws are good and when the development level is high, HBC regime is better. Furthermore, when the laws are worse than σ* and when the development level is higher than a*, the best regime is m and the corresponding steady state is on the σss curve. Otherwise, the best regime is s and its corresponding steady state is the vertical line connected to the σss curve.

Catching-up Dynamics and Cycles Some important observations we have from Figures 2.1-2.4 are related to the dynamics of long run developments of those economies. To analyze the dynamics of the growth model, we linearize the growth equations around their steady state (ā, gf ):

⎛ 1 ⎜ ⎛ at +1 − a ⎞ ⎜ ⎟ ⎜ ⎜g − g ⎟ ≈ ⎜ ρB f ⎠ ⎝ t +1 ⎜ ⎝ a (1 + g f ) where, C * (λ ( a )) =

a 1+ g f B

⎞ ⎟ ⎟⎛⎜ at − a ⎟⎜ g t − g f ⎟⎝ ⎠

⎞ ⎟, ⎟ ⎠

(1)

αϖ (δL − g f ) is optimal R&D cost, and ρ (1 + ρ ) Δ

B=

δL − g f ∂C * a , ρ (1 + g f ) ∂a C *

(2)

is what we called the inertia factor. Very importantly, the inertia factor B appears as the growth autocorrelation, therefore it is observable, i.e. the model is testable. Through this factor, our linearized growth system decomposes growth into two effects: the convergence effect, − ρB [a /(1 + g f )] , through which a larger B leads to faster catching up, and the growth inertia effect, B (gt − gf), through which a larger B leads to low stable growth. B determines the magnitude of the convergence effect in the system (1). When the current development level at is below ā, an economy with a higher B tends to invest more in R&D than other economies; when at is above ā, then an economy with a higher B tends to reduce R&D more than other economies. Moreover, B also determines the magnitude of the growth inertia. An economy with a higher B may expect higher future R&D capital costs (associated with a more rapid exhaustion of opportunities for imitation), hence a higher future valuation of current projects (capital gain). Thus, when a higher B economy has a high (gt − gf), it tends to invest more in R&D, which drives an even faster growth in the future.

How European Financial Institutions Affect Their R&D and Economic Development 35 The combination of the above convergence effect and inertia effect determines the speed of catching up and the stability of an economy. In a catching-up phase (i.e., at < ā and gt > gf), the two effects work in the same direction therefore a higher B implies a higher speed of catching up. However, when there is an overshooting (i.e., at > ā and gt > gf), the two effects work in opposite directions and, importantly, the inertia effect dominates in a divergence direction. Thus, the inertia effect ultimately determines the stability of the system. A key factor which determines B is (∂C ∂ a ) ⋅ (a C *) , the steady-state elasticity of R&D capital costs with respect to the development level. It turns out that the elasticity is affected by the R&D selection mechanism used by the financing regime. One of the major testable predictions of the model is that everything being equal, a regime s has a larger inertia factor than the regime m. Therefore, for two identical economies with different financing regimes, regime s will be catching up faster at earlier stages, but will be unstable at later stages. However, regime m may be slower in catching up at earlier stages, but more stable at later stages.

Empirical and Policy Implications The following preliminary observations suggest that the major prediction of our model is consistent with the data. Figure 3.6 below plots a lagged growth rate vs. the growth rate for the CEE economies (regime s) and the Western European (WE) economies (closer to regime m) for the period from 1950 to 2000. The figure suggests that after controlling for the convergence effect, the average B of the CEE economies is higher than that of the WE economies: the slope for the CEE economies is positive, whereas the slope for the WE economies is negative.

Figure 3.6. Financing Regimes and Growth Inertia Coefficients: EE vs. WE.

36


Calibration When the regime s has a larger B, our model predicts that regime s is more likely to fluctuate around the steady-state development level. Moreover, the steady-state development level is lower than that under regime m. By using real data, the following calibration suggests that our model fits the reality reasonably well. Applying estimated parameters for Romania, we have estimated Romanian growth equation system (for details of the estimation and calibration, please see Tong and Xu, 2004b):

at = 4.6220 × 10 −2 sin 0.10134t − 9.014 × 10 −3 sin(0.10134t − 1.6215) + 0.15378 g t − g f = −6.0673 × 10 −3 sin 0.10134t + 3.1111 × 10 −2 sin(0.10134t + 1.6215) The following figures compare predictions of our calibration model with the actual growth patterns of Romania. Using similar coordinates as those in Figures 3.3 and 3.5, the vertical axis is the difference between the economy’s growth rate and the world frontiers, gt − gf , while the horizontal axis is the ratio between the economy’s development level and that of the world frontiers. With simultaneously endogenized financing and development, this model has rich policy implications. In reality, the choice (or change) of financing regime may be affected by political institutions, legal restrictions, etc. For example, for a regime s, where the economy’s development level has caught up to a level that is close to its steady state, it is optimal to switch to regime m at this time. However, this may not be implemented since some stakeholders who benefit from regime s may have strong incentives to block a regime change. As a result, the economy may decline after an overshooting, which may trigger an economic/political crisis later.

Figure 3.7. Actual Romanian Growth, 1950-2000.

How European Financial Institutions Affect Their R&D and Economic Development 37

Figure 3.8. Calibrated Model Prediction of Romanian Growth, 1950-2000.

Figure 3.9. Actual Romanian Catching-up and Growth Cycle, 1950-2000.

Therefore, a change of financial institutions may occur as a consequence of a political regime change. Related to this, political regime changes (e.g., the collapse of centralized economies) or outside pressures (e.g., reform conditions imposed by international institutions, such as the IMF) may result in a different timing of regime change, which can be much worse than optimal. As is evidenced in the Appendix, our model derives some useful policy

38


implications from the abovementioned scenario (for details of the simulations, see Tong and Xu, 2004a).

Figure 3.10. Calibrated Model Prediction of Romanian Catching-up and Growth Cycle, 1950-2000.

Conclusion The main conclusion of our model is that in order to be successful in catching-up with the developed economies, the choice of the financial system, which is related to the quality of the legal system, is of crucial importance. However, wrong timing of reforms can bring to a halt the catching-up process or cause more violent economic cycles. This is why it is very important for a country to carefully choose the timing of reform of its financial system. In addition, these reforms have to take into account the state or the quality of the country’s legal system. If the latter is very weak, liberalization of financial markets can be harmful. This observation can usefully be applied in explanation of problems in former centrally-planned economies. The theory therefore contributes to a deeper understanding of problems in the development of financial markets and economic development. At the same time, it offers an alternative explanation of some more recent concepts, such as “financial structure irrelevancy” (Beck and Levine, 2002).

Appendix Consider an economy starting from a0 < a* < ā and g0 = gf . The following examples show the impact of deviations from the second-best financing regime on the consequent development of the economy.

How European Financial Institutions Affect Their R&D and Economic Development 39 1. Optimal regime change: at first, regime s is chosen optimally, which delivers a high speed of catching up. Then the regime is changed at the optimal timing to regime m, which guarantees a stable convergence to the steady-state development level and growth rate (Panel a and b of Figure A_3.1 in the Appendix). After the regime change, the development level keeps increasing toward the steady-state level, while there is a moderate initial drop in the growth rate to adjust to the convergence to the steady-state growth rate. Regarding in-house R&D as regime s and venture-capitalfinanced R&D as regime m, this may shed some light on the impact of the rise of venture capital in the U.S. economy since the 1970s. 2. Late regime change: A reform takes place at the end of a positive overshooting (Panel c and d of Figure A_3.1 in the Appendix). Immediately after the regime change, there is a sharp drop in the growth rate which makes the development level decline as well. Then there is a recovery of growth and the development level. Interpreting a centralized financial system as regime s, this may shed some light on the impact of failing to reform the financial system on time, as well as on the sharp decline of the transition economies after the change in financial systems.

Figure A.3.1. Regime Change and Development.

40


References Beck, T., and Levin R. 2002. “Industry Growth and Capital Allocation: Does Having a Market – or Bank Based System Matter.” Journal of Financial Economics 64: 147 - 180. Bergson, A. 1989. Planning and Performance in Socialist Economies: The USSR and Eastern Europe. London: Unwin Hyman. Cameron, R. et al. 1967. Banking in the Early Stages of Industrialization: A Study in Comparative Economic History. New York: Oxford University Press. Carlin, W., and Mayer, C. 2003. “Finance, Investment and Growth.” Mimeo, UCL and Oxford. Dewatripont, M., and Maskin, E. 1995. “Credit and Efficiency in Centralized and Decentralized Economies.” Review of Economic Studies 62: 541-555. Gerschenkron, A. 1962. Economic Backwardness in Historical Perspective. Cambridge MA: Harvard University Press. Hanson, P., and Pavitt, K. 1987. The Comparative Economics of Research Development and Innovation in East and West: A Survey. Switzerland: Hardwood Academic Publishers. Huang, H., and Xu, C. 2003. “Financial Syndication and R&D,” Economics Letters 80: 141 – 146. Huang, H., and Xu, C. May 1999. “Institutions, Innovations, and Growth.” American Economic Review 89: 438-443. Huang, H., and Xu, C. 1998. “Financial Budget Constraints and the Optimal Choices of R&D Project Financing.” Journal of Comparative Economics 26: 62-79. King, R., and Levine, R. 1993. “Finance and Growth: Schumpeter Might Be Right.” Quarterly Journal of Economics 108: 717-737. Kornai, J. 1992. The Socialist System: The Political Economy of Communism. New York and London: Princeton University Press and Oxford University Press. Kornai, J. 1979. “Resource-Constrained versus Demand-Constrained Systems.” Econometrica 47: 801-819. Kornai, J., Maskin, E., and Roland, G. 2003. “Understanding the Soft Budget Constraint.” Journal of Economic Literature 41: 1095 – 1136. Linz, S., and Thorton, J. 1988. “A Preliminary Analysis of the Demand for Innovation: Evidence from the Soviet Interview Project.” Working Paper No. 56, University of Washington. Maddison, A. 2003. The World Economy – Historical Statistics. Paris: OECD Publications Service. Maskin, E., and Xu, C. 2001. “Soft Budget Constraint Theories: From Centralization to the Market.” Economics of Transition 9: 1–27. National Science Board. 1989. Science & Engineering Indicators — 1989. Washington, D.C.: US Government Printing Office. Poznanski, K. 1985. “The Environment for Technological Change in Centrally Planned Economies.” Working Paper No. 718, The World Bank. Qian, Y., and Xu, C. 1998. “Innovation and Bureaucracy Under Soft and Hard Budget Constraints.” Review of Economic Studies 65: 151-164. Robinson, J. 1952. The Rate of Interest and Other Essays. London: Macmillan, 67-142.

How European Financial Institutions Affect Their R&D and Economic Development 41 Romer, P. 1990. “Endogenous Technological Change.” Journal of Political Economy 98: 71-102. Schumpeter, J. 1936. The Theory of Economic Development. Cambridge, MA: Harvard University. Press. Tong, J., and Xu, C. 2004a. “Financial Institutions and The Wealth of Nations: Tales of Development.” LSE STICERD TE/2004/469; CEPR DP4348. Tong, J., and Xu, C. 2004b. “Imitation, Finance and Growth: theory and evidence from panel data.” Mimeo, University of Southampton and LSE.



Chapter 3

AN ALMOST IDEAL DEMAND SYSTEM (AIDS) OF CANADIAN TOURISM TO EUROPE Panagiotis Panagopoulos and Thanasis Stengos University of Guelph, Guelph, Ontario, N1G 2W1, Cananda

Abstract As tourism is the one of the largest growing industries in the world, estimating its demand is very important for policy reasons of the destination countries involved. Disaggregating consumers into groups and estimating their demand function has been posed several times in the tourism context but data unavailability has made it hard to deal within a system approach as the Almost Ideal Demand System (AIDS) model, which has theoretical advantages over its single equation counterparts. In this paper the demand for tourism in Europe from Canadians is estimated, both for aggregated consumers and disaggregated on the purpose of travel. The findings indicate a noticeable difference in elasticity estimates for each group. In particular, the own price elasticities for leisure demand is larger for tourism in all cases but Italy, while the opposite stands for the business tourism. Moreover, higher elasticity values are estimated, indicating that Canadians’ response to prices and income today is increased. Overall, it is clear that more research is needed on AIDS modeling of tourism for purpose of travel to generate accurate forecasts and estimates for policy reasons.

Keywords: Almost Ideal System (AIDS); Tourism demand; Purpose of travel; Europe

Introduction The world tourism industry has experienced remarkable growth in recent years and has attracted the interest of many researchers. The decrease of transportation costs and the increase of the quality of services have increased demand for tourism, making it an important economic activity. Contributions to the economy are significant due to the influx of foreign currency and employment gains by the tourist sector. The recent European economic integration, apart from positive economic consequences, it has also been plagued by various problems such as the declining agricultural sector and the

44

Panagiotis Panagopoulos and Thanasis Stengos

core-growth effects (the situation where economic activity is gathered on a small area), that prevent an equitable and sustainable development across all European regions. It has been argued that tourism could fill in the gap and propel development in problematic regions. With nine million employees and accounting for 5.5% of the European Union’s (EU) total GDP, policies affecting tourism are expected not only to increase, but also to improve the quality of the tourism industry in Europe. Hence, various socio-cultural programs have been introduced aiming to advertise tourism in European regions, decrease the costs of services (for example transportation costs) for isolated regions, increase the quality of services with various educational programs and seminars, support multi-diversity within a European social framework, so as to develop comparative regional tourist appealing advantages and finally take advantage of complementary, and multiplier effects from tourist appealing developments, by focusing on supporting investments in strategic areas. However, there are high costs required for provision and maintenance of facilities and infrastructure that are fundamental in tourism development. Moreover, providing higher quality services requires investment in highly qualified human capital, while the associated relative risk involved, has brought up the question of the amount of investment that tourism could attract. By the “risk involved” we refer to the fact that the demand for tourism is exogenous to the country, so domestic policies can only marginally affect this exogenous demand. Of course, macroeconomic policies such as inflationary policies, exchange rate (de)stabilization and political instability can seriously affect the demand. The answer tourism specialists offer to weaken the dependence on exogenous demand is the development of domestic demand for tourism, which proves to be stronger than international tourism, see Bigano et al (2004) for estimated figures. The successful provision of a common political and economic framework that considerably decreases the exogenous shocks that tourism is sensitive to is also an important factor in European policies. Finally, the existence of strong multiplier effects from the growth of tourism imply that investment and growth in the tourism industry will positively contribute to the development of the economy as a whole, and even more, supporting domestic tourism has complementary and direct effects in international demand for tourist associated services. Therefore, the attention that the development of tourism has recently received by the European authorities is very much justified. Within this framework, after examining a recent dataset (1990-2005) concerning international demand (overseas, Canada) for tourism in the European Union, some useful points can be made. A quick look at the statistics indicates that Canada and the destinations explored in this paper are among the most important tourism markets in the world. More precisely, Canada is the world’s seventh largest country in tourism, see Bigano et al (2004), while France, USA, Spain, Italy and United Kingdom are the five most popular international destinations globally. For example, France is the first inbound market in the world with 75 Million visitors in 2000, whereas Spain is the third, with 48.5 million following USA. An additional fact that justifies our research topic is that strong links and interdependences are apparent between the countries of the North American and European regions. An example of this is Germany, as being the first foreign market for France and Italy, and similarly the UK for Spain. The interest on the results will both lie on the approach of the problem and the estimation in this period where Europe’s Union policies and economic background in general, were supportive for the development of tourism.

An Almost Ideal Demand System (AIDS) of Canadian Tourism to Europe

45

Future research should involve the use of a larger data set, the dynamic structure of demand, while identifying the effect of the European Union policies on the demand for European tourism not only within European countries but also in a global context among the various continental destinations. The rest of the paper is organized as follows; in section 2 we describe the methodology used for the estimation, in section 3 we provide the data set and description of the variables chosen, while in section 4 we discuss the estimation results. Finally in section 5 we conclude. In the appendix, we provide some additional calculations and remarks.

Methodology In this paper an Almost Ideal Demand System (AIDS) is estimated. Unlike its predecessors such as the Rotterdam or the translog models, the AIDS model “gives an arbitrary first order approximation to any demand system; it aggregates perfectly over consumers” and “it satisfies the axioms of choice exactly”, see Deaton and Muellbauer, (1980a, 1980b). However, the problem in the early applications of the model was the frequent rejections of the homogeneity and symmetry restrictions. An explanation, given by the authors, was that “theoretical” bias derives from “the insufficient attention to the dynamic aspects of consumer behavior”, see Deaton and Muellbauer (1980a). Since the system deals with time series data set, which covers a long period, it can be argued that preferences have been changing over time. In response to this argument, Chalfant and Alston (1988) for example, tried to identify non-parametrically structural changes in consumption. They concluded that changes are more likely to occur from the restrictions demand systems impose. Therefore, the appropriate aggregation patterns and the appropriate functional form need to be adopted in each case. In addition to that, from an econometric point of view it is natural to expect in economic time series a degree of autocorrelation. In a demand system autocorrelation can be explained as lag effects arise when a consumer takes into account his decision with last year’s prices. In the context of tourism, the implication is simply that the demand for tourism for a certain year is affected by the overall impression of people who visited the same country the previous year. In response to this specification problem, dynamic versions of the traditional static AIDS model have been proposed. In our data set however, the number of observations was not large enough to account for a dynamic model. The low degrees of freedom, has always been a problem1 in demand system estimations.

Literature Review Although it was only in 1961 when Guthrie first studied the demand for tourism, as he was motivated by the rapid growth of the previous decade, the AIDS methodology for studying tourism was adopted by White (1985) who first studied United States travelers to 1

As a result researchers (Silver and Ali 1989, Tiffin and Balcombe 2002, Chambers, 1990, Baldwin, Hadid and Phillips, 1983) have tried to deal with both autocorrelation and small size problems trying to find appropriate estimators, while other estimation procedures are on research. In this paper we use two sample size corrected statistics.

46


Europe along with O’ Hagan and Harrison (1984). In particular, O’Hagan and Harrison estimated the demand for Europe (15 destinations) during 1964-1981. However, to deal with the reduced degrees of freedom they added additional constrains. White (1985) estimated the same model (US travelers to Europe) for a larger period 1954-1981, while also aggregated the European destinations into five groups. Syriopoulos and Sinclair (1993) again used the AIDS methodology to examine the US and European demand for tourism in Mediterranean countries for the period 1960-1987. After almost six years, complementary research was done by Papatheodorou (1999), who examined for the period 1957-1989 the demand for tourism in Mediterranean countries (with Yugoslavia included) separately for Germany, UK, and France. Lyssiotou (2000), used quarterly data for the period 1979-1991. The destinations examined are USA and Canada as a group, France, Italy , Greece, Cyprus and Malta as the third group, Spain and Portugal and final and fifth group the North European countries. In her specification she included a time trend and a seasonal effect. De Mello et al (2002, 2005) studied the demand for tourism in Spain, France and Portugal from UK consumers for the period 1969-1997. However, they subdivided the sample into two periods assuming structural changes due to Spain and Portugal’s integration in Europe. Similarly with Papatheodorou’s (1999) research, a time trend was included. However, using the same sample in their last paper, an error correction AIDS model was used. The following year four papers were published. Divisekera (2003) estimated the Japanese demand for Australia, New Zealand, UK, and USA, the USA demand for Australia, New Zealand and UK, UK demand for Australia New Zealand and USA and finally New Zealand demand for Australia, UK and USA tourism. Durbarry and Sinclair (2003) for the period 1968-1999 estimated the French demand for tourism in UK, Italy and Spain, using a dynamic AIDS specification. Li et all (2003, 2005, 2006), in both their studies estimated the UK demand for tourism in western Europe where France, Greece, Portugal, Spain and Italy were the main destinations and the rest were grouped as others for the period between 1972-2000. Error correction models and time varying parameter error correction models were introduced and highlighted in their paper. Lanza et al (2003) estimated for the period of 1975-1992 the demand of 13 European countries in a different context though. They estimated the model for the country in focus and others, while they combined the results with the notified growth of industries related to tourism activities. However, in contrast to other methodologies studying tourism, in the AIDS context the research so far has focused on the development of advanced dynamic specifications. This paper focuses on the case where consumers are disaggregated with respect to purpose of travel. As mentioned before, see Papatheodorou (1999), estimating demand on disaggregated data set will provide a better picture of the demand of tourism while the results will have various policy implications. Single equation models have recently been used on disaggregated data to study the effect of various parameters in demand systems, see Witt and Turner (2001) and Prescott et.al. (2005), however not within an AIDS framework.


47

The Theoretical Model2 In its general form the AIDS model is usually written as: n ⎛X⎞ wi = ai + ∑ γ ij log p j + β i log ⎜ ⎟ , ⎝P⎠ j =1

for i,j=1,2,…,n

(1)

where wi is the budget share of the i-th commodity of the group (total n-commodities) examined, while pj includes the prices of all commodities, X is the total per capita expenditure to the system and finally P is an index, accounting for a measure of the prices of the system given by the formula: n

log P = a0 + ∑ ai log p j + j =1

1 ∑∑ γ ij log pi log p j 2 i j

for i,j=1,2,..,n

(2)

Finally α, β, γ, are parameters. In particular the sign of the βi coefficient determines whether the i-good is a luxury, or a necessity, while the β, γij coefficients of prices account for the change of the budget share of the i-good due to a change in price (ceteris paribus). The model is close to being linear and that is why for simplicity most researchers use Stone’s Price Index given by log P = ∑ w j log p j which can be a good approximation in cases j

where prices are close to being collinear. It should be mentioned that in this simple form, dummies or habit effects can be incorporated in the a term as well. However, all possible variations have been used in this paper without statistically significant results.

Elasticities3 The above form of the model makes it easy for estimating all kinds of elasticities. Price elasticity measures the percentage of change in demand for a product with respect to the percentage of change in its price ceteris paribus. Expanding this idea, when the change with respect to the price of another good, or another parameter like income is measured, then one refers to the cross price elasticity or the income elasticity. AIDS is designed in such a way as to simplify the estimation of the various elasticities. On the parameters stated above the ownprice elasticity is the derivative of the logarithm of the budget share in respect to this own price. Thus, it is given from:

Εii =

2 3

γ ii wi

− βi − 1 and eii =

γ ii wi

+ wi − 1

see appendix for its derivation. Elasticities are conditional upon the definition of the budget share allocation process.

48 where


eii

refers to the compensated-elasticity, which is obtained simply from

eii = Εii + wi Η i as it reports the change of the quantity (in our case the budget share) with respect to a change in price assuming that the real expenditure (X/P) is held constant (P includes pi). In the same way both cross price elasticities and expenditure elasticity Нi can be derived:

Εij =

γ ij

⎛ wj ⎞ ⎛β ⎞ − β i ⎜ ⎟ and Η i = ⎜ i ⎟ + 1 wi ⎝ wi ⎠ ⎝ wi ⎠

These two are particularly important in the tourism context. In particular, the cross price elasticity measures the effect of the change in the “price” of one destination on another, i.e. the effect of a change of the opportunity cost for a tourist to visit a country in terms of visiting another. These effects have proved to be very important and in some cases they dominate the own price effects. That establishes the inter-dependencies between countries and offers the main argument for the use of demand systems to single equations. The obvious consequence arising from this fact for policy purposes is the need for co-operation between complementcountries (for the increase of one country’s demand to increase the other’s as well) and for the need of competitive-improvement oriented policy. The analysis of the expenditure elasticity though is different. From an economic theory point of view the expenditure elasticity classifies commodities as being luxury, normal or inferior. This characterization derives from the definition of the elasticity, that is, the change of the quantity demanded due to a change of income of the consumer. Therefore, the case where an increase in income reduces the quantity demanded justifies the “inferior” characterization. In every-day language people would say that a certain good is “for the poor” (or rich for the luxury case). As one might easily infer, in the tourism demand context, this suggests that a country’s services for tourism are “inferior” compared to others. So from a policy point of view the question would be what is defined as “services” and what would be the appropriate policy in such a case. Without going into much detail, it is worth noting that this is a highly debatable issue in the research of tourism demand. For example, it has been argued whether it is possible to deal with tourism as a product since “services” can only be defined strictly by the tourist industry in each country but a far more accurate definition would include everything that a tourist faces and deals once “purchasing” the commoditycountry. So a policy proposed would need to place special attention on which services should be integrated in order for multiplier effects to increase the return of any investment undertaken. To that end, more accuracy would be gained for estimating tourism demand for particular services within a country using a data set like the one employed in the current research project.

The Tourism Model Applying the theoretical model to the tourism context using matrix algebra notation we have

W = A + Γ * CP + Β * E + U


49

where W is the vector of budget shares for each country, A,B, the vector of the coefficients and U the error terms vector, CP is the vector of prices for each country, E the vector of total expenditure per capita deflated with Stone’s Price Index and Г the 6x6 matrix of coefficients. A fundamental issue of the model has to do with its assumptions. The assumptions for applying the AIDS originate from separability and aggregation conditions. In the context of tourism usually one assumes a three stage budget. The consumer first has to allocate his/her choices between consumption in all goods and tourism. In the second stage, the choice is among the various continents, these are, North or South America, Australia, Asia, Europe or Africa. The choice of this stage is fundamental for satisfying the seperability assumption. The geographic and political characteristics between the continents are obvious. There is one more argument though, that is the travel cost. Finding exact flight ticket prices would be outside the scope of this paper, however, it makes sense to assume that travel costs for the various countries within each continent are close enough to be considered identical, so that each destination of the above six mentioned, has a “price” to travel there and differences are large enough to account for the separability assumption. A similar argument is adopted in the third and last stage of the budget allocation process. That is, a consumer has to choose a country or a set of countries within the chosen continent. The same argument for travel costs at this stage, is that, traveling costs across or to any country in a destination are low compared to the choice of the second stage so as not to be such a significant factor at the third stage. Therefore, within country factors determine the choice of the consumer and at this stage the AIDS model is applied. Similar arguments were stated in O’Hagan and Harrison (1984).

Restrictions Implied from theory the following restrictions can be directly tested in the model.

Adding up Adding up in simple words states that the total value derived from demands equals total expenditure or in other words that the budget constraint is satisfied. In practice in the AIDS context adding up requires for all j-equations (number of countries) that:

∑a j

j

= 1,

∑β j

j

= 0,

∑γ

ji

=0

j

Homogeneity This property derives from the assumption that there is no “money illusion” effect for price and income for the consumer, that is for any proportional increase in both money and prices consumer preferences remain constant. For instance, the consumer facing a doubling of his/her income and doubling of prices, purchases the same bundle of products as when the prices and incomes were unchanged. In practice, in this model homogeneity requires that:

50


∑γ

ij

=0

j

Symmetry This is a very important property. Simply stated, symmetry requires that the cross price effects in a demand function, that is the effect of the change in the price of one product on the quantity demanded of another, should be the same for the different product demands. It can both be inferred from the cost function, but can be also be derived from consumer’s consistency in choices (revealed preference). For example, an increase in price of a product should reduce its quantity demanded and increase the demand of a substitute (in order for the consumer to achieve the same level of utility) at the same magnitude proportionally as if the same increase in price occurs in the substitute. Using the matrix formulation of the model, for symmetry we require that the Г matrix must be symmetric, that is γ ij = γ ji

Negativity Negativity simply adds one more restriction that comes along with the most straightforward fact from demand theory, that is, the response to the quantity demanded of an increase in its price should be non positive. This property originates in the concavity assumption of the cost function. Therefore, either assuming cost minimization or utility maximization negativity implies the same thing. Testing for negativity, as a matrix property, we refer to matrix C defined by:

⎛X cij = γ ij + β i β j log ⎜ ⎝P

⎞ ⎟ − wiδ ij + wi w j ⎠

where δij equals unity if i=j and zero otherwise. Practically, negativity implies non-positive own price elasticity.

Estimation Procedure The system with no restrictions can easily be estimated with ordinary least squares (OLS) equation by equation. However, the most common way to deal econometrically with the restricted model is the seemingly unrelated regressions (SUR) as developed by Zellner (1962), since it is expected that there is a degree of correlation (contemporaneous) between the disturbances of the equations in the same period. It is therefore assumed that errors corresponding to different time periods are uncorrelated. Another method that can be used is maximum likelihood, however the SUR estimator with symmetry restrictions converges to the maximum likelihood estimates under conditions of normally distributed errors. However, since the system is formed in an additive way, estimating the complete system is not possible


51

due to the singularity of the matrix. However, the system will give the same results if it is to be estimated with deleting one of the equations and the estimates from maximum likelihood estimation are invariant to the equation deleted. In a case though, where both autocorrelation and small sample size is involved none of the above estimation procedures will yield robust results. In this case, it is suggested to use Generalized Maximum Entropy (GME) Estimator as proposed by Golan et al (2000). Using the basic concept of Shannon (1948) for entropy, the single equation GME is expanded and applied to a system. The proposed estimator is efficient in cases where the sample size is small and errors are highly correlated. The complexity though of both the methodology and the estimation procedure made it hard to adopt in our research. Concerning the restrictions, the adding up property has been imposed on the model as part of the maintained hypothesis, therefore no further estimation is needed. However, homogeneity can be imposed on the coefficients of the unrestricted model, hence an F-test would be appropriate for testing. What is more, imposing the symmetry restriction required the SUR methodology, since it is a restriction on the system, and in particular on the Г matrix. Therefore, a test based on the residual covariance matrix of the unrestricted and restricted model was carried out. Nevertheless, a tendency for over-rejecting this hypothesis has been reported and identified in the literature. Various tests have been considered for either the small sample size or the existence of autocorrelation. A proper estimator is yet to be established. It has been proposed that bootstrapping can account for that, but this is an issue currently under research. Therefore, considering the low number of degrees of freedoms for this estimation to test the theoretical restrictions of the model we use in this paper a two sample-size-corrected statistics4.

Data and Description The Dataset In the very first paper of AIDS modeling in tourism White (1985) observes that the “best estimates of travel expenditures are obtained from sample questionnaires distributed at border crossings”. However, such data sets are not usually available; therefore most researchers have used the receipts that countries report in their national accounts, for tourist expenditure. In particular, Syriopoulos and Sinclair (1993) weighted the total receipts from the destination country with the number of visitors from the country of origin. The accuracy of this dataset is questionable, since both discrepancies in receipts reported as well as the weighting methodology can significantly lead to different results. The present dataset was retrieved through the University of Guelph’s library data resource center. The International Travel Survey is being conducted by Statistics Canada and it is a sample-based micro documentation of various characteristics for people traveling abroad, and it fulfills the requirements of the Balance of Payments of the Canadian System of National Accounts (BOP). In particular, the survey is about Canadians traveling overseas; so the destination under research is Europe. From the various destinations in Europe, 4

see Appendix for the composition of the test.

52


expenditures in Spain, Germany, UK, France and Italy accounted for approximately 73% of total European spending; consequently, this paper will be focused on these five regions. Still, the rest of the countries are grouped as “Others”, with the budget share being the summation of the component countries’ budget shares while the price for the group is estimated using the Stone Price Index (same treatment with Li et al (2005, 2006)). The annual exchange rates were estimated from the daily values5. The series were analyzed using basic diagnostic statistics and no important fluctuations were indicated. Therefore, the use of the average is justified (in any other case a representative average would need to take into account the effect a “shock” might have on the average value). Finally, the CPIs were retrieved from the International Monetary Fund (IMF) database; additional information (immigration, labor force, imports) was retrieved from Statistics Canada.

Description Before going into the demand for tourism from Canadians, it is also interesting to check the total tourist receipts of the countries examined in order to identify trends, while a comparison of the total with the Canadian demand can provide further insights. Since our model refers to budget shares and not to demand quantities, thus budget shares are estimated and depicted in figure 1 for the total demand for tourism in the countries examined. France,UK, Others, B.Shares on world receipts

0.18 0.16 0.14

Budget sha

0.12 0.1

0.08 0.06 France UK

0.04

Italy Spain

0.02

Germany

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

0

Year

Figure 1. Total tourism receipts budget share on main Europe destination.

5

reported annual values usually are the average between the first value and the last .


53

Comparing the patterns here with the Canadian demand case, two main differences are observed. First, the large share of the others group (close to 40%, not included in the figure), while UK’s share is decreasing. On the other hand, Spain is among the most popular destinations for the decade. Furthermore, tourism in Italy seems to become more popular in Canada while Canadians still keep strong ties with their closest ethnic origin country, which is the United Kingdom, with a small decreasing trend justified from the growth of tourism in the rest of the countries considered. Nevertheless, the increased number of receipts can be partly explained by the increased domestic tourism in Europe itself. Similarly, figure 2 depicts the growth of receipts in the European region for recent years, evidence supporting the existence of externalities described in this paper as the voyage effect, accounting for the distance advantages or disadvantages in favor of visiting a country from another as well as the characteristic effect for the case where two destination countries provide complementary or competing services. Europe Total Receipts

250,000

receipts (in millions

200,000

150,000 Europe Total Receipts 100,000

50,000

20 00

19 98

19 96

19 94

19 92

19 90

0

Years

Figure 2. Total receipts in European Region.

At this point, it is useful to stress that among the reasons for this rapid growth in European tourism, is the lowering of transportation costs within the European region (to be more precise, the reference to changes in transportation can not only be in money terms but also in quality level terms, something that describes the overall improvement in transportation, one of the goals of EU’s policies). Indeed, it is worth mentioning two air flight companies that particularly lowered the cost of air transportation within Europe to the minimum. Accordingly, with prices starting from zero euros (only airport taxes included) Ryan Air and Easy Jet took advantage of scale effects in production and building of airplanes, the increasing supply of employees, the reduced cost of old airports due to the building of new ones financed from increased infrastructure spending provided by EU and last but not least marketing and business organization innovations, like the removal of meal offerings on board, electronic tickets and the efficient use of internet for booking. The following two

54


figures give an idea by depicting passengers per year (as reported by the companies at their official sites).

Figure 3. Ryan-Airs passengers reported for the period 1995-2006. 35,000,000 30,000,000 25,000,000 20,000,000 Series1

15,000,000 10,000,000 5,000,000 0 1999 2000 2001 2002 2003 2004 2005

Figure 4. Passengers reported from Easy Jet the last 6 years.

Turning back to the main focus of the paper, the International Travel Survey (ITS), apart from the countries concerned, also provides total overseas expenditures. Hence, it would be interesting to check the spending on the region concerned (Europe) as a fraction to the total spending overseas. In figure 5 we graph the total spending in Europe, as a fraction of total spending overseas for the years 1990-2005. Looking at the graph, the decrease of spending in Europe to spending overseas could simply imply a similar decrease in the demand for European tourism. Another obvious explanation would be that, the demand for overseas demand for tourism has increased more than the European demand. This seems to be more realistic since generally tourism spending has been increasing.


55

%OF SPENDING INEUROPETOOVERSEAS 60 50

%

40 30

Series1

20 10

20 04

20 02

20 00

19 98

19 96

19 94

19 92

19 90

0

YEAR

Figure 5. Canadian spending in Europe to total expenditure Overseas.

By taking full advantage of the dataset at our disposal, on figure 6 we verify that Canadian spending per capita for tourism in Europe is both higher and has been increasing faster than that of other overseas destinations. This is a very interesting finding and interpreting it correctly one could see it as an indication that tourism in Europe is either more expensive or more efficient as it absorbs a higher income of the traveler as a result of higher quality services and more efficient use of the complementary effects in consumption.

Variables of the Model Prices The consumer price index (CPI) has been frequently6 used as the representative “price” for purchasing the tourism product of a country. The CPI, is an index of the prices of certain goods that an average household is facing on a regularly basis. This is used in practice for the estimation of the cost of living. Prices of various goods are what tourists constantly encounter when they are visiting a country and as such CPI is justifiably used to measure the price for purchasing tourism. The argument which has been used against the use of the CPI is that, tourists have a different representative amount of goods than that used to estimate CPI. While this may be true, it is the nature of the tourist appealing products that makes it impossible to restrict to specific categories. For example, will a tourist purchase products that he/she is accustomed to or will he behave like a local consumer in order to better experience the foreign culture? Hence, defining tourist related spending other than consumer spending is hard by definition, and too complex. The definition of tourism provided by the World Tourism Organization is too vague to define tourism 6

An alternative method used by Paraskevopoulos (1977) is the purchasing power parity (PPP) method. Divisekera (2003) used a per diem approximation of the estimated average spending per day adjusted by the exchange rates and the tourism price index, as a representative price.

56


spending, other than the “activities” which involves the use of money on behalf of tourists. Whether tourist related spending can be approximated or not by the CPI pegs the question of whether the lifestyle of tourists can be approximated by the lifestyle of the locals.

TS/P

SPENDING PERCAPITA(TS/P) INEU%OVERSEAS

2000 1800 1600 1400 1200 1000 800 600 400

TS/PINEUROPE TS/POVERSEAS

20 04

20 02

20 00

19 98

19 96

19 94

19 92

19 90

200 0

YEAR

Figure 6. Canadian spending per capita in Europe and Overseas.

Another argument, in favor of using CPI, is that prices in a place are correlated, which implies for example, that the increase of the price of a necessity will affect all prices. An explanation for that is simply that tourism can be considered as a product whose production requires inputs (food for instance) and differences can be approximated from the differences in CPI. The degree of correlation will depend on the type of local markets. The correlation tends to be maximized in conditions of perfect competition where prices equal marginal costs. The recent growth of the tourism sector strengthens the argument of a perfectly competitive market, thus it not surprising that supply side is treated as being infinitely elastic (justified by the great number of suppliers for tourism in all regions of all countries in Europe). However, as Li et al (2006) suggest “this condition needs to be carefully examined in future studies”. Therefore, differences in the cost of living, along with preferences and government policies, are likely to affect the total price that a tourist has to pay. Considering, that Europe supports a perfectly competitive economy model, discrepancies from policies have been reduced, and therefore CPI would constitute a valid approximation. Another variable used in a single equation analysis is the exchange rate which represents the price of the currency that a consumer faces. However, money units have no value to a consumer of a foreign country. What does matter however is the purchasing power that foreign currency has in the destination country. In practice, the ratio of CPI between the origin and destination country is used adjusted with the exchange rate as suggested by Martin and Witt (1987). Highlighting the fact that Europe in the new millennium has a common currency, differences in CPI would represent the difference in price tourists will have to pay among the various European Union destinations.


57

To sum up, tourists, when referred to in this paper, are assumed to be informed of the prices of some basic goods, best approximated by CPI, adjusted by the exchange rate to both choose and plan their spending on a destination.

Expenditure AIDS estimation requires the existence of a “representative consumer”, see Deaton and Muellbauer (1980b). Therefore, expenditures per capita should be estimated and used for the model. As White (1985) argues, population should be used as a measure because the Department of Commerce records every trip as a new “traveler”. Population growth is implicitly considered a factor in single equation analysis, see Prescott et al (2005). The expenditure on this dataset was simply the sum of the money spend by each traveler in each country visited as reported on the survey. Therefore, and in contrast to most studies mentioned above, the data consists of values reported from each traveler rather than aggregate tourist receipts which can suffer from serious bias. Papatheodorou (1999) used the tourism population as a representative consumer. However, we feel that the “representative” consumer should represent all Canadians and not just those who travel. So, the budget allocation and choices, used in this paper, refer to all Canadians in order to estimate their demand.

Omitted Variables As mentioned above, transportation costs, although used as a determinant factor by some authors, see Martin and Witt (1987), are hard to measure and usually excluded in the majority of the recent studies. Price fluctuations and the general decrease of costs owing to scale economies, argue for their exclusion from the model. Syriopoulos and Sinclair (1993) notes the difficulty of making an appropriate index, but it can also be argued that for a transatlantic trip, where the duration of the vacations would be expected to be at a maximum due to the high traveling costs, the differences in traveling to or between the countries would be expected to least affect decisions, compared to the cost of living for example or the choice of traveling overseas in the first place. Similarly, while trends, dummies and other variables have been used in early studies (indicating sporting activities, political incidents or exchange rate shocks), in the present study we tested for their presence (such as the beginning of the use of Euro in 2000, the Olympic Games, the Euro-Cup in soccer or the 2001 decrease of the total tourism resulting from the tragedy of 11/9). Since no significant effects were found and since we only have a limited number of observations we have not included them in the model. Furthermore, dealing with a short-period sample, weakens the argument for the use of a time trend.

Empirical Results Before proceeding to the estimation, it is important to go through the restriction tests that have been used in the literature for the AIDS model. On table 1 the restriction

58


tests7 on symmetry and homogeneity are reported. It can easily be seen that the performance of the model with the restrictions when the leisure demand is estimated is better. Therefore, the representative Canadian consumer’s preferences are more consistently captured when his choices are among the European countries for leisure tourism. Moreover, the case of the visiting-purpose demand is not tested for homogeneity and symmetry due to the rejection of negativity assumption as indicated by the sign of the own price coefficients and elasticities of the model. The problem might occur from the fact that, the share of Spain was relatively too low for the model estimated. Therefore, for checking the applicability of the AIDS in this case, the model was estimated including Netherlands instead while in the appendix the estimates with only Germany, France, Italy, UK and Others groups are given. Table 1. Restrictions Tests for the tourist demand models including Spain

Homogeneity and Symmetry T1 (F-value) T2 (Chi-square values)

Aggr/ate. Demand 1.49** 22.382**

Leisure Demand 1.1863* 17.79*

B. Demand 2.34** 32.8426899

where *,** represents statistic significance at 1% and 5% respectively

Another test used from Papatheodorou (1999) and so it is used here as well, is the generalized R2 measure. This measure is defined as

E'E R 2= 1 − y'y where the nominator is the determinant of the residuals cross products matrix and the denominator is the generalized variance of the dependent variables matrix Y of the examined system. Our values are all high ( R 2=0.999) therefore no more exploration is necessary concerning the fit of the model. Both generalized and conventional R2 give acceptable values8. Concerning the autocorrelation issue, the reduced degrees of freedom would lead to a wide range of uncertainty (0.30-2.70 approximately) for the values of DW statistics to indicate any degree of autocorrelation. Moreover, in a system analysis, autocorrelation might be captured among the equations. Tests for autocorrelation in such a small sample result in test statistics with poor size and power characteristics. Following Papatheodorou’s (1999) testing procedures, a residuals first order vector autoregression is run for the estimated demands and the results are in the appendix. No significant t-value for a first order 7

8

At this point it is useful to report that homogeneity and symmetry restrictions were not rejected using the conventional Wald statistics, though this would be expected, with the exclusion of the homogeneity restriction in Italy. As Muellbauer and Deaton (1980a) observed that the imposition of homogeneity assumption might result in positive serial correlation. By simply observing Italy’s pattern it is expected that positive serial correlation exists, therefore the imposition of the restriction is over-rejected. Therefore, the two sample size corrected statistics’ use is justified and the low values depict the good performance of the model. Wth the exception of leisure demand only the R2 value of Spain is relatively low. Estimates showed that the imposition of symmetry condition for UK and Spain decreases the fit of the model. The existence of serial correlation as well as the big difference of the budget shares between these two countries can partly account as an explanation for this.


59

autocorrelation was found. While signs of serial correlation exist, they exist on the most problematic part of the model which is the business demand case.

The Case of the Aggregate Demand9 The first estimation concerns the aggregate case. All Canadians visiting Europe are being examined for the period 1990-2005. Figures 7 and 8 give the budget shares of each destination. Observing the figures, Italy’s growing budget shares is one of the most noticeable observations in the dataset, starting from 6.6% at 1990 and reaching at 13.5% at 2005. The ‘Others’ group’s budget is having an approximately constant trend (something generally .40 .36 .32 .28 .24 .20 .16 .12 1990 1992 1994 1996 1998 2000 2002 2004 BSHFRA

BSHUK

BSHOTH

Figure 7. Budget shares of France, UK and Others group for the aggregated demand estimation. .14 .12 .10 .08 .06 .04 .02 1990 1992 1994 1996 1998 2000 2002 2004 BSHGER

BSHSPA

BSHITA

Figure 8. Budget shares of Germany, Spain and Italy for the aggregate demand estimation.

9

Note that the Others group has not been examined in any of the parts since its estimates have no practical value as it groups a total of 15 countries.

60


convenient since any changing patterns are not properly interpretable in a group of countries, so inelastic behavior gives the least effect in the rest of the estimates of the model), while Germany appears to lose the highest share as a response to Italy’s rise. However, in the period from 2000-2005, it seems as though Italy and Spain both encountered a rise in Canadian choices, as apposed to the more traditional and larger countries, UK and France, which seem to have fallen back. An explanation for the increased spending might be found from the conversion to EURO. Signs, of increasing prices have been reported thus the higher expenditure might reflect this inflationary effect. Table 2 gives the estimates for the restricted aggregated demand estimation. Overall, as mentioned before, the model performs well with a suspicion for positive autocorrelation in the case of Italy and negative in the case of Germany. However, the values lie within the uncertainty band so there can be no further analysis on this issue. More data in the future, shall allow a dynamic specification of the model thus leading to more accurate estimates and a comparison with the present results would be interesting. Table 2. Restricted Aggregated Demand Estimation Results

coefficient

Spain

The Aggregated Demand Coefficients Germany France Italy

UK

α t-stat zSpain t-stat zGermany t-stat zFrance t-stat zItaly t-stat zUK t-stat zOthers t-stat(NA) β t-stat

-0.89043 -3.45654 -0.04532 -0.72607 0.074297 1.747104 -0.03236 -0.52287 -0.03599 -0.65844 0.007027 0.334073 0.032341 ----------0.055772 2.989987

0.133547 0.657055

0.9458 3.033724

-1.79425 -4.36347

2.225495 7.264299

-0.40286 -4.40819 0.45332 3.804569 -0.09799 -1.8947 -0.02581 -1.66287 -0.00096 ----------0.021224 1.734135

-0.48198 -3.00996 0.183434 2.657297 -0.1386 -5.57828 0.016191 -----------0.01053 -0.50769

-0.15935 -2.46861 0.066194 2.356792 0.0437 ----------0.097748 3.637316

-0.00377 -0.1132 0.094958 ---------0.19197 -7.04506

R2 DW

0.433057 1.458856

0.802949 2.298342

0.710456 1.955137

0.621946 0.704042

0.720413 1.298729

Table 3, reports the uncompensated own and cross price conditional elasticities as well as the expenditure ones denoted with H, as used in section 2. For the estimation of the elasticities the average budget share value is used. The dynamic specification of the model would allow short run estimation and that would be mostly important for the case of Italy. Papatheodorou (1999), used the last year’s budget share observation to report the elasticities and thus to get an estimate of the most recent observation. In this paper however, given the


61

short period examined, it is preferred to follow the model as it was initially proposed without dynamics. Table 3. The Aggregated Demand Income and Price Uncompensated Elasticities

Spain t-values Germany t-values France t-values Italy t-values UK t-values Others t-values H t-values

The Aggregated Demand Expenditure and Price Elasticities10 Spain Germany France Italy UK -2.04146 0.865845 -0.18041 -0.46574 0.049309 -1.49836 1.647873 -0.5073 -0.74585 0.75924 1.517193 -5.96565 2.57024 -1.2191 -0.03164 1.639903 -5.32146 3.812629 -2.05511 -0.71536 -0.91829 5.517737 -3.71705 1.911814 -0.3256 -0.68285 3.773987 -4.11136 2.411138 -4.53088 -0.88814 -1.22532 1.043252 -2.93122 0.257792 -0.74901 -1.93442 2.679764 -3.94644 2.916747 -0.23716 -0.40056 -0.7652 0.40004 -0.81974 -0.49301 -1.99969 -5.03373 1.161161 -7.1618 0.354762 -0.0866 0.108757 0.179534 0.466996 ----------------------------------------------2.213095 1.26049 0.940404 2.124666 0.40289 5.454748 8.391386 8.011319 6.871425 4.753525 11

In bold letters we highlight the elasticities that are statistically significant at the 5% significance level. In particular, the expenditure elasticities indicate France and UK as ‘necessities’. This makes sense since these two destinations are Canada’s two main origin countries. Therefore they have been both popular and also developed as the most popular transportation centers for Canadians visiting Europe. Hence, as it might have been expected, Mediterranean countries, Spain and Italy, have the highest expenditure elasticity indicating them as ‘luxury’ destinations for Canadians. An additional fact, accounting for this result, is that Mediterranean countries are mostly popular during the summer that is both a high season for tourism and when consumption is higher, resulting in higher spending by travelers. Finally, Germany is in between, possibly indicating higher quality transportation services than UK for example, thus, attracting higher income Canadians. A comparison of these results can only be made with O’ Hagan and Harrison’s (1984) research on US tourists in Europe. However, the different time period is expected to give different values. Concerning the expenditure elasticities, UK is inelastic in income changes as well (0.87), while France exhibits almost the same value (0.98). With the exception of Italy (0.51) the other values are pretty close (HSpain=1.99, HGermany=0.99) indicating a similar behavior though both the different composition of tourists from USA and the different time period would justify even higher differences. Possibly, this supports the transportation cost assumption made in this paper (the transportation cost is incorporated in the previous bundle stage of choice and thus the preferences of overseas visitors in Europe is similar). For the Italian difference in value 10

The t-statistics values are calculated with the following formulas: Var(Hi)=(1/wi)2Var(βi), Var (Eii)=(1/wi)2Var(γii)+Var( βi)-2(1/wi)Cov(γii, βi), Var(Eij)=(1/wi)2Var(γji)+(wj/wi)2Var( βi)-2[wj /(wi)2]Cov(γij, βi), 11 With the exception of Germany and Spain where the level of acceptance is around 7.5%.

62


though, the explanation should be sought in the increased immigration flow to United States from Italy as well as the relative underdevelopment of tourism industry in Italy, which, however has improved greatly in recent years. Once more, the need for both dynamic analysis and the use of more up-to-date data is highlighted. However, in the case of the own price elasticities differences are apparent. In the three out of four cases, where values are statistically significant (with the exception of Spain), the values are much higher than O’ Hagan and Harrison’s (1984) apart from UK’s which is pretty close (-0.72). Nonetheless, this can be justified. Leisure tourism demand, which accounts for approximately 50% of total tourism, has increased during the recent decades and since the response to price changes is expected to be higher in this case, the values reported here are as high. Moreover, the information “revolution” of the 90s resulting from the advance in personal computers and internet, as well as the growth of traveling services, both increased consumers’ knowledge of a country’s living costs. Consequently, a higher and swifter response to the price changes is justified. Finally, as it was mentioned earlier, with the development of local markets CPI is justified to be a better approximation of the price for tourism now than before, as it represents a more accurate view of costs. UK is the exception that deals with inelastic price-elasticity values. A possible explanation would be that demand for UK is already well established over time. This is expected to be verified by a large and significant constant term and a look at table 2 indeed, confirms this expectation. Therefore, France and Germany are expected to be intense competitors as well as to be affected by UK. The values of 2.5 and 5.5 of the cross price elasticities illustrate this fact, while the positive sign confirms the assumption that these countries are substitute destinations for Canadians. Moreover, France similarly to UK exhibits a positive and significant constant term in contrast to Germany so as to justify their difference. Further examination of the cross price elasticities indicates, rather surprisingly, that the most popular destinations, UK and France, are complements rather than substitutes while the same holds for Germany and UK. Reduced transportation cost from UK, as mentioned above, to the other large countries probably strengthened the voyage effects between UK and these two countries. The exception is Italy, which is a complement destination with Germany, justified from strong voyage effects as well. Most probably, the characteristic effect dominates in the case of Italy and France, therefore they are substitute destinations. Being both among the largest cultural centers in Europe, Canadians seem to choose between the ‘city of light’, Paris, and the ‘eternal city’ Rome, great fashion and historical site centers. Finally, though marginally significant, Spain functions as a substitute for Germany, while the sign of the other non-significant values implies, as expected, complementarity with the other countries.

The Demand for Leisure Tourism Similarly to the aggregated case, the budget share patterns are depicted in figures 9 and 10. The differences, as expected, are not big with the exception of UK. After 1997 there seems to be a significant decrease. This decline can be partly attributed to the “Open Skies” deregulation of the European Union, that took place in 1996, and allowed Ryan Air and Easy Jet as local UK airlines to expand to European Union destinations. This impact, along with many more characteristics of this period, is obviously important in the leisure tourism case.


63

.36

.32

.28

.24

.20

.16 1990 1992 1994 1996 1998 2000 2002 2004 BSHOTHHOL

FRANCE

UK

Figure 9. Budget shares of Others group, France Spain and and UK for the leisure tourism case.

.20

.16

.12

.08

.04

.00 1990 1992 1994 1996 1998 2000 2002 2004 SPAIN

ITALY

GERMAN

Figure 10. Budget shares of Germany, Italy for the leisure tourism case.

Table 4 reports the results from the estimation of the restricted model in the case of leisure tourism. As expected, Spain’s higher share along with an expected improvement leads to the better response of this model to the theory, that is, prices and income explain more accurately the movements in the demanded shares, expressed in higher R2 and better DW values.

64

coefficient α t-values Spain t-values Germany t-values France t-values Italy t-values UK t-values Others t-values β t-values R2 DW

Panagiotis Panagopoulos and Thanasis Stengos Table 4. The leisure tourism demand estimation The Leisure Demand Coefficients Spain Germany France Italy -0.7394 -0.19005 0.525002 -1.18338 -2.14798 -0.95555 1.999668 -3.27747 -0.07014 -0.49134 -0.03507 -0.46244 -0.68194 -5.49078 0.09282 0.638257 -0.65854 1.12163 5.928584 -4.45827 -0.01149 -0.12094 0.053074 -0.01907 -0.09747 -2.14428 0.676613 -0.16482 0.020717 -0.00964 -0.1115 0.086164 0.514807 -0.48958 -3.58094 2.395919 0.003156 -0.01017 -0.01411 0.012255 -------------------------------------0.05915 0.00261 -0.02739 0.128347 3.05285 0.253961 -1.78065 7.313453 0.67259 0.736875 0.727499 0.925014 1.576104 2.647243 2.350628 1.19742

Uk 2.21675 9.858344

0.00784 0.199701 0.006427 -----------0.1618 -6.31968 0.755059 2.369056

Table 5. The elasticity values for the leisure demand case


The Leisure Demand Expenditure and Price Elasticities Spain Germany France Italy UK -2.18023 -0.69783 0.458006 -0.17641 0.10878 -2.50914 -0.68272 1.125372 -0.16424 0.757744 -0.60823 -10.1625 3.098995 -1.15166 -0.0052 -0.74145 -6.09442 5.948609 -2.26149 -0.07284 1.288465 12.63179 -4.16316 0.240273 -0.27551 0.978912 5.924159 -5.83507 0.340926 -2.37855 -0.2882 -2.40119 0.271819 -1.30071 0.367065 -0.1538 -2.15522 0.725635 -1.25482 3.010414 0.063098 -0.20568 -0.50261 0.449916 -0.81055 0.095106 -0.5129 -3.08861 1.320486 -5.25777 -0.22033 -0.21627 -0.03036 -0.22149 0.186125 -----------------------------------------1.945434 1.051698 0.867308 2.160083 0.429291 6.281905 5.166358 11.63891 13.61766 4.753701

In addition to the above the comparison of the elasticities reported in Table 5 suggests that Germany’s unity expenditure elasticity as well as France reduced value agree even more accurately with O’Hagan and Harrison’s (1984) results which are in agreement with the leisure model. Another striking observation is Germany’s and France’s increase in the elasticity values. In particular, the latter is closer to the aggregate elasticity values; however


65

Germany’s response to the changes of prices has doubled, while Germany’s response to the change of France’s price is four times larger. Generally, responsiveness to own price would have been expected to increase in leisure demand. Italy however, seems to be closely to unity elastic for leisure, indicating a significant decrease. This would represent the establishment of Italy as a popular leisure destination for Canadians. Both being “luxury” and popular leisure destinations, Spain and Italy’s reduced price elasticity is justified. Finally, UK’s pattern is as reported earlier quite stable.

The Demand for Business Tourism As already mentioned in this paper, data sets reporting on the “purpose of trip” are hard to find. The approach initially is the same; the Canadian consumer receives an income and is allocating his budget for traveling abroad for business purposes. As argued earlier, prices reflect the level of the exhibitions and trade fairs, conferences, corporate events and meetings, just as prices reflect the level of leisure services. The debate on selecting the price in this case can be more intense since business spending is more narrowly defined relative to leisure tourism. However, the countries that are studied are of the most developed in the European Union and therefore CPI would seem to work better as an approximation compared to smaller ones. A further argument is that, big hotel organizations have expanded their services in most big countries so that the differences in their prices (especially when countries are under a similar economic policy, that of EU) are most likely to follow a similar pattern with the CPI’s of the other EU countries. .4

.3

.2

.1

.0 1990

1992

1994

BSHOTHBUS

1996

1998

2000

BUSBSHITA

2002

2004

BUSBSHSPA

Figure 11. Budget shares of Italy, Spain and the group of others for the business tourism case.

66

Panagiotis Panagopoulos and Thanasis Stengos .36 .32 .28 .24 .20 .16 .12 .08 1990 1992

1994 1996

BUSBSHUK

1998

2000 2002

BUSBSHGER

2004

BUSBSHFRA

Figure 12. Budget shares of Germany, France and UK for the business tourism case.

A closer examination of the budget shares patterns in this case as well is useful. Differences with the previous patterns overall are not that large, justifying the previous argument that advanced countries are expected to have similar growth patterns in most economic sectors, as these arise from externalities in both production and consumption. So, figures 11 and 12 reveal that the patterns indicate a smaller reduction of the UK’s share, a much smaller increase of the Italian share while Germany’s has both increased in volume (approximately 15%) and there is no trend; what is observed though, is a decline for the years 1995-1999, following an analogous increase for the years 1999-2003. It can be suggested that the European Union common currency establishment in 1999 had a positive effect, since after a pick (1.44 to Canadian dollars) at Germany’s exchange rate in 1992, the decrease of approximately 50% four years later indicates a reaction to Germany’s high living costs. Table 6 reports the estimations of the restricted model, where R2 and DW values are overall, satisfactory. However, these estimates were obtained after relaxing the symmetry restriction for the case of UK and Spain. From an econometric point of view, this might be justified with the reduced budget share of Spain in this model. From a theoretical point of view however, relaxing this restriction would imply that the response of Canadians visiting UK to the change of prices of Spain is not similar to their response when visiting Spain and the prices of UK are changing. A look at the cross price elasticities for UK and Spain seem to confirm the last point. That is while UK seems to work as a complement to Spain, the latter’s elasticity sign indicate that UK is a substitute destination. A possible explanation can be given from the differences in each country’s growth and development. UK as a world leading business center is expected to work mostly as a complement to the rest of the European destinations. That is the case, with the exception of Italy. It seems that the voyage effects in this case, especially considering that the origin country is Canada, overcome the complementarity effects as derived from UK’s wide range of business activities.


67

Table 6. The business tourism demand estimation

Coefficient Α t-values Spain t-values Germany t-values France t-values Italy t-values UK t-values Others t-values Β t-values R2 DW

Spain 0.160305 0.493606 0.040334 0.458556 0.212069 2.367311 -0.29979 -2.37083 0.059301 0.86722 0.048513 1.618637 -0.06043 --------0.004776 0.240742 0.174828 1.623239

The Business Demand Coefficients Germany France Italy -0.63872 1.278906 -1.4781 -1.36771 2.167836 -4.08801

-0.41991 -1.54033 0.588816 1.666009 -0.37125 -3.06164 -0.02877 -0.77325 0.110992 ---------0.01346 -0.47329 0.437703 1.475781

-0.54738 -1.17868 0.433339 2.756163 -0.09496 -2.32652 -0.08003 --------0.00264 -0.09052 0.541909 1.8491

-0.21369 -2.62186 0.129704 4.612048 -0.0374 --------0.088954 4.709819 0.721182 1.569578

UK 0.529696 1.096285 -0.34367 -4.58421

-0.05772 -0.98365 0.395412 ---------0.10385 -2.59829 0.641571 1.088918

Table 7. The elasticity values for the business demand case


The Business Demand Expenditure and Price Elasticities Spain Germany France Italy UK 0.188571 1.610737 -1.70604 0.778597 -1.19347 0.072504 2.385448 -2.35495 0.804281 -4.48856 6.255763 -4.16905 3.353821 -5.29705 -0.05286 2.360244 -2.01929 1.668967 -1.0835 -0.40171 -8.89454 4.480608 -4.11334 5.777297 -0.26916 -2.37574 1.671318 -1.55432 2.655503 -1.86246 1.7443 -2.80638 2.467871 -4.04447 0.481421 0.860946 -3.05153 2.754515 -3.58118 4.347595 1.395062 -0.18895 -0.53626 1.443254 -1.09868 1.550525 -0.6623 -2.30029 3.667836 -5.34174 -1.83046 0.871954 -0.45102 -0.88793 1.497106 -----------------------------------1.141306 0.897978 0.984966 2.230305 0.635647 1.94433 4.165968 5.930661 8.538003 4.532976

Germany’s elasticity values are smaller than the aggregate demand case and much smaller than the leisure demand case. In accordance with the estimates given in the appendix for the model excluding Spain, it seems that apart from Italy, Germany and France’s elasticities are not significant (therefore both are inelastic to the price changes. Italy in this case as well, seems to be a complement destination while that is not the case for Spain and

68


France. An explanation possibly accounting for these differences as well has to do with the nature of production of these countries. For example, German and Italian cars are considered as high class cars imported to Canada, while France and Spain are mostly known to Canadian consumers for fashion products. The values for France are different as well. The largest difference accounts for the relationship with Italy. Simply stated these two countries seem to be strong competitors for Canadian business tourism. The reduced t-values for the relationship with Germany might reflect the latter’s pattern as a strong business candidate for Canadians. What is more, France’s cross price relationships seem to exceed its own price pattern that identifies inelastic behavior with Germany. A quick look at table 6 confirms the implication of a strong constant pattern. Indeed, France has a significant and relatively large constant term value. Therefore, results overall imply the need for France to cater for the relationships with Spain as well as to be on the look out for the competition from Italy and Germany; with an even higher value, the same implication holds for Italy. Finally, concerning the expenditure elasticities, only the reduced value of Spain is worth mentioning. In particular, the elasticity value is close to unity with a reduced t-value. Possibly, Spain business interactions with Canada are to be considered rather as a necessity than a luxury. The luxurious production line of Italy along with the increased leisure popularity probably justifies its classification of a “luxury” destination. Witt and Turner (2001) along with Kulendran and Wilson (2000) papers, present some results from the estimation of business tourism. The latter using Granger-causality and cointegration tests, find a significant relationship between international trade and international travel. Along with these results, Turner and Witt (2001) using structural equation techniques, while they confirm that trade is an important parameter for business tourism they get low R2 values for the business tourism model. What is more, the relative price coefficient is only significant for the US demand case and with a positive sign. These results are contradicted12 by Prescott et al (2005) with an R2 value close to 0.75 and with a negative coefficient to the price, and seem to match the results in our paper as well. One more observation that needs reporting, is the high standard error in the price coefficient and thus the two estimated ownprice elasticities have low t-values. The present fills the gap by estimating the cross-price elasticities, which seem to be an important determinant in business travel.

The AIDS Model for the Visiting-Friends (VF) Consumers The visiting-friends tourism demand has been estimated from the authors mentioned above, see Prescott et al (2005), Witt and Turner (2001). Similarly to the leisure case, estimates seem to account for a negative relationship with prices that have a strong effect in the model. What is more, Prescott et al (2005) highlights the significant effect of the immigrant population of this model. Considering this literature so far, it is to be seen whether similar results are to be found with the estimation of an AIDS model. Figures 13 and 14 give the patterns for the visiting-friends budget shares for Canadians traveling to Europe. What stands out in the budget shares patterns in this case, is the increased 12

An explanation probably accounting for the difference between the estimates could be the time period estimated in the present and Prescott’s paper, 1990-2005, to 1978-1995 for the Witt and Turner paper.


69

budget share of UK (around 50%) and the decreased budget share of Spain (around 2%). In the estimated model, with the possible existence of serial and autocorrelation, the estimates for Spain are not acceptable. Thus the negativity conditions are violated and the performance of the model is not acceptable. A fact that can partly account for this problem is that, migrant population to Canada from Spanish the years examined is reduced. The most popular origin countries are the ones examined in this paper, along with Greece, Portugal and Netherlands. As an example, UK accounts for the 17% of the migrant population while Netherlands for 2%. Prescott et al (2005) compute a significant effect of immigration to tourism; what is more, Spain’s emigrants to Canada are low compared to the other destinations, thus, the reduced acquired budget share of Spain in VF-traveling purpose is justified. .55 .50 .45 .40 .35 .30 .25 .20 1990 1992 1994 1996 1998 2000 2002 2004 VISBSHUK

BSHOTHVIS

Figure 13. Budget shares of UK and Others group in VF demand. .14 .12 .10 .08 .06 .04 .02 .00 1990

1992

1994

1996

VISBSHSPA VISBSHITA

1998

2000

2002

2004

VISBSHGER VISBSHFRA

Figure 14. Budget shares of France, Spain, Italy and Germany in VF demand.

70


In order to get proper estimates, the problem was redefined for the top five popular destinations in the VF purpose travel. As a consequence, the model is estimated including Netherlands, that accounts for 5% of the expenditures budget on behalf of Canadians. Figure 15 presents the budget shares patterns including Netherlands instead of Spain. As it might have been expected, the other patterns are similar to the case where Spain was included. The important difference is that the share of Netherlands is higher and comparable to the rest. Overall the five equations examined in this case account for the 76% of the total budget shares. .14 .12 .10 .08 .06 .04 .02 1990

1992

1994

1996

VISBSHNET VISBSHITA

1998

2000

2002

2004

VISBSHGER VISBSHFRA

Figure 15. The VF budget shares including Netherlands.

Table 8. The estimates for the VF tourism demand case including Netherlands

coefficient α t-values Netherlands t-values Germany t-values France t-values Italy t-values UK t-values β t-values R2 DW

Netherlands 0.550501 2.264899 -0.12522 -1.61909 0.178473 2.350384 -0.03971 -0.37454 0.055099 1.586592 -0.04864 -1.97951 -0.02013 -1.18932 0.518439 2.001268

The VF Demand Coefficients Germany France Italy -0.5052 -0.56976 -0.42417 -1.55803 -1.48069 -1.81344

UK 1.925713 7.321448

-0.74509 -4.46383 0.673065 3.155927 -0.14497 -2.2573 -0.01878 -0.62502 0.065898 2.973242

-0.73935 -2.38618 0.094166 1.065524 -0.0639 -1.87253 0.073047 3.731024

-0.04194 -1.194 0.055368 3.098689 0.036231 2.692901

0.022453 0.637221 -0.16653 -5.72717

0.670076 2.700925

0.828103 2.66435

0.557243 2.090248

0.744755 2.285012


71

Table 8 reports a satisfactory fit of the AIDS model in this case. Moreover, the restriction value test gives a Chi-square of 13.4, which accepts the symmetry and homogeneity restrictions. Table 9 reports the elasticity values. Table 9. The VF tourism elasticities with Netherlands

Netherlands t-values Germany t-values France t-values Italy t-values UK t-values H t-values

The VF Demand Expenditure and Price Elasticities Netherlands Germany France Italy UK -3.27326 2.317773 -0.49643 0.974924 -0.08099 -2.31677 2.310376 -0.41411 1.532771 -1.49251 3.296426 -10.9412 7.582978 -2.71088 -0.01274 2.368001 -4.94393 3.131667 -2.29853 -0.18723 -0.69487 8.843845 -9.47155 1.669691 -0.10084 -0.35738 3.127212 -2.68851 1.0292 -1.4216 1.02919 -1.96906 1.024453 -1.80586 0.131979 1.611089 -2.30765 1.019806 -2.7876 3.270277 -0.71077 -0.67541 -1.13099 0.691494 -0.78749 -1.50829 -1.62043 -2.83092 2.009603 -12.2355 0.631278 1.873405 1.829767 1.664948 0.658944 2.036211 6.377482 8.227496 6.742648 11.0653

The inclusion of Netherlands instead of Spain prevents the direct comparison with the previous estimates. However, and in accordance with the previous literature, the pattern of VF is similar to the leisure case, with similar R2 values and significant own-price effects. UK’s elasticities, though higher, still represent an inelastic behavior. Two main differences are obvious, the higher expenditure elasticities of both Germany and France as well as the latter’s higher price-elasticity values. The implication of these observations is that, for visitingfriends purpose, Canadians seem to give more importance to their income and the prices of these destinations. A possible explanation is that, for VF travel, consumers are informed more precisely on the cost of living from their friends and what is more, they value more the income they are planning to spend in a country most familiar to them. Therefore, elasticities are even higher from the aggregate case.

Concluding Remarks In this paper an Almost Ideal Demand System (AIDS) was estimated for tourism in Europe from Canadians. The period examined, is characterized by many important economic changes in Europe region, resulting from the integration of 1990, the common currency established in the last six years and increased international trade of goods and services that is implied by the formation of an economic union that led to the growth of domestic tourism. The main effect for Canadians traveling to Europe, highlighted in this paper, is their increased response to prices. In other words, their demand for visiting more European countries is increased so that price changes play a bigger role in their decisions. Another issue examined in this paper is the demand “on purpose of travel”. Results showed that elasticities can vary greatly. With the exception of UK, that proves to have a

72


solid demand from Canadians by being a “necessity” and inelastic to prices, the rest of the countries have different estimates on purpose of travel. In this context, it is worthy highlighting Italy’s lowest own-price elasticity in the leisure demand case while Germany has the highest values. One more interesting result indicates Spain and France being business partners while on the contrary, they are tourism competitors. However, the low t-values in own price elasticities poses the question for an inelastic pattern for business. A further point, that is not usually highlighted and seems to be very important in a demand system, is the way that the separability assumption is introduced by the researcher. There are two cases, where the estimation of the true demand for a particular market is efficient. First is the Hicksian separability and second the functional separability. The first suggests that the movement of prices of the relevant products should be proportional so that their relationship is to remain constant. Prices reflect preferences, so if one is to study the preferences for a market, this condition is required for obtaining estimates that are consistent with economic theory. In the tourism context, the methodology used to estimate the price of the tourism product using CPI and exchange rates seems plausible. When using different prices, the chances that prices do not satisfy the Hicksian seperability, are higher (as mentioned, local market prices can vary since there are regional advantages, especially in the context of tourism). In a globalized market with such a well functioning financial system and under conditions of improving mobility and perfect information one can argue that CPI satisfies Hicksian separability. The composite commodity theorem states that a parallel move in prices is sufficient for the aggregation of these commodities. Thus, it is essential when choosing a group of countries studied to be as closely related and “separable” to the highest degree possible from the other countries or commodities that form the corresponding stage budgeting. We also tested different assumptions on stage budgeting, like the ones used in the previous literature for the demand for Mediterranean countries, Syriopoulos and Sinclair (1993) and the estimated model failed to pass the restriction tests. However, the low degrees of freedom, along with the possible existence of serial correlation prevented a deeper analysis on this issue. It goes without saying though, that further attention and research on tourism should be devoted to this issue. The second case is functional separability, which assumes that preferences between a set of two commodities, are independent. This is a strong assumption and needs special attention. Many studies focused on a region including a group of countries (like Mediterranean mentioned before in Syriopoulos and Sinclair (1993) and Papatheodorou (1999)) assuming functional separability. However, it is questionable whether the decision for visiting Mediterranean countries excludes neighboring countries. This might be the case for some consumers while not for others. Therefore, results depend not only on the acceptance of the demand restrictions but also on the acceptance of these two conditions. The methodology of grouping the rest of the countries that are a part of a market was convenient both theoretically and practically, since demand system estimation requires the exclusion of one equation. Finally, an issue arising in this paper is on disaggregating consumers. AIDS was based on aggregation theory developed earlier by Deaton (1974). However, empirically it is highly probable that the estimated representative consumer is not well defined. The examined dataset is most likely to give exact estimates of the representative consumer. Future research should involve the use of a larger data set, the dynamic structure of demand, while identifying the


73

effect of the European Union policies on the demand for European tourism not only within European countries but also in a global context among the various continental destinations.

Appendix The model estimations without Spain. Therefore, approximately 65% of total tourism is estimated on 4 countries and 35% approximately on the other countries. With the exception of the business demand, the values are pretty close to the ones estimated on the paper. Table 10. The aggregate demand estimation without Spain The AG. Demand Coefficients coefficient α t-values zGermany t-values zFrance t-values zItaly t-values zUK t-values β t-values R2 DW

Germany

France

Italy

Uk

-0.05245 -0.22966 -0.33984 -3.43545 0.410208 3.187291 -0.05471 -1.31288 -0.03342 -2.3962 0.036687 2.596572 0.752002 2.108216

0.429403 1.310348

-1.352441 -2.609894

2.518899 7.820461

-0.425073 -2.505757 0.091918 1.62304 -0.131904 -6.175894 0.00349 0.158172 0.672749 1.839628

-0.114392 -1.871742 0.114815 4.15968 0.07884 2.619818 0.707336 0.751145

-0.03051 -0.94192 -0.18745 -5.9247 0.672468 1.185018

Table 11. The elasticity estimates for the aggregate demand The AG. Demand Income and Price Elasticities

Germany t-values France t-values Italy t-values Uk t-values H t-values

Germany

France

Italy

Uk

-5.20772 -4.29168 4.955071 3.136606 -0.71059 -1.38272 -0.55487 -3.03214 1.450273 8.363191

-3.40902 -3.55318 0.518457 1.599298 -0.75281 -5.70106 1.01975 8.165658

-1.31617 -1.84449 1.029404 3.022952 1.907115 5.507866

-0.90746 -8.03844 0.416952 4.236911

74

Panagiotis Panagopoulos and Thanasis Stengos Table 12. The leisure demand estimation without Spain coefficient

α t-values zGermany t-values zFrance t-values zItaly t-values zUK t-values β t-values R2 DW

Germany 0.048054 0.192481 -0.54123 -5.84067 0.627831 5.273068 -0.10615 -2.5116 0.01465 0.671802 0.001775 0.11651 0.58266 1.439383

The L. Demand Coefficients France Italy 0.830808 -2.22885 3.322251 -5.29438

UK 2.117509 6.881963

-0.73681 -4.73264 0.193156 3.926017 -0.13652 -5.20296 -0.00117 -0.06682 0.702677 1.352805

-0.02076 -0.454 -0.17205 -5.7719 0.803909 1.981241

-0.1753 -3.18563 0.070171 2.160698 0.155262 6.866965 0.880868 0.875681

Table 13. The elasticity estimates for the leisure demand

Germany t-values France t-values Italy t-values UK t-values H t-values

Germany -11.7223 -6.39183 12.42869 5.26871 -2.10655 -2.50841 0.280216 0.623446 1.035159 3.430743

The L. Demand Income and Price Elasticities France Italy

-4.56861 -6.05909 0.936448 3.910122 -0.65982 -4.70835 0.994351 11.75892

-2.73969 -5.46268 0.236391 0.754025 2.403358 11.76022

UK

-0.90119 -4.92593 0.393137 3.739142

Table 14. The business demand estimation without Spain coefficient α t-values zGermany t-values zFrance t-values zItaly t-values zUK t-values β t-values R2 DW

Germany 0.543806 1.028097 -0.18138 0.268715 -0.03135 -0.06728 0.021624 0.151835 -0.07652 -2.04864 -0.03127 -0.99079 0.321307 1.238567

The B.. Demand Coefficients France Italy 0.193022 -0.79369 0.29718 -1.61887

UK 1.681519 3.028257

-0.28668 0.251028 -0.0394 -0.21643 -0.12487 -3.03875 0.007669 0.128072 0.433808 2.013166

0.012933 -0.21963 -0.10999 -1.99523 0.24893 0.645965

-0.04479 -0.13889 0.085822 2.312219 0.078288 2.675374 0.626949 1.164084

An Almost Ideal Demand System (AIDS) of Canadian Tourism to Europe Table 15. The elasticity estimates for the business demand


The BUS. Demand Income and Price Elasticities Germany France Italy UK -2.34339 -0.53834 -0.19595 -2.63961 -0.0359 -0.51167 0.181026 -0.22741 -1.69773 0.134213 -0.17752 -1.60787 -0.51243 -0.72329 0.878376 -0.84464 -1.75888 -2.97346 2.113945 -4.15713 0.762994 1.043656 2.082785 0.614101 3.417755 5.722824 6.606408 3.618224

Table 16. The VF demand estimation without Spain coefficient α t-values zGermany t-values zFrance t-values zItaly t-values zUK t-values β t-values

Germany -0.26055 -0.75829 -0.6333 -3.58114 0.696878 3.050855 -0.12304 -1.77293 -0.0019 -0.06747 0.050954 2.215182

R2 DW

0.653322 2.521661

The VF Demand Coefficients France Italy -0.38013 -0.44359 -0.95701 -1.68974

UK 1.95186 7.141767

-0.82788 -2.76805 0.130302 1.431476 -0.08344 -3.21372 0.070267 3.638984

-0.05284 -1.35614 0.062403 3.44592 0.032311 2.228521

0.010654 0.318396 -0.16564 -5.68717

0.816167 2.459722

0.517447 2.077235

0.747091 2.578464

Table 17. The elasticity estimates for the VF demand


The VF Demand Income and Price Elasticities Germany France Italy UK -9.44462 -4.02892 9.176897 -10.4744 3.030516 -3.08232 -1.66758 1.436656 -2.00199 -1.80836 1.386541 -2.78364 -0.35494 -1.33757 0.855735 -0.81255 -0.90695 -4.39133 2.449523 -13.5947 1.675339 1.798188 1.593004 0.660773 5.495322 8.19807 5.986577 11.07792

75

76

Panagiotis Panagopoulos and Thanasis Stengos Restrictions test are calculated using the formula:

tr (Λ r ) −1 (Λ r − Λ u ) T1 =

π

u

tr (Λ r )(Λ ) (n − 1)( N − k )

where, π is the number of restrictions, Λ is the residuals covariance matrix of the restricted and unrestricted model, n is the number of equations of the system, N the number of observations and k the number of estimated variables in each equation. T1 follows an F(π, N − k ) distribution while excluding π, T2 gives a Chi-square distribution with π degrees of freedom.

