The Political Economy of Refugee Migration and Foreign Aid

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The Political Economy of Refugee Migration and Foreign Aid

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The Political Economy of Refugee Migration and Foreign Aid Mathias Czaika

© Mathias Czaika 2009 All rights reserved. No reproduction, copy or transmission of this publication may be made without written permission. No portion of this publication may be reproduced, copied or transmitted save with written permission or in accordance with the provisions of the Copyright, Designs and Patents Act 1988, or under the terms of any licence permitting limited copying issued by the Copyright Licensing Agency, Saffron House, 6-10 Kirby Street, London EC1N 8TS. Any person who does any unauthorized act in relation to this publication may be liable to criminal prosecution and civil claims for damages. The author has asserted his right to be identified as the author of this work in accordance with the Copyright, Designs and Patents Act 1988. First published 2009 by PALGRAVE MACMILLAN Palgrave Macmillan in the UK is an imprint of Macmillan Publishers Limited, registered in England, company number 785998, of Houndmills, Basingstoke, Hampshire RG21 6XS. Palgrave Macmillan in the US is a division of St Martin’s Press LLC, 175 Fifth Avenue, New York, NY 10010. Palgrave Macmillan is the global academic imprint of the above companies and has companies and representatives throughout the world. Palgrave® and Macmillan® are registered trademarks in the United States, the United Kingdom, Europe and other countries. ISBN-13: 978–0–230–57688–9 hardback ISBN-10: 0–230–57688–5 hardback This book is printed on paper suitable for recycling and made from fully managed and sustained forest sources. Logging, pulping and manufacturing processes are expected to conform to the environmental regulations of the country of origin. A catalogue record for this book is available from the British Library. A catalog record for this book is available from the Library of Congress. 10 9 8 7 6 5 4 3 2 1 18 17 16 15 14 13 12 11 10 09 Printed and bound in Great Britain by CPI Antony Rowe, Chippenham and Eastbourne

This work is dedicated to those who currently live as refugees and asylum seekers in desperate situations around the globe, whether living in a camp, a retention centre or somewhere else, but certainly in insecurity and fear. May your voices be heard and your prayers answered.

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Contents List of Tables

ix

List of Figures

xi

Abbreviations

xii

Acknowledgements

xiv

1 Introduction 1.1 Research objective 1.2 Research outline

1 1 5

2 Forced Migration Patterns and Politics 2.1 Introduction 2.2 Migration characteristics 2.3 International actors for refugee protection 2.4 Refugee Politics

10 10 11 16 19

3 International Refugee Burdens 3.1 Introduction 3.2 Assessing the refugee burden to host countries 3.3 Methodology of a refugee burden index 3.4 Concluding remarks

25 25 26 30 40

4 Determinants of Forced Displacement: The Case of Aceh, Indonesia 4.1 Introduction 4.2 Conflict history and displacement in Aceh, Indonesia 4.3 Theoretical background of displacements 4.4 Empirical analysis of displacement in Aceh 4.5 Concluding remarks

43 44 46 50 52 65

5 The Political Economy of Refugee Migration 5.1 Introduction 5.2 The Model 5.3 Counter-asylum policies 5.4 Concluding remarks

67 67 70 77 87

vii

viii

Contents

6 Asylum Cooperation among Asymmetric Countries 6.1 Introduction 6.2 Literature review 6.3 The analytical framework 6.4 Asylum cooperation in the European Union 6.5 Concluding remarks

88 88 90 94 104 112

7 Refugee Movements and Aid Politics 7.1 Introduction 7.2 Aid and refugee migration policies 7.3 Aid allocation patterns and statistics 7.4 UN diplomacy and aid allocation 7.5 Concluding remarks

114 114 115 118 123 129

8 Aid Allocation and Asylum Migration 8.1 Introduction 8.2 Aid allocation literature review 8.3 The model 8.4 Comparative statics 8.5 Empirical evidence 8.6 Concluding remarks

131 131 132 135 137 138 144

9 Refugee Movements and Aid Responsiveness 9.1 Introduction 9.2 Refugee movements: some policy options 9.3 Empirical analysis 9.4 Concluding remarks

146 146 148 151 162

10 Conclusion 10.1 Summary 10.2 Policy implications

164 164 166

Appendix

170

Notes

181

Bibliography

192

Index

203

Tables 2.1 3.1 3.2 3.3 3.4 3.5 4.1 4.2 4.3 4.4 4.5 5.1 6.1 6.2 7.1 7.2 7.3 8.1 8.2 9.1 9.2 A.1 A.2 A.3 A.4 A.5 A.6

International migration stocks (2005) and flows (2000–05), in 1000 Correlation matrix of the RCI components Top 20 burdened countries in 2003 Top 5 intra-regionally most burdened countries in 2003 Inter-regional Refugee Burden Index in 2003 Inter-regional Refugee Burden Matrix (gross/net stocks) in 2003 Conflict and population change in the Aceh sample, 1999–2002 Probit estimation: conflict incidence Determinants of change in village population Quantile regressions of population change Determinants of population change by conflict occurrence Simulation: aid transfers and refugee migration Asylum cooperation: equal population quota, EU-27 Asylum cooperation: equal economic burdens, EU-27 Sector composition of total ODA grants, 3-year average Bilateral and multilateral aid allocation Bilateral donor-specific coefficients Bivariate correlates of German ODA and explanatory variables German aid allocation Aid allocation determinants, 1992–2003, 18 donors, 148 recipients Categorization of aid donors Specifications of the Refugee Capacity Index Descriptive statistics, Aceh sample Derivations of the refugee migration model Descriptive statistics, EU-27 Descriptive statistics, UN speeches sample Data description and sources, UN speeches sample ix

12 33 37 38 40 41 54 58 60 61 62 85 108 110 122 127 128 141 142 157 160 170 171 173 174 175 175

x

Tables

A.7 A.8 A.9 A.10 A.11

Descriptive statistics, German aid allocation sample Data description and sources, German aid allocation sample Descriptive statistics, aid panel dataset Correlation matrix, aid panel dataset Data definitions and sources, aid panel dataset

176 177 178 179 180

Figures 1.1 4.1 4.2 5.1 5.2 5.3 5.4 5.5 5.6 6.1 6.2 7.1 7.2 7.3 7.4 8.1

Global distribution of refugees, in millions Aceh districts Perceived value of displacement Cross-border migration duration of persecuted individuals The refugee migration decision: who, where, and how long Liberalization of self-reliance in the first asylum country Declining asylum recognition rate in the Western asylum country Increasing income level in the country of origin Self-financing proactive asylum policy Asylum policy among countries with asymmetric spillovers Asylum policy outcomes among countries with asymmetric costs Donor motivations of ODA provision, EU public poll, 2007 Net disbursement of ODA, OECD/DAC countries Top bilateral donor states in 2006 Regional distribution of ODA, 1996 and 2006 Asylum applications in EU countries (in 1000)

xi

3 49 51 73 75 79 80 84 87 98 98 118 119 120 121 133

Abbreviations BIS BMZ

BPS CEAS CIA CRS DAC DPKO EC ECHO ELRFI ERC ERF EU FDI GAM GDPI HDI IASC ICARA ICRC IDP ILO IMF IOM NGO OAU OCHA ODA OECD OLS

Bank for International Settlements Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung (German Ministry for Economic Development and Cooperation) Badan Pusat Statistik (Statistics Indonesia) Common European Asylum System Central Intelligence Agency Creditor Reporting System Development Assistance Committee Department for Peace-Keeping Operations European Commission European Community Humanitarian aid Office Ethno-Linguistic-Religious Fractionalization Index Emergency Relief Coordinator European Refugee Fund European Union Foreign Direct Investment Gerakan Aceh Merdeka (Acehnese Freedom Movement) Gross Domestic Product Index Human Development Index Interagency Standing Committee International Conference on Assistance to Refugees in Africa International Committee of the Red Cross Internally Displaced Person International Labour Organization International Monetary Fund International Organization for Migration Non-Governmental Organization Organization for African Unity Office for the Coordination of Humanitarian Affairs Official Development Assistance Organization of Economic Cooperation and Development Ordinary Least Squares xii

Abbreviations

PDI PFI PODES PQLI PSI RBI RBS RCI TNI TPOC UN UNDP UNHCHR UNHCR UNICEF UNRWA WB WFP

xiii

Population Density Index Political Freedom Index Potensi Desa (Village Potential Census) Physical Quality of Life Index Political Stability Index Refugee Burden Index Refugee Burden-Sharing Refugee Capacity Index Tentara Nasional Indonesia (Indonesian military forces) Total Persons of Concern United Nations United Nations Development Program United Nations High Commissioner for Human Rights United Nations High Commissioner for Refugees United Nations International Children’s Emergency Fund United Nations Relief and Works Agency for Palestine Refugees World Bank World Food Program

Acknowledgements To whom shall I give more praise and thank for HIS support than to my heavenly Father! He is and was my solid foundation in all my struggles and times of thinking, writing, and abandoning. He is and was my friend and encourager, my shining light in times of joy and doubt. I praise you, Jesus! I would like to express my sincere gratitude to my supervisor, Prof. Günther Schulze. His detailed and constructive comments as well as all his encouragement and understanding have provided a good basis for the present book. Furthermore, I am grateful to my (former) colleagues and friends from the University of Freiburg, Krisztina Kis-Katos, Eva Deuchert, Antonio Farfán Vallespín, Juliane Fliedner, Grischa Perino, Martin Keller, Michael Trost, Inga Müller, Amy Mayer and Marcus Marktanner for all of the discussions, collaboration, assistance, proofreading and fun. I also appreciated all of the helpful comments of the participants in the Wednesday Doktorandenseminar at the University of Freiburg. I am thankful also to Amy McDonough for proofreading this book. However, I would like to state that any errors in this thesis, be they imprecise quotations or basic misrepresentations, are unintentional and my responsibility alone. My warm thanks are due to Giseltraud Herbert for all her kindness and helpful assistance throughout the past five years. Special thanks to all of my friends from Students for Christ (SfC) for their interest, encouragement and prayers. I owe my loving thanks to my wonderful parents Hartmut and Lydia, for enabling my university education, and particularly, for their love and support throughout the years. Finally, I want to express my special thanks to my beloved wife Catheline. She helped me to concentrate on completing this thesis and supported me in many ways, especially by her prayers. Without her help, encouragement and understanding, this book could not have been completed.

xiv

1 Introduction

If you don’t help the third world, [...] then you will have these poor people in your society. (Paul Rasmussen, Prime Minister of Denmark, Copenhagen Social Summit 1995) This book addresses the causes and incidences of refugee movements and the policy responses of Western asylum and aid-giving countries. In particular, it is focused on the positive and normative aspects of asylum migration and its management by Western industrialized countries. Thereby, asylum policy and aid policy are the main instruments of interest in this work. The book focuses explicitly on the following research questions: What are the driving factors of forced migration movements? How can refugee burdens be assessed and compared across different types of host countries? Who are the actors in international refugee protection and management, and how do they act? Is cooperation among asylum countries necessary, and possible? And finally, how does the phenomenon of international refugee movements, and specifically how do asylum seekers, influence the aid allocation politics of Western industrialized countries? The remainder of this introductory chapter describes the background of these research objectives and also provides an outline of the subsequent analytical parts.

1.1 Research objective The relevance of this work is self-evident. In 2005, the United Nations estimated about 190 million people living in a country in which 1

2

Refugee Migration and Foreign Aid

they were not born (United Nations, 2006b). During the past decades, large migration movements originated from countries and regions ranging from those characterized by economic and social despair or political insecurity to countries that are more stable, secure and economically developed. Continuously increasing disparities in the level of human and social security, on the one side, and improving facilities for communication and transportation, expanding social networks, as well as the emergence of a commercial trafficking industry, on the other side, have led to this steadily growing number of foreign-born populations in developed and developing host countries. This ‘age of migration’ (Castles & Miller, 2003) has caused a growing concern, mainly in Western industrialized states, which are major destinations for all types of migrants, and has brought the topic of international voluntary and involuntary (forced) migration to the top of national and international agendas. Generally, it is difficult to distinguish between forced migration and voluntary economic migration, since it is usually arbitrary to state that people move voluntarily or by force. Basically, voluntary migration movements, which are purely economically motivated, do not largely originate from the least developed regions or countries and poorest populations, but rather from those that are better equipped. This phenomenon, known as the ‘migration hump’, is mainly due to constraints in economic resources, which prevents the poorest from raising the funds necessary to emigrate. During the past decades, these economic constraints have become even more important for emigration decisions since international (or, transcontinental) migration often requires substantial resources for smuggling or trafficking agents. In contrast to pure economically motivated migration, this book is focused on refugee migration, which is by definition forced and involuntary. With respect to this group of migrants, fear, insecurity and individual or mass persecution in times of violent conflicts in many of the most fragile and economically marginalized countries of the developing world have triggered increasing numbers of displaced people. For this subpopulation, the determinants that cause their (involuntary) emigration are comprised of a complex mixture of political, social and economic factors, of which the individual psychological perception of ‘threat’ is only one, but most often the decisive and final factor. Concerning the various push factors at the

Introduction 3

origin site, one can distinguish those rather related to political insecurity such as human rights violation, persecution of societal subgroups, or violent conflict, and those related to social and economic conditions such as indigence, unemployment, lack of access to education, health or social services, or environmental and natural degradation. Analyzing and disentangling this whole range of driving forces of large displacements in conflict-ridden situations is one task of this book. A case study on the conflict-affected province of Aceh, Indonesia, attempts to identify and to categorize this mixture of conflict-related and traditional, socioeconomic determinants. Many persecuted people of this world are not able to migrate at all, or only a small distance, because of lack of resources. Those who are able to emigrate mostly move either only internally or to a neighboring country. Consequently, a number of relatively stable countries in Asia, the Middle East, South America, Central and East Europe or Southern Africa have increasingly become preferred destinations for asylum seekers and refugees. However, although the majority of forced migrants, that is refugees, asylum seekers, or internally displaced persons (IDPs), are in fact compelled to stay close in their origin sites, Western industrialized regions such as the EU and Northern America are also first choice destinations for many forced migrants from the southern hemisphere (Figure 1.1 and Table 2.1). 20 18 16 14 12

Rest of World

10

Asia

8

Africa

6

Western Europe

4

Northern America

2

Figure 1.1

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

0

Global distribution of refugees, in millions

Note: Figure is based on data provided by UNHCR (2005).

4


Particularly during the 1990s, the rise in international refugee migration has become a major challenge to many host countries, and as a consequence, Western countries, but also destination countries in the region of origin, introduced various restrictions intended to impede and dissuade people from accessing their territory for the purpose of seeking asylum. Industrial states often acknowledge the benefits of immigration when it meets the economic requirements of the domestic labor market. However, policymakers and the general public increasingly perceive refugee (asylum) migration as a means to bypass legitimate immigration controls. As claiming refugee status allows the claimant to stay in the asylum country, at least for the years while the application is processed, large-scale asylum procedures generate significant costs for administration and provision of social care, public services and legal advice. This burden of hosting refugees has led to a growing reluctance in most host societies to accept further inflows, particularly in Western asylum countries. However, is the often stated perception of Western asylum countries correct that they carry the bulk of the global refugee burden? This question is addressed by generating a more objective measure to make refugee burdens comparable across countries without having explicit data on the actual political, social and economic costs of hosting a large refugee population. The political economy of refugee management in Western states is mainly driven by general concerns about the apparently negative impact of asylum migration inflows on unemployment, social welfare systems, security and national identity. Despite various benefits from immigration that are put forward either by Western firms and employers with respect to economic gains or by liberal protagonists for sociocultural and humanistic reasons, there remains nevertheless considerable resistance in society and in the political discourse to any liberalization of migration and asylum policies. Since the late 1980s, when the problem of unwanted immigration and asylum flows began to emerge, primarily the EU, the US, Canada, and Australia, have been at the forefront of implementing more restrictive policies to scale down immigration pressures. A wide spectrum of measures has been introduced designed to limit the numbers of asylum seekers, and the costs of hosting them and of processing their claims.1 Based on the experience of the past decade in Western countries, a more restrictive asylum policy in one country leads to a diversion of

Introduction 5

asylum flows to other countries, and consequently, to a race to the bottom of asylum standards, and thus, to an increase of irregular (undocumented) migration flows into Western countries. As a result, unidimensional restrictive asylum and immigration policy approaches seem rather inadequate to curtail the trend of rising South–North asylum migration movements. Since migration flows are mostly self-perpetuating by network effects, they are also unavoidable. Deficiencies of restrictive asylum policies require alternatives to these reactive asylum policy approaches. For instance, it is often argued that promotion of economic, political and social development in poor refugee-sending countries could curb emigration. In particular, development aid and other financial transfers are discussed as alternative instruments to reduce refugee migration rather than restrictive asylum systems and stricter border controls. In this book, we distinguish basically two impact variants of such transfers: first, financial transfers could compensate for non-private spillovers that are generated by refugee-hosting countries (that is, internalization of cross-border externalities), and thus, produce a more efficient allocation of refugees. However, under what conditions do countries actually have incentives to act cooperatively and to join an international burden-sharing regime? And second, foreign aid has the potential to work as an instrument that curbs the socioeconomic and also political causes for emigration. However, do countries actually allocate aid for that purpose of migration prevention? If yes, to what extent are aid policies of Western donor countries influenced by the refugee movements that increasingly take place in the developing world? Furthermore, do Western donor countries distinguish between different types of forced migrants? These questions are discussed in the subsequent chapters.

1.2

Research outline

The remainder of the book proceeds as follows.2 Chapters 2 and 7 provide the respective background information for the two major topics of this book: refugee migration movements and the allocation of foreign aid. While Chapter 2 focuses on the characteristics of migration and its underlying causes as well as on the actors and their asylum policy approaches, Chapter 7 primarily explores the motivations and patterns of foreign aid allocation policies.

6


In Chapter 3, the incidence and distribution of the current global refugee population is investigated. The focus is put on the development of an appropriate measure (that is, an index) that makes refugee-hosting burdens more comparable across countries. We elaborate particularly on the question of how large populations of refugees affect host countries that are highly heterogeneous with respect to their political, economic, and social ‘performance’. These three categories are often the most relevant areas when host countries are confronted with refugee inflows. Since burdens in these areas are not directly measurable, for example, in terms of monetary costs, this analysis corrects for cost heterogeneity by indexing the political, economic and social performance of the host country when providing asylum to a refugee population.3 This methodology generates implicit performance-based refugee burdens that are comparable across host countries. As a result, a Refugee Burden Index displays a more appropriate picture of the actual extent of both global and intra-regional distribution of refugee burdens, and thus, the unequal distribution across asylum countries. Chapter 4 elaborates on the determinants of forced displacement by studying the effects of the protracted conflict in the Indonesian province of Aceh on population movements across villages. As a starting point, this chapter explores the correlates of this longlasting armed conflict. We then identify the determinants of displacement based on various conflict-related and socioeconomic push and pull factors. The study, based on village-level data, disentangles the driving forces behind changes in village populations during the most severe years of the Acehnese civil war (between 1999 and 2003). It is shown that the driving forces of displacement are not only the immediate conflict factors like violence and fear of death, but that a significant part of the population also left their homes because of other political, economic, cultural and religious factors. Thus, traditional migration causes are still relevant factors in explaining large displacements in times of conflict and war. This case study partly unravels the complexity of forced migration determinants in a conflict environment and contributes empirical evidence to the scarce quantitative literature on conflict-induced migration movements. Chapter 5 models the individual migration decision of persecuted people. It analyzes the driving forces of the magnitude, composition and duration of refugee movements that are originally caused by

Introduction 7

conflict and persecution. We presume that the migration decision is based on intertemporal optimization by each persecuted individual. It is shown that ceteris paribus asylum seeking towards Western countries is often a phenomenon of the rather less persecuted people. Furthermore, since all host countries are interested in a sustainable reduction of their potential or actual asylum burdens, Western countries and conflict-neighboring host countries in the region of origin are likely to end up in a ‘race to the bottom’ of restrictive asylum policies. As an alternative, this analysis shows that under some circumstances proactive refugee-related aid transfers are an effective instrument to relieve Western countries of their asylum pressure. Whether financial transfers among asylum countries are an appropriate instrument for instigating a cooperative international refugee regime is the task of Chapter 6. Two alternative international refugee regimes with and without financial cooperation schemes among heterogeneous asylum countries are analyzed. It becomes evident that refugee protection levels are generally suboptimal because of the absence of an institutionalized and efficient refugee cooperation regime. Asylum provision as a ‘public good’ causes non-cooperative deflection and free-riding of (potential) refugee-hosting states. Furthermore, the analysis indicates that, first, asylum cooperation is rather attainable among less heterogeneous countries, and second, financial transfers are, under some circumstances, able to enforce and stabilize a cooperative international asylum regime. These implications of the theory are applied on the context of the enlarged European Union. At least for the (currently) 27 member states of the EU, this exercise indicates that asylum cooperation is attainable under the present voting system, and even more so under the proposed voting principle outlined in the EU reform treaty. Based on the policy implications of the previous chapters, Chapters 8 and 9 analyze whether the normative claim of a proactive refugee-related aid policy is reflected in actual bilateral aidgiving patterns of Western aid donors. The objective is to disentangle bilateral aid patterns and to identify the most relevant aid allocation determinants of Western donors. Particular emphasis is put on the influence of refugee (asylum) migration on aid transfers towards both refugee-sending and conflict-neighboring first asylum countries. In these empirical studies, only aid flows for the period after the Cold War are addressed. This coincides temporally

8


with the period of increasing asylum migration movements towards Western donor countries. In Chapter 8, the focus is put on the means that seems most efficient in triggering donor countries to respond sustainably to urgencies: asylum migration to Western donor countries. The objective of the analysis is to identify the influence of asylum seekers and other foreign-born immigrants on the bilateral allocation of development aid funds. In contrast to most other empirical studies that focus primarily on domestic economic and political variables relating to the recipient countries, this analysis is rather interested in the role of domestic politics in the donor country, without ignoring relevant aspects of the recipient countries. In the literature of political economy, the influence of interest groups has received wide attention. In this study, the relevant interest groups are the permanently settled foreign-born immigrants as well as the asylum applicants that newly enter the Western donor country. Both groups seem to be interested in increasing aid transfers in favor of their home country. This hypothesis is studied in the case of Germany, since it is both one of the largest donors of bilateral aid and a main haven for asylum seekers and other immigrants with a significant stock of foreignborn population and a continuous inflow of asylum seekers. The analysis shows that bilateral asylum burdens have a significant effect on the amount of bilateral aid transfers, and particularly, when the respective recipient countries have – besides the influence of asylum seekers – a significant stock of permanent expatriates living in the donor country. Chapter 9 continues this discussion and analyzes the impact that different characteristics of refugee movements have on the allocation decisions of bilateral donors of (long-term) development and (short-term) emergency aid. The evidence indicates that short-term aid is indeed used in severe situations of violent conflicts and natural disasters, and therefore is predominantly allocated to the home countries of refugees. Furthermore, donor states respond to all types of forced migration (that is internally displaced persons, cross-border refugees or international migrants seeking for asylum in donor states) by increasing their humanitarian aid efforts. With respect to the allocation of long-term development assistance, however, donor states target their aid funds primarily to the countries of origin of refugees, particularly in the case where these refugees have become

Introduction 9

asylum applicants in the donor country itself. Further disaggregation indicates that this effect is stronger for the donor countries that are main havens for asylum seekers. Finally, Chapter 10 summarizes the main insights of these different studies and discusses some further policy implications. Although the complexity of the conflict–refugee–aid linkages permits only modest statements, it becomes clear that there is an unambiguous need for alternatives given the increasing trend of restrictive asylum and immigration policies. More proactive international root-cause strategies for the countries in trouble and burden-sharing initiatives for the overflowing refugee-hosting countries are unavoidable to sustainably address refugee migration movements.

2 Forced Migration Patterns and Politics

Abstract This chapter provides a general overview on migration characteristics, such as different types and causes of migration. Furthermore, the main actors in migration and refugee politics are introduced and the most relevant alternatives in international and national refugee policy are briefly described.

2.1

Introduction

Migration is a global phenomenon. There is no country in the world that does not register either transnational or internal migration movements. The ever increasing integration of world markets works as a major driving force of economically motivated migration movements. Yet, individuals do not only move for economic reasons. Refugees (or, forced migrants) constitute a substantial part of the worldwide migration stocks and flows. By definition, they do not (primarily) leave their home village or country in order to find better economic living conditions for themselves and their families, but because they seek protection from suppression, persecution and violation of their human rights. In general, states try to control migration by various measures that restrict immigration.4 However, the intensity of efforts to limit immigration varies significantly across states; some countries have more liberal immigration laws than others. In principle, all national states try to manage migration flows; no country has completely open borders. Nevertheless, international 10

Forced Migration Patterns and Politics

11

migration continues to increase, and policymakers in many states suggest that more international cooperation is needed to arrest the trend of continuously increasing numbers of all types of (international) migrants. This is particularly critical in view of the proceeding integration of world markets, increasing wealth and income inequalities, as well as political insecurity in many states of the developing world. This chapter begins with an overview of the characteristics, the pattern, and the causes of the migration phenomenon. Next, the implications for national and international migration policies are discussed, though primarily focusing on the subgroup of forced migrants, that is, refugees, asylum seekers and internally displaced persons (IDPs).

2.2

Migration characteristics

The global migration pattern is difficult to describe and can only roughly be estimated due to a lack of quality data on global migration. Even in many Western industrialized countries, the quality of migration-related data is insufficient and fragmentary (Crisp, 1999). For instance, there is a lack of individual data available on the age, origin and educational background of migrants, as well as information on the (intergenerational) characteristics of (re)integration processes. Data collected on global migration stocks and flows are still sketchy; there are deficits in the national statistics, in addition to different definitions and collection methods across countries. Therefore, clear-cut statements on the statistics of the global migration pattern must be interpreted with caution (Crisp, 1999). This data deficiency is also problematic with regard to the formulation of appropriate policy strategies, since without reliable data, the planning and evaluation of political programs and initiatives can hardly be accurate. Nevertheless, keeping this fact in mind with regard to international migration numbers, it can be stated that the migration pattern has changed strongly since the end of the Cold War. By the end of 2005, almost 200 million people lived temporarily or permanently in countries where they were not born (United Nations, 2006b). This corresponds to around three percent of the world population (Table 2.1). Between 2000 and 2005, industrialized countries have had an average annual migration net inflow of approximately 2.6 million people stemming from the developing

12


Table 2.1 1000

International migration stocks (2005) and flows (2000–05), in

Population

Migrant stock (per capita)

Net annual inflow (per capita)

Developed countries

1,211,265

Developing countries

5,253,484

115,397 (9.5%) 75,237 (1.4%)

2,622 (2.2%) –2,622 (–0.5%)

Source:

United Nations (2006b): Population Division.

world, which is around 0.2 percent of the developed countries’ combined population (United Nations, 2006a). In Western industrialized countries, these migration numbers are often perceived as much higher, although they rose only moderately compared to previous decades. Besides modestly increasing migration movements, migration forms have become more varied and more complex. It is difficult for governments and international organizations to distinguish between the different migration forms, for example, to distinguish between economic migrants and refugees, or between temporary, permanent and repeated immigration, as well as legal and illegal (irregular) immigration. 2.2.1

Migration types

Disaggregating global migration movements indicates roughly three major bases of the total stock. First, subsequent immigration (family reunification) of relatives of former immigrants (or refugees) living now permanently in the host country constitutes the major part of the whole migration stock (United Nations, 2005a). In most Western countries, subsequent immigration of family members is limited to spouses and children of migrants with permanent/ unlimited residence rights.5 Some Western countries generously permit the influx of relatives that do not belong to the actual core family. It is expected that subsequent immigration of relatives can facilitate integration in the host country. Other countries, particularly in East and Southeast Asia, are stricter and refuse immigration of family members in principle.


13

The second most important migration form is labor migration, that is, entry for the purpose of a limited or durable practice of an economic activity (United Nations, 2005a). This group consists of, for example, unskilled seasonal workers in the agriculture, production or service industries (tourism). In many countries certain industries would be no longer competitive without such economic migrants. However, labor migrants may also be highly qualified employees such as technicians, scientists or managers. In most countries, but not in the classical immigration countries like the US, Canada, Australia or New Zealand, labor migrants receive only a limited residence and work permit that is often restricted to a certain employer–employee relationship. In recent years, some European states have also begun to provide durable job market-related immigration rights in order to fill structural gaps in their labor force.6 The third category of migrants, which is of particular interest to this book, is the group of asylum seekers and refugees. Together, they constitute around seven percent of global migration stock (UNFPA, 2006). The number of refugees worldwide decreased from 16.3 million people in 1993 to approximately 12.7 million in 2005, of which 8.4 million were assisted by United Nations High Commissioner for Refugees (UNHCR) and an additional 4.3 million were under the responsibility of the United Nations Relief and Works Agency for Palestine Refugees (UNRWA) (UNHCR, 2003, 2006c).7 However, the total number of persons who left their country or region of origin because of fear of persecution, political suppression or violent conflicts was even larger. In 2006, UNHCR was responsible for more than 32 million persons – in addition to the already specified refugees in the narrow sense, there were 596,000 asylum seekers, and 734,000 refugees returned to their home countries. Additionally, internally displaced persons (IDPs) constituted another 12.8 million persons by the end of 2006 (UNHCR, 2006c).8 Since the mid-1990s, the number of refugees has decreased and the number of internally displaced persons increased. This is primarily because of changing flight causes: on the one hand, there were more internal civil conflicts with forced displacements as part of warfare, and on the other hand, asylum policies of most host countries have become more restrictive, which made it more difficult for refugees to access safe and developed countries for the purpose of asylum.

14


2.2.2

Migration causes

The above outlined rather modest increase in total migration numbers established two stylized facts of migration research. First, despite often very desperate living conditions, most individuals or groups remain in their country of origin and do not emigrate or flee their poor situation (Fischer et al., 1998; Straubhaar, 2000). And second, international migrants and refugees do not usually belong to the poorest group of people in their country of origin. Instead, they are rather the more wealthy ones, which have the financial resources and/or family or other social networks for immigrating into the destination country (Bhagwati et al., 1984). Migration decisions are complex because they often have farreaching consequences. Basically, the individuals decide upon leaving their familiar living environment, that is their families and neighborhood, for an unfamiliar situation abroad. However, the migration-determining factors are generally more comprehensive. Traditional migration theory differentiates between factors that take place on various socioeconomic and political levels. These are usually separated into push factors, which are effective in the country or region of origin, and pull factors, which proceed from the potential destination. Since these factors are most often mutually interdependent, they can hardly be separated. In general, the core causes of migration movements are economic, political, sociocultural, demographic or ecological, or a mixture of these. Economic factors are certainly the most important reason for emigration. Demographic, ecological, ethnical or political migration motives frequently become effective only in interaction with economic motives. Even refugees can have economic reasons for escaping their origins, for example, if their economic bases have been destroyed by violent conflicts (see Chapter 4). Mixed motivations make it difficult to differentiate between economic and noneconomic migration. Refugees are often presumed to give political reasons for their subjacent economic motives (Neumayer, 2005b). Thus, this so-called ‘asylum-migration nexus’ reflects the fact that forced (that is involuntary) migrants are often hard to distinguish from those with pure economic motives. Besides economic reasons, political migration factors are often as important as economic factors. International refugee law defines the obligation for protection of transnational migrants according to the


15

distinction of political and non-political migration causes (Hathaway, 1990). The strongest political pressure is exerted on potential host countries when migrants originate from countries in violent conflict and war. However, other aspects of persecution and human rights violations are added to the various definitions of a refugee.9 In most cases conflict and violence lead not only to transnational migrations, but also to mass displacements within the country. These internally displaced persons (IDPs) are often in a worse situation than crossborder refugees, because international refugee laws are not valid for their protection. Loescher & Milner (2005) state that the political inaction of international actors in terms of their lack of interest in intervening in situations of armed conflict and human rights violations and in assisting host countries are the major causes for the increasing number and durations of protracted refugee situations around the developing world. Social, cultural and religious migration factors are closely related to political push factors. Emigration can be forced if, for example, social, cultural, or economic practices of minorities are rejected by significant other groups within a society and the government cannot alleviate these tensions. On the pull side, similar cultural and ethnical backgrounds, such as common language between the origin and destination country, facilitate migration movements substantially. Networks among ethnical communities in the host country offer often crucial support to new immigrants, for example, by providing accommodation or work after the entry (for example, Massey et al., 1998). Obviously, these effects hold for both legal and irregular entry and stay. Demographic development is often mentioned as another migration-inducing factor. However, demographic pressure becomes effective only if certain economic, social, or ecological conditions are also present. Particularly for developing countries, population growth can induce both internal and transnational migration movements depending on its rate and regional distribution. For most industrialized countries, however, pull effects are more relevant because of decreasing fertility rates and a rising life expectancy. Aging societies with contracting populations in the long-run suffer from a diminishing labor force. Therefore, many industrialized countries discuss possibilities to abate these structural gaps by temporal or permanent (high-skilled) immigrants (Borjas, 1995).

16


Environmental factors have increasingly become a cause for emigration. Obviously, this applies particularly to natural disasters such as floods, earthquakes, droughts or hurricanes. Mass movements induced by such factors are usually temporally limited and the (environmental) refugees normally remain in the region or in the country. However, other human caused environmental damages are often even more serious in their consequences. These are, for example, the long-term loss of field and pasture land because of excessive land use and the misuse of water resources, as well as the amassment of serious floods caused by ecological degradation of the soils. Other environment-related migration factors can be environmental degradations by military activities, wide environmental pollution, expansion of industrial or agricultural manufacturing areas, or resources shortages (for example, drinking water).10

2.3 International actors for refugee protection 2.3.1 UNHCR and its historical background National states and international organizations arrange their responsibilities and competencies for migration movements according to whether these take place voluntarily or by force – that is whether they are migrants, or refugees according to the UN Refugee Convention of 1951/1967. The Convention does not obligate the signatory states to grant individual asylum to a refugee but only not to expel or to reject entry into a country, in which his/her life or liberty would be endangered (‘non-refoulement principle’). In 1949, the Office of the United Nations High Commissioner for Refugees (UNHCR) was established. The mandate of the organization is to provide adherence to the principles of refugee protection. It provides long-term assistance in terms of refugee protection and postconflict repatriation and reintegration. It is the most important human rights and advocacy organization for refugees and, more recently, also for internally displaced persons (Loescher, 2002). Within the UN system, UNHCR plays the most important role in refugee protection matters. The mandate of UNHCR, the UN Convention on the Status of Refugees, was adopted in July 1951 (UNHCR, 2000). At that time, the Convention was only applied to individuals who became refugees before 1950. The signing states were given the


17

possibility to limit their obligations to only European refugees, while internally displaced persons were generally excluded (UNHCR, 2000). The spatial and temporal restrictions of the 1951 Convention were only waived with the ratification of the supplementary protocol of 1967, since during the 1960s and 1970s UNHCR expanded its operations to extra-European refugee situations in many regions of the world. As a consequence, UNHCR became the undisputed international authority in matters of refugee protection, and began advising governments in asylum-related legal questions (UNHCR, 2000). In the 1980s, the situation changed again as refugees became involved in the deputy wars in Asia, Africa and Latin America. Some conflict parties recruited refugees as fighters and used refugee camps as military replenishment or retreat areas. At the same time, the maintenance of refugee camps took up substantial resources and organizational capacities, as many refugee situations became protracted and showed no prospect of imminent solution. Furthermore, increasing numbers of refugees sought asylum in Western states. As a consequence of this and the fact that the Western states increasingly considered the work of UNHCR to be unsuitable for the solution of refugee crises, the Western governments endeavored to solve refugee and asylum problems themselves on bi- and multilateral levels, further diminishing the role of UNHCR (UNHCR, 2000). Since the end of the Cold War, Western governments’ perception of refugees has changed. Refugees are no longer regarded as a strategic instrument in the struggle between the two geo-political blocks, but have been increasingly perceived as a burden. The large refugee movements of the 1990s fuelled the fear of political destabilization and became a major issue of international and regional security aspects.11 Starting from 1991, UNHCR intervened in internal affairs of states affected by refugee disasters, in a way that would have been inconceivable during the Cold War.12 UNHCR expanded its assistance to ever more conflict-affected populations, in particular to local populations and internally displaced persons. During the 1990s, the number of persons for whom UNHCR were responsible had almost doubled (UNHCR, 2000). This made UNHCR the dominant actor in forced migration matters, not only in the developing world but also in collaboration with industrialized states.

18 Refugee Migration and Foreign Aid

2.3.2 Other international organizations Beside UNHCR, there are several other UN special organizations and other international organizations working in the field of refugee issues.13 For instance, since 1997, the Office for the Coordination of Humanitarian Affairs (OCHA) has come under the direction of the UN Under-Secretary for Humanitarian Affairs, who is also the Emergency Relief Coordinator (ERC) and the chairman of all important international humanitarian organizations of the comprehensive Interagency Standing Committee (IASC). In 1993, the office of the United Nations High Commissioner for Human Rights (UNHCHR) was established. The task of the High Commissioner is to supervise and promote the adherence of human rights, which also includes issues of forced displacement. The United Nations Development Program (UNDP) steers and coordinates development projects throughout the developing world and is also active in a number of refugee disasters, contributing to the reconstruction of local infrastructure, the promotion of economic development and employment opportunities, fighting poverty, and thus, also to the long-term solution of refugee crises. Since 1992, the mandate of UNICEF also includes the assistance of displaced children and women, who belong to the most vulnerable group in many refugee situations. Apart from establishing initiatives for the improvement of legal protection for children and women, UNICEF is also an operational actor in refugee situations. The task of the World Food Program (WFP) is to supply food and to guarantee basic nourishment, and furthermore, to improve the capability of refugees for self-reliance, for example, by financing food-for-work programs. The Department for PeaceKeeping Operations (DPKO) is responsible within the UN secretariat for the planning and execution of different types of peace missions. In recent years, given the growing complexity of refugee situations and the larger range of auxiliary operations, cooperation and coordination of humanitarian assistance among relief organizations and military actors has become much more important (Angenendt, 2002). Beside these UN organizations, there are many other international organizations active in the refugee assistance. For instance, the International Organization for Migration (IOM), which carries out assistance and repatriation of refugees and asylum seekers on behalf of governments, or the International Committee of the Red Cross


19

(ICRC), which ensures humanitarian protection of the civilian population in armed conflicts. Other important actors are also regional organizations like the European Union, which has become one of the most important donors of international humanitarian assistance in recent years through its office for humanitarian assistance (ECHO), or the Organization for African Unity (OAU), which has contributed to the political resolution of some refugee crises in sub-Saharan Africa. Finally, there are many international and national nongovernmental organizations (NGOs), which also carry out important humanitarian assistance and relief for refugees. 2.3.3 National states The role of national states as actors in international refugee and asylum issues has altered, although they retain their role as important and often essential actors. States still are the key actors in asylum migration and in tackling the root causes of migration for durable solutions to refugee situations. National states are essential actors in efforts to protect the human rights of involuntarily displaced populations (Mertus, 1998). However, the sovereignty of the nation state when it comes to entry of their own territory and the control of their borders is a matter of consensus in the international community of national states. Several multilateral agreements and conventions on human rights basically urge nation states to grant asylum to forced migrants for humanitarian reasons, but discretionary policies of national governments in asylum and immigration matters are generally accepted (Sassen, 1998). For instance, during past periods, the US government considered refugee and asylum policy too important to let the United Nations control it, and they sought to limit the scope and independence of UN agencies. In this respect peace missions and refugee interventions in specific regions are much more generously funded and supported by the US and other Western states when their respective geopolitical interests are significantly affected (Loescher, 2002). Thus, the focus is subsequently primarily on the role and interests of national states and their bilateral activities.

