EU Eastern Neighborhood: Economic Potential and Future Development

EU Eastern Neighborhood

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Marek Dabrowski

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Maryla Maliszewska

Editors

EU Eastern Neighborhood Economic Potential and Future Development

Editors Prof. Dr. Marek Dabrowski Center for Social & Economic Research (CASE) ul. Sienkiewicza 12 00–010 Warsaw Poland [email protected]

Dr. Maryla Maliszewska Center for Social and Economic Research (CASE) ul. Sienkiewicza 12 00-010 Warsaw Poland [email protected]

ISBN 978-3-642-21092-1 e-ISBN 978-3-642-21093-8 DOI 10.1007/978-3-642-21093-8 Springer Heidelberg Dordrecht London New York Library of Congress Control Number: 2011931674 # Springer-Verlag Berlin Heidelberg 2011 This work is subject to copyright. All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks. Duplication of this publication or parts thereof is permitted only under the provisions of the German Copyright Law of September 9, 1965, in its current version, and permission for use must always be obtained from Springer. Violations are liable to prosecution under the German Copyright Law. The use of general descriptive names, registered names, trademarks, etc. in this publication does not imply, even in the absence of a specific statement, that such names are exempt from the relevant protective laws and regulations and therefore free for general use. Printed on acid-free paper Springer is part of Springer Science+Business Media (www.springer.com)

Preface

This volume contains a selection of research output from the Specific Targeted Research Project (STREP) on ‘EU Eastern Neighborhood: Economic Potential and Future Development (ENEPO),’ which was funded under the EU Sixth Framework Program, Priority 7 ‘Citizens and Governance in a Knowledge Based Society’, Contract No 028736 (CIT5). The main objective of the ENEPO project was to examine the potential of the ENP and the EU strategic partnership with Russia to upgrade relations between the enlarged EU and CIS countries in the spheres of trade, investment, labor movement, technical cooperation, and economic and governance reforms in the CIS, with special attention given to mutual interdependence among these cooperation areas. In this volume we use the term ‘Commonwealth of Independent States’ and its abbreviation, CIS, purely for analytical convenience in order to define the group of 12 successor countries of the former USSR (all former Soviet republics except for the Baltic states, which are now EU members). We are aware that the role of the CIS as a regional integration block, founded at the end of 1991 in order to provide a ‘velvet divorce’ from the former USSR, is gradually decreasing. Furthermore, in August 2009, Georgia terminated its membership in the CIS.1 Before the launch of the ENEPO project, the vast majority of previous studies in the areas of trade, investment, and labor migration focused on the economic integration of CEE with the EU, leaving EU cooperation with the CIS outside of the main stream of analysis. With the EU’s Eastern Enlargement in 2004 and 2007, research interests have evolved towards analyzing the economic relations of the entire EU27 with their new neighbors to the East. In the governance sphere, a large body of research has focused on the postcommunist transition of individual CIS countries and the CIS region as a whole, as well as cross-country and cross-regional comparative analyses. However, few

1 The analyzed group of countries has been sometimes referred to as the New Independent States (NIS). However, as almost 20 years have passed since the end of 1991 when they obtained independence, this notion also seems inaccurate.

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Preface

studies have attempted to analyze the role of the European integration process as the potentially most powerful factor determining the success or failure of building a market economy and democratic society in the post-communist world. Few researchers have investigated the adoption of European economic, legal and political institutions by CIS countries, the appropriateness of these institutions to CIS development needs, and their potential to speed up the transition and modernization processes in this region. The ENEPO project broke with the narrow focus of the majority of previous studies and concentrated on achieving the following specific objectives: l l

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Analysis of the development gap between CIS and EU countries Analysis of trade flows (including energy resources) and implications of free movement of goods and services between CIS countries and the EU Analysis of the sources of and obstacles to capital movement between CIS countries and the EU Exploration of the actual and potential labor migration and, more generally, free movement of people, and their implications for CIS and EU countries Identification of the governance gap between CIS and EU institutions and the differences in CIS laws and regulations with respect to the acquis as well as the potential role of the EU and ENP in closing the discrepancies Drawing policy recommendations related to reform strategies in CIS countries and further development of the ENP and related EU policies towards its Eastern neighbors

The innovative approach of the ENEPO research agenda consisted of a deeper investigation of the interrelations between the above-mentioned economic cooperation and policy reform areas along with an underlying assumption of a far-going interdependence between progress achieved within each area of integration. The thematic outline of this volume largely follows the above list of research objectives. In the Chap. 1, Irina Sinitsina identifies various dimensions of the development gap between CIS countries and the EU. In Chap. 2, Arne Melchior brings a geographical economics perspective to the analysis of the income gap and cohesion processes in Europe as a result of the increasing economic integration of the continent. Maryla Maliszewska, Iryna Orlova and Svitlana Taran introduce the concept of ‘deep’ trade and economic integration in Chap. 3 and estimate the impact of the removal of non-tariff barriers between the EU and selected CIS partners. In Chap. 4, Wojciech Paczynski and Vladimer Papava concentrate on a strategically important component of EU-CIS trade relations, namely energy supply and transit. Alina Kudina and Malgorzata Jakubiak provide an empirical analysis of the dominant foreign direct investment (FDI) strategies and major obstacles to FDI in selected CIS economies in Chap. 5. In Chap. 6, Vladimir Borgy and Xavier Chojnicki assess the demographic and economic consequences of migration in Europe and neighborhood countries in the context of population aging, using a multi-regions CGE-OLG model INGENUE2. Matthias Luecke continues the migration topic in the following Chap. 7, concentrating his analysis on the direct and indirect income effects of international labor migration and remittances in