Testing Residual Autocorrelation Results Table 18. Residuals Autocorrelation Regression for AG. Demand RESID05

RESID06

RESID07

RESID08

RESID09

RESID05(-1)

1.049776

-0.03894

0.194393

0.678464

-1.37544

st.error

-0.56979

-0.4005

-0.65199

-0.91733

-0.99377

t-value

[ 1.84240]

[-0.09724]

[ 0.29816]

[ 0.73961]

[-1.38406]

RESID06(-1)

-1.17099

-0.36083

1.267213

-0.21398

0.080335

st.error

-0.58043

-0.40798

-0.66416

-0.93446

-1.01233

t-value

[-2.01745]

[-0.88442]

[ 1.90799]

[-0.22899]

[ 0.07936]

RESID07(-1)

0.5137

-0.11969

-0.11488

0.082456

-0.53541

st.error

-0.4464

-0.31377

-0.51079

-0.71867

-0.77856

t-value

[ 1.15077]

[-0.38145]

[-0.22490]

[ 0.11473]

[-0.68769]

RESID08(-1)

-0.3367

-0.20896

0.09743

0.289815

0.077157

st.error

-0.31377

-0.22055

-0.35903

-0.50515

-0.54725

t-value

[-1.07308]

[-0.94747]

[ 0.27137]

[ 0.57372]

[ 0.14099]

RESID09(-1)

0.333155

-0.12756

0.200042

-0.05528

-0.35961

st.error

-0.37626

-0.26448

-0.43055

-0.60577

-0.65625

t-value

[ 0.88543]

[-0.48229]

[ 0.46462]

[-0.09125]

[-0.54797]

0.361165

0.133359

0.38582

0.442357

0.27163

2

R

An Almost Ideal Demand System (AIDS) of Canadian Tourism to Europe Table 19. Residuals Autocorrelation Regression for L. Demand RESID05

RESID06

RESID07

RESID08

RESID09

RESID05(-1) st.error t-value RESID06(-1) st.error t-value RESID07(-1) st.error t-value RESID08(-1) st.error t-value RESID09(-1) st.error t-value

-0.32626 -0.4406 [-0.74047] -0.44913 -0.78326 [-0.57341] -0.18141 -0.40651 [-0.44627] -0.65188 -0.47193 [-1.38132] -0.07515 -0.26237 [-0.28641]

0.327278 -0.1893 [ 1.72890] -0.18128 -0.33651 [-0.53869] 0.433013 -0.17465 [ 2.47934] 0.009177 -0.20275 [ 0.04526] 0.201841 -0.11272 [ 1.79057]

0.481381 -0.4468 [ 1.07738] -0.47316 -0.79428 [-0.59571] -0.32453 -0.41223 [-0.78725] 0.179072 -0.47857 [ 0.37418] -0.18912 -0.26607 [-0.71079]

0.586408 -0.39214 [ 1.49540] 0.266029 -0.6971 [ 0.38162] -0.04739 -0.36179 [-0.13099] 0.784797 -0.42002 [ 1.86848] -0.16812 -0.23352 [-0.71994]

-0.79306 -0.74535 [-1.06401] -0.53852 -1.325 [-0.40643] -0.70005 -0.68767 [-1.01800] 0.166718 -0.79834 [ 0.20883] -0.60067 -0.44385 [-1.35333]

R2

0.238771

0.597344

0.250033

0.377315

0.324588

Table 20. Residuals Autocorrelation Regression for B. Demand RESID05

RESID06

RESID07

RESID08

RESID09

RESID05(-1)

0.134109

-0.02804

0.865943

-0.52102

0.648912

st.error

-0.47179

-0.504

-0.43288

-0.55135

-1.13336

t-value

[ 0.28425]

[-0.05563]

[ 2.00044]

[-0.94499]

[ 0.57256]

-0.2338

-0.26504

0.210905

0.237354

-0.10751

RESID06(-1) st.error

-0.27733

-0.29626

-0.25445

-0.32409

-0.6662

t-value

[-0.84303]

[-0.89461]

[ 0.82886]

[ 0.73237]

[-0.16137]

RESID07(-1)

0.319525

0.582222

-0.27062

0.025989

-0.18727

st.error

-0.24013

-0.25652

-0.22032

-0.28062

-0.57685

RESID05

RESID06

RESID07

RESID08

RESID09

t-value

[ 1.33064]

[ 2.26968]

[-1.22829]

[ 0.09261]

[-0.32464]

RESID08(-1)

0.485702

0.090042

-1.03921

0.189281

-0.67991

st.error

-0.31254

-0.33388

-0.28676

-0.36524

-0.7508

t-value

[ 1.55404]

[ 0.26969]

[-3.62396]

[ 0.51823]

[-0.90558]

RESID09(-1)

-0.09532

-0.53467

-0.28862

0.01502

0.248289

st.error

-0.15987

-0.17078

-0.14668

-0.18682

-0.38403

t-value

[-0.59627]

[-3.13076]

[-1.96771]

[ 0.08040]

[ 0.64653]

0.335662

0.658973

0.745575

0.139181

0.242345

2

R

77

78

Panagiotis Panagopoulos and Thanasis Stengos Table 21. Residuals Autocorrelation Regression for VF. Demand

RESID05(-1) st.error t-value RESID06(-1) st.error t-value RESID07(-1) st.error t-value RESID08(-1) st.error t-value RESID09(-1) st.error t-value R2

RESID05 0.613461 -0.37694 [ 1.62746] 0.382494 -0.30708 [ 1.24557] 0.937185 -0.33134 [ 2.82848] 0.029054 -0.31653 [ 0.09179] 0.476273 -0.25932 [ 1.83663] 0.466334

RESID06 0.516743 -0.59327 [ 0.87101] -0.42547 -0.48332 [-0.88032] 0.033351 -0.52149 [ 0.06395] 0.305293 -0.49818 [ 0.61282] 0.160893 -0.40814 [ 0.39421] 0.25736

RESID07 -0.67113 -0.44982 [-1.49200] 0.083062 -0.36646 [ 0.22666] -0.56028 -0.3954 [-1.41699] 0.171325 -0.37772 [ 0.45357] -0.1587 -0.30946 [-0.51285] 0.329039

RESID08 0.483577 -0.34914 [ 1.38507] 0.247055 -0.28443 [ 0.86860] 0.260129 -0.3069 [ 0.84761] -0.13554 -0.29318 [-0.46233] 0.293608 -0.24019 [ 1.22240] 0.18595

RESID09 -0.25192 -0.87064 [-0.28935] -0.05367 -0.70929 [-0.07567] -0.26169 -0.76531 [-0.34195] -0.50349 -0.7311 [-0.68868] -0.32542 -0.59896 [-0.54332] 0.080737

The Derivation of the AIDS Specification The expenditure function, e(u,p), which is defined as the least amount of money necessary for reaching utility level u at prices p is the basis of the AIDS and is given by:

ln e( u, p) = α o +

∑ α o ln p k + 12 ∑ ∑ γ *ij ln p k ln p j + uβ o ∏ p βk k

k

k

(1)

k

j

With α, β, and γ∗ are parameters. The demand functions are derived as the derivatives of (1), these are the Hicksian demands. Differentiating with respect to pi results in the share equations:

∂ log e ( u , p ) ∂ e ( u, p ) . pi pi ci = = = Wi ∂ log pi ∂ pi .e ( u, p ) e ( u, p )

(2)

where Wi is the share of the i-good of the total expenditure. In particular,

∂ log e(u, p ) = α i + ∑ λij log p j + β o β i u∏ pkβ ∂ log pi k k

k

(3)

where

λ ij =

1

2

(γ

* ij

+ γ *ji

)

(4)


79

Hence Hicksian demands are:

ci =

e( u, p) ⎛ ⎜αi + p i ⎜⎝

∑ λ ij log p j + β oβ i u∏ p βk k

k

k

⎞ ⎟⎟ ⎠

(5)

With income=expenditures from (1) ⇒

ln y = α o +

∑ α k ln p k + 12 ∑ ∑ γ *ij ln p k ln p j + uβ oβ i ∏ pβk k

k

k

k

j

and solving for utility ⇒

u=

αo +

log y βo

∏

β p kk

∑ α k ln p k + 12 ∑ ∑ γ *ij ln p k ln p j k

−

βo

∏

k j β p kk

(6)

k

k

(2),(6) ⇒

⎛ αo + ∑αk ln pk + 12 ∑∑γ ij* ln pk ln p j ⎞ ⎜ ln y ⎟ k k j − Wi = αi + ∑λij ln p j + βo βi ∏ pkβk ⎜ βk βk ⎟ βo ∏ pk j k ⎜ βo ∏ pk ⎟ k k ⎝ ⎠ hence, budget shares are expressed as a function of prices and income, (Marshallian demands)

⎛ y⎞ Wi = α i + ∑ λij ln p j + β i ln ⎜ ⎟ ⎝P⎠ k where, ln P = α o +

∑ α k lnp k + 12 ∑ ∑ γ *ij ln p k ln p j k

k

j

References Baldwin M. A., Hadid M., Phillips, G. D. A., 1983, “The estimation and testing of a system of demand equations for the UK”, Applied Economics, 15. Bigano A., J. M. Hamilton, M. Lau, R.S.J. Tol and Y. Zhou, 2004, “A global database of domestic and international tourist numbers at national and subnational level”, Working Paper, FNU-54. Chalfant J. and J. M. Alston, 1988 , “Accounting for Changes in Tastes”, The Journal of Political Economy, V96.

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Chambers, M. J., 1990, “Forecasting with demand systems: A comparative study”. Journal of Econometrics, 44. De Mello M. and F. Natercia, 2005, “Testing Alternative Dynamic Systems for Modeling Tourism Demand”, CETE-Research Center on Industrial, Labour and Managerial Economics. De Mello M. M., Pack A. and T.M. Sinclair, 2002, “A system of equations model of UK tourism demand in neighbouring countries”, Applied Economics, V.34. Deaton A. and J. Muellbauer, 1980. Economics and Consumer Behavior, Cambridge University Press. Deaton A. and J. Muellbauer 1980, “An Almost Ideal Demand System”, American Economic Review, 70. Deaton A., 1974, “The Analysis of Consumer Demand in the United Kingdom, 1900-1970”, Econometrica, V.42. Divisekera S., 2003, “A model of demand for international tourism”, Annuals of Tourism research, Vol. 30. Durbarry R. and T.M. Sinclair, 2003, “Market shares analysis: the case of French Tourism demand”, Annuals of Tourism Research, Vol. 30. Golan A., Perloff J. M., Shen E. Z.,2000, “Estimating a demand system with Nonnegativity constraints: Mexican Meat Demand”, manuscript. Kulendran N., K.Wilson, 2000, “Is there a relationship between international trade and international travel?”, Applied Economics, 2000, 32. Lanza A., P. Temple and G. Urga, 2003, “The implications of tourism specialization in the long run: an econometric analysis for 13 OECD economies.”, Tourism Management 24. Li G., H. Song and S. F. Witt, 2003, “Modelling Tourism Demand: A Dynamic Linear AIDS Approach”. Li G., H. Song and S. F. Witt, 2005, “Recent developments in Econometric Modelling and Forecasting”, Journal of Travel Research. Li G. H. Song and S.F. Witt, 2006, “Time varying parameter and fixed parameter linear AIDS: An application to tourism demand forecasting”, International Journal of Forecasting 22. Lyssiotou P., 2000, “Dynamic analysis of British demand for tourism abroad”, Empirical Economics, V.15. M.artin A. C. and S.F. Witt, 1987, “Tourism Demand forecasting models: Choice of appropriate variable to represent tourists’ cost of living”, Tourism Management, V.8, (3). O’Hagan J.W. and M.J. Harrison, 1984, “Market shares of US tourist expenditure in Europe: an econometric analysis”, Applied Economics V. 16. Papatheodorou A.,1999, “The demand for international tourism in the Mediterranean region”, Applied Economics, 31. Paraskevopoulos, G.,1977, “An Econometric Analysis of International Tourism”. Athens, Greece: Center of Planning and Economic Research, Lecture Series 3 I. Prescott D., D. Wilton, C. Dadayli and A. Dickson, 2005, “Travel to Canada: the role of Canada’s immigrant populations”, Applied Economics, 37. Shannon, C. E., 1948, “A mathematical theory of communication”, Bell System Technical Journal 27. Silver J.L., Ali M. M., 1989, “Testing Slutsky symmetry in systems of linear demand equations”, Journal of Econometrics 41.


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Syriopoulos T.C. and T.M. Sinclair, 1993, “An econometric study of tourism demand: the AIDS model of US and European tourism in Mediterranean countries”, Applied Economics, 25. Tiffin R. and K. Balcombe, 2002, “Testing symmetry and homogeneity in the AIDS with cointegrated data using fully-modified estimation and the bootstrap”., working paper University of Reading and Imperial College. White K. J., 1985, “An international travel demand model US travel to Western Europe”, Annals of Tourism Research, V.12. Witt S. F. and L. W. Turner 2001, “Factors influencing demand for international tourism: tourism demand analysis using structural equation modeling, revisited”, Tourism Economics, 7. Zellner A., 1962, “An efficient method for estimating seemingly unrelated regressions and test for aggregation bias.”, Journal of the American Statistical Association, V.57.



Chapter 4

SINGLE MOTHERS IN EUROPE: A DECOMPOSITION APPROACH Libertad González Universitat Pompeu Fabra, Spain

Abstract This chapter explores the sources of the large cross-country variation in the incidence of single mother families in Europe. I describe the prevalence of these families in each country over the period 1994-2001, using data from the European Community Household Panel. Then I analyze the contribution to the overall cross-country variation of its different demographic components: marriage rates, cohabitation, divorce, co-residence, marital and non-marital fertility, re-partnership, etc. The analysis suggests that the variation in the prevalence of single mothers across countries can be attributed to differences in mainly three demographic rates: non-marital fertility, divorce rates, and co-residence. Other factors that were potentially relevant a priori turn out not to be significant, such as marriage rates, cohabitation patterns, marital fertility, or the number of widows.

Keywords: single mothers, marriage, fertility, divorce.

1. Introduction Single mother households have become an increasingly frequent family type in many industrialized nations. This trend has been very pronounced in countries like the United States and the United Kingdom, attracting a great deal of attention from researchers and policy makers.1 The main concern is that single motherhood seems to be associated with poverty and negative outcomes for children.2 1 For recent research on the prevalence of single mothers in the US, see Blau et al. (2004), Neal (2004), Schmidt (2003), Moffitt (2000), Rosenzweig (1999), Hoynes (1997), Akerlof et al. (1996). See Del Bono (2004) for a recent study on pre-marital fertility in Britain. See Burdett and Ermisch (2002) and Willis (1999) for theoretical models of the formation of single mother families. 2 Lerman (1996), McLanahan & Sandefur (1994), Krein & Beller (1988).

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However, not much research has been devoted to understanding what drives the large cross-country variation in the prevalence of single mother families.3 In Europe, the differences across countries are substantial. In 2001, single mothers headed 18 percent of all households with children in Sweden and 15 percent in the UK, compared with 2 percent in Spain, Portugal or Greece. Moreover, the evolution of the number of single parent families over time also varies considerably across Europe. Between 1994 and 2001, some countries experienced a significant decline in the prevalence of single mothers (Denmark, Finland), while it increased in some (The Netherlands) and it remained stable in others (Ireland, Spain). This paper explores the demographic trends behind the large cross-country variation in the prevalence of single mother families in Europe. First I describe the incidence of these families in each country over the period 1994-2001, using data from the European Community Household Panel (ECHP). Then I analyze the contribution to the overall variation of its different components: marriage rates, cohabitation, divorce, co-residence, marital and non-marital fertility, re-partnership, etc. Understanding which of these components are driving the international variation may have relevant policy implications, especially if European Union countries aim to coordinate their family policies. We may think that large differences in the number of young, unmarried mothers would have different implications compared with a large variance in the number of older widowed mothers. Differences in non-marital fertility versus divorce rates may suggest different policies regarding child support enforcement. It is also possible that marriage and fertility decisions may turn out to be less important than decisions about co-residence and household headship, arguably easier to modify through economic incentives. A simple variance decomposition analysis suggests that the variation in the prevalence of single mothers across European countries can be attributed to differences in mainly three demographic rates: non-marital fertility, divorce rates, and co-residence. Other factors that were potentially relevant a priori turn out not to be significant, such as marriage rates, cohabitation patterns, marital fertility, or the number of widows. The remainder of the paper is organized as follows. Section 2 describes the data and the main variables. The analysis is performed using ECHP data for all 8 waves and 15 countries. The next section discusses the number of single parent families in the 15 European countries, as well as its evolution during the 8 years included in the survey. Section 4 presents the variance decomposition analysis and shows the main results. A final section summarizes the findings and concludes.

2. Data and Variables The data set used in the analysis is the European Community Household Panel (waves 1 through 8), spanning from 1994 until 2001.4 This data set is the best available option for international comparisons in Europe, as the same survey was conducted in all 15 (preenlargement) European Union countries. Its main shortcomings are the short time period

3 Gonzalez (2005) looks at the economic determinants of the prevalence of single mothers in 17 countries using Luxembourg Income Study data for the 1980’s and 1990’s. Gonzalez (2006) studies the effect of benefit systems on the incidence of single mothers in Europe using ECHP data. 4 The data start in the second wave for Austria, the third for Finland, and the fourth for Sweden.

Single Mothers in Europe: A Decomposition Approach

85

covered, and the low sample sizes at the country level once we restrict the population of interest. I define a single mother as an unmarried woman aged between 18 and 55 living on her own with her dependent children younger than 18. There are many issues involved in settling on a specific definition of single mother households. In particular, we need to determine the age limit for the children, whether to include single fathers, the marital status of the head, whether to include cohabitants as single, and whether to include single parents who are coresiding with other relatives, such as the grandparents of the children. The main definition, presented above, includes only mothers whose children (in the household) are all below 18, who are not married, not cohabiting with a partner, and not co-residing with other relatives. However, sensitivity analyses are performed using alternative definitions, and especial attention is devoted to the role of co-residence. According to the above-mentioned definition, the number of single mothers in the sample is 10,204. The number of observations per wave varies between 1,150 and 1,387. By country, sample sizes go from 265 in Greece to 1,601 in the UK. Obviously, some country-year sample sizes are very small. This is especially true for Luxembourg and Greece (less than 50 observations per wave), and less so for Spain, Ireland and Portugal. This should be kept in mind when interpreting confidence intervals. The ECHP provides a different definition of single parents, which includes households composed only of a parent and her/his own children under the age of 16. This differs from our definition in the age limit for the children and in that it does not require the head to be unmarried.5 The ECHP also provides two alternative definitions that include single parents with children aged between 16 and 24, and only economically active children between 16 and 24, respectively. I addition to the four above-mentioned definitions, I consider two additional ones that identify households where single parents may be co-residing with older children or other relatives (the grandparents of the children, etc).

3. The Prevalence of Single Mothers in Europe, 1994-2001 The number of single mother families varies a lot across European countries. The present section documents this stylized fact, while section 4 investigates the sources of the international variation. I pay special attention to the definition of single mother, and show whether (and how) the ranking of countries is altered when we modify age limits and coresidence requirements. I also report the number of single parents (both including and excluding single fathers) as a proportion of all households, of all households with children, and of all women.

3.1. The Number of Single Mothers in Europe in 2001 Figure 1 shows the number of single parent households as a proportion of all families with children under 18, by country, in 2001. The ranking of countries is not altered if we use 5

Thus possibly including families where the husband (or wife) is absent although not separated.

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Libertad González

16 as the age limit for the children, or if we calculate the proportion with respect to all households.6 The prevalence of single mothers is very low in Greece, Spain, Portugal and Italy (less than 4% of all families with children). It is very high in Sweden and the United Kingdom (more than 16%), followed by The Netherlands.

0,20 0,18 0,16 0,14 0,12 0,10 0,08 0,06 0,04 0,02 Lu x e Ital y m bo ur g D en m ar Ire k la n Fi d nl an Be d lg iu m Fr an ce G er m an Au y st N ri e U the a rl a ni te nd d s Ki ng do Sw m ed en

Sp a Po in r tu g G al re ec e

0,00

Note: ECHP data, Wave 8 (year 2001). Household weights have been used. Single parent households defined as those composed exclusively of one parent and her/his dependent children, all of them younger than 18. 95% confidence intervals are shown.

Figure 1. Single Parent Households as a Proportion of all Families with Children.

The analysis will focus on single mothers.7 The ordering of the countries does not change when we calculate the number of single mothers as a proportion of all women (aged between 18 and 55). Table 1 displays these proportions using the ECHP definition (with age limit 16 for the children), our main definition, and two additional ones that include single mothers that co-reside with other relatives (only older children or any other co-residents).8 Less than 2% of Spanish, Greek, Portuguese or Italian women head a single mother household, compared with more than 7% in the UK and Sweden. The ranking of countries changes when we allow for the presence of older children or other co-residents (columns 3 and 4). Two things are worth noticing here. First, the numbers if allowing for the presence of older children will be influenced by the cross-country variation in the age when children emancipate. Since this is not the main object of the study, we will focus on families with at least one child younger than 18, who tend to live with the parent(s) in all countries.

6

These tables are available upon request. Only about 10% of all single parents are single fathers. 8 Standard errors are available upon request. 7


87

Table 1. Number of single mothers as percentage of all women 18-55

Spain Greece Portugal Italy Luxembourg Denmark France Finland Germany Belgium Austria Ireland Netherlands United Kingdom Sweden

(1) 0,72 0,84 0,79 1,19 2,07 2,24 2,69 2,64 3,07 2,84 2,7 3,31 4,21 6,49 6,45

(2) 0,87 0,97 1,08 1,08 2,12 2,38 2,81 2,88 3,13 3,37 3,49 3,67 5,01 7,1 7,2

(3) 1,53 1,56 1,75 1,71 2,98 2,77 3,57 3,2 3,59 4,35 4,21 4,63 5,85 8,36 8,05

(4) 3,09 2,07 5,81 2,08 3,92 2,77 4,11 3,38 4,27 4,6 6,01 10,66 5,91 9,48 8,05

Note: ECHP data, Wave 8 (year 2001). Person weights have been used. Single mothers defined as: (1) Women living alone with their own children, all of them younger than 16. (2) Unmarried women living alone with their own children, all of them younger than 18. (3) Unmarried women living alone with their own children, at least one younger than 18. (4) Unmarried women living with their own children (at least one younger than 18) and without a partner (but maybe with other co-residents).

Second, co-residence with other relatives (such as the grandparents of the children) may be an interesting source of variation, thus we will pay attention to the differences across countries in this respect. At first sight, including single mothers in co-residence causes some relevant changes in the ordering of countries. Ireland has now surpassed the UK and Sweden, while at the bottom end Denmark ranks below Spain, and well below Portugal. Thus the headship decision may be an important factor to take into account when searching for the sources of the crosscountry variation. In Ireland, for instance, less than half of all single mothers live by themselves with their children, compared with virtually all Swedish or Danish single moms.

3.2. The Changes in the Number of Single Mothers, 1994 To 2001 The evolution of the number of single mothers over time was also remarkably different amongst the countries in the sample. Figure 2 shows the number of single mothers as a proportion of all women 18 to 55, for all 15 countries and all waves.9 The same four countries at the bottom (Portugal, Greece, Spain and Italy) remain below 2% during the whole period. The UK and Sweden are always at the top, with 6.5 to 7.5 of women being single mothers.

9

The corresponding numbers for the remaining five definitions of single mothers are available upon request, as well as the proportions with respect to all households and all households with children. Standard errors are also available.

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Libertad González

About 6% of women were single mothers in Denmark and Finland in the first and third waves, respectively, but both countries experienced a marked decline over the period. 8% 7% 6% 5% 4% 3% 2% 1% 0% 1994

1995

Spain Luxembourg Belgium Finland

1996

1997

Italy Netherlands France Sw eden

1998

1999

Greece Austria Germany United Kingdom

2000

2001 Portugal Ireland Denmark

Note: ECHP data, all waves. Person weights have been used. Single mothers defined as unmarried women living on their own with their dependent children, all of them younger than 18.

Figure 2. Single Mothers as a Percentage of All Women 18 to 55.

The only country that experienced a significant increase (at the 95% confidence level) was The Netherlands, where the prevalence of single mothers doubled (from 2.5% in 1994 to 5% in 2001). Four countries (Denmark, Finland, France and Germany) suffered significant declines, while the remaining 10 did not experience a significant change. The decline was particularly striking in Denmark and Finland, where the prevalence of single mothers was reduced by more than half.10 Including single mothers in co-residence does not change the main patterns. We still observe significant declines in Denmark, France and Finland, and a significant increase in The Netherlands. However, the observed increase is now significant in Portugal (from 3.7 to 5.8%) and Ireland (from 8.5 to 10.7%).11

4. Decomposing the Cross-Country Variation Single mother families are heterogeneous in many dimensions, one of the most obvious being the marital status of the head. There are three main inflows into single motherhood: an unmarried woman has a child on her own, a married mother gets separated or divorced, or she becomes a widow. Thus, a first step in decomposing the variation across countries involves determining which of these groups can account for the observed disparities. 10

The same trends are observed if the analysis is performed with respect to all households or all households with children. 11 The increase in Ireland is significant only at the 90% confidence level.


89

4.1. Single Mothers by Marital Status Formally, the proportion of women who are single mothers can be decomposed into the number of never married mothers, plus separated or divorced, plus widowed mothers:

P(single mother) = P(never married mother) + P(divorced mother) + P(widowed mother).12

(1)

Figure 3 shows these proportions for all 15 countries in 2001. In all countries, widows are the least frequent type of single mother (between 0 and .5% of women). In 12 countries, divorced mothers are the most prevalent group,13 while never married mothers are more numerous in Ireland, Sweden and France.14 The prevalence of divorced mothers in 2001 varies from .6% of all women in Spain, Portugal and Greece to 3.9% in the UK. As for never married mothers, the proportions diverge from 0 in Greece to 3.5% in Sweden. 5,0%

Never Married

Separated or Divorced

Widow ed

4,0% 3,0% 2,0% 1,0%

Sp a Po in r tu g G al re ec e Ire la nd Lu I x e tal y m bo ur g Fr an D ce en m ar Au k st ria G er m an Fi y nl an Be d lg iu Sw m e N et d en he U r ni te l an ds d Ki ng do m

0,0% -1,0%

Note: ECHP data, Wave 8. Person weights have been used. Single mothers defined as unmarried women living on their own with their dependent children, all of them younger than 18. Observations with missing marital status excluded. 95% confidence intervals are shown.

Figure 3. Single Mothers as a Percentage of All Women, by Marital Status, 2001.

The incidence of never married mothers increased in 9 out of the 15 countries between 1994 and 2001. The increase, however, was only significant in The Netherlands, while Denmark and Finland experienced a significant decline. The proportion of divorced mothers increased only in 6 countries, and the increase was only significant in The Netherlands. There were significant declines in four countries. The changes in the number of widows were small and insignificant in all countries.

12

In short, SM=N+D+W. Divorced mothers are more prevalent than never married at the 95% confidence level only in Spain, Greece, Belgium and The Netherlands. 14 Never married mothers are more prevalent than divorced at the 95% confidence level only in Ireland. 13

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Libertad González

Thus, divorced mothers are the most prevalent group, but never married mothers are the faster-growing one. It seems clear that the three categories of single mothers follow different paths across countries and over time, and thus deserve separate attention. We intend to find out which of the three components are responsible for the reported cross-country variation in the prevalence of single mothers. The proportion of widows shows only a small, not significant correlation with the overall prevalence of single mothers in the last wave (.31, but only .17 if we include all waves). The correlation with the prevalence of both divorced and never married mothers is strong and significant (.91-.92 and .88-.89, respectively), and it remains essentially unchanged whether we include only the last wave or all of them. An alternative measure of how much of the overall variation in the prevalence of single mothers can be attributed to each subgroup is the covariance between the overall prevalence and each specific component, divided by the variance in the overall prevalence of single mothers.15 The advantage of this measure over the raw correlations is that it accounts for the magnitude of the contribution of each component, as well as for the closeness of fit. According to this measure, the prevalence of divorced mothers contributes 50 percent to the overall variance in 2001 (54 in 1994), while the prevalence of never married mothers accounts for 48 percent of the variance in the overall prevalence of single mothers (45 in 1994). When analyzing the changes over time, the results are alike. The change in the proportion of widowed mothers between 1994 and 2001 shows a small positive correlation with the change in the overall prevalence of single mothers, while the correlation is higher for divorced (0.91) and never married mothers (0.87). Divorced mothers account for 52 percent of the overall variance, while never married mothers contribute by 45 percent. The same analysis is performed using the definition of single mothers that includes those co-residing with older children, own parents, or other relatives. This definition increases the variance in the overall prevalence of single mothers by 37 percent. Now the disparity in the number of divorced mothers contributes by 42 percent to the overall variation, while the number of never married mothers contributes by 57 percent. Since both the proportion of never married and divorced mothers account for a large fraction of the cross-country variation, the following subsections analyze in depth the trends behind these two components. Note that the analysis is not stratified by age or any other demographic characteristics. This would be desirable, but its feasibility is limited by the small sample sizes in some countries once we disaggregate by marital status.16

4.2. Never Married Mothers In order to analyze the sources of the variation in the proportion of women who are never married mothers across countries, note that we can interpret this proportion as an estimate of the probability that a woman aged 18 to 55 is never married and lives by herself with her own children, all under 18 years of age, in a given country and year. A simple decomposition will allow us to analyze the contribution of the cross-country variation in marriage rates (defined as the probability that a woman aged 18 to 55 has ever been married), non-marital fertility 15

Note that Var(SM) = Cov(SM,N) + Cov(SM,D) + Cov(SM,W). See fact a) in the appendix.


91

(the probability that a never married woman has children), cohabitation (the probability that a never married woman is cohabiting with a partner), and co-residence (the probability that an unmarried woman with children is co-residing with other relatives). Following the law of total probability, we can decompose the probability that a woman is a never married mother (N) as: N= P(never married, with children, not cohabiting with a partner, not in co-residence) =

(2)

P(nm, k, np, nc) = P(nm) P(k, np, nc | nm) = P(nm) P(k | nm)P(np, nc | nm, k) = P(nm) P(k | nm)P(np | nm, k)P(nc | nm, k, np). Where nm stands for “never married”, k for “children under 18”, np for “not cohabiting with a partner”, and nc for “not in co-residence”. Thus, the probability that a woman is a never married mother is a function of the probability of being never-married, P(nm); the probability of having children, conditional of being never-married, P(k | nm); the probability that an unmarried mother is not cohabiting with a partner, P(np | nm, k); and the probability that a never married, non-cohabiting mother is not in co-residence, P(nc | nm, k, np). These probabilities are estimated for each country in all waves (see table 2 for last wave decomposition). Table 2. Decomposition of Never Married Mothers as a Proportion of All Women, 2001 country Denmark Netherlands Belgium France Ireland Italy Greece Spain Portugal Austria Finland Sweden Germany Luxembourg United Kingdom Average Variance

16

P(nm) 0,296 0,328 0,300 0,377 0,443 0,357 0,306 0,427 0,351 0,336 0,367 0,356 0,323 0,327 0,311 0,3470 0,0019

See section 3 for sample sizes.

P(k, np, nc | nm) 0,032 0,039 0,023 0,039 0,058 0,006 0,000 0,003 0,009 0,042 0,027 0,100 0,041 0,020 0,089 0,0351 0,0008

P(k | nm) 0,264 0,145 0,118 0,224 0,209 0,028 0,001 0,047 0,122 0,146 0,144 0,254 0,099 0,093 0,229 0,1415 0,0067

P(nc, np | nm, k) 0,123 0,267 0,196 0,173 0,277 0,216 0,000 0,065 0,076 0,287 0,185 0,392 0,410 0,217 0,387 0,2180 0,0150

P(np | nm, k) 0,131 0,297 0,234 0,240 0,841 0,525 0,000 0,657 0,649 0,604 0,208 0,397 0,617 0,377 0,514 0,4195 0,0549

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Libertad González Table 2. Continued

country P(nc | nm, k, np) P(np | nm) P(nc, k | nm, np) P(k | nm, np) Denmark 0,939 0,482 0,067 0,072 Netherlands 0,899 0,556 0,070 0,079 Belgium 0,838 0,796 0,029 0,035 France 0,719 0,726 0,053 0,075 Ireland 0,329 0,945 0,061 0,186 Italy 0,412 0,958 0,006 0,013 Greece 0,000 0,954 0,000 0,000 Spain 0,099 0,951 0,003 0,033 Portugal 0,117 0,925 0,010 0,086 Austria 0,475 0,845 0,050 0,102 Finland 0,887 0,694 0,038 0,043 Sweden 0,987 0,683 0,146 0,160 Germany 0,665 0,847 0,048 0,072 Luxembourg 0,575 0,777 0,026 0,044 United Kingdom 0,752 0,709 0,125 0,166 Average 0,5796 0,7899 0,0489 0,0776 Variance 0,1070 0,0222 0,0018 0,0031 Note: ECHP data, Wave 8 (year 2001). Person weights have been used. “P” stands for proportion of women aged 18 to 55. “Nm” stands for never married, “k” for children, “np” for not cohabiting with a partner, and “nc” for not co-residing with other relatives.

In order to decompose the overall variance of the probability of being a never-married mother (N), I propose a first order Taylor expansion about the cross-country average of each of the conditional probabilities in 2001.17 This allows us to express the variance of the prevalence of never married mothers as an additive function of the variance of each component (see fact c) in the appendix). On average, 35 percent of women aged 18 to 55 had never been married in 2001, with a standard deviation of 4 (see column P(nm) in table 2). The cross-country variation in marriage rates accounts for barely 2% of the variance in the proportion of never married mothers. The variation in out-of-wedlock fertility is much larger (see column P(k | nm)). On average, 14 percent of never married women had a child in 2001, and the standard deviation 17

Let Nit be the probability that a woman aged 18 to 55 is never-married and lives on her own with her children younger than 18 in country i and period t. Let NMit = P(nm) be the unconditional probability that a woman is never-married and Kit = P(k, np, nc | nm) the probability that a woman lives on her own with her children younger than 18, conditional on being never-married. Note that equation (2) states that Nit = NMit Kit. Finally,

let

Xt

be

the

average

of

Xit

across

all

15

countries

in

period

t.

Then,

N it = NM it ⋅ K it ≈ NM t ⋅ K t + NM t ( K it − K t ) + K t ( NM it − NM t ). Therefore, the variance of N can be 2 2 Var ( N ) = Var ( NM ⋅ K ) ≈ NM t Var ( K ) + K t Var ( NM )

expressed as:

. Thus, the “contribution” of NM to the

2 K t Var ( NM ) Var ( N ) . The same procedure can be followed for each of the decompositions of N variance of N equals into conditional probabilities in (2) and (3).


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was 8 points. In Greece, this proportion was practically 0, while reaching 26 percent in Denmark. In fact, non-marital fertility contributes by about 40% to the overall variance. An alternative decomposition will offer a better framework for analyzing the contribution of cohabitation rates. Note that: P(nm, k, np, nc) = P(nm) P(k, np, nc | nm) = P(nm) P(np | nm) P(nc, k| nm, np) = P(nm) P(np | nm) P(k | nm, np) P(nc | nm, k, np).

(3)

About 21 percent of never married women were cohabiting with a partner on average, with a standard deviation of 15 (see column P(np | nm) in table 2). In Italy, Greece and Spain, less than 5% of never married women were cohabiting with a partner, compared with more than 50% in Denmark. The sign of the contribution of cohabitation rates to the variation in the number of single mothers across countries is not clear a priori. If cohabitation is a common alternative to marriage and separation from a cohabitant is easier than divorce, then higher cohabitation could lead to more never married (separated) mothers. On the other hand, in the event of a pregnancy, the absence of cohabitation as an alternative to marriage may lead to more women staying on their own. The numbers show a positive, but small correlation between cohabitation rates and the prevalence of unmarried mothers. Cohabitation accounts for just about 5 percent of the cross-country variation. The 15 countries also differ a great deal in the co-residence rates of unmarried mothers (see column P(nc | nm, k, np)). On average, 33 percent of never married women with children (and not cohabiting with a partner) live in the same household with relatives other than their children under 18, but this number fluctuates essentially from 0 to 100. In countries like Greece, all never married mothers are in co-residence, versus virtually none in Sweden or Denmark. This component accounts for about 40 percent of the cross-country variation in the number of never married mothers with children. In sum, the international variation in the prevalence of never married mothers (not cohabiting, and not in co-residence) in 2001 can be attributed mainly to differences in nonmarital fertility and co-residence, while marriage or cohabitation rates play virtually no role. We are also interested in the cross-country variation in the evolution over time of the number of never married mothers. The variance of the changes across countries between 1994 and 2001 can be decomposed in order to analyze the contribution of each of the components in equations (2) and (3). The decomposition requires a first order Taylor series expansion and uses fact d) from the appendix.18 Recall from section 4.1 that the incidence of never married mothers increased in 9 out of the 15 countries between 1994 and 2001. The increase, however, was only significant in The Netherlands, while Denmark and Finland experienced a significant decline. The decomposition shows that changes in non-marital fertility can account for about half of the cross-country variation in the changes of the incidence of never married mothers. The Netherlands, for instance, experienced a substantial increase in the proportion of never married women with children (from 5 to 15 percent). Changes in co-residence are also significant, accounting for almost 30 percent of the overall variation. This component is 18

The table with the different components by country for wave 1 is available upon request. The proportions for wave 8 are displayed in table 2.

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responsible for most of the decline in the number of never married mothers in Finland, since in 1994 only 11 percent of them were living with other relatives, while in 2001 the proportion in co-residence had risen to 50 percent (who were therefore not counted as single mother households).

4.3. Separated or Divorced Mothers A divorced mother is defined as a woman aged 18 to 55 who is either separated or divorced from her husband and lives by herself with her children, all younger than 18. Excluded are divorced mothers that cohabit with a new partner or with other relatives. We can decompose the probability that a woman is a divorced mother as follows: D = P(separated or divorced, with children, not cohabiting, not in co-residence) = P(d, k, np, nc) = P(d) P(k, np, nc | d) = P(d) P(k | d) P(np, nc | d, k) = P(d) P(k | d) P(np | d, k) P(nc | d, k, np).

(4)

Where d stands for “separated or divorced” and the rest are defined as in (2). An alternative decomposition that would allow us to focus on cohabitation rates would be the following: P(d, k, np, nc) = P(d) P(np | d) P(nc, k| d, np) = P(d) P(np | d) P(k | d, np) P(nc| d, k, np).

(5)

The proportion of women who are divorced is probably more informative conditional on women who have ever been married. Thus, we can also express the probability that a woman is a divorced mother as:

P(em) P(d, k, np, nc | em) = P(em) P(k | em) P(d, nc, np | em, k) =

(6)

where P(em) stands for the probability of having ever been married. As in the previous subsection, we can analyze the contribution to the overall variation in the prevalence of divorced mothers of marriage rates, P(em); divorce rates, P(d | em); marital fertility, P(k | em); re-partnership, P(np | d, k); and co-residence, P(nc | d, k, np). The estimated probabilities by country are presented in table 3. On average, 10 percent of ever-married women in the 15 European countries were divorced in 2001, with a standard deviation of 4. These proportions were lowest in Italy and Greece, with less than 5 percent, and highest in the UK with 19 percent. Divorce rates contribute to the overall variation in the prevalence of divorced mothers by 41%. For a given divorce rate, a higher number of marriages would result in more divorced women. The proportion of women that have ever been married is similar in all 15 countries, with an average of 65 percent and a standard deviation of 4. The variance decomposition analysis shows that marriage rates only contribute by 1% to the overall variation. Marital fertility can only account for 1.5% of the variation. A priori, we expect that more marriages having children would result in a higher number of divorced mothers (for given marriage and divorce rates). On average 60 percent of ever-married mothers have children,


95

with a standard deviation of 5. This component however turns out not to be very important. The contribution is also low if we focus on the likelihood that a divorced mother has children, P(k | d). The average is 48 percent and the cross-country dispersion is higher, but still the contribution to the overall variance is a mere 6-7%. Table 3. Decomposition of Divorced Mothers as a Proportion of All Women, 2001 country Denmark Netherlands Belgium France Ireland Italy Greece Spain Portugal Austria Finland Sweden Germany Luxembourg United Kingdom Average Variance Denmark Netherlands Belgium France Ireland Italy Greece Spain Portugal Austria Finland Sweden Germany Luxembourg United Kingdom Average Variance

P(d) 0,0516 0,0965 0,0919 0,0555 0,0457 0,0288 0,0343 0,0333 0,0392 0,0682 0,0888 0,0917 0,0816 0,0737 0,1303 0,0674 0,0009 0,7427 0,7712 0,7719 0,7373 0,8894 0,8567 0,9760 0,8584 0,8157 0,8169 0,7581 0,8113 0,7542 0,8162 0,7227 0,8066 0,0046

P(k|d) 0,4326 0,5428 0,4868 0,4814 0,6117 0,5683 0,4050 0,5044 0,5725 0,4788 0,3398 0,5148 0,3667 0,4108 0,5543 0,4847 0,0064 0,7044 0,6723 0,6998 0,6226 0,5570 0,6429 0,6938 0,5732 0,6489 0,6639 0,6335 0,6443 0,6770 0,6726 0,6886 0,6530 0,0019

P(k|d,np) 0,4484 0,5383 0,4448 0,4433 0,5821 0,5301 0,4129 0,4771 0,5618 0,4474 0,3227 0,5143 0,3527 0,3710 0,5270 0,4649 0,0061 0,5819 0,5966 0,5943 0,5981 0,7441 0,5939 0,5508 0,6377 0,6322 0,5983 0,5767 0,5955 0,5527 0,5768 0,5845 0,6009 0,0021

P(np | d, k) 0,7760 0,7649 0,7053 0,6790 0,8464 0,7698 0,9951 0,8176 0,8004 0,7686 0,7341 0,8104 0,7222 0,7561 0,6971 0,7762 0,0059 0,0732 0,1435 0,1313 0,0891 0,0821 0,0448 0,0494 0,0581 0,0605 0,1027 0,1403 0,1424 0,1206 0,1096 0,1892 0,1024 0,0018

P(nc | d, k, np) 0,8097 0,8256 0,7538 0,6737 0,3312 0,6443 0,4519 0,4474 0,3471 0,6875 0,8358 0,8054 0,8062 0,5269 0,7677 0,6476 0,0325 0,0544 0,1306 0,1075 0,0717 0,0675 0,0428 0,0363 0,0460 0,0548 0,0822 0,0826 0,1231 0,0800 0,0780 0,1794 0,0825 0,0015

Note: ECHP data, Wave 8 (year 2001). Person weights have been used. “P” stands for proportion of women aged 18 to 55. “Em” stands for ever-married, “d” for divorced, “k” for children, “np” for not cohabiting with a partner, and “nc” for not co-residing with other relatives.

Only 20 percent of divorced women are cohabiting with a new partner, on average (see column P(np | d)). The number is barely higher if we condition on divorced women with

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children (22 percent), and the cross-country variation is surprisingly low. Accordingly, repartnership can account for less than 2% of the cross-country variation in the prevalence of divorced mothers.19 Finally, 35 percent of divorced mothers not in cohabitation co-reside with other relatives, on average. The variation is large from country to country (standard deviation of 18). In Ireland and Portugal, more than 65 percent of divorced mothers live with relatives other than their own children, compared with only 16 and 17 percent in Finland and The Netherlands, respectively. Co-residence patterns in fact account for almost 20% of the overall variance. In sum, the cross-country variation in the prevalence of divorced mothers (not cohabiting, and not in co-residence) in 2001 can be attributed mainly to differences in divorce rates, with co-residence also playing a relevant role, while marriage rates, marital fertility, or cohabitation can account for very small fractions of the international disparities. The decomposition of the changes between 1994 and 2001 is also performed as in section 4.2.20 The variance of the change in the prevalence of divorced mothers can also be attributed mainly to changes in divorce rates (55%), followed by co-residence patterns (24%). Recall that the proportion of divorced mothers increased only in 6 countries, and the increase was only significant in The Netherlands. This country experienced a marked increase in divorce rates, so that the percentage of ever-married women who were divorced rose from 9 percent in 1994 to 14 percent in 2001, while co-residence of divorced mothers decreased substantially (from 27 to 17 percent). During the same period, Denmark, Finland, France and Germany experienced significant declines in the prevalence of divorced mothers. The fall in divorce rates was especially large in Denmark (from 19 to 7 percent of ever-married women)21, while co-residence increased substantially in Finland and France.

5. Conclusion The incidence of single mother families varies a great deal across European countries. In 2001, more than 16 percent of families with children were headed by a single parent in the United Kingdom and Sweden, compared with less than 4 percent in Portugal, Spain, Greece and Italy. The evolution of the prevalence of single mothers over time also followed different patterns during the 1990’s. Between 1994 and 2001, the proportion of women aged 18 to 55 who headed a single mother family doubled in The Netherlands, while this proportion fell significantly in Denmark, Finland, France and Germany, and it remain unchanged in Spain or the UK. This chapter is an attempt to understand the sources of the reported cross-country variation. I find that the variation in the prevalence of single mothers across countries can be attributed to differences in mainly three demographic rates: non-marital fertility, divorce rates, and co-residence. Other factors that were potentially relevant a priori turn out not to be

19

Note that we cannot distinguish first marriages from remarriages. The table with the different components by country for wave 1 is available upon request. The proportions for wave 8 are displayed in table 3. 21 This is confirmed by other statistical sources. Eurostat reports that divorces per 1,000 marriages in Denmark fell from 41 in 1993 to 37.5 in 2000. This was mostly a result of stable annual divorces per 1,000 population coupled with increasing annual marriages per 1,000 population. 20


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significant, such as marriage rates, cohabitation patterns, marital fertility, or the number of widows. The low number of single mothers in Spain, Portugal, Italy and Greece can be attributed to very low levels of non-marital fertility (less than 5% of never married women reported having children in 2001), very low divorce rates (less than 6% of ever married women were divorced), and high co-residence (more than 50% of never married mothers were not living by themselves with their children). The most extreme case is probably Greece, where just .5% of never married women had children in 2001 and essentially all of them were in coresidence, only 5% of ever-married women were divorced, and more than half of all divorced mothers were in co-residence. At the other end of the spectrum, the high prevalence of single mothers in Sweden is the result of high non-marital fertility (25% of never married women have children), very low coresidence (just 1% of never married mothers), and high divorce rates (14% of ever married women). The UK has higher co-residence, but very high divorce rates (19% of ever-married women in 2001). The number of single mothers doubled in The Netherlands between 1994 and 2001. This change can be attributed mainly to large increases in divorce rates and non-marital fertility. As for the marked decline in Denmark, a large fall in divorce rates stands out as a key culprit, while in Finland the decline can be attributed mostly to increasing co-residence for both never married and divorced mothers. It is worth pointing out that the analysis focuses on the rough trends, but leaves some variables out that might be potentially relevant. I do not analyze remarriage patterns since the data do not allow for detection of second (or third) marriages, which means that what looks like a decrease in divorce rates might be hiding an increase in remarriage. I do not look into differences in the number of children across countries. A woman with only one child will be a single mother for at most 18 years, while more children will potentially increase the length of the spell. Finally, I do not break down co-residence patterns, but it might be interesting to know whether single mothers are co-residing with their own children older than 18, the grandparents of the children, or other relatives. Once the main sources of the cross-country variation in the prevalence of single mothers have been identified, the next step would be to investigate whether there are economic variables behind those trends. Labor market conditions, benefit systems, and marriage markets have a potential to affect marriage and divorce, fertility and co-residence.

Appendix: Variance Decomposition (a) If x = y + z, then

Var ( x) =

[

]

1 N 1 N ( xi − X ) 2 = ∑ ( xi − X ) ( yi + zi ) − (Y + Z ) = ∑ N i =1 N i =1

1 N 1 N ( xi − X )( yi − Y ) + ∑ ( xi − X )( z i − Z ) = Cov ( x, y ) + Cov( x, z ) ∑ N i =1 N i =1

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Thus, the contribution of y to the overall variance of x equals

Cov( x, y ) . Var ( x)

(b) If xt = yt + zt, where t indicates a time period, Var (Δx) = Var ( xt + s − xt ) = Var ( xt + s ) + Var ( xt ) − 2Cov ( xt + s , xt ) = Cov( xt + s , yt + s ) + Cov( xt + s , zt + s ) + Cov( xt , yt ) + Cov( xt , z t ) − 2Cov( yt + s , yt ) − 2Cov( zt + s , zt )

Thus, the contribution of y to the overall variance of the change in x equals

Cov ( xt + s , yt + s ) + Cov ( xt , yt ) − 2Cov ( yt + s , yt ) Var ( xt + s ) + Var ( xt ) − 2Cov( xt + s , xt ) (c) c) If x=yz, then 2

2

Var ( x ) = Var ( yz ) ≈ Var (Y Z + Z ( y − Y ) + Y ( z − Z )) = Z Var ( y ) + Y Var ( z ). 2

Thus, the contribution of y to the overall variance of x equals Z Var ( y ) . Var ( x )

(d) d) If xt =yt zt, where t indicates a time period,

Var(Δx) = Var( xt + s − xt ) = Var( yt + s zt + s − yt zt ) ≈ 2

2

2

2

Z t + sVar( yt + s ) + Y t + sVar( zt + s ) + Z t Var( yt ) + Y t Var( zt ) − 2Cov( xt + s , xt ). Thus, the contribution of y to the overall variance of the change in x equals

2 2 Z t + sVar ( yt + s ) + Z t Var ( yt ) − 2Z t + s Z t Cov ( yt + s , yt ) Var ( xt + s ) + Var ( xt ) − 2Cov ( xt + s , xt )

References Akerlof, G. A., J. L. Yellen and M. L. Katz. 1996. “An Analysis of Out-Of-Wedlock Childbearing in the United States.” Quarterly Journal of Economics 111(2): 277-317. Blau, F., L. Kahn and J. Waldfogel. 2004. “The Impact of Welfare Benefits on Single Motherhood and Headship of Young Women. Evidence from the Census.” Journal of Human Resources 39(2): 382-404. Burdett, K. and J. F. Ermisch. 2002. “Single Mothers.” Institute for Social and Economic Research Working Paper 30-2002. University of Essex.


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Del Bono, Emilia. 2004. “Pre-Marital Fertility and Labour Market Opportunities: Evidence from the 1970 British Cohort Study.” IZA Discussion Paper No. 1320. Gonzalez, Libertad. 2005. “The Determinants of the Prevalence of Single Mothers: A CrossCountry Analysis.” IZA Discussion Paper 1677. Gonzalez, Libertad. 2006. “The Effect of Benefits on Single Motherhood in Europe.” Forthcoming, Labour Economics. Hoynes, H. W. 1997. ''Does Welfare Play Any Role in Female Headship Decisions?'', Journal of Public Economics 65: 89-118. Krein, S. F. and A. H. Beller. 1988. “Educational Attainment of Children From Single-Parent Families: Differences by Exposure, Gender, and Race.” Demography 25(2): 221-234. Lerman, R. 1996. “The Impact of the Changing US Family Structure on Child Poverty and Income Inequality” Economica 63(250): S119-39. McLanahan, S. and G. Sandefur. 1994. Growing up with a single parent: what hurts, what helps. Cambridge, Mass.: Harvard University Press. Moffitt, R. 2001. “Welfare Benefits and Female Headship in U.S. Time Series”. In Out-ofWedlock: Causes and Consequences of Nonmarital Fertility, eds. L. Wu and B. Wolfe. Russell Sage Foundation. Neal, D. 2004. “The Relationship Between Marriage Market Prospects and Never-Married Motherhood.” Journal of Human Resources 39 (4): 938–957. Rosenzweig, M. R. 1999. "Welfare, Marital Prospects, and Nonmarital Childbearing." Journal of Political Economy 107(6): S3-S32. Schmidt, L. 2003. “Murphy Brown Revisited: Human Capital, Search and Nonmarital Childbearing Among Educated Women.” Unpublished draft. Williams College. Willis, R. J. 1999. ''A Theory of Out-Of-Wedlock Childbearing.'' Journal of Political Economy 107(6): S33-S64.



Chapter 5

OVERREACTION IN VENTURE CAPITAL AND PRIVATE EQUITY MARKETS: EVIDENCE FROM EUROPEAN MARKETS BEFORE THE BURST OF THE BUBBLE Marina Balboa-Ramón* Departament of Financial Economics, University of Alicante, 03690 San Vicente del Raspeig (Alicante), Spain

José Martí-Pellón** Departament of Financial Economics, University Complutense of Madrid, 28223 Pozuelo de Alarcón (Madrid), Spain

Abstract The venture capital and private equity market is made up of a supply curve and a demand curve. According to Gompers and Lerner (2002), the supply curve is given by investors’ wishes to commit funds to this market, whereas the demand curve is given by the number of firms looking for this type of financing. The venture capital and private equity process is basically made up of three stages: fundraising, investment and divestment. Given the permanent need to use the market as a source of funds for new investments, the venture capital and private equity one is a cyclical process activated every two or three years. The inherent cyclical nature of this process may determine the appearance of continual imbalances between supply and demand in venture capital and private equity markets. Given the importance of this market in the launching and development of new industries (Bygrave and Timmons, 1992); or the positive impact it exerts on the economy (Barry, 1994; Fried and Hisrich, 1994), on innovation (Kortum and Lerner, 2000) or job creation (Birch, 1979; Storey, Watson and Wynarczyk, 1989), this work aims to offer an explanation of these imbalances. Except for the work by Gompers and Lerner (2002), who studied this problem for the American case, there is no previous work on this topic.

*

E-mail address: [email protected] E-mail address: [email protected] The authors wish to acknowledge their gratitude to Juan Carlos Gómez Sala and Antonio Rubia Serrano (Universidad de Alicante) for the highly valuable suggestions. Thanks are also due for their valuable comments to Juan Antonio Maroto Acín and Teodosio Pérez Amaral (Universidad Complutense de Madrid). **

102

Marina Balboa-Ramón and José Martí-Pellón The aim of this chapter is to explain the imbalances between supply and demand in private equity markets. We seek to assess whether an overreaction effect is produced in new fundraising when there are changes in demand and in liquidity. The main cause presented for this effect is the inelastic behaviour of the short term supply. The hypotheses are tested on panel data referring to aggregate figures of a group of 20 European countries. The overreaction hypothesis is fully confirmed in the case of demand as regressed coefficients are significantly greater than one. The same is partially confirmed in the case of liquidity. The implications of these results are indeed important since the European Venture Capital and Private Equity market is experiencing a sound growth since the mid of the first decade of the twenty first century, fueled by large levered acquititions that could lead to a shock similar to that experienced by internet companies at the beginning of the century.

Keywords: Private equity, venture capital, fundraising, divestments, overreaction JEL Classification: G24, F00

1. Introduction The term venture capital refers to the professionalised financial activity consisting of investing in firms which are in the start-up or expanding stages. Characteristically, the investor usually provides added value for the firm being financed, by giving it credibility with third parties and providing it with its experience in tight situations. However, from the eighties onwards a more broader term than venture capital is used in Europe so as to take into account the change in the focus of investments towards more consolidated firms. This term is private equity and refers to investments made in non-quoted firms whatever their stage of development or size. Given that this work centres on the study of 20 European countries, for the purposes of this work we will be referring to the concept of private equity. The venture capital and private equity (henceforth VC/PE) market is made up of a supply curve and a demand curve. According to Gompers and Lerner (2002), the supply curve is given by investors’ wishes to commit funds to this market, whereas the demand curve is given by the number of firms looking for this type of financing. The VC/PE process is basically made up of three stages: fundraising investment and divestment. Given the permanent need to use the market as a source of funds for new investments, the VC/PE one is a cyclical process activated every two or three years. The inherent cyclical nature of this process may determine the appearance of continual imbalances between supply and demand in VC/PE markets. Given the importance of this market in the launching and development of new industries (Bygrave and Timmons, 1992), or the positive impact it exerts on the economy (Barry, 1994; Fried and Hisrich, 1994), on innovation (Kortum and Lerner, 2000) or job creation (Birch, 1979; Storey, Watson and Wynarczyk, 1989), this work aims to offer an explanation of these imbalances. Except for the work by Gompers and Lerner (2002), who studied this problem for the American case, there is no previous work on this topic. The aim of this chapter is to provide evidence on the existence of an overreaction effect that could distort the correct functioning of the market under certain circumstances. The scope of the analysis concentrates on European countries on the decade that led to the burst of the internet buble. This chapter contributes to the existing literature in two ways. First, it extends the ideas expressed by Gomper and Lerner (2002) to the particularities of the European markets. It is

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considered that the explanation of the imbalances may vary due to the selfsame characteristics of each VC/PE market. Whereas in the American case the VC/PE market is consolidated, European markets are still maturing. Second, and bearing in mind the VC/PE process itself, it is considered that any attempt to explain the behaviour of the supply of and demand for VC/PE must bear in mind the three stages of this process. This is an aspect that has not hitherto been tackled. The rest of the chapter is structured as follows. The second section provides a brief description of how the VC/PE market works as well as an overview of the theoretical literature on the characteristics of the behaviour of supply of and demand for VC/PE. Taking as a reference the rates of growth of the main magnitudes of VC/PE markets in different countries, in the third section we argue the existence of overreaction in supply when there is a change in demand and liquidity. The fourth section sets out the data used, the methodology applied and the specifications of the model which aims to verify the existence of overreaction. In the fifth section results are shown. Finally, the conclusions are summarised in section six.

2. Theoretical Base In VC/PE markets, three basic proceedings must be identified: raising resources, allocating them to non-quoted firms, and the recouping of the investment to return the resources to those who originally provided them. The process begins when institutional, corporate or private investors decide to take a stake in a specialised investment vehicle designed to invest in firms whose characteristics fit in with the definitions offered above of VC/PE. Given the non-liquid nature of the investments being made, in order to attract investment resources it is customary to set up closed-end investment vehicles. These funds, which in the United States take the form of limited partnerships, usually last between 8 and 12 years. They are launched on the initiative of an experienced management team, which will receive a fixed management fee in the first years of the fund’s life and a stake in the capital gains which may be obtained in the fund as a whole (carried interest). Attracting the volume required by managers to close the fund usually takes around one year. This circumstance is the reason for the inelastic nature of the supply of short-term funds suggested by Gompers and Lerner (2002). Moreover, the stakeholders in these funds do not hand over their percentage immediately. Instead they sign a commitment letter, which will be honoured by stages, when investments are accepted, till the whole sum is provided. In the second stage the management team must identify a sufficient flow of projects to be able to choose from them those who will receive a stake from the new fund. Since these are non-quoted companies, the entry process is laborious and takes between 4 and 30 weeks until the deal is closed. The chosen firms must provide a business plan which will be compared and discussed by the managers of the VC/PE firms. In this process, both parties state their preferences regarding the price of the shares to be issued or sold, as the case may be. If the positions of both sides can be reconciled, a letter of intent will be signed, giving rise to a period of due diligence. As well as the scrutiny of the assets and liabilities of the candidate firm, in this period other special conditions will be approved so as to regulate the relationship between both parties during the time the investor has a stake. Due to the complexity of this

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process, an experienced manager normally needs about 3 years to allocate most of a fund’s resources. In the third stage, the VC/PE managers attempt to recoup the resources invested by the fund to return them to the stakeholders. However, until this moment is reached, their role is to add value to the firm. The added value usually stems from the credibility offered to third parties, by the synergy with other firms in the portfolio and by the assessment in dealing with problems in the firm’s development, which had already shown up in other firms in which the same managers had owned a stake. Nevertheless, the most important contribution made by managers usually is to identify the most suitable means of realising the value of the firm. The most valid alternatives consist of the introduction of the firm’s shares on the stock market (Initial Public Offering- IPO) or selling them to a financial or corporate investor (trade sale). The limited availability of public information on transactions and the relative newness of this industry, even in developed countries different from the United States, makes it difficult to have a substantial number of relevant theoretical and empirical studies focused on how VC/PE markets work. For the American experience, the pioneering works of Bygrave and Shulman (1988), Poterba (1989) and Bygrave and Timmons (1992) have coincided with those developed by Sahlman (1990), Gompers (1998) and Gompers and Lerner (1998). We are indebted to the latter three authors for an interesting review of the present state of the art. Gompers and Lerner (2001) describe what is known and unknown about venture capital. However, there is a limited number of relatively important empirical works on how VC/PE markets operate, and, especially, the limited approach of most of them, attempting to compare relative, specific aspects of some of the three above-mentioned stages in VC/PE activity. Moreover, nearly all the papers take as their exclusive area of study the investment experience in the United States. Works with a wider geographical range are even scarcer because of the heterogeneous nature of the data and the fact that fewer time observations are available. In the European case, it must not be forgotten that the experience of venture capital began in the seventies, and veered towards another activity which became defined as private equity. For this reason, available data refer to a broader concept, limiting comparisons with data relative to the United States experience. Also, the historical series for most countries started in 1987, registering the evolution of a limited number of concepts which was to increase at the start of the nineties. Empirical research taking a more integrating approach to VC/PE markets is limited to an important contribution by Gompers and Lerner (1998), followed by the works of Jeng and Wells (2000) and Martí and Balboa (2006). Gompers and Lerner (1998) analysed the impact on funds raised from a large number of variables such as volume of IPOs, tax rate on capital gains, expenditure on research and development, regulation of pension funds, and returns obtained from stock exchange investments. In addition to a more ambitious approach, the main contribution of this work is that it applies a suitable methodology for dealing with the limitations stemming from the small number of time observations available. The study is developed on three levels, as an aggregate, by geographical states and investors, for data referring to the United States. In the work of Jeng and Wells (2000) an attempt is made to define the equations for VC/PE supply and demand, which are subsequently estimated with panel data for a group of 21 OECD countries, in the period 1986-1995. The variables presented are similar to those studied by Gompers and Lerner (1998). Even though this work has contributed to the scarce literature on how VC/PE markets work, its limitations stem from the scant differentiation


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between the supply and demand equations and the use of a between-groups estimator for the model when the number of countries finally included in the empirical analysis varies between 13 and 14. Finally, Martí and Balboa (2006) present an explanatory conceptual model of how VC/PE markets work, which is tested from the supply viewpoint for a panel of 16 Western European countries in the period 1987-2000. Its novel approach is found in its linking of the three-stages of the VC/PE activity process. The estimate of the coefficients is supported by the within-groups methodology, making it possible to reduce the unobservable heterogeneity deriving from the different degree of development of VC/PE markets in the areas analysed. The approach of the present work does not aim to estimate supply or demand, but rather to study the effects of their interaction in the face of sudden changes in the market. As the main reference in academic literature, the most outstanding work is that by Gompers and Lerner (2002). These authors present an explanation for the behaviour of venture capital markets, based on the experience of the United States market. In their opinion, changes in supply and demand give rise to short term imbalances in the market, since those changes require some time to be digested by agents. According to their approach, supply represents the amount of resources that investors are willing to commit for venture capital investment at different levels of expected return and demand is identified with the number of firms seeking funds for these levels of return.1 Especifically, when the demand curve is displaced, as a result of technological change, such as the appearance of transistors or the birth of Internet, the short-term supply of resources does not increase, producing an initial situation of equilibrium which leads to an increase in expected return from investments. The justification for the verticality of shortterm supply behaviour, when there is a change in demand, is found in the period of time required to close a new fund. As a result, investors are willing to increase the amount of resources committed to new funds, and the long-term supply curve moves to the right. This gives rise to a reduction in expected return at the cut-off point of the new demand curve at a point of smaller expected return.

3. Causes of Overreaction in VC/PE Markets: Proposed Hypotheses As has been mentioned, Gompers and Lerner (2002) show that there is rigidity in the supply of short-term resources when there is a sudden change in demand. As a result, expected return undergoes a double adjustment in the face of the change in demand, due to the delay in the incorporation of fresh funds, since the short-term supply behaves inelastically. However, the market conditions may have changed at just the time when the fresh funds are finally available, after being activated by the need for a higher volume of funds at a previous time. This situation could lead to price imbalances which could reduce the expected return, since there would exist a surplus supply for the new demand situation. It should be made clear that, unless there is agreement by the managers, once a fund has been 1

The expected return depends on the entry price and the expectations of future revaluation. For example, when there is excess supply, entry prices in a limited number of firms would rise, reducing the expected return from the operation.

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made up the managers have complete freedom to claim resources from those providing the fund for a prolonged period, and this should prevent an immediate downward movement in supply. Moreover, demand displacement should not lead in the United States to displacement in the supply curve, but rather to a lagged movement within the same long-term supply curve. This circumstance is understood to take place as a result of the degree of maturity of the United States market, where all potential investors are aware of the VC/PE market. Nevertheless, it is necessary to stress that this is not the situation in European markets, where institutional investors have in practice not yet begun to enter VC/PE markets. By way of reference, local pension funds account for a marginal part of VC/PE resources in most European countries (EVCA, 1988-2001), whereas in the United States they are the main source. Consequently, imbalances due to demand changes can become more significant in developing markets. In the wake of the explanation offered by Gompers and Lerner (2002), a shift of the demand curve would also give rise to an increase in expected return, due to the vertical form of the short-term supply curve. However, as a result of the entry of new investors, attracted by the new investment opportunities, it could also produce a shift to the right of the supply curve. As can be seen in Figure 1, the result would be an equilibrium point for a much lower level of return (r2), due to the existence of a surfeit of funds compared to the volume of investment operations. The delay in the creation of funds could have an even more perverse result if, once the initial impact of technological change wore off, the assessment references argue about the business models of many of the new firms which are hoping to start up. This could lead to a new shift of the demand curve towards previous levels. Since these closed-end funds are at the beck and call of managers for several years, this imbalance would not be easily absorbed by the market, and would cause distortions in prices and returns, which could affect the correct functioning of the market for a long period of time.

r SS1 SL1 r 1’

D1

D2 SL2

r1 r2

Q1

Q2

Q

Figure 1. Changes in supply due to a shift of demand to the right in a developing market.


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Moreover, the alternation between cycles of expansion and recession on the economic scene also impinges on VC/PE markets. Given the temporary nature of resource management in VC/PE organisations, rigid short-term supply can also lead to serious imbalances in periods of recession. As is shown in Figure 2, a recession usually leads to a contraction of demand and a reduction in expectations of returns for the set of projects analysed. This may shift the curve to the left (D2). But resources in circulation are not automatically invested as they are received. It takes around three years for them to be allocated, nor are they returned to the managers until the end of the fund’s life. This latter circumstance determines the verticality of the short-term supply curve (SS1). Thus, the shortage of worthwhile projects for the same supply will mean a short-term fall in the return to be expected from investments, since there exists a surfeit of resources to be invested compared to the fewer opportunities with a certain interest. In the short term, the fall in expected returns may give rise to a more than proportional drop in new fundraising, since there is a glut of resources in circulation. All in all, the existence of a one-year lag for a new VC/PE fund to come on stream and the fact that 3 years are needed for the resources of a newly-raised fund to be allocated may produce imbalances between supply and demand which, in developing VC/PE markets may tend to cause more than proportional variations among supply and demand. This work aims to verify whether the lag in raising extra funds and the blocking of the latter in the hands of a management team for several years may be the cause of situations of overreaction in the supply of VC/PE resources in the face of changes in demand. This effect is considered to be found in countries where VC/PE markets are not fully developed, due to the massive entry of new investors when there are certain changes which might shift the demand curve.

r SS1 SL1 D2

D1

r1 r2 r1’

Q2

Q1

Q

Figure 2. Changes in supply due to a shift of demand to the left in a developing market.

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Given the difficulty of measuring the supply of and demand for venture capital, according to the definition offered by Gompers and Lerner (2002), other variables could be considered as proxy. In particular, the volume of investments really made could be considered as a proxy for demand, whereas the volume of funds definitely committed by investors to recently established funds could constitute a reference to supply. Bearing in mind Gompers and Lerner’s contribution (2002), and the lower transparency found in European markets, the following hypothesis is proposed: Hypothesis 1: Lagged investments have a more than proportional effect on new fundraising in growing VC/PE markets, giving rise to the above-mentioned overreaction effect. Moreover, the volume of investments made would not be expected to be the only determinant of the volume of funds raised, since making investments does not guarantee in itself the recouping of the funds invested (or liquidity) plus a profit which will offset the risk assumed. It is thus fundamental to know historical returns generated by the VC/PE activity. Nonetheless, in countries where the VC/PE market is not fully developed, there is not enough information about profitability since not many managers have had time to close a sufficient number of funds. Besides, even when references of profitability exist, according to Gompers and Lerner (1998), the calculation of returns is hindered by the policies of many venture capital organisations who delay the updating of the value of unsold assets. In any case, the annual reports of the European Venture Capital and Private Equity Association (EVCA) provide information about the divested amounts, albeit valued at cost price. These data can be used, at least as a reference for the end of the investment and divestment cycle described in the previous section. Therefore, with the aim of verifying whether overreaction can also be linked to the data regarding the end of the VC/PE cycle, the second hypothesis proposed is the following: Hypothesis 2: Liquidity, proxied by cost price divestments, also has a more than proportional effect on new fundraising by VC/PE managers in aggregate terms. Given that the annual EVCA (1988-2001) reports do provide information on the breakdown of divestments on the basis of the mechanism used, it is worthwhile analysing whether the mechanism used provides information that is interpreted by agents in the market in a positive or negative sense. First of all, despite the fact that these data are valued at cost price, a study must be made of the volume of divestments stemming from the introduction of shares onto the stock market (IPOs). The evidence from previous empirical studies carried out is conclusive insofar as it identifies it as one of the most profitable forms of divestment (Venture Economics, 1988; Amit et al., 1998). Likewise, other works have already given an account of the effect of this exit route on fresh fundraising. In these works it is inferred that, if there is a high volume of share placements in new firms on the stock market there usually occurs a great volume of new fundraising (Berlin, 1998; Black and Gilson, 1999; Jeng and Wells, 2000). In this same sense, Dittmann et al. (2004) point out that the takeoff of venture capital in Germany took place when companies broke free from commercial banks and that this process coincided with the initial success of the Neuer Markt. In similar fashion, sales to third parties, especially when they are strategic sales to industrial firms, are considered to


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constitute a very desirable form of divestment for VC/PE managers. Because of this, Hypothesis 2 can be supported with the following complementary hypothesis: Hypothesis 3: Divestments through the placement of shares on stock markets or by sale to third parties give rise to a positive reaction, which is more than in proportion, to the volume of fresh funds raised in the following period. On the contrary, experience undergone in several European countries shows that repurchasing of the stake held by VC/PE operators by the original shareholders and/or directors constitutes an exit route linked to gaining modest profits. The use of this divestment mechanism is justified by the VC/PE operator’s need to get out when stock markets are not very keen and there is little merger and take-over activity. The limited size of many of these firms where stakes are held prevents access to the two above-mentioned exit routes, so the VC/PE investor finds himself doomed to a sale to the shareholders and/or directors in not very favourable conditions. Finally, another of the categories included in the EVCA reports is divestment by write-off, which is associated with the loss of a substantial part of the investment. As a result, following the line of argument of Hypothesis 3, the possibility of overreaction in a negative sense should also be broached, since these are considered to be two forms of divestment, completing Hypothesis 2 with the following: Hypothesis 4: Divestments by means of repurchase by the original owners or by writing-off the investment produces a negative reaction, which is more than in proportion, to the volume of new funds raised in the following period. Finally, it must be pointed out that there may be other variables which might impinge on the intended link between supply and demand, including making investments liquid. Therefore, in order to verify the hypotheses proposed, the possible effect of other variables must be borne in mind, such as growth in gross domestic product (henceforth GDP). According to Gompers and Lerner (1998), if the economy is growing there will be more investment opportunities, so there will be a larger number of opportunities that will meet the profitability requirements of investors, thus increasing investors’ willingness to participate in these opportunities. This evidence has also been found in countries where venture capital is rather less developed. Aylward (1998) evidenced that in Central and Eastern Europe many of the amounts devoted to venture capital were produced in the years 1994 and 1995, when the outlook for sustained economic stability improved.

4. Data and Methodology 4.1. Data and Descriptive Analysis In order to verify the proposed hypotheses of overreaction described in the previous section, data on European markets is taken as a reference in order. The scope of the study covers data regarding all the countries included in the annual EVCA reports (1988-2001). These reports register data for a total of 20 countries, with observations ranging from 1987 to 2000. However, the panel is incomplete since no data are recorded for all of them for every

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year. The level of breakdown of the figures is complete for the majority only from 1991 onwards and the Eastern European countries considered only have information available from 1998 onwards. In appendix A the data available by countries is described. Tables 1 and 2 show that the growth of the VC/PE activity in all the countries analysed is certainly spectacular. Table 1. Descriptive statistics of the annual growth of new funds raised by countries

Western countries Austria Belgium Denmark Finland France Germany Greece Iceland Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom Eastern countries Hungary Poland Slovakia

Mean

Median

Maximum

Multiple

340.93% 33.45% 249.21% 70.02% 35.95% 75.64% 84.85% 197.19% 80.80% 35.31% 59.08% 2108.26% 122.59% 75.74% 202.99% 60.55% 34.06%

73.56% 15.59% 91.57% 4.49% 14.31% 29.53% 39.24% 36.56% 13.34% 7.89% 21.17% -3.17% 32.79% 24.38% 25.21% 57.28% 5.67%

1847.69% 159.37% 1750.51% 408.03% 250.12% 664.51% 279.98% 1980.03% 505.29% 143.56% 444.56% 24383.93% 946.46% 645.64% 1869.55% 228.47% 202.62%

2.09% -7.30% -51.86%

2.09% -7.30% -51.86%

23.94% 10.37% -51.86%

20.04 8.87 7.68 13.11 3.40 9.47 6.54 4.34 7.14 7.57 10.48 1.49 5.46 16.10 5.76 3.54 -

Source: Calculations based on data from 1988- 2001 EVCA Yearbooks. Note: Growth rates in this table are calculated from data in constant currency in each country, taking 1995 as base year.

In Table 1 different descriptive statistics are recorded for the annual growth of fundraising by countries. The mean for this figure shows double-digit positive figures in 13 out of the 17 Western European countries. The only exception is that of Norway, which shows negative annual growth figures for some years in the period 1987-2000, but is notable for having the highest annual growth figure of all countries for the figure registered in 1990. Two fast-growth periods may be identified in this group of countries. These periods can be situated between 1987 and 1990 and between 1996 and 2000. Except for the United Kingdom and Ireland, who began their recovery in 1994, the values registered between 1992 and 1995 fell below the figures for the first growth cycle. The multiple column registers how many times the average figure for new funds is higher in the second growth period compared to the


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first, and likewise one can see the enormous growth experienced by the VC/PE activity in this time. This figure cannot be calculated for Greece due to lack of data for the above-mentioned first period. The study also incorporates the data from the first reports made in Eastern European countries. Even though they only provide data for a limited time period, they are also considered to be subject to the same vagaries as the other countries, and their data make a contribution to providing a complete picture of the problems faced by this activity in Europe. Table 2. Descriptive statistics of the annual growth of investments by countries

Western countries Austria Belgium Denmark Finland France Germany Greece Iceland Ireland Italy Netherlands Norway Portugal Spain Sweden Switzerland United Kingdom Eastern countries Hungary Poland Slovakia

Mean

Median

Maximum

Multiple

220.56% 31.97% 29.12% 41.78% 23.08% 39.54% 207.30% 129.85% 19.55% 33.92% 18.41% 353.72% 55.97% 28.54% 90.72% 44.32% 13.59%

-18.87% 11.07% 14.97% 28.27% 17.95% 25.36% 213.81% 27.68% 1.65% 41.90% 27.03% 27.31% 50.16% 34.17% 4.24% 36.50% 11%

2153.63% 196.07% 181.18% 191.48% 85.17% 126.38% 581.52% 490.04% 101.58% 126.53% 69.45% 4412.84% 196.43% 111.58% 520.79% 286% 55.27%

12.24 5.06 3.39 16.10 3.12 8.51 6.36 2.30 6.90 5.19 8.70 4.53 4.29 14.35 6.04 2.80

227.17% 29.37% -21.39%

227.17% 29.37% -21.39%

534.55% 59.22% -18.01%

-

Note: Growth rates in this table are calculated from data in constant currency in each country, taking 1995 as base year.

In similar fashion Table 2 shows the average, the mean, maximum value and the multiple between the previously-mentioned periods for annual growth in investment figures. The mean once again shows double-digit annual growth figures in 13 out of the 17 Western European countries. In this case the exception comes from Austria, which registered negative annual growth figures for several years of the total period considered. If we take into account the multiple relating growth in the volume of investments between the period 1987-1990 and 1997-2000, once more the existence of strong alterations in such a short time period can be observed. The strong variations in new funds raised and investments gave rise to successive displacements of the supply and demand curves, so the balancing performances, as expressed

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in Figures 1 and 2, are situated at acceptable levels for investors. It must not be forgotten that the VC/PE is an asset class competing with other forms of investment to attract resources from institutional and corporate investors. However, the important variations registered, along with the peculiarities of the fundraising and investment processes, have been the result of imbalances in the past in VC/PE markets. Especifically, either the one-year lag, on average, for closing out the new fund, or the three years needed to be able to allocate most of the resources of a fund in firms in which they have a stake must be recalled as a cause of rigid short-term supply. Below we attempt to show that in this growth process, with an adjustment at the beginning of the nineties, a more than proportional impact was made by lagged investments on new fundraising.

4.2. Methodology Given that we have a limited number of time observations for each country, it is necessary to work simultaneously with the information for the group of countries considered in the study, with support in econometric techniques applicable to panel data. The use of this methodology presents a series of advantages. First, it enables the unobservable individual heterogeneity to be controlled, particularly if it remains constant over time, with the estimated coefficients reflecting the real impact of x on y. Second, it provides the researcher with a large amount of information, which makes possible an increase in the degrees of freedom in the test and a reduction in the colinearity between the different explanatory variables. Third, it enables a series of important economic questions to be studied which could not be analysed through the exclusive use of time series or cross-section data. Finally, it enables the effect of omitted or unobservable data to be more naturally controlled. Thus, the regression model is of the form:

yit = α + xit ' β + ηi + vit

i = 1, 2,..., N ; t = 1, 2,..., T

y

x

An endogenous variable, it , and a vector of explanatory variables, it , are observed. The subindex i refers to the individual, in this case a country, and the subindex t to the year.

vit is a zero average residual and ηi represents the specific individual effects of each country

which are unobservable, and which are assumed to be constant over time for a particular

N (0, σ

2

)

ηi . country and with a distribution In order to estimate the model, the great disparity of data relative to each country should be taken into account. Thus, in each of the regressions the corresponding table registers Wald’s modified statistic which compares heteroskedasticity among the different countries. This statistic is still valid when the normality hypothesis is violated. The form of this statistic is given by the following expression: Ng

W = ∑ (σˆ i 2 − σˆ 2 )Vi −1 i =1

Overreaction in Venture Capital and Private Equity Markets −1

where Ng is the number of countries and Vi = Ti (Ti − 1)

−1

113

Ti

∑ (e t =1

it

2

− σˆ i 2 )

2

and

Ti

σˆ i2 = Ti −1 ∑ eit 2 , with Ti the number of time observations in unit i. In all cases analysed, as t =1

will be noted in the tables, the null homoskedasticity hypothesis was rejected, so robust standard errors are used to verify the hypotheses proposed.

4.3. The Models Proposed As has already been mentioned, the hypotheses proposed aim to identify the existence of overreaction on new fundraising when variations in investments and divestments occur. Thus, the dependent variable is the annual volume of new funds raised in each country. In order to facilitate the verification of the hypotheses proposed, three models are estimated. Model I only records the effect of aggregated investments and divestments on fundraising. However, there are also other variables which might impinge on the behaviour of the endogenous variable, such as economic growth and the effect of time. For this reason, the Model II includes GDP growth and a deterministic time trend. Finally, in Model III the volume of aggregated divestments is replaced by the breakdowns of divestments according to different exit mechanisms. In the first model, the first independent variable refers to aggregate volume invested by all VC/PE organisations in the previous year ( INVit −1 ). When there are lags in information,2 it is considered that the existence of variation in demand by firms which might offer reasonable profit expectations may lead to fresh fundraising at a later time. The second independent variable tries to explain supply variation due to the liquidity effect. Given that the presence of investors in these markets is not temporary, when the latter receive repayments from venture capital managers, as divestments take place, the need arises for placement of these funds. This variable is also lagged one year ( DIVit −1 ). Model I:

NFRit = α + β1 INVit −1 + β 2 DIVit −1 + ηi +ν it

With the aim of controlling the effect of economic growth on the dependent variable, GDP growth ( GDPg it ) is also included. Finally, given the remarkable growth experienced by the VC/PE markets in the period analysed, it is considered important to add a time variable ( Tt ), which would register the growth of the endogenous variable caused by the simple passing of time. Model II: NFRit = α + β1 INVit −1 + β 2 DIVit −1 + β 3GDPg it + β 4Tt + ηi + ν it

2

Although the figures referring to a given year are published in June of the following year, it must be stressed that prior to this date there are partial unofficial figures that can also be taken into account by the market.

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Marina Balboa-Ramón and José Martí-Pellón In Model III, the variable DIVit −1 is replaced by the amounts divested at cost through

different exit mechanisms. As has been explained before, this is considered important since the different mechanisms usually imply a different return to investors. The divestment methods that generate the best returns to investors are the ones that take place through placements in stock markets (IPOs) and through trade sales to a third party. Both mechanisms are supposed to positively affect fundraising and are registered in the variable DIVIPO & TRSLit −1 , which is also lagged one year. On the contrary, divestments through owners/managers buy-back and write-offs are considered as exit ways related to poor performance. This variable is identified as DIVOBB & WOFFit −1 and is also lagged one year. Model III:

NFRit = α + β1 INVit −1 + β 2 DIVIPO & TRSLit −1 + β3 DIVOBB & WOFFit −1 + + β 4GDPgit + β5Tt + ηi + ν it The source of data for all the information relative to each country was, as has been mentioned, the official reports of EVCA. These reports register data on each country in current local currency. However, the data finally used in this work is expressed in thousands of 1995 local currency. The reason stems from the fact that if variables are expressed in nominal terms, an observed increase in a variable over time can be due exclusively to a rise in the inflation rate and not to an increase of that variable in real terms. In order to assess the robustness of results, we have also run all regressions using a kind of standardised information from different countries. In this case, each of the variables was divided by the value of the country’s GDP for the related year. Using this data itself, we may partially solve the problem of heteroskedasticity because we are taking into account the great differences among countries that result from the different levels of economic development.

5. Results The results obtained using data expressed in thousands of 1995 local currency are shown in Table 3. Given the great disparity among countries, the modified Wald statistic rejected the null of homoskedasticity, so Whites’ corrected standard errors are shown in the table. Models I and II seem to verify the first two hypotheses proposed. The variable representing the lagged demand shows coefficients of 1.940 and 1.938 respectively, which are significant at 0.1%. In the case of divestments, the coefficient ranges between 1.942 and 1.960, which is also significant at 0.1%. However, these results do not by themselves verify the first two hypotheses, as overreaction only occurs when the coefficients are significatively higher than one. In order to test if the coefficients are significatively greater than one, a one-tailed test based on the robust standard errors is obtained. In the case of the lagged volume of investments, the t-statistic is 3.48 in model I and 3.31 in model II. Regarding this t-statistic for the lagged volume of divestments, these figures are 2.43 in Model I and 2.43 in Model II. Thus, we may conclude that both the coefficient of the lagged volume of investments and the lagged volume of divestments are significatively greater than one, so we can accept both


115

hypotheses 1 and 2. Finally, and although this is not the aim of the paper, we find a positive and significant impact of GDP growth at the 5% level in Model II. In relation to the time variable, it is not significant in neither of the models proposed. Table 3. Summary of results using data at 1995 prices Independent variables

Dependent variable: New Funds Raised Model I Model II Model III 1.940* (0.270) 1.942* (0.387)

1.938* (0.284) 1.960* (0.395)

(3) DIVIPO&TRSLit-1

-

-

(4) DIVOBB&WOFFit-1

-

-

(5) GDPgit

-

(6) Tt

-

(1) INVit-1 (2) DIVit-1

(7) constant R2 p-valuea

-546,252* (157,903) 0.843 0.000

4,869,940** (2,255,157) -11,484 (25,346) -596,516* (140,956) 0.845 0.000

1.993* (0.240) 2.491* (0.292) -1.023 (2.513) 64,696 (2,821,867) 11,792 (37,500) -491,953 (331,572) 0.864 0.000

*

= significant at 1%, ** = significant at 5%, *** = significant at 10% Fixed effects regression of 20 countries. Robust standard errors in parenthesis. The dependent variable is New funds raised. The independent variables are (1) Total amount invested lagged one period, (2) Total amount divested lagged one period, (3) Total amounts divested through IPOs plus sales to third parties lagged one period, (4) Total amount divested through owners buy-back and write-offs lagged one period, (5) GDP growth from "t-1" to "t", (6) Deterministic time trend. All regressors (except time trend) are expressed in 1995 constant local currency. a p-value of the modified Wald statistic for groupwise heteroskedasticity.

Hypothesis 2 could be complemented with the use of the volume of divestments at cost by different exit mechanisms. In this sense, hypotheses 3 and 4 aim to test the incidence of divestments on fundraising by using, respectively, good and bad ways of exiting. This information may complete that provided by the total volume of divestments. Although data are still valued at cost in both cases, these mechanisms of exiting suggest a success or a failure in the investment made. The breakdown of information is available in the EVCA’s annual reports. The results including this breakdown of information are shown in Model III in Table 3. Despite the lesser number of observations used to estimate this model,3 we again find a positive impact of the lagged volume of investments on new funds raised, with a coefficient

3

The breakdown of data required could not be obtained for all years in all countries, thus reducing the number of observations from 214 to 158, once the lag has been considered.

