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Natural Resource Abundance, Growth, and Diversification in the Middle East and North Africa

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Investigates the effect of natural resources dependence and the role of policies in achieving sustained growth and diversification away from oil.

D I R E C T I O N S I N D E V E L O P M E N T


Trade


Natural Resource Abundance,
Growth, and Diversification in the


Middle East and North Africa
The Effects of Natural Resources and


the Role of Policies


Ndiamé Diop, Daniela Marotta, and
Jaime de Melo, Editors


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Natural Resource Abundance,
Growth, and Diversification in the


Middle East and North Africa






Natural Resource Abundance,
Growth, and Diversification
in the Middle East and
North Africa
The Effects of Natural Resources and
the Role of Policies


Ndiamé Diop, Daniela Marotta, and


Jaime de Melo, Editors




© 2012 International Bank for Reconstruction and Development / The World Bank
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Washington DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org


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Attribution—Please cite the work as follows: Diop, Ndiamé, Daniela Marotta, and Jaime de Melo. 2012.
Natural Resource Abundance, Growth, and Diversification in the Middle East and North Africa: The Effects
of Natural Resources and the Role of Policies. Washington, D.C.: World Bank. DOI: 10.1596/978-0-8213-
9591-2. License: Creative Commons Attribution CC BY 3.0


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ISBN (paper): 978-0-8213-9591-2
ISBN (electronic): 978-0-8213-9592-9
DOI: 10.1596/978-0-8213-9591-2


Cover photo: J. Burns/Getty Images; Cover design: Naylor Design


Library of Congress Cataloging-in-Publication Data has been requested.




v


Contents


Acknowledgments xi
Contributors xiii
Chapter Abstracts xvii
Abbreviations xxi


Chapter 1 An Overview of Diversification in MENA: Rationale,
Stylized Facts, and Policy Issues 1


Ndiamé Diop and Daniela Marotta


The Facts: MENA Economies’ Low Level of
Diversification 3


Limited Diversification, Natural Resource Rents,
and Growth Volatility 9


Why Is Greater Diversification Desirable
in MENA? 11


The Role of Rents and Real Exchange Rates 13
The Role of Weak Links in Output Concentration 16
Fiscal Policy and Output Concentration 18
Natural Resources and Incentives for Regional


Trade Reforms 20




vi Contents


Notes 22
References 23


Chapter 2 Resource Abundance and Growth: Benchmarking
MENA with the Rest of the World 27


Jaime de Melo and Cristian Ugarte


Benchmarking MENA’s Long-Term Growth and
Volatility 29


Correlates of MENA’s Growth Performance 36
Conclusion 60
Annex 2A Trade, Structural Change, and Natural


Resources 61
Annex 2B Ten Observations on Successful Growth 71
Annex 2C Applied Tariff Protection Is Still


Relatively High 72
Annex 2D Ad-Valorem Equivalents Estimations 74
Notes 78
References 82


Chapter 3 Rents, Regulatory Restrictions, and Diversification
toward Services in Resource-Rich MENA 87


Ndiamé Diop and Jaime de Melo


Services in MENA: Stylized Facts 89
Relative Roles of Engel’s Effects in Consumption


and Rents 94
The Role of Microeconomic Regulations 102
Export Diversification Opportunities for


Resource-Poor MENA 105
Concluding Remarks 108
Notes 110
References 110


Chapter 4 Patterns of Diversification in MENA: Explaining
MENA’s Specificity 113


Marcelo Olarreaga and Cristian Ugarte


Empirical Methodology 115
Measuring Weak Links and Dutch Disease Effects 116




Contents vii


Data Description 118
Empirical Results 118
Concluding Remarks 130
Notes 138
References 140


Chapter 5 Fiscal Policy and Diversification in MENA 143
Ali Zafar


Role of Fiscal Policy in the Aftermath of the
Arab Spring 144


Fiscal Policy in MENA: Stylized Facts 148
Fiscal Policy and Diversification 156
The Behavior of Fiscal Policy in MENA:


Econometric Evidence 168
Concluding Remarks 169
Notes 170
References 172


Chapter 6 Natural Resource Heterogeneity and
the Incentives for and Impact of Regional
Integration 175


Celine Carrère, Julien Gourdon,
and Marcelo Olarreaga


Trade Agreements in MENA: An Analytical
Setup 177


Empirical Results 182
Concluding Remarks 190
Notes 195
References 196


Appendix A Country Grouping Classifications 199
Notes 201
References 201


Boxes
1.1 How Is MENA Performance Captured and


Benchmarked in This Volume? 5
2.1 MENA in the Natural Resource Curse Literature 40




viii Contents


3.1 Regulatory Restrictions in MENA: Findings from
Other Case Studies 104


3.2 Dubai’s Successful Approach to Diversification 106


Figures
1.1 Changes in the Composition of GDP, 1980–83


to 2007–10 3
1.2 Services Share in GDP by Level of Income 4
1.3 Share of Services in Nonmining GDP 7
1.4 Drivers of Export Growth (Excluding Oil Products),


1998–2008 8
1.5 Natural Resource Rents in 2010, by Region 10
1.6 Growth Volatility, by Region 11
2.1 MENA Long-Run Growth Performance 30
2.2 Estimated Equilibrium Real Exchange Rates 39
2.3 Frequency Ratios, Core NTMs, 2001–10 44
2.4 Average Distance of Trade and Trade Costs 50
2.5 Average Trade Distance of MENA Countries with


Traditional and New Trade Partners 52
2.6 Export Diversification and per Capita Income 56
2.7 Kaplan-Meier Survival Rates 58
2A.1 Predicted Trade Shares in GDP 64
2A.2 Actual versus Predicted Shares of Manufactures and


Manufacture Exports: MENA 69
2C.1 Predicted Applied MFN Protection 73
2D.1 Ad-Valorem Equivalent of NTMs and per Capita Income 77
3.1 Services Value-Added Growth and GDP Growth in MENA 89
3.2 Composition of Exports in MENA and South Asia, 2008 90
3.3 Changes in the Composition of GDP: 1980–83


to 2007–10 91
3.4 Services Share in GDP by Level of Income 92
3.5 Services Share in GDP, MENA versus Rest of the World 93
3.6 Share of Services in Nonmining GDP 95
3.7 Share of Consumption of Services in GDP 97
3.8 Share of Imported Services in GDP 98
3.9 Restrictiveness of Services Trade Policies and Share


of Services in GDP, MENA-GCC, GCC, and
Other Regions 103


4.1 Diversification of Output (Giniout) on GDP per Capita 124
4.2 Diversification of Output (Giniempl) on GDP per Capita 124




Contents ix


4.3 Diversification of Output (Ginivadd) on GDP per Capita 125
4.4 Marginal Effect of GDPpc on Output


Concentration [Giniout] 128
4.5 Marginal Effect of GDPpc on Employment


Concentration [Giniempl] 129
4.6 Marginal Effect of GDPpc on Value-Added


Concentration [Ginivadd] 130
5.1 Fiscal Dynamics in the Middle East on the Eve of the


Arab Spring, 2010 145
5.2 Fiscal Balances in MENA, 2000–10 148
5.3 Real GDP Growth Rates in MENA, 2000–10 149
5.4 Revenue and Expenditures in MENA, 2000–10 150
5.5 GCC Government Fiscal Policy 154
5.6 GCC Sovereign Wealth Funds 155
5.7 Level of Subsidies, 2006–10 Average 158
5.8 Distribution of Subsidies to Poorest 40 percent 159
5.9 Public and Private Capital, 1982–2010 160
5.10 Saudi Arabia’s GDP Decomposition 162
5.11 Number of Exported Products Compared across


Four Countries, 2007 164
5.12 Jordanian Diversification 166
6.1 Predicted Non-Oil Trade Diversion by MENA Countries


Given the Pre-PAFTA Concentration Index Value 189
6.2 Regional Distribution of Export Growth by Sector for


Resource-Rich and Resource-Poor Countries 191


Tables
2.1 Per Capita Income Mobility, 1982–2010 31
2.2 Growth and Its Volatility, 1982–2010 33
2.3 Real Effective Exchange Rate Volatility by Period 36
2.4 Deviations from Estimated Equilibrium Real Exchange 38
2.5 Policy Indicators Affecting Trade in MENA 45
2.6 Correlates of Bilateral Non-Oil Exports 48
2.7 Firm-Level Productivity, MENA and Non-MENA 54
2.8 Correlates of Hazard Rates for 4-Digit Export Flows 59
2A.1 Correlates of Trade Shares in GDP 63
2A.2 Correlates of the Share of Manufactures and


Services in GDP 66
2A.3 Correlates of the Share of Manufactures and Services


in GDP in MENA 67




x Contents


2D.1 Frequency Distribution of the Number of NTMs 75
2D.2 Ad-Valorem Equivalents of NTMs 76
3.1 Share of Rents from Natural Resources in GDP 99
3.2 Correlates of the Share of Services in GDP 100
3.3 Determinants of the Services Share in GDP 101
4.1 Sample Coverage and Number of Observations 119
4.2 Summary Statistics of Measures of Diversification 120
4.3 Correlation between Measures of Diversification 120
4.4 Descriptive Statistics of P(low) 120
4.5 Imbs and Wacziarg’s Results (2003) 121
4.6 Basic Regressions of Concentration on a Quadratic


Function of GDPpc 123
4.7 Regressions of Concentration Including [GDPpc]


3
and [GDPpc]


4 126
4.8 Splitting Samples between Middle East and


North Africa and the Rest of the World 127
4.9 Stages of Diversification and Weak Links versus


Exchange Rate Appreciation 131
4.10 Splitting the Sample after Lower Tails of Productivity 132
4.11 Splitting the Sample after Changes in the Real


Exchange Rate 133
4A.1 Expanding Imbs and Wacziarg’s Samples 135
4A.2 Regressions of Diversification on Income Level for


OPEC Countries 136
4A.3 Regressions of Concentration for Subsamples of


MENA Countries 137
5.1 Selected MENA Economics: Real GDP Projections


and Fiscal Assessment 147
6.1 Trade Creation and Diversion for Each Agreement


Involving MENA Countries, 1990–2009 183
6.2 Decomposition of Intra-PAFTA Trade Creation


and Diversion According to Natural Resources
Endowment, 1990–2009 184


6.3 Decomposition of Intra-PAFTA Trade Creation
and Diversion According to Natural Resources
and Labor Endowment, 1990–2009 186


6.4 Decomposition of Intra-PAFTA Trade Creation
and Diversion 188


6A.1 Agreements Involving MENA Countries as Importer 193
A.1 Comparator Groups 200




xi


The authors are indebted to Alan Gelb, senior fellow at the Center for
Global Development; Tony Venables, professor at Oxford University and
director of the Centre for the Analysis of Resource Rich Countries;
Olivier Cadot, professor at the University of Lausanne and director of
the Institute of Applied Macroeconomics; and Lahcen Achy, of the
Carnegie Endowment for Peace, Beirut, for agreeing to peer review this
work and for their excellent comments and suggestions. We are also
grateful to Caroline Freund, chief economist in the Middle East and
North Africa Region of the World Bank; Jean-Pierre Chauffour, regional
trade coordinator, Middle East and North Africa Region; and Michael
Ross, professor at the University of California, Los Angeles-UCLA, as
well as the participants in the 18th Economic Research Forum
Conference in March 2012 in Cairo for useful comments.


The authors thank Manuela Ferro and Bernard Funck, respectively
director and sector manager of the Poverty Reduction and Economic
Management Department in the Middle East and North Africa Region
of the World Bank, for their useful comments, guidance, and encourage-
ment during the course of preparing this volume.


This book would not have been possible without the financial support
of the Multi-Donor Trade Trust Fund, managed by the World Bank Trade


Acknowledgments




xii Acknowledgments


Department. We thank Bernard Hoekman, director of the Trade
Department, for supporting the funding of this activity. Last but not
least, we would like to thank a large number of colleagues in MENA and
the Trade Department for the discussions we have had around the issues
covered in this volume.




xiii


Ndiamé Diop
Ndiamé Diop is a lead economist at the World Bank. He has worked for
more than six years in the Economic Department of the Middle East
and North Africa Region, as lead economist for Jordan and Lebanon
(2010–12), the Bank’s resident representative in Tunisia (2007–10),
and senior economist (2005–06). Before that, he was senior economist
in the Bank’s Trade Department, undertaking applied research on inter-
national trade and related policy reform issues in developing countries.
Mr. Diop has published in the areas of trade, growth, competitiveness,
and macroeconomics.


Daniela Marotta
Daniela Marotta is a country economist at the World Bank. She has
worked for the past five years in the Economic Department of the
Middle East and North Africa Region on issues related to international
trade and foreign direct investment, firm productivity and innovation,
and poverty and inclusion analysis. Before taking her current position,
she worked for the Latin America and Caribbean Region of the World
Bank, as economic advisor to the British government, as business con-
sultant at Andersen consulting group, and in academia. Dr. Marotta has
a PhD in economics from the University of Pavia in Italy, an MSc from


Contributors




xiv Contributors


University College of London, and an undergraduate degree from
Bocconi University, Milan.


Jaime de Melo
Jaime de Melo is a senior fellow at the FERDI (Fondation pour les Études
et Recherches sur le Développement International) in Clermont-Ferrand,
France. From 1993 to 2012, he was professor at the University of Geneva
and associate at CERDI (Centre d’Etudes et de Recherche sur le
Développement International), University of Auvergne. From 1972 to
1976, he worked at the U.S. Agency for International Development, and
from 1976 to 1980, he taught at Georgetown University. He then held
various positions in the Research Department at the World Bank before
joining the faculty at the University of Geneva in 1993. He studied at the
Johns Hopkins University where he earned a PhD in economics.


Marcelo Olarreaga
Marcelo Olarreaga is a professor of economics at the University of
Geneva and a research fellow at the Centre for Economic Policy Research
(CEPR) in London. Before joining the University of Geneva, he worked
as an economist in the Research Department of the World Bank and in
the Economics Research Division of the World Trade Organization. He
has also been an invited professor at CERDI (France), the Graduate
Institute (Switzerland), INSEAD (France), Institute CLAEH (Uruguay),
SciencePo-Paris (France), Universidad de la República (Uruguay), and the
University of Antwerp (Belgium).


Cristian Ugarte
Cristian Ugarte is currently finishing a PhD in development economics at
the University of Geneva, where he also works as a teaching assistant in
econometrics, development, and trade theory. He has worked for several
years as consultant to the World Bank, the African Development Bank,
the International Labour Organization, and other institutions on issues
related to microfinance, poverty, trade, competitiveness, growth, and
development.


Celine Carrère
Celine Carrère joined the University of Geneva in 2011 as associate pro-
fessor. She is also affiliated with the Centre for Economic Policy Research
(CEPR) in London and the Foundation for International Development
Study and Research (FERDI) in Clermont-Ferrand, France. After receiv-
ing her PhD in economics at CERDI in France (2005), she was an assis-
tant professor of economics at HEC Lausanne, Switzerland (2005–07)




Contributors xv


and then research fellow at National Centre for Scientific Research
(France) from 2007 to 2011. She is currently doing research on develop-
ing countries’ exports, regional integration, preferential market access,
and the impact of infrastructure and transport costs on trade.


Julien Gourdon
Julien Gourdon has been an economist at CEPII (Centre d’Etudes
Prospectives et d’Informations Internationales), Paris, since August 2011.
He holds a PhD in economics from CERDI, France (2007). He was an
economist at the World Bank from 2006 to 2011, working for the Middle
East and North Africa Region and in the International Trade Department.
A specialist in international trade and development economics, his main
topics of interest are trade policies, exports competitiveness, the impact
of international trade on income inequalities, and the impact evaluation
of trade assistance projects.


Ali Zafar
Ali Zafar is currently senior economist in the Africa Region of the World
Bank. He has been a macroeconomist for more than 10 years, with
operational experience in 5 regions of the World Bank, including the
Middle East and East Asia. He has participated in more than 30 missions
to the developing world and provided policy advice to authorities on key
economic issues. His work has focused on macroeconomic policy, public
finance, and competitiveness. He has also been an adviser to the UNDP
in New York, Sudan, and Yemen. He has published extensively on
exchange rate, trade, and macroeconomic issues, and is currently working
with a team to finalize a book on competitive industries around the world
and assess the lessons from Asian labor-intensive manufacturing for the
rest of the developing world. He has an undergraduate degree in econom-
ics from Princeton University and a graduate degree from the University
of Michigan in Ann Arbor.






xvii


Chapter 1


MENA countries set for themselves three interrelated policy shift goals
in the 1990s: a shift from economies dominated by the public sector
to economies led by the private sector; a move from closed economies to
more globally integrated ones; and a transition from oil-dominated to
more diversified economies. This chapter examines the pattern of struc-
tural transformation in MENA and summarizes the role of various factors
examined thoroughly in the rest of the volume.


Chapter 2


Economic performance in MENA has shown significant progress recently,
featuring higher growth rates, less growth volatility, and increased market
shares for its exports than in the past despite competition from fast-
growing countries and exporters such as China and India. MENA’s catching
up is encouraging against the backdrop of a generally disappointing perfor-
mance over the past 50 years, particularly for the resource-rich countries.
Nonetheless, with the exception of Oman, MENA countries have failed to
climb the economic ladder and remain in either the lower-middle- or the
upper-middle-income group. This chapter examines the correlates of this


Chapter Abstracts




xviii Chapter Abstracts


overall disappointing performance. At the macro level, MENA countries
have been unable to maintain depreciated (undervalued) real exchange
rates for long periods, yet such undervaluation has proved important to
offset the market failures and poor institutional environment that severely
hit the dynamic non-resource-intensive traded sectors. In addition, the
volatility of the real effective exchange rate in MENA has been greater than
in comparable groups of countries, contributing to the lack of development
of new activities outside the resource sectors and to short-lived export
spells. Further, despite some progress toward reducing tariffs on industry,
MENA countries have fared poorly in most indicators describing the
domestic microeconomic environment, giving the impression of an envi-
ronment in which trade is not facilitated and of an unfinished reform
agenda. Improved domestic regulatory policies along with improved public
sector governance reflected in better values for key indicators would help
MENA achieve greater integration into the world economy.


Chapter 3


This chapter shows that services sectors in resource-rich MENA countries
have been declining as a share of gross domestic product (GDP) and of
nonmining GDP as per capita incomes increase. This negative relation-
ship between the share of services in GDP and income per capita is
opposite to global patterns and is linked to the large rents generated by
natural resources in these countries. A large number of services sectors
can now be moved offshore or produced by temporary movement of
service providers, implying that countries need to be competitive to
maintain domestic production. Rents from natural resources inflate wages
and nontradable prices, thereby appreciating the real exchange rate and
discouraging domestic production of tradable services. As the chapter
highlights, the negative effect of rents is compounded by the negative
impact of policy and regulatory restrictions on entry, and of business con-
duct on the development of services sectors. These restrictions create
rents captured by “protected incumbents” or increase the real cost of
producing services—in both cases inflating the price of services.
Resource-rich countries need to reduce these restrictions, build strong
human capital, and improve their institutions to create an enabling
environment for diversification in the long run. Meanwhile, they offer
formidable export diversification opportunities to resource-poor
MENA, provided that these countries reduce their own regulatory
restrictions to investments in exporting service industries, improve their




Chapter Abstracts xix


backbone services (such as telecom), and proactively engage resource-
rich countries in reducing barriers to labor mobility within the region.


Chapter 4


This chapter explores the presence of systematic differences between the
patterns of diversification in MENA and the rest of the world. The rela-
tionship between economic diversification and income per capita is non-
monotonic: at early stages of development, countries typically diversify as
income increases and new economic opportunities emerge, but at later
stages the production bundle becomes more concentrated as income rises.
This empirical regularity does not fit the observed pattern of develop-
ment and diversification in MENA countries. At their early stages of
development, production becomes more concentrated as income rises,
and then less concentrated after reaching a certain income-per-capita
threshold. This chapter explores the correlates of these different patterns
of diversification, starting from the role of relative endowments in natural
resources and then investigating the role of Dutch Disease associated
with a strong appreciation of the real exchange rate in contrast with the
role of weak links in the economy. The weak link argument is recent—it
shows that complementarities in production and linkages among sectors
can lead to either multiplier or weak link effects. When the links are
weak, low productivity in one sector can reduce productivity throughout
the economy, depending on the degree of substitutability among inputs.
In a setup with low substitutability, weak links will result in a less diversi-
fied production bundle as downstream sectors are hurt by higher input
prices and factor prices. We test econometrically the relevance of the
Dutch Disease versus weak links in explaining MENA’s peculiar pattern
of diversification.


Chapter 5


This chapter shows that from a historical perspective, fiscal policy has
not contributed significantly to diversification in MENA, because it has
been more oriented toward food and fuel subsidies (consumption)
rather than toward public goods such as infrastructure (investment).
Even at that, fiscal policy has not been well targeted and has been par-
ticularly ineffective at promoting redistribution. Fiscal policy in
resource-rich countries of MENA has also suffered from a lack of
transparency and accountability. The recent oil boom in the Gulf




xx Chapter Abstracts


Cooperation Council (GCC) countries has led to an impressive buildup
of sovereign wealth funds, which have helped mitigate deficits and cush-
ion these countries through crises, but transparency on the governance of
these funds has been limited. Nevertheless, over a longer period, the three
regions in MENA—the GCC oil exporters, the Maghreb countries in the
northwest of Africa, and the Mashreq countries located in the Middle
East—have all improved their overall fiscal management, although they
have all neglected infrastructure investments.


Chapter 6


The benefits of regional integration in the MENA region have been
debated for a long time, mainly in terms of the classic potential trade-
creation effects emanating from the elimination of tariffs and nontariff
barriers among regional partners. In this chapter, the authors emphasize
the different characteristics of the regional partners in terms of their
resource endowments and consider wealth distribution effects within the
region. The MENA region has resource-rich and resource-poor members.
As argued in one recent study, the proximity of resource-rich and
resource-poor countries creates an opportunity, through regional integra-
tion, to even up wealth distribution among these countries. Preferential
trade liberalization is typically more beneficial than unilateral nondis-
criminatory most-favored-nation trade liberalization for the resource-
poor country, because the country gains access to the rents in the
resource-rich country. This would imply that integration between the
resource-rich labor-importing and the resource-poor labor-abundant
countries might be beneficial only for the resource-poor countries in
MENA. The authors test the extent to which economic diversification is
achieved at the expense of trade diversion and, consequently, at the
expense of broader economic efficiency. Results suggest that significant
trade creation is associated with regional integration within MENA, with
no evidence of trade diversion in resource-poor countries. But there is
evidence of trade diversion in both labor-abundant and labor-importing
resource-rich countries. Hence, while further intraregional trade integra-
tion is an important avenue for enhancing diversification of resource-poor
MENA countries, resource-rich countries have no strong incentive for
further preferential regional integration from a purely economic stand-
point, and this may explain their relative reluctance to engage in this type
of scheme.




xxi


ADR average distance ratio
AVE ad valorem equivalent
BOT build-operate-transfer
COMTRADE United Nations Commodity Trade Statistics Database
CPI consumer price index
ECO Economic Cooperation Organization
EU European Union
FTA free trade agreement
GCC Gulf Cooperation Council
GDP gross domestic product
GNI gross national income
H high (income)
HS Harmonized System
ICT information and communication technology
IMF International Monetary Fund
L low (income)
LM lower-middle (income)
LPI Logistics Performance Index
MENA Middle East and North Africa
NTB nontariff barriers


Abbreviations




xxii Abbreviations


NTM nontariff measures
OECD Organisation for Economic Co-operation and


Development
OLS ordinary least squares
OPEC Organization of the Petroleum Exporting Countries
OTRI Overall Trade Restrictiveness Index
PAFTA Pan-Arab Free Trade Agreement/Area
PPP purchasing power parity
QIZ qualified industrial zones
REER real effective exchange rate
RER real exchange rate
RPLA resource-poor labor-abundant
RRLI resource-rich labor-importing
SDR special drawing rights
SWFs sovereign wealth funds
TFP total factor productivity
TRI Trade Restrictiveness Index
TTRI Tariff-only Trade Restrictiveness Index
UAE United Arab Emirates
UM upper-middle (income)
UNIDO United Nations Industrial Development Organization
WDI World Development Indicators
WTO World Trade Organization


Note: U.S. dollars are used unless otherwise indicated.




1


C H A P T E R 1


An Overview of Diversification in
MENA: Rationale, Stylized Facts,
and Policy Issues


Ndiamé Diop and Daniela Marotta


In the early 1990s, most Middle East and North Africa (MENA) countries
acknowledged the failure of the old state-led import-substitution model of
development and set for themselves three interrelated policy shifts to
sustainably boost growth and create jobs outside of the bloated public sec-
tor (Nabli et al. 2007). These shifts were from a public-sector-dominated
economy to a private-sector-led economy; from closed economies to
globally integrated ones, and from oil-dominated economies to more
diversified ones.


This agenda has remained unfinished. Regarding the first shift, the
private sector does have a larger role in MENA today than before.
However, as shown by the World Bank report “From Privileges to
Competition,” it is far from being a strong engine of growth (World Bank
2009). Governments have, by and large, failed to establish rule-based
modes of interaction with the private sector. Private-sector reforms were
initiated, but sclerotic state institutions have continued discretionary
interventions in businesses, resulting in an uneven playing field for the
private sector. Reforms took place in a weak institutional context of
nepotism in most cases, resulting in limited domestic competition and




2 Diop and Marotta


maintenance of market entry barriers, thus undermining innovation and
small and medium enterprise growth. MENA’s situation illustrates the
view that economic institutions evolve very slowly and in accordance
with the social order and the balance of power in economic and political
interests (North, Wallis, and Weingast 2009). In this view, the ongoing
Arab Spring could present an opportunity to foster a more genuine insti-
tutional change, if citizens and other nonstate institutions are truly
empowered.


Turning next to global integration, recent studies unambiguously point
to an unfinished agenda both at the macro level and with regard to regu-
latory reforms. In a recent volume, Lopez-Calix, Walkenhorst, and Diop
(2010) show that “while MENA has increased its pace of trade integra-
tion reforms, compared to fast-growing East Asia and ECA [Europe and
Central Asia], it has not fully exploited the benefits of participating in
global production networks, increasing global trade in services, the rise of
China and India, and regional integration.” At the micro level, Freund and
Bolaky (2008) find that the 12 MENA countries included in their large
sample of 126 countries have among the most regulated economies. On
the reasons for this slow pace of integration reforms, most studies strongly
suggest that oil rents, remittances, and foreign aid act together to lessen
the pressure for reform (Diwan and Squire 1993; Hoekman 1995; Shafik
1995; Nabli 2004; WEF and OECD 2005; and Havrylyshyn 2010). In the
same vein, drawing on the Doing Business data from a large sample of
133 countries, Amin and Djankov (2009) find that the proclivity to
undertake microreforms that reduce unjustified regulatory restrictions is
much lower in countries whose exports are concentrated in abundant
natural resources.


This volume aims to complement the picture by focusing on the third
goal, diversification away from oil, and its relation to MENA’s pace of
structural transformation. Has MENA managed to diversify away from
natural-resource-based sectors toward manufacturing and services over the
past 30 years? More specifically, what is the role of natural resource abun-
dance and macroeconomic policies in the region’s economic diversification
patterns? What is the impact of natural resource rents and real exchange
rates on diversification toward manufacturing and tradable services? Has
fiscal policy been supportive of economic diversification? Beyond Dutch
Disease,1 do weak links (input sectors with low productivity) play a role
in limiting diversification? To what extent can resource-poor but more
diversified MENA countries benefit from complementarities with the
resource-rich MENA countries through trade?




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 3


The Facts: MENA Economies’ Low Level of Diversification


Typically, as countries become richer, the share of agriculture declines,
giving way to a rise in the share of manufacturing and services. This often
happens because of technological advances that increase agricultural pro-
ductivity and drive resources out of agriculture toward manufacturing
and services (Baumol 1967; Chenery and Syrquin 1975). At the same
time, Engel’s Law stipulates that as household income increases, the per-
centage of income spent on food decreases while the proportion spent on
other goods and services increases.2 Thus supply and demand forces sug-
gest that as income rises the share of agriculture in overall GDP (gross
domestic product) should decline while nonagricultural GDP increases.


MENA’s production structures have, however, undergone little diver-
sification over the past 30 years. Contrary to global trends, the relative
size of the manufacturing sector hardly increased at all in MENA coun-
tries while the relative size of the services sector actually shrank between
1980 and 2010 (figure 1.1). Agriculture contracted, as it did in other
regions, but did not give way to vibrant and innovative manufacturing and
services sectors. While MENA’s difficulty in expanding manufacturing is
well documented, the contraction of services as a share of GDP is par-
ticularly striking.


The pattern of change in the services sectors share in GDP is sharply
differentiated within MENA (figure 1.2). This share has increased


Source: World Development Indicators, World Bank.


Figure 1.1 Changes in the Composition of GDP, 1980–83 to 2007–10


–80 –60 –40 –20 0 20 40 60 80


Sub-Saharan Africa


Middle East and North Africa


Latin America and
the Caribbean


South Asiap
er


ce
n


t


Europe and Central Asia


East Asia and Pacific


change in the share of services


change in the share of industry
(including mining, manufacturing)


change in the share of agriculture




4 Diop and Marotta


modestly in resource-poor countries (Jordan, Lebanon, Morocco, and
Tunisia), but it has decreased in those countries that are resource rich
(Algeria, Bahrain, Islamic Republic of Iran, Iraq, Kuwait, Oman, Qatar,
Saudi Arabia, and the Republic of Yemen). (See box 1.1 for a description
of the typology used in this volume.) There is a clear positive correlation
between services shares in GDP and per capita GDP for the resource-
poor group, implying that resource-poor countries conform to theoretical
expectations. In contrast, for the resource-rich country group, the share of
services in GDP decreases with per capita GDP. In other words, the
observed decline of services in GDP for MENA is driven by the resource-
rich countries. The United Arab Emirates constitute a notable exception,
because it has experienced a dramatic increase in the share of services in
the economy (+32 percent) and a moderate shift toward manufacturing
over the past 30 years.


The declining share of services in GDP in resource-rich countries is
not an artifact reflecting the large and growing share of mining (includ-
ing oil and gas) in overall GDP. For the countries for which data are


Figure 1.2 Services Share in GDP by Level of Income


Source: Authors’ calculations.
Note: The figure is showing averages over five-year periods. RPLA = resource-poor labor abundant;
RRLA = resource-rich labor abundant; RRLI = resource-rich labor-importing; Obs = observations; Lowess = locally
weighted scatterplot smoothing = fitting trend. Lowess (band width = 0.8) excludes Iraq, Libya, and Qatar.


100


80


60


40


s
er


vi
ce


s
as


a
s


h
ar


e
o


f G
D


P
(%


)


20


6 7 8


Ln(GDP per capita)


9 10 11


LBN


DJI
DJI


DJI
DJI


TUN
TUN


TUN


TUN
LBNLBN


LBNJOB
JOB


JOB


JOB
JOBJOB


YEM


YEM YEM


EGY
EGY


EGYEGY


MAR MAR


MARMAR


IRQ IRQ


LBY
LBYDZA


DZADZA
DZA


SYR SYR
SYR


SYR
SYR DZA DZA


OMN
IRN


IRN
IRN


IRNIRN


IRNIRNIRN


SAU


SAU


SAUSAU
SAUOMNOMN


OMN OMN
SAU


BHR
BHR


BHR


KWT
KWT


KWT


KWT


KWT


BHR


ARE ARE


ARE
ARE


AREARE


DJI


RPLA Obs.


RPLA Lowess


RRLA Obs.


RRLA Lowess


RRLI Obs.


RRLI Lowess




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 5


Box 1.1


How Is MENA Performance Captured and Benchmarked in
This Volume?


MENA countries differ along several dimensions: resource endowments, internal


market size, policies, and so forth. These differences caution against generalized


observations and justify looking for suitable countries against which to compare


individual countries’ performance. Throughout this volume, we capture country


performance through three classifications.


First, countries are classified according to the three-grouping classifica-


tion: resource-poor labor-abundant (RPLA): Arab Republic of Egypt, Jordan,


Lebanon, Morocco, and Tunisia; resource-rich labor-abundant (RRLA): Algeria,


Islamic Republic of Iran, Iraq, Libya, Syrian Arab Republic, and Republic of


Yemen; and resource-rich labor-importing (RRLI): Bahrain, Kuwait, Oman, Qatar,


Saudi Arabia, and United Arab Emirates.a This last group corresponds to the


members of the Gulf Cooperation Council (GCC) countries.b The GCC holds


about 40 percent of global oil reserves. Its total GDP is about $1 trillion. Oil


accounts for about half of the GCC’s total GDP, 80 percent of government


revenues, and 75 percent of total exports. Whenever relevant, we drop GCC


countries (6 countries) from the analysis and refer to the RRLA countries as


resource-rich countries (6 countries) and compare them with the RPLA or what


we refer to as resource-poor countries (5 countries).


This classification captures only some of the diversity in the region, however.


For example, in the GCC grouping, two of the countries (Bahrain and Qatar) have


a population of around 1 million, two (Kuwait and Oman) have a population of


3 to 4 million, one (United Arab Emirates) has a population of around 7 million,


and one (Saudi Arabia) has a population of 25 million. Hence, we develop four


additional country classifications, which we also use to compare MENA countries


with other developing countries. First, to account for the importance of market


size and the exploitation of economies of scale, we create a group of LARGE devel-


oping countries with a population over 20 million (48 in the world, 6 of which are


in MENA).


Second, we build an OIL group that includes all the major oil exporters in the


world (18) and the region (10); these are countries whose oil exports account for


80 percent or more of total merchandise exports. Third, although they are not


included in the OIL group, Morocco, Syria, and Tunisia have natural resources and


qualify as “point-source natural resource” countries in the classification proposed


(continued next page)




6 Diop and Marotta


by Hausmann, Pritchett, and Rodrik (2005).c This classification distinguishes natu-


ral-resource-rich countries according to whether these resources are “diffuse”


(such as in the United States) and do not give rise to rents or are “point source”


(such as phosphates in Morocco) that give rise to rents. The resulting group, POINT


source (43 countries, 8 MENA countries), is large and includes half of the MENA


countries, including Egypt.


Fourth, to analyze “income mobility” (that is, changes in income category through


sustained growth), we include MENA countries in the World Bank’s four-group clas-


sification: low-income (L), lower-middle-income (LM), upper-middle-income (UM),


and high-income (H) categories in an extended sample that also includes Organisa-


tion for Economic Co-operation and Development (OECD) countries (but excludes


the former socialist countries of Europe and Central Asia). The list of countries in


each grouping is given in appendix table A1 at the end of this book.


a. This three-group classification was introduced in World Bank (2004, chapter 2).
b. The GCC was founded in 1981. Regional integration picked up around 2000, with a quasi-common
market status achieved in 2008.
c. The objective of this classification is to capture the idea that natural riches produce institutional weak-
nesses (the “voracity effect” associated with the attempt at rent-capture by different social groups; see
Tornell and Lane 1999). Point source natural resources such as oil, minerals, and plantation crops are
extracted from a narrow economic base while “diffuse” natural resources are extracted from a large base.
While this voracity effect extends to all sources of rents (natural monopolies, foreign aid, nontariff barri-
ers, financial elites), over the long haul, it makes sense to include a classification of countries along this
dimension.


Box 1.1 (continued)


available, the observation of a shrinking share of services in nonmining
GDP over time is confirmed. In contrast, the share of services in non-
mining GDP in resource-poor countries is either increasing or stagnant.
Figure 1.3 illustrates this contrast, with the share of services in nonmin-
ing GDP declining in Saudi Arabia and Kuwait but increasing in Tunisia
and Jordan.


In oil-rich MENA, the mining sector is large and has gotten bigger in
relative size over time, suggesting that diversification away from oil is still
an elusive goal. In 2010, mining accounted for 37 percent of GDP (up
from 28 percent in 1990), 85 percent of merchandise exports, and
between 65 percent and 95 percent of government revenues (90–95
percent in the six GCC countries and 60–80 percent in non-GCC
resource-rich countries). The size of the mining sector in resource-rich
economies of the region is largest in the GCC, where it represents almost
half of GDP (48 percent). This high concentration of production is




Figure 1.3 Share of Services in Nonmining GDP


Source: World Development Indicators.
Note: Data for Kuwait are not available afer 2000.


75


70


65


60


55


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


2 0
0 2


2 0
0 4


2 0
0 6


2 0
1 0


2 0
0 8


a. Saudi Arabia


60


50


40


10


0


30


20


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


2 0
0 2


2 0
0 4


2 0
0 6


2 0
1 0


2 0
0 8


c. Tunisia


p


e


r


c


e


n


t


p


e


r


c


e


n


t


p


e


r


c


e


n


t


p


e


r


c


e


n


t


66


62


64


60


54


52


58


56


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


2 0
0 2


2 0
0 4


2 0
0 6


2 0
0 8


d. Jordan


100


80


60


40


20


0


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


b. Kuwait


7




8 Diop and Marotta


reflected in the composition and dynamics of exports. Figure 1.4 shows
that export diversification across MENA countries in 1998–2008
occurred through exports of existing processed and primary industrial
products (oil-related) to existing markets. More specifically, export
growth in resource-rich MENA was driven by exports of existing pro-
cessed and industrial goods (mainly crude and refined oil) to existing and
new markets mainly in Asia, the European Union (EU), and within the
GCC. Product diversification (export of new products) occurred exclu-
sively within the industrial sector. For resource-poor MENA, export
growth was driven by existing primary and processed industrial goods as
well as by consumer goods to existing markets mostly in Europe. The


Figure 1.4 Drivers of Export Growth (Excluding Oil Products), 1998–2008


Resource-poor (net oil importers)


0


10


–10


20


30


40


50


sh
ar


e
o


f e
xp


o
rt


g
ro


w
th


(%
)


sh
ar


e
o


f e
xp


o
rt


g
ro


w
th


(%
)


sh
ar


e
o


f e
xp


o
rt


g
ro


w
th


(%
)


sh
ar


e
o


f e
xp


o
rt


g
ro


w
th


(%
)


–10
0


10
20
30
40
50
60
70
80
90


Resource-rich (non-GCC countries)


–10


0


10


20


30


40


50


–10
0


10
20
30
40
50
60
70
80
90


a. export markets b. products exported


c. export markets d. products exported


Af
ric


a
As


ia


La
tin


A
m


er
ica


an
d


Ca
rib


be
an


E
ur


op
e a


nd
C


en
tra


l A
sia


Eu
ro


pe
an


U
ni


on


Un
ite


d
St


at
es




oi
l im


po
rte


r


ot
he


r o
il e


xp
or


te
r


GC
C


re
st


of
w


or
ld


Af
ric


a
As


ia


La
tin


A
m


er
ica


an
d


Ca
rib


be
an


Eu
ro


pe
an


d
Ce


nt
ra


l A
sia


Eu
ro


pe
an


U
ni


on


Un
ite


d
St


at
es




oi
l im


po
rte


r


ot
he


r o
il e


xp
or


te
r


GC
C


re
st


of
w


or
ld


fo
od


p
rim


ar
y


fo
od


p
ro


ce
ss


ed


in
du


str
ial


p
rim


ar
y


in
du


str
ial


p
ro


ce
ss


ed


pa
rts


an
d


co
m


po
ne


nt
s


ca
pi


ta
l g


oo
ds


co
ns


um
er


g
oo


ds


fo
od


p
rim


ar
y


fo
od


p
ro


ce
ss


ed


in
du


str
ial


p
rim


ar
y


in
du


str
ial


p
ro


ce
ss


ed


pa
rts


an
d


co
m


po
ne


nt
s


ca
pi


ta
l g


oo
ds


co
ns


um
er


g
oo


ds


(continued next page)




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 9


Source: Staff calculations based on Comtrade.


Figure 1.4 (continued)


sh
ar


e
o


f e
xp


o
rt


g
ro


w
th


(%
)


sh
ar


e
o


f e
xp


o
rt


g
ro


w
th


(%
)


Resource-rich (GCC countries)


e. export markets f. products exported


–10


0


10


20


30


40


50


–10
0


10
20
30
40
50
60
70
80
90


exports of new products to new markets


exports of new products to exisiting markets


exports of existing products to new markets


exports of existing products to existing markets


fo
od


p
rim


ar
y


fo
od


p
ro


ce
ss


ed


in
du


str
ial


p
rim


ar
y


in
du


str
ial


p
ro


ce
ss


ed


pa
rts


an
d


co
m


po
ne


nt
s


ca
pi


ta
l g


oo
ds


co
ns


um
er


go
od


s


Af
ric


a
As


ia


La
tin


A
m


er
ica


an
d


Ca
rib


be
an


E
ur


op
e a


nd
C


en
tra


l A
sia


Eu
ro


pe
an


U
ni


on


Un
ite


d
St


at
es




oi
l im


po
rte


r


ot
he


r o
il e


xp
or


te
r


GC
C


re
st


of
w


or
ld


extent of growth in exports of new products was limited both in sectors
and in markets (mostly to MENA oil exporters and the EU).


Limited Diversification, Natural Resource Rents,
and Growth Volatility


A mirror image of the overdominance of the oil and gas sector in MENA
is the very large share of natural resource rents in GDP compared with
other regions. In 2010, rents from natural resources reached 24 percent
of GDP in MENA, against about 14 percent in Sub-Saharan Africa and
Eastern Europe and 5–7 percent in East Asia, South Asia, and Latin
America and the Caribbean (figure 1.5).3


MENA’s natural resource dependence also induces large, oil-price-driven
fluctuations of production and growth.4 Indeed, MENA’s growth exhibits
the highest level of volatility among the regions of the world (figure 1.6).
The volatility of growth in the six members of the GCC is twice as high as
the volatility in the other resource-rich MENA countries (Algeria, Islamic
Republic of Iran, Iraq, Libya, Syrian Arab Republic, and Republic of
Yemen) and four times as high as in the resource-poor countries of the
region.5 Compared to the rest of the world, the volatility in growth in the




10 Diop and Marotta


GCC and other resource-rich MENA countries is higher than that observed
across all other World Bank income classification groups (except the low-
income group) in 1982–2010. Likewise, MENA oil exporters’ volatility is
higher than that of the world’s other major oil exporters.


Output volatility is bad for long-term growth and diversification, in
part because reversals in growth trends are sharper and more frequent.
Volatility also is bad for investment and capital accumulation and may
harm productivity growth. In a recent paper, Furth (2010) finds that dif-
ferences in terms of trade volatility account for 25 percent of the cross-
country variations in growth from 1980 to 2007. Using a broad sample of
countries from 1960 to 2000, Aghion et al. (2009) find negative growth
effects of terms of trade volatility, measured in five-year periods, under
fixed exchange rate regimes. Ramey and Ramey (1995) link higher out-
put growth rate volatility to lower average output growth. Consistently,
MENA trails significantly behind East Asia and South Asia in its long-
term per capita growth. Between 1980 and 2010, MENA grew by a mere
1.3 percent a year in per capita terms, compared to 4 percent in South
Asia and 6.9 percent in East Asia.


Figure 1.5 Natural Resource Rents in 2010, by Region


Source: World Development Indicators.


30


25


20


15


10p
er


ce
n


t
o


f G
D


P


5


0


M
EN


A
SS


A


Eu
ro


pe
an


d


Ce
nt


ra
l A


sia


La
tin


A
m


er
ica


an
d


Ca
rib


be
an E


AP


So
ut


h
As


ia




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 11


0.00


1.00


2.00


3.00


4.00


5.00


6.00


7.00


8.00


9.00


GDP growth standard deviation


M
EN


A


Su
b-


Sa
ha


ra
n


Af
ric


a


re
so


ur
ce


-p
oo


r M
EN


A


re
so


ur
ce


-ri
ch


M
EN


A
GC


C


La
tin


A
m


er
ica


an
d


Ca
rib


be
anE


AP


So
ut


h
As


ia


Figure 1.6 Growth Volatility, by Region


Source: World Development Indicators.
Note: Growth volatility is measured as the standard deviation of GDP per capita growth in 1970–2008.


The link with diversification is straightforward. As shown by McMillan
and Rodrik (2011), growth requires both new activities and ongoing
structural changes. MENA’s slow growth implies that the expansion of
new activities or structural change, or both, is limited. Asia’s high growth
is both a cause and a consequence of its structural shift from agriculture
(where productivity is low) to services and manufacturing (where pro-
ductivity is higher).


Why Is Greater Diversification Desirable in MENA?


Many reasons motivate countries’ drive for diversification. One key
objective is job creation. For countries abundantly endowed with natural
resources, diversification away from resource-based sectors is crucial to
widen the scope for job creation. In natural-resource-rich countries of
MENA, arable land and water are scarce, and agriculture is expected to
remain a minor source of labor absorption. The public sector, the largest
employer in the region (29 percent of total employment), has reached a
saturation point in almost every MENA country. Going forward, rapid job




12 Diop and Marotta


creation will have to come from the private sector. Thus for resource-rich
MENA countries, finding ways to develop manufacturing and services
sectors is crucial for widening the scope of job creation.


Another rationale for greater diversification is to reduce macroeco-
nomic volatility. Indeed, as noted, the concentration of activities in
resource-rich MENA is associated with volatility; so the natural policy
response has been to push for export diversification. Empirically, Di
Giovanni and Levchenko (2008) and Loyaza et al. (2008) show for
instance that export concentration is associated with greater volatility of
the real exchange rate, which in turn is associated with greater volatility
in GDP growth. A higher concentration of activities and volatility of the
real exchange rate can also be a channel for a resource curse (Lederman
and Maloney 2008; Hausmann and Rigobon 2002). The latter can under-
mine long-term growth through various channels: if large rents are gener-
ated by few activities, the ensuing “easy life” can lead to lower investment
in human capital, contributing to less learning and innovation (Gylafson
2001); macroeconomic volatility (Hausmann and Rigobon 2002); institu-
tional weaknesses as groups attempt to capture rents (Mehlum, Moene,
and Torvik 2006); corruption in nondemocratic contexts (Bhattacharya
and Hodler 2010); and possibly conflicts (Collier and Hoeffler 2004).6
Finally, as discussed later, if real exchange rates become overvalued, they
undermine tradable manufacturing and services.


For natural-resource-poor countries where economic activities are less
concentrated, diversification is often seen as an important ingredient in
sustaining long-term growth. Strong empirical evidence shows that diver-
sified economies perform better over the long term (Lederman and
Maloney 2007). Some authors suggest that countries can increase their
growth rate through externalities associated with diversifying into prod-
ucts where learning by doing is large (Matsuyama 1992) or into “rich-
country products,” that is, more sophisticated products (Hausmann,
Hwang, and Rodrik 2007). Rodrik (2011a, 2011b) argues that manufac-
turing industries generally produce goods that can be rapidly integrated
into global production networks, facilitating knowledge transfers and
adoption. The “monkey-tree” argument put forth by Hidalgo et al. (2007)
is similar: some products, such as electronics or mechanics, tend to be
exported along with a large range of different products; in contrast, other
commodities, such as oil, tend to be exported alone. This is because the
skills and assets used to produce many manufacturing products can be
much more easily deployed in a large range of other manufactures than
those used to extract oil, for instance.




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 13


Thus diversifying into manufacturing may open up more possibilities
for boosting exports through the “extensive margin,” that is, exports of
new products as opposed to exporting the same products more inten-
sively or through “intensive margin.” In turn, this diversification is associ-
ated with greater economies of scale and opportunities to reap the
benefits of global integration thereby boosting long-term growth.


This volume intends to shed light on the causes and consequences of
limited diversification in MENA (and thus growth) and to identify poten-
tial policy remedies. Much of the recent literature on the determinants of
MENA’s economic performance has focused on weaknesses in the areas
of private sector development and trade integration. Although the limited
dynamism of the private sector and integration to the global economy are
important, they are not the whole story. Natural resource management
and macroeconomic policies also play a decisive role in the economy
through their impact on the incentive framework and the overall environ-
ment for private sector activity. In examining diversification, the volume
does not focus on diversification within the resource sector (such as mov-
ing from oil extraction to natural gas and petrochemical) but, rather, on
diversification toward manufacturing and services, where the scope for
creating jobs is the highest in most countries. An explicit attempt to cap-
ture MENA’s diversity in resource abundance and market size is also
made throughout this volume.


