EU Eastern Neighborhood: Economic Potential and Future Development

EU Eastern Neighborhood

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Marek Dabrowski

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Maryla Maliszewska

Editors

EU Eastern Neighborhood Economic Potential and Future Development

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

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

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

Preface

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

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

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Preface

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

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

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

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

March 9, 2011

Marek Dabrowski Maryla Maliszewska

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Contents

1

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

2

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

3

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

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

5

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

6

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

7

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

8

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

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

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

11

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

12

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

List of Figures

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

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

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

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

List of Figures

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

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

List of Tables

Table 3.1

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

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

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

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

List of Tables

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

180 187 188 189 191 192

List of Abbreviations

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

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

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

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EU EU10 EU12 EU15

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

NACE

List of Abbreviations

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

List of Abbreviations

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

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

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Contributors

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

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Contributors

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

Chapter 1

The Development Gap Between the CIS and EU Irina Sinitsina

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

1.1

Introduction

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

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

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

1

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I. Sinitsina

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

2

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

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

1.2

Quality of Life: Income and Poverty

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

80 Poverty, $2.15 a day 70

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

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

% Gini

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

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

1.3

Human Capital: Health Care and Education

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

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

1 The Development Gap Between the CIS and EU

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

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

1.4

Innovation, Technological and Infrastructural Gap

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

1 The Development Gap Between the CIS and EU

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

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

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

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

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

1.5

Openness and Trade Potential for Development

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

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

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

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

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Fig. 1.5 Logistics Performance Index and GDP (PPP) per capita (2009) (From WB LPI 2010)

the supply chain. The figure also demonstrates the scale of the ‘logistics gap’ between the top performers (EU15) and the low-income landlocked CA countries, which are among the most logistically constrained in the world. Furthermore, the

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15

LPI scores point to noteworthy differences in logistics performances across countries at similar levels of development. Overachievers and underachievers can be identified by whether or not they exhibit a positive or negative LPI gap compared with their potential measured by their GDP per capita (Fig. 1.5). Among less developed countries of the region, Turkey, with its manufactured export-led growth, stands out as a logistics overachiever. On the other hand, Russia and Azerbaijan, which rely heavily on fuel exports, tend to underperform significantly in terms of logistics compared to Romania and Bulgaria, and even to Kazakhstan. One of the reasons may be related to the absence of private sector pressure to implement institutional reforms in the area of trade and transport, reflecting the dominance of oil and raw materials in those countries’ exports.

1.6

Gaps in Environmental Performance

The environmental situation in the region is affected by common challenges such as the persistence of highly-polluting production facilities, the relatively extensive but deteriorating environmental infrastructure, unenforceable regulations, enforcement systems focused on punitive actions and a culture of top-down environmental management (OECD 2005). In the more industrialized countries, pollution issues are generally more important, while in the poorer countries, like Azerbaijan or Moldova, natural resources management linked to the productivity of agriculture play a bigger role. Generally, the more developed the country is, the greater environmental pressure it usually produces. Major environmental trends and challenges in the region can be summarized as follows: 1. Pollution (and environmental pressure in general) has sharply decreased in most transition economies because of the decline in traditional industrial output (scale effect). This effect dominated over composition effects (a shift towards more/ less polluting industries) in all countries for virtually all pollutants. The magnitude of this effect, however, is varied: in some countries (e.g. Russia and Ukraine), pollution was not reduced proportionately to the decrease in GDP, while in most EU12 and CWB countries, pollution continued to decrease even after economic growth resumed (Golub et al. 2003). 2. No clear general pattern on transition and pollution can be identified (composition effect). In many countries, resources have been transferred from heavy manufacturing industries (iron and steel) towards lighter industries and less polluting sectors (food, beverage and tobacco products). 3. In several countries, enterprise restructuring and privatization had a beneficial effect on the reduction of the energy intensity of GDP and pollution per unit of production (Hungary, Latvia, Poland, Armenia, and Belarus). In countries that expanded their energy and/or petroleum-refining sector, their energy and pollution intensities have remained relatively stable or even increased (e.g. Russia,

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Azerbaijan, and Bulgaria). This was the result of two opposite trends: (a) increasing shares of pollution-intensive sectors such as metal smelting and oil production vs. less pollution intensive manufacturing and (b) a decline in pollution intensities within several industrial sectors (Cherp et al. 2003). 4. In some countries there is a legacy of soil contaminated by heavy metals and stockpiles of pesticides and hazardous toxic waste; fine particulate matter and lead are the main pollutants, and transport is responsible for up to 70% of emissions. Emission levels of fine particulate matter are not being monitored at present, but leaded gasoline has been phased out in five EEN countries and in Russia (OECD 2005). 5. The reduction of environmental pressures was accompanied by a budgetary crisis that affected countries’ capacities to maintain environmental infrastructure, and induced environment agencies to focus on raising revenue rather than on changing the enterprises’ behavior. To quantify the existing gaps in the environmental dimension, we used the EPI, which tracks national environmental results for 163 countries on a quantitative basis, measuring proximity to an established set of policy targets using the best data available. The EPI combines 25 performance indicators covering both environmental public health and ecosystem vitality objectives grouped into 10 policy categories. These indicators provide a gauge of how close countries are to meeting environmental policy goals (Emerson et al. 2010). Figure 1.6 demonstrates that even top EEN environmental performers (Belarus and Georgia) rank below most EU and CWB countries while Tajikistan, Uzbekistan 95 SE

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Fig. 1.6 Regression of EPI (2010) on GDP (PPP) per capita (2007) (From Emerson et al. 2010)

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and Turkmenistan represent EPI values comparable to those of Central African nations. At the same time, the figure shows that some countries rank higher or lower than their economic potential would suggest. This means that other factors, such as geography, climate, population density and governance also matter for environmental performance. The EPI allocates 50% of the overall weight to the Environmental Health (EH) objective, which measures environmental stresses to human health, relying heavily on past and current investments in health care, including availability and access to public health infrastructure. The analysis demonstrates that the EU15/EEN gap in EH is larger and more pronounced, reflecting a higher environmental burden of disease, and lower access to drinking water and sanitation in CIS countries. As concerns Ecosystem Vitality (EV), comprised of environmental quality and climate change, the EEN countries do not necessarily lag behind the EU15 or EU12, primarily due to their lower population density and higher environmental capacity. This observation, however, would probably not hold if we moved to a subnational level of analysis, since well-known pollution ‘hot spots’ are highly concentrated in several industrial regions of CIS, particularly in Russia and Ukraine. As compared to the EU12, where environmental improvement has been driven by adaptation of the EU acquis, many CIS countries have limited access to international experience on environmental management and give a low priority to environmental issues in their political agendas. Across the region, legislation is extensive but largely inconsistent and unenforceable. Environmental authorities are weak vis-a`-vis powerful industrial interests. Although a broad range of environmental management instruments is being used, the current policy packages are not aimed at achieving specific targets and are not streamlined. Cross-border cooperation remains difficult, even in cases where its necessity is obvious like in the Aral and Caspian Seas (OECD 2005). The regulatory framework is poorly developed, municipalities cannot afford the required investments, and there are obstacles to inter-municipal co-operation. Thus, environmental issues stay at the bottom of the public priorities list, overshadowed by other important concerns (OECD 2005).

1.7

Institutional Dimension of the Development Gap

Only recently have scholars attempted to answer the question about the role institutions play in narrowing/widening the development gap between various groups of countries. North et al. (2006) divided the contemporary constituent systems composed of economic, political, military, and religious components into those belonging to a ‘limited access order’, and ‘open access order’, that is, one based on competition in politics and economy. The theory predicts that ‘open access’ countries should outperform the ‘limited access’ ones at least in: (1) political performance, including standard democratic norms securing the openness of

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the political system; (2) competition and ‘fairness’, including ease of starting a business; (3) business environment (legal protection, access to capital, etc.), and (4) corruption. The preceding sections have amply demonstrated that among the array of factors affecting the size and evolution of the development gap between the EU and CIS countries, institutional determinants play a critical role across virtually all dimensions. The ‘stability of institutions guaranteeing democracy, the rule of law and human rights’ is the first of the Copenhagen criteria, along with a ‘functioning market economy’ and a ‘capacity to cope with competitive pressures and market forces’, which determines prospects of EU accession. The overall picture of gaps in various components of institutional development could be drawn from the WBGI, where governance quality is measured in six broad areas: voice and accountability, rule of law, control of corruption, regulatory quality, political stability and government effectiveness (Kaufmann et al. 2010). The quality of governance determines, to a great extent, business and investment climate, i.e. ease of starting, running and closing a business, transaction costs and predictability in the application of government rules and regulations. Relative distances in governance areas between the country groups are shown at Fig. 1.7. A more detailed insight into the existing gaps in institutional indicators could be derived from responses to the World Economic Forum’s Global Executive Opinion Survey (GEOS) and the WB/IFC Enterprise Survey (ES) (see Sinitsina et al. 2008). The results are not entirely the same and relative distances between country groups in these datasets can vary a great deal. However, they do not change the overall conclusion on the major governance gap between the enlarged EU and CIS region.

Voice & Accountability 1.50

Control of Corruption

0.50

Political Stability & Absence of Violence/Terrorism

-0.50

-1.50

EU15 Government Effectiveness

Rule of Law

EU12 Candidates & West Balkans EEN Central Asian CIS Russia

Regulatory Quality

Fig. 1.7 Institutions: WBGI (2010) (From Kaufmann et al. 2010)

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Concluding Remarks

The analysis of raw indicators and indices produced by international organizations and research groups has allowed us to highlight substantial gaps that could potentially hinder social and economic cooperation between the EU and CIS and slow down the removal of the remaining dividing lines in Europe. 1. The per capita income figures, which on their own cannot measure the whole specter of disparities in the quality of life, display a close correlation with subjective indicators reflecting people’s perception of well-being. Low scores of subjective indicators in the CIS are indicative of the societal trends not captured by income or poverty scores – collapsing expectations, people’s perceptions of inequalities, social exclusion, widespread corruption and state capture. 2. The expanding gap in the quality of education between the EU15 and EEN calls for enhanced cooperation in the sphere of higher and vocational education, e.g. expanding scholarship opportunities at EU universities, including the Erasmus program, or special scholarship schemes aimed at attracting talented young people in such disciplines as hard sciences, engineering and mathematics. 3. The absorptive capacity for innovations in EEN/Russia on the whole remains low. There is an apparent mismatch between the large number of researchers employed in the region and the results of their activities, as well as low expenditures on R&D. Unlike the EU, EEN countries also demonstrate a high variance in the development of the four pillars of the ‘knowledge economy’ – innovation, education, ICT and institutional regime, with the latter presenting the major bottleneck for innovation absorption and performance. 4. Dramatic gaps in regulatory regimes between the CIS and the EU are omnipresent in virtually every dimension of development resulting in a poor business climate for investors (both domestic and foreign) and low innovation potential. Thus, adapting neighboring countries’ regulatory regimes to that of the EU remains the most important part of the association agreements being negotiated (see Chaps. 3 and 12). CIS countries lag behind the EU considerably in terms of freedom of international trade and tax administration. Formal liberalization of trade policies and regulatory regimes has not been sufficient to close the gap in trade openness and attract foreign investment, while the EU12 is rapidly catching up with EU15 in this area. 5. The quantitative indicators support the anecdotal evidence of poor environmental legislation enforcement, inconsistent policies and inadequate environmental institutions in the CIS. Environmental policies cannot guarantee significant environmental improvements. Weak institutions do not have incentives to achieve environmental objectives. Levels of public awareness and participation are low, and their impact is limited. 6. Institutional development remains the crucial factor affecting the size and evolution of the development gap. In governance quality, the EEN and Russia occupy an intermediate position between CWB and CA. On average, EEN and especially Russia lag considerably behind EU countries in voice and accountability, rule of law and control of corruption.

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References Alam A et al (2005) Growth, poverty, and inequality: Eastern Europe and the former Soviet Union. World Bank, Washington, DC Booysen F (2002) An overview and evaluation of composite indices of development. Soc Indic Res 59(2):115–151 Cherp A, Kopteva I, Mnatsakanian R (2003) Economic transition and environmental sustainability: effects of economic restructuring on air pollution in the Russian Federation. J Environ Manag 68(2):141–151 CMEPSP (2009) 2009 Report by the commission on the measurement of economic performance and social progress. http://www.stiglitz-sen-fitoussi.fr/documents/rapport_anglais.pdf. Accessed November 22, 2010. Easterlin RA et al (2002) Happiness in economics. Edward Elgar, Cheltenham EC (2006) European innovation progress report 2006. European Commission. http://www. proinno-europe.eu/docs/Reports/Documents/EIPR2006-final.pdf. Accessed September 03, 2008. Emerson J et al (2010) 2010 Environmental performance index. Yale Center for Environmental Law and Policy, New Haven, http://epi.yale.edu. Accessed November 14, 2010 Federal Customs Service (2006) On clean-up in customs authorities. http://russian-customs.org/ news/printableaead.html?id695¼10316&print¼1. Accessed September 06, 2008. Franicevic V (2003) Real and perceived inequality, poverty and well-being in South East Europe: challenges of the welfare state and democracy. http://src-h.slav.hokudai.ac.jp/ sympo/03september/pdf/V_Franicevic.pdf. Accessed August 21, 2008. Golub A, Dudek D, Strukova E (2003) Environmental protection in transition economies: the need for economic analysis. S.l.: environmental defense. http://www.edf.org/documents/2879_ transition_06_view.pdf. Accessed August 12, 2008. ITU (2005) Europe & CIS’s telecommunication/ICT markets and trends. International Telecommunication Union. http://www.itu.int/ITU-D/ict/statistics/at_glance/Europe_RPM_2005.pdf. Accessed August 14, 2008. Jaggi R (2005) Innovation in technology lags. Finance Times, October 11. Kaufmann D, Kraay A, Mastruzzi M (2010) Aggregate governance indicators 1996–2009. World Bank. http://info.worldbank.org/governance/wgi/index.asp. Accessed November 14, 2010. Kinoshita Y, Campos NF (2003) Why does FDI go where it goes? New evidence from the transition economies. William Davidson Institute Work Pap 573, June Kuzminov Y (2004) Challenges and opportunities of education reforms: the case of Russia. High School of Economics, Moscow, p1.hse.ru/eng/IMHE_report_eng.pdf. Accessed December 02, 2010 North DC, Wallis JJ, Weingast BR (2006) A conceptual framework for interpreting recorded human history. NBER Work Pap 12795 OECD (2005) Environmental management in Eastern Europe, Caucasus and Central Asia. OECD. http://www.oecd.org/dataoecd/60/16/35873053.pdf. Accessed August 12, 2008. OECD (2006) Education at a glance. OECD indicators 2006. Organization for Economic Cooperation and Development www.oecd.org/edu/eag2006. Accessed on August 8, 2008. OECD (2007) PISA 2006. Science competencies for tomorrow’s world. Vol 1: Analysis. Organization for Economic Cooperation and Development. http://www.pisa.oecd.org/. Accessed August 11, 2008. PovcalNet (2010) PovcalNet: the on-line tool for poverty measurement developed by the Development Research Group of the World Bank. http://go.worldbank.org/NT2A1XUWP0. Accessed December 09, 2010. QS (2010) QS World university rankings 2010. http://www.topuniversities.com/universityrankings/world-university-rankings. Accessed December 1, 2010. Quillin AMB et al (2006) Migration and remittances: Eastern Europe and the former Soviet Union. World Bank, Washington, DC

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Sinitsina I et al (2008) The development gap between the CIS and EU. CASE Netw Rep 81. Sobolevskaya, O (2005) La Russie pourrait exporter plus activement son enseignement (Russia could export its education more actively). ROST, De´cembre 29. http://www.rost.fr/index.php. Accessed August 10, 2008. Soubbotina TP (2004) Beyond economic growth: an introduction to sustainable development. World Bank, Washington, DC UN Millennium Project (2005) Investing in strategies to reverse the global incidence of TB. Task Force on HIV/AIDS, Malaria, TB, and Access to Essential Medicine. Earthscan. www. unmillenniumproject.org/documents/TB-notesreferences.pdf. Accessed August 09, 2008. UNCTAD (2008) UNCTAD handbook of statistics 2008. United Nations, New York and Geneva UNCTADstat (2010) UNCTAD statistics. http://unctadstat.unctad.org/ReportFolders/ reportFolders.aspx. Accessed November 24, 2010. UNDP (2004) Towards a knowledge-based society. Human development report for the Russian Federation 2004. UNDP, Moscow. UNDP (2006) Human development report 2006. Beyond scarcity: power, poverty and the global water crisis. Palgrave Macmillan, New York UNDP (2010) Human development report 2010. The real wealth of nations: pathways to human development. http://hdr.undp.org/en/reports/global/hdr2010/. Accessed November 22, 2010. UNESCO (2007) Global education digest 2007: comparing education statistics across the world. UNESCO Institute for Statistics, Montreal UNESCO IS (2010) UNESCO Institute for Statistics Data Centre. http://stats.uis.unesco.org/ unesco/tableviewer/document.aspx. Accessed November 27, 2010. Veenhoven R (1996) Happy life-expectancy. Soc Indic Res 39(1):1–58 WHO Europe (2010) European health for all database (HFA-DB). World Health Organization Regional Office for Europe. http://data.euro.who.int/hfadb/. Accessed November 27, 2010. WIPO (2006) World Intellectual Property Organization patent report: statistics on worldwide patent activity. http://www.wipo.int/ipstats/en/statistics/patents/patent_report_2006.html. Accessed August 16, 2008. World Bank (2000) Making transition work for everyone: poverty and inequality in Europe and Central Asia. Oxford University Press, Oxford World Bank (2004a) Moldova: trade diagnostic study. Report 30998-MD. World Bank, Washington, DC World Bank (2004b) Ukraine: trade policy study. World Bank, Washington, DC World Bank (2005) From disintegration to reintegration: Eastern Europe and the former Soviet Union in international trade. World Bank, Washington, DC World Bank KAM (2009) Knowledge assessment methodology. KEI and KI indexes (KAM 2009). http://info.worldbank.org/etools/kam2/KAM_page5.asp. Accessed December 2, 2010. World Bank LPI (2010) World Bank logistics performance index 2010. http://info.worldbank.org/ etls/tradesurvey/mode1c.asp?region¼on. Accessed December 4, 2010. World Values Survey (2000) http://www.worldvaluessurvey.org/. Accessed September 09, 2008.

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Chapter 2

East–West Integration: A Geographical Economics Approach Arne Melchior

Abstract Does European economic integration create more inequality between domestic regions, or is the opposite true? A numerical simulation model with 90 regions gives no general answer: the regional impact of trade integration varies across countries and reform scenarios. Integration is better for Eastern regions in the West, and for Western regions in CEE. A reduction in distance-related trade costs is particularly beneficial for the European peripheral regions. Historical data indicates a V-shaped pattern of growth in 1995–2005, with the lowest growth at a longitude passing through Western Germany. In Western Europe, integration has reduced distance-related trade costs, thereby promoting development in peripheral regions. In CEE, such an effect is not present; instead we find growing domestic disparities fuelled by faster growth in capital city regions. For Europe as a whole, integration has promoted income convergence across countries, and this effect is stronger than the increase in domestic regional disparities in the East.

2.1

Introduction

Since the fall of the ‘iron curtain’, Europe has undergone dramatic reforms and changes, most notably East–West integration, the creation of the EU internal market and monetary integration. Two decades after the start of these reforms, we now have data to assess whether they have changed the economic landscape of Europe. Specifically, has European integration contributed to regional convergence or greater income disparities? Using data for 1995–2005, we found a greater EUwide convergence but also greater regional inequality, particularly in CEE, in NMS, and outside the EU. How were these two phenomena related? In this chapter based on Melchior (2008, 2009a, b), we examine how international convergence and

A. Melchior (*) Senior Research Fellow at the Norwegian Institute of International Affairs (NUPI), Oslo e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_2, # Springer-Verlag Berlin Heidelberg 2011

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domestic divergence are related using multi-region model simulation as a theoretical tool for the empirical analysis. As we will show, the regional impact of integration varies across countries and reform scenarios, so there is no general prediction. This is the point of departure for our ‘geographical economics’ approach: we need models that are sufficiently specific to capture the nature of reforms as well as the geographical dimension properly.1 According to the NEG framework, one might expect, for example, more agglomeration for intermediate levels of trade costs. While this may be plausible as part of the general theory, it abstracts from the specific geography of countries and regions, and conclusions from two country models can not necessarily be generalized to multi-regional and more realistic landscapes (Garretsen and Martin 2010). Following Bosker et al. (2010) and the request for higher-dimensional modeling by Fujita and Mori (2005) and Combes et al. (2008), we argue in favor of adding geography to the NEG framework. This approach is in line with other recent contributions using multi-region numerical model simulations as a tool for analysis; see, e.g. Stelder (2005), Brakman et al. (2009), or Behrens et al. (2005, 2007). Following this approach, we do not expect to find universal and simple predictions about trade integration and domestic regional inequality but rather, we hope to find ones that apply to specific reform scenarios and specific countries. We therefore derive such predictions and thereafter consider whether or not they shed light on the growth patterns in Europe that are actually observed. Empirically, we found a pattern of growth among European regions in 1995–2005 that is V-shaped along the East–West axis; with higher growth to the West and to the East of a longitude that passes through prosperous regions in Western Germany and Northern Italy. According to our analysis, economic geography can contribute significantly to the explanation of this pattern; although we do not rule out a role for neoclassical income convergence or technology-related explanations.

2.2

International Integration and Domestic Regions: An Ambiguous Issue

The NEG provides a new micro-foundation for examining regional inequality.2 Some NEG contributions have also examined the relationship between international integration and domestic inequalities. In economic geography models there is typically a centrifugal force working against agglomeration, and a centripetal force promoting a more uneven core-periphery pattern. Appearing in various shapes and

1

Geographical economics aims to explain the spatial distribution of economic activity, often using multi-region model simulation as a tool, see e.g. Brakman et al. (2009) for an introduction. 2 See Ottaviano and Thisse (2004), Fujita and Mori (2005) or Fujita et al. (1999) for overviews, and Puga (1999) for a synthesis of some core models.

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embedded in different models, the standard engine for agglomeration is often the ‘home market effect’ as demonstrated by Krugman (1980): industries with economies of scale and imperfect competition tend to be located where market access is better. In Krugman (1980), it was the home market that created better market access but it may also be a more favorable geographical location. If workers are allowed to migrate in response to real wage differences, as in Krugman (1991), it amplifies market size differences, and regional inequality will increase. In this model, the centrifugal force is that workers in the ‘agricultural’ sector are immobile and maintain an incentive to locate firms close to peripheral demand. Relaxing the assumption on labour mobility across domestic regions (but not across countries) allows for the analysis of how migration and domestic agglomeration are affected by international trade integration. Within such a framework, Paluzie (2001) and Monfort and Nicolini (2000) found that international liberalization makes domestic agglomeration more likely. Monfort and Ypersele (2003) obtained similar results in a model without labor migration but with vertical linkages between industries. It is well known in the literature (see, e.g. Puga 1999) that the NEG labor migration model and the Krugman and Venables (1995) model with vertical inter-industry linkages produce rather similar results. The results outlined above are derived from models where domestic regions are symmetrically placed related to foreign countries or regions, so there is no geographical core-periphery pattern. Crozet and Sobeyran (2004) also examined the asymmetric case, in which one domestic region is closer to the outside world. Now the conclusion about integration and regions is reversed: international integration promotes development in the border region. A similar conclusion was obtained by Krugman and Livas Elizondo (1996), who replaced the centripetal force working against concentration: When agglomeration is dampened by domestic congestion costs instead of immobile farmers, international integration also leads to less domestic concentration. From the still limited amount of theoretical research on this issue, the conclusion as to whether international integration promotes convergence or divergence between domestic regions is ambiguous. This ambiguity also applies to empirics. Some evidence indicates that international integration leads to more inequality. For example, summing up the results from a large-scale United Nations research project, Kanbur and Venables (2007, 2009) conclude that ‘trade has on balance increased spatial disparities’. Hanson (2003) examined the impact of NAFTA on wages in Mexico and found that integration led to greater regional wage dispersion but provided a gain for more skilled labor close to the US border. Egger et al. (2005) found that export openness increased regional inequality with respect to real wages in CEE. On the other hand, there is also evidence suggesting that international integration promotes regional convergence: • Crozet and Soubeyran (2004) interpret their evidence about labor migration in Romania as support for the hypothesis that European integration has been to the advantage of border regions, as predicted by their model.

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• Redding and Sturm (2005) found that the division of Germany during 1945–1990 had a particularly negative impact on border regions; thus indicating that disintegration contributed to a stronger core-periphery pattern. They also found signs of recovery for border regions after reunification. Hence also empirically, the impact of international integration on domestic regions is ambiguous. The Bruelhart (2011) survey arrives at the same conclusion. This ambiguity is no surprise in light of our geographical economics approach, as we do not expect a single answer to the question about international integration and domestic inequality. Different reforms have indeed different consequences, and the spatial patterns vary across scenarios.

2.3

Modeling Approach and Scenarios

2.3.1

The Geography of the Model

In Melchior (2009a), a rectangular grid of nine countries and 90 regions is applied, as illustrated in Fig. 2.1. Each dot represents a region of equal size in terms of population or labor force. Eight of the countries have nine regions each, while the last North-East country, E3, has 18. While the map is highly stylized, the idea is to capture aspects of the true European space. The four countries W1-W4 to the left represent the ‘old EU’ or Western Europe (WE) whereas C1-C2 represent the NMS or Central Europe (CE). Eastern Europe (EE) is represented by E1-E3, of which one (E1) is a large, long and narrow country which is meant to capture some dimensions of Russia. In terms of geographic position, E2 could resemble Turkey or Ukraine and E3 might represent Eurasian countries further east. The 90-region landscape 7 6

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Fig. 2.1 A stylized European space with 90 regions (From Melchior 2009a)

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has distinct North–South and even more East–West dimensions; there is a sufficiently rich regional structure inside each country, and we have a sufficient number of countries to study different integration scenarios, and their impact on insiders and outsiders. As an alternative to such a ‘synthetic’ landscape, true geography could have been applied. The EU27 contains 271 regions at the NUTS-2 level of regional classification (see Eurostat 2007). While using true regions and their geographical coordinates would facilitate the empirical analysis, it would complicate the numerical modeling exercise since solving highly non-linear models with hundreds of unknowns is not a trivial issue. In Melchior (2010), a model with 166 true regions and countries is developed for the analysis of regional disparities in China. Brakman et al. (2009) also uses a model with true geographical space for Europe, using another modeling approach. A similar approach was attempted for the purpose here but we experienced more problems with model convergence due to the presence of many small and similar regions. The stylized landscape in Fig. 2.1 was therefore chosen and it has the advantage that the interpretation of model results is easier than with a more complex spatial pattern. In the presentation of results, we often use a cross-section of the 15 regions along latitude 2; since that illustrates the outcome in a stylized way. In Melchior (2009a), results for all the 90 regions are presented.

2.3.2

Trade Costs and Modeling Scenarios

During the period studied, Europe was affected by several reforms and we use different scenarios in order to shed light on them. For the modeling of these scenarios, we include some trade costs that are a function of distance, and others that are independent of distance. We call the first spatial trade costs, and the second non-spatial. A similar distinction is made by Behrens et al. (2007). As shown by Melchior (2000), when the two types are present simultaneously, one obtains effects on the spatial distribution of activity or incomes that are not present when each is considered in isolation. We may think of spatial trade costs as transport costs and non-spatial costs as trade policy related, but empirically, this is not so straightforward. For example, road transport costs increase almost linearly with distance whereas sea freight costs do not. Similarly, we may think of trade policy barriers as non-spatial and this is certainly the case for a MFN tariff applying to all countries. However, if countries form trade blocs with their neighbors which have similar culture and institutions, there may also be a correlation between trade policy barriers and distance. In our analysis, trade costs represent distribution costs in general, and which of the trade costs have a spatial or non-spatial character is an empirical issue. In the EU context, the single market is a large-scale project containing thousands of reforms, of which some may be spatial and others non-spatial.

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In the model simulations, trade costs always include a spatial as well as a nonspatial component: • Spatial trade costs are present within as well as between nations. We use the notation dij ¼ bd*Dij/Dmax where Dij is the ‘geographical’ distance in Fig. 2.1 varying from one between adjacent regions up to the maximum, Dmax14.03. We divide the distance by Dmax so the right hand side ratio is equal to a maximum of one. bd is a scaling factor, which we use to scale up or down the magnitude of spatial trade costs. • We assume that there are non-spatial trade costs present between all regions, and also within nations. We use three levels; within nations (tdomestic), between regions in different nations but within the same trade bloc (trta, where the rta subscript refers to some regional trade agreement), and between regions in different nations that have made no special integration agreement (tmfn, where mfn refers to MFN tariff). We always assume tdomestic < trta < tmfn and for simplicity we let the level for regional integration be mid-way between the domestic and MFN barriers. If we had allowed tdomestic ¼ trta countries would not exist any more. Since international trade costs are always higher than domestic ones, countries continue to matter in all scenarios. • In the analysis, we will show results from the following scenario:3 • Western integration (WEST): A regional integration agreement is formed among the four countries to the west (W1-W4). This is meant to represent the earlier stages of integration in WE such as the EU15. • West-Central integration (WIDER): The CE countries C1 and C2 are added to the regional integration scheme. This intends to capture aspects of the eastward extension of European integration; through various free trade agreements and finally EU enlargement. According to Herderschee and Qiao (2007), it takes 15 years for the impact of the Europe Agreements of the early 1990s to materialize, so the scenario is relevant also for the period studied empirically. • Multilateral integration (WTO): We examine the impact of changes in tmfn, with no changes in the other trade cost components. This sheds light on the impact of multilateral liberalization and also ‘preference erosion’ whereby the intra-European preference margin is reduced. • Reduced transport costs (SPATIAL): We examine the impact of reduced spatial trade costs, while all other trade costs remain unchanged. In this way, we check how regions could be affected by the ‘death of distance’, or more realistically a reduction in its costs. In most cases, we focus on changes from the initial situation (WEST) and later, we check whether these changes may shed light on the observed growth pattern during the period studied (1995–2005).

3 In Melchior (2009a), parameter values are reported, as well as some additional scenarios; e.g. a base case without integration, and various scenarios with integration in EE.

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2.3.3

29

The Model

As the core model for the simulations, we use a new trade theory approach without ad hoc dynamics such as those in standard NEG models. Motivations for this choice included: • The economic geography of Europe shows a pattern of income differences that are gradual across space and not ‘catastrophic’, and we therefore want a model with gradual outcomes. In some recent work using NEG approaches such as the Krugman (1991) core-periphery model with labor migration, the result is catastrophic agglomeration (Brakman et al. 2009), so that all ‘manufacturing’ production is located in one region. • Second, in the presence of many regions we prefer to use a model without multiple equilibria, as often happens with the NEG model. With three possible outcomes in two country models, there might be many solutions in a multiregion setting and a problem might be how to know whether a simulated equilibrium is the solution or just one out of many possible outcomes (Brakman et al. 2009). A possible alternative to NEG models could be a multilateral version of Krugman’s HME model (1980): In this model, large countries tend to be net exporters with respect to a ‘manufacturing’ sector with scale economies, monopolistic competition and trade costs. A multilateral version of the HME model was applied to the analysis of spatial inequality by Melchior (1997, 2000) or Behrens et al. (2005, 2007).4 In the multi-region setting, the HME model has the advantage of simplicity: It has a simple matrix-form solution so numerical exercises can be carried out with little technical difficulty. Hence the model has some of the virtues requested by Fujita and Mori (2005, p. 396) in their quest for developing highdimensional models: A most desirable model would be one that has solvability at the low dimensional setup and computability even at the fairly high dimensional setup.

However, in the HME model, a solution with positive production in all regions exists only within a restricted range of parameter values. With many regions, this problem is severely aggravated. The implication for numerical modeling is that the model is ‘sustainable’ only for quite high levels of trade costs, limited regional size differences, and a high elasticity of substitution. This severely limits the applicability of the HME model in high-dimensional modeling.

4 In addition to the ‘manufacturing’ sector, there is a numeraire sector which is freely traded at zero cost and produced with constant returns to scale. When labor is the only factor of production, free trade with the numeraire good equalizes wages in all regions/countries (provided they all produce that good). With no nominal wage differences, any advantage in market access or home market size is reflected in larger production in the differentiated goods sector. Since large countries obtain a more than proportionate share of production, we obtain the HME.

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While the HME model and some NEG models share a focus on net export effects, Krugman (1980) demonstrated that market access could alternatively show up in the form of nominal wage differences rather than net trade effects. In their survey of empirical NEG studies, Head and Mayer (2004, p. 2663) conclude that the relationship between market access and wages is more robustly supported than that between market access and production structure. Thus empirical research strengthens the case for models with endogenous wages rather than net trade effects. In our analysis, we use Krugman’s idea about nominal wage effects as a point of departure and develop a multilateralized version, which we call the ‘wage gap model’. In Melchior (2009a), we compare this to a multilateral version of the HME model and argue that the wage gap model is indeed a plausible alternative. Based on these arguments, we drop the numeraire sector in the HME model, and obtain a model where wage differences are driven by differences in market access. Dispensing with sector differences and reducing the economy into one sector, using one sector and one factor of production only, we can think of this as a ‘sector average’ for the economy.5 The technical details of the model are shown in Melchior (2009a, b) and a summary is provided in the Appendix. In the model, large regions will have, ceteris paribus, higher wages as shown already by Krugman (1980). In Fig. 2.1, all regions are of equal size so income levels depend on their geographical location and trade costs. In general, regions with better market access have higher nominal and real wages, and this produces a gravity-like outcome where centrally located regions are better off. As an illustration, Fig. 2.2 shows wage levels in the WEST scenario which forms the initial situation in our analysis. We show averages by longitude. Hence the wage gap model produces an outcome where regions that are centrally located have higher income levels. In addition, the Western countries, with regions with longitudes 1 through 6 benefit from regional WEST integration, whereas especially the Central countries and regions (with longitudes 7–9) lose from being outside the integration scheme. Hence there is an ‘agglomeration shadow’ from regional integration, as shown in standard models of regional integration with imperfect competition (see Baldwin and Venables 1995 for an overview). The inverse U-shaped distribution in Fig. 2.2 actually corresponds to the real pattern of income differences in Europe. This is evident from Fig. 2.3 which shows average income levels by longitude for Europe in 1995 and 2005, using data for 1,204 regions in the EU27 and Norway at the NUTS-3 level of classification (for details about data, see Melchior 2009b).

5 In future research we may add other features: sector differences in trade costs or technology, more production factors, NEG effects, etc. Ideally, we would like to have net trade effects and wage effects simultaneously, but – given the dimensionality of the model – we start with wage effects only.

Wage average by longitude

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Real wage Nominal wage 1

2

3

4

5

6

7

8

9

10 11 12 13 14 15

Longitude

Avergage income per capita

Fig. 2.2 Income levels in the WEST scenario (From Melchior 2009b)

30000 Income level 2005 Income level 1995

25000 20000 15000 10000 5000 0 -9

-6

-3

-0

3

6

9 12 15 Longitude

18

21

24

27

Fig. 2.3 Average income levels in EU27/EEA regions by longitude, 1995 and 2005 (From Melchior 2009b)

Here, Western Spain and Ireland are reflected to the far left and Eastern Romania and Bulgaria to the far right; longitude 0 is in the UK; and the income maximum at longitude 9 corresponds to Western Germany or Northern Italy. Figure 2.3 resembles Fig. 2.2 with an inverse U for most of the range.6 Hence simulated income levels broadly reflect the observed East–West distribution of income in Europe, and the model therefore provides a plausible point of departure for further analysis. In the following, we shall examine whether the simulations also have something to say about observed changes in income.

6 Empirically we see the contours of a W-shaped distribution but this pattern is not very robust and disappears if we consider a ‘central belt’ by dropping regions to the far North and South.

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2.3.4

A. Melchior

Numerical Simulation Results

Change in % from scenario WEST

Figures 2.4–2.6 show results from the WIDER, WTO and SPATIAL scenarios (changes from the WEST scenario). In all cases we show changes for regions along the second latitude (for complete results for all regions see Melchior 2009a). It is clear that the regional impact of trade integration varies strongly across scenarios. Given the initial income levels observed in Fig. 2.2, it is then also evident that the impact of integration on domestic regional inequality varies strongly across different types of reform. Hence there are no universal laws about agglomeration in Europe and one has to examine the specific reforms in question. Wider European integration and WTO-type trade liberalization has a particularly positive impact in CE, by eliminating the former trade discrimination they faced as ‘outsiders’ to the EU. Reduction in transport costs or other distance-related 6 Real wage Nominal wage

5 4 3 2 1 0 -1

W2

W4

C2

E2

E3

Change in % from scenario WEST

Fig. 2.4 Income change in the WIDER scenario (From Melchior 2009b) 3.5 3

Real wage Nominal wage

2.5 2 1.5 1 0.5 0 -0.5

W2

W4

C2

E2

Fig. 2.5 Income change in the WTO scenario (From Melchior 2009b)

E3

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Change in % from WEST scenario

8 Real wage

7

Nominal wage

6 5 4 3 2 1 0 -1

W2

W4

C2

E2

E3

Fig. 2.6 Income changes in the SPATIAL scenario (From Melchior 2009b)

trade costs (SPATIAL) has a decentralizing impact in Europe, by eroding the advantage of regions that are centrally located. European integration casts an ‘agglomeration shadow’ on non-members and the eastward extension of European integration should move this shadow further east. In the following, we will examine whether these predictions correspond to the growth patterns actually observed.

2.4

Growth and Regional Inequality in Europe: Stylized Facts 1995–2005

Using the same data set as for Fig. 2.3, we first sum up some important features of regional growth patterns in Europe during the period studied.

2.4.1

A. V-shaped Pattern of Growth

In Melchior (2009b), we found that growth followed a V-shaped pattern, i.e. inverse to the levels observed in Fig. 2.3. Using regional average growth rates by longitude and the same dataset, Fig. 2.7 shows this pattern. The eastern part of the V is well known: Before the recent financial crisis, economic growth was higher in NMS. It is less known that growth was also higher in the most western regions of Europe. The lowest growth was observed in a central area stretching from Denmark through Germany, Switzerland and Italy; with higher growth to the west and to the east of this area. After 2000, growth has been faster to the east of the minimum longitude but there is still a distinct V-pattern.

34

A. Melchior Average annual growth rate (%)

7 6 5 4 3 2 -10

0

10

20

30

Longitude

Fig. 2.7 Per capita income growth rate averages, 1995–2005 (From Melchior 2009b)

2.4.2

Increased Regional Disparities in CEE

Growth was accompanied by a significant increase in regional disparities in CEE. Figure 2.8 shows changes in (population-weighted) Gini coefficients for regional inequality in per capita GDP (PPP) during 1995–2005, based on Melchior (2008). The darker areas indicate increasing inequality. Similar to Landesmann and Roemisch (2006), we find increasing regional disparities in the CEE. While the increase in regional inequality has been considerable, the levels vary and on average, are still not exceptionally high. However some countries such as Russia (not shown in the Fig. 2.8), Ukraine and Latvia are approaching levels of regional inequality that are exceptionally high from a global comparative perspective. The increase in regional inequality is related to growth: There was little change in regional inequality in the low-growth area in Germany and Italy, but increasing regional inequality was recorded in faster-growing CEE countries. This finding is also statistically supported: economic growth and changes in regional inequality are positively related. The match is however not unambiguous, since, e.g. Spain had relatively fast growth and falling regional inequality. This is in line with the hypothesis of Williamson (1965), suggesting that growth and regional inequality are positively related at lower income levels, but this relationship may be reversed when countries become richer.

2.4.3

European Convergence In Spite of More Regional Inequality

During the 1970s and 1980s, faster growth in lower-income countries within the EU15 led to convergence in WE but regional disparities inside countries did not change much. On the contrary, the 1995–2005 period brought rising regional gaps

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FI

NO

EE

SE

LV LT BY UA

DK PL IE

GB

NL

DE SK

CZ

BE AT

RO

HU

SI HR

BG

FR IT

PT

change

GR

ES

-0.92 - 0.18

0.45 - 2.17

2.39 - 3.15

3.52 - 5.49

6.01 - 13.82

Fig. 2.8 Change in domestic regional inequality in European countries. Trend changes in Gini coefficients during 1995–2005 Note: Darker: higher increase; White areas: not covered by data (From Melchior 2008)

in CEE. Using appropriate measures we can determine whether it is the convergence across countries, or the divergence within countries, that dominates. For the EU27, the conclusion is unambiguous – income convergence across countries is quantitatively much more important than divergence across regions within countries (Melchior 2008). Hence on the whole, there was clearly income convergence in wider Europe – driven by convergence across countries.

2.5

Explanations: Neoclassical Convergence, Technology or Economic Geography?

Three potential explanations of the convergence within the EU27 are often suggested (see Combes and Overman 2004): • The standard neoclassical growth hypothesis says that countries with a lower capital-labor ratio grow faster due to the higher marginal returns to capital.

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• A second possibility is technology catch-up; countries with a lower technology level grow faster as they climb the technology ladder. • A third possibility is the economic geography perspective, in which income changes are related to differences in location and market access: East–West integration could raise income levels in CE due to improved market access. These three explanations are often perceived as competing but it is likely that they are actually complementary. In our analysis, we pursued the economic geography track but without ruling out a role for the other explanations. According to the economic geography hypothesis, CE regions close to the EU should benefit more from integration due to their proximity to WE markets. This, however, is not so obvious since such regions may also be exposed to fiercer competition. Our numerical simulations provided us with consistent hypotheses discussed in the following sections.

2.5.1

Continental Western Europe: The World Becoming Smaller

The simulations could contribute in various ways to explaining the V-shaped pattern of growth in Fig. 2.7: • Wider European integration should contribute to faster growth in CE, as predicted in WIDER. • The U-shaped prediction of the SPATIAL scenario resembles Fig. 2.7 with the exception that the minimum point is in WE. This mismatch could however be repaired if we assume that reduction in spatial trade costs has proceeded further in WE due to promoting the EU internal market, or even monetary integration. Simulations may be adjusted to take this into account. The V-shaped pattern of growth could however also be caused by standard neoclassical convergence. In an attempt to distinguish between the two, we go one step further and test empirically the economic geography predictions about regional disparities inside the countries. For example, if the SPATIAL scenario is relevant, we should expect an East–West gradient of regional growth inside countries according to the scenarios. Alternatively, if the neoclassical explanation dominates, we should not expect these predictions to be confirmed. We check this by running regressions separately for 24 countries and obtain results that are distinct for WE, CE and EE. Is the V-shaped pattern of growth in Fig. 2.7 driven by cross-country differences only, or is it also reflected in regional growth differences inside countries? Figure 2.9 shows East–West growth differences across regions inside each country, based on country-level regression analysis (based on Melchior 2009b). It confirms that for continental EU15, the V-shape is also reflected in growth patterns inside countries: In Germany and Italy (and even in the Czech Republic), growth was higher in Eastern parts. For the Netherlands, France and Spain, the Western regions grew faster. These East–West patterns of growth in WE are in line with the ‘spatial liberalization’ hypothesis which states that reduced geographical trade costs (i.e. trade

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DE CZ

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SI HR

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FR IT

PT

East-west

GR

ES

Higher growth in west Not significant

Higher growth to the east

Fig. 2.9 East–West regional growth differences within countries Note: Based on regression analysis. White areas: Not covered by analysis (From Melchior 2009b)

costs that depend on distance) undermine the advantage of central regions and create decentralization. This creates a U- or V-shaped growth pattern that applies across countries and – for WE – also inside them. In CEE, we find no evidence of faster growth in the Western regions of Poland, Hungary, Ukraine and Bulgaria. One exception is Romania. A closer examination, however, reveals that the faster per capita income growth in Western Romania was mainly driven by faster population decline, and this is hardly in line with the ‘proximity-to-markets’ hypothesis. In 1995–2005, WE underwent very important reforms; particularly launching the Single European Market and enhanced monetary integration. The results suggest that these reforms have reduced the costs of distance and actually ‘made Europe smaller’. By our test we have established that the observed regional growth pattern is in line with this hypothesis but we have not actually measured the reduction in geographical trade costs. Further empirical work is therefore needed to conclude more firmly.

38

2.5.2

A. Melchior

Central and Eastern Europe: The Empire of Capitals

In Melchior (2009b), a scenario with capital city region dominance (CAPITAL) is also included in the numerical simulations. In CEE there has been faster growth in capital regions and a potential explanation is that some nations have a hub-andspoke pattern where the capital is a hub. We capture this in the numerical model by assuming that half of the trade of the peripheral regions has to pass through the capital region. We may think of this literally as if goods have to be transported via the capital city, or – perhaps more plausibly – that other aspects of distribution and sales are related to the capital. We use the three Eastern countries as illustrations, but have no a` priori prediction about where such capital city hub effects are relevant. Br€ulhart and Koenig (2006) tested what they called the ‘COMECON hypothesis’ and found that for wages and service employment, capital city regions in five of the NMS were better off. Hence this scenario may be potentially relevant for CE countries, and we replicate it in the numerical modeling. In the country level regressions, we obtain strong empirical support for the presence of ‘capital effects’. While few East–West or North–South differences in regional economic growth are found in CEE, a persistent finding is that growth was higher in capital city regions and their surrounding areas. In Fig. 2.10, the dark areas show countries where growth was higher in regions close to the capital. Indeed we find ‘capital city effects’ in all CEE countries.7 It is generally the case that larger regional inequality corresponds with higher income in the capital city region. It is not fully clear whether this is a ‘development phenomenon’ or a ‘transition phenomenon’ linked to the particular institutional features of the former communist countries.