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selected CIS countries. In Chap. 8, Inna Melnykovska and Rainer Schweickert analyze the external determinants of institutional change in transition economies such as the EU and NATO membership perspectives, association and trade agreements with the EU, and WTO membership. The topic of institutional change is continued in Chap. 9, in which Anna Kolesnichenko discusses the concept of institutional harmonization and its potential benefits and costs for EU neighbors. In Chap. 10, Roman Mogilevsky and Aziz Atamanov focus on the role of technical assistance (especially that which is provided by the EU) in fostering the economic and institutional transformation of CIS countries. In Chap. 11, Wojciech Paczynski discusses the current and potential role of the ENP in anchoring economic reforms in CIS countries. In the final Chap. 12, Marek Dabrowski provides an overview of EU-CIS economic relations and EU policies towards this region, bringing together the analysis and conclusions of the entire volume. The contributions published in this volume are based on earlier, much larger versions of research papers and reports prepared within the ENEPO project. They were, however, subject to re-editing and updating in late 2010 and early 2011, taking into account the most recent developments in the analyzed areas. The ENEPO project itself was conducted from May 1, 2006 until April 30, 2009 by a consortium of 11 research institutes led by CASE – Center for Social and Economic Research in Warsaw. Apart from CASE, the consortium consisted of Center for Economic and Financial Research CEFIR in Moscow, Center for Social and Economic Research CASE-Kyrgyzstan in Bishkek, Center for Social and Economic Research CASE-Transcaucasus in Tbilisi, Center for Social and Economic Research CASE Ukraine in Kiev, Centre d’Etudes Prospectives et d’Informations Internationales CEPII in Paris, Centre for European Policy Studies (CEPS) in Brussels, Foundation for Social and Economic Research CASE Moldova in Chisinau, Institute for Market Economics (IME) in Sofia, Kiel Institute for the World Economics (IfW) in Kiel and Norwegian Institute of International Affairs (NUPI) in Oslo. The editors of this volume, who also acted as the scientific coordinators of the ENEPO project, would like to express their gratitude to all participating institutes, their researchers, and administrative staff. The editors would like to especially acknowledge CASE Vice-President Sebastien Leclef, who effectively managed the ENEPO project through its entire life span. Special thanks go to Paulina Szyrmer, who provided detailed editorial support in preparing this volume. Needless to say, all the views, opinions and policy recommendations presented in this volume are those of the respective authors only and do not necessarily reflect the position of the project donor (European Commission), project institutional participants, institutions which the authors have worked for or have been associated with, and other contributors to ENEPO project and this publication.

March 9, 2011

Marek Dabrowski Maryla Maliszewska

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Contents

1

The Development Gap Between the CIS and EU . . . . . . . . . . . . . . . . . . . . . . . . 1 Irina Sinitsina

2

East–West Integration: A Geographical Economics Approach . . . . . . . 23 Arne Melchior

3

Deep Integration with the EU: Impact on Selected ENP Countries and Russia . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 Maryla Maliszewska, Iryna Orlova, and Svitlana Taran

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Energizing EU-FSU Relations: Challenges and Opportunities . . . . . . . . 61 Wojciech Paczynski and Vladimer Papava

5

The Motives and Impediments to FDI in the CIS . . . . . . . . . . . . . . . . . . . . . . 71 Alina Kudina and Malgorzata Jakubiak

6

Global Ageing and the Macroeconomic Consequences of Migration from Neighborhood Countries to Europe . . . . . . . . . . . . . . . . 83 Vladimir Borgy and Xavier Chojnicki

7

Income and Distribution Effects of Migration and Remittances . . . . . 101 Matthias Luecke

8

Institutional Convergence of the CIS Towards European Benchmarks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123 Inna Melnykovska and Rainer Schweickert

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Institutional Harmonization in the Context of EU Cooperation with its Neighbors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 145 Anna Kolesnichenko

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Technical Assistance to CIS Countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 Roman Mogilevsky and Aziz Atamanov

11

European Neighborhood Policy and Economic Reforms in the Eastern Neighborhood . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 Wojciech Paczyn´ski

12

Economic Relations Between the EU and CIS . . . . . . . . . . . . . . . . . . . . . . . . 185 Marek Dabrowski

List of Figures

Fig. 1.1 Fig. 1.2 Fig. 1.3 Fig. 1.4 Fig. 1.5 Fig. 1.6 Fig. 1.7 Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 2.4 Fig. 2.5 Fig. 2.6 Fig. 2.7 Fig. 2.8 Fig. 2.9 Fig. 2.10 Fig. 2.11 Fig. 4.1 Fig. 5.1 Fig. 5.2 Fig. 5.3 Fig. 5.4 Fig. 6.1 Fig. 6.2

Poverty levels and income inequality, 2005 . . . . . . . . . . . . . . . . . . . . . . . KI, KEI and constituent pillars across country groups (2009) . . . . KEI performance trends, 1995–2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Logistics Performance Index components (2010) . . . . . . . . . . . . . . . . Logistics Performance Index and GDP (PPP) per capita (2009) . . Regression of EPI (2010) on GDP (PPP) per capita (2007) . . . . . . Institutions: WBGI (2010) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . A stylized European space with 90 regions . . . . . . . . . . . . . . . . . . . . . . . Income levels in the WEST scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Average income levels in EU27/EEA regions by longitude, 1995 and 2005 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Income change in the WIDER scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . Income change in the WTO scenario . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Income changes in the SPATIAL scenario . . . . . . . . . . . . . . . . . . . . . . . Per capita income growth rate averages, 1995–2005 . . . . . . . . . . . . . Change in domestic regional inequality in European countries. Trend changes in Gini coefficients during 1995–2005 . . . . . . . . . . . East–West regional growth differences within countries . . . . . . . . . ‘Capital city effects’ in European countries . . . . . . . . . . . . . . . . . . . . . . A combined scenario: changes from WEST, for regions along the second latitude . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Oil and gas trends in the EU27, 1996–2009 (million tons of oil equivalent) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FDI inflows to the CIS, 1997–2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FDI stock per capita in the CIS in 2007 and in 2009 . . . . . . . . . . . . . Strategic roles of CIS subsidiaries in the operations of their parent companies . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Reasons to invest in the CIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . The ten regions of the INGENUE2 model . . . . . . . . . . . . . . . . . . . . . . . . The individual life cycle . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

3 10 11 14 14 16 18 26 31 31 32 32 33 34 35 37 39 40 63 74 75 77 77 86 87

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Fig. 6.3 Fig. 6.4 Fig. 6.5 Fig. 7.1 Fig. 7.2 Fig. 7.3 Fig. 7.4 Fig. 7.5 Fig. 7.6 Fig. 10.1 Fig. 10.2 Fig. 11.1 Fig. 11.2

List of Figures

Total factor productivity (% of North America level) . . . . . . . . . . . . Results of the UN migration scenario (difference from baseline scenario) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Annual migration flows into WE (in millions) . . . . . . . . . . . . . . . . . . . Selected CIS countries: Total GDP, 1990–2008 (year 2000 set at 100) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Household total consumption, 1990–2008 (year 2000 set at 100) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Gross fixed capital formation, 1990–2008 (year 2000 set at 100) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Migrant remittances, 1997–2009 (USD million) . . . . . . . . . . . . . . . . Russia: migrant remittances, 2001–2009 (USD billion) . . . . . . . . . Georgia: incomes across and within the regions . . . . . . . . . . . . . . . . . Differences in TA supply per capita between CIS countries in 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Mean, maximum and minimum increase in country’s transition score in 1990–2007 by country group . . . . . . . . . . . . . . . . . Attitudes towards democracy in EU and CIS countries . . . . . . . . . Regulatory quality in ENP and non-ENP CIS countries, 1996–2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

89 95 97 103 103 104 105 106 115 160 161 177 179

List of Tables

Table 3.1

Table 3.2 Table 3.3 Table 3.4 Table 3.5 Table 3.6 Table 5.1 Table 5.2 Table 6.1 Table 6.2 Table 6.3 Table 7.1 Table 7.2 Table 8.1 Table 8.2 Table 8.3 Table 8.4 Table 8.5 Table 9.1