The Role of Rents and Real Exchange Rates


At the macrolevel, growth and diversification in most MENA countries
were impaired by frequent overvaluation of the real exchange rate (RER).
The importance of the real exchange rate—the relative price of tradables
to nontradables (RER = PT/PN)—in the growth process cannot be underes-
timated. It operates directly by increasing the relative profitability of trad-
ables, which in turn is associated with higher growth (Prasad, Rajan, and
Subramanian 2007; Rodrik 2009). As Rodrik argues, it also operates indi-
rectly by helping compensate for market failures and poor governance (in
rule of law, property rights, or contract enforcement), which undermine the
competitiveness of tradable sectors in developing countries. A key example
is the penalty arising from weak institutions that result in lower appropri-
ability of returns to investment in tradables. Investment in tradable sectors
depends more on these governance and institutional factors. An increase in
the relative price of tradables therefore boosts the competitiveness of the
tradable sector at the margin, contributing to higher growth.




14 Diop and Marotta


MENA countries have experienced a sustained undervaluation of their
real exchange rates, as shown by an event analysis, discussed in chapter 2,
that identifies the systematic events preceding growth accelerations. This
approach is particularly useful for developing countries, where growth
patterns are much less stable than those portrayed in the standard growth
models.7 In an event analysis study, Hausmann, Pritchett, and Rodrik
(2005) analyzed 83 growth episodes over the period 1957–1992, using
the Penn World Tables.8 Of the 83 episodes, 8 were from the MENA
region (years in parenthesis): Algeria (1975), Egypt (1976), Jordan
(1973), Morocco (1958), Tunisia (1968), and Syria (1969, 1974, and
1989). Hausmann, Pritchett, and Rodrik found that compared with the
seven years before the growth episode, the acceleration period was cor-
related with increases in investment and trade shares in GDP and a sharp
depreciation of the real exchange rate. Investment and trade (imports and
exports) shares of GDP were close to 15 percent higher before the accel-
eration period, and contributed up to one-fifth of the growth accelera-
tion. Rodrik (2009, figure 10) shows that around the event year when
acceleration starts, the RER is undervalued by around 20 percent, and
that undervaluation lasts throughout most of the decade following the
start of the growth acceleration. The event analysis literature thus points
to the importance of trade for growth and of the RER for the growth of
trade, particularly of manufactures.


Between 1980 and 2010, the real exchange rates in MENA were
overvalued frequently in a large number of countries, and there is evi-
dence that this overvaluation has hurt MENA’s competitiveness in
manufacturing. In Morocco, Oman, Saudi Arabia, and Syria, the RER
was overvalued most of the time during the period of analysis.9
Overvaluation was much more widespread during the first periods, with
the number of countries with overvalued RER peaking at 13 of 17 dur-
ing the 1980–85 period and dropping to 5 of 17 in the 2000–05 period.
In any case, since the early 1970s, no MENA country experienced a
period of 10 consecutive years of undervaluation. MENA countries do
not exhibit the kind of undervaluation (or lack of overvaluation) identi-
fied with extended past growth episodes found to be critical in sustain-
ing growth acceleration around the world. Importantly, strong evidence
shows the presence of a Dutch Disease phenomenon that undermines
the competitiveness of the manufacturing sectors in MENA’s resource-
rich economies and even a few resource-poor ones (such as Morocco), as
discussed in chapter 2 and in Havrylyshyn (2010) and Lopez-Calix,
Walkenhorst, and Diop (2010).




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 15


Natural resource rents and RER overvaluation have undermined
MENA’s services sectors as well. The analysis in chapter 3 suggests that
the declining share of services in the nonmining GDP of resource-rich
countries is linked to the large rents generated by natural resources in
those countries. At first sight, this finding appears paradoxical. Indeed,
that domestic service sectors can be developed in resource-rich countries
has been taken as granted for a long time. This belief was underpinned
by at least two combined theoretical considerations. First, Engel’s Law
effects in consumption imply that demand for services tends to increase
with income because of higher income elasticity of demand for services
relative to agricultural products (Chenery and Syrquin 1975; Chenery,
Robinson, and Syrquin 1986). At the same time, services sectors—
implicitly assumed to be largely nontradable in earlier Dutch Disease
models—would be positively affected by an appreciation of the real
exchange rate subsequent to a natural resource boom (Corden and
Neary 1982; Corden 1984).


While Engel’s consumption effects do operate in resource-rich MENA
(see chapter 3), the largely nontradable status of services implicit in the
earlier Dutch Disease literature is no longer valid. A large number of
services sectors have now become “off-shorable” or can be produced by
temporary movement of service providers, implying that countries need
to be competitive to maintain domestic production. Rents from natural
resources tend to inflate wages and prices of nontradables in resource-rich
countries, thereby raising the real exchange rate and discouraging domes-
tic production of tradable goods and services. This explains why resource-
rich MENA countries have become large importers of tradable services
and why only domestic production of nontradable services (such as real
estate, retail trade, hotels, and restaurants) has really developed.


Unfortunately, microeconomic regulations on business have tended to
compound the problem, rather than compensate for it. Restrictions on
business entry, licensing, and business conduct are indeed significant and
correlate negatively with the share of services in GDP. The MENA region,
in particular the resource-rich countries, stands out for heavy and discre-
tionary restrictions on services sectors compared with the rest of the
world. These restrictions either create rents within the services sector that
are captured by “protected incumbents” or increase the real cost of pro-
ducing services—in both cases inflating the price of services and further
reducing competitiveness of tradable services sectors.


It is therefore recommended that resource-rich economies strive to
reduce production costs and to offset the negative effect of rents on




16 Diop and Marotta


production in the nonresource tradable sectors. This can be achieved by
reducing regulatory restrictions on entry and competition in these sectors.
Experience from resource-rich countries around the world also shows the
importance of investing in human capital and strengthening institutions
(see Gelb 2011 for a summary). Finland, the Republic of Korea, and
Norway are examples of countries that have invested to build a high-
quality human capital base and have successfully diversified into high-
tech manufacturing and services. Similarly, strong evidence shows that
institutions matter for diversification. Gelb (2011, 67) argues that manu-
facturing sectors are “heavily dependent on strong contract enforcement,
a rule of law and generally strong business environment.” These argu-
ments equally apply to services, if not more strongly so. Institutions that
prevent or reduce rent seeking are also important, as the example of
Botswana shows (Acemoglu, Johnson, and Robinson 2005).


If the increased tradability of services makes it challenging for resource-
rich MENA to maintain domestic production of services, it offers formi-
dable opportunities to resource-poor countries of the region. Indeed,
given their cultural proximity and common language, these countries are
well placed to capture a share of the large and growing market of tradable
services in resource-rich MENA. To capture these opportunities, however,
resource-poor countries will need to undertake autonomous reforms to
improve their competitiveness and work with resource-rich countries to
reduce barriers to labor mobility within the region. More specifically, they
will need to reduce their own restrictions to entry and competition in
professional services, improve their backbone services (such as telecom
and transport), and proactively engage resource-rich countries in reduc-
ing barriers to trade and mobility through specific bilateral agreements.


The Role of Weak Links in Output Concentration


An influential paper by Imbs and Wacziarg (2003) suggests a U-shaped
relationship between economic development and economic concentra-
tion. At early stages of development, economic concentration falls as
income per capita rises, but starts increasing once income per capita
reaches a certain threshold (around $10,000, according to Imbs and
Wacziarg). This U-shaped relationship is confirmed by recent evidence by
Carrere, Strauss-Kahn, and Cadot (2009) for export diversification.


MENA contradicts this empirical regularity. Chapter 4 empirically
confirms, for MENA, an inverted U-shaped relationship between income
per capita and concentration: at early stages of development, economic




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 17


concentration increases with income per capita and only starts falling
with income per capita at relatively high levels of economic develop-
ment. More specifically, MENA countries start to diversify only after
GDP per capita reaches $17,000 to $22,000. Since most MENA coun-
tries are below this income threshold, concentration of production is the
most common pattern observed in the region—in contrast with what is
observed, on average, in the rest of the world.


To explain these differences in the development process, two alterna-
tives have been tested. MENA is a resource-rich region and subject to
Dutch Disease–type phenomena (à la Corden and Neary 1982). It is also
a region where some sectors have notoriously low levels of productivity,
and these weak links (Jones 2011) can lead not only to lower levels of
growth but also to a higher concentration of production. It was found
that weak links contribute to a more concentrated production bundle
than the Dutch Disease does. Moreover, after controlling for these two
variables, the differences in development patterns between MENA and
the rest of the world become smaller.


The weak link argument is recent. Jones (2011) extends Hirschman’s
“linkage” concept10 and shows that complementarities in production and
linkages between sectors can lead to either multiplier or weak link effects.
When the links are weak, low productivity in one sector can reduce pro-
ductivity throughout the economy depending on the degree of substitut-
ability among inputs. In situations with low substitutability, weak links
will result in a less diversified production bundle as downstream sectors
are hurt by higher input prices and factor prices.


This result has some interesting policy implications, at least in terms of
the timing of industrial policy reforms. Policies aimed at diversifying the
production process should first try to address the region’s weak links.
Otherwise resources may be wasted in trying to diversify into sectors that
are not economically viable. Although more research is needed in this
area, the findings in this volume suggest that if governments first address
the existing weak links in their economy, diversification may naturally
follow. If addressing weak links may sometimes seem like a daunting task
requiring large infrastructure investments with a long-term objective, it is
important to note that one characteristic of weak links is that they are
nontraded goods. If there is an easily imported substitute, then the low
productivity of the domestic input sector is no longer a drag on growth.
Thus, when restrictive trade policies are the ones limiting the tradability
of input sectors, liberalization may be sufficient to address those weak
links. Liberalization of input sectors for easier access to imported inputs




18 Diop and Marotta


or greater efficiency of domestic inputs is important for addressing weak
links and encouraging diversification.


Fiscal Policy and Output Concentration


Beyond rents, Dutch Disease, and weak links, has fiscal policy played a role
in the poor record of diversification of MENA? This question is not trivial,
since fiscal policy can affect diversification through several channels. For
instance, today’s investments in education and core infrastructure are cru-
cial for tomorrow’s private sector capacity and return on investment.
Consistently, fiscal management and the composition of public expendi-
tures matter. If public finance is tied up by high subsidies and short-term
public consumption, fiscal space for investing in human capital and infra-
structure may be limited. At the same time, large expenditures on subsi-
dies, in particular on energy, tend to distort investment incentives in favor
of energy- and capital-intensive sectors, at the expense of labor-intensive
industries. Finally, fiscal policy can adversely affect diversification if it
crowds out private investment, or if private investment is discouraged
when fiscal policy generates or is unable to manage volatility.


Taking a historical view, fiscal policy in the MENA region has not
contributed significantly to diversification, because it has been more ori-
ented toward food and fuel subsidies (consumption) than toward public
goods such as infrastructure (investment), which has historically been
neglected in most MENA economies. Much of government expenditure
in MENA has historically gone to fuel and food subsidies that—while
keeping prices low—have had little impact on the poor because of inef-
fective targeting mechanisms. In Saudi Arabia and the United Arab
Emirates, subsidies represent 8 percent of GDP. Subsidies are generally
not well targeted, making them a costly way to protect the poor. For
instance, in Jordan, where the top two deciles capture 40 percent of the
subsidies for food and fuel, the government spends 5 dinars to channel 1
dinar of subsidy to the poor (Coady, El Said, and Flamini 2011).


Although higher public investment in infrastructure and education
would theoretically be good for diversification, existing literature finds a
weak and short-lived relationship between public spending and private
investment in MENA. For instance, Agenor, Nabli, and Yousef (2005) find
that public infrastructure expenditure has had a small and short-lived
impact on private investment in Egypt, Jordan, and Tunisia. Chapter 5
shows that gross fixed capital formation in the private sector seems more
closely correlated with the environment for private investment than with




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 19


any metric of government capital spending. In Egypt, from 1982 to 2009,
there was a secular decline in investment expenditure by the state, but an
increase in private capital. In Jordan, the relationship between public and
private capital is similarly ambiguous, leading to difficulty in finding any
significant correlation. Overall, there seems to be little empirical evidence
for either crowding in or crowding out over the long run, but there may
be particular spending in individual countries that can play a catalytic
role. In brief, there is no evidence that MENA’s limited output diversifica-
tion is driven by a lack of public investment.


Further, another challenge with fiscal policy in MENA has been a lack
of transparency and accountability in most economies. In the wake of the
recent oil boom in the GCC countries, there has been an impressive
buildup of sovereign wealth funds (SWFs), which have helped mitigate
deficits and cushion these countries through crises. The overall assets of
the SWFs are estimated to be more than $1.3 trillion. Overall, while the
details of the SWF stocks are not publicly available, the available esti-
mates suggest that the Abu Dhabi Investment Authority has more than
$600 billion and Saudi Arabia has more than $400 billion (the Saudi
fund is technically a monetary account and not an SWF).11 However, it
must be noted that these funds suffer from a lack of accountability and
transparency, in part a reflection of the lack of clear fiscal rules and open
governance structures among the GCC countries. Truman (2007) finds
that many wealth funds, particularly in the MENA region, involve large
official holdings of cross-border assets, which are often unknown to the
citizens of the countries and to market participants. In a similar vein,
Elbadawi and Soto (2011) find that the resource-rich but largely democ-
racy-deficient MENA region has been a fiscal-rules-free region, and that
fiscal rules can be valuable fiscal stabilization instruments, especially
with the nascent democracies demanding more accountability. A more
open approach to information on the SWFs will increase the account-
ability and transparency of these revenues.


The long-term challenge for the MENA region is to ensure that fiscal
policy is used to promote growth and diversification. The GCC countries
will need to implement policy reforms to accelerate non-oil growth and
create sustained employment opportunities for a rapidly increasing labor
force. For oil importers in the Mashreq and Maghreb, reorientation of
public expenditure from subsidies that do not aid the poor to both con-
ditional cash transfers and effective public investment programs must be
encouraged. Through fiscal policy targeted toward infrastructure, MENA
countries can help lay the foundation for successful diversification.




20 Diop and Marotta


Natural Resources and Incentives for Regional Trade Reforms


As noted, the MENA region contains both resource-rich and resource-
poor countries. At the same time, as recently argued by Venables (2009),
the proximity of resource-rich and resource-poor countries gives an
opportunity to even out wealth distribution within the group of countries
through regional integration. Indeed, the resource-poor countries have a
strong incentive for preferential trade liberalization with their resource-
rich counterparts next door, as a way to get access to the rents. However,
this can be done only at the cost of trade diversion in the resource-rich
country, with a loss of efficiency there. This would imply that integration
between the resource-rich labor-importing and the resource-rich labor-
abundant countries might be beneficial only for the resource-poor labor-
abundant group of countries in MENA.


Chapter 6 tests this hypothesis by looking at the extent to which eco-
nomic diversification is achieved at the expense of trade diversion and
consequently of broader economic efficiency among RRLI and RPLA
countries of MENA. The main prediction of Venables (2009) is that the
resource-rich countries are more likely to experience trade diversion. This
prediction is supported by data for MENA that show a decline in non-oil
imports from the rest of the world of around 38 percent in the case of
resource-rich members of the Pan Arab Free Trade Area, and no trade
diversion at all in the case of resource-poor countries.12 Resource-rich
countries of MENA generally export only a few products and have a
highly concentrated export bundle.


Hence, while further intraregional trade integration is an important
avenue for enhancing diversification of resource-poor MENA countries,
resource-rich countries have no strong incentive, from a purely economic
standpoint, for further preferential regional integration. This may explain
their relative reluctance to engage in this type of scheme. Future discus-
sions of regional trade agreements should take this into account. In this
context, trade liberalization on a most-favored-nation (MFN) basis may
be the best option for furthering global integration.


In conclusion, several policy recommendations emerge from this vol-
ume. First, policy makers in the region should strive to avoid real
exchange rate overvaluation through consistent fiscal policies, flexible
exchange rates, and adequate product and factor market regulations.
Overvalued real exchange rates often lead to Dutch Disease and under-
mine the competitiveness of the non-oil tradable activities in the manu-
facturing and tradable services sectors. Countries with an export-oriented




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 21


growth model, in particular, should pay attention to the trend in their
real exchange rates. Further, MENA is bound to remain a large consumer
of services because of its financial wealth from natural resource extrac-
tion. But the worldwide revolution in technology, transportability, and
tradability that has occurred over the past 20 years has made a large
number of services tradable. What proportion of the services consumed
in MENA will be produced locally and what proportion will have to be
imported now depends on competitiveness factors, of which the real
exchange rate is an integral part. Reforms aimed at reducing regulatory
restrictions that constrain business activities in most services sectors are
equally crucial.


Second, reforms aimed at reducing competition barriers in upstream
input industries are crucial to prevent real exchange rate overvaluation
and boost diversification. Indeed, there is strong evidence that weak links
act to constrain production in MENA. Competitiveness hinges in part on
the ability to purchase good-quality inputs at the lowest cost possible.
China’s competitiveness, for example, is based in part on the availability
of a large domestic input market. Because weak links hurt only when the
inputs are imported at a high cost, trade liberalization for easier access to
imported inputs is as important as fostering domestic competition for
greater efficiency of domestic inputs.


Further, countercyclical fiscal policies are needed to reduce instability
and create a favorable environment for diversification. There is no fiscal
rule in any country of MENA. Discretion is the rule, due to a lack of
political incentives or institutions that impose constraints on policy mak-
ers. Therefore, fiscal policy has been procyclical most of the time in most
MENA countries and certainly in all resource-rich ones since the early
1970s. It will be crucial for MENA countries to develop institutional
mechanisms that constrain fiscal policy discretion. In the resource-poor
countries, reforms aimed at creating fiscal space to invest in core infra-
structure and human capital will be equally crucial for enabling diversifi-
cation. Indeed, in these countries, public finances are tied up by large
subsidies and short-term consumption expenditures, resulting in limited
fiscal space to invest in growth-enhancing areas.


Finally, while regional trade integration is desirable for political, social,
cultural, and economic reasons, an MFN liberalization (that is, a liberal-
ization of trade vis-à-vis all countries) is the best option for resource-rich
countries of the region. Indeed, these countries have little incentive for a
preferential liberalization because it leads to trade diversion.




22 Diop and Marotta


Notes


1. The term Dutch Disease refers to the adverse effects on Dutch manufactur-
ing of the natural gas discoveries of the 1960s, essentially through the subse-
quent appreciation of the Dutch real exchange rate


2. Engel’s Law was introduced by Ernest Engel in 1857.


3. Box 2.1 in chapter 2 summarizes the literature on political economy of rents
in general and in MENA in particular.


4. Burnside and Tabova (2009) show that five global risk factors, including three
commodity price indexes, and country-specific exposure to each factor can
account for 70 percent of the variation in growth volatility.


5. The GCC was formed in May 1981 to encourage policy coordination, integra-
tion, and unity among the member states. An explicit attempt to capture
MENA’s diversity in resource abundance and market size is made throughout
this volume.


6. Brunschweiler and Bulte (2009) contest this result, suggesting that conflict
increases dependence on resource extraction (captured by the share of pri-
mary exports), while resource abundance (measured by resource stocks) is
associated with a reduced probability of civil war.


7. Event analysis refers to a situation when the data is reordered around an
“event,” which serves as the base year rather than the usual calendar year. In
Hausmann, Pritchett, and Rodrik (2005), data is centered around the year
when the event (growth acceleration) occurred. Jerzmanowski (2006) also
studies extreme growth events using a Markov-switching model that distin-
guishes four different growth regimes and finds that institutional quality helps
determine the transition between these states. His study does not focus on
regions, however, so it is not helpful in detecting a MENA specificity.


8. Growth episodes are defined as an increase in GDP per capita growth of at
least 2 percentage points for an eight-year period, with a postacceleration
growth of at least 3.5 percent a year.


9. Estimates for Lebanon and Iran reflect periods of conflict.


10. In a classic work, Hirschman (1958) developed the concepts of backward and
forward linkages and analyzed their importance for economic growth. In his
own words: “The setting up of an industry brings with it the availability of a
new expanding market for its inputs whether or not these inputs are supplied
initially from abroad.” This enhanced market exerts a backward pressure for
establishing industries that supply the new entrants. He calls this process
backward linkage effects. Similarly, forward linkage effects are created when
one industry uses another industry’s outputs as its inputs. The sum of the two
linkage effects gives the total linkage effect, which can be seen as the growth
in new industries induced from establishing an industry.




An Overview of Diversification in MENA: Rationale, Stylized Facts, and Policy Issues 23


11. These buildups in the SWFs have been aided by unprecedented and relatively
high oil prices, which have persisted since the 2008–09 financial crisis.
Currently, at more than $100 a barrel in mid-2012, the price levels are much
higher than the $35–50 price range assumed by the GCC authorities during
the budget planning process.


12. The Pan-Arab Free Trade Agreement was signed in 1996 and entered into
force in 1998. It was signed by Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon,
Libya, Morocco, Oman, Qatar, Saudi Arabia, Syria, Tunisia, United Arab
Emirates, and Republic of Yemen.


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and North Africa.” World Bank, Washington, DC.


Tornell, A., and P. R. Lane. 1999. “The Voracity Effect.” American Economic Review
89 (1): 22–46.


Truman, E. 2007. “SWF: The Need for Greater Transparency and Accountability.”
Policy Brief, Peterson Institute for International Economics, Washington, DC.


Venables, A. J. 2009. “Economic Integration in Remote Resource Rich Regions.”
OxCarre Working Papers 022, Oxford Centre for the Analysis of Resource
Rich Economies, University of Oxford, Oxford, U.K.


WEF (World Economic Forum) and OECD (Organisation for Economic
Co-operation and Development). 2005. “Arab World Competitiveness
Report, 2005.” Geneva: WEF.


World Bank. 2004. “Trade, Investment, and Development in the Middle East and
North Africa: Engaging with the World.” World Bank, Washington, DC.


———. 2009. From Privileges to Competition. Washington, DC: World Bank.




27


C H A P T E R 2


Resource Abundance and Growth:
Benchmarking MENA with the Rest
of the World


Jaime de Melo and Cristian Ugarte


The years 1995–2006 were for the Middle East and North Africa
(MENA) a period of catching up to the rest of the world. During this
period, export growth, excluding minerals and fuels, was higher in MENA
than in the average developing country, leading to an increase in market
share despite strong competition from Asia. MENA countries were also
catching up along other dimensions, expanding the reach of export mar-
kets at a greater pace than competitors in Europe and Central Asia and
East Asia and the Pacific. A catching up on the policy front took place as
well, as protections—which started from higher averages than
competitors—fell more rapidly than elsewhere, with the region’s average
applied tariff rate (the most-favored-nation, or MFN, rate) falling by a
third during 2000–07, to 15 percent. Finally, this trade growth perfor-
mance has been consistent with MENA’s improved growth performance
over the past 15 years. Growth increased from an average 3 percent in
1995 to 6 or 7 percent in the mid-2000s.


Has MENA’s performance led to structural income change and mobil-
ity across the region (such as movement from low-income to high-income
categories)? How much have policy, an abundance of natural resources,




28 de Melo and Ugarte


and luck driven MENA’s recent performance? Have macroeconomic and
microeconomic underpinnings of growth improved significantly? This
chapter attempts to respond to these questions. It shows that MENA’s
recent good trade and growth performance should be analyzed against
the backdrop of a generally disappointing performance over the past
50 years, especially for the resource-rich countries. This poor perfor-
mance explains the slow structural change occurring in the region.


The next section of this chapter benchmarks MENA’s growth and
volatility performance against a large number of comparator developing
countries over the past 50 years, distinguishing MENA countries by their
abundance of natural resources. MENA’s growth performance is first
examined from an income mobility perspective; that is, whether growth
has been strong and long lasting enough to lift MENA countries up the
income ladder over time. To complement this analysis, MENA’s perfor-
mance is examined from the perspective of volatility. Overall, the sec-
tion shows that although resource-poor MENA countries were at par
with comparator groups except the high-growing Asian countries,
MENA overall has failed to climb up the ladder, remaining either in the
lower-middle- or in the upper-middle-income group (with the exception
of Oman).


The chapter then examines the key macroeconomic correlates of
MENA’s long-term growth performance. The first part of this section
builds on the “event analysis” literature,1 which shows that for developing
countries, sustained growth is accompanied by growth in trade and
investment, which is preceded by a strong and sustained depreciation of
the real exchange rate. Indeed, in developing countries, maintaining equi-
librium or an undervalued real exchange rate often helps offset the mar-
ket failures and poor institutional environment that undermine the
competitiveness of non-resource-intensive tradable sectors. In the case of
MENA, an estimation of equilibrium real exchange rates (RERs) in
1970–2005 shows that RERs were marginally overvalued for most of the
time in most countries. This finding is consistent with the natural resource
curse (Dutch Disease) thesis.


This chapter and the supporting evidence show that the combination
of macro- and micropolicies in a generally weak institutional environ-
ment has produced this outcome. At the macrolevel, MENA countries
have been unable to maintain a depreciated (undervalued) real exchange
rate for long periods, which may be explained on distributional grounds.
The region, but especially the resource-rich group, displayed greater vola-
tility in macro-indicators than comparable groups until the middle 1990s,




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 29


when performance started to pick up. For example, the Gulf Cooperation
Council (GCC) countries, as well as the wider resource-rich group,
adopted fewer countercyclical fiscal policies than did other resource-rich
countries with comparable external shocks. As a result, the greater volatil-
ity of the real effective exchange rate in these MENA countries contrib-
uted to the lack of development of new activities outside the resource
sectors, and to short-lived export spells.


Cross-country evidence shows that the positive correlation between
openness and per capita income holds only for countries with good values
for regulatory reform indicators. The Doing Business data also show that
countries rich in natural resources are less inclined to carry out reforms
than others. Despite some progress toward reducing tariffs on industry,
MENA countries fare poorly in most indicators describing the domestic
microeconomic environment, giving the impression of an environment in
which trade is not facilitated and of an unfinished reform agenda.
Improved domestic regulatory policies along with improved public sector
governance, as reflected in better indicator values, would help MENA to
achieve greater integration in the world economy.


Benchmarking MENA’s Long-Term Growth and Volatility


In this section. Mena’s growth performance is benchmarked against sev-
eral comparator groups on to two main aspects of an economy: income
mobility, or its ability to grow fast and climb up the development catego-
ries, and exchange rate and macroeconomic stability.2


Low-Income Mobility
From the 1960s to the mid-1980s, the region’s growth model was govern-
ment led, featuring high public spending and protected national markets.
This growth strategy was mainly financed by high oil revenues in
resource-rich countries, and workers’ remittances and public borrowing
in resource-poor countries. Figure 2.1 shows the trend of GDP (gross
domestic product) growth rates over the longest period possible for a
large sample of countries.3 Two patterns stand out: on average, the
resource-poor group performed as well as the resource-rich group within
MENA. And, as expected, the resource-poor group experienced less
growth volatility during the oil shocks of the 1970s and early 1980s than
the resource-rich group.


Beyond these observations, one should note that for about 50 years, all
MENA countries belonged to either the lower-middle- (LM) or the




30 de Melo and Ugarte


Figure 2.1 MENA Long-Run Growth Performance


−0.5


0


0.5


1.0


sm
o


o
th


ed
g


ro
w


th
ra


te


19
60


19
70


19
80


19
90


20
00


20
10


trend growth rate in MENA


−0.5


0


0.5


1.0


19
60


19
70


19
80


19
90


20
00


20
10


sm
o


o
th


ed
g


ro
w


th
ra


te


resource poor resource rich


lower middle income higher middle income


resource poor resource rich


Source: Authors’ calculations using WDI data.
Note: Sample is a five-year moving average of the growth rate.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 31


upper-middle-income (UM) group (except for what is now the Republic
of Yemen after 1982). Figure 2.1b compares the trend performance of
MENA countries broken down into resource-poor and resource-rich
groups, with the average growth for the samples of lower-middle-income
(44) and upper-middle-income (25) countries. This comparison confirms
the growth volatility of both resource-poor and resource-rich MENA
compared with the UM and LM groups, but especially for the resource-
rich MENA grouping. However, this greater growth volatility may result
partly from the small sample size and the greater commonality of shocks
in the resource rich and poor groups relative to the two larger samples.


Because of the large amount of missing data for several countries,
table 2.1 takes a shorter time period, starting in 1982, using complete
gross national income (GNI) series for 12 MENA countries. In table 2.1,
MENA’s performance is compared with that of 115 countries over the


Table 2.1 Per Capita Income Mobility, 1982–2010


1982↓⎮1996→ L LM UM H Total (1982)
L 16


(0/1/0)
(YEM)


16
(0/1/0)


LM 19 23
(0/0/3)


(EGY, MAR, TUN)


3 45
(0/0/3)


UM 8
(0/3/1)


(DZA, IRN, SYR, JOR)


13
(1/0/0)
(OMN)


5 26
(1/3/1)


H 1
(1/0/0)
(SAU)


27
(2/0/0)


(ARE, KWT)


28
(3/0/0)


Total (1996) 35
(0/1/0)


31
(0/3/4)


17
(2/0/0)


32
(2/0/0)


115
(4/4/4)


1996↓⎮2010→ L LM UM H Total (1996)
L 22


(0/1/0)
(YEM)


13 35
(0/1/0)


LM 17
(0/1/4)


(SYR, EGY, JOR, MAR,
TUN)


14
(0/2/0)


(DZA, IRN)


31
(0/3/4)


UM 12 5
(2/0/0)


(OMN, SAU)


17
(2/0/0)


(continued next page)




32 de Melo and Ugarte


H 32
(2/0/0)


(ARE, KWT)


32
(2/0/0)


Total (2010) 22
(0/1/0)


30
(0/1/4)


26
(0/2/0)


37
(4/0/0)


115
(4/4/4)


Source: Authors’ calculations using WDI data.
Notes: All data are from the World Development Indicators and refer to gross national income (GNI) per capita
data. The thresholds used in classifying countries by income level are those defined by the World Bank for 1982,
1996, and 2010. Former members of the Commonwealth of Independent States are excluded. In parenthesis,
the number of countries in each cell belonging to the three country groups considered for MENA are in the
following order: GCC (ARE, KWT, OMN, SAU); R-R (DZA, IRN, SYR, YEM); and R-P (EGY, JOR, MAR, TUN). Reading
across a row gives the number of countries in the corresponding row at the beginning of period and down the
corresponding column the number of countries at the end of the period. So there were 28 H countries in 1982
and 32 in 1996. The bottom of the table shows that of the 31 countries in the LM group in 1996, 14 moved to
the UM group while 13 from the L group joined the LM group. Country abbreviations in this table and other
tables and figures in this chapter are the three-digit ISO country codes.


Table 2.1 (continued)


1982↓⎮1996→ L LM UM H Total (1982)


period 1982–2010, split into two 14-year periods. Countries are classi-
fied into four income groups according to the World Bank’s classification
system: LM, UM, low income (L), and high income (H). The 12 MENA
countries account for 10 percent of the sample of 115 countries.4 Table
2.1 shows a clear underperformance of MENA countries. In the
resource-poor group, no country moved up the ladder over the 28-year
period, and one country, Jordan, moved down from UM to LM status.5
Among the resource-rich group, two countries, Algeria and the Islamic
Republic of Iran, moved down from UM to LM, but reverted to their
original status in the second period. The Syrian Arab Republic descended
from UM to LM status, and the Republic of Yemen remained in the
low-income group.


Looking at the whole sample, one sees regression during the first sub-
period (the “lost decade of the 1980s”), with 28 countries below the
diagonal (implying movement down the income-group ladder). The sec-
ond period shows a large improvement, with more than one-third of the
countries in the L group moving up to the LM group, close to half moving
from LM to UM status, and no country moving down the ladder.


For MENA countries, two patterns emerge during this 28-year time
span. First, during 1982–96, 5 of the 28 countries that regressed were
from the MENA region, which is twice as large as the share of MENA
in the sample. Second, during the 1996–2010 period of higher growth,
of the 32 countries that moved up a notch, only 4 came from MENA,
again revealing underperformance. In sum, in both subperiods MENA




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 33


countries underperformed, although MENA’s performance improved in
the post-1995 period compared with its own historical performance.


High Growth Volatility and High Real Exchange Rate Volatility
In addition to low levels of growth and no upward income mobility,
MENA’s growth exhibits a high level of volatility, again especially in the
pre-1996 period. Table 2.2 shows two measures of volatility (coefficient
of variation of growth and real effective exchange rate, REER) for
MENA compared with a large number of developing-country income
groups.6 With an average annual growth rate of 1.4 percent, MENA
underperformed relative to all but the low-income classification group
in 1982–2010, with the Gulf Cooperation Countries and resource-rich
groups mostly accounting for the poor performance (column 1). Despite
the presence of high performer Oman in the GCC, the poor perfor-
mance of this group stands out during the period. As discussed below
and in chapter 5, the region’s difficulty in managing volatility has con-
tributed to lower growth.


The comparisons with the LARGE, OIL, and POINT groups in the
bottom of the table confirm this picture. Populous countries in MENA


Table 2.2 Growth and Its Volatility, 1982–2010


Indicators


Mean
growtha


Coefficient
of variationb


Mean change
in growthc


REER
volatilityd


(1) (2) (3) (4)


(A) GNI per capita 1982
Low (20) 2.03 2.99 4.55 49.46
Lower middle (52) 0.96 5.00 3.31 35.34
Upper middle (32) 1.90 2.36 3.63 21.44
High (33) 1.86 1.52 1.93 8.71
(B) GNI per capita 2010
Low (24) 0.76 8.11 4.18 45.15
Lower middle (38) 1.72 3.62 3.72 33.96
Upper middle (32) 1.96 2.81 3.99 22.15
High (43) 2.24 1.87 2.44 11.12
MENA (17) 1.40 4.76 4.78 52.20
GCC (6) 0.51 11.31 4.59 27.48
Resource rich (6) 1.37 5.58 5.29 110.41
Resource poor (5) 2.28 2.81 4.50 23.66
MENA vs. comparatorse


LARGE (33) 2.04 2.44 3.15 46.49
LARGE MENA (6) 1.32 5.55 5.05 74.01
OIL exporters (6) 1.17 5.59 4.46 46.72


(continued next page)




34 de Melo and Ugarte


(more than 20 million) showed lower growth and higher volatility than
the comparator LARGE group. Likewise, growth is lower and volatility is
higher when the comparison is with the OIL group, where this time the
two samples are of the same size. It is only when MENA countries are
compared with the heterogeneous sample of POINT source countries,
which includes many failed states, that MENA outperforms its compara-
tor group with higher average growth and less volatility.


Columns 2 and 3 of table 2.2 show high growth volatility for MENA
driven by the two groups of oil exporters (GCC and MENA OIL
exporters), while the resource-poor group has low growth volatility.
Particularly striking is the high volatility of growth in the GCC group,
which is twice as high as volatility in the resource-rich group and four
times as high as in the resource-poor group. This high volatility is con-
firmed in the detailed decomposition of growth volatility reported by
Koren and Tenreyro (2010).7 They show that in the GCC, the idiosyn-
cratic component of volatility, which is large and mostly unavoidable in
resource-rich countries, is no larger than the country-specific compo-
nent of volatility, which reflects aggregate domestic policy. Being pegged
to a currency (the special drawing rights for all countries except the


MENA OIL exporters
(10)


0.97 7.23 4.82 65.79


POINT (34) 0.95 5.82 4.19 45.32
POINT MENA (10) 1.54 4.07 4.65 65.30


Source: Authors, using data from WDI Indicators.
Notes: Rows (A) and (B) give the composition of income groupings for the two definition years (1982 and
2010), with the classification in row B reflecting the outcome of the mobility during the period. The L group
has a much lower growth according to the latter classification because it includes all the failed states that
moved down the ladder during the period. Equally, the difference in the mean growth for the L and LM
groups in rows A and B reflects the mobility shown in table 2.1, where nearly half (19) of the LM group joined
the L group during the first period and one-third (13) moved up from L to LM status over the second period.
The Commission for Growth identified 13 countries with stellar performance over the period since 1960.
Oman is in that group.
a. Mean growth is the average growth rate over the period 1982–2010, that is, approximately 28 observations
per country resulting in sufficiently large samples to give significantly different mean growth rates in each
sample.
b. Standard deviation divided by the mean.
c. Mean change is the absolute mean change in growth rates observed between two years.
d. The standard deviation of the monthly real effective exchange rate is computed over the period
1980–2010. The sample is not exactly the same as those for columns 1 to 3.
e. See appendix A for a list of countries included in the comparator groups.


Table 2.2 (continued)


Indicators


Mean
growtha


Coefficient
of variationb


Mean change
in growthc


REER
volatilityd


(1) (2) (3) (4)




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 35


Omani, whose rial is pegged to the U.S. dollar) implies that the GCC
countries have relinquished the use of monetary policy for stabilization
purposes, potentially increasing volatility. Koren and Tenreyro (2010)
also show that compared both with other countries at the same per
capita income level, and also with a smaller group with high export
shares of oil and gas products in total exports, the GCC group is an
outlier along several dimensions of volatility, including the nonuse of
countercyclical fiscal policy.8


Finally, column 4 reports the standard deviation of the REER over the
period across the different country groupings. As expected, REER volatil-
ity falls as per capita income increases. Among MENA countries, the
resource-rich and the GCC groups have much less volatility than the
resource-poor group. Nonetheless, volatility is still three times higher
than for the high-income group. Also, when the comparison is across the
other three groupings (LARGE, OIL exporters, and POINT source),
MENA countries always have a higher REER volatility than the com-
parator group. This suggests that the fundamentals of sound fiscal and
monetary policies—a prerequisite for sustained performance—have been
largely missing in the MENA region over the past 30 years.


Volatility of the REER can also hamper the development of non-
resource-based activities. Gelb (1988) and others studying the resource
curse argue that this volatility has resulted in a “volatility-induced ineffi-
cient specialization” pattern. Hausmann and Rigobon (2003) even sug-
gest that volatility produces a vicious cycle. A volatile REER raises
uncertainty and makes investments in nonresource tradables unattractive,
leading to a concentrated export basket in the resource-based sector,
which then causes volatility in the real exchange rate.


Table 2.3 describes a breakdown by decades of REER volatility for
individual countries in MENA. REER volatility has fallen continually
over the period. Taking REER volatility as a first approximation for insta-
bility (the REER should be flexible to maintain external balance, so this
is not strictly a measure of macroeconomic instability), MENA countries
improved in each of the past two decades compared with the 1980s,
when no country in the region was in the bottom quartile, and half the
group of MENA countries were in the fourth quartile. The GCC coun-
tries had low volatility in the 1990s and 2000s along with Morocco (and
Tunisia in the 1990s). Except for the Arab Republic of Egypt (and
conflict-torn Lebanon), all the countries with high volatility (beyond the
interquartile range) are resource-rich countries, indicating another speci-
ficity for that group.




36 de Melo and Ugarte


Correlates of MENA’s Growth Performance


A key focus of the empirical growth literature in recent years has been to
capture the prerequisites for sustained growth acceleration. What are the
characteristics of growth acceleration episodes? Are they similar across
countries in the world? And have MENA countries experienced them?


Event analysis is useful to identify these characteristics, particularly for
developing countries where growth patterns are much less stable than
those portrayed in the standard growth models. In an early event analysis,
Hausmann, Pritchett, and Rodrik (2005) studied 83 growth episodes over
the period 1957–1992 using the Penn World Tables (PWT).9 Of the 83
episodes, 8 (event years in parenthesis) were from MENA: Algeria
(1975), Egypt (1976), Jordan (1973), Morocco (1958), Tunisia (1968),
and Syria (1969, 1974, 1989). They found that compared with the previ-
ous seven years before the growth episode, the acceleration period was
correlated with increases in investment and trade shares in GDP and a


Table 2.3 Real Effective Exchange Rate Volatility by Period


Volatility
1980–89


(standard deviation)
1990–99


(standard deviation)
2000–10


(standard deviation)


High Syrian Arab
Republic


(120.5) Iran, Islamic
Rep.


(52.6) Libya (97.9)


Iran, Islamic
Rep.


(116.5) Libya (46.1) Egypt, Arab
Rep.


(26.6)


Libya (66.2) Algeria (31.8) Iran, Islamic
Rep.


(21.9)


Algeria (66.2) Egypt,
Arab Rep.


(24.5) Lebanon (14.2)


Oman (38.7) Lebanon (23.0) Bahrain (13.9)
Tunisia (32.7) United Arab


Emirates
(5.6)


Low 0 Morocco (5.3) Jordan (5.5)
Tunisia (1.9) Kuwait (5.3)


Morocco (3.5)


Mean 26.0 15.7 11.7
Interquartile


range
[7.3–32.7] [5.3–18.8] [5.8–13.4]


Source: Authors’ calculations using IMF data.
Notes: The standard deviation of the monthly real effective exchange rate from the IMF’s International Financial
Statistics data for each period is used as the measure of volatility for a sample of 16 countries over the period
1980–2010. High (low) volatile countries are those having a standard deviation higher (lower) than the third
(first) quartile of values observed across countries. Extremely volatile countries (standard deviation higher than
200) were excluded from the sample.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 37


sharp depreciation of the real exchange rate of more than 20 percent.
Investment and trade (imports and exports) shares in GDP were close to
15 percent higher than they were before the acceleration period, contrib-
uting up to one-fifth of the growth acceleration.


Using the same Penn data, Jones and Olken (2008) also studied the
extremes of growth and collapse events using structural break techniques
for time series data. They found an asymmetry between up and down
breaks: growth collapses feature sharp reductions in investment in the
midst of price instability, while growth take-offs are associated with large
expansions in international trade (the latter finding is similar to that of
Hausmann et al. 2005). Jones and Olken identified 73 breaks (30 up and
43 down) in 48 of the 125 countries with at least 20 years of data. Among
these, 4 occurred in MENA.10


Real Exchange Rate Depreciation and Growth Acceleration
The event analysis literature thus points to the importance of trade for
growth and of the RER for the growth of trade, particularly of manufac-
tures. At the macrolevel, the importance of the RER—the relative price
of tradables to nontradables (RER = PT/PN)—in understanding growth
operates directly and indirectly: directly by increasing the relative profit-
ability of tradables, which in turn is indirectly associated with higher
growth (Rajan and Subramanian 2007; Rodrik 2008).11


Since undervaluation of the real exchange rate has been characteristic
of growth accelerations,12 we examine whether MENA country RERs are
more often undervalued or overvalued. Following Rodrik (2008), the
equilibrium RER over the period 1970–200513 was estimated using the
latest PWT 7.0 tables, taking five-year averages for a panel of eight peri-
ods (country and period fixed effects included).14 We obtain:


ln RER = 1.35 – 0.09 ln GDPPC R
2 = 0.12 (2.1)


(15.5) (–9.5)


So a 10 percent increase in income is accompanied by close to a
1 percent fall (that is, appreciation) of the equilibrium RER.15 Following
Rodrik (2008), we take the log of the difference between the actual RER
and the one estimated in equation 2.1, so that a positive (negative) value
implies undervaluation (overvaluation), with a zero value for the indica-
tor corresponding to an equilibrium RER.


The results for MENA are reported in table 2.4, where column 1 gives
the average deviation over the periods (from five to eight depending on the
country), and column 2 gives the percentage of periods with overvaluation




38 de Melo and Ugarte


Table 2.4 Deviations from Estimated Equilibrium Real Exchange


Country
Mean deviation of the RER in


percentage
Percentage (number) of periods


with overvaluation


GCC
Bahrain (8) 4.7 50 (4)
Kuwait (5) 0.6 80 (4)
Oman (8) −18.1 63 (5)
Qatar (5) 10.4 20 (1)
Saudi Arabia (5) −9.0 80 (4)
United Arab Emirates (5) 6.4 20 (1)


Resource rich
Algeria (8) −9.0 75 (6)
Iran, Islamic Rep. (8) 13.3 25 (2)
Iraq (8) 2.7 63 (5)
Libya (5) −1.9 60 (3)
Syrian Arab Republic (8) −17.9 63 (5)
Yemen, Rep. (5) 6.1 40 (2)


Resource poor
Egypt, Arab Rep. (8) 9.4 50 (4)
Jordan (8) −2.4 63 (5)
Morocco (8) −24.3 100 (8)
Tunisia (8) −1.4 63 (5)


Source: Authors’ calculations using PWT and WDI data.
Note: Residuals from equation 2.1: A negative value in column 1 means an overvalued RER on average during the
whole period (up to eight five-year periods 1970–2005). In 1970, 7 of 11 countries with data had an overvalued
RER; in 1985, 13 of 17 countries; and in 2005, 5 of 17.


for each country. Most countries are overvalued about half the time, while
a few countries (Morocco, Oman, Saudi Arabia, and Syria) are overvalued
most of the time.16 Further, except for the Islamic Republic of Iran and the
Republic of Yemen, the estimates suggest that resource-rich countries are
overvalued for most periods. Also, overvaluation was much more wide-
spread during the early periods, peaking with 13 of 17 overvalued during
1980–85 and dropping to 5 of 17 in the last period (2000–05).


Referring to his previous work on growth accelerations discussed
above, Rodrik (2008, figure 10) shows that, around the event year when
acceleration starts, the RER is undervalued by around 20 percent and that
this undervaluation lasts throughout most of the decade following the
start of the growth acceleration. Figure 2.2 traces the deviation of MENA
countries from the estimated relation in equation 2.1 for one year, 1985
with two lines showing the 20 percent limit. It is clear that the distribu-
tion of observations outside the band is on the bottom of the figure,
indicating significant overvaluation. We checked whether any country




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 39


Figure 2.2 Estimated Equilibrium Real Exchange Rates


DZA


BHR
EGY


IRN


IRQ


JOR
KWT


LBN


LBY
MAR


OMN
QAT


SAU
SYR


TUN
ARE


YEM


−1


0


1


2


3


lo
g


o
f R


ER
in


P
PP


6 7 8 9 10 11
log of real GDP per capita


linear prediction +20% UNDER


+20% OVER LnRER


partial plot of RER and real GDP per capita MENA countries in 1985


Source: Authors’ calculations using PWT and WDI data.
Note: Partial plot of estimates in equation 2.1 Points under the line indicate overvaluation of the RER relative to
the PPP (purchasing power parity) equilibrium prediction.


had two adjacent episodes (that is, 10 years of undervaluation), but that
was never the case. MENA countries do not exhibit the kind of under-
valuation identified with extended past growth episodes.


If an overvalued RER is a penalty for manufacturing and for a dynamic
services sector, this should show up in comparisons of MENA’s shares of
manufactures and services relative to other countries at similar income
levels. Chapter 3 estimates predicted shares in both cross-section and
panel. It turns out that dummy variables for MENA countries are never
significant when total exports are considered (so MENA’s openness
accords with the norm). When sector shares are considered, however, the
sizes of the manufacturing and service sectors in the economy diverge
markedly from the estimated shares in both cross-section and panel esti-
mates. In the resource-rich labor-abundant (RRLA) group, the manufac-
turing and services sectors are undersized, and their export shares in these
sectors are also lower than predicted. However, this association is muted
when the share of rents in GDP is taken into account, suggesting the
presence of the resource curse syndrome, as widely discussed in the lit-
erature on growth in MENA (box 2.1).


If an undervaluation of the real exchange rate is found to precede and
to be associated with episodes of significant export growth, one should




40 de Melo and Ugarte


Box 2.1


MENA in the Natural Resource Curse Literature


The manifestations of the natural resource curse are perhaps the most invoked


reason give for the overall underperformance of the Middle East and North


Africa (MENA) in the past 50 years (see, for example, World Bank 2004, chap. 2).


Three channels for the deleterious effects of natural resource abundance have


been emphasized. First, natural resources are often concentrated in sectors that


may be associated with lower productivity growth and fewer spillovers. Second,


natural resources are often extracted from a narrow economic base (“point


source” sectors—see below), giving rise to rents. These rents and the ensuing


“easy life” for the elite in turn are associated with lower investment in human


capital, contributing to less learning and innovative capacity (Gylafson 2001).