2.4 Refugee politics As the international economic integration of markets proceeds, incentives and opportunities for different types of transnational

20


migration movements will continue to increase, and individual cross-border mobility is expected to rise for all regions of the world.14 However, in contrast to the relatively unconstrained mobility of goods and markets, international mobility of people is still tightly constrained by restrictive immigration and asylum policies that restrain a large share of potential migration that otherwise would take place (Chiswick & Hatton, 2002). In the face of increasing numbers of irregular immigrants, asylum seekers and refugees, Western countries have become increasingly interested in preventing large exoduses from developing countries. Policymakers are becoming more and more interested in the political and economic root causes of these unwanted population movements. Münz & Weiner (1997) point out three possibilities for Western states to react: One is to seek better instruments to control, to establish and enforce stricter border regimes and restrictive migration and refugee laws, to try to repatriate those who enter unlawfully or do not qualify for asylum. A second is to accept and absorb a certain number of refugees and economic migrants and to address the social, economic and political consequences posed by the flows. A third response is to develop prevention and intervention strategies towards countries that actually or potentially generate mass emigration. (Münz & Weiner, 1997, p. ix) Subsequent chapters are primarily concerned with all of these policy options. International burden-sharing regimes and the potential for international cooperation in refugee protection are analyzed as well as the potential or actual interest of Western destination countries for migration prevention policies, focusing here on financial and technical aid as major economic instruments. In the next section, we introduce these two pillars of a comprehensive migration and asylum policy; later chapters delve more into these areas of refugee politics. 2.4.1 Burden-sharing and international cooperation Policymakers of many developed and developing countries acknowledge that the global migration challenges are not brought under control without international cooperation and intervention. Numerous states around the world perceive themselves as overburdened and


21

have substantial problems designing adequate immigration and integration policies. Western governments are trapped by competing interests when formulating their immigration and asylum policies. Enterprises urge for a more liberal migration policy in order to overcome job market bottlenecks, while the general public is concerned about additional job market competition, internal security and national identity. Therefore, Western industrialized countries try to establish and to unilaterally improve national capacities for controlling and managing migration movements. However, they also strive for common, international regulations for improving regional cooperation in migration matters (for example, common immigration laws as well as a harmonization of asylum policies within the European Union). In fact, why is refugee burden-sharing and international cooperation desirable? As Chapter 3 will show, refugee burdens are very unequally distributed both internationally between the Southern and Northern hemisphere as well as among developed and developing states. These unequally distributed refugee burdens are primarily caused by standard free-riding incentives and the pure self-interest of national states, as well as the relative attractiveness of the pull factors in the host countries. The public good character of refugee protection refers primarily to the non-excludability and non-rivalry in ‘consuming’ security and political stability benefits spilling over from refugee-hosting states (Suhrke, 1998). However, some authors argue instead that states in fact generate excludable benefits, which are specific to the host country, and thus, asylum provision is more accurately characterized by a joint product (Sandler, 1992; Betts, 2003). In this case, state-specific benefits might be generated through international reputation gains or a host country’s ideological and humanitarian objectives (Thielemann, 2003a). A joint product model predicts that provision of refugee protection, in terms of the number of accepted refugees on its own territory, is proportional to the excludable benefits accruing to the asylum-providing country. Beyond country-specific self-interests, national states might follow norms of solidarity and humanity. States that integrate such principles in their asylum policy have then an increased willingness to accept asylum seekers as refugees (Thielemann, 2003b). A third reason for (cross-country) unequal refugee burdens may refer to structural differences in migration-related pull factors, which determine

22


the relative attractiveness of potential host states for asylum seekers and other migrants. Beside standard economic factors such as income and employment opportunities, other non-economic determinants like a state’s reputation of being liberal in asylum and immigration policies, having a generous welfare system, or well-established social, ethnic, and cultural networks might often be even more important pull factors (see the discussion in Chapter 8). Generally, because of sovereignty, national states are not obliged to agree to any international cooperation schemes, and thus, some often refrain from any refugee protection contributions. Many states, however, participate to some extent in a latent burden-sharing in order to satisfy norms of solidarity, geopolitical self-interests, or the rationale of an ‘insurance’ against future mass inflows by stabilizing a fragile region (Schuck, 1997). Several (explicit) refugee burden-sharing regimes are discussed, ranging from rather ‘soft’ options like the harmonization of asylum policies and legislations (partly realized by the Amsterdam Treaty (1999) in the EU) to rather ‘hard’ options such as financial costsharing with overburdened host countries (Acharya & Dewitt, 1997; Thielemann, 2005; Betts, 2006), or even asylum quotas and a physical ‘refugee reallocation’ (as discussed in Chapter 6). Some of these policy options increase efficiency by equalizing marginal costs across countries for asylum provision and stop the general ‘race to the bottom’ in asylum standards observed since the early 1990s. However, burden-sharing without tackling the root causes of unwanted emigration is generally considered as insufficient and not sustainable for any asylum regime. 2.4.2 Migration prevention and aid assistance Policies that control and restrict migration are costly, and other policy approaches may therefore prove to be more effective in reducing unwanted immigration. Supplementary to the outlined, rather reactive, measures of asylum provision, root cause strategies and migration preventive policies may be more effective in the long term. These include conflict resolution by diplomatic or military interventions, intensive humanitarian assistance before, during and after a conflict and refugee crisis, and enhanced development promotion (for example, by trade, FDI, or aid). Obviously, identification of the root causes and formulation of appropriate policies is a complex task


23

and requires country-specific analysis. One policy instrument for tackling the root causes of mass emigration, development assistance, is at the heart of the subsequent analysis. Western countries have an ongoing interest in the potential of development cooperation as effective migration prevention policy.15 Improving the economic, social, and institutional conditions in the countries of origin by an extended provision of financial and technical aid is central in such proactive migration prevention approaches.16 Aid transfers may reduce migration propensities by improving economic and governance conditions of emigration countries. However, some authors alert to the contrary effect. For instance, de Haas (2006) argues that such interventions may rather accelerate economic-motivated emigration and secondary refugee movements. This argument is related to the ‘migration hump’ theory, which states that migration propensities of the very poor are generally lower than those of the better off. However, empirical evidence of this nonlinear effect of economic development on emigration is rather ambiguous (Rotte et al., 1997; Neumayer, 2005b). Similarly, interventions for the promotion of democracy and good governance institutions are effective only in the long-run, but for the shortrun, they might stimulate further upheaval and refugee outflow (Schmeidl, 2001; Nyberg-Sørensen et al., 2003; Neumayer, 2005b). Furthermore, the effectiveness of aid interventions in reducing the risk of violent conflict is highly disputed. While some authors argue that under certain conditions, particularly in some later periods of post-conflict situations, aid is effective in reducing conflict risks (Collier et al., 2003; Collier, 2007), other authors argue that some types of aid might even increase the probability of conflict occurrence (Esman & Herring, 2001). This debate about the effectiveness of aid with respect to the mitigation of conflict risk and emigration pressure is still unresolved, however, progress in governance structures and economic development are definitely important elements of a comprehensive international refugee policy. Simple evidence shows that almost all refugees originate from countries that are ranked in the lowest two quintiles of global poverty and which experience eminent levels of internal violence (Münz & Weiner, 1997). Since poverty and violence are often interrelated, any aid policy approach needs to tackle both issues simultaneously to be effective. However, proper and clear evidence on the effectiveness of


aid for emigration-prevention purposes is still lacking. Nevertheless, several donor countries have re-designed their development policies and reallocated their aid budgets for migration management objectives. Chapters 8 and 9 further discuss this issue and provide evidence about the influence of different aspects of refugee and asylum migration on the decision of donor governments to re-formulate development policies and to reallocate their respective aid transfers.

3 International Refugee Burdens

Abstract17 Many developing, as well as developed, countries provide temporary asylum to a large population of refugees and most of these host countries claim to be overburdened. The effective burden a country has to shoulder is difficult to determine since the absolute number of refugees must be evaluated in the light of country-specific conditions in the host country. This chapter provides a methodology that makes refugee burdens more comparable on a cross-country basis. Taking into account different aspects of a host country’s capacity we provide a Refugee Burden Index that is based on proxy indicators representing the economic, socio-demographic and politicoinstitutional environment. This methodology is applied on a sample of 174 countries, revealing the extent of inequalities in the pattern of refugee burden-sharing, both globally and regionally.

3.1

Introduction

There is an ongoing debate in academia18 as well as in politics19 focusing on national contributions to the international regime of human protection and financial assistance to refugees and asylum seekers. Citizens and policymakers of many refugee-hosting countries especially in the industrialized world perceive their country to be overburdened and disadvantaged in comparison to other countries. However, most refugee-producing countries – which are in most cases conflict-affected countries-, are situated in the developing 25


world and it is usually their neighboring countries (first asylum countries) that must bear the lion’s share of the global refugee burden in absolute terms. In 2003, developing regions hosted some 6.5 million refugees (67 percent), whereas the developed world protected some 3.2 million refugees (33 percent) (UNHCR, 2004d). Although the largest share of the hosted asylum-seeking and refugee population is borne by the low and least developed countries, some of the high-income OECD countries also provide refuge to a quite impressive number of people. In public discussions, the question of whether a country is rather overburdened or not is most generally answered by subjective feelings instead of more analytical reasoning.20 For establishing the ongoing Refugee Burden-Sharing (RBS) debate on a more solid and impartial foundation, this study provides a methodological framework to make refugee and asylum seeker burdens internationally more comparable. The RBS debate lacks in a common and widely accepted measure reflecting the status quo of the relative refugee burden. For achieving wide acceptance, a refugee burden measure should account for fairness, or equity, as well as performance aspects. In the next section we discuss the extent to which refugees and asylum seekers constitute a burden for host countries by assessing their impact on a host country’s society and economy. In Section 3.3 we develop the methodology for a standardized and internationally applicable Refugee Burden Index (RBI), which takes into account not just the absolute number of hosted refugees and asylum seekers but also includes country-specific capacities in categories such as economic performance, population absorbance capacity, and societal and political stability issues. The fourth section contains the application of that measurement tool based on a sample of 174 developing and developed countries of which 145 countries were refugeehosting countries in 2003. A country-specific and a region-specific RBI enable us to make inferences concerning the ‘degree of overburden’ a country or a region in the sample experienced in 2003.

3.2 Assessing the refugee burden to host countries For assessing the ‘burden’ of asylum seekers and refugees to host societies, which literally implies that there is a negative impact (net costs) of hosting refugees for a receiving country, we must break

International Refugee Burdens

27

down this burden into different core factors. That is, we should clearly distinguish the differences between asylum seekers and refugees, as well as the differences between developed and developing countries as host societies. Although most countries of the world have signed the 1951 Convention relating to the Status of Refugees and its extending 1967 Protocol, the receiving countries’ asylum systems are not at all standardized. The systems differ in their process of adjudicating the claims for refugee status, for example, systems may differ in the status decision duration, the opportunities for second or third appeals and the naturalization procedure. Therefore, we must be cautious with both cross-country comparisons and the asylum and refugee data itself (Crisp, 1999; Hovy, 2001). Secondly, it would be naïve not to take into account the very heterogeneous composition of host societies. Western industrialized countries with well established immigration institutions, powerful economies and stable societies are not directly comparable to developing countries with severe economic, political and bureaucratic deficiencies. That is, however, the task of this study: to make refugee burdens assessable across heterogeneous host countries. The impact of asylum seekers and refugees tends generally to be larger in those countries in which asylum seekers as well as refugees account for a higher proportion of total population. Among industrialized countries there is considerable variation in the numbers of arriving asylum seekers (Neumayer, 2004). However, even if asylum seekers account for a non-negligible share of the population, they are rather unlikely to have significant economic, demographic, environmental and other impact on the host society at least in developed countries. This means that asylum seekers may affect greatly the public opinion about immigration if they are perceived as a growing population, even if not yet a significant one in numerical terms. (Martin et al., 2005, p. 101) Thus, whether in fact or just perceived, asylum seekers and refugees constitute at least a gross burden to the receiving and hosting countries in economic, fiscal, political, institutional, societal and environmental terms. Direct fiscal costs for maintaining an asylum adjudication system, detention, care and maintenance, return of rejected applicants and

28


so on are actually the most readily measurable impacts. These costs vary immensely across countries. One of the few studies elaborating the cost of asylum processing and social security estimated the fiscal costs per asylum seeker in 1994 to be US$ 16,596 in Denmark, US$ 10,299 in Sweden and US$ 4,622 in Austria (Jandl, 1995), whereas Ghana, for instance, estimates its annual costs per refugee to be about US$ 348 (US$ 29 per month) (Betts, 2006). Asylum costs vary across host countries to the degree that they keep asylum seekers in reception or detention centers, maintain them in refugee camps or disperse them throughout the host country, granting them work permissions or providing generous social support and so on In fact, there is no international standard for hosting asylum seekers and refugees and international refugee law does not explicitly prescribe how to host asylum seekers and refugees adequately. Generally, discussions on asylum adjudication costs fall short by focusing predominantly on gross expenditures, not taking into account the possible positive economic impact of asylum seekers on, for example, tax revenues. Depending on the host country’s policy on granting work authorization based on the demographic and educational characteristics of refugee populations, asylum seekers might be net contributors to the host country’s fiscal budget (Martin et al., 2005). However, even if eligible, Borjas (1994) points out for the US that at least in the short run, asylum seekers and refugees have worse economic outcomes (wages) compared to other labor migrants. In the long run, however, the outcomes of these immigrant groups may adjust to be similar (Borjas, 1982). Some studies elaborating on labor market participation rates in developed countries find on average a higher unemployment rate among refugees compared to the local population.21 The impact of immigrants on the host country’s labor market depends highly on whether immigrants are substitutes or complements to the native labor force. Having different characteristics in terms of skills and education, immigrants that are complements to native labor endowment should rather be beneficial and generating a positive impact on employment and earnings if they are allowed to join the workforce. Furthermore, in the long run for industrialized host countries, immigrants may fill demographic gaps caused by aging populations. Positive impacts are often not extensively realized by the native society of a host country and therefore asylum has been one of the most controversial issues in discussions about immigration. Public


29

perception of asylum migrants is mainly determined by the immigration motivations of the asylum seekers (economic vs. forced), cultural and linguistic differences to the resident populations or unfamiliar social and cultural practices of the newcomers tending to create tension either among immigrant groups themselves or between asylum migrants and the native population. Sometimes, these tensions are capitalized on and reinforced by political leaders who expect to benefit from a strict stance towards unwanted arrivals. Though in some particular cases, a large influx of forced migrants may even be welcomed by local residents if the situation the arrivals flee from is well known and the media have already ‘generated some altruism’. While, an influx of a large foreign population generally destabilizes the social structure of a society, traditional immigration countries have typically developed a capability for integrating foreigners without weakening the social cohesion. Nevertheless, large-scale refugee inflows always stress local capacities even in cases of wellestablished formal and informal institutions. The higher the performance of these institutions, the more stable the local community is and the less likely that asylum seeker constitute a threat for the hosting country. For many host countries, especially in the developing world, another factor which is crucial in assessing the impact of refugees is the length of their stay. The average duration of major refugee situations increased from 9 years in 1993 to 17 years in 2003 (Loescher & Milner, 2005).22 Long-lasting or protracted refugee situations are not only a waste of life time and resources of the refugees, but have also serious consequences for the host countries by nurturing instability, tensions and even conflicts. Large, neglected and frustrated populations living in obscurity and on subsistence-level pittances are easy to recruit for armed groups and thereby jeopardizing the societal and political situation of the host country and region (Loescher & Milner, 2005). The above-mentioned factors vary dramatically between the refugee-hosting countries of the world and it seems presumptuous to compare refugee capacities of highly developed Western countries with the weak institutionalized developing countries of, for example, sub-Saharan Africa. However, we venture to tackle this challenge in our next section and develop a simple quantitative measure for making large-sample cross-country refugee burden comparisons assessable.


3.3

Methodology of a refugee burden index

In a large-scale refugee or asylum situation, host countries are placed under tremendous pressure in providing facilities for a high number of people.23 Reception countries are faced with the responsibility of ensuring the protection of human rights to refugees as required by UN Refugee Convention 1951/67, as well as providing for their basic personal economic and social needs such as food and other essential supplies, medical care, accommodation, education and security. A country’s capacity to satisfy these needs differs enormously between the industrialized and the developing world. Probably the best proxy for measuring the capacity to meet these rather economic requirements is the income per capita of a country. We assume that the higher a country’s GDP per capita, the higher its capability to provide basic economic and social resources to a refugee population and the lower the burden per refugee to the host country. However, the effective burden of hosting refugees and asylum seekers is more than solely affording the economic requirements of refugees. In fact, if a large refugee population is received in an area or country marked by poverty, ethno-societal tensions or even civil strife, the political and social environment of the hosting country may deteriorate, giving rise to latent or open conflict (Loescher & Milner, 2005). The potential for destabilization caused by the presence of refugees is often higher in countries with weak governance institutions and democratic structures. A RBI therefore must be a surrogate measure, incorporating all of these important capacity dimensions necessary for providing a safe, appropriate, and nondestabilizing refuge to forced migrants. Before such an RBI itself is composed, several sub-indices have to be created for each of those dimensions; that is, representing economic conditions, population absorbance capabilities, and the quality of governance and democratic institutions. 3.3.1

Refugee capacity index

3.3.1.1 Ability-to-care The economic condition of a refugee-receiving country is highly important for indicating the country’s resource capacity for bearing a large refugee population. We use income per capita as an indicator of a country’s ability to care for refugees, such as providing food,


31

health and education services, maintenance, security and bureaucratic services. Thus, income per capita serves as a proxy for all economic capacity aspects in supporting refugees and asylum seekers. Therefore, we use the income index GDPI as an ability-to-care indicator, which simply reflects the logarithm of a country’s income per capita. For compressing the distribution range, the income per capita of a country is adjusted by the logarithm of the actual value and corrected by minimum (US$ 100) and maximum (US$ 40,000) goalpost values, that is, the GDPI value is zero if a country’s per capita income is US$ 100 per annum.24 This implies that the country’s marginal economic capacity for hosting one additional refugee is zero. An income near the maximum value of US$ 40,000 results in a GDPI value of one, implying that a country’s marginal economic capacity for hosting one more refugee is ‘infinite’. 3.3.1.2 Socio-political acceptance of immigrants We also distinguish between two further factors influencing the level of acceptance of immigrants. First, we use a modified population density measure, the ratio of population per arable land, as an indicator of the capacity of a country to absorb new arrivals. It is often argued that the ability of a host country to absorb refugees is a function of both the national population size, as well as the available arable land which is often an important means of sustaining the nutrition of refugee families in developing countries (Jacobsen, 1997; Kibreab, 1997). Refugees are likely to integrate less easily in a host society where space at their disposal is limited. Population density seems, therefore, an adequate measure for representing both space limitation and population strain. In countries with a high population pressure caused by, for example, a high population growth or large former inflows of migrants, an additional large influx of refugees may over-stress a country’s absorbance capacity and lead to social tensions. As a maximum goalpost value we use one million persons per square kilometer of arable land, which is roughly the population density of some microstates. We use logarithms for compressing the range of the scale at the high end, which gives densely populated countries a relatively lower weight. Thus, a Population Density Index (PDI) of close to zero represents a highly crowded country with very limited additional capacity and space resources for further refugees.


Secondly, host societies often perceive refugees as a threat to their country’s stability and their major societal values (Weiner, 1993). Thus, the degree of acceptance for new migrants might be determined by the existing ethnic composition of a country, that is we assume that a more homogeneous (that is less fractionalized) population is more able to cope with further immigrants as new members of society (see, for example, Annett, 2000). To represent this societal dimension we use an ethnicity dataset by Alesina et al. (2003) that provides country-level data on the shares of major ethnic, linguistic and religious groups. Given these shares, we calculate the ethno– linguistic–religious fractionalization index ELRFI as an equally weighted composite Herfindahl index. The values of the ELRFI score within a range of zero to one. A value near one represents a highly homogeneous ethnic, linguistic and religious structure, implying a high sociocultural stability, enabling the society to cope with new immigrants.25 3.3.1.3

Politico-institutional performance

In many host countries the societal tensions potentially caused by the presence of refugees cannot be adequately articulated because of the absence of well-developed democratic channels and good governance institutions. State performance in terms of providing capable democratic processing and efficient governance structures is extraordinarily important in managing societal stability, particularly in times of a large influx of asylum seekers and refugees. For indicating the quality of democratic institutions, we use the dataset on political rights and civil liberties of FreedomHouse (2005).26 These data are originally scaled from 1 (best) to 7 (worst), and we rescale them so that the sub-index for political freedom, PFI, ranges from zero to one.27 A PFI near one represents well-established democratic institutions, which imply robust capacities for transforming societal and cultural conditions and developments into representative political outcomes. Furthermore, the presence of high-quality governance institutions is an essential factor in the determination of whether a country has the capacity to ensure political stability. A large-scale influx of asylum seekers and refugees demands well-functioning and efficient governance institutions to prevent a destabilization of a society’s structure. Taking this into account, we introduce the governance


33

and political stability index PSI for representing a country’s governance capacity. We use the political stability measure of a dataset provided by Kaufmann et al. (2003) for reflecting this issue.28 The data are originally scaled from –2.5 to +2.5 and we rearrange them to a scale from zero to one. For calculating an aggregate index reflecting the core dimensions of economic, societal, political and institutional aspects relevant for hosting refugees, we generate the refugee capacity index (RCI) by aggregating all of the above-mentioned sub-indices. For reasons of simplicity and the lack of more convincing alternatives, we assess all three dimensions x (economic, societal and politico-institutional) the same weight ax and generate the RCI for country i as: RCI i

␣GDPIGDPI i ␣PDI PDI i ␣ELRI ELRI i ␣PFI PFI i ␣PSI PSI i

economic

societal

polit–institutional

(3.1)

Table 3.1 displays the Pearson correlation coefficients for each component of the RCI based on our dataset of 174 countries. Except for the PDI all sub-indices of the RCI are more or less highly positively correlated to each other, which reveals collinearity between the different dimensions. For our purposes this is not a problem but rather an asset because this diminishes the importance of the weights ax we assign to each component and consequently, assigning the same weight to each one seems less ad hoc.29 Table 3.1 Correlation matrix of the RCI components

GDPI PDI

PDI

ELRI

PFI

PSI

RCI

–0.11 (–1.42)

0.42* (6.15) –0.24* (–3.22)

0.55* (8.70) –0.12 (–1.56) 0.20* (2.68)

0.69* (12.48) –0.09 (–1.15) 0.38* (5.33) 0.58* (9.44)

0.89* (25.18) 0.01 (0.19) 0.54* (8.42) 0.78* (16.15) 0.82* (19.07)

ELRI PFI PSI

Notes: t-values in parentheses; critical t-value: 1.974 (5 percent level). Source: (*) significant at the 5 percent level.

34


Through aggregation, the RCI is likewise scored between zero and one. The higher the score of the RCI the higher is a country’s overall capacity for receiving and hosting a relatively large refugee population. The RCI composites the core factors which are essential for appropriate refugee management and, furthermore, it allows crosscountry comparisons concerning the general capacity of host countries for receiving additional refugees. 3.3.2

Refugee gaps

We are now able to calculate hypothetical country-specific refugee populations based on an egalitarian burden-sharing concept. This equity concept requires that the numbers of refugees for a specific country should be proportional to the country’s population, that is, the applied burden-sharing rule implies equal cross-country per capita ratios of refugees. These ratios are adjusted by some country-specific economic, political and social capacity factors. Taking this into account, we are able to determine country-specific refugee gaps. We define a refugee gap as the quantitative deviation of the ‘de facto’ from the ‘should be’ refugee population of a country. Before introducing the methodology of the refugee gap, we should give a definition of our understanding of the expressions ‘adjusted de facto’ and ‘adjusted should be’, respectively. We define an equitable number of hosted refugees, that is a ‘should be’ refugee burden, as a share s out of the total global refugee population, which is equivalent to a country’s national population share out of the total global population. However, these ‘fair’ shares are adjusted (indexed adj) by the above-mentioned country-specific factors. These factors represent the asylum provision capacities derived in the previous section and expressed in a country’s RCI. The fair and adjusted burden of a country i in terms of the ‘should be’ refugee population ref adj,fair is eventually given by: ref iadj,fair

siadj,fair ¦ ref idf

(3.2)

i

with siadj,fair

sifair RCI i ¦ i sifair RCI i

1

and sifair popi ¦ i popi . Basically, such an exemplary adjusted ‘should be’ figure could be used as a benchmark for an international refugee burden-sharing 1


35

regime based on fairness and host country performance. To determine the virtual refugee burdens of all countries in the sample, we must compare the current de facto (indexed by df) situation with this hypothetical benchmark. Therefore, we calculate in an analogous manner the adjusted de facto refugee population ref adj,df for a country i. For this we adjust a country’s de facto share of the total global refugee population by its value of the RCI: ref iadj,df

siadj,df ¦ ref idf

(3.3)

i

with siadj,df

1 sidf RCI i

¦ i sidf RCI1

1

i

and sidf ref idf ¦ i ref idf . Before calculating the actual RBI, which expresses a country’s effective refugee burden level, we must calculate the virtual gap between the ‘fair’ figures of eq. (3.2) and the ‘de facto’ figures calculated in eq. (3.3). The so-called ‘adjusted de facto’ refugee burden gap for country i refers to: 1

gapiadj,df = ref iadj,df − ref iadj,fair .

(3.4)

We calculate in Section 3.3.4 the Gap 03 value, which states to what extent the number of persons seeking refuge deviates from the calculated ‘should be’ number. Then, we can consider a country with a positive Gap 03 as ‘overburdened’ (at least in 2003). Otherwise, a country is below its equitable number of refugees to host and it would still have capacity to host more refugees without becoming overburdened. 3.3.3 Refugee Burden Index Based on the above methodology, the RBI for country i can be specified as: RBI i

gapiadj,df ref iadj,fair

(3.5)

The RBI is scaled from –1 up to positive infinity. An RBI score of –1 stands for a totally unburdened country in terms of having not hosted any refugee or asylum seeker in 2003. A score of zero stands for a fairly burdened country, that is, the country hosted exactly the

36


number of refugees and asylum seekers it should equitably host. And, for instance, a score of 2.5 reflects an overburden level of 250 percent, that is, this country hosts 2.5 times more refugees and asylum seekers than the calculated fair number demands. For our application of this concept we use aggregate figures documenting the number of refugees and asylum seekers by the end of 2003 in 174 developed and developing countries provided by UNHCR (UNHCR, 2004d). Of these, 145 hosted either refugees or asylum seekers or both in 2003. We can aggregate these two figures because there is no overlap between the two categories. Including only these two categories in our calculations is perhaps rather narrow, however they are the best documented. UNHCR’s broadest figure of a country’s refugee population is the ‘Total Persons of Concern’ (TPOC), taking additionally into account the number of Internally Displaced Persons (IDPs), returned refugees, returned internally displaced persons and others. However, the figures used to calculate the TPOC are vague and rather rough estimates because those people are often not assisted and therefore not registered by UNHCR. Therefore, our calculations are based on the rather narrow aggregate measure of refugees and asylum seekers.30 3.3.4

Results

We now apply the previously outlined concept of a RBI to our set of countries, consisting of 145 hosting countries of refugees and asylum seekers and a further 29 non-hosting countries (in 2003).31 Table 3.2 depicts the results for the top 20 most burdened refugee receiving countries.32 Among the top 20 there are sixteen countries from the African continent, which reveals the main region of concern. In contrast, only two European countries (Armenia included) were among the top 20. In 2003, the absolute runaway of the ranking is Armenia, which was overburdened with approximately 42 times its respective equitable number, corresponding to an absolute excess burden of around 210,000 persons. Also severely overburdened were Djibouti (Gap 03 of about 29,000 persons) and Serbia & Montenegro (Gap 03 of about 278,000 persons), which were both shouldering more than 20 times their equitable number. The refugee population of these countries is rather homogeneous, that is these countries act as first asylum countries for citizens of neighboring countries involved in any latent or open conflict, for


37

Table 3.2 Top 20 burdened countries in 2003 Country

RBI 03

Gap 03

Armenia Djibouti Congo (Brazzaville) Serbia & Montenegro Guinea Chad Zambia Tanzania Liberia CAR Sierra Leone Uganda Burundi Congo (Kinshasa) Iran Pakistan Kenya Gabon Cote d’Ivorie Guinea-Bissau

42.15 29.82 26.80 23.21 23.07 20.88 17.92 17.30 16.96 15.96 13.93 11.33 9.37 9.09 8.73 7.90 6.40 6.22 5.88 5.40

210,443 28,990 114,419 278,408 216,535 188,897 240,417 740,204 51,266 63,622 78,197 294,811 67,216 380,956 860,747 1,297,087 247,716 14,049 96,888 8,581

example, Armenia for Azerbaijani, Djibouti for Somalis and Ethiopians, Serbia & Montenegro for Croatians and people from Bosnia-Herzegovina. However, most Western European countries act rather as third countries of asylum and operate as safe havens for a whole variety of nationalities. Therefore, overburdened countries like, for example, Sweden (RBI 03 of 2.67, rank 29), the Netherlands (RBI 03 of 2.30, rank 31), Denmark (RBI 03 of 2.16, rank 32) and Switzerland (RBI 03 of 1.89, rank 34) host a very heterogeneous refugee and asylum-seeking population, reflecting the particular role such countries play in the international refugee burden-sharing system. There were four other Western European countries that can also be considered as overburdened, they were Norway (RBI 03 of 1.07), Germany (RBI 03 of 0.83), Austria (RBI 03 of 0.46) and the UK (RBI 03 of 0.41). Furthermore, we can apply the concept of the RBI not just globally on all countries of the sample, but we are also able to calculate intra-regional refugee burdens.33 As Table 3.3 illustrates, each world

38


Table 3.3 Top 5 countries in 2003

intra-regionally

most

burdened

Region

RBI 03

Gap 03

Europe Armenia Serbia & Montenegro Sweden Bosnia-Herzegovina Netherlands

43.96 24.23 2.82 2.77 2.44

297,648 394,084 87,782 23,367 120,700

Asia-Pacific Nepal Thailand Papua New Guinea Australia New Zealand

33.14 4.18 3.73 3.31 1.80

156,444 80,183 5,600 27,484 2,811

Americas Canada Belize Costa Rica United States Ecuador

2.80 2.37 1.75 1.07 –0.09

123,171 703 8,953 415,853 –1,110

Caswaname Iran Pakistan Iraq Saudi Arabia Algeria

1.35 1.15 0.25 0.11 –0.28

506,135 717,623 29,173 17,921 –53,551

Southern Africa Zambia Namibia Angola Malawi South Africa

8.46 1.05 –0.46 –0.46 –0.48

224,562 8,148 –14,707 –12,223 –79,180

West Africa Guinea Liberia Sierra Leone Cote d’Ivorie Guinea-Bissau

9.69 6.98 5.63 2.05 1.84

163,464 37,902 56,811 60,866 5,261

Central Africa Congo (Brazzaville) Chad

0.96 0.54

43,174 51,723 Continued


39

Table 3.3 Continued Region

RBI 03

Gap 03

Tanzania CAR Burundi

0.29 0.20 –0.27

131,010 8,227 –20,304

East Africa Djibouti Uganda Kenya Sudan Ethiopia

4.62 1.25 0.35 –0.02 –0.51

18,779 135,837 56,516 –3,104 –149,526

region has regional safe havens for refugees and asylum seekers.34 These countries were much more burdened in terms of the number of hosted refugees than other countries of the same region. In Africa, each of the four regions has its own main havens hosting refugees ‘from the neighborhood’ and which were overburdened up to around 1,000 percent of their equitable share! In Southern Africa, Zambia (RBI 03 score of 8.46) hosted around 158,000 refugees from Angola; in West Africa, Guinea (RBI 03 score of 9.69) hosted around 160,000 refugees from Liberia; in Central Africa and the Great Lakes region, Congo (Brazzaville) (RBI 03 score of 0.96) hosted about 81,000 refugees from the Democratic Republic of Congo, and the already mentioned case of Djibouti (RBI 03 of about 4.62). Discrepancies arise between the global and regional RBI 03 scores, reflecting the inequalities that exist between different regions (Table 3.4). For countries that belong to globally overburdened regions, such as Africa and Caswaname,35 the intra-regional RBI 03 score is lower than their respective global RBI 03 score. For instance, Guinea has an RBI 03 score on a global basis of 23.07, but calculated on a regional basis only of 9.69. On the other hand, countries, which were part of an under-burdened region like Asia-Pacific or the Americas, experience a higher RBI 03 on a regional basis, compared to the score on a global basis. This is the case, for example, for Australia, which has an RBI 03 score of –0.32 on a global basis, but a score of 3.31 on a regional basis. Countries of under-burdened regions have higher RBI scores calculated on a regional basis compared to


Table 3.4 Inter-regional Refugee Burden Index in 2003 Region Europe Asia-Pacific Americas South Africa West Africa Central Africa East Africa Africa Caswaname

Refugees (de facto)

Refugees (fair)

1,629,681 798,469 649,948 418,003 690,073 1,729,803 1,017,107 3,854,985 3,194,777

1,698,171 5,079,545 1,757,084 209,061 306,534 122,011 185,494 823,100 769,959

Gap 03 –68,490 –4,281,077 –1,107,136 208,942 383,539 1,607,792 831,613 3,031,885 2,424,818

RBI 03 –0.04 –0.84 –0.63 1.00 1.25 13.18 4.40 3.68 3.15

those calculated on a global basis. This is due to the different total population accounts, global versus regional, applied in the two exercises. In general, these results reveal a noticeable inter-regional discrepancy of refugee burdens. As Table 3.4 shows, particularly Asia-Pacific and the Americas are under-burdened in an inter-regional view; Europe is slightly under-burdened, whereas Africa (and especially Central Africa) and Caswaname are highly overburdened. Table 3.5 itemizes the regional refugee burdens exposed in Table 3.4. For instance, Europe was a net source region for refugees and asylum seekers towards Asia-Pacific and especially the Americas, whereas it was a net recipient from the other regions. Although the Americas was the only region that was a net recipient of refugees and asylum seekers, vis-à-vis all other regions it was still under-burdened (RBI 03 score of –0.63). Another under-burdened region, Asia-Pacific (RBI 03 score of –0.84), was a net refugee-producing region particularly towards the Americas. Generally, these figures display two important insights: first, the global burden of refugee hosting is primarily shared among the countries within a particular region, and second, unequal distribution of refugee population is a phenomenon that is observed within as well as between the various regions of this world.

3.4

Concluding remarks

The often-voiced statements by policymakers and citizens concerning the national burdens in terms of the number of received asylum

Table 3.5

Inter-regional Refugee Burden Matrix (gross/net stocks) in 2003

Caswaname

27,816 83 13 21 1,146 179 96,778 3,236,293

East Africa

Central Africa

West Africa

South Africa

Americas

2 0 0 11 3 32,253 905,279 46

0 41 0 202,119 244 943,273 152,451 35

5 138 2 60 443,841 51,479 329 26,009

90 30 1 217,749 475 90,100 9,054 161

215,198 100,430 82,130 3,106 33,437 15,047 74,774 98,893

20,068 828,544 658 54 1,043 224 11,602 38,337

1,485,863 143,113 12,621 18,301 36,853 39,977 114,207 472,214

Europe Asia/Pacific Americas South Africa West Africa Central Africa East Africa Caswaname

x 3,105 33,435 15,047 74,774 98,880

x –99,772 24 905 183 11,602 38,254

x –123,045 –202,577 18,211 36,848 39,977 114,205 444,398

Europe Asia/Pacific Americas South Africa West Africa Central Africa East Africa Caswaname

x

x

x –96,732

x 120,198 –144

x 51,235 326 24,863

415 –112,019 9,043 140

Asia/ Pacific

Europe

in from

42


seekers and hosted refugees provides the motivation for this analysis. We focus the effective burden of refugee-hosting countries and investigate the overall picture of the global and regional distribution of the global refugee population. For this, we provide a methodology for indicating a cross-country pattern of effective refugee burdens. ‘Effective’ in this context means that country-specific factors that are significant in determining a country’s refugee capacity are taken into account. The resulting RBI reflects several major dimensions that seem reasonable for measuring a country’s capacity for hosting refugees. This RBI methodology is applicable on all levels of interest: we calculate the RBI for a sample of 174 countries that are categorized into eight global regions. This affords cross-country and crossregional comparisons about the actual burdens expressed in a degree of overburden. The main critique of this concept is twofold. First, the results are driven mainly by the applied egalitarian equity concept. The application of an alternative concept would imply, at least slightly, different results. The second shortcoming is the ad hoc selection of the different economic, political-institutional and social indicators that were used for representing a country’s refugee-hosting capacity, and the determination of their relative importance. Thus, simplicity may be both an advantage and disadvantage of this concept. Finally, this concept of a RBI may enrich the Current RBS debate. In this RBS debate, of which UNHCR’s Convention Plus initiative (established in 2003) is only one part, discussions about resettlement, voluntary repatriation and local integration efforts, supported by international aid allocation for refugees, require a clear understanding of the status quo concerning the global refugee situation and its impact on heterogeneous host countries. Countries differ in their capability to receive asylum seekers as well as in their willingness and ability to share refugee burdens physically, but also financially. If such refugee-related financial transfers from donor countries to other refugee-hosting countries would also be taken into account, the ‘net refugee-related burden’ would be lower for recipients and higher for donors. Such a combined measure of physical and financial contributions to the provision of the global or regional public good ‘asylum’ would probably change the RBI ranking significantly.

4 Determinants of Forced Displacement: The Case of Aceh, Indonesia

Abstract36 The purpose of this chapter is to identify the determinants of displacement behavior based on various push and pull factors at the village level. The study concentrates on changes in village population during three years of civil conflict (1999–2002) in Aceh, Indonesia. The empirical analysis is based on a unique dataset from two census rounds of the Indonesian Village Potential Census (PODES). It uses data on around 5,200 Acehnese villages and relates village-level population change to conflict variables, geographic patterns and traditional socioeconomic determinants of migration. By applying quantile regressions, the push (outflow) factors and the pull (inflow) determinants of migration can also be distinguished. We identify the following factors as the main determinants of the Aceh migration pattern in this period: First, conflict clashes induced large rearrangements of the population between villages in highly affected districts, as well as strong village emigration from the geographically remote regions in Central Aceh towards the less conflict-affected coastal industrial areas. Besides conflict factors, an (ongoing) rural– urban migration process, driven by socioeconomic factors, has taken place during the conflict period. Second, there is also evidence that security considerations, such as the presence of police in a village or neighborhood, were emigration-reducing (or, immigrationinducing). Third, although the presence of ethnic-Javanese has not 43

44


been a primary cause of conflict incidence, their intimidation by the rebel movement has led to a significant outflow, primarily from conflict-affected villages in Central Aceh. These results reveal that, beside a conflict-induced fear of violence, population movements in Aceh have also been an outcome of traditional migration determinants.

4.1

Introduction

Worldwide, the number of international and intra-state conflicts has fallen dramatically since the end of the Cold War. This has led to a continuous reduction in the world’s refugee population over the past years. However, internal displacement has not been reduced to the same extent that cross-border refugee movements have. Globally, at the end of 2005, about 23.7 million internally displaced persons (IDPs) were seeking refuge within their own conflict-affected countries (IDMC, 2006a). Obviously, large displacements are induced by armed conflicts and grave human rights violations, but beyond this, the role of other political, economic or social determinants of internal displacement is still unresolved. Although some hypotheses on the determinants of forced migration decisions have been put forth in the refugee studies literature, the relevance of traditional socioeconomic migration determinants has not been systematically analyzed at a disaggregated level. At the cross-country level, most studies emphasize that violence is the major push factor of forced migration flows, indicating that institutional or economic factors have a relatively small impact (see, for example, Schmeidl, 1997; Moore & Shellman, 2004). However, using Colombian household data, Engel & Ibánez (2007) find that, even in a conflict environment, economic incentives play an important role in household displacement decisions, although the impact of economic incentives is less strong where violence levels are high. This study identifies the determinants of displacement during a period of conflict in Aceh, Indonesia. The Aceh conflict was politically motivated and arose between the Indonesian military forces TNI (Tentara Nasional Indonesia) and the Acehnese Freedom Movement GAM (Gerakan Aceh Merdeka). This conflict was particularly intense between 1999 and 2004. During this period, large-scale displacements

Determinants of Forced Displacement 45

were a recurrent feature, and the number of displaced persons has been estimated at more than 500,000 (IDMC, 2006b). Our main task is to investigate the major determinants of these internal displacements during three years of severe clashes (1999 to 2002) in the Aceh province. For this purpose, we use data on 5,211 Acehnese villages from two rounds of the Indonesian Village Potential Census PODES 2000 and 2003 (BPS, 2000, 2003). Unlike empirical analysis based on household data, this village-level dataset enables us to focus on the village-specific determinants of population changes, which makes this analysis unique. Although we cannot directly interpret our results as explaining the individual migration decision of household members, we are able to identify the most relevant push and pull factors at the village level. This level of aggregation also allows us to cover almost the whole area affected by the Acehnese conflict (91.3 percent of Acehnese villages). We distinguish between the effects of conflictrelated and more traditional socioeconomic determinants of net population change. Furthermore, and in addition to standard OLS estimation, we apply a quantile estimation technique, which enables us to separate the push (outflow) factors from the pull (inflow) factors driving the change in Acehnese village population stocks during this period of civil conflict. Obviously, violence and displacement are strongly linked, and this relationship is reflected in our results: the presence of conflict, which is captured by both reported conflict variables and by district controls, reduces net population increase. Beyond this, we are also able to identify several socioeconomic factors as driving forces of population displacement, indicating an ongoing rural–urban migration movement within the province of Aceh. We find that population displacement in Aceh is not only caused by the fear of violence, but also by traditional socioeconomic migration variables. This corroborates the results of Engel & Ibánez (2007) from their study of Columbian households. The remainder of this chapter is structured as follows. The next section briefly reviews the Acehnese conflict history and its effects on population displacement in the past decade. Then, we shortly review the traditional migration literature and discuss its main implications for our empirical analysis. The empirical section contains an introduction to the dataset, an explanation of the empirical strategy,

46


and a discussion of the results of the regression analysis. Some final remarks conclude this chapter.