2.5.3

Eastern Europe and Eurasia: No Agglomeration Shadows in Sight

According to the ‘agglomeration shadow’ hypothesis, economic development should be weaker in regions outside but close to an integrating bloc. Hence Western regions in EE countries outside the EU should be worse off due to the eastward extension of European integration. Melchior (2009b) did not find systematic evidence confirming the presence of such an adverse impact of integration. In Turkey, income growth is higher in regions to the North and East and this might fit this hypothesis. In Russia and Ukraine, we find distinct patterns of population change; for example, population decline in Russia is stronger in regions to the North and East. This is however likely to be a transition effect that has little to do with the repercussions of wider European integration. On the whole, the results for these

7 In Germany we find higher growth further away from the capital. This may possibly be an effect of the change of capital city.

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DE SK

CZ

BE AT

RO

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SI HR

BG

FR IT

PT

Capital

GR

ES

Higher growth around capital Not significant

Higher growth away from capital

Fig. 2.10 ‘Capital city effects’ in European countries Note: Based on regression analysis for each country. White areas: Not covered by analysis (From Melchior 2009b)

three countries are mixed and at least for Russia and Ukraine, we interpret them as saying that the forces of transition are still stronger than the forces of international market access and economic geography.

2.6 2.6.1

Concluding Remarks The Relevance of Geographical Economics

The empirical results suggest that economic geography effects could contribute to the explanation of the V-shaped pattern of growth in Europe as well as the capital city region dominance in CEE countries.

40

A. Melchior 8

Change in %

6 4 2 0 -2 Real wage

-4 -6

Nominal

W2

W4

C2

E2

E3

Fig. 2.11 A combined scenario: changes from WEST, for regions along the second latitude (From Melchior 2009b)

Several scenarios are indeed relevant, and we may illustrate this by showing a hybrid scenario combining (i) reduced spatial trade costs in WE only; (ii) wider West-Central integration; and (iii) capital city effects in CEE. Compared to the initial WEST scenario, the predicted change is as shown in Fig. 2.11. The ‘capital city effects’ are shown only for the EE countries but might also have been extended to countries C1-C2. Figure 2.11 also replicates the V-shaped growth pattern for EU27 as well as the higher growth in CE. The evidence presented is however tentative and it would be too early to dismiss the possibility that neoclassical convergence effects could also be present in the European growth pattern. Capital city region dominance in CEE could also be a transitional phenomenon as suggested by the Williamson (1965) hypothesis. Further research is necessary to draw strong conclusions, but our results suggest that economic geography effects could be part of the explanation.

2.6.2

Policy Implications

The results presented here support the view that wider European integration has indeed contributed to economic convergence in Europe. On the other hand, growth in CEE is disproportionately concentrated in capital cities, resulting in a pronounced increase in regional inequality. A policy objective should therefore be to promote a more equitable pattern of growth in these countries. In order to achieve this, it is important to obtain more insight about the main drivers that create and sustain the dominating role of capital regions. The results also provide tentative support for the view that increased integration in WE has finally reduced the cost of distance and ‘made Europe smaller’. In WE, this seems to be a decentralizing force that strengthens peripheral regions and

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contributes to convergence. The policy implication is that integration in WE had a positive impact on regional disparities. This conclusion however rests on a macroanalysis of regional income levels only, and further micro-based research is needed to verify that the cost of distance has actually been reduced. Within European nations there is a strong concern about regional development and regional policies constitute an important part of the EU policy toolbox. Such policies will hardly be efficient unless they are founded on an appropriate analysis of regional development, and certain research contributions introduce some questions about their efficiency (Ederveen et al. 2006;; Baldrin and Canova 2001). A similar need for analysis relates to the EU’s policies related to its Eastern neighbors (Dodini and Fantini 2006). Puga (2002) argues that NEG may add to the research foundation for policies, and we share this perception. The analysis has also shown that regional effects vary considerably across scenarios, and if the analysis is to be relevant for policy, it has to be specific enough to capture these differences.

Appendix: The Model Each of the N regions is endowed with a given amount of labor Li with wage wi, so total income in the economy is Yi ¼ wiLi. Following a standard Dixit-Stiglitz approach, labor is used in the production of individual varieties of a differentiated good under increasing returns to scale. Trade costs are expressed as a markup on marginal costs so tij  1, e.g. a trade cost of 10% implies tij ¼ 1.1. For an individual variety xi produced in region i, there is, measured in labor units, a fixed production cost f, constant marginal costs c and trade costs tij for sales in market j. For a good produced in region i and sold in market j, the cost in value terms is equal to wi (f+ctijxij). With standard constant elasticity of substitution (CES) demand functions, demand for a variety from region i in market j is xij ¼ pijePje1Yj where pij is the price of a variety from region i in market j, e is the elasticity of substitution between varieties (with the standard assumption e > 1), Pj is the CES price index in region j. With monopolistic competition, firms maximize profits and we obtain the standard pricing condition pij¼[e/(e  1)] wi ctij. Furthermore, free entry and exit imply that total profits have to equal sunk costs f, and as a consequence, the total value of sales for a firm in region i will be efwi. In order to express the equation system in matrix form, we define 2

1

6 t21 1e 6 TNN ¼ 6 6 ::: 4 ::: tN1 1e

t12 1e 1 ::: ::: tN2 1e

::: ::: ::: ::: :::

::: ::: ::: ::: :::

3 t1N 1e t2N 1e 7 7 ::: 7 7 ::: 5 1

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T expresses the trade costs across all markets. Using this, the N equations expressing the allocation of each firm’s sales across markets can be written as TNN  Diag (wi e1 ÞNN  ½vii N1 ¼ ef  ½wi e N1

(2.1)

where Diag (wie1) NN is the diagonal matrix with wie1 as diagonal elements, [vii] N1 is a vector with the home market sales of firms in each region, denoted vii, as elements, and [wie]N1 is a vector with wie as elements. Given that firm size is determined (see above) and we assume full employment, the number of manufacturing firms must be ni ¼ wiLi/(efwi) ¼ Li/(ef). The sales of all firms in market j must add up to Yj. Expressing this in matrix form, we obtain the N equations TNN 0  Diag (wi 1e ÞNN  ½Li N1 ¼ ef  Diag (vii 1 ÞNN  Diag (wi 2e ÞNN  ½Li N1

(2.2)

This is a non-linear system where no explicit analytical solution can be found (except in special cases with two regions). We therefore use numerical simulation in order to determine the outcome. We call this the wage gap model since differences in market access show up in different wages. With a ‘synthetic’ landscape in Fig. 2.1, simulations are generally well-behaved. For the simulated scenarios, we obtained positive solutions in all cases. The results were also checked by computing trade flows and checking adding-up properties and this indicated a high degree of accuracy. For more details see Melchior (2009a, b, 2010).

References Baldrin M, Canova F (2001) Inequality and convergence in Europe’s regions: reconsidering European regional policies (or title alternatively used: Europe’s regions. Income disparities and regional policies). Econ Policy 16(32):206–253 Baldwin RE, Venables AJ (1995) Regional economic integration. In: Grossman GM, Rogoff K (eds) Handbook of international economics, vol 3. North-Holland, Amsterdam Behrens K, Lamorgese AR, Ottaviano GIP, Tabuchi T (2005) Testing the home market effect in a multi-country world: a theory-based approach. Banca d’Italia. Temi di discussione del Servizio Studi 561 Behrens K, Lamorgese AR, Ottaviano GIP, Tabuchi T (2007) Changes in transport and nontransport costs: Local vs. global impacts in a spatial network. Banca d’Italia. Temi di discussione del Servizio Studi 628. Bosker M, Brakman S, Garretsen H, Schramm M (2010) Adding geography to the new economic geography: bridging the gap between theory and empirics. J Econ Geogr 10(6):793–823 Brakman S, Garretsen H, Van Marrewijk C (2009) The new introduction to geographical economics. Cambridge University Press, Cambridge, UK Bruelhart M (2011) The spatial effects of trade openness: a survey. Rev of World Econ 147(1): 59–83

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Bruelhart M, Koenig P (2006) New economic geography meets Comecon: regional wages and industry location in central Europe. Econ Transit 14(2):245–267 Combes P-P, Overman HG (2004) The spatial distribution of economic activities in the European Union, Chapter 64. In: Vernon Henderson J, Thisse JF (eds) Handbook of regional and urban economics, vol 4, Cities and Geography. Elsevier, Amsterdam, pp 2845–2909 Combes P, Mayer T, Thisse J-F (2008) Economic geography. The integration of regions and nations. Princeton University Press, Princeton. Crozet M, Soubeyran PK (2004) EU enlargement and the internal geography of countries. J Comp Econ 32:265–279 Dodini M, Fantini M (2006) The EU neighbourhood policy: implications for economic growth and stability. J Common Mark Stud 44(3):507–532 Ederveen S, de Groot HLF, Nahuis R (2006) Fertile soil for structural funds? A panel data analysis of the conditional effectiveness of European cohesion policy. Kyklos 59(1):17–42 Egger P, Huber P, Pfaffermayr M (2005) A note on export openness and regional wage disparity in Central and Eastern Europe. Ann Reg Sci 39:63–71 Eurostat (2007) Regions in the European Union. Nomenclature of territorial units for statistics. NUTS 2006/EU27. Eurostat Methodol and Work Pap. Fujita M, Mori T (2005) Frontiers of the new economic geography. Pap Reg Sci 84(3):377–405 Fujita M, Krugman P, Venables AJ (1999) The spatial Economy. Cities, regions, and international trade. MIT Press, Cambridge MA Garretsen H, Martin R (2010) Rethinking (new) economic geography models: taking geography and history more seriously. Spat Econ Anal 5:127–160 Hanson GH (2003) What has happened to wages in Mexico since NAFTA? Implications for hemispheric free trade. NBER Work Pap 9563. Head K, Mayer T (2004) The empirics of agglomeration and trade. In: Henderson JV, Thisse J-F (eds) Handbook of regional and urban economics, vol 4, Cities and Geography. Elsevier, Amsterdam Herderschee J, Qiao Z (2007) Impact of intra-European trade agreements, 1990–2005: policy implications for the Western Balkans and Ukraine. IMF Work Pap WP/07/126. Kanbur R, Venables AJ (2007) Spatial disparities and economic development. In: Held D, Kaya A (eds) Global inequality. Polity Press, London Krugman P (1980) Scale economies, product differentiation, and the pattern of trade. Am Econ Rev 70:950–959 Krugman P (1991) Increasing returns and economic geography. J Pol Econ 99:483–499 Krugman P, Livas Elizondo R (1996) Trade policy and the third world metropolis. J Dev Econ 49:137–150 Krugman P, Venables AJ (1995) Globalisation and the inequality of nations. QJ Econ CX 4:857–880 Landesmann M, Roemisch R (2006) Economic growth, regional disparities and employment in the EU-27.WIIW Res Rep 333. Melchior A (1997) On the economics of market access and international economic integration. University of Oslo, Department for Economics. Diss in Econ 36–1997 Melchior A (2000) Globalisation and industrial location: the impact of trade policy when geography matters. NUPI Work Pap 608, www.nupi.no Melchior A (2008) Regional inequality and convergence in Europe, 1995–2005. CASE Netw Stud and Anal 374 Melchior A (2009a) European integration and domestic regions. A numerical simulation analysis. CASE Netw Stud and Anal 378. Melchior A (2009b) East-west integration and the economic geography of Europe. CASE Netw Stud and Anal 379. Melchior A (2010) Globalisation and the provinces of China: the role of domestic versus international trade integration. J Chin Econ Bus Stud 8(3):227–252

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Chapter 3

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

Abstract In this chapter we estimate the impact of the removal of non-tariff barriers between the EU and its selected CIS partners: Russia, Ukraine, Georgia, Armenia and Azerbaijan (CIS5). First, we discuss methodologies of measuring NTB and the impact of their removal, including a review of previous studies focusing on CEE and CIS regions. Further, we employ a CGE model encompassing the following three pillars of trade facilitation: legislative and regulatory approximation, reform of customs rules, and liberalization of the service sector. We conclude that a reduction of NTB and improved access to the EU market would bring significant benefits to the CIS5 countries in terms of welfare gains (from 1.7% of GDP in Georgia to 5.8% in Ukraine), GDP growth, increase in real wages and expansion of international trade.

3.1

Introduction

The ENP was created with the aim of extending the area of prosperity, stability and security to the new EU neighbors following the EU Enlargement in 2004 (see Chap. 12). The ENP offers deeper political and economic integration to countries

M. Maliszewska Economist at the World Bank and CASE Fellow e-mail: [email protected] I. Orlova Economist at CASE-Ukraine e-mail: [email protected] S. Taran Economist at the Bureau for Economic and Social Technologies (BEST), Kyiv e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_3, # Springer-Verlag Berlin Heidelberg 2011

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bordering the EU. In parallel, the EU has developed a Strategic Partnership with Russia covering four ‘common spaces’. The central elements of the ENP are the bilateral Action Plans that set out an agenda for political and economic reforms with various short to medium-term priorities. The Action Plans contain provisions on the gradual harmonization of the national legislation with the acquis in selected areas, setting, among others, a clear agenda for the harmonization of product standards, customs procedures, state aid and competition policy. Their implementation is to lay the groundwork for the conclusion of the DCFTA with the ENP partners, covering nearly all trade in goods and services and including legally-binding provisions on trade and economic regulatory issues (see Chap. 9). The aim of this chapter is to estimate the likely impact of the DCFTA. We focus on Russia along with four ENP countries: Armenia, Georgia, Azerbaijan and Ukraine, which we refer to as the CIS5.1 We begin with a brief discussion of the NTB along with the progress in the harmonization of product standards and the reform of the conformity assessment infrastructure, customs, and barriers to the foreign provision of services in the CIS5 (Sect. 3.2). In Sect. 3.3 we provide our estimates of the likely impact of a series of DCFTA involving a significant reduction of tariffs and NTB. The final section concludes.

3.2

The Magnitude of NTB in CIS Countries

In this section we analyze NTB such as standard costs, border costs and barriers to trade in services in the CIS5 to derive their numerical tariff equivalents to be used in the modeling of DCFTA.

3.2.1

Standard Costs

The purpose of standards and technical regulations such as product certification requirements, performance mandates, testing procedures, conformity assessments and labeling standards is the protection of consumer safety and the achievement of other legitimate goals. However, sometimes they can be applied as barriers to protect market access. Product standards can substantially raise both start-up (fixed) costs and production (variable) costs by requiring additional inputs of labor and capital. Complying with different standards can add costs and limit the export competitiveness of domestic producers.

1 The country coverage was dictated by the broader research agenda of the ENEPO project and by data availability.

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In our modeling exercise, standards costs are determined as an increase of the cost of production incurred by CIS producers exporting their products to the EU. They are modeled as additional value added in each sector where trade takes place.2 In the absence of business surveys or empirical studies providing quantitative estimates of standards costs in the Caucasus region and Russia, we base our assumptions on the survey of Ukrainian exporters to the EU conducted by CASE and CASE Ukraine in 2006 (Jakubiak et al. 2006). In this survey, over 500 Ukrainian companies from different economic sectors were asked to assess costs associated with meeting EU technical standards and the duplication of efforts related to compliance with both national and EU standards. According to this survey, Ukrainian companies exporting to the EU had to increase their production costs by 13.9% on average in 2005–2006 in order to ensure the compatibility of their products with EU technical requirements (see Table 3.1). By commodity breakdown, companies selling products from the metallurgy and chemical industries spent the least on upgrading their commodities up to the EU requirement, while the appropriate expenses of companies producing agricultural and food products, machinery, apparels, etc. were much higher (Jakubiak et al. 2006). All CIS countries have inherited the same post-Soviet standardization and certification system based on the mandatory GOST standards, which remains excessively bureaucratic and centralized. As a result, CIS exporters incur high costs that arise from compliance with both national and international standards. At the same time, CIS countries gradually undertake the replacement of old GOST standards with international and EU standards and technical regulations, and reform their standardization and certification systems in line with the implementation of their commitments under the WTO and the PCA with the EU. Given the similar regulatory heritage we assume that the estimates of the Ukrainian survey can be applicable to other CIS countries. To develop assumptions on the current level of standards costs in other countries, we adjust the Ukrainian estimations in line with the following considerations: the level of harmonization of national legislation, technical regulations and standards with the EU and international norms and standards, the status of WTO membership, the availability and development of the quality assurance infrastructure and capacities in the country, and the formal EU policy and existing requirements towards the particular products originating from CIS countries. A thorough discussion of the above issues as of mid-2008 is presented in Maliszewska et al. (2009). Below we only summarize the main conclusions that led to the adoption of specific assumptions on Georgia, Armenia, Azerbaijan and Russia. Georgia, a WTO member since 2000, has proceeded further than Ukraine in reforming and liberalizing its standardization and technical regulation system (since Georgian producers are currently entitled to apply EU standards and

2

This approach does not take into account the fixed cost elements of implementation of new standards, for which estimates are not available. However, this should not significantly affect our conclusions.

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Table 3.1 Percentage of yearly production costs spent by exporters to the EU in 2006 in order to ensure product compliance with EU norms (From own assumptions based on the survey described in Jakubiak et al. (2006)) Ukraine NACE Industry (survey) Russia Georgia Armenia Azerbaijan Cost difference as compared to Ukraine – 20% 30% 40% 50% Agriculture, hunting and related 01 service activities 14.0 16.8 18.2 19.6 21.0 Forestry, logging and related service 02 activities 7.0 8.4 9.1 9.8 10.5 14 Other mining and quarrying n/a n/a n/a n/a n/a Manufacture of food products and 15 beverages 10.4 12.5 13.5 14.6 15.6 16 Manufacture of tobacco products n/a n/a n/a n/a n/a 17 Manufacture of textiles 2.3 2.8 3.0 3.2 3.5 Manufacture of wearing apparel; 18 dressing and dyeing of fur 34.4 41.3 44.7 48.2 51.6 Tanning and dressing of leather; manufacture of luggage, and footwear 5.3 6.4 6.9 7.4 8.0 19 Manufacture of wood and of 20 products made of wood and cork 20.9 25.1 27.2 29.3 31.4 Manufacture of pulp, paper and 21 paper products 15.0 18 19.5 21 22.5 Publishing, printing and reproduction 22 of recorded media 0.0 0.0 0.0 0.0 0.0 Manufacture of coke, refined petroleum products and nuclear fuel 10.0 12.0 13.0 14.0 15.0 23 Manufacture of chemicals and 24 chemical products 5.5 6.6 7.2 7.7 8.3 Manufacture of rubber and plastic 25 products 5.6 6.7 7.3 7.8 8.4 Manufacture of other non-metallic 26 mineral products 29.3 35.2 38.1 41.0 44.0 27 Manufacture of basic metals 5.0 6.0 6.5 7.0 7.5 Manufacture of fabricated metal products, except machinery and equipment 6.4 7.7 8.3 9.0 9.6 28 Manufacture of machinery and 29 equipment n.e.c. 4.4 5.3 5.7 6.2 6.6 Manufacture of office machinery and 30 computers n/a n/a n/a n/a n/a Manufacture of electrical machinery 31 and apparatus n.e.c. 11.0 13.2 14.3 15.4 16.5 Manufacture of radio, television and communication equipment and apparatus 10.0 12.0 13.0 14.0 15.0 32 (continued)

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Table 3.1 (continued) Ukraine NACE Industry (survey) Russia Georgia Manufacture of medical, precision and optical instruments, watches 33 and clocks 20.0 24.0 26.0 Manufacture of motor vehicles, 34 trailers and semi-trailers 12.3 14.8 16.0 Manufacture of other transport 35 equipment 4.0 4.8 5.2 Manufacture of furniture; 36 manufacturing n.e.c. 15.3 18.4 19.9 37 Recycling 5.5 6.6 7.2 Total average 13.9 16.7 18.1

Armenia Azerbaijan

28.0

30.0

17.2

18.5

5.6

6.0

21.4 7.7 19.5

23.0 8.3 20.9

technical regulations without duplicating efforts related to compliance with both national and EU standards). On the other hand, Ukraine has achieved more in harmonizing and adopting international and EU technical regulations and standards, in collaboration with international and EU accreditation organizations. Ukrainian producers have better access to more developed conformity assessment and metrology infrastructure and, as a result, they can avoid or reduce the additional costs of calibration, testing, and conformity assessment procedures in other countries. This leads us to assume that the standards costs for Georgian exporters to the EU are higher by 30% as compared with Ukraine. Armenia’s quality control infrastructure system, though undergoing comprehensive reforms, is still underdeveloped and weak, thus creating a significant barrier for EU-Armenia trade and raising the costs for Armenian producers willing to export to the EU. Being a WTO member since 2003, Armenia has been ahead of Ukraine in reforming its standards and technical regulation system and implementing WTO commitments (e.g. introducing the system of voluntary standards, adopting technical regulations based on international standards, etc.). At the same time, underdeveloped conformity assessment infrastructure and the lack of its international recognition makes exports to the EU more costly than in the case of Ukraine. We may also assume that standards’ costs were slightly higher in Armenia as compared to Georgia in 2006. This was due to Georgia’s recognition of mandatory standards and technical regulations being applied worldwide and, in particular, in the EU, while Armenian producers were likely to incur some additional costs as a result of the regulatory gap between adopted domestic mandatory technical regulations and the EU norms. Based on the above analysis, we assume these costs to be higher by 40% for Armenian exporters as compared to Ukraine. Taking into account that Georgia and Armenia have been WTO members since 2000 and 2003 respectively, and our benchmark, Ukraine, has been a member since 2008, and as a result these countries have proceeded more in reforming their standardization and certification systems, Azerbaijani exporters are most likely facing higher costs of compliance for all sectors in comparison to the other two

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Caucasian countries and Ukraine. As a result, we assume 50% higher standards costs for Azerbaijani exporters as compared to Ukraine. The comparison of Russian progress in reforming its standards and technical regulations system with Ukraine shows that Ukraine has proceeded a bit forward in this regard (in terms of harmonization of national legislation to international and EU standards, adoption of technical regulations, cooperation and membership in international accreditation organizations and development and recognition of domestic accreditation system, etc.). At the same time, standards costs are likely to be lower in Russia than in Armenia, Georgia or Azerbaijan since Russian exporters have better access to more developed standardization, conformity assessment and metrology infrastructure. As a result, we suggest increasing Ukraine’s benchmark standards costs by 20% to get appropriate assumptions for Russian exporters. Our assumptions on standards costs are summarized in Table 3.1. It should be noted that in many sectors the analyzed countries do not have any exports to the EU. Specifically, Georgia’s, Azerbaijan’s and Armenia’s exports are highly concentrated in some commodity groups such as oil products, non-energy mineral products, base and precious metals, chemicals, etc. This means either these countries do not produce particular commodities at all or the barriers to their exports to the EU such as technical barriers, SPS measures, transport costs, etc. are too high. Legal, regulatory and institutional harmonization in the area of standards and technical regulation systems between CIS countries and the EU, including the implementation of the EU acquis, conclusion of the ACAA of Industrial Products, membership and cooperation agreements with the European and international bodies, etc., are expected to ensure better access to cheaper conformity assessment procedures. It should also ensure the introduction of mutual recognition agreements between CIS countries and the EU in key sectors, thus considerably reducing existing standardization costs and improving CIS countries’ access to the EU internal market. We assume that the DCFTA may lead to a 50% reduction of the standards costs in trade with the EU (provided the same speed of reforms by each country, while preserving their relative positions). In addition, we assume zero standards costs for exporters supplying their products to CIS partners. The CIS countries are signatories of the Agreement on Mutual Policies in the Area of Standards, Metrology and Certification that provided for the establishment of the Interstate Council on Standards, Metrology and Certifications and for mutual recognition of conformity certificates between CIS countries.3

3 It should be noted that in practice these costs are not always zero, since mutual recognition is applied only to interstate standards, whilst each country may develop and adopt its own national standards thus creating risks for exporters. Moreover, some other countries may question certificates issued by the partner country; they may require certificates to be issued by their own bodies (including conducting the testing procedures). Still, these practices are not so widespread, hence the zero standards costs assumptions between CIS countries seems sensible.

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Border Costs

Customs and administrative procedures have a substantial impact on trade flows. Anecdotal evidence and annual WB Doing Business surveys confirm that countries that have efficient customs, good transport networks and fewer document requirements (all of which ensure faster and cheaper compliance with export and import procedures) are more competitive globally. In the CGE exercise, border costs are modeled as additional purchases of a domestic transportation good, which include shipping, handling and warehousing for customs purchases. Border costs for the selected CIS countries are also based on the study by Jakubiak et al. (2006), which provides the costs of customs clearance faced by the Ukrainian exporters to the EU in 2006. According to the claims of Ukrainian exporters to the EU, their border costs amounted to, on average, 7% of the value of production in 2006. In order to develop assumptions on border costs for other selected CIS countries in 2006 and 2004, we use Doing Business Reports (2006 and 2008), which record every official procedure—and the associated documents, time and cost—for importing and exporting goods. According to these data, in 2004 the cost of export and import was about 30% higher in Georgia than in Ukraine, and 2.6 times higher in Azerbaijan than in Ukraine (see Table 3.2). The same source also shows an important improvement in the import and export procedures in Georgia and Armenia over the 2004–2006 period, while Ukraine,

Table 3.2 Border costs in Ukraine and other CIS countries in 2004 and 2006 (From Doing Business (2006, 2008)) Ukraine Georgia Armenia Russia Azerbaijan 2004 2006 2004 2006 2004 2006 2004 2006 2004 2006 9a 8 7 7 8 8 9 9 Documents for export (number) 6 6 (1.50) (1.33) (1.17) (1.17) (1.33) (1.33) (1.50) (1.50) 54 12 34 30 36 36 56 56 Time for export (days) 31 31 (1.74) (0.39) (1.10) (0.97) (1.16) (1.16) (1.81) (1.81) Cost to export 1,370 1,105 1,600 1,165 2,050 2,050 2,715 2,715 (USD per container)b 1,045 1,045 (1.31) (1.06) (1.53) (1.11) (1.96) (1.96) (2.60) (2.60) 15 7 6 8 13 13 14 14 Documents for import (number) 10 10 (1.50) (0.70) (0.60) (0.80) (1.30) (1.30) (1.40) (1.40) 52 14 37 24 36 36 56 56 Time for import (days) 39 39 (1.33) (0.36) (0.95) (0.62) (0.92) (0.92) (1.44) (1.44) Cost to import 1,370 1,105 1,750 1,335 2,050 2,050 2,945 2,945 (USD per 1,065 1,065 (1.29) (1.04) (1.64) (1.25) (1.92) (1.92) (2.77) (2.77) container)b Doing Business Reports (2006 and 2008) are based on data for January 2005 and January 2007 respectively, thus they measure costs for 2004 and 2006. a Numbers in parentheses are the ratios of individual countries to Ukraine for a specific year. b Total costs (in USD) of exporting/importing a 20-ft container, including costs of documents, administrative fees for customs clearance and technical control, terminal handling charges and inland transport; Does not include tariffs and trade taxes.

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Table 3.3 Assumptions on border costs in Ukraine and other CIS countries in 2004 and 2006 (in % of export value) (From own estimations based on Doing Business (2006, 2008) and Jakubiak et al. (2006)) Ukraine

Georgia

Armenia

Russia

Azerbaijan

Years 2004 2006 2004 2006 2004 2006 2004 2006 2004 2006 Adjustments of Ukraine’s Survey Survey by 2.6 by 2.6 benchmark results results +30% +10% +50% +25% +90% +90% times times Share of border costs in export value 7.0% 7.0% 9.1% 7.7% 10.5% 8.8% 13.3% 13.3% 18.2% 18.2%

Estimates for Ukraine are from Jakubiak et al. (2006); adjustments are based on Doing Business (2006; 2008).

Russia and Azerbaijan have shown no improvements. Our assumptions on border costs in Ukraine and other CIS countries in 2004 and 2006 are presented in Table 3.3.

3.2.3

Services

The availability of a diverse set of services is important for economic growth since it allows domestic firms to purchase them at lower cost. In line with the WTO negotiations and international commitments, CIS countries liberalize market access for foreign service providers and encourage them to increase foreign direct investment. WTO-related reforms in service sectors imply the elimination or substantial reduction of discriminatory measures and barriers faced by foreign service providers. Regulatory convergence with the EU aquis and closer integration between countries will likely further reduce barriers to trade in services between CIS countries and the EU. To develop assumptions on Georgia, Armenia and Azerbaijan we use Ukraine’s estimates of ad valorem barriers to trade in services from Copenhagen Economics, IER and OEI (2005). Ukraine’s assumptions have been adjusted for each country based on (i) the status of its WTO accession/membership (since WTO accession implies a substantial reduction of discriminatory measures against trade in services; we assume that the longer a country has been a member of the WTO, the lower the level of discrimination), (ii) the values of the Heritage Foundation Index of Economic Freedom4 compatible across countries (see Table 3.4).

4

This is the global economic freedom index which covers 10 freedoms in 161 countries. Each of these 10 freedoms (business freedom, trade freedom, fiscal freedom, government size, monetary freedom, investment freedom, financial freedom, property rights, freedom from corruption, and labor freedom) is graded using a scale from 0 to 100, where 100 represents the maximum freedom. A score of 100 signifies an economic environment or set of policies that is most conducive to economic freedom, an absolute right of property ownership, fully realized freedoms of movement for labor, capital, goods and services.

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Table 3.4 Heritage Foundation Index of Economic Freedom, 2008 (From The Heritage Foundation, http://www.heritage.org/research/features/index/countries.cfm) Global economic Investment Financial Country Rank freedom freedom freedom WTO accession status Ukraine 133 51.1% 30% 50% Member since 2008 Armenia 28 70.3% 70% 70% Member since 2003 Georgia 32 69.2% 70% 60% Member since 2000 In accession process since 1993, Russia 134 49.9% 30% 40% final stage of accession Azerbaijan 107 55.3% 30% 30% In accession process since 1997 Evaluation of the level of economic freedom: 80–100 – free; 70–79.9 – mostly free; 60–69.9 – moderately free; 50–59.9 – mostly unfree; 0–49.9 – repressed.

Table 3.5 Assumptions on barriers to trade in services (ad-valorem tariff equivalents of barriers to trade in services), 2006 (From Kimura et al. (2004a, b, c) – for Russia (except railway transportation); Copenhagen Economics, IER and OEI (2005) for Ukraine; own assumptions – for other countries) Railway Financial Country Suggested Adjustments transportationa Telecommunication Services Ukraine – 16.0% 6.0% 24.0% Russia – 24.0%a 10.0% 41.0% Georgia 35% off Ukraine’s estimates 10.4% 3.9% 15.6% Armenia 25% to Ukraine’s estimates 12.0% 4.5% 18.0% Azerbaijan +30% to Ukraine’s estimates 20.8% 7.8% 31.2% a Own assumption (calculated as Ukraine’s appropriate estimate increased by 50%).

In particular, the Heritage Foundation index indicates a high degree of economic freedom (including investment and financial freedom) in Georgia and Armenia, while a repressed investment environment is reported in Ukraine, Russia and Azerbaijan.5 Also Georgia and Armenia have been members of the WTO since 2000 and 2003 respectively, followed by Ukraine, while Russia and Azerbaijan are still at the accession stage. Taking these considerations into account, we can roughly assume the following ranking of countries in respect to the level of barriers to FDI in services (from lowest to highest): Georgia, Armenia, Ukraine, Russia, and Azerbaijan (see Table 3.5). The harmonization of national legislation and policies with the EU acquis will lead to a further reduction of barriers to FDI for both foreign and domestic service suppliers.

5

It should be noted that the Heritage Foundation investment and financial freedom indices capture not only formal regulatory restrictions affecting FDI in CIS countries but also corruption issues, contract enforcement, implementation of laws, etc.

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CGE Estimates of the Impact of the DCFTA

This section estimates the potential impact of institutional harmonization of CIS5 countries with the EU using a CGE model. We focus on three pillars of trade facilitation, i.e. legislative and regulatory approximation, reform of customs rules, and liberalization of the access of foreign providers of services.

3.3.1

The CGE Model

We employ a standard comparative static CGE model built on the basis of the Multiregional Trade Model implemented by Harrison, Rutherford and Tarr (1996) in their evaluation of the impact of the completion of the Single Market, but we have modified it in several ways for the purpose of this analysis. A detailed description of the model equations, calibration and parameters employed is provided in Maliszewska, Orlova and Taran (2009).6

3.3.2

Data

The SAM for Georgia for 2004 was based on Jasper Jensen’s and David Tarr’s submission to the GTAP database.7 The SAM for Ukraine was submitted to GTAP by CASE-Ukraine. The data for all other regions is based on the GTAP7 prerelease 3 database. The GTAP database includes the national and regional input–output structures, bilateral trade flows, the final demand pattern and government intervention benchmarked to 2004. The Georgian SAM has been imposed on the GTAP data using a code developed by Thomas Rutherford (www.mpsge.com/gtap6). The benchmark data includes Georgia, Armenia, Azerbaijan, Russia, Ukraine, the remaining CIS countries, the EU27, Turkey and the Rest of the World (ROW). It includes 33 sectors, 11 of which are subject to increasing returns to scale in the imperfect competition scenarios.8 The data on tariffs in the baseline 2004 and 2006 or 2007 originates from the WITS database. It includes applied trade weighted averages of tariff rates with respect to all regions. In most instances, the GTAP tariff data for 2004 was replaced with the WITS tariffs for 2004 which were much lower. The data on tariffs for the

6

A similar analysis has been recently applied in the feasibility studies for Russia, Georgia and Armenia (Maliszewska et al., 2007, 2008a, b) and Ukraine (Ecorys and CASE-Ukraine 2007). 7 The submission of the SAM for Georgia, Armenia and Azerbaijan by Jaspers Jensen and David Tarr was part of the ENEPO project. 8 These are food, beverages and tobacco; textiles and wearing apparel; leather; paper products, publishing; petroleum and coal products; chemical products, rubber, plastic; mineral products, metal and metal products; transport equipment; machinery and equipment; other manufacturing products.

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set of simulations representing the initial trade policy changes for Georgia, Armenia and Ukraine is from 2006; data for Russia and Azerbaijan is from 2007.

3.3.3

Simulation Scenarios

In each simulation we calculate the impact of a given trade policy change assuming increasing returns to scale in selected sectors and allowing for the adjustment of capital stock in response to a change in return to capital – the long run scenarios. The calculation of steady state growth effects follows Harrison, Rutherford and Tarr (1996). In the steady state scenario, capital stock is allowed to adjust, while the price of capital is held constant at its benchmark level. This approach assumes that there exists an invariant capital stock equilibrium. It is defined as the set of prices, production and investment levels at which the economy is able to grow at a steady rate with constant relative prices. This approach provides an upper bound for the potential welfare gains as it ignores the adjustment costs and foregone consumption necessary to increase investment. Our benchmark assumptions on NTB have been presented in Sect. 3.2. In simulations we study the impact of the initial tariff liberalization of 2006 and the reduction of border costs between 2004–2006 to create a new benchmark equilibrium. Further we study the impact of the simple FTA with a full liberalization of manufacturing tariffs in trade with the EU and a 50% reduction of tariffs in agricultural and food products. The results of these estimations are presented in Maliszewska, Orlova and Taran (2009). Finally, we study the implications of the DCFTA where, in addition to the simple FTA assumptions, we add a 50% reduction of border and standards costs and barriers to trade in services.

3.3.4

Impact of the DCFTA with the EU

There are several reasons why we should expect the elimination of NTB to be beneficial to both CIS5 and the EU. The reductions in barriers to trade and transport costs decrease the prices of goods for consumers, as well as prices of intermediates and capital goods for producers. The extent of these gains depends on the amount of trade between the partners and trade creation and trade diversion effects. Apart from the increased efficiency of resource allocation, as demand shifts to regions with lower-cost suppliers, additional gains stem from increased competition. However all gains from trade also involve adjustment costs and may be associated with potentially painful restructuring in selected sectors and significant redistribution effects. The benefits of DCFTA for an individual country depend on many factors such as the level of initial NTB, trade intensity in sectors mostly affected by the reduction of NTB, the economies of scale in the most affected sectors and others. These net gains from DCFTA are presented in Table 3.6. Every column contains an estimation of the individual country’s trade liberalization scenario, assuming no

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Table 3.6 Welfare, GDP, Wage and Trade Implications of DCFTA between CIS5 and the EU (From own simulations.) Armenia Azerbaijan Georgia Russia Ukraine Welfare (% change) Russia 0.00 0.01 0.00 3.20 0.03 Ukraine 0.01 0.01 0.02 0.45 5.86 Armenia 3.49 0.01 0.01 0.20 0.01 Azerbaijan 0.01 2.98 0.12 0.39 0.00 Georgia 0.13 0.07 2.62 0.17 0.03 Turkey 0.00 0.11 0.03 1.76 0.01 EU27 0.00 0.03 0.01 1.22 0.10 CIS 0.00 0.01 0.00 0.91 0.22 ROW 0.00 0.02 0.00 0.76 0.05 GDP (% change) Russia Ukraine Armenia Azerbaijan Georgia Turkey EU27 CIS ROW

0.05 0.25 3.50 0.01 0.22 0.09 0.03 0.00 0.01

0.05 0.28 0.03 3.00 0.02 0.20 0.06 0.02 0.03

0.05 0.29 0.03 0.10 2.70 0.12 0.04 0.00 0.02

3.25 0.18 0.23 0.41 0.27 1.86 1.26 0.91 0.77

0.08 5.58 0.03 0.02 0.07 0.10 0.13 0.22 0.06

Wages of unskilled workers (% change) Russia 0.00 0.01 Ukraine 0.01 0.02 Armenia 4.08 0.01 Azerbaijan 0.02 4.94 Georgia 0.15 0.08 Turkey 0.00 0.09 EU27 0.00 0.02 CIS 0.00 0.01 ROW 0.00 0.01

0.00 0.03 0.01 0.11 4.83 0.02 0.00 0.00 0.00

3.54 0.25 0.15 0.80 0.24 1.36 0.97 0.85 0.55

0.01 6.51 0.03 0.04 0.04 0.00 0.08 0.12 0.03

Wages of skilled workers (% change) Russia 0.00 Ukraine 0.00 Armenia 2.72 Azerbaijan 0.02 Georgia 0.05 Turkey 0.00 EU27 0.00 CIS 0.00 ROW 0.00

0.00 0.01 0.01 3.99 0.06 0.07 0.02 0.01 0.01

0.00 0.02 0.01 0.10 3.80 0.02 0.00 0.00 0.00

3.00 0.34 0.27 0.33 0.26 1.23 0.91 0.74 0.51

0.01 4.97 0.01 0.01 0.00 0.01 0.08 0.13 0.03

Total exports (% change) Russia 0.00 Ukraine 0.01 Armenia 21.22

0.00 0.02 0.07

0.00 0.04 0.15

19.59 0.39 1.15

0.08 13.73 0.17 (continued)

3 Deep Integration with the EU: Impact on Selected ENP Countries and Russia Table 3.6 (continued) Armenia Azerbaijan 0.09 Georgia 0.56 Turkey 0.01 EU27 0.01 CIS 0.00 ROW 0.00 Total imports (% change) Russia 0.00 Ukraine 0.02 Armenia 13.19 Azerbaijan 0.04 Georgia 0.26 Turkey 0.01 EU27 0.01 CIS 0.00 ROW 0.00

Azerbaijan 13.29 0.25 0.11 0.05 0.03 0.02 0.02 0.03 0.04 2.20 0.22 0.19 0.05 0.02 0.03

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Georgia 0.52 22.95 0.03 0.01 0.00 0.01

Russia 1.91 1.72 2.70 2.34 1.78 1.21

Ukraine 0.01 0.07 0.02 0.26 0.36 0.05

0.01 0.04 0.16 0.23 9.07 0.05 0.01 0.01 0.01

17.45 0.28 0.44 0.54 0.81 2.89 2.54 1.53 1.27

0.03 13.97 0.04 0.04 0.02 0.04 0.28 0.29 0.04

changes in EU relations with the remaining countries. For example, the first column presents the results of an EU-Armenia DCFTA for all countries under consideration and, not surprisingly, the strongest impact is felt by the integrating country, i.e. Armenia. Welfare effects are measured as a percentage change in equivalent variation as a share of GDP compared to the 2006 benchmark scenario. The implications of DCFTA for wages and trade flows are also presented as percentage change with respect to their levels under the benchmark 2006 scenario. Our estimates indicate that Ukraine would be the major beneficiary of the institutional harmonization with welfare gains of up to 5.8% of GDP in the long run. The welfare gains for the remaining countries are also sizeable: 3.1% of GDP for Armenia, 2.8% for Russia, 1.8% for Azerbaijan and 1.7% for Georgia. Even though the estimated NTB in Ukraine are not as high as in other CIS5 countries, the highest reduction of standard costs is taking place in sectors where exports to the EU are particularly high, for example, 60–70% of the production of ‘Textiles and textile products’ and ‘Wood and wood products’ is being exported, out of which 70–80% is going to the EU. The impact on wages of skilled and unskilled workers in Ukraine is also the highest among the CIS5, which contributes to sizeable welfare gains. In the CIS5 countries, sectors using unskilled labor grow faster than sectors using skilled labor, hence the wages of unskilled workers increase at a faster pace. According to our simulations, the DCFTA would also lead to a significant increase in total trade in all analyzed countries (for a full set of results, see Maliszewska et al. 2009). In the long run, the total exports of Georgia, Armenia and Russia might go up by as much as 20%, while the total exports of Azerbaijan and Ukraine might increase by 13%. A negligible increase in trade would also be recorded in the EU27, apart from the EU-Russia DCFTA, where the EU imports

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and exports are expected to grow by 2.5%. In all simulations the trade balance is held fixed hence an increase in total exports is accompanied by a compensating increase in total imports. If DCFTA were implemented, some sectors of the CIS5 economies would record significant increases in total output, while other sectors would see their output decrease dramatically. In Armenia the main winners would be ‘Leather products’, ‘Manufactures NEC (not elsewhere classified)’, and ‘Mineral Products’ and ‘Textiles’. This would be at the expense of ‘Wood products’, ‘Transportation and Storage’, and ‘Communications’. In Azerbaijan, the major beneficiaries would include ‘Textiles’, and ‘Leather and Wood products’. The contracting sectors would likely include ‘Mineral Products’, ‘Mining and quarrying’, and ‘Paper products’. In Georgia the expanding sectors would include ‘Textiles’, ‘Metals’, and ‘Mining and quarrying’. The contracting sectors would include ‘Leather products’, ‘Machinery and Electronic Equipment’, and ‘Paper products’. In Ukraine ‘Textiles and textiles products’, ‘Leather’ and ‘Wood products’ would gain from a DCFTA. Generally most sectors would experience and increase in output, but the output of ‘Manufactures NEC’ or ‘Transportation and Storage’ would contract.

3.4

Conclusions

The analysis presented in this chapter estimates the likely impact of the DCFTA between the EU and five selected CIS countries with the use of a CGE model. The model encompasses tariff reductions as well as the three major pillars of trade facilitation: legislative and regulatory approximation (reduction of standard costs), reform of customs rules and procedures (resulting in a reduction of border costs), and liberalization of the access of foreign providers of services. We conclude that an institutional harmonization with the EU resulting in a reduction of NTB and improved access to the EU market would bring substantial long-term benefits to the CIS5 countries in terms of their welfare gains, GDP growth, increases in real wages and expansion of international trade. Ukraine would be the biggest winner (welfare gain equivalent to 5.8% of GDP), however, the welfare gains for the remaining countries could also be sizeable, i.e. from 1.7% of GDP in Georgia to 3.1% in Armenia. The structural changes would be substantial, with some sectors seeing their output decreasing. However, these output, trade and GDP changes would be expected to fully materialize over a 10–15 year period and therefore the adjustment might be gradual and the transition less costly than indicated by total estimated changes in sectoral outputs.

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References Copenhagen Economics, IER, OEI (2005) Project ‘Analysis of the economic impacts of Ukraine’s accession to the WTO’. Copenhagen Economics, Institute for Economic Research and Policy Consulting, Institute for East European Studies Munich. Presented at World Bank. Doing Business (2006) Doing business 2006. Creating jobs. The World Bank. http://www. doingbusiness.org/~/media/FPDKM/Doing%20Business/Documents/Annual-Reports/English/ DB06-FullReport.pdf Doing Business (2008) Doing business 2008. The World Bank. http://www.doingbusiness.org/ ~/media/FPDKM/Doing%20Business/Documents/Annual-Reports/English/DB08-FullReport. pdf Ecorys, CASE-Ukraine (2007) Trade sustainability impact assessment of the FTA between the EU and Ukraine within the enhanced agreement, The European Commission, DG Trade. http:// trade.ec.europa.eu/doclib/docs/2007/june/tradoc_135010.pdf Harrison G, Rutherford T, Tarr D (1996) Increased competition and completion of the market in the European Union. J Econ Integration 11(3):332–365 Jakubiak M et al (2006) Non-tariff barriers in Ukrainian exports to the EU. CASE Netw Rep 68. Kimura, F, Ando M, Fuji T (2004a) Estimating the ad valorem equivalent of barriers to foreign direct investment in the telecommunication services sectors in Russia. The World Bank. http://siteresources.worldbank.org/INTRANETTRADE/Resources/Topics/Kimura-Ando-FujiiRussiaTelecom.pdf. Kimura, F, Ando M, Fuji T (2004b) Estimating the ad valorem equivalent of barriers to foreign direct investment in the maritime and air transportation service sectors in Russia. The Word Bank http://siteresources.worldbank.org/INTRANETTRADE/Resources/Topics/kimura-AndoFujii-RussiaTransport.pdf. Kimura, F, Ando M, Fuji T (2004c) Estimating the ad valorem equivalent of barriers to foreign direct investment in financial services sectors in Russia. The World Bank. http://siteresources. worldbank.org/INTRANETTRADE/Resources/Topics/Kimura-Ando-Fujii-RussiaFinance.pdf. Maliszewska M et al (2007) Economic feasibility and general economic impact of a free trade agreement between the European Union and the Russian Federation. mimeo. Maliszewska M et al (2008a) Economic feasibility and general economic impact of a free trade agreement between the European Union and Georgia. CASE Netw Rep 79 Maliszewska M et al (2008b) Economic feasibility and general economic impact of a free trade agreement between the European Union and Armenia CASE Netw Rep 80. Maliszewska M, Orlova I, Taran, S (2009) Deep integraiton with the EU and its likely impact on selected ENP countries and Russia. CASE Netw Rep 88.