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of 1.993, which is significant at 0.1%. The t-statistic related to the investment coefficient being greater than one is 4.13, thus confirming the overreaction hypothesis. Finally, and with regard to the introduction of the breakdown of divestments, the coefficient of the variable referring to the lagged volume of divestments through IPO and trade sales registers a value of 2.491, which is significant at 0.1%. On the contrary, the coefficient of the variable that refers to the lagged volume of divestments through owners buy-back and write-offs is negative, as expected, although non-significant. The t-statistic for the hypotheses that both coefficients are greater than one is 1.68 and -0.81 respectively, so the overreaction hypothesis is only verified for the lagged volume of divestments through IPO and trade sales. In order to test the robustness of the results, we have run all the models using the same data but expressed in a different way. In this case, and with the aim of reducing the great disparity existing among the different countries, we have normalised the variables fundraising, investment and divestment expressed in current local currency by the GDP of the corresponding country and year. By using these data, we do not expect an equality of coefficients. Our aim is to verify that the coefficients are greater than one, which would verify the overreaction hypothesis. Despite the possibility of having reduced the heteroskedasticity among countries by using the data normalised by GDP, the modified Wald statistic rejects, again, the homoskedasticity hypotheses, so robust standard errors are shown in Table 4. In this case, the results of the estimation of Model I and II show that the coefficient of the lagged volume of investments is positive and significant at 0.1%. With the aim of testing if the coefficients are significatively greater than one, the t-statistic based on a one-tailed test is 2.42 in Model I and 1.96 in Model II, thus verifying the overreaction hypothesis. Regarding the lagged volume of divestments, its coefficient is again greater than one and significant at 0.1% in Model I. In Model II, although the figure is again greater than one and significant at 5%, we can notice a fall in the magnitude of the coefficient. In order to test the overreaction hypothesis, the t-statistic for the one-tailed test is 1.41 in Model I and 0.74 in Model II. This result does not verify Hypothesis 2. Finally, GDP growth is only significant in Model II whereas the time trend is significant in Models II and III. As in the former case, Model III includes the breakdown of both divestments that imply a good return and divestments that suppose a negative return. Using a lesser number of observations, we find that the coefficient of the lagged volume of investments is 1.57, which is significant at 0.1%. The t-statistic for the hypothesis that the coefficient is greater than one is 2.20, which again confirms the overreaction hypothesis. In relation to the volume of divestments by IPOs and trade sales, the coefficient is positive (1.53) and significant at 0.1%, but the t-statistic for the value of the coefficient being greater than one is 0.99, which does not confirm the overreaction hypothesis. In the case of divestments through owners’ buy-back and write-off, the coefficient is not significant. As a result, Hypothesis 1 is verified in all cases considered, even when we are dealing with data with a different perspective, whereas the evidence found with regard to divestments is not so conclusive. The aggregated divestments only confirm the overreaction hypothesis when data in constant 1995 local currency is used, but the outcome is different when data normalised by the GPD is applied. But their substitution by variables which might represent at least the assumption of success or failure would only provide partial evidence of the overreaction related to information about exits through IPOs and trades sales.


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Table 4. Summary of results using data at current prices normalised by GDP Independent variables (1) INVit-1 (2) DIVit-1 (3) DIVIPO&TRSLit-1 (4) DIVOBB&WOFFit-1

Dependent variable: New Funds Raised Model 1 Model 2 Model 3 1.540* (0.223) 1.846* (0.597)

1.441* (0.225) 1.483** (0.640)

-

-

-

(5) GDPgit

-

(6) T

-

(7) constant R2 p-valuea

-5.52E-4* (2.07E-4) 0.687 0.000

0.013* (0.003) 5.05E-5*** (2.68E-5) -1.11E-3* (1.91E-4) 0.716 0.000

1.574* (0.261) 1.539* (0.547) 1.287 (1.568) 0.007 (0.005) 8.77E-5** (3.56E-5) -1.39E-3* (2.88E-4) 0.713 0.000

*

= significant at 1%, ** = significant at 5%, *** = significant at 10% Fixed effects regression of 20 countries. Robust standard errors in parenthesis. The dependent variable is New funds raised. The independent variables are (1) Total amount invested lagged one period, (2) Total amount divested lagged one period, (3) Total amounts divested through IPOs plus sales to third parties lagged one period, (4) Total amount divested through owners buy-back and write-offs lagged one period, (5) GDP growth from "t-1" to "t", (6) Deterministic time trend. All regressors (except time trend) have been normalised by the variable GDPit. a p-value of the modified Wald statistic for groupwise heteroskedasticity.

6. Conclusion The peculiarities of the fundraising, investment and divestment cycle in VC/PE activity may determine the appearance of imbalances in the markets when there is a change in demand. From the fundraising point of view, it usually takes a year to close out a new investment vehicle. From the resource allocation point of view, investing the available funds from such a vehicle may take up to three years. Both circumstances can be considered as an explanation for the inelastic nature of the short-term supply curve, an observation first mentioned by Gompers and Lerner (2002), that may determine imbalances in VC/PE markets. In this work we attempt to show that disruptions are particularly marked in still-growing VC/PE markets when an alteration in demand provokes strong reactions in supply. If demand is displaced to the right due to technological change, a short-term imbalance may occur due to the rigidity of supply; and rather than a shift within the long-term supply curve there will be a shift of this curve towards the right as a consequence of the entry of new investors. Both circumstances could give rise to an impact which is more than proportional in supply, and this would end in a fall in the expected return from investments due to the glut of

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long-term supply. Moreover, the cyclical appearance of periods of recession may give rise to the demand curve being shifted to the left. Given the inelastic nature of short-term supply, this situation may also produce a reaction, which is more than proportional, on new fundraising, as a result of the strong fall in expected returns due to the surplus of funds in circulation for the new demand situation. All in all, the aim is to confirm the existence of a more than proportional variation (overreaction) in supply when faced with changes in demand, both in the positive and negative sense. Equally, we also aim to test if there is overreaction from the supply side as a result of the divestments becoming liquid. These hypotheses are tested using homogeneous information from 20 European countries, taken from the EVCA annual reports, with the support of an incomplete panel covering the period 1987-2000. Although the initial experiences in most developed countries started in the seventies, except for the United Kingdom, there was no real development to speak of until the end of the eighties. In the empirical analysis new fundraising in a particular country in the corresponding year is taken as the proxy of the supply variable. For the study of liquidity information is used from divestments computed at cost price and use was also made of a breakdown of these according to the exit way. The results obtained provide conclusive evidence of the existence of an overreaction effect on the part of supply to leaps in lagged demand. The coefficients estimated are significantly greater than one in all specifications, even when testing the robustness with a different treatment of the data to limit the problem of heteroskedasticity. On the contrary, no conclusive evidence was found of the more than proportional impact of total divestments on the endogenous variable. It was only verified in the case of presumably successful divestments (IPOs and trade sales), but not in the case of exits related to owner/managerbuyback or write-offs. The implications of these findings for investors is that they must be cautious when taking decisions requiring a commitment of resources for new funds at a time when there are fierce leaps in demand, because such funds could be operative when these variations have been corrected. From another view of point, the evidence found is of interest for the limited studies on VC/PE supply and demand. Especifically, with the exception of the work of Marti and Balboa (2006), the models observed up to now are basically supported by macroeconomic variables or those related to the environment. In view of the results obtained, variables linked to the investment and divestment process itself should be added, and both lagged investments and divestments should be incorporated. Despite no conclusive existence of overreaction being noticed in the latter, the coefficient does take a positive, significant value. The implications are indeed important since the European Venture Capital and Private Equity market is experiencing a sound growth since the mid of the first decade of the twenty first century. The main reason behind this process is an ever-increasing activity of buyout acquisitions, with many transactions developed between private equity houses themselves on the same investee companies. It remains to be seen, however, whether the high-volume of investments recorded by private equity investors in some years could lead to a multiplied increase in the funds raised in the following year. This increase could lead to an even higher presure on prices paid for companies and to an enlargement of the leverage used in the acquisitions. It is also to be tested that this process could end with a shock similar to that experienced by the venture capital markets when the NASDAQ started a dramatic fall in April 2000.


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A final point is that, insofar as the sector matures and all investors will be aware of and consider investment in VC/PE, the overreaction effect will gradually fade away as the information lags that cause imbalances are reduced.

Appendix A Data by country available to estimate the models Country

Year Models I and II

Belgium, Denmark, France, Ireland, Italy, Netherlands, Portugal, Sweden, 1987-2000 Switzerland, United Kingdom Finland, Iceland, Norway 1988-2000 Hungary, Slovakia, Poland 1998-2000 Austria 1987-1990, 1993-2000 Germany 1987-2000 Greece 1991-1992, 1995-2000 Spain 1987-2000

Model III 1991-2000 1991-2000 1988-2000 1993-2000 1995-2000

Source: 1988- 2001 EVCA Yearbooks.

References Aylward, A. (1998). Trends in Capital Finance in Developing Countries. Discussion paper 36: International Finance Corporation. Amit, R., Brander, J., & Zott. C. (1998). Why do Venture Capital Firms Exist? Theory and Canadian Evidence. Journal of Business Venturing, 13, 441-466. Barry, C.B. (1994). New Directions in Research on Venture Capital Finance. Financial Management, 23, 3, 3-15. Berlin, M. (1998). That Thing Venture Capitalists Do. Business Review, 15-27. Birch. D. (1979). The Job Generation Process. MIT Programme on Neighborhood and Regional Change. Cambridge, MA: MIT Program on Neighborhood and Regional Change (mimeo). Black, B.S., & Gilson. R.J. (1999). Does Venture Capital Require an Active Stock Market?. Journal of Applied Corporate Finance, 11, 4, 36-48. Bygrave, W.D., & Shulman, J. (1988). Capital Gains Tax: Bain or Boon. In Kirchhoff, B.A., et al. (Eds), Frontiers of Entrepreneurship Research, 324-338. Wellesley, MA: Babson College. Bygrave, W., & Timmons, J. (1992). Venture Capital at the Crossroads. Boston, MA: Harvard Business School Press.

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Dittmann, I., Maug., E., & Kemper, J. (2004). How fundamental are fundamental values? Valuation methods and their impact on the performance of German venture capitalists. European Financial Management, 10, 609-638. EVCA (1988-2001). EVCA Yearbook 1989-2001. Zavetem, Belgium: EVCA. Fried, V.H., & Hisrich, R.D. (1994). Toward a Model of Venture Capital Investment Decision Making. Financial Management, 23, 3, 28-37. Gompers, P.A. (1998). Venture Capital Growing Pains: Should the Market Diet?. Journal of Banking and Finance, 22, 1089-1104. Gompers, P.A., & Lerner, J. (1998). What Drives Venture Capital Fundraising?. Brookings Papers: Microeconomics 1998, 149-192. Gompers, P.A. & Lerner, J. (2001). The Venture Capital Revolution. Journal of Economic Perspectives, 15, 2, 145-168. Gompers, P.A., & Lerner, J. (2002). Short-Term America Revisited? Boom and Bust in the Venture Capital Industry and the Impact on Innovation. In Jaffe, A. B., Lerner, J., & Stern, S. (Eds.). Innovation Policy and the Economy, 3. Boston, MA: NBER Books, MIT Press. Kortum, S., & Lerner, J. (2000). Assessing the Contribution of Venture Capital to Innovation. Rand Journal of Economics, 31, 674-692. Jeng, L.A., & Wells, P.C. (2000). The Determinants of Venture Capital Funding: Evidence Across Countries. Journal of Corporate Finance, 6, 3, 241-289. Martí, J., & Balboa, M. (2006). Self-regulation in European Venture Capital and Private Equity Markets. International Journal of Entrepreneurship and Innovation Management, forthcoming. Poterba, J.M. (1989). Venture Capital and Capital Gains Taxation. Working paper 2832: NBER. Salhman, W.A. (1990). The Structure and Governance of Venture-Capital Organizations. Journal of Financial Economics, 27, 473-521. Storey, D.J., Watson, R., and Wynarczyk, P. (1989). Fast Growth Businesses: Case Studies of 40 Small Firms in the North East of England. Research paper 67: Department of Employment. London. Venture Economics (1988). Exiting Venture Capital Investments. Needham: Venture Economics.



Chapter 6

THE ROLE OF PUBLIC BENEFITS: A CASE STUDY FOR SEVEN EUROPEAN COUNTRIES Hans Hansen Danish National Institute of Social Research Herluf Trolles Gade 1, DK-1052 Copenhagen, Denmark

Abstract Non-voluntary unemployment is a risk outside the control of the individual. By selecting certain occupations the individual knows that he or she may be exposed to a higher risk of unemployment than in other occupations, e.g. workers in the construction and building industry are exposed to unemployment due to the weather to a higher degree than employees in the IT industry. Such varying risks of unemployment may be reflected in the pay agreements of different industries, but exactly who may be unemployed and for how long, is still beyond the control of the individual. Unemployment insurance benefits are designed to help coping with the financial situation as unemployed, they secure the financial basis for existence in this situation, and enables the unemployed to have time to look for a new relevant job. The primary aim of unemployment insurance benefits is to provide security, the main hypothesis being that human beings perform better when they feel financially secure. Future ageing problems and early retirement from the labour market is a bad mixture. Early retirement is very popular in many countries and there are several roads leading to this situation, in some countries also via public schemes, which almost guide to early retirement. Early retirement may be interpreted as a kind of voluntary unemployment. The incentives to join and to stay in such schemes will be studied. The schemes for sickness and disability are different from those for unemployment and early retirement because they are health related. Compensation for sickness often results in high replacement rates, which could be an invitation to short term leaves.

Introduction Non-voluntary unemployment is a risk outside the control of the individual. By selecting certain occupations the individual knows that he or she may be exposed to a higher risk of unemployment than in other occupations, e.g. workers in the construction and building

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industry are exposed to unemployment due to the weather to a higher degree than employees in the IT industry. Such varying risks of unemployment may be reflected in the pay agreements of different industries, but exactly who may be unemployed and for how long, is still beyond the control of the individual. Unemployment insurance benefits are designed to help coping with the financial situation as unemployed, they secure the financial basis for existence in this situation, and enables the unemployed to have time to look for a new relevant job. The primary aim of unemployment insurance benefits is to provide security, the main hypothesis being that human beings perform better when they feel financially secure. It is widely discussed ‘how’ secure people should be and for how long. If the benefits are too generous and the benefit period too long, the situation as unemployed may tend to become permanent. The financial incentives to look for a job may disappear or become very small. In this situation the society is losing production and the public expenditures will be higher than necessary. The challenge is to design the unemployment insurance schemes in such a way that they strike a good balance between social security as unemployed and good incentives to look for a relevant job. There may even exist a theoretical optimal unemployment insurance scheme. This challenge has to some extend been blurred by the implementation of active labour market measures for unemployed over the last couple of decades. One argument runs as follows: Generous benefits is not a problem when all unemployed are participating in activation, they are ‘forced’ to look for jobs and to participate in educational and training programs etc. Even as a participant in active labour market measures it is evident that the intensity in applying for a ‘real’ job increases when the benefits decrease. There are many good arguments for active labour market measures, but one of them is not that they replace good incentives for work in the benefit schemes. The ideal is to construct active labour market measures and benefit schemes in such a way that they both support the return of the unemployed to a relevant job. It is not the return to any job, implying any bad job, which is the aim. The objective of the active measures is to keep up or improve the employability of the unemployed, implying the return to as ‘good’ a job or even a better job than the previous one. This is not an easy task, and as will become evident in the following, countries use very different designs to obtain this goal. Unemployment insurance benefit schemes are not the only relevant ones in this context. Many countries have unemployment assistance schemes as follow-on schemes to insurance benefits, other countries have social assistance schemes to fulfil this role. These schemes will also be studied, but they are not the only ones. In most West European countries there are hundreds of thousands (many millions in the large countries) outside the labour force even if they are in the ‘working ages’. Some persons are long term ill or disabled, other persons have retired early. Illness and disability are usually caused by health problems and cannot be directly compared with unemployment. Never the less, financial incentives to apply for these schemes as well as to stay in them will be studied. Several countries, e.g. the Netherlands who has particularly many disability pensioners, have tightened the access criteria, reduced benefits and introduced more frequent control to ensure continued eligibility for benefits. Other countries, e.g. Denmark, have turned the medical criteria ‘up side down’, the focus is now on what the disabled can do, not on how disabled in general terms he or she is. The potential gains from avoiding people to enter these schemes ‘incorrectly’ are very substantial.

The Role of Public Benefits: A Case Study for Seven European Countries

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Future ageing problems and early retirement from the labour market is a bad mixture. Early retirement is very popular in many countries and there are several roads leading to this situation, in some countries also via public schemes, which almost guide to early retirement. Early retirement may be interpreted as a kind of voluntary unemployment. The incentives to join and to stay in such schemes will be studied.

Unemployment Insurance Benefit Schemes The countries included in this study, Denmark, Finland, Great Britain, Italy, the Netherlands, Spain and Sweden all have unemployment insurance schemes. Denmark, Finland and Sweden have voluntary schemes or components of schemes, which are voluntary. The schemes of the other countries are mandatory. The access conditions to benefits and the entitlements from these schemes will be described very shortly for each country based on 2000 rules, with significant later changes up to 2004 also being mentioned. The sequence of the countries listed above will be changed in the following to be more in accordance with the established categorization of welfare state regimes. The Nordic countries will be first, followed by the Netherlands, then Italy and Spain and finally Great Britain. Besides the unemployment insurance schemes the countries also have ‘follow-on’ schemes or alternatives to the insurance schemes. These will also be described, but the insurance schemes will be first.

Denmark Main characteristics: Completely voluntary, income related scheme with a low cap and a high floor, long benefit period. Access conditions for benefits: Membership of the voluntary scheme for 1 year, shorter for newcomers, and work for 1 year within the last 3 years before the unemployment spell. Only work periods after joining the scheme count. Entitlements: The general rule is compensation of 90 percent of the lost income up to a cap, which is approximately 65 percent of the full time average wage for an industrial worker equivalent to OECD’s average production worker, the APW. If the unemployed person has a work record of 3 years (full time work) there is a minimum benefit, which is 82 percent of the maximum benefit. The floor is reached at approx. 55 percent of the APW income level. The benefit period is maximum 4 years, in the last 3 of which the unemployed participates in active labour market measures. Lower rates for some groups of young unemployed exist, but are not applied in this study. Recent developments: The described structure has not been changed in recent years.

Finland Main characteristics: Voluntary income related component, a relatively low floor and no ceiling, a relatively short benefit period.

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Access conditions for benefits: The voluntary earnings related component requires 10 months membership of the insurance scheme. Both components, the basic and the voluntary, require a work period of at least 43 weeks (minimum 18 hours a week) within the last 24 months before the unemployment spell. Only work after joining the voluntary component counts. Entitlements: The benefit in the basic component is a pure flat rate. This benefit also constitutes the minimum benefit in the voluntary component. Lost income (95 percent) above the level of the minimum benefit is compensated by 42 percent up to a monthly income of 90 times the daily minimum benefit. 95 percent of the lost income about that level is compensated by 20 percent. There is no maximum for the benefit, the minimum benefit is reached at approx. 21 percent of the APW level. The scheme includes additional benefits for children. This benefit can be received for a maximum of 500 days, 100 weeks for both components. There is a waiting period of 7 days. Recent developments: the 42 percent was increased to 45 percent. A compensation of 55 percent (instead of 45 percent) in 150 days was also introduced in case of lay offs and a membership period of at least 5 years. The work requirement of 43 weeks is now within the last 28 months.

Sweden Main characteristics: Voluntary income related component, a relatively high cap and a relatively low floor. Until recently there were no effective time limitations on the benefit period. Access conditions for benefits: A 12 months membership period is required in the voluntary income related insurance scheme. For both the basic and the voluntary component there are specific work requirements (a certain amount of hours within 6 months), which have to be met in order to be eligible for benefits. Only work periods after joining the voluntary scheme count. Entitlements: The benefit in the basic scheme is a flat rate, which is also the minimum benefit in the voluntary component, just as in Finland. The compensation in the voluntary scheme is 80 percent of the lost income up to a cap of approx. 85 percent of the APW income level. The floor is at approx. 35 percent of the APW income level. The span of income, where the scheme is income related (80 percent of lost income) is much wider than in the Danish scheme. The benefit period is 300 days (5 days a week) with a waiting period of 5 days. Until recently the benefit period could be renewed repeatedly by participation in active labour market measures, there was in practise no time limitation. Recent developments: The possibility of repeated renewal of the rights to benefits through participation in active labour market measures no longer exists. An ‘activity guarantee’ has been introduced and the benefit period can only be renewed once, but there are no time limitations on the ‘stay’ in the ‘activity guarantee’. The cap in the voluntary component has been increased in both 2001 and 2002 for the first 100 days of unemployment and is now approx. equivalent to 100 percent APW income level. The minimum benefit (the flat rate in the basic scheme) was also increased considerably in both 2001 and 2002. The higher cap for the first 100 days of unemployment is similar to the higher compensation rate in the Finnish scheme for the first 150 days in case of lay-offs.


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The Netherlands Main characteristics: Mandatory scheme, composite working requirement, duration of benefit period depends on length of working period and age, high cap and a relatively low floor. Access conditions for benefits: The scheme is mandatory. Access to income related benefits depends on the length of the work period. There are two components in the requirement, one is work for at least 26 weeks out of the last 39 weeks before the unemployment spell, and another is work for at least 52 days in each of 4 years out of the last 5. If only the first component is met, the benefit will be a flat rate. Entitlements: The compensation percentage is 70 in the income related scheme with a cap of approx. 1.4 APW income level. The floor is at approx. 50 percent of the APW income level. The flat rate benefit is related to the minimum legal wage, for a single it is 70 percent of that wage. The benefit period varies with the length of the working period and the age of the recipient, the variation is from 6 to 60 months. After the ordinary benefit period has expired it is possible to receive the minimum benefit for up to 2 years. Recent developments: The described structure has not been changed in recent years.

Italy Main characteristics: Mandatory scheme, several components with varying benefit levels and duration of benefit periods. Access conditions for benefits: General unemployment scheme: Benefits are paid to dismissed workers who have paid contributions for at least 52 weeks in the last 2 years before the unemployment spell and have been registered on the placement lists for at least 2 years (an indication that you belong to the work force). The scheme is mandatory and available for unemployed who have been laid off individually or collectively and who are not eligible for the mobility benefit. First time job seekers are not eligible for benefits from the scheme. Mobility scheme: the benefit is paid to workers who have been laid off from firms eligible for the ‘Wage Supplementation Funds’, primarily large firms. 6 months of work is required. Entitlements: General scheme: Compensation is 30 percent of the former wage up to a cap. The benefit period is up to 6 months. Mobility scheme: Compensation is 64 percent of the former wage up to the same maximum as for the general scheme resulting in a much lower cap, because the compensation percentage is much higher. The benefit period can be up to 4 years. There are several variants of both schemes, the descriptions here cover the mainstream variants. Recent developments: The compensation was increased to 40 percent in the general scheme and the benefit period is now 9 months for unemployed over 50 years.

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Spain Main characteristics: Mandatory scheme, variable benefit period, minimum and maximum benefits depend on number of family members. Access conditions for benefits: This mandatory scheme requires contributions for a minimum of 360 days within the last 6 years for benefit eligibility. Entitlements: Compensation is 70 percent of the former income for the first 180 days, thereafter 60 percent. The minimum benefit is equivalent to the official minimum wage, the maximum is 1.7 times the minimum wage, all for a single. The minimum and maximum benefits are higher for families with children. The benefit period is as a minimum 120 days. Depending on the former work record it can be up to 2 years. Recent developments: The described structure has not been changed in recent years.

Great Britain Main characteristics: mandatory scheme, pure flat rate benefits, short benefit period, same basic benefit level for follow-on scheme. Access conditions for benefits: The Jobseekers Allowance, JSA, scheme is for people who are unemployed and who are seeking for a job. The contribution based component JSA ( c ), requires a former work record, a specific contribution record to be precise, within the last two tax years before the unemployment spell. The income based component, JSA ( ib ), is for unemployed who are seeking for a job, but who do not meet the contribution requirement for the JSA ( c ) scheme or who have been ‘out-insured’, i.e. have exhausted their benefit rights from the JSA ( c ) component. Entitlements: The benefit level depends on age, for those aged over 25 years it was 52.20 GBP/week in 2000 (highest rate) . The basic benefit level is the same in the JSA ( c ) and the JSA ( ib) components, but in the last mentioned it is means tested against other income and net wealth. The JSA ( ib ) scheme contains benefit supplements for dependent spouses and for children. These supplements are also available for recipients from the JSA ( c ) scheme. There is a waiting period of 3 days. The benefit period in the JSA ( c) component is 6 months. There are no time limitations for the JSA ( ib ) scheme. Recent developments: The supplements for children in the JSA ( ib ) component will be replaced by tax credits from the Child Tax Credit and Work Tax Credit schemes from April 2004.

Net Replacement Rates as Unemployed What is the outcome of being unemployed according to the just described insurance rules of the seven countries? The result is contained in table 1 for a single person without children. It is assumed, that the housing costs constitute 20 percent of the gross wage of the average worker (OECD concept, here referred to as the 100 percent APW), both when he or she works and when he or she is unemployed, the person stays where he or she lived when in work. The income after tax, social contributions (paid by the APW) and housing costs (reduced by housing benefits received) in unemployment is related to the corresponding amount when in


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work, the result is the net replacement rate. The wage income varies around the average for industrial workers, more precisely around the gross wage of OECD’s average production worker, the APW. The calculations of net replacement rates have been performed by national teams doing ‘their own’ countries according to a common framework agreed upon by all the teams. Table 1. Net replacement rates for a single in unemployment (insured), 2000. APW inc 0.67 0.80 1.00 1.25 1.50

DK 74 57 46 36 30

FIN 55 50 44 37 34

S 68 71 55 42 35

NL 82 64 60 62 66

IT 31 31 33 33 33

ES 53 60 63 51 41

GB 49 38 29 22 18

Source: ‘7 country study’. Note: DK: Denmark, FIN: Finland, S: Sweden, NL: the Netherlands, IT: Italy, ES: Spain, GB: Great Britain.

Table 2. Net replacement rates for sole provider (one child) in unemployment (insured), 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 89 79 69 57 49

FIN 83 82 70 58 52

S 87 86 74 60 51

NL 83 77 65 50 42

IT 50 50 53 51 50

ES 55 54 61 61 50

GB 50 46 41 33 27

Source: ‘7 country study’.

Two countries, Denmark and Great Britain have flat rate unemployment benefits and the replacement rates decrease accordingly over the entire income interval. The profile is, however, not solely determined by unemployment benefits but also by taxation and housing benefits. The profile for Sweden also shows decreasing replacement rates after the 1.00 APW income level. Italy has almost constant net replacement rates, the cap for the ordinary unemployment benefit, used here, is quite high. The Netherlands also has an almost constant profile after the lowest income level where the income as unemployed is ‘topped-up’ by social assistance benefits. Finland’s stepwise scheme results in a more gradual decrease of the replacement rates than is the case for Denmark and Great Britain. The profile for Spain is an inverted ‘U’, mainly caused by taxation. Taxation is also the reason behind the increase for Sweden from the first to the second income level. Only the Netherlands , Denmark and Sweden reach replacement rates above 70 percent, there are no serious incentives problems in relation to work for this case except for the Netherlands at the lowest income level. The sole provider for children is often regarded as belonging to a socially ‘exposed’ group. The sole provider will be our next family type, it could be interpreted as an extended single family type, children are ‘added’. What are the consequences of adding a child? The result is contained in table 2.

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The sole provider is assumed to be young, 22 years of age. The first to notice is that almost all replacement rates are considerably higher than for the single person, cf. table 1. The reason is that some of the U.B. schemes contain supplements for children, e.g. those for Finland and Great Britain, but also that most housing benefit schemes favour families with children, and that housing benefits in general are higher in unemployment than in work. Finally, sole providers usually receive more in child benefits than married couples and the same amount disregarding income. This also helps to increase the net replacement rate for the sole provider in unemployment. The profile for the Netherlands is now one of decreasing replacement rates, the young mother will only receive the income related benefit for a short period, increasing with age, the rest of the time a flat rate benefit is received. Serious incentives problems in relation to work are now evident at the two lowest income levels in all three Nordic countries and the Netherlands. Taking the possibility that return to work may be at a lower wage than received before the unemployment into consideration, the incentives to work are even more problematic at low income levels in the four mentioned countries. The high net replacement rates have here been used to indicate serious incentives problems, the argument can also be turned around, and then high replacement rates indicate a good protection against drastic reductions in income, when unemployment occurs. A third variation is to augment the single family with one adult, a spouse, who can work or not. We start with a situation where the spouse is not working, it is the ‘classical’ oneearner couple. This case may not be so interesting any more, because the two-earner couple is gradually replacing the classical family type. However, there is still a considerable variation in women’s labour market participation across Europe, and in the Nordic countries, where women’s labour market participation is high, there are many immigrant families with one breadwinner. The case therefore has some relevance and from a system approach, it is also of interest to see what happens, when an adult is ‘added’ to the single family type. The replacement rates for the one-earner couple are higher than for the single, except for Spain where they are slightly lower. This is due to supplements for the dependent spouse, ‘topping-up’ to a minimum income for this family type or to taxation of a couple, where unused tax allowances for the non working spouse can be transferred to the other spouse. Housing benefits might also have an impact. Compared to the sole provider especially Finland, the Netherlands and Great Britain have higher replacement rates for the one-earner couple than for the sole provider. Finland ‘sticks’ out with replacement rates in the 90’es up to the 1.00 APW income level, the Netherlands up to the 0.80 level. Finally we look at the two-earner couple with children. It is assumed that one spouse always has 50 percent of the income of the other. Table 4 contains the results from the cases where the high income spouse becomes unemployed while the lower income spouse continues to work and visa versa. The housing cost assumption is now 30 percent of the gross wage of the 1.00 APW. Before interpreting the net replacement rates it should be noted that the income of the working spouse is included in both the nominator and the denominator when the replacement rate is calculated. It should also be noted that the income of the high income spouse constitutes 2/3 of the gross wage income of the couple, leaving 1/3 to the low income spouse. One feature to be noted is that the difference between corresponding replacement rates from the two cases often is surprisingly small, some times down to less than 5 percentage


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points. Another is that losing 2/3 of the couple’s wage income has a very small effect on disposable income, less than 10 percent at the lowest income levels in Denmark and Sweden. Table 3. Net replacement rates for one-earner couple (no children) in unemployment (insured), 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 78 62 50 39 34

FIN 96 95 95 78 65

S 68 71 55 42 35

NL 99 92 74 67 70

IT 45 44 45 42 41

ES 50 55 61 50 41

GB 73 62 46 35 28


Table 4. Net replacement rates for a two-earner couple (2 children) when one of the spouses is unemployed (insured), while the other works. 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 96/99 90/97 72/90 63/85 58/81

FIN 74/80 71/77 62/77 57/78 57/78

S 90/94 90/93 78/91 67/92 62/92

NL 74/86 76/86 77/86 78/88 80/88

IT 57/74 57/73 56/72 54/71 54/72

ES 67/84 73/87 76/86 77/85 72/83

GB 84/85 72/74 56/60 39/57 34/57

Source: ’7 country study’.

In the Netherlands the effect is a reduction of between 1/5 and ¼ of the disposable income when in work over the entire income span. When the low income spouse loses her or his income, it is hardly visible at the two lowest income levels in Denmark, slightly more visible in Sweden, where the reduction is 7 percent. The loss of 1/3 of the wage income in the Netherlands and Spain implies a loss of approximately 1/6 of the net income of the couple in work over the entire income span. There are some very serious incentives problems in relation to work in two of the Nordic countries, Denmark and Sweden, at the two lowest income levels, the two countries in the world having the highest labour market participation among women also implying the highest proportion of two-earner couples among married or cohabiting couples. In most of the countries in this study the unemployed has to participate in active labour market measures during the benefit period or immediately after. The persons are then typically not registered as unemployed any longer but they receive a benefit, which in most countries is equivalent to unemployment benefits. Spain has no activation programme for unemployed and for the other countries it is only in Italy and the Netherlands that participation in active labour market measures implies a higher benefit than ordinary unemployment benefits. In most of the countries there are no financial incentives to participate in activation, it is a compulsory activity when unemployed.

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Benefit Duration Before the results are summarized below it is important to emphasize the duration of the benefit periods in the countries included in the study, because potential incentives problems are more severe if they can last for a long time. Denmark has a benefit period with a fixed duration of 4 years, for Finland the fixed duration benefit period is almost 2 years. Sweden has a benefit period of 1 year and 2 months but participation in the ‘Activity Guarantee’ is without time limitations. In the Netherlands the length of the benefit period varies with the previous work record and age, it is from ½ to 5 years. The ordinary unemployment insurance scheme in Italy has a benefit period of 6 months, for the Mobility Benefit it is up to 4 years in special cases, usually it is 1 year. In Spain the length of the benefit period depends on the former work record, it varies from less than ½ year to 2 years. In Great Britain the JSA ( c ) scheme has a fixed benefit period of ½ year. The span presented her is from less than ½ year (Spain) to 5 years (the Netherlands) and for Sweden the benefit period combined with participation in the ‘Activity Guarantee’ is without time limitations.

Main Results For the single person there are no serious incentives problems in any of the countries except in the Netherlands at the lowest income level. For the young single provider the Netherlands and the Nordic countries have serious incentives problems at the two lowest income levels. This is also the case for the one-earner couple in the Netherlands and in Finland, in Finland it is for the first three income levels. For the two-earner couple it is especially in Denmark and Sweden that the net replacement rates are very high at the two lowest income levels. The difference between the results when it is the spouse with the high income who is unemployed and the spouse with the low income who works and the reversed situation is often surprisingly small.

Follow-On/Alternative Schemes for Unemployed As just mentioned, the variation in the duration of the benefit period for unemployment insurance benefits in the seven countries is very considerable. In the case of Sweden there was (and probably still is) no effective time limitation, in the Netherlands the duration is up to 5 years, in Denmark it is 4 years and in e.g. Italy, Spain and Great Britain it is ½ year. It is therefore of great importance to include follow-on schemes or ‘exit’ schemes from unemployment insurance benefit schemes to assess the financial situation for long term unemployed. One set of such schemes is social assistance or unemployment assistance another set is early retirement schemes. We start with social assistance and unemployment assistance schemes. The follow-on or alternative schemes of the countries studied here will be presented in a very compact form.


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Denmark Main characteristics: Social assistance is ‘follow-on’ as well as alternative scheme for unemployment insurance, no automatic ‘topping-up’, no time limitations. Access conditions for benefits: Social assistance is a ‘last resort’ scheme for which you are eligible in case of a social event, i.e. unemployment, sickness, divorce, and when there are no other possibilities for financial support for you and your family. Recipients are obliged to participate in active labour market measures. Entitlements: The benefit is related to the maximum unemployment benefits, approx. 60 percent of this if the claimant is a non-provider in relation to children, 80 percent if he or she is a provider in relation to children. In a couple each spouse receives the benefit, and both spouses may receive social assistance even if only one of them has experienced a social event. There is also an allowance for housing costs, it is coordinated with ordinary housing benefits. There are no time limitations for the benefit period. Recent developments: It has always been the aim, that social assistance benefits should not be higher than unemployment insurance benefits. This has been difficult to obtain the way social assistance is allocated to couples. Reductions have therefore been implemented for social assistance for couples and ordinary housing benefits have also been included in the limits for total support.

Finland Main characteristics: Labour market support is a follow-on scheme to unemployment insurance and an alternative scheme, when the access conditions to the insurance scheme are not met. Access conditions for benefits: Labour market support is payable when the rights to benefits from the insurance scheme have expired or when the membership requirements (income related scheme) and work requirements for the insurance scheme (both components) are not met. Entitlements: The benefit level is the same as for the basic component in the insurance scheme. It is, however, tapered when other income, also that of a spouse, exceeds a certain ceiling. There are also additional benefits for children, but rates are lower than those in the insurance scheme. There are no time limitations for the benefit period. Recent developments: The described structure has not been changed in recent years.

Sweden Main characteristics: Swedish social assistance is, as in other countries, a last resort scheme and only available if there is no other way of earning a livelihood. It sets a minimum standard, but there is no automatic topping-up to this standard if other income, e.g. unemployment benefits from the insurance scheme, is below the social assistance level. It is an alternative scheme. Access conditions for benefits: The scheme is, as already mentioned, a last resort scheme, which is available when there are no other possible ways of earning a livelihood. The

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recipient has, if he or she is able to work, an obligation to apply for jobs, to participate in active labour market measures and/or participate in educational programmes to improve the chances for getting back to work. Entitlements: The level of social assistance is determined by the size and composition of the family. There are specific rates for adults, cohabiting adults and children, differentiated according to age. Even if the Swedish social assistance constitutes minimum income levels, there is no automatic topping-up to these levels if other income is lower. Recent developments: The described structure has not been changed in recent years.

The Netherlands Main characteristics: Guaranteed minimum standard, to which other income below this standard is ‘topped-up’. Access conditions for benefits: Dutch citizens and foreigners living legally in the Netherlands and without sufficient means for a living, are granted social assistance also as a ‘top-up’ of other income sources, if their combined level is below the minimum standard. There are no specific labour market obligations attached to the scheme. Entitlements: Dutch social assistance rates are related to the legal minimum wage. The rates are defined as varying proportions of the legal minimum wage according to family size and composition. There are special additions to cover housing costs, maximum 20% of the legal minimum wage. There are no time limitations for the benefit period. Recent developments: The described structure has not been changed in recent years.

Italy Main characteristics: Pilot program with the aim of providing a guaranteed minimum standard. The scheme is also an alternative for the unemployment insurance scheme, which has limitations in the access for benefits. Access conditions for benefits: Italian citizens with a low income and lack of sufficient means for a living are granted social assistance, also as a ‘top-up’ of other income. EU citizens have to stay 1 year, other nationalities 3 years, in Italy to become eligible for social assistance. Recipients are obliged to look for jobs and to participate in active labour market measures. Entitlements: The scheme consists of flat rate benefits according to family size and composition. There is ‘topping-up’ of other income sources to the minimum standard. There are disregards for income from work with the aim of encouraging recipients of social assistance to take paid work. There are no time limitations for the benefit period. Recent developments: The pilot program was terminated in 2001 and replaced by a ‘minimum income support’ scheme from 2002. The new scheme is administered at local level. Its characteristics are not very well defined.


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Spain Main characteristics: The scheme applied here is a follow-on scheme to the unemployment insurance scheme, it is time limited for most recipients. Access conditions for benefits: Only those for whom the rights for the unemployment insurance scheme have expired, are eligible for benefits. Entitlements: The scheme has flat rate benefits related to the legal minimum wage, the rate is 75 percent of the minimum wage. The scheme is time limited typically with a maximum benefit period of 21-30 months, depending on age and family size. For unemployed, who are more than 52 years of age, there are no time limitations for the benefit period. Recent developments: The described structure has not been changed in recent years.

Great Britain Main characteristics: JSA (ib) is a follow-on or an alternative scheme to JSA ( c ) . The basic benefit level is the same in the two schemes. Access conditions for benefits: JSA (ib) is for unemployed looking for a job and who do not meet the contribution requirements for the JSA ( c ) scheme or who are ‘out-insured’ from that scheme. Entitlements: The basic benefit level is the same as in the JSA ( c ) scheme, cf. the description of unemployment insurance schemes, and there are also benefits for a dependent spouse and for children. All benefits are means tested. There are no time limitations for the benefit period. Recent developments: The benefits for children in the JSA (ib) scheme will be replaced by tax credits from the Child Tax Credit and the Work Tax Credit schemes from April 2004.

Net Replacement Rates as Unemployed, Insured/Follow-On Or Alternative Schemes Table 5. Net replacement rates for a single in unemployment, first insured, then follow-on scheme, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 74/60 57/46 46/37 36/29 30/24

FIN 55/32 50/25 44/20 37/15 34/13

S 68/73 71/57 55/41 42/32 35/27

NL 82/88 64/70 60/52 62/40 66/33

IT 31/33 31/27 33/21 33/16 33/14

ES 53/13 60/11 63/9 51/7 41/6

GB 49/49 38/38 29/29 22/22 18/18


The effect of moving from unemployment insurance to a follow-on scheme (for Sweden to apply an alternative scheme) for a single unemployed is contained in table 5.

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The replacement rates in front of the / are identical to those in table 1. At the lowest income level the replacement rates of the follow-on schemes are higher than those for the insurance scheme in the Netherlands, Sweden and Italy, this is also the case for the next income level in the Netherlands, which seems to have an incentives problem at least at the lowest income level also for social assistance. For Great Britain the replacement rates for the two schemes are identical at all income levels, JSA ( c ) and JSA ( ib ) have the same basic rate. At all other income levels the follow-on scheme results in a lower compensation than the insurance scheme for all countries except Great Britain. Table 6. Net replacement rates for sole provider (two children) in unemployment, first insured, then follow-on scheme, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 89/82 82/75 72/66 63/58 55/51 Source: ‘7 country study’.

FIN 87/67 85/63 83/59 71/49 64/43

S 90/58 89/55 79/46 67/42 59/36

NL 84/84 76/78 71/67 62/53 64/45

IT 53/56 51/47 54/42 52/35 50/30

ES 59/22 61/17 59/13 65/10 58/9

GB 65/65 60/60 55/55 46/46 38/38

In some of the countries i.e. Spain, Finland and Italy the follow-on replacement rates are very low at least at middle to higher income levels. It should be noted that for Sweden social assistance is not a follow-on scheme but an alternative, so the interpretation for that country is not a sequence where one scheme follows after the other, but more of two parallel schemes, you are either in one or the other all the time. The next family type is the single provider. This time it is not the young mother with one child but the 40 years old mother with two children who experiences the transition from the insurance scheme to the follow-on scheme. The results are contained in table 6. Table 7. Net replacement rates for one-earner couple (no children) in unemployment, first insured, then follow-on scheme, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 78/60 62/47 50/38 39/30 34/26

FIN 96/96 95/95 95/95 78/78 65/65

S 68/114 71/88 55/64 42/50 35/41

NL 99/98 92/92 74/77 67/62 70/52

IT 45/42 44/35 45/33 42/28 41/24

ES 50/13 55/10 61/8 50/6 41/5

GB 73/73 62/62 46/46 35/35 28/28

Source: ‘7 country study’

For most of the countries the replacement rates for the follow-on schemes are higher than for the single at almost all income levels, Sweden being the exception. The reasons are the same as mentioned for the insured unemployed single provider, cf. table 2. The Netherlands and Denmark have high to very high replacement rates for the follow-on schemes at the two lowest income levels. In Spain and to some extend also in Italy the drop in disposable income


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from the insurance scheme to the follow-on scheme is quite dramatic. This is also the case for Sweden, but here you do not move from scheme to scheme. Now follows the situation for the classical one-earner couple when the breadwinner moves from the insurance to the follow-on scheme. Table 7 contains the results. Table 8. Net replacement rates for two-earner couple (2 children) when the ‘high wage’ spouse is unemployed, first insured, then follow-on scheme, while the other works, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 96/98 90/84 72/67 63/58 58/48

FIN 74/62 71/56 62/49 57/41 57/35

S 90/83 90/72 78/57 67/45 62/38

NL 74/50 76/40 77/31 78/24 80/26

IT 57/72 57/61 56/53 54/32 54/33

ES 67/43 73/42 76/41 77/41 72/41

GB 84/84 72/72 56/56 39/39 34/34


Here we see some very high replacement rates at the lower income levels in Sweden, the Netherlands and Finland for the follow-on schemes (parallel scheme for Sweden). They are close to 100 percent or even over 100 percent. If the non working spouse in the Danish couple is willing to work the replacement rate would be significantly higher both for the insurance scheme (topping-up by social assistance) and the follow-on scheme, at the lowest income level the replacement rates would be 105/109. In contrast there is a dramatic drop in compensation in Spain when moving from one scheme to the next. Table 9. Net replacement rates for two-earner couple (two children) when the ‘low wage’ spouse is unemployed, first insured, then follow-on scheme, while the other works, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 99/101 97/89 90/72 85/65 81/63

FIN 80/72 77/64 77/48 78/46 78/49

S 94/83 93/72 91/58 92/58 92/58

NL 86/55 86/56 86/57 88/57 88/58

IT 74/67 73/66 72/63 71/61 72/61

ES 84/71 87/73 86/72 85/63 83/63

GB 85/85 74/74 60/60 57/57 57/57


For the two-earner couple the ‘high wage’ spouse moves from the insurance scheme to the follow-on scheme, while the ‘low wage’ spouse continues to work. Table 8 contains the results. The replacement rates in front of the / are identical to those in table 4. Denmark, Great Britain and Sweden have high compensations at the two lowest income levels for the followon schemes. In this case Spain and Italy have higher replacement rates than the Netherlands, where the loss of the ‘high wage’ has a very severe impact at the middle to high income levels. What happens when it is the ‘low wage’ spouse who moves from scheme to scheme while the ‘high wage’ spouse continues to work? The results are in table 9.

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Table 10. Net replacement rates for two-earner couple (2 children) when both spouses are unemployed, first insured, then follow–on scheme, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 93/97 87/83 71/66 57/53 48/44

FIN 69/60 67/52 58/41 49/33 43/28

S 84/83 83/72 72/57 62/45 55/38

NL 60/50 62/40 64/31 66/25 68/21

IT 46/48 46/41 47/34 30/27 31/22

ES 50/19 56/15 62/12 62/9 56/8

GB 79/79 67/67 52/52 41/41 34/34

Source:’7 country study’.

The first set of replacement rates are well known, they are identical to those in table 4 behind the /. Denmark, Sweden and Great Britain again show potentially serious incentives problems in relation to work at the low income levels for the follow-on schemes Higher up in the income scale it is surprisingly how large the impact is from losing 1/3 of the gross wage of the family. Finland, Sweden and the Netherlands have replacement rates below 67 percent, which this simple gross wage calculation would suggest. There is no social assistance left, it is tapered away against the income of the working spouse, and no special supplements for housing and children, and the housing costs weigh relatively heavily for the family which is now effectively a one earner couple. It is relatively rare that both spouses in a couple are unemployed at the same time, but it happens and in that case table 10 contains the results. Denmark and Sweden have high replacement rates for both schemes at the two lowest income levels, Great Britain at the lowest. Denmark has the most generous follow-on scheme at all income levels of all seven countries. Danish replacement rates are twice those of Italy, which again are more than twice those of Spain. While the ‘follow-on’ scheme for the Netherlands was among the two most generous for families with one income at all income levels, it is much more modest for families with two income earners, where one or both spouses receive benefits from the ‘follow-on’ scheme. The benefits from the follow-on scheme will be received, when the benefit period from the insurance scheme has expired, but is this sequential approach of any significance for the benefit level? It is in the case of Denmark, where social assistance will be lower when it follows the insurance benefits instead of employment at the income levels used here.

Benefit Duration All follow-on schemes, except for Spain, have no time limitations why potential incentives problems from these schemes are severe, because they can last for a very long time. This should be kept in mind when the results are summarized below.

Main Results The replacement rates are sometimes higher for the follow-on scheme than for the insurance scheme. For the single person only the Netherlands has serious incentives problems at the lowest income level in the follow-on scheme. For the single provider the Netherlands


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and Denmark have high replacement rates at the two lowest income levels. For the one-earner couple it is the Netherlands, Finland and Sweden who have serious incentives problems in the follow-on scheme at the lower to medium income levels. For Sweden the replacement rate is over 100, as it also can be for Denmark in special cases. For the two-earner couple Denmark, Sweden and Great Britain have high to very high replacement rates at the lower income levels whoever of the spouses are unemployed and receiving benefits from the follow-on scheme. If both spouses are unemployed at the same time Denmark and Sweden have incentives problems at the lower income levels in both schemes. The change in compensation from the insurance scheme to the follow-on scheme is sometimes drastic, especially in Spain, but sometimes also in Italy and Finland.

Early Retirement Schemes Early retirement from the labour market is very popular in several European countries. In some countries it is possible to retire early only from a situation of employment, this is the case in the Netherlands and Great Britain. In other countries it is also possible to retire early from a situation of unemployment. The schemes, public or private, used in this study are shortly described in the following.

Denmark Main characteristics: Long contribution record, access from employment as well as unemployment, no labour market obligations attached. Access conditions for benefits: Access to the early retirement scheme requires a long contribution record both for the unemployment insurance scheme and the early retirement scheme, 25 years out of the last 30 with various transition arrangements. It is a condition for benefit eligibility that the early retiree is eligible for unemployment insurance benefits when early retirement benefits are claimed. This implies, that there is no access to the early retirement scheme for recipients of social assistance. Minimum age is 60 years. Entitlements: When entering at the age of 60, or when the initial conditions are met after that age, the benefit is 91 percent of the actual or potential unemployment insurance benefits. If entering at the age of 62, or two years after the initial conditions are met, the benefits are identical to the actual or potential unemployment insurance benefits. Various premiums can be received by further deferral dependent on hours of work in the deferral period. Early retirement according to the rules described here has a maximum duration of 5 years. Recent developments: The described structure is still valid.

Finland Main characteristics: Finland has several schemes for early retirement, the unemployment pension, early old-age pension and an individual disability pension. Only the early old-age pension will be described. No labour market obligations are attached to the scheme.

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Access conditions for benefits: The access rules are the same as for ordinary old-age pension, which has a residence based component and an occupational component. The minimum age is 60 for drawing an early pension. Entitlements: The pension is calculated as an old-age pension, where the maximum for the occupational component is 60 percent of the former income. The pension for the early retiree may be smaller because there are fewer years for accrual of the pension. An early drawn pension is furthermore reduced by 0.4 percent per month it is drawn before the age of 65, this is also the case for the residence based component. The maximum duration is 5 years, but the early drawn pension continues as old-age pension from the age of 65 years. Recent developments: The unemployment pension and the individual disability scheme will be phased out. The minimum age will be 62 years from 2005.

Sweden Main characteristics: Sweden has no dedicated early retirement scheme but the flexible old-age pension scheme can be used for early retirement from the age of 61. No labour market obligations are attached to the scheme. Access conditions for benefits: Swedes, and other nationalities, who meet the ‘opening’ criteria for public pensions, 3 years of residence for the basic pension and 3 years of work for the occupational pension, can draw a pension early from the age of 61. Entitlements: A full pension requires 40 years of residence for the basic pension and 30 years of work for the occupational scheme. If the criterion is met for the occupational component, the residence requirement is reduced to 30 years for a full basic pension. If the requirements are not met, the pension is reduced accordingly, e.g. to 25/30 if the work record is 25 years. If the pensions are drawn early they are reduced by 0.5 percent per month they are drawn before the age of 65 years is reached. At the age of 61 the pension will be reduced by 24 percent for the rest of the life. It is possible to change decision, and return to work without receiving a pension, this will then be adjusted accordingly. The duration of early retirement in Sweden is 4 years as a maximum. Recent developments: Sweden has implemented a new pension system, which gradually will replace the old one. It is also possible to draw some of the components in the new scheme early, but not the Guarantee Pension. The pension is then reduced directly by a demographic factor.

The Netherlands Main Characteristics: The early retirement schemes in the Netherlands are labour market agreed schemes called VUT. There is only access from employment. No labour market obligations are attached to the scheme. Access conditions for benefits: The age range for access to the scheme is from 58 to 63 years, typically 60 years. The usual requirement is 10 years of employment within the same industrial sector immediately before the early retirement.


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Entitlements: The compensation is usually 75 to 80 percent of the former wage, 80 percent have been used for the actual calculations. The benefits continue as old-age pension from the age of 65 in addition to the national pension. Recent developments: The favourable VUT schemes are to an increasing extend being replaced by other ‘pre-pension schemes’, where early drawn pensions are reduced actuarially, also when received as old-age pensions.

Italy Main characteristics: Seniority Pension is possible in the ‘old’ income related pension scheme, which gradually will be replaced by the ‘new’ contribution based scheme. There are no labour market obligations attached to the scheme. Access conditions for benefits: It is a requirement that there is a contribution record of at least 18 years up to and including 1995. Furthermore the requirement is a contribution record of at least 37 years disregarding age or 35 years with contributions and an age of at least 57 years. This is the relevant scheme for our case. Entitlements: The benefits are calculated in the same way as income related old-age pension, the main difference being, that the total contribution record for early retirement usually is shorter than for ordinary old-age pension, where the maximum contribution record is 40 years. The accrual rate is 2 percent of an average of indexed earnings over the last 10 years, up to a relatively high income level, above which the accrual rate drops, but there is no cap on the income generating pension rights. The benefit period can be up to 10-12 years. Recent developments: The Seniority Pension scheme will gradually be phased out, new workers entering the labour market from 1996 will follow a new contribution based pension scheme, where early retirement is also possible but at an actuarial reduction determined by the longer remaining expected life span of an early retiree compared with an old-age pensioner. The Seniority Pension has also been tightened up, a 5 year ‘qualifying period’ which allowed no contributions but counted as years with contributions, has been abolished and the minimum contribution record of 37 years will be prolonged to 40 years from 2008.

Spain Main characteristics: Early retirement in Spain takes place by using the flexible old-age pension scheme. Only workers belonging to the old-age pension scheme from before 1967 have this option. No labour market obligations are attached to the scheme. Access conditions for benefits: The age range for access to the scheme is from 60 to 64 years, typically 60 years (assumed for the actual calculations). A long contribution record is also required. Entitlements: The pension is calculated as an old age pension but is reduced by 8 percent per year the pension is drawn before the age of 65. If the work record has a length of 40 years the reduction is by 7 percent per year, it is the assumption used here. The benefit period is to the age of 65, after which the pension continues as old age pension. Recent developments: The scheme is temporary in the sense that during the coming 5-10 years it will be closed for new entrants.

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Great Britain Main characteristics: Great Britain has no dedicated public schemes for early retirement but private schemes (company schemes) and schemes for public sector employees contain early retirement options. These schemes can be accessed from employment only. Early retirees have no labour market obligations. Access conditions for benefits: 50 years of age seems to be pivotal in Great Britain. Above that age an occupational pension will usually be received by early retirement. A long work record, 30 years or so, is usually required as well. Entitlements: There are many schemes and a considerable variation for the outcome of an early drawn pension. A relatively poor outcome after 30 years of work would be a pension of 50 percent of the final earnings and reduced for actuarial reasons by 25 percent for the 60 years old and by 50 percent for the 55 years old. A relatively generous outcome could be a pension of 2/3 of final earnings with no actuarial reductions. The last mentioned option could be in case of compulsory retirement or in cases where the age plus the length of the working period exceeds the number 85, this is primarily for public sector employees. A pension of 40 percent of the final earnings has been used for the actual calculations. Recent developments: The ‘Minimum Income Guarantee’ (MIG) introduced from 1999 for persons who are 60 or more and who have a low income might be considered as a kind of public early retirement scheme. The MIG was replaced by the ‘Pension Credit’ scheme from 2003, this scheme also includes a benefit component, which can be received from the age of 60.

Net Replacement Rates as Insured Unemployed and Early Retiree It is assumed that the route to early retirement is via unemployment. This is, however, not always possible but then unemployment and early retirement can often be considered as alternatives, at least for relevant age groups, and a comparison of the economic position in either situation becomes relevant. Table 11 contains the results for a single in the relevant age group moving from unemployment to early retirement or having the two schemes as alternatives (the Netherlands and Great Britain). In Italy, the Netherlands, Finland and Great Britain the compensation is higher as an early retiree than as an unemployed, sometimes by a very substantial margin. At the lowest income level Italy and the Netherlands have very strong incentives to join the early retirement scheme. Denmark has a slightly lower compensation for early retirement than for unemployment. In Sweden, the low income earners only receive a reduced basic pension as early retirement benefits. It is lower than the unemployment insurance benefit. Higher up in the income range the early retiree in Sweden also receives a reduced occupational pension, but it is income related, and then the situation as early retiree is financially better than as unemployed. When the replacement rate for early retirement is calculated, it should be remembered that the benefits are obtained after many years of income and/or payment of contributions at the specified APW income levels, a long stable work and income record at the specified income level is required and also assumed.


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Table 11. Net replacement rates for a single as insured unemployed and as early retiree, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 74/68 57/53 46/42 36/33 30/28

FIN 55/67 50/55 44/47 37/51 34/54

S 68/46 71/45 55/47 42/50 35/47

NL 82/86 64/69 60/72 62/74 66/77

IT 31/95 31/65 33/70 33/70 33/70

ES 53/53 60/60 63/63 51/63 41/64

GB 49/56 38/46 29/37 22/30 18/26


There is no case with a single provider as an early retiree, the next case is for the oneearner couple. The results for this family type are contained in table 12. Table 12. Net replacement rates for a one-earner couple when the breadwinner is unemployed and an early retiree, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 78/73 62/58 50/47 39/37 34/32


FIN 96/87 95/75 95/60 78/53 65/54

S 68/46 71/45 55/47 42/50 35/47

NL 99/95 92/84 74/77 67/79 70/78

IT 45/109 44/69 45/75 42/72 41/71

ES 50/51 55/55 61/61 50/65 41/66

GB 73/72 62/61 46/48 35/38 28/33

Italy and the Netherlands, now joined by Finland, have very high net replacement rates for early retirement at the lowest income level also for this family type, but, generally speaking, early retirement is not so favourable compared to unemployment as in the case for the single. However, if the non-working spouse in Sweden is 61 or more he or she is eligible for a reduced basic pension, and then the replacement rates would be 105, 91, 80, 75 and 68 respectively, much higher than for unemployment. If the non-working spouse in Denmark is willing to work, the corresponding replacement rates in early retirement would be 108, 86, 69, 55 and 47 respectively, and the replacement rates for unemployment would also be higher. More or less special situations for this family type in these two countries may result in very strong incentives to join the early retirement scheme. Table 13. Net replacement rates for two-earner couple when the ‘high wage’ spouse is unemployed and then (or) an early retiree while the other spouse works, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 96/95 90/80 72/62 63/55 58/53


FIN 74/69 71/66 62/67 57/70 57/72

S 90/65 90/65 78/66 67/68 62/66

NL 74/79 76/81 77/83 78/84 80/86

IT 57/77 57/77 56/80 54/82 54/81

ES 67/70 73/75 76/77 77/77 72/77

GB 84/63 72/54 56/50 39/46 34/45

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The two-earner couple, this time without children, where first one spouse, then the other and finally both spouses follow the route over unemployment to early retirement or as alternatives are the next cases. The couple should be without children both as unemployed and in early retirement, but for unemployment it is the situation with children, which is contained in the following tables. The net replacement rates are then somewhat higher than they should be in case of unemployment. In table 13 it is the high income spouse, who becomes unemployed and/or an early retiree. Replacement rates for early retirement are high at the lower income levels in Denmark, the Netherlands and Italy. A strict comparison with unemployment is not possible, but early retirement is better than unemployment in the Netherlands, Italy and Spain at all income levels and in Finland, Sweden and Great Britain at the higher income levels. What happens when it is the ‘low income’ spouse, who becomes unemployed and/or an early retiree is contained in table 14. Table 14. Net replacement rates for two-earner couple when the ‘low wage’ spouse is unemployed and then (or) an early retiree while the other spouse works, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 99/94 97/93 90/88 85/82 81/78

FIN 80/106 77/98 77/91 78/87 78/84

S 94/96 93/93 91/89 92/87 92/85

NL 86/89 86/89 86/90 88/91 88/91

IT 74/87 73/88 72/90 71/89 72/89

ES 84/98 87/90 86/84 85/86 83/88

GB 85/85 74/79 60/73 57/69 57/65


There are strong financial incentives quite high up in the income range in all countries for the low wage spouse to join the early retirement scheme instead of working. The replacement rates for unemployment in this case are high for almost all countries at all income levels, cf. the comments for table 4, but those for early retirement are even higher in Finland, the Netherlands, Italy, Spain (except at one income level) and Great Britain. In Denmark and Sweden they are close. It should be remembered that the replacement rates for unemployment are for a family with children and then higher than for a family without children. The last case is when both spouses are unemployed and then (or) early retirees, the results are contained in table 15. Table 15. Net replacement rates for two-earner couple when both spouses are unemployed and then (or) early retirees, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 93/73 87/66 71/50 57/39 48/32


FIN 69/69 67/61 58/57 49/55 43/55

S 84/53 83/53 72/51 62/52 55/49

NL 60/68 62/71 64/73 66/75 68/77

IT 46/67 46/66 47/70 30/71 31/70

ES 50/67 56/64 62/61 62/62 56/65

GB 79/59 67/50 52/40 41/34 34/33


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There are no strong financial incentives for both spouses to join the early retirement scheme at the same time in any of the countries, but it is better for the couple to be early retirees than unemployed in the Netherlands, Italy, Spain (except at one income level) and in Finland (except at two income levels).

Benefit Duration Early retirement lasts up to ordinary old-age retirement age in all the countries. The years which can be spend in early retirement is 5 years in Denmark and Finland, 4 years in Sweden, 5 years or more in the Netherlands, 12 years or more in Italy, 5 years in Spain and 10 years or more in Great Britain. In all the countries, except the Netherlands and Great Britain, the early retirement period can be preceded by a period in unemployment and the benefit period for unemployment insurance benefits is often prolonged for this age group. In e.g. Denmark it is possible to have unemployment benefits for 4 years, then a prolonged benefit for 5 years and finally early retirement benefits for 5 years, in total a combined benefit period of 14 years from the age of 51 to the pension age of 65 years. In Italy this period can be even longer.

Main Results Early retirement is permanent in the sense that only few return to work, the majority stays in the scheme until old-age pension takes over. Early retirement schemes are popular in Europe. The results of this section show that there are strong incentives to join these schemes especially for the low wage spouse in almost all the countries at almost all income levels. The incentives to join the scheme for the ‘high wage’ spouse are concentrated at the lower income levels in three countries, Denmark, the Netherlands and Italy. A ‘pure’ comparison with unemployment is not possible (benefits related to children are included in one situation but not in the other) but it is often so, that the compensation is higher in early retirement than in unemployment, implying financial incentives to select the more permanent scheme if possible and then almost exclude the possibility of returning to work.

Sickness Benefit and Disability Pension Schemes Access to these schemes is different from the former ones described. Sickness and permanent health problems are access conditions, which have to be met. The risk of becoming ill is different from the risk of becoming unemployed, but sickness, especially long term, might lead to unemployment. There are, however, separate schemes for sickness and disability and it is relevant to study the financial incentives of returning to work. The debate about using the sickness benefit scheme as a short term ‘leave’ scheme is well known, e.g. in Sweden. The financial consequences of moving from sickness to disability are also important. If the disability benefits are attractive compared with sickness benefits there might be an incentive at least to try to move to the disability pension scheme.

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The formal access to the disability pension scheme is from relatively long term sickness in most countries and then only after all attempts for rehabilitation have been exhausted. These ‘tests’ may, however, be performed more or less stringent.

Sickness Benefit Schemes These schemes are similar in most of the countries and will only be described very shortly. Labour market agreements will often provide full compensation at least for some time, but here it is the insurance schemes, which will be described.

Denmark Access conditions for benefits: The employer is obliged to pay sickness benefits for the first two weeks if the employee has a certain work record, a minimum number of hours within 8 weeks with the same employer. The obligation to pay benefits is passed on to the municipality after the first two weeks. Entitlements: The benefits are based on an hourly rate, which is multiplied by the weekly working hours in order to derive the weekly benefits. The compensation is 100 percent of the lost income up to a relatively low cap, where the benefit reaches its maximum. The maximum benefit period is 52 weeks.

Finland Access conditions for benefits: A certain work record, 3 months, is required to be eligible for benefits. Entitlements: The benefits are calculated on the basis of 95 percent of the gross wage. Compensation is approx. 70 percent up to APW income level, somewhat lower for income above that level. There is no cap and then no maximum for the benefit. There is a waiting period of 9 weekdays before benefits can be received. The maximum benefit period is 50 weeks.

Sweden Access conditions for benefits: All who work in Sweden have immediate access to sickness benefits, there is no qualifying period. Entitlements: The employer is obliged to pay 80 percent of the lost income for the first two weeks, with no cap. There after the insurance takes over. The compensation is still 80 percent, but now with a cap. The benefits can be supplemented by 10 percent of the lost income in this period. There is one waiting day, before benefits can be received. There is no time limitation for the benefit period but after 1 year and 3 months, a disability pension will be allocated if the criteria for this scheme are met.


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The Netherlands Access conditions for benefits: Dutch workers have immediate access to sickness benefits. The employers are responsible for the scheme and they finance it entirely. Entitlements: The compensation percentage is 70 of the lost income up to a cap. There is a waiting period of 2 days. Labour market agreements often wave the waiting period and have a compensation percentage of 100. This is the situation used for calculation of Dutch sickness benefits in this study. The benefit period is 1 year as a maximum.

Italy Access conditions for benefits: Italian workers have immediate access to sickness benefits. Employers are obliged to pay, but they are reimbursed by the insurance scheme. Entitlements: The compensation is 50 percent of the lost income for the first 20 days of illness, thereafter 66 percent. The benefit period is ½ year as a maximum.

Spain Access conditions for benefits: Spanish workers have access to sickness benefits (temporary incapacity benefits) after a qualifying period of 180 days with payment of contributions. The employer is obliged to pay for the first 15 days. Entitlements: Compensation is 60 percent of the income basis for payment of social contributions for the first 20 days of illness, 75 percent thereafter. There are 3 waiting days in the scheme. The maximum benefit period is 12 months with a possibility of prolongation.

Great Britain Access conditions for benefits: The Statutory Sick Pay (SSP) scheme is immediately available for workers who earn more than the Lower Earnings Limit. Entitlements: The SSP benefit is a flat rate benefit. Labour market agreements often supplement the SSP benefit to provide up to full compensation for the lost wage, this is the situation used for calculation of sickness benefits for Great Britain in this study. The SSP scheme has 3 waiting days and the maximum benefit period is 28 weeks. When the rights for SSP expire short term Incapacity Benefits may be received, if the requirements for this scheme are met, a certain amount of minimum contributions paid or allocated over the last 2 years.

Disability Pension Schemes In some of the countries these schemes are related to the old-age pension scheme, in other countries they are more like sickness or unemployment benefit schemes.

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Denmark Main characteristics: Residence based scheme with flat rate benefits. The scheme is related to the old-age pension scheme. Access conditions for benefits: The working capability must be reduced by at least 50 percent for eligibility and the reduction must be permanent. The scheme is residence based, a ‘full’ pension requires that 4/5 of the ‘theoretical time’ between the 15th year and the time of the pension case is spent in Denmark. There are several degrees of disability in the scheme, the lowest being 50 percent. Entitlements: The benefits are flat rates. The basic one and the pension supplement are shared with those in the old-age pension scheme. Other supplements depend on the degree of disability. The benefit period lasts until the old-age pension starts to be received at the age of 65 for some, 67 for others. Recent developments: A new scheme was introduced in 2003. The rate structure was simplified and the emphasis was changed from degrees of disability to remaining capabilities. The scheme is no longer related to the old-age pension scheme.

Finland Main characteristics: The disability pension is basically calculated as an old-age pension based on ‘anticipated years’. Access conditions for benefits: The working capability must be reduced by at least 60 percent for a full pension, by 40 percent for a partial one. Access is for persons who also have access to the old-age pension, both to the basic component, which is residence based, and to the occupational component, which depends on earlier work and income. For the residence based component Finland applies a 4/5 rule similar to that of the Danish scheme, but from the 16th year. Entitlements: The disability pension is calculated as if it was an old-age pension at the age when the disability occurs. The years from this age until ordinary retirement, at 65, are ‘anticipated’. The accrual rate for the last 15 years is, however, smaller than for ordinary oldage pension. Partial pension is 50 percent of a full one. The benefit period lasts until ordinary retirement age. Recent developments: The described structure has not been changed in recent years.

Sweden Main characteristics: The disability pension is calculated in a similar way as the old age pension, with a difference for the rates of the basic pension and its supplement. Access conditions for benefits: The claimant must have lost his or her working capability partly or completely. Access conditions are the same as for old-age pensions, the basic pension is residence based, the occupational component depends on the former work record. For the residence based component Sweden applies a 4/5 rule similar to that of the Danish scheme but from the 16th year. There are, as already mentioned, several degrees of disability in the scheme.


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Entitlements: The pension is calculated as an old-age pension, but the basic pension component is smaller than for old-age pension, the supplement for the basic pension is, however, largest for the disability pension. This supplement is tapered against the occupational component, which is calculated just as for old-age pensions, the years until oldage retirement, at 65, being ‘anticipated’. There is, however, an option where the occupational pension is only based on very recent income, and this is often an advantage for the claimant, who receives according to the most advantageous option. The pension can be drawn in shares of ¼, ½ , ¾ and 1/1 according to the degree of disability. The benefit period lasts until the ordinary retirement age of 65. Recent developments: The described scheme was replaced by two new schemes from 2003, one for younger persons and another one for older persons. The new schemes are not related to the old-age pension scheme, which was also reformed from 2003 for pensioners having reached the age of 65 years. The new disability pension schemes are more like labour market related schemes, sickness and unemployment.

The Netherlands Main Characteristics: The Dutch disability pension scheme is related to the sickness benefit and the unemployment benefit scheme rather than to the old-age pension scheme. Access conditions for benefits: It is a requirement that at least 15 percent of the working capability has been lost to be eligible for benefits. A full pension requires the loss of at least 80 percent of the working capability. All employees meeting these criteria have access to the scheme, there is no qualifying period. Entitlements: The benefit level depends on the degree of disability, the former income and the age of the claimant. A full pension is 70 percent of the former income up to the same cap as for unemployment insurance benefits. This benefit can be received for less than a year for persons below 38 years of age, for a person who is 59, the benefits can be received until ordinary retirement age, which is 65 in the Netherlands. For those who’s rights for the benefit expire, there is a lower follow-on benefit for the rest of the benefit period, which lasts until the ordinary retirement age. Recent developments: Persons who receive disability pension in the Netherlands have been controlled more vigorously in recent years for meeting the criteria also after allocation of the pension.

Italy Main characteristics: The Italian disability pension scheme is closely related to the oldage pension schemes. Access conditions for benefits: Loss of 2/3 of the working capability to be eligible for the disability allowance, complete or almost complete loss of working capability for disability pension, the case to be described here. Access to the ‘old’ income related pension system requires 18 years of contributions before 1996. If this condition is met the disability pension is calculated just as an old-age pension, ‘anticipated years’ are used for the period from the pension case to the age of 60, maximum 40 years. This might result in lower pensions than a

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corresponding old-age pension where the pension age is 65 for men. If the 18 years are not met, but there are some years with contributions before 1996 the disability pension is calculated as a mix of the ‘old’ income related scheme and the ‘new’ contribution based scheme. With no contribution record before 1996 and at least 5 years after the pension is calculated according to the new scheme. Entitlements: Basis for the income related pension is an average of indexed earnings over the last 10 years. This average is multiplied by the length of the contribution record, maximum 40 years, and by an accrual rate of maximum 2 percent, implying a pension of max. 80 percent of the average earnings. The accrual rate decreases with increasing income but there is no cap. Low pensions can be topped-up to a guaranteed minimum. In the new scheme the accrual rate is constant and the average life income, indexed differently, is basis for calculation of the pension. The benefit period is until the age of 60. Recent developments: Italy is in the process of moving from one pension system to a new one. The new one is already in use for disability pensioners with less than 18 contribution years before 1996.

Spain Main characteristics: Spain has different types of disability pension schemes all dependent on former labour market activities. The type ‘absolute permanent incapacity for any work’ has been applied here. Access conditions for benefits: The mentioned criterion for incapacity has to be met. The claimant must have worked for at least ½ the time between the age of 16 and 26 or ¼ of the time between the age of 20 and the year of the pension case if this happens at an age higher than 26. Entitlements: The compensation is 100 Percent of the contribution base with a guaranteed minimum. Pension income from this scheme is non-taxable implying net replacement rates above 100 percent in all cases. Recent developments: The described structure has not been changed in recent years.

Great Britain Main characteristics: The British Incapacity Benefit scheme (IB) is a follow-on scheme to the Statutory Sick Pay scheme or an alternative to it. The long term Incapacity Benefit constitutes the British disability pension. Access conditions for benefits: The claimant has to pass a personal non-capability test to be eligible for long term Incapacity Benefits. You either get full benefits or none at all. Eligibility for IB also requires a certain contribution record. For those who could not meet the requirements for IB there was a non-contributory scheme, the ‘Severe Disablement Allowance’ with lower benefits. Entitlements: Long term Incapacity Benefits is a flat rate benefit which can be supplemented if there is a dependent spouse (low income) and children. The scheme can also be topped up with Income Support (social assistance) to a guaranteed minimum.


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Recent developments: The Severe Disablement Allowance was abolished in 2001, and replaced with versions of the IB scheme, implying that many who do not meet the requirements for contributions for the ordinary IB scheme receive Income Support as an alternative.

Net Replacement Rates as Sick and Disabled The results for a single person of becoming sick and later disabled are contained in table 16. It is assumed that the person is completely disabled, and then not able to work. This is also the assumption for all other cases in this section. Table 16. Net replacement rates for a single becoming sick and then disabled, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 74/142 57/110 46/88 36/69 30/58

FIN 80/87 80/80 79/72 75/71 72/71

S 68/111 71/92 73/77 73/80 61/80

NL 100/82 100/64 100/60 100/62 100/66

IT 66/107 68/78 73/83 73/81 73/82

ES 64/115 70/121 71/126 71/129 72/132

GB 100/65 100/55 100/41 100/31 100/25

Source: ‘7-country study.

Two countries, the Netherlands and Great Britain, always have replacement rates of 100 percent in case of sickness, this is clearly more than the basic insurance schemes, private or public, would usually provide. The compensation for disability is higher than for sickness in Denmark, Sweden, Italy and Spain, sometimes by a very substantial margin. In Finland the two situations are quite similar as far as compensation is concerned. One country, that is Spain, has increasing replacement rates when income increases. The reason is that disability pensions are non-taxable income while wage income is taxed according to a progressive tax schedule. Corresponding results for the single provider are recorded in table 17. Table 17. Net replacement rates for a sole provider (two children) becoming sick and then disabled, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 89/111 82/101 72/89 63/78 55/69

FIN 102/94 93/92 95/90 89/84 82/79

S 90/93 89/92 88/91 83/87 72/84

NL 100/84 100/76 100/71 100/62 100/64

IT 78/86 78/83 84/87 79/87 77/84

ES 45/110 64/109 68/112 73/118 73/122

GB 100/65 100/62 100/57 100/51 100/45

Source: ‘7-country study’.

It is still so that the compensation in Denmark, Sweden, Italy and Spain is higher for disability than for sickness but the margin is much smaller than for the single in almost all cases. For Sweden the main explanation is the increased access to housing benefits. In the case for the single only the disabled could receive housing benefits, but for the single provider

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housing benefits are also available in the situation when she works and when she is sick. This lowers the replacement rates for the disabled sole provider, especially at lower income levels, and increases the replacement rate for the sick sole provider compared with the case for the single person. The gap has narrowed. The next case is for the one-earner couple when the breadwinner becomes sick and then disabled. Table 18. Net replacement rates for one-earner couple (no children) when the breadwinner becomes sick and then disabled, 2000. APW inc. 0.67 0.80 1.00 1.25 1.50

DK 78/113 62/90 49/72 39/57 33/49


FIN 90/98 90/98 89/98 80/97 74/88

S 68/125 71/109 73/94 73/86 61/80

NL 100/99 100/92 100/74 100/67 100/70

IT 73/81 72/80 76/83 74/82 74/82

ES 61/110 64/111 69/116 70/121 71/125

GB 100/77 100/70 100/63 100/48 100/39

The countries, where the compensation for disability is higher than for sickness, Denmark, Sweden, Italy and Spain are joined by Finland in this case. The replacement rate for the one-earner couple receiving disability pension in Denmark is lower than for the single, the reason being that the pension rate for a married person is lower than for a single. The higher replacement rate in case of sickness for the Danish one-earner couple is due to taxation, where unused tax allowances can be transferred between spouses, in case to the breadwinner. This is also the case for disability, but here the lower pension rate dominates the result. The next case is the two-earner couple with children when the ‘high wage’ spouse is sick and then disabled, while the other spouse continues to work, cf. table 19. Table 19. Net replacement rates for two-earner couple (2 children) when the ‘high wage’ spouse becomes sick and then disabled, while the other works, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 96/118 90/100 72/79 63/68 58/61


FIN 95/97 93/94 88/87 85/83 83/83

S 90/106 90/102 85/88 84/88 77/88

NL 100/74 100/76 100/77 100/78 100/80

IT 86/92 83/89 83/89 83/88 84/89

ES 74/107 79/111 82/115 82/117 82/120

GB 100/90 100/85 100/67 100/54 100/46

The loss of 2/3 of the family gross wage is in most countries compensated in such a way that less than 10 percent of the disposable income is lost at the two lowest income levels, and in no country is more than 21 percent lost in case of sickness at these income levels. For disability the compensation is in many cases more than 100 percent and Spain, Italy and Sweden have very high replacement rates in case of disability all over the income span, just a little more than 10 percent is the maximum loss at the highest income level in Italy and


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Sweden. Denmark and Great Britain have replacement rates declining to almost half the value from the bottom to the top of the income range. What happens when it is the ‘low wage’ spouse who becomes sick and thereafter disabled is contained in table 20. Table 20. Net replacement rates for two earner couple (two children) when the ‘low wage’ spouse becomes sick and then disabled while the other works, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 96/127 91/115 83/101 79/93 76/88

FIN 96/91 94/86 92/76 92/68 92/61

S 94/119 93/106 91/95 92/94 92/94

NL 100/86 100/86 100/86 100/88 100/88

IT 93/97 90/93 91/94 90/93 91/94

ES 87/103 90/108 88/111 88/114 90/117

GB 100/91 100/90 100/78 100/75 100/74


For most countries the loss of 1/3 of the family gross wage due to sickness of the ‘low wage’ spouse does not result in a loss of more than 10 percent of the disposable income except in Spain where it is maximum 12 percent and in Denmark, where it reaches 24 percent at the highest income level. The replacement rates for disability are higher than for sickness in Denmark, Sweden, Italy and Spain and at the lower income levels more than 100 percent in 3 of the countries, Italy is the exception. Finland has surprisingly small replacement rates for disability at the two highest income levels, cf. comments for table 21. It may be a relatively rare event that both spouses are sick and then become disabled at the same time, but it happens. Table 21 contains the results. Table 21. Net replacement rates for two-earner couple (two children), when both spouses become sick and then disabled, 2000. APW inc. 1.00 1.20 1.50 1.88 2.25

DK 93/138 87/118 69/94 56/76 47/63

FIN 91/91 89/86 83/76 77/68 75/61

S 84/114 83/101 79/84 77/82 69/82

NL 100/60 100/62 100/64 100/66 100/68

IT 79/84 77/83 77/83 74/82 74/83

ES 61/110 67/114 70/118 70/120 72/123

GB 100/76 100/73 100/58 100/46 100/37


The replacement rates for sickness are relatively high at all income levels for all the countries except for Denmark at the two highest income levels. Disability pensions are even better in Denmark, Sweden, Italy and Spain. Spain have replacement rates above 100 percent for disability in all cases at all income levels, cf. the description of the disability scheme for Spain. The replacement rates for Finland are exactly the same in disability for the case where the ‘low wage’ spouse is disabled and the case where both spouses are disabled. This can hardly be correct, the figures are probably for the case where both spouses are disabled.

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Benefit Euration The benefit period for sickness benefits is 1 year in almost all the countries included in this study, in Finland it is a little shorter, in Italy only ½ year. These benefit periods can in several cases be prolonged if rehabilitation is promising, otherwise sickness benefits will be replaced by disability pensions. These will last until old-age pension replaces the disability pension typically in the age span 60-65 years. There might be checks during the disability period to confirm that the claimant still meets the access criteria.

Main Results The schemes for sickness and disability are different from those for unemployment and early retirement because they are health related. Compensation for sickness often results in high replacement rates, which could be an invitation to short term leaves. Disability pensions are consistently higher (combined with housing benefits) than sickness benefits in Denmark, Sweden Italy and Spain and net replacement rates are often over 100 percent, in Spain always over 100 percent. This might imply a strong incentive to move to the disability pension scheme. This is, however, not a free choice but schemes with such incentives require stringent controls, also repeatedly, to confirm that the access criteria always are met.

References “Remain in or withdraw from the Labour Market?”: A comparative study on incentives (referred to as: ‘7-country study’). Werkdocumenten no 286, the Dutch Ministry of Social Affairs and Employment, SZW. Also published by the European Commission in European Economy, Economic Papers Number 193, October 2003. “MISSOC 2003”: Mutual Information System on Social Protection. Directorate-General for Employment and Social Affairs. European Communities, 2004.

Related Literature “Taxing Wages”: Annual publication from the OECD “Benefit Systems and Work Incentives”: Annual publication from the OECD. “Unemployment Insurance in the Netherlands, Denmark and Sweden”: DGSZ/FEBO Research Memorandum 93/1, the Dutch Ministry of Social Affairs and Employment, SZW. “Unemployment Benefits and Social Assistance in Seven European Countries”: Werkdocumenten no. 10, the Dutch Ministry of Social Affairs and Employment, SZW. Also published in Chinese. “Income Benefits for early exit from the Labour Market in Eight European Countries”: Werkdocumenten no. 61, the Dutch Ministry of Social Affairs and Employment. Also published by the European Commission in European Economy no. 3 1998. “Transition from Unemployment to Social Assistance in Seven European OECD countries”: Empirical Economics, Volume 23 1998, Issue 1-2.



Chapter 7

SECONDARY MARKET BEHAVIOR OF EUROPEAN SECURITIZATION Andreas A. Jobst# International Monetary Fund (IMF), USA International Capital Markets Department (ICM), USA

Abstract Asset-backed securitization (ABS) is a highly flexible yet complex refinancing technique that involves the issuance of contingent claims with varying seniority and maturity on the cash flow performance of a designated pool of asset exposures. Efficient risk management of ABS obligations requires both investors and issuers to thoroughly understand the inherent spread dynamics in this growing segment of fixed income markets. We model the secondary market spread dynamics of different types of European ABS transactions (synthetic and traditional CDO, MBS and Pfandbrief) in a multi-factor GARCH process, which conditions the mean specification of stationary spreads on their heteroskedasticity for valuation and forecasting purposes. We find that historical spread changes are mostly difference stationary and follow a steady positive trend with asymmetric mean reversion depending on the direction and the lag structure of past spread changes. Model estimates indicate that spread stationarity is statistically and economically more sensitive to past spread levels at one lag if they immediately follow a spread decline rather than a spread increase. However, spread dynamics are mostly stochastic in response to past spread levels beyond one lag, which relegates stationarity to a first order effect. Hence, stochastic inference methods based on autoregressive effects of past spread levels fall short of fully capturing short-term spread dynamics. Most of the conditional spread volatility is informed by asymmetric ARCH effects from negative #

International Monetary Fund (IMF), International Capital Markets Department (ICM), 700 19th Street, NW, Washington, D.C. 20431, USA, e-mail: [email protected]. Large parts of this article were completed when the author held fellowships at the Johann-Wolfgang Goethe Universität in Frankfurt/Main and the Center for Financial Research (CFR) of the Federal Deposit Insurance Corporation (FDIC) in Washington, D.C. The article reflects the views and analysis of the author and does not represent those of the IMF. Any errors and omissions are the sole responsibility of the author. The author would like to thank seminar participants at the University of Frankfurt, the Financial Management Association (FMA), the Center for Financial Studies (CFS) and the Financial Markets Group (FMG) for their feedback. David Webb, Jan-Pieter Krahnen, Gregory Connor, Paul Kupiec and Mark Flannery delivered valuable comments and suggestions, which improved the quality of the paper.

