A partnership with academia

Building knowledge for trade and development

Vi Digital Library - Text Preview

Pathways to African Export Sustainability

Report by The World Bank, 2012

Download original document (English)

African exporters suffer from low survival in international markets.Is this really the case? This report shows that the answer is “no.” When survival performance is controlled for by observable country characteristics such as—among other things—the level of income, Africa is no outlier. African exports have short life expectancies, but not any shorter than comparable countries. Beyond income levels, short export survival is largely explained by the difficult business environment in which African exporters operate. Once measures of this environment are taken into account, African countries are by no means “below the regression line” in terms of export survival. To give an overview of the report: Chapter 1 sets the stage by putting Africa’s export-survival performance into perspective and proposing a framework that will guide the interpretation of empirical evidence throughout the report. Chapter 2 covers country-level determinants of export sustainability at origin and destination, including the exporting country’s business environment. Chapter 3 explores some of the firm-level evidence on what drives export sustainability, including uncertainty, incomplete contracts, learning, and networks. Finally, chapter 4 offers tentative policy implications.

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


Trade


Pathways to African Export
Sustainability


Paul Brenton, Olivier Cadot,
and Martha Denisse Pierola


Pu
bl


ic
Di


sc
lo


su
re


A
ut


ho
riz


ed
Pu


bl
ic


Di
sc


lo
su


re
A


ut
ho


riz
ed


Pu
bl


ic
Di


sc
lo


su
re


A
ut


ho
riz


ed
Pu


bl
ic


Di
sc


lo
su


re
A


ut
ho


riz
ed






Pathways to African Export Sustainability






Pathways to African Export
Sustainability
Paul Brenton, Olivier Cadot, and Martha Denisse Pierola




© 2012 International Bank for Reconstruction and Development / The World Bank
1818 H Street NW
Washington DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org


Some rights reserved


1 2 3 4 15 14 13 12


This work is a product of the staff of The World Bank with external contributions. Note
that The World Bank does not necessarily own each component of the content included in
the work. The World Bank therefore does not warrant that the use of the content contained
in the work will not infringe on the rights of third parties. The risk of claims resulting from
such infringement rests solely with you.


The findings, interpretations, and conclusions expressed in this work do not necessarily
reflect the views of The World Bank, its Board of Executive Directors, or the governments
they represent. The World Bank does not guarantee the accuracy of the data included in this
work. The boundaries, colors, denominations, and other information shown on any map in
this work do not imply any judgment on the part of The World Bank concerning the legal
status of any territory or the endorsement or acceptance of such boundaries.


Nothing herein shall constitute or be considered to be a limitation upon or waiver of the
privileges and immunities of The World Bank, all of which are specifically reserved.


Rights and Permissions


This work is available under the Creative Commons Attribution 3.0 Unported license
(CC BY 3.0) http://creativecommons.org/licenses/by/3.0. Under the Creative Commons
Attribution license, you are free to copy, distribute, transmit, and adapt this work, including
for commercial purposes, under the following conditions:


Attribution—Please cite the work as follows: Brenton, Paul, Olivier Cadot, and Martha
Denisse Pierola. 2012. Pathways to African Export Sustainability. Washington, DC: World
Bank. DOI: 10.1596/978-0-8213-9559-2. License: Creative Commons Attribution CC BY
3.0


Translations—If you create a translation of this work, please add the following disclaimer
along with the attribution: This translation was not created by The World Bank and should not
be considered an official World Bank translation. The World Bank shall not be liable for any
content or error in this translation.


All queries on rights and licenses should be addressed to the Office of the Publisher, The
World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail:
pubrights@worldbank.org.


ISBN (paper): 978-0-8213-9559-2
ISBN (electronic): 978-0-8213-9560-8
DOI: 10.1596/978-0-8213-9559-2


Cover photo: Aircraft on runway in Ghana. ©Anne Hoel/The World Bank.
Cover design: Naylor Design, Inc.


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




v


Contents


Acknowledgments ix
About the Authors xi
Abbreviations xiii


Introduction 1


Chapter 1 Export Survival: What We Know about Africa 7
Export Survival: A First Pass at the Evidence 8
Do African Exports Really Survive Less Long? 10
Understanding Entry, Exit, and Survival Decisions 22
Annex 1A: The Basic Analytics of Survival 29
Annex 1B: The Basic Toolkit of Empirical


Survival Analysis 33
Notes 36
References 36


Chapter 2 Countries, Institutions, and Policies 39
Comparative Advantage 40
Trade Costs and the Business Environment 48
Standards and Their Enforcement 60




vi Contents


Annex 2A: Survey of African Exporters on
Export Survival 66


Notes 69
References 71


Chapter 3 Survival, Contracts, and Networks 77
Exports, Firms, and Survival 77
Learning and Synergies 81
Networks: Migrants and Diasporas 88
Notes 98
References 99


Chapter 4 Policy Implications 105
Thinking Strategically: Export-Expansion Paths 106
Trade Preferences: What Role Should They Play? 108
A Role for Support Services and Technical


Assistance 110
Notes 120
References 121


Boxes
2.1 Examples of Non-Tariff Barriers and Their Costs in


Southern Africa 49
2.2 A Malian Mango’s “Soldier’s Run” 63
4.1 The Agri-Food Product Risk Index 112


Figures
1.1 Average Export Survival by Origin Country, 1979–2010 9
1.2 Average Export Survival by Destination, 1979–2010 10
1.3 Sub-Saharan Africa’s Exports Relative to Comparator


Group, 1960–2010 11
1.4 Average Export Survival by Exporter (Origin) Income,


1979–2010 13
1.5 Average Spell Survival by Importer (Destination)


Income, 1979–2010 14
1.6 Kaplan-Meier Survival Function for Developing-


Country Originating Products in OECD Markets 15
1.7 Kaplan-Meier Survival Function for Sub-Saharan


Africa–Originating Products in OECD Markets 15




Contents vii


1.8 Average Spell Survival and Exporter-Country Income,
1979–2010 16


1.9 Export Survival and Income by Sector, 1979–2010 17
1.10 Sunk Costs and the Frequency of Exits 23
1.11 Number of New Export Lines (HS 6) against


Income Levels 25
1.12 Export Growth Decomposed, 1990–2005 26
1.13 Entry and First-Year Entrants’ Survival Rates at the


Firm Level 28
2.1 Average Spell Survival and Comparative Disadvantage 42
2.2 Traveling through Diversification Cones 44
2.3 Evolution of Sectoral Shares with Income Levels 45
2.4 Constraints to Survival 54
2.5 First-Year Survival Rates and Business Environment


Measures in the Origin Country 56
2.6 First-Year Survival Rates and Financial Development 58
3.1 Source of Client Contact, 2009 87
3.2 Importance of Barriers to Export: Regular Exporters


(Number of Responses), 2009 91
3.3 Occupational Changes of Internal and International


Migrants: Burkina Faso, 2009 94
4.1 Effect of Prior, Non-OECD Experience on First-Year


Survival Rates by Region of Origin 107


Tables
1.1 Cox Regression Results: Estimation of Export Spell


Hazard Rates, All Developing Countries 19
2.1 Survival Versus Business Environment Measures in African


Countries: Correlations 59
2A.1 Survey of African Exporters on Export Survival:


Distribution of Exporters by Exporter Type 66
3.1 Origin and Destination of Emigrant Stocks by Region 92
3.2 Origin and Destination of Emigrant Stocks by African


Region, Percent of Total Emigration 93
3.3 Export-Spell Hazard Rate Estimates: Cox Regressions 96
4.1 Cox Regression Results: The PIP Effect on Survival of


Senegalese FFV Exports to the EU Market 116






ix


Acknowledgments


This report was written by Paul Brenton, Olivier Cadot, and Martha
Denisse Pierola with assistance from the Bank-Netherlands Partnership
Program (BNPP) Trust Fund 092945. The authors are grateful to the
Netherlands government for providing the financial resources necessary to
carry out background research, including an original survey of African
exporters and the collection of data from customs administrations in a
number of African countries. We thank the customs authorities in Ghana
(Ghana Revenue Authority), Malawi (Malawi Revenue Authority), Mali
(Direction Générale des Douanes), Senegal (Direction Générale des
Douanes), and Tanzania (Tanzania Revenue Authority) for providing the
team with the exporter-level transaction data required to conduct research.
We also thank Francis Aidoo, William Baah-Boateng, Caesar Cheelo, Sidiki
Guindo, Anthony Mveyange, and Nelson Nsiku for their very valuable
efforts supporting the team during the data collection and survey imple-
mentation stage of this project. The report is based on background papers
by Felix Arndt, Gaelle Balineau, Céline Carrère, Jaime de Melo, Laure
Dutoit, Leonardo Iacovone, Julien Gourdon, Mélise Jaud, Marie-Agnès
Jouanjean, Madina Kukenova, Jean-Christophe Maur, Marcelo Olarreaga,
Caglar Ozden, Ferdinand Rauch, Yuliya Shakurova, and Ben Shepherd. We
are particularly grateful for the comments and suggestions of the peer
reviewers of this report: Elisa Gamberoni and Javier Suarez.






xi


Paul Brenton is the Trade Practice Leader in the Poverty Reduction and
Economic Management Department of the Africa Region of the World
Bank. He co-edited the recent World Bank volume on De-Fragmenting
Africa: Deepening Regional Integration in Goods and Services. Previously he
served in the Trade Department of the Bank, where he worked for several
years on issues related to trade reform with a focus on regional integra-
tion. Dr. Brenton joined the Bank in 2002, having been Senior Research
Fellow and Head of the Trade Policy Unit at the Centre for European
Policy Studies in Brussels. Before that he was a lecturer in Economics at
the University of Birmingham in the United Kingdom. He has a Ph.D. in
Economics from the University of East Anglia.


Olivier Cadot is a Professor of International Economics and Director of
the Institute of Applied Economics at the University of Lausanne,
Switzerland. Prior to taking up his position at Lausanne, he was Associate
Professor of Economics at INSEAD. He has held visiting appointments at
University of California, Los Angeles (UCLA) and McGill University,
New York University, Université d’Auvergne, Koç University, the Paris
School of Economics, and the Institut d’Etudes Politiques de Paris. He
was a Senior Economist in the World Bank’s Trade Department between


About the Authors




xii About the Authors


2009 and 2011, and has advised the French government, the Swiss federal
government, and the European Commission on trade policy matters. He
also worked for the Organisation for Economic Co-operation and
Development (OECD) and the International Monetary Fund. He was
elected best teacher at HEC (Faculty of Business and Economics)
Lausanne and was nominated three times for the Outstanding Teacher
Award at INSEAD. He has contributed regularly to international execu-
tive programs. He is a Research Fellow of the Center for Economic Policy
Research in London, a Senior Fellow of the FERDI (Foundation for
International Development Study and Research), and Associate Scholar
at CEPREMAP (Center for Economic Research and its Applications). He
serves on the editorial board of the Revue d’Economie du Développement
and on the scientific advisory board of the Fondation Jean Monnet. He
has published numerous scholarly papers on international trade and eco-
nomic development. Professor Cadot holds a Ph.D. in Economics from
Princeton University and a Master’s in Economic History from McGill
University.


Martha Denisse Pierola is an Economist in the Trade and International
Integration Unit of the Development Research Group of the World Bank.
She has published several papers on export growth and exporter dynam-
ics and is currently managing the development of the first-ever global
database on exporter growth and dynamics, based on firm-level export
data. Previously, she worked on issues related to regionalism, trade costs,
and trade and productivity. Before joining the World Bank in 2005, she
worked as an economist for the Peruvian government (INDECOPI) and
also consulted for the private sector and other international organizations.
She has a Ph.D. in Economics from the Graduate Institute of International
Studies in Geneva, Switzerland, and a Master’s in International Law and
Economics from the World Trade Institute in Bern, Switzerland.




xiii


ACP African, Caribbean, and Pacific
ASEAN Association of Southeast Asian Nations
ASYCUDA/SYDONIA Automated System for Customs Data/


Système Douanier Automatisé
CACM Central American Common Market
CMT cut, make, and trim
COMESA Common Market for Eastern and Southern


Africa
COMTRADE United Nations Commodity Trade Statistics


Database
CPC crop-protection chemical
DID difference-in-difference
ECA Europe and Central Asia
ECOWAS Economic Community of West African


States
EMIC exporter country migrants in importing


country
EPA Environmental Protection Agency
EU European Union
EU-REP Euro-Retailer Produce Working Group


Abbreviations




xiv Abbreviations


FDA Food and Drug Administration
FD&C Food, Drug and Cosmetics Act
FFV fresh fruit and vegetables
FSIS Food Safety and Inspection Service
GAP good agricultural practices
HACCP hazard analysis and critical control points
HS Harmonized System
ICM integrated crop management
IPM integrated pest management
IT import tolerance
KM Kaplan-Meier survivor functions
LAC Latin America and the Caribbean
LPI Logistics Performance Index
MENA Middle East and North Africa
MERCOSUR Mercado Común del Sur (Southern Cone


Common Market)
MRL Maximum Residue Levels
NAFTA North American Free Trade Agreement
OECD Organisation for Economic Co-operation and


Development
PIP Pesticide Initiative Program
PPP Purchasing Power Parity
RASFF Rapid Alert System for Food and Feed
RFII Revealed Factor Intensity Indices
ROO rules of origin
RPED Regional Program on Enterprise Development
SACU Southern African Customs Union
SADC Southern African Development Community
SITC Standard International Trade Classification
SPS sanitary and phytosanitary measures
SSA Sub-Saharan Africa
TFP total factor productivity
UAE United Arab Emirates
UEMOA Union Economique et Monétaire Ouest Africaine


(West African Economic and Monetary Union)
UN United Nations
UNCTAD United Nations Conference on Trade and


Development
U.S. United States




1


African exporters suffer from low survival in international markets. This
means that they fail more often than other exporters, undermining their repu-
tation with foreign buyers and condemning themselves to incurring again and
again the setup costs involved in starting new relationships. This high churn-
ing is a source of waste, uncertainty, and discouragement. Can something be
done about it?


Is this really the case? The paragraph above had the pessimistic overtone
of most of the literature on African economic performance, whether on
export markets or otherwise. But does this pessimism withstand scrutiny?
This report will show that the answer is “no.” When survival performance
is controlled for by observable country characteristics such as—among
other things—the level of income, Africa is no outlier. African exports have
short life expectancies, but not any shorter than comparable countries.
Beyond income levels, short export survival is largely explained by the dif-
ficult business environment in which African exporters operate. Once
measures of this environment are taken into account, African countries are
by no means “below the regression line” in terms of export survival.


There is more to dispel the dismal tone of our opening paragraph.
African exporters, like those in other low-income countries, show
extraordinarily vigorous entrepreneurship. Entry rates into new products
and new markets are high in spite of the formidable hurdles created by


Introduction




2 Pathways to African Export Sustainability


poor infrastructure and landlockedness for some or limited access to
major sea routes for others. African exporters experiment a lot, and fre-
quent failure is a price to pay for a chance to succeed. In fact, it is the
basic mechanism through which populations improve, through what
biologists call “Darwinism” and economists call “creative destruction.” In
that sense, low survival is good news.


Why should we worry, then? We should be concerned about low
export survival for the same reason we are concerned with high infant
mortality. Every failure has a cost, and the very high failure rates that we
observe suggest, beyond experimentation, that the environment must be
so rough that it is bound to entail a large proportion of “accidental”
deaths. It is those deaths that we want to reduce through better policies.


Lessons from empirical evidence gathered in background papers to
this report and from a recent survey of African exporters carried out by
the World Bank, also as background to this report, suggest that the envi-
ronment in which African exporters operate can be improved through
traditional prescriptions to improve trade facilitation, the legal environ-
ment of business, better access to credit, and also through more proactive
interventions targeting the firms themselves, provided that those inter-
ventions are well designed.


This report provides tentative leads toward such policy prescriptions,
based on an overview of the empirical evidence. Chapter 1 sets the stage
by putting Africa’s export-survival performance into perspective and
proposing a framework that will guide the interpretation of empirical
evidence throughout the report. Chapter 2 covers country-level determi-
nants of export sustainability at origin and destination, including the
exporting country’s business environment. Chapter 3 explores some of
the firm-level evidence on what drives export sustainability, including
uncertainty, incomplete contracts, learning, and networks. Finally, chapter 4
offers tentative policy implications.


The main conclusions from this overview of the causes of Africa’s low
export sustainability should be taken with caution both because of the
complexity of the issue and because of the very fragmentary evidence on
which the overview is based. We should be more cautious in drawing
policy implications, as hasty policy prescriptions are the most common
trap into which reports of this kind can fall. A first, solid conclusion is that
we need substantial additional work on the nature and causes of low
export survival rates in developing countries to determine the path to
high export sustainability. We close the report with some suggestions of
where new work is most needed.




Introduction 3


Yet, before that, we do propose a number of additional tentative
remarks linking this work with recent analysis on barriers to trade in
Africa presented in the World Bank report De-Fragmenting Africa:
Deepening Regional Trade Integration in Goods and Services (http://go.
worldbank.org/MKK5U1Y2D0). First, Africa suffers from a low-survival
syndrome because its business environment is a difficult one. Trade costs
are high, directly—because of high freight rates and long inland routes;
and indirectly—because of burdensome customs and administrative
procedures and substantial non-tariff barriers. Productive capacities are
constrained by many factors, prominent among which is the lack of access
to critical services, including credit from financial services providers,
which prevents African exporters from responding in time to escalating
buyer demands.


As a result of this difficult environment, Africa may find it difficult to
nurture the kind of midsize exporters that have proved, in other environ-
ments, to be the most adaptable and resilient to changing competitive
situations. Because of the continent’s small and fragmented domestic
markets and low levels of intra-regional trade, African exporters have
little opportunity to gain local experience before being approached by
larger buyers, often resulting in mismatch and premature failure.


However, there are grounds to be optimistic. Intra-regional trade is
growing but remains a small fraction of its potential. Although tariff
barriers have been reduced or removed in many regional communities in
Africa, non-tariff barriers remain a major constraint to trade in goods
while limits on market access and restrictive regulatory regimes limit
trade in services. While still a major issue, the infrastructure deficit in
Africa is coming down—but here it is important to coordinate invest-
ments in infrastructure with policy reforms that deliver competitive
services. In addition, as African diasporas abroad gain in economic status,
they are posited to help the continent’s exporters reach out to new mar-
kets with which they have little familiarity, like other—especially Asian—
diasporas have long done for their home countries.


Governments can help secure and accelerate these positive trends.
They can help, first, by vigorously pursuing trade-facilitation agendas and
by working to improve the business environment in which exporters
operate. They can also work to improve the performance of export-
promotion agencies with a view toward better sustainability of results and
more strategic assistance on optimal long-term export-expansion paths.


Efforts to promote the deeper integration of African markets through
more effective regional agreements will help African exporters accumulate




4 Pathways to African Export Sustainability


experience on markets with which they can rapidly gain familiarity and
where consumers have similar preferences. These efforts should focus on
providing transparent, predictable, and stable trade policy environments
and avoid abrupt changes in non-tariff barriers as a response to temporary
market disruptions or to lobbying demands. They should also target the
simplification and relaxation of rules of origin so as to foster the emer-
gence of regional supply chains populated by firms of similar size and
outlook, as this has been shown in other contexts to be a factor in long-
lasting relationships.


Finally, African countries and international organizations should engage
in dialogue with industrial countries to reduce the current degree of
discretionarity in the application of sanitary and phytosanitary standards
for agri-food products, as is current practice in some Organisation for
Economic Co-operation and Development countries, based on reputation
as much as evidence. This tends to penalize new exporters with no estab-
lished records, creating uncertainty through a constant risk of rejection.


Since this is an early report in the analysis of export survival in Africa,
there is clearly much scope and need for further research. The following
are a number of areas that stand out for additional analysis:


• There is a need to understand the role of export intermediaries. For a
number of products, especially raw agricultural products, exports are
not undertaken by firms that produce products but rather by export
agents that sell overseas the output of a large number of smallholder
producers. These intermediaries may be making strategic decisions on
where to sell such produce according to prices in particular markets. As
such, they may shift exports from one market to another in a way that
suggests regular entry and exit from particular markets in the customs
statistics.


• The analysis thus far has used official customs statistics and information
from firms that are exporting officially. A vast number of traders in
Africa operate in the informal sector, in large part because of the hostile
business environment and the high costs of formally crossing borders.
Part of the pathway to export sustainability will be to facilitate the
movement of exporters from the informal to the formal sector, an effort
that will have to include addressing the key factors that may undermine
their survival as formal exporters. Identifying success stories of export-
ers that have successfully migrated from the informal to the formal
arena can provide important information in this regard.




Introduction 5


• The analysis here has focused entirely on trade in goods. An increasingly
important feature of the global economy and regional markets in Africa
is trade in services. Trade in services holds enormous potential for
regional integration in Africa, especially for landlocked countries whose
opportunities to trade in manufactures are limited relative to large
coastal countries. It would be very useful to look at the survival rates of
services flows across borders and their determinants and to see if and
how they differ significantly from the situation regarding the survival
of goods exports flows.


• More studies are needed that carefully assess the impact of trade pro-
motion and other efforts to encourage exporters, and to include in the
analysis not only the impact on export volumes but also on the survival
rates of the underlying export firms. Of particular use would be studies
looking at impacts of programs that have supported greater participa-
tion in regional trade on the subsequent entry of beneficiary firms into
global markets.






7


C H A P T E R 1


Export Survival
What We Know about Africa


Recent evidence suggests that export survival is, on average, very low—
less than ten years for virtually all trade relationships and around one to
two years for the majority of them. It also varies systemically across both
origins and destinations, with an apparently strong association with
income levels. Is Africa an outlier? A first pass at the evidence suggests a
negative answer. Most African export spells fail to survive past one or two
years, not because there is something unique to Africa, but because the
vast majority of African countries are low-income ones. Why is it that
low-income country exports do not survive as well as exports from high-
income countries? This report will systematically explore a range of con-
jectures in this regard.


In this first chapter, we will content ourselves with one simple observa-
tion: exporters in low-income countries experiment a great deal, and
experimentation is associated with failure as much as with success. To
draw an analogy to population dynamics, export markets are character-
ized by high infant (newcomer) mortality. Low-income countries are
characterized by high export birth rates (an active “extensive margin,” in
the trade jargon). Therefore, they also have high export death rates. The
association between high rates of experimentation and high rates of fail-
ure reflects a sort of Darwinism—what Joseph Schumpeter called “cre-
ative destruction”—and it may well be a healthy process.




8 Pathways to African Export Sustainability


However, the infant mortality of exports may be higher in low-income
countries for a different reason—namely, because the environment for
business generally is too tough. In order to reduce the incidence of “acci-
dental deaths,” we need to understand, at least in a basic manner, the logic
of exit decisions at the firm level. This is the second objective of this
chapter.


Export Survival: A First Pass at the Evidence


In this section, we address survival of export flows by product and desti-
nations. The term export survival is basically defined as follows, with slight
variants. Consider exports of a given product, say, “linens for the bed,
table, toilet and kitchen”—code 6584 in the Standard International Trade
Classification (SITC) nomenclature1—from Pakistan to Japan. Suppose
that no bilateral trade in that product between those two countries was
recorded in available trade data until 1985. Starting in 1985, positive
flows are observed during, say, five years in a row, followed by an inter-
ruption of at least one year. We call this uninterrupted period a spell. That
is, a spell is a product-origin-destination combination for which we
observe nonzero trade values for a number of consecutive years. When
flows stop, the spell is said to “die.” Its duration is the number of years
during which we observe positive flows, which in this case is just five
years. By export survival we mean the average duration of export spells.2


Export spells may have multiple lives. That is, exports of SITC 6584
from Pakistan to Japan may stop for a year and then start again. The inter-
ruption may be genuine, either because firms that were active during the
initial spell decided to exit the “market” (meaning the product-destination
combination) and new ones entered a few years later, or because a single
firm goes through a period (a “dry spell”) with no orders in the market.
At the firm level, this may be frequent for durable goods for which orders
tend to be bulky. Alternatively, an apparent interruption of a bilateral
trade flow may simply be a recording error at customs.3 In the absence of
information beyond trade statistics, the way temporary zero-trade periods
should be interpreted is a matter of judgment. Throughout this report,
unless indicated otherwise, we will define a spell death as an interruption
of one year or more. The results we report for a spell death are largely the
same as the results of a two-year interruption.


By and large, export survival is low. Besedes and Prusa (2006a, b) were
the first to study systematically the survival of export spells at a disag-
gregated level. Using U.S. import data at the SITC 4-digit level, they




Export Survival 9


found, strikingly, that the average spell duration was 4.4 years, with a
median of 2 years, over the 1989–2001 period.4 Part of the reason for the
short average and median survival of export spells is that the majority of
them are small in value, and, as we will document in this report, small-
value spells are less enduring. When individual spells are weighted (by the
square root of their initial value), the weighted-average survival rises to
10.8 years.5 Brenton, Saborowski, and von Uexkull (2010); Fugazza and
Molina (2011); and Subsequent studies by (among others) Nitsch (2009)
largely confirmed their findings on increasingly disaggregated data sets.
The most comprehensive data set in the literature has been compiled by
Carrère and Strauss-Kahn (2011) and yields largely similar results.


Beyond averages, there is substantial heterogeneity across countries at
both origin and destination. Figure 1.1 shows that exports originating
from Sub-Saharan Africa survive, on average, just over two years, the low-
est regional average. Only the Middle East and North Africa comes close
to Sub-Saharan Africa in terms of short survival, followed by Latin
America and the Caribbean. What this means practically is that, whereas
an average exporter in the United States faces a 70 percent probability of
surviving beyond the first year in a given product-destination pair, that


Source: Authors’ calculations based on UN Commodity Trade Statistics database.
Note: Average export-spell duration is calculated as the simple average of the duration of all SITC-4 spells
to the destinations in the group.


1.0


0.0


2.0


3.0


4.0


5.0


6.0


hi
gh


in
co


m
e


Ea
st


As
ia


&
Pa


cif
ic


Eu
ro


pe
&


C
en


tra
l A


sia


La
tin


A
m


er
ica


&
C


ar
ib


be
an


M
id


dl
e E


as
t &


N
or


th
A


fri
ca


So
ut


h
As


ia


Su
b-


Sa
ha


ra
n


Af
ric


a


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)


Figure 1.1 Average Export Survival by Origin Country, 1979–2010




10 Pathways to African Export Sustainability


probability is less than 30 percent for an exporter in Burkina Faso
(Brenton et al. 2009).


Survival also varies by destination market, and again Sub-Saharan
Africa stands out for its low average survival, as shown in figure 1.2.
However, the ranking is slightly different, with South Asia now coming
next to Sub-Saharan Africa in terms of low survival, and only then the
Middle East and North Africa.


Thus, in a crude comparison by regions, Sub-Saharan Africa stands out
at both ends for its low survival, suggesting a tough business environment,
prone to failure and sudden interruption, for both exporters and import-
ers. Should this be construed as implying that there is a specifically Sub-
Saharan African syndrome of low export survival? Clearly, it is too early
to jump to such a conclusion. The crude comparisons shown in figures
1.1 and 1.2 do not control for any covariates—country characteristics
such as low income that apply to many countries in Sub-Saharan Africa
and that also correlate with low export survival.


Do African Exports Really Survive Less Long?


Based on the evidence presented above, in this section we explore
whether Africa—mainly we refer to Sub-Saharan Africa—stands out as
an outlier in terms of low export survival.


Source: Authors' calculations based on UN Commodity Trade Statistics Trade database.


Figure 1.2 Average Export Survival by Destination, 1979–2010


0.0


1.0


2.0


3.0


4.0


5.0


6.0


hi
gh


in
co


m
e


Ea
st


As
ia


&
Pa


cif
ic


Eu
ro


pe
&


C
en


tra
l A


sia


La
tin


A
m


er
ica


&
C


ar
ib


be
an


M
id


dl
e E


as
t &


N
or


th
A


fri
ca


So
ut


h
As


ia


Su
b-


Sa
ha


ra
n


Af
ric


a


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)




Export Survival 11


Setting the Stage: Africa’s Export Recovery
After two decades of steep decline, Sub-Saharan Africa exports as a share
of global trade bottomed out in the early 2000s and have subsequently
been on the rise. In dollar terms, Africa’s exports per capita have risen at
an annual rate of 13 percent between 1994 and 2008, compared with
4 percent for the United States, 8 percent for Germany, 13 percent for
India, and 19 percent for China (Easterly and Resheff 2010). Figure 1.3
shows the turning point by plotting the ratio of Sub-Saharan Africa’s non-
commodity exports relative to a comparator group made of low-income
countries and lower-middle-income countries (excluding those in Sub-
Saharan Africa) after eliminating India and China. A clear break in the
trend appears just before 2000, although the recovery is still far from
offsetting the previous two disastrous decades.


As is often noted, Africa trades little with itself, at least to the extent that
is recorded in official customs statistics. There has been a modest increase
in the share of total goods exports from Sub-Saharan Africa going to other
Sub-Saharan Africa countries, from 11 percent in 1994 to 16 percent in


Source: Authors’ calculations based on UN Commodity Trade Statistics database.
Note: The comparator group is the set of low-income and lower-middle-income countries excluding India and
China—whose size make them noncomparable to any African country—and the line represents the ratio of
Sub-Saharan total exports (intra- and extra-African) to the exports of the comparator group. Petroleum and ores
are excluded from the total.


0.05


0.10


0.15


0.20


0.25


1960 1970 1980 1990 2000 2010


year


ra
ti


o
o


f S
u


b
-S


ah
ar


an
A


fr
ic


a
to


c
o


m
p


ar
at


o
r


Figure 1.3 Sub-Saharan Africa’s Exports Relative to Comparator Group,
1960–2010




12 Pathways to African Export Sustainability


2008 (12 percent to 21 percent of non-fuel exports). Trade within
regional communities is even lower. For example, the share of intra-
regional goods trade in total goods imports is only around 5 percent in the
Common Market for Eastern and Southern Africa (COMESA), 10 percent
in the Economic Community of West African States (ECOWAS), and 8
percent in the West African Economic and Monetary Union (UEMOA).
This compares with over 20 percent in the Association of Southeast Asian
Nations (ASEAN), around 35 percent in the North American Free Trade
Agreement (NAFTA), and more than 60 percent in the European Union
(EU). On the other hand, intra-regional trade in the Southern Cone
Common Market (MERCOSUR) is about 15 percent of total imports
and less than 8 percent in the Central American Common Market
(CACM)—see Acharya et al. (2011). There are, however, substantial
informal flows of goods and people across borders in Africa that are not
measured in official statistics.