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Chapter 4

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

Abstract This chapter discusses the role of the energy sector and, especially, natural gas in the relations between EU and FSU countries. It outlines the EU energy demand outlook and FSU production and export prospects, including the controversies surrounding the competition between new gas pipeline projects. The discussion of opportunities and risks for gas trade and cooperation between the EU and FSU partners concludes with policy recommendations.

4.1

Introduction

The main objective of this chapter is to outline selected energy challenges facing the EU and the prospects of energy cooperation with oil and gas producers and transit countries in the FSU area. The EU energy market has seen some profound changes over the last few years due to a combination of internal and external factors. This has far-reaching implications for the EU’s energy outlook. The key factors and forces at play include: • Rising dependence on imports of oil and natural gas • Large swings in prices of energy commodities

Acknowledgments This chapter partly draws from our joint work with Sabit Bagirov, Leonid Grigoriev, Marcel Salikhov and Micheil Tokmazishvili (Papava et al. 2009). We benefitted from discussions with Marek Dabrowski and Janusz Szyrmer. The usual disclaimer applies. W. Paczynski CASE Fellow e-mail: [email protected] V. Papava Senior Fellow at the Georgian Foundation for Strategic and International Studies e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_4, # Springer-Verlag Berlin Heidelberg 2011

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• Strong growth in global energy demand driven by developing countries, predominantly in Asia • Gradually changing character of the gas market and prospects of emergence of the global gas market at some point in the future driven by the US’s unconventional gas boom and rising importance of LNG • Increase in perceived risks to pipeline gas supplies from the major source of energy imports to the EU, i.e. Russia • Ambitious agenda of reducing the climate and environmental impact related to energy usage, especially through lower greenhouse gas emissions These issues are particularly important for the development of energy relations between the EU and individual EU countries and important gas and oil suppliers from the FSU area, notably Russia, but also Azerbaijan, Kazakhstan and some other CA countries. Relations with the FSU transit countries (Ukraine, Georgia, etc.) are also a vital element here. While cooperation and trade in crude oil and oil products is clearly important and interesting for both EU countries and several FSU economies, the focus of this work will be on natural gas, where the dominance of pipelines as the main mode of transport results in more complicated relations with higher risks for both producers and consumers. The remaining part of this chapter is organized as follows. The next section briefly introduces the current patterns of the EU’s energy demand and the outlook for the future. Section 4.3 discusses the recent trends and outlook for FSU gas and oil production and exports, paying particular attention to gas transportation infrastructure. Section 4.4 outlines some recent EU initiatives aimed at improved energy security. In the concluding section, a brief summary is followed by recommendations both for the EU and FSU actors.

4.2

The Challenge of Meeting the EU’s Oil and Gas Demand

The EU energy market has been affected by several important internal and external developments. First, following a long period of gradual yet consistent increase, energy demand started to decline in the mid-2000s. Second, domestic production of primary energy has been declining since 2000, led by falling production of crude oil, coal, and natural gas. Only renewable energy production has been rising sharply, while nuclear energy has been broadly stable for the last decade. With domestic output declining faster than demand, the EU’s import dependence has been on the rise, reaching close to 55% during 2008–2009. Looking ahead, one observation is that there is currently very large uncertainty concerning the EU’s natural gas demand by 2020 and even more so by 2030. Various foresight/forecast exercises carried out during 2008–2010 by the International Energy Agency and other public and private institutions foresee the EU’s gas demand in 2020 in a very broad range: between 400 bcm and close to 700 bcm.

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The dispersion of 2030 forecasts is equally large – they fall in the wide range of 500–800 bcm. This compares to a consumption of more than 500 bcm in 2010. In line with the trends observed since 2000, higher demand will come mainly from Southern Europe (Papava et al. 2009). The power sector and industry are likely to see the strongest growth in demand, although much uncertainty remains. In the industrial sector, production relocation to the developing world will have a large impact on demand. As the relocation processes are expected to be stronger in WE, the industrial sector in CEE economies might see the strongest rise in gas demand. In power generation, the key question is the extent to which gas will replace coal as the main form of fuel. An alternative scenario might be that new generation capacity will remain predominantly based on renewable resources, with gas-fired power plants acting as a back-up for intermittent renewable sources (Fig. 4.1). Importantly, even in the case of natural gas, the fuel for which there exists no worldwide market as yet, global developments are of key importance for the EU’s current situation and outlook. The key links are through the global climate change debate, the rise of the LNG market, the successful development of new unconventional gas resources in the US and the link between international oil prices and gas delivered in many long-term contracts in Europe. In the short- to medium-term, the global economic crisis also affected the EU’s energy markets, including the natural gas market. For the last couple of years, climate change has become the major topic in international policy dialogue. One implication of this is that policymakers, especially in the most developed countries, are changing their preferences on fuel choices. Many EU countries have introduced various schemes subsidizing the power generated from renewable resources. The logic behind this is that the lifetimes of power plants are very long and hence fuel and technology cost calculations supporting investment decisions taken currently should be based on the relative

Fig. 4.1 Oil and gas trends in the EU27, 1996–2009 (million tons of oil equivalent). Notes: Import figures are calculated as the difference between domestic production and consumption (From BP Statistical Review of World Energy, June 2010)

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costs (including costs of greenhouse gas emissions) of fuels several decades in the future. These policies are having an impact already now. In 2009, renewable energy technologies accounted for 61% of new power generating capacity installed in the EU, while gas only accounted for 26% (EWEA 2010). The combination of the booming unconventional gas production in the US, large increases in global LNG output and the economic crisis leading to a fall in global gas demand at the unprecedented rate of 2.1% in 2009 has resulted in a gas glut and hence significantly falling prices in spot contracts. European consumers were trying to renegotiate longterm contracts where prices are indexed to oil, to obtain temporary rebates or in other ways decrease purchases under long-term contracts and partly replace them with cheaper gas available on the spot market. This mechanism explains the significant drop in the EU’s gas imports from Russia in 2009. The complex interaction of several factors will determine the role played by natural gas in the European power sector. The list includes: • The development of renewable resources (hinging on technological progress, financial support from governments and the development of prices of alternative energy resources and of carbon emissions) • The future of the coal sector, including the commercial success or failure of carbon capture and storage technology • Attitudes towards nuclear energy • Energy efficiency improvements • The potential development of unconventional gas resources in some EU countries • The geopolitics of gas: readiness of EU countries to further increase their import dependence Nevertheless, even under scenarios with stagnant or a very slowly increasing EU gas demand, substantial gas imports will be needed and the FSU region is the most natural supplier, at least for the eastern EU economies. Hence, it is important to understand the production, export and transport potential in this region.

4.3

FSU Supply

The FSU region and Russia in particular have the largest natural gas reserves in the world. Russia is the second largest global gas producer. While domestic gas demand in Russia and other FSU countries (notably Ukraine and Uzbekistan) is very high in comparison with the EU, current production levels are sufficient to ensure significant exports to other European countries. The successful implementation of energy saving measures in the FSU countries could further increase their export potential. In 2009, the FSU accounted for more than 23% of global gas production and Russia alone produced 17.6%. For comparison, the US accounted for 20.1%, but the Middle East region as a whole only for 13.6%, the Asia Pacific region for 14.6%,

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while the EU for 5.7%. Besides Russia, countries like Uzbekistan, Turkmenistan, Kazakhstan and Azerbaijan are important producers. However, Uzbekistan consumes most of its gas domestically and Turkmenistan’s main problem is with access to international gas markets. Russian dominant gas company, Gazprom, was an important buyer of Uzbek and Turkmen gas in 2006–2008 (less so in 2009). Azerbaijan has recently become a significant gas exporter while Kazakhstan is on the verge of becoming one. There is some uncertainty on the gas production and export outlook for Russia given a period of underinvestment in new fields. However, a combination of investment revival and crisis-related demand decline suggests no problems with meeting current or even rising levels of demand from the EU. At the same time, Russia is entering East Asian markets utilizing gas fields located in the eastern part of the country. It also recently started producing LNG. For the CA countries, assuming the problem with their access to markets is resolved and the investment climate improves, there is a potential for significant rises in gas output and exports. Azerbaijan is expected to further increase its gas exports. Overall, the region has the chance to further strengthen its role as a supplier of natural gas, mainly to Europe and South and East Asia. The existing pipeline infrastructure does not allow for the diversion of gas extracted from the Russian deposits currently in use to East Asia or other markets. The situation is somewhat different in CA. The key issue here is market access, or specifically the lack thereof. At present, Russia holds strong monopsony power as large volumes of gas from the region are only allowed to reach final markets through Russian pipelines. Future expansion of the region’s export capacity can take place via a multitude of transit routes (e.g. through Russia, the Caucasus, Iran or China). As a result, gas can be directed towards various markets, one of which could be the EU. Russia is actively pursuing policies of diversifying its export routes to the EU, decreasing its dependence on transit countries (mainly Ukraine and Belarus), and maintaining its control of export routes of CA gas. The apparent importance of limiting the reliance, especially on transit through Ukraine, can be illustrated by the size of investments required for the new planned or already built gas transit corridors. Among them, the Nord Stream project is the most advanced. On the EU side, there are attempts to diversify gas import corridors. In some instances, the views on what constitutes a diversification differ between member states. For instance, Germany considers the Nord Stream an improvement in the security of supply but the opposite view prevails in Poland. The so-called southern gas corridor that is meant to supply Europe with Caspian gas and potentially also gas from the Middle East is an area of particularly fierce project competition. Here too, the views of individual EU member states differ and, e.g. the Nabucco project promoted by some Eastern, Central and Southern European countries receives rather little support from Germany or France. Major potential options for the southern gas corridor include:

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• The Nabucco project with a planned gas pipeline from Turkey through Bulgaria, Romania and Hungary to Austria • South Stream with a planned gas pipeline from Russia running under the Black Sea (i.e. bypassing Ukraine) to Bulgaria and then linking to Greece and Italy, and (a second branch) to Romania, Serbia, Hungary, Slovenia and Austria • A combination of some other proposed pipelines in most cases linking Greece with Italy (ITGI, White Stream and TAP) All of these projects are competing with one another and it is unlikely that more than one of the above options makes economic sense at least in the medium term perspective. All of them are presented as a way of ensuring the diversification of import routes to the EU and hence improving supply security. However, the notion of diversification is understood differently, with South Stream focusing on diversification of transit routes (the gas is to come from Russia), whereas other projects mainly focusing on diversifying the original sources of gas production. The main challenge to the South Stream is that unless a major increase in EU gas demand materializes, this project would effectively replace existing gas corridors through Ukraine and/or Belarus. Hence, very large financial resources would be spent on replacing the pre-existing transit infrastructure rather than diversifying transit routes. The main challenge to Nabucco and other projects relying on gas originally delivered to Turkey is that it is unclear whether there is enough gas to fill this pipeline. Factors determining this include the existing pipeline infrastructure in CA, Caucasus, and Middle East and the interest in and commitment of key gas producers. Given its geographical location, Azerbaijan is seen as a major player among producing countries, but its own resources are unlikely to be sufficient to make the new pipeline projects economically viable. Besides, from its own perspective, the country may prefer to continue diversifying its export markets. Access to gas from Kazakhstan and Turkmenistan could change the outlook. This would require building the Transcaspian gas pipeline from Turkmenistan, across the Caspian Sea to Azerbaijan. The chances of this project being carried out are highly uncertain due to the costs involved, the unresolved status of the Caspian Sea, and the existence of other options for CA gas exports – mainly to Russia and China. Nevertheless the EU has been actively trying to ensure support for the southern gas corridor from the gas producing countries in the region, as evidenced by the series of meetings between the President of the European Commission and the heads of state of Azerbaijan, Turkmenistan and Uzbekistan in January 2011. The critical role played by transit countries and especially Ukraine is unlikely to change for the next few years. The volume of gas transported through the Ukrainian pipeline system to other EU countries fell from around 115 bcm annually in 2006–2008 to 80–90 bcm in 2009–2010 on the back of a fall in crisis-related demand and the partial switch by consumers to the much cheaper gas offered in the expanding spot market. However, a rebound in the next few years is possible. In the longer term (after 2015–2020), serious competition may emerge from Nord Stream, which will have a total capacity of 55 bcm annually if a second line is added

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to the first line, which is already being built. If South Stream also materializes (which is uncertain at present), its planned final capacity of close to 50 bcm annually would mean that Ukraine would no longer play an important role in the transit of Russian gas to EU markets, unless trade volumes increased significantly. The Ukrainian gas transit infrastructure is in poor physical condition following years of underinvestment. From a purely economic perspective, the most advantageous option would be to modernize and upgrade existing infrastructure rather than consider building new pipelines. This is also important for Ukraine given the significance of revenues from gas transit services. The interruptions in gas flows to the EU that happened in the past and especially the January 2009 events have highlighted the risks of tense political and business relations related to the transit pipeline infrastructure. The conflict escalated due to a payment dispute between Russia and Ukraine, but its negative repercussions were serious in a few EU countries that had no easily available alternative gas supplies. These events constituted a substantial blow to the reputation of both Russia and Ukraine as reliable supplier and transit countries and brought energy security (and gas supply security in particular) to the EU-wide agenda. The level of market distortions and mistrust between Russia, Ukraine and the EU with regards to the functioning of the gas transit implies that a solution is needed in which actors see an interest in cooperation. This could take the form of Russia entering into an accord with Ukraine on joint control of the Ukrainian pipeline system, a similar deal between Ukraine and the EU or a trilateral agreement. As it stands, all of these potential solutions have far reaching political repercussions and so it is not surprising that past attempts to implement them have failed. The EUUkraine declaration from March 2009 could be seen as a step towards the second of the above options (Paczynski 2009). However, Russian opposition to being excluded and limited progress in taking the required steps by Ukraine somewhat weaken the chances of its implementation. A broader deal including Russian partners may be necessary and indeed some versions of this were proposed by Russia.

4.4

EU Strategy and Policy

Facing a range of new and intensified challenges in the energy sphere, the EU has responded with some novel initiatives and instruments. Article 194 of the Lisbon Treaty refers to ‘the establishment and functioning of the internal market’ [in energy], ‘the need to preserve and improve the environment’, ‘a spirit of solidarity between Member States’ and defines the aims of energy policy as inter alia ‘security of energy supply in the Union’. Hence one can speak of an attempt to Europeanize the energy policy. One specific aspect of this is related to the promotion of interconnections of energy networks, an objective that is also explicitly included in the same article of the Treaty. This has been reflected in EU funding which is being extended to some energy infrastructure projects (e.g. LNG terminals, gas and

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electricity interconnectors) as part of the European Energy Program for Recovery and a relatively small funding envelope available for Trans European Energy Networks (European Commission 2010). FSU countries have a vast energy reform agenda, beginning with dominant state ownership in most energy subsectors and heavily distorted price structures. These domestic issues also have a large impact on the production and export outlook for oil, oil products, the gas and coal sectors, and on the security of gas supplies. Hence, it is not surprising that the EU has been promoting several energy-related elements of the ENP. Besides, Ukraine and Moldova joined the Energy Community that should lead to the gradual adoption of important elements of the EU energy acquis and the integration of energy markets between these countries and the EU. There are several broad strategies promoted at the EU level. One of these stresses the importance of a common external policy in the energy sphere, up to the point of creating the mechanisms which would allow the EU to act as a single buyer of gas. One example of an idea in this category is to set up a Caspian Development Corporation that would centralize negotiations with Azerbaijan and CA gas producers as well as with Russian independent producers. It would enter into a long-term agreement and hence create conditions for financing major investment projects in the region. This strategy can be based on the paradigm of the ‘Harmonization Energy Supply’, under which the different ways of energy supply to the EU will be considered mutually supplementary (Papava and Tokmazishvili 2008). It could be argued that only the ‘harmonization’ paradigm secures the protection of the interests of all producer, transit and user countries of oil and gas to the maximum extent possible (Papava and Tokmazishvili 2010). The other options stress the importance of finalizing the EU internal energy market and suggest that external suppliers, e.g. of Russian gas have to enter into legally binding agreements ensuring efficient and secure market functioning. Some other voices point to the importance of introducing new, more effective, globally binding norms applying to trade in energy commodities.

4.5

Outlook for the Future

Taking note of the breadth of political and economic challenges briefly presented above, we conclude by first reviewing the main messages determining the outlook for the EU-FSU energy cooperation and then offering some general policy recommendations. The EU will continue to import large volumes of natural gas for the next few decades, although uncertainty with respect to the actual level of demand remains high. Russia will likely remain the number one energy supplier to the EU in the years to come. It may also continue to serve as the transit country for CA gas. Meanwhile, new gas supplies can reach EU markets mainly from Azerbaijan.

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Ukraine will remain the key gas transit country for the next 5–10 years but its position in the longer term depends on the uncertain gas demand growth prospects in the EU and the future of several new gas pipeline projects that are currently at various stages of discussion or planning. Both EU and FSU countries will need very significant investments in many elements of their energy infrastructure, from exploring and developing new production areas to upgrading transportation corridors and distribution infrastructure. The energy-saving potential is huge, particularly in the FSU region but also in several new EU member states that are currently most vulnerable to hypothetical problems with the instability of gas supplies. The conditions for mutually beneficial cooperation in the energy sphere between energy consuming countries in the EU and the main energy producer and transit countries in the FSU region would be greatly enhanced by limiting the political dimension and addressing key market imperfections. However, both of these objectives are extremely difficult to achieve in practice. In view of the above we see the case for the continued greater involvement of the EU in building its domestic energy market, integrating neighbor countries into this market, supporting energy sector reforms and providing long-term predictability to market players. Some creative solutions may need to be found to mobilize sufficient private investment into the energy sector infrastructure that provides a critical backbone for energy trade, particularly in natural gas. Given the global character of the forces shaping the future of energy sectors in the EU and FSU, it is important for all partners to constructively engage in a global debate on the legal, economic and financial framework for trade in energy resources, investment rules, climate change mitigation strategies, and international funds for the development of energy sectors. The domestic policy agenda remains crucial and open dialogue and exchange of best practices on reform between the EU and FSU countries should be encouraged.

References European Commission (2010) Energy infrastructure priorities for 2020 and beyond – A blueprint for an integrated European energy network. Communication from the European Commission, 17 November 2010. COM(2010) 677 final EWEA (2010) Wind in power. 2009 European statistics. The European Wind Energy Association Paczynski W (2009) CIS gas for Europe – the transit issue. CASE Netw E-Brief 2009 (4) Papava V, Tokmazishvili M (2008) Pipeline harmonization instead of alternative pipelines: why the pipeline ‘Cold War’ needs to end, Azerbaijan in the World. ADA Biwkly Newsl I (10) http://www.ada.edu.az/biweekly/issues/150/20090327030535315.html. Accessed 6 February 2011 Papava V et al (2009) Energy trade and cooperation between the EU and CIS Countries. CASE Netw Rep 83 Papava V, Tokmazishvili M (2010) Russian energy politics and the EU: how to change the paradigm. Cauc Rev Int Aff 4 (2). http://www.cria-online.org/11_2.html. Accessed 6 February 2011

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Chapter 5

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

Abstract This chapter examines the motives behind FDI in four CIS countries (Ukraine, Moldova, Georgia and Kyrgyzstan) based on a survey of 120 enterprises which was conducted before the global financial crisis. The results indicate that non-oil MNE are predominantly oriented at serving local markets. Most MNE operate in the CIS as ‘isolated players’, maintaining strong links to their parent companies, while minimally cooperating with local firms. They secure the majority of supplies from international sources. For this reason, the possibility for spillovers arising from cooperation with foreign-owned firms in the CIS is rather low. The lack of efficiency-seeking investment poses further concerns regarding the nature of FDI in the region. The most significant problems identified in the daily operations of the surveyed foreign firms are: the volatility of the political and economic environment, the ambiguity of the legal system, and the high level of corruption.

5.1

Introduction

The importance of transition economies as investment sites for multinational corporations has drastically increased over the last decade. With economic liberalization in CEE and the FSU, and the rapid expansion of East Asian economies, vast

Acknowledgements: The authors would like to thank researchers from CASE Ukraine, CASE Kyrgyzstan, CASE Transcaucasus, and CASE Moldova for their help in conducting the survey, as well as researchers from CASE Ukraine who prepared background material for this study. A. Kudina Assistant Professor at the Warwick Business School and CASE Fellow e-mail: [email protected] M. Jakubiak Economist at the Directorate General for Trade of the European Commission, former CASE V-President e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_5, # Springer-Verlag Berlin Heidelberg 2011

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market and production opportunities have opened up for multinational businesses. Although several MNE have successfully managed to capitalize on these opportunities, other firms have been significantly less successful in their internationalization efforts. Various internal and external factors have determined the success or failure of multinational businesses in transition economies (Peng and Heath 1996; Luo and Peng 1999). Among the transition economies, the CIS region only experienced a FDI boom in recent years. Its magnitude resembles the FDI flows that poured into CEE countries in the 1990s. They contributed to the rapid productivity growth of local industries and services in CEE and were an important source of modern technology and managerial knowledge. The aim of the current analysis is to explore the motives for FDI in the selected CIS countries (Ukraine, Moldova, Georgia and Kyrgyzstan) and to analyse how the business and industry environment in these countries affects foreign investors. The study targets three groups of investors with potentially different investment motives: market-seekers, resource/labour-seekers and efficiency-seekers (classification based on Dunning 1993). This analysis will complement earlier results, which focused mostly on Russia (Rogacheva and Mikerova 2003; Ledayeva 2007), by showing which aspects of the investment climate are of particular concern to investors in the CIS. The novel feature of this analysis is that it will increase our understanding of the problems that investors are facing in the CIS by differentiating among various investment types. We approached this task by surveying foreign-owned companies located in four CIS countries (120 firms in total). The survey took place in 2007–2008 in Georgia, Kyrgyzstan, Moldova and Ukraine. Hence, the analysis is based on responses which were obtained in the pre-global financial crisis conditions. Oil and resourceattracting countries were dropped from the analysis. Thus, we were able to see analogies with CEE or SEE, which have attracted mainly non-oil FDI. The chapter is organized as follows: Sect. 5.2 discusses the rationale behind FDI. Section 5.3 describes key facts about FDI flows into the analyzed region. In Sect. 5.4, we investigate the survey findings. Section 5.5 concludes and offers some suggestions to policymakers.

5.2

Investment Motives

The literature on FDI identifies the three most common investment motivations: resource-seeking, market-seeking, and efficiency-seeking (Dunning 1993). The availability of natural resources, cheap unskilled or semi-skilled labor, creative assets and physical infrastructure promotes resource-seeking activities. Historically, the availability of natural resources, e.g. minerals, raw materials and agricultural products, has been the most important host country determinant of FDI. Although it is a major FDI determinant, the presence of natural resources alone is not always a sufficient reason for FDI to take place. A comparative advantage in natural resources usually gives rise to trade rather than to FDI. Investment usually

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takes place when resource-abundant countries either lack the capital required for resource-extraction or do not have the technical skills needed to extract or sell raw materials to the rest of the world. In addition, infrastructure facilities for getting the raw materials out of the host country and to its final destination have to be in place or need to be created (UNCTAD 1998). Labor-seeking investment is usually undertaken by manufacturing and service MNE from countries with high real labor costs. These MNE set up or acquire subsidiaries in countries with lower real labor costs to supply labor-intensive intermediate or final products. To attract such production, host countries often set up free trade or export processing zones (Dunning 1993). Market-seeking investment is based on factors such as the host country’s market size and growth and per capita income. For firms, new markets provide a chance to stay competitive and grow within the industry as well as achieve economies of scale. In some instances, policies sheltering domestic markets from international competition through high tariffs or import quotas trigger the ‘tariff-jumping’ motive of FDI (UNCTAD 1998, p. 107). Apart from market size and trade restrictions, MNE may engage in market-seeking investment when their main suppliers or customers have set up foreign producing facilities and, in order to maintain their business, they must follow them overseas (Dunning 1993, p. 58). The motivation of efficiency-seeking FDI is to rationalize the structure of established resource-based or market-seeking investment in such a way that the investing company can gain from the common governance of geographically dispersed activities. An efficiency-seeking MNE aims to take advantage of different factor endowments, cultures, institutional arrangements, economic systems and policies, and market structures by concentrating production in a limited number of locations to supply multiple markets. In order for efficiency-seeking foreign production to take place, cross-border markets must be both well-developed and open, thus it often flourishes in regionally integrated markets (Dunning 1993, p. 59). However, it is worth noting that many of the larger MNE are pursuing pluralistic objectives and most engage in FDI that combines the characteristics of each of the above categories. The motives for foreign production may also change as, for example, in the case of a firm that becomes an established and experienced foreign investor (Dunning 1993, p. 56).

5.3

FDI Inflows in the CIS

FDI inflows to the entire CIS averaged about USD 39 billion a year in 2000–2009, yet started to be significant in the second half of the 2000s. Nearly two third of all inflows since 2000 (USD 24 billion a year on average) went to Russia (see Fig. 5.1). This investment was mainly directed towards the extraction and transportation of energy resources. Another CIS country with abundant energy-resources, Kazakhstan, attracted USD 6 billion per annum during 2000–2009. During the same period, Ukraine

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USD million

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Russian Federation Kazakhstan Ukraine Belarus Turkmenistan* Armenia Georgia Uzbekistan Azerbaijan Moldova Kyrgyzstan Tajikistan

10,000 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 -10,000

Fig. 5.1 FDI inflows to the CIS, 1997–2009 Note: *– Turkmenistan was in the CIS in 1991–2005; associate member since 2005 (From UNCTAD)

attracted more than USD 4 billion per annum. FDI inflows to the three countries mentioned went down in 2009 following the global economic crisis; nevertheless in both Russia and Kazakhstan, FRI remained at levels well above those from the early 2000s. The highest FDI stocks per capita in the CIS were recorded by energy-producing and energy-transit countries (Fig. 5.2). Kazakhstan accumulated over USD 4,600 per capita in 2009. The FDI stock per capita in Russia and in Georgia was close to USD 2,000, and those of Turkmenistan, Armenia, Ukraine and Azerbaijan were over USD 1,000. Some CIS economies are very FDI-dependent, although their FDI per capita is not very high. Tajikistan has been the extreme example here. FDI inflows in the 2000s accounted for the majority of investment in this country, which reflected the lack of domestic resources. In 2007–2008, over one third of overall investment in resource-rich Turkmenistan and Kazakhstan and in consumption-driven Moldova and Georgia was brought in by foreigners. On the other hand, Uzbekistan, Belarus and Azerbaijan are not FDI-dependent. Less than 15% of all investment in these countries came from foreign firms in 2006–2008. In our research, oil and resource-attracting countries were dropped from the analysis as we wanted to capture possible analogies with the CEE/SEE countries (which have attracted mainly non-oil FDI). FDI in the CEE/SEE countries contributed to a major growth in productivity, which is why this kind of investment is of interest. Taken together, the survey covers countries that attracted about 16% of the overall FDI flows to the CIS in 2007.

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Kazakhstan Russian Federation Georgia Turkmenistan* Armenia Ukraine Azerbaijan Belarus Moldova Kyrgyzstan Uzbekistan

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Tajikistan

Fig. 5.2 FDI stock per capita in the CIS in 2007 and in 2009 Note: * – Turkmenistan was in the CIS in 1991–2005; associate member since 2005 (From UNCTAD)

5.4 5.4.1

Survey Results Survey Design

The survey was addressed to 120 foreign owned-companies located in Georgia, Moldova, Kyrgyzstan and Ukraine (30 in each country). The median company in our sample had been in business for 8 years, had revenues of about USD 4.7 million, and employed 145 people. Their industry structure reflected the sectoral distribution of FDI in these countries. Most of them are active in the financial services, the food industry, trade, transport and communications, and construction. Representatives of companies were asked identical questions about the reasons to invest in the CIS, their business environment, and impediments to their everyday activities.1 The survey was conducted in 2007 – early 2008. Hence, its results and conclusions do not account for the impact of the global financial crisis.

1

See Kudina and Jakubiak (2008) for the detailed list of questions and results of the survey.

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Factors Attracting Investors into the CIS

One of the main objectives of our survey was to explore the nature of FDI that is flowing into CIS. Following Sect. 5.2, we distinguished three types of investment motives: market-seeking, resource-seeking and efficiency-seeking. We tested the investment motives by asking interviewees to answer several questions: about the strategic role of the subsidiary established in the host CIS country, about their investment motives, and about the share of exported production.

5.4.3

Market Seeking

This motive clearly appeared to be the dominant one in the sample. Most of the companies that participated in the survey held a substantial share of the recipient country’s market. The average domestic market share for Ukrainian and Kyrgyz firms was close to 30%, while Moldovan investors held leading positions with an average market share of about 47%. Only in Georgia did foreign investors estimate that they possessed less than 20% of the local market share. This meant that the majority of the surveyed firms not only managed to supply their host markets, but had also secured dominant positions in these markets. The percentage of exports in the production of final and intermediate goods was rather low at 17% and 30% on average, with the exception of Moldova, where the majority of both intermediate and final goods were exported. About 70% of all production of final goods was earmarked for local markets. Some companies even mentioned that they faced a lot of problems when trying to export their products to other countries, particularly to Russia. The role of the CIS affiliates as suppliers of their parent companies’ products to the host country market and to other CIS markets was found to be rather important (see Fig. 5.3). The companies noted a high level of demand in the growing markets, which was considered positive for the further expansion of their businesses. This outcome is supported by the assessment of investment motives. The interviewees were asked to grade reasons for initiating business activity in the CIS by ranking each of the options on a scale from 1 (unimportant) to 5 (very important). Most companies mentioned the ability ‘to serve the host country market’ as the most important motive in all four economies (see Fig. 5.4). On top of this, companies in Moldova and Kyrgyzstan mentioned the ability to avoid import duties while supplying the domestic market as another reason to invest.

5.4.4

Resource-Seeking

The second and third most important investment motives varied across the countries, although they were predominantly concentrated on the use of low-cost

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not important cannot say very important 0

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supply existing products to host country and other CIS markets

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Fig. 5.3 Strategic roles of CIS subsidiaries in the operations of their parent companies Note: Numbers are simple averages (From survey results)

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5

to serve host country market skilled labor Ukraine Moldova availability of low-cost inputs

Kyrgyzstan Georgia

access to regional market

access the host country research and technological expertise

Fig. 5.4 Reasons to invest in the CIS Note: Numbers are simple averages. A higher number indicates a more important reason (From survey results)

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factors of production (including natural resources) and skilled labour. In Ukraine and in Georgia, the second most important motive was the availability of low-cost input factors, i.e. cheap labour, energy and raw materials. This was explained by the availability of rich natural and a cheap labour force and by the close proximity to the EU in the case of Ukraine. In the case of Georgia, it could probably be explained by high investments in pipeline transportation. The decision to invest in Kyrgyzstan was made based on Kyrgyz skilled labour, followed by the availability of low-cost input factors. Interestingly, the second most important motive for investing in Moldova was the ability to access the new regional market (in CEE), which might relate to the country’s proximity to the new EU member states. This motive could also be attributed to the willingness to exploit Moldovan labour and other resources (ranked as the third most important motive). The possibility to access regional markets was also found to be an important factor for investors in Georgia (meaning access to whole Southern Caucasus) and in Kyrgyzstan (CA).

5.4.5

Efficiency-Seeking

Access to a country’s research and technological expertise was found to be the least important reason to invest in the CIS (see Fig. 5.4), which suggested that investors did not yet seek efficiency in the CIS. This was confirmed by the answer that the exploitation of cost-effective production in the CIS for the purpose of exporting products to the EU was not important for the strategy of the parent companies. Moreover, the surveyed firms exported rather small volumes of intermediate goods (17%), which meant that they are weakly integrated into vertical production chains. There was one exception: foreign subsidiaries producing intermediate goods in Moldova exported over 50% of their output. The survey results indicated that market-seeking is the predominant motive for investing in the four analyzed countries. The second most important motive was seeking resources. Foreign investors did not yet seek efficiency in the surveyed CIS firms.

5.4.6

Industrial Organization of FDI in the CIS

When analyzing industry-specific FDI determinants, we relied upon Jacobides (2008). He assumes that cross-country differences in the use of factors of production in the vertically integrated production chains shape globalisation prospects and the effects that FDI may produce in a host country. Hence, the second part of our questionnaire was designed to reveal the impact of FDI on a recipient country. The companies were asked to estimate the extent to which their businesses could be divided into separate components and the degree of similarity of vertical and horizontal value-chain structures between home and recipient countries. They

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were also asked for feedback on the performance of their CIS subsidiary. Some additional questions allowed us to draw conclusions on the importance of industrylevel FDI determinants. Recipients estimated the similarity of industry value-chain structure at 3.4 points (on a scale from 1 to 5 where 1 is not similar and 5 is very similar). Country averages did not differ much, though the answers given by investors in Moldova suggested a higher degree of similarity. When asked to distinguish between the differences similarities in vertical and horizontal industry structures (referring to the vertical structure in terms of the systems of inbound logistics, manufacturing, outbound logistics, and organised sales), and horizontal industry structures (defined as the number of industry participants, their functions and market shares), the respondents gave similar answers, broadly indicating that they were unable to assess the degree of similarity dissimilarity of vertical vis-a-vis horizontal value chains. The differences between home and host country value-chain structures were not perceived as a significant impediment for business expansion in the recipient country. The total average was estimated at 2.0 points, while the results varied among countries. Foreign companies that established their businesses in Kyrgyzstan estimated the impact of different structures as insignificant (1.2 points), Ukrainian and Georgian ones as rather insignificant (2.1 and 2.0 points respectively), while the impact on Moldovan subsidiaries was unknown (2.8 points). The activities of foreign affiliates depended largely on the parent companies’ multinational businesses. 42% of companies’ value chain components were supplied from the home countries, while only 17% were provided by local suppliers. An especially large share of value chain components (about 60%) was imported by Ukrainian foreign affiliates, whereas Moldovan, Georgian and Kyrgyz companies imported only 21%, 46% and 39% respectively. Ukraine’s reliance on imports could be explained by the fact that many of the firms that participated in the survey were engaged in retail trade. The majority of imported value chain components (received from parent companies) were technologies and know-how (42% of total), followed by materials (24%). Components and parts accounted for about 20% and final products accounted for about 14%. As for the open option, the majority of Ukrainian companies reported that marketing technologies brought from parent companies were highly valuable. Also, in all the countries surveyed, financing and working capital were named as important resources received from a parent company. Among other resources mentioned were consulting services with regard to major business processes and equipment. The companies were also asked to comment on the degree to which the success of their businesses depended on the performance of local and multinational partners. The results showed that, on average, the success of the subsidiary’s operations depended more on the performance of international industry participants (3.4 points) than on the performance of local industry participants (3.0 points). This confirmed the earlier findings about the importance of a parent company and its multinational links to subsidiaries. Unfortunately, the local environment was not

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developed enough to offer the companies products of the required quality for their business, so they had to maintain close links with their international partners. The average number of key local suppliers was significantly below the number of key local customers/distributors; the total averages for the four countries were 18 and 74 respectively. This result supported the previously described finding on the predominantly market-oriented nature of investment in the CIS. While much of the resources were supplied from abroad, the final products were targeted to internal markets, which explained the higher number of local distributors and customers. Overall, the results suggested rather minimal opportunities for technological spillover from FDI to domestic firms. In CEE, the highest productivity-increasing gain for local firms took place when foreign-owned, technologically superior firms bought local supplies, taught suppliers and made them acquire new technologies. Only then did positive technological spillovers occur. However, in our sample, spillovers from FDI, even if they existed, were limited to certain firms and/or sectors. Moldova had the most favourable suppliers-to-customers ratio, which suggested that the potential for spillovers might be the highest there. However, even in Moldova, the average number of domestic customers of a foreign subsidiary was three times higher than the average number of local suppliers. Foreign firms in the surveyed CIS markets seemed to buy supplies locally only when necessary, and concentrated instead on capturing domestic demand.

5.4.7

Major Impediments

In order to investigate the investors’ assessment of the investment climate in the CIS, we asked them to name the greatest impediments to doing business. Each of the respondents ranked the importance of problems from 1 to 5 (with 1 being the least important and 5 being the most important). The most urgent problems named were the volatility of the political environment, the uncertainty of the economic situation, the ambiguity of the legal system and the high level of corruption. However, the top three problems differed among countries. Political and economic instability together with the lack of physical infrastructure were of particular concern for the foreign companies operating in Kyrgyzstan and Georgia. All other problems (with the exception of finding a business partner in Georgia) were deemed relatively less important in light of the three mentioned above. Ukraine and Moldova were more stable in political terms and foreign investors perceived extensive bureaucracy, corruption and uncertainties connected to domestic legislations as the main obstacles for their businesses. Neither the difficulties related to the newly-established Ukrainian government in late 2007, nor the problems with the uncertain status of Transnistria in Moldova were cited by foreigners as major obstacles to expanding business activities in these two countries. A high level of corruption in the CIS was widely acknowledged as a serious deterrent to FDI inflows, as confirmed by the Corruption Perception Index of 2006, in which Ukraine was ranked 104th and Kyrgyzstan 145th out of 163 developed and

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developing countries. Interestingly, Moldova ranked relatively better at 81st (Transparency International 2007). Perception of corruption in Georgia was relatively low, most likely due to the successful efforts of the Georgian authorities to fight petty corruption. Problems in establishing clear property rights appeared to be relatively important obstacles faced by firms operating in Ukraine and Moldova, but were not ranked highly in Georgia or Kyrgyzstan. The existing infrastructure, technology and management skills of the local workforce did not seem to be much of a problem for foreign investors operating in Ukraine and Moldova, however they were perceived as important obstacles in Georgia. Finding a suitable partner did not seem to be a problem either in Ukraine or in Kyrgyzstan, whereas it was identified as a relatively important obstacle in Moldova and Georgia. Among other impediments, investors mentioned problems with tax administration, which involves difficulties in paying taxes, in obtaining VAT refunds, and in dealing with complicated tax regulations (Tables 5.1 and 5.2). Table 5.1 FDI inflows in % of domestic investment in CIS, 1997–2008 (From UNCTAD) Countries 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Armenia 19.5 72.0 40.3 29.6 18.7 22.1 18.7 29.0 16.4 20.0 19.7 24.4 Azerbaijan 71.7 60.0 27.2 10.7 17.3 65.5 85.4 71.0 30.7
9.4
67.1 0.2 Belarus 9.9 5.1 13.9 3.9 3.5 7.9 4.1 2.8 3.8 3.2 12.6 10.9 Georgia 37.4 28.7 11.2 16.9 12.5 19.3 31.4 35.0 25.2 59.1 66.9 54.3 Kazakhstan 36.7 33.4 52.7 40.5 53.9 43.8 29.3 38.2 12.4 26.0 35.3 43.6 Kyrgyzstan 37.2 50.6 22.2
0.9 1.9 1.8 17.1 40.3 10.7 27.4 21.9 22.6 Moldova 20.5 20.2 17.5 64.1 41.7 31.0 20.1 26.6 26.0 24.1 35.9 34.3 Russia 6.6 6.3 11.7 6.2 4.7 5.6 10.0 14.2 9.5 16.2 20.2 20.5 Tajikistan 11.1 16.9 3.7 36.9 9.8 46.9 25.3 126.3 21.2 102.1 68.4 91.2 Turkmenistan 9.7 4.8 8.2 9.1 12.1 22.1 18.6 29.7 30.3 44.3 43.5 39.7 Ukraine 6.2 9.0 8.1 9.6 10.6 8.5 13.8 11.7 41.2 21.1 25.1 22.2 Uzbekistan 3.2 3.1 2.6 2.3 3.5 3.0 3.9 6.6 6.0 5.5 14.9 13.2 Table 5.2 Assessment of problems faced by foreign investors in the CIS (From survey results) Total Problem Ukraine Moldova Kyrgyzstan Georgia average Volatility of the political environment 3.4 3.3 4.5 2.8 3.5 Uncertainty about the economic environment 3.3 3.4 4.4 2.9 3.5 Ambiguity of the legal system 3.9 3.5 3.5 2.7 3.4 Corruption 4.0 3.9 3.1 2.1 3.3 Bureaucracy 3.9 3.9 3.1 2.0 3.2 Lack of physical infrastructure 2.5 2.8 3.9 2.9 3.0 Backward technology 2.4 2.9 3.1 2.4 2.7 Lack of business skills 2.4 2.6 3.1 2.7 2.7 Finding a suitable partner 2.5 2.9 2.3 2.8 2.6 Problems in establishing clear ownership conditions 3.2 2.9 1.7 2.4 2.6 Numbers are simple averages; a higher number indicates a more important impediment.

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Conclusions

In this chapter, we analyzed the motives of FDI flowing into the CIS. We also explored the problems which foreign investors encounter in the four surveyed countries. In our sample, market-seeking was a dominant motive for investors. The companies held substantial shares of recipient country markets, and exported only a small portion of their products. The growing CIS markets produced high demand, which foreign investors aimed to capture by expanding their businesses to this region. This motivation was similar to the motivation of foreign investors in the CEE countries in the early 1990s. The second and third most important investment motives varied across the countries, though they were predominantly focused on the use of low-cost factors of production (including natural resources) and skilled labour. We expect that together with closer integration with the global economy and the EU (especially in case of the European CIS countries involved in the ENP – see Chaps. 3 and 9) and the fall in overall protection, low-cost CIS labour will attract new waves of investments, similar to what has been happening in CEE and SEE. It is very important, however, that the skills of the CIS labour force will be able to match the potential investors’ needs at that stage. Investors have not yet sought efficiency by producing in the CIS, which has been one of the key reasons for investment in CEE/SEE. There is a need to address several impediments including political instability, extensive bureaucracy, corruption and uncertainties connected to domestic legislation so that they do not override the potential profits from using cheap CIS labour.

References Dunning JH (1993) Multinational enterprises and the global economy. Addison-Wesley, Workingham Jacobides MG (2008) Playing football in a soccer field: value chain structures, institutional modularity and success in foreign expansion. Manag and Decis Econ 29 Kudina A, Jakubiak M (2008) The motives and impediments to FDI in the CIS. CASE Netw Stud and Anal 370 Ledayeva S (2007) Spatial econometric analysis of determinants and strategies of FDI in Russian regions in pre- and post-1998 financial crisis periods. Bank of Finland, BOFIT Luo Y, Peng MW (1999) Learning to compete in a transition economy: experience, environment, and performance. J Int Bus Stud 30(2):269–296 Peng MW, Heath P (1996) The growth of the firm in planned economies in transition: institutions, organizations, and strategic choice. Acad of Man Rev 21(2):492–528 Rogacheva E, Mikerova J (2003) European FDI in Russia: corporate strategy and the effectiveness of government promotion and facilitation. OCO Consulting, September Transparency International (2007) Global Corruption Report. http://www.transparency.org/ policy_research/surveys_indices/cpi/2007 UNCTAD (1998) World Investment Report. United Nations Conference on Trade and Development. http://www.unctad.org/templates/WebFlyer.asp?intItemID¼2426&lang¼1

Chapter 6

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

Abstract In this paper, we assess the demographic and economic consequences of migration in Europe and neighborhood countries using a multi-region CGE-OLG model (INGENUE2). Our quantitative results shed some light on the long-term consequences of migration in regions that are not at the same stage in the ageing process. Despite some improvement in the public pension systems of host regions, it appears that a realistic migration scenario does not offset the effect of ageing in host regions, leaving room for more pension reforms. The adverse economic consequences of emigration appear to be more serious if the region of origin is advanced in the ageing process. Finally, we consider and evaluate a policy of immigration in which the decline of the labor force in WE is eschewed.

