62703

A WORLD BANK STUDY

Challenges to Enterprise
Performance in the Face
of the Financial Crisis
EASTERN EUROPE AND CENTRAL ASIA
W O R L D B A N K S T U D Y

Challenges to Enterprise
Performance in the Face
of the Financial Crisis
Eastern Europe and Central Asia
Copyright © 2011
The International Bank for Reconstruction and Development / The World Bank
1818 H Street, NW
Washington, DC 20433
Telephone: 202-473-1000
Internet: www.worldbank.org

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ISBN: 978-0-8213-8800-6
eISBN: 978-0-8213-8801-3
DOI: 10.1596/978-0-8213-8800-6

Library of Congress Cataloging-in-Publication Data
Challenges to enterprise performance in the face of the ﬁnancial crisis : Eastern Europe and Central Asia.
p. cm.
Includes bibliographical references.
ISBN 978-0-8213-8800-6 -- ISBN 978-0-8213-8801-3
1. Industries--Europe, Eastern. 2. Industries--Asia, Central. 3. Business enterprises--Europe, Eastern. 4.
Business enterprises--Asia, Central. 5. Global Financial Crisis, 2008-2009. 6. Europe, Eastern--Economic
conditions--21st century. 7. Asia, Central--Economic conditions--21st century. I. World Bank.
HC244.C42 2011
338.50947--dc23
2011018824
Contents

Acknowledgments ....................................................................................................................ix
Acronyms and Abbreviations .................................................................................................xi
Executive Summary ............................................................................................................... xiii
1. Overview.................................................................................................................................. 1
Introduction.......................................................................................................................... 1
Main Issues Reﬂected in the Report ................................................................................. 2
Data and Methodology ....................................................................................................... 5
The Structure of This Report .............................................................................................. 5
2. Access to Finance.................................................................................................................... 7
Financial Sector Developments in the Eastern Europe and Central Asia Region ...... 8
Data and Methodology ..................................................................................................... 14
Financial Constraints ........................................................................................................ 15
Firm Survival...................................................................................................................... 22
Understanding Foreign Bank Presence .......................................................................... 24
Conclusions ........................................................................................................................ 25
3. Infrastructure Bo lenecks .................................................................................................. 29
Infrastructure in the Eastern Europe and Central Asia Region .................................. 30
Data and Methodology ..................................................................................................... 31
Electricity: A Growing Concern ..................................................................................... 32
Telecommunications Usage on the Rise, but Access and Reliability
Remain Issues ............................................................................................................. 39
The Role of Regulation and Governance ....................................................................... 42
Conclusion .......................................................................................................................... 47
4. Labor: Challenges Ahead of the Crisis ............................................................................ 50
Recent Labor Market Developments in the Eastern Europe and
Central Asia Region ................................................................................................... 51
Data and Methodology ..................................................................................................... 52
Labor Constraints in ECA ................................................................................................ 53
Responding to the Rising Skills Constraints ................................................................. 65
Eﬀects of the Crisis on Labor ........................................................................................... 69
Conclusion .......................................................................................................................... 69
References.................................................................................................................................. 73

iii
iv Contents

Appendixes................................................................................................................................ 81
Appendix 1. Technical Notes and Data Tables .............................................................. 83
Appendix 2. Notes on Sampling and the Survey Methodology............................... 120
Appendix 3. Summary of Obstacles Doing Business ................................................. 123

Boxes
Box 2.1. Eﬀects of Easy Money ................................................................................................12
Box 2.2. Fixed vs. Floating—Example of Bulgaria and Poland ..........................................22
Box 3.1. Cost of Power Outages in Kosovo ...........................................................................37
Box 3.2. Progress in Armenia’s Telecom Sector ....................................................................41
Box 3.3. Weak Governance and Infrastructure Services: Ge ing Electricity
in Ukraine ...........................................................................................................................43
Box 3.4. Transport Constraints Tighten Across ECA ...........................................................46
Box 4.1. Strides in Labor Regulations .....................................................................................54
Box 4.2. Are the Trends Emerging from the BEEPS Data Consistent with Other
Sources? ..............................................................................................................................57
Box 4.3. Wages and Productivity Increases: Do They Reﬂect Rising Skills and
Labor Shortages? .............................................................................................................. 60
Box 4.4. Emigration and Brain Drain in ECA........................................................................61
Box 4.5. Information Technology Use and Skills ..................................................................65
Box 4.6. Early Eﬀects of the Financial Crisis .........................................................................68

Figures
Figure 2.1. Liquid Liabilities as a Share of GDP .....................................................................9
Figure 2.2. Foreign Controlled Banking Sector Assets ........................................................10
Figure 2.3. Access to Credit .....................................................................................................11
Figure 2.4. Foreign Claims as a Share of GDP ......................................................................12
Figure B2.1.1. Private Credit/GDP Change (2004–2007) vs. 2009 GDP Growth ..............12
Figure B2.1.2. Change in Foreign Bank Claims to GDP Ratio (2004-2007) vs.
2009 GDP Growth..............................................................................................................13
Figure 2.5. Firm Perceptions of Access to Finance ...............................................................16
Figure 2.6. Demand for External Funding .............................................................................17
Figure 2.7. Predicted Probability of Access to Finance Being No Obstacle ......................18
Figure 2.8. Predicted Probability of Applying for a Loan ...................................................18
Figure 2.9. Predicted Probability of Access to Finance Not Being an Obstacle, by
Current Account Deﬁcit (Fixed Exchange Rate Regime) .............................................20
Figure 2.10. Predicted Probability of Access to Finance Not Being an Obstacle, by
Current Account Deﬁcit (Crawling Exchange Rate Regime) ......................................20
Figure 2.11. Predicted Probability of Applying for a Loan by Current Account
Deﬁcit and Exchange Rate Regime .................................................................................21
Figure 2.12. Predicted Probability of Firm Exit Based on Firm Size ..................................23
Contents v

Figure 2.13. Predicted Probability of Firm Exit Based on Firm Age ..................................23
Figure 3.1. Electricity as No Obstacle, 2005 and 2008 ..........................................................32
Figure 3.2. Electricity as No Obstacle by Region ..................................................................33
Figure 3.3. Power Outages vs. GDP Per Capita ....................................................................34
Figure 3.4. Breadth of Power Outages ..................................................................................35
Figure 3.5. Losses Due to Power Outages .............................................................................35
Figure 3.6. Losses Due to Power Outages by Income Classiﬁcation .................................36
Figure 3.7. Generator Use ........................................................................................................38
Figure 3.8. Telecommunications as No Obstacle, 2005 and 2008 .......................................39
Figure 3.9. Email Use vs. GDP Per Capita .............................................................................40
Figure 3.10. Email Use vs. Labor Productivity .....................................................................41
Figure B3.3.1. Duration of Procedures for Ge ing an Electricity Connection in
Ukraine ...............................................................................................................................43
Figure 3.11. Outage Costs and Government Eﬀectiveness .................................................44
Figure 3.12. Outage Costs and Control of Corruption ........................................................44
Figure 3.13. Internet Access and Government Eﬀectiveness ..............................................45
Figure 3.14. Internet Access and Control of Corruption .....................................................45
Figure B3.4.1. Transport as No Obstacle, 2005 and 2008 .....................................................46
Figure B4.1.1. Labor Regulations vs. GDP Per Capita .........................................................55
Figure B4.1.2. EPL Rigidity vs. Innovation............................................................................55
Figure 4.1. Skills and Education of Labor as No Obstacle, 2005 and 2008........................56
Figure 4.2. Skills and Education of Labor as No Obstacle by Income Classiﬁcation ......57
Figure 4.3. Skills and Education of Labor as an Obstacle vs. Labor Productivity ...........58
Figure B4.3.1. Productivity and Wage Growth in Poland 2004–2008 ................................60
Figure B4.3.2. Productivity and Wage Growth in Ukraine 2004–2008 ..............................60
Figure B4.4.1. Workers’ Remi ances (% of GDP), 2008 .......................................................61
Figure B4.4.2. Skills and Education of Labor as No Obstacle vs. Remi ances ................62
Figure B4.4.3. Skills and Education of Labor as No Obstacle and Emigration of the
Tertiary Educated .............................................................................................................62
Figure 4.4. Skills and Education of Labor as an Obstacle vs. Unemployment.................63
Figure 4.5. Skills and Education of Labor as an Obstacle vs. Unemployment of
Tertiary Educated ..............................................................................................................64
Figure 4.6. Skills and Education of Labor as an Obstacle vs. Unemployment of
Secondary Educated ..........................................................................................................64
Figure 4.7. Provision of Training by Region .........................................................................66
Figure 4.8. Provision of Training by Income Classiﬁcation ................................................66
Figure 4.9. Skills and Education of Labor as an Obstacle vs. Provision of Training .......67
Figure B4.6.1. Firms Planning to Downsize ..........................................................................68
Figure B4.6.2. Skills and Education of Labor as No Obstacle and Plans to Downsize ...68
vi Contents

Tables
Table 2.1. Foreign Banks in ECA Countries during the First 10 Years of Transition ......24
Table 4.1. Employment Elasticity of Growth 2004–2008......................................................52
Table 4.2. Cross-Regional Comparison: Labor Regulations and Skills and
Education of Labor as a Major or Very Severe Obstacle, 2008 (percentage of
respondents) .......................................................................................................................53
Table 4.3. Skills and Education of Labor as an Obstacle to Doing Business
(relative ranking among 14 obstacles) ............................................................................56
Table A1.1. Regression Controls: Summary of BEEPS-Based Control Variables .............86
Table A1.2. Results from Probit Regression of Firm Exit on Enterprise
Characteristics ....................................................................................................................98
Table A1.3. Probit Models, Financial Constraints Analysis ................................................99
Table A1.4. Comparison of Balanced BEEPS Panel and Full Sample, BEEPS 2002,
2005, and 2008 ..................................................................................................................101
Table A1.5. Regressions on the Eﬀects of Power Outage Costs on Labor
Productivity Based on Varying Firm Characteristics .................................................106
Table A1.6. Regressions on the Eﬀects of Power Outage Costs on Productivity by
Firm Productivity Quintile.............................................................................................107
Table A1.7. Regressions on the Eﬀects of High-Speed Internet Access on Firm
Productivity Based on Varying Firm Characteristics .................................................107
Table A1.8. Regressions on the Eﬀects of Email Use on Productivity by Firm
Productivity Quintile ......................................................................................................108
Table A1.9. Regressions on the Eﬀects of High-Speed Internet Access on
Productivity by Firm Productivity Quintile ................................................................108
Table A1.10. Regressions on the Eﬀects of Power Outage Costs in Countries with
Good Governance, Good Control of Corruption, and Good Government
Regulation on Labor Productivity ................................................................................108
Table A1.11. Regressions on the Eﬀects of High-Speed Internet Access in
Countries with Good Governance, Good Control of Corruption, and Good
Government Regulation on Labor Productivity .........................................................109
Table A1.12. Marginal Eﬀects of Generalized Ordered Logit Models of the Eﬀects
of Various Firm Characteristics on Electricity Perceptions ......................................109
Table A1.13. Regression Results for World Governance Indicators on Power
Outage Costs and Access to High-Speed Internet ......................................................110
Table A1.14. Estimated Power Outage Costs and Access to High-Speed Internet ........110
Table A1.15. Marginal Eﬀects of Primary Variables on Labor Regulations as No
Obstacle and Major Obstacle (General Ordered Logit Models) ...............................115
Table A1.16. Marginal Eﬀects of Primary Variables on Skills and Education of
Labor as No Obstacle and Major Obstacle (General Ordered Logit Models) ........116
Table A1.17. Results of the Innovation and ICT Use Logit Models .................................117
Table A1.18. Regression Results of the Training Logit Models ........................................118
Table A1.19. Regression Results of Labor Productivity Models.......................................119
Table A1.20. Regression Results of the Professionalism of Labor Model .......................119
Contents vii