Although exports have grown strongly over the last decade, and even
though the region’s trade has recovered well from the global crisis, the
impact of this recovery on unemployment and poverty has been disap-
pointing in many countries. Unemployment remains around 24 percent
in South Africa. In Tanzania, the percentage of people living in extreme
poverty (less than US$1.25 a day) appears to have remained broadly
constant—around 35 percent of the population. In Burkina Faso, income
poverty has been stagnant since 1997. This is because export growth has
typically been fueled by a small number of mineral and primary products
with limited impact on the wider economy, and because formal sectors
remain small in many countries, such as Burkina Faso. Hence, key objec-
tives in Africa remain to diversify the export base away from dependence
on commodities and to build on the increasing number of export success
stories, thus allowing more people to participate in trade. Therefore, for
this study, a key issue is whether increased export survival rates in Africa
would not only support sustained export growth but also lead to a more
diversified and inclusive export structure.


Export Survival: An “African Exception”?
We saw that Sub-Saharan Africa stands out for its low survival as both a
source and a destination of exports. Is this low survival truly a specifically
Sub-Saharan African characteristic? We cannot answer this question
before controlling for other determinants of export survival. This will be
the object of much of this study, but we start with the most obvious
suspect—income levels.




Export Survival 13


Average export survival varies substantially with the level of income of
the exporting country, as shown in figure 1.4. At 5.8 years, exports from
high-income countries survive, on average, 3.1 times longer than exports
from low-income countries (1.8 years). This wide difference suggests a
high-risk business environment for low-income exporters, where trade
relationships frequently terminate early. Many factors play a role in this
environment of short-term relationships, including, as we will see later in
this chapter, intensive experimentation by export entrepreneurs in low-
income countries.


Figure 1.5 shows that average survival is also, at 6.3 years, 2.7 times
higher in high-income markets than in low-income ones (2.3 years) at the
destination point, although the relationship is non-monotone, with rela-
tively lower survival observed in high-income countries that are not part
of the Organisation for Economic Co-operation and Development
(OECD).6 High survival in higher-income markets is not directly intui-
tive, as those markets may be tougher in terms of number of competitors,
whereas low-income markets—especially in Sub-Saharan Africa—are
sometimes sheltered from competition by high trade and transport costs.
One countervailing force may be the prevalence of contractual relation-
ships on more structured and sophisticated markets.


More seriously, figure 1.5 does not control for origin countries, and this
may introduce a confounding composition effect. A larger fraction of


Source: Authors’ calculations based on UN Commodity Trade Statistics database.


0


1


2


3


4


5


6


7


high-income
OECD


high-income
non-OECD


upper-middle
income


lower-middle
income


low income


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)


Figure 1.4 Average Export Survival by Exporter (Origin) Income, 1979–2010




14 Pathways to African Export Sustainability


0.0


1.0


2.0


3.0


4.0


5.0


6.0


7.0


high-income
OECD


high-income
non-OECD


upper-middle
income


lower-middle
income


low income


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)


Figure 1.5 Average Spell Survival by Importer (Destination) Income, 1979–2010


Source: Authors’ calculations based on UN Commodity Trade Statistics database.


exports sold in high-income countries originate from high-income origin
countries, and we know that exports from high-income origins survive
better. Therefore, we cannot tell if the higher survival observed in high-
income destination markets reflects their intrinsic characteristics or,
instead, the composition of their suppliers. The same observation applies
to the variation of survival across origin countries shown in figure 1.4: the
variation may well be a reflection of variations in destination portfolios
rather than the intrinsic characteristics of origin countries.


A different look at the data is displayed in figure 1.6, which shows
Kaplan-Meier (KM) survivor functions. Annex 1B provides a brief expla-
nation of these functions; suffice it to note here that KM survivor func-
tions plot the proportion of individuals (export flows) still alive as a
function of time since birth (or onset of risk). As the proportion falls to
less than 0.5 after the first year for all three income categories of develop-
ing countries (upper-middle-income, lower-middle-income, and low-
income countries), the median survival length is just below one year.


Unlike figure 1.4, figure 1.6 controls, albeit coarsely, for destination
effects (all spells have OECD countries as destinations). In spite of this
control, it is apparent that spell survival is everywhere lower for low-
income countries than it is for lower- and upper-middle-income ones,
confirming survival’s dependence on origin-country income.


Figure 1.7 shows the same function for export spells originating from
Sub-Saharan Africa compared with those of other developing countries.




Export Survival 15


Source: Carrère and Strauss-Kahn 2011, Figure A1.


504030
spell duration (years)


20100


0.00


0.25


0.50


p
ro


p
o


rt
io


n
s


u
rv


iv
in


g 0.75


1.00


upper-middle income
lower-middle income
lower income


Figure 1.6 Kaplan-Meier Survival Function for Developing-Country Originating
Products in OECD Markets


504030
spell duration (years)


20100


0.00


0.25


0.50


p
ro


p
o


rt
io


n
s


u
rv


iv
in


g 0.75


1.00


Sub-Saharan African countries
other developing countries


Source: Carrère and Strauss-Kahn 2011, Figure A1.
Note: See annex 1B of this chapter for an explanation of Kaplan-Meier functions.


Figure 1.7 Kaplan-Meier Survival Function for Sub-Saharan Africa–Originating
Products in OECD Markets




16 Pathways to African Export Sustainability


It is apparent that Sub-Saharan Africa–originating spells are shorter-lived
than others.


The pattern of variation apparent in figures 1.4, 1.5, and 1.6 suggests
that export spell survival correlates with income. Does this hold up to
scrutiny? Figure 1.8, a scatter plot of average spell survival against
exporter-country income, together with a quadratic regression curve,
provides an interesting answer. The pattern that emerges is non-
monotone, with a slight decrease up to about US$1,600 purchasing
power parity (PPP) and progression at an increasing rate thereafter. Thus,
for low-income countries, income rises may not be associated with imme-
diate, “mechanical” increases in average export survival, although there is
nothing here to suggest the direction of causality between income and
average export survival rates or the factors that can propel both.


Figure 1.8 also shows that once income levels are controlled for, Africa
is no outlier. In fact, most Sub-Saharan African countries are bunched
around the regression curve. The picture does not change significantly
when disaggregated by broad sectors, as shown by figure 1.9.


Thus, as a first pass there does not seem to be an African specificity in
terms of low export survival. However, the scatter plots in figures 1.8 and 1.9


Sources: For survival data, authors’ calculations based on UN Commodity Trade Statistics database; for GDP
per capita, World Development Indicators.
Note: PPP = purchasing power parity


Figure 1.8 Average Spell Survival and Exporter-Country Income, 1979–2010


AGO
BDI BENBFA


BWACAF


CIV


CMRCOG COM


CPVDJI


DZA


EGY


ERI


ETH GAB


GHA


GIN
GMBGNB


IRN


JOR


KEN
LBN


LSO


MAR


MDG


MLIMOZ
MRT


MUSMWI


NAM
NER


NGA


RWA


SDNSE


SLE SWZ SYC


SYR


TCD
TGO


TUN
TZA


UGA


YEM


ZAF


ZMB


ZWE


1


2


3


4


5


6 7 8 9 10 11


log of GDP per capita PPP (constant 2005 international $)


average export duration and income


fitted values


(mean) average export duration, African countries


(mean) average export duration


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)




Export Survival 17


(continued next page)


Figure 1.9 Export Survival and Income by Sector, 1979–2010


AGO
BDI BENBFA


BWACAF


CIV


CMRCOG
COM


CPVDJI


DZA


EGY


ERI


ETH GAB
GHA


GIN
GMB


GNB


IRN


JOR


KEN
LBN


LSO


MAR


MDG


MLI
MOZ


MRT


MUS
MWI


NAM


NER


NGA


RWA


SDNSE


SLE SWZ
SYC


SYR


TCD
TGO


TUN


TZA
UGA


YEM


ZAF


ZMB


ZWE


1


2


3


4


5


6 7 8 9 10 11


log of GDP per capita PPP (constant 2005 international $)


average export duration of agriculture and income


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)
av


er
ag


e
ex


p
o


rt
s


u
rv


iv
al


(y
ea


rs
)


AGOBDI
BEN


BFA


BWACAF


CIV


CMRCOG


COM
CPVDJI


DZA


EGY


ERI


ETH GAB


GHA


GIN


GMB


GNB
IRN


JOR


KEN
LBN


LSO


MARMDG


MLIMOZ
MRT


MUSMWI


NAM
NER


NGA


RWA


SDNSEN


SLE SWZ
SYC


SYR


TCD


TGO


TUN
TZA


UGA


YEM


ZAF


ZMB


ZWE


1


2


3


4


5


6 7 8 9 10 11


log of GDP per capita PPP (constant 2005 international $)


average export duration of mining and income


fitted values
(mean) average export duration, African countries (mean) average export duration




18 Pathways to African Export Sustainability


can be misleading because, like figure 1.4, they do not control for destination
effects. The dependence of survival on origin-country income shown in figure
1.8 may, again, reflect a portfolio-composition effect and confound it with
origin-country effects. In order to disentangle these effects, we turn to formal
regression analysis.


We assembled for this report a very large database of export spells
originating from all developing countries and exported to all destination
markets, including OECD countries, at the highest level of product disag-
gregation (4- and 5-digit SITC, or over a thousand products) from 1979
to 2009. Mirror trade-flow data were used to improve accuracy.7
Aggregating the trade-flow data into export spells reduced the data’s
dimensionality somewhat, but left more than 5 million observations.
Left-censoring substantially reduced the number of observations. See
annex 1B for a brief discussion of technical issues arising from the exis-
tence of left-censored spells.


A common tool for the causal analysis of survival is a Cox regression,
which uses a maximum-likelihood estimator of hazard rates. We ran a series


Sources: For survival data, authors’ calculations based on UN Commodity Trade Statistics database; for GDP per
capita, World Development Indicators.
Note: PPP = purchasing power parity


Figure 1.9 (continued)


av
er


ag
e


ex
p


o
rt


s
u


rv
iv


al
(y


ea
rs


)


log of GDP per capita PPP (constant 2005 international $)


AGOBDI
BENBFA


BWACAF


CIV


CMRCOG COM


CPV
DJI


DZA


EGY


ERI


ETH GAB


GHA


GIN
GMBGNB


IRN


JOR


KEN
LBN


LSO


MAR


MDG


MLIMOZ
MRT


MUSMWI


NAM
NER


NGA


RWA


SDNSEN


SLE SWZ SYC


SYR


TCD
TGO


TUN


TZA UGA


YEM


ZAF


ZMB


ZWE


6 7 8 9 10 11


average export duration of manufacturing and income


1


2


3


4


5


fitted values


(mean) average export duration, African countries (mean) average export duration




Export Survival 19


of Cox regressions of export-spell hazard rates on bilateral, regional, and
country-level covariates; these suggest a different picture. Results are shown
in table 1.1 in the form of coefficients (not hazard ratios) with z-statistics
in parentheses, which can be interpreted like t-statistics (see annex 1B for
more explanation of the Cox regression and its interpretation).


Table 1.1 Cox Regression Results: Estimation of Export Spell Hazard Rates,
All Developing Countries


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


Spell attributes
Initial spell value −0.0617*** −0.0623*** −0.0634*** −0.0634***


(0.00123) (0.0012535) (0.00123) (0.0012)
Spell value growth 2.4e−08** 2.35e−08** 1.71e−08* 1.71e−08*


(9.68e−09) (9.66e−09) (9.67e−09) (9.67e−09)
Multiple spell 0.6774*** 0.6763*** 0.6827*** 0.6821***


(0.00790) (0.00796) (0.0077) (0.0077)
Gravity variables


ln exporter GDP/cap *
ln importer GDP


0.0035***
(0.00013)


0.0005***
(0.0002)


ln exporter GDP/cap −0.427*** −0.4523***
(0.01824) (0.0199)


ln exporter GDP/cap2 0.026*** 0.0271***
(0.00101) (0.0011)


ln importer GDP/cap 0.0368** 0.0138
(0.01679) (0.0168)


ln importer GDP/cap2 −0.0018* −0.0023**
(0.00095) (0.00095)


Landlocked exporter 0.1126*** 0.1481*** 0.1565*** 0.1974***
(0.00794) (0.0084) (0.0083) (0.0089)


Landlocked importer 0.0332*** 0.0384*** 0.0305*** 0.0343***
(0.00282) (0.00276) (0.0026) (0.0028)


Common border −0.1222*** −0.1199*** −0.0928*** −0.0872***
(0.00418) (0.0043) (0.004) (0.004)


Common language 0.0174*** 0.0244*** 0.0071*** 0.006**
(0.00319) (0.0032) (2.64e−03) (0.0027)


Common colonial past 0.008* 0.0036 0.0107** *0.0002
(0.00454) (0.0046) (0.0052) (0.0052)


ln distance 0.0301*** 0.0231*** 0.0643*** 0.0615***
(0.00179) (0.0018) (0.0021) (0.0021)


Exchange-rate
volatility


9.5e−05*** −0.00007*** −0.0001*** −8.3e−05***


(4.94e−06) (0.0000) (5.08e−06) (0.0000)
ln export cost 0.3918*** 0.3412*** 0.2721*** 0.2303***


(0.00516) (0.0047) (0.0059) (0.0053)
(continued next page)




20 Pathways to African Export Sustainability


Following the logic of the basics of survival explained in annex 1A, we
included a number of gravity-type country-pair covariates in the regres-
sion (common border, common language, common colonial past, dis-
tance) as well as country-level covariates (GDP per capita, landlockedness,
trade costs, exchange-rate volatility) and spell covariates (initial value,


Table 1.1 (continued)


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


Exporter regional
dummies
Europe and Central


Asia
0.2*** 0.2***


(0.0092) (0.0092)
Latin America & the


Caribbean
0.2098***


(0.0071)
0.208***


(0.0068)
Middle East & North


Africa
0.2623***


(0.008)
0.2926***


(0.0075)
South Asia −0.0428*** −0.0303***


(0.0067) (0.0067)


Sub-Saharan Africa 0.2*** 0.2217***
(0.0077) (0.0076)


Importer regional
dummies
Europe and Central


Asia
−0.0201*** −0.0281***


(0.0063) (0.0062)
Latin America & the


Caribbean
−0.0399***
(0.0051)


−0.0364***
(0.0051)


Middle East & North
Africa


0.1292***
(0.0055)


0.12***
(0.0053)


North America −0.0937*** −0.14***
(0.0053) (0.006)


South Asia −0.1552*** −0.0972***
(0.006) (0.0071)


Sub-Saharan Africa −0.0825*** −0.0414***
(0.0049) (0.0057)


Western Europe 0.0501*** 0.0134**
(0.0054) (0.0055)


Observations 2,457,227 2,457,227 2,450,944 2,450,944
Exporting region FE no no yes yes
Importing region FE no no yes yes


Source: Authors’ estimations.
Note: Sub-Saharan African values are highlighted with a gray band. Robust standard errors in parentheses.
FE = fixed effects.
*** p < 0.01, ** p < 0.05, * p < 0.1.




Export Survival 21


value growth, multiple spells) in columns 1 and 2. Then we added
exporter-region and importer-region dummies in columns 3 and 4, as well
as initial-year time effects (not reported).


The initial spell value correlates negatively with the hazard rate, imply-
ing that larger spells tend to survive longer. This is a common finding in
the export-survival literature. The positive correlation of hazard rates
with spell growth is unintuitive, and it is estimated fairly imprecisely,
being significant at the 10 percent level only in some specifications. The
positive effect of multiple spells can be interpreted as reflecting the fact
that multiple spells and short duration are both reflections of a stop-go
pattern in bilateral trade flows.


Results on gravity variables are in line with the predictions of the sim-
ple model in annex 1A. Exporter income levels have a non- monotone
effect on hazard rates (as importer income levels do), although the shape is,
after controlling for other covariates, different from that shown in figure
1.8. Landlockedness raises hazard rates on both the exporter and importer
sides, possibly reflecting both higher costs and more frequent disruption
of land routes. A common border reduces hazard rates, as it is likely to
reflect lower variable costs; by contrast, a common language correlates
with higher hazard rates, as it is likely to reflect lower sunk costs of entry
and exit. Colonial past is largely insignificant, whereas distance and export
costs correlate positively with hazard rates. Exchange-rate volatility cor-
relates positively with hazard rates when exporter size and importer size
are controlled for separately, reflecting the “good-news principle” high-
lighted in annex 1A.


The second part of the table shows that, interestingly, regional effects
remain significant, even after controlling for spell-specific, bilateral, and
country-level covariates. The coefficients on regional dummies reflect
differential hazard rates compared with those of East Asia and the
Pacific, the omitted region.


On the importer side (bottom panel), the Western Europe effect cor-
relates positively with hazard rates, possibly reflecting exporter uncer-
tainty with respect to compliance with standards and other technical
issues. We will return to this issue in chapter 3. All other regions have
negative specific effects, suggesting that survival in East Asian markets is
among the toughest to achieve.


On the exporter side, all except South Asia have higher hazard rates
than those of the omitted category, which is East Asia. The strongest
regional effect is for the Middle East and North Africa region, followed
by Sub-Saharan Africa and Latin America and the Caribbean. Thus, the
intuition of figure 1.8 is largely confirmed in the sense that, even though




22 Pathways to African Export Sustainability


regional specificities do exist, Sub-Saharan Africa is no outlier.
Sub-Saharan Africa suffers from a low-survival syndrome that is not
entirely explained by the usual covariates, and that does not set it apart
from other regions of the world either; in other words, lessons from other
continents on both barriers and policy options to overcome them may
well be relevant to Sub-Saharan Africa as well.


Understanding Entry, Exit, and Survival Decisions


In this section, we move beyond the trade-flow analysis of export survival
and discuss the considerations surrounding survival decisions from a
firm’s perspective. Entry, exit, and survival decisions are logically related
through the interplay of expected returns to the export business, fixed
costs, sunk costs, and uncertainty.


Hysteresis and Sunk Costs
The expected return from exporting is derived from additional sales
revenue, net of variable and fixed costs; possibly, also, from the indirect
effect on other costs—through economies of scale, learning, and so on.
It is affected positively by the price of export sales, and negatively by
variable and fixed costs of production and distribution. Sunk costs arise
when selling abroad involves setting up distribution networks or
investing in initial advertising campaigns whose costs cannot be recov-
ered. Uncertainty arises from a number of factors, including fluctua-
tions in the exchange rate, sudden changes in border taxes and
non-tariff barriers, or unanticipated changes in transport and marketing
costs.


If there were no sunk costs of entry or exit, firms would enter export
markets as soon as returns, net of fixed costs, were positive, and exit as
soon as they turned negative, producing rapid churning and almost zero
survival. Yet this is not exactly what we observe. At the macro level,
import flows display what physicists call hysteresis—a phenomenon by
which temporary shocks have permanent effects. For instance, the tem-
porary dollar overvaluation of the 1980s led to permanent changes in U.S.
market structure, as foreign firms established “beachheads” at a time
when sales in the United States were hugely profitable, and stayed instead
of packing up and going when the dollar fell back down to its long-run
value (Baldwin 1988; Baldwin and Krugman 1989; Krugman 1986). At
the micro level, we do observe rapid churning, but survival rates vary and
are certainly not zero.




Export Survival 23


To better grasp the issues, annex 1A lays out a simple setup combining
uncertainty with fixed and sunk costs. Consider the following situation.
A firm is faced with, say, exchange-rate uncertainty in a foreign market
where it exports. When the exchange rate is high, it makes money. When
it falls, it loses money. If entry and exit were costless, the firm would exit
as soon as the exchange rate fell below the breakeven point. But in the
presence of sunk costs of reentry, “toughing it out” in bad times has an
option value. Annex 1A shows that this option value is (1) increasing
with sunk costs of reentry and (2) decreasing in export operating costs.
Thus, given an initial situation where the firm is “in,” there is a minimum
value of the exchange rate at which it decides to exit—that is, to exercise
the option. The lower this value, the larger the range of exchange-rate
fluctuations within which the firm stays in the export market, which, in
turn, implies a longer survival of export spells.


The reasoning is illustrated in figure 1.10. Consider first the left-hand
side of the diagram. The sunk cost of reentry S is measured on the left-
hand side of the horizontal axis, increasing to the left, and the cutoff value
of the exchange rate below which the firm decides to exit export markets,
emin, is measured vertically. The oblique line is the relationship between
S and emin derived in annex 1A: a high value of S maps into a low value of
emin because high sunk costs make exit followed by reentry a costly
option, making the firm more tolerant of losses.


emin


S e


Shigh S low e
min (Shigh)


range of e where firm
exits with high S


range of e where firm exits with low S


distribution of e


45 o line


emin (Slow)


Figure 1.10 Sunk Costs and the Frequency of Exits


Source: Authors.




24 Pathways to African Export Sustainability


Consider now the right-hand side of the diagram. The exchange rate,
e, is measured on the horizontal axis, increasing to the right. The bell-
shaped curve is the distribution of the exchange rate, a random variable.
Through the 45o line, a low value of emin generates the small, dark range
of exchange-rate draws for which the firm decides to exit the export
market. A high value of emin generates the larger, pale gray range of
exchange-rate draws inducing exit.


Combining the two parts of the diagram, the high sunk cost Shigh maps
into a low emin, which in turn generates a small exit range, whereas the
low sunk cost Slow maps into a high value of emin that generates a large exit
range. Thus, the frequency of exit is higher with Slow than with Shigh,
which implies lower average survival with Slow than with Shigh.


Working the relationship highlighted in figure 1.10 backward, the
evidence of generally low survival reviewed earlier suggests that sunk
costs of entry and exit must be low. Why should we worry about low
survival, then? High turnover would be optimal in the presence of small
sunk costs.


Existing empirical evidence on hysteresis at the firm level suggests that
sunk costs of entry are indeed present, but that they may not be over-
whelming. In a seminal paper that shifted the hysteresis literature’s focus
from aggregate flows to firms, Roberts and Tybout (1997) showed how
expressing a plant’s current export status as a function of the previous
year’s status made it possible to uncover evidence of sunk costs of entry.
For instance, they found that a plant having exported in the previous year
was up to 60 percent more likely to be currently exporting than one
without previous-year export experience. However, they found the expe-
rience effect to be short-lived, vanishing after two years without export-
ing. They also found substantial turnover, with an average year-on-year
exit rate of 11 percent. Similarly, Bernard and Jensen (2004) found, for a
sample of U.S. manufacturing plants, that previous-year experience raised
the probability of exporting by 39 percent, although they also found
substantial turnover (an average annual exit rate of 12.6 percent).


The evidence from the two existing bodies of literature—sunk costs
and survival—is broadly consistent and suggestive of nonnegligible but
moderate sunk costs. This, in turn, is consistent with the recent evidence
on export entrepreneurship.


Experimentation and Failure
Returning to our earlier analogy to population dynamics, in the presence
of high infant mortality rates, overall mortality increases with birth rates




Export Survival 25


because more births mean more infants who tend to die young. Do we
observe high “birth rates” in terms of exports in low-income countries?


A number of papers—including, among others, Brenton, Pierola, and
von Uexkull (2009) and Cadot, Carrère, and Strauss-Kahn (2011) (see
also the survey in Brenton et al. 2009)—showed that export entrepre-
neurship at the “extensive margin,” measured by the introduction of new
products and new destinations, was very active in low-income countries.
This is shown by the hump in figure 1.11, which occurs at less than
US$10,000 PPP.


One would expect active export entrepreneurship to fuel overall
export growth. However, Amurgo-Pacheco and Pierola (2007), Besedes
and Prusa (2011), Brenton and Newfarmer (2007), and Evenett and
Keller (2002) all found that the extensive margin’s contribution to overall
export growth was limited—although, interestingly, less so in Sub-
Saharan Africa, where it accounts for over a third of export growth
(figure 1.12). How can we explain this relatively small contribution, and
does it relate to survival?


The smallness of the extensive margin’s contribution—in spite of its
strong activity—is due, in part, to the fact that many of the new products
and destinations introduced in a given year will fail as early as the


0


50


100


150


200


nu
m


b
er


o
f n


ew
e


xp
or


te
d


p
ro


d
uc


ts


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


GDP per capita PPP (constant 2005 international $)


new - 1period new - 2periods
new - Klinger Lederman


Figure 1.11 Number of New Export Lines (HS 6) against Income Levels


Source: Cadot, Carrère, and Strauss-Kahn 2011.




26 Pathways to African Export Sustainability


next. First-year survival rates are particularly low for low-income coun-
tries (39 percent, on average, as reported by Brenton, Saborowski, and
von Uexkull 2010) and for Sub-Saharan Africa (33 percent). For Burkina
Faso, the first-year survival rate is as low as 27 percent. Clearly, a lot of
the experimentation fails.


The high infant mortality of African exports provides strong evidence of
Darwinism—ensuring the survival of the fittest. It is also the reflection of
a very tough environment, in which many export experiments fail for good
or bad reasons. These reasons need to be understood in order to reduce the
incidence of accidental deaths of viable export spells. This report will
attempt to provide evidence in support of a number of conjectures, but
before we turn to a systematic exploration of the evidence, we can use the
simple setup of annex 1A in a first effort to understand the issue.


Export relationships can be terminated by either the exporter or
the importer side, and importer decisions are likely to be driven by


0


20


40


60


sh
ar


e
in


e
xp


o
rt


g
ro


w
th


(p
er


ce
n


t)


80


100


120


Asia ECA+MENA LAC SSA


extensive, destinations extensive, products


intensive


Figure 1.12 Export Growth Decomposed, 1990–2005


Source: Amurgo-Pacheco and Pierola 2007.
Note: ECA = Europe and Central Asia; MENA = the Middle East and North Africa; LAC = Latin America and the
Caribbean; and SSA = Sub-Saharan Africa.




Export Survival 27


considerations distinct from those of exporters. Importers may,
like exporters, have sunk costs in the relationship with individual
suppliers, making switching to new suppliers costly. This will typi-
cally be the case in markets where search costs are high because of
the presence of heterogeneity in quality and weak signaling (for
example, through lack of enforceable warrantees) on the supplier
side. Alternatively, importers may be able to switch easily from one
supplier to another in deep, liquid markets with little product dif-
ferentiation. This will typically be the case in markets, such as low-
end garments, where search costs are low.


Annex 1A shows that a high probability of the termination of relation-
ships by buyers has two effects on export survival. First, frequent termi-
nations mechanically reduce the life expectancy of export spells. Second,
and more interestingly, the expectation of frequent termination inhibits
the willingness of exporters to tough it out in bad times, in the same way
that it inhibits their willingness to invest in the relationship. This further
reduces the life expectancy of export spells. By contrast, uncertainty in
the sense of a high volatility of profits on the export market raises
exporter willingness to stay in bad times because of an asymmetry in the
effects of upside and downside risk, known in the real-options literature
as the good-news principle. This principle says that only the upside poten-
tial matters for the willingness to keep a business line open, so more
uncertainty makes the business line more persistent (see annex 1A for
more details).


As a last conceptual observation, changes in the environment in which
exporting firms operate can have complex effects on survival because
they may trigger two types of reactions working at cross-purposes:
(1) changes in the incentives faced by exporting firms and (2) changes in
the composition of the population of exporting firms. To see this, suppose
that the business environment becomes, in some sense, gentler—that is,
more conducive to both entry and survival. In this case, on one hand each
firm has a higher survival probability; on the other hand, exporting mar-
kets will attract inexperienced or low-productivity entrants that are more
likely to fail. One might imagine a situation where the second effect
dominates the first, so that an improved business environment reduces
average survival rates.8


Preliminary evidence for this conjecture can be gathered by plotting
average first-year survival rates of entering exporters (the probability of
surviving past the first year of exports conditional on entry) against their
average entry rates (the ratio of new entrants to the stock of exporters).




28 Pathways to African Export Sustainability


Controlling for other determinants of survival (reflecting business envi-
ronment), the relationship is shown in figure 1.13.


On the sample shown in figure 1.13, there is indeed a negative correla-
tion between entry rates and survival rates, suggesting a strong selection
effect. It is possible that both variables react in the same way to changes
in omitted variables; for instance, Araujo and Ornelas (2007) show in a
theoretical model that improved contract enforcement raises both the
entry and the persistence of existing trade relationships. However, the
scatter plot of the figure is drawn from a regression where other covari-
ates are included, limiting the scope—although not eliminating it—for
omitted-variable bias.


0.0


0.2


0.4


0.6


0.8


fi
rs


t-
ye


ar
e


n
tr


an
ts


’ s
u


rv
iv


al
r


at
e


1.0


0.0 0.2 0.4 0.6 0.8 1.0
entry rate


African observations fitted valuesnon-African observations


Figure 1.13 Entry and First-Year Entrants’ Survival Rates at the Firm Level


Source: Exporter Dynamics Database, Trade and International Integration Unit, Research Department,
World Bank (DECTI). Available at: http://econ.worldbank.org/exporter-dynamics-database.
Note: Data points in this graph reflect average entry and survival rates for the period 2004–09 for each available
HS 2-digit–country combination. The graph uses information from the Exporter Dynamics Database built
by the Trade and International Integration Unit of the Research Department of the World Bank (DECTI). The entry
and first-year survival rates used for this graph are for almost 30 developing countries in different regions around
the world. The variable on the vertical axis is the first-year survival rate purged of the influence of other covariates
(variables within the Trading Across Borders topic of Doing Business and variables other than the entry rate
[horizontal axis]) using predicted coefficients from a linear probability model. Triangles indicate African
observations.




Export Survival 29


This discussion can be summarized as follows:


• Sunk costs of entry and exit into export markets, such as search costs,
raise export survival; the empirical evidence on the extent of such costs
is so far limited and ambiguous.


• Fixed and variable costs of exporting (paperwork, representations
abroad, and the array of trade-cost factors typically included in gravity
equations) reduce export survival; the empirical evidence on the extent
of such costs is substantial.


• Different types of uncertainty have sharply different effects on export
survival:


- a high volatility of prices and earnings on export markets raises sur-
vival, and


- a high probability of buyer-determined termination reduces it.
• Survival rates may increase or decrease with entry rates when firms are


heterogeneous in some unobserved ability to survive; thus, changes in
the business environment (which also have an effect on average entry
rates) have an ambiguous effect on average survival.


We will build on these very preliminary considerations in chapter 2 to
explore systematically the drivers of export survival.


Annex 1A: The Basic Analytics of Survival


This annex uses a simple model of uncertainty with sunk cost to illus-
trate the determinants of a firm’s decision to stay in the export busi-
ness or to exit in the face of temporarily negative returns. The model
is based on the real-options approach to decisions under uncertainty,
laid out in Dixit (1989) and Dixit and Pyndyck (1994). A rigorous,
infinite-horizon model can be found in Roberts and Tybout (1997); see
also Clerides, Lach, and Tybout (1998) for a model with entry costs
and learning.