4.2 Conflict history and displacement in Aceh, Indonesia Indonesia’s recent history is characterized by persistent conflicts and population displacements in various provinces. Frequent flare-ups of violence in the different provinces originate from a mixture of ethnic, religious, and social causes. The conflict in Aceh was strongly politically motivated and has been carried out between rebel fighters and the Indonesian state, thus, it is more closely related to the ‘classical’ type of civil war. Of all of the clashes since Indonesian independence in 1945, the armed conflict in the Aceh region has been one of the most persistent conflicts, lasting for more than three decades. Since the mid-1970s, the Aceh Freedom Movement (GAM) grew steadily and gained considerable power. This provoked frequent clashes between the GAM, military forces and paramilitary groups, culminating in the period between 1999 and 2003. During the period of political liberalization that followed the resignation of President Suharto in early 1998, Aceh experienced a short period of political détente. However, following the riots in the city of Lhokseumawe, which was one of the hardest-hit areas in Aceh, the announced process of demilitarization was significantly slowed (Schulze, 2004; IDMC, 2006b). In early 1999, frustrated by the lack of substantive changes, Acehnese student activists initiated a campaign for a referendum on Aceh’s political status, which rapidly gained support throughout the province. In mid-1999, military troops and security forces killed tens of pro-independence Acehnese demonstrators, and plans for renewed counter-insurgency operations were announced. In this context of deteriorating conditions, the election of President Abdurrahman Wahid increased the mobilization of support for independence in Aceh. In November 1999, a pro-referendum rally drew an estimated two million supporters (about one half of the whole Acehnese population) and brought the province to a standstill (Sidel, 1999). In 1999, large numbers of Acehnese began fleeing their homes in response to military and police actions, or out of fear


of being involved in clashes between the security forces and the GAM. The months preceding Indonesia’s general election in October 1999 saw a dramatic increase in the number of IDPs (IDMC, 2006b). In early 2000, the GAM announced the rebels’ willingness to negotiate a ceasefire if military operations, including roadblocks, door-to-door searches, and other actions to locate GAM members, were discontinued. In May 2000, the GAM and the Indonesian authorities agreed on a humanitarian pause. However, on June 1st, a day before this pause was to take effect, more than 6,000 people fled their homes in North Aceh because of renewed fighting (IDMC, 2006b). The following months saw continued sweeping operations as well as ongoing clashes, causing displacement and unrest. In April 2001, Indonesia’s efforts to end the separatist rebellion in Aceh entered a new phase with the launch of a military offensive against the GAM rebels. The majority of victims of this offensive were civilians, and severe atrocities were committed by both sides. The district of Central Aceh was most severely hit during this period, during which hundreds of people were killed by the GAM, the military or local militias (ICG, 2002). In this phase, around 32,000 people fled from Central Aceh and sought refuge in adjacent regions (UNDP, 2006). In early 2002, representatives of the GAM and the Indonesian government agreed to turn the armed conflict into a political dispute with the involvement of other Acehnese groups. In December 2002, an agreement on cessation of hostilities was concluded in Geneva, which resulted in a significant drop in the level of violence. However, in early 2003, the ceasefire failed and severe clashes again followed in Aceh. The Indonesian government reacted by implementing martial law on May 9th, 2003. Since then, enforced military operations have led to widespread human rights violations. Thousands of civilians have fled their homes or have been forcibly relocated by the military (AI, 2004). Martial law introduced a new round of violence in Aceh, during which the internal displacement of population into designated villages or camps emerged as a strategy of war (Hedman, 2005). In this phase, forced displacement has also been openly used by the military for separating GAM members from their civilian base. Counter-insurgency operations have relied extensively on recruiting civilians to join militias, civilian defense groups,

48


and military auxiliary units. These groups are also reported to have carried out severe human rights violations (AI, 2004). Between the introduction of martial law in May 2003, and the eve of the tsunami earthquake in December 2004, an estimated 2,300 people were killed in struggles between the Indonesian government, the militias, and the GAM (HRW, 2005). In the same period, around 150,000 persons became internally displaced. After the tsunami earthquake on 26 December 2004, which killed over 100,000 people and displaced over 500,000 people, a ceasefire was installed (HRW, 2005). In August 2005, the Indonesian government and the GAM signed a Memorandum of Understanding bringing this 30-year old conflict to a preliminary end. According to conservative estimates, the aggravated fighting and violence since 1999 resulted in more than 500,000 internally displaced persons (IDPs) and refugees (IDMC, 2006a). The Acehnese conflict has led to two distinct patterns of displacement. First, within the province, local people have been temporarily displaced when their villages were under attack, and have been sheltered in mosques or community halls, particularly alongside the two main roads that run along the North and East coasts, and along the South and West coasts. These displaced persons have usually remained inside their home province. They typically returned to their villages within a few weeks and started reconstructing their houses and livelihoods. The four main areas of displacement within Aceh were North Aceh, East Aceh, Central Aceh and West Aceh (Figure 4.1). In the period from 1999 to 2000, the average length of stay of the IDPs in the various sites was quite short. Later in 2001, many people who fled their villages had to stay away from their homes for periods ranging from several months up to almost two years due to the destruction of their houses and the loss of other assets (Ramly, 2005). Second, although ethnic-Javanese were not officially considered by the GAM as targets, there were thousands of ethnic-Javanese who left Aceh, fleeing mostly to the neighboring province of North Sumatra or to Java (UNOCHA, 2003; Schulze, 2004). The largest number of such displacements occurred in 2001. By September 2002, there were about 178,000 IDPs outside of Aceh, most of whom found refuge in North Sumatra. Only a small number of Javanese sought refuge within Aceh (Ramly, 2005).


96°0'0''E

6°0'0''N

98°0'0''E

6°0'0''N

SABANG

Map of Aceh, Indonesia

Banda Aceh Sigli ACEH BESAR

Lhokseumawe BIREUEN PIDIE

ACEH UTARA [NORTH ACEH]

ACEH TENGAH [CENTRAL ACEH]

ACEH JAYA ACEH BARAT [WEST ACEH]

Takengon

ACEH TIMUR [EAST ACEH]

Langsa ACEH TAMIANG

NAGAN RAYA

Meulaboh

GAYO LUES

4°0'0''N

4°0'0''N

ACEH BARAT DAYA [SOUTHWEST ACEH]

INDIAN OCEAN

Medan ACEH TENGGARA [SOUTHEAST ACEH]

Tapaktuan

ACEH SELATAN [SOUTH ACEH]

SUMATERA UTARA [NORTH SUMATRA]

ACEH SINGKIL

S

IM

E

U

LU

E

2°0'0''N

2°0'0''N 0

25

50

100 Kilometers

96°0'0''E

98°0'0''E

Provincial Capital Other Towns District Boundaries Note: Simeulue, Sabang, Lhokseumawe, and Langsa all have district status.

I N D O N E S I A

Note: Map boundaries and locations are approximate. Geographic features and their names do not imply official endorsement or recognition by the UN. © 2003 by EAST-West Center www.eastwestcenter.org

Figure 4.1

Aceh districts

Note: Reprinted by permission of East-West Center.


The subsequent empirical analysis concentrates on net population change at the village level for the period between fall 1999 and fall 2002. Thus, we are able to quantify the effects of the upsurge in violence that preceded the preliminary ceasefire in December 2002, but we do not examine the wave of forced displacement that followed the introduction of martial law in May 2003.

4.3

Theoretical background of displacements

Displacement in the context of civil conflict is a consequence of the presence or the threat of a violent attack, and not a voluntary migration decision in a narrow sense. While many individuals or whole households flee out of a conflict area to save their lives, we also observe many people who do not leave their homes to seek refuge. There are at least two explanations for this phenomenon. First, violence is not randomly targeted, that is some individuals or groups within the population are more prone to be violently targeted by armed groups, which make these people more disposed to flee than others. Second, when deciding upon staying or leaving, individuals or households do not only take into account security factors, but other traditional (socioeconomic) determinants as well. These two hypotheses are not mutually exclusive. Whether targeted individuals or households prefer to stay at home also depends on the degree of risk aversion. As socioeconomic determinants are expected to play a significant role in explaining forced migration, we refer briefly to the main implications of the traditional migration literature with respect to these determinants. In traditional rural–urban labor migration models the rate of migration is higher, the larger the urban–rural wage gap, and the higher the perceived probability of finding a job in the modern sector (Harris & Todaro, 1970). In the new economic migration literature, Stark (1991) and other authors refine these early migration models adding new variables, such as income uncertainty, relative deprivation (Faini, 1996), and human capital investment in children. Households spread their risks in structurally different markets by pooling and sharing their incomes afterwards. This is regarded as an insurance against uncertain income flows from specific markets to smooth families’ intertemporal income and consumption (Ghatak et al., 1996). Thus, uncertainty plays an important role in any


migration decision: in pure economic migration as well as in conflict-induced displacement. When considering forced displacement, insecurity creates additional costs that modify the expected outcome, and diminish the relevance of other socioeconomic migration determinants. When deciding on displacement, individuals or households compare alternative sites and choose the site promising the largest net benefits. Thus, early rational-choice models on migration decisions compared alternative locations by calculating the present value difference of individual income reduced by migration costs. Migration is then a result of higher expected net benefits at the reception site (Sjaastadt, 1962). In the context of a conflict, net returns to displacement are determined by the difference between origin and reception site benefits and the influence of insecurity and fear of persecution. Origin site incomes as well as migration costs are directly biased by threats, direct violence, and disruption (Figure 4.2). The influence of the economic push and pull factors on the perceived value of displacement is mitigated by the impact of the conflict environment, but it is not necessarily eliminated. If economic factors do not play any role in a violent conflict environment,

Origin site net benefit

Reception site net benefit

Expected income (assets, wages, remittances, etc.)

Conflict environment (Perception of insecurity, fear)

Expected income (wages, self-employment, asylum and job probability, remittances, networks, etc.)

Expected displacement costs (Opportunity and transport costs. survival risk, etc.)

Figure 4.2

Perceived value of displacement

52


a complete population outflow might occur. However, we generally do not observe this. Social networks also play an important role in explaining the size of a population outflow and the duration of stay (Carrington et al., 1996). According to this approach, migration costs decrease with the number of migrants already settled in the destination country. Established networks of previous (economic) migrants can strongly influence the displacement decision by providing housing, support in finding employment and other contacts. Generally, young, economically active people have stronger incentives to migrate, since their discounted net benefits are larger because of their longer planning horizons (Todaro & Smith, 2006). In conflict environments, young adults, male and female, are the most probable targets for threat, violence, and forced recruitment, which increases the likelihood for displacement of this group of the population. Thus, displacement is driven by both the (non-random) targeting of violence and by economic considerations. In many cases, the fear of violence and non-survival triggers non-transitory displacement by reinforcing the fundamental push factors that drive the rural– urban migration. The relative importance of violence and economic factors is a priori unknown and must be addressed by empirical analysis. The next section investigates the driving forces of potentially conflict-induced displacement at the village level, by considering net population changes in the Aceh province during a period of conflict.

4.4 4.4.1

Empirical analysis of displacement in Aceh Data source and descriptive statistics

The data used in this study are based on the Village Potential Census PODES (Potensi Desa) of the BPS (Badan Pusat Statistik) Statistics Indonesia (BPS, 2000, 2003). This census collects information on a regular basis at the lowest administrative level from all Indonesian villages and urban neighborhoods.37 The information is based on the responses of the village heads and includes a wide range of socioeconomic indicators on population, economic activities, infrastructure, and also on village security. We use data from two subsequent rounds of PODES (2000 and 2003) that were collected in the fall of 1999


and 2002. We consider only those Acehnese villages (5,211, or 93.1 percent of all) where a match between the two census rounds has been possible. The term ‘conflict’ generally has no clear-cut definition. It could describe severe atrocities with significant casualties and damages as well as minor clashes. In our dataset, conflict incidence at the village level is captured by two different questions. In PODES 2003, village officials have been asked for the first time whether the village has experienced any conflict during the previous year. Additionally, they were asked to state the number of casualties (conflict-related deaths or injuries) that occurred during the last year as a result of conflicts. The questionnaire did not give further guidance to exactly what events would define a conflict, and hence misreporting of conflict, depending on the interpretation of each village head, cannot be excluded (Barron et al., 2004). Nevertheless, village heads are very well informed about the presence and extent of civil conflict in their own village. The information that we can draw from a village census is much more general than data from questionnaires targeting selective and small-scale household samples. Based on the data for conflict occurrence, the variable Conflict is set to one if the village head has reported the occurrence of a civil conflict, and zero otherwise. A potential measurement error might also arise because we observe conflict incidence in the village only during the last year before the survey, while our population change variable refers to the period of the last three years. Nevertheless, our reported conflict incidence is arguably still capturing a general proneness to conflict, as it is significantly higher in those regions that are known to have been most severely affected by the Acehnese conflict. In order to reduce potential measurement errors, we also code an alternative measure of conflict labeled as Conflict cluster, which is set to one if at least 20 percent of the villages within a subdistrict (kecamatan) reported conflict in the previous year. In this way, we also capture the effects of conflict clusters within the neighborhood while excluding villages with isolated (and potentially minor) conflicts. Additionally, we include district controls in our regressions to also capture part of the differences in conflict intensity between districts. According to these definitions, between 1999 and 2002, around a quarter of the sample villages have reported the presence of a conflict, while more than one third of the villages belonged to a conflict cluster (Table 4.1).38

54


Table 4.1 Conflict and population change in the Aceh sample, 1999–2002

Total pop. in 1999 Abs. pop. change Rel. pop. change (%) Sample villages (N)

Total

No conflict

Conflict

Conflict cluster

3,684,021 –651 –0.0002 5,211

2,684,672 25,208 0.94 3,972 (76.2%)

999,349 –25,859 –2.59 1,239 (23.8%)

1,381,138 –38,068 –2.70 1,807 (34.7%)

Notes: Own calculations based on PODES 2000 and 2003. The sample covers around 93.1 percent of all Acehnese villages.

We define our main dependent variable as net Population change between the two survey rounds in 1999 and 2002, which measures the absolute change in village population in hundreds of inhabitants. Table 4.1 shows that villages which were involved in a conflict or were located in a conflict cluster during the year preceding the second survey round (around one third of the villages), lost on average around 2.7 percent of their population between 1999 and 2002.39 Based on our sample, we estimate for the period of 1999–2002 a net population outflow from the Aceh province of about 176,600 persons. This figure is based on an average annual population growth rate of 1.46 percent (documented for Aceh by BPS (n.d.) for 1990– 2000), which implies an increase in population of about 4.4 percent over these three years. This would amount to an increase of 163,700 persons in our sample. Instead, total population in the Aceh sample decreased by about 650 persons (see Table 4.1). These numbers indicate a net outward migration of about 164,400 persons in our sample villages (93.1 percent of all villages), and are in accordance with other estimates (Ramly, 2005). 4.4.2 Empirical strategy In our empirical analysis we focus on the determinants of net population change in order to quantify the effects of conflict as well as the effects of other socioeconomic variables. We run the regressions both for all villages and separately for villages with and without conflict during the last year. Additionally, by applying quantile regressions we distinguish between the push and pull factors of migration.


As a first descriptive step, we relate the reported conflict occurrence to the same set of explanatory factors as in our subsequent regressions of population change. 4.4.2.1

Estimation models

In our conflict regressions we model the unobserved levels of conflict intensity Ci* in village i as a latent variable, dependent on the vector of explanatory variables Xi, the unknown vector of parameters b, and the normally distributed error term di. Conflict occurrence Ci is our observed binary variable which is set to one if the village has been involved in a conflict during the previous period, Ci

1 if ( Xi' i ! 0), and zero otherwise,

(4.1)

which is estimated by a probit model. Based on the resulting coefficients we calculate the marginal effects of each explanatory variable on the probability that a conflict in a village has occurred, evaluated at the sample mean. In the second set of regressions, the dependent variable is the population change DPopi that occurred between 1999 and 2002 in village i, which is explained by a set of conflict variables and other migration-related variables Xi: 'Popi

oCi Xi' i .

(4.2)

We estimate the vector of the coefficients g0 and g by two alternative procedures. First, we report estimates from the standard OLS technique, both for all villages and for the subsamples of conflict and non-conflict villages. Second, we apply quantile regressions that minimize the sum of the absolute residuals where values above (below) a given quantile receive weights that are proportional (inversely proportional) to the quantile that is to be estimated. We report estimates from quantile regressions around the first (Q = 0.25), second (Q = 0.50), and third (Q = 0.75) quartile. The great advantage of this technique is that it enables us to distinguish between the push and pull factors of migration. Considering the lowest and highest quartiles is especially useful as these two quartiles roughly coincide with villages with considerable population outflow and inflow, respectively. For instance, when running the regression around the


lowest (Q = 0.25) quartile, our parameter estimates reflect the push factors of migration to a larger extent since observations on villages with a larger than predicted net population decrease (that is, villages with a population outflow considerably larger than expected) receive a threefold weight.40 In the probit and OLS specifications we cluster standard errors on the subdistrict level; by doing this we allow for the correlation of error terms for villages within the same subdistrict. 4.4.2.2 Main control variables Basically, changes in village population are either caused by differences in the fertility–mortality ratio or by (forced) migration movements.41 In all sets of regressions, we apply – in addition to the conflict variables – the same set of socioeconomic, political and geographic controls that could be related to one of these two channels or to both.42 By using explanatory variables mainly from PODES 2000, we are able to reduce problems of reverse causality to a considerable extent. Besides investigating the role of conflict for forced migration flows, this analysis will test whether a strong rural–urban migration pattern is still present in times of major conflict. As our main control for economic opportunities we include the indicator variable Urban, which is based on the official classification of villages and neighborhoods by the Indonesian state, and proxies the availability of public services and the structure of economic activity in the village. The urban dummy is strongly correlated with other measures of economic structure (for example, the share of families in the village living primarily from agriculture, or the share of village land that is in nonagricultural use).43 Additionally, we also include geographic Altitude (in thousand meters above sea level) that might capture economic incentives to emigrate similarly to the urban dummy. Furthermore, we also control for the political remoteness of a village by the Distance to district (kabupaten) office (in hundreds of kilometers). We capture the effect of village-level poverty by the share of Poor families in a village. This variable measures the share of village households that are considered to be poor according to a set of welfare criteria established by the Indonesian National Family Planning Agency (BKKBN).44 This agency categorizes Indonesian households into five classes of welfare status: pre-prosperous families KPS (Keluarga Pra-Sejahtera), and families of prosperity status KS I to IV


(Keluarga Sejahtera) (Perdana & Maxwell, 2004). Poor families are defined by the census as households belonging to the two lowest categories KPS and KS I.45 By this definition, on average around 55 percent of village populations are considered to be poor. Variations in potential fertility are controlled for by the variable Fertile couples, which measures the number of couples of reproductive age in the village, and is also based on information collected for the BKKBN. We include the Transport station variable as a rough proxy for costs of migration by indicating the presence of a bus or train station, airport, or seaport in the village. The variable has been set to one also in neighborhoods of larger cities where a station in another part of the city is available. The security environment is controlled for by the variable Police presence which is set to one if the nearest police station is easy to reach (which applies to 65.8 percent of the villages), and zero otherwise. The conflict mitigation role of the police, as opposed to the influence of the military and paramilitary groups, has been documented for several Indonesian conflicts (see Barron et al., 2004). We expect that population outflow is larger if the nearest police station is far away (especially, in the face of a conflict). Additionally, we also include controls that should capture the migration patterns specific to the Acehnese conflict. For instance, we know that in the three years of conflict ethnic-Javanese have been much more likely to leave Aceh altogether (see above). To capture this pattern of migration, we include in our regressions the Share of Javanese as a further control. The variable is based on information from the 2000 Census of Indonesia, and is only available at the subdistrict (kecamatan) level.46 We also include a set of district indicators for those Acehnese regions which we know were the most affected by the conflict: districts with high conflict incidence (Central, North, West, South and Southwest Aceh) and also neighboring districts that experienced population inflows. The remaining nine, less affected districts in Northern and Southern parts of Aceh serve as a control group. Additionally, we include dummies for the three largest cities, Banda Aceh, Langsa and Lhokseumawe, of which the latter was the hardest-hit area. These large centers may experience very different migration patterns from the rest of the country. Finally, we control for nonlinear effects of the village population size by including a fourth grade polynomial of population size in all regressions.


4.4.3 Estimation results Table 4.2 reports the estimates of two probit models explaining conflict occurrences that differ only with respect to the conflict variable; the dependent variable in column (1) is reported conflict, in column (2) the presence of a conflict cluster in the subdistrict (kecamatan). The likelihood that a conflict arises might be influenced Table 4.2 Probit estimation: conflict incidence Dependent variable

(1) Conflict Marg. eff.

Urban Altitude Distance (dist office) Poor families Fertile couples Transport station Police present Share of Javanese Central Aceh West Aceh Nagan Raya Southwest Aceh South Aceh North Aceh East Aceh Aceh Tamiang Langsa (city) Lhokseumawe (city) Banda Aceh (city) Population polynomial Number of observations Pseudo R2 Observed/predicted conflict

–0.020 –0.029 0.001 –0.015 0.019 –0.079** 0.031 0.093 0.291* 0.044 0.009 0.210*** 0.434*** 0.729*** 0.513*** –0.043 0.274* 0.500*** 0.205

t-stat. –0.57 0.36 0.03 –0.21 1.11 –2.21 1.02 0.52 1.89 0.44 0.09 3.10 3.61 8.15 4.68 –0.44 1.83 2.73 1.39

(2) Conflict cluster Marg. eff.

t-stat.

Mean

–0.027 –0.203 –0.062 0.015 0.042 –0.133*** 0.042 0.067 0.605*** 0.183 0.287 0.546** 0.454*** 0.735*** 0.689***

–0.49 –1.09 –0.66 0.11 1.29 –2.64 0.83 0.25 2.63 0.72 1.09 2.46 2.61 5.70 4.30

0.570** 0.563* 0.354*

2.09 1.95 1.77

0.080 0.178 0.078 0.546 0.986 0.061 0.342 0.087 0.037 0.051 0.040 0.024 0.046 0.157 0.089 0.040 0.010 0.013 0.016

Yes

Yes

5211 0.324

5211 0.342

0.238

0.172

0.347

0.304

Notes: The marginal effects are based on a probit regression, and are evaluated at the sample mean. The regressions include a fourth order polynomial in population size and a constant. The reported values of t-statistics are based on robust standard errors that are clustered at subdistrict level (202 clusters). (*), (**), (***) denote values significant at levels of 10, 5, and 1 percent.


by political, civil, or socioeconomic variables. However, in our case both conflict variables are mainly explained by geographic location. We do not find robust evidence for the role of ethnicity in the Acehnese conflict occurrence either, as we find that the share of ethnic-Javanese (the most populous and economically most important minority) in a subdistrict is not related to conflict occurrence. The distribution of observed conflict over the districts supports our trust in the reliability of the conflict variables: reported conflict occurrence is much more likely in those districts that were actually mostly affected in this period (North, East, Central, Southwest and South Aceh, and the large cities). According to the previous theoretical displacement model, we jointly address the influence of socioeconomic determinants and conflict variables for explaining net population change. We use both OLS and quantile regressions as two alternative estimation techniques, reporting the respective results in Tables 4.3 to 4.5. In Table 4.3, the two specifications differ only with respect to the definition of the conflict variable as measuring individual Conflict or the presence of a Conflict cluster. Surprisingly, we do not find that reported conflict has a robustly significant negative effect on net population change. However, the conflict cluster variable has a significantly negative effect on net population change in most of our specifications. This may be due to the fact that the clustering of conflicts better reflects the relevant push factors for forced migration over the whole three-year period than the reported conflict occurrence in the last year in any given village. From column (2) in Table 4.3, we see that the presence of a conflict cluster in a subdistrict (20 percent or more of the villages reporting conflict) reduced village population on average by about 25 persons ceteris paribus. From the geographic distribution of migration flows, we also see that the single most affected district, Central Aceh, experienced also the highest net population outflow of, ceteris paribus, almost 200 persons per village on average. The largest forced migration flows (within the province) have been going to the Northeastern coast of Aceh: the city of Langsa, and the neighbouring Aceh Tamiang district (c.f. Figure 4.1). The data also supports the evidence that the Javanese population has been more likely to migrate: net outward migration from a village increased by

60


Table 4.3 Determinants of change in village population (OLS) Dependent variable

Conflict Conflict cluster Urban Altitude Distance (distr office) Poor families Fertile couples Transport station Police present Share of Javanese Central Aceh West Aceh Nagan Raya Southwest Aceh South Aceh North Aceh East Aceh Aceh Tamiang Langsa (city) Lhokseumawe (city) Banda Aceh (city) Population polynomial Number of observations R2

Change in village population (in .00) Coeff. (1)

t-stat

–0.120

–0.80

0.902** –0.303 0.039 –0.252 0.652*** 0.943** 0.249*** –1.272** –1.966** 0.283 –0.403 0.222 –0.172 0.253* 0.071 1.223*** 2.752** 0.822 0.382 Yes

2.53 –0.86 0.24 –1.46 3.11 1.97 2.77 –2.15 –2.55 0.88 –1.15 0.63 –0.68 1.81 0.35 2.81 2.53 0.69 0.41

5,211 0.133

Coeff. (2)

–0.248* 0.902** –0.324 0.027 –0.250 0.658*** 0.925* 0.245*** –1.223** –1.879** 0.308 –0.369 0.331 –0.115 0.354** 0.185 1.180*** 2.838** 0.904 0.423 Yes

t-stat.

–1.79 2.53 –0.93 0.17 –1.45 3.17 1.93 2.74 –2.10 –2.50 0.98 –1.13 0.92 –0.43 2.46 0.83 2.72 2.48 0.77 0.45

5,211 0.134

Notes: Regressions are performed by OLS, and include a constant and fourth order polynomial of population size. The reported values of t-statistics are based in robust standard errors that are clustered on subdistrict level (202 clusters). (*), (**), (***) denote values significant at levels of 10, 5, and 1 percent.

about 12 persons on average with a 10 percentage point increase in the share of Javanese population within the subdistrict. By applying a quantile regression technique, the overall effect of conflict-induced displacement provided by the OLS estimation results can be further decomposed. The two alternative model specifications (A and B) of the quantile estimations (Table 4.4) differ only in the inclusion of district dummies: whereas in model B the conflict cluster variable reduces both population outflow and inflow, the

Table 4.4 Quantile regressions of population change Dependent variable

Test Q25 = Q75

Change in village population (in .00)

Model A Conflict cluster Urban Altitude Distance (dist office) Poor families Fertile couples Transport station Police present Share of Javanese Central Aceh West Aceh Nagan Raya Southwest Aceh South Aceh North Aceh East Aceh Aceh Tamiang Langsa (city) Lhokseumawe (city) Banda Aceh (city) Population poly Pseudo R2 Model B Conflict cluster Urban Further controls District dummies Pseudo R2

Q25

t-stat.

Q50

t-stat.

–0.008 0.030 –0.179***

–0.58 0.61 –2.99

–0.028** 0.059 –0.091***

–2.31 1.52 –2.88

–0.024 –0.003 0.273***

–0.87 –0.13 3.19

0.078*** 0.069*** 0.136***

3.43 4.45 3.62

0.110 0.039***

0.79 3.03

0.347*** 0.029***

–0.526*** –3.631*** –0.171*** –0.177**

–2.67 –5.81 –4.31 –2.10

–0.242*** –0.104** 0.001 –0.099 0.218** 0.087 –0.061 0.641*

t-stat.

p-val.

–2.37 0.35 –2.14

0.030 0.920 0.751

0.246*** 0.084* 0.397***

6.18 1.79 5.40

0.000 0.047 0.173

2.64 3.36

0.549*** 0.054**

2.58 2.22

0.043 0.512

–0.164*** –0.683*** 0.026 –0.052***

–4.16 –2.35 0.94 –2.75

–0.123 0.460*** 0.221*** –0.100*

–1.31 2.57 2.68 –1.68

0.037 0.000 0.000 0.408

–3.83 –2.20 0.04 –1.07 2.37 0.20

–0.065 0.046 0.001 0.011 0.172*** 1.142

–0.74 1.10 0.06 0.39 3.31 1.27

0.071 0.075 0.003 0.042 0.301** 3.141***

0.86 1.28 0.09 0.61 2.45 2.86

0.000 0.006 0.946 0.157 0.540 0.001

–0.41

–0.394***

–2.90

–0.649**

–2.22

0.052

2.89

0.112

–2.97 1.61

0.231 0.235

1.86

Yes 0.076 –0.040** 0.072

0.428

1.50

Yes 0.020 –2.48 1.39

–0.043*** 0.078**

Q75

–0.075** 0.043 –0.155**

1.446*** Yes 0.085

–5.10 2.15

–0.070*** 0.233

Yes

Yes

Yes

No 0.043

No 0.012

No 0.069

Quartile range of population change Q0 − Q25 Q25 − Q50 Q50 − Q75 [−3,747;−6] [−5;9] [10;37]

Q75 − Q100 [38;2,116]

Notes: Observations N = 5,211. Reported values of t-statistics are based on bootstrap standard errors (with 1000 replications). All regressions include a constant and a fourth order polynomial in population size, the coefficients on which are not reported. Model B includes the same set of explanatory variables as Model A except for the district dummies; full results are available on request. (*), (**), (***) denote values significant at levels of 10, 5, and 1 percent.


Table 4.5 Determinants of population change by conflict occurrence Dependent variable

Change in population in villages

(1) w/o conflict

Urban Altitude Distance (dist office) Poor families Fertile couples Transport station Police present Share of Javanese Central Aceh West Aceh Nagan Raya Southwest Aceh South Aceh North Aceh East Aceh Aceh Tamiang Langsa (city) Lhokseumawe (city) Banda Aceh (city) Population polynomial Number of observations R2

(2) with conflict

Test (1) (2)

(1) (2)

Coeff.

t-stat.

Coeff.

t-stat. p-val. p-val.

0.473 –0.618* –0.095 –0.297* 0.721** 0.863* 0.225** –0.318 –1.363* 0.255 –0.155 0.001 –0.370 0.094 0.219 0.884** 1.498 –0.065 0.524

1.53 –1.78 –0.63 –1.69 2.47 1.69 2.52 –0.46 –1.95 0.82 –0.47 0.00 –1.10 0.72 0.97 2.09 1.49 –0.08 0.49

1.756** 0.822 0.686 –0.250 0.652** 0.377 0.417** –2.023** –3.893** –0.026 –3.211*** 1.146 0.297 0.422* –0.635 0.597 6.665*** 3.273* 0.540

2.08 0.76 1.21 –0.54 2.08 0.46 2.36 –2.32 –2.56 –0.09 –6.29 1.09 1.21 1.85 –1.58 0.51 2.73 1.75 0.51

Yes 3,972 0.101

0.070 0.101 0.086 0.461 0.432 0.302 0.150 0.052 0.053 0.241 0.000 0.116 0.065 0.103 0.036 0.401 0.043 0.039 0.495

Yes 1,239 0.242

Notes: Regressions are performed by OLS, and include a constant and a fourth order polynomial of population size, the coefficients on which are not reported. The reported values of t-statistics are based on robust standard errors that are clustered on subdistrict level (188/116 clusters). (*), (**), (***) denote values significant at levels of 10, 5, and 1 percent. The last two columns present p-values of pairwise hypotheses tests on the H0 hypothesis of inequality of coefficients in (1) and (2).

effect of conflict in outflow villages is directly captured through the district dummies in model A. This may be due to clustering of conflict affected subdistricts. The impact of conflict on population outflow (near to the first quantile, Q = 0.25) is smaller than its impact on inflow (near to the Q = 0.75 quantile). Furthermore, the quantile regressions also draw a more differentiated picture of the geographic distributions of population


movements. In the districts of Central Aceh, West Aceh, and Nagan Raya, we observe both villages with larger population outflows and villages with larger inflows; this may reflect the presence of innerdistrict migration.47 Beyond that, stronger migration flows can be registered towards the coastal cities of Langsa and Banda Aceh, while Lhokseumawe experienced less population inflow. This may be due to the fact that neighborhoods in the industrial area of Lhokseumawe, home to many Indonesian, foreign and local businesses, have been particularly affected by illegal GAM activities such as the ‘village tax’ (Schulze, 2004). The quantile regressions show that the decrease in village population was larger in subdistricts with a relatively high share of ethnic-Javanese, while ethnic composition does not explain differences in net village population inflow. Thus, although the presence of ethnic-Javanese is not related to the outbreak of conflict (Table 4.2), it explains strongly the outward migration flows. These results are reinforced when distinguishing between conflict and non- conflict villages (see Table 4.5): Population is reduced with the share of ethnic-Javanese, but only in conflict-affected villages. Thus, corroborated by other sources (for example, Schulze, 2004), we can state that although GAM officially denied targeting ethnicJavanese, they were nevertheless intimidated into leaving their homes. Beyond the evidence of conflict-induced displacement, we also find a significant role for other socioeconomic determinants of aggregate displacement movements. The Urban variable, reflecting the concentration of economic activity, shows a clear pattern of explanation: urban villages experience a larger increase in net population (Table 4.3). In the quantile regressions (Table 4.4) the Urban dummy loses significance, but geographic Altitude is significantly related to larger decreases as well as smaller increases in population. This result indicates that migration runs from rural and mountainous central areas to the more urbanized Northern and Southern coastal areas of the Aceh province. These two measures of economic remoteness/urbanization show strong evidence for a rural–urban migration pattern. The same pattern can be retraced in the case of inward migration to the large cities, particularly Langsa. When distinguishing between villages that reported conflict and those that did not (Table 4.5), it is apparent that the mere presence of conflict

64


in large cities or urban regions does not hinder an ongoing urbanization process. Our control for political remoteness, the Distance to the district office turns out to be insignificant in most specifications, although it does lead to larger population inflows among the inflow villages in the quantile regressions. The indicator of availability of any transport station in the community, which we use as a proxy for migration costs, is mostly positively related to population change. However, it is most likely that this variable also captures pull factors of rural– urban migration. Based on the evidence from quantile regressions (Table 4/4), direct availability of transport opportunities seems to play only a minor role in displacement decisions in the case of population outflow, but might act as a significant pull factor for population inflows. However, since actual migration costs include a broad range of expenses, they may not be well captured by a transport station dummy. Displacements are related to a lack of police presence and thus we can infer that they are also due to institutional weaknesses of the state. The presence of a police station has a significant and overall robust positive effect on population change, reducing outflow and increasing inflow (Table 4.4) in both conflict and non-conflict villages (Table 4.5). Thus, public institutions still play a significant role in times of large population displacement. The effect of poverty on net population change in a village acts generally through several channels. The share of poor families in a village has no robustly negative effect on population change in OLS regressions (Table 4.3), although the negative effect becomes significant in villages that did not report conflict (Table 4.5). In quantile regressions the share of the poor in a village even increases population inflows (Table 4.4). Thus, in the face of conflict, poverty as a push factor seems to be less relevant; the results confirm the theory of a ‘migration hump’ that is mainly produced by unavailable resources (and information) for migration of the lower income groups. Furthermore, the increased pull effect of villages with a high share of poor families may also be indicative of a policy bias, since the composition of this variable is based on the registration for social support programs for the poor. Poor families tend to migrate to destination sites where governmental support is more likely, and thus, registered poverty may also be related to a larger inward migration.


Our control variable for fertility, the number of fertile age couples, is positive and significant in all regressions. Obviously, population change is also driven by population growth. In all regressions we also include a fourth grade polynomial for population size that indicates a highly significant nonlinear influence of village size on population changes. Finally, these results demonstrate that internal displacements in conflict situations, at least in the context of Aceh, are not unidimensionally caused by conflict variables. The factors that drive common rural–urban migration movements are still relevant in a conflict situation. Thus, traditional push and pull factors are not suspended in times of conflict, though it is obvious that large displacements are primarily initiated by the conflict. However, without other economic, political, social and institutional factors at work, such conflict-induced population movements would certainly be different in their numbers and magnitudes.

4.5

Concluding remarks

The intention of this study is to contribute to an improved understanding of the determinants of civil conflicts and their related forced migration movements. Our empirical analysis is based on village-level data for the province of Aceh, collected by a regular village census taken throughout Indonesia. Since we can only observe net changes in village population, we are unable to model individual or household migration behavior. Even though we cannot learn from our analysis who migrates in the face of a conflict, we are able to identify which villages are more prone to population outflows and inflows. This meso-level approach is able to indicate the most relevant push and pull factors at the village level. This is an innovation in forced migration research. In Aceh, conflict occurrence was very strongly related to the geography of conflict clusters. The presence of such conflict clusters contributed significantly to the displacements of the Acehnese population during our period of observation. Relatively large displacement flows could be observed from the most conflict-affected regions in central Aceh towards the more stable and prosperous urban areas at the coast. However, there were also large population rearrangements between villages within the most conflict-affected districts.

66


Although often categorized as a political conflict, the intimidation of ethnic-Javanese had a substantial effect on outward migration, particularly from conflict-affected villages with a relatively large Javanese population. Furthermore, security, specifically police presence, has had a significant influence on migration flows. Finally, while we know that the clashes between the GAM, militias and military forces were the major driving forces behind the large displacement of the Acehnese population, we also find convincing evidence for the relevance of conventional socioeconomic migration determinants. These results indicate that economic opportunities and rural–urban migration incentives play an important role even in the context of civil conflict. Traditional migration considerations are still effective, even during periods of severe clashes. Potentially, civil conflict might even work as an accelerator of an ongoing urbanization process.

5 The Political Economy of Refugee Migration

Abstract48 This chapter examines the driving forces of the magnitude, composition and duration of refugee movements caused by conflict and persecution. The decision to seek temporary or permanent refuge in the region of origin or in a more distant asylum destination is based on intertemporal optimization. We find that asylum seeking in Western countries could rather be a phenomenon of comparatively less persecuted people. In an attempt to reduce their respective asylum burdens, Western countries and host countries in the region of origin are likely to end up in a race to the bottom of restrictive asylum policies. As an alternative, this study shows that proactive refugee-related aid transfers are, under certain circumstances, an effective instrument to relieve Western countries from asylum pressure.

5.1

Introduction

Refugee situations are one of the most neglected or even forgotten human sufferings. An involuntarily displaced mass of people often lives for a significant period of time under appalling conditions in a state of limbo, mostly in a country that is not their own. Their dilemma is mostly multiple: going back to their home country is dangerous, primarily because of ongoing insecurity and persecution; in addition, they are often not allowed to settle freely and to be economically self-reliant in the first asylum country, and finally, they are often unable to seek asylum in a Western country, either 67

68


because of a lack of resources or the low prospects of receiving asylum status and permanent residence rights. As a result, in 2005, there were more than 9.2 million cross-border refugees, of which 5.5 million individuals were caught in a protracted or long-lasting situation (UNHCR, 2006c). The number, as well as the duration, of these situations has vastly increased during the past decade. In 1993, 27 protracted refugee situations existed and it is estimated that the average duration of major refugee situations will have increased from nine years in 1993 to an average duration of 17 years by the end of 2003 (UNHCR, 2004c, 2006c). It is generally acknowledged that the major causes of long-lasting refugee situations are political in nature. The United Nations High Commissioner for Refugees (UNHCR) states that Protracted refugee situations stem from political impasses. They are not inevitable, but are rather the result of political action and inaction, both in the country of origin (the persecution or violence that led to flight) and in the country of asylum. They endure because of ongoing problems in the countries of origin, and stagnate and become protracted as a result of responses to refugee inflows, typically involving restrictions on refugee movement and employment possibilities, and confinement to camps. (UNHCR, 2004c) This chapter provides the theoretical background for an analysis of refugee migration and the role of alternative asylum policies in explaining this phenomenon. A review of the economic migration literature reveals a severe lack of research dealing with refugee movements. Most of the early migration literature is based upon static rural–urban or international wage differentials (Sjaastadt, 1962; Todaro, 1969; Harris & Todaro, 1970). According to this, regional or international wage differentials are the driving incentives for voluntary and permanent migration. This does not reflect the reality of refugee movements. By definition, refugees do not flee for purely economic reasons, at least initially; and, refugee migration is not necessarily a one-way movement, and thus, not a permanent phenomenon. For realizing the latter, temporary migration models introduce a special loyalty held by individuals for their home country. Voluntarily return migration is then induced by the migrant’s special preferences for consumption at home being higher than

The Political Economy of Refugee Migration

69

abroad (Djajic & Milbourne, 1988; Dustmann, 2003; Brücker & Schröder, 2006). However, this special loyalty for the home country is ‘reduced’ for individuals that are victims of conflict and persecution. Refugees, unlike economic migrants, leave their home country even if the economic situation abroad is inferior. But, even if consumption at home might be foiled by insecurity and danger, there is some empirical evidence that refugees living in encampments under very poor conditions just across the border often return home despite ongoing repressions and substantial risks of being affected by civil strife (Rogge, 1994). These effects are also not captured by existing migration models. Furthermore, refugees also have the option to seek asylum in a Western country that promises better economic perspectives, although the incentive to move on to a Western country is cushioned by the uncertainty about the asylum application’s outcome. Thus, asylum policies of Western countries, as well as that of the conflictneighboring first asylum countries, need to be taken into account when analyzing the migration decision of persecuted individuals. Consequently, a satisfactory model for understanding the mechanisms of refugee movements and their protraction must contain features of (i) temporary and permanent migration, (ii) economic and non-economic push and pull factors, (iii) uncertainty about the success of an asylum-seeking process, and (iv) a non-static framework for analyzing intertemporal migration patterns. Therefore, a dynamic model is required to approach alternative migration options in a long-term time horizon. The innovation of this study is to model the simultaneous decision on seeking asylum in a Western country, moving temporarily or permanently across the border to a neighboring first asylum country, or staying at home. This framework will enable the analysis of some alternative policies available to both neighboring first asylum and Western destination countries that we indentify as decisive in explaining the pattern and duration of international refugee migration movements. The remainder of this chapter is structured as follows. The next section outlines the migration model and derives some core propositions. Section 5.3 discusses the implications of reactive and proactive counter-asylum policies for the refugee migration pattern. The fourth section concludes.


5.2 The model This basic analytical framework models the intertemporal migration decision of N heterogeneous individuals living in a country of origin O before a conflict breaks out. Heterogeneity of individuals refers to the extent each individual is affected by this conflict, that is individual persecution level p in the home country O is uniformly distributed with density N between [0,1] with p equal to unity reflects full security. According to the respective persecution level, the individuals decide upon several migration options to realize for the rest of their lifetime Ti, which is normalized to unity.49 The three migration options are: first, staying in the conflict affected country of origin O despite the threat of persecution, second, emigrating (with negligible migration costs) for the period 0 < ti # 1 = Ti, that is temporarily or permanently, to a neighboring first asylum country S where security is guaranteed but where confinement to a camp or limitations in self-reliance impose worse economic conditions, or third, the opportunity to migrate (with migration costs MW) to a Western industrialized country W with better economic prospects (and security) to seek asylum, but with the risk of being rejected and deported to the home country O.50 Conditions are such that each individual could receive an income level of yO at home, yS in the first asylum country, and yW in the Western industrialized country, assuming that yW is by far larger than the income levels of the other two developing countries, yO and yS .51 However, income generation is hindered by the first asylum country, that is the level of economic self-reliance s that a refugee enjoys is a policy parameter with 0 < s < 1, indicating the proportion of the standard income level yS that is actually available for refugees. The available income to the refugee in the first asylum country is then given by syS .52 The utility u(⭈) an individual derives from consumption either in S the country of origin (cO i ), the (neighboring) first asylum country (c i ), W or in the Western country (ci ), respectively, are given by: uO (c O i ) u S (c Si ) uW (c iW )

␣

p1i ␣cO i , ␣

c Si ,

(5.1) (5.2)

␣

c iW .

(5.3)


71

Generally, the migration decision and its realization takes place at t = 0 for all N individuals.53 5.2.1 The cross-border option For the first migration option, moving to the (neighboring) first asylum country S, the individual must choose the duration for the stay, ti, and the consumption levels abroad and at home (if migration is temporary). Thus, each individual maximizes its lifetime utility ViS with respect to duration and consumption, that is ViS

␣ O ␣ ␶ i[c Si ]␣ (1 ␶ i )p1i [c i ]

(5.4)

subject to the individual’s budget constraint:

␶ i sy S (1 ␶ i )y O ␶ ic Si (1 ␶ i )c O i t 0.