Percentage of yearly production costs spent by exporters to the EU in 2006 in order to ensure product compliance with EU norms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Border costs in Ukraine and other CIS countries in 2004 and 2006 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assumptions on border costs in Ukraine and other CIS countries in 2004 and 2006 (in % of export value) . . . . . . . . . . . . . Heritage Foundation Index of Economic Freedom, 2008 . . . . . . Assumptions on barriers to trade in services (ad-valorem tariff equivalents of barriers to trade in services), 2006 . . . . . . . . Welfare, GDP, Wage and Trade Implications of DCFTA between CIS5 and the EU . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . FDI inflows in % of domestic investment in CIS, 1997–2008 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Assessment of problems faced by foreign investors in the CIS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Yearly net migration flows by origin and destination countries in 2005 (in thousands) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Net migration flows by regions until 2050 (in thousands) . . . . . . Contribution rates evolution in % . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Moldova: CGE simulation results (base values and percentage changes in real terms) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Georgia: CGE simulation results (base values and percentage changes in real terms) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Exogenous variables used in the regressions . . . . . . . . . . . . . . . . . . Chronology of EU, NATO, and WTO accession . . . . . . . . . . . . . . Aggregate results and comparative EU-indicators . . . . . . . . . . . . . Economic vs. political determinants . . . . . . . . . . . . . . . . . . . . . . . . . . . Reduced model versions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Composition of GDP in % of total, 2008 . . . . . . . . . . . . . . . . . . . . . .

48 51 52 53 53 56 81 81 92 93 98 111 116 130 132 134 136 137 150

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Table 11.1 Table 12.1 Table 12.2 Table 12.3 Table 12.4 Table 12.5

List of Tables

Difference in differences estimates of the ENP impact . . . . . . . EU: selected directions of exports of goods in %, World ¼ 100, 2009 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Share of exports to EU-25 as a proportion of the country’s total exports . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Labor remittances as % of GDP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . Foreign direct investment, inward stock, 2005 . . . . . . . . . . . . . . . . Partnership and cooperation agreements between EU and CIS countries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

180 187 188 189 191 192

List of Abbreviations

ACAA ADB bcm bn BoP CA CE CEE CES CGE CGE-OLG CIS COMECON CWB

DCFTA EA EAAP EaP EBRD ECA EE EEN ENEPO ENP ENP AP ENPI EPI

Agreement of Conformity Assessment and Acceptance Asian Development Bank billion cubic meters billion Balance of Payments Central Asia Central Europe Central and Eastern Europe Constant Elasticity of Substitution Computable General Equilibrium (Model) Computable General Equilibrium Overlapping-Generation (Model) Commonwealth of Independent States Council for Mutual Economic Assistance (CMEA) (EU) Candidates and Western Balkan Countries: Albania, Bosnia & Herzegovina, Croatia, Kosovo, Macedonia, Montenegro, Serbia, Turkey Deep and Comprehensive Free Trade Agreement/s Europe Agreement/s Europe Agreement Additional Protocol/s Eastern Partnership European Bank for Reconstruction and Development Europe and Central Asia Eastern Europe Eastern European Neighbors EU Eastern Neighborhood: Economic Potential and Future Development (FP6 Funded Project) European Neighborhood Policy European Neighborhood Policy Action Plan/s European Neighborhood and Partnership Instrument Environmental Performance Index

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EU EU10 EU12 EU15

EU25 EU27 EUR FDI FE FSU FTA GAMS GDN GDP GOST GSP GTAP HBS HME IFC IFPRI ICT IMF ITGI JHA KAM LNG MAP MENA M&E MFN mn MNE MTO NBKR NGO NMS

NACE

List of Abbreviations

European Union EU12 Minus Bulgaria and Romania See NMS Austria, Belgium, Denmark, Finland, France, Germany, Greece, Ireland, Italy, Luxemburg, Netherlands, Portugal, Spain, Sweden, United Kingdom All Member States of the EU Since 2005 (EU27 Minus Bulgaria and Romania) All Member States of the EU Since 2007 (EU12+EU15) Euro Foreign Direct Investment Fixed Effects (Estimator) Former Soviet Union Free Trade Agreement/s General Algebraic Modeling System Global Development Network Gross Domestic Product Gosudartvennyi Standard (State Standard) Generalized System of Preferences Global Trade Analysis Project Household Budget Survey/s Home Market Effect International Financial Corporation International Food Policy Research Institute Information and Communication Technologies International Monetary Fund Interconnection Turkey-Greece-Italy Justice and Home Affairs Knowledge Assessment Methodology Liquefied Natural Gas Membership Action Plan (in NATO) Middle East and North Africa Monitoring and Evaluation Most-Favored Nation million Multi-National Enterprise/s Money Transfer Office/s National Bank of the Kyrgyz Republic Non-Governmental Organization/s New Member States of the EU: Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Malta, Poland, Romania, Slovakia, Slovenia Nomenclature Statistique des Activite´s E´conomiques (Statistical Nomeclature of Economic Activity)

List of Abbreviations

NAFTA NATO NEG NTB NUTS ODA OECD OLS PAYG PCA PISA POLS PPP R&D SAA SAM SEE SPS TA TAA TAP TACIS TB TC TPF UK UN UNCTAD UNDP UNESCO US USD USSR VAT WB WBGI WDI WE WHO WITS WTO

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North American Free Trade Agreement North Atlantic Treaty Organization New Economic Geography Non-Tariff Barriers Nomenclature of Territorial Units for Statistics Official Development Assistance Organization for Economic Cooperation and Development Ordinary Least Squares (Regression) Pay-As-You-Go (Pension System) Partnership and Cooperation Agreement/s Program for International Student Assessment Pooled Ordinary Least Squares (Regression) Purchasing Power Parity Research and Development Stabilization and Association Agreement/s (with the EU) Social Accounting Matrix South Eastern Europe Sanitary and Phyto-Sanitary (Measures) Technical Assistance Trade and Association Agreement/s Trans Adriatic Pipeline Technical Assistance to the Commonwealth of Independent States Tuberculosis Technical Cooperation Total Factor Productivity United Kingdom United Nations United Nations Conference on Trade and Development United Nation Development Program United Nations Educational, Scientific and Cultural Organization United States (of America) United States Dollar Union of Soviet Socialist Republics Value Added Tax World Bank World Bank Governance Indicators World Development Indicators Western Europe World Health Organization World Integrated Trade Solution World Trade Organization