Let p be the price of a widget the firm is exporting on a foreign mar-
ket; y(p,c), its profit-maximizing supply of widgets; F and c, the fixed and
variable costs of production, respectively; and e, the exchange rate of
the country in which the firm is selling. The exchange rate is expressed
as the price of the foreign currency in home currency, so it goes up when
the foreign currency appreciates.




30 Pathways to African Export Sustainability


Assume two periods, t = 0 and t = 1, with a discount factor d = 1/
(1 + r). At t = 0, the exchange rate is e0. At t = 1, it is a random variable
e~ with the following distribution:



Pr( )


,


.
%e e


q e e
q e e


= =


=


− =


⎧⎨⎪⎩⎪
+




if


if1 (1)


At t = 0, the firm’s profit function is


p0 ≡ p (e0) = (e0 p – c) y (p,c) – F (2)


and its expected profit at t = 1 is


E[p (e~)] = q[(e+ p – c) y (p,c) – F ] + (1 – q)[(e– p – c) y (p,c) – F ]
= (e–p – c) y (p,c) – F, (3)


where e– = qe+ + (1 – q)e–. Assume that


A1. p0 < 0


A2. p0 + dp
– < 0 (4)


A3. p0 + dqp
+ > 0.


The first two inequalities imply that the firm is losing money at
t = 0 given the exchange rate, and expected profit given the distribu-
tion of the exchange rate at t = 1 is also negative. Should the firm stay
in that export market? Given A1 and A2, a simple Net Present Value
(NPV) calculation suggests no. However, this answer, based on a simple
NPV calculation, can be misleading in the presence of irreversibility or
sunk costs or entry.


Consider first irreversibility. The firm has two options: (1) exiting now,
with no possibility of reentry, or (2) waiting one period and then decid-
ing, upon resolution of the exchange-rate uncertainty, to stay or to go.
Under option 1, expected profit is


pout = 0, (5)


whereas under option 2, the firm incurs a loss p0 < 0 in the current period
but keeps the option of staying in the market at t = 1 upon good news,
which will happen with probability q. If the news is bad, then it will exit.
Thus, the value of staying is


pstay = p0 + dqp
+, (6)




Export Survival 31


which is positive by A3. The difference between (6) and (5) is the value
of the option to stay, which is here strictly positive. The firm should
therefore stay in the face of current losses and even a negative expected
payoff.


The stripped-down setup of equations (1) through (4) illustrates
something known as the good-news principle—namely, a decision to quit
a business line is linked only to its upside potential, not to its downside
potential. To see this, observe that neither (5) nor (6) depends on p –, the
value of profits under a low realization of the exchange rate. The reason
is that in that “state of nature,” the firm will quit; so the decision depends
only on p +, the value of profits under a high realization of the exchange
rate, in which the firm will stay. This principle has an important implica-
tion: more uncertainty, in the form of a mean-preserving spread in the
distribution of profits on the export markets, increases hysteresis and
therefore survival. This is highly counterintuitive: one would expect
volatility of the destination-market environment to reduce survival, not
to increase it. How can we make sense of this surprising implication of
real-options theory? With more uncertainty, it is as if our exporting firm
held an option on a more volatile stock—the upside potential, which is
all that matters with a call option, is then higher, which makes the option
more valuable.


Consider now another type of uncertainty—namely, the possibility
that the buyer may terminate the relationship for reasons that are
unknown to the exporter. Let Q be the probability that this happens at
t = 1. Then (6) becomes


pstay = p0 + dq(1 – Q)p
+, (7)


which is decreasing in Q. If Q is sufficiently high, (7) becomes negative
(under A1, it must turn negative when Q approaches 1), in which case
the firm will never decide to stay in the export market, irrespective of
q and p +. This type of uncertainty, which is related to the buyer’s behav-
ior, has a completely different effect on hysteresis than a wider dispersion
of second-period profits has. It depresses the firm’s incentive to stay in
the export market. The reason for this debilitating effect is that uncer-
tainty in the form of a probability of an exogenous termination entails
only downside risk and no upside risk, which means that the good-news
principle does not apply.


This second result about the effect of uncertainty has very important
implications for export survival. Erratic buyer practices—abrupt decisions
to switch from one supplier to another in order to arbitrage very small




32 Pathways to African Export Sustainability


price differences, which are common in sectors, such as garments, where
price competition is intense—have a double effect on export survival,
both direct and indirect. The direct effect is that each buyer-determined
interruption reduces the life expectancy of export spells. The indirect
effect, illustrated here, is that this lower life expectancy makes exporters
less willing to endure hard times, because it cuts into the upside potential
of the real option.


Suppose now that there is a sunk cost of reentry S. Expression (5) now
becomes


pout = dq(p
+ – S), (8)


so the value of the option to stay is worth only


Δp = pstay – pout = p0 – dqS. (9)


We can now derive the lowest value of p0 at which the firm is willing
to stay on the market in the face of losses. The lower it is, the longer the
export survival, our magnitude of interest. This value is such that


p0 – dqS = 0


or, substituting for p0 and rearranging,


(e0 p – c) y (p,c) – F = –dqS, (10)


which gives



e


p
c


F qS
y p c0


min


( , )
.= +


−⎡
⎣⎢



⎦⎥


1 δ


(11)


It is easily verifiable that e0
min is decreasing in the sunk cost, as







=


−d
<


e
S


q
py p c


0 0
min


( , )
.
(12)


That is, higher sunk costs of reentry encourage the firm to tough it out
in the face of losses, because it makes exit and possible reentry tomorrow
a costlier option. By contrast,







= >
e
F py p c
0 1 0
min


( , )
.
(13)


That is, higher fixed costs make staying a more expensive option—
recall that the options we are considering are staying and exiting, not
shutting down while still incurring fixed costs, which is an entirely differ-
ent decision. Thus, higher fixed costs reduce the patience of the firm with




Export Survival 33


low realizations of the exchange rate (the unique source of uncertainty in
our model). Similarly, it is easily verified that







= −





>
e


c p
qS


p y
y
c


0
2


1
0


min


.
δ


(14)


This tension between fixed (or variable) and sunk costs is at the heart
of the analysis of hysteresis (see Baldwin and Krugman 1989 for further
elaboration). The negative effect of fixed costs on the willingness to stay
in export markets during bad times justifies the use of gravity-type vari-
ables as determinants of average export-spell duration. The positive effect
of sunk costs is explored through indirect proxies in chapter 2.


Annex 1B: The Basic Toolkit of Empirical Survival Analysis


This annex introduces the basic techniques of applied survival analysis. A
good introduction is provided by Volpe and Carballo (2009). For a com-
plete, hands-on introduction to survival analysis, see Cleves et al. (2010).
Two key characteristics of survival data sets must be kept in mind to
understand the analysis’ specificities.


First, time is defined as “analytical time” rather than calendar or clock
time. Analytical time is individual-specific and is set at zero when an
individual starts being “at risk.” For instance, the zero of analytical time
may be when an individual enters a treatment, a light bulb is put under
a test, or an export spell begins.


Second, observations are “spells” from the zero of analytical time to
the individual’s death (for example, the failure of the light bulb or the
termination of the export relationship). Outside of laboratory experi-
ments, because the sample period is not infinite, some spells will be “left-
censored” (that is, they will already be active when the sample period
starts) or “right-censored” (that is, they will not be completed when the
sample period ends). Left-censored spells are typically dropped out of
the sample, whereas right-censored ones are treated explicitly by the
econometric procedures of survival analysis. When dealing with trade
data, many countries fail to report trade flows in some years, creating
right- and left-censoring in the middle of the sample period that must be
treated carefully. This can be checked using the data-availability file in
the “support-material” menu of the World Integrated Trade Solution por-
tal. Holes in the data can be partially filled by using mirroring (which
should be done systematically for the entire data set).




34 Pathways to African Export Sustainability


Kaplan-Meier Survival Functions


The Kaplan-Meier nonparametric estimator, sometimes called the
product-limit estimator, approximates the survivor function defined as
follows. Let T be the duration of a given export spell (a random vari-
able) and t be a particular, arbitrary value of T. The survivor function is
the probability that failure takes place at or after t—that is, that survival
is at least T:


S(t) = Pr(T ≥ t) = 1 – F(t), (1)


where F(t) is the cumulative distribution function of the failure time.
Consider now several spells, and let i be an index of time going from


the beginning of spells to their death. That is, suppose we observe a
sample of N spells whose duration varies between one and ten years.
Then i = 1, . . . ,10. Let ki be the number of spells that die exactly at
i years, and ni be the number of spells that are still alive after i years.
The Kaplan-Meier estimate of S(t) is



Ŝ t


k
n


i


ii


t


( ) = −⎛⎝⎜

⎠⎟


=


∏ 1
1


.
(2)


The ratio in the parentheses is the ratio of spells dying to spells at risk;
thus, it is the discrete-time equivalent of a hazard rate. The Kaplan-Meier
estimate of the survivor function in the first year is the proportion of
spells that do not die in the first year. Its estimate in the second year is
the product of that by the proportion of spells that do not die in the
second year (among those still alive); and so on. Obviously, since each
term is less than one, it is a decreasing function (see figures 1.6 and 1.7
for examples).


Differences in survival pattern between two groups can be tested
using, for instance, a log-rank test, which is a comparison of distributions
adapted to a setting where some observations are censored (see above).
The test statistic follows a chi-square with two degrees of freedom for a
pairwise comparison.


Cox Regressions


Cox regressions explain hazard rates (death/termination probabilities) in
terms of individual covariates, under particular assumptions. The hazard




Export Survival 35


rate of a distribution is the probability that an event (here, death) occurs
in the next instant, given that it has not happened yet. Formally,




h t T t t T t


f t


F t


f t


S t


t( ) = ≤ + ≥( )
=


( )
− ( ) =


( )
( )


→lim Pr


.


Δ Δ0


1 (3)


In the discrete-time framework that is relevant for empirical analysis,


h(t) = Pr (T = t⏐T ≥ t), t = 1,2, . . .
The fundamental assumption of Cox regression, called proportional


hazards, is that individual hazard rates can be decomposed into two mul-
tiplicative components:


° a component that varies with analytical time but is common to all
individuals, h0(t); and


° a component that varies across individuals, as an (exponential) function
of a vector of covariates, but not over time, exp (xa).


That is,


h(t) = h0(t)e
xa. (4)


A Cox regression is estimated by maximum likelihood. Parameter
estimates for the b ’s give partial correlations between each of the covari-
ates and the hazard rate; thus, a positive coefficient indicates that a par-
ticular covariate raises the probability of termination and therefore
contributes negatively to survival. Coefficients can also be reported as
hazard ratios—that is, exponentiated coefficients; an exponentiated coef-
ficient above one indicates that a covariate raises the hazard rate. To see
the logic of this, consider a “dummy” covariate that can be either zero or
one and call it I :


E(h⏐I = 1) = h0(t)eb, (5)
whereas


E(h⏐I = 0) = h0(t)e0 = h0(t). (6)
Thus, the ratio of the hazard rates when I = 1 vs. when I = 0 is h0(t)e



h0(t) = e


b. If eb > 1, then b > 0. The Cox functional form, in which hazard
rates vary across individuals independently of time (the proportional-
hazards assumption), can be verified by a Schönfeld test.




36 Pathways to African Export Sustainability


Notes


1. Throughout, we use the SITC nomenclature rather than the newer and more
detailed Harmonized System (HS). This is because the SITC has undergone
fewer revisions over time and offers longer time series, which is preferable for
survival analysis. Our sample period will typically be 1979–2010.


2. The trade data tell us when firms commence, continue, and stop exporting a
particular product to a specific market. They do not tell us how many firms
enter or when particular firms exit.


3. Typically, customs monitors imports better than exports because tariffs are
levied on the former. Accordingly, the survival of export flows is measured
using import-side trade statistics rather than export-side ones, a technique
known as mirroring. For instance, we would measure the survival of our
Pakistan-Japan trade flow using Japan’s import statistics rather than Pakistan’s
export statistics. Still, inputting errors in automated customs recording sys-
tems such as Automated System for Customs Data/Système Douanier
Automatisé (ASYCUDA/SYDONIA) or others are frequent, in particular
when entries are made directly by customs brokers.


4. Their baseline results are obtained on more disaggregated data (22,782 obser-
vations over 1989–2001) but we will stick to SITC-4 data, which will be used
throughout this report because it is the finest level at which data are compa-
rable across countries.


5. Besedes and Prusa (2006b) applied the weighting scheme on SITC-5 data, for
which the unweighted average duration was 4.1 years.


6. High-income non-OECD countries include Croatia, Cyprus, Hong Kong
SAR, China, Kuwait, Saudi Arabia, Singapore, and the United Arab Emirates
(UAE). They are mostly small countries, many of which are platforms for
entrepôt trade (for example, the UAE or Singapore).


7. Mirroring consists of using import flows as reported by the importing country
instead of export flows, which are monitored less accurately since customs
authorities generally give greater attention to products entering their customs
space and eligible for domestic duties.


8. We are grateful to Caroline Freund for attracting our attention to this point.


References


Acharya, Rohini, J. A. Crawford, M. Mariszewska, and C. Renard. 2011. “Landscape.”
In Preferential Trade Agreement Policies for Development: A Handbook, ed. J.-P.
Chauffour and J.-C. Maur, 37–67. Washington, DC: World Bank.


Amurgo-Pacheco, Alberto, and M. D. Pierola. 2007. “Patterns of Export
Diversification in Developing Countries: Intensive and Extensive Margins.”
Policy Research Working Paper 4473, World Bank, Washington, DC.




Export Survival 37


Araujo, Luis, and E. Ornelas. 2007. “Trust-Based Trade.” CEP Discussion Paper
0820. Centre for Economic Performance, London.


Baldwin, Richard. 1988. “Hysteresis in Import Prices: The Beachhead Effect.”
American Economic Review 78: 773–85.


Baldwin, Richard, and P. Krugman. 1989. “Persistent Effects of Large Exchange-
Rate Shocks.” Quarterly Journal of Economics 104: 635–54.


Bernard, Andrew B., and J. B. Jensen. 2004. “Why Some Firms Export.” Review of
Economics and Statistics 86: 561–69.


Besedes, Tibor, and J. Blyde. 2010. “What Drives Export Survival? An Analysis of
Export Duration in Latin America.” Draft, Inter-American Development
Bank.


Besedes, Tibor, and T. Prusa. 2006a. “Product Differentiation and Duration of U.S.
Import Trade.” Journal of International Economics 104: 635–54.


———. 2006b. “Ins, Outs, and the Duration of Trade.” Canadian Journal of
Economics 39: 266–95.


———. 2011. “The Role of the Extensive and Intensive Margins and Export
Growth.” Journal of Development Economics 96 (2): 371–79.


Brenton, Paul, and R. Newfarmer. 2007. “Watching More than the Discovery
Channel: Export Cycles and Diversification in Development.” Policy Research
Working Paper 4302, World Bank, Washington, DC.


Brenton, Paul, R. Newfarmer, W. Shaw, and P. Walkenhorst. 2009. “Breaking into
New Markets: Overview.” In Breaking into New Markets: Emerging Lessons for
Export Diversification, ed. R. Newfarmer, W. Shaw, and P. Walkenhorst, 1–38.
Washington, DC: World Bank.


Brenton, Paul, M. D. Pierola, and J. E. von Uexkull. 2009. “The Life and Death of
Trade Flows: Understanding the Survival Rates of Developing-Country
Exporters.” In Breaking into New Markets: Emerging Lessons for Export
Diversification, ed. R. Newfarmer, W. Shaw, and P. Walkenhorst, 127–44.
Washington, DC: World Bank.


Brenton, Paul, C. Saborowski, and J. E. von Uexkull. 2010. “What Explains the
Low Survival Rate of Developing Country Export Flows?” World Bank
Economic Review 24 (3): 474–99.


Cadot, Olivier, C. Carrère, and V. Strauss-Kahn. 2011. “Export Diversification:
What’s Behind the Hump?” Review of Economics & Statistics 93: 590–605.


Carrère, Céline, and V. Strauss-Kahn. 2011. “Exports that Last: When Experience
Matters.” Draft, University of Geneva.


Clerides, Sofronis, S. Lach, and J. Tybout. 1998. “Is Learning by Exporting Import?
Micro-Dynamic Evidence from Colombia.” Quarterly Journal of Economics
113: 903–47.




38 Pathways to African Export Sustainability


Cleves, Mario, W. Gould, R. Gutierrez, and Y. Marchenko. 2010. An Introduction
to Survival Analysis Using Stata, 3rd ed. College Station, TX: Stata Press.


Dixit, Avinash. 1989. “Entry and Exit Decisions under Uncertainty.” Journal of
Political Economy 97 (2): 620–38.


Dixit, Avinash, and R. Pindyck. 1994. Investment under Uncertainty. Princeton, NJ:
Princeton University Press.


Easterly, William, and A. Resheff. 2010. “African Export Successes: Surprises,
Stylized Facts, and Explanations.” NBER Working Paper 16597, National
Bureau of Economic Research, Cambridge, MA.


Evenett, Simon J., and W. Keller. 2002. “On Theories Explaining the Success of
the Gravity Equation.” Journal of Political Economy 110 (2): 281–316.


Evenett, Simon, and A. Venables. 2002. “Export Growth by Developing
Economies: Market Entry and Bilateral Trade.” Draft, University of St.
Gallen.


Fugazza, Marco, and A. C. Molina. 2011. “On the Determinants of Exports
Survival.” Policy Issues in International Trade and Commodities Study Series
No. 46. UNCTAD, New York and Geneva.


Krugman, Paul. 1986. “Pricing to Market When the Exchange Rate Changes.”
NBER Working Paper 1926, National Bureau of Economic Research,
Cambridge, MA.


Nitsch, Volker. 2009. “Die Another Day: Duration in German Import Trade.”
Weltwirtschaftliches Archiv 145: 133–54.


Roberts, Mark, and J. Tybout. 1997. “The Decision to Export in Colombia: An
Empirical Model of Entry with Sunk Costs.” American Economic Review 87:
545–64.


Volpe, Christian, and J. Carballo. 2009. “Survival of New Exporters in Developing
Countries: Does It Matter How They Diversify?” Inter-American
Development Bank Working Paper IDB-WP-140, IADB, Washington, DC.


World Bank. World Development Indicators. Available at: http://data.worldbank
.org/data-catalog/world-development-indicators.




39


C H A P T E R 2


Countries, Institutions, and Policies


In this chapter, we turn to some of the most policy-relevant determinants
of export survival at the country level—including supply-side factors such
as comparative advantage and the business environment as well as
demand-side ones—with a focus on standards and technical regulations,
and on the way they are enforced in industrial countries.


In traditional Heckscher-Ohlin trade theory, comparative advantage
conditions the viability of exports and therefore their sustainability.
Whereas the relationship of comparative advantage to the direction and
magnitude of trade flows has been subjected to many tests, starting with
the celebrated Leontieff paradox, its relationship to export sustainabil-
ity has not. Yet this latter relationship is important for policy design.
Many recent papers (see, for instance, Dutt, Mihov, and van Zandt 2008
and Hausmann, Hwang, and Rodrik 2007) suggest that the composition
of a country’s export basket matters for its future growth. These find-
ings may be interpreted as meaning that active export-promotion poli-
cies should be used to upgrade the positioning of a country’s export
portfolio. However, we find that products lying far from a country’s
comparative advantage stand less chance of surviving on world markets.
Thus, policies promoting those products may produce false starts and
prove costly.




40 Pathways to African Export Sustainability


Beyond traditional factors of production—labor, physical capital,
human capital, and natural resources—export performance is largely
conditioned by the business environment in the exporting country. We
show, on the basis of a novel data set, that this broad observation carries
over to export survival, which correlates with various measures of trade
costs and the business environment. In particular, we find that Sub-
Saharan countries in the sample distinguish themselves not so much by
their poor survival performance, but by their poor ratings in terms of the
business environment, suggesting that this environment is a key con-
straint to export survival. The results of the statistical analysis align well
with results of a survey of African exporters conducted by the World
Bank as background to this report.


Finally, we highlight the potential role of standards in destination
countries and their enforcement in the context of agricultural exports to
Western countries. We argue through anecdotal evidence that enforce-
ment may be discretionary and may lead to an abrupt interruption of
supply chains, potentially creating the debilitating kind of uncertainty
that was shown in annex 1A of chapter 1 to discourage exporters. We
also suggest a perverse mechanism whereby intermediaries ensure their
own survival essentially by shifting all the risk upstream to farmers.


In the first section of this chapter, we look for new evidence about the
relationship between comparative advantage and the sustainability of
exports. In the second section, we explore ways that the business environ-
ment in the origin and destination markets shapes the ability of firms to
survive in export markets. And in the third section, we consider the effect
that technical regulations and the enforcement of sanitary and phyto-
sanitary standards has on destination markets.


Comparative Advantage


Export products located closer to a country’s comparative advantage tend
to survive, on average, better than those located farther away. Comparative
advantage is the adequacy of a country’s endowment of factors of produc-
tion (labor, capital, human capital, and natural resources) to what is needed
for the production of a particular export good. For instance, if a good is
capital-intensive (e.g., the making of silicon wafers for microchips), a coun-
try has a comparative advantage in the production and export of that good
if the country is capital-abundant. This relationship between factor inten-
sity and factor abundance is, in essence, an expression of the Heckscher-
Ohlin theorem. Comparative advantage translates into competitive advantage




Countries, Institutions, and Policies 41


for firms exporting the good in question, provided that three basic condi-
tions are fulfilled. First, macroeconomic fundamentals—in particular the
exchange rate—must be “right”—that is, the exchange rate must not be
overvalued. If the exchange rate is overvalued, even the production of sili-
con wafers in a capital-abundant country can fail to generate profits.
Second, the basic infrastructure (e.g., roads and telecommunications) must
be adequate. Producing shirts in a labor-abundant country can fail to gener-
ate profits if ports are dysfunctional or roadblocks are prevalent. Third, the
investment climate (ease of doing business) must also be adequate.
Meeting these conditions—comparative advantage, sound macroeconomic
fundamentals, adequate infrastructure, and favorable investment climate—
ensures, in a very broad sense, the sustainability of exports.


In a perfect world, firms would not enter export markets when the
country where they are located has no comparative advantage, so one
would expect comparative advantage to affect the extensive margin of
trade—the probability of observing export flows—but not necessarily
their survival. However, in reality, as we saw earlier in this report, there is
a lot of experimentation. Firms try, fail, and try again. In the presence of
intense experimentation, one might observe more success and therefore
longer survival for goods exported in accordance with the origin country’s
pattern of comparative advantage.


Verifying this conjecture has been difficult, since neither endowments
nor the factor intensities of goods were readily available in public data-
bases. Recently, however, the United Nations Conference on Trade and
Development (UNCTAD) has made public a new database that includes
both endowments and factor intensities.1 Endowments include the dollar
value of physical capital per worker calculated from investment data
using the perpetual inventory method, educational attainments (average
years of education per worker), the labor force, arable land per worker,
and the dollar value of subsoil (oil and mining) resources—although this
last component of endowments, calculated by the World Bank, is avail-
able only for two years, 1994 and 2000.


Factor intensities are calculated as follows. The logic is that of
Hausmann, Hwang, and Rodrik’s (2007) PRODY. That is, the revealed
capital intensity of product k is a weighted average of the capital endow-
ments of all the countries exporting product k, the weights being modified
versions of Balassa’s revealed-comparative advantage index. Formally,


κ̂ ω κκk ii
= ∑ i, (1)




42 Pathways to African Export Sustainability


where ki is country i’s capital/labor endowment and wik is the modified
Balassa index for country i and product k. The reason for not using simple
export shares is that these would give too much weight to large countries,
and the reason for modifying Balassa indexes is to make them add up to
a total of 1, so that endowments and intensities can be shown in the same
space (that is, in the same diagram). Human-capital intensities (that is, hi
for country i) are calculated in a way similar to (1).


We can now define comparative disadvantage (the opposite of
comparative advantage) as the Euclidian distance between a country’s
endowment point (ki, hi) and the intensity of product k( , )κk khˆ ˆ :



d h hik


e


i k i k
=


−( ) + −( )⎡⎣⎢ ⎤⎦⎥ˆ ˆ .2 2
1 2


κ κ (2)


Using that measure of product distance from the exporter’s endow-
ment point and our proxy for comparative disadvantage, figure 2.1
shows that average export spell survival decreases with comparative
disadvantage, although the effect is limited (the vertical axis measures


2


3


4


5


6


7


2.0 2.2 2.4 2.6 2.8 3.0
mean standardized distance from country endowment point


av
er


ag
e


ye
ar


s
o


f s
u


rv
iv


al


(mean) length fitted value


length of trade relationship and distance to comparative advantage


Figure 2.1 Average Spell Survival and Comparative Disadvantage


Source: Authors’ calculations based on UN Comtrade database.
Note: Each point corresponds to an International Standard Industrial Classification (ISIC) Rev.3 industry average
(117 manufacturing industries), aggregated over products and countries.




Countries, Institutions, and Policies 43


average years of survival, which means that going from the left-hand tail
of the distribution of distances to its right-hand tail reduces average sur-
vival only from five to four years).2 Stating the same assertion upside
down, the probability of survival rises with comparative advantage.3


This result is important for its policy implications. Export-promotion
agencies providing assistance to new exporters or to exporters wishing to
expand their portfolio of offerings often perform some sort of informal
screening of proposals based on the success and survival prospects of those
offerings. This screening is often based on the officers’ experience and the
past record of the applying firm, at least for programs that have been run-
ning long enough to have a history. The results in this section suggest that
some comparative-advantage criteria may help to formalize the screening
process in order to improve the survival of promoted exports.


The discussion of comparative advantage so far has been static. Yet
comparative advantage shifts over time as countries accumulate factors of
production. Another way in which comparative advantage can impinge
on survival is if countries travel relatively rapidly through “diversification
cones,” a process that makes the optimal export portfolio evolve rapidly
from a comparative-advantage point of view. This is the gist of an argu-
ment laid out in Schott (2003) and illustrated in figure 2.2.


In the upper panel of figure 2.2, the horizontal axis measures the
country’s capital endowment whereas the vertical axis measures its con-
stant labor endowment (the figure omits human capital for simplicity).
In the bottom panel, the horizontal axis measures per capita income
(which is assumed to be proportional to its capital endowment for a
fixed labor force), and the vertical axis measures sectoral shares in
exports. First, the country exploits its advantage in unskilled labor and
increases its exports of products such as textiles and clothing. As the
exporting country accumulates capital, it then leaves the most labor-
intensive diversification cone and its attendant textile and apparel sector,
which separates the first cone from the second. As textiles and apparels
shrink, automobiles—which are more capital-intensive and separate the
second cone from the third—expand and then shrink as additional capi-
tal is accumulated. As automobiles shrink in turn, chemicals—which are
still more capital-intensive and separate the third cone from the fourth—
also expand; with no other diversification cone, the chemicals sector
does not decline.


Empirical evidence from a large panel of countries, shown in
figure 2.3, supports this conjecture. In panel a, the share of exports of
textiles and apparel in total exports grows very rapidly from the lowest




44 Pathways to African Export Sustainability


textiles &
apparel automobile chemicals


chemicals


labor


capital


textiles &
apparel


automobile


export
shares


income


accumulation of capital


cone 1 cone 2 cone 3 cone 4


Figure 2.2 Traveling through Diversification Cones


Source: Adapted from Schott 2003.


level of income and then declines continuously. In panel b, the share of
transport equipment rises up to about $26,000 Purchasing Power Parity
(PPP), then declines. In panel c, machinery rises to $30,000 PPP, then
stays constant. Finally, in panel d, the export share of chemicals rises
monotonically as a function of GDP per capita of countries.


Thus, a country’s accumulation of capital generates a product cycle
that is responsible for some of the observed births and deaths in
export markets. That product cycle is likely to be long and is therefore
unlikely to explain the short-run churning observed in the data.
However, as some countries travel quickly across cones through rapid




Countries, Institutions, and Policies 45


0


0.2


0.4


0.6


sh
ar


e
o


f t
ex


ti
le


s
in


t
o


ta
l e


xp
o


rt
s


(s
ec


ti
o


n
1


1)


lowess smoother


a. textiles & apparel (section 11)


b. transport equipment (section 17)


0


0.1


0.2


0.3


sh
ar


e
o


f v
eh


ic
le


s
in


t
o


ta
l e


xp
o


rt
s


(s
ec


ti
o


n
1


7)


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


GDP per capita, PPP (constant 2005 international $)


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


GDP per capita, PPP (constant 2005 international $)


bandwidth = 0.8


bandwidth = 0.8


lowess smoother


Figure 2.3 Evolution of Sectoral Shares with Income Levels


(continued next page)




46 Pathways to African Export Sustainability


0


0.2


0.4


0.6


0.8


sh
ar


e
o


f m
ac


h
in


er
y


in
t


o
ta


l e
xp


o
rt


s
(s


ec
ti


o
n


1
6)


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


GDP per capita, PPP (constant 2005 international $)


bandwidth = 0.8


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


GDP per capita, PPP (constant 2005 international $)


bandwidth = 0.8


0


0.1


0.2


0.3


0.4


0.5


sh
ar


e
o


f c
h


em
ic


al
p


ro
d


u
ct


s
in


t
o


ta
l e


xp
o


rt
s


(s
ec


ti
o


n
6


)


lowess smoother


lowess smoother


c. machinery (section 16)


d. chemicals (section 6)


Figure 2.3 (continued)


Source: Cadot, Carrère, and Strauss-Kahn 2011.
Note: PPP = purchasing power parity




Countries, Institutions, and Policies 47


development, one should expect to observe an accelerated product
cycle with exporters trying to go “one bridge too far”—either export-
ing products that will lie in the country’s diversification cone, but too
early, or exporting products in which the country is rapidly losing its
comparative advantage, but too late. Thus, deaths at both tails of the
distribution of factor intensities are likely to be more frequent when
rapid change in the country’s comparative advantage creates a rapidly
evolving environment.


So far, the discussion of shifting comparative advantage has been cast
in traditional Heckscher-Ohlin terms—that is, in terms of largely homo-
geneous products. In reality, most manufactured products are differenti-
ated by their quality. As a country accumulates capital, technology, and
know-how, it moves up the quality ladder within goods as well as across
goods. In much the same way as traveling across diversification cones
generates challenges that firms must meet to adapt to new circumstances,
climbing up the quality ladder—often because lower-wage exporters are
breathing down the neck of more traditional ones—creates adaptation
challenges that firms must meet to survive.