These aspects are central to the Dutch Disease aspect of the curse, whereby


manufacturing (and tradable services) activities are depressed through an


appreciated real exchange rate during resource booms, which is exacerbated


when, during busts, countercyclical fiscal policies are not operative and exchange


rate policies are rigid.a Third is the high level of export concentration leading to


higher price volatility and hence to macroeconomic volatility.b However, this vul-


nerability to changes in a country’s terms of trade is not particular to natural


resource abundance but more to a country’s overall openness to trade.c


These channels have been explored in a vast cross-sectional growth literature.


Early findings revealed a robust negative conditional correlation between growth


and the share of primary exports. More recent contributions have pointed out the


weaknesses of the early estimates, relying on trade-based proxies (that is, primary


exports measured by the share of oil and minerals in total exports) for relative


endowments (Lederman and Maloney 2008). These proxies are outcome vari-


ables that reflect resource dependence rather than resource abundance and, as


such, do not capture resource abundance, resulting in a lack of resource curse


effects when better proxies for resources (such as “resource stocks”) are used (see


the critique in Lederman and Maloney 2007, 2008). Moreover, case studies of high


growth rates by resource-abundant countries (Botswana, Indonesia, and Oman,


to name a few) cast doubt on the early findings, a conclusion that also appears in


this chapter in the mobility analysis.


Less easily apprehended is a fourth channel by which natural riches engender


institutional weaknesses as groups attempt to capture rents (Mehlum, Moene,


and Torvik 2006). This curse-via-politics is largely endogenous to the political


(continued next page)




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 41


environment and not likely to be improved by governments in power that have


a vested interest in blocking institutional change.d Resource riches can also be


the cause of conflicts.e


In the Arab world, curse-via-politics effects could have been important, but


this channel has probably been operative in the destiny of nations before the


ascendance of oil in the world economy. It is striking that the MENA region was a


powerful engine of progress through several millennia, mostly through trade


(World Bank 2004, box 3.1), and that it was the most technologically advanced


region of the world about 1,000 years ago, just about the time that Islamic legal


institutions were introduced. Five hundred years later, that technological leader-


ship was erased, and the Arab region lagged behind both Western Europe and


China, a decline that has continued ever since.f


Several studies have uncovered a positive correlation between natural


resources (proxied by resource rents or the share of exports of fuels and minerals)


and an index of corruption (Isham et al. 2005; Leite and Weidman 2002 in cross-


section). However, Bhattacharya and Hodler (2010) show that this correlation


becomes negative when the sample is split into democratic and nondemocratic


groups, thereby justifying why some resource-rich countries such as Canada, Ice-


land, and Norway have avoided the curse. With panel data covering the period


1980–2004, Isham et al. (2005) show that the relationship between natural


resources and corruption depends on the quality of democratic institutions, the


curse applying only in nondemocratic environments.


Insofar as the inertia in MENA’s institutions is linked to deep-rooted legal


developments, there might be a MENA “specificity” on the institutional side. Two


other pieces of recent evidence are relevant to the natural resource curse and


reforms. Freund and Bolaky (2008) relate business regulations to per capita income


over a large sample of 126 countries in which all 12 MENA countries in the sample


are among the 50 percent most regulated economies. They find that increased


trade is positively correlated with income only for countries that are among


the 50 percent least regulated, indicating that domestic policies that impede fac-


tor mobility blur the positive relation between trade openness and income. This


finding supports the policy prescription that trade and regulatory reform are


complementary and should go hand-in-hand. Given the multiple possibilities for


regulatory capture in high-rent environments, this may explain why all the MENA


countries in the sample are in the most regulated group. Drawing on the Doing


Business data for a large sample of 133 countries, Amin and Djankov (2009) find


(continued next page)


Box 2.1 (continued)




42 de Melo and Ugarte


not underestimate the difficulty of effecting such an undervaluation.
Gaining competitiveness by exchange rate manipulation could well be
politically infeasible. For example, in Egypt, oil is heavily subsidized. In
Morocco, fuel subsidies represented 18 percent of government expendi-
tures (3.7 percent of GDP in 2010). As noted by Augier et al. (2011),
these subsidies are a reward to gas-guzzling cars and to negative exter-
nalities, and they complicate fiscal management because of their high
built-in volatility.


Oil subsidies are prevalent throughout MENA. They have a high
elasticity to the exchange rate, which makes it difficult to raise the com-
petitiveness of manufacturing activities through a devaluation of the
exchange rate. Unless compensation can take place, a devaluation would
have large redistributive effects that would make devaluation politically
unsustainable.


Barriers to Trade: Policy-Related and Others
Given the importance of trade in growth acceleration episodes, it is
important to review MENA’s barriers to trade, in addition to the evolu-
tion of the RER examined above. Past appraisals of trade policies and
barriers to trade in MENA suggest limited reforms, even though all the


that the proclivity to undertake microreforms that reduce regulation is much


lower in countries whose exports are concentrated in abundant natural


resources.


a. The Dutch Disease model is examined in Corden and Neary (1982). Gelb (1988) and many others have
applied it in the context of oil windfalls.
b. A higher volatility of the real exchange rate is typical of natural-resource-abundant countries and can
also be a channel for a resource curse (Lederman and Maloney 2008; Hausmann and Rigobon 2003).
c. As put by Lederman and Maloney (2008), Costa Rica’s microchips are as vulnerable to exogenous
developments in world market conditions as Chile’s copper.
d. Acemoglu, Johnson, and Robinson (2005) present the view that institutions evolve very slowly, largely
as a result of a change in the balance of power between parties with opposing wealth.
e. Collier and Hoeffler (2004) were the first to give evidence that conflicts were more likely to be driven
by greed to get hold of the rents, than by grievance for ethnic or religious reasons. Brunnschweiler and
Bulte (2009) contest this result, suggesting that conflict increases dependence on resource extraction
(captured by the share of primary exports) while resource abundance (measured by resource stocks) is
associated with a reduced probability of civil war. MENA has had its share of conflicts, but over the past
30 years, the count is no higher than in other regions.
f. The technology estimates are from Comin, Easterly, and Gong (2010, table 5). The importance of legal
institutions is developed by Kuran (2010).


Box 2.1 (continued)




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 43


indicators reviewed here indicate progress in reducing barriers to trade.
Reducing government intervention is not the only key to success. Much
recent literature and experience suggests that a comprehensive
competiveness-based approach to exports and growth is also a necessary
ingredient. Governments must still overcome market failures, particularly
with regard to information externalities and to collective action and coor-
dination challenges. The evidence reviewed here suggests that MENA still
has an unfinished agenda. In particular, trade and regulatory barriers are
widespread and trade costs high. Both impede the pace of regional and
global integration.


As shown here, MENA’s reduction in protection has not been accom-
panied by the hoped-for improvement in openness. Reasons for this
outcome are explored below. As a general observation, most Latin
American countries that opened up their economies were disappointed
by their subsequent export performance compared with the rapid growth
in exports experienced by the Asian countries in the 1960s. Wood (1997)
argued that Latin American wages were not sufficiently low to compete
with the low-wage South Asian exporters and China.


If this diagnostic is correct for MENA—if the region is not able to
compete in the high-tech sectors because of lack of skills or in the labor-
intensive sector because of relatively high wages, it suggests that the best
option for MENA is to participate in outward processing. Such a strategy,
however, would require improvements in hard and soft infrastructure,
hence the importance of monitoring closely the evolution of trade cost
indicators relative to those of competitors.


Tariffs and nontariff barriers. Despite recent progress in reduced protec-
tion, the opening of MENA countries to world trade remains an unfin-
ished agenda.17 Augier et al. (2011) report recent data on nontariff
measures (NTMs) for 2010 for 29 countries. Except for Egypt, which has
NTM frequency and coverage ratios of 90 percent, the other four MENA
countries in the sample (Lebanon, Morocco, Syria, and Tunisia) have
ratios around the average valued for the sample. Figure 2.3 shows that the
frequency ratios are down, with a shift away from old-style control-and-
command measures such as quantitative restrictions, prohibitions, and
anticompetitive measures, toward technical regulations—sanitary and
phytosanitary and technical barriers to trade—which have replaced all
other forms of nontariff barriers.


As pointed out by Augier et al. (2011), this shift could be taken as
a modernization of nontariff barrier apparatus, marking a shift from




44 de Melo and Ugarte


protectionist measures to regulatory ones. Whether that is so remains
to be determined by case studies on the ground. On the other hand,
the shift could also hide the increasing use of technical regulations as
barriers to trade, through complex designs that end up being discrimi-
natory de facto although not de jure.


Augier et al. (2011) also estimated product-level price-gap compari-
sons between a country and the average for the sample for products with
a core NTB in which the authors control for tariffs and other country
effects such as differences in cost-of-living indexes. Simple average esti-
mates of price gaps for products with NTMs are 87 percent for Morocco
and 37 percent for Tunisia. These are high. Their econometric estimates
suggest that NTMs are more restrictive than tariffs.


The impression of an unfinished agenda is also apparent from inspec-
tion of the policy indicators affecting trade, displayed in table 2.5. The
table is split into two groups of indicators: trade policy indicators in
columns 2–5 and trade-supporting indicators in columns 6–9. Overall,
individually and by group, MENA countries have poor (that is, high)
rankings. Three patterns stand out. First, the indicators are best for the
GCC and worst for RRLA countries. Second, the rankings for trade


Figure 2.3 Frequency Ratios, Core NTMs, 2001–10


0


20


40fr
eq


u
en


cy
ra


ti
o


s


60


80


100


120


20
01


20
10


20
01


20
10


20
01


20
10


20
01


20
10


Egypt,
Arab Rep.


Lebanon Morocco Tunisia


technical reg. prohibitions quotas licences


Source: Augier et al. (2011, figure 2). Data from World Bank/UNCTAD NTM data.




45


Table 2.5 Policy Indicators Affecting Trade in MENA


Country TAR-AGR TAR-MAN
TTRI Value


(rank: 125)
OTRI Value


(rank: 102)
LPI


(rank: 164)


Doing
Business


(rank: 183)


Trading
across borders


(rank: 183)
Rule of law


(rank: 213)


1990–95/ 1990–95/
2006–09 2006–09 2006–09 2008 2006–09 2006–09 2010 2009


RPLA n.a. n.a. 58.4 67.3 79.6 102 60.6 102.6
Egypt, Arab Rep. 35.8/54.6 23.6/9.2 3.3 (68) 10.0 (59) 94 106 21 97
Jordan —/16.7 —/10.0 4.6 (108) 11.3 (66) 80 100 77 81
Lebanon —/11.4 —/5.1 1.9 (50) — 33 107 95 145
Morocco 66.5/26.7 63.9/10.8 1.8 (48) 14.1 (75) 131 128 80 106
Tunisia 29.6/38.6 28.0/21.0 0.9 (18) 11.7 (69) 60 69 30 84


RRLA n.a. n.a. 29 9 119.2 133.6 135.4 169.2
Algeria 25.4/21.5 21.3/15.9 0.7 (9) 1.5 (4) 135 136 124 156
Iran, Islamic Rep. —/28.5 —/24.6 1.9 (49) 2.7 (14) 104 137 131 171
Iraq — — — — 156 153 179 210
Libya —/0 —/0 — — 137 — — 161
Syrian Arab Republic —/15.8 —/12.8 — — 80 143 120 132
Yemen, Rep. —/7.0 —/5.3 — — 103 99 123 185


GCC n.a. n.a. 49.8 16.8 41 38.5 50 77
UAE —/4.7 —/4.2 3.6 (71) 3.5 (20) 24 33 3 76
Bahrain —/7.7 —/3.9 2.6 (58) 3.3 (19) 32 20 33 77
Kuwait —/3.1 —/4.2 — — 36 61 113 73
Oman 8.2/4.9 5.1/3.7 1.4 (31) 3.1 (16) 60 65 87 66
Qatar —/5.9 —/4.1 1.8 (47) — 55 39 46 81
Saudi Arabia 11.8/3.0 12.4/4.1 1.7 (42) 2.6 (12) 39 13 18 89


Sources: Columns (1–2) WITS: Applied tariffs; columns 3–5: World Trade Indicators; column 6: Doing Business; column 7: World Governance indicators.
Notes: For all ranks, a higher value means a worse ranking. TAR-AGR = Applied tariffs in agriculture; TAR-MAN = Applied tariffs in manufactures. — = missing data. n.a. = not applicable.




46 de Melo and Ugarte


policy barriers are usually better than for the trade-supporting indica-
tors. Third, the rankings are generally poor relative to the lower-middle-
and upper-middle-income comparator averages.


This still relatively high level of tariff protection is reflected in the
values and rankings of the Trade Restrictiveness Index (TRI) for the
MENA countries (table 2.5, column 3), and also for the regional average
when compared with other middle-income countries. On a regional basis,
the overall Tariff-only Trade Restrictiveness Index (TTRI) is still the
second-highest in the world after South Asia. When nontariff measures
are included, the Overall Trade Restrictiveness Index (OTRI) for the
MENA region (based on the countries listed in column 4 in table 2.5) is
the highest in the world. This high value is mostly attributable to the high
ad valorem equivalent (AVE) of nontariff measures for the countries in
the RPLA group. The barriers to trade in MENA particularly penalize
exports from Sub-Saharan Africa (40 percent) and Latin America
(57 percent).


Because reforms are often complementary, improvements in the trade-
supporting indicators (columns 5 to 9) are needed if trade is to be an
engine of growth in MENA. To partake in the rapid growth of offshoring
in services (such as back-office work processes, call-center operators, legal
research, and so forth), restrictions to trade in services must not be higher
than they are in competing countries. Likewise, to participate in the
global production networks, where different stages of production take
place in different locations, a country needs state-of-the-art supply and
logistics chains (high-performance transport, customs, and communica-
tion) and efficiency in the full range of backbone service sectors). Except
for the GCC, none of the MENA countries has good scores on these
trade-supporting indicators.


The pervasiveness of nontariff measures is compounded by the
relatively poor ranking in the indicators capturing the regulatory envi-
ronment: the Logistics Performance Index (LPI)(column 6), the Doing
Business indicators (column 7), the trading across border indicator
(column 8) and the rule of law indicator (column 9). By and large,
both resource-rich and resource-poor countries have low rankings,
according to most of these overlapping indicators that capture the
regulatory environment.18 Taken together, these rankings are consis-
tent with rigid economies and an unfriendly business environment
where, as shown by Freund and Bolaky (2008), more trade is not asso-
ciated with a rise in per-capita income (as is the case in the flexible
economies).




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 47


Trade costs and trade facilitation. MENA manufactures and non-oil
exports, especially those of the resource-rich group, have underperformed
(see the estimates in annex table 2A.1). Two broad categories of factors
have been advanced so far: macroeconomic policies and the unfavorable
regulatory environment. In the standard gravity model, after controlling
for other factors, this lack of trade integration would be reflected in a high
value for a regional dummy variable. To test whether this is the case, we
estimate a gravity model augmented by a proxy for trade facilitation,
which captures the unfavorable environment reflected in the indicators
in table 2.5. The proxy is the time-to-export from the factory to the port
(broken down into number of days for documentation, transit time, port
handling, and customs clearance, available for three years from the Doing
Business database).19


The model estimated for non-oil and nonmineral bilateral exports is:


LnEXijt = a1Xit + a2LnDISTij + a3Zij + a4TIME–EXPi
+ a4DUMR + mj + eijt (2.2)


where EXijt are bilateral exports of manufactures (excluding oil and min-
erals) from i to j, Xit is a vector of exporter-specific variables that includes
GDP, population, and an indicator of remoteness, DISTij is distance
between partners, Zij are the usual bilateral controls (contiguity, common
language, former colony), TIME–EXP is the time to get a standardized
container from factory gate on board to the ship, DUMR is a regional
dummy (the Organisation for Economic Co-operation and Development,
or OECD, membership is the omitted dummy), and mj captures all time-
invariant importer-specific characteristics.20


Results are reported in table 2.6. All the controls have expected mag-
nitudes and expected significance levels. Of interest is the negative coef-
ficient for the MENA dummy in column 1, which implies that MENA
exports about 39 percent less (e−0.89 −1 = −0.39) than expected relative to
the excluded OECD, after having taken into account the effects of all the
other controls. Interestingly, East Asia star performers export 630 percent
more than the OECD does, followed by South Asia. All other regions
export less on a bilateral basis than the reference group, but what comes
out of the estimates is that MENA is the region that deviates the most
from the expected trade volume.


Adding the time-to-export variable (column 2) to capture the effects
of trade facilitation lowers only slightly the coefficient value for the
dummy. Unlike the results by Freund and Rocha (2011) for Sub-Saharan
Africa, where introducing the time-to-export variable takes away much




48 de Melo and Ugarte


of the significance for the Africa dummy (not shown), this is not the case
for MENA manufacture exports. Nor are the results affected by the shift
to a log specification for the time-to-export variable (column 3), or by an
estimation carried out on total exports (not reported here).


The estimates for the MENA subsample of 12 countries are given in
columns 4 and 5. Even though MENA countries are not landlocked, the


Table 2.6 Correlates of Bilateral Non-Oil Exports


Ln(Aggregate
non-oil exports)


(1) (2) (3) (4) (5)


All Time exp. Log time MENA MENA


Ln(GDP) 1.30*** 1.21*** 1.15*** 0.55*** 0.43***
[0.01] [0.01] [0.01] [0.05] [0.05]


Ln(Population) −0.15*** −0.06*** −0.00 −0.13*** −0.14***
[0.01] [0.01] [0.01] [0.03] [0.03]


Ln(Distance) −1.64*** −1.65*** −1.66*** −1.96*** −1.97***
[0.01] [0.01] [0.01] [0.08] [0.08]


Time to export −0.02*** −0.08***
[0.00] [0.01]


Ln(Time) −0.63*** −2.32***
[0.03] [0.15]


MENA −0.89*** −0.83*** −0.74***
[0.04] [0.04] [0.04]


SSA −0.44*** −0.45*** −0.48***
[0.04] [0.04] [0.04]


LAC −0.20*** −0.26*** −0.21***
[0.04] [0.04] [0.04]


EAP 1.99*** 1.94*** 1.91***
[0.04] [0.04] [0.04]


SAS 0.38*** 0.29*** 0.30***
[0.06] [0.06] [0.06]


ECA −0.66*** −0.51*** −0.47***
[0.03] [0.04] [0.04]


Contiguity 0.80*** 0.80*** 0.79*** −0.34 −0.33
[0.07] [0.07] [0.07] [0.26] [0.26]


Partner fixed
effects Yes Yes Yes Yes Yes


Observations 53,359 52,458 52,458 4,808 4,808
R-squared 0.702 0.706 0.707 0.607 0.615


Source: Authors.
Notes: Aggregate non-oil exports are equal to total exports minus exports in HS 2-digit sectors 26 (oil) and 27 (ores
and minerals). All exports are gross exports. MENA, SSA, LAC, EAP, SAS, and ECA are dummy variables for regions:
Middle East and North Africa, Sub-Saharan Africa, Latin America and the Caribbean, East Asia and Pacific, South
Asia, and Europe and Central Asia. The reference region is OECD. Other control variables included in the regression
are common language, colony, landlocked, and remoteness. The estimates for unreported control variables are
always statistically significant with the expected sign. Standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 49


coefficient of the distance variable increases significantly, suggesting
higher-than-average, distance-related trade costs. The contiguity coeffi-
cient is not significant, probably reflecting conflicts in the region as well
as a lack of trade facilitation at the regional level. In MENA, the average
time to get merchandise from the factory gate to the port is 20 days (less
than in the other regions except OECD because of the low values for
inland transit, since MENA countries are not landlocked). According to
the estimates in column 4, a reduction in time-to-export of 10 percent
(that is, two days) would increase trade volume by 8 percent.


Breaking down the time-to-export into each one of the three compo-
nents21 also results in negative and significant coefficient values for each
component. When the respective coefficient values between the esti-
mates for the whole sample and for the MENA sample are compared, the
coefficient value for time related to customs and ports is much higher for
the MENA sample (−0.21) than for the whole sample (−0.05). While
interpretation of the results is subject to endogeneity problems (trade
facilitation may stimulate trade, but trade is also likely to influence trade
facilitation), the large estimated values for the customs and ports compo-
nents suggest benefits from trade facilitation.


As shown in figure 2.4, unlike most other countries and regions, the
average distance of trade for MENA countries has fallen from 7,000 kilo-
meters to 6,000 kilometers over a 30-year period. Referring again to the
gravity trade model, this decline could result from two developments.
One is that the region is losing ground and lagging relative to competitors
(as suggested by the comparative performance along the indicators in
table 2.5). If so, then costs related to international trade are increasing
relative to those of competing partners, and countries would trade more
with closer partners to minimize trade costs (or other countries are taking
their place because their trade costs are falling more rapidly).


An alternative possibility is that MENA countries have decreased
trade-related costs mostly on a regional basis, through reductions in tariff
and nontariff barriers or in creased trade facilitation resulting from the
implementation of regional trade agreements. If so, with relative trade-
related costs falling faster on a regional basis, countries would trade more
with close partners, thereby reducing the average distance of trade.22


The prediction that a fall in border-related costs should lead countries
to increase the volume of international (relative to internal) trade is
largely borne out by the data: over the past 30 years, international trade
has increased by 300 percent while world production has increased by
75 percent. According to the gravity model, in a frictionless world,




50 de Melo and Ugarte


potential trade would be proportional to the trading partners’ GDP.
Multiplying by the distance between the partners and summing over all
partners gives the gravity-predicted average distance of trade for
country i, denoted here as the potential distance of trade (ADOTPi ). This
measure (which takes a maximum value when all countries are of the
same size) will increase when there is less dispersion in the group and
over a long period when there is convergence in incomes.


Figure 2.4 Average Distance of Trade and Trade Costs


1,000


(a)


(b)


9,000


8,000


7,000


d
is


ta
n


ce
(k


m
)


A
D


R


6,000


5,000


1.05


0.95


0.85


0.75


19
75


19
80


19
85


19
90


19
95


20
00


20
05


19
75


19
80


19
85


19
90


19
95


non-mena UMI


MENA


non-mena LMI


MENA and comparators, 1970–2006


20
00


20
05


ADOTP-UMI
ADOTP-LMI


ADOTP-MENAI


ADOT-UMI


ADOT-LMI


ADOT-MENA


Source: Authors’ calculations based on Carrère, de Melo and Wilson (2010).
Note: Country averages over five-year periods during 1970–2004 and a two-year period, 2005–06. The 10 MENA
countries are United Arab Emirates, Algeria, Egypt, Jordan, Kuwait, Morocco, Oman, Saudi Arabia, Syria, and
Tunisia. UMI = upper middle income. LMI= lower-middle income. ADOTP = Potential average distance of trade in
a frictionless world. ADOT = actual average distance of trade. ADR = average distance ratio. ADR = ADOTP/ADOT
= trade cost. ADR is normalized to 1 in 1970.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 51


A reduction in all costs related to distance (including better informa-
tion about distant markets) should lead countries to increase their trade
with distant partners. On the other hand, if the relative costs associated
with distance increase, countries should trade with closer partners. Since
what counts is the evolution of distance-related costs across all partners,
trade costs could be falling for all trading partners, but those for which
trade costs are falling the least would see a regionalization of their trade.
Then if the gravity model is an adequate description of bilateral trade, the
ratio of actual trade (ADOTi) to potential (ADOT


P
i ), here called the aver-


age distance ratio (ADRi), is an indirect measure of trade costs: falling
values of the ratio (that is., a regionalization of trade) then reflect an
increase in relative trade costs.


Figure 2.4 reports these measures for the average of the 10 MENA
countries with data over the period 1970–2004 along with the corre-
sponding average for the upper-middle- and lower-middle-income
groups (UMI, LMI), since all MENA countries except the Republic of
Yemen belong to one of these two groups (see table 2.2). To iron out
fluctuations, each point is a five-year average. For all countries, potential
trade is greater than actual trade, suggesting cost minimization in bilateral
trade patterns by choosing closer partners (with lower trade costs).
MENA countries’ potential (or frictionless) trade is about 2,000 kilome-
ters less than the corresponding estimate for the comparator groups,
reflecting a higher dispersion in GDPs across the partners. Over time, the
potential distance of trade increases slightly for the UMI group, reflecting
a higher growth for distant partners. For the MENA and LMI groups, the
potential distance of trade remains flat.


More interestingly, the indicator of trade costs in the bottom half of
the figure (the ADR is normalized to 1 in 1970) shows a sharp fall of
around 10 percent in the average distance of trade for the two compara-
tor groups. This drop could be either because the trade costs associated
with physically close partners are falling more rapidly (as, for example,
with deep integration), or because the costs of barriers to trade have not
gone down as rapidly as they have for the high-income countries, whose
ADR (not shown here) stayed constant throughout the 30-year period.
However, for MENA, the fall in the ADR is reversed starting in the early
1990s, which is the period when the regionwide preferential trade agree-
ments were put in place (along with others outside the region).


Since the average potential distance of trade stays constant, a change
in the composition of trading partners must have taken place. If new
partners (extensive margin) are geographically close, then one would




52 de Melo and Ugarte


observe a regionalization of trade. A regionalization of trade would also
be observed if existing trade were redirected toward geographically
close partners or if trade among geographically close partners were
growing faster.


Figure 2.5 gives the breakdown between existing (intensive margin)
and new partners (extensive margin). It shows that until 2005, the new
partners are closer. This is in line with the results in Carrère, Gourdon,
and Olarreaga (2011), who find that trade increased following the signing
of preferential trade agreements. It also shows that new partners have an
increasing weight in total import value. Thus the regionalization of trade
has taken place at the intensive margin and the increasing trend in
regional trade noted by Shui and Walkenhorst (2011, table 10.3) has been
in new products.


Exports, Diversification, and Survival
MENA countries have lower shares of manufacturing exports than com-
parator countries. The causes behind this symptom are not clear: is it a
matter of volume of trade, lack of diversity, the difficulty in maintaining


Figure 2.5 Average Trade Distance of MENA Countries with Traditional and
New Trade Partners


30


20


n
ew


p
ar


tn
er


s
as


a
s


h
ar


e
o


f t
o


ta
l (


%
)


10


0 3,000


4,000


5,000


A
D


O
T-


M
EN


A
(k


m
s)


6,000


7,000


8,000


9,000


19
80


19
85


19
90


19
95


20
00


20
05


ADOT–traditional partners


number of observations (%) Import value (%)


ADOT–new partners


Source: Authors’ calculations based on Carrère, de Melo, and Wilson (2010).
Notes: Country averages over five-year periods during 1970–2004 and a two-year period, 2005–06. UMI = upper
middle income. LMI = lower-middle income. ADOTP = potential average distance of trade in a frictionless world.
ADOT = actual average distance of trade. ADR = ADOTP/ADOT = trade cost.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 53


export spells for new products, or perhaps, as pointed out above, the bad
luck of being in the most crowded region for factor endowments? This
section explores the extent to which low productivity levels could be a
barrier to exporting. It also looks at what contributes to the survival of
new exports, since an export surge cannot last if survival rates are low.


Low firm productivity. Many firm-level studies support the view that
increased openness to world markets stimulates productivity levels in
stronger firms and encourages weaker firms to leave the market, thereby
reallocating resources from weaker to stronger firms. The firm-level stud-
ies also show that access to imports (made possible by foreign exchange
earnings from exports) boosts growth by granting access to capital goods
and inputs from many competitive sources (this is probably a reason why
investment rates shoot up after trade liberalization). However, we do not
know if it is exporting that improves productivity at the plant level or
rather that exporters self-select into exporting based on higher productiv-
ity, even if interviews from case studies suggest that quality matters for
exporting to rich markets.23


At the microlevel, much effort has focused on the correlates of the low
level of productivity (technical efficiency) in MENA manufacturing. A
typical finding is that, compared with other middle-income countries,
MENA manufacturing firms have lower technical efficiency, lower pro-
ductivity, and higher labor costs across a wide range of manufacturing
sectors. This is the finding of a recent study by Kinda, Plane, and
Veganzones-Varoudakis (2011), reported in table 2.7,24 which shows a
lesser performance for MENA countries relative to other middle-income
countries across nearly all industries; this finding again suggests a MENA
specificity.


Kinda, Plane, and Veganzones-Varoudakis (2011) also find that in
most sectors, lower technical efficiency is positively correlated with
below-average indicator values for the regulatory and legal environ-
ment captured by the Investment Climate Assessment indicators (qual-
ity of infrastructure, experience and education of labor force, and
different dimensions of the government-business relationship). Given
the poor rankings of most MENA countries in trade and regulatory
indicators, this result is not surprising. In sum, as concluded by several
studies and reports (Nabli 2007; World Bank 2004; World Bank 2009),
over the past three decades, investment has lagged, manufacturing
exports have not diversified, and a largely inefficient manufacturing
sector has developed.




54


Table 2.7 Firm-Level Productivity, MENA and Non-MENA


Textile Leather Garment Agroprocessing


Metal,
machinery


products
Chemicals,


pharmaceuticals
Wood,


furniture
Nonmetal,


plastics


Labor Productivity (LP) (US dollars at current exchange rate)
Non-MENA 10.08*** 6.80*** 6.65* 14.9 16.0 18.5 7.5 11.1**
MENA 7.93 4.91 4.96 15.2 15.6 18.6 7.3 8.8
Unit labor costs
Non-MENA 0.37*** 0.46*** 0.69 0.46 0.44** 0.33* 0.58** 0.54
MENA 0.49 0.82 0.63 0.42 0.50 0.43 0.68 0.48
Technical efficiency
Non-MENA 44.6** 63.9*** 62.3 44.5*** 60.6*** 40.8 48.3*** 61.6***
MENA 42.8 54.7 64.8 40.3 44.4 42.5 37.5 49.8


Source: Kinda, Plane, and Veganzones-Varoudakis (2011, table 4).
Note: Number of firms per industry: 360 (leather) to 1,601 (garments). MENA countries: Algeria, Egypt, Lebanon, Morocco, Saudi Arabia. NON-MENA: (LAC) Brazil, Ecuador, El Salvador,
Guatemala, Honduras, Nicaragua; (AFR) Ethiopia, South Africa, Tanzania, Zambia; (SAS) Bangladesh, India, Pakistan, Sri Lanka; (EAP) China, Philippines, Thailand. * 10 percent significance.
** 5 percent significance. *** 1 percent significance.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 55


Export diversification and survival. Is product diversity (in terms of prod-
ucts, partners, or both) correlated with superior performance? At the
macrolevel, concentration of activities is associated with volatility,25 so the
natural policy response—which has been part of the package of reforms
advocated for MENA countries—has been to push for export diversifica-
tion (export growth at the extensive margin, either from existing products
to new markets or from new products). But the evidence at the microlevel
is still inconclusive. Some have pointed out that productivity increases are
primarily achieved through interindustry spillovers and that these are more
likely in certain product groups—that is, in the product-space language, in
the “denser” part of the “forest,” where there are greater opportunities for
cross-product linkages. Along these lines, Hausmann, Hwang, and Rodrik
(2007) find that, after controlling for intervening factors, notably per capita
income, countries with a more sophisticated (that is, more diversified)
export bundle subsequently grow faster.


These results have not remained unchallenged. For example, Harrison
and Rodriguez-Clare (2009) suggest that the links between diversity and
productivity have not yet been established, and that quality upgrading
(which is essential to remain competitive in rapidly evolving markets)
rather than product diversity may be the key to success. Also, the evi-
dence is mixed about whether productivity increases come through
learning from exporting, or whether initially, at least, the highest-produc-
tivity firms that self-select into exporting (increases in productivity that
might come from first exporting at the regional level).


Figure 2.6, adapted from Cadot, Carrère, and Strauss-Kahn (2011),
estimates an index of concentration in relation to income per capita at the
HS-6 level for 156 countries over two periods: 1990–95 and 1996–2007
(period averages). Their estimates show that diversification takes place
mostly at the extensive margin (new products to old or new partners)
rather than at the intensive margin (old products to existing or new part-
ners). As can be seen from the figure, this decrease in concentration takes
place until income per capita reaches about $22,000. The fit is quite
tight, and the relationship is stable over the two periods, with a slightly
more concave estimated curve for the second period.


It is worth noting that all oil exporters in the resource-rich and GCC
groups are far above the estimated line, while the resource-poor countries
are either on or below the estimated line (meaning they are more diversi-
fied). This is undoubtedly related to the small size of the industrial sectors
in resource-rich countries, and it once more reveals a specificity for the
oil exporters even though the root causes of lack of diversification vary




56


Figure 2.6 Export Diversification and per Capita Income


YEM


MAR


JOR


SYR


EGY


TUN


DZA


IRN
OMN


SAU


a. 1990–95 b. 1996–2007


ARE


KWT


2


4


6


8


0 10,000 20,000 30,000 40,000


(mean) GDPpcppp


YEM


MAR


SYR


JOR
EGY


DZA


TUN


IRN
OMN


SAU


ARE


KWT


2


4


6


8


0 10,000 20,000 30,000 40,000 50,000


(mean) GDPpcppp


Source: Authors’ calculations from Cadot, Carrère, and Strauss-Kahn (2011).




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 57


across countries. But moving to new products is not just a matter of pas-
sive factor accumulation: as emphasized by Hausmann and Rodrik
(2003), it also requires having the capabilities associated with the new
products, capabilities that depend on what you already export. According
to the measures developed by Hidalgo and others (2007), these capabili-
ties are limited for exporters of hydrocarbons.


Algeria’s exports are very concentrated, as Hausmann, Klinger, and
Lopez-Calix (2010) show, even when one excludes oil and minerals. They
reject real exchange rate appreciation and volatility as potential explana-
tory factors. They recognize that high protection and rent-seeking might
have played a role, as well as a business-unfriendly environment, but they
argue that (partial) correlation between Doing Business indicators and
the product diversity of the export bundle still shows that Algeria’s non-
oil export basket is very concentrated, once controlling for the value of
the Doing Business indicator. Using a measure of the connection of prod-
ucts showing that the product space has a core-periphery structure, they
find that hydrocarbons are poorly connected to the rest of the product
space, suggesting that diversification for oil exporters will be inhibited
because new activities are not closely related in the product space (see
their figure 4.9). Thus the pattern in figure 2.6 suggests that exporters of
hydrocarbons have an inherent difficulty in diversifying.


There is, however, an intriguing observation behind the quadratic
shape of the concentration curve in figure 2.6. Cadot, Carrère, and
Strauss-Kahn (2011) show that the search for new products (called “dis-
coveries” by some and “export entrepreneurship” by others), which disap-
pears after the turning point, coincides with a change in the export
bundle to more closely resemble the comparative advantage of countries
(as measured by the distance from their endowments). This would sug-
gest that there is level of development beyond which a country’s com-
parative advantage settles. So, among MENA countries, the oil exporters
might be closer to their long-term comparative advantage, because high
diversification characterizing the middle part of the development process
would be an “out-of-equilibrium” stage between two states, characterized
by specialization according to comparative advantage. But it could also
reflect weak links because of the size of their industrial sector, as sug-
gested in chapter 4 of this book.


Typically, export spells are of very short duration in low-income coun-
tries. The issue then is what accounts for (is correlated with) this lack of
duration of new products. From a policy point of view, knowing this is as
important as (if not more important than) what lies behind the discovery




58 de Melo and Ugarte


phase. Having shown that 80 percent of new exports die within a year,
Besedes and Prusa (2006) suggest that higher survival rates are essential
for achieving faster export growth. This conjecture finds support in
Brenton, Pierola, and Von Uexkull (2009), who show that poorly per-
forming countries are not inferior to stronger countries in introducing
new trade flows, but rather that they experience much lower rates of
survival. The authors find that a strong positive association between
export survival rates and per capita income as well as the probability that
the death of an export flow diminishes the longer the export flow sur-
vives. More recently, Besedes and Prusa (2010) show that differences in
survival rates and the deepening of existing relationships are important
drivers in accounting for long-run differences in performance.


Figure 2.7 compares the survival rates of exports for the MENA group
with those for the sample of upper-middle- and lower-middle-income
groups used earlier in the chapter, using HS-4 level data to remove the
large errors in measurement for low-income countries in the more disag-
gregated data.26 This gives us 1,240 product categories over the years
1998–2007.27 Survival rates increase with income per capita (see Brenton,
Saborowski, and Von Uexkull 2010, figure 1). Since MENA countries
mostly belong to the middle-income groups, we compare survival rates
with those of the UM and LM group averages. Survival rates are lower for
the MENA group than for both the LM and UM groups.28 This low sur-
vival of exports is indeed another MENA specificity.


Figure 2.7 Kaplan-Meier Survival Rates


0


0.25


0.50


0.75


1.00


analysis time
0 2 4 6 8 10


LM MENA UM


Source: Authors’ calculations using International Trade Dataset at the Product Level (BACI).
Note: See text for an explanation of this figure.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 59


We now use the standard Cox proportional hazard model to estimate
the correlates of the hazard rates and estimate:


li(t) = l0(t)exp(zi(t)′b ) (2.3)


where l0(t) is the baseline hazard rate, and zi(t) is a vector of covariates
that has a proportional impact on the hazard function.29


Results are reported in table 2.8 for the high- and middle-income
group of countries in the first two columns, and for 15 MENA countries
in the last two columns. For the control comparator group (columns 1
and 2), greater distance between the partners reduces the duration of the
export spell (that is, increases the hazard rate). Volatility does not affect
the hazard rate.30 Surprisingly, this is not the case for the MENA group
of countries, for which distance does not affect the hazard rate. Contiguity
and common language are associated with longer duration. Interestingly,
for the MENA group, misalignment is associated with a significantly
lower export spell.


It is difficult to arrive at unambiguous conclusions given data trunca-
tion problems (the time series data is only over a 10-year period) and the
fact that some of the coefficient values change when the sample group is
altered. Nonetheless, since earlier evidence shows that diversification and
duration of export flows are associated with superior long-run export
growth, the evidence suggests that MENA countries are disadvantaged by
the short duration of their export flows.


Table 2.8 Correlates of Hazard Rates for 4-Digit Export Flows


Variable


High- and middle-income
countries MENA countries


Coefficient p-value Coefficient p-value


Log(Distance) 1.078 0.000 0.984 0.024
Contiguity 1.007 0.170 0.876 0.000
Common language 1.010 0.003 0.884 0.000
Colony 0.931 0.000 1.013 0.605
Log(Total bilateral trade) 0.800 0.000 0.884 0.000
Volatility 1.007 0.000 1.004 0.000
Misalignment 1.028 0.000 1.118 0.000


Source: Authors’ calculations using International Trade Dataset at the Product Level (BACI).
Note: The dependent variable is the hazard rate of export flows at the HS 4-digit level excluding oil and minerals
(HS-2 digit: 26 and 27). Coefficients are presented in exponential form so a coefficient of 1.07 (0.93) means that,
holding the other covariate values constant, the hazard rate is 7 percent higher (lower) than the baseline estimate.
Total bilateral trade is calculated for the first year of the spell. Volatility is the monthly volatility in the exporter’s real
effective exchange rate with respect to the partner’s volatility. Misalignment is the exchange rate between
exporter and importer in the year the trade relationship starts relative to the period average (1998–2007).




60 de Melo and Ugarte


Conclusion


MENA’s recent performance has shown progress, with higher growth
rates, less growth volatility, and increased market shares for its exports
despite the competition from fast-growing countries and exporters such
as China and India. This catching up is encouraging against the backdrop
of a generally disappointing performance over the past 50 years, espe-
cially for the resource-rich countries. Over the past 50 years, performance
has been better for the resource-poor countries, which have quite closely
tracked comparator groups except the high-growing Asian countries,
while resource-rich labor-abundant countries have lagged. With the
exception of Oman, MENA countries have failed to climb up the ladder,
remaining either in the lower-middle- or upper-middle-income group.


This chapter and the supporting evidence have shown that the com-
bination of macro- and micropolicies in a generally weak institutional
environment produced this outcome. At the macrolevel, MENA coun-
tries have been unable to maintain a depreciated (undervalued) real
exchange rate for long periods; such an undervaluation helps to correct
the market failures and poor institutional environment that hits hardest
the dynamic non-resource-intensive traded sectors. The region, especially
the resource-rich group, displayed greater volatility in macro-indicators
than comparable groups until the mid-1990s, when performance started
to pick up. For example, for the GCC, but also for the resource-rich
group, countercyclical fiscal policies have been less effective than in
other resource-rich countries with comparable external shocks. As a
result, the volatility of the real effective exchange rate has been greater
than in comparable groups, and volatility contributed to the lack of
development of new activities outside the resource sectors and to short-
lived export spells.


Cross-country evidence shows that the positive relation between
openness and per capita income holds only for countries with good indi-
cator values for regulatory reform. The Doing Business data also show
that countries rich in natural resources are less inclined to carry out
reforms than others. Despite some progress toward reducing tariffs on
industry, MENA countries fare poorly in most indicators describing the
domestic microeconomic environment, giving the impression of an envi-
ronment in which trade is not facilitated and of an unfinished reform
agenda. Improved domestic regulatory policies, along with improved
public sector governance (reflected in better indicators values), would
help MENA to achieve greater integration in the world economy.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 61


Annex 2A Trade, Structural Change, and Natural Resources


This annex examines patterns of structural change from traditional sec-
tors toward high-productivity sectors, that is, shifts out of agriculture
and the informal sector into the other sectors of the economy where
the production of most high-productivity tradables takes place. For
countries in the Middle East and North Africa (MENA), the
low-productivity sectors include not only agriculture, but most rent-
generating sectors in oil and minerals, many public sector services, and
some nontradable services.31


If development entails a resource shift toward manufactures and ser-
vices, then there should be a positive correlation between the shares of
manufacturing and services in GDP and per capita income. Likewise,
if exports from these sectors reflect high productivity and the exploita-
tion of spillovers, one would expect a positive correlation between the
shares of exports of manufactures and per capita income. By the same
token, one would also expect a positive correlation between the export
shares of services and per capita income. Controlling for factors associ-
ated with exports, does this positive association hold, and is there a
MENA specificity?


Patterns of growth and structural change are examined by fitting trade
and production shares against per capita GDP, yit, and control variables,
zit (such as population, the share of rents in GDP, an index of trade costs,
conflicts, and/or time and country fixed effects).32 Endogeneity issues are
ignored because the only objective is to see if MENA countries or group-
ings are “different” from average development patterns. The typical esti-
mated equation is:


qit(t) = a + b yit + g zit + eit; i = 1, . . . n, t = 1, . . . T (2A.1)


We expect that the shares will be positively correlated with per capita
GDP. Dummy variables for MENA countries are included to detect
regional specificity (that is, the effect of omitted variables). Data avail-
ability for the control variables determines the sample size. We start with
trade shares and then move on to predictions of manufacturing and ser-
vices shares in GDP.


The first exploration takes a large sample of countries to try to iden-
tify the correlates of the trade share in GDP, taking first the overall trade
share, then the share of manufacturing exports (excluding oil and miner-
als) in GDP as the regressand. To ease comparisons, the same set of




62 de Melo and Ugarte


regressors is used with a dummy variable for MENA added to the list of
regressors, starting in each case with per capita income (column 1 in
table 2A.1) and adding one regressor at a time. For the correlates of the
overall trade share (only reported in figure 2A.1), per capita income
always enters positively. Except for population, which is always nega-
tively related to the trade share, adding regressors improves the overall
fit only marginally even though the share of rents in GDP, the average
rate of protection, and the number of conflicts have the expected signs.33
Noticeably, the Logistics Performance Index (a higher value of the index
means better physical infrastructure) is not significant, although that is
because it is significantly positively correlated with per capita income.
When taken jointly, the control variables (in addition to per capita
income and population) are statistically significant. In conclusion, the
MENA dummy variable is never significant, so there is no MENA spec-
ificity in the overall openness of the countries in the region, and to bor-
row from World Bank (2004, figure 2.3), contrary to what was said in
that report regarding the trade share in GDP, one cannot say that MENA
“failed to ride the wave.”


This conclusion is confirmed in the partial scatter plot of the trade
share against per capita GDP in annex figure 2A.1a after having netted
out the other control variables, where except for conflict-stricken
Lebanon, all MENA countries are bunched around the predicted line.


However, when the same set of regressors is applied to the share of
manufacturing trade (excluding oil and minerals) in GDP, a MENA
specificity does appear (see table 2A.1). The sample is smaller, the fit
less good and less stable, and there are obvious endogeneity problems
with two-way causality between protection and trade and between
the infrastructure index and trade. The significance of per capita
income disappears when the average rate of protection for manufac-
tures is introduced in column 3 because of the significant negative
correlation between protection of industry and per capita income in
the sample. However, the significance of the MENA dummy remains
for all specifications.


The partial scatter plot in figure 2A.1b suggests that the specificity
is related to the distinction between the resource-rich and resource-
poor groups. Most resource-rich members, except for Bahrain and
United Arab Emirates, which are above the line, fall on or below the
predicted relation while all the resource-poor countries are close to or
above the regression line. Particularly significant is Algeria’s low non-oil
trade share. Hausman, Klinger, and Lopez-Calix (2010) argue that




63


Table 2A.1 Correlates of Trade Shares in GDP


Ln(Non-oil exports
as % of GDP)


(1)
Income


(2)
L-lock


(3)
Pop.


(4)
Protect.


(5)
Rents


(6)
Conflict


(7)
LPI


Ln(GDPpc) 0.16*** 0.18*** 0.18*** 0.05 0.05 0.04 −0.25**
[0.05] [0.06] [0.06] [0.07] [0.08] [0.08] [0.10]


Landlocked 0.15 0.16 0.06 0.04 0.03 −0.02
[0.19] [0.19] [0.18] [0.18] [0.18] [0.18]


Ln(Population) 0.01 −0.02 −0.02 0.01 −0.12**
[0.04] [0.04] [0.04] [0.05] [0.06]


Average tariff −0.03* −0.03 −0.03 −0.02
[0.02] [0.02] [0.02] [0.02]


Ln(Rents) −0.01 −0.01 0.00
[0.01] [0.01] [0.01]


Number of conflicts −0.01* −0.01
[0.01] [0.01]


LPI index 0.79***
[0.20]


MENA −0.86*** −0.83*** −0.83*** −0.72*** −0.70*** −0.69*** −0.56**
[0.24] [0.24] [0.24] [0.24] [0.25] [0.25] [0.25]


Constant 2.60*** 2.54*** 2.51*** 3.13*** 3.06*** 3.05*** 1.65***
[0.13] [0.15] [0.18] [0.29] [0.29] [0.29] [0.48]


Observations 124 124 124 122 121 121 113
R-squared 0.142 0.147 0.147 0.167 0.175 0.195 0.304


Source: Authors’ calculations.
Note: The results are estimated in cross-section regressions for year 2005. “Average tariff” is a simple average of applied MFN tariffs on manufactures. “Rents” is the share of total rents in
GDP (2004). Total number of conflicts by country is calculated over the period 1980–2005. Conflicts are counted yearly. LPI is the Logistics Performance Index. Standard errors in brackets.
*** p<0.01, ** p<0.05, * p<0.1.




64


Figure 2A.1 Predicted Trade Shares in GDP


MAR
LBY


JOR


LBN


EGY


SYR
OMN


SAU
BHR


DZA
KWT


ARE
TUN


QAT


IRN


−2


−1


0


1


2


a. total trade b. non-oil trade


e


(


l


n


e


x


p


o


r


t


|


X


)


−3 −2 −1 0 1 2


e(lninc|X)


coef = 0.17904732, se = 0.04173083, t = 4.29 coef = 0.03895179, se = 0.07558655, t = 0.52


LBN


MAR
JOR


SYR


OMN
SAU


BHR


DZA


ARE


QAT


TUN


IRN


−3


−2


−1


0


1


2


e


(


l


n


e


x


p


o


r


t


s


w


o


o


i


l


|


X


)


−2 −1 0 1 2


e(lninc|X)


Source: Authors.
Notes: Partial plot of the share of total and non-oil exports in GDP (in logs) and GDP per capita at constant prices (in logs). Estimation is run on a cross-section of countries in 2005.
For other control variables and estimates, refer to column 6 in table 2A.1.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 65


Algeria’s lagging manufacturing sector is mainly caused by the lack of
connection between hydrocarbons and other sectors


The significance of the MENA dummy also holds when it is applied to
each group one at a time. In conclusion, the three groups display different
non-oil export patterns that are still in need of further exploration.