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Contributors

Aziz Atamanov PhD student at the Maastricht Graduate School of Governance; Economist at CASE-Kyrgyzstan in Bishkek, [email protected] Vladimir Borgy Economist at the Banque de France (Monetary and Financial Analysis Directorate), previously worked at CEPII in Paris and for the French Ministry of Economy, Finance and Industry, [email protected] Xavier Chojnicki Research Fellow at CEPII in Paris; Associate Professor of Applied Economics at the University of Lille 2, [email protected] Marek Dabrowski Co-founder and President of CASE, former Deputy Minister of Finance of Poland (1989–1990), policy consultant to governments and central banks of more than 20 transition and developing countries, Marek.Dabrowski@ case-research.eu Małgorzata Jakubiak Economist at the Directorate General for Trade of the European Commission, previously worked as an Economist at CASE (1997–2008) and served as CASE Vice-President (2007–2008), [email protected] Anna Kolesnichenko Economist at UniCredit/Bank Austria in Vienna; Research Associate at CASE Ukraine since 2001, [email protected], [email protected] Alina Kudina CASE Fellow; Research Associate at CASE Ukraine (since 1998); Assistant Professor of International Business at Warwick Business School; Associate at the UK’s Advanced Institute of Management Research, [email protected]

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Contributors

Matthias Luecke Senior Research Economist at the Kiel Institute for the World Economy and Adjunct Professor at Kiel University; Senior Economist at the International Monetary Fund (2000–2003), [email protected] Maryla Maliszewska Economist at the World Bank; Economist at CASE (1996– 2010) and CASE Fellow specializing in macroeconomic and trade policy related issues, [email protected] Arne Melchior Senior Research Fellow at the Norwegian Institute of International Affairs (NUPI) in Oslo; previously served as the Assistant Director and Head of Department at NUPI, [email protected] Inna Melnykovska Research Associate in the Department of Political Science at the Christian-Albrechts-University of Kiel; Research Fellow at the Kiel Institute for the World Economy, [email protected] Roman Mogilevsky Executive Director of CASE-Kyrgyzstan in Bishkek and CASE Fellow, [email protected] Iryna Orlova Economist at CASE Ukraine in Kyiv, [email protected] Wojciech Paczynski CASE Fellow and Member of the Supervisory Board of CASE Ukraine in Kyiv, CASE Economist since 2000, Wojciech.Paczynski@ case-research.eu Vladimer Papava Senior Fellow at the Georgian Foundation for Strategic and International Studies, member of the CASE Advisory Council, former Minister of Economy (1994–2000) and former Member of the Parliament of Georgia (2004–2008), [email protected] Rainer Schweickert Research Fellow at the Kiel Institute for the World Economy, [email protected] Irina Sinitsina CASE Fellow and the Leading Researcher at the Institute of Economics, Russian Academy of Sciences, [email protected] Svitlana Taran Economist at the Bureau for Economic and Social Technologies (BEST) in Kyiv; Economist at CASE Ukraine in Kyiv (2007–2008), Svtaran@ gmail.com

Chapter 1

The Development Gap Between the CIS and EU Irina Sinitsina

Abstract This chapter aims to identify the development gap between the EU15/ EU12 and CIS countries and EU actual and potential candidates across five dimensions (economic, human, openness, environmental and institutional). Special attention is paid to those gaps that could potentially hamper the ENP. We focus on several areas such as (a) quality of life; (b) human capital (including education and health); (c) innovation potential (including R&D, information and communication technologies); (d) openness and trade potential (including trade regime and performance in logistics and infrastructure); (e) environmental public health and ecosystem vitality objectives and (f) institutional development based on WBGI.

1.1

Introduction

To examine the potential of the ENP and the EU strategic partnership with Russia in upgrading mutual relations in the spheres of trade, investment, labor movement, and technical cooperation between the enlarged EU and the countries of the CIS,1 we need to be aware of major development gaps between these two groups. The identification of these gaps is vital for promoting comprehensive reforms in the CIS

Acknowledgements The author would like to express her gratitude to Azis Atamanov, Alexander Chubrik, Irina Denisova, Vladimir Dubrovskiy, Marina Kartseva, Irina Lukashova, Irina Makenbaeva, Magdalena Rokicka and Michael Tokmazishvili for their contribution to the earlier much broader version of this study (which served as the basis for this chapter). 1

For analytical purposes, this group includes six EEN, i.e., Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine, plus Russia and five CA countries, i.e. Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan. In 2009, Georgia terminated its membership in CIS. I. Sinitsina (*) CASE Fellow and the Leading Researcher at the Institute of Economics, Russian Academy of Sciences e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_1, # Springer-Verlag Berlin Heidelberg 2011

1

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I. Sinitsina

and for facilitating their greater integration into European programs and networks and cross-border cooperation. The notion of development has changed in recent decades. In the empirical literature, the development gap has often been measured as the income gap. However, since the 1990s, there has been a growing consensus that development must also include social and environmental dimensions, leading to the concept of sustainable development (Soubbotina 2004). Further research has expanded the notion of development, complementing it with measures of people’s well-being, social progress and quality of life, as well as their sustainability (CMEPSP 2009). Lately, a more precise set of indicators to measure human development was introduced, capturing important aspects of the distribution of well-being such as inequality, gender equity and poverty (UNDP 2010). The analyzed region is diversified in terms of levels of economic development, institutions, industrial structure, and progress achieved in market reforms. According to the WB country classification (2007), all six EEN belong to the group of lower-middle income countries, and Russia belongs to the upper-middle-income group. Among the new EU members (EU12), only Slovenia belongs to the high-income group. The development gap between the CIS (and Eastern Europe in general) and Western Europe has a strong historical background, at least since the thirteenth century. It widened over the course of the Industrial Revolution, beginning in the nineteenth century. The European part of the former Russian Empire experienced the first stage of industrialization and capitalism before the First World War, while CA was still feudal. During the socialist period, the USSR launched a large-scale forced industrialization, but despite desperate attempts, it failed to overcome the development gap; from the late 1920s to the late 1960s, the main goal of modernization was military superiority rather than development per se. While it was successful in fighting illiteracy and in creating modern industries, Soviet economic policy generated massive distortions and inefficiencies. The latter caused the gap to widen again in the 1960s because of the USSR’s failure to meet the challenges of post-industrialization. Following the USSR’s breakdown at the end of 1991, a profound economic and political crisis accompanied by armed conflicts in some territories severely damaged the physical and human capital of the respective countries. This contributed to the widening of the development gap during the first years of transition. During the transition period (1989–2005), the EU12 were the fastest in catching up with the EU15’s2 GDP per capita level, while the least progress was made by the lowincome CIS economies. In Russia, due to a deeper GDP decline, the catching-up progress was significantly slower than that of the EU12 or the Candidates and West Balkans (CWB). The CIS started to catch up after 2000, when the repercussions of the 1998–1999 financial crisis faded. Thus, the substantial gap in per capita income is likely to persist for a considerable time, especially in low-income CIS countries. Market reforms appeared to be an important determinant in closing the income gap and they remain topical for most of the region’s economies. These issues are

2

The group of 15 member states which belonged to the EU before 2004.