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Andreas A. Jobst errors of forecasted past variance, which contributes most of the economic significance to generally positive GARCH effects. Given significant volatility-induced future spread movements, a GARCH approach permits a more accurate estimation of time-varying confidence intervals for the parametric specification of spread movements. Amid persistently widening spreads, negative shocks associated with unexpected spread declines testify to a significant departure from the historical pattern and induce higher spread volatility, which tends to be propagated over time by further negative past innovations. In light of notoriously weak information transparency and valuation uncertainty in large parts of primary and secondary ABS market segments, the recognition of time-varying volatility is imperative to forecasting ABS spreads especially at times when a sudden contraction of spreads at time of greater investment risk entails a higher risk premium and asset volatility.

Keywords: Securitization, ABS, MBS, CDO, CLO, CBO, Pfandbrief, GARCH, structured finance, spread dynamics JEL: C12, C32, C53, G12, G21

1. Introduction 1.1. Definition of Asset Securitization Securitization describes the process and the result of converting a pool of existing or future assets into tradable liability and equity obligations as contingent claims backed by identifiable cash flows from the credit and payment performance of these assets. This structured finance technique allows issuers to improve their liquidity position without increasing the capital base (funding motive) in the bid to refinance asset origination and/or investments most efficiently (see Figure 1-4). Securitization seeks to substitute capital market-based finance for credit finance by sponsoring financial relationships without the lending and deposit-taking capabilities of banks (disintermediation).1 From an issuer perspective, securitization registers as an alternative, market-based source of refinancing profitable economic activity in lieu of intermediated debt finance. The implicit risk transfer of securitization allows issuers benefit from more cost efficient terms of high-credit quality finance without increasing on-balance sheet liabilities or compromising the profit-generating capacity of assets. Investors in securitization have a wider choice of high-quality investments at their disposal, whose market valuation engenders greater overall efficiency and liquidity of capital markets. The transformation and fragmentation of asset risk through securitization also brings greater diversification within the financial system, since the tradability of securitized debt facilitates the synthetic assembly and dynamic adjustment of credit portfolios via secondary markets.

1

The move towards such capital market-based investment funding is reducible to various causes. First, recent financial crises have led to a general shortage of investment funds and heightened competition for low-risk borrowers. Second, the deregulation and liberalization of international financial markets as well as technological advances have elevated market efficiency to a level amenable to two strands of asset securitization. On the one hand, the issuing of debt securities by banks and non-bank financial institutions as well as corporations has posed a formidable challenge to traditional channels of asset funding through bankbased external finance and deposit business. On the other hand, securitization of balance-sheet assets has also drawn in banks and financial service companies alike as rising sophistication in credit risk management have facilitated continuous innovation in structured finance products and derivative instruments.

Secondary Market Behavior of European Securitization

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Basic Securitization Structure (I) Originators transfer bank assets to a Special Purpose Vehicle (SPV) Asset Asset Originator/ Originator/ Sponsoring Sponsoring Entity Entity

SPV issues debt securities (asset-backed) to investors, typically structured into various classes,

Issuing Issuing Vehicle Vehicle

1

2

Capital Capital Market Market Investors Investors (Primary/Secondary (Primary/SecondaryMarket) Market)

rated by one or more rating agencies, underwritten by the sponsoring bank and sold to institutional investors.

Underlying Underlying reference reference asset asset portfolio portfolio

Figure 1. Basic securitization process.

Basic Securitization Structure (II) Any asset class with a stable stream of cash flow can in principle be included in the reference portfolio and securtized. Asset type determines the type and classification of the ABS structure (ABS, MBS, CDO, etc.) Underyling pools can include the following: aircraft/auto/equipment leases, corporate debt, credit cards, gov't related payments, loans (consumer, home equity, project, student), project finance/operating income, trade receivables

Asset Asset Originator/ Originator/ Sponsoring Sponsoring Entity Entity

Three types of underlying pools: 1.

"Static" (standing) pool asset pool fixed w/o substitution loan balances fixed w/o redraw facilities or adjustable credit limits pre-defined principal amortisation schedule e.g. mortgages (residential & commercial), leases, corporate loans

2.

"Revolving" pool asset pool varies; allows substitution loan balances are adjustable up to maximum limits no pre-defined principal amortisation schedule e.g. credit cards, trade receivables, corp. loans/bonds

3.

"Substituting" pool allows substitution of new loans within defined credit parameters as org. loans pay down loan balances fixed principal does not amortise during substitution e.g. corporate bonds, some residential mortgages/consumer loans

Underlying Underlying reference reference asset asset portfolio portfolio For For loan loan pool: pool: •• credit credit transactions transactions are are complex complex&& highly highly customised, customised, and and require require individual individual procedures procedures •• credits credits are aremainly mainly held held by by the the issuing issuing bank bank until until maturity maturity •• portfolio portfolio management management is is very very difficult difficult

Figure 2. Asset-specific types of securitization.

Aside from being a flexible and efficient off-balance sheet source of funding, which enables issuers to refinance themselves based on securitized asset risk rather than corporate risk, securitisation is understood as an important risk reduction tool in the spirit of Skarabot (2002) as well as Rosenthal and Ocampo (1988).2 The Bond Market Association (2001) considers securitisation “an increasingly important and widely-used method of business 2

See also Leland (1998) and Frankel (1991).

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Basic Securitization Structure (III) 1.

Asset transfer from the originator/sponsor to the issuing vehicle: legal form: legal/equitable assignment, contingent perfection or sub-participation (should represent a "perfection of security interest" (true sale))

2.

Considerations: assets should be immune from bankruptcy of seller (non-recourse financing) originator retains no legal interest in assets, although though some economic benefit may be retained compliance with consumer protection laws regulatory aspects specific to banks, consumer finance companies, etc.

3.

Issuing vehicle is a "bankruptcy-remote" entity: arrangement to prevent issuer from incurring additional liabilities or expenses restrictive covenants: issuer must not pursue voluntary bankruptcy proceedings no "substantive consolidation", i.e. issuer's assets are not considered part of originator's assets

4.

Issuer profiles and legal forms vary according to jurisdiction and asset type: master trust: pool of trusts or a pool of assets in one trust agreement owner trust: cash flows are tailored to create certain maturities of tranches (in a nod to investor targets) grantor trust: cash flows as passed directly through to investors w/o manipulation

Originators transfer bank assets (“true sale”) to a Special Purpose Vehicle (SPV) Asset Asset Originator/ Originator/ Sponsoring Sponsoring Entity Entity

1

Underlying Underlying reference reference (asset) (asset) portfolio portfolio

Figure 3. Organization of asset transfer in the securitization process.

Basic Securitization Structure (IV) SPV issues debt securities (asset-backed) to investors, typically structured into various classes,

Issuing Issuing Vehicle Vehicle

2

rated by one or more rating agencies, underwritten by the sponsoring bank and sold to institutional investors

1.

2.

Markets/Investors -

Euro

-

Global

-

U.S. Domestic

Cash Flow Profile -

fixed/floating rate coupons

-

sequential/pro-rate tranches

-

bullet, sinking fund or pass-through payment structure

-

callable, extendable or “putable”

-

“capped”, “uncapped” or “available funds”

-

“clean-up call” or “step-up coupon”

Figure 4. Structure of securitized debt issue.

financing throughout the world, [given that its] continued growth and expansion ... [generates] significant benefits and efficiencies for issuers, investors, securities dealers, sovereign governments and the general public.” Both mounting competitive pressure over client deposits and a notorious squeeze on interest spreads have led banks to the employ securitisation as a vehicle for balance sheet management. So securitization mainly serves (i) to reduce both economic cost of capital and regulatory minimum capital requirements as a


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balance sheet restructuring tool (regulatory and economic motive), (ii) to diversify asset exposures (especially interest rate risk and currency risk) as issuers repackage receivables into a securitisable asset pool (reference portfolio or collateral) underlying a so-called assetbacked securitization (ABS) (hedging motive), and (iii) to improve asset-liability management thanks to more information-intensive origination of claims and greater operational efficiency. Also the generation of securitized cash flows from securitized debt represents an effective method of redistributing asset risks to investors and broader capital markets (transformation and fragmentation of asset exposures). These issuer incentives engender an alternative investment proposition, which offers higher yields than comparable sovereign or corporate debt. As opposed to ordinary credit-sensitive assets, a securitized claim on a promised credit performance constitutes tradable credit risk, which facilitates the synthetic assembly and dynamic adjustment of credit portfolios via capital markets. Investors of securitized debt can quickly adjust their investment holdings at low transaction costs in response to a change of personal risk sensitivity, market sentiment and/or consumption preferences.

1st step: sales of the assets to SPV

Sale of asset portfolio Originator/ Originator/ Underwriter Underwriter (asset (asset seller) seller)

Current cash-flow from portfolio Purchase price costs

2nd step: issuance of securities on the market

Special Special Purpose Purpose Vehicle Vehicle (SPV) (SPV) –– Selling Selling Agent Agent

Placement Purchase Price

ConConsortium sortium of of banks banks

Placement Purchase Price

Investors Investors

Interests, redemption Placement Purchase price

Basis Basis transaction transaction Credit enhancement, liquidity support

Borrower Borrower Interest, redemption

Cash flow Rating

Servicer Servicer Possible Possible further further participants participants

•• debtor debtor account account •• collection collection of of principal principal and and interest interest

Rating Rating Agency Agency

declares required credit enhancement and rating of tranches

commonly retained by originator

Cash flow

Trustee Trustee

Issued Issued securities: securities:

•• paying paying office office •• administration administration of of transferred transferred assets assets

•• senior senior tranche(s) tranche(s) -- (AAA) (AAA) •• mezzanine mezzanine tranche(s) tranche(s) -- (A) (A) •• subordinated subordinated tranche(s) tranche(s) -- (BB) (BB) •• equity equity (unrated) (unrated)

utilization of assets in case of distress/workout process

Figure 5. Structure of a traditional ABS transaction.

However, securitization involves a complex structured finance technology (see Figure 5), which commands significant initial investment of managerial and financial resources. The complexity of these structures is rooted in regulatory requirements for insulating investors against a multiplicity of impending risks arising from credit default (credit risk), an adverse movement of market prices (market risk) and the inability of the issuer of the security to honour scheduled payment obligations to investors (liquidity risk) in the wake of a securitisation transaction. By convention, these risks are managed by the originating institution on an institutional basis with the backing of the institution’s equity base.

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Mortgage-Backed Securities (MBS)

Agency MBS (quasi-government backing, e.g. FHLMC, FNMA, GNMA)

Non-agency MBS

Asset-Backed Securities (ABS) (narrower sense)

Real estate ABS (second-lien mortgages)

-residential mortgage-backed security (RMBS) -commercial mortgage-backed security (CMBS) -collateralized mortgage obligation (CMO)

-home equity loans -manufactured housing loans

Non-real estate ABS

-credit card receivables -equipment leases -student loans -auto loans -export receivables -tax receivables -insurance premiums -film receivables -health-care receivables -music royalties -lottery winnings -dealer floorplans -whole business -project loans

Collateralized Debt Obligations (CDO)

Collateralized Bond Obligation (CBO)

Collateralized Loan Obligation (CLO)

bonds traded on the market or held on balance by issuer

loans held by bank, non-bank financial intermediary or non-financial institution

Types of assets in underlying reference portfolio

Figure 6. Types of asset-backed securities (Jobst, 2003).

The flexible security design of asset-backed securitisation allows for a variety of asset types to be used in securitised reference portfolios. Mortgage-backed securities (MBS), real estate and non-real estate asset-backed securities (ABS) and collateralised debt obligations (CDO) 3 (see Figure 6) represent the three main strands of asset-backed securitisation in a broader sense. Although securitisation has been traditionally used by commercial banks to finance these simple, self-liquidating assets such as mortgages, bank loans and consumer loan receivables, it is now also used for infrastructure and project finance. Besides securitising a wide variety of bank loans, including short-term commercial loans, trade and credit card receivables, auto loans, first and second mortgages, commercial mortgages and lease receivables, banks have also turned to small business loans and middle-market commercial loans as suitable for securitisable reference portfolios. The evolution of securitisation has produced two prime asset classes that serve as underlying collateral. Apart from structured leasing and project finance, alternative means of external investment finance4 vie for the attention of firms, whose credit standing influences their mode of funding, such as small and medium-sized companies (SMEs)5. Whereas the securitisation of corporate and sovereign loans, auto loans, credit card receivables, project finance or individualised lending agreements and alike (Standard & Poor’s, 1996) are categorised as asset-backed securities (which is also the generic term for securitised assets irrespective of their type), private and commercial mortgages are called mortgage-backed securities (MBS). Especially one type of asset-backed security (see Figure 2) has put securitisation on the agenda of banks and other financial service providers across the world – the collateralised debt obligation (CDO). CDOs are investment vehicles that allow issuers to refinance the 3 4

5

See Jobst (2005b and 2003) for a descriptive and technical review of collateralized debt obligations (CDOs). Similarly mezzanine capital, equity finance and corporate bonds are other popular means of external finance with comparable structural properties. See also Müller-Stewens et al. (1996).


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purchase of debt instruments by repackaging them into different slices of risk and maturity. While CDOs use the same structuring technology as ABS to convert a large, diversified pool of exposures into tradable commercial paper, their underlying collateral pool typically includes a wider and more diverse range of heterogeneous reference assets, such as senior secured bank loans, high yield bonds and CDSs, as opposed to more homogenous titles, such as home equity loans and credit card receivables. In a typical CDO structure (Fabozzi and Goodman, 2001), the issuer repackages (corporate or sovereign) debt securities or bank loans into a reference portfolio (the collateral), whose proceeds are subsequently sold to investors in the form of debt securities with various levels of senior claim on the collateral portfolio. The issued securities are structured in senioritized tranches, which denote a particular class of debt security investor acquire when they invest in a CDO. The tranching can be done by means of various structural provisions governing the participation of investors in the proceeds and losses stemming from the collateral. Particularly banks have embraced a specialised type of CDO, the collateralised loan obligation (CLO), as a means to curb credit risk by outright selling portions of a large loan portfolios to investors. In the conventional type of such transactions a portfolio of pre-selected loans is transferred from the balance sheet of the originator to a special purpose vehicle (SPV)6, which refinances itself by issuing securities on this reference portfolio to capital markets at a margin (Burghardt, 2001)7. Typically institutional investors are the prime investor group for such transactions. Besides the obvious benefit of improved credit risk management, CLOs enable issuers to achieve a broad range of financial goals, which include the off-balance sheet treatment of securitised loans, reduced minimum regulatory capital requirements and access to alternative sources for asset funding of lending activities and liquidity support. Over the last ten years, asset-backed securitization (ABS) has recorded formidable growth rates and established itself as the premier segment of fixed income market for yieldseeking investors looking for greater diversification and lower risk exposure than with traditional investment products. However, growing investment demand has yet failed to translate into a level of market liquidity comparable to conventional fixed income markets. In spite of nascent standardization through synthetic single-tranche CDOs replicating the riskreturn profile of newly formed indices of selected CDSs and collateralized debt instruments (e.g. the Dow Jones iTraxx® and the iBoxx® index),8 primary and secondary ABS markets 6

7 8

The Basel Committee on Banking Supervision (2002a) uses the term “special purpose entity (SPE)” to define the functions of a SPV, which is a corporation, trust, or other entity organised for a specific purpose, the activities of which are limited to those appropriate to accomplish the purpose of the SPE, and the structure of which is intended to isolate the SPE from the credit risk of an originator or seller of credit exposures. SPEs are commonly used as financing vehicles in which credit exposures are sold to a trust or similar entity in exchange for cash or other assets funded by debt issued by the trust. See also Turwitt (1999). Both the Dow Jones iTraxx® Europe index of credit default swap (CDS) contracts and the iBoxx® index of collateralized (fixed-rate) debt obligations have inaugurated the first round of emerging standardization. A recent survey paper by Cousseran and Rahmouni (2005) states that the development of liquid pricing benchmarks has greatly contributed to the maturity of the market for collateralized debt obligations (CDOs) as regards “an improvement in the transparency of the market, as tranche prices are continuously quoted; a significant increase in market liquidity[...], allowing protection to be bought and sold at a lower cost [...], an improvement in the management of market participants’ risk, as they now have access to daily valuations from which they can obtain [...] levels of implied correlation [...], [and] a broadening of the investor base to new market participants such as hedge funds, which use these instruments for their sophisticated trading strategies (correlation trading).” However, large parts of the ABS market in Europe have shed little of their frequently deplored opacity. Market observers do not find a dramatic change in market transparency due to

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remain plagued by significant deterrents to greater transparency and liquidity: The notoriously complex security design of multi-layered synthetic transactions, intricate transaction covenants and the lack of rigorous standardization restrict informed investment and, thus, impairs the predictability of potential risks and fair asset pricing in a market with dominated by a few players and low retail participation.9 Moreover, the prevalent “buy and hold”-strategy of investors and the absence of comprehensive trading platforms prevent market deepening and inhibit efficient information dissemination across different segments of capital markets.10 The upshot of all of these features dulls the efficiency of price discovery in ABS markets and largely compromises the profound analysis and adequate specification of secondary market price dynamics. These shortcomings are particularly troubling given that efficient risk management and diversified asset allocation of ABS requires a thorough understanding of the longitudinal properties of spreads. In light of a general paucity of reliable trading data, informed investment in ABS transactions comes at a premium.

1.2. Motivation of the Research Study For loss of an integrated and exhaustive technical study on secondary pricing in structured finance markets particularly outside the U.S., the examination of spread dynamics of different types of ABS in Europe proves particularly interesting. While recent research has generated some models to determine ABS spreads (Arora et al., 2000; Goodman and Ho, 1997 and 1998), general time series properties of ABS transactions have be addressed only in a cursory fashion. Although papers by Jobst (2005a) and Koutmos (2001 and 2002) specify the spread dynamics of U.K. and U.S. MBS transactions respectively, they do not consider collateralized debt obligations (CDOs) or quasi-ABS transactions (e.g. Pfandbriefe), a prominent onbalance sheet MBS-type deal structure in Europe, which matches the importance of U.S. MBS by any standard of comparison, be it market volume, trading activity or historical track record.

9

10

standardization as much as they acknowledge changing hedging patterns of ABS issues in its wake (Tsui, 2005). For instance, CDOs are generally structured to meet specific investor needs. In the past, issuers would hedge unbalanced positions of customized CDOs through complex subordinated, multi-tranche structures (“transaction-based”), whose complexity inhibited transparent asset pricing. When the Dow Jones iTraxx® Europe index was created in June 2004 from the merger between two existing CDS indices, large issuers began to offer standard CDO tranches on the iTraxx® index. These tranches replicate the behavior of synthetic CDO claims whose underlying reference portfolio replicates the constituent names in the basket of the Dow Jones iTraxx® index. These standardized (synthetic) CDO claims on liquid indices now offer a base correlation measure (“CDO delta”) with the actual equity prices of (underlying) reference assets and constitute a dynamic “market-based” hedge for issuer of bespoke and mostly privately transacted single-tranche transactions (arranged for single investors). Most recently, issuers also began to offer multi-tranche transactions with mezzanine tranches indexed to equity prices and tranche-specific CDS contracts on any retained CDO interest. Although the transformation and fragmentation of credit risk through securitization brings greater diversification within the financial system, the structural complexity arising from multi-layered security designs, diverse amortization schedules and the state-contingent funding of credit risk transfer might obfuscate actual riskiness and inhibit provident investment. Market participants have also been alleged to deliberately cultivate an aura of opacity to maintain a competitive edge at the risk of retarding the scope of mobilizing additional investment to a greater economic benefit in the long-run.


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In contrast to the U.S., where the market for ABS has had a longstanding tradition since the first half of the 1980s11 (Klotter, 2000), European ABS has gained popularity only over the last several years. In Europe, the Pfandbrief market of quasi-“on-balance sheet mortgagebacked securities” is the biggest segment of the euro-denominated private bond market in Europe and rivals in size the individual European government bond markets (Mastroeni, 2005).12 Although many European countries have already put in place legal frameworks for Pfandbrief-style products, the German Pfandbrief (literally “letter of pledge”)13 is the eponym of this type of covered bond, which allows issuers to convert a credit claim or a pool of

11

12

13

The first ABS issue in its modern form was completed by Sperry Corporation, which issued computer leasebacked notes in 1985 (Kendall, 1996). Several structural differences emerge between MBS and Pfandbrief-style transactions. Although they both are refinancing techniques that convert a credit claim or a pool of claims into negotiable securities, they represent two distinct forms of covered bonds obtained from securitizing the same type of reference asset either (i) offbalance sheet (asset-backed securitization), (ii) on-balance sheet (“Pfandbrief-style” securitization), or even through (iii) synthetic securitization. In an off-balance sheet securitization the issuer sells a portfolio of assets to a separate entity, which refinances the acquisition of the assets by issuing debt or equity claims on these reference assets, whose performance secures the issuer’s ability to honor its obligations. In on-balance sheet securitization reference assets remain on the balance sheet of the issuer. While the Pfandbrief is a classical onbalance sheet refinancing tool of mortgages and public loans with both origination and issuance completed by one and the same entity, MBS transactions involve at least one more party (besides the mortgage originator), which sells contingent claims on asset cash flows, so that the reference portfolio underlying the securitized assets is removed from the balance sheet and legally segregated (bankruptcy remote). A typical Pfandbrief transactions, promised repayments to investors are independent from the repayment of securitized assets. In a German Pfandbrief transaction reference assets are ring-fenced on the balance sheet of government-licensed banks. Holders of Pfandbrief bonds are pari passu to each other and benefit from a priority right to these assets in the event of issuer default. Moreover, issuers are fully liable with their registered capital if reference assets fail to generate sufficient cash flows for the repayment of investors. Hence, this arrangement implies a double protection of investors against the solvency of the issuer and the insolvency of the debtors of the original assets. Given the value of this institutional guarantee depending on the issuer’s financial strength, Pfandbrief transactions generally receive high ratings; however, Pfandbrief investors are not insulated from an originator event (insolvency and bankruptcy) of the issuer. In comparison, MBS transactions re devoid of any institutional guarantee and solely return cash flows generated from the pool performance of the designated reference portfolio. Investor claims rank either pari passu to each other in the sense of pass-through (PC) or are prioritized (pro rata) through subordination (but no other parties can declare a moratorium on assets). Issuers of MBS transactions compensate issuers for the higher asset exposure due to the lack of institutional protection by including various kinds of internal and external liquidity and credit support, such as bridge-over facilities, surety bonds, third-party guarantees, yield spreads/excess spreads, over-collateralization and reserve accounts. Finally, Pfandbrief issues are subject to stringent federal laws (requiring a weighted average loan-tomarket or appraised value (LTV) of at least 60% as a statutory benchmark), whilst private-label MBS are free from these legal requirements, except in so-called agency-MBS in the U.S., where the quasi-government agencies Fannie Mae (FNMA), Freddie Mac (FHLMC) and Ginnie Mae (GNMA) provide institutional guarantees in return for certain restrictions imposed on mortgages eligible for purchase in MBS structures. In general, Pfandbrief transactions represent a very secure and liquid asset class of fixed income instruments with an established track record and cyclical resilience. MBS issues are equally liquid (at least in the U.S. market) and feature an unchallenged degree of flexibility allowing for customized features and investor arrangements, such as variations to amortizing repayment (in contrast to bullet repayment structures of Pfandbrief issues). Pfandbriefe serve primarily as funding instruments, whereas MBSs are also employed for credit risk transfer and balance sheet restructuring, with the aim of efficient management of economic and regulatory capital. Although the creation of the first Pfandbrief instrument was attributed to an executive order of Frederick II of Prussia in 1769 (Skarabot, 2002; Anonymous, 1999), it was only when the Mortgage Bank Law was passed in 1899 that the Pfandbrief took its present form (Mastroeni, 2005). The first legal guidance for the issuance of Pfandbrief-style products was adopted in France in 1852 with the Loi sur l’obligation foncière et communale, whereas the oldest mortgage credit market can be traced to Denmark, when the Great Fire of 1789 created vast demand for housing finance in its wake. No specific mortgage bank law has been issued in Sweden, where a mortgage market has existed at least since 1860 under the legal provisions of the more general Law on Credit Companies.

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claims into negotiable securities (Mastroeni, 2005).14 Recently, ABS issues have caught up with Pfandbrief transactions as one of the largest fixed income markets in Europe (see Figure 7). The issue volume of both mortgage-backed securities (MBS) and collateralized debt obligations (CDO) has surged at an impressive scale despite depressed expectations from interest-based income and the search for alternative asset funding and risk management mechanisms. Although both types of structured finance transactions have become distinct segments of the European fixed income markets, the nascent European ABS market still falls short of attracting large secondary trading activity comparable to the Pfandbrief market. A thorough investigation of European ABS price dynamics cannot afford to ignore the distinct track record of on-balance sheet securitization in European structured finance. Hence, we need to censure a comparison of European and U.S. asset-backed securitization on the Pfandbrief market, which measures up to the U.S.-based securitization in liquidity and maturity.15 Pfandbriefe Type of securitization

Asset-Backed Securities (ABS)

On-balance sheet

Off-balance sheet (true sale of assets or synthetic transfer of risk exposure)

Covered bonds, issued and guaranteed by the issuing bank (and the public sector if reference asset are public loans)

Covered bonds backed by a reference portfolio originated by originating bank or acquired by issuing agent

Level of standardization

Very high for Jumbo Pfandbriefe and low for traditional Pfandbriefe. Higher standardization facilitates efficient price formation, high transparency and market liquidity.

Very little. The main feature of ABS is their flexibility to address diverse refinancing needs of issuers and risk expectations of investors.

Reference assets & bankruptcy remoteness

Reference assets (mortgages and public loans) are ring-fenced and must satisfy stringent legal requirements (e.g. >60% LTV); Pfandbriefe are guaranteed by issuers, who are fully liable with their registered capital in the event of default.

ABS are guaranteed by the performance of a designated reference portfolio of securitized asset, which represent only part of the assets of the originating bank (but most or all assets of the issuing SPV in a true sale transaction).

Nature of securities

Figure 7. Comparison of Pfandbriefe and asset-backed securities (ABS).

Recent research (Goodman and Ho, 1998; Koutmos, 2002) indicates that the shape of the interest term structure plays an important role in the determination of fixed-rate MBS yields in the U.S.16 In their study on the determinants of MBS spreads on treasury bond yields, Goodman and Ho (1998) also consider the five-year cap volatility and the ten-year swap

14

15 16

Also Spain, Denmark and Sweden have established a long track record in the issuance of Pfandbrief-style investment products. See also Peppetti and Rinaldi (2001). Bhasin and Carey (1999) were the first to present an empirical study, which analyzed – although in an admittedly rudimentary fashion – the trading behavior of bank loans. In contrast to conventional wisdom of fixed income securities research, credits with a low rating grade were traded the most. This liquidity effect would of course affect the market price (i.e. the spread over some benchmark yield) ex ceteris paribus and its attendant volatility. However, it does not account for the pricing behavior in ABS markets.


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spreads to identify some LIBOR interest rate effect on MBS spreads.17 They find that MBS yields are by and large explained by the yield on government securities and the shape of the yield curve, even though the prepayment of principal and interest by mortgagors induces greater variation of duration (compared to government bonds) due to an uncertain timing of cash flows. Arora et al. (2000) propose a five-factor model that explains nearly 60% of mortgage spreads. In an extended version of the term structure model by Longstaff and Schwartz (1992), Koutmos (2002) showed that the stochastic process of U.S. MBS spreads over the maturity-matched U.S. treasury rate behaves asymmetrically in response to the direction of past spread changes (“asymmetric mean reversion”). In this paper we conduct an empirical analysis of the spread change behavior of several types of European ABS transactions (MBS, synthetic and traditional CDO18 as well as Pfandbrief transactions) in order to assess the determinants of time-varying volatility in a more comprehensive and integrated analysis than previous findings about the time series dynamics of U.S. MBS spreads. So far no study has been completed about the secondary market of European ABS. In extension to Koutmos (2002) as well as Longstaff and Schwartz (1992), we enlist two versions of an asymmetric, multi-factor GARCH process for volatilityinduced dynamics of secondary market spreads (based on logreturns). We ascertain (i) whether spread volatility is constant or time-varying, and (ii) whether the mean specification of observed spreads reveals mean reverting or stochastic process at level and first differences. In this way, we derive accurate parametric estimators for time-varying forecast confidence intervals. In line with Goodman and Ho (1998) we control for LIBOR effects in both the mean and the conditional variance of spread change. In contrast to previous studies, we find no evidence of the stochastically asymmetric spread dynamics, where the existence of mean reversion is contingent on a certain direction of past spread changes. Instead, expected spread changes tend to be largely level stationary 17

18

The pricing of fixed income instruments requires investors to measure the yield-to-maturity (YTM) or even the entire term structure yield curve from a series of permutation of the current spot rate (“key rate durations”), depending on the nature of the obligation, in order to discount future cash flows from holding such assets. The market term structure enters the calculation of YTM as some benchmark yield curve or spot rate curve (e.g. the LIBOR or EURIBOR rate), which reflects the general maturity dependence of interest rates. The spread over the benchmark yield captures the (idiosyncratic) risk contribution of other factors in addition to the market interest rate, which have to be taken into account for the mean-variance efficient pricing of fixed income securities. All ABS structures engross different criteria of legal and economic considerations, which all converge upon a basic distinction of security design: traditional vs. synthetic securitization. Traditional (true sale) securitization involves the legal transfer of assets or obligations to a third party that issues capital market paper on the back of these assets to investors via private placement or public offering. This transfer of title can take various forms (novation, assignment, declaration of trust or subparticipation), which ensures that the securitization process involves a “clean break” (true sale, bankruptcy remoteness or credit de-linkage in loan securitization) between the sponsoring bank (which originated the securitized assets) and the securitization transaction itself. In most cases, however, the sponsor retains the servicing function of the securitized assets. Traditional securitization mitigates regulatory capital requirements by trimming the balance sheet volume. In synthetic securitization only asset risk (e.g. credit default risk, trading risk, operational risk) is transferred to a third party by means of derivatives without change of legal ownership of the designated reference portfolio of assets. Any resulting regulatory capital relief and/or lower cost of economic capital do not stem from the actual transfer of assets off the balance sheet but the acquisition of credit protection against the default of the underlying assets through asset diversification and hedging. Commonly, sponsors of synthetic securitization issue debt securities supported by credit derivative structures, such as credit-linked notes (CLNs), whose default tolerance amounts to total expected losses of the underlying reference portfolio. Hence, investors in synthetic transactions are not only exposed to inherent credit risk of the reference portfolio but also operational risk of the issuer.

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and display asymmetric economic significance of past spread changes. We observe that past spread declines fir the most part have a stronger effect on mean-reverting spreads than past spread increases, which tend to be more significant for non-stationary spread dynamics. We record a positive general GARCH effect on conditional spread volatility, which tends to be biased towards negative past errors of forecasted variance. These spread dynamics imply that negative shocks associated with unexpected upward price corrections (and declining spreads) seem to induce higher spread volatility when persistent (or increasing) spread levels testify to negative investor sentiment. These findings provide useful insights for both the adequate secondary market pricing of ABS issues with varying credit quality and the efficient management of ABS portfolios with respect to risk-return considerations if spread volatility is not constant. In light of notoriously weak information transparency in large parts of primary and secondary ABS market segments, our findings underscore that time-varying volatility is essential for the accurate forecasting of spread dynamics especially at times when a sudden contraction of spreads at time of greater investment risk entails a higher risk premium and asset volatility. The rest of the paper is organized as follows. After a definition of asset securitization and a brief literature review, we present the data set, complete univariate inspection of our pricing data and discuss the effects of data transformation on time series dynamics. Subsequently, we specify two GARCH processes of the heteroskedasticity for selected spread series of CDO, MBS and Pfandbrief transactions. We then present the estimation results and verify the correct model specification by means of residual and coefficient tests. We finally discuss the econometric implications of our findings before we conclude in the last section.

2. Data 2.1. Dataset Our dataset consists of aggregated secondary market data of weekly spread quotes (over 3month LIBOR) of the majority of outstanding European ABS transactions over a time period of almost two years from 5 January 2001 to 18 October 2002 (93 weekly observations). These ABS transactions are classified as synthetic and traditional (true sale) Collateralized Debt Obligations (CDO), Residential Mortgage-Backed Securities (RMBS)19 or Pfandbrief transactions with floating rate tranches of varying rating grades (“AAA”, “A” and “BBB”) and terms to maturity (3, 5 and 7 years) (see Table 1 and Figs. 8-10).

19

As opposed to CDO spreads, RMBS time series data does not consider synthetic and traditional structures individually but represents the weighted-average, aggregated spreads of both classifications. The dominance of traditional transactions in MBS spreads reflects the observed market preference for true sale structures of this kind of ABS. We will use the generic expression of mortgage-backed securities (MBS) as short-hand for this asset class in the remainder of the paper unless otherwise noted.


165

Level Data Transformed Series

Original Series

(log. & Johnson Fit)

CDO Spreads (AAA, 3 years) 70

4.4 4.2

60

4.0 3.8

50

3.6

40

3.4 3.2

30 3.0

20 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

2.8 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

CSAAA3_AD_L

CSAAA3

CDO Spreads (BBB, 7 years) 400

6.2 6.0

350

5.8 5.6

300

5.4 250

5.2 5.0

200

4.8 150 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

4.6 1/05/01

5/25/01

CSBBB7

10/12/01

3/01/02

7/19/02

CSBBB7_AD_L

First Differences Transformed Series

Original Series


15

0.6

10

0.4

5 0.2

0 0.0

-5 -0.2

-10 -15 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

-0.4 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

D_CSAAA3_AD_L

D_CSAAA3

1st differences indicate volatility effects ...

120

0.6 0.4

80

0.2 40 0.0 0 -0.2 -40

-80 1/05/01

-0.4

5/25/01

10/12/01

3/01/02

D_CSBBB7

7/19/02

-0.6 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

D_CSBBB7_A

Figure 8. Data overview of selected original and logarithmic, Johnson-fit adjusted synthetic CDO spread series.

166

Andreas A. Jobst Level Data Transformed Series

Original Series

(log. & Johnson F it)

RM BS Spreads (A A A , 5 years) 32

3.6

28

3.4

24

3.2

20

3.0

16

2.8

12 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

2.6 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

MAAA5_AD_L

MAAA5

RM BS Spreads (BBB, 7 years) 180

5.4 5.2

160 5.0

140

4.8 4.6

120 4.4

100 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

4.2 1/05/01

5/25/01

MBBB7

10/12/01

3/01/02

7/19/02

MBBB7_AD_L


Original Series

(log. & Johnson F it)

4

0.8 0.6

2

0.4 0.2

0

0.0

-2

-0.2 -0.4

-4 -0.6

-6 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

-0.8 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

D_MAAA5_AD_L

D_MAAA5

1st differences indicate volatility effects ... 40

0.4

30 0.2

20 10

0.0

0 -10

-0.2

-20 -30 1/05/01

5/25/01

10/12/01

3/01/02

D_MBBB7

7/19/02

-0.4 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

D_MBBB7_AD_L

Figure 9. Data overview of selected original and logarithmic, Johnson-fit adjusted synthetic RMBS spread series.

The spread series of RMBS and CDO transactions were obtained from the structured finance trading desk of a major European commercial bank, which routinely generates an endof-week indicative secondary spread benchmark from all traded transactions (classified by ABS


167

type, rating and maturity) with the highest market quotes.20 So in some cases the given spread time series of these asset types does not reflect actual transaction data but a market composite specific to the respective ABS type and rating class. We augment our ABS pricing data by Pfandbrief spreads as a maturity-matched pricing benchmark of a virtually risk-free “on balance sheet”-MBS transaction. The time series data of European Pfandbrief spreads are based on the Merrill Lynch EMU Pfandbrief Index database21 (via Bloomberg), whose “maturity buckets” (13 years, 3-5 years and 5-7 years) roughly squared with the terms to maturity of available CDO and RMBS tranches (see Tabs. 1 and 2).22 Since the Pfandbrief indices were not re-balanced by a equally weighted measure, we find different proportions of rating classes at the beginning and the end of the sample periods (see Table 2). On 5 January 2001 all Pfandbrief indices included more than 80% AAA-rated issues compared to 18 October 2002 when roughly 75% of all issues were rated AAA. We redress this issue by computing a mean weighted-average of rating classes for each Pfandbrief index. We obtain daily spreads according to this distribution of rating classes for each maturity classification of Pfandbrief deals. Level Data Transformed Series

Original Series


Pfandbrief Spreads (A A A , 3 years) 32

3.4

28

3.2

24

3.0

20

2.8

16

2.6

12 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

2.4 1/05/01

5/25/01

PAAA3

10/12/01

3/01/02

7/19/02

PAAA3_AD_L

Pfandbrief Spreads (A A A , 5 years) 40

3.8

35

3.6

30

3.4

25

3.2

20

3.0

15 1/05/01

5/25/01

10/12/01

3/01/02

PAAA5

7/19/02

2.8 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

PAAA5_AD_L

Figure 10. Continued on next page. 20

21 22

These secondary market spreads are predominantly indicative and constitute a consensus view of a trading syndicate, which routinely reports the “tightest” spreads of selected transactions to make up the spread series for each asset class. The majority of constituent Pfandbrief issues of the index were originated by German banks. We discarded the possibility of calculating the index composition for each daily spread observation due to shortterm volatility jumps and level effects induced by the accounting scandals surrounding the U.S. corporations Enron and WorldCom. Note that we replaced two missing observations on 14 April 2001 and 29 March 2002 (bank holidays) by the spreads of the previous day. Originally, daily Pfandbrief spreads were obtained for the time period from 13 April 1998 to 18 October 2002, which were later transformed into weekly spreads and shortened to fit the time period of observed CDO and RMBS spreads in order to ensure a reliable statistical analysis.

168

Andreas A. Jobst


Original Series


15

0.6

10

0.4

5

0.2

0

0.0

-5

-0.2

-10 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

-0.4 1/05/01

5/25/01

10/12/01

D_PAAA3

15

0.6

10

0.4

5

0.2

0

0.0

-5

-0.2

-10 1/05/01

5/25/01

10/12/01

3/01/02

3/01/02

7/19/02

D_PAAA3_AD_L

7/19/02

-0.4 1/05/01

5/25/01

10/12/01

3/01/02

7/19/02

D_PAAA5_AD_L

D_PAAA5

Figure 10. Data overview of selected original and logarithmic, Johnson-fit adjusted synthetic Pfandbrief spread series.

Table 1. Nomenclature of the sample spread series associated with certain asset types. Acronym

ABS Type

Rating Class (S&P)

Maturity

Deal Structure

CSAAA3 CSA5 CSBBB7

CDO CDO CDO

AAA A BBB

3 years 5 years 7 years

synthetic synthetic synthetic

CTAAA3 CTA5 CTBBB7

CDO CDO CDO

AAA A BBB


traditional, balance sheet traditional, balance sheet traditional, balance sheet

MAAA3 MAAA5 MA7 MBBB7

RMBS RMBS RMBS RMBS

AAA AAA A BBB

3 years 5 years 7 years 7 years

synthetic & trad. synthetic & trad. synthetic & trad. synthetic & trad.

PAAA3 PAAA5 PAAA7

Pfandbrief Pfandbrief Pfandbrief

AAA AAA AAA


on-balance on-balance on-balance


169

Table 2. Definition of the Merrill Lynch EMU Pfandbrief Index and its rating class composition over time. Pfandbrief (Rating Grade)

Mean Weighted5 Jan. 2001 Average Index Weighted# of Composition Average Index Issues

with maturity 1-3 years AAA AA A Cash Total

78.11% 20.81% 1.08% 100.00%


79.63% 19.76% 0.61% 100.00%


81.03% 18.31% 0.67% 100.00%

81.51% 17.91% 0.45% 0.12% 100.00%

18 Oct. 2002 Weighted# of Issues Average Index

989 191 10

74.39% 23.71% 1.71% 0.19% 100.00%

1,190

82.78% 17.04% 0.18% 0.00% 100.00%

722 126 4

87.07% 12.72% 0.22% 0.00% 100.00%

431 53 2

852

486

815 180 29 1,024

76.48% 22.48% 1.04% 0.00% 100.00%

536 144 15

74.91% 23.90% 1.11% 0.07% 100.00%

329 83 11

695

423

Table 3. Data transformation of logarithmic CDO, MBS and Pfandbrief spread series (1st diff.) by means of the Johnson Fit procedure. The ABS tranche indices are classified as M=“Mortgage-Backed Securities (RMBS)”, P=“Pfandbrief”, CS=“Synthetic Collateralized Debt Obligation (CDO), and CT=“Traditional (True Sale) Collateralized Debt Obligation (CDO)”. The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. Jarque-Bera (JB) and the Doornik-Hansen (Ep) statistics indicate whether the residual series are distributed close to normality. Asset Class ρ with Selected Spread z-value skewness kurtosis original distribution Series distribution

JB

pJB

EP

pE

CSAAA3 CSA5 CSBBB7

0.9515 0.8292 0.8341

SB SB SB

0.9597 0.9173 0.9679

0.0461 -0.0914 0.0972

3.5767 3.2128 3.3628

1.3359 0.5128 0.3051 0.8585 0.6636 0.7176

3.1429 0.2077 1.1243 0.5700 1.8504 0.3965

CTAAA3 CTA5 CTBBB7

0.3029 0.4108 0.9119

SU SB SB

0.4504 0.9330 0.9317

0.2781 -0.2280 0.0360

2.2272 2.7507 3.0430

3.5511 0.1694 1.0582 0.5891 0.0276 0.9863

5.4736 0.0648 1.0287 0.5979 0.4472 0.7996

170

Andreas A. Jobst Table 3. Continued

Asset Class Spread Series MAAA3 MAAA5 MA7 MBBB7 PAAA3 PAAA5 PAAA7

z-value

Selected distribution

0.2864 0.2765 0.0902 0.1507

SB SU SU SU

0.8342 0.6446 0.8041

SB SB SB

ρ with EP skewness kurtosis JB pJB original distribution 0.9912 0.3847 2.5218 3.2142 0.2005 4.9582 0.2511 -0.1348 2.0911 3.5201 0.1720 4.6662 0.2592 0.0701 3.3462 0.5463 0.7610 1.7428 -0.0331 0.1279 7.3745 75.2062 0.0000 49.1697 0.9808 0.9967 0.9902

-0.1098 0.0258 0.0599

3.6506 2.8617 3.0421

1.8466 0.3972 0.0854 0.9582 0.0631 0.9689

pE 0.0838 0.0970 0.4184 0.0000

3.6514 0.1611 0.0657 0.9677 0.4690 0.7910

2.2. Statistical Descriptives and Univariate Diagnostics The quality of our estimation results of time series fundamentally depends on the statistical properties of spread series in our data set, especially, the distribution of spreads and the degree of autocorrelation if applicable. We first inspect the descriptive statistics of sample spreads as a crucial piece of preliminary information for modeling the spread dynamics (see Figs. 8-10). Traditional CLO Reference Pool Credit Quality

Structural Considerations

Originator/ Servicer Considerations

• portfolio composition and payment rate • margin on reference loans • credit enhancement (retention of first loss provision) and liquidity support (advanced payment facilities, etc.) from servicer or third parties (hedging, swap, guarantee, etc.) as cushion for potential losses (including excess spread in traditional CLOs) • loan pool selection and replenishment criteria (eligibility & substitution criteria) • verification of credit event, workout process and redemption of tranches under consideration of early amortization triggers • prioritisation of losses & proceeds under different scenarios • legal integrity and regulatory compliance (bankruptcy remoteness of SPV) of asset transfer ("true sale") • risks from commingling, set-off, interest rate, maturity mismatch, etc.)

Capital Market Considerations

• legal integrity of structured claim with/without SPV as originator ("perfection of security interest in collateral") • loss determination and timing of settlement, allocation • exposure from credit deterioriaton of sponsoring bank • illiquidity and value deterioration of collateral

• impact of loan servicing and reference pool characteristics on the originator: underwriting standards, effectivness of operational routines in dealing with delinquencies, debtor relationship • servicing competencies: administration of collections and payment remittances, policies of bad debt allowance, adherence to servicing duties and obligations (eligibility criteria) • availability of substiute servicer and call provision of notes • marketability (liquidity) and scope for rating downgrade/volatility • potential linkage to the sponsoring bank and market value

Collateral Risk Sponsoring Bank Credit Risk

Synthetic CLO

• historical performance and diversification effects of loans • credit rating of obligors and type of asset

• ability to perform as swap counterparty (if applicable)

• ability to pay credit protection premium for interest deficiency • potential exposure from other roles performed in transaction (e.g. repo or hedge counterparty)

• sensitivity of structured rating and spreads to event risk and so-called "headline risk" • bond profile - pass through structure vs. bullet structure, integration of call option for premature redemption of notes • secondary liquidity • degree of senior note leverage in partially funded structures • reliance on trustee and rating agencies in being able to safeguard investor interests (allocation of proceeds and credit losses) and credit assessment respectively

Figure 11. Differences between traditional and synthetic CLOs.


171

On first inspection, Pfandbrief spreads reflect reasonable stationarity of periodically meanreverting cycles. In contrast, sporadically occurring hikes and infrequent changes of MBS and CDO spreads on level and at first difference hint to arguably lower liquidity of these markets due to buy-and-hold investment strategies. Although some interspersed idle periods in these spread series might jeopardize the appellation of even weak level stationarity, the frequently occurring volatility peaks in the first differences of spreads (both original and transformed) make a strong case for autoregressive constant heteroskedasticity models (ARCH). Nonetheless, bearing in mind the hazards of “stale time series”, we attach great importance to a robust preliminary analysis before we proceed to develop the proposed GARCH approach.

2.3. Test of Normality Our proposed GARCH specification largely relies on the statistical assumptions of linear multivariate analysis.23 Although endogenous variables are not required to fit certain distributional characteristics, robust parametric testing of statistically significant parameter coefficients infers normally distributed residuals (Greene, 1993), which, in turn, implies similar distributional characteristics of the dependent variable (Hair et al., 1998). Otherwise any resulting estimates would not be independent of the residuals and the critical values for parametric tests, such as the t-statistic, would lose their significance (Hair et al., 1998). Various kinds of transformations have been suggested in past research in order to adjust observed data to fit desired distributional assumptions. For instance, Hartung (1987) suggests the logarithmic transformation, g ( x ) = ln ( x + c ) , the reciprocal transformation, g ( x ) = x −1 , and the square root transformation, which comes in various forms, such as g ( x ) = x + c . In this paper, we use logarithmic spread returns. Additionally, we resort to a more complex transformation via a moment-matched adaptation of the Johnson Fit (1949) procedure (see Appendix), which promises greater flexibility of stochastically more precise and reliable transformation. The Johnson algorithm allows us to align the continuous distribution of log returns of spreads (i.e. first differences of logarithmic spreads) closer to normality in order to improve the distributional properties of our spread series for robust statistical inference.24 The descriptive statistics of logarithmic and Johnson Fit-adjusted spread series in Tabs. 4-10 23

Assumptions for linear multivariate regression estimation (Greene, 1993, 170f) in matrix algebra: (i) linear relationship between exogenous and endogenous variables: Y = Xβ + ε , (ii) zero expected residuals:

E ( ε ) = 0 , (iii) homoskedasticity: E ( εε ') = σ 2 I , (iv) independence of residuals: E ( ε X ) = 0 , (v) X 24

represents a non-stochastic n × k matrix of rank k. The Johnson Fit transformation procedure is based on three kinds of distribution functions (Johnson curves) that map a set of equidistant standard normal target variables u to original variables x. We apply the Johnson Fit adjustment to all but the LIBOR spread series for the proposed GARCH model specification.

Table 4. Summary descriptive statistics of all Johnson Fit-adjusted, logarithmic CDO, MBS and Pfandbrief spread series and logarithmic LIBOR rates at first differences. The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The Jarque-Bera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Q-statistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Collateralized Debt Obligations (CDO) synthetic Mean Median


Pfandbriefe

LIBOR

traditional

AAA3

A5

BBB7

AAA3

A5

BBB7

AAA5

AAA3

A7

BBB7

AAA3

0.0083

0.0114

0.0083

0.0021

0.0038

0.0050

0.0000

0.0021

-0.0016

0.0014

-0.0007 -0.0027 -0.0055

AAA5

AAA7 LIBOR_L -0.0040

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

-0.0024

0.4579

Maximum

0.5521

0.3907

0.5521

0.2038

0.3718

0.1485

0.5595

0.4060

0.1702

0.8041

0.4285

0.1764

0.0209

Minimum Std. dev.

-0.3072 0.0770

-0.0935 0.0568

-0.3072 0.0770

-0.1918 0.0446

-0.1247 0.0522

-0.1482 0.0364

-0.5595 0.1598

-0.4060 0.0906

-0.0829 0.0220

-0.5943 0.1252

-0.2241 -0.2312 -0.2141 0.0937 0.0913 0.0754

-0.0691 0.0131

0.8222

3.9327

0.0885

-0.6800

-0.7285

Skewness

4.0859

5.0211

4.0859

4.4868

1.3561

0.8764

1.2008

0.0547

-1.9469

Kurtosis

34.8839

30.5179

34.8839 14.8060 29.8630 12.2877 10.7610

14.7080 43.2074

26.1380

6.5829

8.6559

3.3994

9.0406

Jarque-Bera

4152.87

3325.06

4152.87

539.40

6576.48

2103.06

61.65

146.31

0.66

305.8752

(p-value) LB-Q (lags)

0.0000 0.01 (1)

0.0000 0.09 (1)

0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.0000 0.01 (1) 0.39 (1) 1.19 (1) 0.51 (1) 38.91 (3) 0.82 (1) 1.18 (1)

0.0000 3.99 (1)

AC value

-0.0100

-0.0300

-0.0100

0.0640

0.1110

0.0730

-0.2330

-0.0920

0.1110

-0.2040

0.0560

92

92

92

92

92

92

92

92

92

92

92

Obs.

550.58

3036.00

334.38

240.57

0.0000 0.0000 0.7173 0.0000 22.36 (2) 8.39 (2) 1.47 (1) 1006 (25) -0.0190 -0.1240 92

92

0.1970 92

Table 5. Descriptive statistics of all logarithmic and Johnson Fit-adjusted, logarithmic CDO (synthetic and traditional) spread series (level). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The Jarque-Bera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Q-statistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Collateralized Debt Obligations (CDO) - logarithmic spreads (level) synthetic

- Johnson fit-adjusted, logarithmic spreads (level) -

traditional

synthetic

traditional

Mean Median Maximum Minimum Std. dev.

AAA3 3.7314 3.8286 4.1744 3.4012 0.2617

A5 4.8060 4.9200 5.1648 4.2767 0.2447

BBB7 5.5049 5.5452 5.9269 5.1591 0.2236

AAA3 3.3846 3.3322 3.6636 3.2426 0.1313

A5 4.5263 4.4998 5.0106 4.2767 0.2284

BBB7 5.2839 5.2215 5.7038 5.1358 0.1428

AAA3 3.7314 3.8192 4.3118 3.2583 0.2617

A5 4.8060 4.8697 5.3492 4.1993 0.2447

BBB7 5.5049 5.5449 6.0047 5.0301 0.2236

AAA3 3.3846 3.3769 3.6240 3.0413 0.1313

A5 4.5263 4.5700 5.0782 4.2178 0.2284

BBB7 5.2839 5.2672 5.6205 4.9377 0.1428

Skewness Kurtosis Jarque-Bera (p-value) LB-Q (lags)* AC value Obs.

-0.0271 1.5821 7.80 0.0202 882.6 (26) 0.1850 93

-0.4738 1.9614 7.74 0.0208 909.24 (27) 0.1730 93

0.0181 2.0100 3.84 0.1463 911.34 (28) 0.1710 93

0.6853 1.8238 12.78 0.0017 583.9 (15) 0.1740 93

0.3858 1.7907 8.06 0.0178 1,072.8 (27) 0.1800 93

1.2042 3.7495 24.92 0.0000 768.14 (26) 0.1710 93

-0.0914 3.2128 0.31 0.8585 437.37 (14) 0.1990 93

0.0461 3.5767 1.34 0.5128 587.16 (26) 0.1980 93

0.0972 3.3628 0.66 0.7176 609.65 (25) 0.1980 93

-0.2280 2.7507 1.06 0.5891 420.4 (13) 0.1790 93

0.2781 2.2272 3.55 0.1694 655.46 (16) 0.1840 93

0.0360 3.0430 0.03 0.9863 739.28 (28) 0.1690 93

Table 6. Descriptive statistics of all logarithmic and Johnson Fit-adjusted, logarithmic CDO (synthetic and traditional) spread series (first differences). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high noninvestment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The Jarque-Bera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Q-statistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Mortgage-Backed Securities (MBS) - logarithmic spreads (level) -

- Johnson fit-adjusted, log. spreads (level) -

AAA5

AAA3

A7

BBB7

AAA5

AAA3

A7

BBB7

Mean

3.1244

3.0217

4.1751

4.9367

3.1244

3.0217

4.1751

4.9367

Median

3.1781

3.0910

4.1897

4.9558

3.0854

2.9947

4.1829

4.9514

Maximum

3.3322

3.2189

4.3175

5.1648

3.3879

3.2047

4.3678

5.2871

Minimum

2.8622

2.8332

4.0943

4.7875

2.8285

2.7988

4.0988

4.4829

Std. dev.

0.1446

0.1404

0.0673

0.1207

0.1446

0.1404

0.0673

0.1207

Skewness

-0.2931

-0.4277

0.0080

0.0127

0.0701

-0.1348

0.3847

0.1279

Kurtosis

1.4694

1.3694

1.6577

1.6713

3.3462

2.0911

2.5218

7.3745

Jarque-Bera

10.52

13.28

7.06

6.92

0.55

3.52

3.21

75.21

(p-value)

0.0052

0.0013

0.0293

0.0315

0.7610

0.1720

0.2005

0.0000

967.94 (23)

905.08 (22)

785.63 (21)

733.1 (17)

35.073 (3)

164.22 (7)

645.58 (19)

22.393 (2)

0.1810

0.1820

0.1720

0.1890

0.1410

0.1270

0.1760

0.1300

93

93

93

93

93

93

93

93

LB-Q (lags)* AC value

Obs.

Table 7. Descriptive statistics of all logarithmic and Johnson Fit-adjusted, logarithmic MBS spread series (level). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The Jarque-Bera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Qstatistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Pfandbriefe - logarithmic spreads (level) AAA3

AAA5

- Johnson fit-adj., log. spreads (level) AAA7

AAA3

AAA5

AAA7

Mean

2.9268

3.2045

3.4437

2.9268

3.2045

3.4437

Median

2.9444

3.2189

3.4499

2.9571

3.2111

3.4540

Maximum

3.3673

3.5835

3.8501

3.2652

3.6690

3.9766

Minimum

2.7081

2.8332

3.0910

2.5928

2.8406

2.9558

Std. dev.

0.1065

0.1691

0.1819

0.1065

0.1691

0.1819

Skewness

0.8305

-0.2348

0.0708

-0.1098

0.0258

0.0599

Kurtosis

4.9100

2.4766

2.0795

3.6506

2.8617

3.0421

Jarque-Bera

25.10

1.94

3.40

1.85

0.09

0.06

(p-value)

0.0000

0.3796

0.1829

0.3972

0.9582

0.9689

189.51 (12)

459.52 (15)

617.95 (19)

226.06 (12)

410.94 (14)

543.62 (17)

0.0710

0.1820

0.1910

0.1090

0.1950

0.2130

93

93

93

93

93

93


Obs.

Table 8. Descriptive statistics of all logarithmic and Johnson Fit-adjusted, logarithmic MBS spread series (first differences). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The Jarque-Bera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Qstatistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Collateralized Debt Obligations (CDO) st

- Johnson fit-adjusted, logarithmic spreads (1st diff.) -

- logarithmic spreads (1 diff.) synthetic AAA3

A5

traditional BBB7

AAA3

A5

synthetic BBB7

AAA3

A5

traditional BBB7

AAA3

A5

BBB7

Mean

0.0081

0.0093

0.0082

0.0009

0.0056

0.0041

0.0083

0.0114

0.0083

0.0021

0.0038

0.0050

Median

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Maximum

0.4055

0.2877

0.2231

0.2595

0.2772

0.1508

0.5521

0.3907

0.5521

0.2038

0.3718

0.1485

Minimum

-0.0426

-0.0351

-0.0690

-0.1082

-0.1823

-0.1823

-0.3072

-0.0935

-0.3072

-0.1918

-0.1247

-0.1482

Std. dev.

0.0509

0.0391

0.0341

0.0350

0.0427

0.0312

0.0770

0.0568

0.0770

0.0446

0.0522

0.0364

Skewness

5.2634

5.1544

5.2045

3.3225

0.6595

-1.3280

4.0859

5.0211

4.0859

0.8222

3.9327

0.0885

Kurtosis

33.0000

32.8519

35.3596

33.3007

20.8972

26.5657

34.8839

30.5179

34.8839

14.8060

29.8630

12.2877

Jarque-Bera

7,307.81

3,755.58

2,774.31

4,247.65

1,920.50

1,386.95

4,152.87

3,325.06

4,152.87

550.58

3,036.00

334.38

(p-value)

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

LB-Q (lags)*

0.03 (1)

0.06 (1)

0.02 (1)

0.44 (1)

0.10 (1)

0.67 (1)

0.01 (1)

0.09 (1)

0.01 (1)

0.39 (1)

1.19 (1)

0.51 (1)

AC value

-0.0170

-0.0250

0.0130

0.0680

0.0320

0.0840

-0.0100

-0.0300

-0.0100

0.0640

0.1110

0.0730

92

92

92

92

92

92

92

92

92

92

92

92

Obs.

Table 9. Descriptive statistics of all logarithmic and Johnson Fit-adjusted, logarithmic Pfandbrief spread series (level). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The Jarque-Bera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Qstatistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Mortgage-Backed Securities (MBS) st

- Johnson fit-adjusted, log. spreads (1st diff.) -

- logarithmic spreads (1 diff.) AAA5

AAA3

A7

BBB7

AAA5

AAA3

A7

BBB7

Mean

-0.0036

-0.0022

-0.0017

-0.0025

0.0000

0.0021

-0.0016

0.0014

Median

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Maximum

0.1133

0.1278

0.1128

0.1409

0.5595

0.4060

0.1702

0.8041

Minimum

-0.0564

-0.1112

-0.0408

-0.0847

-0.5595

-0.4060

-0.0829

-0.5943

Std. dev.

0.0251

0.0253

0.0155

0.0230

0.1598

0.0906

0.0220

0.1252

Skewness

1.1376

0.7786

4.0873

2.7928

-0.6800

-0.7285

4.4868

1.3561

Kurtosis

10.0180

15.4485

36.4269

23.5092

10.7610

14.7080

43.2074

26.1380

Jarque-Bera

95.00

464.40

4,028.59

1,180.81

240.57

539.40

6,576.48

2,103.06

(p-value)

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

0.6727 (1)

0.0018 (1)

1.7533 (1)

3.1105 (1)

38.913 (3)

0.8166 (1)

1.1814 (1)

3.9858 (1)

0.0840

0.0040

0.1350

0.1800

-0.2330

-0.0920

0.1110

-0.2040

92

92

92

92

92

92

92

92


Obs.

Table 10. Descriptive statistics of all logarithmic and Johnson Fit-adjusted, logarithmic Pfandbrief spread series (first differences). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. The JarqueBera statistic evaluates the null hypothesis (H0: normal distribution with unspecified mean and variance) based on the sample skewness and kurtosis. We report the Llung-Box Q-statistic (H0: no autocorrelation) at statistical significance of 5% for up to (x) lags (at which the Q-statistic falls within the two standard error bounds of +/-2T-0.5 and no longer flags statistically significant observations with autocorrelation). Pfandbriefe st

- Johnson fit-adj., log. spreads (1st diff.) -

- logarithmic spreads (1 diff.) AAA3

AAA5

AAA7

AAA3

AAA5

AAA7

Mean

-0.0010

-0.0020

-0.0041

-0.0007

-0.0027

-0.0055

Median

0.0000

0.0000

0.0000

0.0000

0.0000

0.0000

Maximum

0.5341

0.3646

0.1924

0.4285

0.4579

0.1764

Minimum

-0.3228

-0.1671

-0.2151

-0.2241

-0.2312

-0.2141

Std. dev.

0.1002

0.0847

0.0701

0.0937

0.0913

0.0754

Skewness

1.4112

1.1500

0.3424

0.8764

1.2008

0.0547

Kurtosis

15.1285

8.1286

3.5867

6.5829

8.6559

3.3994

Jarque-Bera

284.29

35.87

1.96

61.65

146.31

0.66

(p-value)

0.0000

0.0000

0.3758

0.0000

0.0000

0.7173

23.899 (2)

9.0816 (2)

5.1163 (1)

22.358 (2)

8.3868 (2)

1.4672 (1)

0.0680

-0.0020

-0.0230

0.0560

-0.0190

-0.1240

92

92

92

92

92

92


Obs.


179

suggest that average spreads decrease with higher ratings and maturity. Relative spread volatilities (relative variation) are modest, ranging from 1.6% to 7% for the logarithmic spread series of asset classes in the data set. The Jarque-Bera (1980 and 1987)25 test diagnostic (JB) of transformed logspreads indicates that most of the original and transformed time series are not normally distributed. The Doornik-Hansen diagnostic (Ep) of normality for small samples sizes on the basis of a χ df2 = 2 -distributed test statistic confirms this finding. All spread series fail to adhere to normality at first differences. Surprisingly, non-normality does not seem to stem from poor data quality or different market liquidity. For instance, according to our JB and Ep statistics, liquid Pfandbrief price movements deviate more from normality than illiquid, low-rated synthetic CDO tranches. Although the logarithmic transformation does not improve the spread distribution across all spread series, it harmonizes relative spread volatility, V = σ S S , of the standard deviation σ S and the sample mean S of spreads, which decreases for higher ratings and longer terms to maturity. For non-transformed spreads, we derive an average V = 33.96% for S = 16.67% and σ S = 5.66% , which are highly correlated at ρ S ,σ s = 0.947 across spread series. The logarithmic transformation of original spreads mitigates level effects of spread variance and stabilizes the variance of the spread sample for comparative analysis by reducing the correlation ρ S ,σ s to 0.289, with relative volatility V ranging from 1.6% to 7.0% across the sample spread series.

2.4. Test of Autocorrelation and Unit Root We first need to investigate the time series dynamics of all CDO, MBS and Pfandbrief spread series (actual, adjusted and with/without Johnson Fit) of our sample for autocorrelation and stationarity, before we move on to introduce a multi-factor model as an estimation of spread time series (GARCH specification), which allows us to determine asymmetric spread dynamics contingent on past spreads and changes in the risk-free interest rate. Various financial studies (Goodman and Ho, 1997 and 1998) have shown that interest rates follow a random walk, which has a constant forecast value, conditional on time, and associated variance increasing over time. Since a random walk can be integrated at first order it does not succumb to a mean-reverting process of level stationarity (Nelson and Plosser, 1982). According to Koutmos (2001), U.S. MBS price quotes and government bond yields each have unit roots, while U.S. MBS spreads appear to be stationary and co-integrated with U.S. Treasury spreads (i.e. both share a long-term relationship)26 for a sample of weekly observations of U.S. MBS with maturities of five, seven and ten years over a sample period of more than 30 years. Furthermore, his analysis concludes that spread changes exhibit

25

26

The Jarque-Bera statistic evaluates the null hypothesis that a given series has a normal distribution with unspecified mean and variance, against the alternative that the series does not have a normal distribution, based on the sample skewness and kurtosis. If observed variables grow together, spurious correlation might be measured erroneously. However, in the presence of co-integration they might share fundamental economic driver that gives rise to a long-term relationship.

180

Andreas A. Jobst

asymmetric mean reversion, i.e. the first moment of spreads is strongly mean-reverting following spread decreases, but non-stationary following spread increases. We examine the time series dynamics of the different asset class spreads as to the existence of autoregressive effects and their compliance with the stationarity condition of inference procedures. The Llung-Box Q-statistic for the null hypothesis of i.i.d. variables indicates significant and high levels of serial correlation for both observed spreads (up to 26 lags) and logspreads (up to 28 lags). The first moment of spreads sheds most of the serial correlation, with only some spread series flagging autocorrelation at up to two lags (e.g. Pfandbrief spreads for maturity terms of three and five years). Also the correlogramgenerated partial correlation coefficients (PAC) between the current spread levels and past spread levels of up to five lags together with the associated Q-statistics for each period for non-transformed and transformed logspreads confirm this assessment. While partial correlation decreases substantially after one lag for synthetic and traditional CDO and MBS spread series (with the Johnson Fit reducing some of the correlation), Pfandbrief spreads retain partial correlation values of more than 20% up to three lags in some instances. The attempt of stripping all spread series of any autocorrelation by using the residuals of an AR(p) estimation of past spreads for up to p number of lags for further analysis does not prove to be effective. If past spreads absorb all serial correlation effects, residuals of the least squares estimator in an autoregressive (AR) linear specification of lagged spreads should be i.i.d. and absorb all serial correlation effects. For p = 2 for Pfandbrief spread series (AAA3 and AAA5) (given high partial correlation coefficients for up to at least lag two for these spread series in original and adjusted form) and p = 1 for the spread time series of all other asset types, we find that autocorrelation persists in the new spread time series of residuals, with autocorrelation and partial correlation test diagnostics only marginally different from the original spread series. In some cases for CDO and MBS data, this result might be primarily attributable to level persistence, where slight changes over time generate significant autocorrelation, which, at the same time, sustains a mean reverting process. In this instance “stale data” would feign mean reversion, which would otherwise spawn from level stationarity in very liquid and volatile markets. Hence, we abstain from using new autocorrelation-adjusted spread series of AR estimated residuals. This observation, however, has important consequences for the later formulation of the multi-factor GARCH model, where we control for past changes in LIBOR as spread reference base (so we could view the spread series as “excess returns” over LIBOR). We address the issue of autocorrelation by including correction terms in our GARCH specification in order to control for autoregressive effects up to two lags. We also compute heteroskedasticity consistent (quasi-maximum likelihood) covariance matrices, which are needed for several diagnostics of model robustness (i.e. coefficient and residual tests). In order to determine the time trend and the presence of mean reversion of all asset spread series in the data set, two methods emerge – the correlogram or the unit root test. In a finite data sample the correlogram testing procedure is imprecise, because sample autocorrelation will converge to zero for k elements (and indicate mean reversion), even if the time series is non-stationary. In practice it is difficult to tell whether a time series is nonstationary or slowly converging stationary. If values for autocorrelation drop to zero only after some periods, we can reject the random walk hypothesis (unit root). Given the short spread time series in the data set, we choose the classical unit root testing procedures by


181

Dickey-Fuller (1979 and 1981) and Phillips-Perron (1987) – the Augmented Dickey-Fuller (ADF) and the Phillips-Perron (PP) test statistics (Greene, 1993). In our case, the autoregressive ADF test (with deterministic time trend) is defined as

ln ( S t ) = μ + β t + γ ln ( S t −1 ) + ∑ j =1φ j ln ( ΔS t − j ) + ε t p −1

with H 0 : ρ = 1 vs . H1 : ρ < 1 , (1)

with the one-sided hypothesis test H 0 : γ = 0 vs . H1 : γ < 0 (since the differences from the null hypothesis are unidirectional ex ante), with μ as the long run mean, Δ as difference

(

)

operator, and i.i.d. residuals as white noise error term ε ~N 0,σ 2 . The PP test for the specification Δ ln ( S t ) = μ + β ( t − T 2 ) + γ ln ( S t −1 ) + ε t with H 0 : γ = 1 vs . H1 : γ < 1 also corrects the t-statistic of the γ coefficient of the AR(1) process by the serial correlation of residuals ε t under the Newey-West (1987) heteroskedastic and autocorrelation consistent q T ⎛ j ⎞ −1 estimator, where ω 2 = γ 0 + 2∑ ⎜ 1 − ⎟γ j and γ j = T ∑ ε%t ε%t − j with the truncation lag q + 1⎠ j =1 ⎝ t = j +1 q. The PP t-statistic is specified as 2 γ 0 tb (ω − γ 0 ) Ts b , tPP = − 2ωσˆ ω

where tb and s b are the t-statistic and the standard error of

(2)

β respectively and σˆ denotes

the standard error of the test regression. We run the ADF test statistic with a constant and a linear trend on level and first differences of spreads of up to two lags and the PP test diagnostic with a vector of three truncation lags of the autocorrelation consistent variance estimator for the Newey-West correction. For both test we employ MacKinnon (1996) critical values for rejection of hypothesis of a unit root based on one-sided p-values. In our preliminary descriptive statistics of transformed logspreads, we reject the unit root in most weekly spread time series for level data (see also Figs. 8-10), like Koutmos (2002) with respect to U.S. MBS spreads. Autocorrelation effects can almost be entirely eliminated for a test specification of up to four lags. For the first difference of spreads both ADF and PP test diagnostics strongly reject the null hypothesis of a unit root in all cases. Hence, all spread series are integrated of the order zero or at least one. If spreads are mean-reverting, standard statistical hypothesis testing is applicable. Pfandbrief spreads reflect reasonable level stationarity of periodically mean-reverting cycles. In contrast, infrequent changes of spread data on level and first difference and sporadically occurring spikes in level spread series of CDO and MBS transactions testify to arguably higher illiquidity of these markets compared to the Pfandbrief market. Although some interspersed idle periods might jeopardize the appellation of level stationarity, the frequently occurring volatility peaks in the first differences of spreads (both original and transformed) make a strong case for autoregressive constant heteroskedasticity models (ARCH). The relative spread variation of up to 25.8% suggests considerable risk in the spread structure across all asset classes. All first order integrated CDO, MBS and Pfandbrief spread series are difference stationary and preserve

182

Andreas A. Jobst

strong mean reversion even on a level basis, except for traditional CDOs (A5) in both unit root tests and two out of three Pfandbrief spread series (AAA5 and AAA7) in the PhillipsPerron unit root test.27 We also find a level effect in the degree of mean reversion, i.e. spread series with higher sample means entail stronger mean reversion. Both ADF and PP test diagnostics indicate the unit root can be rejected for most level spread series, which are at least integrated at the order of one. LIBOR rates as well as CDO, MBS and Pfandbrief spreads share unique co-integration vectors, indicating difference stationarity of most individual spread series. We identify two possible causes for divergent stochastic properties across spread series in our sample compared to Koutmos (2002): liquidity and data frequency. Our limited number of observations does not invite the same degree of measurability for long range cycles of mean-reversion. The fact that our results are less homogenous compared to Koutmos (2002) could be attributable to the poor data quality.28 Whereas Koutmos used time series data of more than 30 years to substantiate his findings on the level stationary of U.S. MBS spreads, our limited number of observations over a time period of not even two years does not engross the same degree of measurability of long range cycles of mean-reversion. We also need to view cases of level stationary spreads with great caution, given the quality of the underlying pricing information. MBS and CDO markets differ from the Pfandbrief market in investment liquidity. We also recognize that persistent stochastic processes over long spans of time with a small autoregressive component (due to low liquidity and infrequent trading activity), like the occasionally “stale” nature of spread movements of MBS and CDO, could bias the ADF and PP tests towards rejecting the unit root, in the absence of strong statistical power against the alternative of level stationarity (Papell and Prodan, 2003).29 However, strong autocorrelation does not apply for first differences of spreads, so at least first order integration yields satisfactory characteristics of for standard statistical hypothesis testing.

3. Model We explain the heteroskedasticity spread change behavior (term structure of spreads) by a multi-factor asymmetric GARCH process on the basis of two equations for the mean and conditional variance. In extension to Koutmos’ (2002) adaptation of Longstaff and Schwartz (1992), the conditional mean of spread changes is influenced by past spread levels, the past 27

28

29

We find that the non-stationary of the Pfandbrief spread series can only be eliminated by using daily observations over the original time period of four years (with the exclusion of all observations during the second half of 2000 in order to control for exogenous distortions to mean reversion due to the financial crises in summer 2000). Higher ADF and PP test statistics of daily Pfandbrief spreads over the originally generated time period from September 1998 to October 2002 (not reported) indicates that better data quality with respect to data frequency and time period of observations, support the rejection of a unit root. Moreover, the spread series of Pfandbrief spreads over a four-year period include spread quotations of summer 2000, when some German Pfandbrief issues – for the first time in recent history – were downgraded amid the massive liquidity crises in global financial markets. While almost all German Pfandbrief transactions were AAA-rated and regarded similarly safe an investment as government bonds, a re-assessment of credit risk in Pfandbriefe sent spreads markedly higher during the second half of 2000. Also the shorter series of weekly Pfandbrief spreads used in this analysis might still suffer from lagged effects on spread volatility from January 2001 onwards. The danger of type II error misspecification, which also operates in the presence of a nonlinear data generating process, has critical implications on the interpretation of ADF results: the linear specification biases the unit root test into failing to reject the unit root hypothesis (Taylor, 2001).


183

LIBOR rate and the conditional variance. The latter follows a GARCH process defined by past variance (GARCH term), past squared residuals of the mean equation (ARCH term) as well as the LIBOR rate and past spreads as variance regressors. We find both the level and the first differences of LIBOR rates as an appropriate reference base for the given spread series.30 In contrast to Koutmos, however, our sample size is limited to 93 weekly observations of actual secondary market spread data for traded tranches of theses asset types. In order to improve the statistical properties of the analysis we adjusted the spread series and transformed them, so that the subsequent examination could be completed on “raw” data, logarithmic spreads and spreads adjusted by the Johnson Fit. We abstained from the Johnson Fit procedure for LIBOR rates, i.e. the spread series of LIBOR enters the estimation only as observed spot rates and logarithmic spot rate without Johnson Fit. We propose two GARCH specifications, GARCH(1,1) and GARCH(2,1), with one variation each. In the GARCH(1,1) model we incorporate (i) the first moment of LIBOR changes (with indicator function) in the mean equation and (ii) the past LIBOR rate as variance regressors. As a variation to this specification we include past LIBOR rates as either level data or first differences. In the alternative GARCH(2,1) process, we refine the GARCH(1,1) model as we (i) introduce a new set of mean reversion coefficients of lag two for positive and negative past spread levels mean equation (with a corresponding indicator function) and (ii) extend the past forecast variance to two lags in the estimation of conditional variance. Overall, we consider asymmetric effects of explanatory factors through (i) indicator functions for past spreads and past LIBOR rates in the mean equation as well as (ii) two coefficients for positive and negative errors in the expression for conditional variance.

3.1. Model Assumptions and Specifications In this paper we explain the heteroskedasticity spread change behavior by a multi-factor asymmetric GARCH process of the mean and conditional variance. We assume that ABS spreads S t are lognormally distributed after application of Ito’s Lemma to a standard geometric Brownian motion (GBM),

{(

)

}

S t = S 0 exp μS − σ S 2 2 t + σ S tz t ,

(3)

of spread dynamics dS t S t = μ S dt + σ S dWt as stochastic differential process, where μS is the growth rate of S and the volatility σ S tz t of S contains a Wiener process Wt of instantaneous spread change of Δz ~(0, Δt ) .

We measure the volatility of spread changes on the basis of a GARCH multi-factor term structure model as a discrete approximation of linear spread change, provided that the spread change follows a stationary process. Generally, a GARCH(p,q) process models the heteroskedasticity of a given time series x T , whose distribution is specified by

F (x t σ t ) ~(0,1) . The conditional variance is defined by the volatility from previous

30

See also Goodman and Ho (1998).

184

Andreas A. Jobst

period(s), measured as the q lag(s) of the squared residual(s) from the mean equation (ARCH term(s)), and the forecast variance(s) of the last p periods (GARCH term(s)). In a practical context, a GARCH process prescribes that asset volatility is predicted by the weighted average of the long term mean, past volatility and the estimation error associated with forecasted variance of the last period. We specify the GARCH(1,1) model for logarithmic spreads by the following mean and conditional variance equations:

Δ ln S t = α 0 + I tα 1,1 ln S t −1 + (1 − I t )α 1,2 ln S t −1 + Ktα 2,1 ln L t −1 + (1 − Kt )α 2,2 ln L t −1 + α 3σ t2 + ε t

(4)

and

σ t2 = β 0 + β 1ε t2−1 + β 2 ut2−1 + β 3 ln S t −1 + β 4 ln L t −1 + β 5σ t2−1 ,

(5)

for LIBOR level rates, as well as

Δ ln S t = α 0 + I tα 1,1 ln S t −1 + (1 − I t )α 1,2 ln S t −1 + Ktα 2,1Δ ln L t + (1 − Kt )α 2,2 Δ ln L t + α 3σ t2 + ε t

(6)

σ t2 = β 0 + β 1ε t2−1 + β 2 ut2−1 + β 3 ln S t −1 + β 4 Δ ln L t + β 5σ t2−1

(7)

and

for LIBOR at first difference. This GARCH(1,1) specification of the mean and the conditional variance of spreads above captures any past influence on the first difference and the heteroskedasticity of spread change ΔS t , where S t denotes the secondary market spreads of a certain asset class (CDO, MBS or Pfandbrief) at time t and L t is the 3-month LIBOR spot rate at time t. The use of one lag spreads captures first-order autocorrelation. The conditional mean of first order spread changes is influenced by past spread levels S t −1 (conditional on the direction of change), the change of the sport rate (LIBOR) of the previous period and the level of the conditional variance, with error term ε t . The conditional variance follows a GARCH process defined by the volatility forecast of the previous period σ t2−1 (GARCH term), lagged squared residuals ε t2−1 of the mean equation (ARCH term) as well as the LIBOR rate and past spreads as variance regressors (in extension to the standard GARCH(1,1) specification). In particular, we consider asymmetric effects of explanatory factors through indicator functions (i) for past spreads and past LIBOR rates in the mean equation, and (ii) past errors in the expression for conditional variance. If the mean reversion parameter α1 < 0 , the spread series is considered level stationary. Since the traditional twofactor GARCH(1,1) model by Longstaff and Schwartz (1992) does not recognize asymmetric spread dynamics, we follow Koutmos (2002)31 to distinguish our analysis of mean reversion

31

Building on the two-factor model by Longstaff and Schwartz (1992) and the work by Bali (2000) on stochastic volatility of short-term interest rates, Koutmos considers volatility clusters of yield curves (GARCH effect) in the context of asymmetric mean reversion. He finds that spreads commonly behave non-stationary if a positive


185

after positive and negative past spread changes in line with Bali (2000). We break down the mean reversion term α 1 (mean equation) into α1, p I t S t −1 + α 1,n (1 − I t ) S t −1 by imposing the

⎧1 if S t − S t −1 ≥ 0 indicator function I t = ⎨ on spreads at one lag (with indices p and n ⎩0 if S t − S t −1 < 0 indicating positive and negative effects respectively). If the conditions α 1,1 ≠ α 1,2 and

α1,1 + α 1,2 < 0 for the regression coefficients hold, the given spread series is level stationary with asymmetric mean reversion. Moreover, we also introduce asymmetry to the conditional variance equation by discriminating between the coefficient value contingent on positive and negative squared residuals of the previous period. We extend the ARCH term to β 1ε t2−1 + β 2 ut2−1 , where

ut −1 = min ( 0, ε t −1 ) is the indicator function of negative past innovations. β 1 measures any

general sensitivity of the conditional variance σ t2 to past errors, whereas

β 2 measures the

impact of negative past error ε t −1 < 0 on the conditional variance, and hence, reflects any degree of potential asymmetries for β 2 ≠ 0 . The contribution of an (overall) positive error

ε t −1 > 0 is equal to β1 + β 2 . If β 2 > 0 , the conditional variance of spread change is more sensitive to positive past errors (i.e. spread increases) than negative past errors (i.e. spread decreases). However, if β 2 < 0 , negative residuals precipitate a negative reaction to past spread changes. This approach differs only formally from a TARCH process (Glosten et al., 1993; Zakoian, 1990), which allows asymmetric shocks to volatility through the ARCH term ⎧0 if ε t2−1 ≥ 0 32 β1ε t2−1 + γε t2−1dt −1 for d = ⎨ . We also impose the indicator function 2 ⎩1 if ε t −1 < 0

⎧1 if L t − L t −1 ≥ 0 on LIBOR rates L t . The inclusion of the LIBOR rate (at level Kt = ⎨ ⎩0 if L t − L t −1 < 0 and first differences) as a proxy for the general interest rate level helps us control for idiosyncratic pricing effects (Goodman and Ho, 1998), with squared residuals measuring those parts of spread changes, which are common to the pricing of structured debt and independent of interest rate movements. β 3 and β 4 measure the sensitivity of variance to past spreads and LIBOR rates. In extension to the GARCH(1,1) model, we grant greater explanatory power to past volatility in a GARCH(2,1) process. We expand the forecast variance of the conditional variance (GARCH term) to the last two periods, matched by two lag spreads as additional independent variables in the mean equation to control for second⎧1 if S t − S t − 2 ≥ 0 for second order autocorrelation. Based on the indicator function J t = ⎨ ⎩0 if S t − S t − 2 < 0 differences of spreads S t , this extension yields33

32

33

spread change in the past had preceded an external shock, whilst mean reversion is statistically significant after negative spread change. See also Engle and Ng (1993), who introduced original TARCH setting in their research on the asymmetric news impact curve of price dynamics. For brevity we show only the specification of the GARCH(2,1) model for LIBOR at first differences.