A Lagging Manufacturing Sector for the Resource-Rich Group
Next we look for a MENA specificity in the share of manufactures and
services in GDP over the period. This gives a slightly different perspective
and is more directly addressed to the de-industrialization effect associated
with the Dutch Disease while at the same time changes in production are
also a close indicator of changes in exports.34 Evolution of these shares is
also a measure of the speed of structural change.


The regressions in table 2A.2 are for a panel of 167 countries over
the period 1980–2004. The top left panel reports the correlates for
manufacturing shares and the right panel for services shares. Both
have country fixed effects that absorb time-invariant factors specific
to individual countries and time fixed effects for common changes in
the external environment. Table 2A.3 displays the same regressions for
the MENA region as a whole and for the MENA subgroups over the
same periods, but without time fixed effects to preserve degrees of
freedom.


Focusing on manufactures and services has its roots in the dual-economy
vision of development. This view, largely accepted, calls for the move-
ment of resources out of “traditional,” relatively low-productivity activi-
ties into “modern,” high-productivity activities where externalities help
establish a virtuous circle of growth. The modern high-productivity goods
are in manufacturing and more recently in the traded components of the
services sectors (banking, transport, telecommunications, professional
services). As Rodrik (2009, p. 4) put it, “Poor countries get rich by pro-
ducing what rich countries produce.” We comment first on the results for
manufacturing, then for services.


Results for manufacturing for the whole sample are at the top left of
the table. As expected, per capita income is significant, but so is the
share of rents in GDP, which enters negatively as would be expected
from the resource-curse literature. The negative coefficient on rents
holds in table 2A.3, where the sample is restricted to MENA countries.
Since per capita income is significant only for the resource-poor group,
these results suggest resource-curse effects delaying the development of
manufacturing in the resource-rich and GCC group.




66


Table 2A.2 Correlates of the Share of Manufactures and Services in GDP


Whole sample


Share of manufacture (% GDP) Share of services (% GDP)


(1)
All


(2)
All


(3)
All


(4)
All


(5)
All


(6)
All


Ln(GDPpc) 1.82*** 1.69*** 1.69*** 0.78 0.74 0.74
[0.39] [0.38] [0.38] [0.77] [0.78] [0.78]


Ln(Rents) −0.08* −0.08* −0.02 −0.02
[0.05] [0.05] [0.06] [0.06]


MENA Dummy −14.15*** −19.15***
[1.45] [2.00]


Constant 16.19*** 16.21*** 16.21*** 28.34*** 28.34*** 28.34***
[0.78] [0.78] [0.78] [0.90] [0.90] [0.90]


Country fixed effects Yes Yes Yes Yes Yes Yes
Year fixed effects Yes Yes Yes Yes Yes Yes
Observations 3,232 3,232 3,232 3,648 3,648 3,648
R-squared 0.838 0.839 0.839 0.828 0.828 0.828


Source: Authors.
Notes: GDP capita in thousands of $US at constant prices (2005). “Rents” is the share of total rents in GDP in percentage points. The sample is an unbalanced panel of 167 countries for the
period 1980–2004. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * <0.1.




67


Table 2A.3 Correlates of the Share of Manufactures and Services in GDP in MENA


MENA


Share of manufacture (% GDP) Share of services (% GDP)


(1)
All MENA


(2)
Resource poor


(3)
Resource rich


(4)
GCC


(5)
All MENA


(6)
Resource poor


(7)
Resource rich


(8)
GCC


Ln(GDPpc) −1.02 6.07*** −3.34 −2.22 −8.44*** 3.71** −12.45*** 0.64
[1.28] [1.31] [4.14] [1.40] [3.24] [1.72] [2.67] [2.62]


Ln(Rents) −0.33* −0.14 −0.40 −3.67*** −0.82* −0.04 −4.22*** −21.18***
[0.18] [0.08] [1.17] [0.86] [0.42] [0.08] [0.77] [1.84]


Constant 15.17*** 9.63*** 12.77*** 31.30*** 76.26*** 43.04*** 74.96*** 109.85***
[5.10] [1.67] [3.44] [5.31] [12.77] [2.21] [6.18] [8.58]


Country fixed effects Yes Yes Yes Yes Yes Yes Yes Yes
Year fixed effects No No No No No No No No
Observations 304 111 83 110 312 111 91 110
R-squared 0.763 0.539 0.511 0.729 0.762 0.872 0.932 0.726


Source: Authors.
Notes: GDP capita in thousands of $US at constant prices (2005). Rents is the share of total rents in GDP in percentage points. The sample is an unbalanced panel of 16 countries for the
period 1980–2004. Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.




68 de Melo and Ugarte


Returning to the entire sample, there is also a strong MENA specificity
after controlling for country and time fixed effects. Time-varying omitted
country effects must then have been important in the development of
manufactures. Many omitted factors, specific to countries, could account
for the significance of this dummy variable, including different macroeco-
nomic cycles, policy changes, country-specific external shocks, or mea-
surement errors.


Figure 2A.2 takes the longest period of data available for trade and
manufacturing shares in GDP for MENA countries and compares them
with those predicted from a regression of the share on per capita GDP
(PPP). It complements the cross-section results in figure 2A.1.


Two patterns stand out. From figure 2A.2a, one sees that, on average,
MENA countries do not undertrade on an aggregate basis, but that the
spread around the predicted relation is large. Consistently, for all esti-
mated relations, the resource-poor group is closer to the predicted line.
Figures 2A.2b and 2A.2c show that the share of manufacturing in GDP
and the share of manufacturing exports in GDP are under the regression
line, mostly for the resource-rich and GCC groups. Countries in the
resource-poor group are either on or above the 95 percent confidence
interval.


The bottom part of figure 2A.2 shows the evolution of the absolute
deviation of country shares from the predicted relation over the four time
periods. Across all shares, the resource-poor group is closest to the norm
and is relatively stable. The resource-rich group gets closer to the pre-
dicted line for the overall trade share, but distances itself for the value
added share of manufacturing in GDP and especially for the share of
manufactured exports. On the other hand, the GCC group is either
closer or getting closer to the predicted line for both the predicted share
of manufacturing in value added or of the predicted share of manufac-
tured exports.


A Lagging Services Sector in the Resource-Rich Countries
Services and services trade have taken a growing role as a source of
growth around the world, even though this is not evidence that the
services sector is an engine of growth. However, the dramatic changes in
the 3Ts—technology, transportability, and tradability—of many services
activities have contributed to the growing share of the services sectors
in GDP growth (50 percent of South Asia’s GDP’s growth between
1980–85 and 2000–07; see Reis and Farole 2010).35




69


Figure 2A.2 Actual versus Predicted Shares of Manufactures and Manufacture Exports: MENA


ARE


BHR


DZA
EGY


IRN


JOR
KWT


MAR


OMN
SAU


SYR


TUN


ARE
BHR


DZA


EGY
IRN


JOR


KWT


LBN


LBY
MAR OMN


QATSAU


SYR


TUN


3.5


4.0


4.5


5.0


5.5


l


o


g




o


f




m


e


a


n




t


r


a


d


e




s


h


a


r


e




(


%




o


f




G


D


P


)


−2 0 2 4


a. predicted trade share (% of GDP)


ARE


BHR
DZA


EGY


IRN


JOR


KWT


MAR


OMN


SAU


TUN
ARE


DZA


EGY


IRN


JOR


LBN


LBY


MAR


SAU
SYR


TUN


0.5


1.0


1.5


2.0


2.5


3.0


l


o


g




o


f




m


e


a


n




m


a


n


u


f


a


c


t


u


r


e


v


a


l


u


e


-


a


d


d


e


d




(


%




o


f




G


D


P


)


−2 0 2 4


c. predicted share of manufacture value-added (in production)


0


0.2


0.4


0.6


0.8


m


e


a


n




a


b


s


o


l


u


t


e




e


r


r


o


r


1 9
8 0


1 9
8 5


1 9
9 0


1 9
9 5


2 0
0 0


2 0
0 5


b. trade share (% of GDP) on GDP per capita


0.2


0.4


0.6


0.8


1.0


m


e


a


n




a


b


s


o


l


u


t


e




e


r


r


o


r


1 9
8 0


1 9
8 5


1 9
9 0


1 9
9 5


2 0
0 0


2 0
0 5


d. manufacture value-added (% of GDP) on GDP per capita


(continued next page)




70


Figure 2A.2 (continued)


ARE


BHR


DZA


EGY


IRN


IRQ


JOR


KWT


LBN


LBY


MAR


OMN SAU


SYR


TUN


YEM


ARE


BHR


DZA


EGY


IRN


IRQ


JOR


KWT


LBN


LBY


MAR


OMN
QATSAU


SYR


TUN


YEM


−1


0


1


2


3


4


m


e


a


n




s


h


a


r


e




o


f




m


a


n


u


f


a


c


t


u


r


e


s




i


n


e


x


p


o


r


t


s




(


i


n




l


o


g


s


)


−2 0 2 4
log mean GDP per capita, PPP (constant 2005 international US$)


95% confidence interval fitted values
MENA values in 1995 MENA values in 2005


e. predicted share of manufacture in exports


0.5


year


1.0


1.5


2.0


2.5


m


e


a


n




a


b


s


o


l


u


t


e




e


r


r


o


r


1 9
9 5


2 0
0 0


2 0
0 5


resource poor resource rich


GCC


f. share of manufacture in exports on GDP per capita


Sources: Authors’ calculations of the share of manufacture on total exports, based on WDI and BACI International Trade Database at the Product Level. Trade flows cover the period
1998–2007.
Notes: “Manufacture exports” is equal to total exports minus exports in HS 2-digit codes 01-28. The observed value of each variable in t is the average of the variable over the period


(t, t+4). Fitted OLS line of variable on y-axis against log mean per capita income on x-axis.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 71


However, it remains that the services sector is very heterogeneous,
mingling very high- and very low-productivity activities, making services
productivity difficult to capture with a single measure. Overall, one
would expect a growing share of services in GDP as per capita income
increases, capturing Engel effects, among others, in consumption and the
development of human-capital-intensive professional services. In cross-
section data, this should be reflected in a positive correlation between
the share of services in GDP and per capita income (Hoekman and
Mattoo 2008). But this is not the case for the MENA sample because
the income per capita coefficient has a positive, but statistically insig-
nificant sign (right-hand side of table 2A.3)—although the expected
positive pattern between per capita income and the service share holds
for the resource-poor group in the bottom of the table. Rents are also
negatively associated with the share of services in GDP in the resource-
rich and GCC groups in the right part of table 2A.3, a pattern again
coherent with resource-curse effects.


Overall, the pattern across the three groupings is distinctive. As a
region, MENA’s openness to trade is close to predicted norms but, as
expected, the share of non-oil manufactures in trade is below predicted
patterns, and there is a MENA specificity in non-oil trade across all three
groups. There is also evidence of a lagging manufacturing and services
sector for the resource-poor group consistent with resource-curse
effects.


Annex 2B Ten Observations on Successful Growth


General Principles
After four years of inquiry, acknowledging that there are “no recipes, just
ingredients,” the Commission on Growth and Development (2008) iden-
tified five common characteristics of successful growth—the fundamen-
tals of competitiveness:36


1. Committed, credible, capable government: governments must have
the capacity to devise and the institutions to implement a growth
strategy.37


2. Macroeconomic stability: modest inflation and sustainable public
finances.


3. High rates of savings and investment: high and sustained investment
underpinned to a large extent by domestic savings. Countries that had




72 de Melo and Ugarte


achieved high and sustained growth had impressive rates of public
investment in infrastructure, education, and health.


4. Full exploitation of the world economy: knowledge acquired in the
global economy and exploitation of global demand is the fundamental
basis of economic catch up and sustained growth. Promoting foreign
direct investment and foreign higher education can support knowl-
edge transfer.


5. Letting markets allocate resources: policies need to ensure that
product and labor markets are flexible enough to allow structural
transformation of the economy from agriculture to manufacturing
to take place and there is, at minimum, no bias against exports.


Correlates of Success
Evidence—economy-wide, sectoral, firm, and product level studies—has
indicated some of the channels through which increases in productivity
take place:


6. A substantial reduction in barriers to trade (tariffs, nontariff barriers)
is associated with an increase in the growth rate (and in the invest-
ment share in GDP) (Wacziarg and Welch 2008).


7. A sustained real exchange rate depreciation, that is, a competitive cur-
rency (that is subsequently maintained) is a key ingredient to sus-
tained export surges and to higher investments and growth (Freund
and Pierola 2011).


8. A positive causal relationship flows from openness to income levels
and from trade liberalization to medium-term growth.


9. Export spells are likely to last longer when carried out with physically
closer partners and a preferential trading relation is associated with
longer export spells (Brenton, Saborowski, and Von Uexkull 2010).


10. The linkages between a successful export strategy and the pattern of
export expansion are still open to debate and are likely to be context
specific and highly idiosyncratic as evidenced by recent case studies of
firms’ successes.


Annex 2C Applied Tariff Protection Is Still Relatively High


Controlling for per capita income, estimated applied tariffs around the
world were about 10 percentage points lower in 2008 than they were
around 1990 (figure 2C.1). The fit also became tighter, as the 95 percent




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 73


Figure 2C.1 Predicted Applied MFN Protection


MAR


DZAEGY
TUN


0


20


40


60


80


si
m


p
le


a
ve


ra
g


e
A


H
S


ta
ri


ff
(p


er
ce


n
t)


0 5 10 15


GDP per capita, PPP (constant 2005 international US$)


a. in the 1990s


DZA


LBY


MAR EGY


IRN


JOR LBN


SYR
TUN


YEM
0


20


40


60


80


si
m


p
le


a
ve


ra
g


e
A


H
S


ta
ri


ff
(p


er
ce


n
t)


0 5 10 15


GDP per capita, PPP (constant 2005 international US$)


95% confidence interval fitted values


b. in the 2000s


Source: Authors’ calculations from WITS data based on 1995 or closest available year for predicted protection in
the 1990s and on 2008 or latest available year for the bottom figure.
Notes: Countries included have at least 0.5 million inhabitants and GDP per capita in PPP is less than US$15,000.
In the 1990–95 period, only four MENA countries had tariffs. The estimated equations are: Tariff = 22.6 – 1.08
income + 0.04 pop for the 1990s based on 63 countries; and Tariff = 11.84 – 0.44 income + 0.00 pop for the
2000s based on 116 countries. The mean standard deviation of MENA countries has reduced from 44.57 to
15.57 over the period, calculated for Algeria (DZA), Egypt (EGY), Morocco (MAR) and Tunisia (TUN). For the mean
absolute deviation, a reduction is also observed (from 14.91 to 6.28).




74 de Melo and Ugarte


confidence interval bands narrowed. MENA countries improved their
relative position and drew closer to the “norm” average protection level,
even though Algeria, Tunisia, and the Islamic Republic of Iran still had
high protection and their observations are still spread out of the confi-
dence interval.


Annex 2D Ad-Valorem Equivalents Estimations


New estimates of the ad-valorem equivalents (AVEs) are computed for
those countries that impose single nontariff measures (NTMs) on tariff
lines (this gives a better estimate than the usual estimate where the AVE
is computed over the four “core” NTMs).


To get a better idea of the importance of NTMs, the incidence of
NTMs of MENA countries is compared with that of other countries in a
large sample of countries.38 Table 2D.1 shows the frequency distribution
of core NTMs for the MENA countries in the NTM database. First,
although not indicated in the table, the Arab Republic of Egypt had a
core NTM in 4,941 tariff lines out of a potential of 4,961, that is, only 20
tariff lines at the HS-6 level did not have a core NTM in Egypt in 2002–
04. On a comparative basis, this is an extremely high level of incidence
for the core NTMs, even though prohibitions are important, and these are
mostly on the basis of origin (such as ban on imports from Israel).39
Second, whereas 74 percent of the tariff lines in the sample of 91 coun-
tries only had one core NTM, according to the table, only 31 percent of
the tariff lines in the MENA sample had one core NTM. For the MENA
countries, the multiple NTMs are usually a combination of a technical
regulation and a prohibition at the HS-6 level either for the environment,
a suspension of issuance of licenses, or a prohibition on the basis of origin
(embargo).


Carrère and de Melo (2011) have calculated the average tariff equiva-
lent for these single NTM lines (74 percent of the tariff lines in the Kee,
Nicita, and Olarreaga 2009 sample). This has the advantage of estimating
the tariff equivalent of each NTM separately so that one can distinguish
between the AVE for, say, technical regulation and the one for nonauto-
matic licensing. These estimates are reported in table 2D.2 for the MENA
countries that have single NTM lines.


Several patterns emerge. First, the distribution of AVEs is narrow
with most NTMs having an AVE of around 40 percent, which is
almost always greater than the corresponding tariff rate on that prod-
uct line. Second, the simple tariff on the product line with the NTM




75


Table 2D.1 Frequency Distribution of the Number of NTMs


Number of NTMs
per HS6 lines


Egypt,
Arab Rep. of Algeria Morocco Tunisia Saudi Arabia Oman Lebanon Jordan


1 0 0 4,641 1,510 685 642 1,298 2,073
2 9,774 7,354 590 276 106 36 322 764
3 162 2,418 15 30 0 0 3 21
4 0 1,760 0 0 0 0 0 0
5 0 90 0 0 0 0 0 0
Total lines 9,936 11,622 5,246 1,816 791 678 1,623 2,858


Source: Authors’ calculations from Carrère and de Melo (2011).




76 de Melo and Ugarte


is usually high, often above the average tariff for manufactures. Third,
as can be seen from a comparison of the unweighted and import-
weighted AVE, the estimates show that NTMs are associated with
smaller import volumes.


As a final comparison, figure D2.1 plots the estimates of the AVEs
for the sample of countries against per capita income for the two most
important NTMs, nonautomatic licensing and technical regulations.
Nonautomatic licensing is quite widespread, while technical regula-
tions are more common in high-income countries.40 For the other
countries, they are spread around the estimated line, so one can con-
firm that the AVE of single-product-line NTMs are high for the
MENA countries, but that the estimates are in line with those for
other countries.


Table 2D.2 Ad-Valorem Equivalents of NTMs


NTM codes Frequency
AVE


simple (%)
Tariff


simple (%)
AVE


weight (%)
Tariff


weight (%)


31 Administrative pricing
Saudi Arabia 8 42.90 11.69 12.35 15.77


61 Nonautomatic licensing
Jordan 823 47.09 13.90 40.20 9.98
Lebanon 661 40.57 4.61 35.14 5.92
Oman 47 28.57 32.94 16.41 41.90
Saudi Arabia 540 35.50 10.96 16.35 5.45
Tunisia 226 33.21 26.39 15.69 20.59


63 Prohibitions
Lebanon 9 63.65 2.18 4.67 0.05
Oman 27 47.63 6.92 28.95 8.23
Saudi Arabia 56 45.35 5.19 10.16 12.61


71 Single channels for imports
Jordan 2 72.54 6.29 72.54 6.29
Lebanon 10 44.86 3.37 72.43 4.61
Saudi Arabia 3 74.09 5.20 35.99 5.77
Tunisia 34 24.55 19.10 16.83 15.00


81 Technical regulations
Jordan 1,248 40.21 15.06 20.14 14.23
Lebanon 618 44.58 8.51 58.36 9.07
Morocco 4,641 13.99 27.08 7.36 22.50
Oman 568 49.43 8.73 55.97 3.23
Saudi Arabia 78 35.60 11.00 38.61 10.80
Tunisia 1,250 44.22 43.90 27.21 27.69


Source: Authors’ calculations from Carrère and de Melo (2011). The 2-digit NTM classification level has the four
NTMs listed here plus voluntary export price restraint (32), variable charges (33), and quotas (62).
Note: AVEs are calculated only for tariff lines that have a single NTMs.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 77


Figure 2D.1 Ad-Valorem Equivalent of NTMs and per Capita Income
(For HS lines with single NTM)


JOR


TUN


LBN


OMN


SAU


0.1


0.2


0.3


0.4


0.5


0.6 a. 61 nonautomatic licensing


b. 81 technical regulations


N
TM


’s
A


V
E


0 10,000 20,000 30,000 40,000 50,000


per capita GDP (constant $)


MAR


JOR
TUNLBN


OMN


SAU


0


0.2


0.4


0.6


0.8


1


N
TM


’s
A


V
E


0 10,000 20,000 30,000 40,000


per capita GDP (constant $)


Source: Authors’ computation adapted from Carrère and de Melo (2011).
Note: = MENA; O = other countries.




78 de Melo and Ugarte


Notes


1. “Event analysis” refers to a situation when the data is reordered around an
“event” that serves as the base year rather than the usual calendar year. In
Hausmann, Pritchett, and Rodrik (2005), data are centered around the “event”
of growth acceleration. Jerzmanowski (2006) also studies extreme growth
events using a Markov-switching model that distinguishes four different
growth regimes and finds that institutional quality helps determine the transi-
tion between these states. However, his study does not focus on regions, so it
is not helpful in detecting a MENA specificity. See annex 2B for a list of fac-
tors considered essential for sustained growth.


2. Countries are benchmarked against per capita income level. The categories
are OIL, LARGE and POINT (for more details see appendix A). LARGE
countries are those with a population of at least 20 million in 2000; OIL
exporters are the 15 major oil crude exporters listed by the U.S. Energy
Information Administration (IEA 2005); and POINT countries are
those with point source natural resources (classification taken from Isham
et al. 2005).


3. Unfortunately over this period, more than one-third of the data for the two
main groups of interest—resource-rich (6 countries) and resource-poor
(5 countries)—are missing.


4. This sample includes 137 countries (22 countries, of which 5 are MENA
countries, were excluded from the table because of incomplete data). It would
have been desirable to evaluate the performance of nonmineral gross domestic
product, but that would have reduced the sample to 80 countries, with only
4 MENA countries having complete data for the period 1982–2010.


5. Jordan regained its former UM status in 2010.


6. The REER is from the IMF’s International Financial Statistics data, is com-
puted as the relative price of a trade-weighted basket of foreign relative to
domestic goods, and is an indicator of competiveness in world markets and of
sustainability of the current account. It is different from the RER discussed
later in the chapter.


7. See, for example, Koren and Tenreyro (2007), who show a statistically nega-
tive correlation between the volatility of growth and per capita income.


8. In the aggregate sample of Koren and Tenreyro (2010), there is no systematic
correlation between sector and country-specific shocks, whereas there is a
systematic positive covariance between the two in the GCC, indicating a pro-
cyclical fiscal policy that has persisted over the 30-year period they consider
and contributed to greater growth volatility. Interestingly, high-performing
Oman is the exception, displaying a negative covariance between sector-
specific and country-specific shocks, suggesting the use of countercyclical fiscal
policies.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 79


9. Growth episodes are defined as an increase in GDP per capita growth of at
least 2 percentage points for an eight-year period, with a post-acceleration
growth of at least 3.5 percent a year.


10. With years of up breaks separated from years of down breaks by a semicolon,
the following breaks were observed in the MENA region: Algeria (1981),
Egypt (1975; 1970, 1980), Iran (1981; 1976), and Tunisia (1967;1972).
Under this more stringent selection criterion, only three countries had an up
break and the region had four down breaks.


11. As argued by Rodrik (2008), it also operates indirectly through two other chan-
nels: at the microlevel, market failures are likely to be more important in
industrial production; and at the macrolevel, greater penalties from weak insti-
tutions that result in lower appropriability of returns to investment in tradables
(because tradables depend more on property rights, contract enforcement, and
hold-up problems). Thus undervaluation (an increase in the relative price of
tradables) boosts tradables at the margin, contributing to higher growth.


12. And also of export surges—see below for microchannels through which an
RER undervaluation could lead to export growth, especially of new products.


13. Before 1970, data for too many MENA countries were missing.


14. Country fixed effects control for omitted country-specific effects, such as the
differences between resource-rich and resource-poor countries, and period
fixed effects control for common shocks in a period. The five-year averages
smooth fluctuations and attempt to capture the finding that undervaluation
preceding and accompanying export surges typically lasts more than 10 years
(see Freund and Rocha 2011).


15. The result is close to the one obtained by Rodrik (2008), although the RER
appreciation associated with the Balassa-Samuelson effect (an increase in the
productivity of tradables as income rises brings about an RER appreciation)
is about half in value because the latest Penn World Tables have raised sub-
stantially their estimates of price levels for low-income countries.


16. Estimates for Lebanon and Iran reflect periods of conflict. We refrained from
dismissing outlier observations according to a specific criterion (that is, all
observations with an RER over- or undervaluation greater than 200 percent),
not only because we would have lost MENA observations, but also because
the extreme values in column 1 are an indicator of overall macroeconomic
instability in the region. The only excluded observation from the full sample
is Zimbabwe (2005).


17. Annex 2C to this chapter shows that, despite catching up, MENA tariffs are
still high after controlling for per capita income and country size. The ad-
valorem equivalents of nontariff measures in Annex 2D (computed from data
for 2001) also show a high level of incidence of core nontariff barriers com-
pared with other countries.




80 de Melo and Ugarte


18. These indicators are averages of several subindicators, some subjective.
There is overlap, and one could take an average across the indicators in
columns 6 to 9.


19. Because we wish to estimate the model separately for MENA countries, esti-
mation is over a panel of three years covering 2006 to 2008.


20. We thank Caroline Freund and Nadia Rocha for providing the data on time
to export. Our specification is the same as theirs except that we deal only
with exports of manufactures. All control variables in the vectors X and Z not
reported here had expected signs and were usually significant.


21. As done by Freund and Rocha (2011) in their table 4.


22. Carrère, Gourdon, and Olarreaga (2011) detect an increase in intrapartner
trade over the period 1990–2009 for MENA countries following the imple-
mentation of the Pan-Arab Free Trade Agreement and other regional agree-
ments in MENA.


23. Drawing on interviews with 23 successful exporters across the region, Nassif
(2010) concludes that successful export products in the region appear to
depend most on information about new business opportunities and risk taking.


24. In their sample of 22 middle-income countries, 5 are from MENA (Algeria,
Egypt, Lebanon, Morocco, and Saudi Arabia), and the firms are taken from 8
sectors (textiles, leather, garment, agroprocessing, metals and machinery,
chemical and pharmaceuticals, wood and furniture, and plastics). The number
of firms per industry per country is often small (no more than 1,600 in
leather), calling for caution in interpreting the results.


25. Export concentration is associated with greater volatility of the real
exchange rate, which in turn is associated with greater volatility in GDP
growth. See Di Giovanni and Levchenko (2011) and Loayza and Raddatz
(2007).


26. Easterly and Reshef (2010) document the extensive errors in the HS-6 level
data for low-income countries and also opt for aggregation to the HS-4 level.
As customary, we use reporter data. As in Brenton, Saborowski, and Von
Uexkull (2010), we delete left-censored observations (right-censoring is not a
problem).


27. Brenton, Saborowski, and Von Uexkull (2010) use a five-digit SITC product
classification which gives 1,271 products for 82 exporting countries and 53
importing countries for 20 years (1985 to 2005). We only have data for the
10-year period covering 1998–2007 for HS-4 level (1,241 commodities).
However the International Trade Dataset at the Product Level (BACI) from
Centre d’Etudes et de Prospectives et d’Informations Internationales (CEPII)
corrects for the reliability trade flow data. This gives us 142 countries, from
which we exclude countries whose population is less than half a million.




Resource Abundance and Growth: Benchmarking MENA with the Rest of the World 81


28. Survival rates across the three MENA groups are very similar


29. The list of covariates is inspired by Brenton, Saborowski, and Von Uexkull
(2010), but we have taken out those that are consistently insignificant.
Estimates with the more general Prentice-Gloecker model often do not con-
verge because of the large sample size, so we stick with estimates from the
Cox model.


30. The expected effect of volatility on survival is ambiguous. On the one hand,
higher volatility can cause more accidental deaths in trade relations. On the
other, higher volatility means more hysteresis (that is, export spells last longer
because of fixed costs in exporting). When volatility increases, the mean of
the distribution moves up because of truncation of the distribution. A higher
mean results in longer survival.


31. Oil is not necessarily a low-productivity sector with no positive externalities.
Much of the successful experience of Norway, which moved from laggard to
leader among the Nordic countries, has been ascribed to the positive exter-
nalities from the high-technology oil sector (Larsen 2004, 17, cited in
Lederman and Maloney 2008). An example of low-productivity services are
the 800,000 chauffeurs earning around $350 a month who are needed to
drive Saudi women, who are not allowed to drive.


32. Work on patterns of growth and structural change over the long haul was
initiated by Chenery and colleagues (Chenery and Syrquin 1975; Chenery,
Robinson, and Syrquin 1986). That work established several stylized patterns:
strong Engel effects in consumption associated with a diminishing share of
agriculture in GDP at the expense of manufactures and services as a country
develops; less trade in large countries; and a deepening of interindustry link-
ages as per capita income increases.


33. The data on rents are for a sample of 174 countries from the World Bank
database on adjusted net savings (see Bolt, Matete, and Clemens 2002 for
details). They include rents from 15 natural resources, which are calcu-
lated as the difference between the market value of extracted materials
and the average extraction cost and expressed as a share of GDP. As dis-
cussed, rents are an outcome variable and hence not a good proxy for
resource abundance.


34. Freund and Pierola (2011) report a correlation of 0.95 between production
and exports in log levels for a sample of 113 countries over the period
1999–2008. In growth rates, the correlation is still 0.57.


35. Francois and Hoekman (2010) highlight some key characteristics of the
services sector: “services facilitate transactions through space (transport,
telecommunications) or time (financial services)” and “services are fre-
quently direct inputs into economic activities, and thus determinants of the
productivity of the ‘fundamental’ factors of production—labor and




82 de Melo and Ugarte


capital—that generate knowledge, goods and other services. Education,
R&D and health services are examples of inputs into the production of
human capital.”


36. The report identified 13 successes (that is, countries with an average growth
rate of 7 percent over a 30-year period) and notes that pragmatism, skepti-
cism, experimentalism, and persistence have high payoffs.


37. The commission stressed that policies need to be prioritized, reasonably well
implemented, and, tolerantly administered, implying some minimum degree
of probity and absence of the worst excesses of corruption.


38. Kee, Nicita, and Olarreaga (2009). This database covers 91 countries, of
which 21 are OECD countries, for 4,961 HS-6 product categories. With
8 countries, MENA is sufficiently well represented to make comparisons.


39. For comparison, on average across countries, only 1,341 lines have at least one
of the five core NTMs. The Republic of Korea is the country with the least
incidence of core NTMs (two lines). Countries with the highest incidence
(4,941 lines) are Algeria, Côte d’Ivoire, Egypt, Malaysia, Morocco, Nigeria,
Philippines, Sudan, Senegal, and Tanzania. The median number of lines with
at least one of the core NTMs is 799.


40. Because the AVE is computed as the estimated impact of the NTM on trade
via the dummy variable (after controlling for other factors affecting trade
including tariffs) divided by the corresponding estimated import demand
elasticity, the positive correlation between the AVE and per capita income
could reflect a lower elasticity coefficient (in absolute value) for high-
income countries. However, this is not the case (see Carrère and de Melo
2011, figure 6). So the pattern reveals more restrictive technical regulations
in high-income countries rather than differences in import demand elastici-
ties. The scatter plot does not include Algeria and Egypt, the two countries
with the most NTMs (only multiple NTMs per product line)


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87


C H A P T E R 3


Rents, Regulatory Restrictions, and
Diversification toward Services in
Resource-Rich MENA


Ndiamé Diop and Jaime de Melo


For Middle East and North Africa (MENA) countries, especially the
resource-rich ones, finding ways to develop services sectors is crucial for
widening the scope of job creation. First, the public sector, the largest
employer in the region, has reached a saturation point in almost every
single country. Going forward, rapid job creation in the public sector is
likely to hold down aggregate labor productivity and deprive the private
sector of the skilled labor it needs to grow. Second, rents from natural
resources have been an obstacle to the development of manufacturing
industries because of currency overvaluation leading to a low profitability
for tradable manufactures (Rodrik 2008; Havrylyshyn 2010; Lopez-Calix,
Walkenhorst, and Diop 2010). Third, agriculture has become a minor
source of labor absorption and is expected to continue declining as a
share of GDP with income growth and technological advances. Thus, the
services sector is the most promising source of job creation in resource-
rich MENA countries.1


That vibrant local service sectors can be developed in resource-rich coun-
tries has been taken for granted for a long time. This belief was underpinned




88 Diop and de Melo


by at least two combined theoretical considerations. First, Engel’s Law
effects in consumption imply that demand for services tends to increase
with income due to higher income elasticity of demand for services relative
to agricultural products (Chenery and Syrquin 1975; Chenery, Robinson,
and Syrquin 1986). At the same time, services sectors, implicitly assumed to
be largely nontradable in earlier Dutch Disease models, would be positively
affected by an appreciation of the real exchange rate subsequent to a natural
resource boom (Corden and Neary 1982; Corden 1984).


While Engel’s consumption effects are likely to operate, the “largely
nontradable” status of services implicit in the earlier Dutch Disease lit-
erature is no longer valid. The revolutions in technology, transportability,
and tradability of the past 20 years have significantly increased the trad-
ability of services and have led to substantial cross-border “disembodied”
trade in services (Francois and Hoekman 2010). A large number of ser-
vices sectors are now able to be moved offshore, implying an imperative
to be competitive to maintain domestic production. In addition to ser-
vices enabled by information and communications technology (ICT)
(back-office business process services, information technology services,
software development, and so forth) that can be delivered from a distance,
many professional services (engineering, legal, accounting, auditing, and
the like) are now produced through temporary movement of profession-
als. In other words, firms located in a country such as Saudi Arabia are no
longer dependent on domestic service providers and can import many of
the services they need if local producers are not competitive.


This chapter investigates whether MENA has managed to diversify in
services and, if not, why. The first section presents the stylized facts about
the size and performance of services sectors in MENA. It shows that
contrary to the global pattern, the share of services in gross domestic
product (GDP) has been declining with income in the resource-rich
MENA countries. Using econometric analysis, the second section shows
the negative role of rents in explaining this observed structural change.
The next section discusses the role of microeconomic regulations and
shows that rather than alleviating the negative effect of rents on the rela-
tive contraction of services, regulatory restrictions compound the diffi-
culty of developing competitive services sectors in MENA. The chapter
concludes by highlighting the contrast between resource-rich and
resource-poor MENA countries. While rents hinder the development of
services sectors and encourage import of services in resource-rich coun-
tries, they can indirectly widen the scope for diversifying exports and
increasing job creation in resource-poor countries of the region by
demonstrating the potential benefits of regional integration in services.




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 89


Services in MENA: Stylized Facts


Recent Growth and Trade Performance
Although MENA’s services sectors have grown faster than average GDP
in the past decade, they remain much less dynamic than the same sectors
in East Asia, South Asia, and, to a lesser degree, Europe and Central Asia.
During the period 2000–10, services sectors grew at an average annual
rate of 9.5 percent in East Asia, 8.3 percent in South Asia, and 5.4 percent
in MENA. Within MENA, they grew by 1 percent of GDP more in the
Gulf Cooperation Council (GCC) countries than in the rest of MENA.


Figure 3.1 illustrates the importance of services sectors to growth. It
compares growth in the real GDP per capita to growth in services value
added of MENA countries. The positive relationship between the two
variables is by no means causality, but it is a correlation implying that
MENA countries with high growth in services tend to have high GDP or,
conversely, countries with high overall economic growth have high ser-
vices growth. Ghani and Kharas (2010) have found this positive correla-
tion for 136 countries. Finding ways to boost growth in services is thus an
important instrument for increasing overall GDP growth in most coun-
tries, and MENA is no exception.


The relative underperformance of MENA on growth in services is
mirrored by its poor performance in services trade. In 2000–08, trade in
services grew 23 percent annually in South Asia, 16 percent in East Asia,
and 14 percent in Europe and Central Asia compared with 12.4 percent
annually in MENA in the same period. Along with Latin America and
Sub-Saharan Africa, MENA is a minor exporter in the global market for
ICT, finance, and other business services—a market that has exploded
over the past decade. MENA’s share in global services trade stagnated at
around 2.8 percent between 2000 and 2008 (Borchert, Gootiiz, and
Mattoo 2011).


Figure 3.1 Services Value-Added Growth and GDP Growth in MENA


10
8
6
4
2
0


10 12864


average services value-added growth in 2000–08 (%)


av
er


ag
e


G
D


P
g


ro
w


th
in


2
00


0–
08


(%
)


20


Source: World Development Indicators, World Bank.




90 Diop and de Melo


The lack of dynamism of MENA’s services exports reflects its low
value added orientation. Exports of services remain largely dominated by
travel services (tourism-related). In 2008, this category accounted for
53 percent of total services exports. Travel and transport together made
up 78 percent of total exports. This is in sharp contrast with South Asia
(driven by India), where ICT and finance are the leading export services,
making up 55 percent of exports. Transport and travel services account
for a mere 24 percent of total exports in that region (figure 3.2). Although
resource-poor MENA (Jordan, Lebanon, Morocco, and Tunisia) has
shown strong potential for growth in exporting nontraditional services
(such as ICT-enabled and business services) in recent years, they are still
minor players in the global market.


Has MENA Diversified toward Services?
One of the key objectives that MENA countries set for themselves in the
early 1990s was to diversify away from natural resources toward services
and manufacturing (see chapter 1). Typically, as countries become richer,
the share of agriculture declines, giving way to a rise in the share of
manufacturing and services. This often happens because of technological
advances in agriculture, which increase agricultural productivity and
drive resources out of agriculture toward manufacturing and services
(Baumol 1967; Chenery and Syrquin 1975). At the same time, Engel’s


Figure 3.2 Composition of Exports in MENA and South Asia, 2008


25%


53%


9%


13%


a. MENA b. South Asia


12%


12%


21%


55%


transport travel other business ICT finance


Source: International Monetary Fund.




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 91


Law stipulates that as household income increases, the percentage of
income spent on food decreases, while the proportion spent on other
goods and services increases. Thus both supply and demand forces suggest
a decline in the share of agriculture in overall GDP and an increase in
nonagricultural GDP as income increases.


Consistent with theoretical predictions, agriculture has shrunk over
the past 30 years, to varying degrees, in all regions of the world. The
share of agriculture in GDP contracted by more than 60 percent in
East Asia, 58 percent in Europe and Central Asia, 46 percent in South
Asia, 38 percent in Latin America and the Caribbean, 36 percent in
MENA, and 23 percent in Sub-Saharan Africa. Figure 3.3 shows how
the economic structures of the different regions of the world have
changed following the relative contraction of agriculture. In Asia and
Europe and Central Asia, the contraction of agriculture has given way
to a large services sector. In contrast with the rest of the world, the
services sector has shrunk overall in MENA, but the share of industry
(dominated by mining and oil) in GDP has increased. While this
aggregate result certainly conceals large variations across MENA, it
indicates that services have contracted in a least some countries of the
region.


It is important to distinguish different groups of countries within MENA,
given the region’s heterogeneity and the abundance of natural resources in


Figure 3.3 Changes in the Composition of GDP: 1980–83 to 2007–10


–80 –60 –40 –20 0 20 40 60 80


Sub-Saharan Africa


Middle East and North Africa


Latin America and
the Caribbean


South Asiap
er


ce
n


t


Europe and Central Asia


East Asia and Pacific


change in the share of services


change in the share of industry
(including mining, manufacturing)


change in the share of agriculture


Source: World Development Indicators.




92 Diop and de Melo


many of them. Figure 3.4 shows a pooling (five-year averages for each
observation) of the pattern of services in GDP for each of three MENA
groups: resource-poor labor-abundant countries (RPLA), resource-rich
labor-abundant countries (RRLA), and resource-rich labor-importing coun-
tries (RRLI). The figure shows a clear positive correlation between services
shares in GDP and per capita GDP for the resource-poor group, implying
that resource-poor countries—the Arab Republic of Egypt, Jordan,
Lebanon, Morocco, and Tunisia—conform to theoretical expectations. In
contrast, for the two resource-rich country groups, the curve fitting consis-
tently shows that the share of services in GDP is a decreasing function of
per capita GDP. In other words, the decline in the share of services in GDP
in the region is driven by both the resource-rich labor-importing countries
(Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and United Arab Emirates)
and the resource-rich labor-abundant countries (Algeria, Islamic Republic
of Iran, Iraq, Syrian Arab Republic, and Republic of Yemen).


As figure 3.5 shows, the contraction of services sectors in resource-rich
MENA is at odds with global trends. There is indeed evidence of a


Figure 3.4 Services Share in GDP by Level of Income


RPLA Obs.


RPLA Lowess


RRLA Obs.


RRLA Lowess


RRLI Obs.


RRLI Lowess


100


80


60


40


s
er


vi
ce


s
as


a
s


h
ar


e
o


f G
D


P
(%


)


20


6 7 8


Ln(GDP per capita)


9 10 11


LBN


DJI
DJI


DJI
DJI


TUN
TUN


TUN


TUN
LBNLBN


LBNJOB
JOB


JOB


JOB
JOBJOB


YEM


YEM YEM


EGY
EGY


EGYEGY


MAR MAR


MARMAR


IRQ IRQ


LBY
LBYDZA


DZADZA
DZA


SYR SYR
SYR


SYR
SYR DZA DZA


OMN
IRN


IRN
IRN


IRNIRN


IRNIRNIRN


SAU


SAU


SAUSAU
SAUOMNOMN


OMN OMN
SAU


BHR
BHR


BHR


KWT
KWT


KWT


KWT


KWT


BHR


ARE ARE


ARE
ARE


AREARE


DJI


Source: Authors.
Note: The figure is showing averages over five-year periods. RPLA = resource-poor labor-abundant; RRLA =
resource-rich labor-abundant; RRLI = resource-rich labor-importing; Obs = observations; Lowess = locally
weighted scatterplot smoothing = fitting trend. Lowess (band width = 0.8) excludes Iraq, Libya, and Qatar.




93


Figure 3.5 Services Share in GDP, MENA versus Rest of the World


EGY


JOR
MARTUN


DZA


IRN


ARE


BHR
KWTOMNSAU


DJI


EGY
JOR


MAR
TUN


DZA


IRNSYR
ARE


BHR
KWTOMN


SAU


DJI


EGY
JOR


LBN


MAR
TUN


DZA


IRN
SYR


YEM


ARE


BHR
KWT


OMNSAU


DJI


EGY


JOR LBN


MAR


TUN


DZA


IRN


IRQ


SYRYEM
ARE


BHR
KWT


OMN
SAU


DJI


EGY


JORLBN


MAR
TUN


DZA


IRN


IRQ


SYRYEM AREKWT


LBY


OMN
SAU DJI


EGY


JORLBN
MAR


TUN


DZA
IRN


SYR


ARE
LBY


SAU


20


40


60


80


100


20


0


40


60


80


20


0


40


60


80


20


40


60


80


100


20


40


60


80


100


0


50


100


4 6 8 10 4 6 8 10 4 6 8 10


4 6 8 10 4 6 8 10 12 4 6 8 10 12


a. 1980−84: 124 countries b. 1985−89: 141 countries c. 1990−94: 166 countries


d. 1995−99: 168 countries




s


e


r


v


i


c


e


s




a


s




a




s


h


a


r


e




o


f




G


D


P




(


%


)


e. 2000−04: 172 countries f. 2005−09: 167 countries


Ln (GDP per capita)


RoW Obs. MENA Obs. RoW Lowess MENA Lowess


Source: Authors.
Note: The figure shows averages over five-year periods. RoW = rest of world; Obs = observations; Lowess = locally weighted scatterplot smoothing = fitting trend. Lowess (band width =


0.8) excludes Iraq, Libya, and Qatar.




94 Diop and de Melo


positive correlation between the share of services in GDP and income per
capita as shown by Hoekman and Mattoo (2008), who use a large cross-
section sample of countries from around the world.


Before examining the correlates of the apparent relative shrinking of
services in resource-rich MENA in the next section, it is important to
examine whether the above result is a statistical artifact, reflecting a
rapid increase in the share of the mining sector (dominated by oil) in
these economies. In other words, we need to control for the role of oil.
One way to do this is to examine the changes in the composition of
nonmining GDP over time and see how the share of services in nonmin-
ing GDP evolves.2 Unfortunately, we do not have a complete series of
nonmining value added for many MENA countries. For the countries for
which data are available, however, the observation of a shrinking share of
services over time is confirmed. This observation contrasts with resource-
poor countries, where the share of services in nonmining GDP is either
increasing or stagnant. Figure 3.6, which shows the share of services in
nonmining GDP declining in resource-rich Kuwait and Saudi Arabia and
Kuwait while increasing in resource-poor Jordan and Tunisia and Jordan,
illustrates this point.


Relative Roles of Engel’s Effects in Consumption and Rents


There are two traditional explanations for the positive correlation
between the share of services and income. First is Engel’s effects in
consumption: as incomes rise, demand for services tends to rise owing
to higher income elasticity of demand for services relative to agricul-
tural products (Chenery and Syrquin 1975; Chenery, Robinson, and
Syrquin 1986). The second explanation is Baumol’s cost “disease”
effect: fast productivity growth in agriculture as a result of mechani-
zation frees up resources allocated to manufacturing and services
(Baumol 1967).


In addition to these effects, under the assumption that services are
(largely) nontradable, an appreciation of the real exchange rate subse-
quent to a natural resource boom would increase the relative profitability
of the services sectors and promote their development. As noted, how-
ever, the revolutions in technology, transportability, and tradability have
made a large number of the services sectors tradable, weakening this argu-
ment. In any case, empirically, the positive correlation between the share
of services in GDP and per capita income is well established for most
countries in the world (Hoekman and Mattoo 2008).




95


Figure 3.6 Share of Services in Nonmining GDP


75


70


65


60


55


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


2 0
0 2


2 0
0 4


2 0
0 6


2 0
1 0


2 0
0 8


a. Saudi Arabia


60


50


40


10


0


30


20


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


2 0
0 2


2 0
0 4


2 0
0 6


2 0
1 0


2 0
0 8


c. Tunisia


p


e


r


c


e


n


t


p


e


r


c


e


n


t


p


e


r


c


e


n


t


p


e


r


c


e


n


t


66


62


64


60


54


52


58


56


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


2 0
0 2


2 0
0 4


2 0
0 6


2 0
0 8


d. Jordan


100


80


60


40


20


0


1 9
8 0


1 9
8 2


1 9
8 4


1 9
8 6


1 9
8 8


1 9
9 0


1 9
9 2


1 9
9 4


1 9
9 6


1 9
9 8


2 0
0 0


b. Kuwait


Source: World Development Indicators, World Bank.
Note: Data for Kuwait are not available after 2000.




96 Diop and de Melo


Engel’s Effects
We start by examining whether Engel’s effects operate for services in
MENA. Figure 3.7 confirms a rising share of consumption of services in
GDP as GDP per capita rises. Thus, Engel’s effects in consumption oper-
ate for services in MENA. However, a large and growing share of this
consumption of services is satisfied by imports in MENA in sharp contrast
with the rest of the world. Figure 3.8 shows that imports of services have
been growing in MENA, thereby compensating for the low domestic
production of services in resource-rich countries and supplying the local
consumption market.


The Role of Resource Rents
The pattern of production and imports of services observed above may
be associated with a real exchange appreciation that accompanies the
rents from exports of natural resources in the region. With an increasing
share of services becoming tradable as transaction costs have fallen, rather
than observing a general expansion of the services sector when the real
exchange rate appreciates for the resource-rich group, one observes an
increase in the imports of services that cannot be produced competitively
domestically because of the depressed relative profitability of production
in these activities. We turn to the possible effects of rents and apprecia-
tion of the real exchange rate below.


To explore the role of natural resources in the development of ser-
vices sectors, we draw on data on rents from natural resources for a
sample of 174 countries. This data is obtained from the World Bank
database on adjusted net savings (see details in Bolt, Matete, and
Clemens 2002). More specifically, rents are calculated as the difference
between the market value of extracted materials and the average
extraction cost and are expressed as a share of GDP. Rents from 150
natural resource-rich countries are compiled in this way from 1970 to
2004 (services data are available only since 1980). Table 3.1 shows
clearly that resource-rich labor-abundant (RRLA) and especially
resource-rich labor-importing (RRLI) countries in MENA have, by far,
the largest share of rents in GDP in the world. Although data on rents
in the period 2005–10 are not available, one can safely assume that
they have only grown larger as a result of the dramatic increase in oil
price during that period.