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increasingly being addressed by the EU in the ENPI, where institutional and administrative reforms were identified as top priority issues for most of the EEN. This chapter is organized as follows: in Sect. 1.2 we look at quality of life and its components, including poverty and inequality and subjective perceptions of wellbeing. Section 1.3 presents a comparative overview of divergences in human capital development, including education and health. Section 1.4 concentrates on key bottlenecks and the most visible gaps affecting innovation potential. Section 1.5 analyzes openness to the world economy and trade potential, including trade regimes and logistics and infrastructure development. Following the concept of sustainable development, Sect. 1.6 attempts to explain the gaps in environmental performance in terms of environmental public health and ecosystem vitality. Section 1.7 benchmarks the EEN countries across an array of institutional dimensions that are known to be critical determinants of economic growth. Section 1.8 presents some concluding remarks.

1.2

Quality of Life: Income and Poverty

Income and poverty. Within the EU12, Slovenia, the most developed country, had an income per capita that was less than two-thirds of the EU15 average (2005). This differential grows when moving eastwards from the EU15. Russia, the most prosperous CIS country, hardly reaches 40% of the EU27 GDP per capita average. The two CIS countries with the second highest incomes, Kazakhstan and Belarus, still represent only about two-thirds of Russia’s level, while the latter is eight times that of Tajikistan. When assessing income inequality within the CIS and CWB (Fig. 1.1), we should remember that under socialism, income among both social groups and 45

80 Poverty, $2.15 a day 70

Poverty, $2.15-$4.30 a day Income inequality: Gini coefficient

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Fig. 1.1 Poverty levels and income inequality, 2005 (From PovcalNet 2010)

% Gini

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geographic regions was equalized through a massive system of subsidies, transfers, and controlled prices. Initially rather egalitarian, these societies faced an abrupt increase in income inequality which radically changed the relative positions of large layers of society. On the whole, during the transition period (1) the initially low living standards translated into the spread of poverty (2) the economic growth of the late 1990s and early 2000s did not necessarily translate into poverty reduction in most of the CIS and (3) high inequality and low living standards were accompanied by the spread of shadow (unregistered) unemployment, which adversely affected the quality of life not only in most of the CIS but also in some CWB countries. The deterioration in living conditions was aggravated by military and ethnic conflicts in several countries. As a result, the international poverty line of USD 1.25 a day (in PPP), which is widely used for the poorest countries, was still present in 2005 in some of the EU12 (0.4% in Lithuania, 0.1% in Poland, 0.75% in Romania) and CWB (0.85% in Albania, 0.3% in Macedonia, 0.16% in Bosnia and Herzegovina and 2.7% in Turkey). EEN recorded notably higher extreme poverty levels: 4.7% in Armenia, 8.1% in Moldova and 13.4% in Georgia (PovcalNet 2010). According to the USD 2.15 PPP a day threshold that accounted for the basic necessities in the region (World Bank 2000), only the Czech Republic and Slovenia among the EU12 and Croatia among the CWB group were totally free from absolute poverty. In the rest of the EU12 and CWB, absolute poverty headcounts were generally low, varying from less than 1% in Hungary to 2.5–4.5% in Romania and Macedonia. Significant levels of absolute poverty (over 10%) remained only in Albania and Turkey. On the other hand, about a third of the population of Georgia, Armenia and Moldova lived in absolute poverty; in CA these figures were close to two-thirds. A considerable sphere of vulnerability (as measured by the share of the population living on USD 2.15–4.30 PPP a day) remained in the majority of CWB and CIS countries (Fig. 1.1) (Alam et al. 2005). Substantial levels of poverty and unemployment, as well as conflicts, violence, corruption, and ethnic, religion and gender discrimination were among the factors that caused a high level of migration flows across the region. Migrants’ remittances represented over 20% of GDP in Moldova and Bosnia and Herzegovina, and over 10% in Albania, Armenia, and Tajikistan (see also Chap. 7 of this volume). For Albania and Bosnia, remittances were nearly as large as exports. In many transition economies, remittances played a significant role in poverty reduction: in Moldova and Albania 20% of household spending in 2003 came from this source. The EU and the resource-rich CIS are the main sources of remittances, with the former accounting for three-quarters of the total and the latter accounting for 10% (Quillin et al. 2006). Measuring quality of life. To produce a comprehensive measure of quality of life that is analogous to GDP, a number of multi-dimensional indexes have been proposed (Booysen 2002). They measure quality of life by input – the degree to which society provides conditions deemed beneficial. However, nobody knows to what extent the conditions provided are really good for people, or at least perceived as such. An alternative is to measure output – subjective perceptions of life quality, commonly referred to as ‘life satisfaction’ or ‘happiness’ (Veenhoven 1996).

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Leaving aside the many controversial theoretical and methodological issues related to subjective (vs. objective) measurements of well-being (see Easterlin et al. 2002), we tend to admit that subjective assessments of life quality are probably no less important than objective ones. In post-socialist countries, a dramatic break with past income and consumption habits, rights and guarantees, coupled with a significant rise in inequality and uncertainty, accompanied by an emergence of narrow groups of nouveaux riche at the top and broad groups of very poor at the bottom – could have played an important role in subjective assessments of personal wellbeing (Franicevic 2003). The ‘relative dimension’ in subjective assessments of well-being could prove crucial for formulating EU policies towards CWB/EEN. A perception of a more successful neighbor as a model for a country’s development could contribute to an evolution of a ‘national idea’ that could serve to lower disappointment with the results of reforms, enhance optimism, and create forward-looking expectations in the society. For the analysis of subjective measures of personal welfare, we used the data on ‘overall satisfaction with life as a whole’ and ‘freedom of choice and control over peoples’ lives’ based on the latest available series of the World Values Survey (2000). As could be expected, they demonstrate a significant (almost fourfold) disparity in the percentage of people who are satisfied with their lives between the EU15 and EEN/Russia, and a twofold gap in the proportion of those who are ‘unable to control their lives,’ with both shares growing greater the further they are located ‘from Brussels’. Cross-national differences in ‘subjective well-being,’ measured as the mean of the percentage who are ‘Happy’ and the percentage who are ‘Satisfied with life as a whole,’ showed a high correlation with per capita income (R2 ¼ 0.76). This implies that objective and subjective well-being indicators measure basically the same phenomena, albeit from slightly different angles. The regression also illustrates an important characteristic of EEN/Russia: a lower subjective well-being compared to what could be expected based on per capita income. This discrepancy could reflect societal trends not captured by income or poverty scores, such as widespread pessimism, collapsing expectations, perception of not only income inequality, but also of wealth distribution, social exclusion, perception of losing out on the reforms, and, last but not least, a low level of trust in political and public institutions, widespread corruption and state capture.