Table A2.1. Sample Summary 2005 and 2008 ......................................................................122
Table A3.1. Problems Doing Business: Ranking of Problems 2008 ..................................123
Table A3.2. Problems Doing Business: Ranking of Problems 2005 ..................................124
Table A3.3. Factors that are Not a Problem Doing Business, Percentage Point
Changes and Statistical Signiﬁcance ............................................................................125
Acknowledgments

T his report was prepared by Gregory Kisunko (Task Team Leader), George Clarke,
Robert Cull, Kimberly Johns, Marc Schi auer, Erwin Tiongson, and Ricky Ubee un-
der the overall guidance of Yvonne Tsikata and Roumeen Islam. Valuable contributions
were made by Bryce Quillin, William Dillinger, Pedro L. Rodriguez, and Jan Rutkowski.
We are grateful for the advice and comments of the peer reviewers: Leora Klapper,
Arvo Kuddo, James S. Moose, and L. Colin Xu. In addition we would like to thank Randi
Ryterman, James Anderson, and John Giles, who advised on the concept. Comments
provided by the Oﬃce of the Chief Economist, Europe and Central Asia Region of the
World Bank are gratefully acknowledged.
This report uses data from the European Bank for Reconstruction and Development-
World Bank Business Environment and Enterprise Performance Survey (BEEPS), and we
wish to acknowledge those at Enterprise Surveys who worked to ensure the data were
collected in a timely manner and were of high quality, including James Anderson, Jorge
Luis Rodriguez Meza, and Veselin Kunchev of the World Bank and Helena Schweiger of
the European Bank for Reconstruction and Development. Finally we wish to thank the
more than 30,000 enterprise managers who have given their time to this survey over the
years.

ix
Acronyms and Abbreviations

AFR Sub-Saharan Africa
BEEPS Business Environment and Enterprise Performance Survey
BIS Bank for International Se lements
CIS Commonwealth of Independent States
EAP East Asia and the Paciﬁc
EBRD European Bank for Reconstruction and Development
EC European Commission
ECA Europe and Central Asia
EPL Employment Protection Legislation
EU European Union
EU-10 European Union 10 Countries
FCS Financial Crisis Survey
FDI Foreign Direct Investment
FSU Former Soviet Union
FSU-N Northern Former Soviet Union Countries
FSU-S Southern Former Soviet Union Countries
GDP Gross Domestic Product
ICT Information Communication and Technology
IMF International Monetary Fund
KEK Kosovo Energy Corporation
KILM Key Indicators of the Labor Market
LCR Latin America and the Caribbean
LCU Local Currency Units
LME Large and Medium-sized Enterprises
MNA Middle East and North Africa
OECD Organisation for Economic Co-operation and Development
SAR South Asia
SEE South Eastern Europe
SME Small and Medium-sized Enterprises
WDI World Development Indicators
WEF World Economic Forum
WGI World Governance Indicators

Country abbreviations used in ﬁgures and tables:
ALB = Albania
ARM = Armenia
AZE = Azerbaijan
BLR = Belarus
BIH = Bosnia and Herzegovina
BGR = Bulgaria
HRV = Croatia
CZE = the Czech Republic

xi
xii Acronyms and Abbreviations

EST = Estonia
MKD = FYR Macedonia
GEO = Georgia
HUN = Hungary
KAZ = Kazakhstan
KSV = Kosovo
KGZ = the Kyrgyz Republic
LVA = Latvia
LTU = Lithuania
MDA = Moldova
MNE = Montenegro
POL = Poland
ROM = Romania
RUS = the Russian Federation
SRB = Serbia
SVK = the Slovak Republic
SVN = Slovenia
TJK = Tajikistan
TUR = Turkey
UKR = Ukraine
UZB = Uzbekistan
Executive Summary

T his report takes stock of enterprise sector performance in the Europe and Central
Asia (ECA) region and its key drivers: access to ﬁnance, infrastructure, and labor. It
is the second of two complementary reports that examine selected trends emerging from
the Business Environment and Enterprise Performance Survey (BEEPS) data that are of
immediate policy relevance to ECA countries.1 Both reports draw primarily on informa-
tion from data collected prior to the crisis. This report also uses data on employment and
access to ﬁnance collected during the crisis in a subset of ECA countries.2
The global ﬁnancial crisis has had enormous consequences for ﬁrms’ access to ﬁ-
nance, the availability of qualiﬁed workers, and the ability of governments to provide
(and of private sector to obtain) reliable infrastructure services. The extent and impact
of these constraints is yet to be determined—but their presence at a time of economic
growth suggests they may re-emerge during the post-crisis economic recovery.
The BEEPS captures information on a number of aspects of the business environ-
ment. This report highlights the elements of ﬁrm ﬁnance, labor regulations and skills,
and infrastructure that are covered by the BEEPS questionnaire. Where possible, the
data are supplemented with data from other sources. The report covers a lot of ground,
and several broad ﬁndings from the analyses stand out.

Access to Financial Resources
In the period just before the crisis, credit to the private sector grew sharply. In large part,
the rapid growth of credit reﬂects many important reforms in the region’s banking and
ﬁnancial sectors that were carried out during the ﬁrst decade of transition. Increasing
foreign bank participation also provided broader access to credit, and introduced new
ﬁnancial instruments on the eve of the crisis.
Firms who responded to the BEEPS questionnaire reported that access to ﬁnance
was, on average, easiest in 2005. This reﬂects the signiﬁcant change in ﬁnancial market
conditions that took place in the early 2000s, driven in large part by the restructuring of
the banking sectors throughout the region in the 1990s and broadened access to credit
made possible over time by increasing foreign bank participation. This access gradually
tightened over time through 2008/09 on the eve of and during the global ﬁnancial crisis.
In 2008, access to ﬁnance was, on average, perceived as the 5th highest out of 14 obstacles
measured by the BEEPS.
The data collected during the crisis in six ECA countries suggests that access to
ﬁnance was a critical factor in a ﬁrm’s ability to survive the beginning of the economic
downturn. The analysis of these data shows that large, established ﬁrms with stronger
ties to credit institutions tended to be er weather the storm than smaller, newer ﬁrms.
But not all forms of ﬁnance were equally beneﬁcial. The presence of foreign-owned banks
with well-established local networks, ﬁnanced primarily from local deposits, and lend-
ing in local currency tended to have a stabilizing eﬀect, whereas other forms of ﬁnance,
including direct cross-border lending and local lending in foreign currency, did not.

xiii
xiv Executive Summary

Adequacy of Infrastructure
Although the region entered the transition with a relatively large transport and energy
infrastructure endowment, over time it has not kept pace with growing demand. For a
variety of reasons—the recession in the early transition period, pricing policies in the
energy sector, and weak ﬁscal resources—governments in the region have generally
not been able to provide adequate resources for the maintenance and development of
infrastructure.
For example, of 14 obstacles measured by the BEEPS, electricity ranked 3rd in 2008
(behind only tax rates and corruption), up from 12th in 2005. While the electrical infra-
structure constraints found in ECA are similar to those reported in other developing
regions, the consequences to enterprise operation and sales are more substantial, espe-
cially in the lower-income countries of ECA. The magnitude of outages is much larger in
the ECA region—32 hours per month, almost three times higher on average than in LCR,
while for low and lower-middle income countries this diﬀerence is even more signiﬁ-
cant—58 and 16 hours in ECA and LCR, respectively. These outages are tied to losses in
sales, which are particularly large among ﬁrms in lower-income countries with limited
access to secondary sources of energy, such as generators. This is also, at least partially,
the result of limited private investment (relative to other regions) in the electricity sector.
Further, ﬁrms in countries with weaker public sector governance experience short-
ages of and diﬃculties with access to electricity and information communication and
technology (ICT) services that lead to higher losses as a share of sales, compared to ﬁrms
in countries with more eﬀective governance and regulatory frameworks. This ﬁnding
suggests that reforms in the sectors may be more eﬀective if coupled with other gover-
nance and administrative reforms.

Labor Regulations and Adequate Labor Skills
Throughout most of the region, ﬁrms report that labor regulations have become less of
a burden since 2005. The rank of labor regulations as an obstacle to doing business fell
from 9th place in 2005 to 13th in 2008. Only enterprises in Poland and Slovenia reported
that labor regulations have become more of a burden. These improvements may be at-
tributed to active labor market reform eﬀorts, which increased ﬂexibility and lowered
the costs of employing workers.
During the same period, ﬁrms reported a growing dissatisfaction with the quality
of labor. Dissatisfaction with the skill level of the labor force has risen in all but three
countries in the region, becoming the 4th highest obstacle in the region. The inability of
ﬁrms to ﬁnd workers with adequate skills has been accompanied by sharp increases in
wages exceeding the growth of labor productivity. This reﬂects a structural transforma-
tion in the labor markets in the region—the rising number of jobs with higher skill con-
tent—as well as the inability of education systems to respond to the growing demand for
skills, and the migration of skilled workers to higher-income countries. The results of the
analysis provide evidence of an emerging skills shortage in the region. A solution to the
problem may lie in the development of marketable skills, rather than simply increasing
the formal educational a ainment of the workforce.
Executive Summary xv

Are Obstacles to Doing Business Distributed Evenly?
The obstacles to doing business and the beneﬁts of improvements in the business en-
vironment are not distributed uniformly across enterprises. Small ﬁrms, for instance,
complained particularly about diﬃculties in accessing credit and because this problem
is long standing, these ﬁrms were less aﬀected by the sudden tightening of credit that
accompanied the global ﬁnancial crisis. At the same time, diﬃculties in accessing ﬁnanc-
ing may have left them without the means to weather the lean years. As a result, many
young and innovative enterprises failed to survive the crisis.
Large ﬁrms were more likely to complain about labor quality—although they were
also more likely to have suﬃcient resources to mitigate it. In 2008, 80 percent of large
ﬁrms were dissatisﬁed with the skills of the labor force, compared to only two-thirds of
small ﬁrms. At the same time, while larger ﬁrms were more likely to provide training
to their employees, the burden of internally developing their workforce may impact the
productivity and eﬃciency of these ﬁrms.
The impacts of access to reliable infrastructure services vary both by country and
across diﬀerent types of ﬁrms. Firms in low- and middle-income countries appear to face
higher costs for power outages than those in high-income countries. Certain ﬁrms are
also more vulnerable to the electricity bo lenecks. For example, ﬁrms that innovate and
low productivity ﬁrms appear to face higher constraints in terms of how power outage
costs aﬀect labor productivity than their counterparts. Similar trends appear when ex-
amining access to high speed Internet—increasing access to ICT may provide a compar-
atively higher productivity boost for smaller ﬁrms and those in lower-income countries.