6.1

Introduction

In the past few years, the pace of international migration has accelerated and this phenomenon is likely to continue in the coming decades as part of the globalization process. During the twentieth century, WE was one of the major host regions

Acknowledgements This paper reflects the opinions of the authors and does not necessarily express the views of the institutions they are affiliated with. We thank Fre´de´ric Docquier and Abdeslam Marfouk for transmitting their dataset. We are grateful to Marek Dabrowski, Maryla Maliszewska, Toman Omar Mahmoud, Ainura Uzagalieva, Cyrille Schwellnus, Martine Carre´ and Agne`s Be´nassy-Que´re´ for useful comments and suggestions. The usual disclaimers apply. V. Borgy Banque de France e-mail: [email protected] X. Chojnicki Equippe, University Lille 2 and CEPII e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_6, # Springer-Verlag Berlin Heidelberg 2011

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of migrants, together with the US. At the end of the last century, about 30 million foreigners were established in WE. The migrants, who come from diverse backgrounds, have contributed significantly to population growth in WE in recent years, in many cases overtaking natural population increases. The most recent period has been characterized by new migration, especially from CEE and the CIS. Indeed, migration flows between and within these regions increased after the Soviet Union began to disintegrate. In the early 1990s, the annual number of officially recorded net migrants from CEE to Western countries was around 850,000, more than twice the figure of the three preceding decades (Salt 2005). The early period of transition was marked by ethnic and conflict-driven migration, while later, as the political situation stabilized, migration became mainly economically motivated. In this respect, migration and the associated policies became an important socio-economic issue both in receiving and in sending countries. As one of the main channels of interdependency among economies, immigration is a longstanding concern for policy-makers and has been alternately considered as a challenge or an opportunity for WE. Since the frontiers of a larger Europe are not well-defined, it might be relevant to assess the consequences of tighter links between WE and regions perceived to be in its backyard. Such structural change is already in motion through the opening to larger flows of migrants from nearby territories. The ENP could define the migration policy between Europe and some specific countries more precisely. Immigration is one policy option that may alleviate the financial burden on the public retirement system and sustain the growth rate of the working-age population. Some political leaders also seem ready to embrace the idea that an influx of migrants is the best way to save European pension systems by limiting the increase of the dependency ratio and of the contribution rate. Future trends in migration could have more substantial demographic consequences than what has been observed in the past. As fertility is now below replacement in Europe, policies to encourage immigration may become an important means for the EU to moderate its rates of population decline. The EU policy strategy on migration has been presented in several official publications (see European Commission 2006, for instance). The building of a common EU policy on labor immigration is presented in detail by Carrera (2007). This question appears all the more crucial now that numerous CIS countries (which constitute good candidates as emigration countries) are very advanced in the ageing process and are already suffering from a declining population. Discussions on the macroeconomic effects of immigration have become a focus of policy and media attention in WE, following the surge in the labor-force from new member states as well as neighborhood regions. To the best of our knowledge, very few researchers deal with this question using a general equilibrium applied approach. However, there are several reasons to adopt an open economy approach when addressing multi-country issues. First, the world economy is becoming increasingly interdependent. The deepening of the globalization process is reflected in increased levels of international trade, financial integration and international labor mobility. Second, current population structures and demographic projections

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for the various regions of the world show that the ageing process is not synchronous. This difference in the time profiles of demographic changes suggests that one mechanism which could ease the pressure on pension systems is inter-temporal trade in the form of international capital flows (see Brooks 2003; B€orsch-Supan et al. 2006; Aglietta et al. 2007; Krueger and Ludwig 2007). Third, together with international capital flows, international migration is a key feature in the process of income convergence between countries. Hence, international macroeconomic models are required to accurately assess the cost and benefits of such policies. Most of the literature on the macroeconomic consequences of migration is based on CGE-OLG models of ‘closed’ economies.1 Only two studies (Fehr et al. 2003, 2004) have dealt with international migration using multi-country open-economy CGE-OLG models. Even if an open-economy framework is used, the impact of immigration on the sending countries and on inter-country inequalities is not addressed, as the regions losing migrants are not explicitly modeled. Compared to these studies, our approach offers a global vision of the consequences of international migration. Indeed, the value added of our model is that it is able to analyze the effects of international migration on both the destination and the origin regions. Consequently, the following questions are addressed: What is the impact of migration on economic growth, capital accumulation, consumption, pension schemes and the current accounts of sending and receiving countries? Can immigration from neighborhood regions help mitigate the adverse effects of population ageing in WE? We show that the financing of the PAYG pension system is noticeably improved in the regions receiving migrants (WE and the ‘Slavic world’ in the migration scenario presented here). Concerning the regions losing migrants, the adverse consequences of emigration are more important the more the region is advanced in the ageing process (and is already suffering from a declining population). In this regard, our analysis provides some quantitative results that allow us to compare the consequences of emigration for EE and the ‘Mediterranean world’, two regions that are not at the same stage of the ageing process. We also quantify the migratory inflows of workers necessary to stabilize the WE working age population and evaluate the consequences of such an increase in migration flows. The rest of this chapter is organized as follows. The macroeconomic model is presented in Sect. 6.2. Calibration of migration flows is described in Sect. 6.3. The economic study of migration from neighborhood countries follows in Sect. 6.4. Finally, Sect. 6.5 concludes.

1

Storesletten (2000) investigates whether a reform of immigration policies could attenuate the fiscal burden of ageing in the coming decades. Iakova (2007) and Barrell et al. (2007) consider the macroeconomic effects of the migration that followed the enlargement of the EU in May 2004. Chojnicki et al. (2011) examine the economic impact of the second great immigration wave (1945–2000) on the US economy.

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INGENUE2: A Long-Term Model for the World Economy

Our economic simulations were made using the CGE-OLG model INGENUE2.2 The world is divided into ten regions according to mainly geographical and demographic criteria (Fig. 6.1). The neighborhood countries (CIS and Mediterranean countries) are included in three regions: the ‘South Asian world’ (including Kazakhstan and Tajikistan), the ‘Slavic world’3 (including Russian Federation, Ukraine, Belarus and Republic of Moldova) and the ‘Mediterranean world’ (including Armenia, Azerbaijan, Georgia, Kyrgyzstan, Turkmenistan, Uzbekistan, together with the Maghreb and Middle-East countries). Each region is made up of four categories of economic agents: the households, the firms, a fictive world producer of a world intermediate good and a PAYG pension system.

Western Europe

Eastern Europe

North America

Latin America

Japan

Mediterranean World

Eastern Asian World

Africa

Slavic World

South Asian World

Immigration and emigration countries

Migration flows

Made with Philcarto: http://perso.club-internet.fr/philgeo

Fig. 6.1 The ten regions of the INGENUE2 model (from authors sources)

2 For technical features of the new INGENUE2 model, as well as for the baseline scenario and a sensitivity analysis of the main structural parameters, see INGENUE (2006, 2007). For a detailed presentation of the results described in this chapter, see Borgy and Chojnicki (2009). 3 The ‘Slavic world’ of the model corresponds more precisely to the East Slavic area (with the exception of Moldova included in this region for analytical purposes).

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6.2.1

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Household Behavior

The period of the model is set to 5 years. In each region, the economy is populated by 21 overlapping generations who live up to a maximum age of 105. The individual life-cycle of a representative agent is described in Fig. 6.2. Between ages 0 and 19, the agents are children and are supported by their parents. Given the specificities of developing countries, we assume that children can begin to work at age 10 but their income is included in their parents’ income. At age 20, agents become independent and start working. When they become independent, individuals make economic decisions according to the life cycle hypothesis. A voluntary bequest is left to children at age 80 conditional on survival until 80. In the budget constraint, the expenditures consist of consumption (including costs of children) and saving during each age and each period. On the income side there is: first, the return on accumulated savings corrected by one-period survival probabilities. Second, there is non-financial income that depends on age, labor income (after social security taxes) adjusted by a region-specific age profile of labor force participation for people in full labor activity, a mix of labor income and pension benefits for people partially retired (reduced labor activity), and full pension benefits for people entirely retired. The lifetime utility is maximized under the intertemporal budget constraint, taking prices, social contributions and benefits as given.

6.2.2

The Public Sector

The public sector is reduced to a social security department. It is a PAYG public pension scheme that is supposed to exist in all regions of the world. It is financed by

Fig. 6.2 The individual life cycle (from authors sources)

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a payroll tax on all labor incomes and pays pensions to retired households. The regional PAYG systems operate according to a defined-benefit rule. The exogenous parameters are the retirement age and the replacement ratio. They are region-specific and are fixed to their 2000 value through the entire projection period. Regional contribution rates are determined so as to balance the budget, period by period. In the baseline scenario, the European pension system is characterized by two major features, which we have assumed to be untouched all throughout the twentyfirst century: a low legal retirement age (62.5 years on average) and a fairly high replacement rate (45%). The implementation of pension reforms in WE seems absolutely necessary if we look at the evolution of the contribution rate to the public retirement system. In the baseline case, the contribution rate increases from 17.1% in 2000 to 31.9% in 2050 (Table 6.3). EE and the ‘Slavic world’ will also face a strong increase in their contribution rate even if their pension system is less generous at the beginning of the period compared to WE. On the contrary, the ‘Mediterranean world’ and the ‘South Asian world’ are clearly less affected by pension financing difficulties given the dynamics of their populations as well as their underdeveloped pension schemes.

6.2.3

The Production Side and the World Capital Market

We assume that different regions produce different imperfectly substitutable intermediate goods using labor and capital. In the spirit of Backus et al. (1995), we assume that the domestic composite final good of each region is produced according to a combination of the domestic intermediate good and a homogenous world good imported by the region from a world market. In order to simplify the exchanges of intermediate goods between regions, this homogenous world good is produced by a fictive world producer as the output of a combination of all intermediate goods exported by the regions. In each type of sector, firms act on competitive markets. They maximize their profit under their production constraint, taking prices as given. In the domestic intermediate goods sector, the constraint is intertemporal since the production function depends on the stock of capital that is depreciated and accumulated. Intermediate goods producers thus maximize net present value of future cash flows, i.e. production values minus wage cost and capital cost. The latter depends on the depreciation rate, which itself is affected by international capital market imperfections. More precisely, the depreciation rate is asymmetrically dependent on the ownership ratio, defined as the ratio of the total wealth of households to the capital stock. Indeed, firms located in countries that are indebted to the rest of the world borrow at a higher interest rate than the world interest rate and this ‘indebtedness premium’ is proportional to the financial market exposure of each firm (measured by the ownership ratio). At equilibrium, the marginal return of capital thus depends on the net external position.

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6.2.4

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Technological Catch Up

All production functions are augmented by TFP coefficients at constant prices. We define TFP as a Hicksian neutral technological progress in a Solow growth model. This means that there exists a production frontier that shifts over time. The level of TFP is exogenous and grows at a constant rate in each region. From 1950 until 2000, the growth rate of TFP is given by historical data (see Heston et al. 2002). After this date the rate of growth of the TFP is the result of a given, exogenous growth of 1.1% per annum in the North American region, supposed to be the technological leader, and a region-specific exogenous, catching-up factor, reflecting the international diffusion of technological progress. In the baseline scenario of the model, three regions have a sustained catch up process: the takeoff in the Eastern Asian and South Asian regions which started in the 1990s is assumed to gain momentum. EE is also assumed to be a fast-growing region due to its EU membership. Figure 6.3 gathers the profiles of TFP in the world regions of the INGENUE2 model.

6.3

Introducing Migration

Unlike fertility and mortality, which are in transition worldwide from high to low levels in a long historical process, there is much more uncertainty concerning net migration (see Alho and Borgy 2007). Therefore, migration projections have no

100%

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20%

0% 1950 1960 1970 1980 1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100 N. America Africa

W. Europe Russia

Japan China

S. America India

Mediterranean E. Europe

Fig. 6.3 Total factor productivity (% of North America level) (From authors’ estimation)

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strong and consistent trend that can serve as the backbone of credible projection assumptions for the future, particularly in the case of EE and CIS countries, which recently experienced a migration crisis. For this reason, it is important to assess the migration potential of these regions by analyzing the main driving forces of past and recent trends.

6.3.1

Population Projection Method

Population evolution is calculated according to a standard population projection method on the basis of historical and prospective UN data. In the baseline scenario, we implicitly assume that there are no migration flows in the future.4 Then, we build a comprehensive migration scenario to analyze the demographic and economic consequences of international migration for Europe and the neighborhood regions. For that purpose, an immigration shock is introduced into the model as an increase in the number of young adults (aged between 20 and 24). After crossing the border, immigrants automatically become natives in an economic sense, i.e. they have the same preferences and fertility behavior as natives and adjust to the productivity and activity rates of the host region. As in Storesletten (2000), we assume that immigrants move into receiving countries without any capital (note that natives have no wealth at the same age).5 However, this choice seems to have a minor impact for the results since most immigrants actually move before the age of 30, i.e. at the beginning of the wealth accumulation process. In the INGENUE2 model, the neighborhood countries are included in three regions: the ‘Slavic world’, the ‘South Asian world’ and the ‘Mediterranean world’. For the ‘South Asian world’, we only calibrate migration flows for the two CIS countries that are included in this region: Kazakhstan and Tajikistan. For the ‘Mediterranean world’, we distinguish migration flows from North Africa (Algeria, Egypt, Libya, Morocco, Tunisia and Western Sahara) and from the CIS countries (Armenia, Azerbaijan, Georgia, Kyrgyzstan, Turkmenistan and Uzbekistan). The ‘Slavic world’ is composed of the Russian Federation, Ukraine, Belarus and Moldova.

6.3.2

Evaluation of Current Migration Flows

We only focus on migration that concerns Europe and CIS regions. WE has experienced net inflows of migration for four decades and represents the second

4

See Ingenue (2007) for a complete description of the baseline. All these assumptions are necessary to avoid problems of agent heterogeneity that would complicate the computation of the transitory path. 5

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major immigration area with 21% of the total immigrant stock (United Nations 2004). EE and the former Soviet Union have a stock of migrants of around 15% of the total. Migration in these regions follows a broad biaxial pattern: on one axis a migration system developed among the countries of WE, CE and EE and, on the other, a system of movement between the CIS countries (World Bank 2006). Finally, North Africa is characterized by a predominant labor migration to Europe. Following these facts, we distinguish four types of regions in the model: • Pure immigration zones only face inward flows: WE • Pure emigration zones only face outward flows: ‘Mediterranean world’, ‘South Asian world’ • Intermediate zones face in- and outflows simultaneously: EE, ‘Slavic world’ • No migration zones are isolated from the international mobility of workers (there is thus neither immigration nor emigration): North America, Latin America, ‘Eastern Asian world’, Africa We then adopt a calibration process that allows us to make actual net migration flows compatible with our multi-region description of the world using different data sources. First, we aggregate net migration flows by countries used in the medium variant of 2004 UN population projections (United Nations 2004) to correspond to the INGENUE2 regional grouping. Then, we calibrate immigration flows to WE, EE and the ‘Slavic world’ based on UN figures after removing intra-regional flows (for example German migration to France) as well as non-pertinent flows (for instance WE migration to North America). Given the global aspect of our model, immigration in host regions has to correspond to emigration in sending regions. Thus, we have to allocate immigration flows to WE, EE and the ‘Slavic world’ by origin regions. For that purpose, we first use the emigration stocks and rates of 195 origin countries built by Docquier and Marfouk (2006) to allocate the immigration flows to WE between our pure emigration zones. However, Docquier and Marfouk (2006)’s database only focuses on OECD members as receiving countries and there is no information on migration flows to EE and the ‘Slavic world’. Thus, for EE, the ‘Slavic world,’ and the CIS countries, we use data from the World Bank report (2006) and Salt (2005). Table 6.1 presents the calibrated net migration flows by regions in 2005 and Fig. 6.1 shows the direction of migration flows by region as well as countries affected by migration. The calibrated flows appear lower than the UN official net flows because we only focus on WE and neighborhood regions.

6.3.3

Estimation of Future Migration Flows

We have to reproduce this methodology for each future 5-year period. Some migration flows are durable (lasting for decades) and relatively predictable. Conversely, assessing migration potential in CIS countries is a very complex task requiring a careful consideration of factors apart from economic and demographic

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Table 6.1 Yearly net migration flows by origin and destination countries in 2005 (in thousands) (From Docquier and Marfouk (2006), Salt (2005), United-Nations (2004), World Bank (2006); authors’ calculations) Destination countries Region/country

Origin Countries

Mediterranean World – North Africa – CIS & Middle East South Asian World EE Slavic World – Russia – Ukraine Total Immigration

WE

EE

Slavic World

Total emigration

124.6 112.9 11.7 9.8 74.6 47.1 29.6 17.5 256.1

0.8 0.5 0.4 0.2 0.0 23.7 5.0 18.7 24.7

53.2 0.0 53.2 54.6 0.0 18.2 0.0 18.2 126.0

178.6 113.3 65.3 64.7 74.6 89.0 34.6 54.4 406.9

ones. Indeed, a study by the World Bank (2006) suggests that the nature of labor migration in CIS countries has changed since the beginning of the reforms. Initially, emigration from these countries was caused primarily by political and ethnic motives. In recent years, economic reasons have become increasingly more important and workers move mainly for higher incomes, better job opportunities and a better quality of life. However, the size of migration flows observed in the 2000s may be unsustainable in the future due to negative population dynamics in most CIS countries. Consequently, future migration flows from the CIS cannot be evaluated by simply extending recent flows. Thus, the main driving forces of the past and recent migration flows from the CIS have to be fully analyzed. For these reasons, we use Chojnicki and Uzagalieva’s (2008) estimation of potential migration flows from the CIS to WE and Russia based on economic and demographic factors but also on other factors such as the ethnic background of migrants, the political situation and migration policies in CIS countries.6 In concrete terms, the scenarios of future migration flows in CIS countries are determined separately for three population groups: 1. The first group includes those who emigrate for ethnic reasons (nationalities which have ethnic ties with other countries), who will most likely emigrate irrespective of the socio-economic and political situation in the CIS, if they are welcomed by destination countries. The potential of this group is estimated to be in the range of one million for the period of 2006–2025. 2. The second group includes new ethnic minorities who appeared after the establishment of independent CIS states. Their migration potential to Russia will

6

The assessment of the immigration potential to Russia and WE from CIS countries also requires a number of assumptions to be made in order to reflect future differences in economic and political climate, policies and reform progress. More specifically, among the different scenarios suggested by Chojnicki and Uzagalieva (2008), we adopt the status-quo scenario in terms of catching-up, which is close to our assumptions on TFP.

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Table 6.2 Net migration flows by regions until 2050 (in thousands) (From Docquier and Marfouk (2006), Salt (2005), United-Nations (2004), World Bank (2006), Chojnicki and Uzagalieva (2008); authors’ calculations) Region/country 2000–2005 2006–2010 2011–2015 2016–2020 2026–2030 2046–2050 WE 0 256.1 235.1 226.0 204.9 199.9 Mediterranean World 0 178.6 147.6 153.6 159.3 175.8 – North Africa 0 113.3 101.8 101.8 97.3 102.0 – CIS & Middle East 0 65.3 45.8 51.8 62.1 73.8 South Asian World 0 64.7 50.8 50.3 51.6 54.8 EE 0 49.9 45.4 45.4 42.5 36.8 – Inflows 0 24.7 24.7 24.7 20.8 18.0 – Outflows 0 74.6 70.1 70.1 63.3 54.8 Slavic World 0 37.0 8.7 23.3 48.6 67.6 – Inflows 0 107.8 70.7 80.2 93.4 106.0 – Outflows 0 70.8 62.1 56.9 44.8 38.4

depend more on the socio-economic and political situation in CIS, as well as Russian politics. Using a standard gravity model, the estimated migration potential of this group is 6.7 million for the period of 2006–2025. 3. The third group concerns the emigration potential from CIS to WE. Here the assessment is based on the estimation results reported in Fertig (2001), with the underlying intuition that long-run migration perspectives will be driven by economic factors. The potential emigration from CIS to Germany and WE for economic reasons is equal to 1.26 million and 2.18 million, respectively, for the period of 2006–2025. Net migration from other regions (North Africa and EE) after 2005 is relatively steady and predictable. Thus, we simply calibrate our migration flows to match UN projections until 2025. Given the long run feature of INGENUE2, we need to make some assumptions on migration flows far in the future. Between 2030 and 2100, we keep emigration rates constant at their 2025 levels so they follow the number of young workers in the emigration area. After 2100, migration flows are progressively reduced, reaching nil in 2150 in order to allow population to converge towards a stationary level. Table 6.2 gives the dynamics of net migration flows until 2050.

6.4

Consequences of Migration

The results of our comprehensive migration scenario are compared to the baseline with no migration. Then, we consider and evaluate a policy of immigration in which the decline of the labor force in WE is eschewed.

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Results of the Conventional Migration Scenario

The effects of the shock on the main demographic and macroeconomic variables are presented in Fig. 6.4 (in percentage point deviation from the baseline). The introduction of migratory flows from neighborhood countries strongly modifies the international distribution and the age structure of the population for the concerned regions. WE is the only zone that faces large immigration flows (the ‘Slavic world’ is also a net immigration zone in the whole period but to a lower extent) whereas EE, South Asia and the Mediterranean are emigration zones (Fig. 6.4). Thus, in 2050, WE and the ‘Slavic world’ have a total population, respectively, of 6.5% and 2.7% higher than in the baseline case. At the same time, the populations of EE and the ‘Mediterranean world’ are respectively 3.4% and 2.6% lower. The ‘South Asian world’ is barely affected by migration from Kazakhstan and Tajikistan, given that these two countries represented only circa 1% of this region’s population in 2005. International migrations also modify the age structure of the regions’ populations since migrants are assumed to be young workers (aged 20–24). In 2050, the dependency ratio is almost 6.7 points lower than in the baseline case in WE (Fig. 6.4) and 3.4 points lower in the ‘Slavic world’. In 2050, it increases by about 3.9 points in EE and by almost 2 points in the ‘Mediterranean world’.7 It follows that the financing of the PAYG pension system significantly improves in WE and deteriorates in the ‘Slavic world’ in line with the evolution of the dependency ratio. For example, the contribution rate reaches 30.4% in WE in 2050 (compared to 32% in the baseline case) because migrants contribute to its financing. However, the net rates of immigration necessary to offset the effect of ageing in the long run are substantially higher than those in our realistic migration scenario. The impact of migratory flows from neighborhood regions on the GDP growth rate is far from insignificant. The arrival of young workers progressively increases the growth rate in WE. It is more than 0.2 points higher in 2035 and then stabilizes with the ageing of the first migrant cohorts (see Fig. 6.4). The effect on the ‘Slavic world’ growth rate follows the evolution of the working-age population and is thus less marked than in WE. The mirror effect of the improving economic situation in WE and in the ‘Slavic world’ is the deteriorating economic situation in emigration regions, noticeably in EE because this region is advanced in the ageing process and is already suffering from a declining population. Indeed, the magnitude of the deterioration depends on the loss of potential workers relative to the region’s total labor force.

7

Note that the emigration rates in EE and in the ‘Mediterranean world’ are relatively similar. The significant difference in the evolution of the dependency ratio is explained by the different demographic features between these two regions. The former is more advanced in the ageing process whereas the latter is still characterized by the sustained growth of its working-age population. The consequences of young workers emigrating are thus more serious in EE.

6 Global Ageing and the Macroeconomic Consequences of Migration

a

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Japan East Asia

S. America South Asia

Mediterranean E. Europe

Private consumption per capita (level)

N. America Africa

W. Europe Slavic Word

Japan East Asia

S. America South Asia

Mediterranean E. Europe

Fig. 6.4 Results of the UN migration scenario (difference from baseline scenario) (From authors’ calculation)

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Nevertheless, the level of consumption per capita is lower than in the baseline scenario in WE until the very end of the half-century (see Fig. 6.4). The reason lies in the production sector: the inflow of workers reduces capital intensity relative to the baseline. Indeed, immigration can be seen as a supply shock to the labor market, thus impacting the productivity of factors supplied by natives. For a given stock of capital, an increase in labor supply reduces the capital per worker. The marginal productivity of capital is increasing and the interest rate is rising as well. Conversely, labor productivity is diminishing with a lower capital intensity. The real wage rate, being a decreasing function of the return on capital on the factor price frontier, is itself on a slower path than in the baseline. It follows that relative to the baseline scenario, consumption (which depends on labor and capital income) is less augmented than total population; hence consumption per capita is lower until 2040. Indeed, compared to the baseline, investment grows faster than saving in WE until 2040. Around 2025, savings gain momentum in WE (see Fig. 6.4) and the interest rate recedes a bit because savings grow faster than investment from 2040 onwards. Therefore the growth of consumption per capita relative to baseline turns positive from 2020 onwards and the level overtakes the baseline one in 2040. In the ‘Slavic world’, the level of consumption per capita is always lower than in the baseline given its net savings profile. The opposite occurs in emigration regions. But the impact is diffused over several regions and mitigated by the size of the labor force. The fall in the interest rate and the subsequent increase in productivity persist for almost the entire span of the 50-year period. Only EE and the Mediterranean exhibit a non-negligible increase in consumption per capita. The sharp increase in saving in WE (compared to the baseline trajectory) as of 2030 is mainly explained by the fact that the first cohorts of migrants are now entering the ‘high saver’ cohorts (aged between 45 and 69). The opposite occurs in EE where saving decreases substantially as of 2030, due to a combination of net emigration and an advanced ageing process (see Fig. 6.4). Because of the rise in interest rates in WE compared to the baseline, the saving/investment balance in WE stays more in surplus than in the baseline scenario. The current account balance, which was already in surplus in the baseline, is therefore more, reinforcing WE’s creditor position in the world economy. The ownership ratio rises systematically above the baseline (Fig. 6.4). The emigration regions with slightly appreciating exchange rates relative to the baseline stay more in deficit and more in debt.

6.4.2

Replacement Migration Scenario

The conventional migration scenario presented above demonstrates that realistic migration flows to WE are far from being enough to attenuate the consequences of population ageing in this region. Nevertheless, UN and EU technocrats consider immigration a policy that will alleviate the financial burden of the public pension system. So, it seems of the utmost importance to evaluate the impact of migration

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on rejuvenating the ageing European population. For that purpose, we compute the flows of migrants required to stop the decline of the working age population in WE. We assume that the migrants’ distribution by origin countries remains the same as the one observed in 2000. Then, migration flows are calibrated in order to get a constant working age population in WE between 2005 and 2050. As a result, these flows will rise sharply from 2005 (the date when this active migratory policy begins) until 2030 (Fig. 6.5). They will reach roughly 3.5 million migrants in 2030 and will abate thereafter because the fertility rate will recover (due to demographic transition) so that constant reproduction is resumed and because the children of the first cohorts of migrants are entering the labor force. In 2030, these flows represent around 0.63% of the total population of WE against only 0.06% in the conventional migration scenario. The active migration scenario lowers the dependency ratio in 2050 by more than 30 percentage points (from about 1 in the baseline scenario to 0.64). It follows that the financing of the PAYG system will dramatically improve. Indeed, the social contribution rate falls continuously as a percentage of the baseline (see Table 6.3). This means that in 2050 the contribution rate is only 23.6% in the active migration scenario against 31.9% in the baseline because migrants contribute massively to its financing. This is a major improvement as the deterioration of the contribution rate would only be 6.5 percentage points (compared with 17% in 2000). However, the opposite consequences will be observed in the sending regions and will become all the more pronounced as the considered economy is concerned by ageing and by high emigration rates. For example, outflows from EE will be almost

3

2.5

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0.5

0 2005

2010

2015

2020

Benchmark migration scenario

2025

2030

2035

2040

2045

2050

Stabilisation of the working age population in WE

Fig. 6.5 Annual migration flows into WE (in millions) (From authors’ calculation)

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Table 6.3 Contribution rates evolution in % (From authors’ calculation) Region Scenario 2000 2010 2020 WE Baseline 17.1 19.1 22.5 Conventional migration 17.1 18.9 22.1 Active migration 17.1 18.9 21.4 EE Baseline 13.8 14.7 17.8 Conventional migration 13.8 14.8 17.9 Active migration 13.8 14.8 18.2 Mediterranean World Baseline 4.9 5.0 5.9 Conventional migration 4.9 5.0 6.0 Active migration 4.9 5.0 6.1 Slavic World Baseline 10.1 9.6 11.9 Conventional migration 10.1 9.6 11.8 Active migration 10.1 9.6 11.9 South Asian World Baseline 4.6 5.0 5.8 Conventional migration 4.6 5.0 5.8 Active migration 4.6 5.0 5.8

2030 27.6 26.8 23.9 21.0 21.4 22.4 7.4 7.5 7.9 14.5 14.4 14.7 7.3 7.3 7.3

2040 30.7 29.3 24.0 25.2 25.9 28.3 9.2 9.4 10.2 17.7 17.4 18.2 9.2 9.2 9.2

2050 31.9 30.4 23.6 29.3 30.2 34.0 11.3 11.5 12.7 22.5 21.9 23.4 11.5 11.5 11.6

eight times higher in 2030 compared to the conventional migration scenario and thus will negatively affect the demographic trends in this region. Consequently, the gains for WE in pension financing will be mirrored negatively in the increasing contribution rate in EE (Table 6.3). Once again, the consequences for the Mediterranean world are less dramatic given the vitality of its demography.

6.5

Conclusion

In this chapter, we assessed the demographic and economic consequences of migration in Europe and the neighborhood countries using a CGE-OLG model. Our quantitative results shed some light on the long term consequences of migration on regions that are not at the same stage of the ageing process. We have to stress that our results are sensitive to some assumptions of the model. On the one hand, immigrants are assumed to have exactly the same productivity as native workers. The perfect assimilation of migrants in terms of productivity level in the host region thus involves an output productivity located on the upper bound. On the other hand, the INGENUE2 model assumes perfect flexibility in the labor and goods markets. Thus, immigration has no impact on unemployment and economic output is continuously at potential. Furthermore, the multi-region CGE-OLG model that we use makes several assumptions that could limit the scope of our analysis. Firstly, the remittances flows (associated with the migration flows) are not modeled in our framework. Clearly, these flows could be of great economic importance, especially when we focus on some specific countries such as Moldova, Albania, Tajikistan or Armenia,

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where remittances are higher than 10% of GDP. Secondly, the age of migrants is limited to some specific cohort and we do not model return migration. Despite these shortcomings, our analysis allows us to simultaneously study the impact of migration flows on destination countries as well as on sending countries. However, such a framework would also allow us to analyze the relative attractiveness of regions when the timing of the ageing process is asynchronous. Indeed, migration flows change the geographic structure of some economic variables. They increase the labor force in destination countries and entail an increase in capital return, which attracts capital flows. The reverse effect characterizes sending countries. As a consequence, a world general equilibrium model could allow us to evaluate the feedback effect of capital flows and wage changes on migration flows. Integrating endogenous migration flows into such a framework constitutes a challenging improvement that we leave for further research.

References Aglietta M et al (2007) Pension reforms in Europe: an investigation with a computable OLG world model. Econ Model 24:481–505 Alho J, Borgy V (2007) Global ageing and macroeconomic consequences of demographic uncertainty in a multi-regional model. CEPII Work Pap 2007-09. Backus D, Kehoe P, Kydland F (1995) International business cycles: theory and evidence. In: Cooley TF (ed) Frontiers of business cycles research. Princeton University Press, Princeton, pp 331–356 Barrell R, FitzGerald J, Riley R (2007) EU enlargement and migration: Assessing the macroeconomic impacts. NIESR Discuss Pap 292. Borgy V, Chojnicki X (2009) Labor migration: macroeconomic and demographic outlook for Europe and neighborhood regions. Econ Int 119:115–153 B€ orsch-Supan A, Ludwig A, Winter A (2006) Ageing, pension reform and capital flows. Economica 73:625–658 Brooks D (2003) Population ageing and global capital flows in parallel universe. IMF Staff Pap 50:200–221 Carrera S (2007) Building a common policy on labour immigration: Towards a comprehensive and global approach in the EU? CEPS Work Doc 256. Chojnicki X, Uzagalieva A (2008) Labor migration from East to West in the context of European integration and changing sociopolitical borders. Case Netw Stud and Anal 366. Chojnicki X, Docquier F, Ragot L (2011) Should the US have locked heaven’s door? Reassessing the benefits of postwar immigration. J Popul Econ 24(1):317–359 Docquier F, Marfouk A (2006) International migration by educational attainment (1990–2000), Release 1.1. In: Ozden C, Schiff M (eds) International migration, remittances and development. Palgrave Macmillan, New York European Commission (2006) The global approach to migration one year on: Towards a comprehensive European migration policy. Communication COM 735 final. Fehr H, Jokisch S, Kotlikoff L (2003) The developed world’s demographic transition the roles of capital flows, immigration and policy. NBER Work Pap 10096. Fehr H, Jokisch S, Kotlikoff L (2004) The role of immigration in dealing with the developed world’s demographic transition. NBER Work Pap 10512. Fertig M (2001) The economic impact of EU-enlargement: Assessing the migration potential. Empir Econ 26.

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Heston A, Summers R, Aten B (2002) Penn World Table, Version 6.1. Center for International Comparisons at the University of Pennsylvania (CICUP). Iakova D (2007) The macroeconomic effects of migration from the new European Union member states to the United Kingdom. IMF Work Pap 61. INGENUE (2006) The larger Europe: technological convergence and the labour migration. Rev Econ 57(4):823–850 INGENUE (2007) INGENUE 2: A long term intertemporal world model for the 21st century. mimeo. Krueger D, Ludwig A (2007) On the consequences of demographic change for the rates of returns to capital and the distribution of wealth and welfare. J Monet Econ 54:49–87 Salt J (2005) Current trend in international migrations in Europe. Council of Europe Storesletten K (2000) Sustaining fiscal policy through immigration. J Polit Econ 108(2):300–323 United-Nations (2004) World population prospects: the 2004 revision. Department of Economic and Social Affairs, Population Division World Bank (2006) Migration and remittances: EE and the former Soviet Union. The World Bank.

Chapter 7

Income and Distribution Effects of Migration and Remittances Matthias Luecke

Abstract This chapter analyzes the direct and indirect income effects of international labor migration and remittances in selected CIS countries. The analysis is based on CGE models for Moldova, Ukraine, Georgia and Kyrgyzstan. We find that these countries would experience a sharp contraction of private consumption and substantial decline in GDP in the absence of remittances. Because of the important contribution of migration and remittances to stabilizing and sustaining incomes, enhanced opportunities for legal labor migration should figure prominently in any deepening of bilateral relations between CIS countries and the EU under the ENP.

7.1

Introduction

In many member countries of the CIS, international labor migration and remittances received by relatives at home play a large role in financing private consumption and in shaping the labor supply and education decisions of households. Remittances received range from 8% of GDP in Ukraine to more than 30% in Moldova according to the latest internationally comparable estimates (see Sect.7.2 for details). Because of their large size, migration and remittances matter not only at the level of individual households. They are bound to affect output and income distribution not only directly at the level of remittance-receiving households, but also indirectly through general-equilibrium channels. For example, in many CIS countries, a large

Acknowledgements This chapter summarizes the findings of ENEPO Work Package 8: Analysis of Outward Migration in Selected CIS Countries. The country case studies on which this chapter is based are described in detail in Atamanov et al. (2009). They were authored by Matthias Luecke and Toman Omar Mahmoud (Moldova); Vitaliy Vavryschuk (Ukraine); Kseniya Tereshchenko and Ainura Uzagalieva (Georgia); and Aziz Atamanov and Roman Mogilevsky (Kyrgyzstan). M. Luecke (*) Senior Research Economist at the Kiel Institute for the World Economy e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_7, # Springer-Verlag Berlin Heidelberg 2011

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share of government revenues is derived from taxes on imports (especially VAT), which grew rapidly as a result of growing remittance inflows. With higher revenues, governments were able to maintain and expand social transfers so that households may have benefited indirectly from migration and remittances, even if they received no remittances of their own. Furthermore, the growth of remittances since approximately the year 2000 has coincided in most CIS countries with the recovery of GDP from its transition-induced precipitous fall during the 1990s. This coincidence of remittances and GDP growth raises the question of how labor migration and remittances may have contributed to economic recovery in most CIS countries since 2000. This chapter summarizes case studies for several CIS countries that describe the extent of migration and remittances and assess their indirect effects on income distribution and structural change systematically. These case studies are based on single country CGE models. While the direct impact of migration and remittances at the household level has been studied through household-level analyses in many countries, much less work has been done on indirect (or general equilibrium) effects. The selected countries – Moldova, Ukraine, Georgia, and Kyrgyzstan – were chosen on the basis of data availability as well as their diversity in terms of size and geographic location. Section 7.2 describes the macroeconomic context in which the sharp increase in labor migration and remittances occurred. Since Russia hosts most migrants from CIS countries, the development of the Russian economy is also reviewed. Section 7.3 provides a brief methodological introduction to CGE modeling. Section 7.4 presents the case studies and Sect. 7.5 draws out the policy implications.

7.2

The Macroeconomic Context

The stylized facts of macroeconomic development in the CIS countries during the last two decades are straightforward. After the disintegration of the former Soviet Union in 1991, GDP fell sharply in all our selected CIS countries including Russia (Fig. 7.1). This precipitous fall was followed by prolonged stagnation at a low level during the second half of the 1990s, with some further losses as a result of the Russian financial crisis in 1998. A sustained recovery began around 2000; by 2008, GDP in the selected countries had grown cumulatively by 45–78%. In 2009, the global financial crisis caused substantial output losses, ranging from 7% in Moldova and 8% in Russia to 15% in Ukraine. However, output stabilized and even began to grow again in 2010, so that most of the gains made during the 2000–2008 growth spurt were preserved.1

1

Data from the World Development Indicators database (which we use in Figs. 7.1–7.3 for the sake of consistency over time) are only available through 2008. More recent information on output developments in the selected CIS countries is provided, inter alia, by the IMF Regional Economic Outlook for Europe, and IMF Regional Economic Outlook for Middle East and Central Asia: http://www.imf.org/external/pubs/ft/reo/reorepts.aspx

7 Income and Distribution Effects of Migration and Remittances Fig. 7.1 Selected CIS countries: Total GDP, 1990–2008 (year 2000 set at 100) (From World Bank World Development Indicators database; own calculations)

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300 Georgia Kyrgyz Republic

250

Moldova Russia

200

Ukraine

150

100

50

0 1990 1992 1994 1996 1998 2000 2002 2004 2006

Fig. 7.2 Household total consumption, 1990–2008 (year 2000 set at 100) (From World Bank World Development Indicators database; own calculations)

250.0 Georgia 200.0

Kyrgyz Republic Moldova

150.0

Russia Ukraine

100.0

50.0

0.0 1990 1992 1994 1996 1998 2000 2002 2004 2006

In spite of this impressive performance, total GDP in most CIS countries has barely reached or is still below pre-independence levels. However, among the demandside components of GDP, private consumption has grown over-proportionally (Fig. 7.2). Although data are missing for Georgia and Moldova for the early 1990s, it is safe to conclude that private consumption had regained or even surpassed its 1990 levels by 2008 in all the analyzed CIS countries. Fixed investment shared in this buoyant recovery only at a fairly late stage, particularly after 2005 and with the exception of Kyrgyzstan (Fig. 7.3). The driving forces behind these macroeconomic trends have been subject to some debate. The post-independence output collapse is probably explained largely by the rapid disintegration of the central planning system after 1990, including the various institutions that regulated trade among the Soviet republics. In the early 1990s, the necessary institutional infrastructure for market-based economic relations

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M. Luecke 600 Georgia Kyrgyz Republic

500

Moldova Russia

400

Ukraine 300 200 100 0 1990

1992

1994

1996

1998

2000

2002

2004

2006

Fig. 7.3 Gross fixed capital formation, 1990–2008 (year 2000 set at 100) (From WB World Development Indicators database; own calculations)

(functioning currencies, hard budget constraints, enforceable contracts) simply did not yet exist. Sharp reductions in government procurement, which especially affected the military industrial complex, must have contributed to the output collapse. The gradual emergence of market-enabling institutions helps to explain why trade among the CIS countries, along with output, stabilized during the mid-1990s. However, observers continued to note many persistent shortcomings regarding corporate governance, the business climate, and the investment environment. Therefore, the sustained recovery since 2000 is more difficult to explain. One possible reason is that institutional reforms, perhaps contrary to appearance, had surpassed a critical threshold by 2000 (Havrylyshyn 2008). In addition, Russian import demand for CIS exports surged on the heels of rising world market prices for energy materials since the late 1990s. Capacity utilization rates were generally low after the output collapse of the early 1990s; this observation explains how output could initially increase with existing production capacities and fixed investment picked up only after several years of steady GDP growth. Furthermore, rising demand for non-tradable goods and services must have contributed to output growth. In Russia, demand for non-tradables was fuelled by rising oil and gas revenues. In the remaining CIS countries, labor migration and migrant remittances picked up sharply as of 2000, driving up household incomes and demand for non-tradables. The evolution of migration and remittances is best viewed through the lens of BoP statistics. The quality of most national survey data on the number of migrants is low, in part because only household members remaining at home, rather than the migrants themselves, can be interviewed. Furthermore, many migrants travel, work, and remit their earnings through informal channels which are not easily captured by

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5000 4500 4000 3500

Georgia Kyrgyz Republic Moldova Ukraine

3000 2500 2000 1500 1000 500 0 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Fig. 7.4 Migrant remittances, 1997–2009 (USD million)(From IMF BoP Statistics Database; own calculations)

official statistics. In the BoP, remittances are represented by the sum of credit items from compensation of employees (income account) and workers’ remittances (transfer account). All selected CIS countries experienced a substantial increase in migrant remittances from about 2000 until 2008 (Fig. 7.4). The picture for Georgia is more nuanced in that there were substantial remittances as early as the mid-1990s; notably, Georgia’s GDP also began to recover at that time, earlier than elsewhere in the region (Fig. 7.1). In the case of Ukraine, the sharp increase in reported remittances in 2007 clearly represents a statistical break in the underlying time series; the low level of remittances suggested by the pre-2007 estimates is not plausible, given Ukraine’s large migrant population abroad even at that time. BoP data for Russia, which hosts most labor migrants from CIS countries, mirror these developments. Remittances by incoming migrants (debit items) rose to around USD 25 billion in 2008 from USD 1 billion in 2001 (Fig. 7.5). Remittances received from Russian expatriates also increased to USD 4.6 billion in 2008. The impact of the global financial crisis on Russia resulted in a sharp reduction in demand for migrant labor in 2009. Outgoing (debit) remittances declined by approximately USD 7 billion or 29%. Among recipient countries, Moldova was hit particularly badly as remittances declined to USD 1.2 billion in 2009 from USD 1.9 billion in 2008. Ukraine’s loss was similar in absolute terms but far smaller in relation to the level of remittances. Overall, this brief review suggests that remittances received by the analyzed countries have sustained private consumption and contributed to rising demand for non-tradable goods and services and, hence, to the resumption of GDP growth since 2000. Migration and remittances are large enough not only to improve the welfare of remittance-receiving households, but also to affect macroeconomic variables such as the real exchange rate and wage levels and thus alter the sectoral structure of

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M. Luecke 18 Remittances - credit

16

Remittances - debit

14 12 10 8 6 4 2 0 2001

2002

2003

2004

2005

2006

2007

Fig. 7.5 Russia: migrant remittances, 2001–2009 (USD billion)(From IMF BoP Statistics Database; own calculations)

the economies. Indirectly therefore, they generate further effects on the welfare of all households, whether or not they receive remittances. These effects are further analyzed through CGE models.

7.3

CGE Models: Methodology

CGE models are used to analyze the effects of policy changes and other shocks (such as a rise in labor migration) throughout the economy.2 CGE models may be viewed as an extension of multi-sector input–output and fixed price models and incorporate the indirect effects and price effects of policies. They apply to the time period it takes for an economy to move from one equilibrium to another, in response to a policy change or other shock. In this sense, a static CGE model as used in our country studies generates a medium-term solution – a situation where the initial disequilibrium after the shock has disappeared, but before dynamic effects (such as additional investment or disinvestment) set in.3 The database for a CGE model is the SAM for a given year, a square matrix that describes all commodity and monetary flows among the economic agents in an

2

This section draws extensively on Fagernas (2004). We also explored the feasibility of using a recursive-dynamic CGE model to complement our comparative-static simulations. A more explicitly dynamic structure would reflect the investment process more accurately and thereby provide additional insights into the growth effects of remittances. However, the additional assumptions required to implement a recursive-dynamic model turned out to be far-reaching. Overall, we would have introduced a high level of arbitrariness into the analysis such that the more detailed description of the investment process in the recursive-dynamic model would ultimately have been meaningless. 3

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economy at a suitable level of aggregation (production sectors, households, enterprises, government, the ‘rest of the world’). Depending on the chosen level of aggregation, the SAM combines information from input–output accounts, national income and product accounts, HBS, labor force statistics, and fiscal statistics, among other data sources. Although the case studies in this paper are all based on single-country CGE models, CGE models are also implemented for several countries simultaneously or for the global economy disaggregated by regions and countries, requiring multi-country SAMs. Naturally, simulation results based on CGE models depend heavily on assumptions about functional forms such as Leontief vs. CES production functions, underlying parameters (such as substitution elasticities in production and demand functions), and macroeconomic balancing mechanisms. Traditional neoclassical CGE models are based on Walrasian general equilibrium theory: Firms maximize profits and wages and prices adjust to equate supply and demand in factor and product markets with factors of production fully employed. By contrast, models in the structuralist tradition use different assumptions about macroeconomic balancing mechanisms and the way markets clear, incorporating features of short-run macroeconomic models with wage or price rigidities and unemployment.4 Our case studies are based on the IFPRI ‘standard’ CGE model (Lofgren et al. 2002). Its straightforward basic structure with a choice of standard neoclassical and neo-structuralist assumptions, its user-friendly and well-documented code in GAMS software, and its easy adaptability to national circumstances (different levels of aggregation for households, the agricultural sector, etc.) render it wellsuited for the present analysis. The IFPRI standard model is a real-side model without explicitly modeled asset markets or inflation. In constructing SAMs for our case studies, we combine input–output tables, other national accounts information, household budget surveys, labor force statistics, and fiscal statistics, among other data sources. This allows us to deal with the difficulty that official data tend to understate migration and remittances in some countries because a large proportion of remittances are transferred as foreign exchange cash and much migrant employment is informal. The level of aggregation (number of commodities, sectors, factors of production, and household types) differs across the case studies in line with the underlying national data. In the IFPRI standard model, production is carried out under perfect competition by sectors (‘activities’) that maximize their profits, subject to a multi-level production function and given the prices of their inputs, outputs and factors. Within each activity, the top level of the production function consists of a Leontief function that combines an aggregate of the factors of production (value-added) on the one hand and intermediate inputs on the other hand. Factors of production (high-skilled workers, low-skilled workers, capital, etc.) are combined according to a CES

4 For an overview over the main CGE modeling approaches with references to appropriate literature, see Robinson (2003).

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function, while the various intermediate inputs are used in fixed proportions (Leontief function). Domestic output of a given good (say, food) may be produced by different sectors (say, small farms and commercial agriculture). Aggregate domestic output is allocated to exports and domestic sales based on profit maximization with given prices, a given quantity of total output and imperfect transformability between domestic sales and exports in line with a constant elasticity of transformation function. Export supply is therefore determined by the domestic price of exports relative to domestic sales, where the domestic price of exports is the given world price of exports adjusted by export taxes and the exchange rate. Thus the model assumes a small, open (i.e. price-taking) economy. Domestic demand for a given good is the sum of demand for private (household) consumption, government consumption, investment, and intermediate inputs. If a commodity is imported, domestic demand is for a composite commodity of imports and domestically produced goods, with the optimal mix determined through cost minimisation via a CES aggregation function. This so-called Armington assumption allows for some decoupling of domestic from world market prices and ensures that simulated export and import responses to policy changes will be broadly realistic. The supply of imports is infinitely elastic at given world prices. The domestic price of imports is the given world price adjusted by the exchange rate and tariffs. Households receive income from factors of production supplied to production activities (e.g. labor) and transfers from institutions, particularly the government and other households. Households save, consume, and pay direct taxes and transfers; direct tax rates and the propensity to save are determined by the chosen closure rules for the government and savings–investment balances (see below). Household consumption is allocated across different products according to a linear expenditure system (LES), which implies in this case that the consumption of individual commodities is a linear function of total household consumption expenditure. Household types may differ in their demand elasticities and consumption shares for each commodity. The macro closure rules for the CGE model define the mechanisms by which the three macroeconomic balances are determined: (i) the current government balance; (ii) the current account balance, and (iii) the savings and investment balance. First, the government receives its income from taxes and transfers from the rest of the world and decides on the level of current spending (government consumption plus transfers to households) vs. government saving. The IFPRI standard model allows us to choose between two basic closure rules for the government balance. Either direct tax rates are fixed, but government saving (the real fiscal balance) adjusts in response to changes in government revenue; or direct tax rates adjust to maintain a given level of government saving. Second, the savings–investment balance states that total investment is the sum of private investment, government investment, and foreign savings. In this static model (comparative-static with simulation exercises), investment is not driven by the rate of return on capital but by the availability of savings. The standard IFPRI model allows for two basic approaches: (i) private savings are investment-driven

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such that the marginal propensity to save adjusts to a given level of investment; (ii) investment adjusts to the level of savings, given a fixed marginal propensity to save. Third, foreign savings are equivalent to the current account deficit and hence define the external balance (i.e. the balance of inflows and outflows of foreign exchange). Transfers between the rest of the world and domestic institutions and factors (including migrant remittances) are exogenous to the model and given in foreign currency. Thus the current account balance is driven by the balance of exports and imports. The alternative closure rules in the standard IFPRI model leave either the real exchange rate or the current account balance constant, requiring the other variable to adjust. Factor market closures determine the mechanisms that equilibrate the supply and demand of each factor of production. In line with general equilibrium theory, each activity uses a set of factors up to the point where the marginal revenue product of each factor equals its wage. There are two wage variables: the economy-wide wage, and an activity-specific wage that is the product of the economy-wide wage and an activity specific wage or distortion term. There are basically three possible closure rules: (i) a factor is fully employed and mobile, giving rise to a uniform economywide wage; (ii) a factor is fully employed and immobile, giving rise to sectorspecific wage rates; or (iii) a factor is mobile but may be unemployed, allowing the wage to be set as a policy parameter.