186

Andreas A. Jobst

Δ ln S t = α 0 + I tα 1,1 ln S t −1 + (1 − I t )α 1,2 ln S t −1 + J tα 2,1 ln S t − 2 +

(1 − J t )α 2,2 ln S t −2 + Ktα 3,1Δ ln L t + (1 − Kt )α 3,2 Δ ln L t −1 + α 3σ t2 + ε t

,

(8)

and

σ t2 = β 0 + β 1ε t2−1 + β 2 ut2−1 + β 3 ln S t −1 + β 4 Δ ln L t + β 5σ t2−1 + β 6σ t2− 2 .

(9)

Estimation Procedure The estimation of the presented GARCH models requires a non-linear solution algorithm for conditional maximum likelihood (CML). We apply two kinds of maximum likelihood iterative estimation procedures – Berndt-Hall-Hall-Hausman (1974)/Gauss-Newton (1974) and Marquardt (1963). In this paper we mainly report results for the former optimization algorithm. Since the spread series in our data set does not follow a normal distribution, with the exception of some Pfandbrief issue spreads, we use the heteroskedasticity consistent covariance method by Bollerslev and Wooldridge (1992), which is needed for several model diagnostics (coefficient and residual tests). In this way, we derive robust estimators for quasimaximum likelihood (QML) covariance and standard errors, even in the absence of normally distributed spread differences. Since both the maximum-neighborhood procedure of the Marquardt ML algorithm and the approximation of the negative Hessian by the sum of the gradient vectors of the Berndt, Hall, Hall, and Hausman (BHHH) algorithm use random iterative components, the estimation for one and the same spread series could yield different results each time. This holds true especially for short time series, such as in our case of CDO, MBS and Pfandbrief spreads, where disparate local optima misrepresent the overall estimation result. In order to reduce parameter uncertainty and derive estimation results at parameter values that maximize the objective function (global optimum), we devise a specific estimation procedure. After N iterative cycles generate preliminary estimation results, we perpetuate the estimation process until the adjusted R2-measure and the significance of estimators square up with the best results after the first N number of estimations. The determination of N represents a trade-off between computational time and the consistency of the successive estimations given the length of the time series. We set number of estimations to N = 1,000 for the short time series of weekly spreads. The estimation procedure is conducted with starting values different from 1 (i.e. ×0.7,×0.5,×0.3 or × 0 ) for the OLS estimation in cases where the estimation algorithm encountered a singular matrix due to multicollinearity of model factors.34 Upon estimation of the two specifications of GARCH models, we examine the statistical significance of the degree of level stationarity contingent on past positive or negative spread change by means of the Wald coefficient test, which computes the test statistic by estimating an unrestricted regression without imposing the coefficient restrictions specified by the null

34

We apply SQR-GARCH estimation in cases, when multicollinearity of estimation yields a singular matrix for any starting value of simple OLS-estimators. Alternatively, we could have also skipped the intercept term α 0 (i.e. the constant of spread differences) from the estimation equation. This remedial procedural, however, would only be commendable if the statistical significance of the intercept term is negligible for the interpretation of the estimation results. Particularly in context of the GARCH(2,1) specification, high levels of significance of the intercept prohibit this approach.


187

hypothesis.35 We assess overall mean reversion and associated asymmetric effects by testing the hypotheses for the coefficient values α 1,1 + α 1,2 < 0 in GARCH(1,1) and α 1,1 + α 1,2 < 0 and α 2,1 + α 2,2 < 0 in GARCH(2,1). Each hypothesis is comprised of two sub-hypotheses:

H 0,1 : α 1,1 + α 1,2 = 0 and H 0,2 : α 1,1 + α 1,2 = αˆ1,1 + αˆ1,2

for both GARCH models as well as

H 0,1 : α 2,1 + α 2,2 = 0 and H 0,2 : α 2,1 + α 2,2 = αˆ2,1 + αˆ2,2 for GARCH(2,1) in order to account for past spread levels of up to lag two. The time series is stationary overall, if (i) we cannot reject the second null hypothesis, i.e. the sum of the coefficient values is not significantly different from the sum of the calculated test estimators, and (ii) the sum of the coefficients is smaller than zero, so that the first null hypothesis is rejected. Furthermore, in the context of measuring the heteroskedasticity of spreads, we can also assess any asymmetry of spread dynamics. If H 0 : α 1,1 = α 1,2 for GARCH(1,1) as well as H 0 : α 1,1 = α 1,2 and H 0 : α 2,1 = α 2,2 for GARCH(2,1) can be rejected, past spread change influences the sensitivity of future spread change to past spread levels. Finally, we determine the correct specification of both GARCH models by means of several robustness tests to address estimation risk: two standard residual tests (Llung-Box (LB) Q-statistic and Jarque-Bera statistic) and three model specific, parametric residual tests (sign bias test, negative size bias test and positive size bias test). The Llung-Box (LB) Qstatistic for standardized and squared standardized residuals detects any remaining serial correlation in the mean and conditional variance equation. If the mean equation (conditional variance equation) is correctly specified, all Q-statistics of standardized residuals (standardized squared residuals) should be insignificant with no observable autocorrelation. We resort to the Jarque-Bera statistic for standardized residuals as a statistical diagnostic in order to test the null hypothesis of a normal distribution assumption of errors. Since conventional residual tests fail to address asymmetric mean reversion and heteroskedasticity of spread series, we need to consider possible biased effects of past errors. The sign bias test, the negative size bias test and the positive size bias test generate OLS estimates of squared standardized errors, whose statistical significance help indicate in an integrated approach 35

The Wald test diagnostic is calculated from W = ( Rβ − r )′ ×

(σ R ( X′X ) 2

−1

R′

)

−1

( Rβ − r ) χ df2 = m ,

where σ is the variance of unrestricted residuals. Under the assumption of independent and normally ε, we calculate the F-statistic distributed residuals 2

(

−1 F = ( Rβ − r )′ × s 2 R ( X′X ) R ′

)

−1

× ( Rβ − r ) × m−1 , where m = 1 is equal to the value of W and s 2

estimates σ . The Wald statistic tests the validity of linear coefficient restrictions as it measures how close the unrestricted estimates come to satisfying the restrictions under the null hypothesis. In matrix algebra the null hypothesis is generally written as H 0 : Rβ = r , where r denotes the m× 1 vector of the required results 2

of the testable restrictions and m is the number of restrictions. The matrix R ( m×k ) represents the linear combinations of the restrictions, with β as the coefficient vector with k number of coefficients. For instance,

β1 + β 2 = 1 and β 3 = β 4 would require the following specification ⎡ β1 ⎤ ⎢ ⎥ ⎡ 1 1 0 0 ⎤ ⎢ β 2 ⎥ ⎡ 1⎤ of the Wald test for m = 2 and k = 4 : H 0 : ⎢ ⊗ = ⎢ ⎥ (Hamilton, 1994). ⎥ ⎣0 0 1 −1⎦ ⎢ β 3 ⎥ ⎣0 ⎦ ⎢ ⎥ ⎣ β4 ⎦

the validity of the joint hypotheses of

188

Andreas A. Jobst

whether the model estimates are influenced by the size and the sign of past errors (ARCH terms) in a systematic way.

4. Estimation Results After an optimized application of the Johnson Fit transformation (see Appendix), we find that all CDO, MBS and Pfandbrief spread series are difference stationary. Hence, statistical inference procedures are viable for first-order integrated spread series at the least. We estimate the conditional volatility process of logarithmic spread changes of CDO, MBS and Pfandbrief spreads via the Berndt-Hall-Hall-Hausman algorithm for two multi-factor GARCH models (GARCH(1,1) and GARCH(2,1)) with interest rate control based on logarithmic LIBOR rates (at level and first differences) (see Tabs. 12-15). Although the significance of the estimated model parameters varies across spread series, we can clearly identify a strong statistical influence of endogenous factors on the mean and conditional variance, especially for synthetic CDO and Pfandbrief spreads in GARCH(1,1), as well as all CDO and MBS spreads in GARCH(2,1). Generally, most spread series confirm a reasonably high explanatory power of model parameters in both the mean and conditional variance equation of both GARCH specifications. However, we observe relatively weak influence of past spread levels and LIBOR rates (at level and first differences) in the mean equation of traditional CDO spreads under both GARCH(1,1) and GARCH(2,1) and on the conditional variance of MBS spread in GARCH(1,1) and Pfandbrief spreads in GARCH(2,1). Generally, GARCH(2,1) outperforms GARCH(1,1) on the significance of GARCH effects, whereas ARCH effects claim stronger explanatory power for conditional variance in GARCH(1,1). The use of LIBOR rates at first differences (in lieu of level LIBOR rates) intensifies this effect. In the following discussion of the estimation results, we concentrate on GARCH(1,1) and GARCH(2,1) estimation results with LIBOR at first differences, as this model specification is more plausible for greater measurability of spread changes and generally performs better than the same model with level LIBOR rates as explanatory factor. Nonetheless, we will incorporate findings from the application of level LIBOR rates whenever it changes appreciably the economic and statistical significance of the model parameters. The intercept coefficient α 0 is significant for the spread series of all synthetic CDO series as well as all MBS and Pfandbrief spreads (with the exception of MBS (A7) and Pfandbriefe (AAA5)) under the analysis of GARCH (2,1) with level LIBOR rates, while the statistical significance for the GARCH(1,1) specification is limited to highly-rated synthetic CDOs (AAA3) and most MBS and Pfandbrief transactions. Although our results for GARCH (1,1) are persistent if we substitute the first moment of LIBOR rates for level spot rates, the intercept term (as a reflection of the long-term mean) sheds its statistical significance for almost all spread series save for one synthetic CDO (AAA3) and one MBS (AAA5) in GARCH (2,1). The statistically significant influence of past spreads at one lag on future spread change in both GARCH models clearly supports the degree of mean reversion observed in the unit root tests (see Table 11). A large majority of 45 out of a total of 52 spread series across all GARCH model specifications exhibit negative sums of coefficients values (α1,1 ,α1,2 ) for past

spreads levels at lag one, indicating overall level stationarity. However, MBS spread series


189

generate economically stronger results of mean reversion than CDOs in spite of lower average mean spreads. All deviating spread series (traditional CDOs (AAA3) in three out of all four GARCH specifications, traditional CDOs (BBB7) under GARCH(2,1) with level LIBOR rates, synthetic CDOs (AAA3 and BBB7) in GARCH(1,1) with LIBOR at first differences, and MBS (AAA5) in GARCH(2,1) with LIBOR at first differences) follow an explosive stochastic process only after past spread increases. In both GARCH models we find that negative spread changes at lag one (α 1,2 ) have greater explanatory power than positive

changes for all difference stationary spread series with statistically significant mean reversion coefficients. The economic effect of such negative asymmetric mean reversion is more pronounced for traditional CDO, low-rated MBS as well as all Pfandbrief spread series. So future spread movements largely display asymmetric sensitivity to the direction of past spread change - negative bias if spread differences are mean-reverting and mostly positive bias if the spread series are higher order integrated and approach a long-term equilibrium price conditional on time. Our estimation results for mean equation as spread dynamics conditional on past spread changes defy general agreement with an earlier paper by Koutmos (2002) on U.S. MBS spreads., who breaks down overall mean reversion of spreads into stationary spread change after spread decreases and random spread change after spread increases, with the former effect dominating the latter. We find no evidence of stochastically asymmetric spread dynamics, where the existence of mean reversion is contingent on a certain direction of past spread changes. Instead, with almost all pairs of mean reversion coefficients exhibiting the same sign, we find the economic significance of the direction of past spread changes to be asymmetric for either stationary or stochastic spread series. We mostly observe that the influence of declining spreads in the past have a stronger effect on mean-reverting spreads than past spread increases. As we extend the influence of conditional past spread levels (and conditional variance) to two lags in GARCH(2,1), the stochastic time series properties change noticeably. Spread change sheds its mean-reverting behavior and now follows a random walk, regardless of whether second spread differences are positive or negative. While economic asymmetry still remained, stationarity generally succumbs to an explosive spread process in response to conditional past spread levels beyond one lag. The mean reversion coefficients (α 2,1 ,α 2,2 ) at

two lags carry positive signs in all but two MBS spread series (A7 and BBB7). With the exception of one traditional CDO (AAA3) and some MBS (AAA5 and BBB7) series, all asset classes exhibit strong positive sensitivity to the second difference of past spread changes, which relegates stationarity to a first order effect. However, three out of six CDO series and two out of four MBS spread series do not generate significant α 2,1 and α 2,2 coefficients.

Table 11. Unit Root Test. Sample (adjusted): 05/01/2001-18/10/2002 (93 (92) weekly obs.). We report the ADF and PP test results (test statistic and F-statistic) for all Johnson Fit-adjusted, logarithmic CDO, MBS and Pfandbrief spread series (level and first differences) with a constant drift and a linear trend (shift). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard & Poor’s rating system. We selected a three-lag truncation for the Bartlett (1981) kernel in the PP test and two lags in the ADF test in order to control for serial correlation. The test diagnostics are corrected by the Newey-West (1987) autocorrelation consistent variance estimator. We use MacKinnon (1996) critical values (one-sided) for rejection of hypothesis of a unit root at 10%(*), 5%(**) and 1%(***) statistical significance. Augmented Dickey-Fuller (ADF) level Asset Class

t-stat.

Phillips-Perron (PP)

first difference F-stat.

t-stat.

F-stat.

level t-stat.

first difference

F-stat.

t-stat.

F-stat.

Collateralized Debt Obligations (CDO), synthetic AAA3

-2.1156**

1.3658

-5.4391***

21.9381***

-2.1046**

2.4597*

-9.5749***

45.8374***

A5

-3.3677***

2.8945**

-6.2532***

23.6575***

-2.7279***

3.5594**

-9.9196***

49.1234***

BBB7

-2.1521**

1.2455

-5.8862***

23.3047***

-2.3390**

2.5349*

-9.5909***

45.9627***

Collateralized Debt Obligations (CDO), traditional AAA3

-1.8073*

1.0596

-7.0360***

26.7650***

-1.8599*

2.0212

-8.9250***

39.9795***

A5

-1.4271

1.0235

-4.9343***

17.2697***

-1.3810

1.1353

-8.6148***

36.8747***

BBB7

-3.8687***

3.8989*

-7.1995***

28.3019***

-3.5079***

5.2950***

-8.9273***

39.9059***

Mortgage-Backed Securities (MBS) AAA3

-2.6696***

2.3823*

-5.6247***

27.9576***

-4.0668***

7.6201***

-11.7885***

68.7735***

AAA5

-3.9070***

13.6306***

-7.0601***

70.6754***

-6.4086***

19.6483***

-18.2340***

144.1522***

A7

-2.2938**

4.1190***

-6.6164***

21.2514***

-2.7183***

3.6791**

-8.4312***

35.9602***

BBB7

-6.0350***

10.9508***

-7.2394***

35.9587***

-5.7618***

16.8164***

-12.1448***

68.2325***

Pfandbriefe AAA3

-2.5263**

10.6802***

-7.2234***

66.8306***

-5.4183***

14.5363***

-18.0251***

127.5861***

AAA5

-1.5908

3.8489***

-7.1147***

43.7777***

-2.8242***

5.4351***

-13.8917***

85.7927***

AAA7

-1.5950

2.0476*

-6.4679***

32.5846***

-2.9957***

5.5289***

-11.6370***

63.6673***

Table 12. GARCH(1,1) model for all Johnson Fit-adjusted, logarithmic CDO, MBS and Pfandbrief spread series (1st diff.) and interest rate control with LIBOR (level). Sample (adjusted): 05/01/2001-18/10/2002 (92 weekly obs.). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. All GARCH (1,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. We report the coefficient estimates and the associated standard errors. § signifies estimation results without Bollerslev-Woolridge robust standard errors and variance. According to standard inference testing we report statistical significance at the 10%(*), 5%(**) and 1%(***) level. Collateralized Debt Obligations (CDO) Synthetic AAA3#

α0

A5


Pfandbriefe

Traditional BBB7

AAA3§, #

A5

BBB7

AAA3#

AAA5

A7§,#

1.1372 *** -0.0068 0.2671 0.1695

1.5360 ** 0.3371 * 0.1029 0.6766 0.1731 0.1452

0.8264 *** -0.3019 ***

BBB7

AAA3

AAA5#

AAA7#

0.5673 ** 0.8561 0.2449 1.2240

1.0612

0.2943

0.1866

0.4390

0.3424

1.1252

0.2871

0.2430

0.6977

0.2885

-0.0861 ** -0.1161 0.0400 0.1385

-0.1325

-0.0511

-0.0280

-0.0769

0.1230

0.0728

0.0388

0.1114

-0.1897 ** -0.4084 *** -0.0047 0.0919 0.0979 0.0405

-0.3043 ** -0.1328 ** -0.0562 0.1306 0.0623 0.0373

-0.1069 ** -0.1266 0.0438 0.1391

-0.1404

-0.0758

-0.0391

-0.0892

0.1245

0.0712

0.0398

0.1135

-0.1916 ** -0.4302 *** -0.0104 0.0918 0.0977 0.0397

-0.3270 ** -0.1576 ** -0.0936 ** -0.3270 *** 0.1328 0.0619 0.0373 0.0851

-0.1686 ** -0.1990 0.0738 0.3444

-0.2061

-0.0813 ** -0.0369 0.0342 0.0501

-0.0196

0.1656

0.0951

0.0185

-0.0193

0.0439

0.0787

0.2143

0.0701

0.1151

0.0678

0.0574

0.0722

0.0313

0.0810

0.1362

α2,2

-0.1554 ** -0.1812 0.0670 0.3425

-0.1955

-0.0178

0.1630

0.1006

0.0210

-0.0219

0.2809

-0.0762 ** -0.0362 0.0329 0.0502

0.0694

0.1141

0.0679

0.0571

0.0721

0.0483 * 0.0849 0.0287 0.0787

α3

2.4101

-0.5681

-9.0706

11.7871

2.5065

7.9509

-8.5146

-0.1351

-0.0001

1.7645

0.1799

-0.0091

-43.8189 **

1.8073

15.2081

8.0348

11.4198

2.3221

8.4323

6.1543

0.1386

16.6156

2.1671

2.1459

6.7323

23.1309

α1,1 α1,2 α2,1

0.2829

0.2350 0.0847

0.2277 * 0.1361

Table 12. Continued Collateralized Debt Obligations (CDO) Synthetic

β0

β1

β2

β3

β4

β5


Pfandbriefe

Traditional

AAA3#

A5

0.0036

0.0402

** 0.0212

0.0634

0.0197

-0.0224

AAA3

§,

BBB7

AAA3#

0.0012

0.0243 ** 0.0129

-0.0104

0.0324

0.0066

0.0099

0.0167

-0.0026

0.0744

-0.0246

0.0352

0.0228

0.0623

0.1569

0.5707

0.2475

1.7244

BBB7

#

A5

A7§,#

AAA5

AAA3

AAA5#

AAA7#

-0.1489 **

-0.0006 ** -0.0776 ** 0.0128 * -0.0001

0.0045 ***

0.0062

0.0631

0.0003

0.0344

0.0018

0.0051

0.0012

0.1741

0.3558 * -0.0013

0.3987

0.1401

0.2669

0.1103

-0.0367

0.0446

0.0191

0.1778

0.2150

0.1635

0.2160

0.2205

0.2153

0.1230

0.1643

0.0438

0.0623

0.2851

-0.6777 ** -0.1365

0.0494

1.4608

0.0509

0.4774

2.2513 *** -0.0728

0.1465 *

0.4472

0.2046

0.2692

0.3127

0.1839

0.7851

0.9263

0.9044

0.8302

0.0877

-0.0005

-0.0047 ** -0.0026

-0.0008

-0.0036 ** -0.0020

0.0005

0.0457 **

-0.0001 *** 0.0145 ** -0.0020 * 0.0000

-0.0020 ***

0.0112

0.0024

0.0035

0.0016

0.0015

0.0025

0.0004

0.0214

0.0000

0.0002

0.0001

-0.0110 ** -0.0041

0.0013

-0.0046 ** -0.0014

0.0006

** 0.0061

0.0125

0.0053

0.0014

0.0020

0.0003

0.0071

0.5772

0.5468

0.6879

0.2711

0.0083

0.0020

** 0.5387 *** 0.5115 * 0.5237 ** 0.4624

*

BBB7

*

*

0.0065

0.0010

0.1241

0.0006

0.0008 *** 0.0041 *** -0.0038 *** 0.0001

0.0018 **

0.0003

0.0015

0.0012

0.0031

0.0008

0.3978 *** -0.0122

0.5962 *** 0.3891 ** 0.0428

1.0295

0.6096 ***

0.1880

0.2765

0.2360

0.3629

0.1337

0.0157

0.1278

0.2058

0.0891

0.3337

0.0924

1.8377

-10.6029

-2.8923

0.6164

1.3634

4.6639

1.3634

2.7886

21.4040

2.9852

4.2837

asymmetry effect (b1+b2)/b1 -6.0065

-220.1977

Table 13. GARCH(1,1) model for all Johnson Fit-adjusted, logarithmic CDO, MBS and Pfandbrief spread series (1st diff.) and interest rate control with LIBOR (1st diff.). Sample (adjusted): 05/01/2001-18/10/2002 (92 weekly obs.). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. All GARCH (1,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. We report the coefficient estimates and the associated standard errors. & signifies that singular covariance coefficients are not unique. According to standard inference testing we report statistical significance at the 10%(*), 5%(**) and 1%(***) level. Collateralized Debt Obligations (CDO) Synthetic

BBB7#

AAA3&

-0.3359 *** 0.1323 *** -0.0297 *** -0.1968

0.0602

0.0468

0.1010

0.2027

A5

0.0006

BBB7

0.0100

AAA3

0.2929

α1,1 0.0891 *** -0.0249 *** 0.0071 *** 0.0534 0.0306

0.0000

Pfandbriefe

Traditional A5

AAA3

α0


0.0018

0.0810

AAA5&

AAA7#

0.5348 *** 2.1303

-0.5993 *** 0.6466 ** 0.2456

0.1854

0.1315

0.2086

0.0405

0.1584

0.1067

-0.0079

-0.0087

** -0.0122 -0.1828 ** -0.6812 *

-0.6014 *** -0.1954 *

-0.0512

-0.0339

0.0436

0.0247

0.0740

0.0406

0.1030

0.0474

0.0278

-0.2156

-0.2328 ** -0.0891

0.1734

0.1020

AAA5

A7

** 0.0515 * 0.3354 0.2583

0.1095

0.0620

0.0311

0.0023

0.0010

0.0778

0.0426

0.0249

** -0.0178 -0.2256 *** -0.7325 * 0.0849 0.1076 0.0619

α2,1 -1.6439

-1.1522

-0.2637

1.0116

0.2295

0.0762

1.4318

2.0532

0.7260

1.1898

0.7101

0.3656

0.6729

0.8074

0.3353

1.6820

1.6786

0.7439

α1,2 0.0598 * -0.0360 *** -0.0031 *** 0.0288

-0.0225

-0.0219

BBB7#

AAA3

0.3126

* -0.0637 ***

0.0462

0.0275

-0.1725 * -2.8504 *

-0.9247

-2.3784 ***

0.0957

1.7243

1.7026

1.3084

0.2487

-1.6499

-0.1273 ***

1.4914

1.7029

0.6764

** -3.4029 -0.6688 ** 0.0000 * 0.9633 0.2745 0.0000

α2,2 -1.6393

-0.0550

-0.5683 *** -0.3110

-0.1719

-0.2355

-0.6449

1.6713

0.3705

0.1382

0.4837

0.5318

0.2965

0.9409

2.0765

14.9445 *** 4.2287

17.8444 *

-4.9717

14.2827 ** 5.9111

-0.8505

-26.8013

2.9657 *** -7.9308 *

-9.4603

* -8.9689

4.8579

2.7613

10.6871

4.4131

6.1227

1.7863

27.9541

0.2008

4.9305

18.1075

α3

6.9726

6.2994

*

4.3274

*

Table 13. Continued Collateralized Debt Obligations (CDO) Synthetic AAA3

β0

β1 β2

β3

β4

β5

A5


Pfandbriefe

Traditional BBB7

AAA3

A5

BBB7#

AAA3&

AAA5

** 0.0043 *

-0.0432

0.0009 *** 0.7880 *** 0.0295 *** 0.0145 *** 0.0032 0.0000

A7

-0.0120

-0.0056 *** -0.0021 *** -0.0201

0.0038 * 0.0005

0.0087

0.0000

0.0001

0.0514

0.0079

0.0000

0.0182

0.0402

0.0003

-0.0308

-0.0059

0.3203 ** 0.0978

0.2020

0.2535

0.3087 ** 0.1363

0.0181

0.0608

0.0097

0.1579

0.0926

0.1278

0.3167

0.1480

0.2444

0.6496 *** 0.4211 ** 0.3782 *** -0.4667

0.0908

-0.3176

0.1891

0.2195

0.2698

0.0047 * 0.0012 *** 0.0004 *** 0.0000

-0.0007 * -0.0001

0.0026

0.0000

0.0017

-0.2075 ** -0.0043

0.0004 *** -0.0018

0.0883

0.0266

0.0002

BBB7#

AAA7#

0.0067

-0.5056 *** 0.1006 *

0.0518

** 0.2566 ***

0.1562

0.1459

0.0580

0.0239

0.3299

0.4484

-0.2383

0.1516 *** -0.1881

-0.2147

-0.1729 ***

0.3468

0.3985

0.3188

0.0465

0.1550

0.3951

** -0.0006 *

0.0145

-0.0002 *** 0.0000

-0.0098 *** -0.0042 *** -0.0004

0.0000

0.0061

0.0129

0.0000

0.0019

-0.0503

-0.0130

-0.0003

-0.0720

-0.0033 *** -0.0009 *** -0.0974 ** -0.1010

0.0286 ***

0.0370

0.0138

0.0541

0.0808

0.0008

0.0176

-0.1685

0.4014 *** 0.8667 *** -0.0338 *** 0.6705 *** 0.4764

** 0.4093 *

0.2119

0.1421

0.0000

0.0000

0.1073

0.0014

0.5094

0.0000

0.0000

0.0061

AAA5&

0.0002

0.1833

0.1390

AAA3

0.3460

0.0459

0.0000

0.0834

0.0018

0.2605 * 0.7180 *** 0.0005

0.6548 *** 0.6171 *** -0.0229 *** 0.1030

0.0879

0.0110

0.1445

0.1315

0.3109

0.1492

0.1052

0.0007

-70.3487

2.1808

2.0020

1.4493

-0.2528

-0.2538

-0.7487

0.7002

0.1074

0.7367

-3.1448

0.0731

asymmetry effect

(β1+β2)/β1 837.8767

-20.0991

-0.8698

Table 14. GARCH(2,1) model for all Johnson Fit-adjusted, logarithmic CDO, MBS and Pfandbrief spread series (1st diff.) and interest control with LIBOR (level). Sample (adjusted): 05/01/2001-18/10/2002 (92 weekly obs.). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. All GARCH (2,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. We report the coefficient estimates and the associated standard errors. § signifies estimation results without Bollerslev-Woolridge robust standard errors and variance. According to standard inference testing we report statistical significance at the 10%(*), 5%(**) and 1%(***) level.

Synthetic A5#

AAA3 α0 α1,1 α1,2 α2,1 α2,2 α3,1 α3,2 α4

0.4512 0.2507 -0.5265 0.1551 -0.5232 0.1515 0.4876 0.1686 0.4675 0.1640 -0.2089 0.0780 -0.1718 0.0756 0.2385 1.7441

* *** *** *** *** *** ***

Collateralized Debt Obligations (CDO) Traditional BBB7 AAA3 A5 BBB7


AAA5

A7#

0.9770 ** 1.1291 ** -1.3025 *** 0.4565 *** 0.9452 * 0.3194 *** 1.2504 *** -0.0001 0.4810 0.5462 0.5034 0.1422 0.5540 0.1136 0.4408 18.9227 -0.1521 *** -0.3345 *** 0.2034 * -0.1127 0.1230 -0.1772 *** -0.6625 *** -0.0052 0.0572 0.1025 0.1143 0.1056 0.2558 0.0633 0.1076 0.1341 -0.1541 *** -0.3362 *** 0.1891 * -0.1256 0.1139 -0.2051 *** -0.6949 *** -0.0018 0.0567 0.1026 0.1130 0.1045 0.2559 0.0615 0.1037 0.1622 0.0398 ** 0.2047 *** 0.1029 0.0494 -0.2595 0.0630 0.2645 ** -0.0067 0.0155 0.0550 0.0901 0.0950 0.2308 0.0690 0.1153 0.1614 0.0364 ** 0.1979 *** 0.0957 0.0435 -0.2601 0.0400 0.2356 ** -0.0039 0.0151 0.0555 0.0894 0.0933 0.2298 0.0618 0.1144 0.1544 -0.2695 ** -0.2557 ** 0.1374 * -0.0991 *** -0.1412 ** 0.0223 0.0292 -0.0052 0.1300 0.1143 0.0745 0.0314 0.0632 0.0334 0.0775 0.1537 -0.2629 ** -0.2506 ** 0.1479 ** -0.0957 *** -0.1340 ** 0.0390 0.0370 0.0211 0.1279 0.1138 0.0753 0.0299 0.0609 0.0321 0.0740 0.0499 -10.6189 -8.8101 32.3039 *** -1.6554 -10.3923 -0.7260 -1.2715 0.0000 6.8332 5.8372 11.9526 3.5986 8.7046 2.2477 2.3455 0.0000

BBB7#

AAA3

0.7650 0.3647 -0.2182 0.1233 -0.2313 0.1213 0.0429 0.0706 0.0376 0.0707 0.0710 0.0563 0.0728 0.0553 3.5558 1.5163

** -0.1462 0.3178 * -0.4316 0.0795 * -0.4636 0.0767 0.4935 0.0875 0.4677 0.0860 0.0206 0.0391 0.0219 0.0380 ** -2.3015 8.2509

Pfandbriefe AAA5#

*** *** *** ***

AAA7

-0.0888 0.0688 0.1082 0.0947 -0.1856 -0.2950 *** 0.0680 0.0720 -0.2156 *** -0.3139 *** 0.0664 0.0707 0.2231 *** 0.2860 *** 0.0794 0.0749 0.2084 *** 0.2669 *** 0.0772 0.0730 0.0112 -0.0023 0.0606 0.0515 0.0266 0.0090 0.0590 0.0508 -0.1315 7.1095 1.9338 7.7880

Table 14. Continued Collateralized Debt Obligations (CDO) Synthetic

β1 β2

Pfandbriefe

Traditional

A5#

BBB7

AAA3

BBB7

AAA3

AAA5

A7#

BBB7#

AAA3

AAA5#

AAA7

0.0032

0.0067

0.0222 *** 0.0237 *** 0.0171

-0.0181

-0.0458

-0.0004

0.0026

0.0062

-0.0097

0.0070 *

0.0433

0.0061

0.0093

0.0002

0.0092

0.0133

0.0265

0.4130

-0.0319

0.0611

-0.0285

0.1772

0.2941

0.3295

0.0215

0.0973

0.0203

0.1285

0.1787

AAA3 β0


0.0918

**

A5

0.0336

0.0006

0.0112

0.0054

0.0136

0.0038

* 0.1647 ** 0.0841

0.1268

0.2877 *** -0.0030

0.0785

0.0441

0.0668

0.0756

0.1953

0.0488

0.0643

0.0422

0.0599

-0.4774

0.3497

0.1205

0.2067 *** 0.9762

-0.2284

-0.0970

0.4669 *

0.0472

0.0589

0.4379

0.2777

0.3356

0.4427

0.3142

0.0806

0.2931

0.0937

0.2512

1.0448

0.1145

0.2842

0.1469

0.0446

β3

-0.0133 ** -0.0003

-0.0007

-0.0051 *** -0.0037 ** -0.0026

0.0033

0.0135

-0.0001 ** -0.0006

-0.0030

-0.0006

-0.0033 ***

0.0004

0.0011

0.0000

0.0021

0.0069

0.0120

0.0000

0.0021

0.0021

0.0011

0.0009

β4

-0.0254 ** -0.0010

-0.0017

-0.0028 *** -0.0047 ** -0.0020

0.0100

0.0040

0.0006

0.0006

0.0018

** 0.0092

0.0118

0.0026

0.0020

0.0001

0.0097

0.0009

β5

-0.0053

0.7663 *** 0.6896

0.5079

0.2552

0.2029

1.3948

0.2530

0.0301

0.6432

0.1209

0.2048 ** 0.1188

0.0224

0.6477 *** -0.0211

0.1343

0.1014

1.2461

0.1563

0.1030

2.9744

-6.2412

6.5096

0.0064

β6

0.7718 0.0015 0.0019

0.0015

*

0.0408

0.0037 ***

0.0047

0.0005

0.0013

-0.9052 *** 0.8476

0.5337

0.4136 *** 0.2033

0.1861

0.1681

0.9686

0.0736

-0.3504 *** 0.0423

-0.1178 *** 0.5787

0.4551

0.1858

0.1296

0.6117

0.0325

0.2262

0.1844

0.0776

0.2233

0.4114

6.5542

1.3722

1.2049

-145.5082

4.5299

1.9249

** 0.1138 *** 0.5414

0.1180

0.2514

0.0083

0.0012

0.6453 *** 0.8942 *** 0.2380

0.0801

** -0.4895 *** -0.7964 ***

asymmetry effect (β1+β2)/β1 -0.1558

-9.9508

Table 15. GARCH(2,1) model for all Johnson Fit-adjusted, logarithmic CDO, MBS and Pfandbrief spread series (1st diff.) and interest rate control with LIBOR (1st diff.). Sample (adjusted): 05/01/2001-18/10/2002 (92 weekly obs.). The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. All GARCH (2,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. We report the coefficient estimates and the associated standard errors. & signifies that singular covariance coefficients are not unique. According to standard inference testing we report statistical significance at the 10%(*), 5%(**) and 1%(***) level. Collateralized Debt Obligations (CDO) Synthetic AAA3 α0

-0.1941 ** 0.0847


Pfandbriefe

Traditional

A5

BBB7

AAA3#

A5

BBB7

AAA3

0.1842 0.2393

-0.0167 0.0860

-0.1511 0.1087

0.0039 0.1653

0.0153 0.1507

0.7483 *** -1.6262 0.2812 2.0772

AAA5

A7

BBB7&

0.0043 0.0251

1.7863 1.9188

AAA3 &

AAA5&

AAA7

-0.2188 0.2921

-0.0398 0.1227

0.0285 0.0965

α1,1 -0.2280 * 0.1279

-0.2984 ** -0.0463

0.1746 *** -0.0746

-0.0900

-0.2049

1.6073 *** -0.0119 *** -0.0591

-0.4586 *** -0.2253 *** -0.3051 ***

0.1445

0.0139

0.1561

0.1421

0.1421

0.3677

0.1120

α1,2 -0.2445 * 0.1267

-0.3017 ** -0.0478

0.1540 *** -0.0880

-0.0994

-0.2450 *

0.1455

0.0495

0.0195

0.1418

0.1446

-0.7933 *** -0.0148 *** -0.0713 0.1027 0.0019 0.3859

-0.4894 *** -0.2547 *** -0.3224 *** 0.1087 0.0835 0.0908

α2,1 0.2855 ** 0.1338

0.2665

0.0533

-0.1339 *** 0.0735

0.0897

0.0962

-0.8251 *** 0.0119 *** -0.3025

0.5510 *** 0.2611 *** 0.3102 ***

0.0545

0.0415

0.1279

0.1060

0.0973

0.0898

0.0464

-0.1308 *** 0.0728

0.0874

0.0835

0.2909 *** 0.0118 *** -0.3058

0.5246 *** 0.2465 *** 0.2904 ***

0.0544 -0.5769

0.0429 -0.0972

0.1549 -1.2032

0.1264 -0.1281

0.0966 0.3298

0.1091

0.0878

0.0793

0.0841

0.2565 ** 0.3231

3.6380 ** -0.2003

-0.2845

-1.8068

0.8065

0.4732

1.4532

0.1081

0.2512

1.8319

1.5236

1.3372

0.9687

-0.7577 * 0.3130 0.4522 1.0691

1.9604 1.7129

-0.1718 0.2789

-0.1605 0.1140

0.5323 0.9414

-1.1240 1.1108

-0.2118 0.2315

0.0497

*

0.1504

α2,2 0.2776 ** 0.2588 0.1324 0.1504 α3,1 -2.3231 ** -1.1604

*

0.1549 0.1574

0.0010

0.0006 0.0021

0.3877

0.2583 0.2555

0.0856

0.0822

0.0921

0.0868

1.0404

1.0702

0.5730

1.9979

α3,2 -0.8871 1.0569

0.2274 0.8013

-0.0673 0.5156

-0.7562 *** 0.1939 0.2541 0.2870

α4

0.7800

0.2279

0.4265

21.5064

5.1810

-9.0672

-91.6566 **

0.0000

-21.7732

-0.3115

3.0892

-4.2735

7.5425

3.7222

5.4381

7.5001

17.1633

3.6148

6.0785

37.8835

0.0000

48.3463

2.4287

6.6752

4.3949

9.2975

*

Table 15. Continued Collateralized Debt Obligations (CDO) Synthetic Traditional

β0 β1 β2 β3

β4 β5 β6

AAA3

A5

-0.0076

-0.0141

0.0066

0.0081

AAA3#

BBB7 *

BBB7&

AAA3

AAA5&

AAA7

0.0075

0.0024

0.0124

0.0047

0.0019

AAA3

AAA5

0.0080 *** 0.0073 ** 0.0010

-0.0006

0.0000

0.0008 *** 0.0532 *** 0.0209

0.0034

0.0001

0.0034

0.0015

0.0004

0.0000

0.0002

0.0003

*

A7

Pfandbriefe

BBB7

-0.0066

A5


&

*

0.0298 *

0.0228

0.0012

-0.0085

0.0697

0.1278

* -0.0253

-0.0339

0.0451

0.4824

-0.0322

0.0550

-0.1270

0.0180

0.0268

0.0200

0.0254

0.1129

0.0752

0.0161

0.0222

0.0556

0.5913

0.0572

0.0687

0.1113

0.2483

1.3710

2.2296

0.0409

-0.4589

-0.0173

0.0306

0.1296 *** 0.1704

-0.0527

-0.0145

0.3333

0.2854

0.1560

1.5469

1.1668

0.0864

0.3723

0.1194

0.0170

0.0486

1.3259

0.1423

0.2186

0.1338

0.0031 **

0.0032

-0.0002 *** -0.0106 *** -0.0065

-0.0017

-0.0006

3.97E105

0.0005

*

0.0012

*

** -0.0022 *** -0.0015 ** -0.0002

0.0000

0.0397

0.0042

0.0015

-0.0011

0.0027

-0.4234 *** -0.0033 ** 0.0100

-0.1137

-0.1478 ** -0.0090

0.0028

0.0040

0.1129

0.0014

0.0165

0.0901

0.0633

0.0067

0.6902 *** 0.2800

1.3411 *** -0.0485

0.4127

* 0.5189

0.0962

0.6591 **

0.3350

0.8466

0.1923

0.1288

0.2516

0.5239

0.2527

0.3048

0.3050

0.3446

* -0.5758 *** 0.8832 ***' 0.9811 *** 0.0754

0.0758

-0.2124

-0.2247 ** -0.1936

0.0014

0.0018

0.0007

0.0003

-0.1694 *

-0.0541 ** -0.0103 ** -0.0040

-0.0195

0.0949

0.0231

0.0129

-0.1030

0.7313

0.0731

0.3080

-0.1824

-0.4457

0.1639

0.2697

0.0047 **

0.0973 0.0551

*

0.0026 *

0.0005 *** -0.0025

0.2679

4.08E06

6.23E104

0.0006

*

0.2711 *** -0.4029

0.2947 *

0.4933

0.0850

0.0817

0.1997

0.2629

0.0797

0.0865

0.1486

0.3244

0.0627

0.3159

0.0997

0.3333

1793.2830

-3.8183

-5.5816

0.8649

-0.2091

-2.8209

4.7802

1.5461

1.4515

7.0600

-1.2473

asymmetry effect (β1+β2)/β1 9.3323

61.2015

**

**

Table 16. Coefficient and residual tests of GARCH(1,1) model estimates for spread series (1st diff.) and LIBOR (level). The estimated mean (ετ/στ) and conditional variance (ετ/στ)2 of standardized residuals are obtained from the GARCH (1,1) estimation. All GARCH (1,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. LB(x) and LB2(x) denote the Llung-Box Q-statistics (H0: no autocorrelation) for standardized errors and squared standardized errors up to x lags (at which the Q-statistic does no longer flag statistically significant observations with autocorrelation). The Jarque-Bera statistic evaluates the null hypothesis of the residual series being distributed close to normality (H0: normal distribution with unspecified mean and variance) based on sample skewness and kurtosis. We test for sign bias (H0: (εt/σt)2=μ+γΚ+et), negative size bias (H0: (εt/σt)2=μ+γΚεt-1+et) and positive size bias (H0: (εt/σt)2=μ+γ(1−Κ)εt-1+et), and report the γ coefficient values with the associated t-statistic. The three non-parametric bias tests were computed at heteroskedasticity consistent covariance (White, 1980) on 91 observations (instead of 93) after adjusting for endpoints. § no Bollerslev-Woolridge robust standard errors and variance. NA indicates that no results could be generated by the statistics software due to data overflow. We report statistical significance at the 10%(*), 5%(**) and 1%(***) level.

Wald-test H0: α1,1=α1,2 (t-stat.) Wald-test H0: α1,1+α1,2=0 (t-stat.) LB-Q statistic (lags) LB2-Q statistic (lags) Jarque-Bera statistic Sign Bias Test (γ coeff.) (t-stat.) Negative Size Bias Test (γ coeff.) (t-stat.) Positive Size Bias Test (γ coeff.) (t-stat.)

Collateralized Debt Obligations (CDO) synthetic traditional AAA3# A5 BBB7 AAA3§,# A5 4.4406 ** 12.7732 *** 13.3682 *** 83.3839 *** 15.2958 *** 5.3525 ** 0.7641 1.2165 0.7777 0.7311 0.01 (1) 0.02 (1) NA 2.0 (1) 0.10 (1) 0.04 (1) 0.02 (1) NA 0.23 (1) 0.02 (1) 2,759.87 *** 2,346.54 *** NA 660.55 *** 5,902.84 *** 0.4877 0.9000 1.3179 -0.7553 0.3191

BBB7 15.7247 *** 0.5455 NA NA NA 0.5642

0.5726

1.3202

1.0990

-0.7636

0.5286

0.9857

-0.8180

-0.4823

-0.6458

1.1579

-0.0844

-0.3431

-0.5443

-0.4299

-1.1442

2.5419

-0.2548

-0.7493

-0.1375

-0.1162

-0.1833

0.1769

-0.0966

-0.2733

-0.7542

-0.5758

-0.6505

0.5453

-0.5688

-1.5163

Table 16. Continued Pfandbriefe AAA5#

AAA3

Wald-test H0: α1,1=α1,2 (t-stat.)

Mortgage-Backed Securities (MBS) AAA7#

109.4263 *** 164.5061 *** 101.6267 ***

Wald-test H0: α1,1+α1,2=0 (t-stat.)

5.4711

LB-Q statistic (lags)

8.81 (1)

0.31 (1)

LB2-Q statistic (lags)

0.77 (1)

Jarque-Bera statistic Sign Bias Test (γ coeff.)

13.7176 ***

1.4256

11.6254 *** 22.3917 *** 8.4351 ***

BBB7

NA

0.04 (1)

0.12 (1)

0.00 (1)

1.43 (1)

0.00 (1)

NA

0.42 (1)

0.08 (1)

0.02 (1)

7.26 (3)

145.98

*** 3,635.51 ***

NA

329.03 *** 93.28 ***

-0.2359

-0.1875

0.3252

-0.7118

-0.3354

0.7185

(t-stat.)

-0.5517

-0.4716

0.9111

-0.4282

-0.4370

0.9667

Negative Size Bias Test (γ coeff.)

0.0401

-0.1123

-0.0787

-0.1684

0.1982

-2.7148

0.0734

-0.2738

-0.2175

-0.0855

0.6784

-0.8831

-2.2343

0.0266

0.3220

0.1145

0.6560

0.2845

-0.1416

0.4474

0.1353

1.7332

0.4593

1.0222

0.8404

-1.0055

1.5544

(t-stat.)

**

A7§,#

5.7481 **

Positive Size Bias Test (γ coeff.)

4.0279

AAA5

4.3106 *** 18.3921 *** 0.0356

(t-stat.)

**

AAA3#

18,763.8 *** 78.90 *** 3 -0.7211 ** -2.0195

-0.7957 **

Table 17. Coefficient and residual tests of GARCH(1,1) model estimates for spread series (1st diff.) and LIBOR (1st diff.). The estimated mean (ετ/στ) and conditional variance (ετ/στ)2 of standardized residuals are obtained from the GARCH (1,1) estimation. All GARCH (1,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. LB(x) and LB2(x) denote the Llung-Box Q-statistics (H0: no autocorrelation) for standardized errors and squared standardized errors up to x lags (at which the Q-statistic does no longer flag statistically significant observations with autocorrelation). The Jarque-Bera statistic evaluates the null hypothesis of the residual series being distributed close to normality (H0: normal distribution with unspecified mean and variance) based on sample skewness and kurtosis. We test for sign bias (H0: (εt/σt)2=μ+γΚ+et), negative size bias (H0: (εt/σt)2=μ+γΚεt-1+et) and positive size bias (H0: (εt/σt)2=μ+γ(1−Κ)εt-1+et), and report the γ coefficient values with the associated t-statistic. The three non-parametric bias tests were computed at heteroskedasticity consistent covariance (White, 1980) on 91 observations (instead of 93) after adjusting for endpoints. & signifies that singular covariance coefficients are not unique. NA indicates that no results could be generated by the statistics software due to data overflow. We report statistical significance at the 10%(*), 5%(**) and 1%(***) level. Collateralized Debt Obligations (CDO) synthetic traditional Wald-test H0: α1,1=α1,2 (t-stat.) Wald-test H0: α1,1+α1,2=0 (t-stat.) LB-Q statistic (lags) LB2-Q statistic (lags) Jarque-Bera statistic Sign Bias Test (γ coeff.) (t-stat.) Negative Size Bias Test (γ coeff.) (t-stat.) Positive Size Bias Test (γ coeff.) (t-stat.)

AAA3 A5 BBB7 7.5655 *** 23.8282 *** 49.3522 *** 6.0161 ** 712.6768 *** 2.3971 0.05 (1) 0.38 (1) 0.16 (1) 0.09 (1) 0.07 (1) 0.04 (1) 2,206.44 *** 3449.73 *** 6,709.17 *** -0.6351 1.3575 1.2565

AAA3 A5 BBB7# 38.1184 *** 24.9706 *** 2.9836 19.2352 *** 0.1244 0.3817 0.11 (1) 7.14 (3) 0.31 (1) 0.01 (1) 0.14 (1) 0.00 (1) 234.93 *** 5,771.95 *** 149.09 -0.3306 ** -0.6106 0.3139

-0.5143

1.5682

1.0607

-2.1300

0.2629

-4.5647

-1.7765

4.4133

**

-1.0143

1.4513

-0.0332

-0.4986

0.1543

-1.2439

-1.0671

2.1300

-0.1324

-2.1544

-0.2542

-0.2637

-0.3138

0.2089

0.0313

-0.2067

-0.3707

-0.4137

-1.0077

1.0298

0.2195

-1.6609

***

***

** *

Table 17. Continued Pfandbriefe

AAA5&

AAA3


AAA7#

AAA3#

AAA5

A7

BBB7#


79.7204 *** 180.2684 *** 72.2650 *** 114.4901 *** 16.0829 *** 19.4155 ***

20.5709


4.3676

219.3632 ***


1.0605

3.1330

0.01 (1)

0.69 (1)

0.23 (1)

0.00 (1)

0.00 (1)

0.99 (1)

31.06 (20)

LB -Q statistic (lags)

0.03 (1)

0.00 (1)

0.22 (1)

0.01 (1)

145.57 (1)

0.03 (1)

24.29 (15)

Jarque-Bera statistic

139.52

*** 1,263.37 ***

1.87

365.36

10.98

*** 5,119.85 ***

121.25

***

Sign Bias Test (γ coeff.)

0.7538

-0.2359

0.1180

-0.3516

-0.3389

0.8833

-3.1102

*

1.2033

-0.8684

0.5305

-0.7644

-0.8901

0.6674

-1.7690

-1.0058

0.3363

-0.4303

0.1382

-1.0768

0.5956

-1.0065

0.8021

-0.2916

0.1077

0.0015

0.0286

-1.0799

0.8157

0.0092

0.1797

2

(t-stat.) Negative Size Bias Test (γ coeff.) (t-stat.) Positive Size Bias Test (γ coeff.) (t-stat.)

**

*

4.5068

** 42.5402 *** 0.0587

***

***

-1.6038 *** -1.4319 -2.8301

-0.6751

-0.9214

-1.7838

0.7295 *** 0.0643

2.1154

3.4061

0.2479

1.8194

*

*

Table 18. Coefficient and residual tests of GARCH(2,1) model estimates for spread series (1st diff.) and LIBOR (level). The estimated mean (ετ/στ) and conditional variance (ετ/στ)2 of standardized residuals are obtained from the GARCH (2,1) estimation. All GARCH (1,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasi-maximum likelihood estimation was applied. The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high noninvestment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. LB(x) and LB2(x) denote the Llung-Box Q-statistics (H0: no autocorrelation) for standardized errors and squared standardized errors up to x lags (at which the Q-statistic does no longer flag statistically significant observations with autocorrelation). The Jarque-Bera statistic evaluates the null hypothesis of the residual series being distributed close to normality (H0: normal distribution with unspecified mean and variance) based on sample skewness and kurtosis. We test for sign bias (H0: (εt/σt)2=μ+γΚ+et), negative size bias (H0: (εt/σt)2=μ+γΚεt-1+et) and positive size bias (H0: (εt/σt)2=μ+γ(1−Κ)εt-1+et), and report the γ coefficient values with the associated t-statistic. The three non-parametric bias tests were computed at heteroskedasticity consistent covariance (White, 1980) on 91 observations (instead of 93) after adjusting for endpoints. NA indicates that no results could be generated by the statistics software due to data overflow. We report statistical significance at the 10%(*), 5%(**) and 1%(***) level. Collateralized Debt Obligations (CDO) synthetic traditional Wald-test H0: α1,1=α1,2 (t-stat.) Wald-test H0: α1,1+α1,2=0 (t-stat.) Wald-test H0: α2,1=α2,2 (t-stat.) Wald-test H0: α2,1+α2,2=0 (t-stat.) LB-Q statistic (lags) LB2-Q statistic (lags) Jarque-Bera statistic Sign Bias Test (γ coeff.) (t-stat.) Negative Size Bias Test (γ coeff.) (t-stat.) Positive Size Bias Test (γ coeff.) (t-stat.)

AAA3 0.1271 11.7339 *** 4.2960 ** 8.2510 *** 0.54 (1) 0.01 (1) 1,712.16 *** 0.0194 0.0259 -0.0647 -0.1715 0.1299 0.3714

A5# 6.8480 7.2158 3.6399 6.2133 1.41 (1) 1.37 (1) 1,339.42 0.5735 0.5362 -0.8217 -0.7401 -0.0501 -0.2870

BBB7 0.0491 2.6887 0.9576 0.6819 NA NA *** NA 1.4148 1.3862 -2.4588 -1.5519 -0.1188 -0.4077 ** *** * **

AAA3 A5 BBB7 5.4014 ** 39.6823 *** 7.1080 *** 2.9815 * 1.2868 0.2144 2.2902 7.8005 *** 0.0748 1.2250 0.2433 1.2723 NA 100.84 (6) NA NA 11.94 (2) NA NA 808.52 *** NA -0.4545 -0.2699 0.4362 -0.8517 -0.3664 0.7818 -0.0181 0.0869 -0.6148 -0.0747 0.2050 -1.3185 -0.0801 0.0376 -0.1937 -0.6775 0.1489 -0.8053

Table 18. Continued Pfandbriefe AAA3

AAA5#


AAA3

A7#

AAA5

BBB7#


72.0880 *** 60.2948 *** 82.7466 *** 10.8572 ***

5.1387


32.8384 *** 8.9119 *** 18.2086 *** 6.0724

41.4529 ***

0.0007

3.3781

*


65.6099 *** 18.6160 *** 52.9039 *** 0.0965

22.7569 ***

0.4792

4.8648

**


30.6751 *** 7.5959 *** 13.9719 *** 0.0000

4.7428

0.0009

0.3241


0.43 (1)

0.85 (1)

0.39 (1)

12.74 (1)

2.02 (1)

0.00 (1)

0.41 (1)

LB2-Q statistic (lags)

0.03 (1)

1.18 (1)

1.22 (1)

13.03 (1)

0.13 (1)

0.01 (1)

11.28 (6)


603.62 *** 1,557.34 ***

4.25

72.17

20.32

*** 21,186.81 ***

92.30


0.5495

0.0120

-0.0768

0.0809

0.0080

1.1401

0.0875

(t-stat.)

1.0683

0.7392

-0.6710

0.2427

0.0197

0.9996

0.1188


0.1477

-3.6352 *** -0.1000

0.0369

-0.3294

-2.7874

-0.9478

(t-stat.)

0.4088

-11.5813

-0.5797

0.1673

-0.7366

-0.8883

-1.3440


-0.2574

-0.1133

0.0922

0.0927

0.2827

-0.1565

1.1591

(t-stat.)

-1.1594

-0.6068

0.2397

0.4533

0.9361

-1.0206

1.4578

**

***

**

**

14.7772 *** 11.7910 ***

***

Table 19. Coefficient and residual tests of GARCH(2,1) model estimates for spread series (1st diff.) and LIBOR(1st diff.). The estimated mean (ετ/στ) and conditional variance (ετ/στ)2 of standardized residuals are obtained from the GARCH (2,1) estimation. All GARCH (1,1) parameters have been estimated according to the Berndt-Hall-Hall-Hausman (BHHH) algorithm, except in cases marked with #, when the Marquardt quasimaximum likelihood estimation was applied. The range of rating grades “AAA” (high investment grade), “A” (low investment grade) and “BBB” (high non-investment grade) correspond to the Standard&Poor’s rating system. The number associated with each time series indicates the time to maturity in years. LB(x) and LB2(x) denote the Llung-Box Q-statistics (H0: no autocorrelation) for standardized errors and squared standardized errors up to x lags (at which the Q-statistic does no longer flag statistically significant observations with autocorrelation). The Jarque-Bera statistic evaluates the null hypothesis of the residual series being distributed close to normality (H0: normal distribution with unspecified mean and variance) based on sample skewness and kurtosis. We test for sign bias (H0: (εt/σt)2=μ+γΚ+et), negative size bias (H0: (εt/σt)2=μ+γΚεt-1+et) and positive size bias (H0: (εt/σt)2=μ+γ(1−Κ)εt-1+et), and report the γ coefficient values with the associated t-statistic. The three non-parametric bias tests were computed at heteroskedasticity consistent covariance (White, 1980) on 91 observations (instead of 93) after adjusting for endpoints. & signifies that singular covariance coefficients are not unique. NA indicates that no results could be generated by the statistics software due to data overflow. We report statistical significance at the 10%(*), 5%(**) and 1%(***) level.

Wald-test H0: α1,1=α1,2 (t-stat.) Wald-test H0: α1,1+α1,2=0 (t-stat.) Wald-test H0: α2,1=α2,2 (t-stat.) Wald-test H0: α2,1+α2,2=0 (t-stat.) LB-Q statistic (lags) LB2-Q statistic (lags) Jarque-Bera statistic Sign Bias Test (γ coeff.) (t-stat.) Negative Size Bias Test (γ coeff.) (t-stat.) Positive Size Bias Test (γ coeff.) (t-stat.)

Collateralized Debt Obligations (CDO) synthetic traditional AAA3 A5# BBB7 AAA3# A5 BBB7 2.7887 * 0.0081 0.0338 11.9642 *** 24.9096 *** 16.2930 3.4511 * 0.0708 17.2446 *** 0.0500 0.2732 0.4454 10.0836 *** 0.0031 0.8336 0.2597 0.0658 1.7833 4.4756 * 0.0125 0.0620 0.2925 0.2196 0.4849 0.85 (1) 0.00 (1) 5.65 (2) 0.26 (1) 0.07 (1) 24.69 (15) 0.04 (1) 0.04 (1) 0.45 (1) 0.00 (1) 0.09 (1) 5.19 (2) 444.50 *** 4,101.14 *** 7,155.60 *** 696.64 *** 8,004.54 *** 156.61 0.0586 -0.3537 1.2894 0.1155 -0.1469 -0.5264 0.0715 -0.2857 1.0082 0.3073 -0.5864 -5.7286 0.0529 -2.6216 -1.4837 -0.5102 -0.0583 0.3447 0.1319 -0.6303 -0.9179 -1.1867 -0.2865 5.7286 -0.1422 -0.2234 0.3251 -0.0615 -0.0394 0.3974 -0.7323 -1.2217 0.9464 -0.5693 -0.4909 2.0832

***

*** *** *** **

Table 19. Continued Pfandbriefe AAA3&

AAA5&


AAA3

AAA5

A7

BBB7&

2.6785

3.3301


40.9130 *** 95.7934 *** 52.2577

***

13.0022 ***

6.2115

**


10.9676 ***

11.7751

***

2.4650

65.6843

*** 115.8702


44.1682 *** 14.0902 *** 35.8244

***

1.0149

18.9879

***

0.0047


34.0455 ***

0.6529

12.3520

***

0.7886

6.3620

**

124.1782


11.44 (1)

0.21 (1)

0.16 (1)

0.66 (1)

0.27 (1)

1.63 (1)

7.16 (3)

25.69 (1)

0.03 (1)

0.20 (1)

0.11 (1)

98.71 (8)

0.20 (1)

1.18 (1)

2

LB -Q statistic (lags)

0.5399


667.74 *** 1,799.21 ***


-0.2694

(t-stat.)

0.0757 0.1885

***

112.14 ***

18.84

-1.651

-0.294

-1.5201 **

0.273

-0.6340

-1.024

-2.3049

-0.9761

-0.8689

-2.0417

0.5912

-3.1092

-1.3543


1.1131 ***

-0.2041

0.1180

9.3006

-1.8095

(t-stat.)

4.7225

-0.8534

0.5305

1.1671

-3.0014

3.1092


0.1792

0.3682

0.1601

0.7261

0.2820

0.2547

(t-stat.)

1.8682

0.5612

0.5978

1.3255

0.9228

3.3344

*

***

16.0479

***

0.1426

0.91

**

*** 14,168.17

***

*

***

382.03 ***

-0.9492 -0.7905

***

0.6981 0.9915


207

Moreover, we find a positive asymmetric effect of past spread levels at two lags contingent on the direction of the second difference of spread change on future spread movements. The spread reaction after positive past spread changes is now is relatively stronger and indicates significant positive bias if we compare the absolute values of α 2,1 and

α 2,2 in GARCH(2,1). Considering the high speed of mean reversion at lag one, this result seems plausible. The positive pricing bias might be attributable to widening spreads and cautious investor behavior during the time the data sample was taken. Coefficient values of α 0 suggest that this persistent upward trend is complemented by significant level effects only for highly rated CDO and MBS transactions. We verify the statistical classification of spread dynamics with asymmetric mean reversion by means of the Wald coefficient test of α 1,1 + α 1,2 < 0 (for both GARCH(1,1) and GARCH(2,1)) as well as α 2,1 + α 2,2 < 0 (only for GARCH(2,1)) (see Tabs. 16-19). If spread change is level stationary we can reject the sub-hypotheses H 0,1 : α 1,1 + α 1,2 = 0 and

H 0,2 : α 1,1 + α 1,2 = αˆ1,1 + αˆ1,2

(for both GARCH(1,1) and GARCH(2,1)) as well as

H 0,1 : α 2,1 + α 2,2 = 0 and H 0,2 : α 2,1 + α 2,2 = αˆ2,1 + αˆ2,2 (only for GARCH(2,1)). The Wald tests for unrestricted statistical significance of mean reversion coefficients support overall mean reversion, so inference procedures under standard hypothesis testing in our GARCH models can be justifiably applied. For coefficients α 1,1 ,α 1,2 in GARCH(1,1) with LIBOR rates at first difference, all MBS, most Pfandbrief and three out of a total of six CDO spread series generate sums of (unrestricted) coefficient estimators αˆ1,1 + αˆ1,2 that are statistically different from zero at a significance level of 10% or higher. By extending spread sensitivity to second differences of past spread changes in GARCH(2,1) two MBS (AAA3 and BBB7) and one more CDO (AAA3) spread series do no longer support statistically robust mean reversion. In contrast, in a GARCH(1,1) model with level LIBOR rates almost all CDO spread series flag insignificant difference stationarity irrespective of past spread changes. With this in mind, the Wald test statistic of mean reversion coefficient values at lag one under both GARCH(1,1) and GARCH(2,1) corroborates evidence from earlier unit root tests (see Table 11), which generated the most robust results for Pfandbrief and MBS spreads. The statistical significance of mean reversion substantially weakens if we extend our analysis to spread changes at two lags in GARCH(2,1) for the coefficients α 2,1 ,α 2,2 of past spread levels. We can reject the null hypothesis H 0,1 : α 2,1 + α 2,2 = 0 of the Wald coefficient test only for one synthetic CDO (AAA3), two Pfandbrief (AAA3 and AAA7), and two MBS (AAA5 and A7) spread series. Moreover, the sum of estimators αˆ2,1 + αˆ2,2 is positive for most spread series. Almost all CDO spreads behave stochastic at two lags in GARCH(2,1), while some MBS and Pfandbrief spread series maintain little stationarity, though at admittedly lower degrees of economic and statistical significance. We also measure the degree of statistically significant asymmetry of conditional spread change based on time-varying heteroskedasticity. We test the null hypotheses H0 : α 1,1 = α 1,2 (for both GARCH(1,1) and GARCH(2,1)) and

H0 : α 2,1 = α 2,2 (only for GARCH(2,1)) of no asymmetric spread adjustment conditional on past spread change at one and two lags. The unrestricted Wald coefficient test suggests

208

Andreas A. Jobst

different sensitivity to positive (α 1,1 ) and negative (α 1,2 ) first differences of past spreads for all asset classes in GARCH(1,1) and all but three asset classes (two synthetic CDO (A5 and BBB7) and one MBS (A7) spread series) in GARCH(2,1). Asymmetric spread sensitivity even extends to past spreads at two lags conditional on the direction of second differences in GARCH(2,1), though at a lower degree of statistical significance and mostly for Pfandbrief transactions. The estimated coefficient pairs α 2,1 ,α 2,2 (for GARCH(1,1)) and α 3,1 ,α 3,2 (for GARCH(2,1)) of LIBOR at level and first differences measure any asymmetric interest rate effects on spread dynamics. Our estimation results for MBS and Pfandbrief spreads display weak statistical significance and inconclusive variation of spread sensitivity to past changes in LIBOR rates across different asset types. Both traditional and synthetic CDO spreads appear to be inversely related to interest rate changes at high levels of statistical significance and marginal positive bias, especially in GARCH(2,1). In contrast, most coefficient pairs of LIBOR for MBS and Pfandbrief spreads have positive values. The coefficient values of α 3 in GARCH(1,1) and α 4 in GARCH(2,1), which reflect the direct contemporaneous influence of the conditional spread volatility on spread change, are highly significant for all Pfandbrief and half of all CDO spread series in GARCH(1,1); however, it GARCH effects fail to leave a large footprint in a GARCH(2,1) specification, regardless of whether the LIBOR rate is considered at level or first differences. Surprisingly, neither the short time series nor the relative illiquid nature of CDO and MBS transactions in our sample induce pseudo-causalities of conditional heteroskedasticity on spread dynamics – a situation that might, however, explain why the parameter coefficients of most of these asset classes are just shy of reaching 10% significance. No conclusive assessment can be made with regard to economic significance of coefficient values of α 3 and α 4 , whose signs do not seem to be associated with a certain pattern determined by rating quality, maturity or asset class. Our estimation results of the conditional variance equation show that spread volatility is time-varying and depends mostly on variance forecasts and past squared errors of the mean equation (innovations). The economic influence of past spread levels and LIBOR rates is negligible. We obtain capricious estimation results for the constant β 0 in either GARCH model. While most spread series generate positive intercept values, we find significant estimators only for most CDO and the two lowest rated MBS (A7 and BBB7) spread series in GARCH(2,1) , which two Pfandbrief (AAA3 and AAA5) spread series faintly revealing some level effect in both GARCH(1,1) and GARCH(2,1) with LIBOR at first differences. The coefficient β1 indicates the general sensitivity of the conditional variance to past residuals of estimated mean spread change (general ARCH term). Since β1 + β 2 measures the dependence on positive past errors (positive GARCH effect), while β 2 measures the influence of negative past errors only (negative GARCH effect), any β 2 ≠ 0 reflects asymmetries of how past innovations affect conditional variance. We find that a significant general ARCH effect concurs with a positive coefficient value of β1 for some MBS as well as low-rated CDO spread series. Generally, most spread series also show β 2 > 0 (with negative signs for all Pfandbrief, two MBS spreads (AAA3 and A7) and traditional CDOs (A5) in GARCH(1,1) as well as traditional CDOs (A5 and BBB7), MBS (BBB7) and


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Pfandbrief (AAA3) spreads in GARCH(2,1)). Moreover, we observe significant asymmetric effects of past errors on spread volatility. Negative ARCH effects ( β 2 ) are greater than general ARCH effects of past innovations ( β 2 > β1 ) , and statistically significant coefficient value of β 2 always increase conditional volatility in all but the Pfandbrief (AAA7) series for GARCH(1,1). Hence, negative past innovations (associated with spread decline) increase spread volatility more than positive innovations. While nearly all spread series exhibit a positive effect of past residuals, the increase of conditional variance is more pronounced after negative past innovations. So, while spread levels over our sample period appear to follow a general positive trend, negative shocks associated with declining spreads seem to induce higher volatility at times of persistent spread levels. The degree of the asymmetric effect of past errors on spread volatility is captured by the asymmetry factor ( β1 + β 2 ) β1 of conditional variance, which ranges between -1 and 1 for a balanced positive or negative effect of past innovations on conditional volatility. We generally observe an absolute value of the asymmetry factor greater than one in most spread series (with the exception of MBS spread series), which indicates that negative past errors of spread estimates increase the spread volatility disproportionately more than positive past errors. We observe some of the highest levels of asymmetric sensitivity to past innovations for CDO spread series, especially when we incorporate level LIBOR rates in our GARCH specification. Although our estimation results of ARCH effects vary greatly, we find that the asymmetric factor of all spread series (with the exception of synthetic CDOs) in GARCH(2,1) decreases the longer the maturity and the lower the rating grade. Overall, synthetic CDOs in both GARCH models and Pfandbriefe in GARCH(1,1) deliver the most distinctive support for asymmetric effects of past innovations. Acceptable values of the asymmetry factors only apply to one MBS (AAA3) and one traditional CDO (BBB7) spread series in GARCH(2,1) and most MBS series GARCH(1,1) with LIBOR at first differences. Our results also suggest that the GARCH(1,1) model seems to produce a more consistent degree of asymmetry across different spread series within each asset class (CDO, MBS and Pfandbrief spreads) than the GARCH(2,1) model unless LIBOR rates enter the estimation at level.36 In the remaining spread series ARCH effects are statistically weak (in both GARCH models) and exhibit a balanced incidence of positive and negative coefficient values. The statistical irrelevance of past positive errors – particularly for CDO spreads in both GARCH models as well as MBS and Pfandbrief spreads in GARCH(2,1) – is striking and requires a careful review of our model specification as regards GARCH effects on the spread dynamics of the mean equation. The coefficient estimate of β 3 for the statistical influence of past spread levels on conditional variance (“level effect”) carries a negative sign for most spread series in all GARCH specifications. Despite its generally small coefficient value relative to ARCH and GARCH effects, the volatility effect of past spread levels gains in economic significance by rating quality and maturity if it is statistically significant. The influence of past spread levels on spread volatility is most significant for CDOs in both GARCH models with LIBOR at first 36

An unreported test of ARCH effects for longer Pfandbrief spread series from our original sample period (1998 to 2002) confirms that past errors generally have a significantly positive effect on conditional variance, with economically stronger significance of negative past innovations. However, we cannot rule out asymmetric spread volatility as a spurious pattern of spread dynamics in light of the short time series of our general sample.

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differences, while results for MBS spreads are significant in GARCH(2,1) but incoherent across all GARCH specifications. We find positive (negative) β 3 coefficients for synthetic (traditional) CDOs, but no meaningful spread sensitivity for Pfandbrief transactions in GARCH (2,1). In traditional CDO and MBS spread series, statistical significance always coincides with a negative spread effect on volatility. Incorporating level rates of LIBOR lessens the statistical significance, while leaving the economic significance of β 3 coefficients nearly unchanged. Past LIBOR rates ( β 4 ) seem to play some role only for synthetic CDOs in both GARCH models and, to some extent, for low-rated MBS and Pfandbrief spreads in GARCH(1,1). Evidence about a LIBOR level effect on conditional volatility is inconclusive for traditional CDOs. The substitution of level LIBOR rates for LIBOR at first differences generally decreases (increases) the economic (statistical) significance of parameter values in both GARCH models. Our model estimates suggest that past forecast variance sustains a consistently positive GARCH effect on time-varying spread volatility. The coefficient estimate β5 of past

(

)

variance levels at lag one σ t2−1 is almost always positive (with the exception of synthetic CDO (AAA3) and MBS (AAA3) spreads in both GARCH models as well as Pfandbrief (AAA7) spreads in GARCH(2,1)) and highly significant especially for CDO and Pfandbrief spreads. The economic significance of β 5 improves as we extend the GARCH effect to two lags and specify the influence of interest rates based on LIBOR at first differences. The latter modification, however, improves the statistical significance of the parameter estimates in GARCH(1,1) only. The coefficient values of β 6 in GARCH(2,1) document that most of the explanatory power of the GARCH term for CDO spreads (and to a lesser degree for MBS and Pfandbrief spreads) even extends to second order GARCH effects of past variance forecasts, which exert a durable influence on future spread change. However, most Pfandbrief and CDO spreads now exhibit negative GARCH effects, whereas MBS spreads retain positive GARCH effects. The varying coefficient estimates of β 5 and β 6 across different structured finance asset types betrays hints of liquidity-induced GARCH effects, with Pfandbrief transactions being the most liquid and CDO tranches the most illiquid. Overall, our estimation results for the conditional variance of CDO, MBS and Pfandbrief spread dynamics by and large conform to previous findings about U.S. MBS in Koutmos (2002) and Arora et al. (2000). We find a common negative bias of asymmetric ARCH effects at lag one, whose statistical significance is mostly limited to negative past innovations. GARCH effects remain largely positive even up to two lags. So greater past volatility generally implies even greater future uncertainty, which is exacerbated by negative innovations in the past. However, economically insignificant spread and interest level effects on conditional variance negate some of the previous findings in the existing literature on comparable spread series in the U.S. market. We also find stark differences between different asset classes as to the GARCH specification of spread dynamics. Our analysis indicates that the mean equation (with asymmetric effects of past spreads and LIBOR levels) generates the closest approximation for synthetic CDO, Pfandbrief and some MBS spreads, whereas the ARCH and GARCH effects of past innovations and forecast variance in the conditional variance equation seem to display the greatest economic and statistical significance for high-rated CDO and some MBS spread series.


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5. General Model Specification The correct GARCH specification of the mean and the conditional variance of spread dynamics shows in the stochastic properties of residual statistics. In addition to conventional residual tests for normality (Jarque-Bera statistic) and autocorrelation (Llung-Box (LB) Qstatistic), we also examine sign and size bias of squared standardized residuals to the variation of past innovations to see how well the proposed GARCH models capture the asymmetric nature of spread volatility. In almost all cases, past innovations fail to have an effect on estimation errors, which confirms that presented specification of spread volatility in both GARCH models. We first employ residual-based model diagnostics in order to examine the normality and any non-linear structure of estimated standardized residuals. Both LB(x) and LB2(x) Qstatistics of standardized residuals ε t σ t and squared standardized residuals

( ε t σ t )2

indicate small autoregressive effects up to x lags at which serial correlation can be ruled out at a statistical significance of 10% or less (see Tabs. 16-19). Aside from MBS (BBB7) in GARCH(1,1) with LIBOR at first differences and traditional CDO (BBB7) in GARCH(2,1) with LIBOR at first differences, both Q-statistics testify to an almost complete absence of higher order serial correlation for standardized residuals (which specify the model fit of the mean equation) and squared standardized residuals (which specify the model fit of the conditional variance equation) of all spread series in both GARCH specifications. Since most spread series do not exhibit any autocorrelation beyond one lag, the inclusion of one lag spread levels in GARCH(1,1) and spreads up to two lags in GARCH(2,1) suffice for the correct specification of the mean equation without compromising the reliability of inference testing from an extension of the lag structure at the cost of losing degrees of freedom. Moreover, based on the Jarque-Bera statistic we reject the null hypothesis of normally distributed standardized residuals for the estimation results of all time series, with the exception of long-term Pfandbriefe (AAA7) in all GARCH specifications. This observation complies with the descriptive statistics of spread series (see Tabs. 4-10), where we reject the null hypothesis of normally distributed spreads in almost all cases. The correct specification of the conditional variance equation is imperative for a GARCH model to reliably explain the heteroskedasticity of observed spread behavior. Although both the Llung-Box (LB) and Jarque-Bera (JB) test statistics are commonly accepted and well established residual diagnostics for the correct specification of the mean and conditional variance of time series, they fail to test how well our GARCH approach captures asymmetric effects on spread volatility. In the spirit of Engle and Ng (1993), who developed parametric diagnostics for asymmetric effects in the news impact curve, we propose three different testing procedures for asymmetric autoregressive bias: (i) the (negative) sign bias test, (ii) the negative size bias test and (iii) the positive sign bias test (see Tabs. 16-19). All three tests assume that the conditional variance is correctly specified only if the squared standardized errors escape any predictability through past errors and fully incorporate past information (at one lag). In the negative sign bias test, ( ε t σ t ) = μ + γ K + et (dummy variable K = 1 for 2

ε t −1 < 0 else K = 0 ) measures any statistically significant influence of negative past errors ε t −1 on squared standardized residuals. A significant regression coefficient of K signifies that the impact of past errors on spread volatility is not fully specified in the asymmetric ARCH

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effects of conditional variance. The negative size bias test ( ε t σ t ) = μ + γ Kε t −1 + et also 2

includes the size of past estimation errors in the above sensitivity analysis of residual autoregressive effects. A significant regression coefficient γ of past residuals would imply that the conditional variance does not account for the asymmetric effect of small or large negative errors. The same logic applies analogously to the positive size bias test. We estimate all three bias tests based on heteroskedasticity consistent covariance (White, 1980).37 All three residual tests indicate that the set of explanatory variables of our GARCH models correctly specify any asymmetric influence of past information on conditional variance. Nonetheless, one or more significant residual tests of mainly traditional CDO and MBS spread series reveal substantial size bias of squared standardized residuals, which is not fully captured by present specification of the conditional variance equation. With our earlier estimation results in mind, it is not surprising that some MBS (AAA5 and BBB7) spreads in GARCH(1,1) and MBS (AAA5, A7) spreads in GARCH(2,1) show significant negative and positive size bias, whereas sign bias is sporadic. The GARCH model specification of spread heteroskedasticity seems to improve (deteriorate) for synthetic and traditional CDOs (Pfandbriefe) once we replace level LIBOR rates by LIBOR rates at first difference. Overall, the model diagnostics based on common residual tests as well as sign and size bias test methods for asymmetric effects on conditional volatility suggest that our multi-factor model GARCH specification generates robust parameter estimates. Our model for spread dynamics captures any asymmetric impact of past errors on conditional variance in most cases and fits particularly well for Pfandbriefe and CDOs. However, our model estimates under GARCH(2,1) cast doubts as to its appropriateness for the specification of MBS spread dynamics. The GARCH(2,1) process seems superior to GARCH(1,1) in terms of economically and statistically more significant coefficient estimates for the influence of past changes of spreads and LIBOR rates on the mean and time-varying heteroskedasticity of spreads. GARCH(2,1) also generates more consistent evidence of asymmetric effects of past spread levels in the mean equation and past errors in the conditional variance equation. Despite the attendant loss of degrees of freedom, the inclusion of more explanatory factors in GARCH(2,1) promotes higher levels of significance for traditional CDO and MBS spread series. In GARCH(1,1), the mean equation is a better model fit than GARCH(2,1) for synthetic CDO, highly rated MBS and Pfandbrief spread series, while its limitation of past forecast variance to only one lag seems to be better suited to capture the short-term GARCH effects of all Pfandbrief spreads and most CDO spreads. GARCH(2,1) seems to be superior to GARCH(1,1) in terms of economically and statistically more significant coefficient estimates for the influence of past changes of spreads and LIBOR rates on the mean and time-varying heteroskedasticity of spreads. GARCH(2,1) also generates more consistent evidence of asymmetric effects of past spread levels in the mean equation and past errors in the conditional variance equation. Despite the attendant loss of degrees of freedom, the inclusion of more explanatory factors in GARCH(2,1) promotes 37

The heteroskedasticity consistent covariance matrix proposed by White (1980) estimates coefficient covariances in the presence of heteroskedasticity of unknown form. The White covariance matrix is defined by −1 −1 Σ% W = T ( T − k ) ( X ′X )

(∑

T

)

u x t x t′ ( X ′X ) , where T is the total number of observations, k

2 t =1 t

denotes the number of regressors and ut is the error term.


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higher levels of significance for traditional CDO and MBS spread series. In GARCH(1,1), the mean equation is a better model fit than GARCH(2,1) for synthetic CDO, highly rated MBS and Pfandbrief spread series, while its limitation of past forecast variance to only one lag seems to be better suited to capture the short-term GARCH effects of all Pfandbrief spreads and most CDO spreads. Standard residual diagnostics indicate that GARCH(2,1) offers more reliable model estimates for the mean and the conditional variance of traditional CDO and Pfandbrief spreads. The estimation errors in GARCH(2,1) exhibit little or no serial correlation (and do not follow a normal distribution). Also, the examination of the existence of autoregressive standardized residuals attributes lower influence of past errors to the specification of asymmetries in the volatility process through GARCH(2,1). Hence, the extension of GARCH effects to two lags is sensible and statistically preferable. We need to interpret our estimation results with caution due to low data frequency, which might compromise the full stochastic measurability of spread volatility for reliable inference procedures. For instance, our short time period of observations does not match up to a data history of more than 30 years in Koutmos (2002). Furthermore, considering that the European ABS market has only seen active secondary trading for little more than five years, our pricing information lacks a liquidity measure. Hence, we qualify our findings of CDO and MBS spreads on the low trading activity and low relative illiquidity of these asset classes. Although the presented GARCH models yield estimation results with fairly robust parameter estimates, some spread dynamics might be caused by varying liquidity and rating volatility between spread series.38

6. Conclusion In this paper we presented the first empirical investigation of market pricing for ABS transactions in Europe on the basis of actual trading data. In our analysis we modeled the secondary market spread dynamics of different types of European ABS transactions (synthetic and traditional CDO, MBS and Pfandbrief) in a multi-factor GARCH process, which conditions the mean specification of stationary spreads on their heteroskedasticity for valuation and forecasting purposes. Accounting for volatility-induced future spread movements permits a more accurate estimation of time-varying confidence intervals for the inference testing of parametric time series specifications. We estimated the volatility process of Johnson-Fit-transformed logreturns of weekly secondary market spreads by means of two modified GARCH multi-factor models (GARCH(1,1) and GARCH(2,1)). Our GARCH specification assumed that spread dynamics can be represented as a weighted measure of the observed long-run average, the estimation errors of the previous period and the volatility forecast of up to two periods. We found that historical spread changes are mostly stationary and follow a steady positive trend with asymmetric mean reversion depending on the direction and the lag structure of past spread changes. However, they registered asymmetric sensitivity to past spread levels. Model estimates indicated that past spread levels at one lag had a statistically and economically greater effect on mean reversion if they immediately followed a spread 38

e.g. “AAA” rated spread series show less volatility than spreads of “BBB”-rated deals. Hence, the combination of parameter uncertainty due to rating volatility and liquidity considerations distort actual spread dynamics.

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decline. As opposed to the existing literature, we found no evidence of stochastically asymmetric spread dynamics. The existence of mean reversion was independent of a certain direction of past spread changes, i.e. spreads did not follow a random process if we controlled the influence of past spread levels on either spread declines or spread increases. However, spread dynamics were mostly stochastic in response to past spread levels beyond one lag, which relegated stationarity to a first order effect. We observed an explosive spread process especially when positive second differences of past spreads significantly conditioned future spread changes on past spread levels. Also the economic significance of past spread movement reversed. Spread levels after spread increases explained most of the average spread level. Based on these insights, stochastic inference methods through autoregressive effects of past spread levels seemed to fall short of fully capturing short-term spread dynamics. Most of the conditional spread volatility was informed by asymmetric ARCH effects from innovations of forecasted past variance, which contributed most of the economic significance to generally positive GARCH effects. We observed significant asymmetric effects of negative past errors on spread volatility. These negative shocks associated with unexpected upward price corrections (and declining spreads) seemed to induce higher spread volatility at a time when persistent (or increasing) spread levels testified to negative investor sentiment. Amid the prominence of persistently widening spreads in our sample time period, negative shocks associated with unexpected spread declines constituted a significant departure from the historical pattern, which was propagated over time by further negative past innovations. Subsequent standard residual model diagnostics corroborated a correct parametric model specification of the mean and the conditional variance of spread change. The autoregressive examination of past errors through sign and size bias tests confirmed that our GARCH models leave no explanatory power to past innovations in any statistically meaningful way. Our overall estimation results for European ABS spreads largely contradicts previous findings about MBS spread behavior in the U.S. (Koutmos, 2002; Arora et al., 2000). In our case, ABS spreads are difference stationary irrespective of past spread movements and largely follow a stochastic process only in response to past spreads beyond one lag. In addition, we find that a more extensive lag structure of past variance forecasts (GARCH effect) yields more reliable forecasts of future spread dynamics. Furthermore, the absence of economically insignificant spread and LIBOR interest level effects on conditional variance negates some of the previous findings in the existing literature on comparable spread series in the U.S. market. Our study also delivers cross-sectional evidence of varying spread dynamics between different asset classes. Our GARCH estimation indicated that the mean equation generates the closest approximation for synthetic CDO, Pfandbrief and some MBS, while the conditional variance equation seems to display the greatest economic and statistical significance for the GARCH and asymmetric ARCH effects of forecasted variance and past innovations of CDO and Pfandbrief spread series. These findings provide useful insights for both the adequate secondary market pricing of ABS issues with varying credit quality and the efficient management of ABS portfolios with respect to risk-return considerations if spread volatility is not constant. In light of notoriously weak information transparency in large parts of primary and secondary ABS market segments, our findings underscore that time-varying volatility is essential for the accurate forecasting of spread dynamics especially at times when a sudden contraction of spreads at time of greater investment risk entails a higher risk premium and asset volatility.