What Does the Future Hold?
In the recovery period, a new round of emigration may further exacerbate skill and labor
shortages, as younger and higher skilled workers are more likely to leave. Those who
stay behind will be even less qualiﬁed to perform tasks required of jobs in a changing
economy if proper training is not provided on an appropriate scale. At the same time, the
ﬁscal condition, weakened by the crisis, may further constrain the ability of ECA coun-
tries to invest in education (including worker training) and infrastructure.
By the same token, the ample supply of ﬁnancing available to the region prior to the
crisis may not reappear, as banks a empt to recover from the crisis and central banks
strengthen prudential requirements. ECA countries will have to compete for foreign in-
vestment with countries in other regions that have recovered earlier, faster, or were less
aﬀected by the global crisis.
On the eve of the crisis, enterprises in ECA complained about infrastructure bo le-
necks and the lack of qualiﬁed workers, indicating limited reform progress on several
fronts. In a weak recovery period and in an environment characterized by fragile ﬁscal
balances, the ability of the ECA region to carry out needed reforms in these areas may be
signiﬁcantly constrained. The region will therefore have to explore alternative means to
enhance labor skills and upgrade infrastructure. Further improvements in labor regula-
tions will also be critical, despite notable achievements in regulatory reforms just before the
crisis. Measures to further ease ﬁrm entry and the hiring of workers will also be warranted.
xvi Executive Summary

Notes
1. The ﬁrst report, Trends in Corruption and Regulatory Burden in Eastern Europe and Central Asia was
published in January 2011.
2. The BEEPS survey collects comparable ﬁrm-level data across countries in the ECA region to
measure aspects of ﬁrm performance and the business and regulatory environment in which they
operate. First conducted in 1999 and every three years thereafter, the 2008 round of BEEPS includes
over 11,000 ﬁrms in 29 countries. A Financial Crisis Survey was conducted by the Enterprise Sur-
vey Unit of the World Bank in 2009 in six countries, to help assess the impact of the crisis on ﬁrm
survival. BEEPS 2008 results were supplemented with results of this survey conducted in June and
July 2009 in Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey.
CHAPTER 1

Overview

Introduction

O wing to their tight linkages with international labor, trade, and ﬁnancial markets,
countries in the Europe and Central Asia (ECA) region, on average, experienced
strong economic growth in the years just before the global ﬁnancial crisis. Such rapid
growth was underpinned by substantial inﬂows of funds, robust growth in exporting
activity, and in some countries, large remi ance inﬂows sent home by migrant workers
in Western Europe and in the middle-income countries of the Commonwealth of Inde-
pendent States (CIS), notably the Russian Federation.
What started as a crisis in the housing market in the United States in late 2007
quickly became a full-ﬂedged global ﬁnancial crisis. The ﬁnancial crisis led to a mas-
sive worldwide economic slowdown, with gross worldwide product shrinking by 2.1
percent in 2009. Although all regions were aﬀected, economies in the ECA region ex-
perienced some of the steepest declines. After growing at an annual rate of 7.1 percent
in 2007 and 4.2 percent in 2008, GDP in the region contracted by 5.3 percent in 2009—a
larger contraction than in any other region.1 This was accompanied by a large drop in
private inﬂows of capital. In 2007, $491 billion (16 percent of the regional GDP) of private
capital came into the region. By 2009, this had fallen to $84 billion.
Regional Economic Developments and Prospects
Economies in the region are now embarking on a period of economic recovery, though
at a much weaker pace than other emerging and developing countries. While develop-
ing economies in Asia are expected to grow at a rate of 8 percent per year through 2013,
driven largely by China and India, the ECA region is projected to grow by about 4 per-
cent a year over the same period.
Despite the prospect of growth, the jobless nature of ECA’s economic recovery is
a cause for concern.2 Rates of joblessness remain high in much of Eastern Europe and
are rising in some countries of the CIS. In the Russian Federation, for example, unem-
ployment rates are currently projected to worsen before signs of improvement emerge.
Meanwhile, ﬁscal resources in the region remain weak, thus constraining the ability of
governments to provide social protection and support the recovery.
In this diﬃcult, post-crisis economic environment, identifying constraints on the
growth of enterprises will be critical to economic recovery. Understanding the nature of
such constraints can help enterprises in ECA position themselves in what promises to be
a more competitive, post-crisis economic environment.
While there is a large body of survey work on: government ﬁscal performance,
household and individual consumption, labor market activity, and welfare covering the

1
2 World Bank Study

period just before the crisis and during the early stages of economic recovery;3 relatively
li le is known about ﬁrms in the ECA region and their performance during this same
period. There are several enduring questions regarding obstacles to business activity.
What are the key constraints to enterprise growth in the ECA region after two decades of
structural and policy reforms? Do enterprises have suﬃcient access to ﬁnancial resourc-
es? Are ﬁrms able to ﬁnd workers with the requisite skills and qualiﬁcations? And, are
ﬁrms supported by adequate energy, telecommunications, and transport infrastructure?
An understanding of these and other issues will have immediate implications for policy,
particularly in a challenging post-crisis period.
The Scope and Rationale of this Report
In the period just before the crisis, a number of elements, such as access to ﬁnance, skills,
and qualiﬁcations of labor, and the quality of infrastructure, remained or became im-
portant obstacles to enterprise growth. The relative importance of these obstacles has
evolved over time, reﬂecting structural reforms and progress in improving the business
environment, yet they were important constraints to growth, even in a period character-
ized by robust ﬁnancial inﬂows and generally strong ﬁscal balances.
This report focuses on these key drivers of enterprise sector performance. It is the
second of two reports that examine selected trends emerging from the BEEPS data, the
ﬁrst report being an analysis on governance and trends in corruption in the region.
The selection of speciﬁc topics to be covered in this report was driven by the following
considerations:
First, the report addresses the core components of enterprise activity, namely, the
factors of production and the public infrastructure to support them, and seeks to as-
sess the availability and accessibility of these inputs in the ECA region. Second, one can
view these building blocks as major forms of capital—human capital, ﬁnancial capital,
and public, physical capital—which then leads to an assessment of how these factors
impacted ﬁrm survival, productivity, and performance in the face of the ﬁnancial crisis,
and how these impacts vary across diﬀerent types of ﬁrms. One key insight from the ﬁrst
generation of studies of enterprises is that obstacles to enterprise activity and access to
key resources are not uniformly distributed across ﬁrms. Enterprises face varying levels
of pressure depending on their characteristics and location, the resources directly acces-
sible to them, and the quality of services and regulation provided by their governments.4
Third, the report presents an opportunity to evaluate progress made along these three
key dimensions near the end of two decades of transition. Much of the transition period in
the 1990s and the structural transformation associated with the period have consisted of
eﬀorts to promote broader access to capital, deregulate labor markets, and promote skills,
and to maintain if not to upgrade infrastructure. In order for the recovery to be success-
ful, it is critical to understand where the region stands now relative to these elements,
especially as they are perceived by enterprises, the engine of private sector-led growth.

Main Issues Reﬂected in the Report
Trends in Firm Financing
In the period just before the crisis, credit to the private sector grew very sharply. In large
part, the rapid growth of credit reﬂects many important reforms in the region’s bank-
ing and ﬁnancial sectors that were carried out during the ﬁrst decade of transition. One
Challenges to Enterprise Performance in the Face of the Financial Crisis 3

of the most dramatic changes of the 1990s was a restructuring of the banking sectors
in ECA countries from the state-run mono-banking systems inherited from the Soviet
era to private ownership of banks occupying diﬀerent market niches and drawing re-
sources from internal and international ﬁnancial markets. In fact, the deﬁning feature of
the restructuring of the banking sectors in ECA countries was the heavy involvement of
foreign banks. By 1996, majority foreign-owned banks held 20 percent of banking sec-
tor assets in ECA, and by 2003, over 50 percent of ECA’s banking sector assets were in
the hands of foreign controlled banks. Increasing foreign bank participation provided
much broader access to credit prior to the crisis. Not surprisingly, BEEPS data conﬁrm
that ﬁnancial constraints were less binding in countries where foreign banks had been
present for some time.
The most worrisome eﬀect of the ﬁnancial crisis on ﬁrms was the accompanying
drop in demand for products and services. As a result, some ﬁrms did not survive. Us-
ing data from Enterprise Surveys on the ﬁnancial crisis, which revisited the same BEEPS
ﬁrms in six ECA countries, the impact of the crisis was visible. The results show that
access to ﬁnance played a key role in keeping ﬁrms aﬂoat by providing supplementary
working capital during the crisis.
Deterioration of Physical Infrastructure and Growing Disparities in Access to New Infrastructure
At the beginning of the transition, many countries in the ECA region enjoyed be er in-
frastructure, with respect to quality and quantity, compared to many other countries at
a similar level of development. However, the supply and maintenance of infrastructure
services did not keep pace with the rising demand for such services by ﬁrms, even in the
wealthier economies of the region. Enterprises report that electricity is third among the
major obstacles to business activity in 2008, after tax rates and corruption.5 Overall, be-
tween 2005 and 2008, ﬁrms’ views on the quality of electricity, telecommunication, and
transport infrastructure deteriorated the most among all reported obstacles.6
Deterioration of ﬁrm perceptions of electricity may reﬂect in part the escalating en-
ergy prices worldwide during this period, rather than just the state of electricity infra-
structure in the ECA region. However, BEEPS does not suﬃciently cover the cost of energy
to ﬁrms, therefore an analysis of the cost of electricity is outside the scope of this report.
Although the infrastructure constraints found in ECA are similar to those reported
in other developing regions, the consequences for enterprise operation and proﬁtability
are far more pronounced for ECA ﬁrms. The cost of such bo lenecks is hefty, account-
ing for a loss of about 5.4 percentage points of annual output (measured by lost sales
revenue) for ECA ﬁrms. These costs are disproportionately larger among ﬁrms in the
low and lower-middle income countries of ECA (9.7 percentage points vs. 2.8 percentage
points for higher-income countries).
Shortage of Skilled Labor and Easing of Labor Regulations
Until the global ﬁnancial crisis, the ECA region experienced substantial improvements
in labor market conditions along with steady economic expansion. After a number of
years of stalled employment creation in the period following the 1998 Russian crisis, job
opportunities grew rapidly in the period after 2004. For the new EU member countries,
robust economic growth and job creation coincided with their EU accession. For other
countries, job creation coincided with easing labor market regulations and the large
inﬂow of capital. In this environment, BEEPS data suggest that ﬁrms’ views regard-
4 World Bank Study