To explore further the correlates of the share of services in GDP,
table 3.2 reports the results of the regressions of the share of value-
added in services over GDP on country and year fixed effects, the




97


Figure 3.7 Share of Consumption of Services in GDP


EGY


JOR
MAR


TUN


DZA


IRN BHRKWT
OMN


SAU


EGY


JOR


MARTUN


DZA


IRN
SYR


BHR


KWT


OMN


SAU


DJI


EGY


JOR
MAR


TUN


DZA


IRN


SYR


YEM


BHR


KWT


OMN
SAU


DJI


EGY


JOR


MAR
TUN


DZA


IRN


SYR


YEM


ARE
BHR


KWTOMNSAU


DJI


EGY


JOR


LBN
MAR


TUN


DZA


IRN


SYR


YEM
ARE


KWT


LBY


OMN


SAU
DJIEGY


JOR
LBN


MAR


TUN


DZA
IRN


SYR ARE


LBY


SAU


20


40


60


80


20


40


60


80


20


40


60


80


20


40


60


80


20


40


60


80


0


20


40


60


80


4 6 8 10 4 6 8 10 4 6 8 10


4 6 8 10 4 6 8 10 12 4 6 8 10 12


a. 1980−84: 124 countries b. 1985−89: 141 countries c. 1990−94: 166 countries


d. 1995−99: 168 countries e. 2000−04: 172 countries f. 2005−09: 167 countries


s


e


r


v


i


c


e


s




c


o


n


s


u


m


p


t


i


o


n




a


s




a




s


h


a


r


e




o


f




G


D


P




(


%


)


Ln (GDP per capita)


RoW Obs. MENA Obs. RoW Lowess MENA Lowess


Source: Authors.
Note: The figure shows averages over five-year periods. RoW = rest of world; Obs = observations; Lowess = locally weighted scatterplot smoothing = fitting trend. Lowess (band width =


0.8) excludes Iraq, Libya, and Qatar.




98


Figure 3.8 Share of Imported Services in GDP


EGY


JOR


MARTUNDZA
IRNSYR


BHRKWT


LBYOMN


SAU


EGY


JOR


MAR
TUN


DZAIRN
SYR


BHR


KWT


LBYOMN


SAU
DJI


EGY


JOR


MAR
TUN


DZA
IRN


SYR


YEM


BHR


KWT


LBY
OMN


SAU


DJI


EGY


JOR


MARTUN
DZAIRN


SYRYEM


ARE
BHR


KWT


LBY


OMN
QATSAU DJI


EGY


JOR


LBN


MARTUNDZAIRN
SYRYEM


ARE
BHR


KWT


LBY


OMN QATSAU DJIEGY


JOR


LBN


MARTUNDZAIRN


IRQ


SYRYEM


ARE
BHRKWT


LBY
OMN


QAT


SAU


0


50


0


20


40


0


20


40


0


50


0


50


0


50


4 6 8 10 4 6 8 10 4 6 8 10


4 6 8 10 4 6 8 10 4 6 8 10 12


a. 1980−84: 124 countries b. 1985−89: 141 countries c. 1990−94: 166 countries


d. 1995−99: 168 countries e. 2000−04: 172 countries f. 2005−09: 167 countries


i


m


p


o


r


t


e


d




s


e


r


v


i


c


e


s




a


s




a




s


h


a


r


e




o


f




G


D


P




(


%


)


Ln (GDP per capita)


RoW Obs. MENA Obs. RoW Lowess MENA Lowess


Source: Authors.
Note: The figure shows averages over five-year periods. RoW = rest of world; Obs = observations; Lowess = locally weighted scatterplot smoothing = fitting trend. Lowess (band width =


0.8) excludes Iraq, Libya, and Qatar.




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 99


country fixed effects controlling for time invariant omitted variables,
and the time fixed effects for time-dependent common shocks. The
regressions are run for a large number of countries including those in
MENA (first three columns of table 3.2), then for all countries except
MENA, for MENA only, and for the three subgroups within MENA,
namely resource-poor, resource-rich labor-abundant, and resource-rich
labor-importing. The first three columns confirm the positive correla-
tion between the share of services in GDP and per capita GDP. A 10
percent increase in GDP per capita is associated with an increase of 0.2
to 0.5 percent in the share of services in GDP. Column 5 shows that
this correlation turns negative for MENA, mostly because of the
resource-rich countries (columns 8 and 9).


As documented in table 3.1, a large share of the GDP of resource-rich
countries is composed of rents generated by their natural resources. To
control for the association between rents and the share of services in
GDP, table 3.3 introduces the logarithm of the rents among the corre-
lates. In all the specifications, the share of rents in GDP is significantly
negatively correlated with the share of services in GDP. Moreover, the
relationship between the share of services in GDP and GDP per capita
becomes positive for resource-rich labor-importing countries in regres-
sion 7. For resource-rich labor-abundant countries (regression 6), the
relationship is still negative, but much less significant. While these are
only correlations because they do not account for the potential endogene-
ity of rents, nor does the inclusion of fixed effects only control for time-
invariant omitted variables, it would appear that rents associated with
natural resource abundance in MENA countries partially account for the


Table 3.1 Share of Rents from Natural Resources in GDP
percentage


Region 1980–84 1985–89 1990–94 1995–99 2000–04


MENA RPLA 13 5 2 1 2
MENA RRLA 19 16 23 22 32
MENA RRLI 55 35 34 30 41
East Asia and Pacific 4 4 3 3 4
Europe and Central Asia 3 1 1 3 3
Latin America and Caribbean 7 4 3 3 4
North America 6 3 2 2 3
South Asia 5 3 3 2 3
Sub-Saharan Africa 6 4 4 4 6


Source: Authors’ computation from UNCTAD data.




100


Table 3.2 Correlates of the Share of Services in GDP


Services/GDP
(1)


All
(2)


All
(3)


All
(4)


All but MENA
(5)


MENA
(6)


MENA
(7)


RPLA
(8)


RRLA
(9)


RRLI


Ln(GDP Cap) 0.02*** 0.02*** 0.05*** 0.03*** –0.07*** –0.02*** –0.01 –0.08*** –0.14**
(0.00) (0.00) (0.00) (0.00) (0.02) (0.01) (0.01) (0.02) (0.06)


MENA Dummy –0.13*** –0.02***
(0.03) (0.01)


Constant 0.19*** 0.19*** 0.12*** 0.16*** 0.97*** 0.61*** 0.73*** 0.93*** 1.66***
(0.02) (0.02) (0.01) (0.02) (0.10) (0.04) (0.07) (0.14) (0.60)


Country fixed effects Yes Yes No Yes Yes No Yes Yes Yes
Time fixed effects Yes Yes No Yes Yes No Yes Yes Yes
Observations 3,644 3,644 3,644 3,311 333 333 127 88 118
R-squared 0.84 0.84 0.35 0.85 0.85 0.03 0.94 0.95 0.78


Source: Authors.
Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.




101


Table 3.3 Determinants of the Services Share in GDP


Services/GDP
(1)


All
(2)


All
(3)


All
(4)


All but MENA
(5)


MENA
(6)


MENA
(7)


RPLA
(8)


RRLA
(9)


RRLI


Ln(GDP Cap) 0.02*** 0.02*** 0.05*** 0.03*** –0.07*** 0 –0.01 –0.04** 0.08*
(0.00) (0.00) (0.00) (0.00) (0.02) (0.01) (0.01) (0.02) (0.04)


Ln(Rents) –0.002*** –0.002*** –0.005*** –0.005** –0.01* –0.01*** 0 –0.07*** –0.17***
(0.001) (0.001) (0.000) (0.001) (0.003) (0.001) (0.001) (0.017) (0.02)


MENA Dummy –0.12*** 0
(0.03) (0.01)


Constant 0.22*** 0.22*** 0.18*** 0.19*** 1.05*** 0.72*** 0.50*** 2.13*** 3.71***
(0.02) (0.02) (0.01) (0.03) (0.11) (0.03) (0.08) (0.35) (0.40)


Country fixed effects Yes Yes No Yes Yes No Yes Yes Yes
Time fixed effects Yes Yes No Yes Yes No Yes Yes Yes
Observations 3,644 3,644 3,644 3,311 333 333 127 88 118
R-squared 0.84 0.84 0.43 0.85 0.85 0.40 0.94 0.96 0.85


Source: Authors.
Notes: Robust standard errors in brackets. *** p<0.01, ** p<0.05, * p<0.1.




102 Diop and de Melo


negative correlation between services’ production and per capita
income.


The Role of Microeconomic Regulations


The preceding section shows that rents associated with natural resource
abundance for MENA countries partially account for the negative cor-
relation between services production and per capita income. Here we
examine the role that policy and regulations may have played in the
relative underperformance of MENA’s services sectors. Restrictions on
entry and business conduct either create rents within the services sectors
that are captured by “protected incumbents,” or increase the real cost of
producing services. In both cases, these restrictions inflate the price of
services (Dee 2005). Opening up the services sectors to competition
would therefore reduce the behind-the-border “tax equivalent” or the
“productivity loss equivalent” of the restrictions, thereby enhancing the
competitiveness of services.


The underdevelopment of the services sectors in resource-rich MENA
countries has deep policy roots in addition to the effects of rents. Indeed,
regulatory barriers to market entry, licensing, and business conduct
remain significant in MENA. The World Bank has compiled a database of
restrictions to trade in services in five services sectors of 11 countries of
the Pan Arab Free Trade Agreement (PAFTA), consisting of five GCC
countries—Bahrain, Kuwait, Oman, Qatar, and Saudi Arabia—as well as
Egypt, Jordan, Lebanon, Morocco, Tunisia, and the Republic of Yemen.
Using this database, Borchert, Gootiiz, and Mattoo (2011) shed light on
the degree of restrictiveness in five key services sectors in MENA com-
pared with the rest of the world.


Figure 3.9 shows the services trade restrictiveness average indexes
(STRI) calculated by Borchert, Gootiiz, and Mattoo (2011) using the
World Bank database compiled from 102 countries. The GCC stands out
as the region that is consistently more restrictive than the rest of the
world in the five sectors surveyed.3 When the share of services in GDP
is overlaid on the same graph, there appears to be a net negative correla-
tion between the magnitude of restrictions to trade in services and the
share of services in GDP. Countries that have the most open services
sectors are also those in which services make the largest contribution to
overall GDP.


Furthermore, Borchert, Gootiiz, and Mattoo (2011) identified the
“desire on the part of government authorities to retain a considerable




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 103


degree of regulatory discretion” as a distinctive feature of PAFTA coun-
tries’ applied policies. As a result, even in areas free of explicit restrictions,
de jure openness may not always imply a commensurate degree of de
facto openness, and vice versa. In many instances, the restriction is on
business conduct or related to foreign equity limits. Across different sec-
tors, the granting of new licenses remains opaque and highly discretionary
in many countries. This discretion creates uncertainty about the rules of
the game and may discourage domestic and foreign investors in the ser-
vices sectors.


The high restrictions found by Borchert, Gootiiz, and Mattoo
(2011) are consistent with those found by other studies or specific
countries and service sectors in the region (box 3.1). They are also
consistent with recent evidence pointing to a negative relationship
between natural resources and the likelihood of undertaking micro-
economic reforms. Drawing on the Doing Business data in a large
sample of 133 countries, Amin and Djankov (2009) find that the
proclivity to undertake microeconomic reforms that reduce regula-
tion is much less in countries whose exports are concentrated in
abundant natural resources. In the same vein, Freund and Bolaky
(2008) find that the 12 MENA countries sampled have among the 50
percent most regulated economies among a large sample of 126
countries. Here, too, there may be a correlation with the presence of
natural resources, given the multiple possibilities for regulatory
capture in high-rent environments.


Figure 3.9 Restrictiveness of Services Trade Policies and Share of Services in GDP,
MENA-GCC, GCC, and Other Regions


80
70
60
50
40
30
20
10S


TR
I a


n
d


s
h


ar
e


o
f


se
rv


ic
es


in
G


D
P


share of services in GDP (%)


STRI


30


48.2


40.9 46.4 53.5 54.6 60.6 60.8 70


39.4 39.3 41.6 29.6 20.7 19.9 18.9


0


GCC EAP MENA SAR SSA LAC ECA OECD


Source: Borchert, Gootiiz, and Mattoo 2011.
Note: STRI = services trade restrictiveness index.




104 Diop and de Melo


Box 3.1


Regulatory Restrictions in MENA: Findings from Other Case
Studies


Case studies conducted in the Arab Republic of Egypt, Jordan, Lebanon (Marouani


and Munro 2009); Morocco (World Bank 2007); and Tunisia (Dee and Diop 2010)


show that services sectors in the region are liberalized but only to a limited extent.


Governments tend to retain control, which leads to lack of transparency and dis-


cretion in how restrictions are applied:


• Foreign equity limits, for example, have been relaxed in most MENA countries


in recent years, yet many service markets remain dominated by state-owned or


domestic enterprises. High levels of state control persist in such cases through


conflicting regulations that protect current market structures.


• In banking Morocco and Tunisia display many restrictions. In particular, these


countries’ capital accounts are only partially open, leading to constraints for


cross-border and consumption abroad trade in services; Egypt has an intermedi-


ate level of openness driven by mode 2 (domestic consumption) whereas re-


strictions span modes 1, 3, and 4, Jordan’s banking sector is relatively open, with


restrictions only in modes 1 and 4, whereas Lebanon’s banking sector is the most


open in the region, with very few restrictions across modes 1, 2, and 3.


• In insurance, Egypt is among the least restrictive countries in non-GCC MENA,


reflecting the liberalization of the sector in recent years. However, specific


restrictions on commercial presence and economic needs tests are noted. On


the other end of the spectrum, Morocco and Tunisia have among the most


restrictive regulatory environment mainly because of restrictions on cross-


border and consumption abroad. For Morocco, important nondiscriminatory


concessions have been made as part of its free trade agreement with the Unit-


ed States (signed in 2004); once effective, the provisions in that agreement will


significantly open the sector.


• In telecom, Dihel and Shepherd (2007) note that Middle East countries rank


among the most restrictive for entry in fixed telecom services (relative to Asian


and transition economies). However, in line with recent reforms, the sector is


increasingly open, especially for mobile devices. Morocco and Jordan have the


most open telecom sectors in the region.


• In maritime transport, major restrictions exist in Morocco and, to a lesser de-


gree, Egypt. In contrast, Tunisia and Jordan have fairly open maritime sectors.


Across the MENA countries, it is common to award preferential treatment to


(continued next page)




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 105


Export Diversification Opportunities for Resource-Poor MENA


If the increased tradability of services makes it challenging for resource-
rich MENA countries to maintain domestic production of services, it
offers formidable opportunities to resource-poor countries of the region.
Indeed, given their cultural proximity and common language, these coun-
tries are well placed to capture a share of the large and growing market
of tradable services in resource-rich MENA. To take advantage of these
opportunities, however, resource-poor countries will need to undertake
autonomous reforms to improve their competitiveness and work with
resource-rich countries to reduce barriers to labor mobility within the
region.4 The competition with “enclaves” within the resource-rich coun-
tries where strategies are put in place to “force” diversification will remain
stiff. Dubai, of the seven monarchies of the United Arab Emirates, is a
good example (box 3.2).


Resource-poor countries will need to reduce the regulatory restric-
tions on business entry, licensing, and business conduct in their own ser-
vices sectors to foster competition. As noted, the restrictions are
significant, including in sectors that have a high potential for capturing a
growing market in the region’s resource-rich countries (such as profes-
sional services). In addition, improvements of the so-called backbone
services (such as air transport and telecom) will be needed. These services
are core inputs into most economic activities in all sectors, including


ships flying the national flag. Jordanian and Egyptian flag carriers, for instance,


are given discounts on prices such as port services. Egypt also gives flag carrier


priority access to the cabotage market. In Morocco, regular shipping line ser-


vices established in the country must fly the national flag. While open to for-


eign carriers, nonliner shipping is also restricted. Foreign shippers need to


contract Moroccan liner intermediaries who have the exclusivity of chartering


foreign vessels.


• Finally, in air transport, Egypt displays high restriction levels in modes 1 and 2.


On the other hand, Morocco, the most open in modes 1 and 4, has recently


introduced many air service reforms in an effort to promote growth in the


tourism industry, but it remains more closed than Jordan, which overall has


the most open sector.


Box 3.1 (continued)




106 Diop and de Melo


Box 3.2


Dubai’s Successful Approach to Diversification


The United Arab Emirates (UAE) is a federation of seven monarchies: Abu Dhabi,


Ajman, Dubai, Fujairah, Ras Al-Khaimah, Sharjah, and Umm al-Qaiwain. Abu


Dhabi is the real “oil-rich” monarchy of the UAE, accounting for about 90 percent


of national oil production. Capitalizing on the financial support of Abu Dhabi,


Dubai has positioned itself as a world center of finance, commerce, transporta-


tion, and tourism. Dubai’s model was based on attracting foreign direct invest-


ment; free movement of labor, capital, and goods across borders; an efficient


bureaucracy with no hassle to private firms; and state-of-the-art infrastructures


and backbone services. A key instrument was creation of free trade zones, which


offer 100 percent foreign ownership with no taxation.


Dubai’s market-oriented policies have led to significant diversification of the


economy. New sectors have emerged, such as high-class tourism and interna-


tional finance. The Dubai International Financial Centre offers 55.5 percent foreign


ownership, no withholding tax, freehold land and office space, and a tailor-made


financial regulatory system with laws similar to those governing leading financial


centers in New York, London, Singapore, and Zürich. Dubai has also developed


Internet and media free zones, offering 100 percent foreign ownership and


untaxed office space for the world’s leading ICT and media companies, along with


the latest communications infrastructure to service them. Recent liberalization of


the property market allowing noncitizens to buy freehold land has resulted in a


major boom in the construction and real estate sectors.


While Dubai illustrates that diversification is possible in resource-rich coun-


tries, whether the Dubai model is sustainable is not clear. In 2008–09, the UAE was


hit hard by the global banking crisis and a collapse of a real estate bubble. The


economy has slowly rebounded since then. Furthermore, the diversification of


the economy did not fully solve the Emirates’ employment problem. Despite the


establishment of an “Emiratization” program in the early 2000s to boost employ-


ment of nationals in the public and private sectors, UAE citizens account for less


than 1 percent of the labor force in the private sector.


A very positive aspect of the UAE’s strategy for long-term diversification is its


massive investments in education. In 2010, investments in education accounted


for 22.5 percent ($2.6 billion) of the overall budget. Multiple governmental


initiatives actively promote training of high school dropouts and graduates in a


multitude of skills needed in the private sector. Beyond directly sponsoring


(continued next page)




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 107


educational initiatives, major research initiatives are funded by the Emirates


Foundation for Philanthropy through competitive research grants, allowing


universities such as United Arab Emirates University or Dubai School of Govern-


ment to build and disseminate expertise in many areas.


Box 3.2 (continued)


exporting service industries. To enhance their competitiveness, ICT-
enabled and professional services firms will need good quality and low-
cost services.5


Resource-poor governments also need to facilitate business-to-business
contacts and work with their resource-rich partners to reduce barriers to
mobility and promote “contract-based” movement of service suppliers.6
The main impediments to the movement of people arise from labor mar-
ket laws in resource-rich countries, which rarely distinguish between
temporary and permanent labor mobility. Restrictions in this category in
the GCC (the main resource-rich services market) include burdensome
and costly procedures for work permits, limitations on the length of stay,
quantitative limits on work permits and sectoral bans, job nationalization,
educational conditions, and restrictions on foreign investment.7


Although a number of regulated professions feature nationality restric-
tions in both resource-poor and resource-rich countries, large niches exist.
In Libya, for example, jobs such as accountants are reserved for Libyan
nationals. In the United Arab Emirates, regulated professions in the areas
of accounting, engineering, law, and medicine, as well as agricultural, eco-
nomic, fishing, industrial, and managerial and technical consultants, are
open to practitioners and employees from any GCC member state and
are accessible to non-GCC suppliers under certain conditions.


Bilateral agreements are a key vehicle for fostering trade in services
among neighboring countries. For instance, Iraq and Lebanon have con-
cluded an agreement aimed at increasing the economic cooperation
between them, including exchange of expertise, specialists, and trainers
(Article 3). A similar agreement was concluded between Kuwait and
Lebanon, stipulating that the two parties shall facilitate the procedures for
granting entry visas to businessmen in both countries (Article 6). Other
agreements have been signed by Lebanon and Syria that promote labor
mobility between the two countries. Yet even though these agreements
mention some provisions related to exchanging expertise or facilitating




108 Diop and de Melo


visa procedures, they do not include direct provisions organizing the
temporary movement of people. Resource-poor countries interested in
exporting services to the GCC and other resource-rich countries of
MENA should aim at signing comprehensive bilateral agreements that
address the specific barriers their firms are facing in these markets.


In brief, the services sectors of MENA present interesting complemen-
tarities and a huge potential for trade but all countries need to implement
relevant policies to exploit these potential benefits.


Concluding Remarks


This chapter has shown that despite recent good growth performance,
services sectors in resource-rich MENA countries have been declining as
a share of GDP (and as a share of nonmining GDP) over time even as
income per capita has increased. This negative relationship between the
share of services in GDP and income per capita is opposite to observed
global patterns. The analysis here suggests that this result is linked to the
large rents generated by natural resources in these MENA countries. A
large number of services sectors can now be stationed offshore or pro-
duced by temporary movement of service providers, implying that coun-
tries need to be competitive to maintain local production. Rents from
natural resources tend to inflate wages and nontradable prices in
resource-rich countries, thereby appreciating the real exchange rate and
discouraging domestic production of tradable goods and services. This
explains why resource-rich countries of MENA have become large
importers of tradable services and why only domestic production of
nontradable services (such as real estate, retail trade, hotels, and restau-
rants) has really developed.8


Microeconomic regulations on business have tended to compound the
problem, rather than compensating for it. Restrictions on business entry,
licensing, and business conduct are indeed significant and correlate nega-
tively with the share of services in GDP. The MENA region, in particular
resource-rich countries, stands out for heavy and discretionary restrictions
of services sectors compared with the rest of the world. These restrictions
either create rents within the services sector that are captured by “pro-
tected incumbents” or increase the real cost of producing services—in
both cases inflating the price of services and further reducing competi-
tiveness of tradable services sectors.


Regardless of the relative weight of different explanatory factors, the
underdevelopment of tradable services in MENA resource-rich countries




Rents, Regulatory Restrictions, and Diversification toward Services in Resource-Rich MENA 109


creates a big challenge for these countries. Indeed, manufacturing is
difficult to develop competitively in these countries for similar reasons
(rents and Dutch Disease)—oil-related industries are capital-intensive
rather than labor-intensive, and public sectors are bloated. The scope for
creating jobs for nationals in the productive sectors of these countries is
limited. This situation is particularly problematic for resource-rich coun-
tries with abundant labor supply, such as Algeria, Saudi Arabia, Syria, and
the Republic of Yemen. It is crucial for these countries to reduce regula-
tory restrictions in services sectors and invest heavily in education to
compensate for the negative effect of rent on competitiveness.


Resource-rich economies should thus strive to reduce production costs
thus offsetting the negative effect of rents on production in the nonre-
source tradable sectors. This can be done by reducing regulatory restric-
tions on entry and competition in these sectors. Experience from
resource-rich countries around the world also shows the importance of
investing in human capital and strengthening institutions (see Gelb 2011
for a summary). Finland, the Republic of Korea, and Norway are exam-
ples of countries that have invested to build a high-quality human capital
base and have successfully diversified into high-tech manufacturing and
services. Similarly, there is strong evidence that institutions matter for
diversification. Gelb (2011, 67) argues that manufacturing sectors are
“heavily dependent on strong contract enforcement, a rule of law and
generally strong business environment.” These arguments equally apply to
services, if not more strongly so. Institutions that prevent or reduce rent-
seeking are also important, as the example of Botswana shows (see
Acemoglu, Johnson, and Robinson 2005).


If the increased tradability of services makes it challenging for resource-
rich MENA to maintain domestic production of services, it offers formi-
dable opportunities to resource-poor countries of the region. Indeed,
given their cultural proximity and common language, these countries are
well placed to capture a share of the large and growing market for trad-
able services in resource-rich MENA. To capture these opportunities,
however, resource-poor countries will need to undertake autonomous
reforms to improve their competitiveness, and work with resource-rich
countries to reduce barriers to labor mobility within the region. More
specifically, they will need to reduce their own restrictions to entry and
competition in professional services, improve their backbone services
(such as telecom and transport), and proactively engage resource-rich
countries in reducing barriers to trade and mobility through specific bilat-
eral agreements.




110 Diop and de Melo


Notes


1. Resource-rich countries are net receivers of migrant workers, and outmigra-
tion is generally not a source of employment for nationals of these countries.


2. The mining sector in MENA is dominated by oil industries in resource-rich
countries. Jordan, Morocco, and Tunisia, all considered “resource-poor coun-
tries” in this volume, do have a sizable phosphates sector. Rents from
phosphates, however, are limited compared with those from oil.


3. Note that the United Arab Emirates is not included in the sample. This coun-
try’s economy is the most open one in the GCC.


4. See Marouani and Zaki (2011) for a comprehensive discussion of barriers to
mode 4 trade in MENA.


5. For empirical evidence on the impact of backbone services on productivity
and competitiveness, see, for instance, Arnold, Javorcik, and Mattoo (2011)
and Arnold, Mattoo, and Narciso (2008).


6. See Hoekman and Ozden (2009) for details on the distinction between
“contract-based” movement of services providers and employment-based
movements of persons.


7. An elaborate administrative mechanism exists to regulate the inflow and
residence of non-GCC migrant workers to GCC countries. All foreign work-
ers and their dependents entering a GCC country are issued a resident visa
for the number of years stipulated in the work contract. All such visas are
issued under the authority of a sponsor that wishes to hire the foreign
worker.


8. The rapid development of a nontradable service, namely, the real estate sector,
in conjunction with the underdevelopment of tradable services is an illustra-
tion of this point.


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113


C H A P T E R 4


Patterns of Diversification in MENA:
Explaining MENA’s Specificity


Marcelo Olarreaga and Cristian Ugarte


Early stages of development are often accompanied by diversification of
the production bundle as more economic opportunities become available.
There is evidence, however, that the relationship between diversification
and development is nonmonotonic. Imbs and Wacziarg (2003) and Koren
and Tenreyro (2007) show that if the two are positively correlated at low
levels of development, once countries reach a certain income-per-capita
threshold, the concentration of production increases with income levels.
Cadot, Carrère, and Strauss-Kahn (2011) show that the U-shaped rela-
tionship between economic concentration and income holds not only for
production but also for export diversification.


A potential explanation for this empirical regularity is the need to
diversify production (or investment opportunities) in the presence of
incomplete financial markets at very low levels of development. Moreover,
as financial markets develop, the forces of comparative advantage push
toward specialization (Saint-Paul 1992; Acemoglu and Zilibotti 1997).
Faini (2004) suggests an alternative explanation: at early stages of devel-
opment, when income rises, the opportunities for risk diversification
through sectorally diversified investment become stronger, leading ini-
tially to diversification. As economies become richer, however, they also
become economically and institutionally more stable, which mitigates




114 Olarreaga and Ugarte


business risks and diminishes the incentives to diversify. An alternative
explanation in a Ricardian model with a continuum of goods is the ten-
sion that may exist between productivity increases that could lead to a
larger number of products being produced in the home country and fall-
ing trade costs that could lead to a decline in the number of goods that
are cheaper to produce domestically. If productivity increases tend to be
followed by declines in trade policy barriers, then a U-shaped relationship
between concentration and income per capita is likely.


This empirical regularity, however, seems to be at odds with the pat-
terns of diversification in the Middle East and North Africa (MENA)
region.1 The objective of this chapter is twofold: to search for the exis-
tence of a systematic difference in the patterns of development and
diversification between MENA and the rest of the world; and to attempt
to explain these systematic differences beyond the obvious observation
that MENA is relatively more abundant in natural resources than the rest
of the world. The two alternative explanations explored are Dutch
Disease–type phenomena that lead to a strong appreciation of the
exchange rate and the presence of weak links.


The Dutch Disease phenomenon takes its name from the experience
of the Netherlands in the 1960s with the discovery of large reserves of
natural gas. As income from gas increased, the guilder appreciated quickly,
making the rest of the economy less competitive. Moreover, the booming
sector increased its demand for factors of production, making the rest of
the economy even less competitive (see Corden and Neary 1982 for a full
economic model of Dutch Disease). This type of phenomenon will natu-
rally lead to a decline in the share of the manufacturing sector, but it may
also affect the extent of industrial concentration, because some sectors
will see stronger increases in their production costs associated with the
exchange rate appreciation. Whether manufacturing concentration
increases with Dutch Disease is therefore an empirical question.


The weak links alternative explanation is based in the ideas of a recent
paper by Jones (2011). It builds on earlier work by Hirschman (1958)
and Kremer (1993) that emphasizes the important role played by link-
ages and complementarities in economic development. Low productivity
in one nontradable input sector for which there is little substitutability
will act as a weak link in the production chain, hurting all the sectors
downstream and the overall development prospects of the country.
Ugarte (2012) formally shows how the presence of weak links results in
a less diversified production bundle as downstream sectors are hurt by
higher nontradable input prices and factor prices. Examples of sectors




Patterns of Diversification in MENA: Explaining MENA’s Specificity 115


that can be considered weak links are the energy production or oil refin-
ing industries, whose products are broadly used by others sectors as inter-
mediate inputs, and which have a nonnegligible, nontradable component.
Energy is required by almost every sector, and while oil is highly tradable,
energy production can be highly nontradable. Low levels of productivity
in energy production will imply higher costs for users of energy and
might constrain diversification into new sectors as their expected profit-
ability falls. Thus, the presence of weak links may lead to higher levels of
concentration.


The empirical results suggest that MENA’s pattern of development
and diversification is indeed different from the rest of the world. MENA
countries tend to concentrate rather than diversify at early stages of
development, and they seem to start diversifying only at relatively high
levels of income. The evidence also suggests that weak links seem to be a
better explanation than Dutch Disease for this different pattern of devel-
opment and diversification in MENA; the appreciation of the real
exchange rate seems to have no impact.


Empirical Methodology


The empirical methodology of this work closely follows Imbs and
Wacziarg (2003). The following relationship is estimated both parametri-
cally and nonparametrically:


Concentration = f(income) + ε (4.1)


where Concentration measures the lack of sectoral diversification using
different indexes and along different dimensions (output, employment,
or value added); income is gross domestic product (GDP) per capita at
constant prices, noted as GDPpc hereafter; and ε is an error term. The f
relationship is estimated across countries and time, and therefore country
and year fixed effects are generally included. Imbs and Wacziarg (2003)
found a quadratic relationship between sectoral concentration and
income per capita. This is the starting point of our empirical study, but
higher orders of the f function will also be checked.2 The potential het-
erogeneity that may exist in the relationship between MENA and the rest
of the world will also be explored by estimating equation 4.1 for different
samples.3


Economic concentration is measured by Gini coefficients, and
Herfindhal indexes are also used to check for the robustness of the




116 Olarreaga and Ugarte


results. Each of them will be calculated along three different dimensions:
output (Giniout, Herfout), employment (Giniempl, Herfempl), and
value added (Ginivadd, Herfvadd) for each country and year, using the
28-sector disaggregation provided at the 3-digit level of the International
Standard Industrial Classification.4


The Gini coefficient is calculated as follows. After ordering the shares
of output, employment, or value added in increasing order, we calculate



Gini = 1


1
n


− +( )


=



c,t


S Si
c


i
c


i


nc t


1
1


,



(4.2)


where Si
c is the cumulated share of output/employment/value added of


sector i in country c, nc ,t is the number of active sectors in country c at
period t, and S0 = 0. The Gini index ranges between zero and one, where
zero represents a fully diversified economy (where all sectors have an
equal share of total production, employment, or value added) and one a
fully concentrated economy (where all production, employment, or value
added are generated in one sector).


The Herfindhal indexes are presented below:



Herf =


=1


( )Si
i


nc,t
2∑



(4.3)


This index ranges between 1/nc , t and 1 and it also increases with the
degree of concentration. Unlike the Gini index, this index is not affected
by the absence of production, employment, or value added in a sector.5
On the other hand, the Herfindhal is very sensitive to large sectors,
whereas the Gini is more sensitive to what happens in the middle of the
distribution and will better capture changes toward diversification.


Measuring Weak Links and Dutch Disease Effects


This section explores the impact of Dutch Disease phenomena and weak
links on the patterns of development and diversification by testing the
equation below:


Concentration = f(income) + a WeakLinks + b DutchDisease + e (4.4)


Dutch Disease phenomena are proxied here by percentage changes of the
real exchange rate. By considering the change of the real exchange rate




Patterns of Diversification in MENA: Explaining MENA’s Specificity 117


instead of the exchange rate in levels, the variable is more likely to have
an impact on concentration through a Dutch Disease–type phenomenon
because it will give more weight to observations where the appreciation
of the exchange rate has been accelerating. Note that a fall in the
exchange rate signals an appreciation of the exchange rate and implies
that a negative correlation between changes in the exchange rate and
concentration is commonly expected (beta < 0).6


As argued above, the presence of weak links is expected to lead to a
more concentrated manufacturing sector (that is, alpha > 0). Weak links
can be measured in different ways. One is to measure differences between
the mean (or median) productivity and the productivity in the least pro-
ductive sector. Another is simply to use the minimum level of productiv-
ity in the economy as a potential indicator of weak links. Whatever the
measure, it must capture the underlying distribution of productivity
across sectors in a given country: the difference between two points, or
the minimum level of productivity, does not capture the probability of
observing this difference of productivity level. The same minimum level
of productivity or productivity differences may be very likely in one
country but almost never observed in another. This will be missed by the
simple use of a ratio or a difference. Therefore, the measure of weak links
chosen here will be the probability of observing a productivity lower than
a certain threshold in a given country and year. To arrive at this measure,
the kernel density of labor productivity for each country in each year is
estimated.7 The kernel density is weighted as the share of output by sec-
tor in total output, to consider the economic importance of each sector
in every year and every country.8 Thus, this procedure estimates a distri-
bution for each observation in the sample and allows us to independently
calculate moments of the distribution for a given country in a given year.
The proxy for weak links is calculated as a probability of observing pro-
ductivity lower than the mean productivity minus two,9 times the stan-
dard deviation of the distribution:


P(low) = Prob(productivity < meanproductivity − 2 × stddev) (4.5)


An important caveat with this measure of weak links is that it does not
take into account the extent to which sectors are used as inputs by other
sectors, or the degree of tradability of input sectors. This is important
when measuring weak links, because they are by definition nontradable
input sectors. To construct a measure of weak links that takes these two
characteristics into account, however, one would need input-output




118 Olarreaga and Ugarte


tables. This is the path followed in Ugarte (2012) in a cross-section of
developed and developing countries, where the author weights the
productivity of each sector in estimated density function by its impor-
tance as an input in other sectors as well as by its degree of tradability.10
Unfortunately, this measure cannot be used for MENA because of the
lack of input-output data.


Data Description


Table 4.1 lists the names of the countries, including MENA countries, in
the sample, as well as the number of observations available for the period
1963–2003. The unbalanced nature of the panel suggests that after con-
trolling for country fixed effects, the coefficients will mainly capture the
country variability with a large number of observations.11 Tables 4.2 and
4.3 present summary statistics of the different measures of diversification.
Concentration indexes for MENA tend to be slightly higher, but the dif-
ferences from the general mean values are statistically insignificant. The
variation of concentration indexes is less important in the MENA region
than in the rest of the world. The Gini index of diversification based on
output information tends to show more concentration than other Gini
indexes. Even though they are not statistically significant, the Herfindhal
indexes show the same tendency. The correlation between measures is
highly significant and positive in all cases. It is worth pointing out that
output measures are highly correlated with value added measures. The
structure of output and value added is very similar within countries. Even
if different measures of concentration are highly correlated, we can reject
perfect co-linearity among measures, so it is important to provide results
for different measures as robustness checks.


Table 4.4 provides descriptive statistics of the probability of observing
a weak link in the world and the MENA samples. A test on mean values
suggests no statistically significant difference between MENA and the
rest of the world. Weak links are as likely in both.


Empirical Results


The parametric results in Imbs and Wacziarg (2003) are reproduced in
table 4.5. The estimates reported in the table are slightly different from
those in their paper because we use GDP per capita at constant dollars
rather than purchasing power parity (PPP) prices; moreover, we no longer




Patterns of Diversification in MENA: Explaining MENA’s Specificity 119


have access to data for some former Soviet countries.12 The bottom panel
shows that the U-shaped relationship between concentration indexes and
income per capita is robust to the inclusion of country fixed effects, as
in Imbs and Wacziarg (2003). The turning points of the U-shaped curve


Table 4.1 Sample Coverage and Number of Observations


Middle East and North Africa
Algeria (31), Bahrain (1), Arab Republic of Egypt (36), Islamic Republic of Iran (38), Iraq (27),


Israel (39), Jordan (38), Kuwait (36), Lebanon (1), Libya (17), Morocco (28), Oman (11),
Qatar (11), Saudi Arabia (8), Syrian Arab Republic (36), Tunisia (33), United Arab Emirates (11),
Republic of Yemen (13).


Others in Asia
Afghanistan (18), Armenia (17), Azerbaijan (13), Bangladesh (28), Bhutan (1), Cambodia (7),


China (26), Hong Kong SAR, China (40), India (39), Indonesia (33), Japan (39),
Kazakhstan (10), Republic of Korea (39), Kyrgyz Republic (16), Lao PDR (1), Macao SAR,
China (25), Malaysia (33), Mongolia (6), Myanmar (14), Nepal (10), Pakistan (30),
Philippines (35), Russian Federation (12), Singapore (40), Sri Lanka (35), Tajikistan (14),
Thailand (19), Turkey (38), Vietnam (3).


Others in Africa
Angola (7), Benin (17), Botswana (22), Burkina Faso (15), Burundi (19),Cameroon (25), Cape Verde


(14), Central African Republic (19), Chad (1), Côte d’Ivoire (31), Democratic Republic of Congo (5),
Eritrea (10), Ethiopia (38), Gabon (20), The Gambia (13), Ghana (28), Equatorial Guinea (3),
Kenya (40), Lesotho (12), Liberia (14), Madagascar (22), Malawi (35), Mauritania (6), Mauritius (32),
Mozambique (25), Namibia (1), Niger (9), Nigeria (28), Republic of Congo (18), Rwanda (16),
Senegal (30), Seychelles (12), Sierra Leone (5), Somalia (14), South Africa (36), Sudan (11),
Swaziland (26), Tanzania (30), Togo (16), Uganda (14), Zambia (18), Zimbabwe (34).


Americas
Argentina (30), Bahamas (20), Barbados (28), Belize (4), Bermuda(12), Bolivia 31), Brazil (28), Canada


(39), Chile (38), Colombia (38), Costa Rica (40), Cuba (15), Dominican Republic (23), Ecuador (40),
El Salvador (31), Grenada (8), Guatemala (26), Guyana (14), Haiti (27), Honduras (33), Jamaica (34),
Mexico (31), Netherlands Antilles (22), Nicaragua (23), Panama (37), Paraguay (20), Peru (22),
Puerto Rico (36), St. Lucia (7), St. Vincent and the Grenadines (9), Surinam (20), United States (38),
Uruguay (33), Trinidad and Tobago (33), RB Venezuela (34).


Europe
Albania (11), Austria (37), Belgium (38), Bosnia and Herzegovina (5), Bulgaria (40), Croatia (17),


Cyprus (40), Czech Republic (16), Denmark (36), Estonia (10), Finland (38), France (38),
Germany (10), Greece (36), Hungary (38), Ireland (38), Italy (34), Latvia (17), Lithuania (11),
Luxembourg (38), FYR Macedonia (10), Malta (39), Moldova (17), Netherlands (38),
Norway (39), Poland (38), Portugal (38), Romania (38), Slovak Republic (7), Slovenia (16),
Spain (38), Sweden (38), Switzerland (12), Ukraine (10), United Kingdom (34).


Others
Australia (39), Iceland (30), Fiji (29), New Zealand (38), Papua New Guinea (27), Solomon


Islands (7), Tonga (19), Western Samoa (2).


Source: Authors.




120 Olarreaga and Ugarte


Table 4.2 Summary Statistics of Measures of Diversification


Variable Mean
Standard
deviation Minimum Maximum Number


World


Giniout 0.5935 0.0984 0.1499 0.9043 3,438
Herfout 0.1598 0.1090 0.0583 0.9104 3,438
Giniempl 0.5622 0.0980 0.0122 0.8792 3,695
Herfempl 0.1403 0.0981 0.0598 0.8772 3,695
Ginivadd 0.5638 0.0999 0.2030 0.8682 3,264
Herfvadd 0.1384 0.1016 0.0509 0.8878 3,264


MENA countries


Giniout 0.6124 0.0970 0.4158 0.8802 389
Herfout 0.1767 0.1295 0.0673 0.6342 389
Giniempl 0.5649 0.0687 0.3387 0.7396 402
Herfempl 0.1252 0.0488 0.0711 0.2879 402
Ginivadd 0.5874 0.0993 0.3780 0.8682 379
Herfvadd 0.1501 0.1043 0.0584 0.6523 379


Source: Authors.


Table 4.3 Correlation between Measures of Diversification


Variables Giniout Herfout Giniempl Herfempl Ginivadd Herfvadd


Giniout 1.000
Herfout 0.627 1.000
Giniempl 0.650 0.279 1.000
Herfempl 0.467 0.625 0.547 1.000
Ginivadd 0.907 0.584 0.716 0.528 1.000
Herfvadd 0.525 0.897 0.288 0.642 0.625 1.000


Source: Authors.
Note: All correlations are statistically significant at the 99 percent level.


Table 4.4 Descriptive Statistics of P(low)


Statistic World MENA


Average 0.0021 0.0017
Standard deviation 0.0087 0.0067
Maximum 0.1040 0.0611
% of P(low) > 0.01 6.17 5.56


Source: Authors.




Table 4.5 Imbs and Wacziarg’s Results (2003)


Giniempl Herfempl Ginivadd Herfvadd Giniempl Herfempl Ginivadd Herfvadd
Variables (I) (II) (III) (IV) (V) (VI) (VII) (VIII)


GDPpc −0.0245*** −0.0195*** −0.0288*** −0.0162*** −0.0058*** −0.0099*** −0.0085*** −0.0040*
(0.0013) (0.0016) (0.0014) (0.0012) (0.0022) (0.0020) (0.0028) (0.0022)


[GDPpc]
2 0.0011*** 0.0008*** 0.0013*** 0.0007*** 0.0003*** 0.0004*** 0.0005*** 0.0002**


(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)
Constant 0.6209*** 0.1748*** 0.6262*** 0.1555*** 0.5727*** 0.1520*** 0.5739*** 0.1264***


(0.0034) (0.0041) (0.0035) (0.0030) (0.0068) (0.0062) (0.0081) (0.0064)
R2 0.273 0.155 0.320 0.174 0.835 0.892 0.776 0.763


Observations 1,433 1,433 1,349 1,349


Source: Authors.
Notes: Estimates in columns V-VIII include country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. Statistically significant:


*** p<0.01, ** p<0.05, * p<0.1.


121




122 Olarreaga and Ugarte


can be calculated by equalizing the first derivative to zero. It occurs
around $10,000.


Table 4.6 presents the estimates obtained using the full 1963–2003
sample provided in table 4.1. The estimated U-shaped relationship
between concentration and level of development is now inverted. All the
results suggest that at lower levels of development, economies tend to
become more concentrated as income rises, and at later stages to diversify
as income grows.13 This is quite surprising and warrants further investiga-
tion using nonparametric techniques, as in Imbs and Wacziarg (2003).14
The nonparametric estimates are shown in figures 4.1–4.3 for the three
Gini coefficients on output, employment, and value added. The relation-
ship between concentration and income levels does not appear to be
U-shaped, as indicated by the parametric estimates; it appears to be of a
higher order than quadratic. At low levels of income per capita, however,
the relationship indeed appears U-shaped, as found by Imbs and Wacziarg
(2003) in their sample.


Higher orders of income per capita are therefore included in the para-
metric estimations: the number of higher-order terms to include is chosen
by checking their statistical significance and changes in adjusted R-squares
(which stopped at the fourth-order terms). The estimates obtained with
GDP per capita at powers three and four are presented in table 4.7. As
with the nonparametric estimates, economic concentration falls with
GDP per capita at very low levels of development for all concentration
indexes (with the exception of the Gini on employment in the second
column), but then increases.15


At the bottom of table 4.7, the saddle points (minimum and maxi-
mum) of each of these functions are provided.16 There is no analytical
solution, but the numerical solutions suggest that at levels of income per
capita between $2,000 and $6,000, countries tend to diversify (become
less concentrated) as income rises, after which they tend to concentrate
their production bundle at least until they reach levels of income per
capita around $18,000 to $25,000.


Thus the U-shaped relationship between concentration and income
per capita found by Imbs and Wacziarg (2003) is validated in our sample
for levels of income per capita below $20,000 to $25,000. After that
threshold, the relationship changes, but not many countries in MENA are
at that level of development and therefore this different pattern may not
be relevant for them.


To explore the potential heterogeneity of the relationship in equation
4.4 between MENA and the rest of the world, the samples are divided




Table 4.6 Basic Regressions of Concentration on a Quadratic Function of GDPpc


Variables Giniout Giniempl Ginivadd Herfout Herfempl Herfvadd


GDPpc 0.0090*** 0.0080*** 0.0128*** 0.0056*** 0.0028*** 0.0049***
(0.0011) (0.0010) (0.0012) (0.0010) (0.0008) (0.0010)


[GDPpc]
2 −0.0002*** −0.0002*** −0.0003*** −0.0002*** −0.0001*** −0.0001***


(0.0000) (0.0000) (0.0000) (0.0000) (0.0000) (0.0000)
Constant 0.6035*** 0.5205*** 0.6446*** 0.2238*** 0.1408*** 0.3477***


(0.0359) (0.0358) (0.0544) (0.0330) (0.0294) (0.0482)
R2 0.776 0.775 0.747 0.838 0.853 0.798


Observations 3,182 3,357 3,027 3,182 3,357 3,027


Source: Authors.
Notes: GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. Statistically significant: *** p<0.01, ** p<0.05, * p<0.1. All regressions include


time and country fixed effects.


123




124 Olarreaga and Ugarte


Figure 4.1 Diversification of Output (Giniout) on GDP per Capita


1050 15 20 25 30 35


0.45


0.50


0.55


0.60


0.65


GDP per capita at constant prices (2000)


G
in


i
in


d
ex


o
n


o
u


tp
u


t


Source: Authors.


Figure 4.2 Diversification of Output (Giniempl) on GDP per Capita


G
in


i
in


d
ex


e
xa


m
p


le
e


m
p


lo
ym


en
t


1050 15 20 25 30 35


0.40


0.45


0.50


0.55


0.60


GDP per capita at constant prices (2000)


Source: Authors.