(5.5)

The first-order conditions of the corresponding Lagrange function Li (with l as Lagrangian parameter) are: ! wL ␣ 1 ␣ p1i ␣ [cO ␭ 0 i ] O wc i ! wL ␣[c Si ]␣ 1 ␭ 0 S wci ! wL O S ␣ S O [c Si ]␣ p1i ␣[c O i ] ␭( sy ␥ c i c i ) 0 w␭ ! wL ␶ i sy S (1 ␶ i )y O ␶ ic Si (1 ␶ i )c O 0 i w␭

(5.6) (5.7) (5.8) (5.9)

The Lagrangian parameter l, that is the shadow value of income, is also time-invariant. Eqs (5.6) and (5.7) imply that pi c Si

c Oi .

(5.10)

This condition indicates that consumption at home is a linear function of consumption abroad; that is the marginal utility of consumption at home and abroad must be equal. This condition (5.10) generates together with eqs (5.8) and (5.6) explicit consumption levels for at home and abroad: cSi

␣( yO sy S ) (1 p i )(1 ␣ )

c Oi . pi

(5.11)


Thus, for temporary migrants, consumption at home and abroad is related to the bilateral income differential between the country of origin and the first asylum country; consumption abroad exceeds their disposable income abroad (that is negative savings). Thus, in order to not exceed the intertemporal budget, the refugee returns home. Substituting eqs (5.10) and (5.11) into eq. (5.9), the optimal duration of the stay abroad is determined by the other model parameters:

␶ i (p i )

with

w␶ i wpi

y O (1 ␣) . °0, if p ! O y ␣sy S ° °° ␣p i y O (1 ␣) , S ® (p i 1) sy y O ° ° sy S ␣ y O °1, if p . (1 ␣ )sy S °¯ ␣ ( p 1)2

0, and

w2␶ i wpi2

2␣ ( p 1)3

(5.12)

0.

Figure 5.1 displays the duration function ti(pi) of the cross-theborder option. The higher the persecution level (that is the lower pi), the longer the refugee stays abroad. For individuals with a persecution level pi < p, the optimal value of ti is larger unity (that is the total – lifetime), which means that the individual migrates permanently – into the first asylum country; less persecuted individuals with pi > p stay at home for their lifetime. All individuals with medium persecution levels stay only temporarily in the first asylum country: sy S ␣ y O (1 ␣ )sy S

p pitemp p

y O (1 ␣ ) . y O ␣ sy S

(5.13)

Proposition 5.1: The following constraints describe the cross-theborder migration option: (i) There are individuals that do not leave the home country, or only leave temporarily, if there exists a negative income differential, that is, 0 # p– # 1, if 0 # syS # yO. (ii) There are individuals that leave the home country permanently, if the self-reliance level in the first asylum country is not too strict, that is, O p $ 0, if s t ␣yS . y – (iii) There is a total permanent outflow of all N individuals, if the economic situation abroad is definitely superior to the situation at home, O that is, p , p– > 1, if s ! y S . y –


73

τi (pi ) 1

p

0 Permanent in S Figure 5.1

p Temporary in S

1 pi

Staying in O

Cross-border migration duration of persecuted individuals

We assume the parameter values to be such that cases (i) and (ii) apply for the subsequent analysis. Finally, the optimized utility function with respect to the individual persecution level is then given by substituting eqs (5.12), (5.11), and (5.10) into (5.4), taking into account the constraints of (5.13):

S

V ( pi )

[ sy S ]␣ , ° ª y O pi sy S ° ® (1 ␣ ) Z « O S ¬ y sy ° ° pi1␣[ y O ]␣ , ¯

0 d pi d p , º ° » , if ® p pi p , ¼ ° ¯ p d pi d 1,

(5.14)

0 0, 0 d pi d p, ° O S wV ° ° (1␣ )(␣y sy (1 p(␣ 1))) with ! 0, if ® p pi p , ®Z ( y O sy S )(1 p ) wp ° ° ¯ p d pi d 1, °¯ (1 ␣ ) pi␣[ y O ]␣ ! 0, 0 0, 0 d pi d p, ° 2 S O S wV ° ° ␣(1␣ )((1␣ )y sy ( p(1␣ )2)) ! 0, if ® p pi p , and ®Z wp 2 ( y O sy S )(1 p )2 ° ° ¯ p d pi d 1, ° ␣(␣ 1) pi1␣[ y O ]␣ ! 0, ¯ ␣ ª ␣( y O sy S ) º and Z { « » ! 0. ¬ (1 pi )(1 ␣ ) ¼ S


5.2.2 The asylum-seeking option As an alternative to the cross-border option, the refugee may move to a third country to seek asylum. A generally preferred destination for that movement is a Western country W that promises better economic prospects. However, low asylum recognition rates that signal low chances of being accepted may discourage asylum migration.54 Uncertainty about the asylum admission process exposes potential asylum seekers to the risk of being deported to the country of origin.55 In this model, an asylum seeker is accepted with probability r, which is the asylum recognition rate, or with probability (1 – r) she or he is rejected and deported back to the home country where the individual stays for the rest of lifetime Ti = 1.56 To realize this migration option, the asylum seeker must invest migration costs M W. These costs must not be larger than the alternative lifetime income at home, that is M W # yO, since in the case of deportation, the intertemporal budget constraint must still hold.57 The recognition rate is the policy parameter that indicates whether the Western asylum country is rather liberal (r is large) or restrictive (r is small) in its asylum policy. With respect to this asylum option, the expected utility for a (risk neutral) individual is given by: ViW

␣

␣

ric iW (1 ri )p1i ␣c 0i

(5.15)

subject to the respective consumption levels abroad and at home: cO = yO – M W, cW = yW – M W,

(5.16) (5.17)

Substituting eqs (5.16) and (5.17) into (5.15) delivers the expected utility level of an individual: ViW

ri ( y W M W )␣ (1 ri )p1i␣ ( y O M W )␣

(5.18)

wV W w 2V W ! 0, and 0. wp wp2 Figure 5.2 displays the utility curves of the two alternative migration options, ViW (pi) and ViS (pi). Individuals choose the asylumseeking option if, and only if, ViW (pi) > ViS (pi). Depending on the exogenous parameters, the cross-the-border utility function and

with


75

Vs, Vw

Vs(s, yo) Vw(Mw,r, yo)

p*

0

p**

1

pi

τi (pi ) 1

p

0

p

1

pi

Permanent Asylum Temporary Staying in S in W in S in O

Figure 5.2

The refugee migration decision: who, where, and how long

concave-shaped asylum-seeking option can have a maximum of two cut-off values. The two possible cut-off persecution levels p* and p** (with p** $ p*) for the decision between the two migration options, that is asylum seeking in the Western asylum country or cross-border emigration, are determined by the equality of the two utility functions:58 !

V S ( p*) V W ( p *) 0 with

wV S ( p*) wV W ( p*) d 0. wp wp

wV S ( p**) wV W ( p**) V ( p**) V ( p**) 0 with t 0.59 wp wp S

W

!

(5.19)


According to this condition, all individuals become asylum seekers in the Western country, if V W (p

0) ! V S (p

0), and V W (p

1) ! V S (p

1), ␣

(y O )␣ (y O M W )␣ § sy S · r !¨ W . W ␣ O W ␣ W ¸ (y M ) (y M ) y M © ¹ This holds if the value of the asylum option is very high, that is if the migration costs MW are very low, the income gap (yW – yO) is very high, and/or the asylum policy of the Western country is very liberal (high r). There is only one intersection point p**, if

that is, if

V W (p

W

0) ! V S (p

0), and V W (p

1) V S (p

1), ␣

(y O )␣ (y O M W )␣ § sy S · r¨ W . W W O W W ¸ ␣ ␣ that is, if (y M ) (y M ) ©y M ¹ The singular cut-off value p**, with 0 < p** < 1, is caused by low migration costs M W or a ‘moderate’ Western asylum policy r with individuals migrating either to the Western asylum country (those with 0 < pi < p**) and those staying at home (or fleeing temporarily to the neighboring country S). Furthermore, there is only one intersection point p*, if V W (p

0) ViS (p

0), and V W (p ␣

1) ! V S (p

1),

sy S · (y O )␣ (y O MW )␣ that is, if §¨ r W . W W ¸ (y MW )␣ (y O MW )␣ ©y M ¹ In this case, highly persecuted people (that is, pi < p*) leave the home country and enter the neighboring country across the border, while less persecuted individuals choose the long-distance asylum option in a Western country. In this case, highly persecuted and riskaverse individuals that face the risk (1 – r) of involuntary repatriation (deportation) to the home country have a stronger incentive to avoid this risk than less endangered individuals. Therefore, better economic prospects in the Western asylum country have a relatively strong impact for those individuals that have lower opportunity costs arising from the option of living in persecution at home. Thus, those who seek asylum in the Western country might be rather the


77

less persecuted refugees compared to those fleeing to a neighboring first asylum country. Two cut-off values (as in Figure 5.2) are possible, if V W (p

0) V S (p

0), and V W (p

1) V S (p

1),

␣

§ sy S · (y O )␣ (y O MW )␣ that is, if ¨ W !r W . W ¸ (y MW )␣ (y O MW )␣ ©y M ¹ In this case, the Western asylum policy is rather strict. However, if it is too strict, the asylum option is inferior for all N individuals and no asylum migration to the Western country takes place (that is, no intersection with V W (p) = VS (p)).

Proposition 5.2: (i) There are individuals that do not leave the home country, if the income differential between it and the Western country is not too large or migration costs are rather high, that is if V W (p – 1) < VS (p – 1), that is, if yW – M W < yO. (Note: Proposition 5.1(i) must still hold, that is, syS < yO.) (ii) There is no permanent emigration to the first asylum country, but instead individuals migrate to the Western asylum country, if the asylum recognition rate is not too low, that is if V W(p = 0) > VS (p = 0), that ␣ § sy S · is, if r ! ¨ W . ¸ W ©y M ¹ Since the persecution levels of the N individuals of the conflictaffected country of origin are assumed to be uniformly distributed between [0,1], the number of asylum seekers to the Western asylum country ASW is then given by:60 ASW = N(p** – p*).

(5.20)

The subsequent analysis of policy implications is based on comparative statics based on the total differential of eqs (5.19) and the effects on the number of asylum seekers to the Western asylum country ASW.

5.3

Counter-asylum policies

This section analyzes the effects of exogenous changes to some policy parameters on the pattern of refugee migration movements.

78


5.3.1 Defensive asylum policy: asylum restriction 5.3.1.1

Decline in self-reliance in the cross-the-border country

If the level of self-reliance s decreases, the consumption level of the permanent cross-border refugees decreases, while that of the temporary refugees increases, and that of the individuals that stay at home remains unchanged. Correspondingly, the average duration spent abroad increases for the whole conflict-affected population:

wc S ws

w␶ ws

yS ! 0 0 d pi d p , ° ␣ y S ° ° 0 if ® p pi p , and ® (1 p )(1 ) ␣ ° ° ¯ p d pi d 1, ° 0 ¯ 0 0 d pi d p. ° S O ° ° y y (1 ␣ ) ! 0 if ® p pi p. ® S O 2 ° ( sy y ) ° ¯ p d pi d 1. °¯ 0

(5.21)

(5.22)

The upper and lower cut-off values for temporary migration change accordingly: wp ws

␣(1 ␣ )y S y O ␣(1 ␣ )y S y O ! ((1 ␣ )sy S )2 ( y O ␣ sy S )2

wp ! 0. ws

(5.23)

This implies that if the first asylum country becomes more liberal in granting higher levels of self-reliance s, then ceteris paribus the number of hosted permanent refugees increases and the number of returnees (that is, temporary refugees) decreases. Graphically, a liberalization of self-reliance activities for crossborder refugees shifts the VS (pi)-curve upwards for persecution levels pi < p–, but remains unchanged for the less persecuted population pi > p– (see Figure 5.3). Consequently, this positive shift in the value of the cross-the-border option has a reducing effect on asylum migration: dp*(**) ds

wV W wV S ws ws wV S wV W wp wp

t 0 for p*, d 0 for p**,

(5.24)


79

VS,VW

VS(s, yo) syS

0

p* p*′

VW(MW, r, yo)

p**′p**

1

pi

Figure 5.3 Liberalization of self-reliance in the first asylum country

wV W wV S 0 and t 0. 61 Thus, it follows resulting from eqs (5.19), ws ws that the number of asylum seekers decreases if the first asylum country becomes more liberal in its self-reliance policy:

N

d( p** p*) d 0. ds

(5.25)

Equations (5.22), (5.23), and (5.25) imply that if the economic situation for refugees in the first asylum country improves by granting extended economic self-reliance, then (i) the number of permanent cross-border refugees does not decrease, (ii) the number of asylum seekers in the Western country does not increase, (iii) the total stock of emigrants does not decrease, and (iv) the average duration of a refugee situation in the first asylum country does not decrease. Thus, Western asylum countries have a reasonable self-interest in a more liberal refugee policy in cross-the-border countries. This holds particularly when small geographical distances make the migration costs MW for migrating to a Western country relatively affordable to asylum seekers. Proposition 5.3: An increasing degree of self-reliance in the first asylum country in terms of a liberalizing refugee and integration policy (for example, including work permissions, use of local services, and so on) increases ceteris paribus the value of the cross-the-border option and protracts a refugee situation.

80


5.3.1.2 Decline in recognition rates in the Western asylum country With respect to the restrictions on asylum immigration in the Western country, asylum admission policy is an effective instrument for regulating the stock of asylum seekers. As shown in Figure 5.4, the value of the asylum option decreases for all N individuals, and consequently, the number of individuals seeking asylum in the Western country diminishes, increasing the number who choose to stay in the region of origin: dp*(**) dr

wV W wV S wr wr S wV wV W wp wp

0 for p*, ! 0 for p **,

(5.26)

wV S wV W 0, and ! 0. 62 The same wr wr asylum reducing effect holds for increasing migration costs M W.63

by taking into account eqs (5.19),

Proposition 5.4: A more liberal asylum policy in the Western destination alleviates the refugee situation in the cross-the-border country, while rising migration costs for realizing the asylum option to the Western asylum country deflects refugee flows towards the first asylum country. As a consequence of a more restrictive asylum policy in the Western country, the inflow of a large refugee population aggravates the public perception within the first asylum country of refugees as an economic and political burden and a threat for the internal security VS,VW

VS(syS,yO) r VW(MW, r, yO)

0 Figure 5.4 country

p* p*′

p**′ p**

1

pi

Declining asylum recognition rate in the Western asylum


81

of the civil society. Consequently, the first asylum country might intensify encampment and reduce economic self-reliance, worsening the refugees’ situation (Loescher & Milner, 2005). This policy response of the first asylum country is driven by the expectation that a more liberal encampment policy with enhanced opportunities for the economic self-reliance of the refugees would rather protract the refugee situation within its territory. Potential refugees expecting to live under appalling encampment conditions are then ceteris paribus more likely to repatriate earlier or to choose immediately the asylum option in a Western country. As a consequence, the Western country is likely to respond in an analogous manner by restricting asylum conditions (that is, reducing recognition rates). Finally, a race to the bottom is established with highly restrictive asylum policies in the Western world and appalling refugee and encampment conditions in the conflict-affected developing world. The challenge of international refugee politics is to solve this apparent dilemma of restrictive asylum policies, which is an inefficient equilibrium since investment into migration is only rewarded for a subgroup of individuals. Deterrence, deflection, detention and deportation of refugees are counterproductive measures for resolving the appalling conditions for refugees, including the waste of resources for an unsuccessful, long-distance asylum migration endeavor. But beyond this, without dealing with the root causes of the refugee movements, these measures are also costly to the respective asylum countries, for example, by increasing costs for border control, administration and maintenance due to the increasing propensity of refugees and asylum seekers to stay irregularly in the respective asylum country and so on. An alternative to this bilateral asylum restriction policy might be a more proactive approach, particularly on the side of Western countries, that might tackle the underlying causes of asylum seeking in both the country of origin and the first asylum country. 5.3.2 Proactive asylum policy: migration-preventive aid transfers The Western country may invest resources into proactive measures for tackling the root causes in the conflict-ridden country of origin or sharing the refugee burden in the first asylum country, assisting local integration that might also reduce the asylum migration


pressure to the Western country. Although there are various proactive policies available, the focus in this analysis is on migrationpreventive aid transfers from the Western asylum country to the country of origin or the first asylum country. The crucial question of any proactive asylum policy in terms of migration-preventive aid is whether aid can indeed reduce asylum migration flows. Or, in terms of the present model, does an aid-induced increase of income levels in the country of origin or the first asylum country relieve the asylum burden in the Western country? The following analysis presumes that aid is to some extent incomeeffective. A necessary condition for this to be true is that aid, when transferred from the donor to the respective recipient country, is indeed channeled within the recipient country to the refugee population where aid can develop the income-generating effect. This is a necessary condition underlying the subsequent discussion. 5.3.2.1 Aid to the first asylum country First asylum countries are often overstressed and unable to tackle the political and economic challenges that large refugee inflows provoke. A common reaction of these cross-the-border countries is to confine refugees within camps, denying them freedom of movement, access to social services or economic self-reliance. Aid targeted at refugees in first asylum countries will promote the living standards of the refugees. But, according to the previous model, an aid-induced increase in the refugees’ income level implies that ceteris paribus the refugee inflow into the first asylum country increases: wpi wy

␣y O ␣s(1 ␣ )y O ! O S 2 s(1 ␣ )[ y ] ( y ␣ sy S )2

S

wpi ! 0, wy S

(5.27)

However, the net outcome for the first asylum country depends on the effect of asylum migration to the Western country: *(**)

dp dy S

wV W wV S wy S wy S t 0 for p*, S wV wV W d 0 for p**, wp wp

with eqs (5.19) and by taking into account that 64 wV t 0. wy S

S

and

(5.28) wV W wy S

0,


83

Income-increasing aid towards the first asylum country has an unambiguous reducing effect on the number of asylum seekers in the Western country. Thus, if aid is channeled to the respective refugee population (and if it is effective in increasing the refugees’ income level), the asylum pressure to the Western country does not increase (see also Figure 5.3): N

d( p** p*) d 0. dy S

(5.29)

While this policy is beneficial to the Western country, it leaves the first asylum country with a higher number of refugees. Interestingly, although this policy might work for the interests of Western donor countries, they are nevertheless reluctant to provide more resources for that purpose (UNHCR, 2006b; Czaika & Mayer, 2008; see Chapter 9). Why? Possibly, Western countries do not consider aid to be as effective in generating additional income for the refugees or they take the negative consequences for the first asylum countries into account. Obviously, first asylum countries might be even more reluctant for Western countries to adopt this policy, because it shifts and consolidates the refugee burden onto their territory. As a consequence, the first asylum country would respond by imposing further restrictions on economic self-reliance and encampment conditions for refugees. Beyond, governments of refugee-hosting developing countries might oppose a likely diversion of aid amounts taken away from the needs of the native population and reallocated to the refugees within their territory. 5.3.2.2 Aid to the country of origin Alternatively, the Western asylum country could transfer aid towards the country of origin in order to reduce refugee outflow and facilitate a sustainable voluntary repatriation. Pre-conditioned by the fact that countries of origin do not hinder a voluntary repatriation of their population after mass emigration, rapid post-conflict reconstruction with appropriate levels of civil security, basic social services and economic perspectives for returnees might be a prior rationale for targeting foreign aid towards the country of origin (UNHCR, 2006b). Obviously, cross-the-border asylum countries are also interested in sustainable return solutions with voluntary repatriation and reintegration of refugees that would otherwise stay within their territory.


Thus, aid to the country of origin that ensures rising income levels for the conflict-affected population is also in the interest of first asylum countries, since – without considering asylum migration to the Western country – the total stock of refugees into the first asylum country decreases, with the less persecuted people going home first: wpi

␣

wy

(␣ 1)sy

O

S

␣(␣ 1)sy S ␣ sy S y O

wpi 0. wy O

(5.30)

However, the consequences for the Western asylum country are wV W wV S t 0, and t 0, O wy wy O 65 the overall effect on the Western country is ambiguous: less distinct. Taking into account eqs (5.19),

*(**)

dp dy O

wV W wV S O wy O wy ! 0 for p * and p**. wV S wV W wp wp

and thus, N

(5.31)

d( p ** p *) ! 0. dy O

(5.32)

Figure 5.5 shows that in this case both curves shift upwards so that the total effect on Western asylum migration becomes unclear. Aid transfers to the country of origin are unambiguously beneficial for the first asylum country, but not necessarily for the Western aid donor.

VS,VW

VS(s, yO)

yO

0 Figure 5.5

p*′ p*

VW(MW, r, yO)

p**′ p**

1

pi

Increasing income level in the country of origin


85

A numerical simulation of such a policy, shown in Table 5.1, indicates that the asylum pressure exerted on the Western asylum country is generally lower the more liberal the first asylum country is in its self-reliance policy. However, the effect of income-generating aid yO (AO) on the change in the number of asylum-seeking individuals N . d(p** – p*) is not as clear. It is possible that for rather high values of the parameter s, that is when economic self-reliance is widely granted to refugees in the first asylum country, aid to the conflictaffected country of origin could even increase the number of individuals that choose the asylum option in the Western country. Why? Simply stated, the better the economic conditions in the country of origin, the lower the value of the permanent cross-border option relative to the asylum option in the Western country. This occurs when economic self-reliance in the first asylum country is generous (s is high) and therefore permanent cross-border migration is relatively extensive. This would also imply that less persecuted people with medium to high levels of p choose, ceteris paribus, this option. However, for these less persecuted individuals, the risk of deportation (if they would choose the asylum option in the Western country instead) is associated with a lower disutility of living at home than to the highly persecuted individuals. Thus, in this Table 5.1 Income in O

Simulation: aid transfers and refugee migration66 Self-reliance in S

Asylum in W

Permanent in S

Temorary in S

yO (AO)

s

p** = p*

p* (for t(p*) = 1)

100 110 120 130

0.95 0.95 0.95 0.95

0.162 0.542 0.580 0.530

0.693 0.219 0 0

0 0 0.093 0.063

100 110 120 130

0.90 0.90 0.90 0.90

0.609 0.682 0.633 0.558

0.250 0.079 0 0

0 0 0.040 0.035

100 110 120 130

0.85 0.85 0.85 0.85

0.788 0.731 0.655 0.573

0.068 0 0 0

0 0.024 0.018 0.020

p* (for t(p*) < 1)

86


particular case, aid to the country of origin reduces permanent migration to the first asylum country but increases asylum migration to the Western country, because the less persecuted individuals choose this option instead. Consequently, aid to the country of origin is only effective for the Western country if the cross-border option is devaluated by a strict refugee policy of the first asylum country. This ambiguous aid impact might also contribute to the fact that Western donor countries are rather hesitant in transferring aid to countries of origin for migration-preventive purposes (Czaika & Mayer, 2008; see Chapter 9). 5.3.2.3 Self-financing proactive asylum policy In light of these potentially unwanted effects of proactive measures on refugee migration, we should explore whether Western asylum countries would have any incentive to invest in such proactive measures. Given the efficacy of aid as an instrument for increasing the respective income level of conflict-affected individuals living either permanently or temporarily in the country of origin or in the first asylum country, aid transfers require that there must be benefits, for example, in terms of decreasing asylum costs, spilling over to the Western (aid-giving) asylum country. Thus, this postulate presumes double effectiveness: aid is effective in increasing respective income, and consequently, it is effective in reducing asylum migration flows to the Western country. If this double effectiveness holds, a selfinterested Western donor country allocates a positive amount of aid A = AS + AO to the first asylum country (AS) or to the country of origin (AO) according to the following marginality condition: w( p** p*) wy S S wy S wA

w( p** p*) wy O O d 0. wy O wA

(5.33)

However, this allocation rule is not sufficient. Aid is only transferred if the aid-induced reduction in asylum-related costs DC(A) is larger than the transferred amount of aid A.67 Figure 5.6 illustrates this relationship between the aid-reduced asylum-related costs C(A) and the transferred amount of aid A. According to this, migration-preventive aid is cost-effective (‘selffinancing’) if total costs TC(A) = C(A) + A are lower for a positive amount of aid, that is if TC(A) # C(0). Consequently, if aid is migration-effective, aid for refugees is in general a reasonable policy option for Western asylum countries.


87

TC = C + A

TC

45° C(0)

C(A) A* Figure 5.6

5.4

A**

Aid

Self-financing proactive asylum policy

Concluding remarks

This study contributes to the economic analysis of refugee migration. Utility-maximizing individuals that are in danger of their life, leave their home countries not primarily out of economic reasons but to avoid the disutility of persecution. However, refugees also respond to economic incentives. Equivalent economic conditions in a cross-the-border country or the expectation of asylum status in a Western country with better economic perspectives induce longlasting absence from the country of origin. Consequently, refugee situations that are generated by conflict and human rights violations at home and are sustained in neighboring first asylum countries for a long period of time go in hand with a twofold asylum policy failure. First, two-sided restrictive asylum policies force refugees to stay in desperate encampment situations in remote areas. And second, a lack of proactive engagement of Western countries either in the country of origin or the first asylum country hinders the implementation of a durable solution in terms of voluntary repatriation and reintegration at home or of local integration abroad.

6 Asylum Cooperation among Asymmetric Countries

Abstract68 This chapter argues that asylum cooperation for tackling the consequences of large asylum migration inflows is only possible among rather symmetric countries. Highly asymmetric countries have no incentives to join and remain in a stable coalition. The distinction of cost and spillover asymmetries shows that financial transfers may only release participation constraints, and thus, make asylum cooperation feasible, if they are focused on tackling this asymmetry. This result becomes relevant when applied to the context of the enlarged European Union. I argue that there is potential for a future cooperative burden-sharing regime for asylum, and particularly, if unanimity is replaced by the double majority principle in votes of the European Council, as the EU reform treaty suggests.

6.1

Introduction

In the recent past, asylum and immigration affairs have gained increasing importance on the political agenda of the European Union and its member states. There are two major reasons for this: first, a strong pressure of (illegal) immigration and asylum seeking, and second, an unequal (cross-country) distribution of asylum seekers and refugees. Although the number of asylum claims in industrialized countries has constantly dropped from its peak in 1992, it is still twice as high as in 1980, with nearly 340,000 applications per year in the industrialized Western countries by the end of 2005 (UNHCR, 88

Asylum Cooperation among Asymmetric Countries

89

2006b). This increased immigration pressure, particularly from developing countries, has intensified the political debate and led to various proposals for improving cooperation at the supranational level. Furthermore, in addition to managing the overall inflow, the uneven distribution of asylum seekers and refugees among European host countries demands adequate burden-sharing policies.69 Before the end of the Cold War, refugees coming from the East were welcomed in the Western world and reception countries derived strategic benefit from the provision of refugee protection (Loescher, 1989). Today, without the strategic interests of the Cold War era, refugees are considered to be an ever-growing burden for economies and societies and a threat to internal stability. The upward trend in the number of asylum applications during the 1990s has made states reluctant to continue providing generous protection to refugees (Hatton, 2005).70 Especially among the European Union member states, policymakers have adopted more restrictive legislative and administrative measures to control the number of asylum seekers in order to reduce the political, social, and economic costs of refugee protection (Thielemann, 2005). In the supranational EU arena, support is growing for a Common European Asylum System (CEAS) that would help harmonize national asylum policies and share the responsibility for asylum seekers Union-wide. A central pillar of a future CEAS is the implementation of an EU-wide asylum cooperation regime with a common burden-sharing rule for refugee admission. However, since decisions on a cooperative asylum regime would require the rarely attainable unanimity of the European Council, member states still act non-cooperatively and unilaterally in their decisions on national admission levels. In the following, we argue that the reason for non-cooperation on refugee admission is the relative asymmetry of EU member states; yet, in contrast to efforts for a global cooperation regime, asylum burden-sharing among EU member states is nevertheless feasible. In the theoretical part of this paper, we analyze two alternative cooperative asylum burden-sharing regimes: at first, we model a simple cooperative refugee burden-sharing regime, and in a second step, we extend this with a monetary transfer scheme with cross-country payments compensating for asylum provision abroad. This study shows that under some particular circumstances, depending on the


type of heterogeneity (costs vs. spillovers), monetary transfers can enhance national and collective welfare by widening participation in a cooperative asylum regime. Finally, we apply the propositions derived from the analytical framework to the multi-country context of the enlarged European Union. We are rather hesitant to argue that the status quo of non-cooperation could be overcome solely through the implementation of an explicit monetary burden-sharing regime such as the European Refugee Fund. This pessimism is nurtured by the theoretical assumptions. It shows that monetary transfers do not make asylum cooperation profitable if the countries are characterized by cost asymmetries instead of spillover asymmetries. However, cost asymmetries are quite reasonable to assume for the EU member states, particularly after the recent EU enlargement. We are much more optimistic about the introduction of a supranational EU asylum institution which implements a cooperative asylum regime. This optimism is based upon the prospect of the EU reform treaty replacing the unanimity rule by a double majority principle (of at least 15 member states representing 65 percent of EU population minimum) for votes in the EU Council. By simulation, we find that when decisions are made according to this modified voting rule, a ‘double majority’ in the EU Council is plausible, and thus a cooperative asylum burden-sharing regime is feasible. The remainder of this chapter is as follows. Section 6.2 briefly reviews the relevant literature on refugee protection and monetary transfers. Section 6.3 compares analytically two alternative cooperative refugee burden-sharing regimes, with and without monetary transfers. Section 6.4 applies these implications numerically on the context of the enlarged European Union. Section 6.5 concludes the chapter.

6.2 Literature review By its very nature, asylum migration movements impact relations between the various destination countries, as unilateral policy responses by one country tend to generate externalities for other countries. Thus, asylum provision and refugee protection has public good characteristics with free-riding opportunities undermining cross-country cooperation, leading to an under-provision of asylum capacities. Suhrke (1998) discusses this problem of collective action


91

failure in international refugee matters and stresses the relevance of state-specific interests in the establishment of an international refugee burden-sharing system. Furthermore, she argues that the establishment of a collective action system in refugee affairs is generally more difficult to achieve than in other international policy fields, due to the unpredictability of future refugee flows, and thus, uncertainty about future costs. Betts (2003) presumes that refugee admission generates both international (public) benefit spillovers as well as country-specific (private) benefits, stemming both from human rights considerations and norms of solidarity. Betts (2006) postulates cost-benefit perceptions of refugee protection to be country-specific and also dynamic. Hatton & Williamson (2005) and Hatton (2005) refer to this cost-benefit approach for modeling international cooperation in the field of refugee protection. Facchini et al. (2006) extend this cooperation model by taking into account the democratic process through which national asylum policy is determined. All of these approaches are based on the premise that positive cross-border externalities from refugee protection exist and they demonstrate that cooperation leads to a welfare improvement compared to a noncooperative situation. Thielemann (2004) and Thielemann & Dewan (2006) analyze refugee burden-sharing within the EU and therewith focus on mechanisms to overcome the very unequal distribution of refugees across the EU. They argue that the unequal distribution of refugees can basically be attributed to structural pull factors that are beyond the control of states, since refugees often base their destination country choice on non-political criteria (Holzer & Schneider, 2002; Neumayer, 2004). In this regard, most initiatives addressing the unequal distribution of asylum seekers and refugees are insufficient, because they neglect the root causes of unequal burdens. Thus, an optimization of refugee policy must not exclusively rely on cost-benefit schemes, but must also refer to the norms and institutional structures of the EU member states.71 Crisp (2005) argues that the norms and instruments of the refugee system will need to change in a variety of ways in order to cope with the rising number of asylum applications and the increasing irregular (‘illegal’) immigration pressure. He strongly emphasizes the necessity for protection in the regions of origin (for example, by promoting both adequate human rights standards and economic progress) in order to avoid mass influxes into industrialized

92


countries. The empirical evidence on this issue is rather ambiguous. While Neumayer (2005) provides evidence that improving economic and political conditions in the country of origin lower the asylum pressure on the destination countries, Rotte et al. (1997) find that support of democracy and economic development will not necessarily reduce migration, at least not in the short run, and thus, restrictive legal measures seem to be a more efficient instrument for reducing asylum pressure. Hatton (2004) supports this argument, but he further investigates the criteria that influence national refugee policies, as well as the motivations for seeking asylum in Europe. He finds that despite more restrictive asylum policies in the early 1990s, there was an overall increase in applications across EU member states. Hatton concludes that the impact of restrictive asylum policies has been superposed by rising worldwide political oppression and increasingly numerous conflicts. In summary, the literature establishes that burden-sharing and a more equal distribution of refugees among EU member states needs to take into account country-specific conditions of asylum countries (Neumayer, 2004). Furthermore, reactive asylum burden-sharing is surely not sufficient but needs to be paralleled by efforts tackling the root causes and measures to manage the overall influx such as migration-preventive development aid policies or peace-keeping missions. In order to analyze whether a financial burden-sharing institution is able to establish more cooperative asylum outcomes among European countries, we briefly refer to the literature on the role of side payments in cooperation regimes. The conditions for selfenforcing agreements, that is agreements that are not based on the commitment to cooperate, are extensively discussed in the environmental cooperation literature (see, for example, Carraro & Siniscalco, 1993; Barrett, 1994). For establishing such an agreement, countries first decide non-cooperatively whether or not to join an agreement given the burden-sharing rule the agreement adopts, and afterwards they decide upon provision levels of the public good. The problem is that some countries may join such an agreement and others not, so that a coalition is only formed by a subset of all countries. Nonparticipating countries prefer to free-ride on the provision levels of the cooperating members. Barrett (1999, 2001, 2002) extensively analyzes country-specific cooperation incentives for the provision of global (environmental) public goods. He emphasizes that the


93

characteristics of a good determine whether enforced international cooperation or rather unilateral provision is preferable. Furthermore, he states that for a high number of cooperating countries, a selfenforcing agreement can only sustain a superior welfare outcome if the global net benefit between the cooperative and the noncooperative outcome is small. In this context, Carraro & Siniscalco (1993) show that stable coalitions only exist among a small number of countries. However, they show that through cooperation inducing monetary transfers, coalitions could be stabilized and expanded. Harstad (2007) analyzes different types of agreements for the internalization of externalities in political cooperation. He derives some conditions under which side payment schemes are efficient forms of cooperation. For instance, the efficiency of side payments depends on the heterogeneity of the cooperating partners, the amount of externalities and the value of the international agreement. If bargaining incentives are too high for some countries, a side payment scheme can also produce a suboptimal outcome, since a growing conflict of interests might delay the achievement of an agreement. Side payments are more likely to increase efficiency if heterogeneity is low and externalities are large. Jackson & Wilkie (2005) demonstrate that side payments do not always lead to an increase in efficiency, even in the case of complete information, binding contracts and absence of transaction costs. They argue that efficiency is only improved by side payments as long as transfers are used to ensure that other participants internalize their externalities. If there are incentives to misuse transfer payments to manipulate the behavior of the cooperation partner to a country’s own advantage and not necessarily to the socially desirable outcome, side payments can deteriorate the combined welfare situation relative to a situation without side payments. With respect to asylum policy, Facchini et al. (2006) show that cross-country financial transfers as incentives to internalize positive asylum spillover effects may result in a welfare inferior outcome. This stems from strategic delegation that might encourage countries to accept a suboptimal number of refugees. Beyond this, the literature is rather limited regarding the welfare aspects of financial burden-sharing instruments in asylum policy issues, despite the fact that (earmarked) aid is already transferred either via bilateral or multilateral channels (for example, by UNHCR) to share asylum-burdens in some refugee-hosting countries.


Thielemann (2006) distinguishes several concepts of financial burden-sharing systems beyond the reactive ‘physical’ burden-sharing mechanism and the harmonization of asylum laws. Hereby, retrospective monetary compensations to the highly burdened asylum countries are one possible approach. The European Refugee Fund (ERF), established in 2000, is partly based on this concept. This fund aims to balance the costs among member states for hosting and integrating refugees. Thielemann (2005) critically analyzes the transfer payment mechanism of the ERF and its effectiveness in promoting burden-sharing and in enhancing the protection of refugees. He criticizes the ERF for not taking country-specific conditions into account, and emphasizes the importance of considering the member states’ heterogeneity when building a cooperative asylum regime. He concludes that the ERF transfer payment logic is rather limited in its redistributive impact and therewith in its potential as a burdensharing instrument. Additionally, Noll (2003) finds fault with the financial volume of the ERF stating that given the total costs of reception and the share channeled to each member, redistributive effects are not more than a drop in the ocean. Furthermore, a financial burden-sharing regime may also refer to a market-based allocation mechanism where a traditional quota system is amended by the possibility to trade the quota (see Schuck, 1997). However, this concept is often criticized because of ethical reasons (‘trading refugees’), and because of its narrow perspective, focusing only on one aspect of refugee-related financial compensations (Thielemann, 2006). Therefore, Thielemann & Dewan (2006) suggest a more implicit trading of an asylum-related public good bundle, for example, refugee protection, development aid, peace-keeping/making efforts and so on, where countries specialize according to the comparative advantage of their contribution (see Boyer, 1989). Next, we refer in a welfare analysis to the question of whether asylum cooperation among heterogeneous (asymmetric) countries is generally possible, and then, whether financial transfers pose to be an effective tool for enhancing cooperative solutions.

6.3 The analytical framework Based on the overall implications of the literature, cooperation in refugee admission can be welfare enhancing for host countries.


95

This is due to the internalization of cross-border externalities, stemming from refugee protection in other countries than the own. We reproduce this result in a static two-country model where both countries aim to optimize their welfare with respect to the tradeoff between the costs and benefits accruing from refugee protection. Even though it would be interesting to analyze the formation of cooperative asylum regimes with more than two (asymmetric) countries, it is sufficient to analyze the theoretical implications in a two-country model. However, in the empirical application, we use a numerical simulation for analyzing the multi-country case of the enlarged EU. 6.3.1 Asylum cooperation without monetary transfers The model assumes an exogenous stock of asylum seekers. A certain number (share) r of asylum seekers of this stock is offered legal refugee status in country i (r i), or country i* (ri*). The respective admission levels depend on national asylum policies. Each country decides unilaterally on the number (or, share) of refugees to be accepted. The model assumes that asylum seekers are not strategic actors, but are – if necessary – resettled between the two countries.72 Both countries benefit from refugee protection either domestically or abroad. Country-specific benefits are generally assumed to be generated by economic (labor market) or political (international prestige, regional stabilization) causes. Moreover, humanistic attitudes imply that (altruistic) individuals are likely to gain from the knowledge that refugees find protection in the own country or somewhere else (Hatton & Williamson, 2005). Thus, each country generates country-specific (private) benefits as well as positive (public) externalities from refugee protection spilling over to the other country. Costs for refugee protection are assumed to be increasing with the number of admitted refugees and are assumed to capture economic and political factors. Economic costs might reflect administrative costs for asylum processing, juridical support, maintenance costs, nutrition, social security, education and so on, whereas political costs are rather caused by integration endeavors and the promotion of public xenophilia. Finally, we assume that both countries are completely informed about all relevant parameters. The relationship between costs and benefits caused by the admission of asylum

96


seekers can be illustrated by the following welfare functions for each country i: Wi ri i ri * ci ri2 ,

(6.1)

where Wi represents the country-specific welfare function, ri denotes the number (or, share of an exogenously given asylum stock) of admitted refugees in country i, and ci is the country-specific refugee admission cost parameter. We assume increasing marginal costs of hosting refugees: the higher the number of refugees already admitted, the higher the marginal costs are for admitting another refugee. Parameter bi > 0 measures the amount of positive externalities spilling over from abroad. That is, a country’s welfare level increases with the number of admitted refugees abroad. Both countries maximize their respective welfare functions simultaneously by taking the asylum choice of the other country as given. Then, welfare maximization with respect to the number of admitted refugee results in the following noncooperative outcome: riu =

1 , 2ci

(6.2)

The optimal number of refugees increases with decreasing costs for hosting refugees. According to the optimal number of admitted refugees, country-specific welfare levels are given by: Wiu

1 1 , i 4ci 2 ci *

(6.3)

Next, we determine the optimal number of refugees when the authority for national asylum policies is delegated to a supranational institution. Such an institution maximizes collective welfare V = Wi + Wi* by assessing the socially optimal number of refugees ri to be admitted by country i as a cooperative outcome: ric

1 i * riu , 2ci

(6.4)

Cooperation leads to the internalization of positive externalities generated by each country. This amounts in higher numbers of admitted refugees in the cooperative scenario compared to


97

non-cooperation. As before, the optimal number of admitted refugees decreases with rising costs for hosted refugees. Obviously, internalizing the positive spillovers of refugee hosting, asylum cooperation increases national welfare levels of both countries: Wic

1 i2* 1 i i . 4 ci 2 ci *

(6.5)

As V c Wic Wic* maximizes the collective welfare due to the higher overall number of admitted refugees, we know that unilateral actions are jointly inferior, that is V c Wiu Wiu* . Thus, V c describes the welfare benchmark for any international asylum regime. Despite this, it might not be unilaterally optimal to make a cooperation commitment. Each country is only willing to participate in an international asylum regime if cooperation is beneficial for the country. Although the maximization of collective welfare is the social optimal solution, participation is not necessarily incentivecompatible to each country. The profitability of an agreement for all participating countries is a functional prerequisite for the cooperation scheme to be implemented. Therefore, collective welfare maximization must be amended by the following participation constraint: max V c s.t . Wic Wiu, ri

and the necessary condition that ensures that both countries participate in a cooperative asylum regime is then given by:

i*

ci * 2 ci * i i* . 2 ci ci

(6.6)

The fulfilment of this participation constraint requires that both countries are not too heterogeneous in the relevant parameters, that is, the maximum spread in spillovers bi is determined by the respective cost asymmetries. Figure 6.1 indicates by the relatively narrow sector I that for spillover asymmetries (and equal cost parameters) asylum cooperation is only feasible when countries are rather similar in their spillover parameters. Combinations outside this sector are unilaterally inferior and asylum cooperation is not a Pareto-optimal outcome. For countries with asymmetric costs, Figure 6.2 displays


βi

I III

II II I

(Ti −>i * > 0)

1

III (Ti *−>i > 0)

βi*

1 Figure 6.1 spillovers

Asylum policy outcomes among countries with asymmetric

Notes: Sector I represents the participation constraint (6.6) in a regime without monetary transfers, sectors II and III, respectively, indicate participation constraints (6.15) and (6.16) for a financial burden-sharing regime. Cost parameters ci are held constant and are assumed to be equal for both countries. Cost asymmetries, for example, ci > ci*, would turn sectors I and II clockwise.

ci

I II

II I 1

1 Figure 6.2 costs

ci*

Asylum policy outcomes among countries with asymmetric

Notes: Spillover parameters bi are held constant and are assumed to be equal for both countries. Spillover asymmetries, for example bi > bi*, would turn sectors I and II clockwise.