ing labor regulations and the availability of skills have generally evolved in opposite
directions.
Firms’ perceptions of labor regulations have steadily improved since 2005 through-
out most of the region. One of the explanations oﬀered in the literature7 is that labor reg-
ulations become less binding in a period of strong labor demand and economic growth.
At the same time, ﬁrms reported a growing dissatisfaction with the quality of labor
over this period. The average level of dissatisfaction with the skill level of the labor
force has risen in all but three countries in the region, though the average level in ECA
is comparable with those of other developing regions, such as LCR. These unfavorable
views reported by respondents to BEEPS are consistent with other surveys of enterprise
perception. Recent data and studies on labor market trends suggest that there are sev-
eral drivers of the skills shortage, including the rising number of jobs with higher skill
content associated with structural transformation, the inability of education systems to
respond to the growing demand for skills, and the emigration of skilled workers (from
EU-10 countries and some of the low-income countries of the CIS alike).
What Are the Prospects in the Post-Crisis Period?
Some obstacles to doing business may have been eased by the crisis as falling energy use
alleviated the energy crunch and as retrenchments reduced the labor and skill shortages.
Notwithstanding this partial relief, access to ﬁnance worsened, as is evident from data
collected for selected countries through 2009. It is not clear whether such crisis condi-
tions continue to hold during the recovery, or whether pre-crisis conditions will slowly
re-emerge.
Will ﬁrms be more or less constrained in the recovery period, relative to ﬁnance,
labor, and infrastructure obstacles? The answer is not obvious. For example, in the face
of sustained joblessness in the region, labor shortages previously felt in the pre-crisis
period may be temporarily relieved. At the same time, with recovery underway, workers
from Eastern Europe may be compelled to ﬁnd work in growth poles abroad, once again,
as the crisis has widened income disparities across countries. In addition, as enterprises
strive to be more productive during a period of weak recovery, the need for higher qual-
ity labor becomes even more urgent, thus boosting the competition for skilled labor. The
net eﬀect of all these developments is not clear.
In the post-crisis period, access to ﬁnance will be a much larger constraint than it
was previously as banks recover from the negative eﬀects of the crisis and consolidate
their ﬁnances, as central banks strengthen prudential requirements, and as ECA coun-
tries compete for foreign investment with countries in other regions that have recovered
earlier, faster, or were much less aﬀected by the global crisis.
In this weak economic environment, policy measures to improve the business en-
vironment and promote job creation will be essential. An analysis of the region’s policy
options is beyond the scope of this report. However, some preliminary policy implica-
tions emerge from the analysis. First, further improvements in labor regulations will be
critical, despite notable achievements in regulatory reforms just before the crisis. With
job creation stagnant, economic activity still weak, and the recent loss of young, innova-
tive ﬁrms, measures to further ease ﬁrm entry and increase the ﬂexibility of employing
workers will be warranted. Second, the region has to explore alternative means to up-
Challenges to Enterprise Performance in the Face of the Financial Crisis 5

grade infrastructure, in light of limited government resources. Public works and public-
private partnerships aimed at the maintenance and the upgrading of infrastructure may
be one way of employing jobless workers while addressing infrastructure bo lenecks in
the region.

Data and Methodology
The 2008 round of surveys was administered to managers of over 11,000 ﬁrms in 29 ECA
countries. For the ﬁrst time, the BEEPS exercise included Kosovo and Montenegro as
separate countries. The BEEPS project stands out from other private sector surveys done
by the World Bank as the largest simultaneous survey of ﬁrms, virtually covering an
entire region. The BEEPS has been undertaken every three years since 1999.
The 2008 BEEPS questionnaire and sample design were modiﬁed8 from previous
rounds to enhance comparability of indicators with similar ﬁrm-level surveys in other
regions. These changes, however, make it more diﬃcult to track progress over time be-
tween 2005 and 2008. Changes in the sample design make it necessary to drop some
ﬁrms from the 2005 and 2008 samples, so they are suﬃciently similar in composition by
ﬁrm size and industry. Resulting reductions in sample size make it somewhat less likely
that a change over time of a given magnitude will be statistically signiﬁcant.
Another notable change is that many of the questions included in 2005 (and in earlier
BEEPS surveys) were dropped. Many other questions, from the World Bank’s Enterprise
Surveys conducted in other regions, were added. One beneﬁt of, and the main driver
for the changes in the survey, is an enhanced ability to draw comparisons between ECA
countries and countries in other regions.
Each chapter in this report uses a subset of BEEPS questions and supplemental data
sources; therefore each chapter has a speciﬁc section dedicated to explaining the meth-
odological approach, caveats, and data sources used.

The Structure of This Report
The rest of the report is organized as follows: Chapter 2 focuses on access to ﬁnance as
an obstacle to doing business. Chapter 3 examines infrastructure bo lenecks confronting
enterprises in the region and Chapter 4 explores the evolution of labor regulations and
skills constraints.

Notes
1. World Bank (2010).
2. See, for example, World Bank (2010b, 2010c), IMF (2010a and 2010b).
3. See, in particular, EBRD (2002 and 2005). The ECA regional report on productivity (Alam et al.,
2008) includes enterprise data through 2005. Relatively li le is known about enterprises in the
region in the period after 2005, except for the initial analysis of the 2008 BEEPS data in Chapter 5
of the Turmoil at Twenty regional report (Mitra et al., 2009) and Chapter 5 of the recent Transition
Report 2010 Recovery and Reform (EBRD, 2010).
4. See, for example, EBRD (2002).
5. Due to changes in the survey instrument, it is not possible to make robust conclusions about
trends in absolute terms. The relative change, however, is valid. It is derived by using the mean
response across ﬁrms as the basis to evaluate the severity of 14 individual problems doing business
6 World Bank Study

asked about in the survey, which include tax administration, corruption, customs regulations and
labor regulations and skills and education of labor among others. Each obstacle is ranked by sever-
ity, 1 for most severe, 14 for least severe.
6. See Appendix 3.
7. See Rutkowski (2007) as an example.
8. A detailed discussion of these diﬀerences is presented in Appendix 2.
CHAPTER 2

Access to Finance

W hat started as a crisis in the subprime mortgage market in the United States in late
2007 quickly became a full-ﬂedged global ﬁnancial crisis. The ﬁnancial crisis led
to a massive worldwide economic slowdown, with gross worldwide product shrinking
by 2.1 percent in 2009. Although all regions were aﬀected, the Europe and Central Asia
Region (ECA) was hit harder than others. After growing at an annual rate of 7.1 percent
in 2007 and 4.2 percent in 2008, GDP in the region contracted by 5.3 percent in 2009—a
larger contraction than in any other region.1 This was accompanied by a large drop in
private inﬂows of capital. In 2007, $491 billion (16 percent of GDP) of private capital
came into the region. By 2009, this had fallen to $84 billion. The combination of a drop
in demand and a drop in the availability of external ﬁnancing aﬀected ﬁrms throughout
the region.
Because the crisis started out in the ﬁnancial sector and inﬂows of capital slowed, it
should have been more diﬃcult for ﬁrms in the region to maintain access to ﬁnancing.
Although this was the case, access to ﬁnancing, surprisingly, was not what concerned
ﬁrms’ managers most during the crisis.2 When ﬁrm managers were interviewed, most
said that the biggest problem that their ﬁrms faced during the crisis was a drop in de-
mand—between 70 and 80 percent of the surveyed ﬁrms in each country identiﬁed this
as the most serious problem. By comparison, less than 10 percent said reduced access
to credit was the most serious problem (Ramalho et al., 2009).3 This does not mean that
access to ﬁnancing was unimportant: empirical results in this chapter address this issue
along with several other important questions.
The ﬁrst question addressed in this chapter is: what ﬁrm characteristics aﬀected
their access to ﬁnance and consequently their chances to survive the crisis? Some early
answers are:
■ Relative to non-crisis periods, larger and older ﬁrms were less likely to have
stopped operating by mid-2009 than similar smaller, younger ﬁrms.
■ Controlling for ﬁrm performance, ﬁrms that had access to ﬁnancing were be er
able to weather the crisis than those that did not. In particular, ﬁrms that had
loans and overdraft facilities were less likely to have stopped operating, which
is consistent with the hypothesis that ﬁrms that lacked access to external funds
were most vulnerable to the drop in demand.
■ If, in the same analysis, ﬁnancial access and ﬁrm performance are controlled for,
the diﬀerence in survival rates between large and small ﬁrms is substantially
reduced. Because informational asymmetries between borrower and lender are
less severe for larger, older ﬁrms, lenders ﬁnd it easier and cheaper to extend
credit to them. Thus it is not ﬁrm size and age, per se, that reduced the likeli-

7
8 World Bank Study

hood that a ﬁrm stopped operating, but rather the relationships between those
characteristics and access to credit.
The second question addressed in the chapter is: what types of ﬁrms and countries
were most aﬀected by the crisis in terms of ﬁnancial constraints? The analysis is done
mostly along two dimensions: diﬀerences in foreign participation in the banking sector
and diﬀerences in exchange rate regimes.
■ First, given the high foreign bank participation rates in many countries in the re-
gion—and that the ﬁnancial crisis started in developed economies—it is plausi-
ble that foreign banks could have contributed to a contraction in credit. Foreign
bank participation might, therefore, have contributed to problems with access
to ﬁnancing during the crisis. Despite this possibility, the empirical results in
this chapter suggest otherwise.
• Having a well-established foreign bank presence helped to ease ﬁnancial
constraints during the crisis. That is, ﬁrms were less likely to report that ac-
cess to ﬁnancing was a serious problem in countries with high foreign par-
ticipation in the banking sector.
■ Second, relative to local lending in local currency, both direct cross-border
ﬂows to ECA and local lending in foreign currency had been growing much
more rapidly, and the la er grew especially quickly relative to other regions.
The large decline in direct, cross-border inﬂows of capital and local lending in
foreign currency during the crisis aﬀected access to ﬁnancing.
• In contrast to the stabilizing inﬂuence of foreign-owned banks with a strong
local presence, direct, cross-border capital ﬂows appear to be destabilizing.
Those ﬂows fell substantially after the onset of the crisis.4
• This was especially true in countries with pegged exchange rates, where cap-
ital inﬂows contributed to the pre-crisis lending boom and the severity of the
drop in access when the bubble burst.
• Countries with large current account deﬁcits, especially those with pegged
exchange rates had lending booms in the middle of the decade, but became
most vulnerable when the situation unraveled by 2008–2009.
The rest of this chapter is organized as follows. The next section provides a brief
overview of relevant developments in the ﬁnancial sector in ECA over the last 10–15
years. The second section discusses the two sources of data used in the chapter. The third
section provides an analysis of ﬁnancial constraints while the fourth section addresses
the 2009 Financial Crisis Survey and provides a concrete indicator of the eﬀects of the
crisis, therefore complementing the constraint analysis with a “survival” analysis (for
six countries where the follow-up survey was conducted). The ﬁfth section provides the
main conclusions.