Patterns of Diversification in MENA: Explaining MENA’s Specificity 125


Figure 4.3 Diversification of Output (Ginivadd) on GDP per Capita


G
in


i
in


d
ex


o
n


v
al


u
e


ad
d


ed


1050 15 20 25 30 35


0.45


0.50


0.55


0.60


0.65


GDP per capita at constant prices (2000)


Source: Authors.


accordingly. The results, displayed in table 4.8, show some important
differences between MENA and the rest of the world. In the latter sam-
ple, we observe the same pattern as in table 4.7. But in the MENA sam-
ple, the pattern observed in the rest of the world is actually reversed.
MENA countries have a tendency to become more concentrated as
income grows at low and intermediate levels of development, and they
start to diversify only when GDP per capita reaches $17,000 to $22,000.
Given that most MENA countries are below this threshold, this finding
suggests that concentration is the most common pattern of development
in the region. The statistical relation between concentration and income
per capita is thus not convex but concave.17


Figures 4.4, 4.5, and 4.6 plot the derivative of the concentration index
with respect to income per capita using the estimates for the MENA
countries (solid line) and the rest of the world (dashed line). A positive
value of the derivative suggests that concentration increases with income
per capita, whereas a negative value suggests that concentration declines
with income per capita at that level of development. At low and interme-
diate levels of development, the derivative is positive for MENA countries,
suggesting that an increase in income tends to be associated with a more




Table 4.7 Regressions of Concentration Including [GDPpc]
3 and [GDPpc]


4


Giniout Giniempl Ginivadd Herfout Herfempl Herfvadd
Variables (I) (II) (III) (IV) (V) (VI)


GDPpc −0.00946494*** 0.00126482 −0.00928499*** −0.00186707 −0.00441353** −0.00379962
(0.00307744) (0.00255713) (0.00339791) (0.00284778) (0.00209783) (0.00301235)


[GDPpc]
2 0.00131129*** 0.00044252** 0.00167939*** 0.00052016** 0.00052041*** 0.00101347***


(0.00025496) (0.00018029) (0.00028863) (0.00023593) (0.00014791) (0.00025588)
[GDPpc]


3 −0.00004638*** −0.00001879*** −0.00006470*** −0.00002269*** −0.00001788*** −0.00004835***
(0.00000878) (0.00000517) (0.00001018) (0.00000813) (0.00000424) (0.00000903)


[GDPpc]
4 0.00000047*** 0.00000019*** 0.00000071*** 0.00000025*** 0.00000017*** 0.00000064***


(0.00000010) (0.00000005) (0.00000012) (0.00000009) (0.00000004) (0.00000011)
Constant 0.64605132*** 0.53397540*** 0.69463459*** 0.23962964*** 0.15631615*** 0.35924320***


(0.03624372) (0.03620361) (0.05450849) (0.03353905) (0.02970082) (0.04832346)


Minimum (US$) 4,711 — 3,398 2,065 5,900 2,211
Maximum (US$) 23,155 25,068 22,851 19,395 21,273 18,871
R2 0.779 0.776 0.751 0.838 0.854 0.801


Observations 3,182 3,357 3,027 3,182 3,357 3,027


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. — = nonexistent.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.


126




Table 4.8 Splitting Samples between Middle East and North Africa and the Rest of the World


Rest of the world Middle East and North Africa


Variables Giniout Giniempl Ginivadd Giniout Giniempl Ginivadd


GDPpc −0.00810690** −0.00408470 −0.00909016*** 0.01862887 0.08287829*** 0.03597924*
(0.00322383) (0.00265546) (0.00340832) (0.01684508) (0.01719385) (0.02149334)


[GDPpc]
2 0.00090447*** 0.00082254*** 0.00121907*** 0.00039008 −0.00495654*** 0.00046735


(0.00028012) (0.00019007) (0.00029570) (0.00109583) (0.00108921) (0.00145475)
[GDPpc]


3 −0.00002705*** −0.00003039*** −0.00004003*** −0.00004226 0.00012329*** −0.00007064*
(0.00001012) (0.00000550) (0.00001067) (0.00003012) (0.00002848) (0.00004218)


[GDPpc]
4 0.00000019 0.00000029*** 0.00000032** 0.00000058* −0.00000110*** 0.00000108**


(0.00000012) (0.00000005) (0.00000013) (0.00000030) (0.00000026) (0.00000044)
Constant 0.62302618*** 0.58085743*** 0.59270500*** 0.57998615*** 0.30586937*** 0.51914496***


(0.04989810) (0.04966570) (0.01635957) (0.06459647) (0.06918234) (0.08069581)


Minimum (US$) 6,011 2,838 4,809 — 31,192 —
Maximum (US$) 22,743 23,069 22,055 21,400 16,692 21,987
R2 0.781 0.794 0.767 0.848 0.595 0.762


Observations 2,856 3,028 2,711 326 329 316


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. — = nonexistent.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.


127




128 Olarreaga and Ugarte


concentrated production bundle. In the rest of the world, on the other
hand, at low levels of development the derivative tends to be negative,
whereas at intermediate levels it is positive, which corresponds to the
U-shaped curve. At very high levels of development both in MENA and
in the rest of the world, a negative derivative is observed, suggesting that
countries in both samples diversify as income rises.18


To investigate the potential explanation of these differences in the pat-
tern of diversification and development between MENA and the rest of
the world, the explanatory variables for weak links (measured as the
probability of observing a sector with a labor productivity below two
times the average labor productivity in the country in that year), and a
proxy for Dutch Disease–type phenomena (that is, the change in the
exchange rate) are included.19 Table 4.9 presents the results for the full
sample and MENA subsample. In the full sample (the first three col-
umns), the U-shaped relationship at low levels of income per capita is still


Figure 4.4 Marginal Effect of GDPpc on Output Concentration [Giniout]


0.015


0.010


0.005


G
D


P
p


er
c


ap
it


a


–0.005


–0.010


0
10 20


y
30


marginal effect of output concentration


Source: Authors.
Notes: The solid line shows the marginal effect of GDP per capita on output concentration in MENA countries
(column 4 of table 4.8), above. The dashed line shows the marginal effect of GDP per capita on output concen-
tration in the rest of the world (column 1 of table 4.8).




Patterns of Diversification in MENA: Explaining MENA’s Specificity 129


present after controlling for weak links and changes in the exchange rate.
Interestingly, the presence of weak links tends to make economies more
concentrated. On the other hand, exchange rate acceleration (that is, a
decrease in our explanatory variable) has no impact on manufacturing
concentration. When turning to the MENA subsample, neither weak
links nor changes in the exchange rate level have a statistically significant
impact on concentration indexes.


As a further check of the relative explanatory power of weak links and
exchange rate accelerations in the U-shaped relationship reversal, these
specifications are run separately for countries with a higher and lower
than average probability to observe weak links, as well as for countries
with exchange rate appreciation or depreciation. The idea is to see
whether the presence of significant weak links or opposite exchange rate
patterns are consistent with a reversal of the U-shaped relationship.
Results are reported in tables 4.10 and 4.11.


Figure 4.5 Marginal Effect of GDPpc on Employment Concentration [Giniempl]


G
D


P
p


er
c


ap
it


a


10
0


0.01


0.02


0.03


0.04


0.05


0.06


0.07


0.08


20
y


30


marginal effect of employment concentration


Source: Authors.
Notes: The solid line shows the marginal effect of GDP per capita on employment concentration in MENA
countries (column 5 of table 4.8), above. The dashed line shows the marginal effect of GDP per capita on
employment concentration in the rest of the world (column 2 of table 4.8).




130 Olarreaga and Ugarte


Figure 4.6 Marginal Effect of GDPpc on Value-Added Concentration [Ginivadd]


G
D


P
p


er
c


ap
it


a


10


–0.01


0


0.01


0.02


0.03


20


marginal effect of value-added concentration


y
30


Source: Authors.
Note: The solid line shows the marginal effect of GDP per capita on value-added concentration in MENA
countries (column 6 of table 4.8). The dashed line shows the marginal effect of GDP per capita on value-added
concentration in the rest of the world (column 3 of table 4.8).


In table 4.10, it appears that the presence of significant weak links can
explain a reversal in the U-shaped relationship. For countries with mildly
(below average) weak links, the relationship is the classic U shape. For
countries with very weak links, however, the U-shaped relationship is
inverted, with a maximum at around $14,000. On the other hand, table
4.11 suggests that no real difference exists in the relationship between
concentration and income per capita for countries with appreciation or
depreciation of their exchange rate. This suggests that the main explana-
tion behind MENA’s inverted U-shaped relationship between concentra-
tion and income per capita lies with weak links in their production chain,
rather than with Dutch Disease.


Concluding Remarks


An influential paper by Imbs and Wacziarg (2003) suggests that there
is a U-shaped relationship between economic development and




Table 4.9 Stages of Diversification and Weak Links versus Exchange Rate Appreciation


World Middle East and North Africa


Variables Giniout Giniempl Ginivadd Giniout Giniempl Ginivadd


GDPpc −0.00514478 −0.00529379 −0.02084442*** 0.03213498 0.06411107* 0.04358153
(0.00431789) (0.00457301) (0.00531031) (0.02510432) (0.03342117) (0.03702011)


[GDPpc]
2 0.00068925** 0.00044620 0.00200579*** 0.00349863 −0.00286479 0.00254568


(0.00032853) (0.00034794) (0.00040268) (0.00298112) (0.00396874) (0.00438068)
[GDPpc]


3 −0.00002267** −0.00001390 −0.00006319*** −0.00028460** −0.00000595 −0.00023179
(0.00001063) (0.00001126) (0.00001300) (0.00013954) (0.00018577) (0.00020444)


[GDPpc]
4 0.00000019 0.00000011 0.00000060*** 0.00000424** 0.00000082 0.00000349


(0.00000012) (0.00000013) (0.00000015) (0.00000183) (0.00000243) (0.00000267)
P(low) 0.77385947*** 0.00175064 1.00649919*** −0.40209100 −0.28687306 −0.20878525


(0.15979803) (0.16923974) (0.22608409) (0.60802414) (0.80945744) (0.88844989)
Δ (exch.rate) −0.00113460 −0.00051930 −0.00304585 −0.01917969 0.03129908 −0.02867972


(0.00710858) (0.00752859) (0.00963133) (0.02655145) (0.03534772) (0.03934538)
Constant 0.64004582*** 0.57093807*** 0.74197483*** 0.47702340*** 0.38066509*** 0.45261880***


(0.02915177) (0.03087421) (0.04731559) (0.07879516) (0.10489934) (0.11622175)


Minimum (US$) 4,814 1,0573 7,766 — — —
Maximum (US$) 22,685 16,912 23,374 15,310 11,676 16,653
R2 0.882 0.844 0.819 0.928 0.587 0.802


Observations 1,770 1,770 1,651 167 167 162


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. — = nonexistent.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.


131




Table 4.10 Splitting the Sample after Lower Tails of Productivity


P(low) < mean probability P(low) > mean probability


Variables Giniout Giniempl Ginivadd Giniout Giniempl Ginivadd


GDPpc −0.00192358 0.00557568** −0.00548106* 0.04680312** 0.00707754 0.08016101***
(0.00261861) (0.00258135) (0.00301095) (0.02276563) (0.02705268) (0.02816104)


[GDPpc]
2 0.00052438** −0.00008797 0.00117795*** −0.00259070 0.00040708 −0.00451626**


(0.00022416) (0.00022097) (0.00025689) (0.00180861) (0.00214919) (0.00223159)
[GDPpc]


3 −0.00001572** 0.00000493 −0.00004291*** 0.00005035 −0.00004163 0.00009270
(0.00000795) (0.00000784) (0.00000910) (0.00006449) (0.00007663) (0.00007953)


[GDPpc]
4 0.00000010 −0.00000012 0.00000044*** −0.00000042 0.00000056 −0.00000076


(0.00000009) (0.00000009) (0.00000011) (0.00000081) (0.00000096) (0.00000100)
P(low) 7.20139983 11.47958274** 6.97866028 0.64945983*** 0.01800120 0.89740199***


(5.38486269) (5.30824491) (6.27875255) (0.22165798) (0.26339893) (0.29356710)
Constant 0.65789379*** 0.48981432*** 0.67919801*** 0.54584944*** 0.59815816*** 0.38127892***


(0.04065571) (0.04007725) (0.04622884) (0.07817760) (0.09289942) (0.10297652)


Minimum (US$) 2,013 — 2,714 — — —
Maximum (US$) 26,817 (31,054) 25,591 13,624 14,105 13,958 US$
R2 0.837 0.826 0.801 0.948 0.920 0.927


Observations 2,625 2,625 2,477 294 294 275


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. — = Nonexistent.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.


132




Table 4.11 Splitting the Sample after Changes in the Real Exchange Rate


Δ (exch.rate) < 0 Δ (exch.rate) > 0
Variables Giniout Giniempl Ginivadd Giniout Giniempl Ginivadd


GDPpc −0.00398480 −0.00441760 −0.01613240** −0.00667477 −0.00141009 −0.03041253***
(0.00624957) (0.00644974) (0.00742766) (0.00707383) (0.00747091) (0.00890573)


[GDPpc]
2 0.00066400 0.00025944 0.00170760*** 0.00087846 0.00029640 0.00299197***


(0.00046775) (0.00048273) (0.00055392) (0.00056430) (0.00059598) (0.00070774)
[GDPpc]


3 −0.00002153 −0.00000512 −0.00005199*** −0.00003230* −0.00001455 −0.00010429***
(0.00001500) (0.00001548) (0.00001773) (0.00001900) (0.00002007) (0.00002377)


[GDPpc]
4 0.00000016 −0.00000001 0.00000044** 0.00000035 0.00000019 0.00000116***


(0.00000017) (0.00000017) (0.00000020) (0.00000022) (0.00000023) (0.00000028)
P(low) 0.59904842** −0.28359026 1.04717963*** 1.00820494*** 0.33614873 1.13410210***


(0.25001225) (0.25801991) (0.36050322) (0.22722656) (0.23998159) (0.31250446)
Constant 0.62661865*** 0.57258443*** 0.72226081*** 0.58501060*** 0.54826477*** 0.60627680***


(0.03384095) (0.03492484) (0.05118088) (0.04079015) (0.04307985) (0.05129346)
R2 0.889 0.859 0.839 0.893 0.861 0.831


Observations 855 855 796 914 914 854


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.


133




134 Olarreaga and Ugarte


economic concentration: at early stages of development, economic
concentration falls with income per capita, but starts increasing with
income per capita after a certain threshold. This U-shaped relation-
ship is confirmed by recent evidence in Cadot, Carrère, and Strauss-
Kahn (2011) of export diversification. This empirical regularity
contradicts casual observation of the development process in the
MENA region, where the production seems to become more concen-
trated as income rises.


This chapter empirically confirms, for MENA, this inverted U-shaped
relationship between income per capita and concentration: at early stages
of development, economic concentration increases with income per
capita and starts falling with income per capita only at relatively high
levels of economic development. This contrasts with what is observed, on
average, in the rest of the world.


To explain these differences in the development process, two alterna-
tives have been tested. MENA is a resource-rich region and subject to
Dutch Disease–type phenomena (à la Corden and Neary 1982). It is also
a region where some sectors have notoriously low levels of productivity,
and these weak links (Jones 2011) can lead not only to lower levels of
growth but also to a higher concentration of production. It was found
that weak links contribute to a more concentrated production bun-
dle than Dutch Disease. Moreover, after controlling for these two vari-
ables, the differences in development patterns between MENA and the
rest of the world become smaller.


This result has some interesting policy implications, at least in terms
of the timing of industrial policy reforms. Policies aimed at diversifying
the production process should first try to address the region’s weak
links. Otherwise resources may be wasted in trying to diversify into
sectors that are not economically viable. Although more research is
needed in this area, this chapter suggests that if governments first
address the existing weak links in their economies, diversification may
naturally follow. If addressing weak links may sometimes seem like a
daunting task requiring large infrastructure investments with a long-
term objective, it is important to note that one characteristic of weak
links is that they are nontraded goods. If there is an easily imported
substitute, then the low productivity of the domestic input sector is no
longer a drag on growth. Thus, when restrictive trade policies limit the
tradability of input sectors, liberalization may be sufficient to address
those weak links.




Annex Table 4A.1 Expanding Imbs and Wacziarg’s Samples


Giniempl Herfempl Ginivadd Herfvadd Giniempl Herfempl Ginivadd Herfvadd
Variable (I) (II) (III) (IV) (V) (VI) (VII) (VIII)


Sample Extended on Time dimension
GDPpc –0.0235*** –0.0187*** –0.0275*** –0.0162*** –0.0017 –0.0057*** –0.0027 –0.0021


(0.0012) (0.0015) (0.0014) (0.0012) (0.0020) (0.0017) (0.0024) (0.0019)
[GDPpc]


2 0.0010*** 0.0008*** 0.0013*** 0.0008*** 0.0001 0.0002*** 0.0003*** 0.0002**
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)


Constant 0.6180*** 0.1727*** 0.6217*** 0.1546*** 0.5601*** 0.1381*** 0.5547*** 0.1198***
(0.0033) (0.0038) (0.0035) (0.0030) (0.0061) (0.0053) (0.0070) (0.0055)


R2 0.259 0.153 0.266 0.138 0.813 0.882 0.765 0.764
Observations 1,605 1,605 1,502 1,502 1,605 1,605 1,502 1,502
Sample Extended on Country dimension
GDPpc –0.0176*** –0.0183*** –0.0206*** –0.0143*** –0.0020 –0.0044*** –0.0044** –0.0019


(0.0012) (0.0012) (0.0012) (0.0012) (0.0019) (0.0015) (0.0021) (0.0018)
[GDPpc]


2 0.0008*** 0.0008*** 0.0010*** 0.0006*** 0.0002** 0.0002*** 0.0003*** 0.0001*
(0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) (0.0001)


Constant 0.6025*** 0.1859*** 0.6079*** 0.1694*** 0.5619*** 0.1506*** 0.5642*** 0.1365***
(0.0029) (0.0031) (0.0030) (0.0031) (0.0053) (0.0043) (0.0061) (0.0052)


R2 0.106 0.120 0.145 0.079 0.787 0.877 0.761 0.826
Observations 2,649 2,649 2,435 2,435 2,649 2,649 2,435 2,435


Source: Authors.
Notes: Estimates V–VIII) include country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses. Statistically


significant: *** p<0.01, ** p<0.05, * p<0.1.


135




136


Annex Table 4A.2 Regressions of Diversification on Income Level for OPEC Countries


OPEC members in the world and in Middle East and North Africa


Variables Giniout Herfempl Ginivadd Giniout Herfempl Ginivadd


GDPpc –0.02553112 –0.04145684*** –0.06333541** –0.00256212 0.04676321 0.06587556*
(0.01670605) (0.01527436) (0.02456196) (0.03072678) (0.02817441) (0.03747950)


[GDPpc]
2 0.00157978 0.00301565*** 0.00438903*** 0.00039342 –0.00141172 –0.00297038


(0.00109037) (0.00099693) (0.00160311) (0.00175157) (0.00160607) (0.00213651)
[GDPpc]


3 –0.00004292 –0.00008484*** –0.00012361** –0.00001440 0.00001161 0.00005499
(0.00003218) (0.00002942) (0.00004731) (0.00004561) (0.00004182) (0.00005563)


[GDPpc]
4 0.00000040 0.00000081*** 0.00000122** 0.00000013 0.00000003 –0.00000035


(0.00000034) (0.00000031) (0.00000050) (0.00000044) (0.00000041) (0.00000054)
P(low) 2.37809157** 0.76363598 3.75498455** 1.84114404 –0.80956578 7.55797790***


(1.12525591) (1.02882283) (1.65439997) (1.43137131) (1.31247224) (1.74593921)
Constant 0.71045758*** 0.61610932*** 0.83534762*** 0.68692604*** 0.50784998*** 0.43498098***


(0.06119162) (0.05594757) (0.08996657) (0.11866039) (0.09178294) (0.14473801)
R2 0.942 0.907 0.860 0.970 0.897 0.948


Observations 169 169 169 88 88 88


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.




Annex Table 4A.3 Regressions of Concentration for Subsamples of MENA Countries


GCC countries Resource-rich countries in MENA


Variables Giniout Herfempl Ginivadd Giniout Herfempl Ginivadd


GDPpc 0.14142876* –0.05326200 0.46837357** 0.43601900 1.68026933** 1.85225279
(0.07450386) (0.03156220) (0.16984645) (0.96252068) (0.73139736) (1.40517389)


[GDPpc]
2 –0.00855715 0.00392086** –0.02546998** –0.71423004 –1.91413946** –2.40558891


(0.00463079) (0.00179743) (0.01055682) (1.20373896) (0.91469358) (1.74205785)
[GDPpc]


3 0.00023041 –0.00009918** 0.00058363* 0.38979807 0.89289429* 1.23280251
(0.00013230) (0.00004314) (0.00030159) (0.62385671) (0.47405438) (0.89416658)


[GDPpc]
4 –0.00000221 0.00000083** –0.00000477 –0.07141563 –0.14865827* –0.22136144


(0.00000137) (0.00000037) (0.00000313) (0.11474395) (0.08719129) (0.16293497)
Constant –0.02294480 0.73102625*** –2.12637846* 0.60297395** 0.07049948 0.19973671


(0.45108237) (0.20472003) (1.02934527) (0.26330246) (0.20007749) (0.38623254)
Observations 51 57 49 147 147 143
R2 0.993 0.947 0.969 0.788 0.881 0.672


Resource-poor countries in MENA Middle East and North Africa excluding Israel


Variables Giniout Herfempl Ginivadd Giniout Herfempl Ginivadd


GDPpc 12.38241006 –12.50140040** –1.53006248 –0.00882500 0.06455250*** 0.01852906
(8.76513390) (6.01454027) (7.84293129) (0.01821666) (0.01766061) (0.02290436)


[GDPpc]
2 –11.66214385 13.82743647** 2.65724461 0.00096733 –0.00359912*** 0.00038176


(8.96555270) (6.14512808) (7.96496817) (0.00113941) (0.00108819) (0.00154124)
[GDPpc]


3 4.64694100 –6.58550407** –1.70714867 –0.00003510 0.00008686*** –0.00004394
(3.98852226) (2.72895854) (3.52736893) (0.00003208) (0.00002866) (0.00004741)


[GDPpc]
4 –0.66305553 1.14630129** 0.36705754 0.00000037 –0.00000077*** 0.00000066


(0.65165408) (0.44490378) (0.57453171) (0.00000032) (0.00000027) (0.00000051)
Constant –4.16860225 3.25204578* 0.90636716 0.70665730*** 0.39152287*** 0.64078851***


(3.14625478) (1.63004036) (2.66930229) (0.06362725) (0.06430421) (0.07929306)
Observations 89 86 85 287 290 277
R2 0.688 0.869 0.682 0.860 0.656 0.775


Source: Authors.
Notes: All regressions include time and country fixed effects. GDPpc stands for GDP per capita at constant prices (thousands US$). Standard errors in parentheses.


Statistically significant: *** p<0.01, ** p<0.05, * p<0.1.


137




138 Olarreaga and Ugarte


Notes


1. The lack of diversification is often seen as a problem faced by the region,
because a relatively concentrated economy may be heavily exposed to a few
very volatile sectors. Moreover, if only a few sectors are growing fast in the
world economy, a concentrated production bundle minimizes the chances of
benefiting from those high-growth sectors.


2. In fact, the nonparametric analysis suggests that the relationship should be
extended beyond the quadratic specification.


3. The lack of data does not allow for a complete analysis of the heterogeneity
within the MENA region. We also explore the heterogeneity that might be
related to the natural resources endowments of oil producers.


4. The source of the data is the United Nations Industrial Development
Organization’s Industrial Statistics (INDSTAT), rev. 2.


5. If data for some small sectors are missing but are considered instead as a zero,
the value of the Gini index will increase, while the Herfindhal will be unaf-
fected. Given that sometimes the data for small sectors are missing in the
dataset used for this work, these sectors are considered inactive (that is, not
producing) rather than missing when they represented, on average over the
entire 1963–2003 period, less than 2 percent of total manufacturing output,
employment, or value added. Thus, the number of active sectors varies by
country and year in our sample. The Herfindhal index is not significantly
affected by this rule, but the Gini can be downward biased.


6. Besides the variable used here as a proxy for Dutch Disease, we check the
robustness of the results using other proxies, that is, exchange rate in levels
and the dependence of countries on oil exports and oil prices. Our conclu-
sions are unaffected when using these alternative proxies; results are available
upon request.


7. Labor productivity is used instead of total factor productivity (TFP) mainly
because data to estimate TFP are available only for a much smaller number of
countries. Many countries do not have the sufficiently long investment series
that is necessary to build capital stocks. We measure labor productivity as the
ratio of output to the number of employees. An alternative would be to use
value added instead of output, but that would leave us with a smaller sample.
A shortcoming associated with the use of labor productivity is that it is
affected by the capital intensity of the sector. An alternative measure control-
ling for relative factor endowments of countries, the degree of tradability in
each sector, and use as an intermediate input is provided in Ugarte (2012).
This measure corrects for the fact that labor productivity may be affected by
relative factor endowments, and that for a weak link to exist the low produc-
tivity sector needs to be nontradable and intensively used as an intermediate
input by other sectors. The correlation between our simple measure here and




Patterns of Diversification in MENA: Explaining MENA’s Specificity 139


the one in Ugarte (2012) is positive and significant, suggesting that the simple
measure is an adequate proxy for weak links.


8. Otherwise, the estimated productivity distribution will give the same weight
to sectors with very different economic importance. After discarding the
negative values of the estimated density, the mean and the standard deviation
of the distribution are obtained using the estimated density function evalu-
ated over 1,000 points.


9. We try several other thresholds and also calculate deviations with respect to
median instead of mean productivity. We decided to report this version using
the cutoff at two standard deviations, which is close to a usual cutoff for the
normal distribution. Concerning the sensitivity of the proxy with respect to
factor endowments, fixed effects included in the regression analysis control
for this characteristic. The measure is based on manufacturing data, so it is less
subject to productivity shocks than data on oil production. Moreover, we did
not detect a statistical correlation between the estimated likelihood and the
dispersion of the distribution.


10. Input-output tables used there are from Organisation for Economic
Co-operation and Development (OECD) and cover around 40 countries,
none of them from the MENA region. Using this information would imply
imposing an average structure of economy to these countries.


11. The data on production, employment, and value added come from UNIDO’s
INDSTAT 3 (3 digit of the ISIC rev. 2); GDP per capita in constant prices is
from the 2009 World Development Indicators; and the real exchange rate
index is from International Financial Statistics/IMF. Although trade data are
more detailed and generally of better quality, we prefer to use production data
to measure concentration so that our results are comparable to Imbs and
Wacziarg (2003).


12. Measuring GDP per capita at constant dollars rather than PPP allows us to
have a much larger expanded sample later on, and there is no strong reason
to use one rather than the other when looking at the relationship between
production diversification and level of development.


13. Only when we expand the sample along one of the two dimensions (time
or countries) are results robust to the ones obtained in Imbs and Wacziarg
(2003). Along the time dimension, we extend the sample for the period
1993–2003, but we keep the same countries as in the original paper. Along
the country dimension, we keep the same time span as in the original
paper, 1963–93, but allow for more countries for which we have data. The
U-shaped relationship between concentration and income per capita is
maintained (results are available from the authors upon request). However,
when we add the two dimensions simultaneously the relationship seems to
reverse.




140 Olarreaga and Ugarte


14. Our estimation procedure uses the package np from Hayfield and Racine
(2008) in R. It includes optimal bandwidth selection and cross validation of
estimates, as suggested by Racine and Li (2004).


15. The different pattern observed for the Gini coefficient on employment may
be because employment is stickier than production or value added. One can
increase production and value added without changing employment by buy-
ing more of other factors or in by being in the presence of technological
progress. The fact that the Gini coefficient is more sensitive to what is hap-
pening in the middle of the employment distribution suggests that another
potential explanation lies in different changes in concentration at the top and
bottom of the distribution.


16. Note that in these high-order functions, even if different orders have different
signs, the first derivative may never be equal to zero (in real number space).


17. Annex table 4A.2 suggests that this difference in the pattern of diversification
and development is also valid for Organization of the Petroleum Exporting
Countries members inside and outside MENA, although the statistical sig-
nificance of the results is not as strong as for the full MENA and RoW sam-
ples. Table 4A.3 explores the heterogeneity that may exist within MENA
countries. A first subsample is defined by the members of the Gulf
Cooperation Council (GCC), and nonmembers are split into resource-rich
and resource-poor countries. Given the reduced number of observations in
each subsample and the fixed effects included, estimates are not highly sig-
nificant. However, results in annex table 4A.3 do not differ substantially from
the results in table 4.8. Results for resource-poor countries differ from those
in table 4.8 (size and sign), but their interpretation can be misleading, since
only three countries (Jordan, Morocco, Tunisia) are represented in this sub-
sample. The exclusion of Israel from MENA does not have a significant
impact on the pattern described by the results in table 4.8.


18. In MENA, income per capita measured in 2000 constant U.S. dollars ranges
from $434 in the Republic of Yemen to $49,329 in the United Arab Emirates.
The mean income per capita in MENA is $6,308 and the median is $1,528.


19. We also include an interaction term between Dutch Disease and weak links
to explore the idea that weak links may be less harmful in countries with a
rapidly appreciating exchange rate, as imported goods become cheaper. The
interaction is never statistically significant.


References


Acemoglu, D., and F. Zilibotti. 1997. “Was Prometheus Unbound by Chance? Risk,
Diversification, and Growth.” Journal of Political Economy 105 (4): 709–51.


Cadot O., C. Carrère, and V. Strauss-Khan. 2011. “Export Diversification: What’s
Behind the Hump?” Review of Economics and Statistics 93 (2): 590–605.




Patterns of Diversification in MENA: Explaining MENA’s Specificity 141


Corden, M., and P. Neary. 1982. “Booming Sector and De-Industrialisation in a
Small Open Economy.” Economic Journal 92 (December): 825–48.


Faini, R. 2004. “Trade Liberalization in a Globalizing World.” Discussion Paper
4665, Centre for Economic Policy Research, London.


Hayfield, T., and J. S. Racine. 2008. “Nonparametric Econometrics: The np
Package.” Journal of Statistical Software 27 (5): 1–32.


Hirschman, A. 1958. The Strategy of Economic Development. New Haven, CT: Yale
University Press.


Imbs, J., and R. Wacziarg. 2003. “Stages of Diversification.” American Economic
Review 93 (1): 63–86.


Jones, C. 2011. “Intermediate Goods and Weak Links in the Theory of Economic
Development.” American Economic Journal: Macroeconomics 3 (2): 1–28.


Koren, M., and S. Tenreyro. 2007. “Volatility and Development.” Quarterly Journal
of Economics 122 (1): 243–87.


Kremer, M. 1993. “The O-Ring Theory of Economic Development.” Quarterly
Journal of Economics 108 (3): 551–75.


Racine, J., and Q. Li. 2004. “Nonparametric Estimation of Regression Functions
with Both Categorical and Continuous Data.” Journal of Econometrics 119 (1):
99–130.


Saint-Paul, G. 1992. “Technological Choice, Financial Markets and Economic
Development.” European Economic Review 36 (4): 763–81.


Ugarte, C. 2012. “Weak Links and Diversification.” University of Geneva.






143


C H A P T E R 5


Fiscal Policy and Diversification in
MENA


Ali Zafar


One of the central policy instruments that governments have to influence
economic activity is fiscal policy. Over the years, both developed-
and developing-country governments have used fiscal policy to stabilize
economic activity, promote growth, develop trade, and manage
terms-of-trade shocks. In the Middle East and North Africa (MENA),
governments have traditionally played a dominant role in the economies
and have had high expenditures by international standards. Economic
diversification, particularly in the Gulf Cooperation Council (GCC)
countries, has depended on fiscal policy to help lay the foundation for
successful growth. In MENA, governments have traditionally been forced
to allocate budgets between consumption subsidies and longer-term
investment. Fiscal policy in the region has tried to insulate the economy
from shocks while safeguarding long-term development. Fiscal policy
(mainly expenditure policy) has two roles to play in the region—as a
stabilizer for terms-of-trade shocks and as a tool for longer-term growth
and development.


Fiscal policy was very prominent during the recent financial crisis in
2008, which partially reversed the gains from the unprecedented oil
boom that led to significant wealth accumulation in some of the MENA
economies.1 However, the resource-rich countries of MENA have been




144 Zafar


less affected by the global recession than most other developing regions,
especially since oil prices have rebounded and reserves have been
strengthened. The impact of the crisis has depended on the nature of the
economy. The GCC economies, such as Bahrain and Saudi Arabia, with
their large oil exports and reserves, have been the least affected, whereas
oil exporters with larger populations relative to their oil wealth—Algeria,
the Islamic Republic of Iran, Iraq, Libya, and the Syrian Arab Republic—
have had a deterioration in their fiscal and current account positions. The
oil importers, such as the Arab Republic of Egypt, Jordan, and Lebanon,
with tight links to the GCC economies, have been hurt by high oil prices
and reduced foreign direct investment (FDI) and remittances from the
resource-rich countries of the region.


This chapter assesses the relationship between fiscal policy, growth,
and diversification in the MENA region. The chapter begins by briefly
describing the contemporary fiscal dynamics in MENA countries in the
aftermath of the Arab Spring. Stylized facts are then analyzed to assess
the historic behavior of fiscal policy in terms of consumption versus
investment. The goal is to understand the use of fiscal instruments for
diversification. The countries are disaggregated into three groups: the
GCC, the Mashreq (Arab-speaking countries to the east of the Arab
Republic of Egypt and north of the Arabian Peninsula) and the Maghreb
(Arab-speaking countries of North Africa). The impact of the global crisis
on fiscal policy is examined and the behavior of fiscal policies over a
30-year horizon is investigated in relation to previous commodity booms
and busts. Case studies of Algeria, Jordan, and Saudi Arabia provide a
closer analysis of the links between fiscal policy and diversification in
countries representing different parts of the MENA region.


Role of Fiscal Policy in the Aftermath of the Arab Spring


In the first half of 2011, a series of revolts happened spontaneously in the
Arab world, with strong repercussions for economic stability, fiscal man-
agement, and long-term growth in the affected countries. Motivated by a
combination of authoritarian rule, government corruption, police intimi-
dation, and lack of economic opportunities, the uprisings gathered
momentum and succeeded in toppling regimes in Egypt, Libya, and
Tunisia. The demands centered on greater political enfranchisement and
economic opportunity, and to a large extent were motivated by rising
expectations. While the full impact of these revolts is still to be borne out,
some preliminary estimates can be made of their potential impacts on the




Fiscal Policy and Diversification in MENA 145


region based on historical, anecdotal, and available empirical evidence.
Figure 5.1 provides a background chart to show the economic condition
of the different regions before the Arab Spring.


By the spring of 2012, the MENA region had settled into an unstable
equilibrium, with some regime change coupled with uncertainty on many
fronts. In addition to the governments that were overthrown in Egypt,
Libya, and Tunisia, the people of Bahrain, Syria, and the Republic of
Yemen have experienced strong discontent and rebellion, while citizen
demands for political liberalization have been growing in Jordan and
Morocco. Across the board, there has been discontent, regardless of
whether a country is an oil importer, Sunni- or Shia-dominated, or located
in the Maghreb or Mashreq. Governments have tried to react to the rising
tide of popular discontent on the economic front by increasing subsidies,
wages, and pensions and by offering large stimulus programs. Currently,
the macroeconomic horizon is uncertain, although there are strong
downside risks.


One of the major challenges for policy makers over the medium and
long term is the need to maintain fiscal stability while actively trying to
ramp up public investment and help shield the poor from exogenous
food and oil shocks. The recent crises, both international and domestic,
have led to greater demands for government expenditure and service
delivery. The political revolts caught almost all MENA governments by
surprise, and many have reacted with strong fiscal stimulus packages or
expansion of subsidies. Because the Middle East depends on large inflows


Figure 5.1 Fiscal Dynamics in the Middle East on the Eve of the Arab Spring, 2010


fiscal deficit


p
er


ce
n


t
o


f G
D


P


revenue
(excl.grants)


expenditure debt


GCC Maghreb Mashreq


–20


–10


0


10


20


30


40


50


60


70


80


Source: IMF 2011.




146 Zafar


of imported foods, an extensive subsidy system has been set up to shield
local populations from food price inflation. In the wake of the recent
international financial crisis, however, recognition is growing that large
public deficits are not sustainable throughout the world and that fiscal
instability can have bad effects on longer-term growth. The biggest danger
for MENA countries in the postrevolutionary era is to overspend on
improperly targeted subsidies and underspend on vital physical and social
infrastructure. Given the overall history of the region, governments have
to maintain a careful balance between the various expansions in expen-
ditures and the quest for fiscal stability.


The countries with perhaps the greatest risk of fiscal slippages in the
medium term are oil-importing countries such as Egypt, Jordan, and
Tunisia, which have a multiplicity of risk factors. There has been
increasing pressure on government expenditures for subsidies, public
investment, and employment generation. In these countries, where
consumption subsidies absorb a large share of government resources
and have dwarfed public investment spending—frequently reaching
8 percent of gross domestic product (GDP) in Egypt and more than
4 percent of GDP in the others—fiscal pressures will be strong in the
years ahead.2 A combination of fuel subsidies to reduce the impact of
higher oil prices on the population and food subsidies to help cushion
price shocks has hurt the countries’ fiscal positions. In addition, strong
stimulus packages in the wake of the political crisis may have deleterious
fiscal implications down the road. Egypt has reversed its decision to scale
back food subsidies in the aftermath of the crisis, and Tunisia’s interim
government announced in April 2011 that it would adopt a fiscal stimu-
lus package, focusing on a series of fiscal and financial measures designed
to encourage investment, especially in regional development zones; ease
the burden on taxpayers; and simplify tax obligations (table 5.1). In
February 2011, to prevent unrest, the Moroccan authorities announced
an additional $2 billion in subsidies to reduce costs of consumer staples,
such as bread and cooking oils.


The oil-exporting countries have also been affected by the political
crisis, but they have more fiscal space to maneuver (figure 5.2). The
oil-exporting countries of the Gulf and the other oil exporters of the
region, such as Algeria, Iraq, and Libya, can benefit from the recent oil
price boom, and their ample reserves allow them to undertake postcrisis
expansionary fiscal policy without jeopardizing macroeconomic stability.
The availability of sovereign wealth funds for many of the oil exporters
gives an ample fiscal cushion. After responding to the 2008 financial crisis




147


TABLE 5.1 Selected MENA Economics: Real GDP Projections and Fiscal Assessment


Real GDP Projections %


Fiscal changes after Arab Spring2010 2011 2012


Oil exporters 3.5 4.9 4.1
Iran, Islamic Rep.
Saudi Arabia


1.0
3.7


0.0
7.5


3.0
3.0


No measures announced
$100 billion stimulus program including public works; 15% pay increases for civil servants


Algeria 3.3 3.6 3.2 Close to $160 billion in new infrastructure projects; main focus, road and rail
United Arab Emirates 3.2 3.3 3.8 No major shift in policy
Qatar 16.3 20.0 7.1 Continued subsidies on petrol and diesel
Kuwait 2.0 5.3 5.1 Free daily meals to population for a year; cash allocations of $5,000 per citizen
Iraq 0.8 9.6 12.6 No policy articulated
Sudan 5.1 4.7 5.6 No policy articulated


Oil importers 4.5 1.9 4.5
Egypt, Arab Rep. 5.0 1.0 4.0 lncrease in some state wages and pensions by 15% maintenance of all subsidies in


full, costing close to $18 billion; exemptions of late loan and tax payments from fines
Morocco 3.2 3.9 4.6 Doubling of funds allocated to state subsidies; compensation system for wheat importers
Syria, Arab Republic 3.2 3.0 5.1 Significant financial benefits on public-sector employees and on low-wage earners in


general; public sector wages will be increased by S£l,500 ($32) a month and basic state
pension will be increased by S£l,500 a month: some lowering of tax rates


Tunisia 3.7 1.3 5.6 Massive stimulus package combining public investment in special development zones;
tax exemptions for investors in these areas; plans to reduce the value added tax (VAT);
6% rate imposed on imported equipment; some projected increases in subsidies


Lebanon 7.5 2.5 5.0 No key policy departures announced
Jordan 3.1 3.3 3.9 $225 million cuts in fuel, sugar, and rice prices; pay increase for civil servants and


retirement pensions


Source: Real GDP figures form IMF; other information from government authorities and World Bank staff.




148 Zafar


with the largest stimulus of any country in the Group of 20, Saudi Arabia
announced in February 2011 about $36 billion in bailouts and $67 billion
in spending plans to combat social unrest.3 Other countries have
announced similar measures, although on a much smaller scale. Overall,
the evidence is fairly clear that any future fiscal shocks to the oil export-
ers, especially the GCC countries, will be mild, especially given the high
projected international prices of oil until 2015.


Fiscal Policy in MENA: Stylized Facts


The global commodity boom since 2003 has positively affected growth
in MENA, although the region has been strongly impacted by the 2008
economic crisis. Overall GDP growth in the region averaged more than
5 percent during the 2000s. Growth reached more than 4 percent in
2008, fell to 2.3 percent in 2009, and rebounded to more than 4 percent
in 2010. The GDP growth rates of oil importers and exporters followed
similar trends, although volatility was greater among the GCC countries
(figure 5.3). Average per capita income surpassed $10,000 by 2005 for
the oil exporters, and oil importers also made significant progress. Total
oil and gas exports increased significantly from 2000 to 2009, although
the crisis reduced their overall value, in large part, because oil prices fell
to less than $50 a barrel. Fiscal and current account surpluses had reached
record levels before the crisis, with the combined current account top-
ping $350 billion. Inflation, as measured by the consumer price index
(CPI), was in the low single digits for most countries in the 2000s, partly


Figure 5.2 Fiscal Balances in MENA, 2000–10
p


er
ce


n
t


o
f G


D
P


–10


20
07


20
08


20
09


20
10


20
06


20
00


–2
00


5


–5


0


5


10


15


20


25


GCC Maghreb Mashreq


Source: IMF 2010.




Fiscal Policy and Diversification in MENA 149


Figure 5.3 Real GDP Growth Rates in MENA, 2000–10


0


p
er


ce
n


t


2


4


6


8


20
00


–0
5


20
06


20
07


20
08


20
09


20
10


GCC Maghreb Mashreq


Source: IMF 2011.


helped by prudent exchange rates and monetary management. Debt
dynamics on the whole have been quite favorable, but the degree of
overhang has been much lower in the oil-exporting economies than in
the oil importers. In parallel, life expectancy increased in the GCC by
almost 12 years, to 75 years, between 1980 and 2010.


Oil-exporting countries, particularly the GCC countries, maintained a
reasonable fiscal stance over the past decade. During the early boom years
of the 1970s and 1980s, governments recycled the windfall gains through
a welfare system and public investment program, but there also was a
surplus of hastily planned infrastructure projects that did not have the
projected impact on economic growth, coupled with weakening fiscal
positions and growing debt. Fiscal policy expansion strongly tended to be
correlated with revenue increases. By the 2000s, MENA countries made
a strong attempt to build up substantial international reserves through
carefully managed fiscal policy.4


The GCC countries were much more careful than other MENA
countries in their spending and ensured that the commodity booms resulted
in reserve accumulation (figure 5.4a). In the current era, hydrocarbon rev-
enues are being used more carefully to catalyze stronger private sector
investment in infrastructure, with a focus on road and rail projects. In the
wake of the crisis, the GCC countries had strong reserves to tap for stimulus
capital. As a result of stimulus measures during the crisis and a general coun-
tercyclical fiscal policy, the GCC countries have maintained non-oil GDP
growth, averaging more than 3 percent a year. Imports were contained, and
the drop in oil prices led current account surpluses to fall from just under
$380 billion in 2008 to about $50 billion in 2009. The subsequent return of
high oil prices led to a current account surplus of $400 billion in 2011 for




150 Zafar


Figure 5.4 Revenue and Expenditures in MENA, 2000–10


0


p
er


ce
n


t
o


f G
D


P
p


er
ce


n
t


o
f G


D
P


p
er


ce
n


t
o


f G
D


P


10


20


30


40


50


60


a. GCC countries


b. Maghreb


c. Mashreq


0


10


20


30


40


50


0


5


10


15


20


25


30


35


revenuesexpenditures


revenuesexpenditures


revenuesexpenditures


20
00


–0
5


20
06


20
07


20
08


20
09


20
10


20
00


–0
5


20
06


20
07


20
08


20
09


20
10


20
00


–0
5


20
06


20
07


20
08


20
09


20
10


Source: IMF 2011.




Fiscal Policy and Diversification in MENA 151


the GCC economies. Overall, the hydrocarbon sector remains dominant
in the GCC economies, accounting for more than 50 percent of GDP and
two-thirds of fiscal revenue over the last ten years.


Macroeconomic and fiscal management in the non-GCC economies
has been mixed (figure 5.4b and figure 5.4c). For most of the 2000s, the
countries managed their fiscal houses to preserve underlying macroeco-
nomic stability. However, in countries such as Jordan and Lebanon the
primary deficit has remained high even during these high-growth years.
Following the global crisis, Egypt and Jordan temporarily increased capi-
tal expenditures to help stimulate the economy, and quickly thereafter,
with the advent of the Arab Spring, food and energy subsidies were raised
significantly. The Maghreb has fared better than the Mashreq in keeping
its expenditures more closely aligned to revenues. Resource-rich labor
importers like Algeria have had an easier time than resource-poor oil
importers like Jordan and Egypt. In a number of oil-importing countries,
particularly Egypt, Jordan, and Lebanon, economic activity has slowed
and unemployment increased in the wake of the Arab Spring. However,
all oil-importing countries of MENA have seen a sharp deterioration of
their fiscal positions and more limited economic policy options because
of reductions in foreign exchange buffers. This translates into a decrease
in public resources for key infrastructure investments that could propel
diversification.


The big concern for the medium term is the fiscal situation in the
marginal oil exporters and oil importers. The stimulus may have an
adverse long-run fiscal impact, especially on public investment, wages,
and subsidies. The strong pre-crisis fiscal positions may evaporate over the
medium term. Evidence suggests that the overall fiscal balance in MENA
fell from a surplus of 13 percent of GDP in 2008 to a deficit of about
1.2 percent of GDP in 2009 before rising to a small surplus in 2010. A
decomposition of fiscal numbers shows considerable diversity within the
region, with the GCC countries in a strong fiscal position, while the
Mashreq and, to a lesser extent, the Maghreb, ran deficits.


The Complementary Role of Monetary Policy in the
MENA Countries
Complementary to fiscal policy, monetary policy also plays a role in the
MENA region, although its role varies significantly depending on the
exchange rate regime in each country. In the GCC countries, monetary
policy has been used to maintain the peg to the dollar. All six countries
of the GCC union, with the exception of Kuwait, have pegged their




152 Zafar


currencies to the dollar to stabilize exports and government revenues, as
well as to maintain price stability (Khan et al. 2008). The monetary union
was established because of several commonalities among the nations:
dependence on oil exports, low tax and tariff regimes, traditionally low
inflation, free capital convertibility, and similar economic structures. This
regime has served to strengthen the credibility of the monetary authori-
ties. The main goal of monetary policy is to support the fixed regime.
Because the central banks of the GCC countries do not use interest rates
as a monetary tool to control inflation, fiscal policy has been the primary
tool to influence economic performance. Fiscal policy, via subsidies, has
also been an important dampening force on inflation in MENA countries.
Inflation has historically averaged in the single digits, although it has been
higher in oil-importing than in oil-exporting countries and has been man-
aged by tight monetary policy at the central banks.


In the GCC, the U.S. dollar peg has served as the external anchor for
monetary policy and has been used to help maintain overall macroeco-
nomic stability. The policy has served the region well over the years and
has helped shield the economies from excessive volatility. Given that the
currency is pegged to the dollar, monetary policy in the Gulf has closely
followed the American monetary policy. Since the economies are heavily
dependent on oil (priced in U.S. dollars), the monetary arrangement has
helped support that structure. In effect, fiscal and external current
account balances have largely followed movements in the price of oil.
However, some costs are associated with this policy. In light of the weak-
ening dollar in 2009, dollar depreciation reignited inflation in the GCC
through higher import prices, although the degree of exchange rate pass-
through has varied by country. Furthermore, while nominal exchange
rates have not appreciated in respect to other currencies, the trade-
weighted real effective exchange rate has increased.5 The GCC countries
are not expected to alter their current exchange rate policy given that
nominal effective exchange rates have not shown a high degree of appre-
ciation vis-à-vis other currencies.


The oil importers have used monetary policy to keep inflation stable
and to prevent it from undermining export competitiveness. Most oil
importers in the Middle East have had relatively conservative monetary
policies and exchange rate–based stabilization programs to prevent strong
credit expansion and avoid double-digit inflation. While practices differ
depending on the country, monetary policy has acted to safeguard mac-
roeconomic stability. For countries such as Egypt and Syria, where the
exchange rate is not pegged to any currency, monetary policy has been




Fiscal Policy and Diversification in MENA 153


carefully used as a countercyclical tool to control inflation. Over the
years, stable inflation rates in most of the MENA countries have led
central banks to be relatively accommodative. When inflationary surges
have occurred, as in the food price shocks of 2008, monetary policy has
been constrained in these countries to dampen inflationary pressures. For
countries in MENA linked to the euro, such as Morocco, there has been
some overvaluation of currencies but no major inflationary surges.6 In
Jordan, which has a fixed peg to the dollar, monetary policy has been
accommodative and has supported the exchange rate. Overall, oil import-
ers have a strong degree of prudent monetary policy that has helped
complement their fiscal management.