7.4 7.4.1

Case Studies Moldova

Migration and remittances play a key role in the Moldovan economy, with approximately one quarter of the working-age population working abroad for at least part of the year, and remittances equivalent to one third of GDP since the mid-2000s.5 These estimates count only those migrants who are still part of a household in Moldova (i.e. contribute to household income and share in expenditures); remittances may include transfers from individuals who have left Moldova permanently (Luecke et al. 2009). In Moldova, poorer households are more likely to send a migrant abroad than richer ones, contrary to the situation in many other countries where barriers to emigration are more difficult to overcome. The poor and low-skilled have the option of taking up employment in Russia, where travel is visa-free and cheap, although working conditions and wages are often poor. By contrast, the EU (especially Italy and Portugal) offers better pay and conditions, but high up-front costs for illegal travel make this option difficult for many poor households. The

5 The original case study on which this section is based was authored by Matthias Luecke and Toman Omar Mahmoud (see Atamanov et al. 2009).

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incidence of migration in Chisinau, the capital city, is substantially lower than in the rest of the country as more economic opportunities are available there. Our SAM is based on the year 2004; available data contain the national accounts through 2004, including an input–output table, annual HBS through 2004, quarterly labor force surveys through 2005, and a special household survey on migration and remittances conducted in 2004 and 2006. As is the case in many developing countries, coverage of household income by HBS is far from complete, particularly for remittances. Therefore, the national accounts data, which are internally consistent, have been used as the starting point in compiling the SAM. Subsequently, the household sector and labor income have been disaggregated using percentage shares for different household and labor types derived from the HBS. The agricultural sector is subdivided into small-scale (household) agriculture and agricultural enterprises. Our simulations provide an impression of what the Moldovan economy would have looked like without labor migration and remittances (Table 7.1). The first simulation hypothetically eliminates the recent strong TFP growth. TFP growth is apparent from the fact that GDP grew by about one third from 2000 through 2004, while fixed investment remained modest and the labor force declined. In part, TFP growth may have been a natural result of the recovery from the transitioninduced crisis, for example due to the emergence of market-supporting institutions as systemic transformation took hold. To this extent, TFP growth might have occurred even in the absence of migration. However, we consider it plausible that most of the apparent TFP growth resulted from higher utilization rates for existing production capacity that arose as a consequence of remittances-induced demand growth. The second and third simulations separately describe the impact of a sharp reduction in remittances and a larger domestic labor supply (if there is no labor migration). The fourth simulation combines lower remittances and a larger labor supply, and the fifth simulation adds lower TFP. It turns out that the combined effects under the fourth scenario are very similar to the sum of the separate effects under the second and third scenarios; similarly, the combined effects for all simulated shocks under the fifth scenario are very similar to the sum of the separate effects under the first, second, and third scenarios. Therefore, in discussing the results, we focus on the fifth scenario. Our simulation results suggest that household consumption would be reduced sharply in the absence of migration and remittances. This applies both to total consumption, which would fall by 32.1%, and to every household group. In relative terms, the losses would be largest for small farmers (with consumption cut in half) because (i) migration, including for seasonal work, is very widespread in the countryside, and (ii) higher disposable incomes in the population at large are strengthening demand for local food products. GDP would fall by approximately one tenth under the fifth scenario and thus by much less than private consumption. A comparison of the GDP effects under the first, third, and fifth scenarios demonstrates that GDP would decline mainly because we assume that without migration, TFP would not improve as much as it actually did. As discussed above, TFP should be thought of in this context as predominantly reflecting

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Table 7.1 Moldova: CGE simulation results (base values and percentage changes in real terms) (From own estimation)

Base run

TFP reduced by 20% (except in Remittances small-scale reduced by agriculture) 70%

Labor supply increased by 20% (except high-skilled and nonagric. selfempl 10%)

TFP and remittances Remittances reduced, reduced and labor labor supply supply increased increased

Macro variables Domestic absorption Private consumption Fixed investment Government consumption Exports Imports GDP at market prices Real exchange rate

412 276 67

13.8 20.6 0.0

13.2 19.7 0.0

9.0 13.4 0.0

4.1 6.1 0.0

21.6 32.1 0.0

52 155 246 320 95

0.0 26.1 16.4 17.8 4.1

0.0 38.0 2.0 0.2 4.8

0.0 18.4 11.6 11.6 0.4

0.0 58.0 14.6 11.6 2.6

0.0 22.4 7.8 10.9 2.7

19 38 22 6 4 7 4 7 11 32 3 19 17 33 14 35 271

42.0 0.0 24.2 46.5 18.9 19.4 19.7 19.8 4.0 19.3 22.2 20.9 21.8 19.0 5.3 12.2 17.3

20.7 0.0 7.6 226.4 2.1 2.2 0.1 6.9 0.9 2.2 6.4 2.3 9.4 6.7 4.4 9.6 0.1

5.3 20.0 11.2 51.6 7.9 9.7 7.9 11.8 2.4 12.1 12.5 11.4 10.6 10.8 3.4 7.9 11.7

14.7 20.0 1.3 299.0 4.6 6.7 5.8 4.4 1.5 14.2 5.1 8.5 1.6 4.3 1.0 1.7 11.9

42.0 4.0 21.8 199.9 13.5 14.0 10.1 17.6 3.3 9.3 19.1 14.8 22.9 17.9 7.5 16.6 10.5

41.7 9.4 10.7 9.9 17.7 14.9

11.6 15.9 13.8 14.9 14.9 10.0

31.9 7.7 4.1 6.2 2.2 4.6

49.9 25.0 26.2 23.7 32.3 23.3

GDP at factor cost A_AGR_L A_AGR_S A_FOOD A_LIGHT A_WOOD A_CHEM A_MASH A_ELEC A_CONSTR A_TRADE A_REST A_TRANS A_COMM A_FIN A_PUBLIC A_PUB_SERV TOTAL

Household consumption (equivalent variation) HH_SMALL_FARM HH_OTH_RUR HH_OTH_URB HH_RICH_URB HH_PUBLIC_SECTOR HH_TRANSFER

75 69 51 28 17 35

16.7 24.1 23.9 22.1 23.8 14.7

higher rates of capacity utilization, rather than technological change. The positive output effect of migrant workers returning home and adding to the local labor force (under the third scenario) would be insufficient to compensate for the fall in TFP.

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Without migration and remittances and the associated TFP growth, the Moldovan currency would depreciate in real terms, adjusting to lower foreign exchange inflows as remittances were reduced. Our closure rules for these simulations imply that foreign savings, investment, and government consumption are exogenous. While foreign savings are adjusted to reflect lower migrant remittances under the second, fourth and fifth scenarios, investment and government consumption are assumed constant in real terms relative to the base run. The only sector whose output would grow significantly in the absence of migration and remittances is light industry, whose exports would also increase sharply. This simulation result is in line with a (reverse) Dutch disease effect as a result of a smaller inflow of foreign currency through remittances. To put this effect into perspective, it is interesting to compare these simulation results with recent sectoral changes. Textile and clothing exports from Moldova to the EU have in fact expanded substantially since 2007 even though migration and remittances have grown and the Moldovan currency has appreciated further since 2004. These developments suggest that it was barriers to trade, rather than rising production costs because of a Dutch-disease-style real appreciation, that impeded the expansion of the Moldovan textile and clothing sector in the past. With Romania’s accession to the EU, Moldova became a direct EU neighbor, separated by only one border from the Single European Market; it appears that the resulting reduction in informal trade barriers was sufficient to set off the recent wave of foreign direct investment and output and export growth in the textile and clothing industry.

7.4.2

Ukraine

The quality of the available information on migration and remittances in Ukraine6 is much poorer than in Moldova. The number of migrants appears to be less than 5% of the total work force; Russia and some EU countries (Poland, Czech Republic, Italy, Cyprus, Greece, UK) are important destinations (IOM 2006; Poznyak 2002; Starodub and Parkhomenko 2005). These numbers contrast sharply with the much lower official data on employment permits for Ukrainians working abroad: only 61,400 were processed through local employment intermediaries in 2006, with more than two thirds for EU countries, especially Cyprus, Greece, and the UK. From the host country perspective, the scale of Ukrainian labor migrants’ presence was revealed during regularization programs. In 2002 the Italian government ran a 2-month regularization program for domestic and contract workers. Out of 341,000 applications from housemaids, 27% were submitted by Ukrainians. During the regularization program in Portugal from January 2001 until March

6 The original case study on which this section is based was authored by Vitaliy Vavryschuk (see Atamanov et al. 2009).

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2003, more than 62,000 temporary work permits (out of a total of 180,000) were granted to Ukrainians. Within Ukraine, the share of labor emigrants coming from small cities is estimated at 42%, while villagers account for about 29% and people from big cities make up 25% of the total number of labor migrants. Similar to Chisinau in Moldova, Kyiv contributes just 3% of the number of migrants, although it accounts for more than 6% of Ukraine’s resident population. Most Ukrainian men abroad work in construction or agriculture, while women are frequently employed as domestic workers. Incoming remittances are probably larger than suggested by Fig. 7.4, because official data do not properly account for informal transfers. IFAD (2006) estimates total remittances transferred to Ukraine in 2006 at USD 8.5 billion, or close to 8% of GDP; official data are closer to 3% of GDP. About half of the remittances come from ‘old’ EU members, with another 5% from the new members. Slightly more than one third originate from Russia and other CIS resource-rich countries. Remittances are a crucial source of income for many Ukrainian households and regions. Anecdotal evidence suggests that remittances-induced domestic demand was a key factor behind the dynamic development of local manufacturing in Western Ukraine. Remittance income helps create physical and human capital (purchase of real estate and cars, repair of dwellings, financing higher education) and provides support to poorer relatives. Ukraine’s SAM is based on input–output tables, the national accounts, the BoP, and HBS raw data for the fourth quarter of 2004. We distinguish 16 economic sectors (including small-scale and large-scale agriculture) and six factors of production (including low-, medium-, and high-skilled labor). In disaggregating the household sector, we do not separate out agricultural smallholders (in contrast to case studies for Moldova, Georgia, and Kyrgyzstan) because ‘farmers’ account for less than 1% of all households in Ukraine. As the HBS does not identify remittances as a separate source of income, guesstimates had to be employed to arrive at plausible assumptions about their distribution across types of households. Simulation results for the Ukrainian CGE model are qualitatively similar to those for Moldova. Once again, we seek to simulate ‘Ukraine without remittances.’ Reflecting the somewhat smaller role of migration and remittances in Ukraine as compared to Moldova, we assume that TFP is reduced by 10% (scenario 1), remittances are reduced by 70% (scenario 2), and labor supply is increased by 5% (scenario 3). On its own, the impact of reducing remittances (scenario 2) is quite modest. However, the assumption that TFP would be lower without migration and remittances implies their substantial indirect welfare gain. At the same time, ‘without migration’, the economy would have had a larger labor force and higher GDP. Combining all three effects, GDP is simulated to be 7% lower without migration and remittances. Among economic sectors, the light and food industries are the main beneficiaries of higher demand due to remittances. In the absence of remittances, these sectors would contract by 17% and 14%, respectively. By contrast, machinery, construction and public administration services are much less affected by remittancesinduced demand.

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All types of households benefit substantially from remittances: their overall consumption would have been lower by 14–21% in the absence of remittances. Rich urban households benefit the most, whilst households that depend mostly on government transfers gain much less. Rich urban households also benefit the most from the assumed increase in the domestic labor force, with an 8% increase in overall consumption; this finding reflects the high share of skilled, well-paid labor in households of this type.

7.4.3

Georgia

Accumulated net migration in Georgia since the beginning of the 1990s has been estimated at more than 880,000 individuals, with some return migrants in 2004 and 2005 (SDSG 2007).7 As the resident population of Georgia is about 4.5 million, these estimates imply that one in five Georgians has previous or current migration experience. Reported inward remittances amounted to more than USD 800 million in 2006, equivalent to about one tenth of Georgia’s GDP. Including unofficial remittances, total remittances may have been 30% higher (GET 2007; 2008). At the same time, 43% of the population lived below the national poverty line in 2004; that rate was higher in the rural (52%) than in urban areas (35%).Hence, we are looking at how migration and remittances affect the production and consumption patterns of the poor. Two aspects of poverty reduction are emphasized: (i) aggregate and sectoral production growth and (ii) the welfare of poor households. In addition, we pay particular attention to regional differences in terms of market access and transaction costs. Georgia’s SAM is based on the national accounts, including the input–output table, BoP, the annual report on household surveys (SDSG 2005) and the raw data from the 2004 HBS. The model distinguishes 13 production activities; the agricultural sector is split into large and small enterprises. Production factors – capital, labor and selfemployment – are decomposed between agriculture and other production activities. Labor is disaggregated according to skill level (high, medium, low). Furthermore, small agricultural enterprises are grouped into three categories according to their geographic location as this determines their market access and transaction costs. The underlying intuition is that farmers in remote or mountainous areas face higher transportation and marketing margins than elsewhere. Georgia’s topography is marked by sharp contrasts, with the Great Caucasian mountain range rising to more than 5,000 m above sea level, the medium high mountains at about 3,000 m and the inner lowlands (e.g. Kolkheti and Alazani) used predominantly for cultivating tea, citrus, grapes and other agricultural products. Arable land makes up around 11% of the territory. There are 12 administrative regions in the country,

7 The original case study on which this section is based was authored by Kseniya Tereshchenko and Ainura Uzagalieva (see Atamanov et al 2009).

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including the capital region (Tbilisi), two autonomous republics, and nine regular regions. These regions are geographically and economically very diverse; industry and service activities are concentrated mostly in the capital city Tbilisi and a few other regions in the inner lowlands. Accordingly, average incomes as well as the extent of income inequality differ across regions (Fig. 7.6). Agriculture is more widespread across the regions and accounts for about one fifth of gross value added and more than one half of employment. In our CGE model, farms in the mountainous regions are assumed to have high-transaction costs; farms in regions with small cities and arable land are assumed to have medium transaction costs and farms around Tbilisi, low transaction costs. CGE simulations consist of five illustrative scenarios (Table 7.2). A reduction in remittances, looked at in isolation (scenario 2), would have no impact on total GDP, but would cause a substantial fall in private consumption and domestic absorption. Consumption would decline the most in rich rural households as well as in all urban households. By implication, remittances benefit higher-income households more than others. Factors of production would be re-directed towards manufacturing production and exports. Among small agricultural enterprises, output would hold up well in the medium-transaction-cost regions, but would decline in both low- and high-transaction cost regions and in large enterprises. Allowing for the return of workers (increase in the domestic labor force; scenario 3), and lower capacity utilization (lower TFP; scenario 1) exacerbates the effects of lower remittances looked at in isolation. GDP is simulated to decline by 13% and private consumption by as much as 25%. All household groups would be affected,

Percentage of households, %

70,00 60,00 50,00 40,00 30,00 20,00 10,00 0,00 Total

1

2

High: above 200 GEL

3

4 5 6 7 Income level by 10 regions Middle: 75 GEL-200 GEL

8

9

10

Low: below 75 GEL

Fig. 7.6 Georgia: incomes across and within the regions (From Atamanov et al. 2009)

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Table 7.2 Georgia: CGE simulation results (base values and percentage changes in real terms) (From own estimation) Base run

Decrease Reduction in Increase in in TFP by remittances labor supply 20% (1) by 70% (2) by 20% (3)

Combined effect (2) + (3) (1) + (2) + (3)

Macroeconomic aggregates Domestic absorption Private consumption Fixed investment Government consumption Exports Imports GDP at market prices Real exchange rate

11.3 7.3 2.8 1.1 2.6 4.2 9.8 97.1

2.6 4.0

4.7 7.3

7.1 11.0

2.4 3.7

15.9 24.7

12.7 1.3 5.9 2.9

11.9 4.3 0.3 3.3

8.7 5.5 8.2 1.2

20.5 1.2 7.9 4.3

1.6 12.8 13.3 3.7

Disaggregated macroeconomic indicators Large agriculture and other primary sectors Small agriculture –low transaction cost –medium transaction cost –high transaction cost Manufacturing Electricity Processing of products by households Construction Trade and repair of motor vehicles Hotels and restaurants Transportation Communication services Financial, professional, other private services Public administration/ NGOs Public services and private households Total

0.6

3.8

8.7

6.3

14.3

40.5

0.2 0.4 0.3 0.7 0.3

9.6 10.9 9.0 12.5 9.8

2.8 2.5 1.3 14.9 2.8

17.1 22.5 18.7 6.6 11.6

13.5 25.5 17.3 21.5 14.3

12.4 3.1 7.5 5.4 12.7

0.4 0.6

6.3 3.3

1.9 4.1

10.1 7.6

8.4 3.6

19.2 16.5

1.0 0.3 0.9 0.4

5.2 6.8 7.6 7.0

1.1 2.6 0.4 1.2

8.1 11.6 9.5 10.7

7.0 9.1 10.0 9.5

13.6 18.9 16.4 16.6

0.7

6.4

0.8

9.4

8.6

14.7

0.7

3.7

0.4

5.2

4.7

7.9

0.8 8.4

4.5 6.1

0.4 0.0

6.6 8.5

6.1 8.5

9.2 13.1

0.9

10.5

1.0

15.5

14.9

25.6

1.5 1.4 0.6

6.8 3.4 4.0

0.9 7.8 7.4

10.9 10.5 11.0

10.2 2.6 3.4

20.3 26.0 23.9

1.2 1.8

5.8 2.1

5.0 16.9

11.8 8.9

6.8 8.6

22.1 29.0

Household consumption Rural poor households Rural middle-income households Rural rich households Urban poor households Urban middle-income households Urban rich households

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with the rate of decline ranging from 20% for rural middle-income households to 29% for urban rich households. Small farms in regions with medium transaction costs would do relatively better than other small or large farms. Manufacturing output would increase by 5% and exports would decline only slightly.

7.4.4

Kyrgyzstan

Between 1990 and 2005, around half a million individuals left Kyrgyzstan permanently. Many were Russian-speaking Kyrgyz citizens who left Kyrgyzstan for permanent residence in Russia and elsewhere. However, during the last five years, nonpermanent labor migration has increased sharply, especially in rural areas with high unemployment. The information on the scope of labor migration is fragmentary and based mostly on information from Kyrgyz embassies abroad. According to the conservative estimates of the State Committee on Migration, up to 300,000 individuals from Kyrgyzstan worked in Russia and around 100,000 in Kazakhstan in 2008.8 The NBKR compiles the BoP, including statistics on remittances at the macro level. It relies in particular on data on international money transfers to and from Kyrgyzstan by individuals through bank accounts including card accounts (transactions below or equal to USD 3,000 are considered workers’ remittances), MTO, e.g. Western Union, and the postal system. There is scarce information on the status of workers abroad and the economic nature of their transactions (e.g. intrafamily transfer, payment for goods or services, person-to-person loan disbursements, etc.). Hence, official statistics on remittances represent a mix of different international transactions. There is only limited survey evidence on the magnitude of remittances and the profiles of labor migrants. Until recently, the HBS conducted by the National Statistical Committee did not even identify remittances as a separate source of income. Within the framework of the ADB project on remittances and poverty and financial sector development in Kyrgyzstan, Mogilevsky and Atamanov (2008) analyze data on 3,995 households. Extrapolating from this survey, there were just over 250,000 labor migrants abroad, equivalent to 5% of the total population and 8% of the working-age population. The regional distribution of the migrants reflected the regional level of economic development: About 70% were from rural areas, 10% from Bishkek, and 21% from other urban areas. The vast majority of the migrants worked in Russia (83%), followed by Kazakhstan (12%). Most migrants were employed in the private sector, either in construction (45%) or trade (30%). Almost half were seasonal workers. Migrants from Bishkek were frequently employed in sectors requiring higher education and higher qualifications (financial intermediation, public administration, education, health care, etc.).

8 The original case study on which this section is based (see Atamanov et al. 2009) was authored by Aziz Atamanov and Roman Mogilevsky.

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This ADB survey yields lower estimates of remittances than the BoP: between USD 224 and USD 287 million in 2006, with a confidence interval of 95%. Most remittances went to rural areas. Average cash remittances per receiving household amounted to USD 1,331, equivalent to 50% of average household income. By contrast, the sharp increase in remittances according to the NBKR (Fig. 7.4) reflected mostly large transactions, which may be incorrectly attributed to migrants. Remittances come to Kyrgyzstan mainly through banks and MTO (79%) or are brought by household members, friends, or relatives. The role of postal services and informal intermediation is negligible. To obtain a more detailed picture on the impact of remittances on the Kyrgyz economy and on household welfare, we construct a CGE model. The underlying SAM is based on the national accounts for 2000–2004, input–output tables for 2003, and the HBS for 2003–2004. We also draw on an existing SAM prepared by the World Bank for 2003. Our SAM has 14 sectors; we disaggregate the household sector into six groups as in the Moldova SAM (Sect. 7.4.1), using data from the HBS: (i) agricultural smallholders with more than half of their total income from small-plot farming; (ii) other rural households; (iii) rich urban households (top 2 deciles by consumption); (iv) other urban households; (v) public employee households which draw more than half of their income from public administration, health and social services; and (vi) pensioners with more than half of their total income from government transfers. Labor income is disaggregated into (i) income from low-skilled labor: head of household has general secondary or lower education; (ii) income from medium-skilled labor: head of household has special secondary or incomplete higher education; (iii) income from high-skilled labor: head of household has higher education; (iv) income from non-agricultural employment: household head is self-employed. We identify the following impact channels: (i) increase in remittances; (ii) outflow of excess labor from agriculture and higher productivity in this sector (e.g. larger farm size, more commercial orientation of farms, more investment financed by remittances); and (iii) brain drain, i.e. an outflow of high-skilled labor. It is important to note that we assume that there is wide-spread unemployment of low-skilled labor so that the outflow of low-skilled workers does not reduce the effective labor supply. We formulate four scenarios for our simulation exercises: 1. An increase in remittances by 40% 2. An increase in remittances by 40%, accompanied by an increase in TFP in agriculture by 5% 3. An increase in remittances by 40%, accompanied by a 10% reduction in the stock of high-skilled labor and 4. An increase in remittances by 40%, accompanied by 5% higher TFP in agriculture and a 10% reduction in the stock of high-skilled labor The simulation results suggest that higher remittances (scenario 1) positively influence private consumption and production in sectors oriented mainly at the domestic market (e.g. agriculture, food processing, trade). Higher remittances also

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pay for higher imports. At the same time, the inflow of foreign exchange leads to a real exchange rate appreciation, with negative consequences for Kyrgyz exports and production in export-oriented (e.g. mining or machine production) and importcompeting (e.g. light industry) sectors. The overall effect of remittances on GDP appears to be very small. Accounting for higher TFP in agriculture along with higher remittances (scenario 2) results in a more favorable performance of several GDP components, with even stronger (6%) growth of private consumption and a smaller reduction in exports. A combination of remittances and brain drain (scenario 3) leads to much more modest private consumption growth and a severe deterioration in exports (minus 7.5%). Finally, under the last scenario with higher remittances, higher productivity in agriculture, and fewer high-skilled workers, GDP increases marginally while private consumption grows by almost 5% and exports decline by 6%. All types of households gain from an increase in remittances; under the final, most comprehensive scenario, the gains are largest for public sector employee households, farming households, and ‘other’ urban households; and lowest for transferreceiving and ‘other’ rural households. These results probably reflect the fact that migrants are concentrated in farming and poorer urban households.

7.5

Policy Implications

Our case studies of the impact of labor migration and remittances in selected CIS countries demonstrate that aggregate private consumption would decline sharply in the absence of out-migration and remittances. The simulated decline is between 18% for Ukraine and 32% for Moldova (where the share of migrants in the labor force and the ratio of remittances to GDP is especially large). All household groups would be strongly affected, although the impact on the poorest groups varies across countries. Along with the decline in consumption, our selected countries would experience a modest real depreciation (typically below 3%) and associated changes in sectoral output patterns. Furthermore, domestic output would decline substantially if returning workers and lower remittances resulted in lower rates of capacity utilization. In our simulations, this impact channel was approximated by an assumed reduction in TFP. These simulation results demonstrate the major benefits from labor migration and remittances for migrants themselves as well their home countries. Indirectly, at least, host countries such as Russia and the EU also benefit from higher incomes and greater economic security in their neighboring net emigration countries. Whether these potential benefits are realized depends on government policies related to migration in both home and host countries. Against the backdrop of the migration-related policies currently being pursued in the CIS region and in the EU, four major policy implications emerge. First, some CIS country governments faced with large migrant outflows have been reluctant, for political reasons, to even acknowledge that emigration is taking

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place on a large scale. Consequently, they have failed to provide support services to migrants where such services would enhance the benefits from migration, limit the risks, and strengthen migrants’ attachment to their home country. Such services include job placement into legal work abroad through official employment agencies, high-quality consular services for migrants abroad, advocacy with partner governments for limited-term work opportunities for their residents, etc. The absence of such support has made migration more costly to households, without offering attractive alternatives, and has alienated migrants from their home country. By contrast, a forward-looking policy strategy for home countries would be to support migrants where they are most at risk, such as when seeking employment and dealing with host country authorities. This would render it more likely that migrants would favorably consider employment or investment opportunities at home in the future. Second, for economic recovery to take hold in the smaller, natural-resource-poor CIS countries, fixed investments need to be sustained and further increased. Remittances could help to pay for such investment. However, the business and investment climate in many of these countries is so poor that, currently, remittances are only rarely used for productive investment. Government efforts to channel remittances into investment, which are debated in many CIS countries, will succeed only when all investors – migrants and non-migrants, politically well-connected or not – can expect to receive an adequate return on productive investments that is not diminished by parasitic public institutions. Third, to promote social coherence in emigration countries, prudent government policies are called for to ensure that the income gains due to migration are shared, to some degree at least, by all households. Taxes on remittances are usually considered counterproductive as income from legal employment is already taxed in the host country and, in any case, remittances might simply be driven underground. However, since many CIS country governments rely on taxes on imports (especially VAT, but also import duties) for much of their revenue, government revenue typically increases along with remittance-driven imports (which are bought overproportionately by migrant households). The extra government revenue can be used to maintain public infrastructure, provide social services and education (including to the children of migrants left at home or with relatives), and provide targeted income support. Fourth, destination countries will increasingly find themselves competing not only for high-skilled migrants, but also for those willing to perform jobs that are otherwise difficult to fill (such as seasonal work in agriculture, construction, and social services). Russia, the most important host country for migrants from the CIS region, is currently offering legal employment on a fairly broad basis, but migrants’ living conditions are frequently poor and harassment by authorities is endemic. Extending legal residence and employment to a larger share of migrants already in Russia, and strengthening the rule of law and ensuring fair treatment for migrants by authorities, would help to attract the growing numbers of immigrants that Russia will want to rely on as its economic growth continues.

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In EU countries, legal employment opportunities for CIS country migrants are still severely limited but growing. Legalization programs in countries such as Italy and Portugal also create pockets of legal migrants that will probably become the hubs of migrant networks that will attract more immigrants from CIS countries in the future. It would be in the interest of both migrants and EU host countries to replace these haphazard legalizations with a forward-looking strategy for admitting migrants with good job prospects in the EU. Since the EU functions as a single labor market, such programs should be coordinated at the EU rather than the national level. For the benefit of both CIS countries and the EU, the deepening of bilateral relations under ENP should include enhanced opportunities for legal labor migration.

References Atamanov A et al (2009) Income and distribution effects of migration and remittances: an analysis based on CGE models for selected CIS Countries. CASE Netw Rep 86 Fagernas S (2004) Analysing the distributional impacts of stabilisation policy with a CGE model: illustrations and critique for Zimbabwe. ESAU Work Pap 4. Overseas Development Institute, London. GET (2007) Georgian economic trends, quarterly economic trends. Georgian-European Policy and Legal Advice Centre, October. GET (2008): Georgian economic trends, quarterly economic trends. Georgian-European Policy and Legal Advice Centre, February. Havrylyshyn O (2008) Growth recovery in CIS Countries: the sufficient minimum threshold of reforms. Comp Econ Stud 50:53–78 IFAD (2006) Sending money home. Worldwide remittance flows to developing and transition countries. International Fund for Agricultural Development IOM (2006) Human trafficking survey: Belarus, Bulgaria, Moldova, Romania and Ukraine. International Organization for Migration in Ukraine. Prepared by GFK Ukraine Lofgren H, Lee Harris R, Robinson S (2002) A standard computable general equilibrium (CGE) model in GAMS. Microcomput in Policy Res 5, IFPRI. Washington D.C. Luecke M, Omar Mahmoud T, Steinmayr A (2009) Labour migration and remittances in Moldova: Is the boom over? International Organization for Migration, Moldova Office, Chisinau Mogilevsky R, Atamanov A (2008) Social-economic implications of outward migration in the Kyrgyz Republic. CASE-Kyrgyzstan Poznyak O (2002) Mashtaby zovnishnih trudovyh migratsii naselennia Ukrainy (Scale of external labor migration of Ukrainian population). Ukraina: Aspekty Pratsi (Ukraine: Labor Aspects) 6: 37-40 Robinson S (2003) Macro models and multipliers: Leontief, Stone, Keynes, and CGE models. International Food Policy Research Institute (IFPRI). mimeo, September. Online: www.ifpri. org/events/seminars/2003/20031014/robinson_thorbecke_EPIAM.pdf SDSG (2005) Households of Georgia 2003–2004. State Department for Statistics of Georgia, Tbilisi SDSG (2007) Statistical Yearbook of Georgia 2007. State Department for Statistics of Georgia, Tbilisi Starodub A, Parkhomenko N (2005) Ukrainska trudova migratsia do krain ES v dzerkali sotsiologii (Ukrainian labor migration to the EU countries in the mirror of sociology). Center for Peace, Conversion and Foreign Policy of Ukraine, Kyiv

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Chapter 8

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

Abstract While the transformative power of EU accession is widely accepted, there seems to be widespread pessimism concerning the potential impact on institutional convergence through ENP, i.e. for neighboring countries without a membership perspective. This chapter analyzes external determinants of institutional change measured by the WBGI. An econometric panel analysis for 25 transition countries for the period of 1996–2005 reveals a considerable element of path-dependency. However, basic agreements with the EU and NATO and economic liberalization matter most. Hence, ENP can provide incentives for better governance if inconsistencies and shortcomings in action plans can be removed.

8.1

Introduction

The concept of Europeanization, i.e. the adoption of the EU’s rules by transition countries, is possibly ‘the most massive international rule transfer in recent history’ (Schimmelfennig and Sedelmeier 2005). The Copenhagen criteria define a set of political, legal and economic criteria of EU accession (Foders et al. 2002). So far, the EU has indeed been successful in promoting democracy and economic development by fostering institution building in most CEE transition countries (Roland 2006). However, after the completion of the Eastern Enlargement with the accession of Bulgaria and Romania in 2007, the ‘carrot’ of membership for pushing institutional development is currently reserved exclusively for the Western Balkan states. For CIS

I. Melnykovska Research Associate in the Department of Political Science at the Christian-Albrechts-University of Kiel, Research Fellow at the Kiel Institute for the World Economy e-mail: [email protected] R. Schweickert Research Fellow at the Kiel Institute for the World Economy e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_8, # Springer-Verlag Berlin Heidelberg 2011

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countries and the Southern Mediterranean region, the ENP foresees EU support that is dependent on their performance according to governance criteria. However, compared to the big ‘carrot’ of membership, ENP incentives may be too limited to support internal drivers of institutional reform (Afanasyeva et al. 2007; Vinhas de Souza et al. 2006). This rather pessimistic view is supported by the country studies performed in the ENEPO project (Gawrich et al. 2010; Franke et al. 2010): • Gawrich et al. (2010) contribute to the integration of Neighborhood Europeanization into the literature on Europeanization. Based on insights from Membership and Enlargement Europeanization, they reveal important inconsistencies of Neighborhood Europeanization through ENP as well as a lack of robust empirical support for its effectiveness. They define core dimensions and determinants of Neighborhood Europeanization and implement this analytical framework for the case of Ukraine. The analysis clearly demonstrates substantial asymmetries in ENP policy across its three major dimensions – democracy promotion, economic co-operation and JHA, which clearly reflect the inconsistency of the ENP concept: a top-down formulation of EU interests combined with weak conditionality. ENP inconsistencies could however be overcome through widening linkages and improving financial support to mobilize and strengthen local support for EU demands and rewards. • Franke et al. (2010) compare Ukraine with Azerbaijan. Among ENP countries, Azerbaijan is outstanding, because it leans on its resource base and sees the EU at the receiving end of bilateral relations. At the other extreme, Ukraine depends on EU cooperation, especially with respect to trade. The researchers develop a comprehensive theoretical concept for analyzing both types of asymmetries and contrast national strategies with bilateral, regional, and multilateral EU approaches to democracy promotion, economic cooperation, JHA cooperation, and conflict resolution in the European neighborhood. It turns out that the EU strategy of Neighborhood Europeanization has to be increasingly based on nontangible tools, on packages of regional and multilateral initiatives which balance out asymmetries, and on issues other than trade, such as security, in order to be more effective. The econometric evidence on external drivers of institutional change in transition countries is rather limited. Recent papers mainly focus on internal economic, political, and cultural drivers (Di Tommaso et al. 2007; Beck and Laeven 2006), treating EU influence rather as a control variable than as the main driver of institutional change. Hence, this chapter fills an empirical gap by focusing on external influences in the first place. This implies distinguishing between EU cooperation and neighborhood instruments and full-scale accession instruments. In addition, we consider all relevant clubs a European transition country may join that provide positive incentives for better institutions. While papers analyzing the impact of trade relations include membership in the WTO (see, e.g. Busse et al. 2007), accession to the NATO as a driver for institutional change receives little attention. Our panel estimations indeed reveal that, in addition to EU agreements, NATO accession also has a positive impact on institutional change in European transition countries.

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8.2

125

External and Internal Drivers of Institutional Change

The case of European transition countries is clearly different from other developing and emerging market economies. Compared to developed countries, all of them show a backlog in terms of institutional development. However, the reunification of Europe after the breakdown of communist regimes has provided a strong pull effect concerning the development of good institutions. Looking at the various clubs which European transition countries may join, one could argue that the EBRD, WB and IMF are important players, providing incentives for reforms. While this is certainly true, there is no exclusive accession process which would demand institutional preconditions. Therefore, the impact of these institutions seems to be rather permanent and program specific. This is different for the EU but also for NATO and WTO. Concerning the impact of the EU on institutional change, there seems to be little doubt that membership matters. Way and Levitsky (2007) explain the institutional divide between a democratic Central and Southeastern Europe and an autocratic CIS by potential membership in the EU. Similarly, Pop-Eleches (2007) argues that post-communist democratization has been faster and less prone to reversals in the countries where for geographic, historical, cultural, and economic reasons, the promise of deep integration with Western Europe was the strongest at the outset of the transition. According to Haughton (2007), the EU’s ‘transformative power’ is strongest when deciding to open accession negotiations. The EU’s influence is also shown to be stronger in some areas, especially in economic aspects necessary to establish the single market, while it is clearly weaker in other areas like minority protection. Schimmelfennig (2007) argues that only the credible conditional promise of membership has had the potential to produce compliance with liberaldemocratic norms in norm-violating transformation countries. According to case studies on Latvia, Slovakia and Turkey, EU democratic conditionality is shown to work through a strategy of ‘reinforcement by reward’. These arguments are confirmed by Beck and Laeven (2006), who show that a dummy variable for EU membership provides an additional positive effect on institutional change in European transition countries as measured by the WBGI. However, the crosscountry approach adopted by this analysis only allows for the inclusion of control variables like EU membership one-by-one, which creates serious problems of misspecification. In contrast, only a few studies analyze the impact of the EU on institutional change by means of agreements below the membership perspective. The positive effects of links to the EU may be reached via a variety of channels: the promotion of democratic attitudes among citizens, political incentives for elites (in government and in the opposition), domestic power balance shifts in favor of democratic politicians, and the promotion of better democratic governance through incentives for public administration reform (Pop-Eleches 2007). Hence, democracy is promoted by a combination of political conditionality with significant political and economic incentives. Di Tommaso et al. (2007) confirm the positive impact of basic

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agreements between the EU and transition countries. This allows for some optimism regarding weak incentives provided by the ENP. However, the paper uses EBRD indicators, which only measure change in economic institutions, while the Europeanization strategy of the EU targets political and legal institutions as well. Furthermore, the EBRD itself is an actor in the transition process and, hence, may provide biased views on its success or failure. Therefore, the robustness of the result should be checked by estimating the impact of basic EU agreements on a broad set of institutional developments, as measured by the WBGI, and by comparing it to the impact of a membership perspective at the same time. While the process of EU enlargement figured prominently in the transition literature, NATO membership and enlargement is almost omitted. A few studies discuss NATO’s impact in terms of economic aspects of regional security (see, e.g. Sandler and Hartley 1999; Andrei and Teodorescu 2005) and democracy promotion (see, e.g. Barany 2004; Boonstra 2007; Epstein 2005). Interestingly, NATO has also developed an enlargement procedure in the form of the MAP approved at the Washington Summit in 1999. A country’s participation in the MAP entails the annual presentation of reports concerning its progress on five different measures. Four of them relate to organization, resources, safeguards, and compatibility and look at the potential of (military) cooperation between the accession country and NATO. The first and probably the most important criterion refers to a country’s readiness to settle international, ethnic or external territorial disputes by peaceful means and to commit to the rule of law and human rights, and democratic control of the armed forces. Hence, NATO accession requires a kind of minimum institutional standards. The ‘carrot’ in this case is regional security rather than economic cooperation. Hence, it could be argued that NATO accession could have a positive effect, which may be comparable to the impact of EU accession (see, e.g. Schimmelfennig 2007 and Pop-Eleches 2007). In addition to the EU and NATO, the WTO also provides major incentives for institution building. Beyond its direct impact on trade liberalization and macroeconomic policies, WTO membership helps to reduce incentives for corruption by providing countries with powerful institutional checks and balances in the international economic sphere. To become a WTO member, a set of institutions and policies should be implemented. Consequently, these WTO-conforming institutions and policies contribute to the openness of the economy, enhance transparency and promote the rule of law (Bacchetta and Drabek 2004). Actually, a country’s institutional setup is affected long before its WTO accession date by the process of the preparation and accession negotiations. However, as reported in Busse et al. (2007), empirical papers largely fail to show a significant impact once trade flows are controlled for. In addition to membership in international institutions, geographic proximity to the West matters (Way and Levitsky 2007; Vinhas de Souza et al. 2006): • Proximity to the West in terms of cultural norms could be assumed to provide a significant path-dependency concerning institutional development (Di Tommasso et al. 2007; Kitschelt 2001; La Porta et al. 1999). A society’s culture adapts rather slowly to changing economic circumstances because of a high persistence

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of cultural norms and human belief systems. At the same time, religious affiliation, like belonging to a community based on western Christianity, can be thought of as a proxy for a complex set of initial conditions. • Nevertheless, trade and capital flows may impact institutional change by having closer experience with the outside world. Busse et al. (2007) argue that any analysis on the relative impact of trade on income and growth would suffer from a lack of relevant control variables, if important determinants of a successful trade liberalization, such as institutional quality affecting the reallocation of resources, were not included. Their results confirmed earlier work showing that more open economies tend to have better institutions (see, e.g. Wei 2000; Islam and Montenegro 2002; IMF 2005). In the CIS context, Havrylyshyn (2006) claims that openness and sweeping reforms have reduced social pain in CE and in the Baltic states. He suggests that liberalization and openness ensure economic recovery and democratic institutions. • Arguably, FDI inflows may also help to promote good governance in CIS countries. However, focusing on corruption, Hellman et al. (2002) show that foreign firms are more likely to pay kickbacks for public procurement contracts and be engaged in state capture practices than domestic firms. Hence, the presence of foreign firms may not narrow the institutional gap. • The allocation of aid has become more selective in recent years, and has become more responsive to economic fundamentals and the quality of a country’s policy and institutional environment (Claessens et al. 2007; see also Chap. 10 of this volume). Hence, aid should support institutional change. However, by expanding a government’s external resources, foreign aid can weaken institutions by reducing accountability. Evidence suggests that industries which are most sensitive to bad governance grow at a slower pace in countries that receive more aid (Rajan and Subramanian 2007). All in all, proximity in terms of culture and trade is assumed to have a positive impact on institutional change while the impact of capital flows is, at least, open to concerns about potential moral hazard problems related to the inflow of financial resources. So far, an empirical analysis of all relevant external drivers of institutional change in European transition countries is not sufficient. In contrast, the analysis of internal drivers can be based on a variety of papers. The basic distinction is between economic and political aspects. The view that economic performance drives institutional development is supported by the modernization hypothesis (see, e.g. Lipset 1959; Acemoglu et al. 2007). In the same vein, the Grand Transition view sees development as a process in which steady economic growth causes a transition in all institutions (Paldam and Gundlach 2008). However, economic shocks and macroeconomic instability may also be important factors of political transition (Acemoglu and Robinson 2006; Paldam 2002). These shocks give rise to a window of opportunity for citizens to contest power, as the cost of fighting with the ruling autocratic regimes is relatively low. When citizens reject policy changes that are easy to renege upon once the window of opportunity closes, autocratic regimes must make democratic concessions to avoid costly repression (Brueckner and Ciccone 2008, p. 1).

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These arguments directly lead to the importance of economic policy, as opposed to economic performance, in driving institutional change. The typical sequencing of reforms suggests that economic liberalization and privatization and the granting of basic political rights and liberties precede institutional reforms such as the establishment of a competition authority or the adoption of bank restructuring programs and stronger financial market supervision. Hence, policy can break path-dependence, to some extent, through economic and political liberalization (Di Tommaso et al. 2007; Havrylyshyn 2006). A political economy explanation of why institution building has varied so much across transition countries is provided by Beck and Laeven (2006), who argue that political entrenchment and reliance on natural resources critically determines the behavior of the ruling elite and thus whether or not the transition process is catalytic or extractive. While this seems to support the pessimistic view that initial conditions determine future outcomes (Fish 1997; Kopstein and Reilly 2000; Guiso et al. 2006; Zweynert 2006), there is also a more optimistic view on the potential for institutional progress in rent-seeking societies which links economics and politics. Olson (2000) argues that the availability of short-term rents like non-renewable resources provides the basis for the rent-seeking strategy of ‘roving bandits’ but they can transform themselves into ‘stationary bandits’ after reaching the limits of their capacities to accumulate and control wealth on the basis of informal institutions (see also Tornell 1998; Dixit et al. 1997; Aslund 1995 and 1999). Looking at the role of resource abundance, several studies suggest its adverse effect on institutional quality and economic growth. This negative impact is particularly strong for easily accessible ‘point-source’ natural resources with concentrated production and revenues and thus massive rents, i.e. oil, diamonds, minerals and plantation crops rather than agriculture (e.g. rice, wheat and animals), where rents are more dispersed throughout the economy, and with easy appropriation of rents through state institutions (Auty 1997, 2001; Isham et al. 2004; Sala-i-Martin and Subramanian 2003; Murshed 2004; Collier and Hoeffler 2004; Ploeg 2007). Analyzing the political economy of resource-driven growth in the CIS countries, Auty (2001, 2006) finds out that in most resource-abundant countries, governments behave in a predatory way and respond slowly to the challenges of economic reforms, distort the economy in pursuit of rents, force industrialization, etc. This negative influence is explained by rent-seeking behavior and lower pressure for political reform. Other natural resources, such as agriculture commodities, were not found to have a negative impact.