99

sector I in which asylum cooperation is profitable for both countries.73 Subsequently, we analyze whether monetary transfers are able to extend these rather narrow sector I’s to make asylum cooperation more profitable also for more asymmetric countries characterized either by spillover or cost heterogeneity. 6.3.2 Asylum cooperation with monetary transfers A supranational institution with the authority to set adequate monetary incentives can exert a decisive influence on the (autonomous) implementation of cooperative asylum policies of asymmetric countries (Barrett, 2002). Cross-country transfers, provided unilaterally but set collectively, can promote efficiency in political cooperation by increasing the incentives to participate in an agreement. This particularly holds if cooperation has low opportunity costs for the respective countries. Monetary transfers may enhance asylum provision levels and make cooperation a more attractive policy option (particularly for countries that would actually lose from cooperation) by internalizing the positive external effect of their actions. With respect to the implementation of a financial transfer regime, Schuck (1997) proposes a transfer system that consists of a centrally set quota for each state and a subsequent possibility to trade the quota obligation with other states either in terms of financial transfers or other resources (for example, package deals).74 We extend our model by including a monetary transfer scheme and consider the following two-stage game: in the first stage, the two countries simultaneously offer a monetary transfer t for each refugee that the other country hosts; in stage two, each country decides unilaterally upon its optimal level of refugees to be admitted, taking the transfer offers of the first stage into account. As before, countries benefit from the admission of refugees to the other country and likewise generate positive spillovers. This raises the unilateral incentive to compensate the other country financially in order to accept more refugees so that unilateral as well as collective welfare improve. In order to derive the unilateral transfer schedule, eq. (6.1) is adjusted as follows: Wit ri i ri * ci ri2 ti oi * ri* t i * oi ri ,

(6.7)


Wit represents the unilateral welfare of country i including both contributed and received transfers. ti* oi (ti oi* ) reflects the transfer contribution schedule of country i* (i) towards country i (i*). Obviously, unilateral welfare levels are directly increased by received transfers and diminished by contributed transfers. Monetary contributions also have an indirect positive impact on national welfare via spillover gains induced by a higher refugee protection level in the other country. For solving this two-stage game backwards, both countries first anticipate the number of refugees the other country is going to accept in the second stage depending on the transfers offered in the first stage. For this, country i maximizes its welfare function Wit with respect to ri in order to get:

riu

1 t i* oi . 2 ci

(6.8)

Equation (6.8) reflects the optimal number of refugees that country i admits, depending on the transfers it receives from the other country. Obviously, if country i offers country i* a monetary compensation per admitted refugee, country i* is encouraged to increase its admission level above the optimal level without transfers. Anticipating the maximization calculation of the other country, the optimal unilateral monetary transfer offered by country i is derived by inserting eq. (6.8) into eq. (6.7). The subsequent first order condition of eq. (6.7) is then: tu

i oi *

i 1 2

.

(6.9)

t u is the optimal amount of transfers that a country is willing to pay as compensation for its received cross-country spillovers. The optimal number of admitted refugees results from substituting eq. (6.9) into eq. (6.8), respectively: riu

1 i* . 4 ci

(6.10)

It can be seen that, for any spillover parameter bi > 0, the noncooperative admission level with transfer payments is always half of that of the cooperative outcome without monetary transfers. However, when comparing the two alternative non-cooperation


101

scenarios, it becomes obvious that admission levels with transfer payments are only higher if the respective spillover parameter bi is larger than unity. That is, non-cooperative refugee admission levels are higher in the regime with monetary transfers only if refugee protection abroad generates comparatively higher benefits than domestic protection. Accordingly, the amount of monetary compensation country i offers to the other country increases with its externality parameter bi. Net amounts of transfer payments T of country i (that is received minus offered transfers) depend on their respective spillover and cost parameters and are given by Ti oi*

i2 1 4ci*

i2 1 4ci

Ti* oi .

(6.11)

Equation (6.11) shows that for countries with asymmetric spillovers, net transfers are always non-zero. Eq. (6.9) indicates that a country only offers non-negative gross transfers t u if bi is larger than unity. For parameter values smaller than unity, countries do not contribute. Thus, if bi > 1, the respective non-cooperative welfare level Wit , u for country i is given by: Wit ,u

(i* 1)2 (i 1)2 . 16ci 8ci*

(6.12)

Figure 6.1 shows that, outside the square area, at least one country compensates the other country non-cooperatively for the spillover gains; consequently, the country with the larger spillovers parameter is the net contributor of financial transfers. Next, it becomes evident that, under certain circumstances, cooperatively set transfer payments (implemented, for example, by a supranational authority) are a valuable instrument to attain the cooperative benchmark solution without transfers. Under monetary cooperation, payments are set according to equal refugee admission levels with those of the cooperative (benchmark) solution without transfers. Eq. (6.4) is set equal to eq. (6.8) and solve for the respective financial transfers. Thus, by setting unilateral transfers according to tc

i oi *

i ,

the collective welfare V c Wit ,c Wit*,c is maximized.

(6.13)

102


By inserting eqs (6.4) and (6.13) into eq. (6.7), the cooperative welfare level for country i is given by: Wit ,c

(1 i*) 2 , 4ci

(6.14)

A country is only willing to participate in an international asylum cooperation regime if cooperation is unilaterally beneficial. Although the maximization of collective welfare delivers the social optimal solution, participation might not be incentive-compatible for each country. Even if collective welfare is maximized under the monetary transfer regime, the outcome might be inferior for at least one country. In this case, not joining the cooperative financial transfer regime is the dominant strategy. For guaranteeing that the monetary cooperation regime is profitable for both countries the following conditions must hold: Wit ,c Wit ,u

and Wit ,c Wiu .

They are functional prerequisites for the implementation of the monetary cooperation regime. Thus, a cooperative asylum regime is only feasible if the following participation constraints are satisfied:75 2ci* ( i* 1)2 1 i 3ci 1

3ci* ( i* 1)2 1, 2ci

2ci* i * c * * ( i * 2) 1 i i i . ci 2ci

(6.15)

(6.16)

Again, these participation constraints are fulfilled as long as both countries are not too heterogeneous. Constraint (6.15) ensures that for both countries, cooperation within a monetary burden-sharing regime (by offering higher transfers t c > t u) is superior to noncooperation. Constraint (6.16) also ensures that for spillover parameters bi < 1 , monetary cooperation is beneficial for both countries. Figure 6.1 displays the sectors II (relevant for bi 1) and III (relevant for bi < 1), the feasible combinations for which asymmetric countries voluntarily access a cooperative asylum regime with monetary transfers. Obviously, monetary transfers widen the rather strict symmetry requirement of the narrow sector I which represents the cooperative asylum regime without transfers. However, this holds only for low to


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moderate spillovers; if both countries gain from mutually large spillovers, the participation constraint is stricter for the monetary cooperation regime. However, when analyzing the respective constraints for cost rather than spillover asymmetry, as displayed in Figure 6.2, we see that the participation constraints for the monetary cooperation regime are entirely more strict than the asylum cooperation regime without transfers – at least if spillovers are rather equal and mutually low. This is shown in Figure 6.2, where sector I is strictly larger than sector II. Thus, monetary transfers cannot promote cooperation if countries are characterized rather by cost asymmetries than by spillover asymmetries. Instead, a cost-sharing regime would be more appropriate. 6.3.3 Summary and interpretation Asylum cooperation with or without monetary transfers is generally not attainable among highly asymmetric countries. It is also shown that non-cooperative transfers are only contributed when refugee admission abroad generates higher benefits than domestic hosting, that is if bi > 1. The reason that this does not result in a corner solution where only one country provides asylum is that refugee hosting causes (private) costs for this country. Generally, if (public) spillover benefits exceed (private) domestic benefits, countries are willing to perform positive gross compensations to the other country, although clearing gross transfers generates the result that, in the end, only the country with the larger spillover gains is the net contributor. Since, consequently, asylum cooperation is only feasible among rather symmetric countries (that is, solutions within sectors I and II, respectively), it is actually required that countries are simultaneously homogeneous with respect to both (public) spillovers and (private) costs. For instance, for countries with relatively high costs or low spillover gains, cooperation is not profitable and free-riding becomes the superior strategy. In this case, non-cooperative unilateral asylum policies dominate international asylum cooperation, even if it does result in a collectively lower welfare level (Suhrke, 1998). The impact of a monetary transfer regime on the feasibility of cooperative asylum outcomes is ambiguous. The distinction between cost and spillover asymmetries shows that, ceteris paribus, a monetary burden-sharing regime is only able to extend the set of cooperative solutions in the case of spillover heterogeneity; however, this only

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holds among countries with mutually low spillover parameters (see Figure 6.1). For situations where countries are rather characterized by cost heterogeneity, a monetary transfer regime does not widen the set of cooperative solutions. The reason for this result is that transfer schedules do not depend on country-specific costs, but on spillover benefits. Thus, in a multi-country context with relatively high cost asymmetries, a cost-sharing regime is the more adequate tool for generating an asylum cooperation solution. Consequently, asylum cooperation among developing and developed countries seems to be rather unlikely because of the large crosscountry asymmetries with respect to both costs and spillovers. Thus, our analysis implies that asylum cooperation is more likely on a regional level among rather similar countries. Therefore, we will now discuss these implications in a multi-country context by analyzing the asylum cooperation potential among the member states of the European Union.

6.4

Asylum cooperation in the European Union

The purpose of this section is to answer the question why – to date – we have not seen a cooperative asylum regime within the EU. Olson (1965) argues that in a multi-country context, in general, the more actors involved, the more difficult it is to overcome collective action failure. On the other hand, Jackson and Wilkie (2005) point out that for an (endogenous) transfer scheme which internalizes two-sided externalities, the more actors that are involved, the easier it is to achieve efficient strategies, because strategic bargaining is limited due to possible sanctions and reputation losses. Beyond these contrary arguments, we would expect, according to our model implications, that the EU is the regional context for which a cooperative asylum burden-sharing regime may seem possible; though, the degree of heterogeneity has increased significantly among the EU member states since its recent enlargement. However, the evidence of the past is different, and therefore, the focus here is to analyze the reasons for this collective action failure in European asylum cooperation. Under the current status quo of the 27 member states of the enlarged European Union, countries act unilaterally and noncooperatively to decide on national asylum recognition rates by


105

maximizing their (hypothetical) country-specific objective function. To simulate the non-cooperative outcome of the status quo, we define for each EU-27 country i an objective function of a similar type to the welfare function used earlier. The (non-cooperative) welfare in the status quo for each member state is then given by Wju ( j j ) j j j j ¦ ( i i ) ( j j ) j ,

ji

1,! ,27

(6.17)

i\ j

with rj as the country-specific asylum recognition rate, uj as the number of asylum applications, aj as the parameter for the countryspecific attitude towards immigration, bj as the country-specific spillover (altruism) parameter, and gj as the country-specific cost exponent. As before, the function Wju captures the three elements: country-specific (private) benefits, (public) cross-country spillovers, and (private) costs for hosting refugees. We assume that member states interact strategically and non-cooperatively, since countryspecific welfare depends on the asylum provision of all the other member states. Furthermore, the member states’ choice parameter is the asylum recognition rate as asylum application numbers are often beyond the scope of national policies. Consequently, asylum recognition rates are endogenous and the respective reaction functions depend on refugee admission levels of the other member states. As an alternative to this (non-cooperative) status quo outcome, we estimate the potential for an EU-wide (cooperative) burden-sharing rule. Any binding rule requires the support of all 27 member states for its implementation (and enforcement). Thus, and different to the previous analysis, we do not look for the cooperative refugee admission numbers that maximize collective EU-wide welfare, but for the cooperative burden-sharing rule which is supported by most (at its best, by all) member states. A cooperative burden-sharing rule modifies welfare function (6.17) according to: Wjc ( : j ) j : j j ¦ :i ( : j ) j ,

ji

1,! ,27

(6.18)

i\ j

where Vj indicates the country-specific number of refugees to be admitted under a specific burden-sharing rule. We consider two alternative burden-sharing rules, which are (i) an equal population quota in terms of admitted asylum seekers per inhabitant, and (ii) an equal asylum burden with respect to the member states’ economic

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performance (c.f. Chapter 3). A cooperative EU-wide burden-sharing rule becomes authoritative for all member states if the respective supranational authority, the Council of the European Union, achieves the required majority under its legislative statutes. In this analysis, we refer to two voting alternatives in the EU Council: the unanimity rule under the present status quo, and according to the design of the EU reform treaty, the double majority voting principle. Until now, decisions of the EU Council on asylum policy issues have required unanimity, that is, each member state retains its sovereignty to block any cooperative asylum burden-sharing scheme that is not unilaterally beneficial. Obviously, this unanimity voting rule is the core obstacle for more cooperative asylum solutions in the EU; and even more so since the recent enlargement.76 However, the design of the prospective EU reform treaty tackles this issue and envisages the implementation of the double majority principle, which would then also be applicable for asylum policy affairs. The double majority principle requires the backing of at least 15 member states, representing a minimum of 65 percent of EU population. This modification facilitates the potential for effectual coalitions for a cooperative asylum regime below unanimity. We apply this modified voting principle for each of the proposed burden-sharing rules and we ‘forecast’ their expected outcome in a stylized vote of the EU Council. The data we use for this numerical simulation are provided in Table A.4. Data on asylum applications (ui) and recognition rates (ri) for the period 1993–2003 are provided by UNHCR (2004), population data are from the World Bank (2004), and data on countryspecific attitudes on asylum and immigration (ai) and humanistic attitudes in general (bi) are taken from the World Values Survey (WVS, 2000). In its 1999 survey, conducted in 26 of today’s 27 EU member states (the exception being Cyprus for which we take the average of the other 26 countries), the WVS asks interviewees about their attitude towards asylum and immigration and about their general concern about human beings. Country coverage and the relevance of content convinced us to use these country-specific data as indicators for domestic benefit and cross-country spillovers. Additionally, we use unemployment rates of foreign-born population taken from OECD (2006) for adjusting the indicator for countryspecific asylum and immigration attitudes.


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First, we determine the country-specific welfare functions Wju of the status quo. Given the fact that all countries act non-cooperatively in their asylum policies, we assume that during the observation period the average number of admitted refugees (rj uj) maximized the country-specific welfare of each member state. This presumes that EU member states were (and still are) in a stable non-cooperative Nash equilibrium concerning refugee admission. This enables us to calculate the country-specific cost parameters gj (see Column VI, Table A.4) for each country. Both welfare functions are now fully specified, and we are able compare the respective welfare outcomes of the two alternative cooperative burden-sharing rules with the outcome of the status quo. For both burden-sharing rules, we run the numerical simulation for four different levels. For the population quota, we display levels between 0.1 and 0.4 refugees per thousand inhabitants, and for the economic burden-sharing rule levels between 0.005 and 0.012 refugees per million US dollar GDP with the corresponding country-specific number of refugees to be admitted. For c each country j, we calculate the numerical welfare benefit Wj (Vj) 2 u Wj (rj uj) and indicate the hypothetical voting outcome for both burden-sharing rules. The results for each burden-sharing alternative are shown in Tables 6.1 and 6.2. Each table displays the net benefit of the non-cooperative outcome under the status quo (Column I) and the outcomes for the different levels of the respective cooperative burdensharing rule. It becomes evident that under each burden-sharing rule, unanimity of all EU-27 member states is not reached. Although only a few countries (Italy, Spain and Portugal) would oppose an equal EU-wide economic burden-sharing rule (refugees per GDP, Table 6.2), without any financial transfers or other package deals, a cooperative asylum burden-sharing solution is not implementable under the current status quo. However, under the modified voting constitution of the proposed EU reform treaty, asylum cooperation is profitable for a ‘double majority’, that is, for at least 15 member states representing at least 65 percent of the EU population. Then, voting on this economic burden-sharing rule would have the potential for the rule to be implemented. The prospects for the alternative burdensharing rule, the population quota, are less promising. Table 6.1 shows that applying this rule (that is an equal EU-wide population quota) does not even satisfy the double majority

Table 6.1

Asylum cooperation: equal population quota, EU-27

Population quota

EU-27 Australia Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy

status quo

0.1

benefit Vote 0.421 0.606 0.853 0.705 0.750 1.148 0.287 1.074 0.610 0.469 1.637 0.560 1.408 0.572

0 0 0 0 0 0 0 0 0 0 0 0 0 0

0.2

Refugee benefit 805 1,033 797 77 1,021 537 136 520 5,949 8,250 1,063 1,016 392 5,769

0.196 0.282 0.406 0.335 0.368 0.530 0.150 0.508 0.278 0.193 0.809 0.267 0.668 0.391

Vote 1 1 1 1 0 1 0 1 0 0 0 1 1 0

0.3

Refugee benefit 1,610 2,067 1,593 153 2,042 1,075 272 1,040 11,897 16,499 2,126 2,032 784 11,538

0.017 0.035 0.026 0.032 0.013 0.085 0.051 0.049 0.027 0.012 0.036 0.022 0.062 0.499

Vote 1 1 1 1 1 1 1 1 1 1 1 1 1 0

0.4

Refugee benefit 2,414 3,100 2,390 230 3,063 1,612 407 1,560 17,846 24,749 3,189 3,048 1176 17,307

0.222 0.347 0.449 0.399 0.326 0.698 0.012 0.603 0.178 0.117 0.700 0.309 0.787 0.790

Vote 1 1 1 1 1 1 1 1 1 1 1 1 1 0

Refugee benefit 3,219 4,133 3,186 306 4,084 2,150 543 2,080 23,794 32,998 4,252 4,064 1,568 23,076

0.42 0.66 0.86 0.76 0.65 1.31 0.04 1.15 0.35 0.21 1.40 0.59 1.51 1.23

Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

0.361 0.186 0.530 0.685 0.708 0.196 1.139 0.419 0.593 0.770 0.765 0.869 0.937

0 0 0 0 0 0 0 0 0 0 0 0 0

Total 19.256 EU-27 consent (no transfers) 0/0.00

234 347 44 40 1,614 3,863 1,018 2,230 538 196 4,092 892 5,923

0.220 0.121 0.253 0.327 0.325 0.202 1.012 0.517 0.281 0.367 0.493 0.425 0.373

48,394

10.297

0 0 1 1 1 0 0 0 1 1 0 1 1 15/0.30

468 694 89 79 3,229 7,725 2,035 4,460 1,076 393 8,183 1,785 11,846

0.189 0.124 0.024 0.032 0.048 0.347 1.798 1.129 0.027 0.026 0.535 0.009 0.142

96,788

4.123

0 0 1 1 1 0 0 0 1 1 0 1 1 20/0.64

701 1,041 133 119 4,843 11,588 3,053 6,690 1,614 589 12,275 2,677 17,769

0.267 0.187 0.300 0.389 0.416 0.582 3.428 2.136 0.332 0.413 0.790 0.436 0.630

14,5182

0.117

0 0 1 1 1 0 0 0 1 1 0 1 1

935 1,388 178 159 6,458 15,450 4,071 8,920 2,152 786 16,367 3,570 23,692

0.45 0.30 0.58 0.75 0.78 0.89 5.87 3.49 0.63 0.79 1.22 0.86 1.10

193,576

1.95

20/0.64

Notes: Population quota in refugees per thousand inhabitant; ‘Status quo’ refers to year 2004. EU-27 consent: number of countries/EU population share.

Table 6.2

Asylum cooperation: equal economic burdens, EU-27

GDP shares

status quo

EU-27

benefit

Vote

Refugee

benefit

0.421 0.606 0.853 0.705 0.750 1.148 0.287 1.074 0.610 0.469 1.637 0.560

0 0 0 0 0 0 0 0 0 0 0 0

1,299 1,616 110 58 468 1,095 48 855 9,033 12,659 889 425

0.148 0.215 0.315 0.261 0.278 0.405 0.107 0.396 0.239 0.162 0.626 0.205

Australia Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary

0.005

0.008

0.010

Vote Refugee benefit 1 1 1 1 1 1 0 1 0 0 0 1

2,078 2,586 176 92 749 1,752 77 1,368 14,452 20,254 1,423 680

0.004 0.013 0.007 0.005 0.000 0.037 0.001 0.003 0.091 0.066 0.040 0.008

0.012

Vote

Refugee

benefit

1 1 1 1 1 1 1 1 0 0 1 1

2,598 3,232 220 116 936 2,190 96 1,709 18,066 25,318 1,779 850

0.10 0.16 0.22 0.18 0.19 0.33 0.07 0.27 0.01 0.02 0.34 0.15

Vote Refugee 1 1 1 1 1 1 1 1 1 1 1 1

3,117 3,879 264 139 1,124 2,628 115 2,051 21,679 30,381 2,134 1,020

benefit 0.196 0.313 0.436 0.359 0.369 0.624 0.137 0.529 0.056 0.005 0.724 0.291

Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden United Kingdom

1.408 0.572 0.361 0.186 0.530 0.685 0.708 0.196 1.139 0.419 0.593 0.770 0.765 0.869 0.937

Total 19.256 EU-27 consent (no transfers)

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0/0.00

672 7,580 64 101 125 24 2,594 1,186 724 329 174 146 4,405 1,599 9,959

0.522 0.434 0.136 0.070 0.195 0.254 0.242 0.083 0.657 0.158 0.218 0.285 0.437 0.323 0.266

58,237

7.636

1 0 0 0 1 1 1 0 0 0 1 1 0 0 1 15/0.30

1076 12129 102 162 200 38 4150 1897 1158 526 279 233 7048 2559 15935

0.000 0.572 0.005 0.005 0.003 0.005 0.028 0.027 0.599 0.012 0.006 0.003 0.421 0.008 0.085

93179

1.638

1 0 1 1 1 1 1 1 0 1 1 1 0 1 1 22/0.48

1,345 15,161 128 203 250 48 5,187 2,372 1,447 658 349 291 8,809 3,198 19,919

0.34 0.73 0.08 0.04 0.13 0.18 0.20 0.01 0.65 0.08 0.16 0.19 0.46 0.20 0.31

116,474

2.06

1 0 1 1 1 1 1 1 0 1 1 1 0 1 1

1,614 18,193 153 243 300 58 6,224 2,846 1,737 789 418 349 10,571 3,838 23,902

0.688 0.926 0.165 0.079 0.266 0.350 0.381 0.037 0.774 0.174 0.304 0.385 0.542 0.404 0.519

139,768

5.549

24/0.78

Notes: GDP shares in refugees per million US $ GDP; ‘Status quo’ refers to year 2004. EU-27 consent: number of countries/EU population share.

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requirement. In this case, a common population quota could only be feasible if it is amended by a suitable monetary compensation regime. Thus, asylum cooperation was not realized in the past because equal burden-sharing is not profitable to all EU member states. The design of the supplementary monetary compensation regime depends on the characteristics of the heterogeneity among the EU countries. As stated above, monetary transfers that compensate for spillover effects (b) might enforce a cooperative outcome of a particular burden-sharing rule only among countries with similar asylum costs. However, since this presumption of cost symmetry among member states was weakened through the recent EU enlargement, such monetary transfers seem to be a rather inefficient instrument. Instead, a monetary regime that compensates for cost asymmetries among EU member states would be a more appropriate approach to increase the incentives for a cooperative EU-wide asylum solution.

6.5

Concluding remarks

This chapter argues that refugee protection levels are insufficient and suboptimal because of the absence of any institutionalized and efficient refugee cooperation regime. It has been shown that because of the public good character of asylum provision, with its externality spilling over across other countries, non-cooperative deflection and free-riding become collectively welfare diminishing. Asylum cooperation is desirable, but it is less attainable the more heterogeneous the affected countries are. Depending on the type of heterogeneity – cost versus spillover asymmetries – monetary transfers may be able to increase country-specific welfare and stabilize a cooperative asylum regime. In general, this only holds among countries with minor asymmetries. We argue that this condition for asylum cooperation is rather satisfied on a regional level, for instance, among the member states of the European Union. Since the implementation of a multilateral asylum (burden-sharing) regime requires a supranational institution which has the competence (i) to guarantee the steady commitment of the participating countries, (ii) to coordinate the allocation of refugee admission numbers, and (iii) to implement, if efficient, an adequate monetary compensation scheme across participating countries, the European Union is even more appropriate because of its already established institutional authority. However,


113

the status quo requires the unanimity of all member states to enact binding decisions of the EU Council on asylum affairs. This has not been attained so far, resulting in the non-cooperation of the EU member states on refugee admission issues. Since the European Refugee Fund (ERF) does not take into account the heterogeneities in asylum costs among the EU member states, which have further increased since the recent enlargement, it is not adequately designed to fundamentally change this non-cooperative outcome. Consequently, based on the implications of this paper, we can predict that the ERF will have little impact on a more cooperative asylum outcome within the EU, even if its financial volume is multiplied. For the future, however, we are optimistic that cooperative asylum in the EU is possible. The authority of the upcoming EU reform treaty and the introduction of the double majority principle in the EU Council are good reasons that cooperative asylum outcomes can become possible in the EU – at least by means of an appropriate financial transfer regime.

7 Refugee Movements and Aid Politics

Abstract This chapter links the topic of refugee migration to the issue of foreign aid. It provides an overview on aid provision and allocation relevant for the subsequent chapters that are focused on the influence of refugee migration movements on the aid allocation decisionmaking process of bilateral donors.

7.1

Introduction

The motives of foreign aid provision vary from donor to donor and also over time. The spectrum of motives for giving official aid is broad, ranging from ethical and humanitarian motives, to political and security interests, and to economic and commercial considerations. Former (special) interests, for example those of colonial powers to give foreign aid to their former colonies, or influencing voting patterns in the UN, have declined in relevancy, while new objectives like the promotion of global public goods (for example, sustaining and improving the global environment, reducing the risks of epidemics such as HIV/AIDS or Malaria, hindering the flow of narcotics or fighting international terrorism) have risen. In this context, one of the new aid allocation objectives of the post–Cold War era is the management of international asylum and refugee movements, which is of particular interest of the remaining chapters.

114

Refugee Movements and Aid Politics

115

7.2 Aid and refugee migration policies Since the beginning of the 1990s, which saw an arising ‘asylum crisis’ in Europe, the international debate concerning the interactions between migration and aid policies has advanced, particularly in donor countries. Western policymakers across the political spectrum now discuss the use of development policy as an instrument for the management of an increasing migration and asylum pressure. Before the asylum crisis of the 1990s, donor governments were rather reserved in the proactive use of short-term humanitarian aid or long-term development assistance in refugee situations. Previously, aid was provided for tackling large displacements in only a few specific cases and then for limited periods (Klingebiel, 1994). In the meantime, however, this reluctance has changed. Aid interventions are often considered as important instruments for tackling the causes and consequences of refugee movements, and to assist the (re)integration and rehabilitation processes of refugees and returnees. For instance, the German Ministry for Economic Development and Cooperation (BMZ) views refugee-related development assistance as an instrument for avoiding the causes and negative consequences of large refugee movements (BMZ, 2006a; BMZ, 2006b, p. 292). Although the effectiveness of aid for the prevention or cessation of conflicts is still ambiguous (Collier, 2007), there are nevertheless high expectations of the potency of development aid as a central policy tool in refugee migration issues. It becomes increasingly important to condition aid provision on good governance in terms of compelling recipient countries to guarantee, for example, respect for human rights, participation of all social and ethnic groups in political decision-making, the rule of law and provision of legal security, or progress in establishing a market-based and development-oriented economic and social order. In this context, the reliability and the time-consistent behavior of donor governments is most important for improving the credibility of the announced policy measures of aid donors (Collier, 2007). Since the causal link between aid provision and good governance reform is often not robust and ex ante conditionality is also often ineffective (Collier, 2007), Western donor governments are giving more weight to mitigating the consequences of large refugee movements,

116


and thus, increasingly consider local integration and reintegration as durable solutions.77 Basically, the underlying intention of Western donor states, beside altruism, is primarily to limit mass refugee movements to the region of origin before they move towards Western borders. Hence, humanitarian aid and development assistance contributes to a stabilization of fragile regions and conflict areas (Zolberg et al., 1989). A sustainable mitigation of the consequences of mass flight movements includes the repatriation, reintegration, and rehabilitation of refugees and other displaced persons. Most generally, priority is given to return options rather than a prolonged support in the first asylum country. Obviously, repatriation and (post-conflict) reconstruction requires that returnees take an active part; however, as a durable solution it is only attractive if international donors assist in creating housing, workplaces and basic infrastructure (Black & Koser, 1999). For acting more proactively, it becomes increasingly important for Western donors to link short-term relief aid, mid-term (re)integration efforts, and long-term (post-conflict) reconstruction and economic promotion for closing the time gaps between the different forms of aid provision (Crisp, 2001). In addition, new activities to create conditions for a sustainable return movement of refugees, but which do not correspond to the traditional conception of development assistance, are required of international donors. These activities include removing landmines, integrating demobilized soldiers in aid programs and the enforcement of housing and property rights (Black & Koser, 1999). For that purpose, aid interventions in (post-)conflict situations often go hand in hand with political siding in favor of the target group. In order to become active, compromises with state armed forces – and also with paramilitary groups or even clan militia – are often unavoidable (de Mello, 1995). Secondary movements to and asylum seeking in Western states are caused by a decelerated or failed (post-conflict) reconstruction process in the country of origin and desperate situations in first asylum countries. Aid interventions, inter alia, will reduce these unwanted migration flows (see Chapter 5). However, with respect to the high complexity of refugee migration movements in general and secondary movements in particular, some evidence shows that aid assistance is often rather ineffective in meeting secondary


117

migration prevention objectives, at least in the short run (Rotte et al., 1997). Therefore, short-term interventions for fighting structural migration causes are very likely to fail. This applies to various emigration causes such as ecology (environmental degradation), economy (poverty), demography (population growth), or politics (civil wars, human rights’ violations and so on). Even if donor states take the task of mitigating the causes of refugee and secondary migration movements seriously, they might only become effective in the long term (Rotte et al., 1997; Burgdorff, 2002). Nevertheless, the interest of Western governments in using development policy for migration prevention objectives is undiminished. Particularly the EU member states have emphasized the need to use foreign aid to reduce migration and refugee flows from different parts of the world into the EU or, as an alternative approach, to reward other states (either within or outside the EU) that accept refugees from other sending or first asylum countries (Castles, 2003). The ambition to manage unwanted migration movements by means of various forms of aid is present in both public debates and national policies. For instance, this is shown by a recent public poll run by the European Commission in spring 2007, where EU citizens were asked what they consider to be the major motivations for providing development aid to poor countries (EC, 2007). In most European countries, citizens state that the main rationale for development aid is based on the self-interest of the donor countries, such as improving (bilateral) trade relationships, contributing to global and regional stability or promoting good governance in the recipient countries. Avoidance of emigration movements from development countries to industrialized countries ranks fourth, while only 11 percent of EU citizens believe that the main motivation for giving development aid is to help people in need (Figure 7.1). This EU-wide poll shows that particularly in the large European migration destinations, France and Germany, using aid for migration prevention purposes is highly ranked. But also in the other EU countries, avoiding large migration movements from poor developing countries to industrialized countries seems to be a major motivation of donors for allocating aid for refugee-related purposes. We will come back to this implication in the subsequent chapters.

118


45 40 35 30 %

25 20 15 10 5 0 EU 27

Germany

France

Italy

Sweden

Economic self-interest

28%

23%

31%

18%

41%

35%

Global stability

28%

38%

25%

26%

33%

28%

Democracy/governance

22%

29%

16%

32%

30%

28%

Avoid emigration

20%

24%

27%

17%

10%

10%

Prevent terrorism

19%

24%

14%

28%

17%

17%

Gain political allies

15%

13%

22%

6%

10%

16%

Clear conscience’

12%

12%

19%

9%

16%

10%

Poverty reduction

11%

5%

9%

19%

13%

1%

9%

4%

7%

7%

6%

13%

Don’t know

Figure 7.1

UK

Donor motivations of ODA provision, EU public poll,78 2007

Note: Data are based on EC (2007).

7.3

Aid allocation patterns and statistics

The international aid business is complex in a sense that participating actors often have diverse interests and motivations, unequal bargaining power, and very different means and instruments. The central actors in development cooperation are national states, multilateral institutions, and non-governmental organizations. On the donor side, the subsequent analysis primarily focuses on official (or, governmental) bilateral aid organizations and the donor governments to investigate the underlying motivations for the allocation of Official Development Aid (ODA) or humanitarian aid. On the recipient side, national states are generally the main cooperation partners of donor states. This means that the whole analysis is state-centered and does not take into consideration the aid allocation procedures on sub-national levels.


119

120 100 80 60

Total ODA A. Bilateral ODA A.1. Grants

40

A.2. Non Grants B. Multilateral ODA

20

2006

2004

2002

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

−20

1980

-

Figure 7.2 Net disbursement of ODA, OECD/DAC countries, current US$ (in bn) Note: Figure is based on data provided by OECD (2007a).

Based on data provided by OECD (2007a), Figure 7.2 displays total amounts of Official Development Assistance contributed since 1980. It is remarkable that net disbursements of bilateral and multilateral aid from OECD/DAC countries to developing countries have almost doubled since the end of the Cold War. Total official development aid in 1990 was about US$ 54.3 billion; in 1997, total aid distribution reached its lowest level during the 1990s of around US$ 48.5 billion (in current US$). Since then, total aid levels have increased to more than US$ 104 billion in 2006. Furthermore, it is shown that most ODA is channeled through bilateral grants; multilateral aid makes up only around a third of the total ODA amount. This preference of donor states for bilateral aid allocation is also evident in Figure 7.3. In 2006, the top ten donor states allocated between 55 percent (Italy) and 90 percent (USA) of their total ODA transfers through bilateral channels. The figure also shows the importance of the five largest bilateral donors. The United States, the United Kingdom, Japan, France and Germany contribute more than two thirds of the total global ODA volume. In absolute terms for 2006, these five countries transferred around US$ 68.2 billion to the developing world. However, in relative terms, the picture is less admirable. Comparing the absolute aid volumes with the respective economic performance reveals that these five, but also most other DAC donor states, are far from realizing the target of 0.7 percent as


Norway: 3.0 (74%)

Other DAC donors: 16.5 United States: 23.5 (90%)

Italy: 3.6 (55%) Canada: 3.7 (69%)

United Kingdom: 12.5 (70%)

Sweden: 4.0 (72%) Netherlands: 5.5 (79%)

Germany: 10.4 (67%)

Japan: 11.2 (65%) France: 10.6 (75%)

Figure 7.3 Top bilateral donor states in 2006, in current US$ (in bn; share of bilateral ODA) Note: Figure is based on data from OECD (2007a).

ODA–GNP ratio, which was passed by the UN General Assembly in 1970 as the goal for aid budgets of Western donor states. The regional distribution of global aid flows, shown in Figure 7.4, indicates that over the past decade there has been a strong trend for reallocating aid volumes to the African continent. While, in 1996, sub-Saharan Africa received around a third of the global aid budget, ten years later it had increased to around 45 percent of the increased global ODA volume. This reallocation of ODA in favor of sub-Saharan Africa was mainly at the expense of Eastern Asia, and Central and South America. These two regions lost between 6 and 7 percent of total ODA, while other regions’ shares largely remained constant. However, the geographic allocation of development aid varies considerably among donor states. While ODA from former European colonial powers is relatively concentrated to ex-colonial states, the United States primarily allocates on the basis of national and international security considerations, and Japan primarily allocates to Asian recipients to further commercial interests (DegnbolMartinussen & Engberg-Pedersen, 2003). Generally, in order to qualify for Official Development Assistance (ODA), aid transfers from donor countries to recipients in the


Oceania 4%

121

Eastern Europe 4% Central & South America 15%

1996

Sub-Sahara Africa 33%

Middle East & North Africa 17%

South & Central Asia 13%

East Asia 14% Eastern Oceania Europe 1% 6%

2006

Sub-Sahara Africa 45%

Central & South America 8%

Middle East & North Africa 19%

East Asia South & 8% Central Asia 13%

Figure 7.4

Regional distribution of ODA, 1996 and 2006, current US$

Notes: Chart sizes are proportional (that is, in 2006 ODA was around double that of 1996) to the respective total ODA volumes of all DAC countries in that year. Figures are based on data provided by OECD (2006a).


developing world or to multilateral agencies must fulfill three basic conditions: first, they must be provided by the official sector, second, its main objective needs to be economic development and welfare promotion, and third, they are generally provided on concessional terms, that is with at least a 25 percent grant element (OECD, 2007b). Besides financial transfers, technical assistance as well as long-term humanitarian aid is also included in official aid statistics. There have been several modifications in the definition of ODA by the DAC. For instance, already since the early 1990s, financial and technical assistance to refugees in developing countries as well as temporary expenses to refugees from developing countries during the first 12 months of stay in the donor country, and all costs associated with eventual repatriation to a developing country, are reportable by the donor countries as ODA (OECD, 2007b). However, it is still rare that donor states report their respective figures of refugee-related aid, only some rough estimates are available indicating a share of about two percent (Table 7.1). Probably because Table 7.1 Sector composition of total ODA grants, 3-year average, current (bn) US$ 25 20 15 10 5 0

1995–97

1998–00

2001–03

2004–06

13.8

13.0

15.8

20.6

Project & Programme Aid

8.5

7.5

8.6

17.0

Debt Forgiveness

3.4

2.4

5.1

16.9

Humanitarian Aid

1.9

2.6

3.0

6.4

Administrative Costs

2.8

3.0

3.2

4.1

Refugees in Donor Country

0.7

1.0

1.4

2.0

Support to NGO’s

1.0

1.1

1.3

1.9

Food Aid

1.1

1.0

1.1

1.0

Technical Co-operation

Note: Figure is based on data from OECD (2007a).


123

of better reporting by donor states, this figure has also increased tremendously since the mid-1990s. In general, figures for the size of official aid must be taken cautiously; they are sometimes highly politicized and several donors have attempted to inflate the amount of their aid assistance by including activities not strictly categorized as ODA (Degnbol-Martinussen & Engberg-Pedersen, 2003). Basically, the sectoral composition of ODA has also undergone significant changes during the previous decade. Table 7.1 displays threeyear averages for the core sectors of ODA grants, indicating that technical cooperation is still the most important sector in bilateral development assistance. Aid devoted to this sector rose to more than one fifth of total spending. Debt forgiveness, which is the major cause of the tremendous increase of ODA volumes in recent years, has more than tripled since 2000.

7.4 UN diplomacy and aid allocation This section examines correlations between bilateral and multilateral aid-giving patterns and the number of speeches country representatives delivered in the two core bodies of the United Nations, the UN Security Council and the UN General Assembly, respectively.79 For both developing and developed countries, the United Nations bodies play an integral role as a forum of international communication and diplomacy, as well as arranging international partnerships serving country-specific concerns. Particularly, the UN Security Council and the UN General Assembly are the core arenas where both developing and developed countries can receive international attention for their issues. These two bodies offer an international audience for country representatives and other delegates to promote their country-specific political affairs and economic urgencies. Once on the agenda, a country-specific concern has the diplomatic forum to be promoted to its special needs. However, do these diplomatic activities actually correspond to the political, economic or humanitarian efforts of bilateral and multilateral donors of aid? This section seeks to analyze this issue by focusing on the question whether UN diplomacy is in line with the transfer pattern of development aid for countries in need. Hereunto, we investigate the correlations between the number of speeches in the UN Security Council and the UN General Assembly in the 1990s and the bilaterally and


multilaterally allocated development aid funds for those countries. We presume that the more often delegates get the chance to speak either in the UN Security Council or in the UN General Assembly for country-specific concerns, the more these countries are also privileged by financial and technical support in terms of bilateral or multilateral Official Development Assistance (ODA). The literature on aid allocation determinants has become increasingly voluminous in recent decades. A common approach in the empirical literature differentiates between donor self-interests and recipient needs as the main explanatory factors for bilateral and multilateral aid patterns. In particular, McKinlay & Little (1977, 1978) and Maizels & Nissanke (1984) represent this concept, reflecting the idea that aid-giving motivations of donor countries might be neither entirely altruistic nor entirely selfish, which is why aid allocations can be expected to be both promoting donor economic and political interests as well as the economic and technical needs of recipient countries. In this context, two well-known biases are extensively discussed. First, the population bias reflects a discrimination of more populous countries in aid allocation patterns, and second, the middle-income bias refers to a concave relationship between aid and income per capita of a recipient country, especially disfavoring the least developed countries.80 More recent studies have proposed some additional aid allocation determinants such as good governance and human rights rewarding effects (for example, Svensson, 2000; Alesina & Weder, 2002; Neumayer, 2003), or expressing the importance of socio-political stability (Chauvet, 2003). Collier & Hoeffler (2004) provide some evidence for the discrimination of post-conflict societies, whereas Czaika (2005a; see Chapter 8) discusses the influx of asylum seekers and refugees as an influential determinant in bilateral aid decisions. In the following, we take some of these previously tested variables into consideration and analyze the bilateral aid allocation patterns of 18 donor countries to 148 recipient countries, as well as the total multilateral aid allocation between 1991 and 1999, and introduce the number of speeches held in both the UN Security Council and the UN General Assembly, respectively, as the main variables of interest. Furthermore, we control (i) for some donor self-interest variables like trade promotion, colonial ties, geographical proximity, bilateral development cooperation duration and country size in terms of


125

population, (ii) for some recipient need variables like income per capita, human development level and intensity of internal conflicts during that time period, and (iii) for some good governance variables like the respect for political freedom or the quality of institutional capacities. We expect a positive correlation of our two main variables of interest, that is the number of speeches in the UN Security Council and the UN General Assembly, and the amount of aid developing countries received during the 1990s. Since we consider the UN Security Council to be the more powerful arena, we anticipate a stronger correlation with those concerns raised in this arena compared to the UN General Assembly, which receives in general less global publicity. Hence, we expect that UN Security Council speeches are more important in reflecting the international development assistance pattern than its UN General Assembly counterpart. 7.4.1 Data All data on ODA flows are taken from the OECD Geographical Distribution of Financial Flows to Aid Recipients database (OECD, 2004).81 We use the nine-year average of the total amounts allocated on a bilateral basis from 18 donors to 148 recipient countries and alternatively the total amount of ODA disbursed multilaterally. UN Security Council (Scouncil) and General Assembly (Gassembly) speeches made by country representatives or delegates are indexed in the UN Bibliographic Information System (United Nations, 2005b). Figures for Income per capita and Population were taken from World Development Indicators (World Bank, 2004). We use the physical quality of life index (PQLI) as a control variable for human development needs, which is calculated, based on data from World Bank (2004), according the formula provided by Neumayer (2003, p. 51). Possible good governance rewarding effects are captured twofold. First, we control for political freedom allowance, represented by the Democracy variable, which is measured as the equally weighted sum of the political rights and civil liberties index from FreedomHouse (2005).82 Second, good Governance reflected in the country-specific politico-institutional quality is calculated as an equally weighted composite index based on governance data from Kaufmann et al. (2003).83 In addition, we take into account the severity of past or running internal clashes by Conflict intensity data extracted from the Armed Conflict dataset of PRIO (2005). In addition, we control for

126


some bilateral factors like the development cooperation history (ODA history) between donor and recipient countries before 1990 in number of years and for former colonial ties (Colony). A geographical proximity (Distance) variable controls for close political and economic interest spheres, and lastly, bilateral trade volumes (Trade) are used in regressions to represent the special commercial interests of donor countries. Data on geographical distances are taken from CEPII (2005), whereas export data for the 18 donor countries are taken from the Bilateral Trade Database of OECD (2005).8 We run the regressions with a pooled least squares fixed effects model with heteroscedasticity robust standard errors estimating the following regression equation: ODAij a0 f(Scouncil, Gassembly; b) f(Xij, Qj; g) «ij

(7.1)

where ODAij denotes the total amount of aid given by country i to country j, the Scouncilj and Gassemblyj variables express the number of speeches held in the respective arena in favor of country j, Xij is a vector of bilateral control variables and Qj is a vector reflecting unilateral control variables. b and g are estimation coefficient vectors, and «ij stands for the error term. Since the number of speeches and the amounts of aid might have similar underlying determinants, we have possibly a problem of endogeneity; thus, we cannot identify causality, but only correlation between the ODA allocation pattern and the pattern of speeches held in these two UN bodies. 7.4.2 Results Table 7.2 reports the regression results for both the bilateral aid allocation decisions (Regressions I–III) and the multilateral aid allocation patterns (Regression IV). The regressions for the bilateral allocations display throughout not unambiguous effects of speeches in UN bodies in reflecting international aid support for countries in need. Speeches in the UN Security Council correlate significantly with both bilateral aid and multilateral aid allocations. On the other hand, the UN General Assembly variable exhibits insignificant (negative) coefficients. During the 1990s, about 30,000 UN General Assembly speeches on concerns of the 148 recipient countries in our dataset (approximately 200 on average) had no measurable positive correlation, neither with bilateral nor with multilateral development aid transfer patterns.