Fiscal Policy Cyclicality in Oil Exporters and Oil Importers:
A Long-Term View
To understand recent fiscal policy, it is important to understand the his-
torical fiscal dynamics in the MENA region. This section examines the
historical response to trade shocks and assesses the degree of pro- or
countercyclicality in the past. By definition, cyclicality means the response
of fiscal policy to shocks. Ideally, fiscal policy should contract during
booms and expand during recessions to ensure a smooth output. The dif-
ferences between the conduct of fiscal policy before and after the finan-
cial crisis are assessed. The effect of fiscal policy on output volatility is
also considered. Given the strong linkages among the MENA countries,
the fiscal analysis sheds light on the interrelationships between the oil
exporters and the oil importers.


An understanding of the GCC’s economic structure is vital to under-
standing the evolution of the members’ fiscal policy over several decades.
The GCC economies, particularly Oman, Saudi Arabia, and the United
Arab Emirates (UAE), represent the founding structure of the Organization
of the Petroleum Exporting Countries (OPEC) and have been the leading
oil producers. Oil has been the foundation of the GCC economies over
the last four decades, contributing to more than half of their GDP, and
more than three-fourths of government revenue and exports. Accounting
for close to half of proven world oil reserves, GCC countries have been
important beneficiaries of the oil booms and shocks of the last decade,
particularly the two oil shocks of the 1970s and the recent commodity
boom of the 2000s.


A historical analysis of the stylized facts for the GCC shows inter-
esting patterns. During the 1970s oil boom, as the OPEC-led oil
shocks led to higher oil prices, the GCC countries launched ambitious




154 Zafar


programs of public spending on infrastructure and the welfare sys-
tem. The boom in oil prices led to a corresponding boom in spending,
with strong correlations between expenditure changes and revenue
changes (figure 5.5). With the declines in oil prices during the 1980s
and 1990s, budgets contracted somewhat as the economies adjusted.
By the 2000s, GCC governments were more careful with fiscal policy,
and the correlation between revenue changes and expenditure
changes declined. Thus, a significant part of revenues has been saved,
although spending on wages and salaries has increased. It is important
to note in this regard that the post-2000 boom has been more pro-
nounced in terms of revenue expansions, while the booms in the
1970s were more short lived.


Fiscal policy in the GCC in the 2000s has been significantly different
from what it was during the 1970s boom, with important implications for
growth and diversification. In the 1970s, GCC governments spent more
than two-thirds of their oil revenues, but by the 2000s, they had put
about one-fourth into savings and stabilization funds, or sovereign wealth
funds, which increased exponentially over the 2000s. (By contrast, much
of the money in the 1970s was put into international banks, which
engaged in reckless lending.) The prudent management in the wake of the
2000 commodity boom led to a decline in the combined public debt of
the GCC countries, from 60 percent of GDP in the early 2000s to less
than 15 percent by 2010, with Saudi Arabia reducing its debt overhang
by close to $100 billion. Many of the GCC countries maintained fiscal
surpluses, rather than the deficits they ran in the earlier period, and some


Figure 5.5 GCC Government Fiscal Policy


0


0.5


ex
p


en
d


it
u


re
c


h
an


g
e/


re
ve


n
u


e
ch


an
g


e


1.0


1.5


2.0


2.5


Ba
hr


ain


Ku
wa


it


Om
an


Qa
ta


r


Sa
ud


i A
ra


bi
a


Un
ite


d
Ar


ab


Em
ira


te
s


1975–80 1994–96 2000–02 2006–08


Sources: Fassano 2002; IMF database.




Fiscal Policy and Diversification in MENA 155


countries had surpluses in the double digits. It is fair to say that macro-
economic management of the commodity booms was better in the 2000s
than in the 1970s.


Over the past decade, one of the major fiscal policy successes of the
GCC governments has been the buildup of savings in the sovereign
wealth funds, which demonstrates their countercyclical fiscal behavior.
The SWFs were originally started in 1980 to cushion the effects of vola-
tile oil revenue and build up reserves. Their growth has been a testament
to prudent macroeconomic management in the Gulf. An important ratio-
nale behind the formation of the SWFs in the Gulf countries was to cre-
ate a source of revenue that could replace oil revenue after the depletion
of oil reserves.7 While each country is different, the SWF has become an
instrument for both financing deficits and building long-term financial
assets. Given the lack of taxation in the GCC, the funds play an impor-
tant role. The overall assets of the various funds are estimated to be more
than $1.3 trillion (figure 5.6). Details of the SWF stocks are not publicly
available, but the available estimates suggest that the Abu Dhabi
Investment Authority has more than $600 billion and that Saudi Arabia
has more than $400 billion (although the Saudi fund is technically a
monetary account and not an SWF).8 Despite a strong shock in the wake
of the financial crisis, the SWFs remain major players in the Gulf
economies. Overall, the use of the reserve funds have helped cushion
budgets, protect the economies from volatile oil prices, and help diversify
government investments for each country’s long-term future.


However, one major issue with the SWFs has been a lack of transparency
and accountability. Because they are the main allocators of state capital in
the region, the lack of transparent governance of the funds raises some


Figure 5.6 GCC Sovereign Wealth Funds


U
S$


b
ill


io
n


s


Ba
hr


ain


Ku
wa


it


Om
an


Qa
ta


r


Sa
ud


i A
ra


bi
a


Un
ite


d
Ar


ab


Em
ira


te
s


0
100
200
300
400
500
600


Sources: Government authorities; Goldman Sachs; World Bank.
Note: Technically, the Saudi account is not a SWF but an account in the central bank.




156 Zafar


issues in relation to governance and control. Since funding for the SWFs
comes from the central bank reserves that accumulate as a result of budget
and trade surpluses, proper fiscal rules should be applied to these funds.
Recent literature casts doubt on whether this is the case. Truman (2007)
finds that many wealth funds, particularly in the MENA region, involve
large official holdings of cross-border assets, which are often unknown to
the citizens of the countries and to market participants. In a similar vein,
Elbadawi and Soto (2011) find that the resource-rich but largely democ-
racy-deficient MENA region has been a fiscal rules-free region, and that
fiscal rules can be valuable fiscal stabilization instruments, especially with
the nascent democracies demanding more accountability. In a world of
greater transparency regarding natural resource management, the resource-
rich governments in the MENA region will need to demonstrate more
openness and transparency about the use of oil revenues in order to reduce
corruption and rebuild trust in government institutions. A more open
approach to information on the SWFs will increase the accountability and
transparency of these revenues.


The oil exporters have also shown greater efficiency in capital expen-
ditures in the 2000s, especially in their infrastructure investments, than in
the earlier periods. The 1970s saw a plethora of investment projects, but
without strong project viability or cost efficacy. The available empirical
evidence suggests that many of the projects had low rates of return. Some
noteworthy investments were made in roads and airports, but the indus-
try consensus is that the level of efficiency in execution of the capital
budget was low. Moreover, the lack of savings resulted in slippages in the
infrastructure budget in the 1980s and 1990s as oil prices declined.9
However, the latter period saw much more pronounced government
investment in real estate projects, which helped pave the way for addi-
tional infrastructure investment. In the post-2001 oil boom period, an
inventive mix of government entities—including ministries, municipali-
ties, and national oil companies—has been involved in public-private
partnerships, including build-operate-transfer projects and joint ventures.
Close to 1,000 government projects have been launched in GCC coun-
tries in the past decade, with more than 50 percent completed or in the
final execution phase.


Fiscal Policy and Diversification


One of the channels by which fiscal policy affects the economy is through
its role in diversification. A growing body of international literature




Fiscal Policy and Diversification in MENA 157


discusses the impact of public expenditure and expenditure composition
on growth, but there is no strong consensus on the impact and the sec-
toral drivers. Devarajan, Swaroop, and Zou (1996) find a positive link
between current spending and growth, and a negative link between
capital spending and growth, in a sample of 43 developing countries,
suggesting problems with capital allocations and investment efficiency.
However, Bose, Haque, and Osborn (2007) find a significant positive link
between capital spending and growth in a sample of 30 developing coun-
tries, with a weak link between current spending and growth. Other
studies are similarly mixed. For the MENA region, there are links between
fiscal policy and non-oil growth. For the oil producers, this diversification
relates to the development of the non-oil sectors and the reduction of the
proportion of government revenue and export proceeds from oil and gas
sector. For the oil-importers, the story is more complex and depends on a
variety of channels of transmission.


Furthermore, the available literature on MENA finds weak links
between public infrastructure investment and private growth, although,
again, there is not a strong consensus on this issue. Agenor, Nabli, and
Yousef (2005) find that public infrastructure expenditure has a small and
short-lived impact on private investment in Egypt, Jordan, and Tunisia.
Using a relatively sophisticated empirical model, they find that public
capital is unproductive and that many MENA countries have a bad
investment climate, which is a deterrent to private investment. Other
empirical work reaches similar conclusions, both for the MENA region
and for non-MENA countries.10 While public investment can potentially
crowd out private capital, the reverse may also occur when public spend-
ing on useful infrastructure allows private operators to emerge.


The stylized facts on the links between fiscal policy and diversification
are interesting. First, an examination of public expenditure trends in the
MENA region shows that fiscal policy is oriented more toward subsidies
and consumption and less toward public investment. The MENA region
historically has underinvested in infrastructure—MENA spending on
infrastructure amounted to less than 5 percent of GDP in 2010 compared
with 15 percent of GDP in East Asia. The World Bank finds that the
spending has been so limited that the region’s actual infrastructure needs
are between $75 billion and $100 billion a year from 2012 onward, in
contrast to the low investment volume of $6 billion that flowed to
MENA in 2009. This low investment has impeded diversification. In
MENA countries, 55 percent of businesses identify lack of reliable power
networks as a main constraint to running their enterprises; in Egypt,




158 Zafar


Jordan, and Morocco, road congestion is also a significant obstacle—the
average speed of vehicles in Cairo is as low as 9 kilometers an hour.


The limited investment in infrastructure in MENA is the mirror image
of the large public spending in subsidies and public consumption. Most
MENA countries have much higher subsidies than their international
comparators, with subsidies in Egypt, Jordan, Morocco, and Tunisia
equivalent to more than 3 percent of GDP in the late 2000s (figure 5.7).
The region’s water scarcity means that food production is low and that
the region is dependent on imported food. In parallel, fuel subsidies have
been used to build support for governments among their people. As a
result, the region has had very high food and fuel subsidies, sometimes
even bigger than the country’s allocations to education, health, or infra-
structure. Unfortunately, these subsidies are generally poorly targeted and
fail to reach lower-income people, with questionable cost-effectiveness in
terms of social protection. Many subsidies, especially fuel, generally go to
middle-class urban residents, and less than 50 percent of the subsidies
actually reach the poor (figure 5.8). For obvious reasons, food subsidies
are better targeted than fuel subsidies.


One contentious area is the nature of the linkages between public
spending and private sector investment. An examination of the
econometric evidence about such links leads to mixed and ambiguous
conclusions. Overall, the relationship appears weak and short lived. In
many of the countries, there are periods of congruence and periods of
divergence between the two series. Gross fixed capital formation in the


Figure 5.7 Level of Subsidies, 2006–10 Average
(percent of GDP)


0


p
er


ce
n


t
o


f G
D


P


1


2


3


4


5


6


7
8


9


Eg
yp


t


M
or


oc
co


Tu
ni


sia


Jo
rd


an
In


di
a


In
do


ne
sia


Se
ne


ga
l


Co
sta


Ri
ca


Source: IMF 2011.




Fiscal Policy and Diversification in MENA 159


private sector seems more closely correlated with investment climate
data than with any metric of government capital spending. In Egypt, from
1982 to 2009, investment expenditure by the state declined while private
capital formation increased (figure 5.9a). Many of the intervening years
show a mixed and uncertain path. Dobronogov and Iqbal (2005) find
that while some reduction in public investment was justifiable during the
period, the reduction of budget deficits in the first half of 1990s was
achieved largely through a fall in public investment. In Jordan, the rela-
tionship between public and private capital is similarly ambiguous, sug-
gesting difficulty in finding a correlation (figure 5.9b). Overall, there
seems to be little empirical evidence for either crowding in or crowding
out of private investment over the long run, but there may be particular
spending in individual countries that can play a catalytic role.


Fiscal Management and Diversification: Case Studies
While the key channels through which fiscal policy influences diversifica-
tion appear to be infrastructure spending and the provision of fiscal
incentives for business expansion, this is difficult to test empirically for a
large sample of countries. Because of the paucity of long-term time series
on fiscal policy and diversification, this section reviews the experience
from three case studies—Saudi Arabia (a large oil producer in the GCC),
Algeria (a smaller oil producer in the Maghreb), and Jordan (an oil
importer in the Mashreq). In some cases, fiscal policy can act as a brake
on growth by crowding out the private sector, although the evidence is
not strong in that regard.


Figure 5.8 Distribution of Subsidies to Poorest 40 percent


0 10 20
percent


30 40 50


Cross-country MENA (2010)


Jordan (Fuel) (2005)


Lebanon (Bread) (2008)


Egypt (Bread) (2008)


Source: IMF 2011.




160 Zafar


Case study 1: Saudi Arabia. Saudi Arabia’s macroeconomic performance
has remained strong over the past few decades. As the world’s largest oil
exporter, Saudi Arabia has benefited from protracted windfalls from the
large increases in oil prices in recent years. As a result of the sharp rise in
petroleum revenues in 1974 following the 1973 Arab-Israeli war, Saudi
Arabia became one of the richer economies in the world. Real GDP
growth has averaged more than 5 percent from 1970 onward, and real
annual growth in the non-oil sector has also been strong, averaging close
to 7 percent between 1975 and 2010. The petroleum sector is very
strong, accounting for roughly 75 percent of budget revenues, 40 percent
of GDP, and 90 percent of export earnings in the past two decades.


Figure 5.9 Public and Private Capital, 1982–2010


0


19
82


19
84


19
86


19
88


19
90


19
92


19
94


19
96


19
98


20
00


20
04


20
02


20
06


20
08


20
10


5


p
er


ce
n


t
o


f G
D


P
p


er
ce


n
t


o
f G


D
P


10


15


20


25


0


19
82


19
84


19
86


19
88


19
90


19
92


19
94


19
96


19
98


20
00


20
04


20
02


20
06


20
08


20
10


5


10


15


20


30


25


private public


private public


a. Egypt


b. Jordan


Source: World Development Indicators.




Fiscal Policy and Diversification in MENA 161


Current account surpluses became a stable feature of the Saudi economy,
and the government built up a huge reserve. For many years, ample gov-
ernment revenues were available for development, as reflected in the
various five-year plans.11 Given the limited tax regime and the strong
reliance on oil receipts and customs duties to produce government reve-
nue, Saudi Arabia has been historically very sensitive to oil price fluctua-
tions. In many ways, Saudi Arabia mirrors the behavior of other GCC
countries in its growth and macro management.


After the oil boom, there was a strong budget surplus, which was
replaced by deficits during the 1980s decline in oil prices. The decrease
in Saudi oil production, from 10 million barrels a day (b/d) during
1980–81 to less than 2 million b/d in 1985, had a fiscal impact on the
budget. However, rising prices and strong macroeconomic management
led to a decline in budget deficits as a share of GDP, from 25.3 percent
in 1987 to 2.9 percent in 1997. By 2000, the country had reached a
strong fiscal surplus for the first time in 17 years and achieved solid inter-
nal and external balances. Since the mid-2000s, the government has used
higher revenue from oil to fuel the non-oil economy, especially construc-
tion and infrastructure development.


In relation to fiscal management, Saudi Arabia has improved its ability
to confront crisis, partly due to lessons learned in the 1980s in the
aftermath of the oil price collapses. The buildup of both the SWF and
fiscal surpluses indicates that Saudi fiscal policy has become quite pru-
dent and effective.12 The Saudi central bank also has been prudent in
monitoring inflation, and countercyclical macro-prudential policy became
a defining parameter of central bank management.


In parallel, over the years, the Saudi economy has undergone a structural
transformation. Difficulties in diversifying the economy have given way to
greater success in recent years. The private sector response, both domestic
and foreign, has been strongly positive, laying the foundation for continued
economic health beyond the oil price boom. The domestic private sector
has grown, from about 20 percent of GDP in the 1970s to about 30 per-
cent of GDP by 2010 (figure 5.10). Resilience in the non-oil sectors, par-
ticularly real estate, construction, tourism, and trade, has been helpful for
the country’s economic growth. Mostly, the private sector in Saudi Arabia
involves the growth of heavy industry—petrochemicals, fertilizer, and steel,
although the nontradable service sector and, to a lesser extent, the small-
scale manufacturing sector, are relatively strong. Non-oil exports of goods
from both the private and the public sectors (as a share of imports of
goods) have increased to 40 percent in the first half of 2012.




162 Zafar


A casual analysis of the correlation between fiscal policy and
diversification (especially that resulting from the rise of the private
sector) shows a clear link. When fiscal policy has been strong and focused,
infrastructure development has followed, and diversification has deepened.
During periods of contraction, as in the 1980s and late 1990s, fiscal con-
trol has hurt diversification efforts. Oil windfalls have been used to help
promote diversification and boost growth. In the 1970s, Saudi fiscal
policy was essentially procyclical, as 90 percent of the income from the
commodity booms was spent with mixed results. With the income from
the oil booms, Jubail on the Persian Gulf coast and Yanbu on the Red Sea,
once fishing villages, were built into large industrial cities; since the
mid-1970s, they have helped attract total investments of close to $150
billion. In parallel, there were many projects, some of dubious investment
quality. By the mid-1980s, the oil price collapse led to a deficit and a
strong decline in infrastructure spending. Government estimates show a
25 percent decline in infrastructure expenditure in the 1980s, because it
was easier to cut than other sectors. Many of the cuts related to capital
expenditure and maintenance. The annual growth rate in the transport
sector, which reached 19.3 percent between 1975 and 1979, had fallen
to a bit more than 1 percent by the late 1980s and early 1990s.


During the 2000s boom, Saudi authorities manifested a more prudent
and focused attitude toward diversification. Fiscal policy changed positively.
There was significant investment in infrastructure, especially roads, health
care, water, power generation, telecom, and air transport. More than $40
billion was spent on infrastructure in the 2000s, representing only half the


Figure 5.10 Saudi Arabia’s GDP Decomposition
p


er
ce


n
t


o
f G


D
P


19
79


19
89


19
99


20
10


oil sector private sector (non-oil)


government sector manufacturing


0


10


20


30


40


50


60


Source: Government of Saudi Arabia.
Note: Government sector includes drilling and oil refining.




Fiscal Policy and Diversification in MENA 163


revenues from the commodity boom, while the rest was saved or used to
pay down the debt stock. There was also a greater emphasis on the quality
of investment expenditure. Saudi Arabia’s 2009 budget increased invest-
ment spending by 36 percent, in line with these objectives. The largest
projected growth in infrastructure spending is in education, with many
new universities and schools being built and old facilities being revamped.
Finally, the goal has been to increase the government’s partnership with
the private sector in providing infrastructure. Very generous fiscal incen-
tives, especially cheap land, low corporate taxes, and no income taxes have
also served to attract FDI.


The prevailing modality with which the Saudi government is cur-
rently promoting diversification is through public-private partnerships.
Essentially, the government sets policies and procedures, while the private
sector takes responsibility for the technical aspects of project delivery.
The government is planning to spend more than $70 billion on infrastruc-
ture development between 2010 and 2015. It is planning the construction
of six new urban centers (Eastern province, Hail, Jizan, Madinah, Rabigh,
and Tabuk). Public-private partnerships are being formed to address
infrastructure needs such as transportation, energy, and water.13 Under
these arrangements, the government provides a percentage of the cost,
while the remainder is financed by the private sector and commercial
banks. Saudi Arabia has also reduced its dependence on fiscal policy by
allowing companies to access the debt capital markets for project finance.
Overall, the Saudi story shows that focused fiscal policy can help diversi-
fication efforts.


Case study 2: Algeria. Another interesting case study is Algeria, an oil
producer in the Maghreb, where diversification has been very limited.
Algeria is an oil exporter with close proximity to Europe and far from the
GCC, and it has a checkered political history. The story of Algeria is one
of missed opportunities for diversification and growth, although recent
evidence suggests a change of policy. Throughout Algerian history, the
government has not been able to diversify away from hydrocarbons. In
addition to the poor business climate, fiscal policy played a central role in
not allowing the private sector the necessary infrastructure to expand.
Only recently, as a result of strong oil windfalls and careful fiscal expan-
sion in infrastructure, is Algeria seeing private sector growth and a
strengthening of domestic and foreign investment.


A relatively large economy in North Africa, Algeria has the 14th-largest
oil reserves and the 7th-largest reserves of natural gas in the world. The




164 Zafar


hydrocarbon sector is the mainstay of the economy, accounting for about
60 percent of budget revenues, 45 percent of GDP, and more than
95 percent of export earnings. After years with a centrally planned econ-
omy, Algeria is slowly attempting to dismantle the socialist edifice and
install a market economy, but it has not been an easy journey.


For a variety of reasons, diversification has not been very successful in
Algeria. The share of oil exports in the economy has remained roughly
constant. Manufacturing output remains a low percentage of GDP, even
for an oil economy. The growth of labor-intensive industries such as tex-
tiles, manufacturing, and food production has been minimal. The private
sector share in GDP has not been strong and has frequently decreased
over the past three decades.14 Hausmann, Klinger, and Lopez-Calix
(2010) find that the country is overspecialized in oil given its small
endowment; its non-oil export basket is small and highly unsophisticated
and offers little growth potential (figure 5.11). The private sector share
of GDP was still only 12 percent in 2010. The failure of diversification
can be seen in the country’s unemployment rate, which currently is close
to 30 percent. Moreover, unemployment among those under age 25 has
reached 70 percent. In a strange twist, the country has moved from hav-
ing state-controlled, centrally planned industries to having a model with
an inefficient, hydrocarbon export–dependent public sector. The Algerian
model has been one of using public employment programs to create jobs,
and although these programs created 1.4 million jobs between 1997 and
2001, they are mostly temporary (Ait Youness and Annane 2004).


Fiscal policy in Algeria has remained a challenge. Despite favorable oil
prices, the country has experienced pronounced fiscal imbalances, a result


Figure 5.11 Number of Exported Products Compared across Four Countries, 2007


n
u


m
b


er
o


f p
ro


d
u


ct
s


0


500


1,000


1,500


2,000


2,500


3,000


Algeria Saudi Arabia Morocco Indonesia


Source: Hausmann, Klinger, and Lopez-Calix 2010.




Fiscal Policy and Diversification in MENA 165


of poor fiscal policy and lending to weak public enterprises. In the 1980s
and 1990s, deficits averaged more than 55 percent of GDP, and the lack
of resources for infrastructure hurt diversification prospects. However,
much more prudent fiscal management over the past decade has led to a
string of surpluses. In 2009, Algeria posted its first overall fiscal deficit in
a decade, at 8 percent of GDP, following a surplus of 8 percent in 2008
(IMF 2009). Annual overall and nonhydrocarbon GDP growth has been
respectable, averaging close to 4 and 5 percent, respectively, largely driven
by public spending, along with low inflation, averaging around 3 percent.
A good share of hydrocarbon export revenues was saved in reserves and
in the oil stabilization fund or used to drastically reduce external debt.
After years of negative or low productivity, by the mid-2000s, the
Algerian economy had started turning the corner with regard to fiscal
management.15


The low level of government investment in infrastructure in the 1990s,
amounting to less than 0.5 percent of GDP a year, contributed to weak-
nesses in the private sector.16 An analysis of Algerian public expenditure
data shows a correlation between low government investment and lim-
ited diversification. Only recently has fiscal policy made a distinct shift
toward infrastructure, with investment increasing to more than 3 percent
of GDP. Since 2006, after the last oil boom, the government has pushed
to build roads, railways, dams, houses, and airports. As a result, public
investment in infrastructure remained strong in 2009.


With more attention to the provision of infrastructure, the Algerian
private sector has generated more than 50 percent of GDP in the past
few years and accounts for a growing percentage of imports. The country
also has implemented strong corporate tax relief, which has acted as a
catalyst to bring in valuable FDI. Moreover, the country is building a
1,200-kilometer east-west motorway, most of which has been opera-
tional since 2009; and tramways are being constructed in the major cities
of Algiers, Constantine, and Oran.17


Case study 3: Jordan. Jordan is another interesting case study because it
is a small oil-importing economy in the Mashreq with a degree of diversi-
fication. Despite having one of the smallest economies in the Middle East
along with a weak resource environment, Jordan has crafted a positive and
ambitious development strategy to achieve rising standards of living in
recent decades. Growth was close to 7 percent annually in 2000–08.
Although highly dependent on foreign aid, the country has emerged as
the business capital of the Levant. Relying on foreign trade to solve its




166 Zafar


energy requirements, the country has one of the most liberal economic
regimes in the Middle East. Many international ratings give Jordan high
numbers for a competitive business climate. Unlike Algeria and Saudi
Arabia, Jordan cannot rely on hydrocarbons to provide the bulk of its
revenues and thus has had to forge strong trade and finance links with the
outside world. Jordan has a good track record with economic diversifica-
tion, and it now exports varied goods such as phosphates, pharmaceutical
products, textiles, and fruits.18 A resilient service economy, including tour-
ism and construction, and a growing industrial sector have also helped
Jordan become a diversified and vibrant economy (figure 5.12). Although
defined as a lower-middle-income country with significant levels of pov-
erty, Jordan has displayed vitality and innovation in recent years despite
limited access to the sea and other geographical disadvantages.


Jordan’s fiscal regime has improved following a severe fiscal crisis in
the late 1980s (which saw the debt-to-GDP ratio reach more than 200
percent) and has helped support the government’s development objec-
tives. Like Saudi Arabia and Algeria, Jordan suffered from persistent
deficits in the 1970s and 1980s. Large aid inflows and poor fiscal manage-
ment, partly stemming from high subsidies, had led to sharp increases in
external debt. By the 1990s, however, the country succeeded in reducing
its deficit and achieving successful growth. By the 2000s, the govern-
ment’s strong fiscal policy with regard to infrastructure lending helped to
spur the rise of manufacturing.


Fiscal policy has been key to Jordan’s diversification program. In the
wake of the oil boom in the 1970s, the Jordanian government received
grants from its neighbors, with which it increased spending to a high of


Figure 5.12 Jordanian Diversification


p
er


ce
n


t
o


f G
D


P


19
70


19
90


20
10


agriculture industry services


0


20


40


60


80


Sources: Government authorities; World Bank staff.




Fiscal Policy and Diversification in MENA 167


40 percent of GDP, with some emphasis on military and wages. However,
the government had to retrench as the grants declined, and it cut vital
capital expenditure. Infrastructure investment was then neglected for
decades, until the government started investing heavily in infrastructure
again in the 2000s. The public sector also began supporting private sector
industrial activity in Jordan’s qualified industrial zones.


As the government began to address historically neglected sectors, it
also began to enact fiscal measures to attract foreign investors and stimu-
late business activity. These included a reduction in corporate taxes for
industry, long-term tax breaks for foreign direct investment in industrial
and free zones, and a strengthened state fiscal system. Jordan has also
announced a massive $30 billion plan to invest in infrastructure projects
by 2030, starting with a water desalination plant. Overall, government
investment in infrastructure has helped the rise of the manufacturing sec-
tor and has accelerated diversification. However, increases in spending on
salaries, food, and energy subsidies in the wake of the Arab Spring
threaten to lessen fiscal stability and reduce resources for long-term
public investment in infrastructure. In 2011, the fiscal deficit, excluding
grants, reached 12 percent of GDP and the debt-to-GDP ratio increased
to 65 percent from 59 percent in 2010. Jordan has no fiscal space to cover
current expenditures fully, not to mention capital spending, which was
curtailed by 29 percent in 2011. The country will be unable to realize its
ambitious infrastructure program in the medium to long term without
seriously jeopardizing fiscal stability.


Comparison among the Three Countries
A comparison among the three case studies yields some important
insights. Each study provides a typical story regarding the relationship
between fiscal policy and diversification among a specific set of countries.
Saudi Arabia shows the dilemmas of the large oil exporters, Algeria shows
the challenges faced by the smaller oil exporters, and Jordan reveals the
difficulties of the oil importers in the MENA region. It is important to
note that while all three have faced shocks, the impact of the shocks has
been asymmetric depending on each country’s resource endowment.


• Over the past decades, all three countries improved their fiscal policies
through better expenditure control and debt management. In parallel,
the boom-and-bust cycles caused difficulties for each government’s
diversification efforts. Since 2000, however, diversification efforts have
been more successful in Saudi Arabia and Jordan than in Algeria.




168 Zafar


• All three experiences show increased allocation to infrastructure
investment in recent years, along with greater reliance on the private
sector, stronger fiscal incentives for domestic and foreign investment,
and more focus on the efficiency of interventions. In the absence of
strong cross-country econometric evidence of crowding out and crowd-
ing in, a cursory examination of the evidence shows no strong relation-
ships between public and private capital, suggesting questions about
the quality of public investment.


• In all the case studies, infrastructure expenditure expanded when oil
prices were high (1970s and 2000s) and was cut back when oil prices
were low (1980s and 1990s). The earlier wave of infrastructure
investment was uneven and sporadic, with greater success in Saudi
Arabia. Infrastructure remained a neglected sector until the 2000s in
Algeria and Jordan.


• A comparison between the Saudi and Algerian experiences suggests
that the former’s role as a strong oil producer gives it greater invest-
ment capital and its policy makers have a greater degree of freedom to
invest in the necessary infrastructure. Jordan’s experience suggests that
diversification is easier for an oil importer given the right government
policy mix.


• The case studies suggest that although the relationship between fiscal
policy and diversification is not linear, there are clear links. The optimal
policy is one that provides a modicum of subsidies but does not neglect
good public investment.


The Behavior of Fiscal Policy in MENA: Econometric Evidence


Procyclical fiscal policy has been viewed as pernicious to long-term
growth. By leading to high output variability, it can have detrimental
effects on the aggregate economy and social welfare. Moreover, during
revenue slumps, the first cuts are often to public investment. This section
empirically measures the cyclicality of fiscal policy. The theoretical
rationale is well defined in a growing literature (Perry, Serven, and
Suescon 1998).19 The analysis seeks to understand the degree of volatility
in MENA fiscal expenditures and the various drivers of that volatility.
The precise objective of the analysis is to examine the link between fiscal
behavior and macroeconomic fluctuations. The goal is to document
empirically the properties of cyclical fiscal policy. According to a




Fiscal Policy and Diversification in MENA 169


well-developed body of theoretical and empirical work, the goal of fiscal
policy should be to save during good times to prepare for a rainy day, and
to be expansionary during bad times to stimulate economic activity.


Fiscal policy should, in essence, be countercyclical to help to dampen
business cycle swings.20 While literature is scant on fiscal policy in the
MENA region, there are a few important contributions. Studying the
fiscal policies in 19 oil-exporting countries over the period 1965–2005,
Sturm et al. (2009) find support for the existence of procyclical conduct
of fiscal policy, including a more pronounced response during the
1985–2005 subperiod. Modeling cyclical fluctuations (output gaps) by
comparing actual production data with the Hodrick-Prescott smoothed
series, the authors find strong fiscal response to oil prices increase.21
Abdih et al. (2010), in a recent review of fiscal policy in oil economies,
find positive average fiscal policy responses of oil-producing countries to
the recent oil price hike. Their estimates show that non-oil primary bal-
ances worsened substantially in the oil economies during 2003–08,
driven by an increase in primary spending, but this trend was partially
reversed when oil prices went down in 2009. Macroeconomic volatility
in the region has historically been the highest in the world in large part
because of oil price changes. Aizenman and Marion (1993) find that
macroeconomic volatility has an adverse impact on investment and
economic growth. Expenditure volatility associated with fluctuations in
oil revenue was found to be a key factor explaining slower growth in
oil-producing countries compared with resource-poor countries (Auty
and Gelb 2001).


Concluding Remarks


The long-term challenge for the MENA region is to ensure that fiscal
policy is used to promote growth and diversification. The GCC countries
will need to implement policy reforms to accelerate non-oil growth and
create employment opportunities for a rapidly increasing labor force in a
sustained fashion. For oil importers in the Mashreq and Maghreb, fiscal
management must ensure that the pressing demands on the state from
the citizenry must be accommodated without jeopardizing longer-term
macroeconomic stability. Reorientation of public expenditure, away from
subsidies that do not go to the poor and toward both conditional cash
transfers and effective public investment programs, must be encouraged.
Through fiscal policy targeted toward infrastructure, MENA countries
can help lay the foundation for successful diversification.




170 Zafar


Notes


1. In the past, current account and fiscal surpluses had reached large propor-
tions, and there had been a large expansion of sovereign wealth funds.
International Monetary Fund (IMF) estimates suggest that sovereign wealth
funds, or SWFs, control as much as $3 trillion, and that a significant part of
this investment has been in the Gulf states.


2. Preliminary IMF and government estimates from Egypt projected a fiscal
deficit of more than 9 percent for 2011. The general perception among indus-
try experts is that the deficit will be manageable in the short term but will
lead to higher debt-servicing costs in the medium term and endanger the
country’s sovereign rating.


3. The massive spending packages include a 15 percent pay raise and two-month
salary bonus for government workers.


4. In the GCC countries, the dollar peg prevents the use of independent mon-
etary policy. The central banks of the GCC countries, which are in charge of
monetary policy formulation and implementation, keep the peg to maintain
price stability, with the medium-term objective of having a monetary union
with the other GCC countries. While GCC member countries officially
pegged their national currencies to the U.S. dollar on January 1, 2003, as an
explicit step toward monetary integration, and have defined a series of con-
vergence criterion, the common currency still faces a number of obstacles.


5. However, as seen in chapter 3, the real exchange rates—that is, the price of
tradable versus nontradable goods—were often overvalued in 1980–2010.
The nontradable sectors’ prices (including wages) are so high in the GCC
that no tradable industries other than oil and gas and related industries
survive. Thus, there has been an adverse impact on competitiveness. Using
the methodology explained by Zafar (2007), there are various ways to
detect the extent of GCC misalignment, including purchasing power parity
(PPP) and time series econometrics. GCC states have tried to resolve some
of these exchange rate issues through a policy of importing labor, which has
allowed them to have very flexible labor markets with strong nominal wage
adjustment.


6. This is not systematic for all oil importers. Although there have been episodic
overvaluations in Tunisia, in 2000–08, the policy has been to depreciate the
currency by about 4 percent per year vis-à-vis the euro.


7. Most of the oil economies in the Gulf use conservative oil price estimates to
protect the budget, and use excess revenues to pay down debt.


8. These buildups in the SWFs have been aided by unprecedented relatively
high oil prices, which have persisted postcrisis. Projected to rise to nearly $70,
the price levels are much higher than the $35–50 price range assumed by the
GCC authorities during the budget planning process.




Fiscal Policy and Diversification in MENA 171


9. In the 1980s and 1990s, in the wake of the oil price declines, the GCC faced
a slump and made systematic cuts in infrastructure expenditures, especially
for development projects, land purchases, and equipment. Smaller cuts were
also made in the wage bill and in operations and maintenance.


10. It is a challenge to obtain reliable disaggregated fiscal data for a range of coun-
tries over a large enough time series. However, the available data show that the
quality of governance is vital in explaining the efficacy of public expenditures.


11. In the first two five-year plans (1970–80), the government emphasized infra-
structure, with the building of highways and power generation plants and
expanding seaports. In the third plan (1980–85) two industrial cities—Jubail
and Yanbu—were constructed for the production of steel, petrochemicals,
fertilizer, and refined oil products. The seventh plan (2000–05) focused on
diversification and an increased role for the private sector in the Saudi econ-
omy. The eighth plan (2005–10) included economic and social infrastructure,
with a focus on transportation, energy, and water.


12. While a discussion of Saudi oil policy is beyond the scope of this paper, it is
important to note that the policy has been guided by a strong desire to main-
tain market and quota shares and to support stability in the international oil
market.


13. This is best illustrated with a typical example. In July 2008, the Saudi gov-
ernment licensed the establishment of Saudi Landbridge Company, which
was to build a freight and passenger rail network between Riyadh and
Jeddah, and Dammam and Jubail on a build-operate-transfer (BOT) basis,
for an investment of $7–$9 billion. In April 2008, the Tarabot consortium,
consisting of seven Saudi companies and Asciano of Australia, was selected
as the preferred bidder for the 50-year BOT concession for the Landbridge
project.


14. In an interesting study on Algerian diversification, Hausmann, Klinger, and
Lopez-Calix (2010) find that the conventional Dutch Disease explanations
for oil dependence, leading to an appreciated real exchange rate and strong
macroeconomic volatility, are not strong arguments. On the contrary, they
argue that Algeria’s limited diversification is due to a poor business climate, a
highly protected internal market, and strong competition for oil rents, which
dulls the incentives for private sector investment in new export activities.


15. The country also has a restrictive attitude toward foreign investment. There
are tight restrictions on imports, and new foreign investment must be in the
form of joint ventures, with Algerian partners owning at least a 51 percent
share.


16. This reflects, to a great extent, the civil war during that period.


17. International calls for tender have been issued for the construction of a
1,300-kilometer high-speed train line. In addition, existing railways are




172 Zafar


undergoing modernization and electrification, and work has begun on
400 kilometers of new railways.


18. In a study, Nassif and Walkenhorst (2006) find that Jordan was very successful
with the creation of qualified industrial zones (QIZs), which accounted for
almost a quarter of total exports and helped exports surge to close to $1 billion
by 2005. In the process, the number of employees in QIZ enterprises increased
to more than 46,000, or almost 30 percent of the country’s manufacturing
workforce.


19. The work follows a growing literature on fiscal policy and volatility. Gavin
and Perotti (1997) find that, in sharp contrast to industrial countries, procycli-
cal fiscal policy has been the norm in several developing countries, particularly
in Latin America.


20. A methodology used by the IMF to link macroeconomic output and fiscal
policy looks at the change in the non-oil primary fiscal balance when it is
decomposed into the change in the cyclically adjusted non-oil primary bal-
ance (fiscal impulse) plus the change in the cyclical non-oil primary balance
(automatic stabilizers). While the construction of such a cyclically adjusted
fiscal balance is laudable, in practice it is problematic given the technical and
econometric difficulties in establishing a benchmark indicator such as an
output gap. Overall, fiscal policy in oil economies has often been defined as
expansionary/contractionary when the change in the non-oil primary balance
is negative/positive. Following Barnett and Ossowski (2002), the non-oil pri-
mary balance is calculated by subtracting the non-oil revenue from total
government expenditure.


21. Several of the studies use public consumption rather than public expenditure
due to data difficulties.


References


Abdih, Y., P. Lopez-Murphy, A. Roitman, and R. Sahay. 2010. “The Cyclicality of
Fiscal Policy in the Middle East and Central Asia: Is the Current Crisis
Different?” IMF/WP/10/68, International Monetary Fund, Washington, DC.


Agenor, P-R., M. K. Nabli, and T. Yousef. 2005. “Public Infrastructure and Private
Investment in the Middle East and North Africa.” World Bank, Washington, DC.


Ait Younes, A., and S. Annane. 2004. “La question de l’emploi et de l’intermédiation
sur le marché du travail.” International Labour Organization, Algiers.


Aizenman, J., and N. Marion. 1993. “Macroeconomic Uncertainty and Private
Investment.” Economics Letters 41 (2): 207–10.


Auty, R., and A. Gelb. 2001. “The Political Economy of Resource-Abundant
States.” In Resource Abundance and Economic Development, ed. R. Auty.
Oxford, U.K.: Oxford University Press.




Fiscal Policy and Diversification in MENA 173


Barnett, S. A., and R. Ossowski. 2002. “Operational Aspects of Fiscal Policy in
Oil-Producing Countries.” IMF Working Paper, International Monetary Fund,
Washington, DC (October).


Bose, N., M. E. Haque, and D. R. Osborn. 2007. “Public Expenditure and
Economic Growth: A Disaggregated Analysis for Developing Countries.”
Manchester School 75 (5, September): 533–56.


Devarajan, S., V. Swaroop, and H. Zou. 1996. “The Composition of Public
Expenditures and Economic Growth.” Journal of Monetary Economics 37
(2, April): 313–44.


Dobronogov, A., and F. Iqbal. 2005. “Economic Growth in Egypt: Constraints and
Determinants.” World Bank, Washington, DC.


Elbadawi, I. A., and R. Soto. 2011. “Fiscal Regimes in and outside the MENA
Region.” Documentos de Trabajo 398, Instituto de Economia. Pontificia
Universidad Católica de Chile.


Fassano, U. 2002. “Testing the Relationship between Government Spending and
Revenue: Evidence from GCC Countries.” IMF/WP02/201, International
Monetary Fund, Washington, DC.


Gavin. M., and R. Perotti. 1997. “Fiscal Policy in Latin America.” In NBER
Macroeconomics Annual 1997, 11–72. Cambridge, MA: National Bureau of
Economic Research.


Hausmann, R., B. Klinger, and J. Lopez-Calix. 2010. “Export Diversification in
Algeria.” In Trade Competitiveness of the Middle East and North Africa, ed.
J. Lopez-Calix, P. Walkenhorst, and N. Diop. Washington, DC: World Bank.


IMF (International Monetary Fund). 2009, 2011. Regional Economic Outlook:
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Khan, M., et al. 2008. “The GCC Monetary Union—Choice of Exchange Rate
Regime.” International Monetary Fund, Middle East and Central Asia
Department, Washington, DC.


Nassif, C., and P. Walkenhorst. 2006.“Trade, Competitiveness, and Employment in
Jordan.” World Bank, Washington, DC.


Perry, G., L. Serven, and R. Suescon. 1998. “Fiscal Policy, Stabilization, and
Growth: Prudence or Abstinence.” World Bank, Washington, DC.


Sturm, M., et al. 2009. “Fiscal Policy Challenges in Oil Exporting Countries: A
Review of Key Issues.” European Central Bank, Frankfurt.


Truman, E. 2007. “Sovereign Wealth Funds: The Need for Greater Transparency
and Accountability.” Policy Brief, Institute of International Economics,
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World Bank, Washington, DC.






175


C H A P T E R 6


Natural Resource Heterogeneity
and the Incentives for and Impact
of Regional Integration


Celine Carrère, Julien Gourdon, and
Marcelo Olarreaga


At low levels of development, economic growth is often accompanied by
a diversification of the production structure (Imbs and Wacziarg 2003;
Cadot, Carrère, and Strauss-Kahn 2011). In resource-abundant countries,
however, rising commodity prices can slow down the process of economic
diversification, at least when rising foreign exchange is not properly man-
aged (see chapter 2). The development of regional markets in MENA has
therefore been seen as a potential (second-best) solution to resource-
dependence-induced low diversification, at least since the creation of the
Arab League in 1945.1


Despite numerous regional trade agreements, intraregional trade in
MENA is only a 10th of the region’s total trade and is below the level that
a standard gravity model2 would predict (Miniesy, Nugent, and Yousef
2004; Péridy, 2007). This chapter explores the extent to which regional
trade agreements have contributed to intraregional trade in MENA, and
whether this contribution has been at the expense of trade diversion and
therefore of broader economic efficiency.




176 Carrère, Gourdon, and Olarreaga


In a recent theoretical paper, Venables (2011) argues that some degree
of trade diversion is expected when a resource-rich country enters into a
preferential trade agreement with a relatively labor-abundant country. In
such a situation, the preferential agreement will create incentives for
labor-intensive goods to be sourced from the resource-poor country. This
will help the resource-poor country diversify its production bundle and
reach a higher level of economic growth. But this diversification process
will be achieved at the expense of the resource-rich country, which will
experience trade diversion, as it replaces imports from the relatively more
efficient rest of the world with those from the regional partner.


To empirically test this assumption, this chapter builds on a standard
panel gravity model where aggregate imports of MENA countries are
explained using bilateral fixed effects and year-specific importer and
exporter fixed effects. These fixed effects control for, among other things,
the traditional determinants of a gravity equation, such as distance, colo-
nial links, and common language, as well as gross domestic product
(GDP), population, and most-favored-nation (MFN) tariffs of the
exporter and the importer. Different types of dummies are then intro-
duced to capture the impact of the creation of trade agreements on
intraregional imports and imports from the rest of the world (as in
Carrère 2006). The coefficient on the variable capturing the impact on
intraregional imports measures the extent of trade creation (in the Lipsey
rather than Viner sense),3 and the coefficient on the variable capturing
the impact on imports from the rest of the world measures the extent of
trade diversion (again, in the Lipsey sense).


The results of our basic specification suggest that there is trade creation
in most agreements, and that trade diversion may be a problem only in the
Pan-Arab Free Trade Area (PAFTA),4 in particular when considering non-
oil imports. As predicted by Venables (2011), trade diversion seems to be
concentrated in resource-rich importers. These are generally countries that
export only a few products and that have a highly concentrated export
bundle. Interestingly, these countries have also significantly increased their
exports of non-oil goods to resource-poor countries, but these increases
were not accompanied by trade diversion in resource-poor countries.


Thus, MENA’s regional integration has been mainly trade-creating, and
both resource-poor and resource-rich countries have seen increases in
their exports of non-oil goods to other countries in the region. Trade
diversion has been observed only in resource-rich countries, suggesting
that MENA’s preferential agreements have been associated with income
redistribution from resource-rich to resource-poor countries.




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 177


Trade Agreements in MENA: An Analytical Setup


Our theoretical setting is a three-country world with two countries that
have abundant natural resources and form a preferential trade agree-
ment.5 If the two countries have a comparative advantage in the same
natural resource, there is no reason for these countries to trade, and there-
fore little trade creation or trade diversion should be expected from such
an agreement. If the countries are abundant in different natural resources,
however, then trade creation can be expected and will be accompanied
by little trade diversion. Thus, the first prediction for regional integration
among natural-resource-abundant countries is that integration should be
accompanied by no trade diversion and mild levels of trade creation.


If, on the other hand, the preferential trade agreement is signed by a
natural-resource-abundant country and a natural-resource-poor country
with a small but developing manufacturing sector, then the introduction
of tariff preferences will probably lead to some trade creation in the
resource-poor country, because it will be able to import more natural
resources from the resource-rich country. There is little scope for the
resource-poor country to suffer from trade diversion if the resource-
abundant country is specialized in the natural resource good. On the
other hand, the resource-rich country may suffer from a significant
amount of trade diversion, because the resource-poor country benefiting
from the preferential access can increase its exports of manufacturing
goods to the resource-rich country while continuing to export labor-
intensive goods to the rest of the world.


As suggested by Fouquin, Langhammer, and Scweickert (2006) and
Venables (2011), the fact that resource-poor countries benefit more from
preferential trade agreements than resource-rich countries explains why
the latter have not been a driver of regional integration schemes in the
developing world. Such schemes would imply income redistribution
from resource-rich to resource-poor countries. Indeed, preferential access
allows producers in resource-poor countries to benefit from higher prices
in the resource-rich country, which increases producer surplus in the
exporting resource-poor country while reducing tariff revenue in the
importing resource-rich country. Therefore, while the resource-poor
country is better off, the resource-rich country tends to be worse off.


Whether this effect is desirable for the region as a whole is an empiri-
cal question. In the pure trade-diverting case, where the increase in
exports from the resource-poor country to the resource-rich country is
accompanied by an equivalent decline in resource-rich-country imports




178 Carrère, Gourdon, and Olarreaga


from the rest of the world, the region will unambiguously be worse off.
Thus, a necessary condition for the region to be better off is that the
increase in intraregional trade be larger than the decline in trade with the
rest of the world.


We use the empirical model presented here to test Venables’s (2011)
theoretical proposition that when resource-rich countries sign preferen-
tial trade agreements with resource-poor countries, the former are more
likely to suffer from trade diversion than the latter. We then investigate
whether in such a case, the increase in exports from the resource-poor
country to the resource-rich country is larger than the fall in the resource-
rich country imports from the rest of the world.