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In extension to this paper, we envisage the possible use of the presented model to ascertain significant determinants of time-varying volatility of new derivative indices, such as iBoxx® index of collateralized bonds, and deal-based secondary market spreads with higher data frequency over a longer sample period across a wider range of ABS asset types. This application could yield more instructive insights about the cross-sectional comparison of ABS spreads once idiosyncratic risk of issuers and cyclical effects of spread dynamics in the long run are controlled.

7. Appendix Appendix 1. Security Design of ABS – The Distinction between Synthetic and Traditional Structures All ABS structures engross different criteria of legal and economic considerations, which all converge upon a basic distinction of security design: traditional vs. synthetic securitisation (see Figure 10). Traditional securitisation involves the legal transfer of assets or obligations to a third party that issues bonds as asset-backed securities (ABS) to investors via private placement or public offering. In its second working paper on the treatment of asset-backed securities the Basel Committee on Banking Supervision (2002a) defines traditional securitisation as a structured finance transaction that “involves the (economic transfer of assets and other exposures through pooling and repackaging by a special purpose entity (SPE) into securities[, which] can be sold to investors. This may be accomplished by legally isolating the underlying exposures from the originating bank through sub-participation.” The conventional type of loan securitisation is always predicated on a clean break between the bank originating the assets and the securitisation transaction itself, i.e. it epitomises the legal and economic separation of the seller from the securitised assets via a true sale (novation, assignment, declaration of trust or sub-participation). Granting regulatory capital relief through the transfer of assets off the balance sheet in standard transactions represents the most fundamental regulatory issue for the originating bank of a securitisation transaction. According to the revised proposal of the Basel Committee (2001a and 2001b) regulatory capital relief by means of removing assets from the balance sheet for purposes of determining minimum capital requirements takes effect once the following minimum conditions are satisfied39: (i) the transferred assets have been legally isolated from the transferor; that is, the assets are put beyond the reach of the transferor and its creditors, even in bankruptcy or receivership. This must be supported by a legal opinion, (ii) the transferee is a qualifying special-purpose vehicle (SPV) and the holders of the beneficial interests in that entity have the right to pledge or exchange those interests, and (iii) the transferor does not maintain effective or indirect control over the transferred assets.

39

See also Findeisen and Ross (1999).

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These conditions are essentially the equivalent to the provisions in IAS 39/FASB 140/FASB 125, and therefore, there is no new restriction or qualifying condition being laid down by the regulators. Unless the three previously listed conditions are met, the Basel Committee proposes to retain the respective assets on the books of the originating bank for regulatory accounting purposes (RAP), even if the assets are removed from the books in compliance with GAAP. In order to better manage regulatory and risk capital banks and financial services companies increasingly turn to what is frequently termed the newest wrinkle of securitisation and structured finance – the synthetic security. An increasing number of structured finance transactions are such compound products, which amalgamate properties of both asset-backed securitisation and credit derivatives in one coherent structure. In synthetic securitisation only asset risk (e.g. credit default risk, trading risk, operational risk) is transferred to a third party by means of derivatives without change of legal ownership, i.e. no legal transfer of the designated reference portfolio of assets.40 Hence, any resulting regulatory capital relief41 does not stem from the actual transfer of assets off the balance sheet but the acquisition of credit protection against the default of the underlying assets through asset diversification and hedging.42 Commonly, sponsors of synthetic securitisation issue debt securities supported by credit derivative structures, such as credit-linked notes (CLNs)43, whose default tolerance amounts to total expected loan losses in the underlying reference portfolio. Hence, investors in CLOs are not only exposed to inherent credit risk of the reference portfolio but also operational risk of the issuer.44 Recently, also traditional securitisation transactions included elements of synthetic securitisation (such as credit derivatives) in order to preserve the creditlinkage of issued securities to the originator and realise on-balance sheet financing to fund assets.45 In defection from conventional forms of selling claims on a reference pool of assets, synthetics effectively sidestep the legal quagmires, mainly because most or all of the assets are never sold to capital market investors. Under this scheme of loan securitisation the originating bank merely transfers the inherent credit risk of the loan book by means of a credit 40

41

42

43

44

45

For instance, sellers of credit default swaps (CDS) receive a premium for their obligation of compensating buyers of credit protection for any default losses up to a specified amount. Since the compensation payment through credit default swaps (CDS) is contingent on a certain credit event, derivative components in the security design of synthetic transactions are termed “unfunded”, while bonds directly issued to investors as “credit-linked notes” (CLN) are “funded”. According to the Basel Committee on Banking Supervision (2002a and 2002b) synthetic securitisation “generally involves the transfer of credit risk though the use of funded (e.g. credit-linked notes) or unfunded (e.g. credit default swaps) credit derivatives or guarantees that serve to hedge the credit risk to which the originator is exposed.” This property of synthetic CLOs is attractive to large banks, which tend to have access to on-balance sheet assets at competitive spreads. “Credit linkage” signifies credit risk transfer without a corresponding change of title (legal ownership) of the underlying asset claims. The absence of asset transfer to a special purpose vehicle (SPV) as in traditional CLOs aids the cost efficient administration of synthetic securitisation. Synthetic structures also garner issuers with a wider choice of leveraging the underlying reference portfolio, so that on average the nominal total value of issued debt securities of such transactions is significantly outstripped by the nominal tranche volume in conventional securitisation. The marginal difference in senior risk exposure between partially funded synthetic securitisation and traditional securitisation does not extent to junior noteholders with subordinated security interest. While partial funding structures bear more risk emerging from the sponsor’s role, the credit enhancement (first loss provision) and subsequent junior tranches (the second loss position) are no more exposed to credit risk in synthetic deals than they are in traditional CLOs.


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default swap, in which the counterparty agrees upon specific contractual covenants to cover a predetermined amount of losses in the loan pool. A significant portion of the global U.S. $300 billion business of risk transfer comes from collateralised debt obligations (CDOs), whose prime sub-categories are forms of synthetic and traditional CLO structures (The Economist, 2002). Apart from this credit derivative, also credit-linked notes, credit spread options and total return swaps are further financial instruments, which allow issuers to shift isolated credit risk to guarantors, thereby making the risks marketable while leaving the original lenderborrower relationship untouched (Burghardt, 2001), as the reference asset is the loan pool retained by the bank. In case a sale does not come about, many of the bankruptcy and other securities laws become moot. As the credit risk of the loans is transferred to a special purpose vehicle (SPV) and from there on to the investors, the originating bank (the sponsor of the transaction) achieves regulatory capital relief through a transfer of credit risk the underlying loan portfolio, which would otherwise qualify for a minimum capital requirement to cover credit risk exposure. The SPV as securitisation conduit does not purchase the reference portfolio of securitised bank assets and, hence, forgoes financial outlay in raising funds for financing what is considered to fall outside the definition of an off-balance sheet transfer of assets. Usually a synthetic transaction is complemented by a third-party credit default swap agreement, which protects the sponsor against asset default of the reference portfolio in nature much akin to an insurance contract with exogenized damage claims. In synthetic structures credit derivatives, e.g. CLNs, credit spread options, credit default swaps and total return swaps, are used as vehicles to shift isolated credit risk to guarantors, thereby making credit risk marketable, while the original credit relationship between creditor and debtor remains unchanged. Since bank assets are retained on the balance sheet, a synthetic transaction does not constitute a credit de-linkage between the servicer of the loan pool and the issuer of the tranches offered to investors. At the same time, the credit default swap removes large portions of credit risk from the balance sheet of the sponsor and mitigates the minimum capital requirements for credit risk cover, albeit the issuer sells credit-linked notes in the capital market. So “synthetication” is a more fine-tuned approach, which only addresses the regulatory sensitive element of the loan book, namely credit risk. Generally, synthetic securitisation amounts to on-balance sheet credit hedging by means of a transmission mechanism of payment claims on a portfolio of assets that defy conventional forms of securitisation. Among the reasons certainly are restrictive provisions that prevent transfer or assignment, i.e. the sale of loans to an issuer, the SPV, might compromise client relationships or restrictive contractual covenants on the transfer of the underlying loans. Concerns surrounding the retention of client relationships associated with certain bank loans lead issuers to include those assets in the reference pool of synthetic transactions, whose off-balance sheet treatment would impede the generation of future business. If credit-linked notes are issued the legal integrity of perfected security interest in the reference portfolio underlying these debt securities does not only give rise to the benefit of regulatory capital relief, as investors assume a synthetic and prioritised share in default loss. The “synthetication” of structured claims also wins out over conventional, true sale securitisation transactions in terms of efficiency gains from the legal and economic treatment of the reference portfolio. Possible heterogeneity of loan characteristics, which would otherwise entail legal obstacles, complicates the legal definition of a true sale and its effective

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completion. In synthetic transactions, however, the absence of an outright transfer of legal title to the loan pool purports to a reduction of structural risk and administrative cost. For loss of loan transfer to a bankruptcy-remote SPV the legal issues associated with the notification of obligors and the perfection of legal transfer are evaded altogether in establishing both bankruptcy remoteness (perfected security interest) and true sale properties, essential to conventional transactions. The ability of the sponsor to retain legal title in the framework of a synthetic securitisation particularly lends itself to loans that have been originated in different jurisdictions. Consequently, issuers avoid the cost of complex transfer arrangements of loans that do not lend themselves to a straightforward sale. As collateral assets of synthetic transactions are frequently unfunded, the popularity of synthetic structures as a carrier of regulatory capital mitigation is largely due to the favourable funding properties of large banks, who typically have access to on-balance sheet funds at competitive spreads especially in the area of mortgage-based financing and Pfandbrief issues.46

Appendix 2. Moment-Matched Johnson Fit Adjustment The Johnson Fit adjustment aligns the continuous distribution of logreturns of spreads (i.e. first differences of logarithmic spreads) closer to normality by means of data scaling. This transformation procedure is based on three kinds of distribution functions (Johnson curves) – an unbounded ( SU ) , a bounded ( S B ) and a lognormal distribution ( S L ) – each associated with transformation function u = γ + ηki ( x ; λ ,ξ ) , where u denotes a standard normal target variable and x represents the original variable (of changes in logarithmic spreads). Parameters γ and η define the shape of the fitted curve, the scale factor λ defines the variance and ξ the expected value of the distribution, respectively. Slifer and Shapiro (1980) propose a simplified estimation procedure for all four parameters in each distribution function ( SU , S B , S L ) . First, we randomly pick a value z > 0 from a standard normal distribution to derive three intervals of equivalent distance between the values −3z , −z , z and 3z . We then determine the values x −3z , x −z , x z and x 3z of the original, nonnormal distribution, which correspond to −3z , −z , z and 3z . The differences between the values x −3z , x −z , x z and x 3z (which are not equidistant by definition of the transformation mechanism) define the parameters m = x 3z − x z , n = x −z − x −3z and p = x z − x −z for the selection of one of the following distribution functions

SU : k1 ( x ; λ ,ξ ) = sinh−1 ( ( x − ξ ) λ −1 ) if SU : mn × p−2 > 1 ,

46

(10)

Consequently, the “synthetication” of structured claims squares with both regulatory arbitrage and improved risk-adjusted returns, as the diversification effect of risk transfer by means of credit derivatives requires enhanced internal pricing methods of expected default loss (Rösch, 2001). Even if proposed regulatory changes to the standard credit risk weightings for bank loans renders the regulatory arbitrage aspect of securitisation obsolete, it constitutes no rebuttal to the benefits associated with loan securitisation per se, as efforts of boosting the economic rents from loan origination are not scuppered.


(

S B : k2 ( x ; λ ,ξ ) = ln ( x − ξ )( λ + ξ − x )

−1

) if S

B

219

: mn × p−2 < 1 , and

(11)

S L : k3 ( x ; λ ,ξ ) = ln ( x − ξ ) if S L : mn × p−2 = 1 .47

(12)

We compute the parameters γ ,η , λ and ξ , with z small enough48 so that we can compute the transformed values u within the interior of the original distribution up to x ±3z based on the following system of equations:

(

)

For SU : u = γ + η sinh−1 ( x − ξ ) λ −1 —

⎛

⎞ ⎟, ⎜ 2 ( mn × p−2 − 1)0.5 ⎟ ⎝ ⎠ 2z for η>0 , η= −1 cosh ( 0.5 ( m + n) p−1 )

( n − m) p−1

γ = η sinh−1 ⎜

λ=

2 p ( mn × p−2 − 1)

( ( m + n) p

− 2 ) ( ( m + n) p−1 + 2 )

−1

and

ξ=

0.5

x z + x −z 2

+

(

for λ>0 ,

0.5

p ( ( n − m) p−2 )

2 ( ( m − n) p−1 − 2 )

)— ) − 4 ) ⎤⎥⎦ ( 2 ( p ( mn)

S B : u = γ + η ln ( x − ξ )( λ + ξ − x )

For

(

γ = η sinh−1 ⎛⎜ ⎡( pn−1 − pm−1 ) (1 + pm−1 )(1 + pn−1 ⎝ ⎢⎣

(

(

λ= p

(((1 + pm )(1 + pn ) − 2) −1

−1

2

−4

)

0.5

−1

0.5

η = z ⎛⎜ cosh−1 0.5 (1 + pm−1 )(1 + pn−1 ) ⎝

.

)

0.5

) ⎞⎟⎠

2

−1

))

−1

−1

⎞, ⎟ ⎠

for η>0 ,

⎡ p2 ( mn)−1 − 1⎤ ⎣ ⎦

−1

for λ > 0 ,

and

ξ=

x z + x −z 2

48

λ

(

)

−1 + p ( pn−1 − pm−1 ) ⎡ 2 p ( mn) − 1 ⎤ ⎣ ⎦ 2

−1

S L : u = γ * + η ln ( x − ξ ) —

For

47

−

Since the probability of mn p = 1 borders to zero, it seems reasonable to use certain tolerance levels around the critical value of 1 for this selection process. Slifker and Shapiro (1980) recommend z = 0.5 . 2

220

Andreas A. Jobst 0.5 ⎛ ⎞ γ * = η ln ⎜ ( mp−1 − 1) ⎡⎢ p ( mp−1 ) ⎤⎥ ⎟ , ⎣ ⎦ −1

⎝

⎠

η=

2z , ln ( mp−1 )

and

ξ=

x z + x −z 2

−

p mp−1 + 1 × . 2 mp−1 − 1

The application of the Johnson Fit routine on our data set of weekly spread series indicates that the quality of the desired adjustment to normality is highly sensitive to the choice of the random z-value. Hence, we resort to an iterative procedure to determine the optimal z-value at six decimals. We compute a preliminary optimal z-value for the best approximation of the original distribution to the normal distribution as we count from 0 to 2 by staggered two-digit increments of 0.02. We refine the z-value through another iterative cycle of three-digit increments of 0.001 within a band of +/-0.02 around the interim z-value. This procedure continues until the parameterization of z yields an optimal approximation of the normal distribution measured by the Jarque-Bera statistic after transformation. For our data set, the transformation via the Johnson Fit procedure yields adjusted spreads, whose first two moments deviate significantly from the original spread series. Consequently, we match the mean and standard deviation of the individual Johnson-fitted spread series to the original distribution without compromising the approximative normal distribution of transformed spreads. We recalibrate the differences between fitted spreads and original spreads by means of an adequate scaling factor in order to reinstate the variance of each original spread series. We also adjust the mean of the fitted spread distribution to a new starting value that conforms with the original mean value. The new adjusted spread series bear great resemblance to the original spread series for all asset classes in our data set. The correlation coefficient between both exceeds 90% in most cases. Only the matched pairs of traditional CDOs (A5) and three out of four MBS time series (AAA3, AAA5 and BBB7) exhibit weaker correlation. Table 3 shows the chosen z-values, the type of transformation function (Johnson curve), the correlation between the fitted and the original spread series as well as normality test diagnostics (Jarque-Bera and Doornik-Hansen statistics).49 We particularly address any violation of the normality assumption by means of heteroskedasticity consistent (quasi-maximum likelihood) covariance matrices (White, 1980) in our GARCH estimation. This restriction is also needed for several model diagnostics (coefficient and residual tests) of our model specification.

Appendix 3. The Jarque-Bera and Doornik-Hansen Tests of Normality The Jarque-Bera (JB) test diagnostic examines whether the null hypothesis of normally distributed spreads holds. The Jarque-Bera test statistic,

49

Note that we have not applied the Johnson Fit to LIBOR rates. So the LIBOR rates our GARCH model estimations of Johnson Fit-adjusted spread series include logarithmic LIBOR rates only.


JB =

221

N −k ⎛ 2 1 2⎞ ⎜ S − ( k − 3) ⎟ , 6 ⎝ 4 ⎠

(13)

measures the degree to which a time series is normally distributed based on the difference of the skewness S and kurtosis K between the normal distribution and the spread series, where k represents the number of estimated coefficients used to create the series. The probability of the JB test indicates the likelihood of the JB statistic to exceed (in absolute value) the observed value of a normal distribution. Since the JB statistic is particularly suitable for large samples, our limited number of observations suggests an alternative test procedure, which would promises greater robustness as regards the normal distribution assumption. We apply the test procedure by Doornik and Hansen (1994), which was developed for small sample sizes. Similar to the Jarque-Bera test statistic, the Doornik-Hansen diagnostic (Ep) computes the deviations from the normal distribution on the basis of transformed higher moments of skewness z 1 and kurtosis z 2 :

E p = z 12 + z 12

app

χ df2 = 2 .

(14)

Doornik and Hansen define the transformation of skewness S and kurtosis K for n number of observations as

(

)

z 1 = δ ln y +

y2 − 1 ,

(15)

where

δ= y=S

1 ln (ϖ )

,

ω 2 − 1 ( n + 1)( n + 3) 2

ϖ 2 = −1 + 2 ( β − 1) , β =

6(n − 2)

,

3 ( n2 + 27n − 70 ) ( n + 1)( n + 3)

( n − 2 )( n + 5 )( n + 7 )( n + 9 )

,

and 1 ⎛ ⎞ 3 χ 1 ⎟ ⎛ ⎞ −1+ z2 = ⎜⎜ 9α , ⎟ ⎜ ⎝ 2α ⎠ 9α ⎟ ⎝ ⎠

where

(16)

χ = 2k ( K − 1 − S 2 ) ,

α = a + S 2c , k=

( n + 5 )( n + 7 ) ( n3 + 37n2 + 11n − 313) 12δ

,

222

Andreas A. Jobst

δ = ( n − 3)( n + 1) ( n2 + 15n − 4 ) , a=

( n − 2 )( n + 5 )( n + 7 ) ( n2 + 27n − 70 ) 6δ

c=

( n − 7 )( n + 5 )( n + 7 ) ( n2 + 2n − 5 ) 6δ

,

.

The Doornik-Hansen test rejects the null hypothesis of normally distributed spreads if the approximate χ df2 = 2 -distributed test statistic is significantly different from zero.

Appendix 4. The Llung-Box Test of Autocorrelation The main statistical diagnostic for autocorrelation in time series is the Llung-Box test. Llung-Box Q-statistic at lag k represents the test statistic for the null hypothesis of no autocorrelation up to order k (i.e. whether the series is white noise) for

QLB = T ( T + 2 ) ∑ j =1 r j ( T − j ) , k

−1

(17)

where r j is the jth autocorrelation and T is the number of observations. The Q-statistic is asymptotically distributed as χ 2 with the degrees of freedom equal to the number of autocorrelations, since the observations are not the result of an ARIMA estimation. We augment this test statistic by the AC-value of autocorrelation (with the null hypothesis of no autocorrelation). The AC-value confirms the Q-statistic of absent serial correlation if it cannot be rejected at 5% level, i.e. falls within the two standard error bounds of ±2T −0.5 . We assume 36 lags as default test setting for all test statistics of autocorrelation for the given time series. We estimate the autocorrelation of series y with lag k and sample mean y as the correlation coefficient over k periods

rk

(

∑ =

T t = k +1

where yt −k = ∑ t =k +1 yt −k × ( T − k ) T

( yt − y)( yt −k − yt −k ) (T − k ) , T 2 ∑ t =1 ( yt − y) T

−1

) relies on the same overall mean

(18)

y as the mean of

both yt −k and yt (which would bias the result towards zero for finite series) for matters of computational simplicity. Hence rk ≠ 0 means that the series is first order serially correlated. A geometric decrease of rk in an increase of lags k would constitute a low-order autoregressive (AR) process, whereas as rapid decline of rk to zero flags a low-order moving average (MA). We determine the degree of autocorrelation at the statistical threshold of


223

significant Q-statistics (p-value) and AC values (together with the partial correlation measure PAC) for the null hypothesis of no autocorrelation. This threshold level entails the maximum number of lags until which either the associated AC value or the Q-statistic no longer indicate a rejection of the null hypothesis of at least the 5 % level – whichever occurs first, with the Qstatistic being the primary criterion.

References Anonymous (1999), “Pfandbriefe, Germany’s Secret Gamblers,” Euromoney Magazine (April). Arora, A., D. K. Heike and R. Mattu, “Risk and Return on the Mortgage Market: Review and Outlook,” The Journal of Fixed Income, 10, No. 1, 5-18. Bali, T. G. (2000), “Testing the Empirical Performance of Stochastic Volatility Models of the Short-Term Interest Rate,” Journal of Financial and Quantitative Analysis, 35, No. 2, 191-215. Bartlett, J. F. (1981),“A Non-Stop Kernel,” in Proc. 8th Symposium on Operating Systems Principles (SOSP). Basel Committee on Banking Supervision (2002a). Second Working Paper on the Treatment of Asset Securitisations. Bank for International Settlements (October). Basel Committee on Banking Supervision (2002b). Quantitative Impact Study (QIS) 3 Technical Guidance: Credit Risk – Securitisation Framework. Bank for International Settlements (October) (www.bis.org/bcbs/qis/qis3tech.pdf). Basel Committee on Banking Supervision (2001a). First Working Paper on Asset-Backed Securitisation. Bank for International Settlements (October). Basel Committee on Banking Supervision (2001b). Basel Consultative Paper on Securitisation. Bank for International Settlements (January). Berndt, E. K., H. B. Hall, R. E. Hall, and J. A. Hausman (1974), “Estimation and Inference in Nonlinear Structural Models,” Annals of Economics and Social Measurement, 653-66. Bhasin, V. and M. Carey (1999), “The Determinants of Corporate Liquidity,” Working Paper FNMC/Federal Reserve Board, May (1 November 1997). Bollerslev, T. and J. M. Wooldridge (1992), “Quasi-Maximum Likelihood Estimation and Inference in Dynamic Models with Time Varying Covariances,” Econometric Reviews, 11, 143–72. Burghardt, M. (2001), “ABS: Pedigree Beasts or Trojan Horses ?,” Investment Management Newsletter, PricewaterhourseCoopers (June), 6. Cousseran, O. and I. Rahmouni (2005), “The CDO Market – Functioning and Implications in Terms of Financial Stability,” Banque de France Financial Stability Review, No. 6 (June), 43-62. Dickey, D. A. and W. A. Fuller (1981), “Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root,” Econometrica, July, 1057-72. Dickey, D. A. and W. A. Fuller (1979), “Distribution of the Estimators for Autoregressive Time Series with a Unit Root,” Journal of the American Statistical Association, 74, 427-31. Doornik, J. A. and Hansen, H. (1994), “An Omnibus Test for Univariate and Multivariate Normality,” Working Paper, Nuffield College, Oxford University.

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Engle, R. F. and V. K. Ng (1993), “Measuring and Testing the Impact of News on Volatility,” Journal of Finance, 48, 1022–82. Fabozzi, F. J. and L. S. Goodman (2001) (eds.). Investing in Collateralized Debt Obligations. Wiley Europe. Findeisen, K.-D. and N. Ross (1999), “Wirtschaftliche Zurechnung und Anhangangabe bei Asset-Backed Securities,” Der Betrieb, 52, No. 21 (28 May), Dusseldorf/Frankfurt am Main, 1077-79. Frankel, T. (1991), “Securitisation: Structured Financing, Financial Asset Pools and AssetBacked Securities,” Journal of Financial Intermediation, 6, 64-86. Glosten, L. R., R. Jagannathan and D. Runkle (1993), “On the Relation between the Expected Value and the Volatility of the Normal Excess Return on Stocks,” Journal of Finance, 48, 1779-1801. Goodman, L. and J. Ho (1998), “A LIBOR-based Approach to Modeling the Mortgage Basis,” The Journal of Fixed Income, 8, No. 2, 29-35. Goodman, L. and J. Ho (1997), “Modeling the Mortgage-Treasury Spread,” The Journal of Fixed Income, 7, No. 2, 85-91. Hamilton, J. (1994). Time Series Analysis. Princeton University Press. Kendall, L. (1996). A Primer on Securitisation. MIT Press, Cambridge. Klotter, D. J. (2000), “Der Markt für ABS in Deutschland Wächst,” Börsenzeitung, No. 209 (28 October), B. 7. Koutmos, G. (2002), “Modeling the Dynamics of MBS Spreads,” The Journal of Fixed Income, 12, No. 2, 43-49. Koutmos, G. (2001), “Common Volatility in MBS Returns: A Factor GARCH Approach,” The Journal of Fixed Income, 10, No. 4, 59-65. Jarque, C. M. and A. K. Bera (1980), “Efficient Tests for Normality, Homoscedasticity and Serial Independence of Regression Residuals.” Economics Letters, 6, 255-259. Jarque, C. M. and A. K. Bera (1980), “A Test for Normality of Observations and Regression Residuals,” International Statistical Review, 55, No. 2, 163-172. Jobst, A. (2005a), “Correlation, Price Discovery and Co-movement of ABS and Equity,” Working Paper 2005-11, Center for Financial Research (CFR), Federal Deposit Insurance Corporation (FDIC). Forthcoming in Derivatives Use, Trading & Regulation, 11, No. 4. Jobst, A. (2005b), “Tranche Pricing in Subordinated Structured Finance, ” Journal of Structured Finance, 11, No. 2, 64-96. Jobst, A. (2003), “Collateralized Loan Obligations (CLOs): A Primer,” The Securitization Conduit, 6, Nos. 1-4. Leland, H. (1998), “Agency Cost, Risk Management and Capital Structure,” Journal of Finance, Vol. 52, 1213-43. Longstaff, F. and E. Schwartz (1992), “Interest Rate Volatility and the Term Structure: A Two-Factor General Equilibrium Model,” The Journal of Finance, 47, No. 4, 1259-82. Marquardt, D. W. (1963), “An Algorithm for Least-Squares Estimation of Nonlinear Parameters,” Journal of the Society for Industrial and Applied Mathematics, 11, No. 2, 431-41. Mastroeni, O. (2005), “Pfandbrief-style Products in Europe,” Occasional Papers Series, European Central Bank, Frankfurt/Main. Müller-Stewens, G., Roventa, P. and G. Bohnenkamp (1996). Wachstumsfinanzierung für den Mittelstand. 2nd ed., Stuttgart.


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Nelson, C. R. and C. I. Plosser (1982), “Trends and Random Walks in Macroeconomic Time Series,” Journal of Monetary Economics, 10, 139-62. Newey, W. and West, K.D. (1987), “A Simple, Positive, Heteroskedasticity and Autocorrelation Consistent Covariance Matrix,” Econometrica, 55, 703-05. Papell, D. H. and R. Prodan (2003), “The Uncertain Unit Root in U.S. Real GDP: Evidence with Restricted and Unrestricted Structural Change,” Working Paper (October), University of Houston. Peppetti, A and R. Rinaldi (2001), “I covered bond in Europa e le ipotesi di sviluppo in Italia.” Bancaria, No. 6, Associazione Bancaria Italiana, Rome. Phillips, P. C. B. and P. Perron (1988), “Testing for a Unit Root in Time Series Regression,” Biometrika, 75, 335–46. Rösch, D. (2001), “Transfer von Kreditrisiko,” Kreditpraxis, 26, No. 1, Wiesbaden, 8-13. Rosenthal, J. and J. Ocampo (1988). Securitization of Credit. John Wiley & Sons, New York. Skarabot, J. (2002), “Securitisation and Special Purpose Vehicle Structures,” Working Paper, Haas School of Business, University of California at Berkeley (29 April) (http://faculty.hass.berkeley.edu/skarabot/unix/ch2.pdf). Taylor, A. (2001), “Potential Pitfalls for the Purchasing-Power-Parity Puzzle ? Sampling and Specification Biases in Mean-Reversion Tests of the Law of One Price,” Econometrica, 69, 473-498. The Bond Market Association (2001), “Transaction Reporting Standards: Analysis and Preliminary Recommendations of the European Securitisation Forum,” London. The Economist (2002), “Bombe Surprise – Who Can Understand, Let Alone Manage, Credit Risk ?,” (7 February). Tsui, E. (2005), “Asia poised for take-off in CDOs,” Financial Times, Capital Markets (15 July). Turwitt, M. (1999). Asset-Backed Finanzierungen und Handelsbilanzielle Zuordnung. Wiesbaden. White, H. (1980), “A Heteroskedasticity-Consistent Covariance Matrix and a Direct Test of Heteroskedasticity,” Econometrica 48, 817-38. Zakoian, J. M. (1990), “Threshold Heteroskedastic Models,” mimeo, CREST, INSEE, Paris.



Chapter 8

AN ATTEMPT TO MEASURE THE INFLUENCE OF THE MAASTRICHT CRITERIA AND THE STABILITY PACT ON GROWTH AND UNEMPLOYMENT IN THE EUROPEAN UNION Vítor Castro∗ and Elias Soukiazis Faculty of Economics, University of Coimbra, Coimbra, Portugal

Abstract In this chapter we attempt to measure the direct effects of the Maastricht criteria and the Stability and Growth Pact (SGP) on growth and unemployment in the European Union (EU) member countries, covering the period of 1980-2001. The evidence from a empirical analysis panel suggests that growth in the EU has been negatively influenced by the imposition of the Maastricht criteria and by the SGP restrictions. Estimating the pre and post-Maastricht periods separately, it was found that the higher fiscal discipline after Maastricht did not benefit the growth of real output as expected. In that sense, we find evidence that the effort that has been made by all EU countries to reduce the budget deficit has been negatively affecting the growth of real output. Exchange rate stability also seems to have a negative effect on growth. On the contrary, the efforts to reduce the inflation have been beneficial to the output growth, giving support to the price stability requirement of the SGP. Nevertheless, the growth objective of the SGP is far from being achieved, as our results demonstrate. Finally, the unemployment analysis through growth confirms the idea that unemployment follows the economic cycle and that the fall in unemployment due to growth of real GDP was smaller in the post-Maastricht period. Our main conclusion is that the Maastricht criteria and the SGP have been somehow unfavourable to growth and unemployment in Europe, so we argue that a more flexible SGP is ∗

Corresponding Author. Faculty of Economics,University of Coimbra, Av. Dias da Silva, 165, 3004-512 Coimbra, Portugal. Telef.: +351 239 790525; Fax: +351 239 403511; E-mail: [email protected]

228

Vítor Castro and Elias Soukiazis needed to stimulate aggregate demand and productive investment in order to achieve full employment and a higher growth in Europe.

Keywords: stability and growth pact, maastricht criteria, optimum currency areas, asymmetric shocks, growth, unemployment, panel data analysis.

Abbreviations aus: a_av: bel: c_av: den: fin: fr: ger: gr: ire: it: lux: neth: por: sp: sw: uk:

Austria annual average Belgium country average Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden United Kingdom

1. Introduction The Stability Pact was designed in order to achieve price stability and sustainable growth in the long-term for members of the euro zone. Its main issue is to achieve zero public deficits in the medium-term with the aim of producing greater budget flexibility when members suffer asymmetric shocks and fall into recession. A balanced public budget allows economic adjustments to be made in the short-term, when the economies involved are affected by asymmetric shocks, without disturbing price stability. In the absence of the exchange rate mechanism and a centralized budget, national government budgets are the only available means for state members to cope with asymmetric shocks. National budgets have to play the role of automatic stabilizers when an economy is hit by a recession. Theoretically this is the main advantage of the Stability Pact: it gives the members of a monetary union more room for manoeuvre and compensates for a loss of both monetary independence and exchange rate policy. However, the Maastricht criteria regarding nominal convergence and the Stability Pact requirements made on public deficits have imposed some important constraints which are neither favourable to growth nor to unemployment performance in the short-term. Before the EMU was established, members could use monetary policy to control inflation, fiscal policy

An Attempt to Measure the Influence of the Maastricht Criteria…

229

to deal with unemployment and exchange rate policy to bring their Balance of Payments into equilibrium. Since the EMU was implemented, member countries still had to deal with the same problems of internal and external equilibrium but now less economic policies areavailable to work with. Exchange rate policy was vanished and monetary policy is no longer national or independent for member states. The only national and independent policy remaining, is fiscal policy, but even that is constrained by the requirements of the Stability Pact. Hence, member countries are not free to choose the exchange rate they want, the inflation rate and interest rate suitable to their economic needs or the unemployment rate they wish to have. Besides that, the EMU monetary authorities have proved to be more concerned with maintaining price stability than with promoting growth or reducing unemployment in Europe. Statistical evidence from the last decade shows that since the imposition of the Maastricht criteria in 1992, economic growth in Europe has been modest and unemployment has increased. This has been especially true since 2001 and now many economists agree that Europe is entering into recession. Which are the responsibilities of the Stability Pact for the declining of economic performance in Europe? What are the direct short-term effects of the Maastricht criteria and the Stability Pact on growth and unemployment? Are there any positive expected growth effects in the long-term horizon? Is there any alternative Stability Pact more favourable to growth and employment? These are some of the fundamental questions that this chapter attempts to answer. Almost all contributions in this area have been theoretical and most of them do not give any clear answer about the impact of the Maastricht criteria or the Stability Pact on the economic activity.1 With this study we try to contribute to the debate providing solid empirical evidence in order to answer the questions raised above. The present chapter is organised as follows: Firstly, we present some theoretical considerations on the Stability Pact and discuss its relevance in a Monetary Union; Then we analyse the historical data related to the Maastricht criteria since 1980, detecting some obvious trends; The next task is to estimate the direct effects of the Maastricht criteria on growth by using panel data analysis and discuss the obtained results; In a similar analysis, we also consider the effects of the Maastricht criteria on unemployment through growth performance; And in the last part, we summarize the main findings and discuss some alternative ideas that could lead to a more flexible Stability Pact that might favour both growth and employment.

2. Some Theoretical Considerations Regarding the Stability Pact The Stability Pact, which is the extension of the Maastricht Treaty requirements, establishes the necessary conditions for more efficient monetary integration. It is consistent with the Optimum Currency Area (hereafter, OCA) doctrine, which was developed by Mundell (1961). The OCA defines a set of rules in order to minimize the costs of achieving the macroeconomic goals of internal balance (low inflation and low unemployment) and external balance (a sustainable balance-of-payment position) in a currency union without 1

See, for instance, Alho (2001), Artis and Buti (2001), Bassanini and Scarpetta (2001), Blanchard and Perotti (2001), Brück et al. (2002), Casella (1999), Gali and Perotti (2003), and Perotti (2002).

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Vítor Castro and Elias Soukiazis

using the exchange rate as an adjustment mechanism. The pre-accession requirements to the OCA are related to the nominal or monetary convergence of low inflation and long term interest rates, exchange rate stability and public finance discipline, as have been adopted by the Maastricht Treaty. If the objective of the union is to guarantee price stability then nominal convergence is a necessary condition. According to OCA norms, wage flexibility and labour mobility are some additional conditions necessary in dealing with asymmetric shocks, which may affect the members of the union differently. A fall in real wages will be necessary if a country that has been affected by a negative demand shock wants to improve its competitiveness, as the exchange rate changes option is no longer available. Migration of the unemployed forces may solve the problem of social costs in depressed areas and also contribute to the convergence of real wages within the union. Problems will arise, however, if real wages are not flexible enough, or labour mobility not high enough, as is the case with the EU. Under these rigidities, the areas affected by a demand shock will have to support the costs of unemployment and current account deficit, and restore equilibrium, by deflating their economies. This could be avoided if an efficient centralised budgetary policy and an active regional policy, at union level, are able to restore equilibrium by transferring resources from the surplus countries to the deficit countries. The Stability Pact is thought to play this automatic adjustment role (replacing the exchange rate policy) in the case where wages are rigid, labour mobility is limited and federal budgetary policy is not significant. After a negative demand shock, equilibrium can be restored by public spending (or tax reduction) during a recession, and by a reverse policy during expansion to reduce inflationary pressures. Therefore, the Stability Pact substitutes the exchange rate policy to give some flexibility to a system that would otherwise be overly rigid. This is the main argument to the creation of the Stability Pact as a useful policy instrument for macroeconomic adjustments. The degree of openness is also an important criterion to define OCA. According to McKinnon (1963) the greater the openness of an economy, the greater the benefits of a fixed exchange rate system will be, avoiding the consequences of imported inflation, eliminating speculative crises and uncertainty on trade. On the other hand, Kenen (1969) argues that fixed exchange rates are more adequate for diversified economies. Countries with a less diversified output structure and supply of exports are subject to more asymmetric shocks, making them less suitable to form a monetary union. This is also Krugman’s(1991)2 view, who stresses that when economic integration increases, the countries involved become more specialized, so that they will be subjected to more, rather than fewer, asymmetric shocks. However, this is not the view of the European Commission, presented in the report “One Market, One Money”. According to them, differential shocks on demand will occur less frequently in a monetary union as trade is, to a large extent, intra-industry trade (based on product differentiation). This trade structure leads to a situation where most demand shocks will affect each country of the union in a similar manner. Economic integration will make asymmetric shocks between nations less likely and instead of being asymmetric these shocks will tend to be more symmetric (DeGrauwe, 2000). 2

Krugman (1991). This is an old idea that was developed by Myrdal (1957) and Kaldor (1966) based on the cumulative causation principle. Trade integration benefits regions which specialise in increasing both static and dynamic returns to scale activities. The result of free trade will be a higher concentration of economic activity in regions with substantial gains in productivity.


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Frankel and Rose (1998) also showed that the synchronisation of economic cycles is an important element in evaluating the costs of the monetary union. When all economies of the union are moving along the same pace of the economic cycle, the impact of the specific asymmetric shocks will be less significant. In terms of the efficiency of the common monetary policy, the synchronisation of business cycles is a very important element. Centralised monetary policy is more effective when the economies are all on the same cycle and affects equally all members of the union. Monetary policy can affect the members of the union differently when they find themselves in a different position on the cycle, and can be more suitable to some and less suitable to others. According to the theory of optimum currency areas, a monetary union should proceed with some kind of centralization of the national budgets to allow for automatic transfers to regions and countries that are affected by negative shocks. Monetary unification in Europe has been realized without having any significant central European budget system. In the absence of the exchange rate mechanism and an efficient centralized European budget, national government budgets are the only available instruments for member states to confront asymmetric shocks. Therefore, national budgets must play some role of automatic stabilizers when an economy is hit by recessions (DeGrauwe, 2000). Theoretically, this is one good reason for the existence of the Stability Pact, in sense it may offer to the members of a monetary union more room of manoeuvre to absorb negative shocks, once monetary and exchange rate policies are not national policy instruments. Another reason for the existence of the Stability Pact is to avoid the occurrence of asymmetric shocks, which may arise from the different fiscal policies of the union members. The objective of a zero public deficit will oblige a higher harmonisation of the public spending and tax revenue policies between the members of the union. This reduces the possibility of negative shocks caused by differences in taxation and public spending policies. Consequently, another concern of the Stability Pact relates to the spill over effects of unsustainable national debts and deficits in particular countries (especially large countries), which may harm other member countries and may exert pressure on the ECB policy to ensure price stability.

3. Historical Analysis of the Maastricht Criteria The Maastricht Treaty came into force in 1992 and represented the first great step toward the creation of an economic monetary union (EMU) in Europe. The countries that signed the Treaty had to accomplish some criteria in order to be accepted as members of the EMU: The well-known Maastricht criteria. These were established in order to achieve nominal convergence and to reduce the economic disparities that exist between the European Union (EU) countries before the creation of the EMU in 1999. Having committed themselves to these criteria, the countries involved surrendered a degree of their national sovereignty to the monetary authorities of the EMU, and lost a great deal of flexibility at economic policy level. By 1999, they had further lost all control over the exchange rate and monetary policies with their adhesion to the EMU. The European Central Bank (ECB) is now having a total control of these policies. The only policy remaining in the hands of members is fiscal policy, but even this is limited by the requirements of the Stability and Growth Pact, which aims to guarantee the equilibrium of public finances, avoid inflationary pressures, and promote economic growth in the euro area. Despite its relative success in controlling public deficits and

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achieving price stability in the Euro land, there is no evidence of great concern shown for economic growth. Statistical evidence clearly shows a declining economic activity and increasing unemployment. Economic growth in Europe has, in fact, been tendentiously moderate since 1980. Table A.1 (see Appendix) shows that during the period 1980 to 2001 the average growth in the actual EU member countries was about 2.6%. It is convenient to divide the analysis of this period into two sub-periods, before and after the Maastricht Treaty. The volatility of growth was more apparent before the imposition of the Maastricht criteria in 1992 than afterwards for all countries. In the early 80s economic growth was relatively slow but it increased significantly in most countries until 1990. Since then (and mainly after the Maastricht Treaty) growth of real GDP has been modest but the trends between countries were more similar (see graph A.1 in the Appendix). This close pattern in the growth trend can be taken as evidence that increased integration produced a higher degree of harmonization in growth performance. However, growth was slower than before. This was more apparent in 1993 (due to the EMS financial crisis) and again in 2001 (due to world recession), when all member countries suffered a drastic slowdown on growth. After 1993 the European economies recovered from the crisis, but the majority of EU countries never reached the levels of economic performance of the late 80s.3 In 2001 another decline in economic activity occurred. This made some economists to believe that Europe was entering into recession once again. However, unlike the crises of 1993, the EU countries have to accomplish strictly the rules established by the Stability and Growth Pact. They can use neither exchange rate policy nor an independent monetary policy, and the use of fiscal policy is severely restricted. Therefore, a question arises: how will member states deal with crises when they face such economic and policy constrains? Certainly, some institutional reforms allowing a greater flexibility in the Stability Pact and structural changes to improve competitiveness will be necessary to solve such a problem.4 The historical data on unemployment also shows that unemployment rates increased moderately after the imposition of the Maastricht criteria in a majority of EU member states (see table A.2 and graph A.2 in the Appendix). Beside the different rates between countries, there is a tendency for unemployment to persist after economic recessions.5 In the early 80s the rate of unemployment in Europe was relatively low, but it increased in the middle of the decade, and declined again by the end of the decade, following the cycle of economic growth. However, after the imposition of the Maastricht criteria in 1992, unemployment increased again and reached even higher levels than those attained in the 80s.6 The 1992-1993 financial crisis and economic recession affected the unemployment records significantly, and these were extended to the following years. In the late 90s unemployment went down again in all EU countries, although the levels remained higher than those recorded at the beginning of 80s and 90s, in most member states. Unemployment increased again with the economic recession that began in 2001. As with growth performance of real GDP, the unemployment rates also seem to be affected by the constrains of the Maastricht criteria and the Stability and Growth Pact obligations. 3

The main exception was Ireland, whose growth of real GDP reached its higher ever levels. The solutions can also be found in the OCA`s norms as explained in section 2. 5 This phenomenon is known in the literature as hysteresis of the unemployment rate. 6 Average unemployment before the Maastricht Treaty period was 7.3% and after the Maastricht period it increased to 8.3%. 4


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One of the Maastricht criteria stipulated a reduction in government deficits to below 3% of GDP by 1999. The Stability Pact reinforced this target for the years following 1999 in order to avoid the deregulation of state public accounts and to create higher budget flexibility. This is especially necessary, when countries suffer asymmetric shocks and fall into recession. It is clear from table A.3 and graph A.3 (see Appendix) that the EU members made a great effort to meet this criterion. Before the Maastricht Treaty came into force a majority of the EU countries had unbalanced public accounts, and some of them recorded large public deficits. After the imposition of the Treaty, government deficits were reduced and all countries reached their targets of having a deficit less than 3% of GDP by 1999. Despite this relative success, the recession that began in 2001 seemed to create certain difficulties in reaching zero public deficit in some countries, such as France, Germany and Portugal. In order to avoid a deepening crisis it is necessary to use a more active (growth inducing) fiscal policy which is, however, inconsistent with the Stability Pact obligations. As some countries still need to balance their public accounts they may have some additional problems in improving their economic performance in the future without a more flexible fiscal policy favouring aggregate demand through public investment. With the high levels of public deficits registered in the 80s, national debt continued growing until early 90s. Therefore, since 1992 another concern of the EU members has been the reduction of their national debts to below 60% of GDP. Almost all countries had reached this target by the end of 1999, and those which failed (Belgium, Greece, and Italy) at least showed a substantial declining tendency in their public debts (see table A.4 and graph A.4 in the Appendix). In the 80s inflation rates were relatively high in almost all of the countries analysed here. Therefore, the monetary authorities of the EU had the concern of including another criterion into the Maastricht Treaty to guarantee price stability through a higher coordination of monetary policies among the members. The reduction and convergence of inflation rates between EU members became another necessary target as exchange rate policy gradually disappeared. Table A.5 and graph A.5 (see Appendix) clearly show the reduction of inflation in the 90s and a strong commitment with this criterion by all countries since 1992. In fact, price stability continues to be one of the main preoccupations and is indeed one of the main achievements of the European Central Bank. During the process of nominal convergence towards a European Monetary Union it was also necessary to ensure the convergence of interest rates between member states. In the 80s there were wide differences between countries. Some had interest rates of nearly 20% while others presented rates of about 10% (see table A.6 and graphA.6 in the Appendix). Indeed, there was a large discrepancy in the interest rates between these countries due to widely differing inflation rates. After 1992 a convergence of the interest rates was achieved in the EU due to a more coordinated monetary policy and a higher exchange rate stability. The target of interest rate convergence was finally achieved in 1999, and since then it has been the ECB that is responsible for fixing interest rates in the euro zone seeking for price stabilisation in the whole area. The process towards EMU also required a reduction in the variation of the exchange rates before the creation of the fixed exchange rate system and the introduction of the single currency. Therefore, it was necessary to create a mechanism to ensure such stabilization by controlling the fluctuations in parities within certain established bands. This mechanism – based on the European Monetary System (EMS) and the exchange rate mechanism – had

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already been created before the implementation of the Maastricht Treaty. The EMS was founded in 1979. This system established margins of currency fluctuations relatively to an assigned value. As can be seen from graph A.7, there was a higher degree of fluctuation in the exchange rates within the members of the EMS in the 80s. This difference was even greater between member and non-member states. After a period of relative stability registered in early 90s, speculative attacks forced the considerable devaluation of some currencies in 1993. This forced the EMS authorities to widen the fluctuation bands. This was only a temporary measure to avoid speculative attacks, once the main objective was to achieve exchange rate stability. The countries taking part in the EMU actually won this battle. Table A.7 and graph A.7 (see Appendix) clearly show the reduction in the variation of exchange rates among these countries in the late 90s. The exchange rates were finally and irrevocably fixed in 1999, and a new European currency was introduced: the Euro. The EMU members consequently lost the use of their exchange rate policy as a mean of improving external competitiveness, so they now have to use other structural policies to bring their Balance of Payments into equilibrium.

4. Measuring the Direct Effects of the Maastricht Criteria on Growth In the previous section, by just observing the data, we were able to detect some unfavourable tendencies on growth and unemployment in the period following the imposition of the Maastricht criteria. In this section, an attempt will be made to measure the direct effects of the Maastricht criteria on growth using a more formal way based on a panel data analysis. Our intention is not to estimate a complete growth equation which is mostly supply-driven in the long-run, but rather to test the influence of policy measures on growth7. Therefore, the equation we estimate relates directly the growth of real GDP on the variables which determine the Maastrich criteria, namely, the ratios of the national debt (DBT) and budget deficit/surplus (DEF) (as a percentage of GDP), the inflation (INF) and long-term interest rates (IR), and finally, the annual variation of real exchange rate (EXC). The sample of the panel series considers the fifteen EU countries, and covers the period 1980-2001. In order to capture the specific effects of the Maastricht criteria we use a dummy variable which takes the value of one from 1992 to 2001 and zero elsewhere. The growth equation related to the Maastricht criteria can be specified in the following simple way: RYit = a0 + a1DBTit + a2DEFit + a3INFit + a4IRit + a5EXCit + uit

(1)

with i = 1,……,15 and t = 1,……,22. Table 1, presents analytically the estimated results of the Growth/Maastricht equation. Starting from an Autoregressive Distributed Lag specification and following a down testing procedure, we obtained a model similar to the partial adjustment specification where the increment8 of the debt ratio (ΔDBT), rather than the ratio itself explains better the growth of real output. 7

Also, we do not consider any possible feedback effects through policy restrictions, e.g. the budget deficit and the public debt can be affected by the growth rate. This reverse causality is out of the scope of this work. 8 This hypothesis was tested by a Wald test and was accepted at 5% significance level.


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Three different methods were used to estimate the partial adjustment growth equation related to the Maastricht criteria: the classical OLS method using pooling data; the Least Squares Dummy Variable method allowing for different intercepts in each country (Fixed Effects); and the GLS method allowing the heterogeneity between countries to be captured in the error term (Random Effects).9 The estimated results are quite satisfactory in terms of the expected effects of the explanatory variables and the significance level of the estimated coefficients. The effect of the increment of public debt ratio is negative on the growth of real output and highly significant. It is interesting to note that the magnitude of the negative effect of the debt ratio is higher than the negative effects of the other Maastricht criteria. An increase of one percentage point in the increment of the national debt ratio reduces output growth by more than 0.2 percentage points. So the impact of the Maastricht restrictions on the debt ratio has a positive effect on growth. This shows that the Stability Pact should give more attention to the public debt criterion which projects the financial situation and indebtedness position of each country in relation to the exterior. Competitiveness problems of the national economies and external trade performance are also reflected in the national debt position.10 The effect of the budget (deficit/surplus) ratio lagged for one period is also negative on the growth of real output, although its magnitude is less than the effect of the debt ratio. A reduction of one percentage point in the budget deficit ratio provokes a reduction in the output growth of approximately 0.1 percentage points. Or alternatively, an increase of one percentage point in the budget surplus provokes a reduction in the output growth of approximately 0.1 percentage points. This can be taken as evidence that the efforts made by all countries to reduce their budget deficits have not been favourable to the growth of real output. The effect of inflation is also negative on the growth of real output as expected but the impact of exchange rate variation (lagged for one period) is positive. This can be taken as evidence that exchange rate flexibility has benefited growth: the higher the variation in the exchange rate, the higher is the growth of real output. However, the degree of influence is small, showing an elasticity of less than 0.05 percentage points. On the other hand, interest rates have not shown any significant effect on growth in all three methods of estimation. This means that monetary policy in the EU has been efficient in bringing price stabilisation but inefficient in generating growth. Finally, the evidence regarding the unfavourable impact of the Maastricht criteria on growth is supported by the negative sign of the dummy variable for the post-Maastricht period and by its statistical significance in the LSDV and GLS methods of estimation. The imposition of the Maastricht criteria has been responsible for a fall of about 0.5 percentage points in the rate of growth of real output. The speed of adjustment of the actual output growth to the desired level is quite high showing a relative high degree of harmonisation in the growth rates of real GDP among the EU countries.

9

The econometric Package RATS, version 5(2000) was used to estimate the equations. This idea is in accord with the work of Casella (1999), who argues that the Stability Pact may also lead to a cumulative downward trend in the ratio of the public debt to GDP. She also concludes that the Stability Pact must be designed to penalize countries with higher debt to GDP ratios.

10

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Vítor Castro and Elias Soukiazis Table 1. Estimated direct effects of the Maastricht criteria on growth, 1980-2001

RYit = a0 + a1ΔDBTit + a2DEFit-1 + a3INFit + a4IRit + a5EXCit-1 + a6 RYit-1 + a7D1992-2001 + uit Variables D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15

Constant ΔDBTit DEFit-1 INFit IRit EXCit-1 RYit-1 D1992-2001 Adjustment speed R2 F-statistic D.F. D-W Chi-square(1)

OLS (Pooling)

1.778 (3.95) -0.219 (-8.11) -0.071 (-2.66) -0.118 (-3.13) 0.049 (1.00)* 0.055 (2.56) 0.418 (8.55) -0.423 (-1.79)* 0.582 0.481 40.60 307 1.85 3.31 (0.0691)

LSDV (Fixed effects) 2.116 (3.94) 1.297 (2.21) 2.037 (3.31) 3.113 (4.96) 2.241 (3.92) 2.032 (3.90) 2.668 (3.41) 3.715 (5.54) 1.685 (2.52) 4.617 (7.57) 1.931 (3.55) 3.044 (4.15) 2.871 (4.45) 2.019 (3.36) 2.108 (3.63) ––– -0.249 (-9.35) -0.150 (-4.39) -0.147 (-3.81) 0.019 (0.37)* 0.037 (1.77)* 0.276 (5.38) -0.651 (-2.64) 0.724 0.568 18.37 293 1.91 3.83 (0.0501)

GLS (Random effects)

2.185 (4.47) -0.237 (-8.91) -0.109 (-3.61) -0.136 (-3.60) 0.037 (0.75)* 0.045 (2.16) 0.332 (6.65) -0.541 (-2.28) 0.668 0.454 36.52 307 1.84 4.76 (0.0290)

Notes: D1,……,D15 are individual dummies for each of the 15 countries. D1992-2001 is a dummy variable to capture the effects of the Maastricht criteria on growth. * indicates that the estimated coefficient is not statistically significant at 5% significance level. Numbers in brackets are t-ratios. Chi-square is derived from an alternative LM test for autocorrelation in the case of a lagged dependent variable in the estimation (the number in bracket represents the significance level). Data Source: European Commission, Statistical Annex of European Economy, Autumn 2002.

In order to better understand the growth performance of the European Union countries, the same Growth/Maastricht equation was estimated for two different periods: the preMaastricht period of 1980-1991 and the post-Maastricht period of 1992-2001, and the results are reported in Table 2.


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Table 2. Estimated direct effects of the Maastricht criteria on growth, before and after the implementation of the Maastricht Treaty RYit = a0 + a1ΔDBTit + a2DEFit-1 + a3INFit + a4EXCit-1 + a5IRit + a6RYit-1 + uit Variables D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15 Constant ΔDBTit DEFit-1 INFit EXCit-1 IRit RYit-1 Adj. speed R2 F-statistic D.F. D-W x2 (1) Sign. level

OLS (Pooling) 1980-1991 1992-2001

1.168 1.000 (1.77)* (1.89)* -0.226 -0.234 (-5.10) (-6.28) -0.089 -0.023 (-2.66) (-0.38)* -0.134 -0.123 (-2.58) (-1.08)* 0.016 0.105 (0.55)* (3.17) 0.117 0.102 (1.57)* (0.98)* 0.391 0.479 (5.28) (6.06) 0.609 0.521 0.415 0.550 18.67 26.12 158 128 1.83 1.85 4.38 1.37 (0.0363) (0.2416)

LSDV (Fixed effects) 1980-1991 1992-2001 1.18(1.40)* 2.13(3.27) -0.23(-0.21) 1.34(2.01) 0.67(0.61)* 2.44(3.34) 2.06(1.86)* 3.69(5.02) 1.22(1.24)* 2.27(3.50) 1.15(1.41)* 1.97(3.09) 2.21(1.61)* 3.82(3.83) 1.02(0.87)* 6.36(6.51) 0.38(0.29)* 1.57(2.26) 4.08(4.34) 5.72(6.77) 0.49(0.54)* 2.41(3.54) 2.89(1.97)* 3.02(4.17) 1.92(1.65)* 3.16(4.38) 1.26(1.23)* 1.98(2.78) 0.64(0.65)* 3.04(4.39) _____ _____ -0.257 -0.249 (-5.68) (-7.01) -0.214 -0.184 (-3.59) (-2.85) -0.251 -0.219 (-4.09) (-1.89)* -0.018 0.062 (-0.58)* (2.10) 0.170 -0.081 (1.92)* (-0.81)* 0.239 0.175 (2.96) (2.22) 0.761 0.825 0.531 0.719 8.14 14.6 144 114 1.95 2.09 5.24 8.95 (0.022) (0.0028)

GLS (Random effects) 1980-1991 1992-2001

1.197 2.119 (1.60)* (3.68) -0.245 -0.253 (-5.56) (-6.92) -0.122 -0.109 (-2.96) (-1.70)* -0.178 -0.147 (-3.23) (-1.26)* 0.003 0.086 (0.10)* (2.78) 0.152 0.009 (1.90)* (0.09)* 0.314 0.317 (4.16) (3.98) 0.686 0.683 0.406 0.461 18.0 18.3 158 128 1.84 1.83 5.54 2.80 (0.0185) (0.0941)

Notes: D1,……,D15 are individual dummies for each of the 15 countries. * indicates that the estimated coefficient is not statistically significant at 5% significance level. Numbers in brackets are t-ratios. Chi-square is derived from an alternative LM test for autocorrelation in the case of a lagged dependent variable in the estimation. Data Source: European Commission, Statistical Annex of European Economy, Autumn 2002.

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The first point to note is that the increment of the debt ratio affects both periods equally, thus confirming the 0.2 percentage points negative impact on growth which has been found for the whole period (see Table 1). However, the influence of the budget (deficit/surplus) ratio on growth is different in the two periods, affecting relatively more the pre-Maastricht period than the post-Maastricht period, both in terms of the negative value of the estimated coefficient and its significance level. The slowdown of real output is slightly higher in the pre-Maastricht period than in the post-Maastricht period as a result of fiscal policy contraction.11 After all, the evidence is still showing that the higher fiscal discipline after the Maastricht Treaty did not benefit the growth of real output greatly: its impact continues to be negative. The picture in relation to the inflation rate is analogous. The negative effect of inflation on growth is slightly higher in the pre-Maastricht period and in the post-Maastricht period has no statistical significance. The fall in output growth due to exchange rate variability is higher in the post-Maastricht period where higher exchange rate stability was achieved. This is evidence that the loss of the exchange rate policy has been not favourable to economic growth in Europe. The same effect regarding the exchange rate variation in growth is not significant in the pre-Maastricht period. Similarly to the whole period, interest rate has not any significant impact on growth for either of the distinct periods. From this separate analysis it can be argued that the efforts made to meet the budget deficit criteria and achieve exchange rate stability in the post-Maastricht period were not as favourable to the growth performance of the EU countries as expected.

5. The Effects of the Maastricht Criteria on Unemployment In this section a similar process is used to find how the Maastricht criteria affected unemployment performance by relating unemployment rates directly to growth of real output. The idea is to test the hypothesis that unemployment follows the cyclical tendencies of economic performance in Europe. If the Maastricht criteria were unfavourable to economic growth, and unemployment followed the economic cycle, then unemployment itself would be influenced by the restrictions imposed on economic policy due to Maastricht obligations. The estimated equation relates the rate of unemployment (UR) to the growth of real output (RY) and to the rate of unemployment lagged for one period to account for the stock of the unemployed forces. The dummy variable D1992-2001 has also been introduced to capture the effects of the period related to the imposition of the Maastricht criteria. A panel analysis is subsequently used considering the 15 EU countries for the period 1980-2001. The estimated equation then takes the following form: URit = b0 + b1RYit + b2UR it-1 + b3D1992-2001 + vit

(2)

with i = 1,……, 15 and t = 1,……,21. Estimating equation (2), by using the common methods of Panel analysis and by correcting for serial correlation, we obtained the results presented on Table 3.

11

This result is in accord with the conclusions of Perotti (2002). Using a VAR approach for 5 OECD countries, he concludes that the effects of the fiscal policy on GDP have become substantially weaker over time.


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Table 3. Estimated effects of the Maastricht criteria on unemployment through Output growth, 1980-2001 URit = b0 + b1RYit + b2URit-1 + b3D1992-2001 + vit Variables D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15

Constant RYit URit-1 D1992-2001 Adjustment speed R2 SEE D.F. D-W RHO

OLS (Pooling)

0.933 (4.39) -0.198 (-9.03) 0.927 (43.3) 0.143 (0.15)* 0.073 0.968 0.6974 295 2.01 0.558 (9.20)

LSDV (Fixed effects) 0.951 (2.59) 1.252 (2.82) 0.890 (2.20) 1.742 (3.94) 1.629 (3.43) 1.403 (3.40) 1.670 (3.85) 2.178 (4.13) 1.625 (3.52) 1.287 (3.44) 0.836 (2.09) 1.196 (3.00) 2.348 (3.93) 1.082 (2.81) 1.231 (2.80) ––– -0.201 (-8.74) 0.870 (27.1) 0.152 (1.05)* 0.130 0.970 0.6968 281 2.028 0.541 (6.44)

GLS (Random effects)

1.136 (4.24) -0.201 (-9.03) 0.905 (34.9) 0.147 (1.01)* 0.095 0.949 0.6907 295 2.02 0.553 (7.97)

Notes: D1,……,D15 are individual dummies for each of the 15 countries. D1992-2001 is a dummy variable to capture the effects of the Maastricht criteria on growth. * indicates that the estimated coefficient is not statistically significant at 5% significance level. Numbers in brackets are t-ratios. SEE is the standard error of estimation. RHO is the serial correlation coefficient obtained after correcting the serial correlation by using the HildrethLu method. Data Source: European Commission, Statistical Annex of European Economy, Autumn 2002.

The results are robust in terms of the degree of explanation, the statistical significance of the estimated coefficients (except the dummy variable) and the expected effects. There is strong evidence that unemployment follows the cycle of economic performance but the effect of growth of real output on unemployment is rather modest. An increase of one percentage point in the growth of real output implies only 0.2 percentage points fall in the unemployment rate. The dummy variable introduced to capture the post-Maastricht period effects, has its expected positive influence on the unemployment rate but without any statistical significance.

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On the other hand, the speed of adjustment of the actual unemployment rate to its desired level is extremely low, showing the difficulties involved in reducing unemployment to the desired levels. This evidence gives support to the so-called hysteresis phenomenon of unemployment rate. Table 4. Estimated effects of the Maastricht criteria on unemployment through output growth, before and after the Maastricht Treaty URit = b0 + b1RYit + b2URit-1 + uit Variables D1 D2 D3 D4 D5 D6 D7 D8 D9 D10 D11 D12 D13 D14 D15 Constant t-ratio RYit t-ratio URit-1 t-ratio Adj. speed R2 SEE D.F. D-W RHO t-ratio

OLS (Pooling) 1980-1991 1992-2001

0.868 0.619 (4.03) (2.80) -0.191 -0.127 (-6.61) (-4.35) 0.956 0.905 (40.89) (39.7) 0.044 0.095 0.972 0.980 0.6659 0.5398 146 116 2.11 2.12 0.426 0.465 (5.61) (6.60)

LSDV (Fixed effects) 1980-1991 1992-2001 1.44(4.03) 0.85(2.08) 2.26(3.59) 1.42(2.71) 1.93(3.78) 0.54(1.24)* 1.77(4.34) 1.26(1.91)* 2.76(4.52) 1.68(2.82) 1.98(4.18) 1.59(3.06) 2.13(4.30) 2.34(4.13) 4.50(4.87) 0.91(1.66)* 2.58(4.36) 1.93(3.17) 1.79(4.71) 0.76(1.87)* 1.98(3.53) 0.53(1.26)* 2.08(4.04) 1.08(2.43) 4.29(4.59) 1.76(2.28) 1.11(3.21) 0.81(1.58)* 2.69(4.22) 0.81(1.73)* _____ _____ -0.199 -0.092 (-6.58) (-2.61) 0.765 0.816 (12.9) (18.2) 0.235 0.184 0.976 0.983 0.6401 0.5300 132 102 2.12 2.26 0.352 0.496 (2.98) (3.86)

GLS (Random effects) 1980-1991 1992-2001

1.254 0.639 (4.11) (2.61) -0.197 -0.126 (-6.79) (-4.17) 0.923 0.895 (28.4) (35.3) 0.077 0.105 0.940 0.975 0.6526 0.5367 146 116 2.12 2.13 0.412 0.469 (4.93) (6.32)

Notes: D1,……,D15 are individual dummies for each of the 15 countries. (*) indicates that the estimated coefficient is not statistically significant at 5% significance level. Numbers in brackets are t-ratios. SEE is the standard error of estimation. RHO is the serial correlation coefficient obtained after correcting the serial correlation by using the HildrethLu method. Data Source: European Commission, Statistical Annex of European Economy, Autumn 2002.


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Since the evidence of the unfavourable effects of the Mastricht criteria are not so clear from this analysis (trough the effect of the dummy variable), we proceeded with two different estimations, distinguishing the pre-Maastricht period from the post-Mastricht period. Table 4 shows the estimated results of these two different periods and provides some interesting insights. Again the results are robust according to the usual criteria. The most important point to note from these separate estimations is that the fall in unemployment due to growth performance is slightly smaller in the post-Maastricht period than in the pre-Maastricht period and this evidence is apparent in all three methods of estimation. With this result it can be argued that the Maastricht restrictions were unfavourable to employment creating some additional difficulties in reducing further the high levels of unemployment in Europe. In other words, growth performance in the post-Maastricht period was not sufficient enough to reduce unemployment to the desirable levels.

6. An Alternative Growth-Induced and Employment-Induced Stability Pact The Stability and Growth Pact which was introduced during the third stage of economic and monetary union, and the introduction of the Euro in January 1999, have been criticised for being responsible for lower economic activity and higher unemployment in the EU in recent years (Thirlwall, 2000, Arestis et al., 2001 among others). The Stability Pact based on nominal convergence to meet the Maastricht criteria, focuses excessively on price stabilisation within the euro-zone and pays less attention to employment and growthorientated strategies at the EU level. The elevation of monetary policy as the only policy instrument which can be exercised at the European level by the ECB to combat inflation (by raising the interest rate) will tend to generate a deflationary economic environment which will be deteriorated by the lack of an active fiscal policy and the absence of other mechanisms (such as the promotion of investment) to stimulate aggregate demand. The ECB interest rate policy can influence the pace of inflation by suppressing aggregate demand, but this, in turn, may have detrimental effects on investment and unemployment. However, the constraints imposed on budget deficits by the Stability Pact severely reduce national fiscal independence and limit the use of national fiscal policy as an instrument for achieving higher levels of economic activity. The Stability and Growth Pact establishes the dominance of the ECB monetary authorities over the fiscal authorities of national governments, without taking into account specific problems and needs of individual economies.12 In this context, the euro-zone members face the following situation in implementing macroeconomic policy adjustments: exchange rate policy can no longer be used as an adjustment mechanism for macroeconomic stabilisation; monetary policy is not independent, since it is the ECB that decides which will be the inflation rate and interest rate for all participating countries in the EU, regardless of their individual economic circumstances; fiscal policy is national but subject to budget deficit constraints imposed by the Stability Pact. 12

Alho (2001) and Casella (1999) also appoint that the Stability Pact has been mostly criticised from the point of view of imposing a strict constrain on short-run stabilization policies. In addition Brück el al. (2002) argues that fulfilling only the deficit target does not ensure output stabilization achievement.

242


Therefore, the euro-zone members have less economic flexibility in making the macroeconomic adjustments necessary to stabilise their own economies, to reduce regional disparities, and to cope with asymmetric shocks that may affect them. It is our view that, the objectives of the ECB should be reformulated in a way to encourage full employment and higher economic activity. The ECB should have the responsibility of setting interest rates in a manner which promotes growth and full employment, rather than merely fighting inflation. Higher growth and employment can be achieved through a less rigid Stability Pact, which allows a higher flexibility in the national fiscal policy necessary to cope with the specific needs of the individual economies. We also consider that public deficit must be reduced cutting current expenditures and not productive investment. In this context, the Stability Pact should distinguish investment from expenditure. The idea that investment expenditures issues debt payments to new generations is not at all a valid argument since future generations will also benefit from these public investments. So, we agree that public investment should be promoted, especially in recession periods, in order to encourage economic activity. On the other hand, as our evidence shows, the Stability Pact needs to pay more attention to public debt performance, which reflects problems related to the competitiveness of the economies. With the removal of exchange rate variations as an adjustment mechanism and the constraints on fiscal policy, the development of a larger and more progressive tax system at the EU level is necessary, and the use of the tax revenue in growth generating activities. Fiscal policy should work as a stabiliser, with negative shocks leading to lower taxation and higher social security payments in the regions that are adversely affected. The tax and social security systems operating at the EU level would automatically make transfers between rich and poor regions in an attempt to reduce regional disparities. These budgetary transfers would only have to be made temporally in order to deal with negative shocks that may affect some economies. The convergence process can be reinforced by extending the European Investment Bank activities to the objectives that stimulate investment in the less prosperous regions where unemployment is higher. The EIB should also invest in health, education, urban renewal and the urban environment and not only in Trans-European Transport and Communications networks13. Thirteen of the 15 present member states do not count such EIB investment against national debt. Thereby, a social investment recovery programme in these areas need not be constrained by the Maastricht national debt limits.

7. Summary and Conclusion The main scope of this chapter is to contribute to the debate on the effectiveness of the Maastricht criteria and Stability Pact on the economic and unemployment performance in Europe. Firstly, we analyzed the statistical data related to the Maastricht criteria, the growth of real output and unemployment for the period 1980-2001. It was possible to detect a higher convergence of the nominal criteria in the post-Maastricht period, but the performance of the real criteria related to the growth of output and unemployment was not satisfactory. The 13

This case was made by Stuart Holland in his original proposals to the Commission in 1993, and underlay the proposals for the issue of Euro Bonds to finance social investment programmes. The full content of the proposals can be found in Holland, S. (2003).


243

empirical analysis based on panel data attempted to explain these tendencies by estimating the direct effects of the Maastricht criteria on growth and unemployment. Considering the whole period, the most significant negative effect on growth arises from the increment of the debt ratio. Our argument is that the Stability Pact should give more importance to this criterion, which reflects the indebtedness position of the countries and the degree of competitiveness of their economies. Moreover, the debt ratio variable affects the pre and post-Maastricht periods to a similar extent. The budget (deficit/surplus) ratio also affects the growth of real output negatively but in a somewhat modest way. Nevertheless, evidence sustains that the efforts which have been made to reduce budget deficits were not as favourable to growth of real output as expected. Inflation has an expected negative effect on growth (which was higher in the preMaastricht period) but interest rate has not any significant influence in any of the periods studied. Our argument is that EU monetary policy was successful in achieving price stability but it was not favourable to growth. Interest rate policy has shown to be efficient in bringing price stability but was rather unsuccessful in encouraging higher economic activity. On the other hand, our evidence does, however, suggest that exchange rate variability was beneficial to growth, although its effect was not substantial. The loss of the exchange rate policy as an instrument for improving external competitiveness may bring some additional difficulties regarding export performance and hence growth. The negative effects of the Maastricht criteria on growth are obvious from the dummy variable representing the post-Maastricht period. It was found that 0.5 percentage points of the fall in real output resulted from the efforts made to meet the demands of the Maastricht norms. The effects of the Maastricht criteria on unemployment through growth were clear. Our evidence suggests that unemployment follows the economic cycle of the EU economies, but the fall in unemployment due to growth of real output is not very significant. Unemployment falls by only 0.2 percentage points for every increase in real output of one percentage point. The fall in unemployment was even lower in the post-Maastricht period suggesting that the period of nominal convergence was not beneficial in reducing unemployment. The low speed of adjustment of actual unemployment to its desirable level lends support in favour of the hysteresis phenomenon of the unemployment rate. As our empirical analysis suggests, the Maastricht criteria and the Stability Pact were not as favourable as expected to growth and unemployment performance in Europe. Thus, we argue that the Stability Pact should be reformulated to allow for a more flexible fiscal policy, which would take into account the specific needs of each member state and the economic cycle in which each country stands on. The ECB monetary policy should also encourage growth and employment and not merely fight inflation. A more efficient distributed tax system at the EU level, which is able to transfer financial resources in such a way as to reduce regional asymmetries, should be created. A social system is also needed at the EU level which is capable of providing compensation to regions which have been adversely affected by asymmetric shocks. The European Investment Bank should also participate in social investment programmes, such as, health, education, urban and environment improvements. Projects financed by the EIB through enhanced issues of Euro Bonds could promote social investment-led recovery and contribute to economic and social cohesion in Europe.

244


Appendix Table A.1. Growth of Real GDP in the EU by country (1980-2001) aus 2.2 -0.1 2.1 2.9 0.4 2.4 2.1 1.6 3.4 4.2 4.7 3.3 2.3 0.4 2.6 1.6 2.0 1.6 3.9 2.7 3.5 0.7 2.3

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 c_av

bel 4.4 -0.3 0.6 0.3 2.5 1.7 1.8 2.3 4.7 3.5 3.1 1.8 1.5 -1.0 3.2 2.4 1.2 3.6 2.0 3.2 3.7 0.8 2.1

den -0.6 -2.1 2.7 1.7 3.5 3.6 4.0 0.0 1.2 0.2 1.0 1.1 0.6 0.0 5.5 2.8 2.5 3.0 2.5 2.3 3.0 1.0 1.8

fin 5.1 2.1 3.1 2.7 3.4 3.1 2.5 4.2 4.7 5.1 0.0 -6.3 -3.3 -1.1 4.0 3.8 4.0 6.3 5.3 4.1 6.1 0.7 2.7

fr 1.6 1.2 2.6 1.5 1.6 1.5 2.4 2.5 4.6 4.2 2.6 1.0 1.5 -0.9 2.1 1.7 1.1 1.9 3.4 3.2 3.8 1.8 2.1

ger 1.0 0.1 -0.9 1.8 2.8 2.0 2.3 1.5 3.7 3.6 5.7 5.0 2.2 -1.1 2.3 1.7 0.8 1.4 2.0 2.0 2.9 0.6 2.0

gr 0.7 -1.6 -1.1 -1.1 2.0 2.5 0.5 -2.3 4.3 3.8 0.7 3.1 0.7 -1.6 2.0 2.1 2.4 3.6 3.4 3.6 4.2 4.1 1.6

ire 3.1 3.3 2.3 -0.2 4.3 3.1 0.3 4.7 4.3 6.2 7.6 1.9 3.3 2.7 5.8 10.0 8.1 10.9 8.8 11.1 10.0 5.7 5.3

it 3.5 0.8 0.6 1.2 2.8 3.0 2.5 3.0 3.9 2.9 2.0 1.4 0.8 -0.9 2.2 2.9 1.1 2.0 1.8 1.6 2.9 1.8 2.0

lux 0.8 -0.6 1.1 3.0 6.2 2.9 10.0 4.0 8.5 9.8 5.3 8.6 1.8 4.2 3.8 1.3 3.7 7.7 7.5 6.0 8.9 1.0 4.8

neth 1.2 -0.5 -1.2 1.7 3.3 3.1 2.7 1.4 3.0 5.0 4.1 2.5 1.7 0.9 2.6 3.0 3.0 3.8 4.3 4.0 3.3 1.3 2.5

por 4.6 1.6 2.1 -0.2 -1.9 2.8 4.1 6.4 7.5 6.4 4.0 4.4 1.1 -2.0 1.0 4.3 3.5 3.9 4.5 3.5 3.5 1.7 3.0

sp 1.3 -0.1 1.2 1.8 1.8 2.3 3.3 5.5 5.1 4.8 3.8 2.5 0.9 -1.0 2.4 2.8 2.4 4.0 4.3 4.2 4.2 2.7 2.7

sw 1.7 -0.2 1.2 1.9 4.3 2.2 2.7 3.3 2.6 2.7 1.1 -1.1 -1.7 -1.8 4.1 3.7 1.1 2.1 3.6 4.5 3.6 1.2 1.9

uk -2.1 -1.5 2.0 3.6 2.5 3.6 3.9 4.5 5.2 2.2 0.8 -1.4 0.2 2.5 4.7 2.9 2.6 3.4 2.9 2.4 3.1 2.0 2.3

Source: European Commision (2002), Statistical Annex of European Economy.