1.3

Human Capital: Health Care and Education

Human capital is usually defined as the knowledge, skills, and health of people which make them economically productive. Health and education are recognized as key components of national human capital. The level of human capital inherited from the socialist past in all transition countries was generally considered high relative to other countries at similar levels of economic development. During the

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transition, scarce public funding appeared to be unable to support the existing public healthcare and education systems. This, coupled with inconsistent reforms, led to a progressive deterioration of outcomes, and the quality of health and education services suffered. Health. Throughout the twentieth century, national indicators of life expectancy were closely associated with GDP per capita, although this relationship does not explain the trends in transition countries, especially EEN: in the course of the 1990s, the gap in average life expectancy dividing EEN and the EU15 increased, exceeding 10 years. The situation looks dramatic if we compare the respective data on male life expectancy at birth: in EEN (2007) it was on average 12 years lower compared to most of the EU15 (and female life expectancy was 7 years lower). The average difference in life expectancy between the CA and Western Europe is respectively 11 and 10 years. The major external factors leading to increased mortality, particularly among working age males, include excess consumption of alcohol, smoking, and high poisoning and accident rates. Infant mortality rates, albeit declining, still remain high: on average, about 13 infants per 1,000 live births die in the CIS, while for the EU, the corresponding figures are at least 2.5 times lower. Death rates related to pregnancy and childbirth in CIS are several times higher than in the EU15. In 2005, the maternal mortality rate per 100,000 live births averaged 5.6 in the EU25 (4.8 in the EU15 and 8.9 in the EU10). At the same time, this rate amounted to 28.2 in the CIS (25.4 in Russia), while in CA it was even higher at 36.7. There is also sound statistical evidence on the spread of dangerous infectious diseases in the CIS, especially HIV and TB. The TB incidence rate per 100,000 grew steadily, reaching 83 in Russia and 107 in CA in 2005, as compared to 44 in the EU12 and 10 in the EU15 (WHO Europe 2010). An alarming increase in multi-drug resistant TB rates, especially in prisons, posed additional threats to TB control in the region (UN Millennium Project 2005). Notwithstanding an improvement in some indicators, we can observe a considerable gap between the EU15 and CIS countries in human health, especially in the case of low-income households. There is ample anecdotal evidence on lower life expectancy in these households, their exposure to dangerous diseases, etc. Hence, the visible gap in health status between the analyzed country groups could be just the tip of the iceberg. Significantly larger health gaps are most probably hidden inside intracountry inequalities, with their magnitude greatly exceeding that in Western Europe. The major factors affecting the growing gap in human health between the EU and CIS countries could be summarized as follows: 1. Deterioration of health care services as a result of poor financing and inconsistent reforms: (a) low government health expenditures in the CIS as share of GDP: throughout the whole decade of the 2000s, in the EEN, expenditures were consistently lower than in the EU15 by about 3 percentage points; in Russia this difference was about 4 p.p.; (b) very low absolute per capita total health expenditures in the CIS, differing six to seven times (EEN) or four to five times (Russia) from the EU15 averages (WHO Europe 2010); (c) misallocation of resources due to the preservation of outdated health care networks; (d) delays

1 The Development Gap Between the CIS and EU

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or failure in introducing health insurance systems; limited and inequitable health risk protection and coverage. In the reforms of health insurance systems, Russia/ EEN are lagging well behind the EU12. 2. Differences in the accessibility to healthcare services, especially for low-income households: (a) shrinking availability of quality public services due to the deterioration of health infrastructure; (b) rapid ‘marketization’ of health services and growth of out-of-pocket payments bringing an escalation in health care costs for households. Furthermore, in poorer countries, up to 50–80% of the costs are financed out of pocket and informally. As a result, in most of the CIS, health financing became less equitable. Education. Post-Soviet education is characterized by two contradictory features: (i) a huge number of students, especially in higher education, and (ii) one of the lowest levels of per capita financing in the world. By the number of students per 10,000 (525), Russia ranked second in the world after the US in 2006 (UNESCO IS 2010). However, compared to the EU15 and EU12, less public resources are available to education in general, and particularly to higher education in EEN/Russia. The post-Soviet education system is perceived as both one of the world’s largest in terms of scale and coverage, and one of the worst affected by a shortage of funds (Kuzminov 2004). During 1990 –2004, when EU25 countries increased their share of public spending on education relative to GDP by more than 1 p.p., CIS countries saw a decline in this share of roughly 2 p.p. to below 4%, with Georgia (2.9%) and Kazakhstan (2.4%) being the worst performers (UNDP 2006). More recently, however, several CIS economies, particularly Moldova and Ukraine, raised their education spending to 8.1% and 6.2% of the GDP (2006) respectively. In real terms, total expenditure on education increased in all EEN, but in many of them, spending levels are still far below 1991 levels (UNESCO IS 2010). Public expenditure per student relative to per capita GDP in Russia is about 27% compared to 34% in France and 42% in Germany. Middle-income countries usually maintain this ratio at much higher levels than affluent countries: around 50% for medium professional education, and between 100% and 150% of per capita GDP per undergraduate student. This enables them to reduce, at least partially, the gap in absolute financing compared to richer countries and to compensate for quality differences. In Russia, however, this indicator is even lower than in developed countries (UNDP 2004). The consequence is the deterioration of the quality of education and its inability to meet society’s growing needs (UNDP 2004). Thus, despite remarkable achievements, public education in EEN/Russia does not adequately provide students with the capabilities they need to compete in a market economy. The Soviet education system stressed memorizing factual and procedural knowledge instead of learning skills that allow the labor force to adapt flexibly to changing labor market needs; very little changed in this respect during the transition (World Bank 2000). As a result, Russia has repeatedly received the lowest ratings on the PISA, according to tests conducted by OECD among 15-year old pupils. In 2000, it ranked