In a rapidly shifting environment, export sustainability depends
largely on the capacity of individual firms to adapt. Useful lessons on
what drives firm resilience to shocks can be drawn from a case study of
India’s knitwear industry, which underwent a severe crisis in the early
1990s as a result of the loss of guaranteed markets in the Soviet Union
and of India’s trade liberalization. Tewari (1999) shows that the industry
recovered very rapidly, shifting product mixes to suit the more demand-
ing Western markets and getting back onto a strong growth trajectory.
She provides an interesting account of the factors that contributed to the
industry’s resilience. Exporters that withstood the shock better than
average included those with a strong position in the domestic market as
well. In Tewari’s words:


The striking point in this regard is that these learning effects occurred in a
segment of the domestic market that was dynamic despite being sheltered
behind high tariff walls in a country that has long been maligned as an exces-
sively dirigiste regime that has shackled productivity through its “disastrous”
policies of import substitution (Tewari 1999, p. 1661).


Second, the more robust exporters included firms that adapted not only
their investment policies, but also—and perhaps more important—their




48 Pathways to African Export Sustainability


managerial practices. Third, she highlights the importance of a strong fab-
ric of midsize companies:


Middle-level firms with up to 300 to 500 workers, who have accumulated
capital and have the resources to invest in new processes, have been able to
take the fullest advantage of the opportunities created by the crisis of 1991
to restructure themselves quite radically. In contrast to the largest firms, this
middle tier is more closely tied to the vast network of small firms and sup-
pliers, and influences them in incremental ways—by forcing organizational
changes among their suppliers, creating demand, and by “vacating” some
segments of the export market where smaller firms are now able to compete
(Tewari 1999, p. 1656).


Thus, industry structure interacts with individual firm capabilities to
generate a sustainable and resilient export basis. It is therefore critical for
the business environment in the exporting country to be conducive to the
blossoming of this critical layer of midsize firms. In the Sub-Saharan
African context, corruption, which is mentioned by many exporters as a
key constraint (see below), is likely to be a particular hurdle precisely for
those midsize firms that are too big to hide in the informal market and
too small to have political connections.


Trade Costs and the Business Environment


The institutional environment in which business transactions take
place—the term transactions taken in a broad sense to mean all the
conditions and events surrounding a firm’s activity, including, for exam-
ple, customs and trade facilitation, contract enforcement, access to
credit, and the tax and procedural environment—has also been shown
in the recent literature to be a key determinant of trade flows. For
instance, Levchenko (2007) and Nunn (2007) showed how institu-
tional quality and contract enforcement could create endogenous com-
parative advantage.


In the case of Africa, while the predominance of primary-product
exports is consistent with natural resource–based comparative advantage,
the small share of exports of labor-intensive manufactured goods and the
lack of transition to labor-intensive diversification cones reflect the poor
infrastructure and weak regulatory environment that characterizes the
continent and lead to very high trade costs. According to the ranking of
183 countries by the World Bank’s Ease of Doing Business (2011), only
3 out of 46 African countries are within the top 50 countries (Mauritius




Countries, Institutions, and Policies 49


in the 21st position, followed by South Africa in the 36th position, and
Rwanda in the 50th); 30 are in the bottom 50, and almost all of the last
10 slots are all occupied by African nations. Overall, most of the African
nations show a less-favorable business environment than countries in
other regions.


Trade costs weigh heavily on the overall export performance of Sub-
Saharan Africa, especially for cross-border trade within Africa. World Bank
(2011) compares the prices of agricultural products in a wide range of
markets in Burundi, the Democratic Republic of Congo, and Rwanda. The
report concludes that the effect on relative prices of crossing the Burundi-
Rwanda border is equivalent, on average, to pushing the two markets an
additional 174 kilometers or 4.6 hours farther apart. However, crossing
the Burundi–Democratic Republic of Congo border is equivalent, on aver-
age, to pushing markets in each country 1,824 kilometers or 41 hours
farther apart, and crossing the Democratic Republic of Congo–Rwanda
border is equivalent to adding an extra 1,549 kilometers and an additional
35 hours. This reflects the very high financial and physical costs associated
with crossing the Democratic Republic of Congo border.


Non-tariff barriers to trade are pervasive throughout Africa. Box 2.1
provides examples from southern Africa and indicates the costs that are


Box 2.1


Examples of Non-Tariff Barriers and Their Costs in
Southern Africa


Inefficiencies in transport, customs, and logistics raise trade costs: For regional trade


agreements to be effective, it is critical that intraregional trade be able to move
without hindrance. However, high transaction costs are being incurred because


of inadequate transport infrastructure and inefficiencies in customs procedures


(including delays at road checks, borders, and ports) as well as poor quality and


costly logistics that are the result of weak competition among service providers.


For example, the supermarket chain Shoprite reports that each day that one of its


trucks is delayed at a border costs the company US$500.


Restrictive rules of origin limit preferential trade: Onerous local content require-


ments in rules of origin (ROOs), particularly in labor-intensive sectors (such as


clothing) that use capital-intensive inputs not produced competitively in the


region (such as fabrics), reduce the incentive to trade regionally. For example, the


(continued next page)




50 Pathways to African Export Sustainability


implementation of more restrictive rules (double transformation) on selected


clothing imports from Malawi, Mozambique, Tanzania, and Zambia resulted in


some clothing producers in these countries (for example, Bidserv in Malawi)


losing their ability to compete in the regional market. For other products where


ROOs have been so contentious (such as wheat flour) or simply not agreed (for


example, certain electrical products for which rules were finalized only in April


2010), preferential trade within the region has been effectively prohibited (Nau-


mann 2008).


Further costs arise from the administrative requirements for certificates of ori-


gin, which can account for nearly half the value of the duty preference. For exam-


ple, Shoprite spends US$5.8 million per year in dealing with red tape (for example,


in filing certificates and obtaining import permits) to secure US$13.6 million in


duty savings under the Southern African Development Community (SADC).


Woolworths does not use SADC preferences at all in sending regionally produced


consignments of food and clothing to its franchise stores in non–Southern Afri-


can Customs Union (SACU) SADC markets. Instead, it simply pays full tariffs


because it currently deems the process of administering the ROO documentation


to be too costly.


Poorly designed technical regulations and standards limit consumer choice and


hamper trade: Standards regimes in southern Africa are often characterized by


an over-reliance on mandatory inspections and certifications; unique national


(rather than regional or international) standards and testing; overlapping


responsibilities for regulation; and, occasionally, heavy government involve-


ment in all dimensions of the standards system. These factors create unneces-


sary barriers to trade, especially when technical regulations and standards are


applied in a discriminatory fashion against imports. One example is shoes in


Mauritius, where the Chamber of Commerce has proposed the development


of a regulation to govern the quality of shoes to prevent the entry of low-cost


Chinese sandals that are perceived to have a tendency to wear more quickly


than domestically produced ones. However, these lower-quality shoes are


often the only ones that the poorest people in Mauritius can afford to buy.


Other non-tariff barriers restrict opportunities for regional sourcing: Other


barriers—such as trade permits, export taxes, import licenses, and bans—also


persist. Shoprite, for example, spends US$20,000 per week on securing import


(continued next page)


Box 2.1 (continued)




Countries, Institutions, and Policies 51


permits to distribute meat, milk, and plant-based goods to its stores in Zambia


alone. For all the countries in which it operates, approximately 100 (single-entry)


import permits are applied for every week; this can rise to 300 per week in peak


periods. As a result of these and other documentary requirements (for example,


ROOs), up to 1,600 documents can accompany each truck Shoprite sends with


a load that crosses an SADC border. Lack of coordination across government


ministries and regulatory authorities also causes significant delays, particularly


in authorizing trade for new products. Another South African retailer took three


years to get permission to export processed beef and pork from South Africa to


Zambia.


In SACU, national protection for infant industries has often been used to justify


import bans. Namibia has used the provision to protect a pasta manufacturer and


broilers and maintains protective bans on ultra-high temperature (UHT) milk,
extending its ban beyond the recently expired eight-year limit for this protection.


Botswana has recently limited imports of specific varieties of tomatoes and UHT


milk. Seasonal import restrictions on maize, wheat, and flour also ensure that


domestic production is consumed first. For example, Swaziland’s imports of wheat


flour were effectively prohibited for half of 2009, since no import permits were


issued since June of that year.


Export taxes also impose costs and inhibit the development of regional supply


chains. A case in point is small stock exports from Namibia. Since 2004 the Namib-


ian government has limited exports to encourage local slaughtering. Quantity


restrictions were originally used but have recently been replaced by a flexible levy


of between 15 percent and 30 percent, effectively closing the border for the export


of live sheep to South Africa. The impact of this restriction is affecting the small


stock industry in both Namibia and South Africa. In the former, farmers have


switched to alternative activities such as cattle and game farming. Those sheep


farmers that remain have become almost entirely dependent on the four Namib-


ian export abattoirs, while they were previously able to sell more sheep to the


South African market where they received higher prices (PriceWaterhouseCoopers


2007). In South Africa, 975 full-time jobs are at risk because of the scheme, espe-


cially in the bigger abattoirs in the Northern and Western Cape that focus on


slaughtering Namibian sheep during the low season to better utilize their capacity


(Talijaard et al. 2009).


Source: World Bank 2012.


Box 2.1 (continued)




52 Pathways to African Export Sustainability


representative of the barriers faced by firms and individuals in crossing
borders throughout the continent. These non-tariff barriers impose
unnecessary costs on producers and undermine the predictability of the
trade regime.


Empirical work on the relationship between export performance and
the business environment highlights the importance of this relationship as
a constraint on the continent’s exports. For instance, regarding time delays
in the handling and shipping of merchandise, Djankov, Freund, and Pham
(2010) estimate that if Uganda reduced its factory-to-ship time from
58 to 27 days (the median for their sample), exports would be expected
to increase 31 percent and Uganda would be closer to its main trading
partners by 2,200 kilometers. More recently, using detailed data on differ-
ent types of delays in customs, Freund and Rocha (2010) examined the
impact of the functioning of customs in African nations. In particular, they
examine whether delays in transit, delays in documentation, or delays in
port handling have significant and differentiated effects on exports. They
find that delays in inland transit do have a significant and negative impact
on exports. In particular, they find that a one-day reduction in travel time
inside the exporting country leads to a 7 percent increase in exports. The
other types of delays have a smaller impact on exports.


Beyond overall export performance, the business environment directly
affects incentives to enter export markets. Edwards and Balchin (2008)
studied in detail the impact of various components of the business envi-
ronment on the propensity to export in African countries. Using firm-
level data from the World Bank’s Enterprise Surveys for eight African
nations, they generated principal-component indexes to test whether
(1) physical infrastructure, (2) micro-level supply constraints, (3) macro-
economic conditions, (4) legal environment, and (5) trade-related infra-
structure and services have a significant impact on the propensity to
export in these eight African nations.


They found a negative and significant relationship between the
propensity to export and the principal components that reflect the
following:


• Micro-level supply constraints: This index captures access to land, tax
rates and administration, labor regulations, business licensing and oper-
ating permits, and the cost of access to financing.


• Unfavorable macroeconomic conditions: The index captures percep-
tions regarding macroeconomic instability and economic and regulatory
policy uncertainty.




Countries, Institutions, and Policies 53


• Weak legal environment: This index captures crime and anti-
competitive behavior.


Does the business environment affect export survival as well? One might
conjecture that once a firm decides that the environment is sufficiently
favorable to generate a positive return on exporting—that is, conditional on
the decision to enter—survival would not be affected. In fact, one might
even expect a negative relationship between the quality of the business
environment and average survival. The reason is the selection effect dis-
cussed in chapter 1 of this report—namely, that an easier environment may
prompt low-productivity, low-quality firms to try to export on a hit-and-run
basis, thus reducing export survival averaged over all exporting firms.


Preliminary but direct evidence from a survey conducted in 2009 by
the World Bank in five African countries (Ghana, Malawi, Mali, Senegal,
and Tanzania) suggests that the quality of the business environment—in
particular freight costs, burdensome customs, and corruption—looms
large among the factors constraining the ability of African firms to thrive
in export markets.


The survey covered both active exporters and those who recently
exited export markets, thus potentially singling out factors that domi-
nated the picture for the second category. Figure 2.4 shows the number
of respondents mentioning each of a series of possible constraints on
survival (out of a menu of 10—multiple responses were allowed) in the
form of a scatter plot with the number of respondents among failed
exporters (those who exited) on the horizontal axis and the number of
respondents among active exporters on the vertical axis.4 Thus, a con-
straint factor appearing above the 45° line is revealed as more important
to active exporters, while a factor appearing below it is revealed as more
important to exited exporters.


Most exporters, whether active or exited, identified freight costs
(whether internal or external) as the primary limitation to their expan-
sion (panel a). While in absolute terms this was the answer most marked
by both groups of exporters, in relative terms it was more frequent within
the group of existing exporters (this result is expected, considering that
members of this group are currently dealing with more export transac-
tions than members of the exited group, given their exporting status).
The second most frequent answer was “costs and burdensome procedures
by customs” in the country of export, followed by “bribes.” The relative
importance of the most frequent answers is consistent with the evidence
in the literature. The frequency with which exporters mention corruption




54 Pathways to African Export Sustainability


as a constraint is particularly important, given that corruption tends to be
most damaging to firms that are simultaneously too small to have the
connections it takes to go around corrupt officials or tame them, and too
large to operate in the less-visible informal market.5


SPS


Burden Customs Home


Burden Customs Foreign


Export permit
Import permit


Duties


Freight costs


Refund M duties


Refund X VAT


Bribes


0


20


40


60


80


100


120


140


160


0 20 40 60 80


exporters that failed


a. trade-related costs as a constraint to exports


b. supply-side constraints to exports


ex
p


o
rt


er
s


th
at


s
u


rv
iv


ed


unreliable
inputs supply


inputs' volatility


not meeting quality


not meeting deadlines


not meeting
quantities


lack access to
finance


tough
competition


bad services (energy, telecom,
roads, and ports)


bad
marketing


ex
p


o
rt


er
s


th
at


s
u


rv
iv


ed


0


20


40


60


80


100


120


140


160


3010 50 70


0 30 40 60 80
exporters that failed


2010 50 70


Figure 2.4 Constraints to Survival


Source: World Bank survey of African exporters, conducted in 2009.
Note: Refund M = refund of import duties; Refund X VAT = refund of export VAT; SPS = sanitary and phytosanitary
standards.




Countries, Institutions, and Policies 55


Regarding supply-side constraints, exporters were given a menu of
nine options covering a wider array of issues (panel b). Access to finance
is clearly the major factor identified by both active and exited exporters,
suggesting that all exporters, active or failed, hit that constraint—again,
this is fully consistent with the empirical literature and with casual
observation as well. In absolute terms, bad infrastructure services (mainly
representing energy services and port infrastructure) was the most com-
mon response marked by the existing exporters. Volatility in input prices
and tough competitive behavior from others are the next most marked
answers by both groups of exporters.


Using a novel data set of exports at the firm level for countries in
Africa, Asia, Eastern Europe, Latin America, and the Middle East, figures
2.5 and 2.6 present more evidence on the relationship of different busi-
ness environment variables (at the country-year level) and a more direct
measure of export survival: first-year export survival rates of new export-
ers (by country-year).6


Export costs per container correlate negatively with export survival, as
do delays. Panels a and b of figure 2.5 present the relationship between
survival rates and the World Bank’s Doing Business indicators of export
costs: costs per container and number of days to export. The data for
African countries are indicated by triangles. The graphs show a negative
relationship between survival rates and these indicators: the lower the
export cost and the smaller the delay, the higher the survival rate. This
negative relationship is more pronounced in the case of costs per con-
tainer and is further supported when using a summary measure of trade
costs—the Logistics Performance Index (LPI), shown in panel c7—which
correlates positively, across countries, with export survival. Note that, in
all three panels, African countries do not stand out as outliers in terms of
low survival. Those lying on the left-hand part of the diagrams, with
export costs, delays, and LPIs comparable to those of non-African coun-
tries, seem to have, by and large, comparable export survival. What stands
out for African countries is not their low survival rates by themselves, but
rather the high values of their costs and delays.


Lack of financial development is also an important factor. Figure 2.6
shows the relationship between survival rates and two measures of finan-
cial development and structure: private credit of financial institutions to
GDP and the value of shares traded in stock market exchange to GDP.
These data are taken from the updated version of the New Database on
Financial Development and Structure (Beck, Demirgüç-Kunt, and
Levine 2000).8 Overall, there is a positive relationship between survival




56 Pathways to African Export Sustainability


0


0.2


0.4


0.6


0.8


o
n


e-
ye


ar
s


u
rv


iv
al


ra
te


o
f e


n
tr


an
ts


0 1,000 2,000 3,000 4,000


cost of export (US$ per container)


0


0.2


0.4


0.6


0.8


o
n


e-
ye


ar
s


u
rv


iv
al


ra
te


o
f e


n
tr


an
ts


10 20 30 40 50 60


time to export (days)


a. export costs per container


b. number of days to export


Figure 2.5 First-Year Survival Rates and Business Environment Measures in the
Origin Country


(continued next page)




Countries, Institutions, and Policies 57


0


0.2


0.4


0.6


0.8


o
n


e-
ye


ar
s


u
rv


iv
al


ra
te


o
f e


n
tr


an
ts


2.0 2.5 3.0 3.5


LPI infrastructure


c. Logistics Performance Index: infrastructure


non-African countriesAfrican countries


Figure 2.5 (continued)


Source: Exporter Dynamics Database (DECTI) forthcoming. Available at: http://econ.worldbank.org/exporter-
dynamics-database.


rates and these two variables. Again, most African countries in the sam-
ple stand out as being to the left rather than below the regression line,
highlighting the explanatory power of these factors in determining the
ability of exporters to survive.


Thus, the relationships highlighted in figure 2.6 and the particular
patterns they suggest for Africa are consistent with the direct evidence
from the survey of exporters—lack of credit and financial development
constrains not only entry, but also survival. This can be understood as
follows. Often, large-scale buyers in industrial countries “test” new sup-
pliers by ordering limited amounts. When the supplier’s performance is
satisfactory, the buyer rapidly ramps up orders. It is at this stage that the
credit constraint hits the suppliers, who need to invest rapidly to
respond to the buyers’ scaled-up demands. Ample anecdotal evidence
suggests that even with letters of credit and guarantees from reputable
buyers—such as supermarket chains in Europe, Japan, or the United
States—local banks in Sub-Saharan Africa will refuse to grant credit, or
will accept credit applications only with unrealistic collateral require-
ments and prohibitive interest rates. The supplier will typically not have




58 Pathways to African Export Sustainability


time to gather the required financing before the buyer switches to
alternative sources of supply—e.g., in East Asia, where the supply
response is less constrained. The African supplier’s inability to ramp up
production will thus lead to rapid termination of the export relation-
ship, showing up in the data as low survival—precisely in the sense in


0


0.2


0.4


0.6


0.8


o
n


e-
ye


ar
s


u
rv


iv
al


ra
te


o
f e


n
tr


an
ts


0 0.5 1.0 1.5 2.0
private credit of financial institutions to GDP


0


0.2


0.4


0.6


0.8


fir
st


-y
ea


r s
u


rv
iv


al
ra


te
o


f e
n


tr
an


ts


0 0.5 1.0 1.5 2.0 2.5
total shares traded on the stock market exchange to GDP


a. private credit by deposit money banks
and other financial institutions to GDP


b. value of listed shares to GDP


non-African countriesAfrican countries


Figure 2.6 First-Year Survival Rates and Financial Development


Source: Beck, Demirgüç-Kunt, and Levine 2000.




Countries, Institutions, and Policies 59


which low survival is defined in this section—the ability to survive past
the first year.


In order to confirm these findings through formal regression analysis,
table 2.1 shows the results of correlations between the variables pre-
sented above and survival. All regressions report ordinary least squares
results with the unit of observation being the proportion of flows that
survive past the first year of export, by product group (Harmonized
System [HS] 2-digit level), firm, and destination. The estimations con-
trol for the number of entries in order to take into account the selection
effect discussed in chapter 1, namely, that an easier environment may
induce entry by relatively low performers, thus dampening the induced
improvement in average survival rates.


As expected, the number of survivors is highly correlated to the
number of entrants in each country-year-HS 2-digit trio, and the
squared term also presents a positive and significant relationship.9
Regarding the business environment variables, after controlling for
entry, country, year, and HS 2-digit, we find that only export costs
per container are significant in their relationship with survival.


Table 2.1 Survival Versus Business Environment Measures in African Countries:
Correlations


Dependent variable: Log natural (number of surviving entrants)


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


Log natural (number of
entering firms)


0.328*** 0.321*** 0.330*** 0.490*** 0.343***
(0.0189) (0.0187) (0.0162) (0.0274) (0.0164)


Log natural (number of
entering firms)2


0.0874*** 0.0875*** 0.0864*** 0.0667*** 0.0846***
(0.00298) (0.00297) (0.00262) (0.00347) (0.00261)


Log natural (export costs
per container)


–0.228***
(0.0541)


Log natural (days to export) 0.130
(0.149)


Private credit by deposit
money banks and other
financial institutions/GDP


–0.279
(0.172)


Total shares traded on
the stock market
exchange/GDP


–0.107
(0.0705)


LPI infrastructure –0.0646
(0.0507)


Observations 2,534 2,534 3,098 1,188 3,020
R2 0.94 0.94 0.94 0.97 0.94


Source: Authors.
Note: LPI = Logistic Performance Index
Significance level: *** = 1 percent




60 Pathways to African Export Sustainability


This relationship is negative, as expected, indicating that the higher
the cost to export, the lower the number of firms that will be able to
succeed. In particular, a 10 percent increase in the export costs per
container would mean a 2 percent decrease in the number of surviving
firms in Africa.


Standards and Their Enforcement


The discussion of the business environment so far has focused largely on
trade facilitation, which is crucial for the long-term viability of export
businesses in developing countries. Equally crucial is the regulatory envi-
ronment in destination markets, in particular for agri-food products, to
which we now turn. We show, through anecdotal evidence, that African
exporters of agri-food products may face considerable discretion and
uncertainty on destination markets. Consider the following case study,
discussed in Ashraf, Giné, and Karlan (2009).


DrumNet is a program designed by Pride Africa to help small-scale
farmers develop long-term business relationships with exporters.10 The
dual problem that DrumNet addresses is that overseas buyers fear that
once they provide inputs on credit, farmers will sell their crop to other
buyers and refuse to repay their loans; at the same time, farmers fear that
buyers will use the exclusive-purchase understanding to “hold them up”
and offer low prices for the crop, especially when it is perishable. As a
result of this “double moral hazard” problem, farmers do not invest in
agricultural improvement or utterly fail to adopt export crops, even when
these crops could earn them higher incomes. The DrumNet program
aims to build trust between farmers and their buyers and to foster long-
term, sustainable business relations between them.


Ashraf, Giné, and Karlan (2009) report the results of a rigorous impact
evaluation of DrumNet. They find “positive but not overwhelming one-
year impacts” from the program in terms of adoption of export crops and
overcoming barriers to market access. However,


[t]he epilogue to this project is more dismal. One year after the evaluation
ended the export firm that had been buying the horticultural produce
stopped because of lack of compliance with European export requirements
(EurepGap). This led to the collapse of DrumNet as farmers were forced to
undersell to middlemen, leaving sometimes a harvest of unsellable crops to
rot and thus defaulting on their loans. Afterward it was reported to us anec-
dotally that the farmers returned to growing local crops (Ashraf, Giné, and
Karlan 2009, p. 974).11




Countries, Institutions, and Policies 61


The incident related by Ashraf, Giné, and Karlan, which illustrates
some of the contradictions of industrial countries’ development and trade
policies, may not be wholly representative in its starkness. However, it
points to a real problem—discretion in the administration of sanitary and
phytosanitary (SPS) regulations in industrialized countries.


The problem this discretion imposes is better described in the case of
the United States by Jouanjean, Maur, and Shepherd (2011). Three fed-
eral agencies are involved in the oversight of food safety in the United
States: the U.S. Department of Agriculture’s Food Safety and Inspection
Service (FSIS), the U.S. Food and Drug Administration (FDA), and the
U.S. Environmental Protection Agency (EPA). The FSIS ensures the
safety of imported meats, poultry, and processed egg products. The FDA
covers all other products. The EPA licenses pesticide products and moni-
tors pesticide residues in products. The FDA enforces compliance with
limits on pesticides residues, food hygiene, additives, and contamination.


Under Section 801 of the Federal Food, Drug and Cosmetics Act
(FD&C), products are subject to inspection when imported. Imported
food products are expected to meet the same standards as domestic
products; in addition, since 1997 fruit and vegetable producers must fol-
low the FDA’s good agricultural practices (GAP) for the control and
management of microbial food safety. Likewise, since 1995 imported fish
products must meet hazard analysis and critical control point (HACCP)
standards, as must domestic producers. In the United States, HACCP
standards apply to processors only, in contrast to the European Union,
where they apply to the entire supply chain (see below). Other measures
applying to seafood include traceability requirements such as the iden-
tity preservation system for mollusks, and labeling of origin and method
of production (wild harvest or farm-raised).


All the measures described above are in accordance with the principle
of national treatment, as they apply equally to importers and domestic
producers. There is, however, a significant difference: The FD&C Act
allows for the rejection of imported FDA-regulated products for “appear-
ing” to be adulterated or misbranded. According to Jouanjean, Maur, and
Shepherd (2011),


The law is interpreted in a broad sense as allowing the FDA to make admis-
sibility decisions based not only on physical evidence such as examination,
facility inspection, or laboratory results, but also based on historical data,
information from other sources (e.g. about a disease outbreak), labeling, and
any other evidence. . . . Factors such as reputation can clearly come into play
in this decision. In other words, if there is the faintest suspicion that a




62 Pathways to African Export Sustainability


product from a given origin will not meet FDA standards, it can be detained.
Therefore the standard of proof for determination of refusal for food import
products is much less strict than for domestic products, which must be based
on an actual violation (Jouanjean, Maur, and Shepherd 2011, p. 9).


Such a discretionary regime is bound to generate periodic sales disrup-
tions for exporters and—perhaps more important for African producers—
to overpenalize those who are climbing up the quality ladder because
their history weighs on them. It thus creates uncertainty of the kind that
was shown in annex 1A of chapter 1 to have negative effects on survival,
both direct (unanticipated termination) and indirect (discouragement
effect making exporters less resilient to bad news).


Beyond discretionary regimes, the evolution of standards in destina-
tion countries can put gradual pressure on exporters in low-income
countries in a way that threatens their sustainability, pushing them to
adopt deep changes in the structure of value chains as survival strategies
and making it very difficult for smaller players to survive in the tougher
regulatory environment.


Senegal’s exports of French beans, studied in detail by Maertens and
Swinnen (2009) and Jaud and Cadot (2011), is a case in point. EU stan-
dards for fresh fruit and vegetables (FFV) have been steadily rising, put-
ting enormous pressure on exporters. EU legislation imposes (1) common
marketing standards for FFV; (2) SPS; (3) general hygiene rules based on
HACCP control mechanisms’, and (4) traceability standards. These mea-
sures became notably more stringent in the 1990s. Particularly relevant to
FFV is reduced tolerance for chemical residue levels.12 First, about
350 active substances initially approved for use in the European Union
have been gradually withdrawn (out of the 823 initially allowed). Second,
Maximum Residue Levels (MRL),13 as well as import tolerances (ITs),14
are imposed at levels specific to particular protection chemical–crop
combinations. The registration of an IT is a complicated process involving
the submission of a complete residue dossier, including field trials and lab
analysis results. For exporters of minor crops—most tropical crops except
bananas—from developing countries, the challenge is compounded by
the fact that agrochemical companies have little incentive to provide
registration residue data for those crops because the benefit would not
cover the cost.


The HACCP and traceability requirements came into force with the
General Food Law of 2002 (EC/178/2002). Traceability means that EU
food companies must document from (to) whom they are buying (sell-
ing) so that products can be traced back to their origin if they prove




Countries, Institutions, and Policies 63


defective or dangerous. Although traceability is legally limited to a “one
step forward, one step back” principle within the European Union
(with no obligation to keep records in third countries), in practice EU
buyers tend to go beyond the strict legal requirement. Complete trace-
ability throughout the chain all the way up to the overseas producers is
part of many private standards such as those of the GlobalGAP.


Maertens and Swinnen’s analysis shows that the European Union’s
rising standards have profoundly altered the structure of the supply
chain in Senegal’s horticulture sector. For instance, the financial con-
straints generated by the need to comply with increasingly stringent
standards have induced consolidation at the intermediation stage, with
only the larger firms able to cope. Between 2002 and 2005, the number
of French bean exporters dropped from 27 to 20, and the market share
of the three largest companies rose from less than half to two-thirds
(Maertens and Swinnen 2009).


Rising standards have complex implications for export survival and
risk-sharing along the value chain, as the overall sector’s sustainability as
a supplier to the increasingly regulated market can be obtained by shifting
risk over to the producers upstream. In the case of Mali’s mango exports,
for instance, complete traceability all the way up to the farmer comes
with precise tracking of quality records. Farmers that are recorded to have
supplied less than 60 percent of “export-grade” mangoes during a speci-
fied period are automatically tossed out of the supply chain (see box 2.2),
notwithstanding the fact that every single mango shipment is screened for


Box 2.2


A Malian Mango’s “Soldier’s Run”


In order to reach EU markets, a mango must be patient, tough, and spotless. The


run starts with a manual sorting that eliminates fruits with visible defects and those


that have lost their peduncle, which would allow moisture to infiltrate them. Those


fruits that survive the first sorting are immersed for 5 minutes in a 50o pool in order


to eliminate insects and anthracnose risk. A second, 2-minute immersion in


prochlorase—a bacteria-killing detergent—further reduces infection risk. After


drying, a new round of selection eliminates all fruits bearing any trace of cochineal.


Cochineal contact involves no health risk for consumers, but traces might possibly


put them off, and the retailers will take no chances. The second round of selection


(continued next page)




64 Pathways to African Export Sustainability


is followed by calibration, which is necessary to condition the fruits in cardboard


boxes subjected to very strict weight restrictions. The calibration team’s work is


then verified by another team; any errors send the fruits back to the previous stage.


Finally, each box is stamped with a seven-digit traceability code identifying the


conditioning station, the week of conditioning, the day of the week, and the lot. Lot


identification includes reference to a producer ticket, thus guaranteeing total trace-


ability. The producer ticket identifies the name of the collector, the village of origin,


the producer’s name, the land plot number, the date of harvest, and the number of


cages or containers containing the mangoes supplied by the producer.