127

Table 7.2 Bilateral and multilateral aid allocation. Dependent variable: log of ODA I Constant Scouncil Gassembly Income Income (sq.) Population Population (sq.) PQLI Democracy Governance Conflict ODA history Trade Distance Colony R2 adjusted Number of observations (pooled)

2.363 (0.897) 0.116* (2.540) 0.182 (1.663) 1.614** (3.052) 0.154** (4.474) 0.290 (1.229) 0.005 (0.406) 0.218 (0.452) 0.335* (2.117) 0.713* (2.330) 0.122 (1.846) 0.473** (8.803) 0.266** (5.650) 0.308** (3.124) 1.372** (9.878) 0.663 2592/144

II 1.573 (0.580) 0.091* (1.973) 1.587** (2.972) 0.154** (4.419) 0.162 (0.681) 0.002 (0.151) 0.301 (0.599) 0.363* (2.304) 0.594* (2.056) 0.119 (1.767) 0.459** (8.201) 0.264** (5.531) 0.311** (3.084) 1.365** (9.648) 0.661 2592/144

III 1.308 (0.492) 0.111 (1.038) 1.673** (3.271) 0.155** (4.699) 0.226 (0.951) 0.001 (0.099) 0.463 (0.936) 0.328* (2.083) 0.675* (2.283) 0.120 (1.734) 0.466** (8.373) 0.279** (5.827) 0.337** (3.339) 1.344** (9.688) 0.659 2592/144

IV 1.945 (0.681) 0.138** (2.889) 0.076 (0.616) 1.895** (3.300) 0.184** (4.561) 0.430 (1.712) 0.007 (0.539) 0.795 (1.765) 0.580** (3.132) 0.671* (2.486) 0.054 (0.931) – – – – 0.727 148

Notes: Regression I–III results are for 18 donor and 148 recipient countries, Regression IV results for total multilateral aid to 148 recipient countries. t-values are shown in parentheses. (*) and (**) denote statistical significance at the 5 percent and 1 percent level, respectively. Estimates for country dummies are available on request.


Table 7.3 lists donor-specific regression estimates for UN Security Council speeches, which shows significant results for some bilateral donors. It is striking that explicitly those donor countries that were non-permanent UN Security Council members during the observed time period have positive and significant estimates. We find for the non-permanent UN Security Council members Sweden (member in 1997/1998), Italy (1995/1996), Spain (1993/1994, not significant), Austria (1991/1992) and Canada (1989/1990, that is shortly before the observation period) as well as for the permanent members France, UK and USA (not significant) rather strong correlation, whereas for the non-members Australia, Switzerland and Denmark (both even with negative signs), Finland, Ireland, Netherlands and Norway estimates are rather low and not significant.85

Table 7.3 Bilateral donor-specific coefficients. Dependent variable: log of ODA Donor Australia Austria Belgium Canada Switzerland Denmark Spain Finland France UK Germany Ireland Italy Japan Netherlands Norway Sweden USA R2 adj Number of observations

Scouncil 0.032 0.182* 0.067 0.188** 0.009 0.039 0.131 0.121 0.186** 0.158 0.208** 0.088 0.214* 0.027 0.091 0.091 0.208* 0.164

(0.423) (2.263) (0.995) (2.599) (0.133) (0.460) (1.462) (1.620) (2.763) (1.887) (2.944) (1.495) (2.543) (0.317) (1.095) (1.145) (2.444) (1.462) 0.670 2592/144

Notes: Estimates of the other explanatory variables are the same as those given in Table 7.2. t-values are shown in parentheses. (*) and (**) denote statistical significance at the 5 and 1 percent level, respectively.


129

For the other control variables, we can confirm the middle-income bias for both bilateral and multilateral aid, whereas a large country bias is not evident in our regressions. The recipient need variables show no significant signs indicating an inferior importance in the aid decision-making process. However, we find some evidence for the good governance and democracy rewarding effects, respectively, for both aid allocation channels. For bilateral donors, we can show some strong evidence for distinctive self-interest, reflected in higher aid amounts where long-lasting development cooperation relationships exist. Commercial, political and cultural ties in terms of special preferences for trade partners, geographically close countries, and former colonies are other factors of aid determination. Thus, elaborating on the correlation of UN speeches delivered by representatives and delegates of developing and transition countries with the international allocation pattern of bilateral and multilateral development assistance provides some indication for an underlying relationship. The evidence for the hypothesis that the allocation of development assistance follows the pattern of international diplomacy activities is rather weak – at least for the two main arenas of the United Nations. However, if this hypothesis is valid at all, then it is for the UN Security Council, where a positive relationship was found for several bilateral donors as well as for total multilateral aid. Correlation with diplomatic activities was particularly found for the aid allocations of – at least temporary – UN Security Council members. In contrast, diplomatic activities in the UN General Assembly are a rather poor indicator for aid allocation decisions. Both bilateral and multilateral aid allocations are not in line with the appeals put forward through this body. This result indicates the superior repute of the UN Security Council as a ‘donor institution’ compared to the UN General Assembly. However, this rather weak result might also be interpreted as an indicator of the different time perspectives of UN diplomacy and international development assistance. While, contrary to diplomatic activities that might have been determined by political urgencies, international aid provision seems rather influenced by long-term considerations.

7.5

Concluding remarks

‘More aid’ is a permanent calling of development aid organizations and other stakeholders, which has been repeated time and time again


at various UN bodies and international summits.86 More aid for first asylum countries and countries of origin seems to be only a refinement of this call. For instance, the UNHCR Convention Plus Initiative promotes an enhanced burden-sharing with refugee host countries and countries of origin of refugee movements (UNHCR, 2004a). With respect to countries of origin, facilitating a sustainable voluntary repatriation by alleviating reintegration, rehabilitation and (post-conflict) reconstruction is the primarily rationale for increased aid volumes. More aid for countries of origin will ensure rapid post-conflict reconstruction with appropriate levels of civil security, basic social services and better economic potential for those refugees willing to return (UNHCR, 2007). In fact, voluntary repatriation is often not possible for years, and thus, protracted refugee situations are a major consequence. Refugees are confined to camps and closed settlements mostly for economic and security reasons. First asylum countries lack assistance (and financial incentives) to overcome these political and economic impediments to allow refugees greater individual freedom, such as access to basic social support or the opportunity to unfold productive capacities and self-reliance in the host country (UNHCR, 2007). Western donor states are generally highly interested in the process of political stabilization, economic reconstruction and peacebuilding in a (post-)conflict context. However, for various reasons, such as free-riding interests, they are rather reluctant to increase their aid funds for (refugee-related) challenges in the developing world. Therefore, the remaining parts of this book discuss the main determinants of bilateral aid allocation decision, and whether donors respond to different types of refugee movements differently.

8 Aid Allocation and Asylum Migration

Abstract87 In this chapter, the influence of immigrants and asylum seekers on the aid allocation pattern of bilateral donors is investigated. Immigrants constitute a respected interest group in the aid allocation process in the donor country and seek to influence bilateral aid flows in favor of their country of origin. We distinguish between immigrants (with foreign citizenship) living permanently in the donor country and asylum applicants who are non-permanent in their status, but who play an important role as information and ‘urgency indicators’. For Germany, some empirical evidence can be found indicating that permanent immigrants as well as asylum seekers influence the bilateral aid allocation pattern.

8.1

Introduction

Since the end of the Cold War and the demise of the Soviet Union, transnational migration has become a major concern of Western European governments. In particular, the large increase of asylum seekers from the various developing and transition countries has had a noticeable impact on European societies and politics. The policy response of European governments to the growing influx of unwanted immigrants from the South and East was twofold: First, there were attempts to reduce immigration by legal means, tightening asylum laws and cracking down on illegal migrants. 131


Second, the migration issue led to a reorientation of development policies toward a broader perspective of development, including economic, social and political elements. (Rotte et al., 1997) This chapter focuses on the influence of immigration (permanent and asylum) with respect to the aid allocation policy of a donor country that is at the same time a migrants-receiving country.88 For many donor countries, immigration in general and asylum migration in particular constitute very relevant determinants of bilateral aid allocation that have never been specifically studied.89 Immigrants (permanent and asylum) form a respected group in many societies, especially in classical immigration countries like the US, Canada, Australia, or New Zealand, but also in the non-classical immigration countries of Western Europe. Worldwide, two-thirds of all asylum applications were lodged in the EU-15 countries, and the absolute number of applications confronting Western Europe has risen dramatically since the end of the Cold War (UNHCR, 2003).90 Among the EU member countries, Germany plays a dominant role as a main migration-receiving country. During the 1990s, Germany was confronted with about 50 percent of all European asylum applications. The total number of asylum seekers in Germany rose dramatically in the early 1990s and then stabilized at a relatively high level (Figure 8.1). Germany, as one of the main havens for immigrants in Europe in the 1990s, is thus an interesting case for studying the influence of immigrants and asylum seekers on bilateral development aid allocation policy. We presume that Germany, as a major host country for asylum seekers and other immigrants, concentrates its development aid efforts, along with other criteria, on recipient countries that are the main sending countries of asylum-seeking people to Germany. In the aid allocation literature, the question of whether asylum seekers have an impact on the aid allocation decision of their host country is a rather neglected one. This chapter seeks to fill this gap.

8.2

Aid allocation literature review

The aid allocation literature of the last thirty years offers numerous determinants for explaining aid allocation patterns, with varying degrees of relevancy. A cornerstone for explaining donors’ aid

Aid Allocation and Asylum Migration

133

500 450 400 350 300 250 200 150 100 50

Germany

France

UK

Italy

2000

1998

1996

1994

1992

1990

1988

1986

1984

1982

1980

0

Sweden

Figure 8.1 Asylum applications in EU countries (in 1000) Source: Data provided by MPI (2005), http://www.migrationinformation.org/

allocation motivation is the recipient need and donor self-interest approach (for example, Maizels & Nissanke, 1984). This approach postulates that donors care about the needs of the recipients, but at the same time also use aid to advance their self-interest. Based on various empirical studies, some evidence exists that donor governments mainly pursue their political, economic and strategic selfinterests in the way they allocate aid. Objectives of poverty reduction and development promotion are less important, especially in the bilateral aid allocation of the large donors. The dominance of self-interest in the motivation of donors partially excludes the ‘better’ motivation of focusing on the recipients’ needs and constrains poverty-oriented aid allocation (World Bank, 1998; Collier & Dollar, 2002). However, Trumbull & Wall (1994), Alesina & Dollar (2000), McGillivray (2003b), and others show that even larger bilateral donors favor developmental criteria and, especially since the end of the Cold War, have refocused their aid support away from nondevelopmental concerns to more poverty and need-oriented aspects of the recipient countries (McGillivray, 2003a). Beside these indigence factors, both economic factors – like trade promotion in terms of tied aid (McGillivray, 2003a) – and political


factors might have a major influence on aid allocation patterns (Neumayer, 2003).91 Svensson (2000) and Alesina & Weder (2002) find some evidence that good governance structures are rewarded by higher aid amounts as in cases where well-established civil and political rights or low corruption levels are observed. Arvin & Drewes (1998, 2001), studying Canadian and German aid allocations, provide evidence for a large country bias, which indicates a negative relationship between per capita assistance and the population of aidreceiving countries. While Chauvet (2003) expresses the importance of socio-political stability on the aid allocation of donors, Collier (2000) and Collier & Hoeffler (2004) provide some evidence that donors do not respond appropriately to (post-)conflict societies in their allocation of bilateral aid. In this context, Neumayer (2003) accentuates the relevance of human rights aspects in the recipient country for the donors’ aid decision. In this study, we attempt to extend this set of aid allocation determinants. We propose a novel hypothesis and extend the standard empirical framework used for explaining bilateral aid allocation patterns by adding the hitherto neglected impact of immigrants on the donors’ aid allocation decision. Generally, donor governments are exposed to the influence of immigrant groups interested in the welfare of their country of origin. We differentiate between immigrants with permenant residence permits in the donor country and immigrants without permanent residence permits (such as asylum seekers). The driving force of allocating aid in favor of a home country is the collusion of these two groups, both of whom care for their country of origin. Although asylum seekers, as well as permanently residing ethnic groups in the host country, might be skeptical about the government and the political system of their country of origin, it seems reasonable that they would still have strong links to and interests for their compatriots at home. Despite an aversion to the ruling governmental system at home, immigrants encourage aid and support in favor of their home country, especially when it is transferred on a sub-state level, for example, by non-governmental organizations (NGOs). Beyond this, asylum seekers play an important role as ‘first hand’ information providers and ‘urgency indicators’ for the donor country. When people are forced to leave their home country, donor governments acknowledge the special need for assistance in these countries.


135

In the next section, we theoretically set out these ideas in a political economic framework to illustrate the core determinants of a bilateral aid allocation decision. From this model, we formally derive the hypotheses to be tested in Section 8.5 for the case of German aid allocation in the 1990s. Section 8.6 concludes this chapter.

8.3 The model The focus of this section is to elaborate on the relationship between official bilateral aid, the stock of legally immigrated foreigners, and the influx of asylum seekers. To do this, we use a multi-household three-country model. Within this model, we assume five types of households: three types in the donor country, including two immigrant households (types 1 and 2) and one native household (labeled type 3), and two types of households in two recipient countries, labeled a and b. The households differ in incomes and utilities. All three domestic household types, as well as the households in the – recipient countries, each receive exogenous incomes Y i (with i = 1, 2, – 92 3) and Y j (with j = a, b). Li (with i = 1, 2, 3) stands for the number of households of type i in the donor country and Lj (with j = a, b) for the number of households in recipient country j. The donor government levies a proportionate and exogenous income tax t on all domestic (native and permanent immigrant) households. The tax revenue T t ( L1 Y1 L2 Y2 L3 Y3 ) is transferred abroad as Official Development Assistance (ODA). The (indirect) household utilities ui (with i = 1, 2, 3) are expressed by the following functions: u1 u2 u3

V[(1 t)Y1 ] ␭(A␣ )L␣ u␣ , V[(1 t)Y2 ] ␭(A ␤ )L ␤ u␤ , V[(1 t)Y3 ].

(8.1) (8.2) (8.3)

The immigrant households are altruistic towards their country of origin, and the altruism depends on the number of asylum seekers from their home country residing in the donor country (that is, the presence of more asylum seekers enhances the altruism of the permanent immigrants for their country of origin).93 We assume, therefore, that l(Aj) > 0 and l9(Aj) > 0, with j = a, b. This functional relationship reflects the compassion of the permanent immigrants


for their compatriots.94 This ‘concern effect’ increases with the number of new immigrants. The immigrant households’ utility function incorporates an additional term reflecting their altruism for their compatriots in the home country. The ODA is allocated as a share µ of the total ODA volume to the two recipient countries, according to the marginal utility of income the immigrant households receive through their altruism for their countries of origin. In the recipient country, the transfer is allocated to the households using a lump sum approach, that is, each household receives the same amount additional to their exogenous income. The efficiency of the aid transfer process is expressed by the parameter dj (with j = a, b).95 The indirect utility functions uj (with j = a, b) of the households in the two recipient countries are given as: (1 ␮ )T ␦␣ L␣

u␣

V ª« Y␣ ¬

u␤

␮T ␦␤ º ª V « Y␤ . L ␤ »¼ ¬

º, »¼

(8.4) (8.5)

Finally, we assume positive and diminishing marginal utility of income for all households. Hence, by assuming Ya > Yb, we get V’(Yb) > V’(Ya) with V’’(.) < 0. The growing concern caused by the influx of foreign nationals is reflected in the social welfare function the donor government seeks to maximize. The objective function of the donor government is an additive separable welfare function maximizing total welfare W of all households in the donor country by determining the allocation parameter µ: max W ␮

L1u1 L 2 u 2 L 3u 3

(8.6)

The government maximizes total welfare by choosing the optimal allocation parameter µ, which determines the share the two recipient countries receive. Assuming an inner solution, we get the optimal m* from eq. (8.6): wW w␮

!

L 2␭(A␤ )V'(Y␤ )␦␤ L1␭(A␣ )V'(Y␣ )␦␣ 0

(8.7)


137

Equation (8.7) determines the conditions for a corner solution, in which the poorer country b receives all the ODA (m* = 1), if • the number of permanent immigrant households from the poorer country (L2) is relatively large, • the number of asylum seekers from the poorer country b is relatively large generating relatively strong altruism of the compatriots living permanently in the donor country, • the efficiency parameter dj of the poorer country is relatively high. The corner solution is of some practical interest because some countries receive either no aid or only a negligible amount from various donor countries.96 In order to derive the core hypotheses that we are going to test, the interior solution of the allocation parameter must before be analyzed.

8.4 Comparative statics The main focus of this model is the influence of immigrant households and asylum seekers on the outcome of the social welfare function, in terms of the equilibrium value of the allocation parameter µ. We begin with the impact of asylum migration on the aid allocation parameter. By enhancing the concern of the permanent immigrants, asylum seekers can influence the aid allocation decision in favor of their home country. Thus, we derive the following core hypothesis from eq. (8.7): Hypothesis 1: bilateral aid: d␮ dA ␤

Refugee-sending countries receive a greater share of

L 2␭ '(A ␤ )V'(Y␤ )␦␤ ! 0, D

␦␤

2

␦␣2

T 0. L␤ L␣ Before testing this hypothesis empirically, we can derive some further testable hypotheses from eq. (8.7), namely: L2 ␭(A ␤ )V''(Y␤ )

where D

T L1␭(A␣ )V␣''(Y␣ )

(8.8)

Hypothesis 2: Countries with a large immigrant base in donor countries receive a greater share of bilateral aid: d␮ dL1

␭(A␣ )V'(Y␣ )␦␣ D

! 0.

(8.9)

138


Hypothesis 3: More-populous countries receive a greater share of bilateral aid: d␮ dL␤

L2 ␭(A ␤ )V''(Y␤ )␦␤2 D

! 0. ␮T L2␤

(8.10)

Hypothesis 4: The good governance rewarding effect exists only for countries with an initially low efficiency parameter. If such countries improve their governance structure, they can expect a larger share of aid, simultaneously reducing the share of bilateral aid received by countries with a high governance level: d␮ d␦␣

L1␭(A␣ )V'(Y␣ ) + L1␭(A␣ )␦␣V''(Y␣ )

(1 ␮ )T L␣

D

!

0.

(8.11)

Hypothesis 5: Countries with a low per capita income receive ceteris paribus more aid: d␮ dY␣

L1␭(A␣ )V''(Y␣ )␦␣ ! 0. D

(8.12)

We can now summarize the marginal impacts of major determinants of interest on bilateral Official Development Assistance (ODA) allocations: ODAij

( ) ( ) ( ) r) () P P (P P· §P ODAij ¨ Lji , Aji , Yj , ␦ j , Lj ¸ . ¨ ¸ © ¹

8.5 Empirical evidence This section empirically tests the hypotheses formally derived above. The objective is to explain the behavior of Germany as a bilateral donor of foreign aid. Therefore, we focus on its bilateral aid allocation pattern in terms of amounts of aid given to recipient countries. The following variables are controlled for: First, we assume that the longer the development assistance relationship between Germany and a recipient country has lasted, the stronger the bilateral ties and the more aid this country receives. Development cooperation history, therefore, should play an important role in current aid allocation decisions. Furthermore, we expect that geographical distances


139

between donor and recipient country should influence the aid pattern. The geographical distance variable, which is well known from gravity models in international trade, suggests that countries that are situated geographically closer to the donor country receive more aid (see Chapter 7.4). In addition, bilateral trade volumes should be taken into account as a variable representing the commercial interest of the donor country. We assume that Germany allocates more ODA to trade partners according to bilateral trade volumes.97 Lastly, we control for the strategic interest of Germany in Eastern Europe, where most of the countries were treated as potential EU-accessing countries. Thus, we expect that Germany allocates relatively more aid to the Eastern European transition countries. 8.5.1 Data and methodology The data used for the dependent variable represent the annually disbursed bilateral Official Development Assistance (ODA) amounts from Germany to 148 developing countries and countries in transition during the 1990s (from 1991 until 1999). In order to balance out the large variations in the annual volumes, we take nine-year averages. The ODA data are obtained from the Development Assistance Committee (DAC) of the OECD.98 We include both Part I and Part II countries in our sample.99 For the purpose of controlling the significance of the explanatory variables on the allocation of German ODA in the 1990s we use OLS estimation. It is remarkable that Germany (as with other major donors, such as Japan) gives at least some aid to almost every developing and transition country.100 Although the Development Assistance Committee (DAC) of the OECD (2004) provides ODA data for more than 150 countries, in the case of aid flows from Germany in the 1990s the sample of countries is smaller because data for some explanatory variables are not available. If not constant, all variables represent nine-year averages from 1991 to 1999. We estimate the following regression equation: ODAj

␤0 f (asyl j , forpop j ; ␤ ) ␥ X j ⑀ j ,

where j indexes aid-receiving countries and immigrant-origin countries, and b and g are estimation coefficients. We focus our regression on the impact of the asylum seeker and foreign population variables, and the interaction of the two. The asyl variable expresses the

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logarithm of the average number of asylum applications from citizens of country j in Germany from 1991–1999, whereas the forpop variable denotes the logarithm of the average stock of population with citizenship of country j living permanently in Germany.101 With the vector Xj we capture other exogenous control variables that might affect the allocation of aid:102 • Population: the logarithm of the average stock of population of recipient country j.103 • Income: the logarithm of the average per capita income in constant 1995 US dollars in recipient country j.104 • PQLI (Human well-being): we use the logarithm of the physical quality of life index (PQLI) for capturing human needs in the recipient country j. The PQLI is an aggregate measure of life expectancy, infant mortality and literacy, all equally weighted on a 0 to 100 scale.105 • Distance: the logarithm of the geographical distance of recipient country j from Germany in kilometers.106 • Democracy: the logarithm of the democracy variable reflects the average of both the political rights and civil liberties index for country j.107 • Trade: the logarithm of average export volumes in goods from Germany to recipient country j.108 • Aid ties: the logarithm of the number of years of development cooperation between Germany and recipient country j before 1990.109 • Eastern Europe: dummy controlling for Germany’s strategic interest in the region of Eastern European transition countries. Table 8.1 provides Pearson correlation coefficients for the dependent variable and all explanatory variables. Most of the correlations are not very high, except for the correlation between foreign population and asylum seekers, which reveals some evidence for a network and size effect, respectively. To avoid multicollinearity, we exclude one of these variables in the regression specifications I, II and III (Table 8.2). Several studies (for example, Neumayer, 2003) point out a nonlinearity in population size and demonstrate a bias towards less populous countries that is reversed for very populous countries. Furthermore, we test for nonlinearity in the asylum seeker variable.

Table 8.1 Bivariate correlates of German ODA and explanatory variables

ODA Asyl Forpop Population Income PQLI Distance Democracy Trade

Asyl

Forpop

Pop

Income

PQLI

0.15 (1.78)

0.19* (2.31) 0.62** (9.58)

0.25** (3.23) 0.05 (0.57) 0.04 (0.43)

–0.01 (–0.08) –0.07 (–0.86) 0.00 (0.002) –0.08** (–0.96)

0.14 (1.74) 0.09 (1.10) 0.09 (1.14) 0.03 (0.31) 0.52** (7.40)

Distance –0.25** (–3.08) –0.27** (–3.35) –0.21* (–2.57) 0.04 (0.47) –0.03 (–0.30) –0.02 (–0.22)

Dem

Trade

Aid ties

–0.11 (–1.38) 0.10 (1.26) 0.03 (0.34) 0.10 (1.20) –0.20* (–2.52) –0.42** (–5.64) –0.19* (–2.34)

0.70** (11.78) 0.14 (1.74) 0.34** (4.35) 0.36** (4.72) 0.29** (3.63) 0.32** (4.15) –0.22** (–2.78) –0.17* (–2.07)

–0.18 (–2.21) 0.04 (0.48) 0.06 (0.72) 0.06 (0.78) –0.05 (–0.64) –0.35** (–4.50) 0.26** (3.30) 0.21* (2.60) –0.14 (–1.76)

Notes: t-statistics in parentheses. Critical t-values at 1 percent (5 percent) level: 2.61 (1.98). (*), (**) statistically significant at the 5 percent (1 percent) level.

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Table 8.2 German aid allocation. Dependent variable: log of aid, 1991–1999

log(Population) [log(Population)] (sq.) log(Income) log(PQLI) log(Asyl) [log(Asyl)] (sq.)

I

II

III

IV

1.39*** (3.44) –0.04** (–2.10) –0.40** (–2.23) –0.49 (–0.62) 0.08** (2.29) –

1.32*** (3.27) –0.04* (–1.93) –0.40** (–2.24) –0.35 (–0.43) 0.16* (1.69) –0.007 (–0.91) –

1.36*** (3.42) –0.04** (–2.04) –0.45*** (–2.63) –0.65 (–0.80) –

1.47*** (3.63) –0.05** (–2.26) –0.42** (–2.39) –0.60 (–0.76) –

log(Forpop)

–

log(Asyl) ∙ log(Forpop)

–

log(Aid ties) log(Distance) Democracy log(Trade) Eastern Europe R2 adjusted Number of observations

–

–

–

0.11* (1.83) –

–

0.35*** (3.57) 0.18 (–0.68) –1.26*** (–4.20) 0.13 (1.18) 2.62*** (5.28)

0.37*** (3.54) 0.20 (0.73) –1.24*** (–4.10) 0.13 (1.20) 2.70*** (5.53)

0.31*** (2.99) –0.03 (0.13) –1.18*** (–4.07) 0.10 (0.91) 2.44*** (4.93)

0.007** (2.31) 0.33*** (3.23) 0.13 (0.49) –1.24*** (–4.13) 0.12 (1.13) 2.50*** (5.03)

0.70 148

0.69 148

0.70 148

0.70 148

Notes: Standard errors tested for heteroscedasticity (White test). Absolute t-values in parentheses. A parameter estimate indicated with (*), (**), (***) is statistically significant at 10, 5, or 1 percent level.

We capture nonlinearity by adding squared terms for these two variables. 8.5.2 Empirical results Table 8.2 provides the OLS estimation results for the average bilateral aid flows of Germany. The four regression models differ in the specification of the immigration variables, foreign population and asylum seekers. Besides these two variables, all other control variables


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are included in all four specifications identically. Obviously, the variables of special interest concern the influence of the immigration determinants. In Regression I and II we test for the influence of asylum seekers on the ODA allocation pattern. This reveals a positive and significant impact of the linear asylum variable, whereas the squared term in specification II is insignificant, rejecting the hypothesis of a diminishing marginal impact of asylum immigration. In Regression III, we test for the influence of the population with foreign citizenship and durable residence permits on the aid allocation decision. As Lahiri & Raimondos-Møller (2000) theoretically point out, ethnic groups (that is, immigrants) influence aid allocations in favor of their countries of origin through lobbying activities. We find some empirical evidence, indicated by a positive and significant elasticity of about 0.11, suggesting that immigrant groups play a non-negligible role in the aid allocation process in Germany: the average ODA volume Germany allocates to a recipient country increases by around 11 percent if the stock of immigrants from that country doubles. Specification IV includes an interaction term of asylum applicants and the foreign population, which confirms the results of specifications I, II and III: the marginal impact of asylum migration on allocated aid is positive and increasing with the stock of foreign population from the country of origin. Therefore, we can reject the null hypothesis that asylum migration has no influence on the bilateral aid allocation pattern of a donor country. Concerning the other control variables, we can confirm the existence of a large country bias, which states that large, populous countries receive a relatively smaller amount of aid compared to less populous countries.110 As for the influence of the recipients’ needs, we find that only the income variable indicates a negative and significant effect in all regressions (with elasticity between –0.39 and –0.45), whereas the physical quality of life index (PQLI) variable is not significant at all. However, to conclude that per capita income as a monetary parameter plays a more important role in the allocation decision than human well-being is rather vague since both variables are significantly correlated. The variables expressing trade ties between Germany and the recipient country, as well as the geographical proximity, display the expected signs but are not significant. However, our regressions do confirm that the good governance rewarding effect reported, for example, by Alesina & Dollar (2000) or

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Neumayer (2003), is found at least partially in German ODA allocations. The democracy variable, as a quality parameter for the existence of democratic institutions, delivers positive and significant coefficients in all specifications. Finally, two control variables show remarkable explanatory power in explaining Germany’s bilateral aid allocation. First, development cooperation history between Germany and recipient countries seems to be rather important, meaning that the longer a country receives aid, the more aid that country receives. And second, our regression shows that Germany has a strong strategic interest in Eastern Europe, manifested by the huge amount of official aid (OA) allocated to this region.

8.6

Concluding remarks

This chapter focuses on the hitherto-unexplored question of whether the bilateral aid allocation pattern of a donor country is determined by the influence of immigrant groups. We differentiate between the foreign population that lives permanently in the donor country, but still has foreign citizenship, and immigrants seeking asylum who do not have permanent residency status. We assume that the interests of both types of immigrants are symbiotic, but the asylum seekers have a weaker status and less means to influence the donor in favor of their country of origin. Therefore, asylum seekers’ ‘agents’ are their compatriots already living in the donor country. Our results suggest that the development policy of a donor country is significantly influenced by permanent immigrants and asylum applicants. In the case of Germany, we can assert that people with foreign citizenship (as the core interest group) as well as asylum seekers (as transmitters of information about human needs and urgencies) play a decisive role in forming public opinion, and through this, in shaping the political aid allocation decision-making process in the donor country. This applies in particular to donor countries which are – like Germany – major host countries for asylum seekers. This result can be viewed in two ways. On the one hand, bilateral aid allocation decisions could simply be seen as influenced (‘immigration bias’) by the different immigrant groups in the donor country. On the other hand, the influx of asylum seekers could be seen as helping to overcome information problems of donor governments about the severity of a specific political and economic situation in the migrants’ sending


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country. Development policymakers in the donor countries should take this into account and scrutinize the relative neediness between countries that send and do not send asylum seekers. In this context, donor governments should take also into consideration the potential for a rather perverse incentive to force emigration in less-developed recipient countries. If countries receive more aid when they ‘send’ asylum seekers to donor countries, development aid can itself have a counterproductive effect, which is definitely not intended by the donor country. However, in order to fully corroborate theory and empirical evidence, aid allocation patterns of other major donor countries – which are also major host countries for asylum seekers – should also be examined. This is the task of the next chapter.

9 Refugee Movements and Aid Responsiveness

Abstract111 This chapter analyzes the impact of refugee migration movements on the long-term and short-term aid allocation decisions of bilateral donors. We distinguish between different types of forced migrants: internally displaced persons (IDPs) that stay in their country of origin, cross-border refugees that flee to neighboring countries, and asylum seekers in Western donor states. For the period 1992 to 2003, empirical evidence on 18 donor and 148 recipient countries suggests that short-term emergency aid is given to all types of refugee situations, but is predominantly directed towards the countries of origin. For the allocation of long-term development aid, donor states focus even more on the sending countries of forced migrants; they increase aid volumes only for the home countries of refugees, not for the hosting countries. This preference for the countries of origin is even stronger when they are sending countries of asylum seekers to the Western aid-giving states.

9.1

Introduction

Foreign aid serves a multitude of objectives. For some donor states, the allocation and type of aid is largely shaped by concerns for the development needs of recipient countries, while other states use aid rather as an instrument of foreign and commercial policy interests. Since the early 1990s, the criteria for bilateral aid allocation decisions have shifted towards some new objectives (Hjertholm & White, 146

Refugee Movements and Aid Responsiveness 147

2000). One of these new goals of development policy is mitigating the root causes of the heightened migration pressure from refugees and other migrants coming from developing countries to Western industrialized countries. The prevention of refugee movements and the cessation of long-lasting refugee situations have gained priority in international politics as primary development and foreign policy objectives, although bilateral donor governments have, as yet, been rather slow in implementing these objectives (UNHCR, 2006b). Western countries that are preferred destinations for asylum seekers and refugees are, in principle, more sensitive to addressing migration and refugee issues in the formulation of their development aid strategies. For instance, the German government frequently expresses its concerns regarding large refugee movements (for example, BMZ, 1994; BMZ, 2006b). During the height of the asylum crisis in the early 1990s, the German Federal Ministry for Economic Cooperation and Development (BMZ) phrased a concept paper for their development assistance policy, which explicitly aims to (i) achieve the elimination of the reasons that cause flight and unwanted emigration, (ii) provide support for neighboring first asylum countries in coping with refugee crises and the alleviation of their burdens, and (iii) provide aid for reintegrating returning refugees into their country of origin (BMZ, 1994). Other Western asylum destinations have similarly rephrased their development policies to focus more on migrationrelated issues.112 This chapter examines the question of whether Western donor states indeed responded to forced migration flows, and if yes, how their aid allocation policies have changed in face of this ‘age of migration’ (Castles & Miller, 2003) since the end of the Cold War. In particular, we investigate how different types of refugee populations influenced the respective cross-country aid allocation decisions of bilateral donors. We analyze whether internal displacements, cross-border refugee migration movements or flows of asylumseeking people towards Western countries have had any significant influence on the bilateral aid-giving patterns of the affected donor states. Aid allocation determinants and differences between donor states are tested using a large panel dataset covering the post–Cold War period from 1992 until 2003. Three relatively robust results are discovered: first, donors differentiate between internal and external displacements, that is, cross-border refugee outflows create

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significantly greater donor responsiveness than displacements that are confined within the country of origin. Second, cross-border refugee movements attract significantly more aid to the respective countries of origin than to the (neighboring) first asylum countries that host the bulk of the global refugee stock. And third, refugee movements that reach the Western donor countries ‘physically’ as asylum seekers provoke an even larger aid response. The remainder of this chapter is structured as follows: the next section provides a brief review of aid allocation and forced migration literature. In Section 9.3, a description of the empirical framework is outlined with a short discussion of the set of explanatory variables that is used to explore and test bilateral aid-migration patterns. Finally, the regression results for both the allocation of Official Development Assistance (ODA) and emergency aid are discussed. Section 9.4 concludes and discusses policy implications.

9.2

Refugee movements: some policy options

The 1951 ‘UN Convention on the Status of Refugees’, amended by the Protocol of 1967, defines a refugee as an individual, who owing to a well-founded fear of being persecuted for reasons of race, religion, nationality, membership of a particular social group or political opinion, is outside the country of his nationality and is unable or, owing to such fear, [ ... ] is unwilling to return to it. (Article 1, The 1951 Convention Relating to the Status of Refugees) The interpretation and implementation of this convention is contingent on the country where an asylum application is placed. But the principle of ‘non-refoulement’ denies a signing country the right to refuse protection to asylum seekers, even when refugee status is not granted.113 Although the basic classification of refugee status has not changed, the number of persons seeking protection abroad has multiplied in recent decades (Widgren, 1989; Schuck, 1997; Hatton, 2004). Additional to the increasing number of crossborder refugees, the global population of internally displaced persons (IDPs) has also increased tremendously during the same period (UNHCR, 2004b). However, IDPs and cross-border refugee movements


have predominantly been a phenomenon of the developing world, particularly since the end of the Cold War. Although these refugee movements have become increasingly intercontinental, the majority of displaced populations remain in the region of origin (Hatton & Williamson, 2005). Only a small, but increasing, share of the total population of these forced migrants has the resources to seek asylum in Western industrialized countries.114 There, particularly since the growing numbers of asylum seekers in the early and mid-1990s, domestic pressure to restrict asylum immigration continues to increase due to the perception that socioeconomic costs of integrating people with completely different ethno-cultural background are too high to accommodate the rising influence of refugees (Stein, 1986; Boswell, 2003). Consequently, Western industrialized countries are confronted with a balancing act between humanitarian aspects and their ‘selfprotection interests’: on the one hand, concerned with the prevention of large inflows of asylum seekers towards the own country, they are also impelled in upholding solidarity with these highly vulnerable individuals. On the other hand, many (mostly African) developing countries perceive that they shoulder a disproportionate burden of hosting refugees in comparison to the international community, since the largest part of the global refugee population is hosted in the region of origin, mostly in conflict-neighboring countries, and only a small share finds refuge in Western industrialized countries (UNHCR, 2004b; Betts, 2004). Many donor governments address this problem and express their intention to share this refugee burden of developing countries either financially or ‘physically’. In fact, the attempt to prevent asylum migration to Western countries by stabilizing the region of origin is the overarching interest of Western donors. What drives the international refugee migration pattern? Rotte et al. (1997) find evidence that the emigration pressure of refugees is reduced by both a decreasing per-capita income gap between the country of origin and the intended country of asylum and an increase in the absolute level of per-capita income. Furthermore, the migration propensities of refugees are lessened by diminishing levels of economic hardship, political oppression, violence, and human rights abuses in the country of origin and increasing restrictiveness of asylum policies in the host country (Hatton, 2004; Neumayer, 2005b).115


In contrast, Gosh (1992) provides evidence that improving economic conditions along with political reforms may initially lead to a rise in emigration, while in the long run, at a higher level of income, the desire to leave the home country decreases. However, neither Hatton (2004) nor Neumayer (2005b) find empirical evidence for this ‘refugee hump’ effect. Besides this ambiguity on the relative importance of these root causes on emigration, there is also a controversy regarding the effectiveness of development aid in tackling and relieving these causes of refugee movements. On the one hand, it is highlighted that the influence of aid intervention on the root causes of emigration and the success of altering deeply rooted political and economic grievances in the country of origin are rather limited (for example, Byrne, 2003). On the other hand, development assistance is, under certain conditions, effective in reducing conflict risk by promoting economic progress. That is, even if aid does not have a significant (positive) impact on the quality of policy in a recipient country, it nevertheless might enhance economic growth, which might reduce conflict risk, and as a consequence, lower the propensity to emigrate (for example, Collier & Hoeffler, 2002). Rotte et al. (1997) and Neumayer (2005b) provide some evidence that aid is effective for the support of democracy or economic development to reduce refugee migration flows, at least in the long run. Consequently, donors with a long-term perspective might use the aid instrument for their migration-preventive interests. Beyond these questions of aid effectiveness, the facts about the determinants of aid allocation policies of donor governments are much more stylized. Generally, and throughout the empirical literature of the last decades, convincing evidence has been put forward that donor self-interest variables like trade relations, political similarity, geographical distance, military expenditures or colonial ties are the most important determinants of bilateral aid allocations.116 Beside these ‘hard’ factors of economic and political self-interest, the influence of recipient need on bilateral aid allocations is less distinctive. The evidence shows that donor preference for poverty alleviation or other human development factors, like the promotion of democracy and human rights, are rather volatile with respect to time and differ largely between donor states (Alesina & Dollar, 2000; Neumayer, 2003; Nunnenkamp & Thiele, 2006).