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27th among 32 countries in reading abilities (including comprehension, analysis and formulating own viewpoint), 26th in natural sciences and ranked 21st in math; in 2006, it ranked 39th, 34th and 34th accordingly among 57 countries, with a noticeably deteriorating performance across all areas (OECD 2007). The same is true for higher education: according to the 2010 QS World University Ranking, the list of the top 200 universities in the world includes only one from Russia, Lomonosov Moscow State (93d), with two more, St. Petersburg and Novosibirsk universities, included into the top 400 (210th and 375th respectively). To compare, the same rating includes 15 universities in France, six in India, five in South Korea, and none from the EEN (QS 2010). The international marginalization of Russian universities is also reflected in the declining number of international students coming to Russia: Russia’s share in the world education market does not exceed 0.5% (Sobolevskaya 2005). The basis of the Russian higher education system are newly-formed, low-calibre universities (in fact, oversized colleges) and ‘diploma mills’, where 50–65% of students do not even dream of getting a job that matches their qualifications. According to the polls among university graduates, over 50% of them are not using competencies learned during their studies in their work (Kuzminov 2004). Another dividing line between the analyzed country groups lies in a substantial growth of private spending on education in many CIS countries. Not surprisingly, countries that are under the greatest fiscal pressure shift education costs to families more often than those that are less fiscally constrained. Unfortunately, these are the same countries that tend to have higher levels of family poverty. For example, in Moldova, private spending on education in 2005 accounted for 28% of total (and 79% of higher) education funding, or 1.7% of GDP. By comparison, in EU15 countries, private funds contributed to ca. 10% of the total education spending (UNESCO 2007). In Russia, the growth in the number of students was accompanied by a sharp increase in paid admission: in 2006/2007, it surpassed 66% (both in the public and private sectors). Although comparable data on household spending on education in CIS and the EU are not available, anecdotal evidence demonstrates that this spending in EEN became comparable to government expenditures allocated to this end. Russian household survey data demonstrate that households’ investments in higher education in 2003 exceeded budget expenditures by nearly 40% (Kuzminov 2004). This is in stark contrast with the situation prevailing both in the EU15 and EU12, where household expenditures on education generally do not exceed 10–15% of total education expenditures (OECD 2006).

1.4

Innovation, Technological and Infrastructural Gap

The potential of nations to build a knowledge-based economy is an important prerequisite for both economic growth and human development. The ratio of research and development (R&D) expenditures to GDP is one of the most prevalent

1 The Development Gap Between the CIS and EU

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input indicators used to identify key bottlenecks and the most visible gaps in innovation performance. Median R&D expenditures in EU15 countries far exceed corresponding values for all other country groups, staying at the level of about 2%, with considerably higher figures in most developed countries – reaching 3% and even exceeding 4%. All other countries, including the EU12, have considerably lower expenditures, with only Russia and Ukraine exceeding 1%. If we exclude Ukraine, the median for the rest of the EEN would stay at just 0.3% GDP. Despite a fall in the number of researchers (by more than half during 1992–2002), Russia is traditionally ranked first in the number of researchers per million people, surpassing the EU15 median level (although the gap is narrowing) and exceeding the level of the EU12 more than twice. Retaining post-Soviet R&D institutions, Georgia, Ukraine, and Belarus also still preserve high employment in R&D. These input numbers, however, do not translate into high innovation outputs. In the area of ICT, the differences in the number of personal computers per 1,000 people in the mid-2000s remained almost unchanged since 1998: over 100% between the EU15 and EU12; 1.7 times between the EU12 and Russia, 1.9 times between Russia and the CWB countries, and almost five times between Russia and the EEN. The level of internet use in the CIS is only 13% of that in the EU15. The gap is slightly smaller in mobile telephony, where the CIS penetration rate stands at one-fifth of that in the EU15. It is the smallest in ‘traditional’ ICT (fixed lines), where CIS’ penetration level represents 37% of that of Western Europe (ITU 2005). The two indicators reflecting the outcomes of innovation performance (‘Patent applications filed by residents’ and ‘Scientific and technical journal articles per million people’) point to considerable gaps between the EU15 and other country groups. In the case of patent applications, they range from roughly 2.5 times between EU15 and Russia to 8–10 times between EU15 and EEN/Russia. At the same time, these differences are not as wide as could be expected judging by relative GDP figures or R&D expenditures. Data for 2004 published by the World Intellectual Property Organization indicate that Russia, Ukraine and Belarus have relatively high rates of patent activity: Russia ranks 6th in the world in the absolute number of resident patent filings, with Ukraine ranking 11th. The scores in patent filings per USD 1 billion of GDP (in PPP terms) are 17.6 for Russia (6th rank), 16.9 for Belarus and 14.7 for Ukraine (8th and 9th rank accordingly) (WIPO 2006). However, in terms of the absolute number of patents issued, these countries (even taken together) still lag far behind Germany. And more importantly, the structure of Russian patent applications radically differs from that of developed countries: just 9% of applications in Russia were in ICT and electronics (compared with 40–50% in OECD countries) with a majority filed in the ‘food and agriculture’ and ‘materials and instrumentation’ sectors (Jaggi 2005). Weighted indicators on scientific and technical journal articles demonstrate an even bleaker picture: a fivefold gap that is still growing between the EU15 and EU12/Russia. A more generalized picture of international differences in innovation performance could be drawn from composite indices developed in accordance with the WB KAM (Knowledge Index, KI, and Knowledge Economy Index, KEI), as well as their main components (pillars). Three components of KI represent key variables

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Fig. 1.2 KI, KEI and constituent pillars across country groups (2009) (From WB KAM 2009)

characterizing a country’s ability to generate, adopt and diffuse knowledge. These are: (1) Education and human resources; (2) Innovation system; and (3) ICT. In addition, KEI takes into account whether the environment is conducive for knowledge to be used effectively for economic development. This is achieved by adding one more pillar, ‘Economic incentive and institutional regime’ (Institutions), which includes tariff and non-tariff barriers, regulatory quality and rule of law (World Bank KAM 2009). The results comparing country groups’ scores for each of the pillars as well as scores of KI and KEI are presented at Fig. 1.2. The four KEI pillars provide further insight into the relative innovation strengths and weaknesses of individual country groups. Many of them, particularly Russia, CA and EEN are characterized by the uneven development of innovation pillars. On the other hand, the differences between KEI pillars’ scores are minimal for the EU15 and EU12, and are only slightly larger for CWB. Meanwhile, recent evidence suggests that countries with even scores on each of the innovation pillars perform better overall than countries with an uneven distribution, since an obstacle in one field, such as poor knowledge creation, could cause a deterioration in general performance. This suggests, in particular for countries lagging behind, that within given resources, policy efforts should concentrate on improving areas of weakness (like institutional regime) rather than on making further improvements in areas of relative strength (EC 2006). In Fig. 1.3, countries’ KEI 2009 scores are plotted against KEI percentage change over the preceding 15 years, showing the current innovation performance on the horizontal axis against the trend of KEI performance on the vertical axis. This enables us to select, from the viewpoint of the EU/CIS gap, at least three distinct groups of countries. The first one includes countries which are above both the average ECA trend performance and the average ECA KEI (most of EU12 except Bulgaria and Romania). They are moving ahead, rapidly catching up, and, in

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Fig. 1.3 KEI performance trends, 1995–2009 (From WB KAM 2009)

some cases, outperforming the EU15. The countries belonging to the second group have below average KEI scores but close to average (or above average) trend performance. These include CWB, Russia and some other CIS countries. Finally, the third group consists of countries which have both below average KEI values and a negative trend (Belarus, Azerbaijan, Uzbekistan and Tajikistan). The second group is more likely to catch up, at least in the long run, while the third group is falling further behind. Both the above indicators and anecdotal evidence illustrate the consequences of inadequate reforms in the R&D sector in CIS. One of many examples is the proportion of applied research with a commercial potential, which is remarkably low compared to developed countries. A poor institutional environment and low entrepreneurship potential discourage R&D in the private sector and the commercialization of innovative ideas, which markedly reduces overall innovation capacity.