Boxes are piled on wood pellets and then stored in a cold-storage facility at 9o,


with a temperature sensor put in one of the fruits for constant monitoring. They


are then shipped by refrigerated truck, and then by truck, rail, and cargo ship via


Abidjan (during the troubled period in Côte d’Ivoire, they were shipped by an


alternative route that included rail transshipment at Ferkessedougou, which


involved several days of inland transit). The sea journey usually takes 12 days with


the best shipping lines, and can take up to 20 days, which makes the supply chain


very tight, given that the life of a mango typically does not exceed 28 days. The


fruits may also be shipped by air freight, but that makes them very expensive,


given their weight.


Fruits that were rejected from the export supply chain end up on the local


market with a substantially marked-down price. The conditioning unit tracks the


“export-grade” ratio of each producer—that is, the ratio of fruits that make it to the


total supplied by that producer. This ratio must hover around 60 percent, with a


tolerance around +/–10 percent. When a producer’s ratio dips below this toler-


ance, he is tossed out of the supply chain. Thus, the supply chain’s survival is, at


least in part, the result of the fact that the risk of failing its extraordinarily strict


requirements is largely pushed up the chain, to the farmers. In that sense, the


sustainability of the export flow and income security of the producers may even


be inversely correlated.


Source: Cadot 2008.


Box 2.2 (continued)


defects and non–export grade fruits are thrown away or sold locally. Thus,
the price for the sector’s survival is the shifting over of the survival risk
onto farmers. The resulting uncertainty does not appear in the product-
level statistics, since the overall flows are not affected; nor does it even
appear in the firm-level statistics that can be gathered from customs
administrations, as the firms appearing in those statistics are the interme-
diaries, not the ultimate producers. Yet it is not something that should be




Countries, Institutions, and Policies 65


glossed over, as the risk is unlikely to be fully diversifiable or insurable at
the household level.


In the case of Senegal, this tension between sustainability at the sec-
tor level and individual risk for farmers has given rise to new contrac-
tual and market-structure arrangements. First, the relationship between
intermediaries and producers has changed, so there is now more con-
trol by intermediaries over farming methods. Tighter control was
implemented through increasingly precise contracts, technical assis-
tance, and the provision of credit and farm inputs. Second, the induced
changes also affected the structure of upstream farm production, with
a sharp decrease in the incidence of contract farming and a rise in that
of large-scale estate production. Maertens and Swinnen show, from
data from a survey they conducted in Senegal’s French bean–producing
region, that the share of agricultural households that took part in
export production through contract farming dropped from 23 percent
in 2000 to less than 10 percent in 2005, whereas the share of house-
holds that had at least one member working as a salaried employee on
a plantation rose in the same period from 10 percent to 34 percent.
Interestingly, Maertens and Swinnen show that these transformations
have been accompanied by reductions in the incidence of extreme pov-
erty. However, there are no data on the effect of these transformations
on the volatility of income, which is one of the key potential effects of
low export survival.


Overall, this chapter shows the following:


• A poor business environment—in particular, high trade costs—is associ-
ated with lower rates of export survival. This suggests that government
interventions that seek to promote the export of particular products
may have little impact in the face of a hostile business environment and
a high rate of export failure. We return to policy implications later in the
report.


• Discretionary application of trade and regulatory policies in overseas
markets increases uncertainty about market access and so has negative
impacts on export survival. This chapter gives the example of standards
in Western markets but applies equally to standards applied in Africa
and other developing-country markets, as well as to policy instruments
such as rules of origin and periodic import and export bans.


• The impact of higher standards is complex, but such standards can
lead to a shift of risk within export value chains to poor producers in
developing countries and affect survival possibilities for particular
suppliers.




66 Pathways to African Export Sustainability


Annex 2A: Survey of African Exporters on Export Survival


The objective of the Survey of African Exporters on Export Survival was
to obtain information from both successful exporters and firms that have
exited from export markets on specific factors that, in their opinion,
affected their ability to survive and thrive in foreign markets.


This survey was conducted in 2009 in five African countries: Ghana,
Malawi, Mali, Senegal, and Tanzania. The sample of exporters who par-
ticipated in this survey was randomly drawn from the total population of
exporters in each country, based on their customs data. The preestablished
criteria taken into account to draw the sample of exporters were


• exporting status of the firm,
• its size,
• its location, and
• the economic sector in which it operates, at the 2-digit level of the


Harmonized System (HS) Code.


In particular, all the exporters in each country were classified into four
groups according to their exporting status in 2008, the last year covered
by the customs data available at that time for these four countries:
(1) regular exporters are those exporters with consecutive exports until
2008; (2) past exporters are the exporters who were exporting consecu-
tively for at least two years and then exited the market permanently
before 2008; (3) intermittent exporters are those who exported erratically
during the period included in the sample; and finally (4) new exporters are
those exporters who appear for the first time in the sample in 2008.


Over 200 firms were contacted in each country. However, because of
low cooperation and identification problems with some of the firms, the
final sample by country and exporting group was the following:


Table 2A.1 Survey of African Exporters on Export Survival: Distribution of
Exporters by Exporter Type


Country Intermittent New Past Regular Total


Ghana 6 4 37 65 112
Malawi 9 13 15 54 91
Mali 12 18 20 48 98
Senegal 28 24 31 39 122
Tanzania 22 8 16 38 84
Total 77 67 119 244 507


Source: Authors.




Countries, Institutions, and Policies 67


The questionnaire administered during this survey contained basic
questions about the firm and questions about specific factors affecting
the survival of the exporters and potential opportunities for exporters’
expansion. Regarding general information about the characteristics of the
firms interviewed, the answers to the first question of the questionnaire
reveal the following:


• Sixty-eight percent of the firms are domestically owned, 27 percent are
foreign owned, and 5 percent have mixed or other type of ownership.


• Although the vast majority of the participating managers have pursued
undergraduate or graduate studies (78 percent), the proportion of
responding managers who had only elementary or high school educa-
tion was higher among the group of exporting companies that had
exited the market before 2008 (39 percent of all responding managers
within this group).


• The share of managers who are male within the entire group of respon-
dents was an overwhelming 92 percent.


• Only 21 percent of the responding managers revealed that they had
received training from the Export Promotion Agency.


• Almost a third (30 percent) of the participating managers responded
that their exporting company operated in an Export Processing Zone.


Regarding the specific factors affecting survival, the following two
questions were asked:
On trade costs:


Are any of the following costs relating to exporting a constraint to the
expansion of your sales abroad?


1. Costs related to complying with sanitary,
phytosanitary, and technical regulations? Yes ® No ®


2. Costs that arise from delays and burdensome
procedures in home customs? Yes ® No ®


3. Costs that arise from delays and burdensome
procedures in customs abroad? Yes ® No ®


4. Difficulties and costs in getting an export permit
or license? Yes ® No ®


5. Difficulties and costs in obtaining an import
permit abroad? Yes ® No ®




68 Pathways to African Export Sustainability


On supply-side costs:


Regarding specific supply constraints (in addition to the export costs
covered in the previous question), are any of the following a constraint
to the expansion of the exports of your company?


6. Duties in markets abroad higher than expected? Yes ® No ®


If “Yes,” is this due to:


6.1. Inability to get tariff preferences the company
is eligible for? Yes ® No ®


6.2. Authorities in the export market do not
accept company’s certificate of origin? Yes ® No ®


6.3. Difficulties with customs in the overseas
market regarding valuation of the export or
its classification? Yes ® No ®


6.4. Unexpected changes in the tariff on the
company’s main product(s)? Yes ® No ®


7. High costs of freight to the export market? Yes ® No ®


8. Difficulties in getting refund of import duties on
imported inputs that the company is eligible for
under a duty drawback scheme? Yes ® No ®


9. Difficulties in getting refund of VAT on exports
for which the company is eligible? Yes ® No ®


10. Problems with unforeseen payments to officials? Yes ® No ®


11. Was there another important factor not mentioned
above? Yes ® No ®


1. Unreliability of input supply? Yes ® No ®
2. Volatility of input prices? Yes ® No ®


3. Problems in meeting the buyers’ requirements
regarding quality? Yes ® No ®


4. Problems in meeting deadlines to deliver the
product to the buyer? Yes ® No ®


5. Problems in providing the quantities requested by
the buyer? Yes ® No ®


6. Inability to access the finance necessary to support
the export activity? Yes ® No ®




Countries, Institutions, and Policies 69


6.1. Lack of finance limits the expansion of the
company’s production to a larger scale Yes ® No ®


6.2. Lack of finance limits the investment into
improving the products’ quality Yes ® No ®


7. Competition that undermines your company’s
ability to export? Yes ® No ®


8. Problem with key services inputs (such as energy,
telecom, etc.)? Yes ® No ®


If “Yes,” what is the main problem?
8.1. Energy? Yes ® No ®
8.2. Telecommunications? Yes ® No ®
8.3. Roads? Yes ® No ®
8.4. Ports? Yes ® No ®


9. High costs of marketing abroad? Yes ® No ®


10. Was there another important factor not
mentioned above? Yes ® No ®


The answers to these questions are presented and discussed in the scatter
graphs of figure 2.4 in this chapter.


Notes


1. The database of Revealed Factor Intensity Indices (RFII) is available in
Excel and Stata format at http://r0.unctad.org/ditc/tab/research.shtm
and has recently been updated to 2007. A complete description of the
original database can be found in Tumurchudur, Shirotori, and Cadot
2006.


2. In interpreting this result, one should keep in mind that in a Heckscher-
Ohlin-Vanek model with more goods than factors, the underlying frame-
work for the revealed factor intensities constructed by UNCTAD—since
it uses 5,000 products, based on the classification of products at the
6-digit level of the Harmonized System (HS) and only three factors of
production—trade patterns are formally indeterminate (unlike in a two-
good, two-factor framework). Trade flows are only correlated with factor
intensities.


3. Preliminary results by Nicita, Shirotori, and Tumurchudur-Klok (2011) sug-
gest, however, that this effect may be temporary.


4. The detail of the questions asked to exporters during this survey can be found
in annex 2A.




70 Pathways to African Export Sustainability


5. In this case, most of the responding firms that indicate corruption as their
main constraint are located in the bottom third of the size distribution of
firms in the sample for this survey.


6. The first-year survival rates of entrants, in each country, were calculated as the
ratio of the number of firms that enter in year t (so they were not present in
year t–1) and survived at least until year t+1 over the total number of firms
that entered in year t. The data contained in these graphs cover the following
23 countries: Burkina Faso, Cameroon, Kenya, Mali, Mauritius, Malawi, Niger,
Senegal, Tanzania, Uganda, and South Africa from Africa; Cambodia and
Pakistan from Asia; Albania and Bulgaria from Eastern Europe; Chile,
Colombia, Costa Rica, the Dominican Republic, Ecuador, Mexico, and Peru
from Latin America; and Jordan from the Middle East. The coverage in terms
of years varies per country, although only three cases have data before 2000
(Costa Rica, Mexico, and Peru); in most cases, reliable data are available only
from 2005 onward. Source: Exporter Dynamics Database (DECTI)


7. The Logistic Performance Index (LPI) for infrastructure is a measure of per-
ceptions of the quality of physical infrastructure. This number indicates a
score between 1 and 5, where 1 is the worst possible scale of performance and
5 is the best. See http://go.worldbank.org/88X6PU5GV0.


8. See http://go.worldbank.org/X23UD9QUX0.


9. We use the product-related information from the Database on Export
Growth and Dynamics at the HS 2-digit because of its availability at the time
when these regressions were estimated.


10. Pride Africa is a nongovernmental organization founded in 1988. Its first
activity consisted of developing microfinance schemes in Kenya, after which
it expanded into other Sub-Saharan African countries and into a general
model of business franchises whose objective is to encourage local entrepre-
neurship. DrumNet started in 2004 in Kenya as one such franchise, designed
to deliver services to agro-buyers, banks, farm input retailers, and farmers. It
developed over time into a full program. See http://www.prideafrica.com.


11. EurepGap, which was made mandatory in January 2005, is part of the good
agricultural practices (GAP) protocol of the Euro-Retailer Produce Working
Group (EU-REP), a group of EU supermarkets.


12. In the late 1990s, an updated harmonized legislation package on pesticide
Maximum Residue Limits (MRL)—EC Directive 91/414 and subsequent
Regulation 396/2005—created concern for African, Caribbean, and Pacific
horticultural exporters because of its stringency.


13. The MRL is the level of residue legally permitted to remain in/on a food or
animal feedstuff following the use of a crop-protection chemical (CPC)
under good agricultural practice (GAP)—that is, under the specific label
instructions of the approved product.




Countries, Institutions, and Policies 71


14. An import tolerance is an MRL set for imported products containing active
plant-protection substances not authorized in the European Union for rea-
sons other than public health, or when a different level is appropriate
because the existing Community MRL was set for reasons other than public
health.


References


Albornoz, Facundo, H. F. Calvo Pardo, G. Corcos, and E. Ornelas. 2010.
“Sequential Exporting.” CEP Discussion Papers dp0974, Centre for Economic
Performance, LSE.


Alvarez, Roberto, and R. Lopez. 2005. “Exporting and Firm Performance: Evidence
from Chilean Plants.” Canadian Journal of Economics 38: 1384–400.


Araujo, Luis, and E. Ornelas. 2007. “Trust-Based Trade.” CEP Discussion Papers
dp0820, Centre for Economic Performance, LSE.


Ashraf, Nava, X. Giné, and D. Karlan. 2009. “Finding Missing Markets (and a
Disturbing Epilogue): Evidence from an Export Crop Adoption and
Marketing Intervention in Kenya.” American Journal of Agricultural Economics
91: 973–90.


Aw, Bee Yan, and A. Hwang. 1995. “Productivity in the Export Market: A Firm-
Level Analysis.” Journal of Development Economics 47: 313–32.


Baldwin, John R., and W. Gu. 2003. “Export-Market Participation and Productivity
Performance in Canadian Manufacturing.” Canadian Journal of Economics 36:
634–57.


Beck, Thorsten, A. Demirgüç-Kunt, and R. Levine. 2000. “A New Database on
Financial Development and Structure.” World Bank Economic Review 14:
597–605.


Bernard, Andrew B., J. Eaton, J. B. Jensen, and S. Kortum. 2003. “Plants and
Productivity in International Trade.” American Economic Review 93: 1268–90.


Bernard, Andrew B., and J. B. Jensen. 1995. “Exporters, Jobs, and Wages in U.S.
Manufacturing: 1976–1987.” Brookings Papers on Economic Activity:
Microeconomics 67–119.


———. 1999. “Exceptional Exporter Performance: Cause, Effect, or Both?”
Journal of International Economics 47: 1–25.


———. 2004. “Why Some Firms Export.” Review of Economics and Statistics 86:
561–69.


Bigsten, Arne, P. Collier, S. Dercon, M. Fafchamps, B. Gauthier,
J. W. Gunning, A. Oduro, R. Oostendorp, C. Pattillo, M. Söderbom, F. Teal, and
A. Zeuf. 2004. “Do African Manufacturing Firms Learn from Exporting?”
Journal of Development Studies 40 (3): 115–41.




72 Pathways to African Export Sustainability


Bigsten, Arne, and M. Söderbom. 2006. “What Have We Learned from a Decade
of Manufacturing Enterprise Surveys in Africa?” Policy Research Working
Paper 3798, World Bank, Washington, DC.


Blalock, Garrick, and P. Gertler. 2004. “Learning from Exporting Revisited in a
Less Developed Setting.” Journal of Development Economics 75: 397–416.


Boermans, Martijn. 2010. “Learning-by-Exporting and Destination Effects:
Evidence from African SMEs.” Unpublished. Hogeschool Utrecht University
of Applied Sciences. Available at SSRN: http://ssrn.com/abstract=1612770 or
http://dx.doi.org/10.2139/ssrn.1612770.


Brambilla, Irene, D. Lederman, and G. Porto. 2010. “Exports, Export Destinations,
and Skills.” NBER Working Paper 15995, National Bureau of Economic
Research, Cambridge, MA.


Cadot, Olivier. 2008. “Stratégie de Développement Commercial, Rapport de
cadrage.” Unpublished report. Bamako: Rapport pour le Gouvernement du
Mali.


Cadot, Olivier, C. Carrère, and V. Strauss-Kahn. 2011. “Export Diversification:
What’s Behind the Hump?” Review of Economics & Statistics 93: 590–605.


Cadot, Olivier, L. Iacovone, D. Pierola, and F. Rauch. 2011. “Success and Failure
of African Exporters.” Policy Research Working Paper 5657, World Bank,
Washington, DC.


Castellani, Davide. 2002. “Export Behavior and Productivity Growth: Evidence
from Italian Manufacturing Firms.” Review of World Economics 138: 605–28.


Clerides, Sofronis, S. Lach, and J. Tybout. 1998. “Is Learning by Exporting
Important? Micro-Dynamic Evidence from Colombia.” Quarterly Journal of
Economics 113 (3): 903–47.


Collier, Paul, and J. W. Gunning. 1999. “Explaining African Economic Performance.”
Journal of Economic Literature 32: 64–111.


Crozet, Matthieu, K. Head, and T. Mayer. 2009. “Quality Sorting and Trade: Firm-
Level Evidence for French Wine.” CEPII Working Paper 2009–14, CEPII,
Paris.


Damijan, Joze P., S. Polanec, and J. Prasnikar. 2004. “Self-Selection, Export
Market, Heterogeneity and Productivity Improvements: Firm Level Evidence
from Slovenia.” LICOS Discussion Paper 14804, Katholieke Universiteit,
Leuven.


De Loecker, Jan. 2004. “Do Exports Generate Higher Productivity? Evidence
from Slovenia.” LICOS Discussion Paper 151, Katholieke Universiteit,
Leuven.


Delgado, Miguel A., J. Farinas, and S. Ruano. 2002. “Firm Productivity and Export
Markets: A Non-Parametric Approach.” Journal of International Economics 57:
397–422.




Countries, Institutions, and Policies 73


Djankov, Simeon, C. Freund, and C. Pham. 2010. “Trading on Time.” The Review
of Economics and Statistics 92 (1): 166–73.


Dutt, Pushan, I. Mihov, and T. van Zandt. 2008. “Trade Diversification and
Economic Development.” Unpublished. INSEAD, Fontainebleau, France.
http://faculty.insead.edu/dutt/Research/diversification.pdf.


Eaton, Jonathan, M. Eslava, M. Kugler, and J. Tybout. 2008. “Export Dynamics in
Colombia: Firm-Level Evidence.” In The Organization of Firms in a Global
Economy, ed. E. Helpman, D. Marin, and T. Verdier, 231–72. Cambridge, MA:
Harvard University Press.


Edwards, Lawrence, and N. Balchin. 2008. “Trade-Related Business Climate and
Manufacturing Export Performance in Africa: A Firm-Level Analysis.” Journal
of Development Perspectives 4 (1): 67–92.


Freund, Caroline, and N. Rocha. 2010. “What Constrains Africa’s Exports ?” Policy
Research Working Paper 5184, World Bank, Washington, DC.


Girma, Sourafel, D. Greenaway, and R. Kneller. 2004. “Does Exporting Increase
Productivity? A Microeconometric Analysis of Matched Firms.” Review of
International Economics 12: 855–66.


Graner, Mats, and A. Isaksson. 2007. “Firm Efficiency and the Destination of
Exports: Evidence from Kenyan Plant-Level Data.” Unpublished document.
UNIDO. http://www.unido.org/fileadmin/user_media/Publications/Pub_free
/Firm_efficiency_and_destination_of_exports.pdf.


Greenaway, David, and R. Kneller. 2007. “Firm Heterogeneity, Exporting and
Foreign Direct Investment.” The Economic Journal 117: 134–61.


Hagemejer, Jan, and M. Kolasa. 2008. “Internationalization and Economic
Performance of Enterprises: Evidence from Firm-Level Data.” MPRA Working
Paper 8720, Munich.


Hansson, Par, and N. Lundin. 2004. “Exports as Indicator on or as Promoter of
Successful Swedish Manufacturing Firms in the 1990s.” Weltwirtschaftliches
Archiv 140: 415–45.


Harding, Alan, M. Söderbom, and F. Teal. 2004. “Survival and Success among
African Manufacturers.” CSAE Working Paper Series 2004–05, Centre for the
Study of African Economies, University of Oxford, Oxford.


Hausmann, Ricardo, and D. Rodrik. 2003. “Economic Development as Self
Discovery.” Journal of Development Economics 72: 603–33.


Hausmann, Ricardo, J. Hwang, and D. Rodrik. 2007. “What You Export Matters.”
Journal of Economic Growth 12: 1–25.


Heritage Foundation. 2011. Index of Economic Freedom (database). http://www
.heritage.org/index/default.


Jaud, Mélise. 2011. “Food Safety, Reputation, and Trade.” Working Paper
halshs-00586310, HAL Paris School of Economics, Paris.




74 Pathways to African Export Sustainability


Jaud, Mélise, and O. Cadot. 2011. “A Second Look at the Pesticides Initiative
Program: Evidence from Senegal.” Policy Research Working Paper 5635,
World Bank, Washington, DC.


Jouanjean, Marie-Agnes, and O. Cadot. 2011. “A Second Look at the Pesticides
Initiative Program: Evidence from Senegal.” Policy Research Working Paper
5635, World Bank, Washington, DC.


Jouanjean, Marie-Agnes, J.-C. Maur, and B. Shepherd. “US SPS Enforcement: Do
Refusals Harm Reputation?” Forthcoming in Non-Tariff Measures: New
Analysis for Trade Policy’s New Frontier, ed. O. Cadot and M. Malouche.
London/Washington, DC: The World Bank and CEPR. (Center for Economic
and Policy Research).


Levchenko, Andrei. 2007. “Institutional Quality and International Trade.” Review
of Economic Studies 74: 791–819.


LPI (the Logistics Performance Index). Database. World Bank, Washington, DC.
http://go.worldbank.org/88X6PU5GV0.


Maertens, Miet, and J. Swinnen. 2009. “Trade, Standards, and Poverty: Evidence
from Senegal.” World Development 37 (1): 161–78.


Mayer, Thierry, M. Melitz, and G. Ottaviano. 2011. “Market Size, Competition,
and the Product Mix of Exporters.” Working Papers 2011-11, CEPII research
center.


Melitz, Marc. 2003. “The Impact of Trade on Intra-industry Reallocations and
Aggregate Industry Productivity.” Econometrica 71: 1695–725.


Mengistae, Taye, and C. Pattillo. 2004. “Export Orientation and Productivity in
Sub-Saharan Africa.” IMF Staff Papers 51 (2): 327–53.


Naumann, Eckart. 2008. “Intra-SADC and SADC-EU Rules of Origin: Reflections
on Recent Developments and Prospects for Change.” Draft. TRALAC,
Stellenbosch.


Nicita, M. Shirotori and B. Tumurchudur-Klok. 2011. “Survival Analysis of LDCs’
Exports: The Role of Comparative Advantage.” Unpublished document.
UNCTAD, Geneva.


Nunn, Nathan. 2007. “Relationship Specificity, Incomplete Contracts, and the
Pattern of Trade.” Quarterly Journal of Economics 122: 569–600.


Pisu, Mauro. 2008. “Export Destinations and Learning-by-Exporting: Evidence
from Belgium.” NBB Working Paper 140, National Bank of Belgium, Brussels.


PriceWaterhouseCoopers. 2007. “Evaluation of the Implementation of the Small
Stock Marketing Scheme in Relation to the Namibian Government’s Value
Addition Goals and Objectives.” Windhoek: PriceWaterhouseCoopers.


Schott, P. 2003. “One Size Fits All? Heckscher-Ohlin Specialization in Global
Production.” American Economic Review 93: 686–708.




Countries, Institutions, and Policies 75


Talijaard, P., Z. Alemu, A. Joote, H. Jordaan, and L. Botha. 2009. “The Impact of
Namibian Small Stock Marketing Scheme on South Africa.” National
Agricultural Marketing Council, South Africa.


Tewari, Meenu. 1999. “Successful Adjustment in Indian Industry: The Case of
Ludhiana’s Woolen Knitwear Industry.” World Development 27: 1651–71.


Tumurchudur Klok, Bolormaa, M. Shirotori, and O. Cadot. 2010. “Revealed
Factor Intensity Indices at the Product Level.” Policy Issues in International
Trade and Commodities Study Series No. 44, UNCTAD, New York and
Geneva.


Van Biesebroeck, Johannes. 2003. “Exporting Raises Productivity in Sub-Saharan
Manufacturing Plants.” NBER Working Paper 10020, National Bureau of
Economic Research, Cambridge, MA.


Volpe, Christian, and J. Carballo. 2009. “Survival of New Exporters in Developing
Countries: Does It Matter How They Diversify?” IDB Working Paper
WP-I140, Inter-American Development Bank, Washington, DC.


Wagner, Joachim. 2002. “The Causal Effect of Exports on Firm Size and Labor
Productivity: First Evidence from a Matching Approach.” Economics Letters
77: 287–92.


———. 2007. “Exports and Productivity: A Survey of the Evidence from Firm-
Level Data.” World Economy 30: 60–82.


World Bank. Ease of Doing Business. Website. World Bank, Washington, DC.
http://www.doingbusiness.org/.


———. Enterprise Surveys. Website. http://www.enterprisesurveys.org/.


———. 2010a. Doing Business 2011: Making a Difference for Entrepreneurs.
Washington, DC: World Bank.


———. 2010b. “Connecting to Compete: Trade Logistics in the Global Economy.
The Logistic Performance Index and Its Indicators.” World Bank, Washington,
DC.


———. 2011. “Facilitating Cross-Border Trade between the DRC and Neighbors
in the Great Lakes Region of Africa: Improving Conditions for Poor Traders.”
Report No. 62992-AFR, http://siteresources.worldbank.org/INTAFRREGT
OPTRADE/Resources/Great_Lakes_Final_Report_21_June_11.pdf.


———. 2012. De-Fragmenting Africa: Deepening Regional Trade Integration in
Goods and Services, P. Brenton and G. Isik, ed. Washington, DC: World Bank.






77


C H A P T E R 3


Survival, Contracts, and Networks


In chapter 2, we analyzed some of the determinants of export
sustainability at the country level—mainly comparative advantage, the
business environment, and standards and technical regulations. In this
chapter, we further evaluate the determinants of survival by assessing the
links between survival and firm strategies and informal institutions, pri-
marily discussing the role of linkages and networks.


Exports, Firms, and Survival


The survival of an export spell and that of the exporting firm are linked
by the simple fact that if the firm ceases to exist, so will the products that
it exports. However, there is more than this to the relationship, as exit
from export markets can be a survival strategy for the exporting firm—
getting out of money-losing markets may be part of a strategic reposition-
ing or restructuring process. Thus, exit from export markets may reflect
either the firm’s exit from business or a decision to cut losses that condi-
tions its survival. The following section provides a brief overview of the
empirical literature on firm exit decisions, looking for possible linkages to
export survival.




78 Pathways to African Export Sustainability


What Do We Know about Firm Survival?
Beyond the simple observation above, the literature is largely silent on the
relationship between export survival and firm survival. However, some
indirect inferences can be made from the literature on firm exit decisions,
especially those of multinational firms. The gist of the argument is that
exporting firms are typically stronger than non-exporting ones, so firm
exit is unlikely to be a key differential driver of short export survival,
although it may, of course, contribute somewhat. Instead, the most fre-
quent reason for the exit of an export spell from a product-origin-desti-
nation combination is likely to be a managerial decision to shift outlets
for a given production facility or to move that production facility to a
different location.


Exporting firms are typically larger and more productive than non-
exporting ones (see, among others, Bernard et al. 2007). By the same
token, they are also stronger and able to survive longer. Firm attributes
that correlate with survival also correlate with the decision to export. For
instance, Audretsch and Mahmood (1995); Audretsch, Santarelli, and
Vivarelli (1999); Disney, Haskel, and Heden (2003); Dunne, Roberts, and
Samuelson (1988); and Mata and Portugal (1994) find that firm charac-
teristics such as productivity, capital intensity, wage levels, and size cor-
relate with survival. We already saw that these characteristics correlate
with the decision to export. Thus, because exporting firms are the largest
and most productive firms, they are also the best equipped to survive, and
the high turnover observed in export activities cannot be primarily
explained by a particular vulnerability of exporting firms. If anything,
exporting firms are stronger than non-exporters; therefore, a substantial
part of the observed churning has to be caused by voluntary decisions to
exit export markets because costs are too high or profitability is too
low—in other words, because export markets are particularly tough.


Export-oriented investments by multinational companies in labor-
intensive sectors might be conjectured to contribute to improve average
export survival in the host country, because these companies are likely to
be among the strongest. However, that may well not be the case, because
they are also footloose. In one of the early papers, Flamm (1984) showed
that multinationals hedge against local country risk by spreading produc-
tion sites over several locations even when doing so entails higher costs
(his object of study is semiconductor assembly, which is highly labor-
intensive and uses largely unskilled entry-job female labor). By reducing
cross-location switching costs, multinationals make their production
facilities footloose. Easy switching reduces sunk costs. While in the model




Survival, Contracts, and Networks 79


presented in annex 1A of chapter 1 uncertainty was found in the export
market, in Flamm’s model uncertainty emanates from the production
site. However, the logic remains the same. With lower sunk costs of entry
and exit (an easier switching of production from one site to another),
multinationals make themselves less willing to put up with losses on the
production site, contributing to shorter export spells. Maintaining a diver-
sified portfolio of production sites induces multinationals to keep several
sites active at all times, but the location of those sites may shift around.


Later studies confirmed that multinationals were more footloose than
domestic firms after controlling for firm and industry differences (Bernard
and Sjöholm 2003; Görg and Strobl 2003b; Girma and Görg 2004),
although Mata and Portugal (2002) and Özler and Taymaz (2007) found
no significant difference in survival rates between domestic and multina-
tionals. Ferragina, Pittiglio, and Reganati (2011) found substantial varia-
tion across sectors on Italian data. All in all, the literature suggests that
export-oriented investment by multinationals does not appear to be, in
itself, necessarily conducive to better export survival.


The literature surveyed so far has not uncovered a strong causal link
between patterns of firm or plant closure and patterns of exit from
export markets or from a particular product-origin-destination combi-
nation. In fact, causation may run the other way around. Bernard and
Jensen (2002, 2007) showed that the probability of firm exit decreases
with the number of products produced and the number of markets
served. Thus, exporting and export diversification, in particular when
this diversification takes place into several geographical markets,
enhances firm survival, either through hedging of risk or through better
access to inputs.


Could it be, however, that firm survival correlates negatively with
export survival at the product-origin-destination cell level? As the
diversification of production facilities, in Flamm’s argument, makes
multinationals footloose, one might expect that simultaneously operat-
ing in different cells might make firms more willing to close one and
open another, making expected survival lower at the firm-cell level.
Volpe and Carballo (2009) and Cadot et al. (2011) find the opposite
pattern—namely, that a firm-cell’s survival correlates positively with
the number of markets the firm serves with that product and with the
number of products that it ships to the same destination. Low-income
countries have mainly single-product, single-market exporters, whereas
in middle-income countries there are more multi-product, multi-market
exports.