We contribute a new aspect to this literature on aid allocation determinants with its particular focus on the influence of refugee movements. There are basically two alternative explanations for the underlying motives for donors to respond to refugee migration. Western donor states might either respond to various types of refugee migration by transferring different forms of aid (that is shortterm emergency versus long-term development aid) for reasons of ‘self-protection’ against large emigration movements, or because of altruistic (humanitarian) burden-sharing motives. Thus, the underlying argument of the subsequent analysis presumes that the more a donor state perceives itself as (directly or indirectly) ‘affected’ by refugee movements, the more it reacts by transfering aid, either to the country of origin or to any other first asylum country for migration prevention and/or burden-sharing purposes. We presume that donor countries perceive themselves to be more affected by refugee migration when refugees travel farther away from their homes into other developing countries, since their propensity for secondary movements towards Western countries is larger. According to this assumption, donor countries should, ceteris paribus, transfer less aid to countries with internally displaced persons and more to countries with a large number of cross-border refugees, and even more to countries that already send a significant number of asylum seekers to Western countries (see also Chapter 8).

9.3 Empirical analysis The following empirical analysis tests whether bilateral aid allocation decisions are influenced by any type of refugee migration. Based on the core hypothesis that donor states respond more generously if they perceive themselves as affected by any type of refugee movement, we will test whether ceteris paribus countries with internally displaced persons (IDPs) receive less bilateral aid than countries that receive cross-border refugees. With respect to cross-border refugee populations, we distinguish between aid that is received by the countries of origin and aid that is received by the first asylum countries. Although the two underlying motives of migration prevention and burden-sharing might overlap, we suggest that aid to the first asylum countries tend to be motivated by altruistic burden-sharing intentions, while aid to the country of origin is rather motivated by


migration prevention interests in terms of reducing emigration pressure and inducing voluntary repatriation of cross-border refugees and asylum seekers.117 In this context, we also test whether asylum seekers that enter the Western (donor) countries provoke a particular donor response to the respective sending countries. 9.3.1 Methodology For testing these outlined hypotheses, three different estimation techniques are applied to check for robustness of the results: A standard two-part model, a Heckman sample selection model and a linear fixed effects model. For a discussion of these alternative estimation methods in the context of foreign aid allocation decisions see, for example, Neumayer (2003) and Berthélemy (2006). The reason that the robustness of the results is controlled for by these alternative estimation models is that the truncated dependent aid variable is not fully continuous with a positive probability mass at the value zero (given that aid disbursements are non-negative). The literature provides different estimation techniques to account for nonlinearity and the existence of a censored dependent variable (Apodaca & Stohl, 1999; Neumayer, 2003; Berthélemy & Tichit, 2004).118 The problem can be formally described as follows: Pr (Yijt ! 0) F (␣ ␥ Xijt ⑀ijt ), Yijt ␤ ␦ Zijt ␮ijt .

(9.1) (9.2)

Bilateral aid transfer Y is estimated in the first stage as a binary dependent variable, while in the second stage, the aid allocation equation is only based on the sub-sample with positive observations. The set of independent variables is given by X and Z, a and b are constants, g and d are vectors of coefficients, and e and m are independent and normally distributed error terms. F(.) represents the cumulative distribution function. Indices i and j specify respective donor and recipient countries, and t indicates the time period. A first, and common, approach is to estimate the binary estimation model and the level model separately, that is the binary first stage of aid eligibility applies a logit (or alternatively a probit) model, while on the second-level stage a linear model is run with the reduced dataset. Here, the crucial assumption is that the choice of an aid


recipient is independent from the respective aid level. However, the error terms are not necessarily independent, and thus, it is likely that the second-stage regression on aid levels is biased. Nevertheless, this model is used as a benchmark; its results are checked by the following two alternative approaches. A Heckman sample selection model estimates the two stages simultaneously, assuming that the error terms e and m of the eligibility stage and the level stage regressions are not independent, which is a plausible assumption. Therefore, the binary stage is still run with a logit estimation, while the level stage includes additionally the inverse Mill’s ratio from the first stage in order to correct for selection bias. Aid allocation eq. (9.2) is then modified to be: Yijt

␤ ␦ Zijt ␳␴ f (␣ ␥ Xijt ⑀ijt ) / F(␣ ␥ Xijt ⑀ijt ) ␮ijt ,

(9.3)

with r representing Cov(e, m), s denoting Var(m), and f(.)/F(.) capturing the inverse Mill’s ratio.119 Since the results of the Heckman model are qualitatively not very different from those obtained from the linear regression on the reduced dataset (eq. 9.2), the properties of the selection equation and of its error term are not particularly important when estimating the regression on aid levels.120 This result is similar to those of, for example, Alesina & Dollar (2000) or Berthélemy (2006), who all conclude that a linear regression was basically as good as Heckman estimation. Third, a fixed effects model is applied for the aid level equation to control for donor-recipient-specific heterogeneity, which might not be captured by the set of explanatory variables. The inclusion of donor-recipient fixed effects may also correct for the problem of unobserved as well as missing variables. Compared to the other two approaches, some results for the fixed effects regression are significantly different, indicating that donor-recipient heterogeneity matter and should not be neglected, particularly not in a large dataset.121 Furthermore, as donor decisions on aid allocations and the actual aid transfers are predominantly not settled in the same year, most variables enter with a one-year time lag. This also reduces potential simultaneity problems. Estimation results are reported with standard errors which are robust in terms of arbitrary heteroscedasticity and serial correlation.

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9.3.2 Data The sample contains 18 donor countries and 148 recipient countries, covering the years from 1992 to 2003. Data on both bilateral (longterm) development aid and (short-term) emergency aid are provided by the Development Assistance Committee of the OECD (2006a). Both dependent variables are defined in per capita terms. ODA is characterized by its composition of grants and highly concessional loans, the objective of promoting economic development and welfare, and its distribution by the official sector.122 We use ODA gross disbursements instead of commitments, since they reflect the aid volume actually transferred to the recipient country.123 For emergency aid, we use disbursement data provided by the Creditor Reporting System (CRS) of OECD (2006a). For the dependent variables (only for aid levels) and most explanatory variables, we use logarithms for capturing possible nonlinear effects and to interpret estimates as elasticity (Neumayer, 2003). As the core explanatory variables, four respective categories of refugee migration are distinguished: (i) internally displaced persons (IDP), that is refugees who did not (yet) cross borders, (ii) total stock of refugee outflow (Ref origin), that is number of cross-border refugees by country of origin, (iii) total stock of refugees living in a recipient country (Ref asylum), that is the number of hosted refugees, and (iv) bilateral asylum applicants in the Western donor country (Asylum seeker), that is, persons applying for asylum status according to UN convention in the donor country stemming from the respective aid recipient. For all categories, the respective variables are defined in absolute numbers and all data are provided by UNHCR (2006c).124 It is presumed that donor states respond differently towards each one of the four refugee categories. According to the previous discussion, basically for all categories of refugees both motivations for aid provision can underlie, that is migration prevention interests and altruistic (humanitarian) burden-sharing, although burden-sharing with first asylum countries and migration prevention towards the countries of origin seems more plausible. The other control variables reflect categories of donor self-interest, recipient need and good governance.125 The Bilateral trade variable captures the major commercial interests of the donor. It is defined by goods and services exported to the recipient country as share of total exports. Hereby, we assume that larger trade volumes result in larger aid amounts. Although a


reversal effect may exist due to aid-tying, Lloyd et al. (2001) find no evidence that tied aid increases bilateral trade volumes significantly.126 Taking into account the economic potential, total Population of the recipient country is included, assuming that larger countries receive more aid. The External debt variable measures the total debt of the recipient country as percentage of GDP.127 The most common recipient need variable is Income per capita, which approximates the economic needs of the recipient country’s population: the worse the economic hardship, the more aid is allocated. However, since the income per capita is evidently only an incomplete measure for basic human needs, the Physical Quality of Life Index PQLI is additionally included as a proxy for human well-being in a recipient country (Morris, 1979; Moon, 1991).128 The number of fatalities caused by natural disasters (NatDis deaths) is used as a proxy for the immediate basic needs after a natural catastrophe. It measures deaths by natural disasters such as droughts, famines or earthquakes. Obviously, the allocation of aid, particularly short-term emergency aid, is expected to correspond positively to the extent of a calamity. Good governance and democratic structures are controlled for by the CPRI variable, which is generated by the civil and political rights indices from FreedomHouse (2006), indicating for example the right to participate freely in the political process, or the freedom of expression and belief without interference by the state.129 According to good governance rewarding policies, Western donor states are committed to promoting better democratic institutions in recipient countries by transferring larger amounts of aid to countries that adopt these institutions. A proxy for the political fragility and instability of the recipient country is taken into account by the number of Battle deaths caused by armed combats. Armed conflicts destabilize the political and economic conditions, which results in major failures of states to insure the basic needs of the population. Donor states are often rather reluctant to support politically fragile states since the allocation of development assistance is more delicate in conflicting countries (McGillivray, 2003b). We nevertheless assume that, ceteris paribus, peace-building interests (as a precondition to sustainable migration prevention) of donor states prevail, so that we expect that development and emergency aid amounts increase with the number of conflict fatalities.

156


9.3.3 Results 9.3.3.1 Aggregate aid allocation Table 9.1 provides the estimation results for the allocation equation for both long-term ODA as well as short-term emergency aid.130 It is shown that there is not much correlation between the error terms of the aid eligibility and aid level stages; thus, the results of the OLS and the Heckman two-stage procedure are basically consistent and robust in the use of these two alternative estimation methods.131 However, the introduction of donor-recipient fixed effects into the panel regression significantly changes some of the results. The outcome for the fixed effects model suggests that we have unobserved effects that are not captured by the set of the explanatory variables. Consequently, in the subsequent discussion we refer primarily to these regression results. The coefficients for the four refugee categories show a clear-cut pattern of explanation. With respect to both aid types, the strongest effects refer to the Asylum seeker variable, which indicates that donor states significantly increase their aid volumes for the respective sending countries of their asylum applicants. In fact, the effect for short-term emergency aid is even threefold larger than for long-term aid.132 This result, compared with the outcome for the Ref origin variable, shows that donors have a stronger reaction towards migration movements if they are directly affected by the refugee outflows. Accordingly, donor states do not respond to the same extent if refugees are only displaced internally. In this case, donors respond – if they respond at all – only with short-term emergency aid without regarding the long-term need to relieve protracted internal displacement situations. This pattern of interest of donor states becomes even clearer when comparing the assistance directed towards the home countries of cross-border refugees with that for (neighboring) refugee-hosting countries (Ref asylum). This latter variable, which reflects the refugee burden of first-asylum countries, is only positive and significant in the fixed effects regression on emergency aid allocation. This means that conflict-neighboring host countries are scarcely ‘compensated’ by the international community for providing (often long-lasting) protection of the displaced population. Development aid allocations are – on average – not affected by these cross-border refugee populations,


Table 9.1 Aid allocation determinants, 1992–2003, 18 donors, 148 recipients Dependent variable

ODA OLS

Ln(Asylum seeker) Ln(Ref asylum) Ln(Ref origin) Ln(IDP) Ln(Population) Ln(Income pc)° Ln(Bilateral trade)° CPRI External Debt PQLI Ln(Battle deaths)° Ln(NatDis deaths)° Intercept

Heckman

0.054** (6.52) –0.030** (5.64) 0.020** (2.80) –0.001 (0.15) –1.068** (58.86) –0.807** (22.26) 0.403** (35.53) –0.077** (5.93) –0.024** (4.16) –0.003* (2.34) 0.019** (2.67) 0.075** (9.27) 25.65** (53.85)

0.053** (5.47) –0.030** (5.47) 0.018* (1.98) –0.001 (0.19) –1.070** (52.23) –0.787** (9.60) 0.396** (13.78) –0.073** (3.84) –0.023** (3.48) –0.003* (2.02) 0.020* (2.57) 0.074** (9.24) 25.50** (35.45) –0.141 (0.27)

16,990

21,520

Imills Number of observations Uncensored observations R2 adj. Hausman test

16,990 0.250

0.251

Emergency aid Fixed eff. 0.054** (6.93) 0.001 (0.16) 0.014* (2.21) 0.000 (0.10) –1.373** (8.59) –0.167** (2.93) 0.059** (4.82) –0.034* (2.20) –0.014 (1.04) –0.001 (0.25) 0.015** (3.07) 0.015** (3.60) 22.21** (9.37)

OLS 0.129** (10.88) –0.014 (1.59) 0.108** (9.13) 0.020** (2.84) –1.017** (34.14) –0.387** (6.61) 0.002 (0.10) –0.025 (1.14) 0.053* (2.20) 0.000 (0.07) 0.082** (8.77) 0.047** (3.92) 14.221** (17.94)

16,990

0.854 272.79**

Heckman 0.143** (9.79) –0.017* (1.95) 0.122** (8.31) 0.022** (3.17) –0.994** (30.32) –0.578** (4.43) 0.072 (1.54) –0.059* (1.96) 0.045 (1.83) –0.002 (0.73) 0.075** (7.09) 0.050** (4.11) 15.63** (13.35) 1.526 (1.64)

Fixed eff. 0.178** (7.58) 0.065** (3.31) 0.086** (3.95) 0.025** (2.77) –2.102** (4.00) –0.321 (1.57) –0.022 (0.58) –0.049 (1.06) –0.029 (0.38) 0.011 (0.94) 0.040** (3.32) 0.037** (3.16) 30.45** (3.77)

21,520

0.406

5,103 0.406

0.653 33.17**

Notes: Lagged variables are signified by (°). The Heckman estimation is run with two-stage estimation. OLS without sample selection correction. Coefficients for fixed effects are not reported. t-values are in parentheses. (*), (**) significant at the 5 (1) percent level.


although promotion of local integration in the first-asylum country is often considered as a durable solution, and thus, also as a possible migration prevention strategy for Western countries. Aid assistance is shown to be primarily focused on the source countries of refugee movements and not so much on those countries bearing the burden of hosting refugees. This may suggest that donor governments rather support voluntary repatriation of cross-border refugees to their home country instead of local integration in the first asylum country. Source countries receive larger amounts of emergency and development aid for larger refugee exoduses. However, aid responsiveness of Western donor states towards the source countries of refugees is even stronger if parts of the population spill over to their own territory as asylum seekers, indicating motives of selfprotection. This pattern of explanation actually holds for both shortterm emergency aid and long-term development assistance. However, if the displaced population does not leave the home country, but instead remains within its own borders, additional aid is – on average – only provided in the form of short-term emergency aid, but not as long-term development aid. In the next section, however, disaggregation of these results will show that the willingness to respond to refugee movements by providing additional aid varies tremendously among donor states. For the non-migration variables, the results are consistent with many other cross-country studies on foreign aid allocation. The findings assert that poorer countries receive more aid per capita throughout the different regressions. However, this result holds rather for income poverty, while the influence of the composite (non-pecuniary) human development indicator PQLI is rather weak. That is, while the Income variable shows high significance for almost all regressions, the PQLI coefficients do not exert a significant and robust influence on the donor’s decision to allocate ODA or emergency aid. Furthermore, countries with better developed democratic institutions are likely to receive more development aid than those with rather autocratic tendencies. This reflects the interest of bilateral donors in rewarding good governance structures through enhanced ODA transfers. Bilateral trade volume has a predominantly positive and significant influence on the allocation of ODA, but not on the allocation of emergency aid. Thus, ODA per capita is a robust correlate to the donors’ share of exports to the recipient country. The influence


of external debt, which plays a significant role in determining whether a country is eligible to receive aid (not reported), displays only a weak influence at the allocation stage. Furthermore, with respect to population size, although large countries receive more aid in terms of total volume (see, for example, Neumayer, 2003), they receive less in terms of aid per capita: countries that are one percent larger than others receive on average around one percent less ODA per capita. This indicates the existance of a large-country bias. Finally, humanitarian catastrophes generally initiate solidarity with the affected country, regardless of whether natural disaster or violent conflict is the root cause. However, donors seem to distinguish between victims of natural disasters and those of violent clashes. While donor countries tend to allocate more ODA in the aftermath of natural catastrophes than in violent conflict situations, the pattern for emergency assistance is the other way around:133 countries with a high number of conflict casualties attract significantly more attention from bilateral donors in terms of emergency aid assistance than those with an equivalent number of natural disaster victims. Hence, human rights violations and armed conflicts are generally not disciplined by aid cuts, that is, compassion seems to dominate reprehension. 9.3.3.2 Donor-specific aid allocation For the purpose of testing whether there are significant differences among donor countries with respect to aid responsiveness towards refugee movements, we modify the previous fixed effects model by introducing interaction terms between the set of explanatory variables and a donor-specific dummy Da:134 Yijt

␤ ␦ ' D␣ Zijt ␦ Zijt ␮ijt

(9.4)

with Da (i) = 1 if i = a, and Da (i) = 0 otherwise. Based on this extended fixed effects regression, Table 9.2 reports the coefficients (as elasticities) of these interaction terms for the Asylum seeker variable as well as the IDP variable for both ODA and emergency aid allocations. The overall picture of this exercise is that donors respond very differently to these two types of refugee migration. For instance,

160


Table 9.2 Categorization of aid donors Donor

Elasticity of aid to number of Asylum seeker

Norway Austria Italy Switzerland USA Australia Sweden Netherlands Ireland Germany UK Spain Japan Finland Canada France Belgium Denmark

Aid Responsiveness

IDPs

ODA

Emerg Aid

ODA

Emerg Aid

0.177*** (5.32) 0.175*** (5.77) 0.110*** (3.15) 0.078*** (2.88) 0.059*** (2.74) 0.027 (0.85) 0.146*** (4.75) 0.127*** (3.14) 0.026 (0.68) 0.057 (1.21) –0.017 (0.64) 0.071* (1.79) 0.067 (0.84) 0.056 (1.11) 0.025 (0.59) 0.023 (0.59) –0.017 (0.39) –0.105* (1.71)

0.121* (1.74) 0.533*** (5.43) 0.076 (0.75) 0.218*** (4.14) 0.245** (2.35) 0.209** (2.06) 0.082 (0.90) 0.098 (0.82) 0.311 (0.49) 0.262** (2.30) 0.193*** (2.66) 0.136 (0.64) –0.054 (0.20) 0.158 (1.06) 0.234 (1.64) 0.228 (1.43) –0.182 (0.87) – –

0.048*** (2.80) –0.039** (2.41) 0.019 (1.00) –0.012 (0.76) 0.032* (1.95) 0.055** (2.56) 0.025 (1.54) –0.006 (0.39) 0.055*** (2.92) –0.031* (1.83) 0.000 (0.00) –0.005 (0.25) –0.004 (0.22) –0.017 (0.91) 0.000 (0.00) 0.009 (0.57) –0.034* (1.87) –0.056** (2.32)

0.073*** (2.68) 0.017 (0.40) 0.103*** (2.74) 0.005 (0.19) 0.042 (0.89) –0.072 (1.01) 0.008 (0.26) 0.012 (0.36) 0.083 (0.53) 0.063** (2.10) –0.014 (0.37) 0.055 (0.77) –0.039 (0.29) 0.013 (0.39) –0.007 (0.21) 0.064 (1.09) 0.032 (0.78) – –

ODA

Emerg Aid

++

++

++

+

+

+

+

+

++

+

+

+

+

0

+

0

+

0

–

++

0

+

+

0

0

0

0

0

0

0

0

0

–

0

––

Notes: Elasticity estimates are based on fixed-effect regression with additional donor-specific parameters that are estimated together. t-values are in parentheses. (*), (**), (***) significant at the 10, 5, and 1 percent level. The results for Denmark are based on a rather small number of observations.


countries such as Norway, Austria, or the US stand out as countries with a strong migration-sensitive aid allocation. These and other havens for asylum applicants react very strongly to increasing numbers of asylum seekers by boosting respective foreign aid budgets towards the sending countries. At least eleven donor states increase their aid budgets significantly (either long-term development assistance or short-term emergency aid) for the countries of origin of their respective asylum applicants. Other donor states like Japan, France, Canada and Finland do not respond at all to any of these different categories of refugee migration, while Denmark may even partially reduce its bilateral aid budget towards refugee-sending countries. With regard to internal displacement, aid responsiveness is much weaker and not always in favor of the respective recipient country. Only a third of all observed donor states react positively to internal displacements by allocating any additional aid towards these countries, while four countries even significantly reduce their corresponding short-term or long-term aid budgets. In particular, Italy and Norway become more generous towards countries that experience large internal displacements. As violent conflicts as well as natural catastrophes are controlled for as possible root causes for internal displacements in these countries, it is recognizable that these donors have a special interest in supporting recipient countries with a significant number of IDPs. This behavior might originate from altruism towards these people, who are generally poorly assisted by the recipient country’s government or any other external donors. However, these donors may also have farsighted migrationprevention interests since protracted and unassisted internal displacements could further destabilize the country or the whole region, which would force parts of the population to cross borders and become refugees in the region of origin or even asylum seekers to the Western countries. In general, countries of origin tend to receive more aid when their citizens leave the country as refugees and asylum seekers. As stated in Chapter 8, this might induce counterproductive incentives, as sending countries could be tempted to enhance refugee flights (or, at least, not to prevent them) to attract more short-term and long-term aid. Countries of origin receive even more foreign aid when refugees

162


seek asylum in the donor states and less when they stay in the region of origin. At least two explanations for this result are possible: first, urgency of a conflict situation is transmitted to the donor countries by the inflow of asylum seekers. Aid might then be an instrument for stabilizing the crisis situation in the country of origin, so that emigration might become a less attractive option and voluntary repatriation of refugees is supported. A second explanation is that asylum seekers, together with other foreign-born compatriots living in the donor country, proactively lobby the aid allocation decision-making process for the sake of their compatriots at home (Lahiri & RaimondosMøller, 2000; Anwar & Michaelowa, 2006; Chapter 8).135

9.4

Concluding remarks

This chapter focuses on the influence of refugee movements on the allocation of long-term development aid and short-term emergency aid by Western donor countries. Until now, the aid allocation literature has not addressed this issue. Increasing refugee flows, particularly within the most fragile regions of the developing world, highlight the need of both the sending and the hosting countries for international support. Two underlying motives for an increased aid support towards conflict-affected countries are often discussed: altruistic burden-sharing towards first asylum countries and countries of origin (in case of IDPs), or migration management interests by preventing further de-stabilization of the countries of origin and reducing the emigration pressure towards the Western industrialized countries. This study shows that bilateral aid allocation policies are primarily focused on countries of origin. This indicates that the underlying interests of donor states are rather focused on migration prevention instead of on altruistic burden-sharing motives, since first asylum host countries, as well as countries with a high number of IDPs, are widely neglected. Beyond this, what do these results imply? In fact, some donor governments seem to be motivated to act proactively, seeking to alleviate the root causes of cross-border refugee movements by transferring aid towards refugee-sending countries. They probably do this with an intention of decreasing further refugee outflows and to increase repatriation willingness of refugees living abroad (preferably those living in the donor country itself). However, this analysis shows that


this policy approach is not common to all donor countries, since they are actually very heterogeneous in their migration-related aid allocation pattern. There might be several reasons for this heterogeneity among donors. First, if donor states are not affected or insignificantly affected by refugee movements and asylum seekers (like Japan), then there is obviously no need for any migration-preventive aid policy. Second, even if a donor state is affected by refugee movements (for example, by asylum seekers), it does not necessarily trust in the effectiveness of aid for migration prevention purposes. And third, even if a refugee-affected donor state trusts in the effectiveness of aid-induced migration prevention, it has an incentive to free-ride on aid provisions of other donor states that are more willing to channel aid towards refugee hot spots.

10 Conclusion

10.1 Summary This book addresses the causes and incidences of refugee movements and the policy responses of Western asylum and aid-giving countries. In particular, we focus on the positive and normative aspects of refugee migration and its management by Western industrialized countries. Thereby, asylum policy and aid policy are the two main instruments of interest. The analysis began by analyzing the worldwide incidence of refugee movements. We develop a so-called Refugee Burden Index that reveals the heterogeneity of both the global and intra-regional distribution of the refugee population, and thus, unequal burdens across host countries. The causes of refugee migration are explored using a case study on the civil conflict in Aceh, Indonesia, identifying the driving forces of forced migration. This shows that the main determinants of displacement are not only the immediate conflict factors like violence and fear of death, but also other traditional migration causes like socioeconomic factors. The focus of the theoretical part of the book is twofold. First, we elaborate the driving forces of the magnitude, composition and duration of refugee movements, as well as its implications for the Western asylum countries and the conflict-neighboring first asylum countries. Since both types of asylum countries are interested in reducing their respective burdens, it is likely that they end up in a ‘race to the bottom’ of restrictive asylum policies that are intended to divert refugees and asylum seekers from the own territory. However, without 164

Conclusion

165

tackling the root causes of emigration, refugees and other migrants switch to alternative, mostly illegal migration routes. Therefore, it is argued that more proactive policy alternatives like aid transfers should be applied to overcome this race to the bottom. Furthermore, it is shown that aid transfers towards the conflict-neighboring countries are clearly beneficial to the Western donor and (potential) asylum country. However, this approach shifts the burden of hosting refugees even more to the first asylum countries since the magnitude as well as the average duration of the refugee situation increase as a consequence of this policy. Instead, aid towards the country of origin would release both neighboring first asylum countries and the Western donor country from their refugee burdens, although under some certain circumstances the asylum pressure towards the Western asylum country might also increase. Next, we analyze an international refugee regime with and without financial cooperation schemes among heterogeneous asylum countries. The analysis shows that asylum cooperation is rather attainable among less heterogeneous countries with respect to their humanitarian attitude and their asylum cost schemes. Financial transfers are able to stabilize a cooperative international asylum regime if they compensate the respective asymmetries in crossborder externalities or country-specific costs. This means, if countries are characterized by not too heterogeneous asylum costs, an international cost-sharing regime is able to implement a cooperative asylum solution. The empirical section identifies the influence of refugee migration movements on the aid allocation decisions of bilateral donors. First, it has been stated that both permanently settled foreign-born immigrants and asylum seekers influence aid allocation decisions in favor to their home country. The analysis shows that it is important to acknowledge influence of foreign population on domestic aid policies. This discussion is continued by analyzing the general influence of refugee movements on long-term development aid and short-term humanitarian aid allocation decisions of bilateral donors. It is shown that donor states respond to all types of forced migration (that is internally displaced persons, cross-border refugees or international migrants seeking for asylum in donor states) by increasing their humanitarian aid efforts, while the allocation of long-term development assistance is rather targeted only to the countries of origin,

166


particularly when these countries are major sending countries of bilateral asylum applicants in the donor country.

10.2

Policy implications

Although the complexity of the linkages between conflict, refugee migration and aid permits only modest statements, it becomes clear that there is an unambiguous need for alternatives to restrictive asylum and immigration policies, given the increasing trend of reliance on restrictive policies. Proactive international root-cause strategies and burden-sharing initiatives towards conflict-affected and destabilized regions are thus unavoidable. Despite the relative openness of their societies and economies, asylum seeking and other low-skilled immigration are increasingly unpopular in the public perception in Western countries. This is related to the apparently negative economic, political and social effects. Through the establishment and consolidation of transnational migration networks, unwanted migration movements have become increasingly difficult for governments to avoid or to control. Many Western governments still focus only on one-dimensional restrictive asylum and immigration policies that are, however, severely insufficient and overstrained since they often neglect to address the root causes of such migration movements. Instead of curbing emigration, restrictive asylum policies tend to stimulate irregular immigration, human trafficking and smuggling, or alternatively but often not better, force refugees to live for a protracted period of time in remote areas and desperate situations in the regions of origin. Therefore, the author of this book pleads generally for a more comprehensive asylum and refugee policy approach of Western countries. Such an approach would combine the following nonsubstitutable policies. A stepwise liberalization of asylum and migration policies, including the expansion of legal access opportunities for low-skilled migrants, should reduce both the pressure on asylum systems and the number of irregular (undocumented) immigrants. This would diminish inefficiencies in the allocation of economic (and human) resources, mitigate the pressure on asylum systems in Western asylum countries, and avoid repatriation (deportation) costs when more asylum seekers are allowed to stay. However, it is questionable whether Western governments are willing to follow

Conclusion

167

this path, since migration movements are not finite and a significant decrease in South–North migration pressure is hardly to be expected in the near future. Since a liberalized asylum policy alone is hardly implementable and sustainable, it needs to be complemented by some further proactive initiatives such as promoting refugee protection in the region of origin. This would require that Western countries extend the necessary financial and technical assistance to share the burden of host countries in the conflict-affected regions. Acknowledging their coresponsibility for refugees in poor and overburdened first asylum countries is not only an ethical proviso for Western countries, but also a long-term investment in conflict prevention and regional stability, and thus, in the prevention of future asylum migration flows that might reach the own territory. Therefore, directing significant financial and technical support to highly burdened first asylum countries for promoting their economic and human development, stabilizing their political and social coherence, and assisting local integration and self-reliance of hosted refugees, needs to be another integral part of a comprehensive refugee and asylum policy. However, these efforts are not sufficient since they tend to shift refugeehosting burdens even more to the first asylum countries, which there may increase social, political and ethnic tensions and overstrain local institutional capacities. Consequently, these efforts need to be complemented by activities that aim to address the root causes of conflict, persecution and economic despair in countries of origin. A targeted and country-specific use of appropriate aid interventions, that is, long-term development assistance and short-term emergency aid – beside other policies such as peace-building (diplomatic or military) interventions for human rights protection and conflict prevention and resolution as well as more market-based instruments such as FDI and trade promotion – might be effective for tackling the root causes of emigration. Ideally, aid interventions should be extended before large emigrations are expected, but latest, once mass emigration outflux has begun. Concerted aid interventions must be implemented to reduce further outflows and encourage voluntary return of refugees back to their countries of origin. The crucial and most controversial aspect of such a policy approach is the migration-preventive function of aid. Aid effectiveness is often

168


considered as restricted since the scope and duration of aid programs are too limited to have any significant effect (de Haas, 2006). Aid for the improvement of living conditions, that is the root causes of refugee movements, may only be migration-preventive if a relatively small geographical area with a relatively small number of individuals is assisted with relatively large amounts of aid. However, if only modest aid amounts are available for highly fragile conflict-ridden and post-conflict countries, the migration-preventive potential of aid is rather limited to improve the local living conditions in such a way that substantial outflows could be avoided. Furthermore, the measurement of the migration-preventive effects of aid might be one reason why donor states are so far rather hesitant to increase aid amounts for that purpose. Since preventive policies aim to reduce an unwanted incidence, aid must prove to be effective in reducing migration flows. The evaluation of migration-preventive policies, however, is only possible with an unambiguous predefined benchmark. Additionally, since refugee migration is a complex phenomenon that is determined by a multiplicity of factors, it is difficult to evaluate ex post to what extent aid assistance has contributed to a reduction in refugee migration movements. That is, even if emigration numbers from a region or a country decrease during an ‘aid cure’, it cannot necessarily be concluded that it has taken place due to the provision of aid. Moreover, as has been shown in Chapter 5, aid towards the countries of origin could also increase asylum migration movements towards the Western countries, which would run contrary to the donor interests. Thus, uncertainty about the impact of aid on migration propensities makes donors still rather passive to tackle this issue by foreign aid. Thus, more research needs to be done on the migration-preventive effectiveness of foreign aid for improving knowledge on its efficacy and reducing the uncertainty and unwillingness of Western countries to implement this policy option. Finally, asylum migration movements often happen in the context of transnational, social networks. These cannot be changed by one (affected) country alone, and thus, international cooperation is essential. Generally, it is obvious that the control of transnational migration movements and the mitigation of their consequences must take place in an environment of intergovernmental and international cooperation, integrated with non-governmental groups and

Conclusion

169

civil society. It is particularly important that instruments for the control of migration and asylum movements are improved, that is clear and transparent immigration regulations and their intergovernmental coordination and harmonization. For monitoring and coordinating forced migration movements, an international refugee regime is necessary, which specifies the obligations sending, transit and asylum countries have with respect to admission, repatriation and reintegration. Since, as laid out in Chapter 6, the willingness of highly heterogeneous host states to set up a cooperative regime is rather low, intra-regional cooperation among less asymmetric countries seems more feasible in this respect. For Western industrialized countries, a comprehensive asylum and migration policy approach that also contains proactive (aid) policy elements is definitely more promising than the status quo of onedimensional restrictiveness of asylum and immigration regulations. Although this is not a cure-all of the deficiencies of the international refugee regime, it has the potential (i) to improve the individual protection and living conditions of those that flee from conflict, violence and persecution, (ii) to reduce the pressure on (and abuse of) asylum systems in Western countries, and (iii) to alleviate the burdens of overstrained first asylum countries in the developing world.

Appendix Table A.1 Specifications of the Refugee Capacity Index Specification

Description and Source

GDPI i

log yi log y min log y max log y min

Income per capita in constant US$, purchasing power parity, 2002. Goalposts: 100 US$ (min) and 40,000 US$ (max); Data: UNDP (2004): Human Development Report

GDPI i

log yi log y min log y max log y min

Population in mill. in 2003 per square kilometer of arable land. Maximum goalpost: 1 mill. per km2; Data: World Bank (2004): World Development Indicators

ELRI i

1 § ei2,m 3 ¨© ¦ m

PFI i

di 7

PSI i

gi 5

·

¦ li2,n ¦ ri2,o ¸¹ n

o

Shares of major ethnic, linguistic and religious groups in country i; Data: Alesina et al. (2003) Average score of civil liberties index and political rights index, redefined on a range from 0 to 1; Data: FreedomHouse (2005): Country ratings Original score is re-scaled on a range from 0 to 1; Data: Kaufmann et al. (2003)

170

Appendix 171

Table A.2 Descriptive statistics, Aceh sample Variable

Definitions

Mean St Dev

Min

Max 21.16

Population change

Change in village population (in hundreds) between 1999 and 2002

0.004

2.523

–37.47

Conflict

Dummy variable set to one if a conflict with or without casualties has been reported (within the previous year, reported in 2002)

0.238

–

0

1

Conflict cluster

Dummy variable set to one if at least 20% of the villages within the same subdistrict (kecamatan) reported conflict (within the previous year, reported in 2002)

0.347

–

0

1

Urban

Dummy variable set to one if municipality was officially considered as urban in 1999 (classification is based on economic structure and access to services)

0.080

–

0

1

Altitude

Altitude in thousand meter above sea level

0.178

0.311

0

2

Distance (dist office)

Distance to the district (Kabupaten) office measured in hundreds of kilometers in 1999

0.463

0.345

0

4.85

Poor families

Share of families in the village who were officially registered as poor in 1999

0.546

0.268

0

1

Fertile couples Number of fertile age couples in 1999 in the villages (in hundreds)

0.986

1.179

0

15.1

Transport station

0.061

–

0

1

Dummy variable set to one if transport station (bus, train, airport, seaport) is available in the village in 2002 (for large towns is also set to 1 if other parts of the town have a station)

Continued

172

Appendix

Table A.2 Continued Variable

Definitions

Mean St Dev

Police presence

Dummy variable set to one if police station was not far/very far to reach in 1999

0.658

Share of Javanese

Share of Javanese population within the subdistrict (kecamatan), estimations are based on Indonesian Census 2000

0.087

Population

Village population in 1999 (in thousands)

0.707

Central Aceh

District dummies

0.037

–

Min

Max

0

1

0.140

0

0.689

0.801

0.047

9.681

0

1

–

West Aceh

District dummies

0.051

–

0

1

Nagan Raya

District dummies

0.040

–

0

1

Southwest Aceh

District dummies

0.024

–

0

1

South Aceh

District dummies

0.046

–

0

1

North Aceh

District dummies

0.157

–

0

1

East Aceh

District dummies

0.089

–

0

1

Aceh Tamiang District dummies

0.040

–

0

1

Langsa (city)

District dummies

0.010

–

0

1

Lhokseumawe District dummies (city)

0.013

–

0

1

Banda Aceh (city)

0.016

–

0

1

District dummies

Notes: All descriptive statistics pertain to the N = 5,211 villages in our sample.

Appendix 173

Table A.3 Derivations of the refugee migration model (1)

wV W wp

(2)

w 2V W wp 2

(1 ␣ )(1 r )( y O M W )␣ p ␣ ! 0.

␣(1 ␣ )(1 r )( y O M W )␣ p ␣ 1 0. 1

§ ( y O M W )␣ r ( y M M W )␣ sy S · 1␣ ¨ ¸ r 1 © ¹

if 0 d p* d p.

(3)

p*

(4)

p**

§ r ( y W M W )␣ · 1 ␣ ¨ O␣ O W ␣ ¸ © [ y ] (1 r )( y M ) ¹

wV S ws

␣ y S ( sy S )␣ 1 0 d pi d p. ! 0, ° ° ° (1 ␣ )y S ( p ␣ 1)y O ␣ psy S ! 0, if ® p pi p. ®Z O S 2 ( y sy ) ° ° 0, ¯ p d pi d 1. ° 0 ¯

1

(5)

wV S ( p )

Proof:

ws

␣ y S ( sy S )␣ 1 ! 0, and

(6)

wV W wr

(7)

wV wy O

(8)

wV W wy O

␣(1 r ) p1␣ ( y O M W )␣ 1 ! 0.

(9)

wV W wr

§ r ( yW y O ) y O M W · ¨ ¸ (1 r ) p r © ¹

wV S ( p ) ws

0.

( y W M W )␣ p1␣ ( y O M W )␣ ! 0.

0 0 d pi d p. 0, ° O ° ° (1 ␣ )(␣ y sy S (1 p(␣ 1))) ! 0, if ® p pi p. ®Z O S 2 ( ) y sy ° ° ! 0, ¯ p d pi d 1. ° ␣ p1␣[ y O ]␣ 1 ¯

S

** (10) wp wy O

if p d p** d 1.

␣ 1

( y W y O ) ! 0. ␣

r ( yW M W )␣ ·1␣ ([ y O ]␣ 1 ( r 1)( y O M W )␣ 1 ) ¸ © [ yO ]␣ ( r 1)( yO MW )␣ ¹ 0. (1 ␣ )([ y O ]␣ ( r 1)( y O M W )␣ )2

␣r( yW M W )␣ ¨§

Proof: [ y O ]␣ 1 ( r 1)( y O M W )␣ 1 ! 0, since y O M W t 0. (11) ␦ p* ␦yO

␣

§ ( y O MW )␣ ( [ sy S ]␣ ) r ( yW MW )␣ ·1 ␣ ¸ ( r 1) © ¹ 0. (1 ␣) ( r 1)

␣( y O M W )1 ␣ ( [ sy S ] r ( yW M W )␣ ) ¨

174

Appendix

Table A.4 Descriptive statistics, EU-27 EU-27 Austria Belgium Bulgaria Cyprus Czech Rep. Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK

I

II

III

IV

V

VI

15.0 31.7 63.4 14.1 6.2 63.6 16.7 54.6 19.2 12.3 9.8 32.5 11.1 15.0 8.8 16.9 13.8 48.4 49.4 9.0 16.3 13.3 31.7 9.7 15.0 48.3 37.1

17,874 21,072 1,259 1,011 6,499 7,981 14 1,924 35,028 95,817 3,554 4,546 5,915 10,519 22 221 1,069 183 33,494 3,568 325 1,223 3,315 1,403 7,208 17,136 68,003

8.6 17.6 14.3 4.6 13.0 14.1 10.3 23.3 16.4 14.6 15.4 4.2 11.1 14.1 7.4 11.1 7.0 11.1 12.2 15.8 7.2 7.0 24.2 7.5 19.4 15.5 9.5

8,048 10,333 7,965 0,765 10,210 5,374 1,358 5,199 59,485 82,495 10,631 10,159 3,920 57,69 2,338 3,469 0,444 0,397 16,144 38,626 10,177 22,300 5,379 1,964 40,917 8,924 59,229

0.017 0.021 0.016 0.002 0.021 0.011 0.003 0.011 0.123 0.170 0.022 0.021 0.008 0.119 0.005 0.007 0.001 0.001 0.033 0.080 0.021 0.046 0.011 0.004 0.085 0.018 0.122

1.45 1.36 1.47 1.66 1.50 1.35 3.75 1.46 1.36 1.34 1.57 1.32 1.54 1.48 4.30 1.83 1.70 1.63 1.34 1.47 1.90 1.68 1.40 1.69 1.54 1.42 1.32

VIIa VIIb VIII 4.8 6.1 8.8 7.2 2.9 7.4 6.9 10.4 5.6 4.8 6.2 2.1 8.3 9.7 7.0 4.4 7.4 1.5 3.9 6.2 11.6 17.8 2.6 4.6 16.5 16.3 4.4

51.6 31.2 30.8 37.7 30.4 23.8 36.7 34.7 33.7 32.5 44.9 12.1 46.7 47.4 26.9 36.5 51.9 30.0 35.5 18.5 61.4 38.8 27.3 48.1 59.6 54.5 34.0

4.2 6.4 9.2 7.6 8.1 12.7 3.1 11.6 6.4 4.6 17.7 6.1 15.2 6.0 3.9 2.0 5.7 7.4 7.4 2.1 12.3 4.5 6.4 8.3 8.1 8.5 10.7

Notes: (I) Average Asylum Recognition Rates, 1993–2002, in percent, (UNHCR, 2004b), (II) Average Asylum Applications, 1994–2003, (UNHCR, 2004b), (III) Unemployment rate, foreign-born population, average 1995–2005, (OECD, 2006b), (IV) Population, 2003, in million, (World Bank, 2004), (V) EU population share, 2003, in percent, own calculation, (VI) Country-specific cost exponents, own calculation, (VII) Immigration policy attitude: ‘How about people from other countries coming here. What do you think the government should do?’ (VIIa) Response (in percent): ‘Let anyone come’ (VIIb) Response (in percent): ‘As long as jobs available’. (WVS, 2000), (VIII) General altruism: ‘To what extent do you feel concerned about the living conditions of human kind’, Response (in percent): ‘very much’, (WVS, 2000).