Graph A.1: Growth of Real GDP % 12 10 8 6 4 2

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

-2

1980

0

-4 -6 -8

aus

bel

den

Year fin

fr

ger

gr

ire

it

lux

neth

por

sp

sw

uk

a_av


a_av 1.9 0.1 1.2 1.5 2.6 2.7 3.0 2.8 4.4 4.3 3.1 1.9 0.9 0.0 3.2 3.1 2.6 3.9 4.0 3.9 4.4 1.8 2.6


245

Table A.2. Unemployment Rate in the EU by country (1980-2001)

aus 1.0 1.4 2.3 2.9 2.8 3.0 3.2 3.4 3.2 2.8 3.0 3.3 3.3 3.9 3.8 3.9 4.4 4.4 4.5 3.9 3.7 3.6 3.3

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 c_av

bel 7.4 9.4 11.0 10.7 10.8 10.1 10.0 9.8 8.8 7.4 6.6 6.4 7.1 8.6 9.8 9.7 9.5 9.2 9.3 8.6 6.9 6.6 8.8

den 4.9 7.9 8.4 8.4 7.9 6.6 5.0 5.0 5.7 6.8 7.2 7.9 8.6 9.5 7.7 6.7 6.3 5.2 4.9 4.8 4.4 4.3 6.6

fin 4.7 4.9 5.4 5.5 5.2 4.9 5.2 4.8 4.2 3.1 3.2 6.6 11.7 16.4 16.6 15.4 14.6 12.7 11.4 10.2 9.8 9.1 8.4

fr 6.2 7.3 7.9 7.9 9.4 9.8 9.9 10.1 9.6 9.0 8.6 9.1 10.0 11.3 11.8 11.3 11.9 11.8 11.4 10.7 9.3 8.5 9.7

ger 2.7 3.9 5.6 6.9 7.1 7.2 6.6 6.4 6.3 5.6 4.8 4.2 6.4 7.7 8.2 8.0 8.7 9.7 9.1 8.4 7.8 7.7 6.8

gr 2.7 4.0 5.8 7.1 7.2 7.0 6.6 6.7 6.8 6.7 6.4 7.1 7.9 8.6 8.9 9.2 9.6 9.8 10.9 11.9 11.1 10.5 7.8

ire 8.0 10.8 12.5 13.9 15.5 16.8 16.8 16.6 16.2 14.7 13.4 14.7 15.4 15.6 14.3 12.3 11.7 9.9 7.5 5.6 4.2 3.8 12.3

it 7.1 7.4 8.0 7.4 7.9 8.2 8.9 9.6 9.7 9.7 8.9 8.5 8.7 10.1 11.0 11.5 11.5 11.6 11.7 11.3 10.4 9.4 9.5

lux 2.4 2.4 2.4 3.5 3.1 2.9 2.6 2.5 2.0 1.8 1.7 1.7 2.1 2.6 3.2 2.9 3.0 2.7 2.7 2.4 2.3 2.0 2.5

neth 6.2 8.6 11.5 9.2 8.9 7.9 7.8 7.6 7.2 6.6 5.8 5.5 5.3 6.2 6.8 6.6 6.0 4.9 3.8 3.2 2.8 2.4 6.4

por 7.6 7.3 7.2 8.2 8.9 9.1 8.8 7.2 5.8 5.2 4.8 4.2 4.3 5.6 6.9 7.3 7.3 6.8 5.1 4.5 4.1 4.1 6.4

sp 8.5 11.2 13.0 14.1 16.5 17.7 17.3 16.7 15.9 13.9 13.1 13.2 14.9 18.6 19.8 18.8 18.1 17.0 15.2 12.8 11.3 10.6 14.9

sw 2.0 2.5 3.2 3.7 3.3 2.9 2.7 2.2 1.8 1.6 1.7 3.1 5.6 9.1 9.4 8.8 9.6 9.9 8.3 7.1 5.8 4.9 5.0

uk 5.6 8.8 10.1 10.8 10.9 11.2 11.2 10.3 8.5 7.1 6.9 8.6 9.8 10.2 9.4 8.5 8.0 6.9 6.2 5.8 5.4 5.0 8.4


Graph A.2: Unemployment Rate % 20 18 16 14 12 10 8 6 4 2

2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

1991

1990

1989

1988

1987

1986

1985

1984

1983

1982

1981

1980

0

aus

bel

den

finYear

fr

ger

gr

ire

it

lux

neth

por

sp

sw

uk

a_av


a_av 5.1 6.5 7.6 8.0 8.4 8.4 8.2 7.9 7.4 6.8 6.4 6.9 8.1 9.6 9.8 9.4 9.3 8.8 8.1 7.4 6.6 6.2 7.8

246

Vítor Castro and Elias Soukiazis Table A.3. Government Deficit/Surplus in the EU by country (1980-2001)

aus bel den fin fr ger gr ire it lux neth por 1980 -1.7 -9.5 -2.4 3.5 0.0 -2.9 -2.6 -11.6 -7.1 -0.4 -4.1 -8.4 1981 -1.8 -15.7 -5.9 4.8 -2.2 -3.7 -9.0 -12.2 -11.1 -3.1 -5.2 -12.4 1982 -3.4 -12.5 -8.4 3.0 -2.9 -3.3 -6.8 -12.6 -10.2 -1.0 -6.4 -8.3 1983 -4.3 -14.8 -6.4 1.4 -2.8 -2.6 -7.5 -10.7 -10.3 2.0 -5.6 -6.7 1984 -2.7 -10.9 -3.7 3.2 -2.8 -1.9 -8.3 -8.9 -11.7 3.2 -5.3 -10.2 1985 -2.8 -10.2 -1.4 3.3 -3.0 -1.2 -11.6 -10.2 -12.7 6.3 -3.5 -10.1 1986 -4.0 -10.1 3.3 3.8 -3.2 -1.3 -9.4 -10.1 -12.2 4.4 -4.9 -5.7 1987 -4.5 -7.9 2.5 1.4 -2.0 -1.9 -9.1 -8.1 -11.8 2.8 -5.3 -5.3 1988 -3.5 -7.3 1.5 5.1 -2.5 -2.2 -11.4 -4.2 -11.3 2.6 -4.2 -3.4 1989 -3.1 -7.7 0.3 6.7 -1.8 0.1 -14.2 -1.7 -11.7 2.8 -5.0 -2.3 1990 -2.4 -6.8 -1.0 5.3 -2.1 -2.1 -15.9 -2.8 -11.8 5.1 -5.3 -4.9 1991 -3.0 -7.5 -2.4 -1.1 -2.4 -2.9 -11.4 -2.9 -11.7 1.4 -2.7 -5.8 1992 -2.0 -8.1 -2.2 -5.6 -4.2 -2.6 -12.6 -3.0 -10.7 0.1 -4.2 -2.9 1993 -4.2 -7.3 -2.9 -7.3 -6.0 -3.1 -13.6 -2.7 -10.3 1.9 -2.8 -5.9 1994 -5.0 -5.0 -2.4 -5.7 -5.5 -2.4 -9.9 -2.0 -9.3 2.7 -3.5 -5.9 1995 -5.3 -4.3 -2.3 -3.7 -5.5 -3.5 -10.2 -2.2 -7.6 2.6 -4.2 -4.5 1996 -4.0 -3.8 -1.0 -3.2 -4.1 -3.4 -7.4 -0.2 -7.1 2.0 -1.8 -4.0 1997 -2.0 -2.0 0.4 -1.5 -3.0 -2.7 -4.0 1.2 -2.7 2.8 -1.1 -3.0 1998 -2.4 -0.7 1.1 1.3 -2.7 -2.2 -2.5 2.4 -2.8 3.1 -0.8 -2.6 1999 -2.3 -0.5 3.1 1.9 -1.6 -1.5 -1.9 2.1 -1.8 3.6 0.7 -2.4 2000 -1.5 0.1 2.5 7.0 -1.3 1.1 -1.8 4.4 -0.5 5.6 2.2 -2.9 2001 0.2 0.4 3.1 4.9 -1.4 -2.8 -1.2 1.6 -2.2 6.1 0.1 -4.2 c_av -3.0 -6.9 -1.1 1.3 -2.9 -2.2 -8.3 -4.3 -8.6 2.6 -3.3 -5.5 Source: European Commision (2002), Statistical Annex of European Economy.

sp -2.5 -3.7 -5.4 -4.6 -5.2 -6.2 -5.5 -3.7 -3.3 -3.5 -4.2 -4.3 -4.0 -6.7 -6.1 -6.6 -4.9 -3.2 -2.7 -1.1 -0.6 -0.1 -4.0

sw -3.9 -5.1 -6.8 -4.9 -2.9 -3.7 -1.2 4.1 3.4 5.2 4.0 -1.1 -7.5 -11.9 -10.8 -7.7 -3.1 -1.6 1.9 1.5 3.7 4.8 -2.0

uk -3.2 -4.4 -2.7 -3.4 -3.6 -2.9 -2.6 -1.8 0.5 0.8 -1.6 -3.1 -6.4 -7.9 -6.7 -5.8 -4.4 -2.2 0.2 1.1 4.0 0.7 -2.5

Graph A.3: Government Deficit/Surplus %

8 6 4 2 2001

2000

1999

1998

1997

1996

1995

1994

1993

1992

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-2

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-4 -6 -8 -10 -12 -14 -16 Year

aus

bel

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it

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a_av


a_av -3.8 -6.0 -5.8 -5.4 -4.8 -4.7 -3.9 -3.4 -2.7 -2.3 -3.1 -4.1 -5.1 -6.0 -5.2 -4.7 -3.4 -1.6 -0.6 0.1 1.5 0.7 -3.4


247

Table A.4. Government Debt in the EU by country (1980-2001) aus bel den fin fr ger gr ire it lux neth por 1980 36.2 78.6 36.5 11.7 19.8 31.7 25.0 75.2 58.2 9.3 46.0 32.3 1981 37.9 91.9 48.2 11.8 21.9 35.4 29.7 81.2 60.2 9.7 49.9 41.1 1982 40.3 102.3 60.2 14.1 25.5 38.7 33.5 91.3 65.1 9.7 55.3 44.0 1983 44.7 113.2 69.2 15.7 26.9 40.2 38.5 102.0 70.0 10.2 61.4 49.1 1984 47.3 117.3 72.9 15.5 29.1 41.0 45.9 106.5 75.2 10.2 65.5 54.2 1985 49.2 122.3 70.0 16.2 30.8 41.7 53.6 109.6 81.9 9.7 70.1 61.5 1986 53.7 127.6 62.1 16.9 31.3 41.6 55.7 121.8 86.2 9.4 72.0 60.3 1987 57.6 132.4 58.0 18.0 33.4 42.6 62.6 123.1 90.4 8.2 74.6 58.1 1988 59.0 132.4 60.2 16.9 33.4 43.1 68.4 118.4 92.6 6.5 77.4 57.6 1989 58.0 129.1 58.0 14.6 34.1 41.8 72.0 108.2 95.4 5.4 77.3 56.4 1990 57.2 129.2 57.8 14.3 35.1 43.5 79.6 101.5 97.2 4.4 77.0 58.3 1991 57.5 130.9 62.5 22.6 35.8 40.4 82.3 102.9 100.6 3.8 76.9 60.7 1992 57.2 132.5 66.3 40.6 39.6 42.9 87.9 100.2 107.7 4.7 77.8 54.4 1993 61.8 138.2 78.0 56.0 45.3 46.9 110.2 96.3 118.1 5.7 79.0 59.1 1994 64.7 135.9 73.5 58.0 48.4 49.3 107.9 90.5 123.8 5.4 76.3 62.1 1995 69.2 134.0 69.3 57.2 54.6 57.0 108.7 82.7 123.2 5.6 77.2 64.3 1996 69.1 130.2 65.1 57.1 57.1 59.8 111.3 74.1 122.1 6.2 75.2 62.9 1997 64.7 124.8 61.2 54.1 59.3 61.0 108.2 65.0 120.2 6.1 69.9 59.1 1998 63.7 119.6 56.2 48.8 59.5 60.9 105.8 54.9 116.3 6.3 66.8 55.0 1999 64.7 114.9 52.7 46.8 58.5 61.2 105.1 49.3 114.5 6.0 63.1 54.4 2000 63.0 109.6 46.8 44.0 57.3 60.2 106.2 39.3 110.6 5.6 55.8 53.4 2001 62.7 108.6 44.7 43.4 57.3 59.5 107.0 36.7 109.9 5.6 52.8 55.5 c_av 56.3 120.7 60.4 31.6 40.6 47.3 77.5 87.8 97.2 7.0 68.1 55.2 Source: European Commision (2002), Statistical Annex of European Economy.

sp 16.8 20.6 25.6 31.0 37.1 42.3 43.7 44.0 40.3 41.7 43.6 44.3 46.8 58.4 61.1 63.9 68.1 66.6 64.6 63.1 60.5 57.1 47.3

sw 40.3 48.4 57.9 61.6 62.9 62.4 61.9 54.8 49.1 44.0 42.3 51.3 65.2 73.5 76.2 76.2 76.0 73.1 70.5 65.0 55.3 56.6 60.2

Graph A.4: Government Debt % 150

125

100

75

50

25

aus

bel

den

fin Year

fr

ger

gr

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sp

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a_av


2001

2000

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1997

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1995

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1993

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1987

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1985

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1981

1980

0

uk 53.2 53.5 52.4 52.7 54.5 52.7 51.2 48.7 42.3 36.7 34.0 34.4 39.2 45.4 48.5 51.8 52.3 50.8 47.7 45.1 42.1 39.1 46.7

a_av 38.1 42.8 47.7 52.4 55.7 58.3 59.7 60.4 59.8 58.2 58.3 60.5 64.2 71.5 72.1 73.0 72.4 69.6 66.4 64.3 60.6 59.8 60.3

248

Vítor Castro and Elias Soukiazis Table A.5. Growth of CPI (inflation rate) in the EU by country (1980-2001)

1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 c_av

aus 5.1 6.6 5.1 3.4 4.7 3.0 2.9 2.2 1.2 2.9 3.3 3.8 3.6 2.9 2.7 2.5 1.3 0.9 0.5 0.7 1.4 1.6 2.8

bel 4.1 5.1 7.6 5.6 5.4 4.6 2.8 1.7 2.2 4.8 2.8 2.9 3.4 4.0 2.1 1.3 1.2 1.3 1.7 1.4 1.3 2.0 3.2

den fin fr 8.6 9.7 11.1 11.8 11.0 11.0 11.1 9.0 11.5 8.4 8.4 9.0 6.0 8.5 7.0 4.9 5.5 5.4 4.0 4.3 5.1 5.1 4.2 2.9 2.5 8.1 3.0 5.2 6.1 3.1 3.7 5.4 2.9 2.8 1.8 3.0 2.9 0.9 2.0 1.4 2.3 2.3 1.7 2.0 1.7 1.8 4.1 1.7 2.5 -0.2 1.4 2.2 2.1 1.3 1.0 3.0 0.9 2.7 -0.2 0.5 3.7 2.6 0.7 2.8 3.0 1.4 4.4 4.6 4.0

ger 5.0 4.2 4.4 3.2 2.1 2.1 3.2 1.9 1.5 2.4 3.2 3.9 5.0 3.7 2.5 2.0 1.0 0.7 1.1 0.5 -0.3 1.4 2.5

gr 19.3 21.6 27.2 20.6 21.9 19.0 18.9 15.3 16.7 14.5 20.6 19.8 14.8 14.5 11.2 9.8 7.4 6.8 5.2 3.0 3.4 3.4 14.3

ire 14.8 17.5 15.2 10.8 6.4 5.3 5.8 2.2 3.4 5.1 -0.3 1.8 2.8 5.2 1.7 3.0 2.1 4.2 6.2 4.1 4.3 5.3 5.8

it lux neth por sp sw uk a_av 21.4 7.9 5.5 20.9 13.4 11.7 19.4 11.9 19.0 7.2 5.4 17.6 12.3 9.5 11.3 11.4 17.2 10.8 5.4 20.7 13.6 8.1 7.4 11.6 15.1 6.8 2.1 24.6 11.9 10.1 5.4 9.7 11.5 4.4 1.4 24.7 10.9 7.5 4.5 8.5 8.9 3.0 1.8 21.7 8.6 6.5 5.7 7.1 7.9 -0.1 0.1 20.5 10.9 6.5 3.3 6.4 6.2 0.1 -0.7 10.1 5.9 4.8 5.3 4.5 6.8 2.8 0.9 11.2 5.9 6.4 6.1 5.2 6.5 4.0 1.1 10.5 6.9 8.0 7.5 5.9 8.2 2.5 2.2 13.1 7.3 8.8 7.5 6.1 7.6 1.8 2.8 10.1 6.9 7.3 6.6 5.5 4.5 3.7 2.3 11.4 6.7 1.0 4.0 4.6 3.9 6.0 1.8 7.4 4.5 2.7 2.6 4.3 3.5 3.5 2.3 7.3 3.9 2.4 1.4 3.3 5.0 2.4 2.0 3.4 4.9 3.5 2.6 3.3 5.3 1.6 1.2 3.0 3.5 1.4 3.3 2.4 2.4 3.3 2.0 3.8 2.3 1.7 2.9 2.5 2.7 2.1 1.7 3.8 2.4 0.9 2.9 2.4 1.7 3.1 1.6 3.2 2.7 0.7 2.5 1.9 2.1 2.8 4.2 3.1 3.5 1.0 2.2 2.4 2.6 2.3 5.3 4.9 4.2 2.0 1.9 2.9 7.7 3.7 2.4 11.7 7.0 5.1 5.3 5.6


Graph A.5: Growth of CPI (inflation rate)

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Table A.6. Interest Rate in the EU by country (1980-2001) aus bel den fin fr ger gr ire it lux neth por 1980 9.3 12.2 18.7 11.6 13.1 8.5 17.1 15.4 16.1 7.4 10.7 16.3 1981 10.6 13.8 19.3 12.4 15.9 10.4 17.7 17.3 20.6 8.7 12.2 16.0 1982 9.9 13.5 20.5 12.4 15.7 9.0 15.4 17.0 20.9 10.4 10.5 16.8 1983 8.2 11.8 14.4 13.1 13.6 7.9 18.2 13.9 18.0 9.8 8.8 20.9 1984 8.0 12.0 14.0 14.0 12.5 7.8 18.5 14.6 15.0 10.3 8.6 22.5 1985 7.8 10.6 11.6 12.7 10.9 6.9 15.8 12.7 14.3 9.5 7.3 27.7 1986 7.3 7.9 10.6 11.7 8.4 5.9 15.8 11.1 11.7 8.7 6.4 19.5 1987 7.0 7.8 11.9 11.2 9.4 5.8 17.4 11.3 11.3 8.0 6.4 16.8 1988 6.7 7.9 10.6 10.6 9.0 6.1 16.6 9.4 12.1 7.1 6.3 15.5 1989 7.1 8.7 10.2 12.1 8.8 7.0 18.7 8.9 12.9 7.7 7.2 16.9 1990 8.7 10.1 11.0 13.2 9.9 8.9 19.9 10.1 13.4 8.6 9.0 16.8 1991 8.6 9.3 10.1 11.7 9.0 8.6 22.7 9.2 13.0 8.2 8.7 18.3 1992 8.3 8.6 10.1 12.0 8.6 8.0 23.5 9.1 13.7 7.9 8.1 15.4 1993 6.6 7.2 7.2 8.2 6.7 6.4 23.5 7.8 11.1 6.8 6.3 9.5 1994 6.7 7.8 7.9 8.4 7.3 6.9 24.6 8.1 10.4 7.2 6.9 10.4 1995 7.2 7.5 8.3 8.8 7.5 6.8 16.4 8.3 11.9 7.2 6.9 11.5 1996 6.3 6.5 7.2 7.1 6.3 6.2 13.8 7.3 9.2 6.3 6.2 8.6 1997 5.7 5.8 6.2 6.0 5.6 5.7 12.8 6.3 6.7 5.6 5.6 6.4 1998 4.7 4.7 4.9 4.8 4.6 4.6 8.5 4.8 4.8 4.7 4.6 5.0 1999 4.7 4.8 4.9 4.7 4.6 4.5 6.5 4.6 4.8 4.7 4.6 4.8 2000 5.6 5.6 5.6 5.5 5.4 5.3 6.5 5.4 5.6 5.5 5.4 5.6 2001 5.1 5.1 5.1 5.0 5.0 4.8 5.9 4.9 5.2 4.9 5.0 5.2 c_av 7.3 8.6 10.5 9.9 9.0 6.9 16.2 9.9 11.9 7.5 7.4 13.9 Source: European Commision (2002), Statistical Annex of European Economy.

sp 16.0 15.8 16.0 16.9 16.5 13.4 11.4 12.8 11.7 13.7 14.7 12.4 12.2 10.1 10.1 11.3 8.7 6.4 4.8 4.7 5.5 5.1 11.4

sw 11.7 13.5 13.0 12.3 12.3 13.0 10.3 11.7 11.4 11.2 14.2 11.8 10.0 8.6 9.5 10.2 8.1 6.7 5.0 5.0 5.4 5.1 10.0

uk 13.9 14.8 12.7 10.8 10.7 10.6 9.8 9.5 9.3 9.6 11.1 9.9 9.1 7.3 8.1 8.2 7.8 7.0 5.5 5.0 5.3 4.9 9.1

a_av 13.2 14.6 14.2 13.2 13.2 12.3 10.4 10.6 10.0 10.7 12.0 11.4 11.0 8.9 9.4 9.2 7.7 6.6 5.1 4.9 5.5 5.1 10.0

Graph A.6: Interest Rate % 30

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Vítor Castro and Elias Soukiazis Table A.7. Variation of the Exchange Rate in the EU by country (1980-2001)

aus bel den fin fr ger gr ire it lux neth por 1980 -1.9 1.1 8.6 -3.1 0.7 0.5 17.1 1.0 4.5 1.1 0.4 3.8 1981 -1.4 1.7 1.2 -7.0 2.9 -0.4 3.7 2.2 6.2 1.7 0.5 -1.5 1982 -5.7 8.3 3.0 -1.8 6.5 -5.5 6.0 -0.2 4.8 8.3 -5.8 13.9 1983 -4.4 1.6 -0.3 5.1 5.3 -4.4 19.5 3.7 2.0 1.6 -2.9 26.5 1984 -1.5 0.0 0.2 -4.5 1.5 -1.4 13.2 1.5 2.3 0.0 -0.5 17.2 1985 -0.6 -1.2 -1.6 -0.6 -1.1 -0.5 19.6 -1.5 4.8 -1.2 -0.5 12.6 1986 -4.3 -2.5 -1.0 6.1 0.1 -4.4 30.0 2.6 1.0 -2.5 -4.4 12.9 1987 -2.6 -1.8 -0.6 1.7 1.9 -2.7 13.7 5.7 2.3 -1.8 -2.8 10.5 1988 0.1 1.0 0.8 -2.4 1.6 0.1 7.2 0.0 2.8 1.0 0.0 4.6 1989 -0.1 -0.1 1.2 -4.5 -0.2 -0.2 6.7 0.2 -1.8 -0.1 0.0 2.0 1990 -0.9 -2.2 -2.4 2.8 -1.6 -0.9 12.6 -1.2 0.8 -2.2 -1.0 4.4 1991 -0.1 -0.5 0.7 3.0 0.9 -0.1 11.8 0.0 0.7 -0.5 0.0 -1.4 1992 -1.5 -1.5 -1.3 16.1 -1.8 -1.5 9.7 -0.9 4.1 -1.5 -1.6 -2.2 1993 -4.2 -2.7 -2.8 15.3 -3.1 -4.2 8.7 5.2 15.4 -2.7 -4.4 7.8 1994 -1.0 -2.0 -0.7 -7.5 -0.8 -0.6 7.2 -0.8 4.0 -2.0 -0.8 4.5 1995 -3.0 -2.8 -2.9 -7.8 -0.9 -2.6 5.2 2.8 11.2 -2.8 -2.8 -0.4 1996 2.7 1.9 0.4 2.1 -0.5 1.9 0.8 -2.7 -8.0 1.9 2.0 -0.2 1997 2.9 3.1 1.7 0.9 1.8 2.9 1.2 -5.8 -1.5 3.1 3.3 1.4 1998 0.2 0.2 0.2 1.7 -0.2 0.2 6.9 5.2 0.7 0.2 0.4 1.6 1999 -0.7 -0.7 -0.9 -0.6 -0.6 -0.7 -1.5 0.2 -0.4 -0.7 -0.7 -0.6 2000 0.0 0.0 0.2 0.0 0.0 0.0 3.3 0.0 0.0 0.0 0.0 0.0 2001 0.0 0.0 0.0 0.0 0.0 0.0 1.2 0.0 0.0 0.0 0.0 0.0 c_av -1.3 0.0 0.2 0.7 0.6 -1.1 9.3 0.8 2.5 0.0 -1.0 5.3 Source: European Commision (2002), Statistical Annex of European Economy.

sp 8.4 3.0 4.7 18.5 -0.7 2.0 6.4 3.4 -3.2 -5.2 -0.8 -0.7 3.2 12.5 6.6 2.6 -1.4 3.2 0.8 -0.5 0.0 0.0 2.9

sw 0.2 -4.2 9.0 11.0 -4.5 0.2 7.3 4.5 -0.9 -2.0 5.9 -0.5 0.7 21.1 0.5 1.8 -8.8 1.6 3.1 -1.2 -4.1 9.6 2.3

uk -7.4 -7.6 1.3 4.7 0.6 -0.3 14.0 4.9 -5.7 1.3 6.0 -1.8 5.2 5.7 -0.5 6.8 -1.8 -14.9 -2.3 -2.6 -7.5 2.0 0.0

Graph A.7: Variation of the Exchange Rate %

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a_av 2.3 0.1 3.1 5.8 1.6 2.0 4.1 2.4 0.5 -0.2 1.3 0.8 1.7 4.5 0.4 0.3 -0.6 0.3 1.3 -0.8 -0.5 0.9 1.4


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References Alho, Kari E. O. (2001), “The Stability Pact and Inefficiencies in Fiscal Policy making in EMU”, Journal of Public Finance and Management, 1, 4-33. Arestis, P., K. McCauley, and M. Sawyer (2001), “An alternative Stability Pact for the European Union”, Cambridge Journal of Economics, 25, 113-130. Artis, M. J. and M. Buti (2001), “Setting Medium-Term Fiscal Targets in the EMU”, Journal of Public Finance and Management, 1, 34-57. Baltagi, Badi H. (2002), Econometric Analysis of Panel Data, Willey. Bassanini, A. and S. Scarpetta (2001), “The Driving Forces of Economic Growth: Panel Data Evidence from OECD Countries”, OECD Economic Studies No. 33, 2001/II. Blanchard, O. and R. Perotti (2001), “An Empirical Characterization of the Dynamics Effects of Changing in Government Spending and Taxes on Output”, mimeo, Massachusetts Institute of Technology. Brück, T., A. Cors, K. F. Zimmermann, and R. Zwiener (2002), “Stability Criteria and Convergence: the Role of the System of National Accounts for Fiscal Policy in Europe”, DIW Berlim Discussion Papers, 318, German Institute for Economic Research. DeGrauwe, Paul (2000), Economics of Monetary Union Forth Edition, Oxford University Press. Frankel, J.A. and A. Rose(1998), “The Endogeneity of the Optimum Currency Area Criteria”, Economic Journal, 108, 1009-1025. Gali, J and R. Perotti (2003), “Fiscal Policy and Monetary Integration in Europe”, NBER Working-Paper 9773. Gros, D. and A. Steinherr (1997), “Openness and the cost of Fixing Exchange Rates in a Mundell-Fleming World” in Optimum Currency Areas. New Analytical and Policy Developments (Ed.), M. Blejer, J.A. Frenkel,L.Leiderman, A.Razin and D. Cheney, Washington DC: International Monetary Fund, pp.69-71. Holland, Stuart (2003), Financial Instruments and European Recovery – Current Realities and Implications for the New European Constitution, CEUNEUROP Discussion Paper No 17, Faculty of Economics, University of Coimbra. Kaldor, N. (1966), “Causes of the Slow Rate of Economic Growth in the United Kingdom”, in The Essential Kaldor (Ed. 1989), F. Targetti and A.P. Thirlwall, Duckworth, pp.282-310. Kenen, P.B. (1969), “The Theory of Optimum Currency Areas: an eclectic view”, in Monetary Problems of the International Economy (Ed.), R.A. Mundel and A. Swoboda, Chicago: University of Chicago, Press, pp.41-60. Krugman, Paul (1991), Geography and Trade, Cambridge, Mass.: MIT Press. McKinnon, R. (1963), “Optimum Currency Areas”, American Economic Review, 53, 717725. Mundell, R.A.(1961), “A Theory of Optimum Currency Areas”, American Economic Review, 51, 509-517. Myrdal, G. (1957), Economic Theory and Underdeveloped Regions, New York: Duckworth. Perotti, R (2002), “Estimating the Effects of Fiscal Policy in OECD countries”, mimeo, European University Institute. Thirlwall, A. P.(2000), “The euro and regional divergence in Europe”, New European Trust, www.ne-europe.co.uk.



Chapter 9

THE ORIENTATION OF THE FINANCIAL SYSTEM: ALTERNATIVE APPROACHES AND THE CASE OF THE EUROPEAN MONETARY UNION Bert Scholtens* Department of Finance, University of Groningen, PO Box 800, NL 9700 AV Groningen, The Netherlands

Abstract The financial system is generally held responsible for the smooth operation of the real economy. Monetary and financial authorities aim at securing this smooth operation and use the financial system as a transmission vehicle for their policies. This paper tries to find out about the orientation of the financial system within the European Monetary Union. To this extent, we construct a conglomerate index of the financial system. It is compared with two previously constructed measures of cross-country differences in financial systems. We find that there is substantial heterogeneity as to the structure of the financial system, it seems wise to take account of the fact that the – generic – ESCB policies will affect the financial institutions and markets of the participating countries differently.

Keywords: Comparative Financial Systems; Regulation; Financial Markets; Financial Institutions; Financial System Architecture; European Monetary Union. JEL: G15, G20, L84

Acknowledgments I wish to thank François Bourguignon, M. Kumar, Robert Lensink, Elmer Sterken, and Sweder van Wijnbergen for their helpful comments and suggestions on previous versions of this paper. Of course, the usual disclaimer applies.

*

E-mail address: [email protected]. Phone ++ 31 (0)50 363 7064, fax ++ 31 (0)50 363 8252.

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1. Introduction The orientation of the financial system plays a crucial role in two debates in the modern economic literature. First, it is thought to impact on the effectiveness of the transmission mechanism of monetary policy (De Bondt, 2000). Second, it is thought to affect the ways in which financial development is related to economic growth (Allen and Gale, 2000). In both types of literature, ideas have emerged about how to assess and measure the financial system of a particular country or group of countries. On the one hand, there is the market-oriented system. Here, financial markets are assumed to play a key role in the redistribution of savings within the economy and in the allocation of capital along alternative uses. Archetypes are the UK and the US. On the other hand, there is the bank-oriented system, where banks allegedly play this role; Germany and Japan are the archetypes here. However, is there really a clear-cut distinction between bank- and market-oriented financial systems? And what is the orientation of the financial system of the EMU member states? The answers to these questions are of the utmost importance for the European monetary and financial authorities (see Fligstein and Merand (2002) for an overview of the major policy issues in this respect). Not only to safeguard the key functions of the financial system within the economy, but also to employ sound monetary and financial policies (for a recent analysis, see Holman, 2004). This paper tries to analyze the de facto orientation of the financial system within the European Union. We discuss the dominant existing metrics. It appears that these are subject to important methodological flaws. To this extent, we come up with an alternative conglomerate index. Our alternative is the first metric that aims at incorporating different dimensions to assess the characteristics of the financial system per se. In this respect, it is worthwhile to reflect upon the notion of ‘financial system’. This notion relates to the position of financial institutions and markets within the economy and to their development. It is concerned with their relative importance for the economy. As such, we opt for an interpretation of the financial system that deviates from that of, for example, Levine (1999) and Allen and Gale (2000, page 3 ff.). The former explicitly includes the quality of the legal system within the description of the financial system, whereas the latter include corporate governance. In our opinion, both elements are important indeed but are not to be regarded as belonging to the financial system itself. Laws and regulations impose restrictions and directions upon the operations of the financial institutions and markets. But they are produced outside the realm of the financial sector. Governance is embedded within the socio-economic and political system. These have been thoroughly analyzed elsewhere in the literature (Rodrik, 2000). Of course, both legal requirements and governance will find their way in how institutions and markets develop (see Beck and Levine, 2003). But, in our opinion, it is somewhat too far fetched to view them as basic elements of the financial system. We will analyze the orientation of the financial system, that is the face value of the operations and activities of the financial intermediaries within the economy. The structure of the paper is as follows. Section 2 is a brief overview of the ideas about the importance of the orientation of the financial system. Section 3 introduces indices of the orientation of financial systems, as developed by Kashyap and Stein (1997) and by DemirgüçKunt and Levine (1999). A problem with both indices is that they only account for a small part of the financial system. As a result, they do not come to grips with the complexity of modern financial systems. In section 4, we develop an alternative index to classify European

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countries according to the orientation of their financial system. Japan and the US will be used as benchmarks in this respect. This section also discusses the merits of the different indices. The conclusion is in section 5.

2. Background Basically, there are four different views on the interrelation between the financial and the economic system: market-based, bank-based, financial services, and legal-based (Levine, 2000). The market-based view stresses the importance of well-functioning securities markets in providing incentives for investors to acquire information, impose corporate control, and custom design financial arrangements. According to this view, market-based financial systems are better at promoting long-run economic growth than bank-based financial systems (Gerschenkron, 1962; Allen, 1993). The bank-based view stresses the importance of financial intermediation in ameliorating information asymmetries and intertemporal transaction costs. According to this view, bank-based financial systems – especially in the countries that are at early stages of economic development – are better than market-based financial systems at promoting growth. This results from the information acquisition and procession potential of banks vis-à-vis financial markets and from their ability to establish long-term and close relationships with firms (Hellwig 1991; Goodhart, 1994). Bank-based systems also are thought to be more effective in revealing information and in providing external resources to new firms that require stage financing (Stulz, 2000). The financial services view regards banks and markets as complementary in allocating funds within the economy and in managing financial risk (Boot and Thakor, 1997; Levine, 1997). The legal-based view rejects the bank-based versus market-based debate. It argues that finance is a set of contracts. The contracts are defined and made more or less effective by legal rights and enforcement mechanisms. LaPorta et al. (1997, 1998) argue that a well-functioning legal system facilitates the operation of both markets and intermediaries. From the literature, it appears that the main characteristics distinguishing bank- and market-oriented financial systems are: -

dominance with respect to corporate finance, maturity of the loan contract, market structure and dynamics, depth and width of financial markets, cross-holdings between banks and firms, firm indebtedness, government policy and regulation.

The empirical analysis of financial systems counts a large number of country studies1 as well as many time-series and cross-sectional analyses.2 Raymond Goldsmith (1969) 1

2

Cable (1985), Edwards and Fischer (1994), Chirinko and Elston (1995), and Gorton and Schmid (1996) for Germany; Hoshi et al. (1990, 1991), Berglöf (1991), Berglöf and Perotti (1994), Horiuchi en Okazaki (1994), and Weinstein and Yafeh (1995) for Japan; Gilson and Black (1996), Jayaratne and Strahan (1996), Hawley and Williams (1996), and Harrison et al. (1999) for the US. Examples are Mayer (1988), Frankel and Montgomery (1991), Corbett and Jenkinson (1994), Perotti (1994), Steinherr and Huveneers (1994), Rajan and Zingales (1995), Sussman (1995), Demirgüç-Kunt and

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undertook the first systematic examination of financial structure and economic development for a large cross-section of countries during a considerable time period (over 100 years). Goldsmith showed that in the course of a country's economic development his measure of financial development, the financial interrelations ratio - the quotient of the aggregate market value of all financial instruments to the value of a country's tangible net national wealth - tends to increase. In the 1990s, interest in these issues revived. King and Levine (1993abc) analyzed macro-economic indicators for developed and developing countries during the 1960s-1980s. They, as well as numerous other studies,3 found a significant positive association between financial and economic development. In the late 1990s, attention shifted to the question: Does financial structure matter for economic growth? Many authors answer this question in the affirmative (for example Levine, 1999 & 2000; Beck and Levine, 2000; Rajan and Zingales, 2002). In general, the country studies as well the cross-sectional ones suggest that the differences mentioned above do not affect economic performance as expected or predicted. For example, Edwards and Fischer (1994) find no empirical evidence for the generally alleged merits of the German (bank-based) financial system. In contrast, the time-series studies that include a larger number of countries generally do corroborate the hypothesis. E.g., Steinherr and Huveneers (1994) conclude that universal banks achieve a better riskreturn trade-off than specialized banks. Rajan and Zingales (2002) explicitly go into the European financial system. However, they only look at a few very rough indicators of financial development and do not relate them to one eachother. Nevertheless, they conclude that the European financial system has become more market-based. From this overview, it appears that views about the importance of the orientation of the financial system differ but the empirical literature is inconclusive. Therefore, the remainder of this paper concentrates on how to come to grips with the orientation of the financial system.

3. Existing Indices Most studies make a priori classifications of countries as either bank- or market-oriented systems. Furthermore, differences between the two systems are hard to operationalize and findings are difficult to interpret. Here, we will look into two often-cited studies that come up with indices that are used to characterize the financial system, i.e. the potency of the lending channel by Kashyap and Stein, and the conglomerate index of financial structure by Demirgüç-Kunt and Levine.

3.1. Predicted Potency of the Lending Channel Kashyap and Stein (1997) argue that the degree of bank dependence in the economy and the extent to which central bank actions move loan supply are the key factors determining the importance of the bank lending channel in the transmission of monetary policy. In the light of the vast differences in institutions across Europe, they find the need for a measure of these Maksimovic (1996), Levine and Zervos (1996), Perotti and Van Oijen (1996), Beck and Levine (2000), and Rajan and Zingales (2002). 3 Examples are Pagano (1993), Arestis and Demetriades (1996), Berthélemy and Varoudakis (1996ab), Boyd and Smith (1996), Demirgüç-Kunt and Levine (1996), Galetovic (1996), and Levine and Zervos (1998).


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differences. As such, they come up with their predicted potency of the lending channel, hereafter PPLC. Kashyap and Stein infer the degree of bank dependence in different European countries by looking at the size distribution of firms and the availability of nonbank finance. To gauge loan supply effects, they study the size distribution of the banking industry and the health of the banks (see also Cecchetti, 1999, and Cecchetti and Krause, 2000). Kashyap and Stein proceed as follows. First, they define four factors that are crucial for the responsiveness of loan supply to policy changes: the importance of small banks, bank health, the importance of small firms, and the availability of non-bank finance. These are derived from the literature on the transmission of monetary policy. Then, they come up with a number of indicators for each of these factors. From this, they derive a general picture and qualify the responsiveness for the country in this area. After they have investigated all four areas, they come up with a general assessment, i.e. the PPLC. From the literature, it appears that small banks are more responsive to monetary tightening than large banks. In this respect, bank size can be regarded as a proxy for the ability to access noninsured sources of funds. Kashyap and Stein compare the C3-, C5-, and C10-ratios (that is, the share of total commercial bank assets controlled by the 3, 5 or 10 largest banks respectively). As they assume that well-capitalized or healthy banks have easier access to securities markets to raise funds, they also look into bank profitability and bank capital. Small firms rely on bank loans as it is very costly for such firms to attract non-bank finance. To assess how dependent firms might be on bank finance, Kashyap and Stein compare the importance of small firms within Europe. To this extent they investigate the fraction of employment accounted for by small firms. Furthermore, they look into the ratio of each country’s share of total European employment to its share of the total number of enterprises. Another indicator of bank dependence is based on the structure of the capital markets across Europe. Here, Kashyap and Stein examine the size of stock market capitalization and public bonds in relation to GDP. Table 1. Factors affecting the potency of the lending channel Country Belgium Denmark France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain U.K.

Importance of small banks A B B C B B B C A B B A

Bank health B B C B B B C A A C B A

Importance of small firms B B B A C B C A C C C A

Availability of non-bank finance A A B B C B C B B C B A

Overall predicted potency (PPLC) A/B B B/C B B/C B C B A/B C B A

A indicates low effect of lending channel sensitivity to monetary policy, C indicates high sensitivity. Source: Kashyap and Stein, 1997: 15.

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Kashyap and Stein assign each country a letter grade (from A to C) for each of their four factors. Grade A indicates the least sensitivity to monetary policy, grade C indicates the most sensitivity. These grades are used to derive the overall predicted potency of the lending channel under EMU (PPLC). The results are reproduced in table 1, which gives the grades on the four factors and the overall grade based on the weighting of the factors. The UK emerges as the country for which the evidence most clearly suggests a relatively weak lending channel. UK banks are in relatively good shape, there are not a lot of small firms, and firms have many other financing options. Belgium and The Netherlands also appear to be on the insensitive end of the spectrum. Kashyap and Stein expect strong effects of monetary policy in Italy and Portugal. In the remaining countries, the picture is less clear. Kashyap and Stein (1997) pioneer in an important direction of the research on the transmission of monetary policy. They depart from the intuition that it not only is the bank itself that is important in assessing monetary policy transmission. Alternative financing mechanisms have to be taken into consideration too. Furthermore, they acknowledge the specialness of countries, which might affect the one-size-fits-all policy of the monetary authorities in the EMU. However, the PPLC also has some serious weaknesses. First is that Kashyap and Stein abstract from other views on the workings of monetary policy. Taking into consideration the interest rate channel and the asset price channel too would probably result in a much ‘richer’ description. Second is that they use data for quite different years and periods. For example, the data on the size of firms relate to 1990, whereas those on the stock market are for 1995; the C3-ratio is calculated for 1993, the C5- and C10-ratio for 1995. Third is that, in order to derive the grades for each of the four factors, a consistent methodology lacks. For example, Germany receives a high score concerning competition in the banking system (C). This assessment seems to follow from the interpretation of thousands of savings banks and cooperative banks as competing with each other. For the purpose of measuring competition, the German savings banks would have to be counted as one institution, and similarly for the cooperative banks. Furthermore, the grading cannot be reproduced from the data.

3.2. Conglomerate Index of Financial Structure Demirgüç-Kunt and Levine (1999) analyze the financial structure for a large number of countries as part of a research project on financial development and economic growth. To this extent, they construct a conglomerate index of financial structure (CIFS) based on measures of size, activity, and efficiency of financial institutions and markets. They study ratios of stock market development relative to banking sector development. Countries with below average ratios are classified as bank-based. Countries where the ratio of stock market development to banking sector development is above average are classified as market-based (Demirgüç-Kunt and Levine, 1999). This grouping produces two categories: bank- and market-based countries. Given the fact that there are numerous countries with very small, poor or weak banks or stock markets, they introduce a third category, namely that of underdeveloped systems. A country’s financial system is underdeveloped if it has below median values of both bank and market development. Demirgüç-Kunt and Levine, hereafter DL, come up with 17 indicators with respect to the structure of the financial system. On the basis of their association with economic development, they select five to derive the CIFS. They average Capitalization vs. Bank (i.e. stock market capitalization / domestic assets of


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deposit money banks), Trading vs. Bank Credit (total value traded on the stock market / claims of deposit money banks on the private sector) and Trading vs. Overhead Cost (total value traded/ bank overhead costs), and call the result Structure. Higher values of Structure (i.e. CIFS) signify a higher degree of stock market development relative to the financial system. Table 2. Financial structure across the EMU (CIFS) Country Portugal Austria Belgium Italy Finland Greece France Germany Ireland Spain Netherlands Denmark Sweden UK Japan US

GDP per Structure Index: Capitalicapital ($) CIFS zation vs. Banks 4822 -0.75 0.17 13177 -0.73 0.10 14482 -0.66 0.30 11505 -0.57 0.22 15892 -0.53 0.37 6552 -0.34 0.36 15232 -0.17 0.32 16573 -0.10 0.20 9014 -0.06 0.73 7286 0.02 0.31 13955 0.11 0.61 17023 0.15 0.42 18982 0.91 1.16 11794 0.92 0.97 15706 19412

-0.19 1.96

0.60 1.09

Trading vs. Bank Credit 0.09 0.08 0.09 0.15 0.15 0.32 0.19 0.29 0.48 0.34 0.47 0.42 0.72 0.48

Trading vs. Overhead Costs 0.00 0.00 0.00 0.00 0.00 0.00 0.01 0.01 0.00 0.01 0.00 0.01 0.01 0.01

0.24 0.96

0.00 0.04

Source: Demirgüç-Kunt and Levine, 1999, Table 11.

Table 2 gives the CIFS, as well as the constituting three indicators, of the EMU countries and that of Japan and the US. It also shows the average GDP per capita (in US dollars at constant prices) over 1990-95. The ranking is from bank-based to market-based. Countries with a negative score on Structure are qualified as bank-based, and countries with a positive score are market-based. Thus, Portugal (CIFS = -0.75) is viewed by DL as the EMU-country with the most bank-based financial system. Great-Britain (CIFS = +0.92) is the county with the most market-based financial system in the EMU. On the basis of this exercise, Spain, The Netherlands, Denmark, Sweden and the UK are market-oriented financial systems; Portugal, Austria, Belgium, Italy, Finland, Greece, France, Germany, and Ireland are bank-oriented financial systems (NB: Luxembourg is excluded from the analysis by DL). However, probably mistakenly, Spain is put among the bank-based systems by DL in their Table 12 as well as in the accompanying text. The financial system of both Denmark and Ireland is underdeveloped. DL derive their measures from the finance and growth literature (Levine, 1997; Beck et al., 1999). They find a high correlation between all measures and income per capita. As such, they conclude that it does not matter much what measures are to be used further. When CIFS is to be used exclusively for establishing the relationship between the financial system and economic development, this approach may be efficient. However, in the case of shedding

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light on the de facto structure of the financial system it is of extreme importance to include the information in a proper way. DL filter the data on the basis of their assumption that finance and development are highly interrelated. Second is that DL aim at including efficiency measures of the financial system but, unfortunately, they do not come up with unambiguous ones. This especially is a problem with Overhead Cost. Overhead costs equal the ratio of bank overhead costs to the total assets of the banks. DL view lower overhead costs as a sign of greater efficiency. However, overhead costs are likely to be related to the types of financial services provided by the financial intermediary as well as to the investments undertaken by competitive banks to keep or to create competitive advantages. In that case, lower (than average) overhead costs reflect insufficient competition and insufficient investment in providing superior banking services. Furthermore, banks’ production functions will differ among the different countries (Altunbaş and Chakravarty, 1998). This negatively affects the information content of CIFS. Third is that DL neglect some crucial features of the financial system. For example, risk management and the maturity of financing are neglected.

4. Assessing the Orientation of the Financial System 4.1. Bank-Market Orientation Score We suggest an alternative index for the orientation of the structure of the financial system to come to grips with the real-life financial systems of EMU countries. We also compare them with those of the US and Japan to put the EMU in perspective. We focus on quantitative aspects of the main attributes of the financial system. We concentrate on financial indicators, as these characterize the financial system most. We investigate size and activity, as these generally are readily available and easy to interpret. We have seven indicators for each of the two constituents of the financial system, i.e. financial institutions and financial markets. We briefly discuss the methodology and consecutively the results for each indicator. The methodology is straightforward. We rank the fifteen EMU countries for each of the fourteen indicators. Ranking is on the basis of the relative position of the individual country. Countries for which the indicator is nearest to the a priori one of a bank-based system get rank 1; countries for which the indicator is nearest to the a priori one of a market-oriented 4 financial system get rank 15. The a priori classification is as follows. On the basis of the literature in section two, we assume that bank finance is relatively well-developed in bankoriented financial systems, and that institutional investors and financial markets are relatively well-developed in market-oriented systems. For illustrative purposes, we come up with the so-called bank-market orientation score (BMOS). This is the sum of the scores of a particular country on all items divided by 1.4. As such, we have a score that has 1 as its minimum value and 10 as its maximum value. A low value of BMOS means that the country in question is relatively bank-oriented, a high score signals a high degree of market orientation.

4

When there are two countries with a similar value of the indicator, they get the same score. The score of the US and Japan is based on the position they would have earned within this ranking scheme.


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Table 3. Bank-market orientation scores for EMU-15, Japan and the US

1

1

2

3

4

Financial system indicators 1 5 6 7 8 9 10 11

Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden UK

5 2 13 12 6 8 15 11 10 1 4 7 9 14 3

4 8 14 10 5 2 15 6 11 9 3 12 7 13 1

9 6 12 8 2 1 4 10 15 6 2 4 14 11 12

12 6 1 1 7 11 14 9 4 15 8 5 12 1 10

2 6 13 9 1 6 11 3 8 12 15 10 3 3 14

7 9 12 5 10 8 1 2 4 10 14 2 6 13 15

6 13 3 1 14 6 5 2 4 15 9 8 10 11 11

1 10 6 7 8 5 2 11 4 15 13 3 9 12 14

5 4 8 8 11 11 3 10 5 1 15 2 5 13 14

10 7 6 9 11 12 1 1 1 1 14 1 8 15 13

5 13 15 11 10 5 7 3 12 5 9 2 1 14 4

6 11 13 8 10 1 4 3 2 15 14 7 5 9 12

1 9 11 8 14 5 1 13 1 1 6 15 10 7 12

Japan US

10 14

1 11

12 1

6 10

8 14

10 10

5 14

12 13

11 15

11 15

9 5

12 15

7 15

12

13

14 3 1 11 10 5 7 2 9 7 15 14 6 4 12 13

BMOS value 3.88 5.36 7.04 5.46 5.82 4.49 4.34 4.74 4.49 6.17 7.14 4.29 5.26 7.55 7.55

BMOS st.dev. 3.27 3.67 4.33 3.30 4.02 3.54 5.38 4.18 4.34 6.01 4.76 4.17 3.61 4.20 4.52

1 12

5.87 8.37

3.79 4.18

1 = Total bank assets (% of GDP); 2 = Bank credit to the private sector (% of GDP); 3 = Short-term assets (% of total bank assets); 4 = Foreign bank assets (% of total bank assets); 5 = Pension funds financial assets (% of GDP); 6 = Insurance companies financial assets (% of GDP); 7 = Investment companies financial assets (% of GDP); 8 = Stock market capitalization (% of GDP); 9 = Trade in stocks as a percentage of GDP; 10 = Notional value of stock options and index options (% of GDP); 11 = Bond market capitalization (% of GDP); 12 = Corporate bond market capitalization (% of GDP); 13 = Corporate money market capitalization (% of GDP); 14 =New capital raised on the stock market (% of gross fixed capital formation); st.dev. is standard deviation.

Sources: 1 Total bank assets as a percentage of GDP; IMF, 1997: 23 2 Bank credit to the private sector as a percentage of GDP; IMF, International Financial Statistics 3 Short-term assets as a percentage of total bank assets; Bank of Greece, Bank of Ireland, Bank of Portugal, Borio, 1995: 21 4 Foreign bank assets as a percentage of total bank assets; Claessens et. al, 1997, Table 1 5 Pension funds financial assets as a percentage of GDP; OECD, 1997: 22 6 Insurance companies financial assets as a percentage of GDP; OECD, 1997: 21 Investment companies financial assets as a percentage of GDP; OECD, 1997: 23 Stock market capitalization as a percentage of GDP; IMF, 1997: 23 9 Trade in stocks as a percentage of GDP; FIBV, 1997: 65 10 Derivatives markets (notional value of stock options and index options) as a percentage of GDP; FIBV, 1997: 104 11 Bond market capitalization as a percentage of GDP; IMF, 1997: 23 12 Corporate bond market capitalization as a percentage of GDP; ING, 1997; 31-35 13 Corporate money market capitalization as a percentage of GDP; Bank of Japan, Federal Reserve Board, ING, 1997: 13-15 14 New capital raised on the stock market as a percentage of gross fixed capital formation: FIBV, 1997: 94

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Table 3 gives the ranks of the fifteen EMU member countries and those of the US and Japan for our fourteen indicators. The gray column gives the BMOS. The first seven indicators in table 3 relate the size and activity of financial institutions to GDP, the remaining seven are related to financial markets. Indicator 1 is the size of the banking sector, measured as total bank assets as a percentage of GDP. Countries with a relatively large banking sector (i.e. bank-oriented) are Luxembourg, Belgium, the UK, the Netherlands, and Austria. The relative amount of private bank credit is highest in the UK, the Netherlands, and Germany (i.e. bank-oriented). The share of short-term assets (< 1 year) in total bank assets signals asset maturity. Long-term assets prevail (i.e. bank-oriented) in the Netherlands, Germany, and France, whereas short-term assets are substantial in Italy and Spain. Note that this also is the case in the US (Borio, 1995). The share of foreign banks in total bank assets of a country is low (i.e. bank-oriented) in Scandinavia. The indicators 5-7 relate the size of three types of institutional investors to GDP. When this size is relatively small, the country is qualified as bank-oriented. As to pension funds, this is the case in France, Austria, Italy, and Ireland. With insurance companies, Ireland, Italy, Luxembourg, and Spain, are most bank-oriented. With investment companies, it is Ireland, Denmark, and Finland. In contrast, institutional investors are widespread in the UK and the Netherlands. Indicators 8-14 go into the size and activity level of financial markets. Stock market capitalization to GDP is highest in Luxembourg and in the UK. It is relatively low (i.e. bankoriented) in Greece, Portugal, and Austria. The trade in stock is high in the UK, Sweden, and the Netherlands. The derivatives market is a reflection of risk management practices. They are well-developed in Sweden and the Netherlands. Bond markets are large in Belgium, Denmark, and Sweden. The market for corporate bonds is largest in Denmark, Luxembourg, and the Netherlands. Companies make most use of the money market in Ireland, France, and Portugal. The last indicator is the amount of new capital raised on stock exchanges in relation to gross fixed capital formation. As such, the stock market proves to be important most for UK, Dutch, and Luxembourgean firms. The stock market is of little importance (i.e. bankoriented) for firms in Austria, Belgium, and Greece. Overall, table 3 reveals that there is a wide variety in the financial systems within the European Union. We conclude that the financial systems of the EMU member states are heterogeneous. This is not only the result of our methodology. There truly are substantial differences in the size and significance of financial institutions and markets within Europe. On the basis of table 3, we may conclude that the financial system of Austria, Portugal, Italy, Greece, Germany, and Italy is most in the direction of the bank-oriented model. The financial system of the UK, Sweden, the Netherlands, and Denmark mainly is in the direction of the market-oriented model. The standard deviation of the scores on the fourteen indicators (last column of table 3) shows that there is relatively little variation in the scores of Austria, Belgium, Finland, Germany and Spain. There is substantial variation in the scores of Luxembourg and Greece. There is no ‘pure’ bank- or market-oriented financial system within the EMU. The bank-market orientation score of the most market-oriented countries within Europe (Sweden and the UK) is quite close to that of the US. The median countries in Europe have almost the same degree of bank-market orientation as the financial system of Japan. Countries like Austria, Germany, and Italy, however, are much more bank-oriented than Japan. Financial markets in Japan are larger than those in Europe, whereas Japanese institutional investors are underdeveloped. Compared to the US, European countries clearly are much


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more bank-oriented. The US scores in the fifth or fourth quintile in all cases, except for shortterm credit as a percentage of total credit and for the general size of the bond market.

4.2. Comparing PPLC, CIFS and BMOS As the three indices lack a common objective or aim, it is difficult to assess which of them is the ‘superior’ index with respect to reflecting the orientation of the financial system. Therefore, we compare the three indices on a number of items: aim, methodology, interpretation, efficiency, and robustness. Table 4 gives their main characteristics. Table 4. Characteristics of the three indices Theoretical background Method Methodology Number of variables used to achieve the index score Number of countries studied Interpretation of variables and index Robustness

PPLC Monetary transmission Ranking Index 13

CIFS Finance and development Value Index 5

BMOS Financial system architecture Ranking Index 14

12 Ambiguous Weak

57 Ambiguous Weak

17 Clear Moderate

Table 5. PPLC, CIFS and BMOS: score and rank Country Austria Belgium Denmark Finland France Germany Greece Ireland Italy Luxembourg Netherlands Portugal Spain Sweden UK Japan US

PPLC Score* Rank 1.50 11 1.75 8 2.25 4 2.00 6 2.50 3 2.00 6 2.75 1 1.75 8 1.75 8 2.75 1 2.25 4 1.00 12 -

CIFS Score -0.73 -0.66 0.15 -0.53 -0.17 -0.10 -0.34 -0.06 -0.57 0.11 -0.75 0.02 0.91 0.92 -0.19 1.96

Rank 2 3 12 5 7 8 6 9 4 11 1 10 13 14

BMOS Score Rank 3.88 1 5.36 8 7.04 12 5.46 9 5.82 10 4.49 4 4.34 3 4.74 6 4.49 5 6.17 11 7.14 13 4.29 2 5.26 7 7.55 14 7.55 15 5.87 8.37

* This score has been derived as follows: for an A on one of the four factors, a country is given 1 point, 2 points for a B, and 3 for a C. The results on all four factors have been aggregated and divided by 4.

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The aim of the PPLC is to assess the structure of the financial system in relation to the effectiveness of monetary policy. It is a ranking of countries with respect to their responsiveness of loan supply to (monetary) policy changes. The aim of the CIFS is to classify countries as either bank-based or market-based. It is the average value of three ratios that are made up of five different variables. It results in a dichotomy of countries. The CIFS is an absolute value that can be used as a variable or parameter. The aim of the BMOS also is to give an impression of the orientation of the financial system of a country within the EMU. However, it does not regard the financial system of a country as either bank- or market-based. Table 5 gives the scores on bank and market-orientation of the PPLC, the CIFS, and the BMOS for the EMU countries, and, if possible, for Japan and the US too. Furthermore, it ranks these countries from the most bank-based one (rank 1) to the most market-based country. For the PPLC, we rank from most sensitive to monetary policy to least sensitive. It turns out that the CIFS and the BMOS place the countries from the European Monetary Union in quite similar positions along the bank-market spectrum. A simple test on the association between the rankings of the CIFS and the BMOS is the Spearman rank correlation coefficient. This coefficient is 0.78, indicating significant positive association between the two indices. The rank correlation between PPLC and CFIS is 0.23, between PPLC and BMOS it is 0.53. Kashyap and Stein basically select their indicators and factors on the basis of their theory of the banking lending channel for monetary policy transmission. However, the indicators are .. no doubt highly imperfect proxies (Kashyap and Stein, 1997: 9). The selection of the measures that make up the indicators of the CIFS by Demirgüç-Kunt and Levine is based on their association with per capita income. The measures of relative size, activity, and efficiency place the countries into slightly different places along the bank-based versus market-based spectrum. As such, DL see little reason to favor one particular measure of financial structure over another. Unfortunately, they do not explicitly account for the choice of the final measures they include in their index. The BMOS explicitly tries to derive measures that characterize bank- and market-based structure from the literature on financial system architecture. The methodology employed basically is an index approach but it differs somewhat between the three indices. The PPLC has 13 indicators that result in grades on the 4 factors. Kashyap and Stein (1997) come to the grades on each of the factors and on the resulting PPLC on a purely subjective basis. DL use 5 indicators (from a set of 17) on the basis of their association with per capita income. As such, they select the indicators on an a priori basis. The BMOS uses 14 measures on the basis of the literature and incorporates all of these in the ultimate index. Here, it is the relative position (rank) within the group of countries that matters in assigning the scores. It is the unweighted average of ranks, divided by 1.4. All three indices lack a standardized procedure to derive the variables that are included in the final score. Although there is a theoretical basis for the variables, their selection and operationalisation is subjective. Also, the weighting procedure is simple and not rationalized. Here, it appears that the literature on multiattribute utility theory might be helpful (e.g. Chicken and Hayns, 1989; Dyer et al., 1992), especially in coming up with legitimate weights for the different variables. However, this theory assumes that there is at least some consensus about what objects are key in determining the, in the underlying case, orientation of the financial system. The review of the literature in section 2 showed that such consensus is missing.


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All three indices heavily lean on size and activity indicators. The CIFS and the PPLC also include an efficiency measure. Unfortunately, this measure (overhead costs in relation to total bank costs) is ambiguous. Differences in overhead costs reflect differences in efficiency, but only when the institutions being compared have identical production structures and production functions. In the underlying case, this is quite unlikely. This negatively affects the signaling quality of the CIFS. The PPLC has a pre-tax return on assets as an efficiency measure, which also is ambiguous. A high return reflects profitable and efficient banks (as is to be expected in a market-oriented financial system), but it also can result from a monopolistic environment (as is expected to be the case with a bank-oriented system). None of the approaches allows for analyzing relationships among the factors, as suggested by Hackethal and Tyrrell (1998). This critique especially affects the PPLC and the BMOS, as these weigh each item on an equal basis, whereas the CIFS relates the constituting items to GDP. Therefore, it needs to be elaborated upon whether there truly are some dominant features or characteristics of the financial system that drive the position on the bank-oriented / market-oriented scale. Furthermore, it appears that variable choice in general is subjective. In part, this reflects the different relationship of each of the indices with (parts of) the economic literature. However, in case theory does not drive the selection of the variables, it is important to investigate how sensitive the outcomes are for this selection. To this extent, we randomly left out 50% of the indicators in each of the three indices. We found that deviations from the scores in table 5 were largest for the PPLC and smallest for the BMOS. On the basis of this assessment, it is not possible to conclude which of the three indices is ‘superior’. This is to be related to the aim of the analysis. CIFS can be regarded as a useful parameter to be used in further empirical analysis, especially on the finance-growth nexus. Redoing the PPLC with more accurate and consistent data can result in an index that shows how sensitive EMU financial systems are for the bank lending channel. But other transmission channels would still be neglected. BMOS is most helpful if one wants to assess the structure of the financial system per se.

5. Conclusion It appears that the being in place of a well-functioning financial system is a conditio sine qua non for the efficient operation of modern market economies. The orientation of a financial system varies from bank-based to market-based. Most empirical studies classify economies within the spectrum on an a priori basis. We analyze ways in which the orientation is assessed on an a posteriori basis. We assessed three indices to assess the orientation of the financial system: The Predicted Potency of the Lending Channel (PPLC) by Kashyap and Stein (1997), The Conglomerate Index of Financial Structure (CIFS) by Demirgüç-Kunt and Levine (1999), and, as an alternative, the Bank Market Orientation Score (BMOS). The PPLC appears to be somewhat flawed as it heavily leans on one single view of the transmission channel (viz. the lending view). The CIFS seems fit as an intermediary variable in financegrowth research. The BMOS is a very informative reflection of the de facto orientation of the financial system. It also appears that there is a wide variety in the financial systems of the EMU member states. The financial systems of Austria, Portugal, Italy, Greece, and Ireland show a dominant role of the banking sector, have relatively small institutional investors, and

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their financial markets tend to be not very well-developed. In contrast, the financial systems of the UK, the Netherlands, Sweden and Denmark have well-developed financial markets and their institutional investors are relatively large players. Belgium, Germany, and Luxembourg have some well-developed financial market segments. Some EMU-countries are even more bank-oriented than Japan (Austria, Germany, Italy), whereas other tend to go in the direction of the US financial system (Sweden, UK). Relating the indices to the four different views presented in this section, it appears that CIFS is most helpful in analyzing the market-based and the bank-based view, given its dichotomous character. PPLC and BMOS opt for a more gradual approach and put countries on a spectrum of policy responsiveness and bank/market orientation respectively. As such, they fit well with the financial services view on the interrelation between finance and the economy. None is related to the legal-based view. The results of our analysis have important implications for monetary and financial policies within the EMU. As there is substantial heterogeneity as to the structure of the financial system, it seems wise to take account of the fact that the – generic – ESCB policies will affect the financial institutions and markets of the participating countries differently.

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Chapter 10

COMPARISON OF CULTURAL PROFILES OF YOUNG MANAGERS FROM THE COUNTRIES OF SOUTHEASTERN EUROPE AND RUSSIA Janez Prašnikar, Marko Pahor and Hugo Zagoršek University of Ljubljana, Slovenia

Introduction Mapping national cultures is a comprehensive, demanding and time consuming project. Until now only a few researchers have managed to collect comprehensive databases of comparable data for the majority of countries. On the basis of research conducted at the end of the 1970s, Hofstede (2001) researched four basic dimensions in approximately 60 countries worldwide. Ten years later, under the influence of Asian researchers, he added the fifth dimension. The research consortium GLOBE (House et al., 2004) has collected data on nine cultural dimensions for 62 countries. Trompenaars (1993; 2004) draws from a constantly growing database of more than 55,000 managers from over 50 countries.1 Eastern European countries are mainly not included in the above mentioned research. This particularly holds for countries on the territory of the former Yugoslavia, which represent a “blank spot” on the international map of national cultures. With the exception of Slovenia, internationally comparable data concerning the cultural profiles for these countries do not exist. In this chapter we present the results of a comparative study of cultural profiles of young managers from certain countries on the territory of the former Yugoslavia (Slovenia, Croatia, Bosnia and Herzegovina, Serbia and Montenegro) and Russia. These are managers who have recently completed their education or are still included in post graduate studies of business sciences and therefore have little or even no work experience. Additionally, they come from transitional countries and perceive new social circumstances similarly, thus forming similar values, beliefs and points of view. On the other hand, they belong to different national 1

More on various models of culture see Zagoršek and Štembergar (in this book).

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cultures. Our research confirms the influence of both types of factors on the formation of managers’ cultural profiles. Its significance is also in that it shows the usefulness of the methodology employed in such research. In the next part we first present the results of the existing studies in selected countries. In the third part we present the methodology. We follow with the presentation of the results and the comparison of cultural profiles of different groups of managers. The concluding part contains the main observations.

Brief Introduction of Hofstede’s Study of Cultural Profiles in Studied Countries The cultures of the studied countries, all of which are Slavic, have a lot of commonalities as well as differences. However, being a large country and having played a substantial role in the history, Russian culture has excelled for centuries. On the other hand, cultures of the former Yugoslav countries have mainly been influenced by the Central European, Mediterranean, Eastern and Dinaric cultures (Goić, 2005). The Central European culture comes from the northwest, from the territory as well as the institutions of the former AustroHungarian monarchy. Eastern cultural influences were primarily brought by the Turks in times of their invasions from the southeast and have left an important impact in the invaded area. The Dinaric culture, present in the central hilly parts of the Western Balkans, could be described as autochthonous to this area. It had presumably existed long before the arrival of the Slavs. The dominant and distinctive culture in individual countries of the Western Balkans has certainly developed under the influence of a specific combination of all these influences. It may be broadly presumed that the intensity of individual influences was proportional to the distance from the transmitting source. According to this presumption the Central European and (to a lesser extent) the Mediterranean culture would have the greatest influence in Slovenia. In Croatia one can talk about a combination of the Central European, the Mediterranean and the Dinaric (especially in central parts) cultural components in different concentrations in individual parts of the country. In Bosnia and Herzegovina a combination of the Dinaric and the Eastern culture is present. The Dinaric culture is dominant in Montenegro, with some characteristics of the Eastern and (on the coast) the Mediterranean culture. In Serbia one can talk about a combination of the Dinaric and the Eastern culture, with specific (slightly less important) characteristics of the Central European culture (Goić, 2005). Besides historical and geographical determinants, the contemporary culture in these countries has also been influenced by recent experiences and events. In the second half of the 20th century they were socialist countries. Four of these countries (on the territory of the former Yugoslavia) spent the majority of the previous century in the same country and therefore belonged to a uniform ideology. After the breakup of Yugoslavia at the end of the 20th century three of them were involved in a bloody ethnic conflict. Little empirical data concerning the level of similarities or differences among cultures of the countries on the territory of the former Yugoslavia exist. Some stress that geographical and linguistic proximity and a shared history in the 20th century are reflected also in the cultural proximity of the nations in this area. Since people concentrate more on differences while taking similarities for granted, the former are often unjustly emphasized. Only by

Comparison of Cultural Profiles of Young Managers...

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stepping out of a regional framework and observing ourselves in the world-wide context can we see how similar the cultures of these countries are compared to other European, American, Asian or African countries (Wachtel, 1998). Others point out that the fact that we speak similar languages does not mean that we ascribe the same meaning to events around us. A nation’s culture is rather stable. It is questionable to what extent the forced ideological homogenization during the time of Yugoslavia left its traces in the national cultures of individual nations. Significant disagreements at the breakup of Yugoslavia show that there might be more mutual differences than we imagine (Debeljak, 1986). At the end of the 70s Hofstede (1980) included three of the then Yugoslav republics in his research: Slovenia, Croatia and Serbia (Table 7.1). Considering the intense social changes within the last 30 years the data are likely to be slightly out of date. They are also unreliable because of small samples, as in some republics fewer than 40 people answered the questions. The iteration of Hofstede’s research, performed for Slovenia by Jazbec (in this book), shows that at least in Slovenia the current state on some dimensions differs from the one presented in Table 7.12. However, the results of the original study show a high similarity in the way of thinking and beliefs of people of the then republics. Substantial power distance or respect for hierarchy and authority is present in all three countries that arose from the former Yugoslavia. It is the highest in Serbia and slightly lower in Slovenia and Croatia. All three countries are quite collectivistic (putting community ahead of an individual). Croatia is the most individualistic although differences among countries on this dimension are almost negligible. Typically feminine values, such as good relationships, cooperation and general quality of life (in contrast with achievement, heroism, work and material success), prevail in all three countries. Slovenia stands out in the direction of femininity. In all three countries strong uncertainty avoidance is present – risk aversiveness and avoidance of unstructured, non-transparent and uncertain procedures and circumstances. In summary, the data from Hofstede’s research confirm the thesis about substantial cultural proximity and similarity among the countries of the former Yugoslavia. Table 7.1. Dimensions of National Culture According to Hofstede (2001) for Slovenia, Croatia and Serbia

Slovenia Croatia Serbia Russia

Power distance (PDI)

Individualism (IDV)

Masculinity/ Femininity (MAS)

71 73 86 90

27 33 25 50

19 40 43 40

Uncertainty avoidance (UAI) 88 80 92 90

Note: Numbers present an index of a country’s ranking approximately on the scale between 1 and 100, where a higher number indicates a higher intensity of a specific dimension. Numbers for Russia are based on estimates (Hofstede, 1993).

2

Results of the research performed on the sample of 563 Slovenian managers are: Slovenes are highly individualistic (IDV 107), inclined towards low power distance (IPD 28) and uncertainty avoidance (UAI 72), with prevailing feminine values (MAS 20) and relatively short-term oriented (LTO 33). For more on this see Jazbec in this book.

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Russia is rather different. Power distance and the presence of individualism are high. The uncertainty avoidance is similar to the countries of the former Yugoslavia; however it differs from Slovenia in the degree of femininity – in Russia masculine values are more evident.

Methodology Questionnaire. In our research we have used a questionnaire about national culture developed by Trompenaars (1993). The questionnaire includes various types of questions, such as short scenarios where an individual is forced to choose between two alternatives, questions where an individual among several statements chooses the most suitable one and classic Likert-type questions where an individual expresses his/her agreement or disagreement with a certain statement on a one-to-five scale. The questionnaire has been tested on numerous samples and has good psychometric characteristics (Trompenaars and Wooliams, 2004). Trompenaars (1993) distinguishes seven basic dimensions of culture that represent key challenges of existence of a human as a social being. Dimensions of organizational culture are added to the dimensions of national culture. With their aid it is possible to form various types of current and ideal organizational culture. The four types of organizational culture are formed by combining two dimensions: 1) orientation towards a task or person and 2) hierarchy or egalitarianism (Trompenaars and Wooliams, 2004).3 Sample and course of research. The research of cultural dimensions was performed on ten groups of current and former postgraduate students of business sciences in seven countries. A total of 650 people participated in filling out questionnaires. Countries were not represented equally. Initially Turkey was also included in the research but was excluded due to insufficient number of participants (13). The surveying took place in June and July of 2005. In each country young managers completed a questionnaire in their mother tongue but the method of acquiring the data differed among countries. In Slovenia the participants answered the questionnaire through a computer interface while in other countries they completed a paper version of the questionnaire (with identical questions) and the data was later transferred to a computer system. Table 7.2. Basic Demographic Data of the Sample Age Share of males Years of education Share of managers

Slovenia 35.5 63% 17.4 71%

Croatia 31.9 38% 16.2 34%

B&H 33.7 55% 16.2 61%

Serbia 28.6 45% 16.6 23%

Montenegro 29.5 34% 16.7 46%

Russia 31.9 49% 16.2 57%

Source: THT and own analysis.

The choice of the sample is reflected in the demographic data – the participants in this comparison are relatively young, around 30 years old and well educated since they have on average completed more than 16 years of education, i.e. they have on average at least an 3

See Zagoršek and Štembergar (in this book) for a detailed description.


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undergraduate degree. The average education is highest in the Slovenian sample, which indicates the dominant share of active managers. The sample is well balanced in regards to gender. In Slovenia, and Bosnia and Herzegovina the share of men is slightly higher while in Croatia, Montenegro, and Serbia women prevail. In researching cultural profiles of countries we use two types of data, both of which are based on the same questionnaire. When comparing only groups of young managers from the chosen countries we use raw data. By raw data we do not mean the answers to the questions but rather the dimensions derived directly from them. When groups of participants from the studied countries are placed in a broader group of countries, we use transformed data. In transforming raw data we have accounted for distributional characteristics of a certain dimension for all 55,000 managers from the Trompenaars database as well as demographic characteristics of an individual participant.

Cultural Profiles of Young Managers Placement into a Broader Group of Countries In this part we compare young managers in the studied countries with studies of managers’ profiles around the world where Trompenaars’ methodology has been used. The selected countries have been placed into a broader context of countries on the basis of the so called cultural profiles that are based on transformed cultural dimensions, which take into account the distributional characteristics of an individual dimension as well as the demographic specifics of an individual group – average age, gender structure and the like. From the data in Table 7.3 below we can conclude: 1. Young managers included in the research are very similar among themselves on many dimensions. Index values range mainly between 50 and 60 and can therefore be placed somewhere in the middle compared to other countries.4 Although they belong to a group of transitional countries, they differ from the already performed studies in those countries that used the same methodology by displaying higher adherence to the rules and procedures (universalism), higher care for the colleagues (lower individualism) and higher intertwinement of business and personal relationships (less relationship specific). Young managers give more importance to the internal locus of control and proactive behavior (mechanistic view of the environment). On the other hand, they differ from the old EU members (young managers: higher intertwinement of business and personal relationships and lower importance of achievements) as well as from Asian countries (young managers: higher universalism, lower intertwinement of business and personal relationships, lower importance of status and power). The results of a study of Russian young managers, who on the majority of dimensions agree with others, also lead to the conclusion that these groups of managers are similar. An individual’s age and profession play an important role in determining a cultural profile, which is included also in the Trompenaars’ research methodology. 4

Due to the use of transformed data we assume the mean of 50.

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Janez Prašnikar, Marko Pahor and Hugo Zagoršek Table 7.3. Comparison of Cultural Profiles – Transformed Data

Old EU members

Austria Greece Denmark Ireland Italy France Germany The Netherlands Spain Sweden Great Britain New EU The Czech members Republic Poland Hungary Australia Other Western Switzerland countries USA

Other countries Bulgaria of the Eastern Russia block Israel Middle East Turkey Far East China India Japan Young Slovenia managers Croatia included in the Bosnia and research Herzegovina

U (h) – P (l) 67 38 65 69 70 43 62 56 52 78 66 34

Ind (h) – C (l) 45 44 65 44 46 36 53 63 57 58 60 90

N (h) – A (l) 58 38 52 46 45 40 59 46 49 54 62 50

S (h) – D (l) 49 54 76 71 73 67 67 84 65 64 68 80

Ach (h) – Int (h) – E Asc (l) (l) 56 55 50 54 70 62 79 49 62 57 55 66 61 49 61 48 65 70 77 49 75 56 50 47

43 62 84 82 76

83 74 65 61 67

55 29 61 54 68

72 63 65 70 90

47 44 74 62 78

49 37 71 46 76

18

79

54

74

30

30

18

84

57

57

37

31

76 57 44 33 55 52

64 41 40 17 29 57

45 46 51 57 80 59

65 69 22 26 33 51

81 45 46 42 40 55

61 59 31 42 10 56

53 50

59 60

59 61

50 51

52 47

50 51

Serbia 48 54 65 51 47 54 Montenegro 66 66 61 52 51 64 Russia 38 59 65 60 53 46 Note: U – universalism, P – particularism, Ind – individualism, C – communitarianism, N – neutral, A – affective, S – specific, D – diffuse, Ach – achievement, Asc – ascription, Int – internal, E – external. The sign (h) means that this pole of the dimension is denoted by high values and the sign (l) means that this pole of the dimension is denoted by low values. Source: THT Consulting, 2005 and own work.

2. Similarities in value dimensions of the Slovenian, Croatian and Bosnian young managers with certain dimensions of Austrian managers (exceptions being higher universalism and higher inclination towards communitarianism in the Austrian research) point to the similarities of national cultures. This is consistent with historical reasons, which was already pointed out by Hofstede’s results. It is worth


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adding that the results of young Serbian managers do not vary much from the stated ones. 3. Although similar on many dimensions, the data on young Russian managers depart in particular on two dimensions, where they reach notably lower values: the bigger role of personal relationships over rules (particularism) and relationship with the external environment. The values on both dimensions are approaching values in the Trompenaars’ Russian research and point out the significance of dimensions of national culture. Disregard for the rules and organic perception of the environment, on which an individual does not have much influence, are much more present in Russia than elsewhere. 4. The departures evident in the data from Montenegro are also interesting. Higher inclination towards respect for rules, higher emphasis on individualism than communitarianism and higher active involvement of an individual in the environment are slightly contrary to our understanding of Montenegrin culture.5

Differences among Selected Countries In the previous part we saw that cultures of young managers in selected countries are relatively similar. Considering that this is a geographically limited area with a lot of common history, this is in a way understandable. However, certain differences among them exist. The similarities and differences of individual groups of young managers from different countries, are seen from Table 7.3. Here, in contrast with the previous part, we use raw untransformed data that are comparable among themselves but not directly with the data from other countries. Universalism – particularism. Young managers from Montenegro stand out most in regards to respecting the standardized rules, with Slovenian and Croatian managers also scoring high on this dimension. On the other hand, young Russian managers stand out for their inclination towards particularism. With them the importance of individual circumstances and personal relationships is most evident. Individualism – communitarianism. Young managers from Montenegro stand out on giving preference to individualism. Vukotić (2005) emphasizes that this is only partially true since they have a strong desire for individualism, but they have to behave in a traditional way despite their strong desire to dominate in a group. However, this level of individualism is not accompanied by the appropriate tendency to take risks. Closest to them are young managers from Russia. Others are quite similar among themselves and emphasize collectivistic values to a greater extent. Neutral – affective. Differences among groups on this dimension are relatively small. Only groups from Russia and Serbia stand out slightly. They are more neutral, as they try to exclude emotions from business relationships. Specific – diffuse. Russian managers have the most specific culture among young managers. Croatian managers show the highest intertwinement of business and personal

5

As mentioned by Cerović and Aleksić (2005), the difference between the expected and the actual results could be a result of the sample of young managers in Montenegro who belong to a specific group that supports liberalism in this country.

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relationships (the difference is not statistically significant) while Slovenes are somewhere in the middle. Achievement – ascription. Status is given the biggest weight by the young managers from the more eastern countries – those from Bosnia and Serbia. Young managers from Slovenia stand out for assigning bigger weight to achievements than status. Cerović and Aleksić (2005) interpret this as a consequence of people in Slovenia being evaluated mainly on the basis of what they have themselves achieved, while in Serbia they are evaluated according to their status and/or connections with certain social groups. This result is expected and in line with the findings from previous research. Another explanation for this could also be an undeveloped market and the industrial structure that still values inherited positions (Čičić, 2005). Relationship towards the external environment. Managers from Montenegro are more mechanistic. They think they own their own destiny and can proactively affect the environment. According to Vukotić (2005) people in Montenegro have a feeling that they can change everything in a moment, although this is more a revolutionary zeal than the real state of affairs. Montenegrins are followed by the Slovenian and Serbian managers. Managers in Russia have the most organic culture, characterized by giving in to fate. Further we describe a cultural dimension that we omitted in the previous part due to more difficult comparability. We also describe the current and ideal organizational culture as perceived by young managers from selected countries. Relationship towards time. All young managers from the selected countries are more directed at the present and the future and consider past less important. Future is most emphasized in Montenegro. In their culture one does not live a present life now and for now but for the future and the future generations (descendents). Fruits of present labor and battles are meant for the future generations (Vukotić, 2005). Past is given most attention by young managers from Bosnia and Serbia. Young managers from Slovenia mainly perceive time synchronically. It is interesting that young Croatian managers stand out most on this dimension. Young managers in the majority of the selected countries currently see the Family and the Eiffel Tower as the prevalent types of organizational culture. Those are hierarchical cultures, the former oriented towards employees and the latter towards task implementation. It is interesting that here young managers from Croatia and Serbia stand out, the Croats ascribing more significance to task implementation and the Serbs to family relationships. Young managers from Slovenia and Montenegro compare the current organizational culture more with the Guided Missile, characterized by orientation towards task implementation and the achievement of goals, which includes teamwork. However, they hardly notice any characteristics of the Incubator. In their judgment, formalization and hierarchy prevail over creativity and equality of mutual relationships. In the opinion of young managers, the ideal types of organizational culture are mainly the Guided Missile and the Incubator, while the Family and the Eiffel Tower are much less desired. Their preference is therefore given to a more egalitarian organization compared to a hierarchical. Since the Guided Missile is in most cases ahead of the Incubator it seems that young managers want their work to be directed towards achievement of goals and tasks, which presents them a bigger challenge than developing conditions for creativity and their own development. Here the Slovenian managers, who see developing conditions for creativity as a contribution to ideal organizational culture, stand out.


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Table 7.4. Cultural Profiles of Young Managers in the Studied Countries – Raw Data

U (high) – P (low) Ind (high) – C (low) N (high) – A (low) S (high) – D (low) Ach (high) – Asc (low) M (high) – O (low) Relative meaning of past Relative meaning of present Relative meaning of future Perception of time – Syn (high) – Seq (low) GM – current

Slovenia (n = 153) 63.9 (20.1) 52.9 (23.2) 54.6 (18.0) 64.2 (23.1) 59.2 (13.3) 61.6 (21.4)

Croatia (n = 114) 63.3 (22.6) 52.6 (19.2) 54.4 (17.0) 59.4 (23.6) 54.3 (14.6) 55.4 (20.7)

B&H (n = 198) 58.6 (22.7) 56.2 (21.6) 56.9 (15.5) 60.2 (27.2) 48.1 (13.8) 56.5 (20.3)

Serbia (n = 31) 57.2 (25.0) 51.0 (19.9) 62.9 (17.2) 61.3 (25.7) 49.2 (15.8) 59.4 (24.8)

Montenegro (n = 67) 76.9 (19.0) 66.6 (21.6) 57.0 (14.8) 63.8 (23.1) 54.2 (13.9)

Russia (n = 74) 45.6 (19.9) 59.7 (20.3) 61.8 (15.7)

72.1 (16.7)

69.6 (24.2) 56.3 (15.2) 49.9 (19.8)

26.1 (6.6)

26.4 (7.2)

28.2 (7.3)

28.4 (6.4)

27.1 (6.5)

28.1 (10.4)

35.5 (6.2)

35.2 (7.5)

32.9 (6.7)

32.7 (5.1)

32.1 (6.6)

32.9 (7.3)

38.4 (6.3)

38.3 (8.5)

38.9 (8.6)

39.0 (7.0)

40.8 (6.6)

39.0 (10.0)

64.5 (21.2)

55.7 (24.9)

59.3 (24.3)

60 (29.4)

57.0 (25.3)

66.4 (22.7)

3.7 (1.9)

4.3 (2.7)

3 (2.1)

3.4 (2.4)

3 (2.4)

4.4 (2.4)

F – current

3.9 (2.1)

4.6 (1.9)

4.5 (2)

4.5 (2.1)

3.3 (1.8)

3.8 (1.9)

ET – current Inc – current

4 (2.5) 1.7 (1.6)

4.8 (2.5) 1.6 (1.3)

4.1 (2.3) 1.9 (1.4)

4.4 (2.4) 2.1 (1.2)

4 (2.2) 2.3 (1.7)

4.2 (2.2) 2.3 (1.6)

GM – ideal

5.9 (2.3)

6.3 (1.9)

5.9 (1.9)

6.9 (2)

6.2 (1.7)

5.9 (1.9)

F – ideal ET – ideal Inc – ideal

1.3 (1.1) 0.8 (1.2) 6.0 (2.4)

1.8 (1.2) 1.2 (1.3) 4.7 (1.9)

1.5 (1.3) 1.6 (1.4) 5 (2)

1.1 (1.0) 1.2 (1.8) 4.7 (1.8)

1.4 (1.0) 1.2 (1.2) 5.2 (2.0)

1.6 (1.2) 1.9 (1.6) 4.6 (1.9)

Note: U – universalism, P – particularism, Ind – individualism, C – communitarianism, N – neutral, A – affective, S – specific, D – diffuse, Ach – achievement, Asc – ascription, M – mechanic, O – organic, Syn – synchronic, Seq – sequential; GM – Guided Missile, F – Family, ET – Eiffel Tower, Inc – Incubator. Arithmetic means of raw data (standard deviations in parentheses). Bolded values are higher with statistical significance, italicized values are lower with statistical significance. Source: THT and own analysis.

Differences among Young Managers and Managers from Three Companies in Slovenia A group of young Slovenian managers in the study is comprised of three different populations. The first are the members of the Alumni Association of the International Full Time Graduate Program in Business Administration at the Faculty of Economics in Ljubljana (70 replies out of approximately 300 members). The second group is represented by the participants of the Consortium MBA (KMBA) (40 replies out of 75 participants)6 while the third group includes participants at last year’s business conference in Portorož (43 replies out of 200 registered participants). The three populations differ among themselves in some characteristics. The first group primarily consists of young managers who are at the beginning 6

Consortium MBA is a special form of MBA at the Faculty of Economics in Ljubljana, which consists of groups of managers from selected companies.

280


of their careers, included in the second one are managers with a few years of experience who mainly hold positions in middle or higher management and are preparing to take over more responsible duties. In the third group top management is more represented than in the other two.7 In the Table 7.5 we first present a comparison between the whole group of young managers (including all three mentioned groups) with the data on dimensions of organizational culture for the managers of the three companies that participated in our research and are covered later in this book (Droga, Helios and Trimo). Table 7.5. Comparison between Managers from Companies and Young Managers Slovenia (n = 508) Universalism (high) – Particularism (low) Individualism (high) – Communitarianism (low) Neutral (high) – Affective (low) Specific (high) – Diffuse (low) Achievement (high) – Ascription (low) Mechanistic (high) – Organic (low) Relative meaning of past Relative meaning of present Relative meaning of future Perception of time – Synchronic (high) – Sequential (low) Guided Missile – current Family – current Eiffel Tower – current Incubator – current Guided Missile – ideal Family – ideal Eiffel Tower – ideal Incubator – ideal Source: THT and own analysis.