80 Pathways to African Export Sustainability


Thus, diversification does not make firms footloose across cells and
does not explain churning. Quantitatively, in Volpe and Carballo (2009),
adding one destination does as much as adding three products on survival;
in Cadot et al. (2011), it does as much as adding six products. These
results are confirmed by Carrère and Strauss-Kahn’s 2011 study. Looking
at spell survival on Organisation for Economic Co-operation and
Development (OECD) markets, they find that one non-OECD destina-
tion—for the same origin-product pair at the Standard International
Trade Classification (SITC)-5 level—adds 11 percentage points to the
probability of first-year survival, from 35.7 percent to 46.8 percent, while
a second destination adds another 6 percentage points. To have the same
effect on survival, the range of products exported to the same destination
would have to shoot up from the [0–30] bracket to the [30–200]
bracket.1


Contracts, Reputations, and Trade Relationships
Incomplete enforcement of contracts across borders affects export sur-
vival in two ways: (1) by inducing termination of relationships when one
party reneges on a contract and (2) by reducing transaction volumes,
which, as we saw earlier, correlate with survival.


International trade is particularly vulnerable to incomplete contract
enforcement. In the words of Dani Rodrik:


Transaction costs [in international trade] arise from various sources, but per-
haps the most obvious is the problem of contract enforcement. When one of
the parties reneges on a written contract, local courts may be unwilling—and
international courts unable—to enforce a contract signed between residents
of two different countries. Thus national sovereignty interferes with contract
enforcement, leaving international transactions hostage to an increased risk
of opportunistic behavior (Rodrik 2000, p. 179, quoted in Araujo and
Ornelas 2007).


In the presence of incomplete contract enforcement, market mecha-
nisms typically emerge to alleviate moral hazard and adverse selection.
Moral hazard is typically dealt with through self-enforcing contracts with
credible non-legal sanctions (later in this chapter, we will discuss the role
of ethnic and other networks in this regard), while adverse selection is
typically dealt with through signaling and the building of reputations.


Reputations are built in the course of repeated interaction. In the case
of producers located in developing countries, empirical evidence sug-
gests that buyers in industrial countries typically first test them through




Survival, Contracts, and Networks 81


small orders, and then ramp up transaction volumes upon satisfactory
performance in the pilot-order phase. Sometimes, small volume is the
choice of the exporter himself. Tewari (1999) cites the case of an Indian
knitwear exporter who was approached by JCPenney for large orders
and turned down the offer, preferring to stick with a small-scale business
relationship with Ferrucci and other Italian buyers.


JCPenney promised big orders, but very low rates. The European firms were
small; they gave small orders but the rates were better, and they had built
up a relationship with us. They gave advice, and the small orders made it
possible [for us] to learn from our mistakes (Rai Bahadur interview, 1990,
1998, cited in Tewari 1999, pp. 1663–64).


As Tewari further discusses, learning at the firm level typically takes
place in small steps, which means that the “one mistake and you are out”
environment of the supply chains manned by volume retailers is hardly
those where reputations could be built progressively.


These insights have been formalized by Rauch and Watson (2003) in
a theoretical model showing that buyers who are uncertain about the
capabilities of their suppliers start relationships with small orders, and
then ramp up transaction volumes. Their model shows that the initial
“underordering” is more severe when the cost of searching for new part-
ners increases. It also shows that large-volume transactions correlate with
longer persistence. The reason is that both correlate with lower costs. We
saw in annex 1A of chapter 1 that the higher the profitability is of an
export business, the more the exporter will persist during bad times.
Similar reasoning applies to buyers: the lower the supplier’s cost, the
higher the transaction’s profitability. This will raise both volumes pur-
chased and persistence. The positive relationship between transaction
volumes and the reputation of the party with uncertain quality or moral
hazard is also found in Araujo and Ornelas (2007) and Jaud (2011).


Learning and Synergies


If firms do not learn from exporting, it does not matter how long they
stay in export markets: high churning does not deprive them from
dynamic economies of scale. But if firms learn from exporting, keeping
them in the market is important, because exiting has an opportunity cost
that goes beyond the lost sales. Put another way, the learning effect
might well be what drives the decreasing hazard rates observed in some
of the empirical work on survival. That is, give exporters time to learn




82 Pathways to African Export Sustainability


and they will figure out how to survive. We will see in this section
whether the empirical literature offers any guidance on how to maxi-
mize learning effects, the lessons of which may be relevant for policies
to raise survival rates.


If there are synergies between exporters, the dynamic learning
effect is compounded by a market failure—namely, the positive exter-
nality that exporters exert on each other. Intuitively, it is not obvious
why exporters should reinforce each other: one would rather expect
them to compete with each other. But channels of interaction other
than price—which is unlikely to be a strong one for small-scale
African exporters anyway—may exist, whereby exporters reinforce
each other’s credibility. The following two sections will explore these
conjectures in turn.


Do African Firms Learn from Exporting?
Whether firms learn from exporting or simply self-select into exporting
markets when they are more efficient has been a subject of controversy
for a while in the international trade literature. Exporters are typically the
upper tail of the distribution of firms in terms of size, capital intensity,
productivity, wage levels, and survival (see, for example, Bernard and
Jensen, 1995, 1999, 2007). Until recently, the weight of the empirical
evidence supported the theoretical insight initiated by Melitz (2003):
that more efficient firms exported because they were more efficient, not
the other way around, because learning effects appeared small or insig-
nificant. Most of the papers in this strand of literature (Aw and Hwang
1995; Castellani 2002; Delgado, Farinas, and Ruano 2002; Hansson and
Lundin 2004; Wagner 2002) used data from industrial countries, which
means that their applicability to an African context was limited. But a
few used data from developing countries. Clerides, Lach, and Tybout
(1998) used data from Colombia and Mexico; Isgut (2001) from
Colombia; and Alvarez and Lopez (2005) from Chile, all with pretty
much the same message.


More recently, however, new empirical evidence has emerged sug-
gesting that firms do learn from exporting, in particular when they
operate from emerging markets. Most of this literature relies on match-
ing techniques.2 Blalock and Gertler (2004) found that the total factor
productivity (TFP) of Indonesian exporters rose more than that of
non-exporters. De Loecker (2004) and Hagemejer and Kolasa (2008)
also found that exporting firms had larger TFP growth than matched
non-exporting ones.




Survival, Contracts, and Networks 83


More relevant to us, Bigsten et al. (2004), Graner and Isaksson (2007), and
Mengistae and Pattillo (2004) found support for the learning-by-exporting
hypothesis in terms of various performance measures including TFP growth
on various samples of Sub-Saharan African manufacturing firms in the
1990s.


How can we reconcile these two strands of conflicting evidence? First,
the new literature’s interpretation of learning-by-exporting is fairly wide.
Papers in this strand identify transformational processes including, in
particular, capital deepening. They also identify performance improve-
ments including higher sales, earnings, and employee salaries, but none
of these changes constitutes direct evidence of “learning” in the strict
sense, even though they may well go with learning by managers and
employees. There is no direct evidence, for instance, of exporting firms
organizing or participating in training programs.3 There is also little guid-
ance from the theoretical literature. Albornoz et al. (2010) propose a
model in which firms learn about the profitability of export, which is
correlated over time within destinations and across destinations as well.
Entry generates information that firms use to revise their profitability
expectations for future entry and exit decisions. The model suggests that
firms are more likely to exit a destination market after one year when
that year is their first try, because information is then minimal. As firms
accumulate experience over export spells and markets, they become less
sensitive to bad news. Thus, in Albornoz et al., learning is a reduction in
uncertainty about key parameters, not an improvement in production
processes.


Some anecdotal evidence on the channels through which learning-
by-exporting takes place can be found in the literature on cross-border
supply chains. Rhee, Ross-Larson, and Pursell (1984) described, in their
classic survey of manufacturers from the Republic of Korea (a survey
carried out in 1975, at a time where Korea was still a relatively poor
country), how technology was transferred by foreign buyers to their
suppliers:


The relations between Korean firms and the foreign buyers went far beyond
the negotiation and fulfillment of contracts. Almost half the firms said they
had directly benefitted from the technical information foreign buyers pro-
vided: through visits to their plants by engineers or other technical staff of
the foreign buyers, through visits by their engineering staff to the foreign
buyer, . . . (Rhee, Ross-Larson, and Pursell 1984, p. 61; quoted in Rauch
2001).




84 Pathways to African Export Sustainability


Gereffi (1999) also described how least-developed-country producers
of textile and apparel products producing under CMT (“cut, make, and
trim,” meaning assembly of clothing products with precut pieces of fab-
ric) typically learn how to source fabric themselves and produce to the
buyer’s design, progressively transforming themselves into branded
manufacturers. The anecdotal evidence is thus suggestive of learning
through the interaction that takes place between buyers and subcontrac-
tors in global value chains. There is little evidence, even anecdotal, of
learning through arms-length relationships. The lack of evidence does not
imply that learning does not take place either, however—it means simply
that we do not know whether it does or does not.


A second issue is that the two literatures referred to above describe
different things. The earlier one draws mainly from samples of firms
located in industrial or middle-income countries, whereas the later one
uses samples of firms from developing countries. One may thus conjec-
ture that the scope for learning-by-exporting somehow relates to the
exporter’s distance from the technological frontier. The more backward a
firm’s technology and management culture are (as proxied by the income
level of the origin country), the more that firm can learn from contact
with demanding buyers located in higher-income markets.


Indeed, a number of papers have identified “destination effects”
whereby firms exporting to higher-income countries appear to differ in
both characteristics and production methods from those exporting only
to lower-income ones. Crozet, Head, and Mayer (2009); Damijan,
Polanec, and Prasnikar (2004); Graner and Isaksson (2007); and Pisu
(2008), among others, all find evidence of destination effects. Brambilla,
Lederman, and Porto (2010) use the Brazilian devaluation of the 1990s
as a natural experiment to identify destination effects on Argentine firms.
They do find that, as firms redirected their sales away from the less-
profitable Brazilian market toward higher-income destinations, they
became more skill-intensive in a way that is measurable at the firm level.
Mengistae and Pattillo (2004) also find that exporting to non-African
markets generates stronger improvements in performance measures than
exporting solely within Africa. Using the World Bank’s Regional Program
on Enterprise Development (RPED) survey of manufacturing firms
located in five Sub-Saharan African countries, Boermans (2010) finds
evidence of learning-by-exporting and of destination effects. That is, those
exporters with sales outside of Africa report a higher capital intensity;
they also report, upon starting to export, higher increases in employment
and earnings than matched control firms.4




Survival, Contracts, and Networks 85


As in the case of learning-by-exporting, we do not know much about
the channels through which destination effects take place. It may be that
selling to demanding markets induces transformational changes in export-
ing firms, possibly with technical assistance from buyers (see our previous
discussion on food standards). Alternatively, Mayer, Melitz, and Ottaviano
(2011) have developed a model showing how tougher competition in a
destination market—reflected in lower markups on all products sold on
that market—induces firms to concentrate on their best-performing
products in that market. They also provide supporting evidence from
French firms. Thus, it may be that the improvement in performance mea-
sures is a composition effect within the firm (across products) rather than
a transformation of all production processes.


All in all, it is fair to say that the weight of the evidence is increasingly
on the side of the existence of learning economies (dynamic economies
of scale) in exporting, particularly for producers in poor countries that
may start far away from the efficiency frontier. These learning economies
can interact with survival in two ways: (1) learning makes exporters
stronger over time (higher TFP and earnings), which contributes to their
ability to survive; and (2) reducing the infant mortality of exports can be
expected to have two effects: a direct one (reducing the number of exits)
and an indirect one (giving exporters access to a second phase of life
where hazard rates are lower).


Synergies
Synergies between exporters can take several forms. First, exporters’
decisions to enter foreign markets and their subsequent success or fail-
ure may reveal information about the parameters of demand on those
markets and the capabilities (costs and productivity) of similar produc-
ers from the same country to serve that demand. In that way, exporters
help the “self-discovery” process discussed by Hausmann and Rodrik
(2003). Second, they may—if one believes the learning-by-exporting
hypothesis discussed in the previous section—adopt new processes and
products that allow them to better survive in foreign markets. These
“discoveries” may be imitated by other firms, making those imitating
firms also better able to enter and survive in foreign markets. Third,
there may be “critical mass” effects. When a sufficiently large number
of firms serve a given market with a given product, they may get
noticed by buyers who will then interpret signals in a more favorable
way. Likewise, the presence of several firms in a product-destination
niche may help convince bankers in the origin country that there is




86 Pathways to African Export Sustainability


business potential, which may ease access to finance for each one of the
exporting firms.


In one of the first papers exploring synergies, Clerides, Lach, and
Tybout (1998) tested for spillovers from exporting by including an
industry-level export-intensity index as one of the explanatory variables
in firm-level export-participation and cost functions. A significant effect
on the participation function would signal externalities on the cost of
entry—consistent with the “self-discovery” conjecture—and a significant
effect on the cost function would signal externalities on production pro-
cesses. Results were disappointing—effects on both the probability of
entry and costs were imprecisely estimated, partly because the industry-
level externality variable was highly collinear with the exchange rate and
sometimes went the wrong way, leading the authors to conclude that
there might even be crowding out via factor markets.


Closer to our concerns here, Cadot et al. (2011) tested directly for
cross-exporter externalities on the ability to survive using a firm-level
data set of exporters from four African countries.5 Their performance
variable is the probability of surviving past the first year at the level of a
(firm × product × destination) cell. Controls include the number of prod-
ucts the same firm exports to the same destination, the number of desti-
nations to which the same firm exports the same product, the export
spell’s initial value, and the product’s share in the firm’s overall exports.
The regression also includes time and origin-destination effects. Synergies
across exporters are measured by the number of firms from the same
origin exporting the same product to the same destination—direct
national competitors. Their point estimate implies that, for example, dou-
bling the number of national competitors at the cell level, from 22 to 44,
would raise the first-year survival probability of a Senegalese firm from
22 percent to 26 percent. This effect is highly significant, although quan-
titatively limited.


What is the source of the cross-exporter synergy? There is no direct
evidence on information spillovers. Audretsch (1991) and Doms, Dunne,
and Roberts (1995) relate innovation to firm survival, so products and
process improvements may be one of the things that firms steal from
each other when they operate in similar markets. Beyond conjecture, the
World Bank survey of African exporters already discussed in chapter 2
can be used to get a glimpse at information sharing on one dimension—
contacting buyers. Figure 3.1 shows that competitors’ networks are men-
tioned as the primary source of contacts with clients by only 15 percent
of the survey’s African respondents, coming after “third party”—a




Survival, Contracts, and Networks 87


catch-all category—but also trade fairs (17 percent) and online research
(16 percent).


Thus, direct informational spillovers do not appear as a driving force
of the synergy identified. Cadot et al.’s (2011) preferred hypothesis is,
instead, the presence of indirect spillovers operating via the banking
system. Consider the following scenario. A Senegalese firm is approached
by a U.S. buyer to provide a small trial order of t-shirts. Upon success-
ful delivery and sale, the buyer is satisfied and again contacts the
Senegalese firm for a larger order. Now the Senegalese firm has to
ramp up capacity and, for that, it needs support from financial institu-
tions. In Sub-Saharan Africa, financial institutions may not take letters
of credit from the buyer at face value, because they are aware of the
risk of all sorts of glitches—quality or others—that may appear down
the line. In fact, anecdotal experience suggests that the bank’s response
will typically be “no” irrespective of the proofs of profitability that the
exporter shows, and the trade relationship with the U.S. buyer will end
before it had a chance to bear fruit. However, if several Senegalese
firms already sell t-shirts on the U.S. market, the same financial institu-
tions may be more easily convinced about the chances of success of
this venture and better evaluate the potential risks involved in this
transaction.


0


type of contact source


p
er


ce
n


t
o


f s
u


rv
ey


re
sp


o
n


d
en


ts


5
10


15
20
25
30


35
40


th
ird


p
ar


ty


tra
de


fa
ir


on
lin


e


co
m


pe
tit


or
s’


ne
tw


or
k


EP
A


ex
po


rte
r a


ss
oc


iat
io


n
ot


he
r


Figure 3.1 Source of Client Contact, 2009


Source: World Bank survey of African exporters, conducted in 2009.
Note: EPA = Economic Partnership Agreement.




88 Pathways to African Export Sustainability


If this scenario is representative, the synergy effect should be stronger
for products that are highly dependent on external finance, because
initial financial constraints would be more binding on those sectors.
Cadot et al. (2011) test this conjecture by interacting their synergy vari-
able with the measure of dependence on external finance proposed by
Rajan and Zingales (1998).6 The interaction term is positive and sig-
nificant, suggesting that, indeed, synergies are stronger for finance-
dependent products. As an alternative way of getting a handle on the
degree of dependence from finance, Cadot et al. (2011) use a proxy for
“asset tangibility” proposed by Braun (2003).7 The idea is that firms
with more tangible assets present lower risks because these assets pro-
vide real guarantees for bank loans, and information asymmetries
(adverse selection or moral hazard) are less important with good col-
lateral, so synergy effects should play a lesser role. The interaction of
asset tangibility with the synergy effect has a negative and significant
coefficient, implying that firms belonging to industries with high asset
tangibility (essentially these are capital-intensive industries) are less
sensitive to the synergy effect.


Networks: Migrants and Diasporas


In this section we discuss the effect of migrants and diasporas on export
survival, with special attention given to the role they play in the African
continent.


Networks and Trade
Networks, whether based on kinship, ethnic groups, or city-states, are as
old as trade itself. Greif (1989, 1993) provides a detailed description,
based on historical records, of the eleventh-century Maghribi Jewish
traders’ network in the Arab-dominated Mediterranean. The literature
conjectures that traders’ networks serve essentially two functions:


• provide information to members about market opportunities and risks
and facilitate the matching of buyers and sellers (see, for example,
Rauch and Casella 2003).


• overcome moral hazard through various informal incentive
mechanisms.


There is plenty of anecdotal evidence in support of both conjectures (see
Rauch 2001 for a complete review of the literature). As for information,




Survival, Contracts, and Networks 89


Saxenian (1999), for instance, argued that the development of software
exports from southern India was linked to the existence of a network of
Indian immigrant entrepreneurs in the United States. As for moral hazard,
Cohen (1971) showed how the Hausa, a network of ethnically related trad-
ers with roots in northern Nigeria, organized its diaspora in Ibadan (a city
in Yoruba land, in southwest Nigeria) to enforce obligations among mem-
bers:


Among the Hausa, the creditworthiness of a business landlord is measured
first by his housing assets. He cannot dispose of these assets without the
mediation of the chief of his community. The chief also acts as arbitrator in
business disputes. A landlord cannot sell his houses overnight and leave the
community after embezzling the money of traders. On the other hand, when
it is necessary, the Chief can put a great deal of pressure on a landlord in
difficulties to sell some of his housing assets in order to meet his financial
obligations to traders (Cohen 1971, p. 274).


In this case, network relations are not the only mechanism for contract
enforcement: the cultural tradition of accumulating wealth through an
illiquid asset—land—makes community-based contract enforcement
easier, although ultimately there is no court-based or other hard enforce-
ment mechanism. Similarly, in their study of Chinese networks of traders
abroad, Weidenbaum and Hughes (1996) note, “If a business owner
violates an agreement, he is blacklisted. This is far worse than being sued,
because the entire Chinese network will refrain from doing business with
the guilty party” (p. 51, cited in Rauch 2001).


The bonds that hold diasporas together go well beyond business rela-
tions. Cohen (1971) stresses particularly the role of religion in West
Africa:


Islam has been associated with long-distance trade in West Africa because it
provided a blue-print for the establishment of networks of communities. . . .
[T]o the extent that it has been interconnected with trade, it has done so not
as an epiphenomenon, but as the blue-print of a politico-economic organiza-
tion which has overcome the many basic technical problems of the trade.
Indigenous traders become Moslems in order to partake in the moral com-
munity of other traders. In both Ibadan and Freetown, nearly half the popu-
lation are Christians. Yet in both cities all the butchers without any exception
have converted to Islam, because only in this way can they participate in the
chain of trade in cattle which extends from the savannah down to the forest
area (Cohen 1971, pp. 277–78).




90 Pathways to African Export Sustainability


Quantitatively, the effect of diasporas on international trade flows has
been estimated by including stocks of migrants in gravity equations.
Gould (1994) estimated one-way trade to and from the United States
using gravity determinants as well as the stock of migrants from each of
the partner countries in the United States. He found that a 10 percent
increase in the stock of migrants from a given origin country raised that
country’s exports to the United States by 8.3 percent. Performing a simi-
lar exercise for Canada, Head and Ries (1998) found somewhat smaller
but also significant elasticities, with a 10 percent increase in the stock of
migrants from a partner country, raising that country’s exports to Canada
by 3.3 percent.


Gravity estimates are largely black boxes. Rauch and Trindade (2002)
tried to disentangle the two conjectures mentioned earlier about the
function of networks by running separate gravity equations for differenti-
ated products versus reference-priced ones, following Rauch’s definition,
using ethnic Chinese population shares in 57 countries for 1980 and
1990. They conjectured that if networks helped essentially in matching
buyers with sellers, the effect would be stronger for differentiated prod-
ucts, for which matches are harder because of the heterogeneity of the
products, than for reference-priced ones. They found that the effect of
Chinese diasporas in trading countries had a significant positive effect on
bilateral trade worldwide for both types of products, but more for dif-
ferentiated ones, providing support for the informational-role (matching)
hypothesis. Interestingly, they also found that the effect shrunk in magni-
tude between 1980 and 1990, raising the issue of whether improved
access to information could reduce the importance of networks. This is
surely the case with the generalization of Internet access.


In sum, recent research on trade and networks suggests that they
provide both better information and alternative mechanisms for con-
tract enforcement in weak legal environments. In the case of African
exporters, these mechanisms are of interest for several reasons. First,
low-income exporters are often assumed to suffer from difficult access
to information. However, figure 3.2 shows that, according to the World
Bank’s survey of African exporters previously mentioned, marketing
difficulties are not perceived, by the respondents’ own account, as an
important constraint to exporters’ expansion, especially compared with
access to credit (the first concern) and port, customs, and logistics (the
second).


Second, African exports to developing countries with low levels of
legal security may be affected by moral hazard, which may be alleviated




Survival, Contracts, and Networks 91


by the presence of local diasporas. In order to explore these conjectures
more formally, we first turn to some stylized facts about African migra-
tions within and outside of the continent.


African Migration Patterns
African migration patterns have varied considerably over the last 30 years
as a result of conflicts and violent disruptions, but a constant feature is
the relatively high proportion of intra-African migration—precisely the
result of local disruptions. However, this high proportion of intra-African
migration is not reflected in enhanced levels of intra-African trade flows,
at least not through formal channels. Overall migration patterns are
shown in table 3.1. These patterns are strikingly skewed, with South-
North migrations largely dominating the picture, a majority of zeroes, and
only a few nonzero flows to southern destinations, including those from
Africa to the Middle East.


Table 3.1 has important implications for our conjectures on the role of
migrant diasporas. The bulk of the diasporas are to high-income countries,
which are generally characterized by high levels of legal security. Thus,
one might think that the scope for informal enforcement mechanisms
described in the previous section might be limited. However, the transac-
tion costs of litigation in high-income countries are high, and access to


0


type of barrier


n
u


m
b


er
o


f r
es


p
o


n
se


s


10
20
30
40
50
60
70


fin
an


ce


po
rts


, c
us


to
m


s, &
lo


gi
sti


cs


qu
ali


ty
&


co
m


pe
tit


io
n


pr
od


uc
tiv


e c
ap


ac
ity


&
in


pu
ts


tra
de


p
ol


icy
a


m
ar


ke
tin


g/
fin


di
ng


b
uy


er
s


Figure 3.2 Importance of Barriers to Export: Regular Exporters (Number of
Responses), 2009


Source: World Bank survey of African exporters, conducted in 2009.
a. Trade policy is composed of tariffs, taxes, procedures, and regulations.




92 Pathways to African Export Sustainability


complex legal systems may not be easy for relatively inexperienced
immigrant-entrepreneurs or for small-scale traders with relatively low-
level business disputes. Thus, even in high-income environments, infor-
mal dispute-resolution and contract-enforcement mechanisms may be
efficient.


Intra-African migration patterns are shown in table 3.2. With the
exception of North Africa, which essentially migrates outside of the con-
tinent, migration patterns are heavily intra-regional, especially for West
and southern Africa. This reflects cultural proximity, to some extent, but
also the incidence of forced migration. According to Ratha et al. (2011),
2.2 million Africans live as refugees in countries other than their own
because of wars and natural disasters.


As of end-2009, the main refugee groups were located in Chad, the
Democratic Republic of Congo, Kenya, and Sudan, which together
“hosted” about a million refugees. The main origins of refugees were the
Democratic Republic of Congo, Eritrea, Somalia, and Sudan, which
together account for about 1.8 million refugees (Ratha et al. 2011). These
emigrant stocks are likely to be too vulnerable, impoverished, and
enclaved to serve as trade facilitators.


What do we know about individuals who migrate and their house-
holds? The World Bank’s Living Standards Measurement Surveys,
together with surveys carried out in Burkina Faso, Nigeria, and Senegal


Table 3.1 Origin and Destination of Emigrant Stocks by Region
percent of total emigration


Origin


Destination


Africa
East Asia &
the Pacific


Europe &
Central


Asia
Latin Am.
& the Car.


Middle
East


South
Asia


High-
income


countries


Africa 50 0 0 0 4 0 46
East Asia


& the Pacific 0 15 0 0 1 0 83
Europe &


Central Asia 0 0 59 0 0 0 41
Latin America &


the Caribbean 0 0 0 13 0 0 87
Middle East 2 0 1 1 45 0 51
South Asia 0 2 0 0 8 30 61
High-income


countries 1 1 3 5 2 0 87


Source: Ratha et al. 2011.




Survival, Contracts, and Networks 93


as part of the World Bank’s Africa Migration Project, provide some
information. Unsurprisingly, larger households are more likely to send
one member into emigration. Income and education seem to have, at
least to some extent, opposite effects. An educated head of household
makes emigration by at least one member of the household more likely;
for instance, in Ghana, an additional year of education for the head of a
household raises the probability of emigration by one household mem-
ber by 8 percent (Ratha et al. 2011). However, past a certain level,
income rises make emigration less likely. These may reflect the positive
effect of education on access to information needed for emigration and
the negative effect of local opportunities associated with income on the
incentive to move. Survey results for Burkina Faso, shown in figure 3.3,
highlight the occupational changes that go with migration.


The fact that the majority of the respondents were farmers before
migration is due to sample selection, as the survey was administered in
rural areas. Keeping this caveat in mind, the survey highlights interesting
occupational changes, in particular for internal migrants, among whom
the proportion of farmers drops from 85.7 percent to 36.4 percent,
whereas the proportion of traders—the case that is most interesting in
terms of effects on trade—rises from 4.5 percent to 20.9 percent. Among
international migrants, however, occupational changes are—surprisingly—
smaller, with the proportion of traders rising only from 2.4 percent to 6.8
percent. The rise in the proportion of professionals is much larger, from
2.1 percent to 23 percent. This may reflect a better ability of migrants to
leverage their level of education in environments where the demand for
skills is higher than in their area of origin.


These stylized facts highlight a complex picture in which emigration
reflects highly diverse incentives—voluntary or not—and where networks


Table 3.2 Origin and Destination of Emigrant Stocks by African Region, Percent of
Total Emigration


Origin


Destination


North Africa
Central
Africa East Africa


Southern
Africa West Africa


Out of
continent


North Africa 6 0 0 0 0 93
Central Africa 0 23 26 9 3 39
East Africa 3 1 52 3 0 41
Southern Africa 0 0 7 66 0 28
West Africa 0 5 0 0 71 24


Source: Ratha et al. 2011.




94 Pathways to African Export Sustainability


of traders such as the eleventh-century Maghribi or today’s Nigerian
Hausa or Chinese diasporas may, actually, be fairly rare among African
migrants. These should serve as caveats for the analysis of the effect of
migrations on the survival of exports, which we consider next.


0
10
20
30
40
50
60
70
80
90


p
er


ce
n


t
o


f a
ll


m
ig


ra
n


ts
p


er
ce


n
t


o
f a


ll
m


ig
ra


n
ts


fa
rm


er
s


tra
de


rs


pr
of


es
sio


na
ls


se
m


i-s
kil


led
w


or
ke


rs


un
sta


bl
e o


cc
up


at
io


ns
ot


he
r


0
10


occupation


occupation


a. internal migrants


b. international migrants


20
30
40
50
60
70
80
90


100


fa
rm


er
s


tra
de


rs


pr
of


es
sio


na
ls


se
m


i-s
kil


led
w


or
ke


rs


un
sta


bl
e o


cc
up


at
io


ns
ot


he
r


before migration after migration


before migration after migration


Figure 3.3 Occupational Changes of Internal and International Migrants: Burkina
Faso, 2009


Source: Ratha et al. 2011.




Survival, Contracts, and Networks 95


Migrants, Diasporas, and Export Survival
We now turn to a quantitative exploration of the relationship between
export-spell survival and migrant stocks in destination countries based on
the large set of export spells used in chapter 1. We modified this data set
in order to make it compatible with our migration data. Bilateral (origin-
destination) migrant stock data are from the World Bank and include two
waves of migration measurement: 1990 and 2000. Accordingly, we kept
only those spells that started in 1990 and 2000 and attributed to each one
the relevant bilateral migrant stock evaluated in the corresponding year.
This, of course, sharply reduced the number of observations to little more
than 61,000.


Control variables include the log of the spell’s initial dollar value, the
growth of its value during its lifetime, a dummy variable marking multi-
ple spells, and a number of typical gravity variables. These include, for
both origin and destination countries, the dollar value of GDP (in logs);
landlockedness dummies; import and export costs, in U.S. dollars per
container; distance; common language, border, and colonial history; and
exchange-rate volatility. The justification for including gravity-type vari-
ables in a survival regression follows annex 1A of chapter 1. Results are
shown in table 3.3. The three specifications differ only by the inclusion
of migration variables, shown in the second part of the table.


Results are largely in line with expectations. Large initial spell values
correlate with lower hazard rates. Spell value growth, however, and mul-
tiple spells correlate with higher hazard rates. Although the coefficient
on spell value growth is unintuitive, the coefficient on multiple spells is
to be expected since both multiple spells and higher hazard rates mean
more stop-and-go in the export relationship. Coefficients on gravity vari-
ables are in line with the predictions of the simple model in annex 1A of
chapter 1. Exporter and importer income levels correlate with lower
hazard rates. Landlocked exporters have higher hazard rates, which may
reflect both the mechanisms highlighted in the model and also the fact
that land routes may be more susceptible to sudden disruptions. The
variables on proximity (common border, language, and colonial past)
have unintuitive coefficients, where exchange-rate volatility has a very
small effect.