8.3

The Empirical Model

In line with the theoretical and empirical literature outlined above and as described in Sect. 8.2, we model the impact of external and internal drivers on institutions as measured by the WBGI. We argue that this is the most comprehensive measure of institutional development which is available for international comparisons. The

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WBGI is calculated as the average of six single indicators1 on (i) voice and accountability, (ii) political stability and absence of violence, (iii) government effectiveness, (iv) regulatory quality, (v) rule of law, and (vi) control of corruption. Hence, the aggregate indicator integrates legislative, administrative and legal aspects as well as political and economic institutions (Schweickert 2004). At the same time, the calculation of the indices considers measurement errors and provides standardized measures. By using the WBGI, we follow Beck and Laeven (2006) but we will consider a full model in terms of external and internal drivers of institutional change. In this respect, we modify and extend the framework of Di Tommaso et al. (2007). Due to the fact that key variables are comparable, we are able to provide a kind of robustness check with respect to different measures of the endogenous variable, i.e. institutional change. As can be seen in Table 8.1, we group our explanatory variables into external drivers, internal economic drivers, and internal political drivers. This will allow us to distinguish between economic and political models of institutional change. According to Sect. 8.2, we consider both membership and proximity variables as external drivers. Membership is determined by accession to the EU, NATO, and WTO. In Table 8.2 at the end of this chapter, we distinguish between the EU Accession variable reflecting the full-scale accession process and which varies from 0 (no agreement at all) to 5 (membership) and the EU Basic variable which is a dummy for agreements which can be concluded by all sample countries, i.e. association agreements below a membership commitment on the part of the EU (see Table 8.2 for details). The EU Basic variable is similar to the EU variable used by Di Tommaso et al. (2007). In addition, we use an EU Potential dummy variable for all actual members or countries which have, by now, a membership perspective.2 Concerning NATO, the dummy variable reflects participation in a MAP assuming that this provides an incentive for institutional reform in participating countries (comparable to EU Basic). Unlike the EU and NATO, WTO membership is open to all sample countries. Hence, this dummy variable is one for all countries starting from the year when they entered WTO. Since we code the variables on a year-byyear basis, we can use them to find country fixed effects to assess the impact of the changes in these variables over time. We expect that these variables should have a positive impact on institutional development. Proximity is measured by cultural proximity, i.e. the dummy variable reflects whether or not countries belong to the western Christian community.3 We do not consider other cultural variables because we found them to be highly correlated

1

The data as well as details on the calculation procedure is provided by the WB at http://web. worldbank.org/WBSITE/EXTERNAL/WBI/EXTWBIGOVANTCOR/ 0,,contentMDK:21499997~ pagePK:64168445~piPK:64168309~theSitePK:1740530,00.html 2 The countries not eligible for membership are Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, Ukraine and Uzbekistan. 3 Physical distance as in gravity models has been used in robustness checks only, since it provided little additional explanatory power when membership effects are properly controlled for.

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Table 8.1 Exogenous variables used in the regressions Variable Description Dependent Variable Arithmetic average of the six WBGI WBGI sub-indices External drivers

Source

WBGI; http://www.govindicators.org

Membership Time varying variable measuring the degree of association with the EU on the basis of agreements such as PCA, SAA or ENP AP. Ranging from 0 ¼ no agreements to EU Agreement 5 ¼ membership. EU Agreement Database Dummy Variable, equals 1 for ‘potential members’ if SAA was ratified in the previous year or for other countries if PCA has been in force since previous EU Basic year EU Agreement Database Time-invariant dummy; equals 1 for all countries except Armenia, Azerbaijan, Belarus, Georgia, Kazakhstan, Kyrgyzstan, Moldova, Russia, Tajikistan, Turkmenistan, EU; http://europa.eu/abc/history/ EU Potential Ukraine and Uzbekistan 1990–1999/index_en.htm NATO; www.nato.int; Berlin Information Center for Transatlantic Security; http:// www.bits.de/frames/databasesd. Dummy variable equals 1 for all years htm NATO following a membership action plan WTO; http://www.wto.org/english/ thewto_e/acc_e/completeacc_e. Dummy variable equals 1 for all years htm WTO following WTO or GATT accession Economic Relations FDI, Net Inflows (% of GDP), average FDI over current and past 2 years WB: WDI Online ODA and Official Aid (% of GDP), AID average over current and past 2 years WB: WDI Online Distance CIA World; https://www.cia.gov/ library/publications/the-worldDominance of Protestant or Catholic factbook/ Western Christianity (¼1, otherwise 0) Internal drivers Economics Economic Policy

Liberalization

Inflation

Average of price liberalization and trade and foreign exchange liberalization, EBRD; http://www.ebrd.com/ ranging from 1 to 4.66 country/sector/econo/stats/tic.xls Inflation, consumer prices (annual %), geometric average over current and WB: WDI Online and, if missing, past 2 years EBRD (continued)

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Table 8.1 (continued) Variable Description Source Economic Performance GDP per capita at PPP divided by initial Income GDP per capita, normal PPP WB: WDI Online GDP growth, geometric average over Growth current and past 2 years WB: WDI Online Politics Incentives Heidelberger Institute f€ur Internationale Konfliktforschung; http://www.hiik.de/start/index. Tensions Binary variable: conflict yes or no. html Political rights as an average score of two indicators (civil liberties and political rights) in the early years of Freedom House; www. independence (1989 or 1991) freedomhouse.org Rights Dummy variable, indicate if communist party or successor party is in government, 0 ¼ yes; 0,5 ¼ influence; 1 ¼ no; na ¼ not Turnover clear EIU Country Profiles; www.eiu.com (absolute value of largest non communist party vote) – (ex KP vote in first post-transition election) EBRD Transition Report (1999) Cohesion Opportunities (Resources) WDI – Fuel exports (% of merchandise Fuel Exports exports) WB: WDI Online WDI – Ores and metals exports (% of Metal Exports merchandise exports) WB: WDI Online

with our proximity variable while they do not reflect proximity to the same extent. We expect cultural proximity to have a positive impact on institutional change in European transition countries. In addition, economic relations constitute a form of proximity and are measured by FDI and aid inflows. We also test the impact of exports to non-transition countries in order to check for possible institutional spillovers through trade or to detect any evidence of learning by doing. While we would expect the trade variable to be positively related, this seems less clear for the variables reflecting resource inflows which could create rent seeking effects. Concerning economic internal drivers, we consider both policy and performance variables. In line with Di Tommaso et al. (2007), we measure economic policy in terms of the aggregated EBRD liberalization indicator. Not surprisingly, they found a positive impact of an aggregated EBRD indicator on institutions. We hypothesize that the EBRD liberalization indicator should reveal a comparable impact on the broader concept of institutions not constructed by the EBRD itself. With respect to economic performance, we took the usual suspects, i.e. initial income at the start of the transition process and economic growth. While we tried 3 year averages of growth and a recession variable for robustness checks, our main variable is

Czech Rep. Estonia Hungary Latvia Lithuania Poland Slovakia Slovenia Bulgaria Romania Albania Croatia Macedonia Moldova Ukraine Armenia Azerbaijan Georgia Russia Belarus Kazakhstan Kyrgyzstan Tajikistan Turkmenistan Uzbekistan

Country

2004 2004 2004 2004 2004 2004 2004 2004 2007 2007

2002 2002 2002 2002 2002 2002 2002 2002 2004 2004 2005

1998 1998 1998 2000 2000 1998 2000 1998 2000 2000

Accession negotiations begin 1997 1997 1997 1997 1997 1997 1997 1997 1997 1997 2003 2003 2003

1995 1995 1995 1995 1995 1995 1995 1996 1995 1995 2006 2001 2001

EA/ Strong notice EAAPa/ on SAA membership signed 1993 1993 1993 1993 1993 1993 1993 1993 1993 1993 2000 2000 2000 2005 2005 2006 2006 2006 2003

1993 1993

1991 1991

1991

1991

Weak notice ENPAP/ 4CS/EA on membership agreed

1998 1998 1998 1999 1999 1999 1997 (1995) 1999 1999 (2005) (2004) 1999

1992

1993

PCA/ CA in force

1997 1994 1994 1996 1996 1996 1994 1995 1995 1995 2004 1998 1999

1992

1993

PCA/ CA signed

1998

2000

1947 1999 1973 1999 2001 1967 1947 1994 1996 1971 2000 2000 2003 2001 2008 2003

GATT WTO member

WTO

1999 2004 1999 2004 2004 1999 2004 2004 2004 2004 2009 2009

1997 1999 1997 1999 1999 1997 1999 1999 1999 1999 1999 2002 1999

MAP/ official Member invitation

NATO

EA signed in 1991 with Poland, Hungary and CSFR did not involve a membership perspective and, therefore, could not be evaluated in the same way as EA signed after 1993. The EA of 1991 were updated in 1995 with the EAAP that included a membership perspective. — ( ) ratified by the country but not by the EU. Definitions: CA – The Cooperation Agreement; 4CS – The Four Common Spaces; ‘weak notice on membership’– The Copenhagen Summit of 1993 for countries that became EU Members until 2007 or the Zagreb Summit of 2000 for Western Balkans; ‘strong notice on membership’ – the Luxembourg Summit of 1997 for CEE countries or the Thessaloniki Summit of 2003 for Western Balkans; other definitions – see ‘Abbreviations’.

a

Central Asia

EU North East Neighbors

Southern Caucasus

EU East Neighbors

Balkans

EU Members 2007

EU Members 2004

Group

Accession negotiations Member end

EU

Table 8.2 Chronology of EU, NATO, and WTO accession (From EU Agreements Database (http://europa.eu/abc/history/1990-1999/index_en.htm; own summary); WTO (http://www.wto.org/english/thewto_e/acc_e/completeacc_e.htm ); NATO (www.nato.int; http://www.bits.de/frames/databasesd.htm))

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accumulated growth during transition, assuming that short run ups and downs do not impact relatively persistent institutions. We also use inflation as a measure of macroeconomic stability. While initial income revealing path-dependency can be expected to have a positive impact on institutions, this is less clear for growth and inflation.4 Either growth or stability increase demand for good institutions or recession and instability push institutional reform. As argued by Beck and Laeven (2006), the influence of political drivers may come from either incentives to create good institutions or from opportunities to misuse political power for extracting rents in the case of resource-rich economies. Regarding incentives, we consider that the way a country gained independence, i.e. whether there were any tensions during the declaration of independence and in the following transition years, defines the incentives to build up good institutions. We expect that tensions would reduce incentives for good governance, while a peaceful independence movement would promote them. Furthermore, turnover from communist to democratic parties can be assumed to have a positive impact on institution building in contrast to situations where the communist party or its successor party stays in power either on its own, or in coalition with democratic parties. Finally, the cohesion of political parties is assumed to have a positive influence on institutional reform. Concerning opportunities, we use a dummy variable measuring endowment. However, endowment does not necessarily reflect the exploitation of resources and the related resource flows, which may create moral hazard problems. Therefore, we alternatively also include flow variables like fuel exports and metal exports. We expect that resource measures would have a negative impact on institution building.

8.4

Empirical Results

The empirical estimates are based on a panel of 25 transition economies in the years 1996–2005.5 The empirical results based on Pooled OLS (POLS), Instrumental Variables (IV) and (country) FE estimators are presented in Tables 8.3–8.5 – see Drautzburg et al. (2008) for details of model specification. We proceed in three steps. Table 8.3 (at the end of this chapter) contains the results of the full model specification considering external as well as internal economic and internal political drivers of institutional change in transition countries. Tables 8.4 and 8.5 present partial models. First we show the results for separate economic and political models which, in addition to external drivers (which are of both an economic and political nature), contain only variables from the two blocks of economic and political drivers respectively (Table 8.4). Finally,

4

The officially recorded GDP decline in transition countries was overestimated (see Aslund 2001; Dabrowski 2002). However, we assume that the error value is the same across transition countries. 5 Before 2002, WBGI data is only available each 2 years, i.e. 1996, 1998, and 2000.

Rights

Tensions

Inflation

Income

Growth

Liberalization

Western

Aid

FDI

WTO

NATO

1,051 (1,32) 0,676 (0,98) 0,027 (0,77) 0,108 (0,72) 3,790*** (3,90) 1,291 (1,28) 0,226 (0,21) 0,000 (0,49) 0,020 (0,08) 1,522*** (4,37) 0,046 (0,22)

1,017*** (3,24) 0,019 (0,04) 0,000 (0,03) 0,201*** (2,86) 3,839*** (5,13) 1,376*** (3,48) 0,106 (0,14) 0,000 (0,18) 0,026 (0,15) 1,617*** (6,65) 0,006 (0,04) 0,098 (0,75)

1,188** (2,18) 0,264 (0,14)

1,431*** (4,73) 0,084 (0,24) 0,010 (0,66) 0,270** (2,43)

1,263* (1,67) 0,418 (0,70) 0,040 (1,20) 0,163 (1,33) 4,248*** (5,45) 1,285 (1,41) 0,677 (0,83) 0,000 (0,19) 0,006 (0,02) 1,685*** (5,48) 0,067 (0,37)

1,024*** (3,22) 0,057 (0,14) 0,002 (0,13) 0,216*** (3,62) 4,015*** (6,36) 1,322*** (3,27) 0,397 (0,69) 0,000 (0,08) 0,013 (0,07) 1,655*** (6,94) 0,007 (0,04)

Table 8.3 Aggregate results and comparative EU-indicators (From authors’ estimations) IV OLS FE IV OLS EU Agreement 1,035* 0,617* 0,005 0,648* 0,498*** (1,75) (1,72) (0,02) (1,83) (3,02) EU Potential 1,089 0,405 (1,02) (0,42) EU Basic

0,000

0,098 (0,75) 0,000

1,188** (2,18) 0,264 (0,14) 0,000

1,431*** (4,73) 0,084 (0,24) 0,010 (0,66) 0,270** (2,43) 0,000

FE 0,005 (0,02)

OLS

1,277*** (2,85) 1,223*** (3,71) 1,364*** (3,85) 0,132 (0,32) 0,004 (0,30) 0,172*** (3,03) 3,901*** (6,24) 0,934*** (2,90) 0,067 (0,10) 0,000 (0,15) 0,095 (0,47) 1,867*** (6,49) 0,056 (0,36)

IV

1,789** (2,17) 2,415*** (2,98) 3,463*** (3,10) 1,360 (1,34) 0,058 (1,11) 0,035 (0,27) 5,270*** (5,44) 0,544 (0,39) 1,445 (1,23) 0,000 (0,15) 0,282 (0,89) 2,317*** (4,93) 0,133 (0,66)

0,062 (0,37)

0,734** (2,07) 0,347 (0,22)

0,928*** (2,87) 1,410*** (4,86) 0,386 (0,90) 0,003 (0,22) 0,287*** (3,95)

FE

134 I. Melnykovska and R. Schweickert

0,383 0,482 (1,02) (1,55) Cohesion 0,552 0,544* (1,56) (1,68) Fuel exports 0,012* 0,016** (1,71) (2,57) Metal exports 0,045 0,051** (1,10) (2,09) R-squared 0,97 0,97 Adj R-squared 0,96 0,97 Observations 103,00 103,00 *P < 0.10, **P < 0.05, ***P < 0.01.

Turnover

0,019 (1,01) 0,009 (0,24) 0,62 0,54 103,00

0,105 (0,21)

0,469 (1,47) 0,565 (1,63) 0,007 (0,98) 0,048 (1,25) 0,97 0,96 103,00

0,527* (1,80) 0,542 (1,66) 0,014** (2,40) 0,050** (2,04) 0,97 0,97 103,00 0,019 (1,01) 0,009 (0,24) 0,62 0,54 103,00

0,105 (0,21) 0,000

0,400 (0,83) 0,651 (1,24) 0,012 (0,90) 0,043 (0,81) 0,93 0,91 113,00

0,526 (1,58) 0,590 (1,63) 0,021*** (3,43) 0,057** (1,99) 0,96 0,95 113,00 0,004 (0,19) 0,037 (0,80) 0,57 0,50 113,00

1,078 (1,39)

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Table 8.4 Economic vs. political determinants (From authors’ estimation) IV OLS IV EU Agreement 1,522*** 1,370*** 0,943*** (6,05) (6,23) (2,65) EU Potential 1,664** 1,960*** 0,102 (2,39) (2,91) (0,12) EU Basic NATO 0,718 0,343 0,323 (1,03) (0,98) (0,51) WTO 0,299 0,136 0,248 (0,61) (0,47) (0,35) FDI 0,004 0,015 0,019 (0,11) (1,11) (0,88) Aid 0,007 0,005 0,073 (0,11) (0,13) (1,30) Western 2,936*** 3,086*** 4,071*** (7,86) (8,82) (7,60) Liberalization 1,862*** 1,348*** (4,65) (6,03) Growth 2,201*** 1,808*** (4,19) (3,63) Income 0,000*** 0,000*** (2,67) (3,16) Inflation 0,385** 0,204* (2,25) (1,87) Tensions 1,513*** (7,07) Rights 0,016 (0,16) Turnover 0,827*** (3,02) Cohesion 0,732** (2,35) Fuel exports 0,011 (1,61) Metal exports 0,007 (0,68) R-squared 0,94 0,95 0,97 Adj R-squared 0,93 0,94 0,96 Observations 127,00 127,00 106,00 *P < 0.10, **P < 0.05, ***P < 0.01.

OLS 0,965*** (3,16) 0,201 (0,22) 0,734** (2,24) 0,029 (0,07) 0,039** (2,48) 0,077* (1,81) 4,100*** (7,45)

1,546*** (6,99) 0,009 (0,09) 0,868*** (2,94) 0,644* (1,98) 0,016** (2,37) 0,005 (0,45) 0,97 0,96 106,00

we show a reduced model which only includes variables from the full model which are either significant in all IV or in all fixed effects estimates (Table 8.5). Starting with the full model presented in Table 8.3, a first important result is that the EU variables show the expected signs and are significant in all versions of the model at least in the POLS estimates. EU Agreement clearly has a significant positive effect on institutional development across countries. As can be seen, our

0,385 (0,77) 0,103** (2,19) 3,627*** (7,25) 1,915*** (6,74) 0,425 (0,79)

R-squared 0,93 AdjR-squared 0,93 Observations 164,00 *P < 0.10, **P < 0.05, ***P < 0.01.

Tensions

EU Potential

Liberalization

Western

Aid

NATO

0,213 (0,29) 1,382*** (4,24) 0,90 0,89 164,00

0,337 (0,61) 0,110 (1,54) 4,356*** (11,02)

0,524 (1,06) 0,079 (1,42) 3,889*** (9,57) 1,818*** (5,63) 0,411 (0,71) 0,895 (1,65) 0,94 0,93 164,00

Table 8.5 Reduced model versions (From authors’ estimations) Variable OLS OLS OLS EU Agreement 1,060*** 1,413*** 0,998*** (5,03) (5,99) (4,60) EU Basic

0,32 0,27 164,00

0,975** (2,56)

1,362*** (3,78) 0,035 (0,27)

FE 0,026 (0,07)

0,92 0,91 164,00

2,101*** (5,25) 1,690*** (3,75) 0,086 (1,26) 5,143*** (10,64)

1,168** (2,53) 1,318** (2,75) 0,132** (2,45) 4,260*** (6,90) 1,939*** (5,51) 1,331** (2,36) 2,504*** (4,59) 1,623*** (3,68) 0,89 0,89 164,00

OLS

OLS

1,127** (2,57) 1,433*** (3,19) 0,097 (1,51) 4,544*** (9,28) 1,795*** (4,55) 1,228** (2,19) 1,144 (1,63) 0,92 0,92 164,00

OLS

0,32 0,27 164,00

0,970*** (2,85)

0,019 (0,05) 1,336*** (3,24) 0,035 (0,28)

FE

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alternative specification of the EU impact shows that the EU Potential dummy is significant in the case of inclusion of the EU Basic variable which was tested in Di Tommaso et al. (2007). This implies that a basic relationship with the EU, as shown by the significance of the EU Basic variable, is positive for institutional development. Hence, there is some optimism that the ENP can have an impact on governance in neighboring countries such as in the CIS. At the same time and as a matter of geography, proximity to the EU in terms of both distance and potential membership has an additional benefit. Our EU Accession indicator picks up both effects which confirm the specification of this variable. The results for the remaining membership variables are strikingly different. As in other papers, the positive impact of WTO membership cannot be confirmed by our regressions (see, e.g. Busse et al. 2007). On the contrary, the NATO membership action plan is, with one exception, shown to be significantly positive. Arguably, the perspective of NATO membership provides different incentives from those offered by the EU: regional and international security. This result is of special interest because of ongoing discussions on the future role of NATO and its relation to the UN and the EU (see, e.g. Sandler and Hartley 1999). It seems important to note the different effects of the EU and NATO variables. While the NATO variable generally fails to have a significant impact when both cross-sectional and within-country variation is taken into account, the opposite holds true for the EU Agreement variable. This might indicate that the models fail to approximate satisfactorily for country-specific effects. The NATO-effect is however more than a pure time-effect since the models control for time-fixed effects common to all countries. The full model presented in Table 8.3 shows the results for FDI and aid inflows. While FDI is insignificant in all specifications, our results seem to confirm the negative impact of aid found by other authors. Controlling for potential endogeneity problems, the significance of the negative impact shown in the POLS regressions disappears in the IV versions. However, there is a negative impact shown in our fixed effects versions of the model. While this effect is not robust across all estimated specifications, it is plausible to assume that the level of aid flows does not have a positive impact on institutional performance as measured by the WBGI. One possible explanation is that inflows of aid offer an opportunity to relax fiscal control and to lower reform efforts in a given country. However, since we do not include major recipients of aid flows, i.e. African countries, far-reaching generalizations based on our sample are not warranted. Another striking result is the significance of cultural proximity as shown by countries belonging to the group of countries sharing western Christianity as a cultural feature of their societies. This is perhaps the most robust result of all the specifications which we tried (see also below). Of course, this adds pathdependency as an important feature of institutional development. With respect to the economic variables, it is rather surprising that we do not find much significance regardless of model specification or the regression method applied. There is one exception. Liberalization policies seem to matter. As was the case with aid, the IV regressions show that there may be endogeneity problems

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involved when regressions show significant results. Especially for economic liberalization, it seems plausible to assume that a ‘reform shock’ to institutional development might simultaneously affect liberalization policy. However, since liberalization is not very predictable with its own lags, it might be that the different IV results are due to the additional prediction uncertainty. Focusing on the POLS and FE estimates, the results indicate that across countries as well as within countries, economic liberalization leads to better governance over time. (We cannot see any systematic differences between the FE and POLS estimates). The political variables in the full model reveal substantial path-dependency. Looking at the incentive variables, there are tensions at independence which seem to determine future institutional development negatively. There is only one case in which the turnover variable showing the involvement of democratic parties in government is weakly significant (this might be caused by missing values in the turnover variable and therefore changes in the sample composition). The resource variables seem to work slightly better. At least, fuel exports are shown to have a weakly significant negative effect on institution building as expected by the relevant literature on resource curse. More resources seem to be detrimental but unlike aid, the types of resources seem to matter more between countries than over time.6 In Table 8.4, we present two sub-models because due to our limited data set and a quite substantial number of exogenous variables, the significance of coefficients may suffer from a relatively low degree of freedom in the small sample. Hence, having potential misspecification in mind, we have run economic and political models. The results are quite surprising. First, the results from the sub-models confirm the significance of the external impact variables, especially EU accession and belonging to the Western community. Second, the economic and political blocks work strikingly better when alternative explanations are excluded. Looking at the IV versions of the regressions, all variables from the economic block are significant. The significantly positive effect of higher inflation seems to lend support to the crisis argument by Acemoglu and Robinson (2006), i.e. instability may create momentum for institutional reform. However, the growth variable goes in the opposite direction. It shows that better growth performance stimulates better governance. This would be consistent with the findings of Paldam (2002) and Paldam and Gundlach (2008). With respect to politics, the significance of tensions and cohesion point to a considerable path-dependency while turnover indicates that a higher degree of involvement of democratic parties leads to better institutions. Fuel exports show the expected negative effect which is, however, weak in the IV version. Looking at resource endowment as a dummy variable does not reveal better results for the resource impact on governance. As has been argued above, restricting the model to either economic or political variables runs the risk of misspecification. Nevertheless, it provides a kind of

6

However, this might be due to the lack of variation of resource exports within countries over time.

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overall robustness check to see that some variables remain significant when switching to the full model. In the last step, we deviated from the full model by presenting a reduced model which had only variables which worked in the full model either in its IV or in the fixed effects specification and independent of the EU variable included. The variables which have been picked from the full model are the EU variables plus NATO, aid flows, western Christianity, economic liberalization, and tensions at independence. As can be seen in Table 8.5, the EU variables worked as before. The NATO variable provides strikingly good results in the specification using EU Basic and EU Potential variables. This seems to reflect the fact that basic agreements with the EU and NATO both and independently of each other provide incentives for better governance while there is definitely something left to be explained for those countries which, in addition, have the chance of becoming full members of the EU. While Western Christianity again provides significantly positive results throughout our specification, the aid variable seem to be less robust. The same applies to tensions at independence, which work well when economic liberalization is excluded but, overall, economic liberalization seems to provide a robust and positive impact on institution building, which is also confirmed when checked by IV regressions.

8.5

Summary and Policy Conclusions

All in all, our models confirm the positive impact of the EU on institutional change in European transition countries. However, unlike Di Tommaso et al. (2007) and Beck and Laeven (2006), we are able to show that both membership and accession matters and that there is an additional and independent positive impact from NATO accession on institution building in transition countries. Other potential external drivers do not show a positive impact in our regressions while foreign aid, especially, may even create moral hazard problems. Differences between countries seem to matter less, reflecting different absorption capacities while increase in aid flows over time was found to negatively impact institutional quality. In line with the majority of previous empirical studies, we did not find that WTO membership had a significant impact. As opposed to other papers, we are not able to detect a robust impact for trade flows. Among our proximity variables, belonging to the community of Western Christianity provided the most robust and positive effects. Among potential internal drivers, two determinants show the most robust impact on institutions: economic liberalization as measured by the EBRD liberalization indicator and tensions at independence. Clearly, economic reform matters not only for economic performance and for the building of economic institutions as shown by Di Tommaso et al. (2007), but also for institutions measured on the basis of the comprehensive WBGI as well. At the same time, countries starting with unfavorable political conditions need more effort or external incentives for institution building.

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Hence, there is a considerable element of path-dependency. Clearly, countries belonging to the western community with respect to their cultural features, which could become EU members, and which do not suffer from political conflict at the start of the transition process, have better starting conditions. In this respect, geography matters. However, there are additional variables which have an impact on good governance in any country in our sample: basic agreements with both the EU and NATO and economic liberalization matter most. All in all, the most important message of this paper is that even basic EU and NATO agreements can provide positive incentives for better governance. These are reasons for optimism: both institutions can have an impact below membership incentives and basic incentives are available for most European transition countries. Whether or not the ENP can build on this depends on country-specific action plans. However, it is the impact of NATO agreements which is most surprising – at least at first sight – and calls for further research. Beyond the EU, NATO may be able to trade (regional) security for development in a wider Europe.

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Chapter 9

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

Abstract This chapter discusses the concept of institutional harmonization, its expected agenda under the ENP, as well as the costs and benefits of such harmonization. At the very least, harmonization will have to focus on the areas that relate to improving market access, i.e. removing restrictions to trade, harmonizing product standards, applying systems of quality control, etc. But in order to implement the new standards and rules, the EU neighbors will have to reform many other related areas so that the harmonization will encompass the whole system of economic governance. Such a revamp would not only help these countries get better access to the EU market but also, and probably more importantly, it would stimulate the modernization of their economies and bring much needed efficiency gains.

9.1

Introduction

Institutional harmonization is an important part of European integration, and its effects are more far reaching than trade liberalization alone. In its policies towards its Eastern neighbors (the ENP and EaP), the EU puts a lot of stress on the desirability of institutional harmonization, at least in certain areas. It is widely recognized that simple trade liberalization with the EU (removal of tariffs) is not going to bring much benefit to the European neighbors and to the EU. On the other hand, DCFTA can have a much stronger effect due to institutional harmonization between these countries and the EU. The implementation of DCTFA would result in the reduction of NTB and the general modernization of their economies (see Chap. 3 of this volume), bringing about major efficiency and welfare gains.

A. Kolesnichenko (*) Economist at UniCredit/Bank Austria in Vienna, Research Associate at CASE, Ukraine e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_9, # Springer-Verlag Berlin Heidelberg 2011

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Despite its huge importance, institutional harmonization in the sphere of economic integration and trade liberalization often remains quite vaguely or narrowly defined. This chapter aims to operationalize this concept in the context of EU relations with its Eastern neighbors and discuss its costs and benefits.

9.2

Concept of Institutional Harmonization and Its Applications to European Integration

Institutions are an important factor determining economic performance. Numerous studies have shown a positive correlation between the level of development of institutions in individual countries and the performance of their economies (the earliest and most famous of which was published by North in 1990). According to North, institutions are the formal rules, informal constraints, and enforcement mechanisms that provide the basic structure according to which human beings create order and attempt to reduce uncertainty. By reducing uncertainty, institutions help reduce transaction costs and, hence, the profitability and feasibility of engaging in economic activity. The concept of institutional harmonization is very comprehensive, as it touches upon diverse political and economic issues. In the political domain, it means the convergence of political institutions towards a single model. There is a large body of literature discussing the spread of democratic institutions and the mechanisms of regime change. The economic dimension of this concept takes account of globalization and the worldwide spread of the liberal market model of economic governance. In Europe, the process of institutional harmonization has acquired some special features and has received a special name – Europeanization. Broadly defined, Europeanization is the process of internalizing European values and policy paradigms (see also Chap. 8). Europeanization involves not only formal institutions but also values and informal institutions. It takes place within the EU itself, as well as beyond its borders. Enlargement, for example, stimulated Europeanization in the acceding states. The ENP attempts to bring the same forces into play beyond the EU frontiers. Attempts to measure the effects of Europeanization in the Western European countries have not provided any conclusive results (Boerzel 2003). It is undeniable that there is an EU influence in these countries but its impact is far from revolutionary and is rather an ‘adaptation with national colors’. By contrast, a range of authors have found evidence of the positive effects of institutional harmonization on the CEE countries that joined recently the EU (e.g. Piazolo 1999; Cernat 2006).1 In these countries, Europeanization meant, in many cases, institution-building rather than institutional change. Unlike in the old EU members, Europeanization in CEE

1 A detailed discussion of the impact of Europeanization/ European integration on growth is provided by Radziwill and Smietanka (2009).

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was strongly affected by the conditionality of the EU accession process, mostly due to the ‘market economy’ and ‘democracy’ Copenhagen criteria (Grabbe 2001). In the case of accession, there is also a major difference in the structure and agency nature of Europeanization: while EU members can influence the EU policies that affect them, acceding countries cannot. Finally, the accession countries do not have an opportunity to opt out of some EU policies, like some old EU members do. All these factors suggest that Europeanization has stronger effects on the new entrants than on the old member states. The EU influence is strongest in the area of economic governance, particularly in the set of regulations related to the Single Market (Grabbe 2003). The major question in the ENP debate is whether and how Europeanization can work beyond the EU borders. Emerson and Noutcheva (2004) claim that Europeanization can work similarly to the trade gravity model in the economic sphere and explain democratization in Europe by the degree of proximity and possibility of anchorage and integration with the EU. There are, however, some major differences between Europeanization within and beyond EU borders, and also between the accession and non-accession option. Like in the accession countries, but unlike in the member states, outsider countries cannot effectively influence EU norms and policies that affect them. At the same time, the lack of EU membership prospects posits the major challenge for Europeanization in the EU neighborhood; As Emerson and Noutcheva (2004) note, this may prove to be the litmus test of how far the Europeanization model can be extended. The absence of a membership perspective substantially weakens the conditionality mechanisms (which proved to be the most effective instrument of Europeanization in CEE). Yet, there is still some room for conditionality, especially with regard to the countries that want to modernize and develop according to the Western model. EU norms and institutions are increasingly setting the world’s standards (Economist 2007), so converging to them can serve as a tool of integration into the global economic system for neighboring countries. Moreover, given the inadequacy of many institutions that these countries inherited from the Soviet era, the European model can serve as a good alternative. Therefore, even in the absence of membership prospects, there are other potential benefits of Europeanization. The questions that remain are how they can materialize, where and how conditionality can be applied and which other mechanisms can be used.

9.3

Agenda for Institutional Harmonization of EU Eastern Neighbors

The debate on the prospects of integration and cooperation between the EU and its Eastern neighbors has been ongoing within the ENP framework. More recently, the EaP initiative launched in May 2009 has also been offered as a potential response to the more ambitious integration aspirations of Europe’s Eastern neighbors. The EaP

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adds a multilateral dimension to the ENP agenda and has a clear focus on institutional harmonization. The official economic objective of the ENP is to help neighbors develop and modernize their economies by anchoring them to the European model of economic governance. The EU proposes doing so by concluding DCFTA and offering EU internal market access to its neighbors and undertaking deep integration in several sectors, starting with energy and transport. The key promise of the ENP is that economic integration can go beyond free trade in goods and include ‘behind the border’ issues: eliminating NTB and progressively achieving comprehensive convergence in trade and regulatory areas such as technical norms and standards, sanitary and phytosanitary measures, rules of origin, customs procedures, and others (European Commission 2006; see also Chap. 3). Bilateral Action Plans have been the main instruments guiding the implementation of the ENP. The EU concluded them with all of its Eastern neighbors except for Belarus. The exact content of DCFTA with neighbors is in the process of being developed. The first comprehensive study that developed the idea of a ‘deep’ free trade area for an EU neighbor (Ukraine) was prepared by CEPS (2006). The authors stressed the importance of harmonization in several backbone services sectors, i.e. the sectors that provide crucial infrastructure for the rest of the economy (financial services, transport, energy and telecoms). Another important point advanced in this study was that the harmonization should be comprehensive, as there are important synergies to be achieved by simultaneous reform in various areas.

9.4

Proposal of a Harmonization Package

Based on the experience with the existing integration arrangements within the EU, as well as the policy debate and proposals within the ENP, we assume that the institutional harmonization in the neighboring countries is going to be driven by the agenda of facilitating market access, especially in the goods sector, and integration in infrastructure sectors, notably energy and transport. Some integration is also likely in certain service sectors (especially financial and telecom services), and possibly, to a much smaller extent, in agriculture. It should be stressed that harmonization should involve not only the transposition of certain norms from the EU, but also their proper application, which will require drastic changes in the whole system of economic governance of neighboring countries. We assume that in the medium term (10–15 years), institutional harmonization between the EU and its neighbors will involve (apart from a simple trade liberalization, i.e. reduction/ elimination of import tariffs) the following measures: 1. Industrial products: adoption of regulations in EU harmonized areas and voluntary harmonization and mutual recognition in non-harmonized areas 2. Partial harmonization in agriculture

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3. Partial liberalization of trade in services; a high degree of liberalization and harmonization in financial services and telecoms; integration with EU energy and transport networks 4. Harmonization of customs procedures 5. A high degree of competition and public procurement policies convergence 6. Environment: limited convergence Below we elaborate on the details of the proposed agenda.

9.4.1

Industrial Products

In order to be able to export to the Single European Market, companies in ENP countries will have to comply with EU product regulations and standards. They have been introduced in respect to higher-risk products such as vehicles, pharmaceuticals, medical devices, and chemicals. Detailed parameters of products have also been established. ENP countries will have to adopt these regulations to be able to export to the EU and improve the level of product safety at home. In some areas the EU has introduced voluntary product standards. CEPS (2006) suggests that these standards should be adopted by ENP countries but without becoming mandatory, so the companies which are interested in exports to the EU can use them and others will remain free to choose local standards. We generally agree with this proposal although it is not without a cost: if a company exports to different markets, it will have to bear higher compliance costs due to differences in standards. In the remaining areas, the EU uses the principle of mutual recognition, so that every EU member state must accept goods on its territory that are legally marketed in another member state. For non-EU members, getting mutual recognition requires compliance with EU standards that are developed by EU standardization bodies. To get EU recognition of compliance, ENP countries will need to sign the ACAA of Industrial Products and will have to establish conformity assessment centers accredited by the EU.

9.4.2

Agriculture

Market access in agriculture is likely to be quite limited due to the sensitive nature of this sector for both sides: so far the EU largely excluded agriculture from the FTAs, and its ENP neighbors have also exhibited a high degree of protectionism in this area. Moreover, the agricultural sector in the ENP countries is undergoing modernization, and it will take some time before it can comply with EU standards. These obstacles, however, should not impede harmonization efforts.

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The harmonization agenda in agriculture is going to be shaped around compliance with EU food safety requirements and WTO SPS measures. First, the ENP countries have to comply with WTO SPS measures, as all of them are either members of the WTO or are in the process of WTO accession. Then, to be able to access the EU market, they will have to comply with EU food safety regulations that are normally much more stringent than SPS. It is unlikely that harmonization in the agricultural sector can go beyond the outlined costly measures in the medium term. Yet, deeper compliance is likely to happen at the firm level in companies wishing to export to the EU. The government’s role would be to provide the necessary legislation and infrastructure to support these firms.

9.4.3

Services

Integration and harmonization of the services sector can bring potentially large gains because of its substantial share in ENP countries (Table 9.1). We believe the proposal presented in CEPS (2006) for a comprehensive harmonization in the financial services, telecommunications, transport and energy transit for Ukraine can be extended to all Eastern ENP countries. In particular, in the financial services, ENP countries should aim to adopt relevant EU acquis and also allow the substantial participation of foreign investors in their financial markets. This would facilitate better competition and the spread of best practices. The same applies to telecoms: adopting the EU acquis and attracting FDI into this sector would improve the quality of its services. In the transport sector, the agenda should include a full integration into the EU single aviation market and pan-European transport corridors. Finally, ENP countries can be integrated into European energy networks for gas and electricity; they should also aim to implement the EU energy sector acquis, as this will not only help them integrate with European networks but will also facilitate domestic market reforms. Yet, the major component and indeed a prerequisite of the institutional harmonization in services will be the reform of economic governance; It is not the differences in formal rules, as much as the red tape, corruption, and poor regulatory quality that hamper sector development and its integration into global and European markets. Table 9.1 Composition of GDP in % of total, 2008 (From European Commission, Trade Directorate (http://ec.europa.eu/trade/creating-opportunities/bilateral-relations/statistics)) Country Agriculture Industry Services Armenia 17.8 45.0 37.2 Azerbaijan 6.3 69.9 23.8 Belarus 9.8 44.4 45.8 Georgia 10.0 21.2 68.8 Moldova 10.9 14.7 74.5 Russia 5.0 37.2 57.8 Ukraine 8.3 36.9 54.8

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151

Customs Procedures

Facilitation of border crossings has to be placed high on the harmonization agenda in Eastern Neighborhood. Currently, the cross-border movement of goods and people is hampered by inefficient border procedures and pervasive corruption. The ENP countries should aim for the highest possible degree of harmonization of their customs procedures with those of the EU. This will require substantial infrastructure investments but, more importantly, capacity building and curbing corruption. Some countries have done better than others in fighting corruption and improving customs procedures. For example, Georgia achieved substantial progress in the mid-2000s, according to the World Bank Doing Business survey, which is evidence that the problems are not insurmountable.

9.4.5

Competition, Public Procurement and State Aid

ENP countries should aim to fully harmonize their competition, state aid and public procurement policies with those of the EU by adopting the respective acquis. A high degree of harmonization will facilitate better market access for both trading sides, and an equality of rules will help reduce discrimination. Also, by adopting EU rules in these areas, ENP countries will be able to improve the competitiveness of their internal markets. Yet, as stressed earlier, the key to success will be the proper implementation of the new norms, something that will require substantial administrative costs and, most likely, technical assistance from the EU.

9.4.6

Environment

The ENP countries should aim to implement a comparable level of environmental protection to that of the EU. Yet, this is going to be a medium-to-long-term goal as full harmonization is rather costly. In the context of DCFTA, what could and should be achieved is stronger trans-border cooperation and harmonization with selected European norms. In particular, ENP countries should aim to adopt international and European standards in the handling and storage of radioactive materials. Another priority should be improvement in energy efficiency, which is going to be aided by participation in Kyoto mechanisms (to which all analyzed countries have subscribed). We expect the content of DCFTA between the EU and all analyzed countries to be similar as all of them have comparable problems and priorities. The priorities of the EU with regard to these countries are also very similar. In particular, all of these countries still use post-Soviet product standards, so harmonization in this area is going to be an important component of their economic relations with the EU. All

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Eastern neighbors are important for the energy security of the EU (either as energy suppliers or transit countries – see Chap. 4) so cooperation and integration in the energy sector will apply to all of them. Obviously, there are cross-country differences, which emerged during the transition period and exist objectively due to natural resource endowments, location and other factors. However, these factors are more likely to influence the speed and the costs and benefits of harmonization, but not its initially set goals.

9.5

Benefits of Institutional Harmonization

The experience of previous integration initiatives, both in the EU and in other parts of the world, could give insight into the potential effects of institutional harmonization in the EU neighborhood. Below we discuss the most important benefits.

9.5.1

Improved Market Access

Institutional harmonization, especially in the economic sphere, will improve mutual market access between the EU and the partner country. This effect is due to the reduction in NTB as a result of harmonization in economic regulations and standards. Better market access brings efficiency gains that promote growth. Moreover, compliance with EU standards will also open ENP countries to other world markets, thus allowing them to participate in global value chains. For example, in the case of CEE countries (which can serve as a comparison for the CIS countries), Lejour et al. (2001) found that an improvement in their access to the EU market led to a 5–9% GDP welfare improvement. Maliszewska (2004) obtained a similar result: 3–7% GDP.

9.5.2

Increased Investment

The estimated efficiency and growth gains from institutional harmonization are going to be even larger if one incorporates their dynamic effect, in particular, on investment. First, institutional harmonization makes the business environment in ENP countries more familiar to investors. Second, as the quality of imported institutions will be better than that of old domestic ones, the business environment will become more hospitable to investors. This will result in the reduction of the risk premia and, thus, of interest rates. Lower risk premia will attract risk-averse investors and will also bring efficiency gains due to higher predictability. Furthermore, reduction in interest rates will make investment more affordable. All these effects will stimulate capital accumulation and growth.

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Baldwin et al. (1997) estimated that, for the CEE, the effect from the reduced risk premium would increase the welfare gain from 1.5% (obtained due to the elimination of all trade barriers and the adoption of a common external tariff) to 18.8% (obtained under the assumption that the risk premium decreases by 15%). CEPS (2006, p. 72) estimates for Ukraine give about a 4–5% welfare improvement from the reduced cost of capital (CEPS’s study assumes a fall in the risk premium by 17%).

9.5.3

Increased Competition

Integration into the EU market and the accompanying institutional harmonization can spur competition in the economy. These effects lay at the core of the original idea of the EU common market. They come from trade liberalization as the common market demands the removal of protective trade barriers and exposes companies to strong competition from other companies in the single market. Also, the adoption of EU competition and state aid rules is going to have procompetitive effects. Furthermore, integration and harmonization with the EU can help the government overcome domestic protectionist pressures by referring to the need to comply with external commitments. Finally, competition promotes efficiency and growth although there are still many unresolved questions in the empirical research related to these issues.2

9.5.4

Reduction in Corruption

One of the most important benefits of institutional harmonization is likely to be a reduction in corruption. There are different ways in which harmonization with the EU norms is going to facilitate this process. One of the instruments is ‘tied hands’: the restrictions that harmonization imposes leave less room for a discretionary interpretation of rules and, thus, decrease opportunities for corruption. Moreover, increased competition due to freer trade reduces monopoly rents and, therefore, removes incentives for companies to bribe politicians. Furthermore, closer cooperation will lead to a diffusion of European norms and values and local companies will learn a different way of doing business.

2 See Aghion and Griffith (2005) for a good overview of different studies and an attempt to reconcile them.

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Increased Credibility of Reforms and Policy Predictability

The credibility of reforms is a major prerequisite for their success. If economic agents do not believe in the announced reform plans, they will not adjust their economic behavior accordingly and the reform will not have the desired effect. The literature suggests several strategies to deal with the credibility problem: to signal commitment, to change governmental incentives, and to reduce the room of governmental maneuvering. Integration with a more advanced partner, such as the EU, can help enhance the credibility of reforms. In particular, Piazolo (1999) argues that integration with an advanced partner such as the EU gives an opportunity to use all of the above-mentioned strategies to improve credibility. First, commitment to integrate serves as a government signal of limiting its room for maneuver, including deviation from reforms. Second, integration involves taking on obligations that reduce the possibility of arbitrary changes in policies. Finally, integration may change the incentive structure of the government, especially if it brings benefits, so that it becomes reluctant to deviate.

9.5.6

Improved Domestic Institutions and Economic Governance

It is not only trade-related areas that will see the gains from harmonization with EU norms, but the entire system of economic governance in ENP countries. Often, the requirements for getting better market access can serve as catalysts of the internal reform. For example, improvement in product standards and their effective implementation would require the modernization of production processes in the private sector and the enhancement of the quality of public regulatory bodies. Indeed, it is only in the synergy of formal harmonization and internal reform that economic integration can proceed. This link between European integration and institutional reform can stimulate the modernization of ENP countries’ economies and help them achieve developmental objectives. The resulting growth and welfare gains can be substantial, as has been analyzed, for example, by Radziwill and Smietanka (2009) and in Chap. 3.

9.6

Costs of Institutional Harmonization

Institutional harmonization with the EU may involve some costs in neighboring countries. Harmonization in the economic domain, i.e the adaptation of standards, policies and regulations, will require companies to make additional investments and the government to conduct a lot of work on the harmonization of legislation and its implementation. The assessment of the costs of harmonization is a very difficult exercise, both conceptually and technically (for details see Dimitrov 2009). The

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major methodological difficulty lies, as with the assessment of benefits, in separating the effect of integration from the effect of the general reform and modernization. Another difficulty relates to the definition of costs. For example, should expenses on improvement in product safety be considered a cost or an investment? Should compliance with higher environmental standards be treated as a cost or an investment? From a long-term prospective, many expenses related to improving product safety, environmental quality, administrative procedures and the like are not costs, but rather investments, as they lead to the improvement of the economic environment and quality of life. Therefore, a more appropriate name for costs would be ‘investment in the short run’. These should be clearly separated from costs that emerge due to unproductive losses. There were some attempts to estimate the costs of compliance in the CEE countries in the course of their accession to the EU. The cost of compliance in the agricultural sector was especially high. So, in Poland the costs of the dairy sector adjustment were estimated at PLN 15.5 billion (EUR 3.7 billion) in 1999 (CEN 2003, p. 126) and the investment in the area of environment at EUR 30.4 billion (CEN 2003, p. 155).3 The total cost of compliance in the agricultural sector in Poland and Lithuania was estimated at 2–2.5% of GDP (CEPS 2006, p. 89). In order to help accession countries to make the adjustment, the EU provided a lot of technical and financial aid. In the case of ENP countries, the amount of support is likely to be substantially lower. Therefore, they should carefully plan the size and the speed of their institutional harmonization efforts.