Financial Sector Developments in the Eastern Europe and Central Asia Region
When ECA countries began transitioning from planned to market-based economies the
very notion of private banks was all but alien to many policy makers there. Therefore,
one of the most dramatic changes of the 1990s was a restructuring of the banking sectors
in ECA countries from the state-run mono-banking systems inherited from the Soviet
Challenges to Enterprise Performance in the Face of the Financial Crisis 9

era to private ownership of banks occupying diﬀerent market niches and drawing re-
sources from internal and international ﬁnancial markets.
Among ECA countries, the average ratio of liquid liabilities in the ﬁnancial system
relative to GDP stood at just 27 percent in 1996, almost as low as levels in Sub-Saharan
Africa. The ratio of liquid liabilities to GDP climbed steadily, more than doubling to
reach almost 50 percent by 2008 (Figure 2.1). Although other regions also experienced
gains in this ratio during the same period, none was as dramatic as the gains in ECA, and
thus the region had moved well past Africa and closed the gap in ﬁnancial development
relative to the rest of the developing world.
Indeed, these averages hide even bigger country-level changes. For example, the ra-
tio of liquid liabilities to GDP has almost quadrupled over this period in Georgia—from
the region’s lowest (6 percent) in 1996 to almost 24 percent in 2008. The largest relative
increase was seen in FYR Macedonia—from 11 percent in 1996 to almost 55 percent in
2008. Among ECA countries, Albania had the highest liabilities to GDP ratio in 2008—
over 80 percent. The only country in the region where this ratio went down was the
Slovak Republic—from 59 percent in 1996 to 56 percent in 2006.
Perhaps the deﬁning feature of the restructuring of the banking sectors in ECA
countries was the heavy involvement of foreign banks. Lacking capital to begin with and
often faced with banking sector instability in the early phase of the transition to private
ownership, many ECA countries turned to foreign investors.5 The transformation was
remarkable—prior to the fall of the Berlin Wall there was no foreign ownership of banks.
By 1996, majority foreign-owned banks held 20 percent of banking sector assets in ECA
(Figure 2.2). Though that ﬁgure continued to increase in regions such as Latin America
and Sub-Saharan Africa, it skyrocketed in ECA. By 2003, over 50 percent of ECA’s bank-
ing sector assets were in the hands of foreign controlled banks. Only Sub-Saharan Africa
came close on that dimension.

Figure 2.1. Liquid Liabilities as a Share of GDP

60
Liquid Liabilities/GDP (%)

0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
East Asia & Pacific Europe & Central Asia Latin America & Caribbean
Middle East & North Africa South Asia Sub-Saharan Africa

Source: World Bank Database of Financial Development and Structure.
10 World Bank Study

Figure 2.2. Foreign Controlled Banking Sector Assets

60
Percentage of Assets Held by Foreign Banks

0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005
Year
East Asia & Pacific Europe & Central Asia Latin America & Caribbean
Middle East & North Africa South Asia Sub-Saharan Africa

Source: Claessens, et al. (2008).

The diversity among ECA countries was stunning. Whereas just over 1 percent of
banking sector assets was held by foreign-controlled banks in Uzbekistan in 2005, in
countries as diverse as Albania, Bosnia and Herzegovina, Estonia, and the Kyrgyz Re-
public at least 75 percent of banking sector assets were controlled by foreign-owned
banks. Overall, in 2005, among 26 ECA countries for which data is available, 15 countries
had the majority of banking sector assets held in the hands of foreign controlled banks.
The empirical evidence shows that foreign banks in ECA were more eﬃcient than
domestic banks and the remaining state-owned banks with private ownership, espe-
cially with respect to minimizing their costs.6 As the later ﬁrm-level evidence conﬁrmed,
lending by foreign banks during this period was associated with growth in ﬁrms’ sales,
assets, and leverage.7
Regarding the outreach of the banking sectors in the ECA region, the beneﬁts of for-
eign bank participation were slower to develop. Foreign banks had long been accused of
cream-skimming in developing countries, that is, lending only to top-rated clients. While
granting that foreign banks had contributed to the overall development and stability
of ECA’s banking sectors, as late as 2005 some observers lamented that credit markets
remained shallow and access to credit for SMEs limited.8 Interviews with managers of
foreign banks in the region revealed an intent to incorporate lending to small ﬁrms over
time, and thus over the medium to long-term there was no intended bias toward lending
to large multinational ﬁrms.9 Evidence has also conﬁrmed that the links between foreign
lending and growth in ﬁrms’ sales, assets, and leverage were less pronounced for small
ﬁrms through 2002. At the same time, those links were more pronounced for young
ﬁrms indicating that foreign banks were broadening access to ﬁnance for new (though
not necessarily small) upstarts.10 In that sense, they could be described as broadening
access to credit.
Challenges to Enterprise Performance in the Face of the Financial Crisis 11

The eﬀects of expanded access to credit became evident late in the period, especially
in the four years leading up to the recent crisis (Figure 2.3). As early as 2006, some ob-
servers had warned that the next major ﬁnancial crisis would occur in Eastern Europe
because the classic pa ern—large current account deﬁcits, large foreign debt, currency
mismatches, and misaligned exchange rates—had already emerged.11
Between 1996 and 2008, in countries like Albania, Kazakhstan, and Latvia, the credit
to private sector to GDP ratio increased more than tenfold. Only in the Slovak Republic
was this increase less than 10 percent. The overall ratio went down only in the Czech
Republic, from about 66 percent in 1996 to 48 percent in 2008.
The vulnerability of the ECA region and the early eﬀects of the crisis were appar-
ent in data reported by the Bank for International Se lements (BIS) on the gross claims
of foreign banks on the ﬁnancial and non-ﬁnancial sectors of developing countries.12
From 1996 to 2004, the ratio of credit to the private sector relative to GDP rose modestly
from 18 percent to 25 percent. From 2004 to 2008, that ﬁgure climbed from 25 percent to
50 percent (Figure 2.3). Although other regions experienced increases on this measure,
none were as dramatic as ECA’s and, unlike for liquid liabilities, the region stood on
par with or ahead of other developing regions on this measure by 2008. Of course, the
rapid growth in credit late in the period ushered in a new set of concerns about foreign
banks and their impact on stability. At the same time, the ratio of foreign claims to GDP
in ECA grew two-fold between 2004 to 2007 when it peaked, and had already begun its
decline in 2008 (Figure 2.4). Box 2.1 illustrates eﬀects of “easy money” on selected ECA
countries.

Figure 2.3. Access to Credit

50
Credit to Private Sector/GDP (%)

0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
East Asia & Pacific Europe & Central Asia Latin America & Caribbean
Middle East & North Africa South Asia Sub-Saharan Africa

Source: World Bank Database of Financial Development and Structure.
12 World Bank Study

Figure 2.4. Foreign Claims as a Share of GDP

60
Percent of Total Foreign Claims Relative to GDP

0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008
Year
East Asia & Pacific Europe & Central Asia
Latin America & Caribbean Middle East & North Africa
South Asia Sub-Saharan Africa

Source: Bank of International Se lements.

Box 2.1. Effects of Easy Money

The averages presented in Figure 2.4 do not show the dramatic diversity of the speciﬁc
country conditions. There is a strong connection between the increase of credit to the private
sector, foreign bank claims, and the effect of the ﬁnancial crisis on economic growth (Figures
B2.1.1 and B2.1.2).

Figure B2.1.1. Private Credit/GDP Change (2004–2007) vs. 2009 GDP Growth

45
40 LVA
Credit to Private Sector/GDP
(Difference 2004-2007) (%)

35
30 EST
LTU KAZ
25 SVN BGR
20
ALB
15 ROM HRV
HUN GEO
CZE MKD
RUS MDA TUR
10 SVK SRB
POL
5 ARM KGZ

0
-20 -15 -10 -5 0 5
GDP Growth 2009 (%)
Source: World Bank Database of Financial Development and Structure, World Development Indicators.

(Box continues on next page)
Challenges to Enterprise Performance in the Face of the Financial Crisis 13

Box 2.1 (continued)

Figure B2.1.2. Change in Foreign Bank Claims to GDP Ratio (2004-2007) vs. 2009 GDP Growth

100
Foreign Claims on the Banking Sector/GDP

HRV
80
BIH
(Difference 2004–2007)

60 LVA
ROM SVKBGR
SVN

40 LTU HUN ALB
EST
UKR CZE
20
RUS
TUR MKD KAZ
POL
ARM MDA BLRKGZ
0 GEO TJK AZE
UZB
-20
-20 -15 -10 -5 0 5 10 15
GDP Growth 2009

Source: Bank of International Settlements, World Development Indicators.

Latvia, Lithuania, and Estonia experienced the largest increase of credit to the private sec-
tor over the 2004 to 2007 period and the largest economic contraction in 2009. There are
only two countries that do not ﬁt this relatively clear pattern—Armenia and Kazakhstan. The
former had a very modest increase in credit, but a very signiﬁcant contraction, while the latter
had a credit expansion at the level of Lithuania, and maintained positive economic growth.
The situation was about the same with the foreign bank claims, although the two countries
with the highest increase of foreign bank claims in 2004-2007—Croatia and Bosnia and Her-
zegovina—managed to contain their economic contractions within single digits, while Latvia,
Lithuania, and Estonia experienced double-digit drops in GDP.
Source: Authors’ calculations based on World Bank data.

When foreign claims are separated into direct cross-border ﬂows (i.e., overseas
lending by the parent of a multinational bank) versus local lending in host markets by
the subsidiaries and branches of foreign-owned banks, it becomes clear that the decline
in 2008 was a ributable to reductions in cross-border ﬂows and local lending in for-
eign currency. Local lending in foreign currency in ECA had been growing much more
quickly than in other regions, and substantially more quickly than local lending in local
currency. As in other regions, especially South and East Asia, direct cross-border ﬂows
to ECA had also grown robustly prior to the crisis.13 The analysis of ﬁnancial constraints
that follows will try to distinguish the rapidly growing direct cross-border ﬂows and lo-
cal lending in foreign currency from the relatively more stable long-term local lending in
local currency by subsidiaries and branches of foreign banks.
The eﬀects of the crisis generally were more muted in countries where: (i) foreign
banks conducted a larger share of their lending in local currency, (ii) a larger share of
the funding of foreign-owned aﬃliates came from expanding the domestic deposit base
rather than from the parent bank or from wholesale funding, and (iii) the roster of for-
eign banks was not dominated by banks from the United States and Europe.14 For all
of these reasons, LCR, which had over half of total lending in local currency and where
14 World Bank Study

domestic deposits equaled 105 percent of all loans, has emerged relatively unscathed.
Conversely, ECA, with only a third of its lending in local currency and domestic depos-
its equal to 75 percent of all loans, has suﬀered. Similarly, Sub-Saharan Africa was also
less aﬀected by the crisis than ECA, though foreign banks in Africa also did not extend
credit as widely as in ECA or in LCR prior to the crisis.
Observers remained seriously concerned about the eﬀect of foreign banks on the
stability and outreach of banking sectors throughout the region. One concern was that
foreign banks might shift capital away from aﬃliates in response to real sector shocks
that aﬀect the proﬁtability of lending in a host country. While there was evidence of
some funds being pulled back to the home country when proﬁtable lending opportuni-
ties were prevalent there,15 multinational bank lending did not slow during systemic cri-
ses in the host countries, while lending by domestic banks did. Domestic banks in ECA
contracted their credit bases during crises, whereas greenﬁeld foreign banks did not.16
In addition, parent banks were in a stronger position to assist aﬃliates when their other
aﬃliates were performing well.17
The evidence also indicated that lending relationships for foreign banks tended to
be more stable than those of domestic banks. Foreign banks were less likely to drop their
clients, even in the aftermath of an acquisition, and over time, competition from foreign
banks produced changes in the lending policies of domestic banks, making their lending
relationships more stable and generally improving access to credit for all ﬁrms.18 The
analysis in this chapter will show that foreign bank participation is in fact associated
with less severe obstacles to ﬁnance as reported by ﬁrm owners.19 In that sense, foreign
bank participation could be seen as a stabilizing force during diﬃcult times, in part due
to the stable lending relationships highlighted in the empirical literature.