Migration variables have effects that are largely in line with the dis-
cussion above. The stock of exporter country migrants in the importer
country (EMIC) correlates negatively with hazard rates. This result sup-
ports the hypothesis that migrant networks contribute to overcome
moral hazard in trade relationships. The stock of migrants from the




Table 3.3 Export-Spell Hazard Rate Estimates: Cox Regressions


(1) (2) (3)


Spell attributes
Initial spell value –0.0811*** –0.0842*** –0.0843***


(0.00236) (0.00236) (0.00236)
Spell value growth 4.58e–05*** 4.69e–05*** 4.70e–05***


(1.68e–05) (1.67e–05) (1.67e–05)
Multiple spell 0.754*** 0.751*** 0.751***


(0.0169) (0.0169) (0.0169)
Gravity variables


ln exporter GDP/cap –0.0673*** –0.0638*** –0.0641***
(0.00810) (0.00821) (0.00821)


ln importer GDP/cap –0.0398*** –0.0485*** –0.0481***
(0.00737) (0.00748) (0.00748)


Landlocked exporter 0.363*** 0.353*** 0.352***
(0.0210) (0.0210) (0.0211)


Landlocked importer –0.0464*** –0.0455*** –0.0462***
(0.0162) (0.0163) (0.0163)


Common border 0.0178 0.0168 0.0165
(0.0141) (0.0142) (0.0142)


Common language 0.102*** 0.0981*** 0.0980***
(0.0121) (0.0121) (0.0121)


Common colonial past 0.141*** 0.131*** 0.132***
(0.0198) (0.0199) (0.0199)


ln distance 0.0792*** 0.0754*** 0.0755***
(0.00647) (0.00654) (0.00655)


Exchange-rate volatility –0.000282** –0.000311** –0.000311**
(4.34e–05) (4.32e–05) (4.32e–05)


Migration and product type variables
Exporter migrants in importing


country (EMIC)
–0.00480*** –0.00297** –0.00303*
(0.00144) (0.00144) (0.00175)


Importer migrants in exporting
country


–0.00368** –0.00239 –0.00250
(0.00151) (0.00155) (0.00155)


EMIC*SSA –0.0280*** –0.0252***
(0.00340) (0.00425)


Differentiated goods –0.127*** –0.142***
(0.0188) (0.0398)


EMIC*Differentiated goods 9.05e–05
(0.00217)


Differentiated goods*SSA 0.176*
(0.0951)


EMIC*Differentiated goods *SSA –0.00505
(0.00562)


Observations 61,179 61,179 61,179
Exporting region FE (not reported) yes yes yes
Importing region FE (not reported) yes yes yes
Time effects (spell start year) yes yes yes


Source: Authors.
Note: Robust standard errors in parentheses. FE = fixed effects; SSA = Sub-Saharan Africa.
Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.


96




Survival, Contracts, and Networks 97


importer country in the exporter one also has a negative effect in the first
specification, which excludes interaction terms, but is insignificant in the
other two specifications, shown in columns 2 and 3.


The second specification includes a dummy for differentiated goods as
defined by Rauch (1999). The coefficient shows that differentiated goods
tend to have longer spells (because the differentiated-goods dummy cor-
relates negatively with hazard rates); this is consistent with the logic of
the model in annex 1A of chapter 1, where larger sunk costs of entry raise
the persistence of trade relationships. One may expect that the cost of
searching for partners—and establishing trust—is higher for differenti-
ated goods that vary, by construction, in terms of quality and attributes,
than it is for homogeneous or reference-priced goods.


However, the interaction of the differentiated-goods variable with the
stock of migrants from the exporting country in the importing one is
insignificant, suggesting that diasporas do not significantly reduce search
costs (otherwise they would reduce the survival of export spells, which
would be reflected in a positive coefficient on the interaction term—here
the coefficient is indeed positive but not significant).


As for Sub-Saharan Africa–specific effects, the higher survival
observed for differentiated goods is weaker for African exports, suggest-
ing that the type of differentiated goods exported from Africa may be
less search-intensive.


Overall, this chapter shows how firm strategies and informal institu-
tions have emerged to alleviate the information asymmetries (moral
hazard and adverse selection) that plague cross-border transactions and
often lead to premature, accidental terminations, which account for their
low survival. These include the following:


• Geographical diversification, which helps firms hedge risks and access
better inputs


• Self-enforcing contracts with credible non-legal sanctions, signaling,
and reputations, which help alleviate moral hazard and adverse selec-
tion in the presence of incomplete contract enforcement


• Synergies (indirect spillovers) operating through cluster effects, which
help alleviate credit rationing


• Migrant networks, which contribute to reduce moral hazard in trade
relationships and overcome informational barriers.


These informal arrangements are even more critical to small-scale
trade—largely unrecorded in statistics—which is a vital source of income
to the poor and to vulnerable groups such as women traders. Providing an




98 Pathways to African Export Sustainability


enabling environment for the development of such arrangements is key
to ensuring that the gains from trade are inclusive and that the volatility
of trade-related income is reduced through improved survival of trading
relationships.


Whereas many, if not all, of these informal arrangements are market-
driven, mediated through ethnic, national, or cultural networks, their
efficacy can be encouraged by the right type of policies in terms of
regulation, contract enforcement, banking supervision, and the provi-
sion of adequate information. In fact, the increasing evidence on the
positive aspects of “learning from exporting” in developing countries
suggests that programs that encourage the mentoring of potential
exports by existing successful exporters in those countries and by firms
in destination markets could be useful in increasing export survival
rates.


Notes


1. In interpreting these results, it should be kept in mind that products in both
studies are defined at very high levels of disaggregation, so adding one product
does not mean a great deal of diversification.


2. Matching involves a two-step procedure to estimate the effect of a treatment
that controls for selection into the treatment based on observable individual
characteristics. In the first step, a probit is used to estimate the probability of
being “treated” (here, the probability of being an exporting firm). The pre-
dicted probability of treatment, called a propensity score, is retrieved for use in
the second stage. In the second stage, treated individuals (exporting firms) are
matched with the controls (non-exporting firms) that have the closest pro-
pensity score (one or many, depending on the technique). The last stage of the
procedure is to test whether the average performance outcome of treated
firms (total factor productivity [TFP], capital intensity, and so on) signifi-
cantly differs from that of matched control ones.


3. One should also keep in mind that the panels used in most of the studies
cited, especially those from Sub-Saharan Africa, are very short in the time
dimension, which does not help for the estimation of time-bound effects such
as learning. Moreover, the matching differences-in-differences estimator used
in most of the studies measures the impact effect of starting to export (that
is, the matching differences compare the change in an outcome variable from
the year before treatment to the treatment year, usually taken as the first year
of export, between treated and control firms). It is unlikely that learning-by-
exporting would have a traceable impact on performance outcomes within
just one year.




Survival, Contracts, and Networks 99


4. Increased capital intensiveness may seem to be a puzzle, since African countries
are unlikely to have a comparative advantage in capital-intensive goods. It
should be kept in mind that these levels of capital intensiveness remain modest,
with levels for control firms below the minimum level for efficient production
of even labor-intensive products such as textiles and apparel. Higher capital
intensity may also reflect the need for capital investment to satisfy more
demanding standards in higher-income markets.


5. The data set was obtained directly from customs administrations in Ghana,
Malawi, Senegal, and Tanzania as part of a World Bank research project.


6. Rajan and Zingales’ measure of financial dependence is an industry-level
variable calculated for 27 3-digit ISIC industries and 9 4-digit ones using
Compustat data for the United States. Let k be capital expenditure and x
be operational cash flow at the firm level. Rajan and Zingales’ index for
industry j, rj, is the median value of (k − x)/k across all Compustat firms in
industry j. Index values, given in table 1 of Rajan-Zingales (1998), range
from −45 for tobacco (ISIC 314) to 1.49 for drugs (ISIC 3522).


7. Braun proxies asset tangibility by the ratio of net property, plant, and equip-
ment to market value at the firm level, using U.S. Compustat data. The
industry-level variable is constructed, as in Rajan-Zingales, by taking the indus-
try median at the ISIC 3-digit level. Index values, given in table 1 of Braun
(2003), range from 0.09 (leather products) to 0.67 (petroleum refineries).


References


Albornoz, Facundo, H. F. Calvo Pardo, G. Corcos, and E. Ornelas. 2010. “Sequential
Exporting.” CEP Discussion Papers dp0974, Centre for Economic Performance,
LSE.


Alvarez, Roberto, and H. Görg. 2009. “Multinationals and Plant Exit: Evidence
from Chile.” International Review of Economics and Finance 18: 45–51.


Alvarez, Roberto, and R. Lopez. 2005. “Exporting and Firm Performance:
Evidence from Chilean Plants.” Canadian Journal of Economics 38: 1384–
400.


Araujo, Luis, and E. Ornelas. 2007. “Trust-Based Trade.” Centre for Economic
Performance (CEP) discussion paper 820.


Audretsch, David. 1991. “New Firm Survival and the Technological Regime.”
Review of Economics and Statistics 73: 520–26.


Audretsch, David, and T. Mahmood. 1995. “New-Firm Survival: New Results
Using a Hazard Function.” Review of Economics and Statistics 77: 97–103.


Audretsch, David, E. Santarelli, and M. Vivarelli. 1999. “Start-Up Size and
Industrial Dynamics: Some Evidence from Italian Manufacturing.” International
Journal of Industrial Organization 17: 965–83.




100 Pathways to African Export Sustainability


Aw, Bee Yan, and A. R. Hwang. 1995. “Productivity in the Export Market: A Firm
Level Analysis.” Journal of Development Economics 47: 313–32.


Baldwin, John R., and W. Gu. 2003. “Export-Market Participation and Productivity
Performance in Canadian Manufacturing.” Canadian Journal of Economics 36:
634–57.


Bernard, Andrew B., J. Eaton, J. B. Jensen, and S. Kortum. 2003. “Plants and
Productivity in International Trade.” American Economic Review 93: 1268–90.


Bernard, Andrew B., and J. B. Jensen. 1995. “Exporters, Jobs, and Wages in U.S.
Manufacturing: 1976-1987.” Brookings Papers on Economic Activity:
Microeconomics 67–119.


———. 1999. “Exceptional Exporter Performance: Cause, Effect, or Both?”
Journal of International Economics 47: 1–25.


———. 2002. “The Deaths of Manufacturing Plants.” NBER Working Paper 9026,
National Bureau of Economic Research, Cambridge, MA.


———. 2004. “Why Some Firms Export.” Review of Economics and Statistics 86
(2): 561–69.


———. 2007. “Firm Structure, Multinationals and Manufacturing Plant Deaths.”
Review of Economics and Statistics 89: 193–204.


Bernard, Andrew B., J. B. Jensen, S. Redding, and P. Schott. 2007. “Firms in
International Trade.” Journal of Economic Perspectives 21: 105–30.


Bernard, Andrew B., and F. Sjöholm. 2003. “Foreign Owners and Plant Survival.”
NBER Working Paper 10039, National Bureau of Economic Research,
Cambridge, MA.


Bigsten, Arne, P. Collier, S. Dercon, M. Fafchamps, B. Gauthier, J. W. Gunning, A.
Oduro, R. Oostendorp, C. Pattillo, M. Söderbom, F. Teal, and A. Zeuf. 2004.
“Do African Manufacturing Firms Learn from Exporting?” Journal of
Development Studies 40 (3): 115–41.


Bigsten, Arne, and M. Söderbom. 2006. “What Have We Learned from a Decade
of Manufacturing Enterprise Surveys in Africa?” Policy Research Working
Paper 3798, World Bank, Washington, DC.


Blalock, Garrick, and P. Gertler. 2004. “Learning from Exporting Revisited in a
Less Developed Setting.” Journal of Development Economics 75: 397–416.


Boermans, Martijn Adriaan. 2010. “Learning-by-Exporting and Destination
Effects: Evidence from African SMEs.” MPRA Paper 22658, University
Library of Munich, Germany. Revised May 9, 2010.


Brambilla, Irene, D. Lederman, and G. Porto. 2010. “Exports, Export Destinations,
and Skills.” NBER Working Paper 15995, National Bureau of Economic
Research, Cambridge, MA.


Braun, Mathias. 2003. “Financial Contractibility and Asset Hardness.” Unpublished
dissertation, Harvard University.




Survival, Contracts, and Networks 101


Cadot, Olivier, L. Iacovone, D. Pierola, and F. Rauch. 2011. “Success and Failure
of African Exporters.” Policy Research Working Paper 5657, World Bank,
Washington, DC.


Carrère, Celine, and V. Strauss-Kahn. 2011 “Exports that Last: When Experience
Matters.” Unpublished document. ESCP Europe - CEPII.


Castellani, Davide. 2002. “Export Behavior and Productivity Growth: Evidence
from Italian Manufacturing Firms.” Review of World Economics 138: 605–28.


Clerides, Sofronis, S. Lach, and J. Tybout. 1998. “Is Learning by Exporting Import?
Micro-Dynamic Evidence from Colombia.” Quarterly Journal of Economics
113 (3): 903–47.


Cohen, Abner. 1971. “Cultural Strategies in the Organization of Trading
Diasporas.” In The Development of Indigenous Trade and Markets in West Africa,
ed. C. Meillassoux, 266–78. London: Oxford University Press.


Collier, Paul, and J. W. Gunning. 1999. “Explaining African Economic Performance.”
Journal of Economic Literature 32: 64–111.


Crozet, Matthieu, K. Head, and T. Mayer. 2009. “Quality Sorting and Trade:
Firm-Level Evidence for French Wine.” CEPII Working Paper 2009-14,
CEPII, Paris.


Damijan, Joze P., S. Polanec, and J. Prasnikar. 2004. “Self-Selection, Export
Market, Heterogeneity and Productivity Improvements: Firm Level Evidence
from Slovenia.” LICOS Discussion Papers 14804, Katholieke Universiteit,
Leuven.


De Loecker, Jan. 2004. “Do Exports Generate Higher Productivity? Evidence from
Slovenia.” LICOS Discussion Paper 151, Katholieke Universiteit, Leuven.


Delgado, Miguel A., J. Farinas, and S. Ruano. 2002.“Firm Productivity and Export
Markets: A Non-Parametric Approach.” Journal of International Economics
57: 397–422.


Disney, Richard, J. Haskel, and Y. Heden. 2003. “Entry, Exit and Establishment
Survival in UK Manufacturing.” The Journal of Industrial Economics
51: 91–112.


Doms, M., T. Dunne, and M. J. Roberts. 1995. “The Role of Technology Use in the
Survival and Growth of Manufacturing Plants.” International Journal of
Industrial Organization 13: 523–42.


Dunne, T., M. J. Roberts, and L. Samuelson. 1988. “Patterns of Firm Entry
and Exit in US Manufacturing Industries.” Rand Journal of Economics
19: 495–515.


Eaton, Jonathan, M. Eslava, M. Kugler, and J. Tybout. 2008. “Export Dynamics in
Colombia: Firm-Level Evidence.” In The Organization of Firms in a Global
Economy, ed. E. Helpman, D. Marin, and T. Verdier, 231–72. Cambridge, MA:
Harvard University Press.




102 Pathways to African Export Sustainability


Ferragina, Anna, R. Pittiglio, and F. Reganati. 2011. “Multinational Status and
Firm Exit in the Italian Manufacturing Sectors.” Unpublished draft, University
of Salerno.


Flamm, Kenneth. 1984. “The Volatility of Offshore Investment.” Journal of
Development Economics 16: 231–48.


Gereffi, Gary. 1999. “International Trade and Industrial Upgrading in the Apparel
Commodity Chain.” Journal of International Economics 48: 37–70.


Girma, Sourafel, and H. Görg. 2004. “Blessing or Curse? Domestic Plants Survival
and Employment Prospects after Foreign Acquisition.” Applied Economics
Quarterly 50: 89–110.


Girma, Sourafel, D. Greenaway, and R. Kneller. 2004. “Does Exporting Increase
Productivity? A Microeconometric Analysis of Matched Firms.” Review of
International Economics 12: 855–66.


Görg, Holger, and E. Strobl. 2003a. “ ‘Footloose’ Multinationals?” The Manchester
School 71 (1): 1–19.


———. 2003b. “Multinational Companies, Technology Spillovers and Plant
Survival.” Scandinavian Journal of Economics 105: 581–95.


———. 2004. “Foreign Direct Investment and Local Economic Development:
Beyond Productivity Spillovers.” Globalisation, Productivity and Technology
Research Paper No. 2004/11. Available at SSRN: http://ssrn.com
/abstract=715981 or http://dx.doi.org/10.2139/ssrn.715981.


Gould, David. 1994. “Immigrant Links to the Home Country: Empirical Implications
for U.S. Bilateral Trade Flows.” Review of Economics and Statistics 76: 302–16.


Graner, Mats, and A. Isaksson. 2007. “Firm Efficiency and the Destination of
Exports: Evidence from Kenyan Plant-Level Data.” Unpublished draft,
UNIDO. http://www.unido.org/fileadmin/user_media/Publications/Pub_free
/Firm_efficiency_and_destination_of_exports.pdf.


Greenaway, David, and R. Kneller. 2007. “Firm Heterogeneity, Exporting and
Foreign Direct Investment.” The Economic Journal 117: 134–61.


Greif, Avner. 1989. “Reputation and Coalitions in Medieval Trade: Evidence on
the Maghribi Traders.” Journal of Economic History 49: 857–82.


———. 1993. “Contract Enforceability and Economic Institutions in Early Trade:
The Maghribi Traders’ Coalition.” American Economic Review 83: 525–48.


Hagemejer, Jan, and M. Kolasa. 2008. “Internationalization and Economic
Performance of Enterprises: Evidence from Firm-Level Data.” MPRA Working
Paper 8720, Munich.


Hansson, Par, and N. Lundin. 2004. “Exports as Indicator on or as Promoter of
Successful Swedish Manufacturing Firms in the 1990s.” Weltwirtschaftliches
Archiv 140, 415–445.




Survival, Contracts, and Networks 103


Harding, Alan, M. Söderbom, and F. Teal. 2004. “Survival and Success among
African Manufacturers.” CSAE Working Paper Series 2004–05, Centre for the
Study of African Economies, University of Oxford.


Hausmann, Ricardo, and D. Rodrik. 2003. “Economic Development as Self-
Discovery.” Journal of Development Economics 72 (2): 603–33.


Head, Keith, and J. Ries. 1998. “Immigration and Trade Creation: Econometric
Evidence from Canada.” Canadian Journal of Economics 31: 47–62.


Isgut, Alberto. 2001. “What’s Different about Exporters? Evidence from
Colombian Manufacturing.” The Journal of Development Studies 37 (5):
57–82.


Jaud, Mélise. 2011. “Food Safety, Reputation, and Trade.” Working Paper
halshs-00586310, HAL Paris School of Economics, Paris.


Kimura, F., and T. Fujii. 2003. “Globalizing Activities and the Rate of Survival:
Panel Data Analysis on Japanese Firms.” Journal of Japanese International
Economies 17: 538–60.


Kimura, F., and K. Kiyota. 2006. “Exports, FDI and Productivity: Dynamic
Evidence from Japanese Firms.” Review of World Economics 142: 695–719.


Mata, J., and P. Portugal. 1994. “Life Duration of New Firms.” Journal of Industrial
Economics 42: 227–46.


———. 2002. “The Survival of New Domestic and Foreign-Owned Firms.”
Strategic Management Journal 23: 323–43.


Mayer, Thierry, M. Melitz, and G. Ottaviano. 2011. “Market Size, Competition,
and the Product Mix of Exporters.” Working Papers 2011-11, CEPII research
center.


Melitz, Marc. 2003. “The Impact of Trade on Intra-Industry Reallocations and
Aggregate Industry Productivity.” Econometrica 71: 1695–725.


Mengistae, Taye, and C. Pattillo. 2004. “Export Orientation and Productivity in
Sub-Saharan Africa.” IMF Staff Papers 51 (2): 327–53.


Özler, Sule, and E. Taymaz. 2004. “Does Foreign Ownership Matter for Survival
and Growth? Dynamics of Competition and Foreign Direct Investment.”
ERC Working Papers 0406, ERC (Economic Research Center), Middle East
Technical University, Ankara, Turkey, revised March.


Pisu, Mauro. 2008. “Export Destinations and Learning-by-Exporting: Evidence
from Belgium.” NBB Working Paper 140, National Bank of Belgium,
Brussels.


Rajan, Raghuram, and L. Zingales. 1998. “Financial Dependence and Growth.”
American Economic Review 88: 559–86.


Rankin, Neil, M. Söderbom, and F. Teal. 2006. “Exporting from Manufacturing
Firms in Sub-Saharan Africa.” Journal of African Economies 15: 671–87.




104 Pathways to African Export Sustainability


Ratha, Dilip, S. Mohapatra, C. Özden, S. Plaza, W. Shaw, and A. Shimeles. 2011.
Leveraging Migration for Africa: Remittances, Skills, and Investments. Washington,
DC: World Bank.


Rauch, James E. 1999. “Networks versus Markets in International Trade.” Journal
of International Economics 48 (1): 7–35.


———. 2001. “Business and Social Networks in International Trade.” Journal of
Economic Literature XXXIX, 1177–203.


Rauch, James E., and A. Casella. 2003. “Overcoming Informational Barriers to
International Resource Allocation: Prices and Group Ties.” Economic Journal
113: 21–42.


Rauch, James E., and V. Trindade. 2002. “Ethnic Chinese Networks in International
Trade.” Review of Economics and Statistics 84: 116–30.


Rauch, James E., and J. Watson. 2003. “Starting Small in an Unfamiliar
Environment.” International Journal of Industrial Organization 21: 1021–42.


Rhee, Yung-Whee, B. Ross-Larson, and G. Pursell. 1984. Korea’s Competitive Edge:
Managing the Entry into World Markets. Baltimore, MD: Johns Hopkins
University Press.


Rodrik, Dani. 2000. “How Far Will International Economic Integration Go?”
Journal of Economic Perspectives 14: 177–86.


Saxenian, AnnaLee. 1999. Silicon Valley’s New Immigrant Entrepreneurs. San
Francisco, CA: Public Policy Institute of California.


Taymaz, Erol, and S. Özler. 2007. “Foreign Ownership, Competition, and Survival
Dynamics.” Review of Industrial Organization 31 (1): 23–42.


Tewari, Meenu. 1999. “Successful Adjustment in Indian Industry: The Case of
Ludhiana’s Woolen Knitwear Industry.” World Development 27: 1651–71.


Van Biesebroeck, Johannes. 2003. “Exporting Raises Productivity in Sub-Saharan
Manufacturing Plants.” NBER Working Paper 10020, National Bureau of
Economic Research, Cambridge, MA.


Volpe, Christian, and J. Carballo. 2009. “Survival of New Exporters in Developing
Countries: Does It Matter How They Diversify?” IDB working paper WP-I40,
Inter-American Development Bank, Washington, DC.


Wagner, Joachim. 2002. “The Causal Effect of Exports on Firm Size and Labor
Productivity: First Evidence from a Matching Approach.” Economics Letters
77: 287–92.


———. 2007. “Exports and Productivity: A Survey of the Evidence from Firm-
Level Data.” World Economy 30: 60–82.


Weidenbaum, Murray, and S. Hughes. 1996. The Bamboo Network. New York, NY:
The Free Press.




105


C H A P T E R 4


Policy Implications


Improving the survival of African exports requires effort to make the
environment in which African exporters operate friendlier through
reduced trade costs and better upstream services, including, above all,
better access to credit. These are standard prescriptions that would be
reached by any study of African export performance, be it about entry,
value, or survival, and are consistent with an agenda limiting the role of
the government to setting clear rules, enforcing contracts, and providing
base infrastructure.


This chapter will explore the question of whether there can be any
role for more proactive policies. We will see that although preferential
market access seems to have a limited role to play, any encouragement to
trade with neighbors and regional trade may have far-reaching conse-
quences for the ability of African firms to gradually build the capabilities
that would allow them to serve more distant and more demanding mar-
kets, through gradual export-expansion paths.


However, we will also see that the very fragmentary evidence about
the role of technical assistance in helping African firms cope with rising
demands in terms of traceability and quality (in particular in agriculture)
is ambiguous. More research is clearly needed to evaluate the impact of
technical assistance, especially in view of the basic ambiguities of impact
evaluation in this context.




106 Pathways to African Export Sustainability


Likewise, we will see that export promotion may have ambiguous
effects on the sustainability of exports, possibly at times inducing export-
ers to spread themselves too thin. If export-promotion agencies are to
contribute effectively to improve export sustainability, they probably
need to include it as an explicit objective in the design and follow-up of
assistance.


Thinking Strategically: Export-Expansion Paths


We saw in chapter 3 that the scale and scope on which firms export mat-
ter for survival at the product-origin-destination level (by scale we mean
the number of destinations to which a product is shipped, and by scope
we mean the number of products shipped to a given destination).
However, these effects—at least as they are modeled in empirical
papers—are essentially static and so do not tell a full story. In addition,
they may well reflect omitted variables; for instance, scale may pick up
the quality of a product, although quality may not be explicitly included
as a separate variable. We now turn to dynamic effects—how to develop,
grow, and survive.


The sequence in which exporters expand across foreign markets can
make a difference to their survival prospects, according to findings by
Carrère and Strauss-Kahn (2011). How much more sustainable are
exports benefiting from prior product experience is shown in figure 4.1,
which displays average first-year survival rates for product-origin cells on
Organisation for Economic Co-operation and Development (OECD)
markets (1) without prior extra-OECD experience (lower part of the
bars) and (2) with one year of extra-OECD experience for the same
product and origin. The effect is positive for exports originating from all
regions (and significant, as these are regression results), but its magnitude
varies substantially. For exports originating from East Asia and the Pacific,
the first-year survival rate jumps by 11.6 percentage points, from
29.2 percent to 40.8 percent, a rise of over a third. For Sub-Saharan
Africa, one year of experience outside the OECD raises the first-year
survival rate by 7.4 percent—from 22 percent to 29.4 percent—a propor-
tional increase of about one-third.


Carrère and Strauss-Kahn (2011) extend these first-pass results using
Cox regressions with various controls, including gravity-type variables
(distance, contiguity), initial values, origin-country income, and so on.
Results confirm that one year of prior extra-OECD experience signifi-
cantly enhances survival (in a broader sense than just first-year survival
probabilities, since Cox regressions take into account hazard rates




Policy Implications 107


throughout a spell’s life). A second year provides an additional boost, but
the effect stops there, as additional years of prior experience have no
significant effect on hazard rates.


In line with intuition, Carrère and Strauss-Kahn also find that experi-
ence effects are stronger for more differentiated products.1 However,
surprisingly, they find that a spell that has a destination market with
imports of the same product from more origin countries lasts longer, as if
tougher competition could be associated with longer survival. They inter-
pret this as picking up unobservable market attractiveness effects; such
effects could include more readily available and cheaper information on
market access conditions and opportunities.


All in all, Carrère and Strauss-Kahn’s analysis suggests that export-
expansion strategies starting with Southern markets before targeting
OECD ones may be a winning strategy. Experience in exporting to other
developing countries can contribute to higher survival rates for firms
when they subsequently enter OECD markets. Nevertheless, firms may
get distracted from such a strategy by the large size of OECD markets and
if the potential returns in those markets are higher than in neighboring


Figure 4.1 Effect of Prior, Non-OECD Experience on First-Year Survival Rates
by Region of Origin


Source: Carrère and Strauss-Kahn 2011.


0


su
rv


iv
al


ra
te


(p
er


ce
n


t)


10


20


30


40


50


So
ut


h
As


ia


Eu
ro


pe
&


C
en


tra
l A


sia


M
id


dl
e E


as
t &


N
or


th
A


fri
ca


Ea
st


As
ia


&
Pa


cif
ic


Su
b-


Sa
ha


ra
n


Af
ric


a


additional survival rate past first year due to
experience in year t–1


first-year survival rate




108 Pathways to African Export Sustainability


developing countries. Even though we saw in chapter 3 that learning
effects are limited when exporting to destination markets at similar
incomes, there seems to be some learning not reflected in firm-level per-
formance outcomes—total factor productivity, size, capital intensiveness,
and so on—that helps later survival in OECD markets. These learning
effects, which may be tacit knowledge, deserve further research. Also, one
should keep in mind that these effects may be highly relevant for export-
ers who rely on their own resources for learning. When exporters get
direct assistance from OECD buyers, they may be able to make the jump
directly to demanding markets. This is clearly an area where export
promotion programs should provide strategic assistance to exporters. In
addition, the nature of a firms’ prior experience of exporting should be an
important indicator for export promotion agencies and donors when
identifying priority firms to support.


Trade Preferences: What Role Should They Play?


Trade preferences, which have traditionally been an important factor in
the landscape of African exports, can have many effects on their survival.
Following the logic of the model in annex 1A of chapter 1, inasmuch as
trade preferences reduce trade costs, they can be expected to reduce the
incidence of exit decisions in the face of bad news and therefore to
enhance survival. However, the empirical literature’s results are ambigu-
ous, suggesting, in particular, that selection and facilitating effects may
work at cross-purposes.


In addition, Cadot, Olarreaga, and Tschopp (2009) showed that
regional trade agreements have the effect of reducing the volatility of trade
policy. They identified policy volatility as the absolute value of the year-
on-year changes in the wedge between international and domestic prices,
measured for agricultural products by the World Bank’s agriculture distor-
tions database (Anderson and Valenzuela, 2008).2 They find that as coun-
tries sign more regional agreements—after controlling for the endogeneity
of those agreements with instrumental-variable techniques—they have, all
else being equal, lower policy volatility. Following again the logic of annex
1A of chapter 1, reducing the volatility of a key parameter of the export-
er’s profit function can have conflicting effects. On one hand, sudden
policy or regulatory shifts can induce the premature termination of trade
relationships. Reducing the frequency of such shifts on destination mar-
kets can contribute to better export survival. On the other hand, the vola-
tility of export earnings encourages persistence through the good-news




Policy Implications 109


principle derived in annex 1A. Reducing this volatility may then dampen
the positive effect of improved policy stability just outlined.