Appendix 175

Table A.5 Descriptive statistics, UN speeches sample Mean ODA bilateral ODA multilateral Scouncil Gassembly Income Population PQLI

Median

Max

Min

St Dev

136.74

11.80

14,795.60

–0.60

644.58

1,054.49

586.55

9,704.60

2.80

1463.28

26.16

8.50

298.00

0.00

43.89

198.32

135.00

922.00

2.00

195.41

2,230.12

1,046.39

23,023.33

100.56

3,249.91

32,387.53

6,750.17

1,203,797.00

72.33

126,531.60 13.67

69.47

74.41

87.51

33.72

Democracy

4.20

4.11

7.00

1.00

1.73

Governance

2.20

2.18

4.18

0.50

0.63

Conflict

5.03

0.00

85.00

0.00

10.47

ODA history

9.76

7.00

30.00

0.00

10.08

Trade Distance Colony

268,819 7,247.12 0.04

17,367

55,002,671

7,074.27 0

18,587.08 1

2 59.62 0

1,533,846 3873.05 0.21

Table A.6 Data description and sources, UN speeches sample Description

Source

ODA bilateral

Log of sum of bilateral ODA disbursements (in mill. constant US$ of 2002)

OECD (2004): International Development Statistics

ODA multilateral

Log of sum of multilateral ODA disbursements (in mill. constant US$ of 2002)


Scouncil

Number of Speeches in the UN Security Council

United Nations (2005b): Bibliographic Information System

Gassembly

Number of Speeches in the UN General Assembly

United Nations (2005b): Bibliographic Information System

Income

Log of GDP per capita (in constant US$ of 1995)

World Bank (2004): World Development Indicators

Population

Log of average number of inhabitants (in thousands)


PQLI

Average of literacy rate, adjusted mortality rate and adjusted life expectancy (scaling from 0 (worst) to 100 (best)).

Own calculation based on data from World Bank (2004): World Development Indicators Continued

176

Appendix

Table A.6 Continued Description

Source

Democracy

Average of Civil Rights Index and Political Rights Index ranging from 1 (best) to 7 (worse)

FreedomHouse (2005): Freedom in the World

Governance

Average of good governance indices on voice and accountability, political stability, government effectiveness, regulatory quality, rule of law, and corruption control

Kaufmann et al. (2003): Governance matters III: governance indicators for 1996–2002

Conflict

Number of conflicts times conflict intensity (1=low, 2=medium, 3=high)

PRIO (2005): Armed Conflict Database

ODA history

Number of bilateral development cooperation between 1960 and 1989

Own calculation based on OECD (2004): International Development Statistics

Trade

Bilateral trade volume (in thousand US$)

OECD (2005): Bilateral Trade Database

Distance

Bilateral geographical distance between countries, that is their main cities or agglomerations (in kilometer)

CEPII (2005): Geographical Distances Dataset

Colony

Dummy variable on bilateral colonial history

CIA (2004): World Factbook

Table A.7 Descriptive statistics, German aid allocation sample Mean ODA Population Income PQLI Asylum seeker Foreign pop ODA ties Distance Democracy Trade Eastern Europe

388.67 32,387.53 2,230.12 69.47 12,047.56 34,292.29 18.35 6,293.63 4.20 681,599 0.13

Median 149.45 6,750.17 1,046.39 74.41 533.00 2,497.50 25.00 5,941.79 4.11 68,085 0

Max

Min

St Dev

7,019.60 1,203,797.00 23,023.33 87.51 450,775.00 2,066,917.00 30.00 16,654.11 7.00 8,836,294 1

0.30 72.33 100.56 33.72 0.00 2.40 0.00 546.00 1.00 225 0

819.88 126,531.60 3,249.91 13.67 45,739.65 182,900.40 11.52 3,430.66 1.73 1,498,291 0.34

Appendix 177

Table A.8 Data description and sources, German aid allocation sample Description

Source

Log(ODA)

Log of sum of German ODA disbursements (in mill. constant US$ of 2002)


Log(Population)

Log of average number of inhabitants (in thousands)


Log(Income)

Log of GDP per capita (in constant US$ of 1995)


Log(PQLI)

Log of average of literacy rate, adjusted mortality rate and adjusted life expectancy scaling from 0 (worst) to 100 (best)

Own calculation based on data from World Bank (2004): World Development Indicators

Log(Asylum seeker)

Log of average number of asylum applications (also pending cases)

UNHCR (2003): Statistical Yearbook, Database

Log(Foreign pop)

Log of average stock of foreign-born population

MPI (2005): Migration Information Source

Log(ODA ties)

Log of number of years German bilateral development cooperation between 1960 and 1989

Own calculation based on OECD (2006a): International Development Statistics

Log(Distance)

Log of bilateral geographical distance between countries, that is the main cities or agglomerations (in kilometers)

CEPII (2005): Geographical Distances Dataset

Democracy



Log(Trade)

Log of average German export volumes (in thousand current US$)

OECD (2005): Bilateral Trade Database

Eastern Europe

Dummy variable for Eastern European countries

178 Appendix

Table A.9 Descriptive statistics, aid panel dataset Mean

Median

Max

Min

St Dev

ODApc

2.34

0.07

883.09

0

16.00

EMApc

0.06

0.00

44.65

0

0.57

Asylum seeker

175

1

115,395

0

1,596

Ref asylum

70,291

1,987

4,150,723

0

242,891

Ref origin

72,484

857

4,552,153

0

288,685

IDP

32,081

0

1,290′

0

133,514

Population

33.60

7,265′

1,290′′

71,079

130

GDPpc

4,684

3,446

25,168

440

4265

Bilateral trade

0.0014

0.0001

0.37

0

0.0063

CPRI

4.12

4.00

7.00

1

1.82

External debt

1.13

0.59

47.09

0.00

3.36

PQLI

69

77

98

3

22.5

Battle deaths

484

0

48,034

0

2,858.3

Natdis deaths

328

0

30,005

0

2,245.2

Table A.10 Correlation matrix, aid panel dataset

(1) ODApc (2) EMApc (3) Asylum seeker (4) Ref origin (5) Ref asylum (6) IDP (7) Population (8) GDPpc (9) Bilateral trade (10) CPRI (11) External debt (12) PQLI (13) Natdis deaths (14) Battle deaths

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(11)

(12)

1.00 0.08 0.02 –0.03 –0.03 –0.02 –0.03 0.06 –0.01 –0.07 –0.01 0.04 –0.02 –0.02

0.08 1.00 0.02 0.12 –0.01 0.04 –0.02 –0.06 –0.02 0.03 –0.00 –0.04 –0.00 0.01

0.02 0.02 1.00 0.03 0.06 0.03 0.08 –0.00 0.11 0.04 –0.02 0.02 0.02 0.04

–0.03 0.12 0.03 1.00 0.10 0.31 0.03 –0.22 –0.01 0.34 –0.00 –0.25 0.01 0.16

–0.03 –0.01 0.06 0.10 1.00 –0.01 0.15 –0.12 0.03 0.25 –0.03 –0.12 0.06 0.05

–0.02 0.04 0.03 0.31 –0.01 1.00 –0.02 –0.08 –0.01 0.14 –0.02 –0.06 –0.03 0.16

–0.03 –0.02 0.08 0.03 0.15 –0.02 1.00 –0.04 0.31 0.12 –0.04 0.04 0.40 0.05

0.06 –0.06 –0.00 –0.22 –0.12 –0.08 –0.04 1.00 0.17 –0.41 0.18 0.59 –0.06 –0.06

–0.01 –0.02 0.11 –0.01 0.03 –0.01 0.31 0.17 1.00 –0.02 0.05 0.15 0.07 0.03

–0.07 0.03 0.04 0.34 0.25 0.14 0.12 –0.41 –0.02 1.00 –0.11 –0.42 0.02 0.16

–0.01 –0.00 –0.02 –0.00 –0.03 –0.02 –0.04 0.18 0.05 –0.11 1.00 0.01 –0.02 0.00

0.04 –0.04 0.02 –0.25 –0.12 –0.06 0.04 0.59 0.15 –0.42 0.01 1.00 –0.03 –0.09

(13)

(14)

–0.02 –0.02 –0.00 0.01 0.02 0.04 0.01 0.16 0.06 0.05 –0.03 0.16 0.40 0.05 –0.06 –0.06 0.07 0.03 0.02 0.16 –0.02 0.00 –0.03 –0.09 1.00 0.04 0.04 1.00

180

Appendix

Table A.11 Data definitions and sources, aid panel dataset Variable

Definition

Source

Ln(ODApc)

Log of Official Development Assistance per capita (in 2002 US$)

OECD (2006a): International Development Statistics

Ln(EMApc)

Log of Emergency aid per capita (in 2002 US$)

OECD (2006a): Creditor Reporting System

Ln(Asylum seeker)

Log of number of bilateral asylum applications registered in donor country

UNHCR (2006c): Statistical Yearbook, CD-ROM

Ln(Ref Asylum)

Log of number of refugees registered in recipient country


Ln(Ref Origin)

Log of number of refugees originating from recipient country


Ln(IDP)

Log of number of internally displaced persons in recipient country


Ln(Population)

Log of number of inhabitants (in mill.)


Ln(GDPpc)

Log of GDP per capita (in current US$, PPP)


Ln(Bil. trade)

Log of bilateral trade volume (exports in current US$) as share of total exports

OECD (2005): International Trade by Commodity Database

CPRI



External debt

Ratio of External debt (in mill. of current US$) to GDP (in mill. of current US$)

JEDH (2007): Joint External Debt Hub

PQLI

Average of literacy rate, adjusted mortality rate and adjusted life expectancy, scaling from 0 (worst) to 100 (best)

Own calculations based on data from World Bank (2006): World Development Indicators

Ln(Battle deaths)

Log of number of annual battle fatalities

PRIO (2005): Armed Conflict Database

Ln(NatDis deaths)

Log of number of casualties caused by natural disasters

CRED (2006): EM-DAT: Emergency Event Database

Notes 1. The restrictions that have been introduced are mostly concerned with the reception conditions of asylum seekers for the purpose of reducing costs and to prevent the abuse of the asylum system by so-called ‘bogus refugees’ (Neumayer, 2005b). For instance, restrictive measures related to housing, detention, dispersion, deportation, reduction of welfare benefits, access to the labor market, re-definition of the term ‘refugee’, introduction of ‘temporary protection’ and ‘safe third country’ principles, intensified border and internal controls, and so on (Boswell & Crisp, 2004). 2. Each chapter, except Chapters 2 and 7, is based on an article already published or currently under review in a peer-reviewed economics or political science journal. Therefore, it has to be acknowledged that each of these chapters can (and should) be read and understood as self-contained units. 3. Political and social costs, such as anxieties on the influence of large-scale immigration on identity or security, are much too abstract to be estimated in monetary terms. Only the monetary costs for the reception and application processing of asylum seekers have roughly been estimated for some Western asylum countries by Martin et al. (2005). 4. Despite the fact that some authoritarian states also constrain emigration, they are not included in this analysis. 5. For instance, in Germany, the Residence Act (Section 29) defines the general conditions for the subsequent immigration of family members accordingly by (a) the foreign family member already living in Germany has a residence or settlement permit, (b) sufficient living space is available, (c) the family member has sufficient means to support him or herself without recourse to public funds, and (d) no grounds for expulsion exist. 6. For instance, the German green card program for IT-specialists has been in place since 2000, or the proposed pan-European program of the European Commission that is targeted at highly skilled workers. 7. This number covers persons who were recognized according to the Geneva Refugee Convention or the Refugee Convention of the Organization for African Unity (OAU), as well as on persons with a temporary residence right for humanitarian reasons. 8. Irregular migration is another type of migration, which probably consists of the fastest growing group of migrants. However, since they are statistically not recorded, the number can only be roughly estimated. It is estimated that there are between 15 to 30 million illegal or irregular immigrants worldwide (ILO, 2004). 9. According to the 1951 Convention and the 1967 Protocol relating to the Status of Refugees, a refugee is someone who ‘(i) has a well-founded fear of 181

182

10.

11.

12.

13. 14.

15.

16.

17. 18.

Notes

persecution because of his/her race, religion, nationality, membership in a particular social group, or political opinion; (ii) is outside his/her country of origin; and (iii) is unable or unwilling to avail him/herself of the protection of that country, or to return there, for fear of persecution.’ In 1969, the Convention Governing the Specific Aspects of Refugee Problems in Africa of the Organization of African Unity (OAU), added to this definition of a ‘refugee’ of the UN Refugee Convention a broader consideration of any person ‘compelled to leave his/her country owing to external aggression, occupation, foreign domination or events seriously disturbing public order in either part or the whole of his country of origin or nationality.’ In 1984, the Cartagena Declaration adds a more objectively based consideration to the 1951 UN Convention by including persons who flee their countries ‘because their lives, safety or freedom have been threatened by generalized violence, foreign aggression, internal conflicts, massive violation of human rights or other circumstances which have seriously disturbed public order.’ (UNHCR, 2001, p. 9). Although the estimated number of environmental refugees is expected to double between 1995 and 2010 (Myers, 2002), this type of refugee is not included in this study, since they are not captured by the official UN definitions and therefore not recognized by national states as eligible for asylum. For this reason, the UN Security Council authorized humanitarian interventions, for example, in Northern Iraq, in Somalia, in ex-Yugoslavia and in Haiti, in order to prevent mass flight movements. For instance, several times military support was engaged in order to be able to manage the immense logistical challenges of caring for large numbers of refugees (de Mello, 1995). See, for example, the overview by Angenendt (2002). Several indicators for this development are identifiable, for example several millions waiting for legal immigration to the US (Smith & Edmonston, 1997), and the continuous flow of irregular immigration into Western countries (estimated at 300,000 per year in the US, and 400,000–500,000 per year in the EU) (see Hatton & Williamson, 2002). In 2005, after the dramatic refugee situation in Ceuta and Melilla, José Manuel Barroso, president of the European Commission, stated that ‘The problem of immigration, the dramatic consequences of which we are witnessing, can only be addressed effectively [ ... ] through an ambitious and coordinated development [plan] to fight its root causes’ (cited from de Haas, 2006). UNHCR and ILO started a project in 1992 with the working title ‘Aid in place of migration?’ to investigate whether more or different development aid could diminish unwanted emigration (see Böhning & SchloeterParedes, 1994). This chapter is based upon Czaika (2005b). See, for example, the issue of Journal of Refugee Studies, Vol. 16, No. 3, 2003.

Notes 183

19. See, for example, UNHCR’s Convention Plus Initiative, founded in 2003, or the debate in the EU about asylum reception centers in North Africa. 20. For instance, UNHCR’s recent statistical yearbooks contain three different indicators of host country capacity and contributions (for a discussion see UNHCR, 2002) that give a good first hint as to a country’s borne refugee burden. 21. See, for example, Bach & Carroll-Seguin (1986) and Wooden (1991) for South-East Asian refugees in the US and in Australia, or Hauff & Vaglum (1993) for Vietnamese refugees in Norway. 22. UNHCR defines a protracted refugee situation as a refugee population of 25,000 or more persons living in exile for five or more years in the host country. At the end of 2003, there were 38 major protracted situations affecting about 6.2 million refugees (UNHCR, 2003). 23. The author is aware of the economic and sociocultural contributions of refugees for a host country. Particularly young immigrants can produce a net benefit for the host country notably if they stay in that country throughout their lifetime (Borjas, 1994). 24. See Table A.1 in the Appendix for further information about the RCI component specifications and the data sources. 25. See Table A.1 in the Appendix for the methodology of the ELRI. 26. For instance, political rights refer to fairness in elections with a real possibility to overtake power via elections, freedom for organizing in parties, the existence of party competition and allowance for opposition, and so on. Civil liberties reflect the freedom of assembly, freedom for any religious activities, freedom of the media, protection from political suppression, and so on. 27. See Table A.1 in the Appendix. 28. We took this indicator from the governance indicators data set of Kaufmann et al. (2003). This indicator is based on several different sources, including polls of experts and surveys of residents and entrepreneurs of a specific country. A linear unobserved components model is used to aggregate these various sources into one aggregate indicator. It is then normalized such that it ranges from –2.5 to +2.5 and has a mean of zero with a standard deviation of one. A higher value signals a higher degree of political stability. 29. Sensitivity analysis has shown that changes in the weights of the RCI components have little effect on the country rankings (that is Spearman rank correlations of alternative weight combinations are always positive and strong significant). 30. It is to be noted that a small amount of data that was missing from the data sets for some sub-indices has been sourced from the CIA World Factbook (CIA, 2004). 31. Countries at the bottom of our RBI 03 ranking (not reported) were either actual refugee-producing countries like Sri Lanka (RBI 03 score of about –1.00) or countries that are located more in a regional periphery which is the case for most island countries.

184

Notes

32. The complete output of RBI scores for all 174 countries is not reported, but is available on request. 33. This regional classification of the countries reflects UNHCR’s arrangements of regional bureaus and operations. This classification takes into account cultural similarities and geographical coherence. 34. The complete results for RBI 03 on a regional basis are available from the author on request. 35. ‘Caswaname’ describes the region of Central Asia, South-West Asia, North Africa and the Middle East. 36. This chapter is based upon Czaika & Kis-Katos (2009). 37. In what follows, we use the term village for both villages and urban neighborhoods. 38. Villages with conflict constitute nearly the same proportion of Aceh as a whole and in our matched 91.3 percent sample. As a comparison, PODES 2003 reports a share of conflict of 23.8 percent in the whole Aceh province. 39. The variable ‘population change’ is corrected by the numbers of deaths because of conflicts or epidemics. 40. Another possibility to examine the push and pull factors of migration would be to define indicator variables of large population outflow (or inflow), and estimate probit or ordered probit models. However, by applying this procedure we would lose information on the intensity of population change. Additionally, as our population change measure is relatively noisy, we find it less obvious to identify a discrete regime switch between outflow and inflow villages. This is the main reason why we prefer the use of quantile regressions, which allows us a smoother identification of inflow and outflow villages. 41. The number of deaths by conflict remains far below the population flows due to displacement. However, changes in fertility behavior might have played a significant role as well, since fertility might have been strongly reduced in conflict-ridden areas. For village-specific mortality factors we partly correct by subtracting from population decrease the deaths by conflict and by epidemics over the last period. 42. Descriptive statistics and definitions of variables are presented in Table A.2 in the Appendix. 43. We experimented with these alternative measures and all yielded very similar results. 44. These welfare criteria include food consumption habits, access to health care, the possession of alternative sets of clothing, information on the floor material of the dwelling, and on the household members’ ability to practice their religion. 45. These families were the main targeted beneficiaries of the Social Safety Net Program of the Indonesian government, which addressed rising poverty during and after the economic crisis. Thus, the variable might also reflect a certain policy bias; villages with a larger share of poor families might also be those with better social safety coverage.

Notes 185

46. For a few subdistricts with missing data, district-level averages or averages of the neighboring districts have been imputed. 47. This pattern might be amplified by measurement errors in village population if they were larger in these most affected regions. 48. This chapter is based upon Czaika (2008b). 49. This assumption does not change the qualitative results of the subsequent model. 50. For simplicity reasons, asylum recognition rates are not specific to individuals but to the whole population of asylum seekers. 51. We further assume that before the conflict broke out at time t = 0 – e, only economic migrants that could afford the high migration costs M W left the country of origin towards the Western country. For the remaining N individuals staying permanently at home is the superior solution in times of peace. 52. For instance, economic self-reliance might be restricted by confinement to a refugee camp or a closed settlement, constrained work permission, limited land ownership, and so on, all of which lessens human capital productivity and the potential to generate income in the first asylum country. 53. At this point of time, both asylum countries have already decided on their respective asylum policies. 54. This implication has been confirmed in several empirical studies providing evidence for the negative impact of low recognition rates on asylum applications in EU member states (Vink & Meijerink, 2003; Neumayer, 2004; Neumayer, 2005a). 55. In this model, we consider only the case in which rejected asylum seekers are deported to their country of origin, acknowledging that this is often not accomplished by asylum states because of the non-refoulement proviso of the 1951/67 UN Refugee Convention. In fact, rejected asylum seekers are often sent back to a safe third country. 56. For reasons of simplicity we assume that the migration movement itself and the asylum procedure are not time-consuming. 57. In fact, we exclude the case that a rejected and deported asylum seeker emigrates again to the first asylum country. This model variation is possible, but makes it less tractable. 58. See Table A.3 in the Appendix for some explicit calculations of p* and p**. wv S ( p ) wv W ( p ) 59. If the two curves osculate, that is, if wp , it follows p* = p**, wp indicating no asylum migration to the Western country. 60. Obviously, the number of admitted asylum seekers is ASW* = r N(p** – p*). 61. See derivation of the latter in Table A.3 in the Appendix. 62. See derivation of the latter in Table A.3 in the Appendix. 63. If the Western asylum country held no bias for asylum seekers from a specific background, that is if V W (p) is equal for asylum seekers from different countries, then asylum recognition rates tend ceteris paribus to be lower for asylum seekers from countries close to the Western

186 Notes

destination, since migration costs for them are presumably more moderate (Proof: drW > 0 for dV W = 0). dM

64. See derivation of the latter in Table A.3 in the Appendix. 65. See derivations in Table A.3 in the Appendix. 66. The simulation is run with the following parameter values: a = 0.8; r = 0.38; yS = 100; yW = 300; M W = 70. 67. These costs might capture all type of costs that the Western asylum country accrues for hosting asylum seekers (for example, administration, maintenance, repatriation and deportation and so on). 68. This chapter is based upon Czaika (2009). 69. Countries like Norway or Sweden received around 15 refugees per 1000 inhabitants during the period 2000 to 2004, while the European average has been about 2.7 refugees per 1,000 inhabitants, with France (3.9) and Germany (4.7) as the main havens in Europe in total numbers (UNHCR, 2006a). 70. During the last decade, the EU countries introduced a wide range of regulations towards asylum seekers. For instance, measures to tighten external border controls based on the Schengen Convention (1990) and the Maastricht Treaty (1993), universal carrier sanctions, extension of visa restrictions, the safe third country principle, and asylum procedures only to take place in the country of first entry according to the Dublin Convention in 1990 (Hatton, 2005). The Amsterdam Treaty (1997) was a first step to delegate asylum decision and enforcement power on the supranational level. In 2000, the European Refugee Fund (ERF) as a first step to an institutional and financial cooperation regime has been established. 71. Noll (1997, 2003) analyzes the problem of collective action failure in the EU and discusses the normative aspects of refugee protection. He points out the conflicting aspects between international, EU, and national asylum laws, and emphasizes the need for further harmonization of asylum legislations. 72. In 2001, an EU directive (Council Directive 2001/55/EC, OJ L 212) referred to the practical and ethical problem of ex post resettlement of asylum seekers by establishing the non-binding mechanisms based on ‘double voluntarism’ (cf. Thielemann, 2006). This means that both asylum seekers and recipient states are required to agree before an asylum seeker can be moved between countries. Although this directive has not yet been applied in the EU, this mechanism may underlie the present model to implement a burden-sharing rule. 73. Sector lines I in Figure 6.2 are derived from rearranging participation 2 2 constraint (6) according to: i*2 c i * c i 2 2i* c i * . 2 i

i

74. This aspect of concrete implementation of a financial burden-sharing regime is beyond this general welfare analysis. For a discussion of narrow asylum trading system, see, for example, Schuck (1997) whereas a more comprehensive transfer system based on the comparative advantages of states in providing public goods (cf. Boyer, 1989) is analyzed by

Notes 187

Thielemann & Dewan (2006). The latter approach may be relevant in the context of the EU, where implicit package deals and intertemporal compensations are often used to find consensus. 75. Corresponding constraints for costs asymmetries are derived from rearranging participation constraints (6.15) and (6.16), respectively: 2c i * (i * 1)2 3 (i 1)2

ci

3c i * (i * 1)2 2 (i 1)2

, and

2i *c i * c ( 2) c i i * i *2 i * . i (i 2) i

76. Note that the so-called ‘Nizza rules’ on voting in the EU Council, which release the tight corset of unanimity, are still not yet applicable on asylum affairs. 77. See, for example, the Convention Plus initiative initiated and coordinated by the United Nations High Commissioner for Refugees (UNHCR). This initiative pursues to generate multilateral agreements for (i) a strategic use of resettlement as a tool of protection, (ii) an effective targeting of humanitarian and development assistance, and (iii) a clarification of responsibilities among nation states in case of irregular secondary movements. 78. The corresponding question was: ‘Development aid means giving grants or loans to developing countries which aim to promote economic development and human welfare. We are not talking here about humanitarian aid (that is assistance provided in emergency situations like war, natural disaster, famine, and so on), but about development aid. What in your opinion are the two main motivations for richer countries to provide development aid to poor countries? (max. 2 answers).’ 79. This section refers to the article by Czaika (2008a). 80. See, for example, Isenman (1976), Dowling & Hiemenz (1985), Trumbull & Wall (1994), Arvin & Drewes, (1998, 2001). 81. Official Development Assistance (ODA) to developing countries as well as Official Assistance (OA) to transition and newly industrialized countries is comprised of net amounts of both grants and concessional loans minus amortization, converted to constant 2002 US$. 82. Political rights refer, for example, to fairness in elections with real feasibility to overtake power, freedom for founding a party, party competition and allowance of political opposition. Civil liberties relate to freedom to assemble, religious freedom, freedom of the media, or protection from political suppression. 83. The governance variable composites data for voice and accountability, political stability, government effectiveness, regulatory quality, rule of law and corruption control. These data are originally normalized so that they range from –2.5 to +2.5 with mean of zero and a standard deviation of one. 84. See Tables A.5 and A.6 for data description, sources, and descriptive statistics. 85. However, Belgium (member in 1991/1992) and Japan (member in 1992/1993), which have no significant estimates for the Scouncil variable, weaken the idea that UN Security Council membership makes donor countries more sensitive to appeals from developing countries.

188

Notes

86. For instance, at the Financing for Development Summit in Monterrey (Mexico) in 2002, or at the G-8 Summit in Gleneagles (Scotland) in 2005, where G-8 countries pledged to increase the overall aid to developing countries by US$ 50 billion by 2010. 87. This chapter is based upon Czaika (2005a). 88. This chapter neglects legal policy issues of immigration management, like the tightening of asylum laws, speeding up application and decision procedures, or the introduction of the safe third country principle. 89. The theoretical paper of Lahiri & Raimondos-Møller (2000) is an exception. Their model illustrates the impact of ethnic groups (that is, permanent immigrants by definition) on the political process in donor countries through their lobbying activities, which influence the aid allocation decision in favor of their country of origin. 90. Asylum seekers partially emigrate because of economic reasons (‘bogus refugees’, see Neumayer, 2005b), however, most asylum applicants are victims of war, oppression, natural disasters and so on, that is, they are forced to leave and are therefore non-voluntary emigrants. As such, we can recognize asylum seekers as worldwide transmitters of information about urgent physical needs and human atrocities. 91. In several empirical studies, political factors are very significant for both aid allocation decisions and aid effectiveness (see, for example, Boone, 1996; Burnside & Dollar, 2000; Svensson, 1999; Collier & Dollar, 2002). 92. We assume all markets to be perfectly competitive and all countries to be small, open economies with exogenously determined commodity prices. Furthermore, all factors are inelastically supplied, which assures exogenously given gross factor incomes. 93. Altruism, in this context, indicates the concern of immigrant households for their country of origin. As more natives are forced to leave the home country to seek asylum in the new, common host country, the immigrant households become increasingly concerned about their original home country. 94. This altruism parameter may also contain self-interested motivations of the immigrant groups in strengthening their status as a special immigrant group. However, this interpretation is rather secondary in this study. 95. This parameter represents the political, economic and civil environment in terms of income-enhancing factors, like a low level of corruption, democracy, political freedom, civil rights and so on. Or, from another angle, this parameter stands for the efficiency of a recipient country in transferring aid to its households. 96. In the 1990s, Germany actually gave a positive amount of aid to all developing countries, and therefore the corner solution has no explanatory relevance for the German aid allocation pattern. Other donors, like the US or the Nordic countries, are more selective. 97. We do not take into account the fact that at least a small part of ODA volumes are tied to imports from the donor country.

Notes 189

98. The OECD/DAC defines ODA according to three criteria: first, aid must be given by official national or international (development) agencies; second, aid must have the objective of promoting economic and social development in the recipient country; and third, ODA must have a minimum grant element of 25 percent (Cassen, 1994). 99. The DAC separates recipient countries into two parts. Part I countries can be distinguished into least developed countries (LDCs), low income countries (LICs), low-middle income countries (LMICs), upper-middle income countries (UIMCs) and high income countries. Part II countries are countries in transition (all Eastern European countries and countries of the former Soviet Union) and other advanced developing countries. Bilateral and multilateral aid to Part I countries is called official development assistance (ODA), whereas Part II countries receive official aid (OA). 100. In the case of a small number of recipients (and therefore many zeros in the dependent variable) a tobit or probit estimation model testing for aid eligibility could be appropriate. 101. The data source for these two immigrant variables are the Migration Information Source (http://www.migrationinformation.org/) and the Federal Statistical Office of Germany (http://www.destatis.de/). 102. See Tables A.7 and A.8 for data description, sources, and descriptive statistics of the sample. 103. Data from World Bank (2004). 104. Ibid. 105. The PQLI is computed from data of World Development Indicators (World Bank, 2004) according to the formula provided by Neumayer (2003, p. 51). 106. Data from Centre d’études prospectives et d’informations internationales (CEPII, 2005). 107. Political rights refer to fairness in elections with a real possibility to overtake power via elections, freedom for organizing parties, the existence of party competition and allowance for opposition, and so on. Civil liberties reflect the freedom of assembly, freedom for any religious activities, freedom of the media, protection from political suppression, and so on (see FreedomHouse, 2005). 108. Data from OECD (2005). 109. Data from OECD (2004). 110. The calculated peak of transferred aid amounts is at a population level of about 40 million people. 111. This chapter is based upon Czaika & Mayer (2008). 112. In 2002, Prime Ministers Tony Blair of the United Kingdom and Jose María Aznar of Spain announced a plan to link aid provision to migrants sending countries to their willingness to cooperate in containing illegal immigration (see Bhagwati, 2003). Furthermore, French Foreign Minister, Bernard Kouchner, on his first visit to Africa in June, 2007, called for more development aid to help curb emigration (Reuters, 2007).

190

Notes

113. Many individuals who are not granted refugee status according to Article 1 may still not be deterred as the Conventions’ Article 33 declares that ‘no contracting state shall expel or return (“refouler”) a refugee in any manner whatsoever to the frontiers of territories where his life or freedom would be threatened on account of his race, religion, nationality, membership of a particular social group or political opinion.’ 114. As there tend to be multiple reasons for emigration, the distinction between refugees and economic migrants becomes more and more delicate. A common but not unproblematic way to separate voluntary from forced migrants refers to the original motivation of the migrant to leave the place of origin. According to this, the underlying root cause of voluntary migration is economic, while either internal or cross-border refugee movements are rather caused by non-economic factors as mentioned in the Geneva Convention (see above). 115. Czaika (2008b) models the influence of economic incentives and individual persecution on the migration decision-making of refugees (see Chapter 5). 116. See, for example, Dudley & Montmarquette (1976), McKinlay & Little (1977, 1978), Maizels & Nissanke (1984), McGillivray (1989), Trumbull & Wall (1994), Alesina & Dollar (2000), Neumayer (2003), Berthélemy & Tichit (2004), or Berthélemy (2006). 117. An alternative argumentation, which states that aid towards first asylum countries serves rather migration-prevention interests, while aid for countries of origin is rather intended by burden-sharing motivations, is basically possible, but seems less plausible to the authors. 118. For a discussion on different approaches for tackling sample-selection biases, see, for example, Heckman (1979), Manning et al. (1987), Leung & Yu (1996) or Puhani (2000). 119. See, for example, Neumayer (2003) for a discussion of some caveats of this approach in the context of aid allocation. 120. The coefficient of the inverse Mill’s ratio is insignificant, indicating that selection bias does not seem to be a problem in our sample. 121. We tested the fixed-effect model against random-effects, but the Hausman test rejected the latter. 122. Chang et al. (1999) discuss the many shortcomings of the official development aid data from the OECD that include the underestimation of the aid content, the over-representation of loans with high concessionality, as well as the constant interest rate of 10 percent used to calculate the grant element of the highly concessional loans. They developed the aid measure EDA (effective development assistance) to eliminate most of the failures of ODA. However, as Ovaska (2003) proves that both concepts yield essentially the same results, we use the standard ODA measures. 123. Neumayer (2003) states that gross data are non-negative and conceptually closer to commitments than net ODA disbursements, but the problem with gross data is that parts of the amounts disbursed are not at the

Notes 191

124.

125. 126.

127.

128.

129. 130. 131. 132.

133. 134.

135.

country’s unrestricted disposal, as they are used to repay current loans. Table A.10 in the Appendix displays the correlation matrix for this set of explanatory variables. It indicates that most of the cross-correlations are not significant. Therefore, we should not have multicollinearity problems between the different refugee variables. Descriptive statistics and information on the composition and sources of the dataset are provided in the Tables A.9 and A.11 of the Appendix. Since the correlation between the two variables is negligible (Table A.10), potential aid and trade simultaneity should not exert too much influence on the estimation results. In former studies, including external debt was problematic due the poor data availability. However, this has improved significantly, since the Bank for International Settlements (BIS), the International Monetary Fund (IMF), the Organization for Economic Co-operation and Development (OECD) and the World Bank (WB) operate a new joint database on external debt. Morris (1979) develops the PQLI as an aggregate measure of infant mortality, literacy and life expectancy. All three variables are transformed to a scale from 0 (worst) to 100 (best), from which the average is taken. We prefer the PQLI to the Human Development Index (HDI), since the latter is available for fewer countries, and additionally, income per capita is not a component of the PQLI. Thus, collinearity between the two variables is largely avoided. See http://www.freedomhouse.org/ for an explanation of the index methodology. Results for the first stage logit regression are available on request. The inverse Mill’s ratio is in both specifications not significant. This indicates similarity of the OLS regression and the Heckman approach. This result provides additional evidence to earlier results reported in Chapter 8, where a similar result for German aid allocation pattern is found. At least when referring to the OLS and Heckman regressions. This procedure refers to the methodology described and used by Berthélemy (2006). He applies a similar exercise for the categorization of donor countries with respect to the influence of trade volumes on bilateral aid transfers. This argument is based on the theory by Lahiri & Raimondos-Møller (2000). They argue that political support is maximized by politicians (or, the government) by considering the lobbying activities of ethnic groups who may carry out propaganda or financial contributions in exchange for preferred aid policies (see also Anwar & Michaelowa, 2006). Their theoretical framework refers to the approach of Grossman & Helpman (1994).

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Index aid allocation bilateral, 119, 124, 126, 130, 131–5, 138, 143–4, 146, 150, 151, 162 decision, 114, 126–30, 132–5, 137–8, 143–4, 146–7, 151–2, 162, 165, 188 determinant, 7, 124, 134, 147, 151, 157 foreign, 5, 83, 114, 117, 138, 146, 152, 158, 161, 168 multilateral, 124, 126, 127, 129 pattern, 118, 124, 126, 131–4, 138, 143, 144–5, 163, 188, 191 aid responsiveness, 146, 158, 159, 160, 161 altruism, 29, 95, 105, 116, 124, 135–7, 151, 154, 161–2, 174, 188 asylum applicant, 8, 9, 131, 143, 144, 154, 156, 161, 166, 188 application, 69, 89, 91, 105–6, 132, 140, 148, 174, 177, 180 country, 4, 67, 69, 70–87, 116, 151, 158, 165, 185, 186 crisis, 115, 147 law, 94, 131, 186, 188 policy, 1, 4, 5, 19, 20, 21, 74–7, 78–80, 81–2, 86–7, 91, 93, 98, 106, 164, 167 pressure, 7, 67, 83, 85, 92, 115, 165 proactive asylum policy, 81–7 procedure, 4, 185, 186 provision, 7, 21, 22, 34, 89, 90, 99, 105, 112 recognition rate, 74, 77, 80, 104, 105, 174, 185 regime, 7, 22, 89, 90, 94, 95, 97, 102, 104, 106, 112, 165 restrictive asylum policy, 4, 5, 7, 9, 67, 80, 81, 87, 92, 164, 166

seeker, 1–4, 8–9, 11–13, 18–22, 25–32, 35–42, 74–7, 79–81, 83, 88–91, 95, 105, 124, 131–4, 135–44, 146–63, 164–6, 181, 185–8 standard, 5, 22 system, 5, 27, 89, 166, 169, 181 asylum migration, 1, 4, 7, 8, 14, 19, 24, 74, 77, 78, 81, 82, 84, 86, 132, 137, 143, 149, 185 flow, 82, 86, 88, 167 movement, 5, 8, 90, 168 benefit of immigration, 4 net, 51, 52, 93, 107, 183 spillover, 21, 101, 103, 104 strategic, 21, 89, 103, 106 burden-sharing financial/monetary, 90, 94, 98, 102, 186 regime, 5, 20, 22, 88, 89, 90, 103, 104, 112 rule, 34, 89, 92, 105, 106, 107, 112, 186 conflict armed, 6, 15, 19, 44–7, 125, 155, 159, 176, 180 civil, 13, 43, 45, 50, 53, 65, 66, 164 internal, 125, 182 risk, 23, 150 victim, 47, 53, 69, 159, 171, 180, 188 violent, 2, 3, 8, 13, 15, 23, 51, 159, 161 cooperation asylum, 7, 88, 89, 90, 94, 95–9, 102, 103, 104, 107, 112, 165 development, 23, 118, 124, 129, 140, 144

203

204

Index

cooperation – continued financial, 7, 165, 186 history, 126, 138, 144 international, 11, 20–2, 91, 168 technical, 123 cost asylum, 28, 86, 112–13, 165 economic, 4, 89, 95, 149 heterogeneity, 6, 90, 97, 98, 99, 103, 104, 112 migration, 51, 52, 64, 70, 74, 76, 77, 79, 80, 185, 186 monetary, 6, 22, 181 opportunity, 76, 99 parameter, 96, 97, 98, 101, 107 -sharing, 103, 104, 165 transaction, 93 country destination, 14, 15, 52, 91 first asylum, 67, 69, 70, 71, 72, 77–87, 116, 151, 158, 185 host, 6, 12, 15, 21, 25, 26, 28–31, 35, 130, 132, 134, 149, 183, 188 of origin, 8, 14, 23, 68–77, 81–7, 92, 116, 130–5, 143–54, 162, 165, 182, 185, 188 Western asylum, 74, 75–7, 80–5, 165, 185, 186 development assistance, 8, 23, 115–16, 123, 125, 129, 138, 147, 150, 155–8, 161, 165–7, 187, 190 economic, 18, 23, 92, 122, 150, 154, 187 human, 125, 150, 158, 167 social, 5, 189 donor country, 8–9, 86, 122, 131–4, 135–7, 139, 143–5, 154, 162, 165–6 government, 24, 115, 118, 133–6, 144–5, 147, 149–50, 158, 162 self-interest, 124, 133, 150, 154 European Union asylum policy, 89, 104, 132 Council, 90, 106, 113, 187

double majority, 88, 90, 106, 107, 113 enlargement, 90, 95, 122 European Refugee Fund, 90, 94, 113, 186 member states, 89, 90, 91, 92, 104, 106, 107, 112, 113, 117, 185 population, 90, 106, 107, 109, 111, 174 reform treaty, 7, 88, 90, 106, 107, 113 unanimity, 88, 89, 90, 106, 107, 113, 187 good governance, 115, 117, 124, 125, 129, 134, 154, 155, 158, 176 institution, 23, 30, 32 rewarding, 125, 138, 143, 155 human rights, 18, 19, 30, 91, 115, 124, 134, 167 violation, 3, 10, 15, 44, 47, 48, 87, 117, 149, 159, 182 immigration group, 28–9, 134, 143, 144, 188 household, 135–7, 188 irregular, 20, 166, 181 permanent, 131, 135, 137, 144, 188 income differential, 68, 72, 76, 77, 149 middle-income bias, 124, 129 insecurity, 2, 3, 11, 51, 67, 69 integration local, 42, 81, 87, 116, 158, 167 reintegration, 16, 83, 87, 116, 130, 169 internally displaced person, 3, 8, 11, 13, 15, 16, 17, 36, 44, 48, 146, 148, 151, 154, 165, 180 labor market, 4, 28, 95, 181 migration flow, 5, 10, 44, 56, 59, 63, 66, 116, 147, 150, 166–8 forced, 1, 2, 6, 8, 17, 44, 50, 56, 59, 65, 147, 164, 165, 169

Index

migration – continued management, 24, 162, 188 prevention, 5, 20, 22, 23, 117, 151, 152, 154, 155, 158, 162, 163, 190 policy, 5, 21, 169, 174 return, 68 unwanted, 116, 117, 166 network social, 2, 5, 14, 15, 22, 51, 52, 140, 166, 168 Official Development Assistance, 118, 119–25, 138, 139, 148, 180, 187, 189 volume, 119–23, 125, 136, 143, 188 package deal, 99, 107, 187 persecution, 2, 3, 7, 10, 13, 15, 51, 67, 68–78, 87, 167, 169, 182, 190 fear of, 13, 51, 182 population conflict-affected, 17, 78, 84 foreign-born, 2, 106, 174, 177 poor, 1, 2, 14, 23, 56, 57, 64, 117, 137, 158, 167, 171, 184 refugee, 4, 6, 26, 28, 30–40, 42, 44, 80–3, 147, 149, 151, 156, 164, 183 post-conflict reconstruction, 83, 116, 130 situation, 23, 124, 168 recipient country, 8, 82, 115, 117, 124–7, 132–45, 146–50, 152–5, 158–61, 180, 188, 189

205

need, 124, 125, 129, 133, 143, 150, 154, 155 refugee environmental, 16, 182 movement, 1, 5, 6–8, 17, 23, 44, 67–9, 81, 114–16, 130, 146–51, 158, 162–5, 168 protection, 1, 7, 16–22, 89, 90, 91, 94–5, 100, 101, 112, 167, 186 repatriation, 16, 18, 116, 122, 162, 166, 169, 186 voluntary, 42, 83, 87, 130, 152, 162 self-reliance, 18, 72, 78–9, 85, 130, 167 economic, 70, 79, 81–5, 185 spillover asymmetry, 88, 90, 97, 98, 103, 112 parameter, 93, 97, 98, 100, 101, 102, 104, 105 transfers aid, 7–8, 23–4, 67, 81–6, 120, 153, 165 financial, 5, 7, 42, 88, 93, 94, 99, 101, 107, 122, 165 monetary, 90, 93, 95, 98–103, 112 welfare analysis, 94, 186 collective, 90, 96, 97, 99, 101, 102 criteria, 56, 184, 187 function, 96, 100, 105, 107, 136, 137 maximization, 96, 97 system, 4, 22

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