Data and Methodology
The BEEPS20 survey covers a broad range of issues about the business environment,
including questions about access to, and use of, ﬁnancial services and thus provides an
excellent vehicle for studying the eﬀects of the dramatic structural changes in banking
described in the previous section.
The analysis undertaken for this chapter required an indicator of “access to” or “use
of” ﬁnancial services that is comparable over the survey rounds. The best candidate
comes from the response to the following question:
“Is access to ﬁnance, which includes availability and cost, interest rates, fees and
collateral requirements, no obstacle, a minor obstacle, a moderate obstacle, a major
obstacle, or a very severe obstacle to the current operations of this establishment?”
A variable equal to one if the manager of the ﬁrm responded that access to ﬁnancial
services was no obstacle is the dependent variable in the analysis of ﬁnancial constraints
that follows.21
An advantage of this question is that it provides a comprehensive measure of the
ﬁrm managers’ perceptions of ﬁnancial constraints. The second advantage of using this
question is that responses to questions about perceptions of ﬁnancial constraints (or oth-
er topics that aﬀect ﬁrms’ operations) are likely to be forward looking.22 Thus, concerns
that the ﬁnal round of the BEEPS survey coincided with the onset of the crisis are less
worrisome than they would be for other types of questions.23
Challenges to Enterprise Performance in the Face of the Financial Crisis 15

One potential concern in using this question was a change in the number of re-
sponse categories between the 2005 and 2008 rounds of the survey: the category “very
severe obstacle” was added. Since the statistical analysis in this chapter is focused on
relative movements in the “no obstacle” indicator for diﬀerent types of ﬁrms rather than
on absolute levels and the wording of the question was identical for all ﬁrms in a given
round, the relative changes across rounds provide a reliable comparison of the severity
of ﬁnancial constraints over time.
Another potential concern with the comparability of BEEPS results across diﬀerent
rounds of the survey was the changes in sampling methodology between 2005 and 2008.
In order to mitigate this concern, the analysis of ﬁnancial constraints presented in this
chapter utilized the balanced panel of ﬁrms that responded to all three rounds of the
survey. Inferences drawn from tracking responses to the same questions by the same
ﬁrms over time are likely to be more reliable than those that could be drawn from the
full BEEPS samples.24
While panel data provides for good cross-period comparability, using it narrows
down opportunities to discuss the speciﬁcs of individual countries as country samples
dwindle. Therefore, this chapter oﬀers a limited number of country-speciﬁc examples.
The results of BEEPS 2008 were supplemented with results of the Financial Crisis
Survey (FCS) conducted in June and July 2009 by Enterprise Surveys in six countries:
Bulgaria, Hungary, Latvia, Lithuania, Romania, and Turkey. These follow-ups permit
an investigation of the determinants of ﬁrm survival during the crisis in which the de-
pendent variable indicates that the ﬁrm was no longer operating—either because it had
closed, had ﬁled for bankruptcy or insolvency, or was temporarily shut—at the time of
the second survey. This occurred in 296 of 2,501 possible cases (11.8 percent).25

Financial Constraints
The BEEPS data indicate that ﬁnancial constraints eased from 2002 to 2005 and then
tightened, in 2008 (Figure 2.5). Of course, these averages mask wide variations in ﬁnan-
cial constraints reported by ﬁrms participating in the surveys. Some ﬁrms reported no
constraints, despite the crisis, while others were constrained across all three waves of
the BEEPS. The main results that emerge from the ﬁnancial constraints analysis revolve
around three themes: the relative reliance on external ﬁnance by large versus small ﬁrms,
the eﬀects of foreign bank participation, and the impact of exchange rate regimes.26 Each
is discussed in turn.
Large and Small Firms
Informational asymmetries between borrower and lender tend to be more severe for
small, young ﬁrms, and thus lenders charge them higher interest rates or do not extend
credit to them at all. Small ﬁrms therefore use less external ﬁnance than others. Protec-
tion of property rights helps mitigate these informational asymmetries, and increases ex-
ternal ﬁnancing for all ﬁrms but more so for small ﬁrms, mainly due to its eﬀect on bank
ﬁnance.27 The World Bank’s Investment Climate Surveys reveal that access to ﬁnance
ranks high among constraints to ﬁrm growth as reported by ﬁrm managers, and relaxing
ﬁnancial constraints has a proportionately greater eﬀect on sales growth for small ﬁrms
than for others.28 Overall, recent ﬁndings indicate that larger ﬁrms ﬁnd it easier to trade
oﬀ between internal funding and external borrowing than small ﬁrms.
16 World Bank Study

Figure 2.5. Firm Perceptions of Access to Finance
Percentage of Firms Indicating Access to Finance

60
as Not an Obstacle to Doing Business

0
2002 2005 2008
BEEPS Year
All Firms Large Firms Small/Medium Firms

Source: BEEPS panel data, 2002, 2005, 2008.

Figure 2.5 shows that ﬁnancial constraints eased for ﬁrms from 2002 to 2005, but
then tightened again in 2008.29 It also shows that large ﬁrms were four percentage points
more likely to report that ﬁnance was no obstacle to their operations than small or medi-
um-sized ﬁrms in 2002. Firm size categories are deﬁned in the BEEPS as small, medium,
and large; ﬁrms having 19 or less workers, 20–99 workers, and 100 or more workers,
respectively.
By 2005, that gap had widened. Although all ﬁrms reported less severe ﬁnancial
constraints, nearly half of the large ﬁrms reported no constraints compared to less than
40 percent of the small and medium-sized ﬁrms. In 2008, the share of large ﬁrms that
reported ﬁnance was no obstacle plummeted to 28 percent, a level slightly below that of
the smaller ﬁrms.30 The regressions described in Appendix 1, which control for a host of
ﬁrm and country characteristics, conﬁrm the same pa ern. In 2002, large ﬁrms were 15
percent (4.5 percentage points) more likely to report access to ﬁnance as no obstacle, a
gap that widened slightly by 2005. By 2008, the share of large ﬁrms that were ﬁnancially
unconstrained had fallen, and was no longer statistically distinguishable from that of
smaller ﬁrms.
Greater reliance on external ﬁnance, however, also makes ﬁrms more vulnerable to
sudden stoppages in funding. In normal times, larger ﬁrms ﬁnd it easier to access exter-
nal ﬁnancing than small ﬁrms,31 but the severity of the current crisis makes it likely that
demands for ﬁnance from large ﬁrms exceeded the available supply.
Foreign banks in developing countries tend to lend less to SMEs, and thus large
ﬁrms in the panel sample are the ones most likely to have received loans from foreign
banks (though the BEEPS data do not permit this to be checked directly). Moreover,
large ﬁrms were much more likely to rely on funding from foreign banks based in de-
veloped countries.32 Lacking external ﬁnance prior to the crisis, smaller ﬁrms’ sources of
funding were less likely to have been aﬀected.
Challenges to Enterprise Performance in the Face of the Financial Crisis 17

Figure 2.6. Demand for External Funding

80
Percentage of Firms that Applied for a Loan

70
in the Previous Fiscal Year

60
50
40
30
20
10
0
2002 2005 2008
BEEPS Year
All Firms Large Firms Small/Medium Firms

Source: BEEPS panel data, 2002, 2005, 2008.

Though it is not certain what motivated loan applications during the crisis, large
ﬁrms were much more likely than others to apply for external sources of funding in re-
sponse to the dramatic drop in demand in the ECA region (Figure 2.6).33 Whereas large
ﬁrms had been about one-third more likely to apply for a loan than small and medium-
sized ones in 2002 and 2005, they were twice as likely to do so in 2008. The share of small
and medium-sized ﬁrms that applied for a loan also increased, but much less sharply.34
Taken together, the steep decline in the share of large ﬁrms that reported ﬁnance
was not an obstacle to their ﬁrms’ operations in 2008 and the sharp increase in the share
of large ﬁrms applying for loans suggests that large ﬁrms were able to ride out the crisis
by relying on external ﬁnancing; certainly more so than smaller ones.
Foreign Bank Participation
The extent of foreign participation in bank ownership varies widely among ECA coun-
tries. A high degree of foreign bank ownership was associated with relative diﬃculty in
accessing credit at the outset of the decade, but less so at the end. The BEEPS data reveal
a distinction between countries that had a well-established foreign bank presence by 1999
and those that did not in terms of reported ﬁnancial constraints (Figure 2.7). In 2002,
an estimated35 one-third of the ﬁrms in countries with low levels of foreign bank pres-
ence (in 1999) reported that ﬁnance was not an obstacle to their operations. In the same
year, in countries with medium and high levels of foreign bank participation, even fewer
ﬁrms reported facing no obstacle accessing ﬁnance—27 percent and 19 percent, respec-
tively.36 But by 2008 the scales tipped in favor of countries with relatively high levels of
foreign bank participation. About half of ﬁrms in these countries reported that ﬁnance
was not an obstacle to their operations, whereas only a third of the ﬁrms in countries
with low levels of foreign participation reported the same. These results suggest that
foreign banks entered relatively credit-starved countries in the ﬁrst part of the decade
and substantially succeeded in addressing this constraint as the decade proceeded.
18 World Bank Study

Figure 2.7. Predicted Probability of Access to Finance Being No Obstacle

60
Probability of a Firm Indicating Access to Finance
as Not an Obstacle to Doing Business (%)

0
Low Medium High
Level of Foreign Ownership
2002 2005 2008

Source: BEEPS panel data, 2002, 2005, 2008.

Figure 2.8. Predicted Probability of Applying for a Loan

80
Probability of a Firm Applying for a Loan (%)

0
Low Medium High
Level of Foreign Ownership
2002 2005 2008

Source: BEEPS panel data, 2002, 2005, 2008.