9.7

Conclusion

Institutional harmonization with the EU offers numerous benefits to ENP countries, and serves as a catalyst for the modernization process. The challenge for the EU and its partners is how to instrumentalize this idea: what can be done and how? Based on our analysis, we suggest that in the medium-term, the partners aim to reach a high level of harmonization in the areas related to market access (product standards, safety, security, competition, public procurement and customs procedures), a high level of integration in the aviation, energy and transport sectors, and partial harmonization in services and agriculture. This agenda is quite ambitious and broad, but it is the synergy of different components that is going to bring the strongest effects.

3

At the same time, it is expected that by 2020 the accumulated benefits from the improvement of environmental standards will accrue to EUR 41–208 billion (mainly due to the improved health of the population).

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References Aghion P, Griffith R (2005) Competition and Growth: Reconciling Theory and Evidence. MIT Press, Cambridge, MA Baldwin R, Francois J, Portes R (1997) Costs and benefits of eastern enlargement: the impact of the EU and Central Europe. Econ Policy 24 Boerzel TA (2003) How the European Union interacts with its member states. Institut f€ur H€ohere Studien Polit Sci Ser 93, www.ihs.ac.at/publications/pol/pw_93.pdf CEN (2003) Costs and benefits of Poland’s membership in the European Union. European Center Natolin, Warsaw CEPS (2006) The prospect of deep free trade between the European Union and Ukraine. Centre for European Policy Studies, Brussels Cernat L (2006) Europeanization, varieties of capitalism and economic performance in Central and Eastern Europe, Studies in economic transition series. Palgrave Macmillan, New York Dimitrov V (2009) Cost of institutional harmonization in the ENP countries, CASE Netw Stud and Anal 388 Economist (2007) How the European Union is becoming the world’s chief regulator. The Economist. 20 September Emerson M, Noutcheva G (2004) Europeanisation as a gravity model of democratisation. CEPS Working Document 214. Centre for European Policy Studies, Brussels European Commission (2006) Communication from the Commission to the Council and the European Parliament on strengthening the European Neighbourhood Policy. COM(2006) 726, Brussels http://ec.europa.eu/world/enp/pdf/com06_726_en.pdf Grabbe H (2001) How does Europeanization affect CEE governance? Conditionality, diffusion and diversity. J Eur Pub Pol 8(6):1013–1031 Grabbe H (2003) Europeanisation goes east: power and uncertainty in the EU accession process. In: Featherstone K, Radaelli C (eds) The politics of Europeanisation. Oxford University Press, Oxford Lejour A, de Mooij RA, Nahuis R (2001) EU enlargement: economic implications for countries and industries. CPB Netherlands Bureau for Economic Policy Analysis, CPB Doc 11 Maliszewska M (2004) EU enlargement: benefits of the single market expansion for current and new member states. CASE Netw Stud and Anal 273 North DC (1990) Institutions, institutional change, and economic performance. Cambridge University Press, New York Piazolo D (1999) The credibility and growth effects of EU institutions on Eastern Europe. EIB Econ and Financ Rep 99/P2. Radziwill A, Smietanka P (2009) EU’s eastern neighbors: institutional harmonization and potential growth bonus. CASE Netw Stud and Anal 386.

Chapter 10

Technical Assistance to CIS Countries Roman Mogilevsky and Aziz Atamanov

Abstract During the last two decades, CIS countries have received very significant amounts of technical assistance from international development organizations and bilateral donors. While this has played a positive and important role in the transformation of CIS societies, practically all stakeholders share the opinion that serious problems have accumulated in this area. This chapter intends to outline these problems, analyze their underlying reasons (including the changing environment for technical cooperation in the CIS) and the interaction of the interests of beneficiaries, donors and providers in the process of implementing technical cooperation projects. The analysis suggests that a good understanding, recognition and coordination of the interests of all TC stakeholders and a reduction in the information gap between the various participants in the technical cooperation process are necessary for improving the effectiveness of technical cooperation.

10.1

Introduction

Technical cooperation (TC) and assistance (TA) between the EU and CIS countries provide a key link between these two groups of countries. Since 1992, the European Commission and EU member countries have allocated significant resources for technical cooperation with CIS countries. On the recipients’ side, TC has influenced

Acknowledgments The authors would like to express their gratitude to Elena Rakova and Magdalena Rokicka, who provided valuable input to this chapter. R. Mogilevsky Executive Director of CASE-Kyrgyzstan and CASE Fellow e-mail: [email protected] A. Atamanov PhD student at the Maastricht Graduate School of Governance and Economist at CASE-Kyrgyzstan, Bishkek e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_10, # Springer-Verlag Berlin Heidelberg 2011

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many government and civil society institutions and has contributed to human capacity building in these countries. Of course, the EU is not the only supplier of technical assistance to the CIS. Many other international financial organizations as well as the governments of developed countries have played a very important role in this region. The TC process is expected to meet the demands of donor and recipient societies for institutional and human development, reacting to the ongoing social-economic changes in these countries. In practice, however, this has not always been the case. This has been an important cause for growing dissatisfaction with TC performance and has been noticeable among all TC stakeholders: donors, providers and recipients. The CIS is not the only region in the world where TC performance is widely considered unsatisfactory. During the last 15–20 years, the global development community has been discussing TC problems, paying a great deal of attention to its concepts and capacity development, as well as to the technologies of TA delivery (e.g., OECD 1991; Oxford Policy Management 2003). Very often these discussions have been based on the experiences of developing countries with TC in other parts of the world. While these issues are also relevant for the CIS, it seems that in the context of this region another facet of TC, stakeholders’ interests and how they interact, has not received enough attention yet. This chapter explores the existing problems of TC in the CIS countries and discusses possible solutions to them. Section 10.2 contains statistical data on the size and patterns of TC flows to CIS. Section 10.3 reports on the results of a stakeholder survey on TC performance in these countries. Section 10.4 discusses possible links between TC problems and some of the deeper political, economic and social changes taking place in CIS countries and in their vicinity. Section 10.5 addresses the political economy of TC, which seems to be the reason for many TC problems. Finally, Sect. 10.6 identifies some possible ways for improving the performance of technical cooperation in the CIS context.

10.2

Dynamics of TC Flows to CIS Countries

TA is provided by various bilateral and multilateral donor agencies. It goes to a variety to recipients (beneficiary country government agencies, civil society organizations) and is managed in different ways (implemented by donors, TA providers, recipients themselves, etc.). As a result, information on TC flows produced by different sources is heterogeneous, with various definitions of TC, timeand recipient-type coverage, and country and sector disaggregation. Information on TC provided by multilateral organizations is particularly fragmented; in some cases these organizations do not have reporting systems providing a breakdown of their TA by recipient country and sector (see, for example, IMF 2005). Therefore, it is impossible to draw any comprehensive picture of TC by summarizing data from the separate reporting systems of different donor or recipient countries and organizations.

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In recent years the OECD has made efforts to harmonize the reporting systems of donors related to official development assistance and official aid, including technical assistance. The OECD proposed the definition of TC used in this chapter: Technical Co-operation: This is defined as activities whose primary purpose is to augment the level of knowledge, skills, technical know-how or productive aptitudes of the population of developing countries, i.e., increasing their stock of human intellectual capital, or their capacity for more effective use of their existing factor endowment. Accordingly, the TC figures relate mainly to activities involving the supply of human resources (teachers, volunteers, experts in various sectors) and action targeted on human resources (education, training, advice) (OECD 2006, p.11).

According to OECD data (which should be considered slower bound estimates of TC flows), in 1992–2004, CIS countries received as much as USD 14.6 billion in TC from all donors. Such resources are obviously huge and capable of inducing changes in the political, economic and social life of CIS countries. The EU has made a very significant contribution to the total amount of TC flows. According to the OECD database, the overall contribution of the European Commission and EU member countries (on a bilateral basis) in 1992–2004 was USD 5.6 billion, or almost 40% of total TC received by CIS countries. The peak of TC flows was in the second half of the 1990s, when the CIS countries’ demand for TC was the highest. By the 2000s some transition problems in the CIS had already been resolved, so the need for TC gradually diminished and demand for TC in some CIS countries (especially Russia) faded away for economic and political reasons. Among donor countries, the US was the main provider of TA resources for CIS countries; its contribution is almost 60% of total TA supplied by bilateral donors. The second largest donor has been Germany (12%), followed by Turkey (7%), Japan (7%), the UK (3%) and France (3%). The list of leading TA donors seems to reflect the political priorities of the donor countries and their interests in the CIS region. For example, Germany has been the absolute leader among European countries, providing more TA than all the other European donors combined, a fact that corresponds closely with its well-known interest in the EU’s Eastern neighborhood and in CA. An analysis of the sector distribution of TA to CIS countries shows that the majority of TA (57%) went to the support and development of social sectors. However, economic management, general governance and other issues also received a considerable share of total TA. Distribution of TA by recipient country appeared to be highly uneven. In 2006 alone, in per capita terms (Fig. 10.1), the leader (Armenia) received 11 times more TA than the back marker (Uzbekistan). The per capita TA amounts correlate very well with the values of the EBRD transition indicators and with the Heritage Foundation’s rankings of economic freedom. This correlation inclines one to believe that the willingness of recipient country governments to implement market economy-oriented and democratic reforms is a key factor influencing the relative size of TA received by CIS countries. Of course, there could also be a reverse causality in this relationship: those countries which received more TA were able to make greater progress in their economic transitions. TA amounts allocated for the

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80 70 60 50 40 30 20 10 0

3.0 2.5 2.0 1.5 1.0 0.5 0.0 Armenia

Georgia

Kyrgyz Republic Turkmenistan

Three CIS countries receiving largest TA

Belarus

Uzbekistan

Three CIS countries receiving smallest TA

TA per capita, USD (2006, lhs) HF economic freedom score (2007, lhs) Average EBRD transition index (2007, rhs)

Fig. 10.1 Differences in TA supply per capita between CIS countries in 2006 (From OECD, EBRD Transition Indicators and the Heritage Foundation)

major (in per capita terms) recipient countries are very large and comparable or even exceed government budget allocations for public administration. For example, in 2006 in Kyrgyzstan, government budget expenditures for general government services were USD 84 million (at the market exchange rate) in comparison to TA of USD 100 million, according to the OECD database. A distribution of total TA amounts by country can be obtained from TACIS (2007). In 1991–2006, the major recipient of TA was Russia, which got half (50.3%) of all funds. Ukraine was the second large recipient of the EC’s TA, with almost a quarter (24.3%) of all TACIS resources. Kazakhstan and Uzbekistan received 4.1% each; smaller CIS countries received from 3.8% (Azerbaijan) to 0.4% (Belarus). These shares seem to reflect the size of the recipient countries in terms of their population and GDP.

10.3

Performance of Technical Cooperation with CIS Countries

Assessment of the performance of TC in CIS countries and its impact on their development is complicated by the fact that TC flows (regardless of their size) have not been the only or even most important factor affecting developments in the CIS countries during their period of transition. Nevertheless, one could expect to see a visible positive change in the capacity of these countries to implement political, economic and social transitions to a market economy and democracy, directly underpinned by substantial TA. A general picture of the progress of CIS countries in their transition can be drawn from two sources: the EBRD’s transition indicators and the WBGI. According to indicators in Fig. 10.2, the progress of CIS countries in transition appeared to be much more modest in comparison to other transition

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3.0 2.5 2.0

Mean Min Max

1.5 1.0 0.5 0.0 CIS

New EU members

Other transition countries

Fig. 10.2 Mean, maximum and minimum increase in country’s transition score in 1990–2007 by country group (From EBRD Transition Indicators, authors’ calculations)

countries; the increase in the average country’s transition score (average value for all EBRD transition indicators) for the period 1990–2007 appeared to be considerably lower for the CIS region than for the new EU member countries and lower than for other transition countries (Western Balkans and Mongolia). In terms of the WBGI, their average values improved in 2006 in comparison to 1996 in seven CIS countries out of 12 and a decline in governance quality was registered for the other five countries. These are just two examples demonstrating that the performance of CIS countries in the transition period can be seen as mixed, at best. Thus, large donor resource allocations for the CIS have not resulted in comparably convincing progress in their governance. In order to get a more objective balance of accomplishments and problems, a qualitative survey of TC stakeholders was implemented within the framework of this study in two CIS countries: Belarus and Kyrgyzstan. These two countries represent two very different cases, which allowed us to highlight the different environments within which TC operates. Belarus is an EE industrialized country with one of the least open political regimes in the CIS; perhaps for the latter reason it received relatively little TA during the transition period. In contrast, Kyrgyzstan is a CA country whose main economic sector is agriculture; the Kyrgyz government and society are very open and the country has been one of the largest TA recipients in the CIS. TC stakeholders in the survey have been divided into three groups: (i) beneficiaries – representatives of government agencies or civil society organizations receiving different forms of TA (on-the-job training, advice, formal education abroad, etc.), (ii) providers – representatives of international or local companies and/or think tanks which provide training and advisory services to beneficiaries, (iii) donors – representatives of multilateral and bilateral organizations which provide funding for these services. Experts included both CIS and non-CIS citizens and represented different policy areas (economic policy, governance, civil society development, etc.). A semi-structured questionnaire was used for 22 interviews with respondents in

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Belarus and Kyrgyzstan using traditional evaluation criteria: relevance, effectiveness, efficiency, sustainability and impact. The results of interviews are summarized in the remaining part of this section; the interviews also provided some input into the discussion in Sects. 10.4–10.6. In addition to the survey, the content of this section is also drawn from numerous discussions on selected TC aspects with TC stakeholders in countries other than Belarus and Kyrgyzstan.

10.3.1 Relevance A key issue in the area of TC relevance, as noted by many of the interviewed experts, is the donor-driven agenda of TC. This is not a new issue in the TC-related discussions in this and other regions of the world. The political interests of donor organizations and/or the providers’ desire to utilize the available stock of expertise often results in the implementation of projects that are untimely or of low priority for beneficiaries. Examples of such projects are efforts to introduce multi-year expenditure planning in countries with serious flaws in annual budget processes or the introduction of security markets in economies where the majority of enterprises have a non-transparent ownership structure and management. However, the interviewees noted that recently donors have become more open to the beneficiaries’ requests regarding the types of technical assistance they need. Some improvement in TC relevance can be attributed to the stronger capacity of beneficiaries who are now better able to identify existing knowledge gaps and articulate their needs. Theoretically, the transition from a donor- to beneficiary-driven TC agenda should be facilitated by strategic planning processes (Poverty Reduction Strategy Papers and the like) in the beneficiary countries. Countries are expected to clearly formulate their priorities and donors have to concentrate their aid (including TC) around these priorities. In practice, however, too often the strategic planning suffers from a multiplicity of priorities. This is partially a consequence of the balance of interests within the beneficiary government or society: assigning non-priority status to any policy area (with budgetary and power implications) could violate the interests of influential groups in the government/society. But another factor contributing to weak prioritization by beneficiaries is donor pressure: every donor organization tries to ensure that its own priorities are included in the priority list of the beneficiary government. For the government it may often seem better to keep all donors happy rather than decline some TC project proposals, and moreover there is usually no hard budget constraint on the TC amount supplied to a country. A needs assessment is now a standard component of any capacity building program, which is to ensure the relevance of supplied TA. However, imperfect governance structures in many CIS countries create a gap between the declared (corresponding to the statutory set of an agency’s goals and activities) and real (based on political-economy considerations including rent seeking by agency staff) needs of a beneficiary agency. Donors’ needs assessments are rarely able to identify

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this kind of gap and TC projects appear to deliver training and provide advice that are not really demanded by the recipient organization and therefore not absorbed properly. Another problem of TC relevance is the lack of supply of some TC that is actually sought by beneficiaries. This includes insufficient support in capacity building in engineering or other production-related types of expertise for smaller CIS countries that lost or never had domestic capacity in these areas. Another example relates to the problems of building administrative systems in many CIS countries with a large informal economy, where one cannot rely on detailed information on incomes, employment, etc. The design of systems appropriate for these conditions is a complicated task and so far, the CIS countries have received too little technical support in this direction.

10.3.2 Effectiveness Key issues raised in the discussion on TC effectiveness include: (i) oversupply of TA to central government bodies of the beneficiary countries, (ii) donors’ capacity to supply expertise of required quality, and (iii) management of the TC process including issues of size and number of TC projects, use of M&E results. In some CIS countries, TC flow was and continues to be so massive that it creates a well-known problem of insufficient absorption capacity of beneficiaries (Oxford Policy Management 2003, p. 14). This is especially relevant for smaller countries, which are large recipients of TA (in per capita terms). In most cases, the larger part of TC activities is concentrated at the central level of government, reflecting the desire of many donors to work and establish close working relations with those who influence policies and practices in the most direct way. But, according to the unanimous opinion of experts, reform success too often depends on implementation at the local government level, where capacity is usually much weaker and requires a lot of support. For example, the development of a computerized database of taxpayers or social benefit recipients would not be successful, however good the design of the system might be, if it is not accompanied by accurate and timely data entry at the local level. The over-concentration of TC at the central level of government at the expense of local governments and civil society also has longerterm political and economic implications. Uneven capacity on different levels of government or in government and civil society puts central governments in an advantageous position in public discussions on important policy issues, especially when these issues are technically complicated (e.g., possible changes in tax rates and their impact on government revenues and the economic activities of taxpayers). The quality of supplied TA is another area of concern. While interviewed experts do recognize that the input of many TC projects is of appropriate quality, there are also many situations in which TC outputs did not satisfy beneficiaries. One typical problem with TC outputs is the attempt to introduce the experience of developed countries as a model to be copied by recipient countries, without considering differences in the levels of economic development, political economy, government

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set-up and culture. Examples of such mechanical importing of institutional solutions into less appropriate CIS environments include attempts to introduce income-test-based social benefit schemes in countries with very poor information on household incomes or complicated administrative systems in countries without a well-trained and well-paid civil service. Many interviewees complained about the complicated, over-technical language used by foreign experts, the low quality of translations and insufficient expert knowledge of local languages and/or Russian, which is the ‘lingua franca’ for most people in CIS countries. It should be noted that this mismatch of experience and mentality is less of an issue now with the increased use of local experts and specialists from Central and Eastern Europe and the CIS in TC projects. Moreover, more reliance on local experts could also positively affect TC efficiency as it would allow a reduction of some unnecessary operational costs (excessive international travel, etc.). The majority of experts believe that the current practice of having many relatively small projects with predetermined outputs is counter-productive. Fewer larger projects with clearly defined outcomes and sufficient flexibility left for the TC provider seem to be a more promising option for TC organizations. These days M&E is a mandatory component of any TC project design. However, the experts commented that too often M&E relies on either input/output, or impact indicators only and may not provide sufficient information on a given project’s performance. For example, a set of trainings/ document drafts/ study tours, etc. may not be enough to produce a sustainable improvement in the beneficiary organization’s capacity; on the other hand, changes in impact indicators (e.g., a reduction in the poverty rate) may often not be attributable to a specific TC intervention (e.g., new pension scheme design). There is an acute need for project-specific outcome indicators that would directly capture any TC-linked change in the beneficiary situation. It seems that the careful selection of these outcome indicators and, if necessary, built-in systems for their measurement are to become an integral part of TC project design. Part of such indicators may be oriented towards measuring beneficiaries’ feedback. One simple way to increase transparency of TC and provide necessary feedback is making all TC products (consultants’ reports, legislation drafts, training materials, etc.) public by putting them on the websites of TC stakeholders. This would provide access to TC products and would support capacity building for a broader audience, not limited to the narrow circle of representatives of beneficiary organizations. Publication of TC outputs would serve as an important tool for TC quality check as it would make it more difficult to hide the consequences of inappropriate TC performance.

10.3.3 Efficiency The efficiency of TC is closely linked with the issue of donor coordination and the elimination of duplicate TC projects. Lack of donor coordination used to be a very big problem in the 1990s, but according to the experts, the situation has been improving somewhat recently. As a result of the Paris Declaration on Aid

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Effectiveness (2005), the majority of donors have taken some steps towards TC coordination, including joint planning, division of labor, use each other’s results, etc. TC oversupply in relation to absorption capacity, as discussed above, also has efficiency implications as it lowers returns on TC resources spent. This suboptimal allocation of resources may be related to the fact that TC size is often determined on the basis of political considerations, rather than the demands of beneficiaries. It seems obvious that any attempts to use TC for political purposes would unavoidably reduce its efficiency as a capacity building tool. TC should benefit not only the (un)friendly governments, but also the people of the recipient countries. Another problem with efficiency addressed by almost all respondents is the insufficient flexibility of the TC process. Excessively rigid donor rules prevent TC providers from optimizing available resources and make it difficult to adjust project resources according to M&E results. One striking revelation of this rigidity is the use of TC amounts spent (regardless of the results achieved) by some donors as a performance indicator of their activities. It seems that donors have to pay much more attention to TC’s outcomes and impact and should not care too much about financial control, which is often impossible or highly ineffective. It is advisable to simplify financing procedures, especially in relations with smaller non-governmental providers/beneficiaries, by using, for example, lump-sum contracts with well-defined and verifiable outputs and outcomes – a practice already used by some donors.

10.3.4 Sustainability In government agencies, the key TC sustainability issue relates to the low salaries of staff and their frequent rotation in official positions. Government officials who received proper training in the framework of TC projects often change their jobs either for higher positions in the government (not necessarily requiring the technical skills they acquired during TC interventions), or for much better paid private sector jobs. The only real solution seems to be a commitment on the part of the government to build up a professional civil service including, among other things, payment of competitive salaries to civil servants who have strong technical skills. Many CIS government agencies with more financial resources have made significant progress towards increasing the remuneration of government officials and this has resulted in an observable improvement in technical skills in these agencies. From this point of view, the level of salaries in a given agency is a good predictor of the longer-term success in capacity building. Lack of institutional memory in government agencies is a by-product of frequent staff rotation and generally weak administrative systems in some CIS countries: not only human capacity, but also other TC products (organizational changes, consultants’ reports, computerized models, etc.) are often hard to find some time after the TC provider has left an agency. Another unintended consequence of the leakage of trained professionals from civil service to commercial companies and NGOs is the strengthened capacity

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of the non-governmental sector; this partially compensates for the overconcentration of TC in government agencies. In some cases, TC projects provide technical expertise to a very limited number of beneficiaries. Apart from problems related to political economy as mentioned above, this also has implications for sustainability as the eventual loss of only a few trained experts by the beneficiary organization could substantially reduce its capacity. Lack of TC diversification seems to be one of the most serious problems in the area of sustainability. One sustainable way to build local capacity is through the development of the local consulting industry. While some donor programs do address this issue by encouraging the creation and operation of companies staffed with national experts, some other donor programs effectively undermine the development of this industry by, for example, using individual rather than institutional contracts for local expert services.

10.3.5 Impact Interviews revealed a mixed picture of TC’s impact on capacity building in the governments of CIS countries. The presence of two factors seems to positively influence TC’s impact on a government agency’s capacity: the commitment of the agency’s leadership to utilize the provided knowledge and advice and the agency’s demand for specialized and politically neutral expertise (e.g., in central banks or statistical agencies). In the absence of these factors, the probability of TC failure becomes significant. The impact of technical cooperation on government capacity also depends on the extent to which TC is linked with other forms of aid, e.g., with budget support programs accompanied by policy conditionality. If this conditionality requires developing some technical capacity in the recipient government, then government demand for corresponding capacity building becomes higher and the provided technical assistance has a greater chance of having an impact. TC has had a significant impact on the capacity development of non-governmental stakeholders. Indeed, in the majority of the CIS, the development of civil society and local consulting organizations’ potential and ability to provide critical feedback to governments is closely correlated with the support they received through TC channels.

10.4

Changing Environment for Technical Cooperation

Some of the problems discussed above may be caused by inertia in TC delivery and insufficient attention to the important changes taking place in the CIS countries. The most important change in the CIS is, perhaps, the end of the transition from the

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Soviet system. The scale of reforms is now much less radical and the direction of change is much more diverse than at the beginning of the transition. Virtually all CIS countries started their transitions explicitly declaring an intention to build democracies and market economies. Twenty years later, this course of transition is on the agenda for only a handful of CIS countries. Many countries have deliberately chosen (semi-) authoritarian political systems and economic systems with a large interventionist role for the government. This has important implications for the process of TC. Initially, TC was built on the underlying assumption that basic development values of donors and recipients are the same: democracy, human rights, and market economy. This assumption implied that the direction of transition is a common goal shared by beneficiaries and donors and that CIS countries simply needed technical support to move faster in this direction. This assumption is not 100% valid anymore. Of course, the development values of the OECD countries and those of the CIS are not contradictory, but they may differ significantly. This difference in values cannot be overcome by means of TC and the efforts to use TC for this purpose could and should result in failure. The development agendas of CIS countries have changed not only because of the shift in values, but simply because some development problems have been successfully resolved and new problems have emerged. For example, the problem of responsible macroeconomic management, which was a very hot issue in the 1990s, had become much less acute, when, after a series of failures like the 1998 financial crisis, proper institutions and policies were introduced and sustained. On the other hand, the relatively new problem of labor migration is now at the top of the policy agenda. This change in development priorities does change TC goals, but often with a significant time lag, which reduces the relevance of the TC. Another important change in the environment for TC is the accumulation of experience and human capital in CIS countries during the period of transition. The period of the early and mid-2000s was characterized by an increase in economic strength and an improvement in the situation of the public finances in CIS countries. Therefore, the significance of TC resources has been relatively reduced for many (although not all) CIS governments and TC programs have lost a part of their leverage on policy making. This should be acknowledged and reflected in the changing TC design. It has also created some new opportunities to increase the sustainability of TC, making feasible the partial substitution of TC resources with government financing. Apart from increased domestic revenues, additional resources for the development of some CIS countries are also provided by the new donors, especially China and Russia, which have a different development perspective than ‘traditional’ donors. While these new donors have not yet established their own development industry comparable to that of the OECD, these countries are in many ways closer economically and culturally to many smaller CIS countries than the OECD countries; often they influence policy making and institution building in other countries just by providing an example from their own experience, which is attractive for many elites. This creates a situation of competition for the development models supplied by TC originating from OECD countries. The role of Russia

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is particularly important as it has traditionally played the role of providing expertise and institutional design for the majority of smaller countries which used to be part of Soviet Union. As the largest country in the region, it has substantial domestic capacity, which offers alternatives to the experience of developed countries, which are often better suited to the mentality and interests of the national elites. As elites of other CIS countries often share the background, incentive structure, mentality and language with the Russian elite, it is worthwhile and easy for them to borrow Russian institutional solutions (e.g., electoral systems or pieces of legislation). Close economic collaboration and political ties, which many of these countries maintain with Russia, is another factor explaining the strong Russian institutional influence. It is worth noting here that insufficient economic cooperation with OECD countries (limited mainly to exports of raw materials to these countries and Western investments into primary industries in CIS) is an important background factor which reduces the impact of TC: CIS countries have insufficient incentives to use OECD benchmarks for their institution building. Apart from this indirect, but very important influence, Russia also makes some direct contributions to the capacity building of other CIS countries through the provision of scholarships to CIS citizens for study at Russian universities, the establishment of jointly funded universities in some CIS countries, the donation of books in Russian to schools, etc. Russia also plays a multiplier role in TC; successful TC projects implemented in Russia provide products to be easily disseminated in other former Soviet republics (e.g., an ECfunded translation of an econometrics textbooks into Russian done in Russia made this textbook immediately available and accessible in all of these countries). This example suggests that TC with Russia has the potential of producing a greater impact than could have initially been planned. The changes in environment for TC discussed above relate mainly to the situation in the CIS. However, important changes are also taking place in the TC supplier countries. One of the most important changes relevant for the CIS is the EU accession of many Central and East European countries that used to be a part of the Soviet Bloc. Many of these countries are now emerging donors. While they do not yet have financial resources and TC management and delivery capacity comparable to that of ‘old’ donors, they have a very important asset, the experience of a successful transition to a democracy and a market economy. This transition experience may comprise the core of their contribution to TC supply.

10.5

Political Economy of Technical Cooperation

It follows from the previous discussion that accounting for the interests of different parties involved in the TC process is key to understanding many of the problems of TC. Therefore, the main stakeholders and their interests need to be clearly identified and the interaction of these interests considered. One can see development aid (including TC) as an interaction of two principal stakeholders and three of their agents. The principals are the societies of the donor

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and recipient countries and the agents are (i) the government of the donor country or multilateral donor agency, (ii) the government of the recipient country, and (iii) the TC provider, which is often a private company/NGO. The ultimate goal of development aid is to increase welfare and support the human development of the recipient country’s population in a sustainable way. Both principals share this goal (otherwise TC is irrelevant), while their vision of the ways of achieving it may be somewhat different. It is important to note that these societies usually interact indirectly, via their agents. It is also important to remember that the performance of TC is difficult to measure and that this creates information asymmetry between principals and agents. Thus, the process of TC may be seen as an example of a ‘principal-agent’ problem, in which the agent has an incentive structure that is non-identical to that of the principal. Then, the agent, led by its own incentives, may operate in a way that is suboptimal from the point of view of achieving the principal’s objectives. There are several ‘principal-agent’ relationships in the TC process. Firstly, this is the relationship between a donor society and a donor government. Due to existing democratic accountability mechanisms in donor countries this is, perhaps, the smallest problem, at least in the long-term. In the short-term, however, this problem could exist – donor government agencies responsible for TC may have their own objectives not limited only to building the capacity of beneficiaries. These other objectives may include, for example, typical bureaucratic incentives for strengthening the agency in terms of influence and available resources. One revelation of the problem is, for example, the above-mentioned use of the amounts spent on TC programs as an indicator of TC’s effectiveness; this corresponds well with the donor agency’s interests, but is not necessarily the best way to use money from the point of view of achieving the ultimate goal of technical cooperation – building the capacity of beneficiaries. Another example of that kind is the use of TC resources for political purposes, e.g., for building relationships with senior officials in counterpart governments, which is not an infrequent situation. This has obvious negative consequences for the performance of TC, especially in the long-term as this practice has reputation implications in the eyes of beneficiaries. Interestingly, many participants of the survey discussed in Sect. 10.3 do see a direct and positive link between the lack of political agenda in TC projects and their successful results. For example, projects funded by the government of Switzerland are referred to as the most effective in comparison to projects of other bilateral donors, and one of the positive features of Swiss projects is seen to be precisely their lack of any explicit or hidden political agenda. As was mentioned earlier, in the CIS context, democratic accountability institutions are rather weak. Therefore, governments/elites in the recipient countries may have interests that significantly differ from the long-term interests of their populations. As a consequence, the demand for TC from these governments and their general attitude towards the technical expertise provided may or may not be the most beneficial for achieving the capacity building objective. The usual assumption that the government of a recipient country adequately represents the development needs of its society should not be taken for granted, and it is the responsibility of

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donors to make sure that the position of the recipient government corresponds to the long-term interest of its country. From this perspective, the generally legitimate trend of increasing the recipient government’s role in TC coordination should not be oversimplified; donors still have to use their own judgment on what kind of TC may positively contribute to the beneficiary country’s development. TC providers have a special place in these ‘principal-agent’ relationships as they are the only type of TC stakeholder that has explicit commercial interest in the process. Therefore, in conditions of a possible mismatch of interests of the TC donor and recipient and lack of clear performance assessment criteria, TC providers have incentives and possibilities to minimize their costs by supplying simple solutions (like copy-paste reform proposals) and seeking the support of their counterparts from the recipient government by meeting their personal interests. A combination of these interests may lead to low-level equilibrium, when three stakeholders-agents maximize their utility (donor government’s TC delivery rates are high, vested interests of recipient government officials are satisfied and TC provider’s profit is high) at the expense of stakeholders-principals, who bear the costs of that equilibrium in monetary form (in the case of the donor society) or in the form of under-development (in the case of the recipient society).

10.6

Ways to Increase TC Effectiveness

The general solution for the ‘principal-agent’ problem, which minimizes welfare losses, consists of reducing information asymmetry between stakeholders and establishing a proper incentive structure for agents. For that, stakeholders’ interests should be explicitly accounted for in the TC program design. This approach requires donors to undertake a political economy analysis of the recipient. This is associated with a risk of intervening in domestic politics, which may be an undesirable activity for many donors, especially multilaterals. Still, this risk needs to be taken and controlled by, for example, outsourcing the stakeholder analysis to independent research organizations. Moreover, such analysis is usually performed informally by donors/TC providers anyway, so it is rather a matter of making it truly impartial, and of acceptable quality. It should also be accessible for all interested parties. One way to reveal the real interests of the stakeholders on the recipient side is to encourage their co-financing of TC activities. The readiness of the recipient government (or other TC beneficiary) to contribute some resources with non-zero opportunity costs to TC implementation could be seen as a clear signal of its true ownership of a TC program or project, and, vice versa, refusal to co-finance could be an indicator of low real interest in the TC results. In order to reduce information asymmetry, TC monitoring systems must be substantially strengthened. A transition from input/output to properly designed outcome indicators and establishing effective beneficiary feedback channels would create conditions for a radical improvement in measuring the performance of TC. Currently, a similar methodology is used for evaluating the development

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impact of major aid programs; this approach needs to be extended to the area of TC. Another information-related issue is the disclosure of data on TC flows and products to the general public. The ability of third parties to make judgments and provide feedback on the amount, quality and utilization of provided expertise would become an effective tool preventing some forms of TC stakeholder misbehavior. To establish healthier incentive structures of stakeholders it is useful to maintain a competitive environment in all segments of the TC market. This is already the case with regards to TC supplier selection, while oligopoly-type market structures are often present; this may lead to various types of inefficiencies. But it is also worth nurturing the creation of competition on the recipient side, which could be done by diversifying TC delivery channels, supporting not only central, but also local governments, involving civil society/private sector representatives into capacity building programs and so on. In the view of the TC over-supply problem and the insufficient absorption capacity of some beneficiaries, it might make sense to consider the introduction of some ceilings on TC amounts allocated to given beneficiaries. This would create a strong incentive for beneficiaries to prioritize their demands for TC on one side, and would create a competitive environment among donors, whose contributions have to be limited by these ceilings, on the other. The introduction of such ceilings, however, would require a very high degree of coordination between donors. Thus, it seems that the general way to improve the performance of TC is by ensuring a good understanding, recognition and coordination of the interests of all TC stakeholders and by reducing the information gap between the various participants of the TC process.

References IMF (2005). Evaluation of the technical assistance provided by the IMF. International Monetary Fund. http://www.jica.go.jp/cdstudy/library/pdf/20071101_18.pdf. OECD (1991) Principles for new orientations in technical co-operation. Organisation for Economic Co-operation and Development, Paris OECD (2006). Geographical distribution of financial flows to aid recipients 1960–2004. Disbursements. Commitments. Country Indicators. Documentation. Oxford Policy Management (2003). A vision for the future of technical assistance in the international development system. http://www.opml.co.uk/document.rm?id¼956. Paris Declaration on Aid Effectiveness (2005). Ownership, harmonisation, alignment, results and mutual accountability. High level forum. Paris, February 28 – March 2, 2005. TACIS (2007). TACIS in tables. http://tacis.uz/docs/Tacis_tables_EN.pdf.

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Chapter 11

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

Abstract This chapter discusses the current and potential role of the ENP in anchoring economic reforms in CIS countries. It analyzes support for European integration in these countries and the similarity of norms and values between the EU and countries of the Eastern Neighborhood. The attempt to estimate the ENP’s impact on governance in CIS countries quantitatively produced mixed results, suggesting a very small positive effect, at best. Some recommendations for strengthening the reform-anchoring potential of the ENP are offered in the concluding section.

11.1

Introduction

The main objective of this chapter is to discuss the extent to which the ENP can serve as an effective instrument furthering democratic changes, market reforms, and modernization processes in CIS countries in light of subsequent EU enlargements, especially the accession process of the CEE countries that joined the block in 2004 and 2007. For CEE countries, the EU accession process (that started in the 1990s) coincided with a major modernization and reform effort. The clearly defined final objective of EU membership and the fact that its accomplishment depended on fulfilling a number of institutional and policy criteria is commonly believed to have been instrumental in speeding up the reform process and improving its

Acknowledgements: This work benefited from my discussions with Joanna Konieczna and Marek Dabrowski and comments received during the GDN conference in Prague in January 2010. The usual disclaimer applies. W. Paczyn´ski (*) CASE Fellow e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_11, # Springer-Verlag Berlin Heidelberg 2011

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effectiveness (Dabrowski and Radziwill 2007; ECFIN 2009). With the ENP borrowing certain elements from the enlargement process (‘a stake in the EU’s Internal Market’) without the explicit offer of EU membership (‘everything but institutions’), the question of what this policy can actually achieve is a valid one. The ENP’s aim of influencing the modernization process is explicitly stated in Commission’s documents. For example the European Commission (2006, p. 2) non-paper contains the following passage: The economic integration of ENP partner countries with the European Union is not only an incentive and reward for economic and regulatory reform in our neighbouring countries; in a globalised economy, it is also of interest to the EU to build a common EU-ENP area of economic integration where the same or similar regulatory standards apply. Yet the challenge with regard to most ENP partners is how to engage them to pursue their ongoing reform processes and to lock in the results.

There are clearly limitations to the external anchoring of reforms. Firstly, a mechanism similar to the one that materialized during the EU accession process required a certain level of governance built on cultural belief systems, ethical norms, and conventions, etc. (Ahrens 2007; Wagener 2004). Secondly, the EU membership objective had genuine public support in the candidate countries, which allowed the governments to implement a difficult reform agenda. Section 11.2 provides a snapshot of underlying social and political realities and the configuration of interests and powers of the key stakeholders. Section 11.3 attempts to evaluate the ENP effects so far, and some conclusions and recommendations are provided in Sect. 11.4.

11.2

The Interests at Play

The relative effectiveness of external pressures on reform processes depends on the degree of support for European integration or cooperation processes, the attractiveness of the ENP, and the similarity of social, political and other realities in the EU and the region covered by the ENP. These issues are discussed below.

11.2.1 CIS Approaches to the EU Stakeholders in CIS countries (ruling elites, other political forces, the business community and population at large) differ in respect to knowledge about, interest in and attitudes towards the EU and its policies in respect to the CIS region or individual countries. This affects the process of shaping bilateral relations between the EU and respective CIS countries and also the potential role of the ENP as a reform-promoting mechanism. Practical knowledge of the EU and its modes of operation among the ruling elites and administrations of CIS countries may be fairly limited as suggested by the generally low quality of governance in the CIS countries (Kaufmann et al. 2010).

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The often prevailing passive approach of some CIS governments to dialogue with the EU, where the CIS side confines itself to responding to EU initiatives but does not come up with its own proposals, may also suggest the limited capacity and know-how of the CIS (Menkiszak et al. 2008). On the business side, it appears that only the largest companies involved in trade and investment in the EU have the capacity to monitor relevant developments and lobby for favorable solutions. Another complication is that the mechanisms of transparent and effective stakeholder consultation are underdeveloped. As a result, CIS businesses may have limited opportunities to learn about likely outcomes of any planned decisions or negotiations, e.g. on FTA with the EU and articulate their interests (Shumylo 2006). At the same time, some business lobbies in the CIS are very powerful and able to affect domestic policies, including areas that are critical from the perspective of relations with the EU. White et al. (2010) reports results of comparable surveys carried out during 2005–2009 in Belarus, Ukraine and Russia which trace the evolution of general feelings about potential membership in the EU.1 In the first two countries, 50–60% of respondents declared at least some support for the idea of EU membership and this share was broadly stable over time. In Russia, the 2005 results were similar to the ones in the other two countries, but by 2008, support decreased. In all cases, negative attitudes were expressed by a rather small share of respondents, while many found it difficult to answer or declined to answer. In some CIS countries, surveys asking a direct question on voting in a hypothetical referendum have been held. For example, in Moldova, surveys have shown support for EU membership fluctuating in the 60–70% range most of the time since 2003, while in Ukraine, support has hovered at around 50% (47–64% range) since 2005 (IPP 2010; Democratic Initiatives Foundation 2008, 2009). Survey-based measures of attitudes to EU accession and especially responses to answers on voting in a hypothetical accession referendum require careful interpretation. However, one interesting observation is that the levels of support cited above are not very different from the levels seen in the early 2000s in the group of countries that eventually joined the EU in 2004–2007. Yet another way of analyzing the European attitudes would be to see to what extent people acknowledge feeling ‘European’ as individuals. White et al. (2010) report the results of comparable surveys in Belarus, Ukraine and Russia over the 2000–2009 period containing the question ‘Do you think of yourself as a European?’ Overall, Belarus emerged as the most European country by this measure, with around 40% of respondents declaring at least partial European identity. In Russia and Ukraine, less than 30% of respondents declared this. Over time, one can observe some decline in the popularity of European self-identity, especially in Russia and Belarus, where, in 2000, the majority of respondents reported at least partial European self-identity. A comparison with self-declared identities in the EU

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and EU candidate countries confirms a large gap in this respect (White et al. 2010). This conclusion is underpinned by results from the World Values Survey, where in response to the question ‘Which of the following best describes you,’ the answer ‘European’ was selected far more often in EU member states and in candidate countries than in the CIS countries covered by the survey (Armenia, Belarus, Georgia, Moldova, Russia and Ukraine) (E&WVS 2006). This brief review of survey evidence suggests that the level of support for the idea of EU membership is broadly stable in the European CIS countries for which data are available. This level is only slightly lower from the percentage of positive declarations in the then-EU candidates (current new members) in the early 2000s. However, the degree of European self-identification (the ‘feeling European’ of individuals) in the European CIS countries is very low, well behind levels observed in the EU and candidate countries. This might provide support to the interpretation that the societies of several CIS countries are potentially not less interested in hypothetical EU membership than the current new members were at the early stages of their integration processes. However, this potential interest may have weaker backing in deeper feelings related to self-identity.

11.2.2 Similarity of Norms and Values As indicated above, some similarity in major underlying values, belief systems, ethical norms and cultural conventions may be a pre-condition for effective cooperation between EU and CIS countries. Eurobarometer (2006, 2007) surveys confirm the existence of a fairly well defined set of values that are associated with the EU. These are human rights, democracy and peace, and possibly also the market economy. When asked whether neighboring countries (defined as countries covered by the ENP) share most of these EU values, a significant majority of EU citizens disagreed (57%) and only 30% said they believed that ENP countries share European values. On the other hand, the interpretation of this result should take into account that EU citizens are also not particularly strongly convinced that EU member states share the same values. 41% of respondents believe that EU countries are distant from each other on this score. The World Values Survey provides several indications on attitudes towards democracy in the EU and CIS countries (E&WVS 2006). Figure 11.1 shows the heterogeneity of responses to questions on the relative merits of the democratic system and on the preferences as to which system would be best for respondents’ own countries. The ‘old’ member states appear to be characterized by significantly stronger support for democracy (on both counts) than new EU members and CIS countries. The difference between NMS and CIS countries is less pronounced. This observation can also be supported by other results of the same survey. Respondents in CIS countries are significantly more likely than EU citizens to believe that, among the possible ways of governing a country, ‘army rule’ or ‘a strong leader

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Fig. 11.1 Attitudes towards democracy in EU and CIS countries Notes: EU15 is the average for 15 countries that formed the EU until 2003. NMS is the average for EU members that joined in 2004–2007. EE is the average for the four direct EU eastern neighbors: Belarus, Moldova, Russia and Ukraine. Other NC is the average for other CIS countries for which data was available: Armenia, Azerbaijan, Georgia and Kyrgyzstan.The horizontal axis shows the percentage of respondents who strongly agree with the following statement: democracy may have problems, but it is better than any other political system. The vertical axis shows the percentage of respondents who believe the democratic political system is very good for their country (other possible answers were: fairly good, fairly bad or very bad) (From World Values Survey 1999–2004)

who does not have to bother with parliament and elections’ could be good options. At the same time, ‘a democratic political system’ was considered a good option by a large majority of respondents in all countries, albeit also here the level of support among respondents in CIS countries was somewhat lower than in the EU. Attitudes towards the market economy are typically analyzed based on questions regarding some aspects of the free market, such as competition, the role of the state and private ownership in business, etc. The advantage of such an indirect approach is that the term ‘market economy’ itself can be understood in various ways by respondents with different historical and cultural backgrounds. There is no statistically significant difference among the groups defined in Fig. 11.1 in terms of their support for economic competition. On the other hand, in CIS countries, there is far more support for state ownership and control role than in the EU. As far as the government’s controlling role is concerned, the actual boundary appears to be between the EU15 on the one side and NMS and direct EU eastern neighbors on the other (E&WVS 2006).