Which effect dominates is an empirical matter, and other effects can
also be at play. Carrère and Strauss-Kahn (2011) explored empirically the
effect of market access on export survival and found no positive effect.
They introduced a market-access variable in a Cox regression of export
survival estimated on a very large sample of 423,328 export spells from
165 non-OECD origin countries to OECD destinations over 47 years
(1962–2010). These export spells came from 1,268 products at the very
disaggregated Standard International Trade Classification (SITC) 5-digit
level after eliminating raw materials and arms, left-censored spells, and
trade flows of an annual value of less than US$1,000. The proxy is calcu-
lated at the origin-country level and is a GDP-weighted average of dum-
mies for preferential access enjoyed by the origin country on any of its
OECD destinations. After controlling for usual determinants of spell sur-
vival—see chapter 1—the market-access variable turns out to be correlated
negatively with survival, as in Brenton, Pierola, and von Uexküll (2009).


This result may reflect the conflicting effects of reduced policy volatil-
ity outlined earlier. It may also reflect additional effects through which
preferences may, somewhat paradoxically, reduce average export survival.
Preferential trade agreements reduce the sunk costs of market entry and
consequently—as documented empirically by Rakhman (2010)—raise
the probability of entry. By reducing sunk costs, preferential market
access can be expected to have two effects, both going in the same direc-
tion. First, lower sunk costs reduce the cost of entry and exit and can be
expected to generate more churning in and out of partner export markets
at the level of the firm (see, again, annex 1A of chapter 1). Second, easier
entry may have a selection effect, inducing low-productivity firms to try
their luck on the preferential market. As the distribution of exporting
firms expands to the left—to low-productivity ones—it can be expected
to generate more failures and therefore to reduce the aggregate survival
probability.


However, it should be kept in mind that Carrère and Strauss-Kahn
(2011) control for prior export experience, proxied by the existence of
export spells of the same product originating from the same country. Thus,
inasmuch as preferential agreements raise the likelihood of entry—which
they do, according to Rakhman—they raise experience and therefore have
an indirect positive effect on survival that is not picked up by the coeffi-
cient on the preferential-access proxy. In addition, Molina (2010) shows
that the experience acquired in preferential trade is leveraged through




110 Pathways to African Export Sustainability


entry on other markets as well. However, Molina does not explore
whether the experience also translates into higher survival on those other
markets.


Carrère and Strauss-Kahn also find that a larger number of countries
exporting to a single destination reduces export survival at the cell
(product-origin-destination) level. This suggests a crowding-out effect
whereby different origin countries compete with each other in a given
destination market, making the environment tougher. It stands in appar-
ent contrast to the “crowding-in” effect identified at the firm level by
Cadot et al. (2011) and discussed in chapter 1, although the crowding-in
or synergy effect in question was between producers from the same origin
country, not across origin countries. The mechanisms suggested by these
authors to explain the synergy effect are unlikely to play between origin
countries.


All in all, it is fair to say that the recent evidence on the effect of
trade preferences on survival is ambiguous, reflecting multiple channels
of influence. On one hand, trade preferences (1) reduce trade costs and
(2) generate experience, both of which can be expected to enhance
survival. On the other hand, they reduce entry costs, which may be
expected to reduce survival through both (1) a more severe reaction to
random fluctuations in profitability and (2) a selection effect that
draws in weaker players. If these forces balance each other, trade pref-
erences are unlikely to have a strong effect, either way, on export sur-
vival. This tentative—and qualitative—conclusion is consistent with
the casual observation that African exporters have had preferential
access to many OECD markets for several decades now and have, nev-
ertheless, not shown particular resilience. However, this may also
reflect weaknesses in the particular preference programs whereby
products that are sensitive in OECD countries—often those with the
largest preference margins—are excluded from the scheme or are sub-
ject to restrictive rules of origin. Thus, the number of products included
with rules of origin that are not a major constraint may be small and
the preference margins may not be sufficient to offset the costs of an
adverse trade and business climate.


A Role for Support Services and Technical Assistance


Policies can take forms other than trade preferences. In this section we
consider the role that technical assistance and other forms of support can
play in promoting the survival of exports.




Policy Implications 111


Upstream Services and Export Survival
Export performance is the product of managerial capabilities and firm
resources, which include access to quality inputs. This was illustrated
recently in a paper by Ferro, Portugal-Perez, and Wilson (2011), who
showed that aid for trade directed at service sectors—including transport,
information and communication technologies, energy, banking, and
business—raised the export performance of downstream manufacturing
sectors. While ostensibly aimed at overcoming reverse-causality problems
in the estimation of aid effectiveness, those authors’ identification strat-
egy uncovered an interesting causal chain from upstream services to
downstream export performance in manufacturing sectors.


Can we similarly link the performance of upstream service sectors to
the ability of downstream exporters to survive in foreign markets? Jaud
and Kukenova (2011) show that the answer is “yes.” Their paper high-
lights the low level of access to credit in Africa, with credit/GDP ratios
about five times lower in Africa than in OECD countries. Indeed, we saw
in chapter 3 that access to finance was highlighted as the single most
important constraint reported by African exporters in the recent World
Bank survey. In particular, 47 percent of past (failed) exporters cited lack
of access to finance as a major constraint. We also saw, in chapter 2, that
the importance of access to credit is likely to rise as agricultural exporters
are forced to comply with increasingly stringent standards and technical
regulations.


In order to identify how improvements in the functioning of credit
markets contribute to overcome the high infant mortality of African agri-
food exports, Jaud and Kukenova rely on a measure of dependence on
finance at the product level. Specifically, they use a measure of the riski-
ness of products in terms of their probability of being rejected at the
European Union (EU) border for sanitary reasons (see box 4.1).


The policy hypothesis tested by Jaud and Kukenova is that financial
development improves disproportionately the sustainability of “risky”
exports, because those products are likely to be particularly dependent on
credit, whether in order to invest in quality upgrading or to weather sudden
crises, such as bans or product rejections. They do this by regressing the
hazard rate of African agri-food exports on the EU market using a Cox
regression on the product riskiness index, the level of financial development
of the origin country, and an interaction term between the two. Their work-
ing hypothesis suggests that the interaction term should be negative, indi-
cating a reduction in hazard rates for risky products over and above the
general effect that financial development has on the survival of all exports.




112 Pathways to African Export Sustainability


Their estimation exercise uses a firm-level data set already used in
chapter 2 for four African countries—Ghana, Mali, Senegal, and
Tanzania—compiled by the World Bank from customs transaction-level
records. Their base index of financial development is the credit/GDP
ratio taken from Beck, Demirgüç-Kunt, and Levine (2000). They find
that, indeed, whereas financial development has, on average, a negative
and significant effect on export hazard rates for all products and countries
in the sample, the effect is reinforced by the interaction term with prod-
uct riskiness, which is also negative and significant. In order to get a feel
for the magnitudes involved, we note that in 2003 Senegal’s financial


Box 4.1


The Agri-Food Product Risk Index


The agri-food product risk index measures the product’s ability to comply with EU


agri-food standards and technical regulations. Put differently, this index measures


the gap between mandated and actual quality. It is constructed from a database


of EU food alerts as reported in the Rapid Alert System for Food and Feed (RASFF)


at the Harmonized System (HS)-8 level of disaggregation. A naïve count of food


alerts at the EU border might correlate with riskiness as defined above (a higher


count of alerts meaning a riskier product), but such an assessment would be con-


founded by many influences, including the quality of sanitary controls in the ori-


gin country and possibly hidden protectionist motives at the EU border. In order


to filter out these confounding influences, Cadot, Jaud, and Suwa (2012) ran a


regression of the count alerts at the (product × exporter) level during the RASFF’s


eight years of records (2000–08) on the past occurrence of bans, import shares on


the EU market, and proxies for protectionist motives (for example, quotas or World


Trade Organization disputes). Product fixed effects were then retrieved from the


regression, providing unbiased estimates of the propensity of products to be


stopped at the EU border.


“Risky” products are defined by positive fixed effects. These high-propensity


products are characterized by a large gap between EU standards and what is cur-


rently offered by exporters to the European Union; these products are therefore


likely to require substantial investments in quality control. Alternatively, exporters


of such products may expect periodic disruptions caused by alerts and shipment


rejections.


Source: Cadot, Jaud, and Suwa 2012.




Policy Implications 113


development—as proxied by its credit/GDP ratio—was about three
times that of Tanzania. If the latter were to reach the former’s level of
financial development, the hazard rate for shrimp exports—a highly risky
product as measured by the riskiness index—would go down by 7
percent for Tanzania. That is, the probability of surviving one more year
would go up by roughly 7 percent. Depending on the initial hazard rate,
this could be a very substantial increase in the survival probability. For
instance, with an initial hazard rate of 0.7, the survival rate would jump
by 16 percent, from 0.30 to 0.35.


Our discussion of the role of technical regulations and standards in
chapter 2, in particular in agro-food products, highlighted their potential
to disrupt trade flows through sudden regulatory changes or through the
arbitrary application of regulations at the border. U.S. food safety regula-
tions, in particular, explicitly allow for discretionary and informal “profil-
ing” in the application of regulations at the border (see Jouanjean, Maur,
and Shepherd 2011).


Technical Assistance: Does It Help?
In chapter 3 of this report we discussed how the administration of
sanitary and phytosanitary (SPS) measures could be a factor in the low
survival for African agri-food exports. Recognizing this, the European
Union has put in place programs designed to help producers in low-
income countries, in particular African, Caribbean, and Pacific (ACP)
countries,3 to cope with those measures. If well designed, such pro-
grams have the potential of helping to secure market access for pro-
ducers who would otherwise have difficulty following, and complying
with, the rising tide of sanitary and technical regulations in their EU
markets.


Recent research by Jaud and Cadot (2011) suggests that, when sub-
jected to rigorous impact evaluation, technical assistance programs may
turn out to have less of an impact than expected, as treatment-effect
methodologies uncover no significant performance improvement for
“treated” firms. In this section, we consider a similar exercise using export
survival—rather than export growth—as the performance measure, and
find similarly insignificant treatment effects. However, impact-evaluation
results should be interpreted very cautiously, for reasons that we will
discuss later on in this section.


Jaud and Cadot focused on the Pesticide Initiative Program (PIP).
Financed by the European Development Fund (EDF) with an overall
budget of 34.1 million euros, the PIP started in 2001, initially for a




114 Pathways to African Export Sustainability


five-year period; it was extended by two additional years, and a new wave
was launched in 2009 following a positive evaluation of the program’s
first phase.


The PIP has two main objectives. The first is to enable ACP exporters of
fresh fruit and vegetables (FFV) to comply with European traceability and
food safety requirements (in particular as regards pesticide residues). The
second is to consolidate the position of small-scale producers in the ACP
horticultural value chain. Support activities are organized around five com-
ponents: (1) good company practices, (2) training, (3) capacity building,
(4) regulation and standards, and (5) information and communication.


The core of the support (almost 30 percent of the program’s budget)
goes to component 1, which consists of helping producers and exporters
to set up internal food safety management systems in production and
marketing operations. The regulation and standards component ensures
that all substances recommended in crop protocols (“technical itinerar-
ies”) are authorized in both the European Union and the origin country.
Finally, the capacity-building component aims at developing national
capacity to provide the services needed by the industry. Beneficiaries of
capacity-building activities include private consultants (training courses
on food safety, pesticide use, and integrated pest management [IPM]);
accredited laboratories (pesticide residue analysis); public services
(including extension services and pesticide registration bodies); and
strong professional organizations.


Eligibility starts with the completion and submission of a request for
PIP intervention addressing the applicant’s particular needs and objec-
tives. The request identifies by self-assessment the problem to be
resolved—for example, maximum residue levels (MRLs), non-accredited
plant-protection products, or traceability—and puts forward possible
fixes such as training in integrated crop management (ICM)/IPM systems
or the safe use of pesticides, implementation of food safety and traceabil-
ity systems, or “technical itineraries.” To be accepted, a requested inter-
vention must help to achieve product compliance with EU traceability
and food safety (pesticide residues) regulations. Upon acceptance, a pro-
tocol stating the actions to be implemented by each party on a cost-
sharing basis (50 percent for each, except for smallholders who are
expected to contribute only 20 percent) is signed. The actions listed in
the protocol are chosen from among a menu offered by PIP under its five
components; however, the combination in each protocol is specific to a
firm and varies across beneficiaries.




Policy Implications 115


So far, most of the financing has gone to training costs, technical sup-
port and the development of a food safety toolbox containing crop pro-
tocols, good agricultural practice (GAP) guidelines, and Hortitrace, a
traceability software developed by PIP. The program’s first phase has
covered 21 countries,4 along with 320 export companies. Out of those,
219 firms benefited from the “good company practices” component
(advice and assistance for setting up sanitary quality and traceability
systems, and certification pre-audits), and 153 benefited from training
under the capacity-building component.


An evaluation of PIP’s first phase was undertaken in June 2008.
Overall, the evaluation report drew up a very positive image of the
program’s impact, contributing to the launch of a second five-year
phase in 2009. However, although fairly comprehensive, PIP’s evalua-
tion suffers from a typical drawback of this type of exercise—namely,
the lack of a counterfactual to benchmark the performance of treated
firms and products. Like many technical assistance programs, PIP has
never been subjected to a rigorous impact evaluation. As part of a
World Bank work program on impact evaluation, Jaud and Cadot
(2011) used a combination of customs and program data to assess, using
difference-in-difference (DID) regression with propensity-score match-
ing if PIP had an impact on the export performance of beneficiary
firms, relative to nonbeneficiary (control) ones. The comparison with a
control group is crucial in the case of Senegal’s FFV exports, because
the whole industry underwent a boom starting in 2000–01, at the same
time PIP was launched. If the performance of beneficiary (treatment)
firms was compared only with their own performance prior to the
treatment, the estimated treatment effect would be very large. However,
this estimated effect could also reflect many factors other than the
treatment. Only a comparison of the performance of treated and con-
trol firms over the same time period can filter out confounding influ-
ences common to all firms. Using this technique, Jaud and Cadot found
no significant impact.


For this report, we ran DID regressions similar in spirit to those in Jaud
and Cadot, but instead of considering export growth as the outcome
(performance) variable, we used their survival, using Cox regressions.
Results are shown in table 4.1. The regressions control for several firm
attributes (initial sales, employment, and number of FFV products) as
well as destination effects. The unit of observation is a firm-product-
destination combination.




116 Pathways to African Export Sustainability


The regressions show significant destination effects, with hazard rates
significantly higher (than in countries not shown on the list, the omitted
category) on the German market and lower on the French market.
But—and this is the important finding for this report—they show no
significant effect of the treatment.


These results, which are based on a single impact evaluation, have no
claim to external validity. In addition, the absence of treatment effects
should be interpreted very cautiously, and the caveats may actually be
more important than the result itself.


On one hand, the absence of treatment effects may reflect program
ineffectiveness, in which case the impact evaluation says what it is meant
to say. On the other hand, the absence of treatment effect may reflect


Table 4.1 Cox Regression Results: The PIP Effect on Survival of Senegalese FFV
Exports to the EU Market


(1) (2) (3) (4) (5) (6)


Treatment variable 0.221 −0.467 −0.314 −0.225 0.213 −0.244
(0.74) (1.48) (0.75) (0.63) (0.53) (0.70)


Destination effects
Germany 0.349 0.456 0.755 0.766 0.886 0.704


(1.11) (1.47) (2.58)*** (2.53)** (2.10)** (1.90)*
Spain 0.423 0.681 0.443 0.380 0.306 0.189


(1.77)* (1.80)* (1.26) (1.26) (0.77) (0.60)
France −0.336 −0.512 −0.589 −0.588 −0.192 −0.209


(2.02)** (2.00)** (1.75)* (1.71)* (0.59) (0.57)
United Kingdom 0.472 0.436 0.414 0.346 0.889 0.964


(1.05) (0.91) (0.88) (0.71) (2.52)** (2.98)***
Italy −0.138 0.103 0.738 0.779 1.053 0.842


(0.37) (0.20) (1.40) (1.40) (2.97)*** (3.09)***
Netherlands 0.321 0.140 −0.032 −0.034 0.352 0.385


(1.72)* (0.38) (0.10) (0.09) (0.97) (1.07)
Firm attributes


Initial sales −0.218 −0.675 −0.273 −0.658
(0.83) (0.69) (0.38) (0.68)


Initial employment −0.092 0.495 0.462 −0.075
(0.39) (0.50) (0.60) (0.10)


Initial export
growth −0.366 −0.498


(3.58)*** (5.32)***
Log natural of FFV
products


1.305
(3.31)***


Source: Authors.
Note: FFV = fresh fruit and vegetables. Dependent variable: robust z-statistics in parentheses.
Significance level: * = 10 percent, ** = 5 percent, *** = 1 percent.




Policy Implications 117


the presence of externalities. Externalities are involved when technical
assistance programs include changes in managerial or technical practices
by beneficiary firms that are easily imitated by other firms. Unless the
control group can exclude “imitator” firms, its performance will be
affected (positively) by the treatment, thus reducing the performance
differential between the treatment and control groups—that is, the treat-
ment effect. Thus, for a given true value of the treatment effect, the
stronger the externality, the weaker the value of the estimated treatment
effect.


The bias introduced by externalities in the measurement of treatment
effects is a crucial issue because externalities provide the ultimate justifi-
cation for government intervention—if all the treatment’s benefits were
internalized by beneficiary firms, the treatment could be provided by the
private sector without the need for public funds. Thus, how to interpret
the absence of the estimated treatment effect depends on the underlying
externality.


To see this, consider two alternative sources of market failures. In the
first case, improved practices are easily imitable, and hence investment in
upgraded practices is not appropriable. In other words, firms will not
invest in upgrading practices because they can wait to imitate others. Let
us call this case no-appropriability. In the second case, practices can be
improved only upon a large investment in knowledge production that
involves indivisibilities and no “rivalry” (zero marginal cost), such as invit-
ing foreign experts to give a one-week training course. However, these
practices are not observable or imitable by other firms, so there is no
appropriability issue. In that case, no individual firm has the resources to
undertake the investment because it is too large, but a coordinating
mechanism, such as a government program, may work.


Suppose that a government program has been implemented in each
case, and consider the interpretation of either significant or insignificant
treatment effects. In the first case (non-appropriability), a significant
treatment effect means that the program was successful, but that benefi-
ciaries managed to internalize the benefits. Then it must be the case that
the non-appropriability problem in fact does not exist, so the market
could (at least in principle) be relied on to provide the training. By con-
trast, a non-significant effect can be either good news or bad news: it may
mean that the treatment worked but that there was indeed a problem of
appropriability, in which case it would be justified; or, simply, that it
failed. In the second case (where investment is too large and knowledge
cannot be imitated), a significant treatment effect is consistent with the




118 Pathways to African Export Sustainability


existence of the market failure. This is unambiguously good news, and the
opposite is true if the treatment effect is insignificant. Thus, estimated
treatment effects provide unambiguous policy signals only when the
externality is of the second type.5


What are we left with? First, there is a serious knowledge gap
concerning the effectiveness of technical assistance programs. Most
technical assistance program-evaluation designs are content with satis-
faction surveys, which are subject to many biases. Programs should be
readied for rigorous impact evaluation at the design stage. Only with
the accumulation of impact evaluation results will we know if the
programs actually have an effect or not. Second, the estimation of
treatment effects should be, whenever possible, complemented by an
attempt to estimate the strength of externalities. Cadot et al. (2012)
provide an example, which is discussed in the section below. Third,
beyond impact effects (the usual focus of DID estimation), sustain-
ability should be an area of focus for impact evaluation, because sus-
tainability is a second area where knowledge gaps are very large. Last
but not least, anecdotal evidence highlights the importance of manage-
rial factors in the viability of export relationships. For instance, Egan
and Mody (1992) relate:


Buyers looking for either new sources of supply or joint venture partners
search for suppliers who manage their factories efficiently, often regardless of
the level of technology those factories currently employ; interviewees com-
monly felt that new machines could easily be installed so long as workers
already had the ability to use them efficiently and absorb training readily. For
many buyers, management was the most important factor in defining an ideal
supplier. . . . As one buyer phrased it, “I do not invest in plant X but in Mr Y.
it all depends on the people” (Egan and Mody 1992, p. 326, quoted in Rauch
and Watson 2003).


Thus, useful technical assistance programs should emphasize manage-
rial training as much as technology—in accordance, incidentally, with the
buyers’ rising emphasis on quality and traceability, which have strong
implications for management structure within the firm and along the
entire supply chain. Thus, there may be a useful role for government to
encourage and facilitate successful exporters in the country to share their
knowledge and experiences with potential exporters. A further role
would be that of setting up a mentoring scheme whereby successful
exporters—at home or overseas—support nascent exporters in poor
African countries.




Policy Implications 119


Export Promotion
Widely used around the world, export-promotion agencies have been set
up essentially to help inexperienced exporters to establish initial contacts
abroad or to expand from a narrow base. Recent evidence (see Lederman,
Olarreaga, and Payton 2009) suggests that these agencies have been, by
and large, fairly successful in that regard, at least provided some key
conditions—such as private sector involvement in their management
structure—were met. But can export-promotion agencies help improve
export sustainability?


Recent evidence from an impact evaluation of Tunisia’s export-
promotion scheme, FAMEX, suggests that sustainability may precisely be
the program’s Achilles heel. Using matching-DID estimation on a data set
that combines firm-level export performance from customs with pro-
gram data, Cadot et al. (2012) show that the program’s effect typically
vanishes after two years. This does not mean that the trade relationships
it generated were interrupted, however: the treatment effect on the num-
ber of products and destinations served by the beneficiary firms is more
persistent than its effect on monetary export values. What the finding
suggests is that beneficiaries may have tended to spread themselves
too thin.


This conjecture is confirmed by the observation that treated firms took
a beating at the outset of the global financial crisis, in 2009, compared
with control firms. Indeed, even though treated firms were more diversi-
fied than control firms after the treatment—as measured by the
Herfindahl or Theil indexes on product-destination cells—they face
no less price risk in their overall export portfolio, suggesting that their
marginal products and destinations were either heavily correlated with
existing ones in terms of price risk, or even riskier.6


If one follows the logic of Rauch and Watson (2003), by increasing the
ease of matching, successful export promotion may even reduce—albeit
indirectly—the persistence of trade relationships (because it reduces sunk
costs of entry and exit in particular trade relationships). However, even if
it makes particular trade relationships less persistent, it may make aggre-
gate ones more persistent, as firms shift more easily across partners but
within a product-destination cell.


Thus, improving the contribution of export promotion to the sustain-
ability of exports may involve some additional emphasis on the long-term
sustainability of the trade relationships. This may involve, first, additional
criteria, such as consistency with comparative advantage (see chapter 2),
or adequacy of the match in terms of firm size. To quote Tewari,




120 Pathways to African Export Sustainability


From a policy perspective […] it may be more fruitful for development
agencies to encourage small and medium-sized local firms that are first-time
exporters to develop relationships with medium-sized overseas buyers,
rather than merely go for large retail chains. The latter may work better for
more experienced or already established exporters (Tewari 1999, p. 1664).


It may also involve stronger follow-up and long-term technical assistance
to accompany the firm’s technology and management upgrading.


The previous chapter also discussed how diasporas can overcome some
of the constraints that undermine export survival. However, export pro-
motion agencies have in general done little to work with and encourage
links between exporters and the diasporas, especially within Africa. One
important opportunity comes from using overseas diplomatic missions to
support export promotion efforts by helping to link exporting firms to
diasporas.


Notes


1. Carrère and Strauss-Kahn measure product differentiation by the size of the
cross-country elasticity of substitution as measured by Broda and Weinstein
(2006).


2. The World Bank’s Agricultural Distortions Database is available at www
.worldbank.org/agdistortions.


3. ACP countries have enjoyed long-standing preferential access to EU markets,
first through the Lome Convention, then through the Cotonou Convention,
and finally through a set of Economic Partnership Agreements currently
under negotiation.


4. Benin, Burkina Faso, Cameroon, Côte d’Ivoire, the Dominican Republic,
Gambia, Ghana, Guinea, Jamaica, Kenya, Mali, Mauritius, Mozambique,
Namibia, Senegal, Surinam, Tanzania, Togo, Uganda, Zambia, and
Zimbabwe.


5. We are grateful to Daniel Lederman for helping us to clarify this point.


6. Cadot et al.’s measure of price risk was constructed as follows. Consider a
Tunisian firm selling men’s cotton t-shirts in Germany. The import unit values
of all men’s cotton t-shirts imported into Germany from origins other than
Tunisia were aggregated into an import-weighted average price, which was
tracked over the entire sample period. This calculation was repeated for all of
the firm’s active product-destination cells. Then, a scaled measure of the port-
folio’s risk was generated out of the 10-year variances and covariances of the
average unit values of all those cells. That scaled risk measure is the coefficient
of variation of the entire export portfolio. As the firm shifts its portfolio over




Policy Implications 121


time (for instance, as a result of FAMEX assistance), the risk measure shifts
under a pure composition effect (shifting cell shares). The risk measure was
then introduced in the matching-DID estimation as an outcome measure. The
treatment effect was found insignificant, meaning that treated firms, in spite
of their reduced concentration, failed to reduce the price risk to which they
were exposed.


References


Anderson, Kym, and E. Valenzuela. 2008. “Estimates of Global Distortions to
Agricultural Incentives, 1955 to 2007.” World Bank, Washington, DC.


Beck, Thorsten, A. Demirgüç-Kunt, and R. Levine. 2000. “A New Database on
Financial Development and Structure.” World Bank Economic Review
14: 597–605.


Brenton, Paul, M. D. Pierola, and E. von Uexküll. 2009. “The Life and Death of
Trade Flows: Understanding the Survival Rates of Developing-Country
Exporters.” In Breaking into New Markets: Emerging Lessons for Export
Diversification, ed. R. Newfarmer, W. Shaw, and P. Walkenhorst, 127–44.
Washington, DC: World Bank.


Brenton, Paul, C. Saborowski, and E. von Uexküll. 2011. “What Explains the Low
Survival Rate of Developing Country Export Flows.” The World Bank Economic
Review 24: 474–99.


Broda, Christian, and D. E. Weinstein. 2006. “Globalization and the Gains from
Variety.” The Quarterly Journal of Economics, MIT Press 121 (2): 541–85,
May.


Cadot, Olivier, A. Fernandes, J. Gourdon, and A. Mattoo. 2012. “Are Export
Support Programs Effective? Evidence from Tunisia.” Unpublished draft,
World Bank, Washington, DC.


Cadot, Olivier, L. Iacovone, D. Pierola, and F. Rauch. 2011. “Success and Failure
of African Exporters.” Policy Research Working Paper 5657, World Bank,
Washington, DC.


Cadot, Olivier, M. Jaud, and A. Suwa. 2012. “Do Food Scares Explain Supplier
Concentration? An Analysis of EU Agri-Food Imports.” In Non-Tariff
Measures: A Fresh Look at Trade Policy’s New Frontier, ed. O. Cadot and
M. Malouche, Washington, DC and London: World Bank and CEPR.


Cadot, Olivier, M. Olarreaga, and J. Tschopp. 2009. “Does Regionalism Reduce
Trade-Policy Volatility?” Agricultural Distortions Working Paper 88, World
Bank, Washington, DC.


Carrère, Céline, and V. Strauss-Kahn. 2011. “Exports that Last: When Experience
Matters.” Draft, University of Geneva.




122 Pathways to African Export Sustainability


Egan, M. L., and A. Mody. 1992. “Buyer-Seller Links in Export Development.”
World Development 20: 321–34.


Ferro, Esteban, A. Portugal-Perez, and J. Wilson. 2011. “Aid for Trade and Export
Performance: The Case of Aid in Services.” In Where to Spend the Next Million?
Applying Impact Evaluation to Trade Assistance, O. Cadot, A. Fernandes,
J. Gourdon, and A. Mattoo, 207–19. Washington, DC and London: World
Bank and CEPR.


Jaud, Mélise, and O. Cadot. 2011. “A Second Look at the Pesticides Initiative
Program: Evidence from Senegal.” Policy Research Working Paper 5635,
World Bank, Washington, DC.


Jaud, Mélise, and M. Kukenova. 2011. “Financial Development and the Survival
of African Agri-food Exports.” Policy Research Working Paper 5649, World
Bank, Washington, DC.


Jouanjean, Marie-Agnes, J.-C. Maur, and B. Shepherd. “US SPS Enforcement: Do
Refusals Harm Reputation?” Forthcoming in Non-Tariff Measures: New
Analysis for Trade Policy’s New Frontier, ed. O. Cadot and M. Malouche.
London/Washington, DC: The World Bank and CEPR (Center for Economic
and Policy Research).


Lederman, Daniel, M. Olarreaga, and L. Payton. 2009. “Export Promotion
Agencies Revisited.” Policy Research Working Paper 5125. World Bank,
Washington, DC.


Molina, Ana-Cristina. 2010. “Are Preferential Agreements Stepping Stones to
Other Markets?” Graduate Institute of International and Development
Studies Working Paper 13-2010, Geneva.


Rakhman, Anna. 2010. “Export Duration and New Market Entry.” Unpublished
draft, George Washington University, Washington, DC.


Rauch, James E., and J. Watson. 2003. “Starting Small in an Unfamiliar
Environment.” International Journal of Industrial Organization 21: 1021–42.


Tewari, Meenu. 1999. “Successful Adjustment in Indian Industry: The Case of
Ludhiana’s Woolen Knitwear Industry.” World Development 27: 1651–71.






ISBN 978-0-8213-9559-2


SKU 19559


African exporters suffer from low survival rates on international markets. They fail more
often than others, incurring the setup costs involved in starting new relationships. This
high churning is a source of waste, uncertainty, and discouragement.


However, this trend is not inevitable. The high “infant mortality” of African exports is
largely explained by Africa’s low-income business environment. Once properly
benchmarked, Africa’s performance in terms of exporter failure is no outlier. Moreover,
African exporters show vigorous entrepreneurship, with high entry rates into new
products and markets despite formidable hurdles created by poor infrastructure,
landlocked boundaries for some, and limited access to major sea routes for others.
African exporters experiment frequently, and they often pay the price of failure. What
matters for policy is how to ensure that viable ventures survive.


Pathways to African Export Sustainability shows how governments can and should help to
reduce the rate of failure of African export ventures through a mixture of improvements
in the business environment and well-targeted proactive interventions.


The business environment can be made more conducive to sustainable export
entrepreneurship through traditional policy prescriptions such as reducing
transportation costs, facilitating trade through better technology and workflow
in border management, improving the effectiveness of banking regulations to ensure
the availability of trade finance, and striving for regulatory simplicity and coherence.


In addition, governments can help leverage synergies between exporters. Original
research featured in this book shows that African exporters improve each other’s chances
of survival when a critical mass penetrates a given market together. They also benefit
from diaspora presence in destination markets. With adequate donor support and private
sector engagement, export promotion agencies and technical assistance programs
can help leverage those synergies.


Pathways to African Export Sustainability will be of interest to policy makers, government
officials, and new and potential exporters in the Africa region.




Login