Managers from Young managers companies (n = 355) (n = 153)

65.6 (22)

66.4 (22.7)

63.9 (20.1)

53.7 (21.9) 55.3 (15.9) 65.2 (23.2) 58 (14.1) 57.5 (20.8)

54 (21.4) 55.5 (14.9) 65.5 (23.2) 57.5 (14.5) 55.8 (20.4)

52.9 (23.2) 54.6 (18) 64.2 (23.1) 59.2 (13.3) 61.6 (21.4)

26.1 (6.8) 36.3 (6.1) 37.6 (6.8)

26.1 (6.9) 36.6 (6) 37.3 (7)

26.1 (6.6) 35.5 (6.2) 38.4 (6.3)

67.1 (20.5) 4.4 (2.4) 3.7 (1.9) 3.8 (2.1) 2 (1.6)

67.8 (20.4) 4.4 (2.2) 3.7 (1.9) 3.8 (2) 2.1 (1.7)

64.5 (21.2) 4.3 (2.7) 3.9 (2.1) 4 (2.5) 1.7 (1.6)

5.6 (2) 1.6 (1.1) 1 (1.3) 5.7 (2.2)

5.5 (1.9) 1.7 (1.2) 1.1 (1.3) 5.6 (2.1)

5.9 (2.3) 1.3 (1.1) 0.8 (1.2) 6 (2.4)

Index values of individual dimensions that are lower with statistical significance are italicized and underlined. Statistically significant higher values for the group of young managers are bolded. The groups are different with statistical significance on four out of six dimensions of organizational culture. Young managers are more inclined towards respecting the rules and put higher emphasis on collectivistic values.8 They value achievement over status and think to a higher extent that they can actively influence events in the environment. Young managers give more attention to the future than the present, which is understandable, considering the age difference between the two groups. Lack of work experience is probably also a reason that they perceive time more sequentially. 7 8

45% of the participants at the last year’s conference identified themselves as top management (chairman of the board, members of the board). 75% of participants identified themselves as managers. As will be seen later, this is mainly due to the KMBA students.


281

Young managers are more critical towards the current organizational culture than the group of managers from participating companies. The Family type of culture is according to them more present in the current organizational culture. They also estimate that there is a lack of atmosphere for the development of creativity, which is a characteristic of the Incubator. Although both groups put a strong emphasis on non-hierarchical organizational structure in the ideal scheme, the importance of the Guided Missile and the Incubator is higher with the young managers. In Table 7.6 we compare groups of young managers among themselves. Members of the Alumni Association of the International Full Time Program in Business Administration are the most individualistic, separate their business and personal lives the most and believe most that the environment can be controlled and directed. Compared to others they perceive time more as a sequence of events and less as a harmony of past, present and future. The group of KMBA students is interesting. They accept acting according to the rules more, are significantly more collectivistic and accept the intertwinement between business and other areas of life and work. They also go more with the flow than their colleagues (organic culture). They do not put much emphasis on the past but they perceive time more synchronically than other groups. According to them, the prevalent types in the current organizational culture are the hierarchical types of culture, in the mode of “respect your boss and the existing rules”. Participants at the business conference are more than others in favor of respecting the general rules and put less importance on personal relationships (universalism) but do not deviate on other dimensions of culture. They perceive time as a sequence of events more than others. Among all current types of organizational culture they perceive the Guided Missile the most, i.e. orientation towards the implementation of tasks and goals. The types of hierarchical organizational cultures are less important to them. That is why they favor culture that is oriented towards people and the development of their creativity, i.e. the Incubator. Let us at the end point out the differences among the three companies (Droga Kolinska, Helios and Trimo). A detailed analysis of the organizational culture of individual companies will be presented in later contributions. We compare them taking in account the organizational units in Slovenia, only. Our data reflects very well the issue of merging Droga and Kolinska into a new company, which was underway just in the period of our data collection. Managers who participated in the survey (their response rate was very low) have the lowest values on the neutral/affective dimension, which shows a high tolerance for expressing feelings in both companies. They are also in favor of less mixing of business and personal relationships. According to their opinion, the hierarchical types prevail in the current organizational culture, with the prevailing family component. Managers of Droga value the organizational culture that would be oriented towards equality and individuals, i.e. the Incubator, the highest among all companies. Color was recently acquired by Helios. A high degree of respect for rules is characteristic of both companies. A high level of impartiality in relationships is present in Helios. Managers give in to the environment less (the highest level of mechanistic culture) and in the current organizational culture see the highest value in the type of culture that builds on achieving goals and implementing tasks (the Guided Missile). Currently in Color a more formalistic and centralized type of culture (the Eiffel Tower) is present than elsewhere, for which one of the reasons might be the process of takeover by Helios.

282

Janez Prašnikar, Marko Pahor and Hugo Zagoršek Table 7.6. Comparison among Different Groups of Young Managers

Universalism (high) – Particularism (low) Individualism (high) – Communitarianism (low) Neutral (high) – Affective (low) Specific (high) – Diffuse (low) Achievement (high) – Ascription (low) Mechanistic (high) – Organic (low) Relative meaning of past Relative meaning of present Relative meaning of future Perception of time – Synchronic (high) – Sequential (low) Guided Missile – current Family – current Eiffel Tower – current Incubator – current Guided Missile – ideal Family – ideal Eiffel Tower – ideal Incubator – ideal Source: THT and own analysis.

Young managers (n = 153) 63.9 (20.1)

MScBA Alumni (n = 70) 60.5 (18.6)

Conference Participants (n = 43) 66.5 (19.6) 67 (22.5)

52.9 (23.2) 54.6 (18) 64.2 (23.1) 59.2 (13.3) 61.6 (21.4)

58.3 (23) 53.6 (18.8) 66.8 (25.1) 58.5 (13.5) 63.3 (21.2)

46.5 (21.9) 55.4 (20.1) 60.6 (21.8) 60.6 (13.5) 59.5 (23.1)

50.2 (23.2) 55.6 (14.5) 63.4 (20.7) 59.2 (12.9) 60.7 (20.4)

26.1 (6.6) 35.5 (6.2) 38.4 (6.3)

26.7 (7.3) 34.8 (6.9) 38.5 (6.7)

24.8 (6.2) 36.5 (5.1) 38.7 (5.8)

26.5 (5.6) 35.6 (6.1) 37.9 (6.4)

64.5 (21.2)

62.6 (22.7)

69.6 (17.4)

62.8 (21.6)

4.3 (2.7) 3.9 (2.1) 4 (2.5) 1.7 (1.6)

4.2 (2.7) 3.9 (2.2) 4 (2.5) 1.9 (1.7)

3.6 (2.5) 4.5 (1.7) 4.5 (2.1) 1.5 (1)

5.2 (2.8) 3.3 (1.9) 3.6 (2.6) 1.8 (1.8)

5.9 (2.3) 1.3 (1.1) 0.8 (1.2) 6 (2.4)

6.1 (2.5) 1.3 (1.1) 0.8 (1.4) 5.8 (2.5)

6.2 (2) 1.3 (1) 0.7 (1.1) 5.8 (2.4)

5.3 (2) 1.5 (1) 0.8 (0.9) 6.3 (2)

KMBA (n = 40)

Table 7.7. Comparison among Managers from Different Companies

Universalism (high) – Particularism (low) Individualism (high) – Communitarianism (low) Neutral (high) – Affective (low) Specific (high) – Diffuse (low) Achievement (high) – Ascription (low) Mechanistic (high) – Organic (low) Relative meaning of past

Managers from Kolinska companies (n = 6) (n = 355) 57.2 66.4 (22.7) (35.2) 53.3 54 (21.4) (16.3) 45.8 (10.2) 55.5 (14.9) 87.5 65.5 (23.2) (13.7) 57.3 (14.5) 57.5 (14.5) 51.7 55.8 (20.4) (14.7) 26.1 (6.9)

29.6 (5.1)

Relative meaning of present

36.6 (6)

39.2 (6.9)

Relative meaning of future

37.3 (7)

31.2 (5.5)

Droga (n = 33)

Color (n = 55)

63.7 (19.8) 47.3 (17.2)

67.8 (23.3) 54.5 (19.4)

47 (8.8) 71.2 (17.8)

51.7 (15) 68.6 (23.2) 58.4 (14.2) 49.1 (22.7) 26.7 (7.7) 36.5 (6.8) 36.8 (7.4)

53 (12) 56.1 (16.2) 28.4 (5.4) 37.1 (6.2) 34.5 (6.3)

Helios Trimo (n = 112) (n = 149) 68.9 (21.6) 53.2 (21.9) 56.8 (14.7) 64.7 (21.1) 59.8 (15.9) 59.3 (19.9) 26 (6) 36.7 (5.3) 37.3 (5.8)

64.9 (23.4) 55.8 (22.6) 58.3 (15.2) 62.8 (25.4) 56.5 (13.7) 55.8 (20.4) 25.3 (7.6) 36.3 (6.2) 38.4 (7.7)


283

Table 7.7. Continued Managers from Kolinska companies (n = 6) (n = 355) Perception of time – Synchronic (high) – Sequential (low) Guided Missile – current Family – current Eiffel Tower – current Incubator – current Guided Missile – ideal Family – ideal Eiffel Tower – ideal Incubator – ideal Source: THT and own analysis.

Droga (n = 33)

Color (n = 55)

Helios Trimo (n = 112) (n = 149)

67.8 (20.4)

61.1 (19.2)

65.7 (18.2)

71.1 (20.8)

64.5 (21.3)

69.9 (19.8)

4.4 (2.2) 3.7 (1.9) 3.8 (2) 2.1 (1.7)

2.7 (1.6) 5.3 (1.9) 4.5 (1.5) 1.5 (0.5)

2.9 (1.2) 5.3 (1.7) 4.9 (1.8) 0.9 (0.6)

3.4 (2.3) 4.3 (1.7) 5.2 (2) 1.2 (1)

5 (2.1) 3.8 (1.9) 3.4 (1.6) 1.7 (1.3)

4.8 (2.1) 3 (1.6) 3.2 (1.9) 3 (1.8)

5.5 (1.9) 1.7 (1.2) 1.1 (1.3) 5.6 (2.1)

5.7 (1) 1.7 (0.8) 1.5 (2.1) 5.2 (1.6)

5.5 (1.8) 1.7 (0.9) 0.6 (1.1)

5.9 (1.8) 1.5 (1.2) 0.9 (1.1) 5.7 (1.9)

5.8 (2.1) 1.5 (1.1) 1.3 (1.3) 5.4 (2.3)

5.2 (1.8) 1.9 (1.2) 1.2 (1.3) 5.7 (2.1)

6.2 (1.7)

Trimo differs from others on two cultural dimensions. The relationships in Trimo are the least partial and the least under the influence of emotions. On the other hand, business relationships intertwine with personal relationships. It is a company with the most democratic current organizational culture. There exists high orientation towards implementing tasks and achieving stated goals (the Guided Missile). The importance of orientation towards people and their creativity (the Incubator) is also the highest in Trimo among all companies. The opposite is true for both hierarchical organizational cultures, which are the least important in the current organizational culture. For Trimo the Incubator and the Guided Missile are the most desired types of organizational culture.

Conclusions Groups of young managers who are still studying or have recently completed the study of business and organizational sciences have at least two common characteristics: they do not have significant work experience and have developed their cultural profile in similar circumstances, i.e. in the process of transition and business education. That is why the groups are relatively similar on cultural dimensions. Through analysis we have also observed the cultural differences that stem from differences in national cultures. Young managers from Slovenia, Croatia, and Bosnia and Herzegovina, followed closely by young managers from Serbia, are on cultural dimensions very similar to Austrian managers. Differences can be observed on dimensions of universalism – particularism and individualism – communitarianism. Young managers are more particularistic and individualistic. On the other hand, young managers from Russia are on some dimensions approaching the profile of Russian managers, which was identified using the same methodology in other studies. Young managers from Russia are much more particularistic and less ready to intervene in social developments (they subjugate themselves more to the environment). Our research therefore shows that elements of national culture play an important role in defining young managers.

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With the employed methodology we have identified differences and similarities in the culture of younger and older managers in Slovenia. There are slight differences between young managers and the managers from the three Slovenian companies, especially in the calmer judgments of the managers from these companies regarding certain questions about national and organizational culture. On the other hand, there are significant differences in judgments regarding individual dimensions of culture and types of organizational culture among the three studied groups of young managers. It seems that they are influenced by their position within a company. Young managers, members of the MScBA Alumni, are more particularistic and individualistic and emphasize the point of view that they can actively participate in changing the environment. Young managers, members of the KMBA, are more universalistic, put a larger emphasis on the organic view of the environment and on the hierarchy of the existing organizational structures. Because they mainly hold positions in middle and top management and have been employed for a longer period of time, their judgment is more influenced by practical experience. Participants at the Portorož business conference hold higher managerial positions compared to the other two groups. Their attention is focused towards higher universalism; among the current types of organizational culture they emphasize the inclusion of employees in the implementation of tasks and accomplishment of goals, while among the ideal cultural types they emphasize the creation of circumstances for creativity and innovativeness of individuals. By using the Trompenaars’ methodology we have in our research pointed out differences in organizational cultures of the three selected Slovenian companies: Droga Kolinska, Helios and Trimo. The first company is in the process of merging. The data display the post-merger uncertainties of the managers from both companies. The second company is due to the nature of its activity (follower in the area of R&D, orientation in market niches) very oriented towards the implementation of stated tasks and implementation of goals. The third company, which is based on project work for individual buyers, is more oriented towards the individual and creating circumstances for their creativity. Taken as a whole, the methodology has proven itself to be a useful tool in the study of cultural profiles of various groups of managers.

References Cerović, B., and Aleksić, A. 2005. “Komentar rezultatov za Srbijo.” In Prašnikar, J. and Cirman, A. (eds): “Globalno gospodarstvo in kulturna različnost.” Časnik Finance, Ljubljana: 135–140. Debeljak, A. 1986. “Zastarelost jugoslovanstva.” Nova revija 5 (52/53): 1389–1396. Goić, S. 2005. “Komentar rezultatov za Hrvaško: pogled iz Splita.” In Prašnikar, J. and Cirman, A. (eds): “Globalno gospodarstvo in kulturna različnost.” Časnik Finance, Ljubljana: 147–151. Hofstede, G. 1980. “Culture’s Consequences: International Differences in Work-Related Values.” Beverly Hills: Sage Publications. House R. J. et al. 2004. “Culture, Leadership, and Organizations: The GLOBE Study of 62 Societies.” Thousand Oaks: Sage Publications. Jazbec, M. 2007. “Slovenian National Culture And Cross-Cultural Training.” In this book. Trompenaars, F., and Woolliams, P. 2004. “Business Across Cultures.” Chichester: Capstone.


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Trompenaars, F. 1993. “Riding the Waves of Culture: Understanding Cultural Diversity in Business.” London: The Economist Books. Vukotić, V. 2005. “Komentar rezultatov za Črno goro.” In Prašnikar, J. and Cirman, A. (eds): “Globalno gospodarstvo in kulturna različnost.” Časnik Finance, Ljubljana: 131–134. Wachtel, A. B. 1998. “Making a Nation, Breaking a Nation: Literature and Cultural Politics in Yugoslavia.” Stanford: Stanford University Press. Zagoršek, H., and Štembergar, M. 2007. “Culture and its Influence on Business Performance.” In this book.

INDEX A AAA, 166, 176, 177, 178, 179, 181, 182, 187, 188, 189, 190, 191, 192, 193, 207, 208, 210, 213, 215, 217, 219, 221, 224, 233 academic, 109 access, 113, 126, 127, 135, 136, 141, 142, 143, 148, 149, 150, 151, 152, 154, 158, 169, 236, 239, 279 accounting, 48, 51, 57, 65, 72, 97, 179, 236 accuracy, 52, 55 Ach, 298, 301 achievement, 263, 295, 298, 300, 301, 302 acquisitions, 122 activation, 126, 133 activity level, 284 actual output, 257 actuarial, 143, 144 adaptation, 186, 197 adhesion, 253 adjustment, 19, 109, 116, 160, 165, 186, 227, 239, 242, 252, 256, 257, 262, 263, 264, 265 administration, 165, 184, 236 administrative, 239 adult, 132 adults, 4, 7, 8, 136 Africa, 53 age, 89, 90, 94, 129, 130, 132, 134, 136, 137, 141, 142, 143, 144, 148, 151, 152, 153, 158, 297, 302 ageing, ix, 125, 127 agent, 8, 173 agents, 2, 3, 9, 12, 15, 18, 19, 23, 109, 112 aggregate demand, x, 63, 71, 77, 250, 255, 263 aggregates, 49 aggregation, 49, 53, 76, 85 agricultural, 47 agricultural sector, 47 aid, 296 AIDS, viii, 47, 49, 50, 51, 53, 55, 57, 59, 61, 62, 63, 65, 67, 69, 71, 72, 73, 75, 76, 77, 79, 81, 82, 83, 84, 85 air, 57, 186 airports, 57

algorithm, 186, 202, 205, 208, 210, 213, 215, 217, 219, 221, 224 alternative, 6, 35, 42, 59, 89, 94, 97, 98, 134, 135, 136, 137, 138, 153, 154, 160, 165, 167, 169, 173, 194, 197, 198, 243, 251, 258, 259, 273, 276, 282, 287 alternatives, 108, 127, 144, 147, 296 amortization, 170, 184 Amsterdam, 26, 290, 291 appendix, 49, 51, 62, 71, 94, 96, 97, 114 application, 84, 198, 205, 235, 242 appraised value, 171 arbitrage, 239 argument, 12, 49, 52, 53, 59, 60, 61, 69, 70, 113, 126, 132, 252, 264, 265 Asia, vii, 53, 247 Asian, 293, 295, 297 Asian countries, 297 assessment, 108, 110, 184, 195, 228, 279, 280, 287 assets, 107, 112, 160, 161, 163, 165, 167, 169, 170, 171, 173, 174, 235, 236, 238, 239, 279, 280, 282, 283, 284, 287 assignment, 163, 174, 235, 238 assumptions, 36, 53, 76, 186 asymmetry, 200, 203, 206, 209, 212, 214, 216, 227, 229 asymptotically, 244 Athens, 84 atmosphere, 303 attacks, 256 aura, 170 Australia, 50, 53, 298 Austria, 4, 31, 88, 91, 92, 95, 96, 99, 114, 115, 123, 250, 281, 283, 284, 285, 287, 288, 298 authority, 17, 295 autocorrelation, 35, 38, 49, 55, 62, 63, 64, 73, 80, 81, 82, 184, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 199, 200, 203, 207, 217, 219, 221, 224, 231, 244, 245, 247, 258, 259 availability, 108, 184, 279 average earnings, 153 avoidance, 295, 296 Azerbaijan, vii

288

Index

B

C

backwardness, 35, 37 balance sheet, 164, 165, 169, 170, 171, 174, 235, 236, 238 Bali, 199, 200, 245 Balkans, 294 Bank of Japan, 283 banking, 29, 279, 280, 282, 284, 286, 287, 288, 289, 292 bankruptcy, 163, 171, 173, 174, 184, 235, 238, 239 banks, 30, 112, 160, 163, 164, 165, 167, 168, 169, 171, 179, 236, 239, 276, 277, 278, 279, 280, 281, 282, 284, 287, 290, 291 base case, 21 base year, 114, 115 Basel Committee, 169, 235, 236, 245 BBB, 176, 178, 181, 182, 187, 188, 189, 190, 191, 192, 193, 207, 208, 210, 213, 215, 217, 219, 221, 224, 233 behavior, 4, 7, 9, 15, 24, 49, 64, 65, 72, 75, 170, 173, 174, 197, 198, 206, 227, 231, 234, 297 Belgium, 4, 8, 20, 31, 91, 92, 93, 95, 96, 99, 114, 115, 123, 124, 250, 255, 279, 280, 281, 283, 284, 285, 288 beliefs, x, 293, 295 benchmark, 18, 171, 173, 174, 178, 179 benchmarks, 169, 277 benefits, ix, 4, 6, 27, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 140, 141, 142, 143, 144, 148, 149, 150, 151, 152, 153, 154, 155, 158, 163, 239, 252 bias, 7, 49, 61, 85, 197, 203, 206, 217, 219, 221, 224, 227, 228, 230, 231, 232, 234, 244 birth, 8, 109 births, 7, 8 BIS, 289 black market, 2 Board of Governors, 26 boils, 37 bond market, 171, 283, 285 bonds, 161, 167, 168, 169, 171, 173, 174, 197, 235, 236, 279, 284 bootstrap, 85 BOP, 55 borrowers, 160 Bosnia, x, 293, 294, 297, 298, 300, 305 Bosnian, 298 Boston, 123, 124 bounds, 187, 188, 189, 190, 191, 192, 193, 244 breakdown, 112, 114, 119, 120, 122 Britain, 87, 131, 139, 148, 281 Brownian motion, 198 budget deficit, 256, 257, 263, 265 budget surplus, 257 business cycle, 1, 2, 3, 6, 7, 23, 253 business education, 305 business model, 110

calibration, 20, 40 Canada, 6, 48, 50, 55, 56, 57, 65, 70, 72, 73, 84 capacity, 160 capital accumulation, 12 capital cost, 38, 39 capital gains, 107 capital markets, 37, 160, 165, 169, 170, 279, 290 Capitalism, 290 carrier, 239 cash flow, ix, 160, 161, 163, 165, 171, 174 cast, 232 casting, 7, 32 catching-up, 31, 32, 33, 35, 39, 42 categorization, 127 causality, 256, 288 causation, 252 CEE, 29, 32, 39 Census, 102 Central Bank, 246, 253, 255, 278, 291 Central Europe, 294 centralized, 29, 30, 31, 32, 34, 35, 36, 41, 43, 250, 253, 303 channels, 7, 160, 287 cheating, 34, 35 child benefit, 132 children, 8, 87, 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100, 101, 128, 130, 131, 132, 133, 135, 136, 137, 138, 139, 140, 147, 148, 153, 154, 155, 157 China, 298 Chi-square, 62, 75, 80, 258, 259 circulation, 111, 122 citizens, 136 civilian, 33, 34 classes, vii, 1, 2, 3, 18, 161, 163, 167, 179, 194, 196, 206, 228, 230, 233, 234, 242 classical, 132, 139, 171, 195, 257 classification, 72, 161, 179, 227, 282 cleaning, 16 CLO, 160, 167, 169, 184, 238 closed economy, 6 clusters, 199 CML, 202 Co, 273 cohesion, 265 collateral, 165, 167, 169, 184, 239 Collateralized Debt Obligations, 167, 187, 188, 191, 207, 208, 209, 210, 212, 213, 214, 215, 216, 217, 219, 221, 224, 246 Columbia, 25 Columbia University, 25 commercial bank, 112, 167, 178, 279 commodity, 3, 11, 18, 51, 52, 76 communication, 84 Communism, 44 communitarianism, 298, 299, 301, 305

Index community, 295 compensation, 6, 127, 128, 129, 138, 139, 141, 143, 144, 148, 149, 150, 153, 154, 155, 156, 236, 265 competition, 72, 160, 280, 282 competitive advantage, 282 competitiveness, 252, 254, 256, 264, 265 complement, 52, 66, 70, 71 complementarity, 66, 70 complexity, 55, 107, 166, 170, 276 compliance, 184, 195, 236 components, viii, 3, 8, 87, 88, 94, 97, 100, 127, 128, 129, 130, 135, 142, 202, 236, 294 composition, 55, 65, 136, 179, 182, 184 computing, 179 concentrates, 106, 278 concentration, 252 conceptual model, 109 conditional mean, 197, 199 confidence, ix, 89, 90, 92, 93, 160, 174, 233 confidence interval, ix, 89, 90, 93, 160, 233 confidence intervals, ix, 89, 90, 93, 160, 233 conflict, 294 consensus, 179, 286 consolidation, 163 constant prices, 281 Constitution, 273 constraints, 35, 84, 250, 263, 264 construction, ix, 125 consumer price index, 59 consumer protection, 163 consumers, viii, 11, 14, 16, 19, 23, 36, 47, 49, 50, 66, 72, 75, 76 consumption, 6, 7, 9, 11, 12, 14, 16, 17, 19, 20, 21, 23, 24, 49, 53, 59, 65, 70, 165 contracts, 36, 169, 170, 277 control, ix, 125, 126, 174, 195, 197, 200, 205, 207, 208, 210, 213, 215, 235, 250, 253, 277, 290, 297 convergence, 35, 38, 39, 43, 250, 252, 253, 255, 263, 264, 265, 289 conversion, 64 cooking, 16 coordination, 255 Copenhagen, 125 corporate finance, 277, 291 corporate governance, 276 corporate sector, 16 corporations, 160, 179, 290 correlation, 6, 7, 54, 60, 62, 63, 76, 94, 97, 169, 170, 194, 195, 196, 203, 207, 231, 233, 242, 244, 245, 260, 261, 262, 281, 286 correlation coefficient, 195, 242, 261, 262 correlations, 94 cost minimization, 54 cost of living, 59, 60, 61, 75, 84 costs, 2, 36, 38, 39, 47, 48, 53, 57, 60, 61, 66, 70, 130, 135, 136, 140, 165, 251, 252, 253, 277, 281, 282, 287, 290 country of origin, 55 couples, 6, 7, 8, 133, 135

289

coverage, 2 covering, x, 122, 249 CPI, 59, 60, 61, 66, 69, 76, 270 creativity, 300, 303, 305, 306 credibility, 106, 108 credit, 160, 161, 165, 166, 167, 169, 170, 171, 172, 174, 176, 184, 185, 197, 234, 236, 237, 238, 239, 283, 284, 285 credit card, 161, 167, 169 credit market, 172 credit rating, 185 creditors, 235 critical value, 186, 207, 241 Croatia, x, 293, 294, 295, 296, 297, 298, 300, 301, 305 cross-country, viii, x, 87, 88, 90, 94, 96, 97, 99, 100, 101, 275 cross-sectional, 234, 235, 277, 278 cultural differences, 305 cultural influence, 294 culture, 59, 293, 294, 295, 296, 299, 300, 303, 306 currency, 47, 60, 70, 75, 114, 115, 118, 119, 120, 165, 250, 251, 253, 255, 256 current prices, 121 cycles, vii, 26, 31, 32, 35, 111, 186, 196, 197, 202 cyclical process, viii, 105, 106 Cyprus, vii, 50

D danger, 197 data analysis, 250, 251, 256 data collection, 303 data set, 49, 50, 52, 55, 69, 76, 88, 176, 184, 194, 195, 202, 242 database, 56, 83, 293, 297 debt, 160, 161, 163, 165, 167, 168, 169, 170, 171, 173, 174, 184, 200, 236, 238, 256, 257, 260, 264, 265 debtors, 171 decision making, 289 decisions, 34, 61, 75, 88, 122 decomposition, 88, 94, 95, 97, 98, 100 deficiency, 184 deficit, x, 249, 252, 253, 255, 257, 260, 263, 264, 265 deficits, 250, 253, 255, 257, 263, 265 definition, 11, 12, 51, 52, 59, 89, 90, 94, 112, 176, 238, 239 degrees of freedom, 49, 50, 55, 62, 76, 116, 231, 232, 244 demand, viii, 6, 8, 9, 18, 19, 20, 47, 48, 49, 50, 51, 52, 54, 56, 57, 58, 61, 62, 63, 64, 66, 68, 69, 71, 72, 73, 74, 75, 76, 77, 78, 79, 82, 83, 84, 85, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 117, 118, 121, 122, 169, 172, 252 demand curve, viii, 105, 106, 109, 110, 111, 115, 122

290

Index

demographic characteristics, 94 demographic data, 296 Denmark, 6, 8, 20, 88, 91, 92, 93, 95, 96, 97, 99, 100, 101, 114, 115, 123, 125, 126, 127, 131, 133, 134, 135, 138, 139, 140, 141, 144, 145, 147, 148, 149, 151, 154, 155, 157, 158, 172, 173, 250, 279, 281, 283, 284, 285, 288, 298 density, 2 Department of Commerce, 61 dependent variable, 62, 117, 119, 121, 186 deposits, 164 depreciation, 11, 12 depressed, 173, 252 deregulation, 66, 160, 255, 290 derivatives, vii, 82, 174, 236, 238, 239, 284 desire, 20, 299 detection, 101 deterrence, 34 devaluation, 256 developed countries, 9, 108, 122 developing countries, 9, 278 deviation, 22 dichotomy, 286 differentiation, 108, 252 disability, ix, 125, 126, 141, 142, 148, 149, 151, 152, 153, 154, 155, 156, 157, 158 disabled, 126, 154, 155, 157 discipline, x, 249, 252, 260 Discovery, 246 discretionary, 6 dispersion, 99 displacement, 110 disposable income, 133, 138, 156, 157 distortions, 18, 110, 197 distress, 165 distribution, 80, 116, 179, 182, 184, 186, 187, 188, 189, 190, 191, 192, 193, 194, 198, 202, 203, 217, 219, 221, 224, 233, 239, 240, 242, 243, 279 distribution function, 186, 239 disutility, 12, 14 divergence, 39, 273 diversification, 160, 169, 170, 174, 184, 236, 239 division, 8, 11 division of labor, 11 divorce, viii, 7, 8, 87, 88, 97, 98, 100, 101, 135 divorce rates, viii, 87, 88, 98, 100, 101 domestic demand, 48 dominance, 176, 263 draft, 103 duration, 2, 61, 129, 134, 141, 142, 174 duties, 184, 302

E early retirement, ix, 125, 127, 134, 141, 142, 143, 144, 145, 147, 148, 158 earnings, 4, 128, 143, 144, 153 Eastern Europe, vii, 29, 31, 32, 113, 114, 293

econometric analysis, 84 economic activity, 8, 47, 48, 160, 251, 254, 263, 264, 265 economic cycle, x, 42, 249, 253, 260, 265 economic development, 29, 38, 42, 118, 277, 278, 280, 281 economic growth, vii, 29, 117, 251, 254, 260, 276, 277, 278, 280, 291 economic incentives, 88 economic integration, 47, 252 economic performance, 251, 254, 255, 260, 261, 278 economic policy, 69, 253, 260 economic rent, 239 economic resources, 24 economic stability, 113 economic theory, 52, 76 economic union, 75 economics, 26 educational programs, 48 egalitarianism, 296 elasticity, viii, 10, 39, 47, 51, 52, 65, 68, 70, 71, 72, 75, 76, 77, 78, 79, 257 eligibility criteria, 184 emerging markets, 291 emotions, 299, 305 employability, 126 employees, ix, 48, 57, 125, 126, 144, 152, 300, 306 employers, 150 employment, x, 2, 7, 8, 15, 16, 47, 140, 141, 142, 144, 250, 251, 263, 264, 265, 279 England, 124 enlargement, 122 Enron, 179 entrepreneurs, 34, 36 entrepreneurship, 291 entropy, 55 environment, 15, 18, 122, 263, 264, 265, 287, 297, 299, 300, 302, 303, 305, 306 equality, 120, 300, 303 equilibrium, vii, 1, 2, 3, 7, 9, 11, 12, 16, 23, 24, 35, 36, 37, 38, 109, 110, 206, 251, 252, 253, 256 equilibrium price, 206 equipment, 161 equity, viii, 105, 106, 108, 122, 160, 161, 166, 167, 168, 169, 170, 171 equity market, viii, 105, 106 estimating, viii, 47, 50, 51, 52, 54, 72, 85, 202, 265 estimation process, 202 estimator, 54, 55, 109, 195, 196, 207 estimators, 49, 174, 202, 203, 227, 228 Euro, 61, 254, 256, 263, 264, 265, 290 Euromoney, 245 Europe, vii, viii, x, 2, 3, 4, 6, 7, 8, 11, 14, 17, 19, 21, 23, 25, 27, 29, 44, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 59, 60, 61, 63, 65, 66, 67, 69, 71, 72, 73, 75, 77, 79, 81, 83, 84, 85, 87, 88, 89, 91, 93, 95, 97, 99, 101, 103, 106, 115, 132, 148, 169, 170, 171, 173, 233, 246, 249, 250, 251, 253, 254, 260, 263, 264, 265, 273, 278, 279, 284, 288, 289, 290

Index European Central Bank, 246, 253, 255 European Commission, 158, 252, 258, 259, 261, 262 European Community, viii, 87, 88 European Investment Bank, 265 European Monetary System, 255 European Monetary Union, x, 179, 182, 250, 251, 253, 255, 256, 273, 275, 276, 280, 281, 282, 284, 286, 287, 288 European Union, vii, x, 26, 48, 49, 60, 66, 70, 77, 88, 249, 253, 258, 273, 276, 284, 290 Eurostat, 100 evolution, vii, 29, 88, 91, 97, 100, 108, 167 excess supply, 109 exchange rate, 48, 56, 60, 61, 70, 76, 250, 251, 252, 253, 254, 255, 256, 257, 260, 263, 264, 265 exchange rate mechanism, 253, 255 exchange rate policy, 251, 252, 254, 255, 256, 260, 263, 265 exchange rates, 56, 76, 252, 255, 256 exclusion, 61, 62, 76, 197 exercise, 281 expenditures, 55, 56, 58, 61, 74, 83, 264 exports, 252 exposure, 103, 169, 171, 173, 184, 237, 238 external environment, 300 externalities, 57, 70

F failure, 119, 120 family, vii, 1, 2, 3, 4, 6, 7, 8, 9, 11, 12, 14, 15, 16, 23, 87, 88, 100, 130, 131, 132, 135, 136, 137, 138, 140, 145, 147, 156, 157, 300, 303 family behavior, 14 family budget, 12 family members, 4, 6, 11, 130 family relationships, 300 family structure, 6, 8, 11 Fannie Mae, 171 Far East, 298 FASB, 236 fax, 275 February, 247 Federal Deposit Insurance Corporation, 159 federal law, 171 Federal Reserve, 26, 245, 283, 289, 290, 291 Federal Reserve Bank, 289, 291 Federal Reserve Board, 245, 283, 290 fee, 107 feedback, 159, 256 feelings, 303 femininity, 295, 296 fertility, viii, 87, 88, 94, 96, 97, 98, 100, 101 fibers, 32 finance, 34, 150, 160, 161, 163, 166, 167, 168, 170, 172, 173, 178, 230, 235, 236, 264, 277, 279, 281, 282, 287, 288, 291 financial crises, 160, 197

291

financial crisis, 254 financial development, 276, 278, 280, 289 financial distress, 290 financial institution, vii, 29, 34, 36, 37, 38, 41, 160, 276, 280, 282, 284, 288 financial institutions, vii, 29, 34, 36, 38, 41, 160, 276, 280, 282, 284, 288 financial intermediaries, 276 financial markets, 42, 160, 197, 276, 277, 282, 284, 288 financial resources, 30, 166, 265 financial sector, 289 financial support, 135 financial system, x, 30, 42, 43, 76, 160, 170, 275, 276, 277, 278, 280, 281, 282, 284, 285, 286, 287, 288, 291 financing, vii, viii, 3, 18, 19, 29, 30, 34, 35, 36, 37, 38, 39, 40, 42, 105, 106, 163, 169, 236, 238, 239, 277, 280, 282, 289 Finland, 88, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 114, 115, 123, 127, 128, 131, 132, 134, 135, 138, 139, 140, 141, 144, 145, 147, 148, 149, 151, 154, 155, 157, 158, 250, 281, 283, 284, 285 firms, vii, viii, 1, 3, 6, 8, 10, 12, 18, 19, 23, 37, 105, 106, 107, 108, 109, 110, 112, 113, 116, 117, 129, 167, 277, 279, 280, 284, 289 fiscal policy, 20, 23, 250, 251, 253, 254, 255, 260, 263, 264, 265 five-factor model, 174 flexibility, 18, 171, 173, 186, 250, 252, 253, 254, 255, 257, 264 flight, 53, 57 floating, 163, 176 flow, ix, 16, 66, 107, 159, 161, 165, 166, 303 fluctuations, 9, 21, 24, 56, 61, 255, 256 FMA, 159 focusing, 48 food, 60 forecasting, ix, x, 84, 159, 160, 176, 233, 234 foreign banks, 284 foreigners, 136 formal sector, 23 fragmentation, 160, 165, 170 France, 1, 2, 4, 6, 7, 8, 9, 12, 16, 18, 20, 22, 24, 26, 28, 31, 48, 50, 56, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 91, 92, 93, 95, 96, 99, 100, 114, 115, 123, 171, 245, 250, 255, 279, 281, 283, 284, 285, 298 Freddie Mac, 171 free choice, 158 free trade, 252 freedom, 49, 50, 62, 76, 80, 110, 116, 231, 232, 244 full employment, 264 funding, 160, 161, 167, 169, 170, 171, 173, 237, 239 fundraising, viii, 105, 106, 111, 112, 114, 116, 117, 118, 119, 120, 121, 122 funds, viii, 105, 106, 107, 108, 109, 110, 111, 112, 113, 114, 115, 117, 119, 121, 122, 160, 163, 169, 238, 239, 277, 279, 283, 284

292

Index

G GAAP, 236 gauge, 279 GDP per capita, 281 gender, 297 generation, 165, 238 Germany, vii, 4, 6, 8, 48, 50, 56, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 91, 92, 95, 96, 99, 100, 112, 114, 115, 123, 245, 250, 255, 276, 277, 279, 280, 281, 283, 284, 285, 288, 289, 298 Globalization, 290 goals, 57, 169, 251, 300, 303, 305, 306 goods and services, 75 governance, 276, 288, 290 government, 4, 8, 9, 17, 18, 20, 60, 164, 171, 174, 194, 197, 250, 253, 255, 263 government budget, 250, 253 government securities, 174 GPD, 120 grades, 176, 182, 187, 188, 189, 190, 191, 192, 193, 207, 208, 210, 213, 215, 217, 219, 221, 224, 280, 286 grading, 280 grandparents, 89, 91, 101 graph, 58, 254, 255, 256 Great Britain, 127, 130, 131, 132, 134, 137, 138, 140, 141, 144, 147, 148, 150, 153, 154, 157, 298 Greece, 7, 8, 20, 50, 73, 84, 88, 89, 90, 91, 92, 93, 95, 96, 97, 98, 99, 100, 101, 114, 115, 123, 250, 255, 279, 281, 283, 284, 285, 287 gross domestic product, vii, x, 4, 6, 31, 34, 35, 48, 113, 117, 118, 119, 120, 121, 247, 249, 254, 255, 256, 257, 260, 266, 279, 281, 283, 284, 287 gross national product, 6, 7, 8, 33, 34 grouping, 76, 280 groups, viii, xi, 47, 50, 62, 63, 92, 127, 144, 290, 294, 296, 297, 299, 300, 301, 302, 303, 305, 306 growth, vii, ix, x, 6, 7, 8, 25, 29, 30, 31, 32, 34, 35, 37, 38, 39, 40, 43, 47, 48, 49, 50, 57, 60, 61, 66, 70, 75, 106, 107, 113, 114, 115, 116, 117, 119, 120, 121, 122, 163, 169, 198, 249, 250, 251, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265, 276, 277, 278, 280, 281, 287, 288, 289, 290, 291 growth rate, 31, 35, 39, 40, 43, 169, 198, 256, 257 growth theory, 30 guidance, 171

H hands, 34, 111, 253 harm, 253, 303 harmonization, 254 harmony, 303 Harvard, 28, 44, 45, 103, 123, 290

hazards, 186 health, ix, 12, 14, 125, 126, 148, 158, 264, 265, 279 health insurance, 12, 14 health problems, 126, 148 heart, 2 hedge funds, 169 hedging, 165, 170, 174, 184, 236, 238 heme, 128, 135, 137, 138, 139, 140, 141, 142, 144, 145, 147, 150 Hermes, 289 heroism, 295 heterogeneity, x, 11, 109, 116, 238, 257, 275, 288 heterogeneous, 92, 108, 169, 284 heteroskedasticity, ix, 116, 118, 119, 120, 121, 122, 159, 176, 186, 195, 196, 197, 198, 199, 202, 203, 217, 219, 221, 224, 227, 228, 231, 232, 233, 242 higher quality, 65 Holland, 264, 273 homogeneity, 49, 53, 55, 62, 75, 85 homogenous, 9, 16, 18, 169, 197 horizon, 251 House, 293, 306 household, 2, 3, 6, 7, 8, 14, 16, 17, 20, 21, 24, 25, 59, 88, 89, 90, 97 household sector, 2, 6 households, 4, 9, 18, 19, 23, 87, 88, 89, 90, 91, 92, 98 housing, 130, 131, 132, 135, 136, 140, 154, 155, 158, 167, 172 housing benefit, 130, 132, 135, 154, 155, 158 human, ix, 30, 33, 48, 125, 126, 296 human capital, 30, 33, 48 Hungarian, 294 Hungary, 32, 33, 114, 115, 123, 298 husband, 8, 89, 98 hypothesis, viii, ix, 55, 106, 112, 113, 116, 117, 120, 125, 126, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 203, 207, 217, 219, 221, 224, 227, 231, 242, 244, 245, 256, 260, 278 hypothesis test, 196, 197, 227 hysteresis, 254, 262, 265

I ice, 170, 173, 287 ICM, 159 id, 260 ideology, 294 idiosyncratic, 11, 12, 14, 174, 200, 235 imbalances, viii, 105, 106, 107, 109, 110, 111, 116, 121, 123 imitation, vii, 29, 34, 35, 38 immigration, 56, 66, 73 implementation, 126, 256, 259, 300, 303, 306 imports, 56 in transition, 35 incentive, vii, 1, 4, 8, 12, 23, 35, 148, 158

Index incentives, ix, 4, 34, 40, 125, 126, 127, 131, 132, 133, 134, 138, 140, 141, 144, 145, 147, 148, 158, 165, 277 incidence, viii, 87, 88, 93, 97, 100, 119, 229 inclusion, 75, 200, 231, 232, 306 income, vii, viii, ix, 1, 3, 4, 6, 9, 12, 14, 16, 17, 19, 20, 22, 23, 24, 47, 51, 52, 53, 59, 65, 67, 69, 75, 83, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 138, 139, 140, 141, 142, 143, 144, 145, 147, 148, 149, 150, 151, 152, 153, 154, 155, 156, 157, 159, 161, 169, 171, 173, 174, 281, 286 income support, 136 income tax, 3, 4, 6, 20, 23 incomes, 11, 53 increased access, 154 indebtedness, 257, 265, 277 independence, 186, 250, 263 independent variable, 117, 119, 121, 200 India, 298 indication, 35, 59, 129 indicators, 278, 279, 280, 281, 282, 283, 284, 286, 287 indices, 169, 170, 179, 182, 200, 235, 276, 277, 278, 285, 286, 287, 288 individualism, 296, 297, 298, 299, 301, 305 industrial, 112, 127, 131, 142, 289, 290, 300 industrialized countries, 1 industry, ix, 47, 48, 52, 66, 108, 125, 126, 279, 289 inelastic, viii, 64, 65, 66, 71, 72, 75, 76, 106, 107, 121, 122 inertia, 35, 38, 39 infinite, 36 inflation, x, 118, 249, 250, 251, 252, 255, 256, 257, 260, 263, 264, 265, 270 inflationary pressures, 252, 253 informal sector, vii, 1, 2, 3, 8, 20, 23 Information System, 158 infrastructure, 48, 57, 167 inherited, 300 innovation, vii, viii, 20, 29, 34, 35, 105, 106, 160 Innovation, 44, 124, 290 insight, 22 inspection, 176, 186 Institutional Investors, 291 institutional reforms, 254 institutionalization, vii, 1, 7, 8, 11, 23 institutions, x, 29, 34, 35, 36, 40, 41, 275, 276, 278, 287, 291, 294 instruments, 160, 169, 171, 174, 238, 253, 278 insurance, ix, 3, 12, 14, 18, 19, 125, 126, 127, 128, 130, 134, 135, 136, 137, 138, 139, 140, 141, 144, 148, 149, 150, 152, 154, 238, 284 insurance companies, 284 integration, 47, 50, 75, 185, 197, 251, 252, 254 integrity, 184, 238 intensity, 33, 126, 294, 295 interaction, 12, 15, 109 interactions, 8, 72 interest rates, 174, 230, 252, 255, 256, 264

293

interface, 296 intermediaries, 277, 288, 289 internal financing, 34 International Monetary Fund, vii, 24, 28, 41, 56, 159, 273, 283, 290 international trade, 72, 75, 84 internet, ix, 57, 66, 106, 109 interpretation, 138, 197, 202, 276, 280, 285, 290 interrelations, 278 interval, 11, 131 intuition, 10, 280 investment, viii, x, 3, 6, 12, 16, 18, 21, 34, 36, 48, 52, 105, 106, 107, 108, 109, 110, 112, 113, 115, 116, 119, 120, 121, 122, 123, 160, 165, 166, 167, 168, 169, 170, 173, 176, 182, 186, 187, 188, 189, 190, 191, 192, 193, 197, 207, 208, 210, 213, 215, 217, 219, 221, 224, 234, 250, 255, 263, 264, 265, 282, 284, 290 investors, viii, ix, 105, 106, 107, 108, 109, 110, 111, 112, 113, 116, 117, 118, 121, 122, 123, 159, 161, 163, 165, 166, 169, 170, 171, 173, 174, 235, 236, 238, 277, 282, 284, 287, 288 ions, 9, 49, 54, 62, 66, 101, 109, 127, 135, 148 IPO, 108, 120 IPOs, 108, 112, 118, 119, 120, 121, 122 Ireland, 88, 89, 91, 92, 93, 95, 96, 99, 100, 114, 115, 123, 250, 254, 279, 281, 283, 284, 285, 287, 298 Israel, 298 Italy, viii, 1, 4, 6, 8, 31, 47, 48, 50, 56, 57, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 90, 91, 92, 95, 96, 97, 98, 99, 100, 101, 114, 115, 123, 127, 129, 131, 133, 134, 136, 138, 139, 140, 141, 143, 144, 145, 147, 148, 150, 152, 153, 154, 155, 156, 157, 158, 250, 255, 279, 280, 281, 283, 284, 285, 287, 288, 298 iteration, 295

J January, 176, 179, 197, 245, 263 Japan, 32, 33, 35, 276, 277, 281, 282, 283, 284, 285, 286, 288, 290, 292, 298 Japanese, 50, 284, 288, 290 job creation, viii, 105, 106 jobs, 7, 8, 126, 136 judgment, 300, 306 jurisdiction, 163 jurisdictions, 239 justification, 109

K keiretsu, 288 kernel, 207 King, 24, 26, 44, 278, 291 Kirchhoff, 123

294

Index

L

M

labor, vii, ix, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 26, 56, 125, 126, 127, 128, 132, 133, 135, 136, 141, 142, 143, 144, 152, 153, 252, 300 labor force, 2, 56, 126 labor markets, 2, 23 labour market, ix, 24, 125, 126, 127, 128, 132, 133, 135, 136, 141, 142, 143, 144, 153 land, 254 language, 52 large banks, 236, 239, 279 Latin America, 26 law, 2, 95, 172 laws, 2, 38, 163, 171, 238 lead, ix, 35, 55, 62, 97, 106, 109, 110, 111, 117, 122, 148, 238, 251, 257, 297 legal issues, 239 leisure, viii, 3, 16, 17, 19, 21, 47, 62, 66, 67, 68, 69, 71, 72, 75, 76, 78 lending, 160, 167, 169, 278, 279, 280, 286, 287 liberalism, 299 liberalization, 42, 160 life cycle, 7 life span, 143 lifestyle, 60 likelihood, 54, 55, 99, 195, 202, 208, 210, 213, 215, 217, 219, 221, 224, 242, 243 limitation, 128, 134, 149, 232, 233 limitations, 108, 128, 130, 134, 135, 136, 137, 140 linear, 11, 51, 84, 186, 195, 196, 197, 198, 203, 207 linguistic, 294 linkage, 184, 236 links, 48 liquidate, 36 liquidation, 36 liquidity, viii, 106, 107, 112, 117, 122, 160, 165, 166, 169, 170, 171, 173, 184, 185, 186, 194, 197, 233, 290 living standard, vii living standards, vii loans, 35, 161, 167, 169, 171, 173, 184, 238, 239, 279 locus, 297 lognormal, 239 London, 28, 44, 124, 247, 288, 289, 290, 307 long period, 49, 110 long-term, 109, 110, 121, 122, 194, 205, 206, 231, 250, 251, 256, 277 losses, 169, 174, 184, 236, 238 low power, 295 low-income, 4 Luxembourg, 88, 89, 91, 92, 95, 96, 99, 250, 279, 281, 283, 284, 285, 288

Maastricht criteria, x, 249, 250, 251, 253, 254, 255, 256, 257, 258, 259, 260, 261, 262, 263, 264, 265 Maastricht Treaty, 251, 252, 253, 254, 255, 256, 259, 260, 262 macroeconomic, 2, 8, 48, 122, 251, 252, 263, 264 macroeconomic adjustment, 252, 264 macroeconomic models, 2 macroeconomic policies, 48 macroeconomic policy, 263 macroeconomic stabilisation, 263 Madison, 31, 34 mainstream, 129 maintenance, 48 males, 296 Malta, 50 management, ix, 107, 111, 159, 160, 161, 164, 165, 169, 170, 171, 173, 176, 234, 282, 284, 302, 306 management practices, 284 manipulation, 163 marginal costs, 60 marginal product, 11, 16, 19 marital status, 89, 92, 93, 94 market, vii, viii, ix, x, 1, 2, 3, 4, 6, 7, 8, 9, 10, 11, 12, 14, 16, 17, 18, 19, 20, 21, 22, 23, 24, 30, 31, 35, 37, 48, 60, 76, 101, 105, 106, 107, 108, 109, 110, 111, 112, 117, 122, 125, 126, 127, 128, 132, 133, 135, 136, 141, 142, 143, 144, 149, 150, 152, 153, 159, 160, 165, 166, 167, 169, 170, 171, 172, 173, 174, 176, 179, 184, 194, 196, 197, 198, 199, 230, 233, 234, 235, 236, 238, 278, 279, 280, 281, 282, 283, 284, 285, 287, 288, 289, 291, 300, 306 market capitalization, 279, 280, 283 market economy, 10 market prices, 76, 166 market segment, x, 160, 176, 288 market structure, 23 market value, 184 marketability, 184 marketing, 57 markets, ix, x, 48, 60, 66, 101, 106, 107, 108, 109, 110, 111, 112, 113, 116, 117, 121, 122, 159, 160, 165, 169, 170, 173, 186, 195, 196, 197, 275, 276, 277, 279, 280, 283, 284, 288, 291 marriage, viii, 7, 8, 12, 87, 88, 94, 96, 97, 98, 100, 101 marriages, 8, 98, 100, 101 married couples, 132 married women, 96, 97, 101 MAS, 295 Massachusetts, 273 matrix, 52, 53, 54, 55, 62, 80, 186, 202, 203, 232 matrix algebra, 52, 186, 203 means tested, 130, 137 measures, x, 51, 52, 126, 127, 128, 133, 135, 136, 173, 200, 203, 228, 231, 243, 256, 275, 280, 281, 282, 286, 288

Index median, 280, 284 Mediterranean, 50, 65, 76, 84, 85, 294 Mediterranean countries, 50, 65, 76, 85 membership, 128, 135 men, 2, 153, 297 metric, 276 Mexican, 84 Middle East, 298 migrant, 73 migrant population, 73 military, 33, 34 minimum wage, 130, 136, 137 MIT, 24, 27, 123, 124, 246, 273, 288, 290 mixing, 303 mobility, 76, 129, 252 model specification, 176, 186, 205, 229, 232, 234, 242 modeling, viii, 47, 55, 184 models, 2, 3, 7, 8, 16, 18, 19, 20, 23, 49, 50, 62, 84, 87, 110, 117, 119, 120, 122, 123, 170, 186, 196, 198, 202, 203, 205, 206, 227, 229, 230, 231, 232, 233, 234, 293 momentum, 35 monetary policy, 250, 251, 253, 254, 255, 257, 263, 265, 276, 278, 279, 280, 286, 289, 291 monetary union, 250, 252, 253, 263, 291 money, 53, 57, 60, 61, 82, 281, 283, 284, 291 money illusion, 53 Montenegro, x, 293, 294, 296, 297, 298, 299, 300, 301 moratorium, 171 mortgage, 161, 167, 168, 171, 172, 173, 174 mortgages, 161, 167, 168, 171, 173 mother tongue, 296 motherhood, 87, 92 mothers, viii, 87, 88, 89, 90, 91, 92, 93, 94, 96, 97, 98, 100, 101 motion, 198 movement, 76, 110, 166, 234 multidimensional, 290 multiplicity, 166 multiplier, 48, 52 multiplier effect, 48, 52 multivariate, 186 myopic, 10, 18

N NASDAQ, 122 nation, 295 national, vii, x, 6, 30, 55, 83, 131, 143, 250, 251, 253, 255, 256, 257, 263, 264, 278, 293, 295, 296, 298, 299, 305, 306 national culture, 293, 295, 296, 298, 299, 305 national debt, 253, 255, 256, 257, 264 national economies, 257 natural, 15, 18, 49 negative outcomes, 87

295

negative relation, 72 negativity, 54, 62, 73 neglect, 3, 282 net income, 4, 6, 133 net present value, 37 Netherlands, 6, 62, 73, 74, 75, 88, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 114, 115, 123, 126, 127, 129, 131, 132, 133, 134, 136, 138, 139, 140, 141, 142, 144, 145, 147, 148, 150, 152, 154, 158, 250, 275, 279, 280, 281, 283, 284, 285, 288, 298 New York, 44, 247, 273, 289, 290 New Zealand, 50 Ni, 101 noise, 196, 244 non-linear, 197, 202, 231 normal, 52, 186, 187, 188, 189, 190, 191, 192, 193, 194, 202, 203, 217, 219, 221, 224, 233, 239, 242, 243 normal distribution, 187, 188, 189, 190, 191, 192, 193, 194, 202, 203, 217, 219, 221, 224, 233, 239, 242, 243 norms, 252, 254, 265 North America, 2, 48 Norway, 8, 114, 115, 123 novelty, 35 nuclear, 32 nuclear power, 32 null hypothesis, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 203, 217, 219, 224, 227, 231, 242, 244, 245

O obligation, 136, 149, 167, 168, 169, 171, 174, 236 obligations, ix, 136, 141, 142, 143, 144, 159, 160, 166, 167, 168, 169, 170, 171, 173, 174, 184, 235, 238, 254, 255, 260 observations, vii, xi, 29, 38, 39, 49, 61, 63, 75, 80, 89, 108, 113, 116, 117, 119, 120, 176, 179, 187, 188, 189, 190, 191, 192, 193, 194, 197, 198, 217, 219, 221, 224, 232, 233, 243, 244, 291, 294 obsolete, 239 occupational, 142, 144, 151, 152 OECD, 3, 4, 6, 7, 26, 27, 44, 84, 108, 127, 130, 131, 158, 260, 273, 283, 291 old age, 143, 151 Olympic Games, 61 opacity, 169, 170 openness, 252 operator, 11, 113, 196 optimization, 15, 202 organic, 299, 300, 301, 303, 306 organization, 57, 300 organizational culture, 296, 300, 302, 303, 305, 306 organizations, 69 orientation, x, 275, 276, 277, 278, 282, 283, 284, 285, 286, 287, 288, 296, 300, 303, 305, 306 ownership, 174, 236

296

Index

oxygen, 32

P paper, 1, 2, 3, 15, 23, 24, 25, 27, 29, 44, 47, 48, 49, 50, 51, 53, 55, 56, 57, 58, 61, 64, 65, 69, 72, 73, 75, 76, 77, 83, 85, 88, 102, 103, 119, 123, 124, 159, 169, 174, 176, 186, 198, 202, 206, 233, 235, 245, 246, 247, 273, 275, 276, 278, 288, 289, 290, 291, 292, 296 parameter, 17, 38, 50, 51, 72, 84, 186, 199, 202, 228, 230, 232, 233, 286, 287 parameter estimates, 230, 232, 233 parents, 8, 89, 90, 94 Paris, 27, 44, 66, 247, 289, 291 partnership, 100 partnerships, 107 payroll, 2 PDI, 295 penalty, 10 pension, 108, 110, 141, 142, 143, 144, 145, 148, 149, 150, 151, 152, 153, 155, 158, 284 pension system, 142, 152, 153 pensioners, 4, 8, 126, 152, 153 pensions, 142, 143, 151, 152, 153, 154, 157, 158 PER, 60 per capita, 31, 51, 53, 59, 60, 61, 281, 286 per capita expenditure, 51 per capita income, 286 perception, 299 perfect competition, 60 performance, ix, 2, 35, 62, 73, 118, 124, 159, 160, 165, 171, 173, 184, 250, 251, 254, 255, 257, 258, 260, 261, 263, 264, 265, 278, 289, 291 personal, 4, 20, 23, 66, 153, 165, 297, 299, 303, 305 personal computers, 66 personal relations, 297, 299, 303, 305 personal relationship, 297, 299, 303, 305 planning, 75 platforms, 170 play, 8, 75, 97, 230, 250, 252, 253, 276, 297, 305 Poland, 32, 33, 114, 115, 123, 298 policy instruments, 253 political crisis, 40 political instability, 48 polyolefins, 32 pools, 161 poor, 52, 62, 118, 144, 194, 197, 264, 280 population, 16, 61, 72, 73, 89, 100 portfolio, 108, 161, 163, 165, 167, 169, 170, 171, 173, 174, 184, 236, 238 portfolio management, 161 portfolios, 160, 165, 167, 169, 176, 234 Portugal, 6, 20, 50, 73, 88, 89, 90, 91, 92, 93, 95, 96, 99, 100, 101, 114, 115, 123, 249, 250, 255, 279, 280, 281, 283, 284, 285, 287 positive correlation, 94

post-Maastricht period, x, 249, 258, 260, 261, 263, 264, 265 poverty, 6, 87 poverty trap, 6 power, vii, 32, 59, 60, 62, 197, 200, 205, 206, 230, 234, 295, 297 predictability, 170, 231 prediction, 39 preference, 18, 54, 176, 299, 300 pregnancy, 97 premium, x, 160, 170, 176, 184, 234, 236 premiums, 141, 167 present value, 37 pressure, 164, 253 price changes, 66, 71, 75 price effect, 52 price elasticity, 51, 54, 69 price index, 59 price movements, 194 price stability, x, 249, 250, 251, 252, 253, 254, 255, 265 prices, viii, 16, 17, 20, 47, 49, 51, 53, 57, 59, 60, 61, 64, 67, 69, 70, 72, 75, 76, 82, 83, 109, 110, 119, 122, 166, 169, 170 pricing behavior, 173 private, viii, 17, 36, 105, 106, 107, 108, 122, 141, 144, 154, 167, 171, 174, 235, 281, 283, 284 private sector, 281, 283 privatization, 291 proactive, 297 proactive behavior, 297 probability, 2, 8, 11, 16, 36, 94, 95, 96, 98, 240, 243 production, vii, 1, 2, 3, 6, 7, 8, 9, 10, 12, 16, 18, 19, 20, 21, 22, 23, 24, 25, 27, 57, 60, 70, 72, 126, 127, 131, 282, 287 production function, 9, 10, 282, 287 productivity, 7, 10, 11, 252 profit, 10, 19, 112, 117 profitability, 112, 113, 279 profits, 3, 6, 19, 37, 113 program, 136 progressive tax, 154, 264 progressive tax system, 264 promote, 253, 265 property, 21, 53, 54, 55, 236 proposition, 165 protection, 2, 132, 163, 169, 171, 174, 184, 236 proxy, 112, 122, 200, 279 public, ix, 36, 108, 125, 126, 127, 141, 142, 144, 154, 164, 171, 173, 174, 235, 250, 252, 253, 255, 256, 257, 264, 279 public debt, 255, 256, 257, 264 public expenditures, 126 public finance, 252, 253 public investment, 255, 264 public pension, 142 public sector, 144, 173 punishment, 35 purchasing power, vii, 59, 60

Index purchasing power parity, vii, 59

Q quality of life, 295 quality of service, 47, 48 questionnaire, 296, 297 questionnaires, 55, 296

R random, 17, 194, 195, 202, 206, 234, 242 random walk, 194, 195, 206 range, 4, 8, 62, 70, 108, 142, 143, 144, 147, 157, 169, 182, 187, 188, 189, 190, 191, 192, 193, 197, 207, 208, 210, 213, 215, 217, 219, 221, 224, 235, 297 rating agencies, 184 ratings, 171, 194 real terms, 118 real wage, 252 reality, 40 recession, 111, 122, 250, 251, 252, 254, 255, 264 recessions, 8, 19, 253, 254 recognition, x, 160 reconcile, 34 recovery, 43, 114, 264, 265 redistribution, 276 reduction, 21, 70, 109, 111, 116, 133, 143, 151, 161, 239, 252, 255, 256, 257 REE, 10, 11 reflection, 205, 284, 287 reforms, 42 regional, 48, 76, 252, 264, 265, 273, 295 regional policy, 252 regression, 116, 119, 121, 186, 196, 200, 202, 231, 232 regressions, 54, 85, 116, 118 regular, 8, 15, 31 regulation, 108, 277, 290 regulations, 3, 276 regulators, 236 regulatory capital, 169, 171, 174, 235, 236, 238, 239 regulatory requirements, 166 rehabilitation, 149, 158 rejection, 62, 196, 197, 207, 245 relationship, 20, 29, 36, 72, 76, 84, 107, 185, 186, 194, 238, 281, 287, 297, 299 relationships, 72, 160, 238, 277, 287, 295, 297, 299, 300, 303, 305 relative prices, 19 relative size, 286 relatives, 89, 90, 91, 94, 95, 96, 97, 98, 99, 100, 101 relevance, 132, 251 reliability, 231 remittances, 184 rent, 12

297

repackaging, 169, 235 replacement rate, ix, 2, 125, 131, 132, 133, 134, 137, 138, 139, 140, 141, 144, 145, 147, 153, 154, 155, 156, 157, 158 repo, 184 research, viii, x, 47, 48, 49, 50, 52, 55, 65, 76, 84, 87, 88, 108, 170, 173, 186, 200, 280, 287, 293, 294, 295, 296, 297, 298, 299, 300, 302, 305, 306 research and development, vii, 29, 30, 31, 33, 34, 35, 36, 37, 38, 39, 41, 43, 44, 45, 108, 306 researchers, 47, 49, 51, 55, 87, 293 residential, 21, 161 residuals, 62, 80, 186, 195, 196, 198, 199, 200, 203, 217, 219, 221, 224, 228, 229, 231, 232, 233 resilience, 171 resource allocation, 121, 288 resource management, 111 resources, 2, 3, 16, 18, 23, 24, 30, 34, 107, 108, 109, 110, 111, 116, 122, 166, 252, 265, 277 responsibilities, 251 responsiveness, 69, 279, 286, 288 restructuring, 165, 171 retail, 170 retention, 184, 238 retirees, 144, 147, 148 retirement, ix, 125, 127, 141, 143, 144, 147, 148, 151, 152 retirement age, 148, 151, 152 returns, 16, 108, 110, 111, 112, 118, 122, 186, 195, 239, 252 returns to scale, 16, 252 revaluation, 109 revenue, 4, 253, 264 revolutionary, 300 rigidity, 109, 121 risk, ix, x, 3, 9, 12, 14, 17, 48, 112, 125, 148, 159, 160, 161, 165, 166, 169, 170, 171, 173, 174, 176, 185, 196, 197, 203, 234, 235, 236, 237, 238, 239, 277, 278, 282, 284, 295 risk aversion, 17 risk management, ix, 159, 160, 169, 170, 173, 282, 284 risk sharing, 3, 9, 12, 14 risks, ix, 125, 126, 165, 166, 170, 184, 238, 299 robustness, 118, 120, 122, 195, 203, 243, 285 Romania, 32, 33, 40 Rome, 66, 247 routines, 185 royalties, 167 Russia, x, 293, 295, 296, 298, 299, 300, 301, 305 Russian, 294, 297, 299, 305

S safeguard, 184, 276 salary, 16 sales, 112, 118, 119, 120, 121, 122, 165

298

Index

sample, 49, 50, 55, 61, 62, 89, 91, 94, 95, 179, 181, 184, 187, 188, 189, 190, 191, 192, 193, 194, 195, 197, 198, 217, 219, 221, 224, 227, 228, 229, 234, 235, 243, 244, 256, 295, 296, 297, 299 sample mean, 194, 197, 244 savings, 12, 276, 280 savings banks, 280 scaling, 239, 242 Scandinavia, 284 Schmid, 277, 290 scientists, 33, 34 scores, 282, 283, 284, 285, 286, 287 search, 173 searching, 91 securities, 160, 161, 163, 165, 166, 167, 168, 169, 171, 173, 174, 176, 235, 236, 238, 277, 279 securitisation, 161, 162, 164, 166, 167, 168, 235, 236, 237, 238, 239 security, ix, 2, 3, 4, 6, 11, 23, 125, 126, 163, 166, 167, 168, 169, 170, 174, 184, 235, 236, 237, 238, 239, 264 selecting, ix, 36, 69, 125 sensitivity, 7, 24, 89, 165, 185, 200, 203, 206, 227, 228, 229, 230, 232, 233, 279, 280 separation, 97, 235 Serbia, x, 293, 294, 295, 296, 297, 298, 299, 300, 301, 305 Serbs, 300 series, 20, 31, 49, 56, 97, 108, 116, 170, 174, 176, 177, 178, 179, 181, 182, 184, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 200, 202, 203, 205, 206, 207, 208, 210, 213, 215, 217, 219, 221, 224, 227, 228, 229, 230, 231, 232, 233, 234, 242, 243, 244, 256 service provider, 168 services, 3, 18, 47, 48, 52, 57, 59, 65, 66, 69, 75, 236, 277, 282, 288 shadow economy, 7, 8 shape, 173, 174, 239, 280 shareholders, 113 shares, 53, 56, 62, 63, 67, 69, 70, 72, 73, 74, 83, 84, 107, 108, 112, 113, 152 sharing, 3, 8, 9, 12, 14 shock, ix, 7, 56, 106, 122, 200, 252 shocks, ix, 10, 11, 14, 48, 61, 160, 176, 200, 229, 234, 250, 252, 253, 255, 264, 265 short period, 65 short run, 64 shortage, 111, 160 short-term, ix, 107, 109, 110, 111, 116, 121, 122, 159, 167, 199, 232, 233, 234, 250, 251, 283, 284, 295 short-term interest rate, 199 shy, 228 sign, 51, 62, 66, 70, 72, 97, 107, 203, 205, 206, 217, 219, 221, 224, 229, 231, 232, 234, 257, 282, 298 signaling, 287 signals, 282, 284

significance level, 65, 227, 256, 257, 258, 259, 260, 261, 262 signs, 63, 206, 228 similarity, 295 simulations, 2, 21, 42 sine, 287 singular, 202, 210, 215, 219, 224 sites, 58 skewness, 182, 184, 187, 188, 189, 190, 191, 192, 193, 194, 217, 219, 221, 224, 243 Slovakia, 114, 115, 123 Slovenia, x, 293, 294, 295, 296, 297, 298, 300, 301, 302, 303, 305, 306 Sm, 239 small banks, 279 small firms, 279, 280 SMEs, 167 smoothing, 3, 19 soccer, 61 social assistance, 126, 131, 134, 135, 136, 138, 139, 140, 141, 153 social change, 295 social cohesion, 265 social costs, 252 social development, 305 social group, 300 social security, 2, 4, 6, 11, 23, 126, 264 social security payments, 264 social structure, 2 socialist, 294 software, 217, 219, 221, 224 solutions, 3, 254 solvency, 171 South America, 53 Southampton, 45 sovereignty, 253 Spain, 4, 6, 8, 20, 48, 50, 56, 57, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 78, 79, 88, 89, 90, 91, 92, 93, 95, 96, 97, 99, 100, 101, 105, 114, 115, 123, 127, 130, 131, 132, 133, 134, 137, 138, 139, 140, 141, 143, 147, 148, 150, 153, 154, 155, 156, 157, 158, 173, 250, 279, 281, 283, 284, 285, 298 specialization, 84 spectrum, 101, 280, 286, 287, 288 speed, 35, 39, 43, 227, 257, 258, 259, 261, 262, 265 sponsor, 163, 174, 237, 238, 239 sporadic, 232 spouse, 132, 133, 134, 135, 137, 139, 140, 145, 147, 148, 153, 155, 157 stability, x, 2, 39, 113, 249, 250, 251, 252, 253, 254, 255, 256, 260, 265 Stability and Growth Pact, x, 249, 253, 254, 263 stabilization, 48, 255, 263 stabilizers, 250, 253 stages, viii, 30, 32, 36, 39, 105, 106, 107, 108, 277 stakeholders, 40, 107, 108 standard deviation, 20, 96, 97, 98, 99, 100, 194, 242, 283, 284, 301

Index standard error, 72, 90, 91, 117, 118, 119, 120, 121, 187, 188, 189, 190, 191, 192, 193, 196, 202, 208, 210, 213, 215, 217, 244, 261, 262 standard model, 7 standardization, 169, 170, 173 standards, 185, 247 statistical analysis, 180 statistical inference, 186 statistics, 48, 49, 56, 62, 114, 115, 182, 184, 186, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197, 217, 219, 221, 224, 231, 242, 244 statutory, 6, 20, 171 steady state, 31, 32, 35, 38, 40 steady-state growth, 43 steel, 32 stochastic, ix, 6, 10, 11, 16, 1725, 28,, 159, 174, 197, 198, 199, 206, 227, 231, 233, 234, 245 stochastic processes, 16, 197 stock, 12, 16, 17, 19, 21, 37, 108, 112, 113, 118, 260, 279, 280, 281, 283, 284, 290, 291 stock exchange, 108, 284 stock market development, 280, 281 stock markets, 113, 118, 280, 290 strategic, 15, 36, 48, 112 strategies, 169, 186, 263 strength, 2, 171 stress, 57, 110, 294 structural changes, 49, 254 structural equation model, 85 structural equation modeling, 85 structuring, 169 students, 296, 302, 303 subjective, 286, 287 subsistence, 8 substitutes, 66, 252 substitution, 7, 120, 161, 184, 230 summer, 65, 197 supplements, 130, 132, 140, 151 suppliers, 60 supply, viii, 6, 7, 11, 12, 14, 15, 18, 19, 23, 57, 60, 105, 106, 107, 108, 109, 110, 111, 112, 113, 115, 116, 117, 121, 122, 252, 278, 279, 286 supply curve, viii, 105, 106, 109, 110, 111, 121 surplus, 109, 122, 252, 256, 257, 260, 265 sustainable development, 48 sustainable growth, 250 Sweden, 8, 88, 90, 91, 93, 95, 96, 97, 99, 100, 101, 114, 115, 123, 127, 128, 131, 133, 134, 135, 137, 138, 139, 140, 141, 142, 144, 145, 147, 148, 149, 151, 154, 155, 156, 157, 158, 172, 173, 250, 281, 283, 284, 285, 288, 298 switching, 19 Switzerland, 6, 44, 114, 115, 123 symmetry, 49, 54, 55, 62, 70, 75, 84, 85 syndicated, 34 syndrome, 30 synthesis, 290 synthetic fiber, 32 system analysis, 62

299

systems, 2, 4, 29, 49, 50, 52, 84, 88, 101, 264, 277, 278, 280, 281, 282, 287

T takeover, 303 tangible, 278 target variables, 186 targets, 163, 255 tax credit, 130, 137 tax credits, 130, 137 tax evasion, 10 tax policy, 2 tax rates, 2, 4, 6, 8, 17, 20, 22 tax receipt, 4 tax system, 3, 8 taxation, 2, 3, 6, 8, 9, 11, 16, 17, 20, 21, 131, 132, 155, 253, 264 taxes, 3, 4, 9, 11, 17, 20, 23, 57 taxonomy, 291 Taylor expansion, 96 Taylor series, 97 technological change, 29, 109, 110, 121 technology, vii, 9, 10, 18, 19, 29, 35, 166, 169 temporal, 3 territory, x, 293, 294 test procedure, 243 test statistic, 62, 194, 196, 197, 202, 227, 231, 242, 244 The Economist, 238, 247, 307 theory, vii, 24, 29, 34, 35, 36, 42, 45, 53, 54, 67, 76, 84, 253, 286, 287, 288, 289 thinking, 295 third party, 118, 174, 235, 236 threshold, 244, 245 threshold level, 245 time, ix, x, 3, 6, 12, 14, 16, 17, 18, 19, 31, 32, 36, 40, 42, 43, 49, 50, 54, 61, 65, 66, 72, 88, 91, 94, 97, 100, 102, 107, 108, 109, 110, 112, 115, 116, 117, 118, 119, 120, 121, 122, 125, 126, 127, 128, 129, 130, 132, 134, 135, 136, 137, 138, 140, 141, 147, 148, 149, 151, 153, 157, 160, 170, 174, 176, 179, 180, 182, 184, 186, 187, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 199, 202, 203, 206, 208, 210, 213, 215, 217, 219, 221, 224, 227, 228, 229, 231, 233, 234, 238, 242, 243, 244, 260, 278, 293, 295, 300, 301, 302, 303, 304, 305, 306 time consuming, 293 time periods, 54 time series, 31, 49, 116, 170, 174, 176, 179, 182, 184, 186, 188, 189, 190, 191, 192, 193, 194, 195, 196, 197, 198, 202, 203, 206, 208, 210, 213, 215, 217, 219, 221, 224, 228, 229, 231, 233, 242, 243, 244 timing, 41, 42, 43, 174, 184 title, 174, 236, 239 tolerance, 174, 236, 240, 303 top management, 302, 306

300

Index

total revenue, 4 tourism, viii, 47, 48, 49, 50, 52, 53, 55, 56, 57, 58, 59, 60, 61, 62, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74, 75, 76, 77, 84, 85 tourist, 47, 48, 52, 55, 56, 59, 60, 61, 62, 83, 84 trade, 69, 72, 75, 84, 108, 118, 120, 122, 161, 167, 252, 257, 284 trade-off, 202, 278 trading, 169, 170, 173, 174, 178, 179, 197, 233, 236 tradition, 171 training, 126 tranches, 163, 166, 169, 170, 176, 179, 184, 194, 198, 230, 237, 238 transaction costs, 36, 165, 277 transactions, ix, 108, 122, 159, 161, 169, 170, 171, 173, 174, 176, 178, 179, 196, 197, 205, 227, 228, 230, 233, 235, 236, 238, 239 transfer, 6, 17, 160, 161, 163, 170, 171, 173, 174, 184, 235, 236, 238, 239, 265 transformation, 17, 160, 165, 170, 176, 182, 186, 194, 205, 239, 242, 243 transformations, 186 transistors, 109 transition, 30, 43, 138, 141, 305 transition economies, 43 transitional countries, x, 293, 297 transmission, x, 238, 275, 276, 278, 279, 280, 285, 286, 287, 289, 291 transparency, x, 112, 160, 169, 170, 173, 176, 234 transparent, 170 transportation, 47, 48, 57, 61, 65, 66 travel, viii, 47, 50, 53, 55, 61, 72, 74, 75, 76, 84, 85 Treasury, 194 trend, ix, 50, 57, 61, 63, 70, 87, 117, 119, 120, 121, 159, 195, 196, 207, 227, 229, 233, 254, 257 triggers, 184 trust, 163, 169, 174, 184, 235 trusts, 163 Turkey, 296, 298 type II error, 197

U U.S. economy, 43 U.S. Treasury, 194 uncertainty, x, 34, 62, 64, 160, 202, 230, 233, 252, 295, 296 undergraduate, 297 underground economy, 2, 9, 18, 19, 22 unemployment, ix, x, 2, 125, 126, 127, 128, 129, 130, 131, 132, 133, 134, 135, 136, 137, 138, 141, 142, 144, 145, 147, 148, 150, 152, 158, 249, 250, 251, 252, 254, 256, 260, 261, 262, 263, 264, 265 unemployment insurance, ix, 125, 126, 127, 134, 135, 136, 137, 141, 144, 148, 152 unemployment rate, 251, 254, 260, 261, 262, 265 unification, 253 uniform, 294

United Kingdom, vii, 4, 32, 48, 50, 56, 57, 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 73, 74, 75, 78, 79, 83, 84, 87, 88, 89, 90, 91, 92, 93, 95, 96, 98, 99, 100, 101, 114, 115, 123, 250, 273, 276, 280, 281, 283, 284, 285, 288 United States, vii, 1, 2, 3, 4, 6, 7, 8, 11, 14, 17, 19, 21, 23, 24, 25, 27, 28, 49, 66, 87, 102, 107, 108, 109, 110, 290 univariate, 176 unmarried women, 92, 93 updating, 112 urban renewal, 264 USSR, vii, 29, 31, 32, 33, 34, 44

V validity, 203 values, viii, x, 47, 56, 61, 62, 64, 65, 66, 67, 68, 70, 71, 72, 75, 76, 77, 114, 124, 186, 195, 196, 202, 203, 205, 207, 217, 219, 221, 224, 227, 228, 229, 230, 239, 240, 245, 280, 281, 293, 295, 296, 297, 298, 299, 300, 301, 302, 303 variability, 260, 265 variable, 17, 60, 84, 116, 117, 118, 119, 120, 121, 122, 130, 186, 231, 239, 256, 257, 258, 259, 260, 261, 263, 265, 286, 287 variables, 21, 49, 61, 80, 88, 101, 108, 112, 113, 116, 117, 118, 119, 120, 121, 122, 186, 194, 195, 232, 256, 257, 285, 286, 287 variance, ix, 62, 88, 94, 96, 97, 98, 99, 100, 102, 160, 174, 176, 187, 188, 189, 190, 191, 192, 193, 194, 196, 197, 198, 199, 200, 203, 205, 206, 207, 208, 213, 217, 219, 221, 224, 228, 229, 230, 231, 232, 233, 234, 239, 242 variation, viii, 87, 88, 89, 90, 91, 92, 94, 96, 97, 98, 100, 101, 117, 122, 129, 132, 134, 144, 174, 194, 196, 198, 228, 231, 255, 256, 257, 260, 284 vector, 53, 62, 116, 196, 203 vehicles, 107, 168, 169, 238 venture capital, viii, 34, 43, 105, 106, 108, 109, 112, 113, 117, 122, 124 violent, 42 visa, 132 visible, 133 volatility, ix, x, 6, 159, 160, 173, 174, 176, 177, 178, 179, 184, 186, 194, 196, 197, 198, 199, 200, 205, 228, 229, 230, 231, 232, 233, 234, 235, 254 voluntary unemployment, ix, 125, 127

W wages, 2, 252 wealth, vii, 29, 130, 278 welfare, 2, 23, 127, 288 welfare state, 127 Western countries, 6, 114, 115 Western Europe, vii, 31, 39, 85, 114, 115

Index wisdom, 173 women, 2, 8, 89, 90, 91, 92, 93, 94, 96, 97, 98, 99, 100, 101, 132, 133, 297 workers, ix, 3, 4, 12, 19, 34, 125, 129, 131, 143, 150 working hours, 4, 149 World Bank, vii, 44, 288, 289, 291, 292 WorldCom, 179

301

Y yield, 55, 169, 171, 173, 174, 199, 202, 233, 235 yield curve, 174, 199 young adults, 4, 7, 8 Yugoslavia, vii, x, 50, 293, 294, 295, 296, 307

European Economic and Political Issues, Volume 9

Understanding British and European political issues

India: Economic, Political and Social Issues

India: Economic, Political and Social Issues

Thailand: Economic, Political and Social Issues

Europe: Financial Crisis and Security Issues (European Political, Economic, and Security Issues)

Asian Economic and Political Developments (Asian Political, Economic and Security Issues)

Issues in Economic Thought (Economic Issues, Problems and Perspectives)

Economic Forecasting (Economic Issues, Problems and Perspectives)

Accounting Theory: Conceptual Issues in a Political and Economic Environment

Transitional Puzzles: Reflections on Social, Economic and Political Issues

Political Events and Economic Ideas

Political Competition and Economic Regulation

Issues in Economic Thought

Political Issues (Contemporary Native American Issues)

Economic Issues And Development (Contemporary Native American Issues)

Economic Rights: Conceptual, Measurement, and Policy Issues

Economic and Social Issues in Agricultural Biotechnology

Currency Interventions, Fluctuations and Economic Issues

Economic and Social Issues in Agricultural Biotechnology

Bioethical Issues, Sociological Perspectives (Advances in Medical Sociology, Volume 9)

European Economic Integration: Limits and Prospects

OECD Economic Surveys: Sweden - Volume 2005 Issue 9

China: Economics Political and Social Issues

European Integration and Political Conflict (Themes in European Governance)

Vietnam: A Guide to Economic and Political Developments (Guides to Economic and Political Developments in Asia)

European Yearbook of Minority Issues: Volume 4, 2004 5

The State: Theories and Issues (Political Analysis)

European Union: An Historical and Political Survey

Emerging Economic Systems in Asia: A Political and Economic Survey

European Economic and Political Issues, Volume 9

Understanding British and European political issues

India: Economic, Political and Social Issues

India: Economic, Political and Social Issues

Thailand: Economic, Political and Social Issues

Europe: Financial Crisis and Security Issues (European Political, Economic, and Security Issues)

Asian Economic and Political Developments (Asian Political, Economic and Security Issues)

Issues in Economic Thought (Economic Issues, Problems and Perspectives)

Economic Forecasting (Economic Issues, Problems and Perspectives)

Accounting Theory: Conceptual Issues in a Political and Economic Environment

Transitional Puzzles: Reflections on Social, Economic and Political Issues

Political Events and Economic Ideas

Political Competition and Economic Regulation

Issues in Economic Thought

Political Issues (Contemporary Native American Issues)

Economic Issues And Development (Contemporary Native American Issues)

Economic Rights: Conceptual, Measurement, and Policy Issues

Economic and Social Issues in Agricultural Biotechnology

Currency Interventions, Fluctuations and Economic Issues

Economic and Social Issues in Agricultural Biotechnology

Bioethical Issues, Sociological Perspectives (Advances in Medical Sociology, Volume 9)

European Economic Integration: Limits and Prospects

OECD Economic Surveys: Sweden - Volume 2005 Issue 9

China: Economics Political and Social Issues

European Integration and Political Conflict (Themes in European Governance)

Vietnam: A Guide to Economic and Political Developments (Guides to Economic and Political Developments in Asia)

European Yearbook of Minority Issues: Volume 4, 2004 5

The State: Theories and Issues (Political Analysis)

European Union: An Historical and Political Survey

Emerging Economic Systems in Asia: A Political and Economic Survey

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