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11.3

Evidence of the ENP’s Impact

Identifying the ENP’s impact on policies and the economic, social and institutional changes in the ENP countries is difficult. First, the ENP is a relatively new instrument, given that the first Action Plans were agreed on only in 2004. In terms of the actual implementation of the ENP-related agenda, progress appears to be quite limited so far. A more intense political dialogue between the EU and ENP partners does not immediately translate into new economic and social realities. As regards initiatives with a concrete economic content, one can mention mainly the autonomous trade preferences granted to Moldova (since 2008), the EU-Ukraine negotiations on a free trade agreement (started in 2008), the still rather distant prospects of FTA offers to Georgia, Armenia and Moldova, the limited visa facilitation agreements with Russia (since mid-2007), Ukraine, Moldova (both since 2008), and Georgia (since 2010), and Moldova and Ukraine’s accession to the Energy Community (2010 with transition periods). Second, the ENP works through a multiplicity of channels that are not always easy to identify. Third, in the analyzed period, several other factors may have acted as external anchors motivating policy changes: relations with the IMF and WB, other bilateral donors (e.g. the US), the WTO accession process (in the case of Ukraine, which joined in 2008, and Azerbaijan, which by the end of 2010 was still in the process of bilateral negotiations), the financial and economic crisis, etc. Separating the reform anchoring impacts of ENP and these other processes is in practice extremely difficult. The above limitations imply that a more robust ex-post impact analysis will only be possible after several years and even then it will be a difficult exercise to carry out. One approach for identifying the ENP’s effects or, more broadly, EU influence on domestic policy developments in CIS countries is through in-depth studies, tracing particular incentives or advice offered by the EU and its effects in a given country. This chapter proposes another method based on comparing policy outcomes in a number of countries, some of which have been actively involved in the ENP process. Identification of the possible effects of the ENP requires at the very least that the relevant control group is found. (While necessary, this is clearly not a sufficient condition.) Two natural candidates are CIS economies that are not participating in the ENP and the past experience of those CIS countries currently participating in the ENP (from the period before the initiative was launched). The intuition behind the approach is illustrated in Fig. 11.2 and compares the scores of the two subgroups of CIS counties with respect to the ‘regulatory quality’ measure of Kaufmann et al. (2010). Two features of the graph are striking. First, until 2003 or so the dynamics of change in regulatory quality indicators were similar for the ENP and non-ENP groups. Since 2003 there has been a clear divergence, with ENP countries continually improving their positions whereas non-ENP CIS countries have seen a stagnation in their scores. More formally, the difference between gains in a given governance indicator between ENP and non-ENP countries can be considered as an estimator of the

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Fig. 11.2 Regulatory quality in ENP and non-ENP CIS countries, 1996–2009 Notes: The ENP group contains the countries for which ENP AP were agreed upon before September 2008: Armenia, Azerbaijan, Georgia, Moldova and Ukraine. The non-ENP group consists of Belarus, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Turkmenistan, and Uzbekistan. The figure plots the arithmetic average of point estimates for countries in the respective groups. The scale for scores ranges from 2.5 to 2.5, with higher values corresponding to better governance (From calculations based on Kaufmann et al. (2010))

average impact of a given policy (here, the ENP). This is called the difference in differences (DD) estimator. Given that there may be systematic differences between the ENP group covered and the control group of non-ENP CIS countries, an improvement of the estimator can be obtained by subtracting from the DD estimator the difference between gains in governance indicators in the treated group over a period before the treatment was introduced and an analogous gain in the control group. This modified estimator is denoted by DDD. In the studied case, the formulas for DD and DDD estimators are as follows. DDENP;X ¼ ðXENP;2009  XENP;2003 Þ  ðXcontrol;2009  Xcontrol;2003 Þ DDDENP;X ¼ ðXENP;2009  XENP;2003 Þ  ðXcontrol;2009  Xcontrol;2003 Þ  ½ðXENP;2003  XENP;1996 Þ  ðXcontrol;2003  Xcontrol;1996 Þ

(11.1)

where X group, year is the average value of the indicator X in the group of countries either covered by the policy (ENP) or not covered by it (control) in the given year. The DD estimator is based on rather strong identifying assumptions. In particular, in the absence of the treatment (in the absence of the ENP), it is required that the average governance average outcomes in the treated and control group would have developed in parallel over time. This would be very hard to justify in the case of the two groups of countries analyzed here and taking DDD is only a partial solution to this problem. What this implies is that DD and DDD should be treated with extreme

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caution as estimators of the actual effects of the ENP. An additional argument for caution is related to the measurement error inherent in all governance indicators. Some differences in scores may not be statistically significant. At the same time, given the character of the ENP and the existing data, a version of the DD appears to be a reasonable approach to attempt the question on the effects of the ENP and hence is used in this chapter. Table 11.1 summarizes the results for the DD and DDD estimators of the ENP effect for a range of relevant governance indicators (Kaufmann et al. 2010) and Transition Indicators (EBRD 2009). EBRD data are more focused on aspects of economic policy and the business environment. As part of the sensitivity analysis, the results for both the means and arithmetic averages of the indicators are presented. A comparison of 2003–2009 and 1996–2003 trends is also repeated with 2-year averages, i.e. a change between the 2003–2004 average and the 2008–2009 average and a change from the 1996–1997 average (pre-ENP period) to the 2003–2004 average. The results suggest the possibility of small positive effects of the ENP on governance. With respect to economic reforms, the picture is more mixed. At best, the ENP countries were found to have fared only minimally better (0.02) than non-ENP CIS countries. This would correspond to less than a one-notch improvement of one of the EBRD indicators, i.e. on average, during 2003–2009, the gap between ENP and non-ENP countries increased by less than one notch in one dimension. Given the low magnitude of estimates and problems with the validity of assumptions underlying the approach, one cannot draw any solid conclusions from this exercise. The results are not driven by the exclusion of Belarus from the ENP group. However, dividing the CIS countries into two groups based on their geographical proximity to the EU produces similar results as the ENP / non-ENP division, with nearby countries faring better on governance scores than far away regions. In part this may be because the ENP covers most of the CIS countries that are Table 11.1 Difference in differences estimates of the ENP impact (From own calculations based on Kaufmann et al. (2010) and EBRD (2009) data) Voice and Regulatory Rule of Control of Transition Indicator accountability quality law corruption Indicators (EBRD) DD medians 0.47 0.42 0.45 0.23 0.18 DDD medians 1.04 0.39 0.47 0.11 0.32 DD means 0.29 0.46 0.23 0.29 0.18 DDD means 0.4 0.43 0.41 0.52 0.32 DD means 2-year averages 0.23 0.42 0.19 0.23 0.18 DDD means 2-year averages 0.26 0.44 0.18 0.28 0.32 For definitions of DD and DDD, see main text. The treated (ENP) group contains the CIS countries for which Action Plans were agreed upon before end-2010: Armenia, Azerbaijan, Georgia, Moldova and Ukraine. The control group is made up of the remaining CIS countries: Belarus, Kazakhstan, Kyrgyzstan, Russia, Tajikistan, Turkmenistan, and Uzbekistan.

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geographically close to the EU, but an alternative explanation is that longestablished institutional, cultural, religious and other factors related to the influence of various European and non-European centers and unrelated to the EU/ ENP is also plausible.

11.4

Conclusions and Recommendations

It is probably too early to assess the success of the ENP in the sphere of promoting economic policy changes in the Eastern Neighborhood countries. The actual progress of the ENP agenda has been limited to date, due to a variety of factors, both on the EU and the CIS sides. While one can find examples of positive influences of EU policies towards particular CIS countries and simple comparative analyses suggest that the ENP may have had slightly positive effects, the evidence is too weak to prove the ENP’s strength in helping to promote and support the sustainability of broader and deeper reforms. The ENP offers different incentives than the EU accession process in CEE in the 1990s and early 2000s and, since EU membership is not currently a part of the ENP offer, both sticks and carrots are weaker. Still this does not preclude the reform anchoring mechanism at work, given that the ENP’s offer of ‘a stake in the EU Internal Market’ could provide meaningful economic benefits, even if only in the long term. Also, support for integration with the EU is not significantly weaker than it was in the current new EU member states several years before their accession. A longer horizon of strategic planning in CIS countries, a stronger commitment to advancing real progress by the EU, and a broader presentation of potential ENP benefits could strengthen the reform anchoring potential of the ENP. Existing information on the values and norms that are important for societies in the EU and CIS countries confirms the existence of significant differences in some dimensions. More specifically, support for democratic and free market principles tends to correlate with a country’s geographic location along the east–west diagonal and its historical experience (e.g. the duration of communist rule), with some new EU member states appearing to be not that different from some CIS countries. The perception of the ENP in CIS countries matters a lot. Therefore, the EU approach should allow for a real dialogue on ENP design and implementation. It should be responsive to the initiatives of CIS partners and encourage such initiatives where local capacity is limited. A more friendly EU visa regime could play a particularly important role in shaping a more positive and more realistic picture of the EU among the citizens of ENP countries. People tend to believe, at least to some extent, what they hear and see in the media (Gentzkow and Shapiro 2004). Well-designed, fully objective information campaigns about the EU, its objectives and actions, could play a positive role. Ensuring easier access to Western news sources in the CIS region (e.g. via availability of translations or subtitles) could be an effective tool.

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Since the ENP’s birth, questions have been asked about whether it would evolve into a strong policy or rather a fake one. Emerson (2004) poses the question by referring to ‘strategy vs. placebo’. This is not necessarily the best analogy. A placebo can actually lead to real change via the so-called ‘placebo effect’. Moreover, it is tempting to extend the analogy between the ‘placebo effect’ in medicine and in promoting economic reforms a bit further. The 2008 Ig Nobel Prize in medicine was awarded to Dan Ariely for demonstrating that expensive placebos are more effective than inexpensive placebos (Waber et al. 2008).2 The lesson for the ENP, if there is a placebo element in it, is therefore to present, promote and implement the ENP as a difficult and demanding program for deepening bilateral relations. It could then be expected to bring about a real improvement in the prosperity of the countries involved.

References Ahrens J (2007) Governance in the process of economic transformation. In: Jovanovic M et al (eds) System transformation in comparative perspective. Affinity and diversity in institutional, structural and cultural patterns. LITVerlag, Muenster Dabrowski M Radziwill A (2007) Regional vs. global public goods: the case of post-communist transition. CASE Netw Stud Anal 336 Democratic Initiatives Foundation (2008) Results of surveys on Ukraine’s membership in the EU and NATO (in Ukrainian). http://dif.org.ua/uploads/HD2008-04.doc. Accessed 10 November 2010 Democratic Initiatives Foundation (2009) Results of survey on Ukraine’s membership in the EU and NATO (in Ukrainian). http://dif.org.ua/ua/press/hdthdrt. Accessed 10 November 2010 EBRD (2009) Transition indicators. European Bank for Reconstruction and Development ECFIN (2009) Five years of an enlarged EU. Economic achievements and challenges. European Economy No 1/2009, European Commission, Directorate-General for Economic and Financial Affairs Emerson M (2004) European neighbourhood policy: strategy or placebo? CEPS Work Doc 215 Eurobarometer (2006) Eurobarometer 66. Public Opinion in the European Union Eurobarometer (2007) The EU’s relations with its neighbours. A survey of attitudes in the European Union. Special Eurobarometer 285 European and World Values Surveys (2006) Four-wave integrated data file, 1981–2004, v.20060423. Surveys designed and executed by the European Values Study Group and World Values Survey Association. File Producers: ASEP/JDS, Madrid, Spain and Tilburg University, Tilburg, the Netherlands. File Distributors: ASEP/JDS and GESIS, Cologne, Germany European Commission (2006) ENP – a path towards further economic integration. Non paper. http://ec.europa.eu/world/enp/pdf/non-paper_economic-integration_en.pdf Accessed 24 February 2011

2

A parody of the Nobel Prizes, the Ig Nobel Prizes are given annually for achievements that ‘first make people laugh, and then make them think’. The Ig Nobel Prizes are organized by the magazine Annals of Improbable Research.

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Gentzkow MA, Shapiro JM (2004) Media, education and anti-Americanism in the Muslim world. J Econ Perspect 18(3):117–133 IPP (2010) Barometer of public opinions. Various editions 2003–2010. Institute for Public Policy. http://www.ipp.md. Accessed 2 November 2010 Kaufmann D, Kraay A, Mastruzzi M (2010) Worldwide governance indicators for 1996–2009. http://info.worldbank.org/governance/wgi/index.asp. Accessed 15 October 2010 Menkiszak M, Kononczuk W, Kaczmarski M (2008) CIS countries’ interests vis-a-vis the European Union and its eastern policy. CASE Netw Stud and Anal 365 Shumylo O (2006) Ukraine and the European neighbourhood policy. Ensuring the free movement of goods and services. CEPS Work Doc 240 Waber RL et al (2008) Commercial features of placebo and therapeutic efficacy. JAMA 299 (9):1016–7 Wagener H-J (2004) Good governance, welfare, and transformation. Eur J Comp Econ 1 (1):127–143 White S, McAllister I, Feklyunina V (2010) Belarus, Ukraine and Russia: east or west? Br J Polit Int Relat 12:344–367

.

Chapter 12

Economic Relations Between the EU and CIS Marek Dabrowski

Abstract The Eastern Enlargement of the EU increased the importance of the EU’s economic and political relations with the CIS countries The CIS countries located in EE and the Southern Caucasus were invited to participate in the ENP and in the EaP. However, a major weakness of both policy frameworks is the imbalance between the EU’s far-reaching expectations with respect to its neighbors’ policies and reforms, and the limited and distant rewards that can potentially be offered. Thus, making the policy frameworks more effective requires a serious enhancement of the rewards using, to the extent possible, the positive experience of previous EU enlargements. The nature of economic relations in a globalized world calls for a more complex package-type approach to economic integration rather than limiting cooperation to a few narrow fields.

12.1

Introduction

The purpose of our analysis is to examine the economic aspects of the EU policies towards its Eastern neighbors. We are going to present a general overview of relations between the enlarged EU and CIS countries in the spheres of trade, investment, labor movement, technical cooperation, and influence of the EU’s economic and institutional model on the course of CIS economic reforms and institutional modernization. Thus this chapter will serve as a partial summary of the previous 11 chapters in this volume. Obviously, the issues mentioned above cannot be fully separated from the political context and agenda – domestic, bilateral and multilateral. However, our analysis will concentrate on economic cooperation

M. Dabrowski (*) President of CASE e-mail: [email protected] M. Dabrowski and M. Maliszewska (eds.), EU Eastern Neighborhood, DOI 10.1007/978-3-642-21093-8_12, # Springer-Verlag Berlin Heidelberg 2011

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and its impact on economic reforms in CIS countries and will refer to political developments only to the extent justified by their direct impact on economic ones. Section 12.2 characterizes the economic importance of both regions in their bilateral economic relations. Section 12.3 examines EU-CIS trade and economic relations before the EU Eastern Enlargement. Section 12.4 analyses the basic concept of ENP and EaP and their implementation. Section 12.5 provides a brief note on EU-Russia relations and EU policy towards CA. In Sect.12.6, we discuss possible directions for enhancing and upgrading the ENP, EaP and EU-CIS economic relations. Finally, Sect. 12.7 offers brief conclusions.

12.2

Importance of the CIS Region for the EU and Vice Versa

The 2004 and 2007, EU enlargements moved the EU external borders to the East and Southeast, radically altering the EU’s geopolitical and economic perception of the CIS region and its potential importance as an economic and political partner. Until these enlargements, CIS countries formed the second, outer ‘ring’ of EU neighbors, being geographically separated from the EU by the EU accession countries of CEE. Their economic and political importance for the EU15 (i.e. ‘old’ member states) was quite limited with the exception of Russia, the largest (territorially) country in the world with huge natural resources and nuclear weapons, directly bordering one of the EU members (Finland). To simplify, the real economic and foreign policy interests of the EU15 in cooperation with CIS countries concentrated primarily on the supply of oil and natural gas from Russia, and on the relative geopolitical stability of the post-Soviet area (avoiding proliferation of regional and ethnic conflicts). The picture changed with the Eastern Enlargement of the EU. First, in purely geographical terms, four CIS countries – Russia, Ukraine, Belarus and Moldova – became direct EU neighbors, sharing long land borders. In a slightly longer time horizon, with Turkey’s expected accession, three Caucasian countries (Armenia, Azerbaijan and Georgia) will also share land borders with the EU. They already share the Black Sea with the enlarged EU. This means that all but the CA CIS countries have already moved, or will move, geographically from the second to the first ring of EU neighbors. Most of the new members states (NMS) of the EU have a political and economic history similar to the countries of the former USSR, not only due to their experience with communism during the second half of the twentieth century; some of them were part of the Russian empire (part of Poland, Baltic countries, Finland) before World War I. There are close ethnic and cultural links between the NMS and EU candidate countries on the one hand and CIS countries on the other (Romania with Moldova, Poland with Belarus and Ukraine, the Russian speaking minority in Baltic countries with Russia, Turkey with Azerbaijan and most of post-Soviet Central Asia). However, looking at the aggregate trade indicators, the importance of the CIS for the EU27 is not much higher than it was for the EU15. This is a result of the limited

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economic potential of both the NMS and the CIS. In 2003, the NMS-10 (all NMS but Bulgaria and Romania) constituted only 4.7% of the EU-25’s total GDP and a small share of its total extra-EU export. On the other hand, even including Russia, the overall CIS share in the world economy is quite limited. It accounted for 4.6% of world GDP in 2009 (PPP-based estimation) and 3.4% of global exports of goods and services, of which Russia’s share amounted to 3.0% and 2.2% respectively (see WEO 2010; Table A).1 According to the EEF (2010), only 2.8% of the total exports of EU27 goods in 2009 was directed to the CIS (see Table 12.1). For comparison, another EU ‘neighborhood’ region, the MENA received 4.8% of EU27 exports.

Table 12.1 EU: selected (2010), Table 57) Region/Country EU27 Austria Belgium Bulgaria Cyprus Czech Republic Denmark Estonia Finland France Germany Greece Hungary Ireland Italy Latvia Lithuania Luxembourg Malta Netherlands Poland Portugal Romania Slovakia Slovenia Spain Sweden UK

1

directions of exports of goods in %, World ¼ 100, 2009 (From EEF EU27 66.5 71.4 77.0 67.9 74.6 84.5 67.3 71.1 56.0 61.6 62.2 66.2 79.4 60.0 56.7 68.4 64.6 86.8 46.0 78.0 79.8 73.9 73.1 85.8 74.7 70.3 59.1 53.3

EU candidates 1.7 2.0 1.0 10.7 0.2 1.3 1.1 1.3 1.4 1.6 1.7 8.6 2.7 0.7 2.8 0.7 0.8 0.8 3.6 1.0 1.7 0.9 6.3 2.2 9.5 1.9 1.7 1.1

CIS 2.8 3.3 1.1 6.1 3.4 3.4 2.0 11.1 9.6 1.9 3.3 3.1 5.6 0.5 3.3 15.4 21.3 0.9 0.5 1.6 7.3 0.5 5.8 4.7 6.1 1.3 2.1 1.3

MENA 4.8 2.8 3.5 4.5 10.3 2.1 2.5 1.3 3.7 8.3 3.9 7.6 2.5 1.8 9.5 5.5 1.6 1.7 7.5 3.1 1.7 3.3 5.5 0.9 3.2 7.3 4.1 5.8

In WEO (2010) the CIS also includes Mongolia, which is beyond the scope of our analysis.

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In CIS countries, the shares of EU exports as part of their total exports are higher, at times much higher, than the share of EU exports to the region, as illustrated by Table 12.2. Such an asymmetry can be considered normal when less-developed or middle-income countries trade with a larger, more developed partner or a large and highly integrated trade block. However, the aggregate and average statistics presented in Table 12.1 may be misleading, for at least three reasons. First, the concept of EU27 exports in this table also includes intra-Union trade, which accounts for 66.5% of the total. Thus, when analyzing the structure of EU external trade, the shares of non-EU countries/ regions should be tripled, at least. Second, Table 12.1 demonstrates that some of the EU member countries represent higher shares of trade with the CIS than the EU average. This relates to the three Baltic countries, Finland, Poland, Bulgaria, Slovenia, Romania and Hungary. Consequently, these countries can gain more from the development of EU-CIS trade relations. However, they are also more vulnerable vis a vis any potential episodes of political, economic or social destabilization in the CIS.2 CIS countries also differ among themselves in terms of the importance of their trade relations with the EU (see Table 12.2). In 2004, the share of exports to the EU as part of a country’s total exports varied from 3.2% in Kyrgyzstan to 51.0% in Russia. However, in most cases, the high share of exports to the EU is determined by just one commodity or group of commodities: energy resources in the cases of Azerbaijan, Kazakhstan and Russia, aluminum in Tajikistan, diamonds in Armenia, and metal products in Ukraine. The monoculture structure of CIS countries’ exports can be considered a serious source of vulnerability to external shocks.

Table 12.2 Share of exports to EU-25 as a proportion of the country’s total exports (From http://unctadstat. unctad.org/TableViewer/ tableView.aspx (accessed on March 7, 2011))

Country

2009

Armenia Azerbaijan Belarus Georgia Kazakhstan Kyrgyzstan Moldova Russia Tajikistan Turkmenistan Ukraine Uzbekistan

45.2 50.7 43.6 33.3 48.7 3.2 49.5 51.9 11.7 25.3 24.3 10.2

2 At the beginning of the 1990s, the former communist countries and Finland were heavily affected by a disruption of COMECON and the collapse of the USRR. The next shock came in 1998 with the Russian and CIS financial crises, which had a negative impact on the Baltic countries and, to a lesser extent, on Poland and Bulgaria.

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Third, the special importance of the energy sector also must be taken into account. Many EU countries are very dependent on imports of CIS energy resources from Russia and, to a smaller but systematically increasing extent, also from the Caspian Sea region (see Chap. 4). Going beyond trade in goods and services, labor migration from the CIS to the EU represents another potentially important field of economic cooperation (see Chaps. 6 and 7). In spite of restrictive migration and visa policies in the EU, the flow of labor migrants (primarily irregular or illegal migrants) from European CIS countries to the EU is systematically increasing. As in the case of trade flows, migration has an asymmetric impact on both sides and its importance differs country by country. For the EU as a whole, the immigrants of CIS origin still constitute a small share of total migrant inflows. Intra-EU flows (particularly from the EU12 to EU15) and migration from the Middle East, Africa and Asia represents a much larger share. However, migration flows from the CIS are unevenly distributed between EU member countries, with the majority of migrants settling in NMS and Mediterranean countries (factors of geographical, cultural and language proximity play an important role here). Considering the ‘export’ side, outgoing migration has become a serious economic and social phenomenon for some low-income CIS countries, where one quarter to one third of the working age population works abroad, at least on a seasonal basis (in Russia, the EU, Turkey and Kazakhstan). Emigrants’ remittances constitute a substantial portion of GDP and an important balance-of-payment item, especially in Tajikistan, Moldova and Kyrgyzstan (see Table 12.3 based on the UNCTAD database and Chap. 7 based on national sources and own estimations3). Table 12.3 Labor remittances as % of GDP (From http://unctadstat.unctad.org/TableViewer/ tableView.aspx (accessed on March 7, 2011)) Country 2004 2005 2006 2007 2008 2009a Armenia 12.1 10.2 10.3 9.2 8.9 8.8 Azerbaijan 2.6 5.2 3.9 3.9 3.4 3.0 Belarus 1.1 0.8 0.9 0.8 0.7 0.7 Georgia 5.9 5.4 6.3 6.8 5.7 6.7 Kazakhstan 0.4 0.3 0.2 0.2 0.1 0.1 Kyrgyzstan 8.5 13.1 17.0 18.8 24.4 19.6 Moldova 27.1 30.8 34.7 34.0 31.4 22.4 Russia 0.4 0.4 0.3 0.4 0.4 0.4 Tajikistan 12.1 20.2 47.6 74.6 102.6 72.7 Ukraine 0.6 0.7 0.8 3.2 3.2 4.4 Workers’ remittances are goods and financial instruments transferred by migrants living and working (as residents) in a new economy to residents of the home economy; a – preliminary estimates

3

Sources differ in remittances’ estimates, which is hardly surprising taking into consideration the unofficial character of labor migration and various channels of transferring remittances to one’s home country (primarily outside the formal banking sector).

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Capital flows are also important for CIS countries as potential importers of capital and at times exporters as well, due to capital flight. For the EU economies, the size of capital movement between them and the CIS represents a negligible amount. For many years, CIS countries lagged behind CEE in attracting foreign direct investment (FDI). This was mainly due to the poor business and investment climate in this region which was the effect of many problems, including high inflation, high fiscal deficits, currency instability, poor protection of property rights, insideroriented privatization, numerous bureaucratic obstacles (including those directly affecting foreign investors), delays in adopting market-oriented legislation and its effective enforcement, pervasive corruption, a fragile financial sector, and underdeveloped infrastructure (see Chap. 5). A substantial part of recorded FDI had, in fact, post-Soviet origins, even if it was formally recorded as originating in other countries (repatriation of capital, which earlier fled CIS countries). Most investments were concentrated in only a few sectors such as energy or mobile telephony. The situation began to change in the mid-2000s, with rapid capital inflows to the largest CIS economies such as Russia, Ukraine and Kazakhstan. Their sectoral destination was much broader than before, including various manufacturing industries, retail trade, financial services, etc. Also, this FDI was accompanied by increasing portfolio capital flows (see Lozovyi and Kudina 2007). On the other hand, some smaller CIS economies managed to increase FDI flows either due to investment in the energy sector (Azerbaijan), or as a result of privatization and some improvement in the investment climate (Armenia, Georgia and Moldova). However, the capital inflows to CIS (including FDI) have been either slowed down or even partly reversed (in the case of Russia) as a result of the global financial crisis of 2008–2010 and its future intensity is difficult to predict. Overall, as demonstrated by Table 12.4, CIS countries continue to experience a substantial gap in the size of FDI flows, not only with respect to EU NMS, but also with respect to EU candidate countries. Furthermore, as discussed in Chap. 5, FDI in the CIS differs from that in the two other regions: foreign investors in the CIS predominantly seek domestic market opportunities in the host country (with most production supplies coming from abroad) rather than efficiency considerations (low-cost opportunities for developing global production chain). Therefore, the potential innovation and efficiency spillovers to domestic producers are limited. Such a strategy is caused by a continuous poor business climate in the CIS region.

12.3

EU–CIS Economic Relations Before 2004

Cooperation between the EU15 and the new independent states of the former USSR was built on the basis of bilateral PCA negotiated during the 1990s. Nine of them entered into force between 1997 and 1999, and one in 2010 (see Table 12.5). The

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Table 12.4 Foreign direct investment, inward stock, 2005 (From http://unctadstat.unctad.org/ TableViewer/tableView.aspx (accessed on March 7, 2011)) Percentage of GDP USD per capitaa Country CIS countries Armenia Azerbaijan Belarus Georgia Kazakhstan Kyrgyzstan Moldova Russia Tajikistan Turkmenistan Ukraine Uzbekistan

2007

2009

2007

2009

797 990 461 1,229 2,900 153 504 3,461 151 789 822 81

1,177 1,024 878 1,771 4,626 196 723 1,792 125 1,194 1,138 132

26.6 25.8 9.9 52.7 42.6 21.5 42.0 38.0 44.7 49.5 26.7 9.7

41.6 21.0 17.2 70.3 67.7 23.9 48.2 20.3 36.2 .. 45.0 12.4

4,955 23,484 10,947 12,493 19,809 4,779 4,488 20,642 4,679 2,935 8,389 7,151

6,724 33,771 11,177 12,123 24,886 5,213 4,210 23,037 4,801 3,478 9,297 7,542

95.7 84.9 64.6 80.3 143.2 37.7 38.7 112.4 42.0 37.2 60.3 30.5

107.7 116.1 59.2 86.3 192.8 44.8 37.0 120.0 42.5 46.1 56.8 31.2

1,121 2,075 8,288 2,208 7,336 2,090 1,039

22.8 45.5 76.9 47.2 _ _ 23.3

28.5 45.9 58.1 51.7 105.1 42.3 12.4

EU NMS Bulgaria Cyprus Czech Republic Estonia Hungary Latvia Lithuania Malta Poland Romania Slovakia Slovenia

EU candidates and potential candidates Albania 792 Bosnia and Herzegovina 1,822 Croatia 10,175 Macedonia 1,833 Montenegro _ Serbia _ Turkey 2,097 a At current prices and current exchange rates.

PCA with Belarus was signed in March 1995 and the PCA with Turkmenistan in May 1998; yet, to date, neither has entered into force due to political reasons. The PCAs offered little in the area of economic integration: the MFN clause, some technical, legal and institutional cooperation in such sectors as transportation, energy, competition policy, some legal approximation in the areas of custom law, corporate law, banking law, intellectual property rights, technical standards and

192 Table 12.5 Partnership and cooperation agreements between EU and CIS countries (From http:// europa.eu/ legislation_summaries/ external_relations/ relations_with_ third_countries/eastern_ europe_and _central_asia/ r17002_en.htm (accessed on March 7, 2011))

M. Dabrowski

Country Armenia Azerbaijan Georgia Kazakhstan Kyrgyzstan Moldova Russia Tajikistan Ukraine Uzbekistan

Entered in force 1.07.1999 1.07.1999 1.07.1999 1.07.1999 1.07.1999 1.07.1998 1.12.1997 1.01.2010 1.03.1998 1.07.1999

certification, etc. This was even recognized in the official communication of the European Commission (2003, p. 5): In contrast to contractual relations with all the EU’s other neighbouring countries, the Partnership and Cooperation Agreements (PCAs) in force with Russia, Ukraine and Moldova grant neither preferential treatment for trade, nor a timetable for regulatory approximation.

This differed from the agenda and implementation mechanism of the TAA signed by the EEC/EU with CEE countries at the beginning and middle of the 1990s, as well as the SAA negotiated with the Western Balkan countries in the 2000s. Both TAAs and SAAs were aimed at building DCFTA, and included a broad agenda of institutional harmonization (adopting the acquis by EU partners) and, most importantly, offered the perspective of EU membership. Some of the TAAs and SAAs were negotiated and signed simultaneously with respective countries’ accession to the WTO, in a few cases even before the formal conclusion of the latter. This was in sharp contrast to the EU attitude towards CIS countries: their WTO membership was considered by the EU as a basic precondition to start negotiating any kind of bilateral FTA. The WTO accession process of the largest CIS countries went slowly (only Ukraine completed it successfully in 2008) so the perspective of trade liberalization between the EU and these large countries remained distant until very recently. However, the same concerned the smaller countries – Kyrgyzstan, Moldova, Armenia and Georgia – which joined the WTO in the late 1990s or early 2000s. To have a complete picture, one must admit, however, that all CIS countries could benefit, to various degrees, from the GSP offered unilaterally by the EU to less developed countries. These are primarily preferential import tariffs. From the very beginning of their independence, CIS countries also benefited from generous European aid programs delivered both by the EEC/EU as a whole and its individual member states (Light 2007). Among these programs, the TACIS was aimed at supporting the democratic and market transition, economic and social modernization, cross-border cooperation and solving numerous regional/ sub-regional issues.

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193

European Neighborhood Policy and Eastern Partnership

The EU attitude towards the CIS region began to change at the beginning of the 2000s. The imminent EU Eastern Enlargement stimulated an intra-EU debate and conceptual effort to upgrade relations with both its Eastern and Southern neighbors. The debate highlighted the notion that the CIS region is far from being homogeneous in political, economic and social terms, and CIS countries require a more individualized approach (Light 2007). The Communication on Wider Europe of March 11, 2003 (European Commission 2003) was the first attempt to propose a new policy framework towards the countries which were to become direct geographical neighbors after the Eastern Enlargement. This document was followed by the official launch of the ENP on May 12, 2004 (European Commission 2004). Interestingly, the 2003 Communication on Wider Europe, which reflected the initial position of the Commission, offered a wider and more far-reaching vision of cooperation with neighbors and clearer incentives for them than the subsequent 2004 ENP Strategy Paper, which also took into account the views of the individual member states. The first paper used clearer language regarding access to the EU internal market, perspectives of free movement of people, visa facilitation, and other potential incentives, while the Strategy Paper put more emphasis on EU security interests, fighting illegal migration, etc. (Schweickert et al. 2007). According to the ENP Strategy Paper, the declared ENP objective was to avoid the emergence of new dividing lines between the enlarged EU and its old and new direct neighbors, as well as strengthening stability, security and well-being in the entire mega-region. The European Commission (2004, p. 3) offered its neighbors a privileged relationship built upon mutual commitment to common values principally within the fields of the rule of law, good governance, the respect for human rights, including minority rights, the promotion of good neighbourly relations, and the principles of market economy and sustainable development. [. . .] The level of ambition of the EU’s relationships with its neighbours will take into account the extent to which these values are effectively shared.

Originally this general declaration was followed by a clear statement that the ENP is not concerned with the next EU enlargements nor does it offer neighbors an EU accession perspective. At the end of 2006 it was replaced by a more flexible approach: the ENP remains distinct from the process of enlargement although it does not prejudge, for European neighbors, how their relationship with the EU may develop in future, in accordance with Treaty provisions. (http://ec.europa.eu/world/enp/policy_en.htm)

In fact, this can be considered a return to both the language and spirit of the 2003 Communication on Wider Europe. As a result, the door became hypothetically opened for those CIS countries which are participants in the ENP (see below) and which will be ready to harmonize their political, economic and legal systems with the acquis. This seems to be, however, a very distant and unclear perspective, particularly if one takes into consideration the phenomenon of ‘enlargement fatigue’ that has recently been observed in some EU member states. Although the

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anti-enlargement sentiment works particularly strongly against the EU membership aspirations of Turkey (for historical and cultural reasons), one can expect a similar reaction to the EU membership aspirations of Ukraine, Moldova or Caucasus countries when they begin to materialize. So, if the perspective of EU membership is either very weak and distant (the case of European CIS countries) or non-existent (the case of the Southern Mediterranean neighbors), what are the alternative incentives provided by the ENP to encourage neighboring countries to undertake a costly modernization effort (see Chap. 9), accept European values in the area of democracy, human rights and market economy, and cooperate closely with the EU on security issues? The general answer is: access to the EU internal market: The approach proposed by the ENP [. . .] offers neighbouring countries the prospect of a stake in the EU Internal Market based on legislative and regulatory approximation, the participation in a number of EU programmes and improved interconnection and physical links with the EU (European Commission 2004, p. 14)

However, so far there has been no clear interpretation of what a stake in the EU Internal Market means in practice. Furthermore, taking into consideration the poorly developed institutional basis of trade and economic relations between the EU and CIS countries (so far based only on PCAs – see Sect. 12.3), it is very unlikely that the ENP can offer the latter full participation in the EU internal market, similar to that of Norway, Iceland or Switzerland. The negotiation of a more or less ‘deep’ FTA and selective participation in some segments of the EU internal market seem to be a more realistic option at the moment. The Council of the European Union (2007a) went exactly in this direction by suggesting the institutional framework of the DCFTA as a tool of modernization and support to economic and institutional reforms in neighborhood countries. However, so far Ukraine is the only CIS country which is negotiating a DCFTA with the EU (since 2008) and this process seems to still be far from conclusion. The ENP is conducted through bilateral Action Plans and the principle of bilateralism is deeply rooted in this policy framework, contrary to the regional approach, which governed the recent EU Eastern Enlargement. This does not mean, however, that third-country externalities of bilateral agreements will be completely ignored. For instance, simultaneous negotiations and signing action plans between the EU and all three Caucasus countries (in mid-November 2006) serve as an example of a coordinated sub-regional approach. The ENP has covered five CIS countries to date: Armenia, Azerbaijan, Georgia, Moldova and Ukraine. All of these countries agreed and signed bilateral Action Plans (AP) with the EU in 2005–2006. However, as evidenced by the rather slow pace of economic and institutional reforms in the ENP countries in the second half of the 2000s4 and the even slower pace of negotiating the DCFTA (see above), the

4 See Jakubiak et al (2006a, b) for early results of the Action Plans for Ukraine and Moldova, and Chaps. 1 and 11 for a more general analysis of the CIS reform process.

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implementation of ENP AP cannot be considered particularly successful. The lack of a clear set of external incentives and cooperation timetable can be considered one of the major reasons for this unsatisfactory performance. Progress in the area of travel facilitation has also been modest, with only four visa facilitation agreements signed: EU-Russia (May 2006), EU-Ukraine (June 2007), EU-Moldova (October 2007) and EU-Georgia (June 2010). These agreements have been used by the European Commission as an instrument to encourage its partners to sign the readmission agreements, which mostly serve the EU’s policy of fighting illegal migration (Trauner and Kruse 2008). Belarus is a potential ENP participant but it has a ‘frozen’ status for political reasons (an autocratic regime and violation of human rights); Libya and Syria in the Mediterranean region are similar cases. A general weakness of the ENP is the lack of balance between far-reaching expectations in respect to neighbors’ policies and reforms, and the limited and distant rewards which it can potentially offer (see Chaps. 8 and 11). This imbalance is especially acute in such areas as migration policy, where the EU is looking for the extensive cooperation of neighboring countries in fighting illegal migration to the EU (very often, against the interests of their own citizens), while offering very little in the realm of facilitating legal migration and freer movement of people (see Guild et al. 2007). More generally, there is doubt as to whether the lack of a clear offer of EU membership can mobilize governments of the neighboring countries to conduct difficult and sometimes unpopular economic and institutional reforms required to align with the acquis (Milcher et al. 2007). On the other hand, one may ask whether the perspective of EU membership, even if hypothetically provided, would be interesting and attractive enough for all the neighboring countries, many of which have different historical and cultural backgrounds, and different geopolitical and economic priorities than those shared by EU members (Chap. 11). Another controversial aspect of the ENP relates to the geographical concept of this initiative addressed only to countries that share either land or maritime borders with EU members and candidates. As a result, post-Soviet Central Asia has been left outside the ENP in spite of its close historical, economic and political links to CIS ENP countries and Russia, and its increasing economic importance for the EU as a prospective energy supplier. In addition, combining these two very different regions under one policy framework does not necessarily make the ENP more coherent, easier to manage and able to generate regional externalities. In the short term, however, the experience of EU cooperation with the Mediterranean region under the Barcelona process, and with the quite complex Association Agreements concluded between the EU and individual Middle Eastern and North African countries in the 1990s and early 2000s may provide useful lessons on how best to upgrade the less advanced economic cooperation between the EU and CIS countries. To address part of the critical comments in respect to the ENP and take account of the regional specifics of Eastern neighbors, the EU launched the EaP initiative in May 2009. The EaP is the supplementary cooperation framework (in addition to the

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ENP) aimed at deepening both the bilateral and multilateral integration of six Eastern neighbors (Armenia, Azerbaijan, Belarus, Georgia, Moldova and Ukraine) beyond the original ENP design. It involves, among others, the perspective of bilateral association agreements and the DCFTA, close cooperation in various sectors, visa facilitation and (in the long-term perspective) visa liberalization, and the launching of Comprehensive Institution-Building Programs aimed at improving the administrative capacity of the Eastern partners (Council of the European Union 2009). So far it is too early to say whether the EaP will share the weaknesses of the ENP or will prove more successful in fostering faster reforms and the European integration of the Eastern neighbors.

12.5

Russia and Central Asia

In spite of an initial offer from the EU, the Government of the Russian Federation opted out of participating in the formal ENP and EaP frameworks, preferring to have separate, strategic partnership relations with the EU. This framework is to be built on the concept of the Common European Economic Space between the EU and Russia, as defined by joint declarations of subsequent EU-Russia summits in 2001 and 2003. The next step was the joint EU-Russia declaration on May 10, 2005, which defined the so-called road maps for the four common spaces (Road Map 2005): • Common Economic Space (including environmental and energy issues) • Common Space of Freedom, Security and Justice (including migration and visa issues) • Common Space of External Security • Common Space on Research, Education and Culture Beginning in 2007, Russia was also a beneficiary country of the ENPI, which replaced TACIS, the previous aid program. In June 2008, negotiations were launched on the new EU-Russia Agreement which would replace the old PCA. However, details of this new treaty have yet to be determined. In particular, it is unclear whether and when it will include an FTA and how ‘deep’ this agreement might be. The main obstacle to start free trade negotiation is the unfinished process of Russia’s WTO accession. In 2010, the Government of Russia raised the new idea of the Partnership for Modernization, which obtained a general political backing at the EU-Russia summit on May 31 – June 1, 2010. The details of this partnership, however, need to be discussed by both sides in the future. Generally, Russia has the chance to develop a broad agenda of economic, political and institutional cooperation with the EU, comparable to that of the most advanced ENP countries (Moldova and Ukraine in Eastern Europe; Morocco and Tunisia in the Mediterranean region) or even going beyond this benchmark. Given the large size of the Russian economy and the country’s middle-income status, the

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key role of Russia’s energy exports in meeting EU energy demand, and the geopolitical importance of this country (without EU membership aspirations at the moment), the EU may be potentially interested in closer economic integration with Russia within the EU internal market. This, in turn, could help the Russian economy to complete its market transition, and advance its modernization and diversification. However, the future of EU-Russia cooperation will depend on the speed of domestic economic and political reforms in Russia, as well as on the geopolitical interest of the latter to build closer links with the EU. Five Central Asian countries have been left outside the ENP. During its meeting on June 21–22, 2007, the European Council approved the program document, which outlines the EU strategy towards this sub-region (Council of the European Union 2007b). Its agenda is, however, narrower and less ambitious than the ENP.

12.6

How to Make the ENP Effective?

The fundamental weakness of the ENP, i.e. its internal imbalance between the efforts needed to harmonize neighboring countries’ institutions with the acquis and incentives provided (see Sect. 12.4), have led many experts to call for a serious enhancement on the ‘reward’ side. For example, Emerson et al. (2007) have proposed the concept of the ENP Plus, which would add the following elements to the existing ENP design: • • • •

An advanced association model for the able and willing partner states, The strengthening of regional-multilateral schemes, An upgrading of the standard instruments being deployed, and The offer of an ‘ENP light’ model for difficult states or non-recognized entities

Indeed, in order to have a real impact on the development, modernization and reform of CIS countries, the ENP initiative must go beyond the narrowly defined cooperation agenda in some selected sectors and areas considered a priority by the EU (examples of these areas include energy supply and fighting illegal migration). The contemporary global economy is much more sophisticated than it was a few decades ago and its complexity calls for a broader concept of economic liberalization and cooperation such as the DCFTA discussed in Chaps. 3 and 9, which involves freer movement of services, investments and labor based on a far-reaching institutional harmonization/ alignment package. Let us take a brief look at how this web of mutually dependent policies works: 1. Trade expansion between the EU and its Eastern neighbors will depend not only on trade liberalization per se (first membership of all the CIS countries in the WTO, then their FTA with the EU), but also on the investment climate in the CIS region, the speed of institutional harmonization and at least the partial liberalization of the movement of people (important for trade in services).

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2. The intensification of foreign investment inflow to the CIS region will depend not only on improving the investment climate in individual countries (determined by the speed of institutional harmonization), but also on liberalizing trade, offering investors in CIS economies easy access to European markets. 3. The intensification of trade and FDI and the resulting decrease in the income gap can weaken the income motive of labor migration in several CIS countries and make the free movement of people less politically and socially controversial in the EU countries. 4. The free movement of people is important not only for balancing national labor markets (both in ‘origin’ and ‘destination’ countries) and the current account, GDP and fighting poverty (in ‘origin’ countries). It is also significant for the development of the domestic SME sector in ‘origin’ countries and the learning experience of more mature market economies and democratic societies, thus, strengthening domestic constituencies in favor of democratic and market reforms (in ‘origin’ countries). 5. Institutional harmonization very often involves substantial social, political and economic costs (see Chaps. 8, 9, and 11). Without strong incentives/potential rewards, these costs may be considered too high by societies and politicians in neighboring countries. The traditional pay-off offered by the EU to the CIS countries (a very gradual improvement of their trade regime with the EU and technical assistance) seems to be insufficient. A stronger set of incentives should probably include at least a faster pace of trade liberalization and the liberalization of the movement of people. In the case of countries that are explicitly interested in EU membership, such a perspective should not be ruled out a priori, as it is potentially an important and powerful incentive. It is a quite recognizable fact that the perspective of EU membership (even if it is very distant in time) can become a very powerful incentive, which speeds up political, economic and institutional reforms, aids in solving ethnic and political conflicts, and mobilizes societies and politicians to accept the most unpopular reform measures and undertake the most difficult modernization efforts. This is the observation which can be drawn from the previous EU enlargement experiences, particularly those of the Northern Mediterranean countries in the 1970s and 1980s, and the CEE countries, which joined the EU in 2004 and 2007. The same can be said for the CWB countries, despite their quite distant prospects of accession. The situation of the CIS countries seems to be less favorable in this respect. Apart from Moldova, Ukraine and Georgia, most of the CIS societies have expressed limited interest in the idea of deep European integration. But, more importantly, there has been a lack of a serious ‘European offer’ from the EU addressed to these countries and societies, which has made the pro-reform integration incentive unrealistic. At the moment, it is hard to say whether the ENP and EaP will ultimately provide such an incentive, but this cannot be totally ruled out (see Chap. 8). Very much will depend on the real interest and determination of individual CIS countries to deepen their economic and political relations with the enlarged EU.

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Summary and Conclusions

Until very recently, CIS countries did not belong to the first ring of EU neighbors and their economic importance as potential partners of the EU was very limited (with the exception of supplying energy resources to the EU, primarily from Russia). This situation began to change with the Eastern Enlargements of the EU, which were completed in 2004 and 2007. The European and Caucasus countries of the CIS region moved geographically from the second to the first ring of neighbors. The NMS have closer economic, social and cultural relations with the CIS region than most of the EU15. In addition, after a decade of severe adaptation output decline, the CIS countries have entered a phase of rapid growth, which has generated more demand for EU imports and investments and offers more benefits of enhanced economic cooperation for both sides. In spite of the recession caused by the global financial crisis in 2008–2009, the CIS region is likely to return to economic growth although, most likely, at a bit slower pace than in the previous decade. The new geopolitical and economic circumstances have led the EU to offer a new cooperation framework, the ENP, to part of the CIS and the Southern Mediterranean region, which has been supplemented by the EaP for Eastern neighbors. Simultaneously, it has launched a similar cooperation framework with Russia. However, the main weakness of the ENP thus far has been its lack of internal balance: the EU expects far-reaching cooperation in areas considered of priority importance for the EU, while it offers very few incentives to neighborhood countries in exchange. Thus, making this cooperation framework more effective requires a serious enhancement of the rewards using, to the extent possible, the positive experience of the previous EU enlargements. The nature of contemporary economic relations calls for a more complex package-type approach to economic integration, rather than limiting cooperation to a few narrow fields.

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