Data on the share of ﬁrms that applied for loans also suggest that foreign banks were
instrumental in meeting demands for credit at the beginning of the decade. In 2002, loan
demand was highest in countries with a high level of foreign participation (Figure 2.8).
It then dropped to levels prevalent in the region in 2005 and 2008. Overall, the results
suggest that well-established foreign banks contributed both to the growth of ﬁrms prior
to the crisis and were a stabilizing force, or at least an element of more stable banking
environments, during the crisis.
Challenges to Enterprise Performance in the Face of the Financial Crisis 19

Current Account Deﬁcits and Exchange Rate Regimes
While foreign banks were instrumental in providing ﬁnance to ﬁrms in credit-starved
countries at the outset of the decade, the role of foreign capital during the recent crisis
has been more complex. The earlier discussion of the ratio of credit to the private sector
relative to GDP (Figure 2.3) makes it clear that there was a steep decline in foreign lend-
ing in 2008, and that most of the reduction was in direct cross-border lending and local
lending in foreign currency. In that sense, the rapid increase in certain types of foreign
lending prior to the crisis was destabilizing.
The current crisis was in fact triggered by banks in developed countries, though
its manifestation in the ECA region and the lead-up to it shared many features of other
crisis episodes. Accumulation of foreign debt, excessive borrowing and lending, and
a mismatch in the maturities and currency denomination of assets and liabilities of ﬁ-
nancial institutions and corporate ﬁrms are elements common to many recent crises,
and all were evident in ECA.37 These pressures came to be reﬂected in large current ac-
count deﬁcits, which were further exacerbated by pegged exchange rates. As described
in Aslund (2009), “the illusory safety of the pegged exchange rate a racted large inﬂows
of short-term lending from European banks…. The temptation for international banks
was irresistible. They could lend to consumers in Ukraine for 50 percent per annum with
minimal ﬁnancing costs.” As foreign exchange inﬂows accelerated imports and balance
of payments deﬁcits increased, inﬂation soared, thus pricing countries with ﬁxed ex-
change rates out of international markets. The situation was not sustainable and even-
tually resulted in large devaluations in a number of countries, although others such as
Latvia chose to maintain its peg by implementing a program of deep austerity measures
and wage adjustments.
The following analysis, therefore, will distinguish the rapid, ultimately destabiliz-
ing increase in some types of foreign lending from the steady, gradual growth in lending
through the subsidiaries and branches of foreign banks. Due to lack of detailed, bank-
level data on the nature and currency composition of foreign banks’ loans, country-level
proxies are used to capture these two eﬀects. The literature on crisis episodes suggests
readily available proxies for capturing destabilizing lending: the current account deﬁcit
and the exchange rate regime. As noted, overheating in the banking sector was most pro-
nounced in countries with pegged exchange rates (i.e. ﬁxed and crawling exchange rate
regimes) and it came to be reﬂected in large current account deﬁcits. Those variables are
used as proxies for the “hot” destabilizing lending that was occurring prior to the crisis,
while foreign bank participation in 1999 is a proxy for the more stable, rooted lending
by foreign banks.38
The results of the analysis summarized in Figures 2.9 and 2.10 indicate that early on,
i.e. up until 2005, reported ﬁnancial constraints were substantially less severe in coun-
tries with exchange rates determined by a ﬁxed or a crawling peg that also had high
current account deﬁcits.39 This ﬁnding is consistent with the notion that the pre-crisis
lending boom was largest in countries that had pegged exchange rates and large current
account deﬁcits, as was noted by many observers.40
So, too, is the ﬁnding that in 2005 the share of ﬁrms that applied for loans was much
higher in countries with pegged exchange rates and large current account deﬁcits (Fig-
ure 2.11). In countries with a peg and a current account deﬁcit greater than 8 percent of
GDP, over 60 percent of ﬁrms applied for a loan. In those with relatively small current
account deﬁcits (0–4 percent of GDP), only about a quarter did so.
20 World Bank Study

Figure 2.9. Predicted Probability of Access to Finance Not Being an Obstacle, by
Current Account Deﬁcit (Fixed Exchange Rate Regime)

Source: BEEPS panel data, 2002, 2005, 2008.

Figure 2.10. Predicted Probability of Access to Finance Not Being an Obstacle,
by Current Account Deﬁcit (Crawling Exchange Rate Regime)

Source: BEEPS panel data, 2002, 2005, 2008.
Challenges to Enterprise Performance in the Face of the Financial Crisis 21

Figure 2.11. Predicted Probability of Applying for a Loan by Current Account
Deﬁcit and Exchange Rate Regime

Source: BEEPS panel data, 2002, 2005, 2008.

By 2008, there were no longer statistically signiﬁcant diﬀerences in reported ﬁnan-
cial constraints for ﬁrms in countries with pegs, regardless of the size of their current
account deﬁcit. The mechanism that brought about the lending boom in countries with
pegged exchange rates and high current deﬁcits had unraveled.
The lending boom as reﬂected in the BEEPS data on ﬁnancial constraints and loan
applications coincides with the increase in foreign claims depicted in Figure 2.4, an in-
crease that was fueled by direct, cross-border lending and local lending in foreign cur-
rency by the parents of multinational banks. This suggests that the beneﬁcial aspects of
foreign bank participation summarized in Figure 2.7 reﬂect the relatively more stable
lending in local currency by the subsidiaries and branches of the multinational banks
in the ECA region. This is consistent with the conclusions of other observers about the
volatility of cross-border ﬂows and the severity of the crisis.41
In other words, countries that kept their exchange rates pegged and borrowed heav-
ily directly from the parent foreign banks might have beneﬁted in the run-up to the
crisis when funding was plentiful, but were hit harder when the crisis arrived and cross-
border ﬂows and local lending in foreign currency dried up.
The regression models estimate the eﬀects of ﬁxed and crawling exchange rate re-
gimes relative to the reference category, countries with ﬂoating exchange rate regimes.
The eﬀects of current account deﬁcits for ﬂoating exchange rate countries are negligible,
as reﬂected in the insigniﬁcant coeﬃcients for the yearly dummies and a predicted prob-
ability of reporting that access to ﬁnance was no obstacle near 25 percent for all three
waves of the BEEPS survey (not shown in a ﬁgure).
22 World Bank Study

Box 2.2. Fixed vs. Floating—Example of Bulgaria and Poland

To illustrate, in Bulgaria, which maintained a ﬁxed exchange rate, only 13.5 percent of BEEPS
respondents indicated that access to ﬁnance was no obstacle in 2002 and the current ac-
count deﬁcit stood at 2 percent of GDP. In 2005, 65.8 percent of those same ﬁrms responded
that ﬁnance was not an obstacle as the current account deﬁcit grew to 12.3 percent of GDP.
By late 2008, the share of those ﬁrms (that participated in all three rounds of survey) indicat-
ing ﬁnance was not an obstacle had plummeted to 31.6 percent and the current account
deﬁcit stood at 25.2 percent. In contrast, in Poland, which maintained a ﬂoating exchange
rate, the share of ﬁrms that reported ﬁnance was no obstacle went from 25 percent in 2002, to
22.2 percent in 2005, and increased to 33.3 percent in 2008. Poland’s current account deﬁcit
remained relatively stable at 2.8 percent of GDP in 2002, 1.2 percent in 2005, and 5.5 percent
in 2008. In other words, the Bulgarian current account deﬁcit grew steadily between 2002 and
2008; a sample of the same Bulgarian ﬁrms went from reporting relatively severe ﬁnancial
constraints in 2002, to much less severe constraints in 2005, and then back to relatively se-
vere constraints in 2008. In Poland, where the current account deﬁcit has grown at a much
slower pace, there were no such ﬂuctuations in reported ﬁnancing constraints.
Bulgaria was also somewhat slower to embrace foreign bank participation than Poland, and
this might have contributed to the relative mildness of the crisis in Poland, in line with the
results from the previous section. In 1999, foreign owned banks controlled 59.6 percent of
banking sector assets in Poland compared with 41.4 percent in Bulgaria.
Source: BEEPS 2002, 2005, 2008, World Bank data and author’s calculations

Firm Survival
As mentioned earlier, the BEEPS 2008 survey was followed up by the Financial Crisis Sur-
vey conducted in six ECA countries in June-July 2009. The results of the la er survey show
that most ﬁrm managers saw the contraction in demand to be the most important eﬀect
of the crisis on their business rather than the credit crunch. It also shows that the sales
growth of innovative and young ﬁrms was signiﬁcantly more adversely aﬀected by the cri-
sis than that of other enterprises.42 It was these ﬁrms that tended not to survive the crisis.
As shown in Figure 2.12, ﬁrms with ﬁve or fewer employees were 63 percent more
likely to fail than those with 100 or more employees. The analysis shows that an increase
in the number of workers from 1 to 10 reduced the likelihood that a ﬁrm was no longer
operating at the time of the follow-up survey by about one ﬁfth—from 14.6 percent to
11.6 percent.43 By the same token, ﬁrms that had been in business for ﬁve years or less
were three times more likely to fail than those that had been in business for over 25
years. While volatility among start-ups is high even in the best of times, the results for
ﬁrm survival are consistent with the view that small, young start-ups suﬀered most as
a result of the crisis.
But size and youth are not the most important explanation of ﬁrm failure. Empiri-
cal evidence found in the academic literature indicates that larger and older ﬁrms tend
to have greater access to external ﬁnance than others.44 Therefore, a close relationship
between ﬁrm age, size, and access to external ﬁnance would be expected. BEEPS-based
results suggest that one of the reasons why larger and older ﬁrms fared be er in terms
of survival was that they had be er access to ﬁnance than smaller ﬁrms. When ﬁrm age,
size, and access to external ﬁnance are all included in the ﬁrm survival regressions, the
ﬁnance variable explains more variation than the two ﬁrm characteristics. In fact, ﬁrm
Challenges to Enterprise Performance in the Face of the Financial Crisis 23

Figure 2.12. Predicted Probability of Firm Exit Based on Firm Size

14 13.1%

12 11.9%
10.5%
Probability of Firm Exit (%)

10 9.3%
8.1%
8

0
1-5 6-10 11-50 51-100 101+
Number of Workers

Source: BEEPS 2008, Financial Crisis Survey 2009.