1.5

Openness and Trade Potential for Development

Trade potential and ‘openness’ play quite different roles across the region, being dependent on a country’s size, geographical location, etc. However, some specific features of all EEN countries could be emphasized, with a focus on those which could affect future trade and investment relations with the EU.

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Comparative measures of openness can be either outcome-based (actual flows of goods, services and capital) or policy-based (analyzing policy instruments essential to effective participation in international trade and capital exchange). A widely used measure of openness is the share of exports and imports in GDP (PPP). All countries and country groups demonstrated considerable growth in openness during the last decade. Still, there is a wide gap in the ratio of merchandise exports plus imports to GDP between the EU15 and other country groups. However, the gap between EU15 and EU12 was rapidly narrowing since 2002 and became quite small by 2006, with openness in the EU12 approaching 70%. Among the CWB group, Turkey and Serbia demonstrated high trade growth, bringing the median rate close to 36%. The rest of the groups (EEN/Russia and CA) demonstrated very similar performances with median scores growing from 16–18% in 2002 to 26–28% in 2006, about one-third of EU15 (UNCTAD 2008). Trade in services has been growing steadily both in CA and in the EEN groups, but at a much lower rate compared to the EU12: in 2006, the respective ratios to GDP for EU12 amounted to 11%, while for EEN – less than 6% and for CA – close to 8%. To compare, this ratio for EU15 reached 18%. The role of the services sector remains very low in Russia, where the share of trade in services to GDP PPP barely exceeds 4% (UNCTAD 2008). The exports structure of some CIS countries continues to rely on raw materials and fuels – 92% in Azerbaijan, 86.2% in Kazakhstan, 69.3% in Russia and 63.5% in Uzbekistan (all data for 2008, see UNCTADstat 2010). The high natural resource and low skilled-labor intensity of exports (as compared to all other country groups) puts the future growth prospects of CIS countries at risk. Despite an increased openness and the growing geographic diversification of their trade, a kind of ‘bifurcation’ of the analyzed region into two major trade ‘poles’ – ‘Euro-centric’ and a ‘Russia-centric’ – has emerged (World Bank 2005). The stock of attracted FDI per capita is another indicator of openness. FDI flows into transition economies have been consistently rising, but their magnitude and importance remain highly unequal among the analyzed country subgroups (see also Chap. 5). The gap between EU15 and other country groups remains huge. Only EU12 countries managed to significantly improve the situation with FDI, opening their firms and banks to foreign investors, but the difference between them and the EU15 is still fourfold. FDI stock per capita in Russia and the CWB group is 10–15 times smaller compared to EU15, while in the EEN and CA it represents 3.8% and 0.9% of the EU15 level, respectively (UNCTAD 2008). Relatively larger FDI inflows to some EEN counties reflect their transition to market-led economies, with privatization programs involving sales to foreign investors. The region’s FDI is also determined by the availability of natural resources and low labor costs. On the other hand, the poor business climate and restrictions on FDI increase transaction costs and therefore discourage foreign investors. (Kinoshita and Campos 2003). Differences in ‘openness’ between the analyzed country groups are determined by policy-based factors related to trade regimes and institutions as well as logistical support infrastructure. The most liberal trade regimes are in the WTO members (i.e. Albania, Armenia, Croatia, Georgia, Kyrgyzstan, Moldova, Ukraine and EU).

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At the other extreme there are countries with highly protectionist regimes such as Uzbekistan, Turkmenistan and Belarus. Russia falls in between. In several countries, trade regimes were severely affected by armed conflicts (e.g. in Abkhazia, Southern Ossetia and Transnistria) that significantly damaged formal trade transactions in Georgia and Moldova and led to the formation of ‘black holes’ in terms of illegal arms transactions and the smuggling of tobacco, liquor, and oil products, thus resulting in significant trade revenue losses for these countries (World Bank 2004a). In the mid-2000s, NTB on imports were common in the CIS (see Chap. 3). Ukraine used its technical standards system as a vehicle for controlling imports into various sectors (World Bank 2004b). Moreover, NTB were frequently used not just as an economic protection measure but also for political pressure, e.g. cases of banning imports of Georgian and Moldovan wine to Russia. Besides formal trade barriers, there exist informal ones, many of which resulted from institutional weaknesses, such as the limited availability of trade finance and insurance or the low transparency of customs procedures. In the CIS, these difficulties are compounded by serious governance problems. Customs procedures over-rely on physical inspection. They often change, leaving room for arbitrary interpretation and corruption. For example, in 2005 alone, the internal security unit of the Federal Customs Service of Russia initiated over 530 criminal proceedings against customs officials (Federal Customs Service 2006). Many of the CWB and CIS countries suffer from the poor quality of trade facilitation infrastructure such as transport and communication and the deficit of modern logistical operation services (e.g. multimodal transport). In CIS the transport network is relatively extensive, but it was developed to meet the industrial and military needs of the USSR, with a strategic focus on connecting the individual republics with Moscow through the capitals of neighboring republics. As a result, there are often no straightforward connections between locations in the same country. Many lower income CIS countries have small and fragmented transport markets that seldom enjoy scale economies in their operations. These problems are exacerbated by corruption and multiple unofficial and semi-official fees and payments, leading to nearly prohibitive additional transportation costs especially for mass commodities such as agricultural products (World Bank 2005). The composite WB Logistics Performance Index (LPI) is based on a survey of more than 800 logistics operators worldwide. The LPI represents an average of country scores on a scale from 1 to 5 in six dimensions: (1) efficiency and effectiveness of the customs clearance process; (2) quality of transport and ICT infrastructure for logistics; (3) competence of the local logistics industry; (4) ability to track and trace shipments; (5) domestic logistics costs; and (6) timeliness of shipments in reaching destination. Figure 1.4 demonstrates the similar performances of the EEN, Russia and CWB, with the worst performance in customs and infrastructure and a better one in international shipments and their timeliness. Still, even the best results of these groups lag more than one point behind the EU15 and about 0.5 points behind the EU12. Not unexpectedly, there is a strong correlation between logistics performance and per capita GDP (Fig. 1.5): poorer countries tend to perform worse in all links of

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Fig. 1.4 Logistics Performance Index components (2010) (From WB LPI 2010)

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3.0

EE

CY

HU

2.8

UZ

2.6

MD

MK GE

KG AM

2.4 0

5

BA UA AZ

BGRO KZ RS

HR

GR MT

SL

R²=0.75

RU

Statistically significant at