The Empirical Model
A standard gravity equation approach is applied here to assess the extent
of trade creation and diversion associated with MENA’s preferential trade
agreements. Bilateral imports of MENA countries with respect to each of
their regional and nonregional partners are explained by a series of bilat-
eral fixed effects that capture the effects of distance, colonial links, and
any other time-invariant characteristic of each bilateral pair, as well as
year-specific importer and export fixed effects that capture the impact of
the evolution of GDP, population, most-favored-nation (MFN) tariffs, or
any other importer and year or exporter and year characteristic. In par-
ticular, the importer-year and exporter-year fixed effects make it possible
to avoid the bias associated with the omission of exporter and importer
remoteness terms (Anderson and VanWincoop 2003). More formally:


In RTAintra RTArowMijt ij it jt
k


ijt
k


k


k
ijt
k


k
ijt= + + + + +∑ ∑α δ γ φ φ ν1 2 (6.1)


where Mijt is country i (¨MENA) imports from j in year t; RTAintra
k
ijt = 1


if i and j belong to the same regional trade agreement (RTA) k in t, oth-
erwise 0 (intraregional trade); and RTArowkijt = 1 if i but not j belongs to
the RTA k in t, otherwise 0. The coefficient of the first term (F k1) cap-
tures trade creation in the Lipsey sense, and the second term (F k2) trade
diversion. aij is bilateral fixed effects, dit is the importer and year-specific
effects, and gjt is the exporter and year fixed effects. nijt is an independent
and identically distributed error term.


The k regional trade agreements explored here include PAFTA; the
Gulf Cooperation Council (GCC), involving Bahrain, Kuwait, Oman,
Qatar, Saudi Arabia, and United Arab Emirates, or UAE; the Agadir




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 179


Agreement (involving the Arab Republic of Egypt, Jordan, Morocco, and
Tunisia); the Common Market of Eastern and Southern Africa (COMESA),
which also involves some Sub-Saharan African countries; all Euromed
(Euro-Mediterranean Partnership) agreements signed by MENA coun-
tries; all free trade agreements (FTAs) with European Free Trade
Agreement countries; and all FTAs with Turkey (for a complete list of
these agreements, see annex table 6A.1).


As a further step, we explored, within the same gravity setup, the varia-
tion in patterns of trade creation and trade diversion across bilateral pairs,
one resource rich and one resource poor. This could be done only for
PAFTA, because that is the only trade agreement within MENA involving
both types of countries.6 PAFTA is also one of the few well-functioning
regional trade agreements in MENA. Indeed, as argued by Hoekman and
Zarrouk (2009), intra-PAFTA trade barriers have come down substantially
since the agreement was initiated.7 The gravity equation becomes:



In intraM RR RR PAFTA RR RPijt ij it jt i j ijt i j= + + + ⋅ ⋅⎡⎣ ⎤⎦ + ⋅ ⋅α δ γ β β1 2 PAFTA


RP RP PAFTA RP RR P


ijt


i j ijt i j


intra


intra


⎡⎣ ⎤⎦
+ ⋅ ⋅⎡⎣ ⎤⎦ + ⋅ ⋅β β3 4 AFTA


RR PAFTArow RP PAFTArow


ijt


i ijt i ijt


intra⎡⎣ ⎤⎦
+ ⋅⎡⎣ ⎤⎦ + ⋅⎡β β5 6 ⎣ ⎤⎦
+ + +∑ ∑φ ν1k φ2kijtkk ijtk ijtkRTAintra RTArow (6.2)


where RR and RP capture whether the importer or the exporter is con-
sidered resource rich or resource poor. As before, RTAintrakijt = 1 if i and j
belong to the same RTA k in t, otherwise 0, and RTArowkijt = 1 if i but not
j belongs to the RTA k in t, otherwise 0. The intravariables of PAFTA are
then interacted with RRi and RPi, as well as with RRj and RPj, to explore
the degree of heterogeneity on trade creation within MENA depending
on whether the importer and exporter are resource rich or poor. Then b1
captures trade creation between resource-rich countries in PAFTA; b2
when the importer is resource rich and the exporter is resource poor
within PAFTA; b3when both PAFTA countries are resource poor; and b4
when the importer is resource poor but the exporter is resource rich.


The specification in equation 6.2 also allows for heterogeneity in trade
diversion within PAFTA depending on whether the importer or the
exporter is resource rich or poor. The term b5 captures the extent of trade
diversion if the PAFTA importer is resource rich, and b6 if resource poor.
Since within PAFTA we can further distinguish between resource-rich
labor-abundant (that is, developing oil exporter) and resource-rich labor-
importing (that is, GCC oil exporter) countries, the heterogeneity in




180 Carrère, Gourdon, and Olarreaga


trade creation and diversion after this further decomposition is also inves-
tigated. The robustness of the results to the use of alternatives to the
World Bank’s resource-poor and resource-rich categories is tested.


As a last addition, a variable capturing the degree of export concentra-
tion of the exporter and the importer is also introduced. The rationale is
that countries that are relatively abundant in natural resources will tend
to have a more concentrated export bundle, whereas countries less abun-
dant in natural resources will have a more diversified export bundle. This
will lead to effects similar to the ones described in Venables (2011), with
more concentrated countries suffering from trade diversion and more
diversified countries benefiting from trade diversion to their more con-
centrated partners. As proxies for the degree of concentration of the
export bundle, we apply the Herfindahl index of export concentration,
and the average number of exported goods at the six-digit level of the
Harmonized System (HS) over the three-year period preceding the
entrance in force of the PAFTA agreement. The estimated gravity equa-
tion then becomes:


In intra
export


M PAFTA
CI


CIijt ij it jt
ijt


jt


it


= + + + ⎡⎣ ⎤⎦ +α δ γ λ λ1 2 0
0


export


export


intra⋅


⎢⎢




⎥⎥




PAFTA


PAFTArow CI


ijt


ijt itλ λ3 4 0 PAFTArow


row


ijt


k
ijt
k


k


k
ijt
k


ijt
k


⎡⎣ ⎤⎦
+ +


+ +


+∑ ∑φ φ ν1 2RTAintra RTA (6.3)
where CIjt0 is the measure of the exporter’s export bundle concentration
(Herfindahl index or number of lines exported) in year t0, with t0 being
an average over the three years preceding the entry of country j in the
agreement. When the CI is indexed I, it captures the concentration of the
export bundle of the importer in the three years previous to the signing
of the agreement. Thus, l 2 captures the extent to which one could expect
a strong degree of trade creation when the exporter is relatively more
concentrated than the importer (if l 2 > 0). And l4 captures whether
trade diversion is expected to be larger (if l4 < 0) when the importer has
a highly concentrated production structure.


Finally, because all specifications imply controlling for a very large
number of dummy variables, it was decided for computational reasons not
to introduce thousands of fixed effects, but to compute deviations from
the mean for each of these variables. But because there are several dimen-
sions in the fixed effects (bilateral, importer-year, and exporter-year), the




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 181


calculation of the deviations to the mean is not straightforward. Each vari-
able was transformed as follows:



%y y y y y y y y yijt ijt ij t t jt i j t= − − + + + + −⎡⎣ ⎤⎦. . . .. . . .. ... (6.4)


A simple ordinary least squares (OLS) estimator was applied to the
transformed variables in each of the specifications in equations 6.1, 6.2,
and 6.3. To control for potential correlation of the error term within
country pairs, standard errors were corrected for clustering within the
country pairs. Indeed, the country pair i–j has the same determinants as
the country pair j–I, which may lead to correlation of the errors for these
observations.


Data and Variable Construction
Bilateral import data for 18 MENA countries (all except Iraq and West
Bank and Gaza) and 239 partners are from the COMTRADE database
and were obtained through the World Bank’s web platform, World
Integrated Trade Solution. Data were used for the 20-year period 1990–
2009, because MENA regionalism did not exist in the 1980s. Data for
Libya are mirrored because Libya does not report to the United Nations
system. These differences in data sources for Libya are partly controlled
for in the empirical specification by the importer-year and exporter-year
fixed effects.


Total import data were used, as well as data on non-oil imports. In a
robustness check, data that subtract re-exports from bilateral import data
were also used, but results are almost identical to the ones reported in the
next section.


World Trade Organization (WTO) notifications were used as a proxy
to capture the year of entry into force of the agreement (see annex table
6A.1). It was decided not to include dummies for the FTAs signed by
some MENA countries and the United States because these are too
recent to meaningfully estimate their impact. In addition, we did not
control for the Economic Cooperation Organization (ECO), for three
reasons. First, the only MENA country in ECO is the Islamic Republic of
Iran. Second, ECO starts in 1992 and therefore captures almost the entire
time variation. Including ECO would require expanding the time frame.
Third, and most important, it is well known that ECO has suffered from
serious implementation problems, and therefore not much should be
expected (Pomfret 1997). It is worth noting, however, that the results
reported in the next section are robust to the inclusion of ECO.




182 Carrère, Gourdon, and Olarreaga


Data used to define countries as resource poor, resource rich labor
abundant, or resource rich labor importing were taken from World Bank
(2008). Of all trade agreements, only one includes both resource-poor
and resource-rich MENA countries, and that is PAFTA. The Herfindahl
indexes of export concentration and the number of export lines at the
six-digit HS level before the entry into force of the agreement are com-
puted using HS six-digit data from COMTRADE on exports of each
country to the world.


Empirical Results


Table 6.1 reports the results of the estimation of equation 6.1 for seven
preferential trade agreements involving MENA countries. Both intrare-
gional and rest-of-the-world effects are reported for each of the seven
agreements. The first column reports results using total imports, whereas
the second column reports results for non-oil imports. The first point to
notice is that there are no statistically significant differences between the
coefficients reported under the two columns for total imports and non-oil
imports.


In all agreements except Agadir and GCC, a positive, large, and statis-
tically significant coefficient on intraregional trade was found. That
Agadir and GCC do not show a statistically significant coefficient for
intraregional trade can be partly explained by the fact that all Agadir and
GCC countries are part of PAFTA and entered into the other agreements
after PAFTA was in force. So the advantages in terms of intraregional
liberalization that Agadir and GCC offer may be limited.


The only agreement to show a negative and statistically significant
coefficient for imports from the rest of the world is PAFTA, and that is
for non-oil imports only. For all other trade agreements, the coefficient is
either positive or statistically insignificant, suggesting that trade diversion
is not an important problem.


In the case of PAFTA, the coefficient on imports from the rest of the
world is statistically significant at the 11 percent level. It is much smaller
than the coefficient on trade creation. Indeed, the estimated percentage
increase in intraregional trade due to PAFTA is around 195 percent
(e1.082 – 1 = 1.95).8 The percentage decline in imports from the rest of
the world is 18 percent. It is important, however, to caution about the
basis on which these numbers are calculated. Intra-PAFTA imports are
only 11 percent of PAFTA imports from the world. So an 18 percent
decline in something that is almost 10 times larger is not too far off a




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 183


Table 6.1 Trade Creation and Diversion for Each Agreement Involving MENA
Countries, 1990–2009


ln (Mijt)


Trade agreement Total imports Non-oil imports


PAFTA
Intra 1.039***


(0.17)
1.082***


(0.17)
Rest of world –0.181


(0.12)
–0.195*
(0.12)


GCC
Intra 0.166


(0.17)
0.260


(0.17)
Rest of world 0.954***


(0.12)
0.956***


(0.12)
AGADIR
Intra –0.051


(0.24)
0.042


(0.23)
Rest of world –0.383


(0.22)
–0.247
(0.21)


COMESA
Intra 0.532***


(0.20)
0.522**


(0.21)
Rest of world 0.469***


(0.12)
0.395***


(0.12)
Euromed
Intra 0.325**


(0.15)
0.266**


(0.15)
Rest of world 0.102


(0.14)
0.041


(0.14)
FTA with EFTA
Intra 0.535**


(0.24)
0.570**


(0.24)
Rest of world 0.237


(0.19)
0.218


(0.19)
FTA with TUR
Intra 0.619***


(0.30)
0.512*


(0.29)
Rest of world 0.226


(0.22)
0.073


(0.21)


Observations 31,054 31,016
Number of importersa 18 18
Number of exporters 239 239
Years 1990–2009 1990–2009
Fixed effects (ij) Yes Yes
Fixed effects (it) Yes Yes
Fixed effects (jt) Yes Yes


Source: Authors.
Notes: Estimation with OLS; standard errors in parentheses: heteroscedasticity consistent and adjusted for
country-pair clustering; for a list trade agrements, see annex table 6A.1; * p = 0.1, ** p = 0.05, *** p = 0.01.
a. Only MENA countries; mirror data for Libya; no data for Iraq and West Bank and Gaza.




184 Carrère, Gourdon, and Olarreaga


195 percent increase in something that is 11 times smaller. In other
words, most of the increases in intraregional trade within PAFTA appear
to be simply substituting for imports from the rest of the world and could
therefore be an important source of inefficiency.


If the increase in intra-PAFTA trade is fully compensated by a fall in
PAFTA imports from the rest of the world, then it is clear that PAFTA has
been welfare reducing for the region. This is a hypothesis that the esti-
mates for PAFTA in the second column of table 6.1 cannot statistically
reject.


To assess the degree to which trade diversion in PAFTA may be con-
centrated in resource-rich countries, table 6.2 reports results of the esti-
mation of the specification in equation 6.2. Again, the first column reports
results for total imports and the second column for non-oil imports only.


Table 6.2 Decomposition of Intra-PAFTA Trade Creation and Diversion According
to Natural Resources Endowment, 1990–2009


PAFTA


ln (Mijt)


Total imports Non-oil imports


Intra
RRi-RRj 1.09***


(0.24)
1.21***


(0.23)
RRi-RPj 0.80***


(0.20)
0.84***


(0.21)
RPi-RRj 1.45***


(0.26)
1.40***


(0.24)
RPi-RPj 0.79***


(0.23)
0.91***


(0.23)
Rest of world


RRi –0.29***
(0.13)


–0.32***
(0.1274)


RPi 0.005
(0.15)


0.01
(0.1485)


Observations 31,054 31,016
Number of importersa 18 18
Number of exporters 239 239
Years 1990–2009 1990–2009
Fixed effects (ij) Yes Yes
Fixed effects (it) Yes Yes
Fixed effects (jt) Yes Yes


Source: Authors.
Notes: All regressions include, in addition to PAFTA, all other agreements; dummies also introduced in table 6.1,
but coefficients are not reported to save space; estimation with OLS; standard errors in parentheses:
heteroscedasticity consistent and adjusted for country-pair clustering; * p = 0.1, ** p = 0.05, *** p = 0.01
a. Only MENA countries; mirror data for Libya; no data for Iraq and West Bank and Gaza.




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 185


Results are not statistically different from each other across columns. The
intra-PAFTA trade creation is now disentangled into four possible catego-
ries: trade creation among resource-rich countries in the first row; trade
creation when the importer is resource rich and the exporter is resource
poor in the second row; trade creation when the importer is resource poor
and the exporter is resource rich in the third row; and finally, trade cre-
ation among resource-poor countries in the last row.


The coefficients on intra-PAFTA trade creation are all positive and
statistically different from zero. They are not very different from each
other, and after performing the six possible tests of equality among intra-
PAFTA trade creation coefficients, only two were found rejecting the null
hypothesis that they are equal. Those were the tests for H0: RPi − RRj =
RRi − RPj, and for H0: RPi − RRj = RPi − RPj. Note, however, that a joint
test of the six equalities simultaneously cannot be rejected, suggesting
that the coefficients on intraregional trade creation may not be statisti-
cally different from each other after all.


Interestingly, the largest coefficients are found for imports of resource-
poor countries from resource-rich countries. The coefficient when the
importer is resource rich and the exporter is resource poor (the second
row) is 0.84, and the coefficient when the importer is resource poor and
the exporter is resource rich (the third row) is 1.40; the difference is
statistically significant, as discussed above. This finding implies that intra-
PAFTA trade when the importer is resource rich and the exporter is
resource poor increased by 132 percent, whereas the increase in intra-
PAFTA trade when the importer is resource poor and the exporter is
resource rich increased by 305 percent—or more than two times larger.


Venables’s (2011) main prediction is that resource-rich countries are
more likely to experience trade diversion. This prediction is supported
by the data from MENA, which show a decline in non-oil imports from
the rest of the world of around 38 percent in the case of resource-rich
PAFTA countries, and no trade diversion at all in the case of resource-
poor countries.


Table 6.3 reports results of the same specification as in table 6.2, but
the resource-rich countries are disaggregated further into GCC oil
exporters and developing oil exporters. As expected, there are no signifi-
cant differences from the results reported in table 6.2, but the decompo-
sition is interesting in itself. The top panel reports results for total imports
and the bottom panel for non-oil imports. Again, there are no statistical
differences between the coefficients in the two panels. The decomposi-
tion suggests that the main driver of the large trade creation coefficient




186 Carrère, Gourdon, and Olarreaga


in table 6.3 for imports of resource-poor countries from resource-rich
countries comes from imports of GCC countries.


The largest trade diversion effects are to be found in developing oil
exporters, not in GCC oil exporters, and the extent of trade creation in the
GCC is also much smaller than in developing oil exporters. Thus, in GCC
countries, the increase in imports from other PAFTA countries is, on aver-
age, 107 percent, whereas the decline in imports from the rest of the world
is estimated at 25 percent. However, to assess the relative importance of
these two reductions, it is necessary to consider the difference in the base.
Given that initial non-oil imports from the rest of the world are at least
five times the imports of non-oil imports from other PAFTA countries, this
again suggests a fully trade-diverting PAFTA for GCC members.


Table 6.3 Decomposition of Intra-PAFTA Trade Creation and Diversion According
to Natural Resources and Labor Endowment, 1990–2009


Total imports


Importer


RPLA RRLA RRLI


Exporter
RPLA 0.66


(0.26)**
1.78


(0.65)***
0.25


(0.22)**
RRLA 0.75


(0.37)**
0.17


(1.23)
0.38


(0.46)
RRLI 1.54


(0.24)***
2.81


(0.61)***
0.26


(0.29)***
Rest of world 0.01


(0.12)
–0.41
(0.20)**


–0.26
(0.11)**


Non-oil imports


Importer


RPLA RRLA RRLI


Exporter
RPLA 0.78


(0.26)***
1.91


(0.65)***
0.53


(0.24)**
RRLA 0.76


(0.36)**
1.73


(1.22)
0.77


(0.37)**
RRLI 1.48


(0.24)***
1.62


(0.61)**
0.89


(0.25)***
Rest of world 0.03


(0.12)
–0.43
(0.20)**


–0.29
(0.11)***


Source: Authors.
Notes: All regressions include, in addition to PAFTA, all other agreements; dummies also introduced in table 6.1,
but coefficients are not reported in order to save space; estimation with OLS; standard errors in italics:
heteroscedasticity consistent and adjusted for country-pair clustering; RRLA = resource-rich labor-abundant
(that is, developing oil exporter) countries; RRLI = resource-rich labor importing (GCC oil exporter) countries;
RPLA =resource-poor labor-abundant countries; * p = 0.1, ** p = 0.05, *** p = 0.01.
a. Only MENA countries; mirror data for Libya; no data for Iraq and West Bank and Gaza.




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 187


In the case of developing oil exporters, the percentage decline in
imports from the rest of the world is in fact much larger: around
35 percent for non-oil imports. But the average increase in intra-PAFTA
trade is much larger as well: around 479 percent. Given that non-oil
imports from the rest of the world are nine times imports from PAFTA at
the beginning of PAFTA’s implementation, this implies that the increase
in intra-PAFTA trade is not fully compensated by the decline in imports
from the world in the case of developing oil exporter PAFTA members.9


Resource-poor PAFTA members experience no trade diversion and
quite significant trade creation. While the trade creation is not a predic-
tion of the Venables (2011) model, the model does predict the absence
of trade diversion among resource-poor PAFTA members.


To check whether our results regarding trade diversion and trade cre-
ation are sensitive to the use of predetermined categories of countries
(resource rich, resource poor, and so forth), the estimation of the specifica-
tion in equation 6.3 was recalculated—instead of using predetermined
categories of countries, measures of the extent of concentration in the
export bundle of each country before the creation of PAFTA are interacted
with the PAFTA variable. The results are shown in table 6.4. Two measures
of concentration of exports are applied: a Herfindahl concentration index,
and the number of HS six-digit goods that the country exports (the latter
being a measure of diversification rather than concentration). The first two
columns of table 6.4 report results for total imports and non-oil fuel
imports using the Herfindahl concentration index as a measure of concen-
tration. The last two columns report results for total imports and non-oil
imports using the number of HS six-digit goods that the country exports
as a measure of the diversification of exports before PAFTA was signed.
The idea is simply to explore whether there is some heterogeneity in trade
creation and trade diversion when countries with different degrees of con-
centration in their export bundle sign a preferential trade agreement.


Results suggest very little heterogeneity in trade creation, with the
coefficients on trade creation being all positive and statistically different
from zero, but not statistically different from each other across the esti-
mates in the four columns. The interaction of relative concentration of the
importer and the exporter is not statistically different from zero. This sug-
gests that there is little evidence of heterogeneity in trade creation across
country pairs with different relative degrees of export concentration.


However, there is some statistically significant heterogeneity in trade
diversion, as illustrated by the fact that all the coefficients in the fourth
row of table 6.4 are statistically significant. More concentrated countries




188 Carrère, Gourdon, and Olarreaga


(as measured by a higher Herfindahl index, or a lower number of prod-
ucts exported) tend to suffer from a larger degree of trade diversion. It is
difficult to interpret the size of the coefficients because the variables are
multiplied by the Herfindahl index or the number of exported lines, but
figure 6.1 provides an idea of the size of trade diversion in the PAFTA
countries, as well as the standard error of the estimate for each country.


When concentration is measured using the Herfindahl index, Kuwait,
Libya, Oman, Saudi Arabia, UAE, and the Republic of Yemen all have
levels of trade diversion that are statistically different from zero, with a
more than 20 percent average decline in imports from the rest of the
world. When the number of export lines is instead used as a measure of
diversification of exports before the agreement was signed, Bahrain,
Jordan, Kuwait, Lebanon, Libya, Oman, Qatar, Sudan, and the Republic
of Yemen all have levels of trade diversion that are statistically different
from zero, with an average 30 percent decline in imports from the rest of
the world.10


Table 6.4 Decomposition of Intra-PAFTA Trade Creation and Diversion


PAFTA


ln (Mijt)


Total imports Non-oil imports Total imports Non-oil imports


Intra 1.051***
(0.18)


1.083***
(0.17)


1.186***
(0.20)


1.247***
(0.20)


CIj/CIi.intra 0.009
(0.01)


0.013
(0.01)


–0.009
(0.06)


–0.028
(0.06)


Rest of world –0.005
(0.15)


0.017
(0.14)


–0.656***
(0.15)


–0.647***
(0.15)


CIi.row –0.383**
(0.18)


–0.461***
(0.18)


0.0003***
(0.00)


0.000***
(0.00)


Concentration index Herfindahl Herfindahl Number of lines Number of lines
Observations 31,054 31,016 31,054 31,016
Number of importersa 18 18 18 18
Number of exporters 239 239 239 239
Years 1990–2009 1990–2009 1990–2009 1990–2009
Fixed effects (ij) Yes Yes Yes Yes
Fixed effects (it) Yes Yes Yes Yes
Fixed effects (jt) Yes Yes Yes Yes


Source: Authors.
Notes: All regressions include, in addition to PAFTA, all other agreements; dummies also introduced in table 6.1,
but coefficients are not reported in order to save space; estimation with OLS; standard errors in parentheses: het-
eroscedasticity consistent and adjusted for country-pair clustering; * p = 0.1, ** p = 0.05, *** p = 0.01.
a. Only MENA countries; mirror data for Libya; no data for Iraq and West Bank Gaza.




189


Figure 6.1 Predicted Non-Oil Trade Diversion by MENA Countries Given the Pre-PAFTA Concentration Index Value


Herfindahl number of lines


20


t


r


a


d


e




d


i


v


e


r


s


i


o


n




(


i


n




%


)


concentration index before PAFTA’S implementation


MAR
LBN


SDN
BHR


EGY SYR
QAT


SAUOMN
KWT


YEM


ARF
KWT


JOR
TUN


–20


–10


0 0.2 0.4 0.6 0.8


0


60


40


20


0


–20


–40


t


r


a


d


e




d


i


v


e


r


s


i


o


n




(


i


n




%


)


concentration index before PAFTA’S implementation
0 1,000 2,000 3,000 4,000


LBY


YEM


KWT


LBN


SDN


BHR
JOR


SYR TUN


MAR


EGY


ARE


SAU


QAT
OMN


Source: Authors.
Note: The figure shows predicted values: exponential of coefficients for trade diversion presented in table 6.4, columns 2 and 4.




190 Carrère, Gourdon, and Olarreaga


Finally, to understand the types of goods in which we observe trade
creation and trade diversion in resource-rich and resource-poor countries,
we report the distribution of export growth by sector between resource-
rich countries in PAFTA and the rest of the world in the top panel of
figure 6.2, and between resource-poor countries in PAFTA and the rest of
the world in the bottom panel.


Interestingly, the bottom panel suggests that exports of resource-
poor countries to GCC countries are not as well correlated with their
exports to the world as they are to developing oil exporters, or to other
resource-poor countries. This again suggests that some significant trade
diversion could be taking place when GCC countries import from
resource-poor countries within PAFTA. The correlation between the
distribution of export growth from resource-rich countries to resource-
poor countries with export growth from resource-rich countries to the
world in the top panel is also quite strong, suggesting again that
resource-poor countries within PAFTA may not be subject to a signifi-
cant amount of trade diversion.11


Concluding Remarks


Regional integration is sometimes seen as an instrument to help diver-
sify the economies of resource-abundant MENA countries. A recent
theoretical study by Venables (2011) suggests, however, that when
resource-rich and resource-poor countries give preferences to each
other, the resource-rich country is very likely to suffer from trade
diversion.


This chapter explores the extent to which MENA’s different integra-
tion schemes have led to trade creation and trade diversion. Significant
evidence of increase in intraregional trade following the entry into force
of the agreements was found in most cases, while evidence of trade diver-
sion appeared in only one agreement, the PAFTA.


Consistent with what Venables (2011) predicts in theory, the empir-
ical work presented in this chapter confirms that the main source of
trade diversion in PAFTA has been the replacement of imports of
resource-rich countries from the rest of the world by imports from
other PAFTA members. Resource-poor counties have suffered no trade
diversion.


This finding seems to suggest that the main beneficiaries from PAFTA
have been resource-poor countries, which experienced only trade cre-
ation and benefited from the trade diversion of resource-rich countries. In




191


Figure 6.2 Regional Distribution of Export Growth by Sector for Resource-Rich and Resource-Poor Countries
percentage


export to RPLA export to RRLA export to RRLI export to world


a. from resource-rich countries


–10 0 10 20 30 40 –10 0 10 20 30 40–10 0 10 20 30 40–10 0 10 20 30 40


17: Vehicles
16: Machinery and electrical equipment


18: Optical and medical instruments
19:Arms and munitions


8: Raw hide and skins
12: Footwear


9: Wood
20: Miscellaneous goods


11: Textile
2: Vegetable products


1: Live animals
3: Fats and oil


13: Stone and cement


4: Prepared food
10: Paper


6: Chemical products
15: Base metals


14: Pearl
5: Minerals products


7: Rubber and plastics


(continued next page)




192


Figure 6.2 (continued)


b. from resource-poor countries


0 10 20 30


12: Footwear
8: Raw hide and skins


19: Arms and munitions


18: Optical and medical instruments


5: Minerals products
9: Wood


3: Fats and oil
11: Textile


7: Rubber and plastics
17: Vehicles


13: Stone and cement
14: Pearl


10: Paper
20: Miscellaneous goods


4: Prepared food
2: Vegetable products


1: Live animals
6: Chemical products


15: Base metals


16: Machinery and electrical equipment


0 10 20 30 0 10 20 30 0 10 20 30


export to RRLI export to RRLA export to RPLA export to world


Source: COMTRADE.




193


Annex Table 6A.1 Agreements Involving MENA Countries as Importer


Name Member countries Coverage Type
Date of


notification WTO legal cover


Date of
entry into


force


FTA intra-MENA
Pan-Arab Free Trade Area (PAFTA) Bahrain; Egypt, Arab. Rep.; Iraq; Jordan; Kuwait; Lebanon; Libya;


Morocco; Oman; Qatar; Saudi Arabia; Sudan; Syrian Arab
Republic; Tunisia; United Arab Emirates; Yemen, Rep.


Goods FTA 3-Oct-06 GATT Art. XXIV 1-Jan-98


Gulf Cooperation Council (GCC) Bahrain; Kuwait; Oman; Qatar; Saudi Arabia; United Arab Emirates Goods CU 6-Oct-09 GATT Art. XXIV 1-Jan-03
AGADIR Egypt, Arab. Rep.; Jordan; Morocco; Tunisia Goods 3-Oct-06 GATT Art. XXIV 1-Jan-04
Arab Magheb Union (UMA) Algeria; Libya; Morocco; Tunisia; Mauritania Goods 2-Jan-12
BTA intra-Mena (and not already


included in intra-MENA FTAs above)
Algeria-Jordan Algeria-Jordan Goods FTA 2-Jan-02
FTA with non-MENA countries
Economic Cooperation


Organization (ECO)
Afghanistan; Azerbaijan; Iran, Islamic Rep., Kazakhstan; Kyrgyz;


Pakistan; Tajikistan; Turkey; Turkmenistan; Uzbekistan
Goods PTA 10-Jul-92 Enabling Clause 17-Feb-92


Common Market for Eastern and
Southern Africa (COMESA)


Angola; Burundi; Comoros; Djibouti; Egypt, Arab. Rep. (1999),
Eritrea; Ethiopia; Kenya; Lesotho; Libya (2005); Madagascar;
Malawi; Mauritius; Rwanda; Sudan; Swaziland; Tanzania;
Uganda; Zambia; Zimbabwe


Goods PTA 4-May-95 Enabling Clause 8-Dec-94


COMESA Free Trade Burundi (2004); Comoros (2006); Djibouti; Egypt, Arab. Rep.,
Kenya; Libya (2006); Madagascar; Malawi; Mauritius; Rwanda
(2004); Sudan; Zambia; Zimbabwe


Goods FTA 22-Jun-00


Euromed Agreements
EC Treaty Austria (1995); Belgium; Bulgaria (2007); Cyprus (1995); Czech


Republic (1995); Denmark (1973); Estonia (2004); Finland (1995);
France; Germany; Greece (1981); Hungary (2004); Ireland (1973);
Italy; Latvia (2004); Lithuania (2004); Luxembourg; Malta (2004);
Netherlands; Poland (1995); Portugal (1986); Romania (2007);
Slovak Republic (2004); Slovenia (2004); Spain (1986); Sweden
(1995); United Kingdom (1973)


Goods
FTA 24-Jul-06 GATT Art. XXIV 1-Sep-05


EC - Algeria EC - Algeria Goods FTA 24-Jul-06 GATT Art. XXIV 1-Sep-05


(continued next page)




194


EC - Egypt, Arab. Rep. EC - Egypt, Arab. Rep. Goods FTA 3-Sep-04 GATT Art. XXIV 1-Jun-04
EC - Jordan EC - Jordan Goods FTA 17-Dec-02 GATT Art. XXIV 1-May-02
EC - Lebanon EC - Lebanon Goods FTA 26-May-03 GATT Art. XXIV 1-Mar-03
EC - Morocco EC - Morocco Goods FTA 13-Oct-00 GATT Art. XXIV 1-Mar-00
EC - Palestinian Authority EC - Palestinian Authority Goods FTA 29-May-97 GATT Art. XXIV 1-Jul-97
EC - Syrian Arab Republic EC - Syrian Arab Republic Goods FTA 15-Jul-77 GATT Art. XXIV 1-Jul-77
EC - Tunisia EC - Tunisia Goods FTA 15-Jan-99 GATT Art. XXIV 1-Mar-98
FTA with EFTA
European Free Trade Iceland; Liechtenstein; Norway; Switzerland Goods FTA 30-Jan-70 GATT Art. XXIV 1-Mar-70
EFTA - Egypt, Arab. Rep. EFTA - Egypt, Arab. Rep. Goods FTA 17-Jul-07 GATT Art. XXIV 1-Aug-07
EFTA - Jordan EFTA - Jordan Goods FTA 17-Jan-02 GATT Art. XXIV 1-Jan-02
EFTA - Lebanon EFTA - Lebanon Goods FTA 22-Dec-06 GATT Art. XXIV 1-Jan-07
EFTA - Morocco EFTA - Morocco Goods FTA 20-Jan-00 GATT Art. XXIV 1-Dec-99
EFTA - Palestinian Authority EFTA - Palestinian Authority Goods FTA 23-Jul-99 GATT Art. XXIV 1-Jul-99
EFTA - Tunisia EFTA - Tunisia Goods FTA 3-Jun-05 GATT Art. XXIV 1-Jun-05
EFTA - Turkey EFTA - Turkey Goods FTA 6-Mar-92 GATT Art. XXIV 1-Apr-92
BTA with Turkey
Turkey - Morocco Turkey - Morocco Goods FTA 10-Feb-06 GATT Art. XXIV 1-Jan-06
Turkey - Palestinian Authority Turkey - Palestinian Authority Goods FTA 1-Sep-05 GATT Art. XXIV 1-Jun-05
Turkey - Syrian Arab Republic Turkey - Syrian Arab Republic Goods FTA 15-Feb-07 GATT Art. XXIV 1-Jan-07
Turkey - Tunisia Turkey - Tunisia Goods FTA 1-Sep-05 GATT Art. XXIV 1-Jul-05
BTA with US
US - Bahrain US - Bahrain Goods & services FTA & EIA 8-Sep-06 GATT Art. XXIV & GATS V 1-Aug-06
US - Jordan US - Jordan Goods & services FTA & EIA 15-Jan-02 GATT Art. XXIV & GATS V 17-Dec-01
US - Morocco US - Morocco Goods & services FTA & EIA 30-Dec-05 GATT Art. XXIV & GATS V 1-Jan-06
US - Oman US - Oman Goods & services FTA & EIA 30-Jan-09 GATT Art. XXIV & GATS V 1-Jan-09


Source: Authors, based on WTO data.
Note: FTA = free trade agreement; BTA = bilateral trade agreement; CU = customs union; PTA = preferential trade agreement; ElA = economic integration agreement.


Annex Table 6A.1 (continued)


Name Member countries Coverage Type
Date of


notification WTO legal cover


Date of
entry into


force




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 195


this way, the trade agreement has helped redistribute income from
resource-rich countries to resource-poor countries within PAFTA itself. It
also explains why resource-rich countries may be reluctant to further
deepen these types of agreements. Indeed, there are certainly more effi-
cient means of redistributing income to resource-poor countries in the
region than through trade diversion.


Notes


1. Clause 2 of the Arab League protocol reads: “the Arab States. . . shall closely
cooperate in . . . commercial exchange, customs. . . .” In 1982, league mem-
bers reached an agreement for the development of intraregional trade (Decree
848 of 27/2/1982).


2. The model explains bilateral trade using distance between two partners and
their economic size.


3. Trade creation in the Viner sense occurs only when the regional partner is the
lowest cost supplier. This is not necessary to observe trade creation according
to Lipsey’s definition; trade creation will be observed whenever intraregional
trade increases are conditional on not displacing imports from the rest of the
world. Thus, trade creation in the Viner sense is a sufficient but not necessary
condition to observe trade creation in the Lipsey sense


4. PAFTA was signed in 1996 and entered into force in 1998. It was signed by
Bahrain, Arab Republic of Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya,
Morocco, Oman, Qatar, Saudi Arabia, Syrian Arab Republic, Tunisia, United
Arab Emirates, and Republic of Yemen. See annex table 6A.1 for more
detail.


5. This section draws heavily from Venables (2011) and WTO (2010).


6. According to World Bank classification, resource-poor countries in PAFTA
include Djibouti, Egypt, Jordan, Lebanon, Morocco, Sudan, Tunisia, and
West Bank and Gaza. Resource-rich countries can be divided into two sub-
categories. GCC oil exporters include Bahrain, Kuwait, Oman, Qatar, Saudi
Arabia, and United Arab Emirates. Developing oil exporters include Algeria,
Islamic Republic of Iran, Iraq, Libya, Syrian Arab Republic, and Republic of
Yemen.


7. Although, as argued by Hoekman and Zarrouk (2009) and Chauffour (2011),
there is still some important work to be done to reduce nontariff barriers.


8. Because the left-hand variable (imports) is in logs and the right-hand vari-
able is a dummy (trade agreements by different types of countries), the
percentage increase in imports is given by the exponent of the coefficient
minus 1. All percentage changes discussed below are computed as dis-
cussed here.




196 Carrère, Gourdon, and Olarreaga


9. More precisely, 67 percent of the intraregional trade increase is at the expense
of the rest of the world, allowing for one-third of pure trade creation.


10. In the case of the United Arab Emirates, imports from the world seem to
increase after the creation of PAFTA, with the use of a number of export lines
as a measure of diversification. However, this could be partly explained by the
country’s large amount of re-exports.


11. The goods with the higher growth in exports of resource-poor countries to
other PAFTA countries are machinery and equipment and base metals and
equipment. Rubber and plastics seem to dominate exports of resource-rich
countries to other PAFTA countries.


References


Anderson, J., and E. VanWincoop. 2003. “Gravity with Gravitas: A Solution to the
Border Puzzle.” American Economic Review 93 (1): 170–92.


Cadot, O., C. Carrère, and V. Strauss-Kahn. 2011. “Export Diversification: What’s
Behind the Hump?” Review of Economics and Statistics 93 (2): 590–605.


Carrère, C. 2006. “Revisiting the Effects of Regional Trade Agreements on Trade
Flows with Proper Specification of the Gravity Model.” European Economic
Review 50 (2): 223–47.


Chauffour, J. P. 2011. “Trade Integration as a Way Forward for the Arab World.”
Policy Research Working Paper 5581, World Bank, Washington, DC.


Fouquin, M., R. Langhammer, and R. Scweickert. 2006. “Natural Resource
Abundance and Its Impact on Regional Integration: Curse or Blessing?”
Paper presented at the ELSNIT/Fundacao Getulio Vargas Conference in Sao
Paulo.


Hoekman, B., and J. Zarrouk. 2009. “Changes in Cross-Border Trade Costs in the
Pan-Arab Free Trade Area, 2001–2008.” Policy Research Working Paper 5031,
World Bank, Washington, DC.


Imbs, J., and R. Wacziarg. 2003. “Stages of Diversification.” American Economic
Review 93 (1): 63–86.


Miniesy, R. S., J. B. Nugent, and T. M. Yousef. 2004. “Intra-Regional Trade
Integration in the Middle East: Past Performance and Future Potential.” In
Trade Policy and Economic Integration in the Middle East and North Africa:
Economic Boundaries in Flux, ed. H. Hakimian and J. B. Nugent. London:
Routledge.


Péridy, N. 2007. “Toward a Pan-Arab Free Trade Area: Assessing Trade Potential
Effects of the Agadir Agreement.” The Developing Economies 43 (3): 329–45.


Pomfret, R. 1997. The Economic Cooperation Organization: Current Status and
Future Prospects.” Europe-Asia Studies 49 (4): 657–67.




Natural Resource Heterogeneity and the Incentives for and Impact of Regional Integration 197


Venables, A. 2011. “Economic Integration in Remote Resource-Rich Regions.” In
Costs and Benefits of Economic Integration in Asia, ed. R. Barro and J. W. Lee.
New York: Oxford University Press.


World Bank. 2008. MENA Economic Developments and Prospects: Regional
Integration and Global Competitiveness. Washington, DC: World Bank.


WTO (World Trade Organization). 2010. World Trade Report, 2010. Trade in
Natural Resources. Geneva.






199


A P P E N D I X A


This appendix defines the different classifications used in the text. For
most comparisons, countries are classified into three groups: resource-
poor labor-abundant (RPLA) countries (Arab Republic of Egypt, Jordan,
Lebanon, Morocco, and Tunisia); resource-rich labor-abundant (RRLA)
countries (Algeria, Islamic Republic of Iran, Iraq, Libya, Syrian Arab
Republic, and Republic of Yemen); and resource-rich labor-importing
(RRLI) countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and
United Arab Emirates).1 This last group corresponds to the members of
the Gulf Cooperation Council (GCC) countries.2 Because there is no
ambiguity, we refer to the groups as resource rich (6), resource poor (5),
and GCC (6). To signal missing data leading to a reduced sample, we
indicate each time how many countries are included in the group in
parenthesis.


This three-group classification captures only some of the diversity in
the region. For example, in the GCC grouping, half of the countries
have a population of approximately 1 million, two of 3 million–4 mil-
lion, and Saudi Arabia has 25 million. To account for the importance of
market size and the exploitation of economies, we constitute a group of
LARGE (48, 6) developing countries with a population over 20 million.
(The numbers in parentheses indicate the number of large developing
countries in the world and then the number in the Middle East and


Country Grouping Classifications




200 Natural Resource Abundance, Growth, and Diversification


North Africa) Likewise, we build an OIL (18, 10) group that includes all
the major oil exporters (that is, those with oil exports accounting for
80 percent or more of total merchandise exports). Although they are not
included in the OIL group, Morocco, Syria, and Tunisia have natural
resources and qualify as “point-source natural resource” countries in the
classification proposed by Isham et al. (2005).3 This classification distin-
guishes natural-resource-rich countries according to whether these
resources are “diffuse” (such as the United States) and do not give rise to
rents, or “point-source,” like Morocco (phosphates), that do give rise to
rents. The resulting group, POINT (43, 8), is large, with half of the
MENA countries, including Egypt.


Finally, for the mobility analysis, we use the World Bank four-group
income classification: low (L), lower middle (LM), upper middle (UM),
and high (H). We break MENA countries into these four categories in
an extended sample that also includes member countries of the
Organisation for Economic Co-operation and Development (OECD)
countries (but excludes the former socialist countries of Europe and
Central Asia).


The list of countries in each grouping (except income) is given in
table A1. It corresponds to the groupings used in table 2.3 in chapter 2.


Table A.1 Comparator Groups


Countries


Middle East and North
Africa (MENA) (17)a


Algeria, Bahrain, Arab Republic of Egypt, Islamic Republic of Iran,
Iraq, Israel, Jordan, Kuwait, Lebanon, Libya, Morocco, Oman, Qatar,
Saudi Arabia, Syrian Arab Republic, Tunisia, United Arab Emirates,
Republic of Yemen.


Resource-rich, labor-
abundant (6)


Algeria, Islamic Republic of Iran, Iraq, Libya, Syria, Republic of Yemen


Resource-poor labor-
abundant (5)


Egypt, Jordan, Lebanon, Morocco, Tunisia


Resource-rich labor-
importing, or GCC (6)


Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, United Arab Emirates


LARGE
Large countries


(48, 6)b


Afghanistan, Algeria, Argentina, Bangladesh, Brazil, Canada, China,
Colombia, Egypt, Ethiopia, France, Germany, India, Indonesia,
Islamic Republic of Iran, Iraq, Italy, Japan, Kenya, Republic of Korea,
Morocco, Mexico, Myanmar, Malaysia, Nigeria, Nepal, Pakistan, Peru,
Philippines, Poland, Dem. Rep. of Congo (Zaire), Dem. Rep. of Korea,
Romania, Russian Federation, Spain, Saudi Arabia, South Africa,
Sudan, Tanzania, Thailand, Turkey, Uganda, Ukraine, United
Kingdom, United States, Uzbekistan, República Bolivariana de
Venezuela, Vietnam


(continued next page)




Country Grouping Classifications 201


Notes


1. This three-group classification was introduced in World Bank (2004, ch. 2).


2. The GCC was founded in 1981 with security and economic cooperation as
main objectives. Regional integration picked up around 2000, with a quasi–
common market status reached in 2008.


3. The objective of this classification is to capture the idea that natural riches
produce institutional weaknesses (the “voracity effect” associated with the
attempt at rent-capture by different social groups—see Tornell and Lane
(1999). “Point source” natural resources such as oil, minerals, and plantation
crops are extracted from a narrow economic base, while “diffuse” natural
resources are extracted from a large base. While this voracity effect extends
to all sources of rents (natural monopolies, foreign aid, nontariff barriers, and
financial elites), over the long haul, it makes sense to include a classification
of countries along this dimension.


References


Isham, J., M. Woolcock, L. Pritchett and G. Busby. 2005. “The Varieties of Resource
Experience: Natural Resource Export Structures and the Political Economy of
Economic Growth.” World Bank Economic Review 19 (2): 141–74.


Tornell, A., and P. R. Lane. 1999. “The Voracity Effect.” American Economic Review
89 (1): 22–46.


OIL
Oil exporters (18, 10)c


Angola, Algeria, Bahrain, Canada, Islamic Republic of Iran, Iraq,
Kazakhstan, Kuwait, Libya, Mexico, Nigeria, Norway, Russian
Federation, Oman, Saudi Arabia, United Arab Emirates, República
Bolivariana de Venezuela. Republic of Yemen


POINT
Point source natural


resources (43, 8)d


Algeria, Angola, Benin, Bolivia, Botswana, Burkina Faso, Chad, Chile,
Republic of Congo, Dem. Rep. of Congo, Dominican Republic,
Ecuador, Egypt, Fiji, Gabon, Guinea, Guyana, Indonesia, Islamic
Republic of Iran, Iraq, Jamaica, Jordan, Liberia, Malawi, Mauritania,
Mauritius, Mexico, Morocco, Namibia, Niger, Nigeria, Oman, Papua
New Guinea, Paraguay, Peru, Saudi Arabia, Sierra Leone, South
Africa, Sudan, Syria, Togo, Trinidad and Tobago, Tunisia, República
Bolivariana de Venezuela, Zambia


Notes: When comparisons are made with countries in the LARGE, OIL, and POINT groups, MENA members
belonging to the group are excluded.
a. Middle East and North Africa definition is based on the World Bank definition of the MENA region. Number of
MENA countries in the group is indicated.
b. Large countries are those with a population of at least 20 million in 2000. The category excludes OECD coun-
tries except for the Republic of Korea, Mexico, and Turkey.
c. Oil exporters are the 15 major oil crude exporters listed by the U.S. Energy Information Administration in 2005,
to which we added Bahrain (80 percent), Oman (90 percent), and Republic of Yemen (93 percent); share of oil in
merchandise exports in parenthesis.
d. Classification taken from Isham et al. (2005).


Table A.1 (continued)


Countries








ISBN 978-0-8213-9591-2


SKU 19591


The Middle East and North Africa (MENA) region is one of the richest in the world


in terms of natural resources. It holds more than 60 percent of the world’s proven oil


reserves, mostly located in the Gulf region, and nearly half of gas reserves. Not


surprisingly, oil represents close to 85 percent of merchandise exports of the region,


making it highly susceptible to fluctuations in international prices. A long strand of


economic literature suggests that such dependence may hurt a country’s growth


prospects and job creation by reducing the scope of economic diversification.


Natural Resource Abundance, Growth, and Diversification in the Middle East and North


Africa: The Effects of Natural Resources and the Role of Policies investigates how the


region can overcome this situation and encourage greater economic diversification.


The authors explore analytical questions, such as: the impact of the real exchange


rate on manufacturing and tradable services competitiveness in MENA; the role of


fiscal policy in supporting diversification; how “weak links” (input sectors with low


productivity) play a critical role in explaining the concentration of economic activities,


in addition to the classical Dutch Disease effect; and the impact of macroeconomic


factors on the drive for regional integration.


Several policy recommendations emerge. Policy makers should strive to avoid real


exchange rate overvaluation through consistent fiscal policies, flexible exchange rates,


and adequate product and factor market regulations. Reforms to strengthen the


competition and efficiency of upstream input activities are crucial for improving the


performance of downstream activities and diversification. Consistent and transparent


fiscal policy is essential to reduce instability, build the fiscal space needed to invest


in core infrastructure and human capital, and create a favorable environment for


diversification. While regional trade integration leads to greater diversification and


welfare gains for the resource-poor countries of the region, for resource-rich countries,


deriving benefits from regional integration requires going beyond trade, since trade


preferences tend to generate net income losses for them. It is hoped that the findings


of this work will be of interest to policy makers, civil society, donors, and practitioners


in MENA countries.




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