Figure 2.13. Predicted Probability of Firm Exit Based on Firm Age

20
18.2%
18
16
Probability of Firm Exit (%)

14
12.0%
12
10
8.6%
8
6.1%
6
4
2
0
1-5 6-10 11-25 26-50
Age of Firm (Years)

Source: BEEPS 2008, Financial Crisis Survey 2009.

size is no longer signiﬁcant. The analysis therefore indicates that apart from inﬂuencing
access to external ﬁnance, a ﬁrm’s size did not exert a strong inﬂuence on its survival.
Everything else being equal, ﬁrms with loans or overdrafts were 3.8 percentage
points more likely to still be operating in mid-2009 than ﬁrms without. In relative terms,
their exit rate was about one third lower than for other ﬁrms.45 Retaining access to ﬁ-
nancing was therefore important for ﬁrm survival. Given that the most common concern
about the impact of the crisis on ﬁrm performance was the eﬀect of a drop in demand,
one plausible explanation is that ﬁrms with access to ﬁnance were be er able to maintain
access to working capital and manage the drop in demand.
24 World Bank Study

Understanding Foreign Bank Presence
Results from the foregoing analysis strongly suggest that a well-established foreign bank
presence helped to ease ﬁnancial constraints during the crisis. This section examines the
determinants of foreign bank participation levels in 1999 to be er understand why this
should be so. The WGI index, the measure of broad institutional development created by
Kaufmann, Kraay, and Maztruzzi (2007), was substantially lower for low-foreign bank
participation countries than for high ones from 1996 to 1998 (Table 2.1), while per capita
income levels are similar for both groups.
However, other things being equal, foreign bank participation in 1999 was signiﬁ-
cantly higher in countries with low initial levels of per capita income.46 Controlling for
the level of institutional development using the WGI index, this pa ern is consistent
with foreign banks seeking out relatively underdeveloped banking markets.
Foreign bank participation was also likely to increase in the wake of ﬁnancial cri-
ses, as local government oﬃcials sought to re-capitalize insolvent banking sectors.47 Yet,
Table 2.1 indicates that most countries in the ECA region experienced a systemic bank-
ing crisis at some point during 1990 to 1998, and thus crises explain li le variation in

Table 2.1. Foreign Banks in ECA Countries during the First 10 Years of Transition
% Assets GDP Per
Held by Capita, 1998 Banking German French Socialist
Foreign WGI, 1996- in Constant Crisis, Legal Legal Legal
Country Banks (1999) 1998 (Avg) 2000 $US 1990–1998 Origin Origin Origin
Estonia 92.3 0.72 3,734.3 X n.a. n.a. n.a.
Hungary 88.3 0.88 4,262.6 X X
Czech Republic 65.8 0.83 5,245.4 X X
Latvia 65.6 0.32 2,903.6 X X
Croatia 61.6 −0.21 4,673.5 X X
Kyrgyz Republic 59.8 −0.39 261.2 X X
Poland 57.7 0.78 4,065.0 X X
Armenia 56.0 −0.53 562.0 X X
Bulgaria 41.5 −0.11 1,415.2 X X
Moldova 39.7 −0.12 357.6 X
Romania 30.5 −0.05 1,632.3 X X
Bosnia and Herzegovina 28.4 −0.57 1,326.4 X X
Slovak Republic 17.4 0.49 5,250.7 X X
Uzbekistan 15.4 −1.12 528.5 X
Albania 13.5 −0.48 1,014.6 X X
Ukraine 13.2 −0.63 589.9 X X
Russian Federation 11.4 −0.68 1,510.5 X X
Kazakhstan 5.4 −0.71 1,076.3 X
Belarus 5.0 −0.91 1,156.3 X
Slovenia 3.5 1.09 9,120.2 X X
FYR Macedonia 1.4 −0.38 1,648.8 X
Turkey 1.4 −0.36 4,021.6 X X
Lithuania 0.0 0.47 3,148.5 X X
Georgia 0.0 −0.87 600.2 X X
Azerbaijan 0.0 −0.93 558.4 X X
Top 13 (Avg) 54.2 0.16 2,745.4 0.85 0.58 0.08 0.33
Bottom 12 (Avg) 5.9 -0.46 2,081.1 0.75 0.25 0.25 0.5
Sources: WDI Database; Caprio and Klingebiel (2003)*; Claessens et al. (2008); La Porta et al (1999).
Note: *Caprio and Klingebiel deﬁne a systemic crisis as occurring when much or all of the capital of the
banking system is exhausted. In the case of ECA, this situation often coincided with the breakup of the
mono-banking systems inherited from the Soviet Era. Hence, many of those crises were qualitatively dif-
ferent from more recent ones.
Challenges to Enterprise Performance in the Face of the Financial Crisis 25

foreign bank participation in 1999, though this was likely an important factor relative to
other developing regions. Cultural similarity, as reﬂected in origin of legal code, also can
explain variation in foreign bank participation levels. Countries of German legal origin
tend to rank near the top of Table 2.1 (ﬁve of the top seven spots) while none of those of
French legal origin rank higher than eleventh.48
With only 25 countries in the sample, it is diﬃcult to draw deﬁnitive conclusions,
but at least notionally, in the early stages of the transition, foreign banks were drawn to
countries with be er established institutions, greater potential for growth and develop-
ment and closer cultural ties. All of these factors enhance the likelihood that these early
entrants were commi ed to pursuing proﬁt opportunities in these host markets over
the long haul. Against this backdrop, it does not seem far-fetched that during the recent
crisis, ﬁrms in countries with well-established foreign banks faced less severe ﬁnancial
constraints than those in countries where this presence was limited.

Conclusions
Analysis of the BEEPS data shows that, in line with predictions drawn from the litera-
ture, access to ﬁnance was a critical factor in a ﬁrm’s ability to survive the crisis. As a
result, large, older ﬁrms with their well established credit relationships tended to do bet-
ter than smaller, newer ﬁrms. However, not all forms of ﬁnance were equally beneﬁcial.
Foreign-owned banks with well-established local networks, ﬁnanced primarily from
local deposits, and lending in local currency tended to have a stabilizing eﬀect. Other
forms of ﬁnance, including direct cross-border lending and local lending in foreign cur-
rency, did not.

Notes
1. World Bank (2010).
2. This is based on the 2009 Financial Crisis Survey implemented by Enterprise Survey Unit of the
World Bank. Some caution is warranted in interpreting the results, as they mainly reﬂect the view
of surviving ﬁrms and not the ﬁrms that failed to survive the crisis.
3. Note that comparisons of binding constraints are only valid for ﬁrms that survived the crisis. In-
formation is not available on the most binding constraints for ﬁrms that are no longer in operation.
4. Cross-border capital ﬂows reﬂect not only ﬁnancial sector issues, but are endogenous to cross-
border trade and sensitive to the economic performance of trading partners.
5. In some cases (particularly in Eastern Europe), foreign banks followed the entry and growth of
large foreign corporates and thus had additional motivation to enter certain markets.
6. See Bonin et al (2005); Grigorian and Manole (2006); Kiraly, et al (2000): Nikiel and Opiela (2002);
and Semih, Yildrim, and Phlippatos (2007). That evidence also indicated that pre-existing banks
that were acquired by foreign owners performed worse than foreign banks that initiated greenﬁeld
operations. Both the greenﬁeld operations and the relative eﬃciency of foreign banks overall sug-
gested banking competition had improved as a result of their entry. Had the ECA countries just
sold their pre-existing banks without permi ing greenﬁeld entry, it is likely that banking sector
outcomes would have been worse. See Bonin et al. (2005).
7. Gianna i and Ongena (2009a).
8. See Marton and McCarthy (2008).
9. De Haas and Naaborg (2006).
10. Gianna i and Ongena (2009a). One might be concerned that foreign banks will revert to lending
primarily to blue-chip clients in response to the crisis. However, evidence from other parts of the
developing world indicates that credit growth rates were higher and lending volatility lower for
foreign banks than for domestic banks during recent crisis episodes, especially in Latin America.
26 World Bank Study

See, for example, Dages et al. (2000) and Crystal et al. (2001, 2002).
11. The warning came from Morris Goldstein and is reported in Aslund (2009).
12. Claims include not only bank loans, but also ﬁnancing through debt securities and equity. Bank
loans are by far the largest component, however.
13. Detailed breakdowns of the foreign bank claims data are available in Cull, Martínez Pería, and
Tepli (2011) and Kamil and Rai (2010).
14. See analysis in Kamil and Rai (2010).
15. The evidence comes from a study of 45 of the largest multinational banks from 1991 to 2004 (de
Haas and van Lelyveld, 2010).
16. The evidence comes from 1993 to 2000 (de Haas and van Lelyveld, 2006).
17. De Haas and van Lelyveld (2010). Of course, this also means that credit growth slows when
other aﬃliates are less proﬁtable.
18. The evidence comes from a study of banks and ﬁrms in 13 countries from 2000 to 2005 (Gi-
anne i and Ongena, 2009b).
19. Foreign banks also contributed to increased ﬁnancing through new techniques. For example
in Turkey, until recently, there was very limited project ﬁnancing; the Turkish banks, for the most
part, only loaned to the Government. Local banks were reluctant to ﬁnance infrastructure and
when they did they usually required collateral or other bank guarantees, which were in excess
of the amount loaned. It was only after exposure to competition from foreign banks using project
ﬁnancing that they have been moving in that direction.
20. The ﬁrst round of the BEEPS was conducted in 1999 but the coverage of ﬁrms and format of
questions was not suﬃciently similar to later rounds to include that data in this analysis. In what
follows, the 2002 survey is referred to as being the ﬁrst round.
21. Although the response categories vary slightly across the rounds of the BEEPS (see Appendix
1), the “No Obstacle” category appears in all three.
22. For example, Clarke (2010) shows that concern about the long-term viability of the power sector
in South Africa led to a rapid and sharp deterioration in views during the ﬁrst weeks of a power cri-
sis. Before the ﬁrst blackouts, about 10 percent of ﬁrms said power was a serious problem. Shortly
thereafter, almost 50 percent of ﬁrms said it was a serious problem. The decline was so rapid and
steep that it seems unlikely to be a ributable to only one week of blackouts but rather to predic-
tions of years of future problems. Similarly, respondents to the ﬁnal rounds of the BEEPS survey
had likely received enough signals about the impending ﬁnancial crisis (indeed some responded to
the survey after Lehman Brothers had failed) that it seems likely that their views regarding ﬁnan-
cial constraints would reﬂect their predictions about the severity of the crisis.
23. The main concern is that the BEEPS 2008 occurred too early to be a useful tool for studying
the eﬀects of the crisis. The focus on relative changes over time in responses to the same question
mitigates these concerns, even if respondents to the 2008 survey could not perfectly forecast the
severity of the crisis. A secondary concern is that the 2008 round of the BEEPS occurred at diﬀer-
ent times across countries, some completed by mid-2008, others in spring 2009 when the eﬀects of
the crisis had become more evident. Therefore, a variable is included in the regression indicating
whether ﬁrms were surveyed prior to the Lehman Brothers failure in August 2008, viewed by most
observers as the deﬁning moment of the recent crisis. That variable (and other variants on survey
timing) was not signiﬁcant and its inclusion did not alter the results of the analysis.
24. However, Appendix 1 shows that the characteristics of the balanced and the full sample (i.e.,
any ﬁrm that responded to a BEEPS survey in 2002, 2005, or 2008) are very similar and regression
results from the full sample tend to be very similar to those for the balanced panel.
25. See Appendix 1 for a more detailed description of the construction of the dependent variable
for the ﬁrm survival analysis.
26. A regression analysis was used to be er understand the types of ﬁrms that were constrained,
and how those constraints evolved over time. The regressions are described in detail in Appendix
1. Note that the regressions control for a host of factors including ﬁrm characteristics (number of
employees, the age of the ﬁrm, its sector, exports as a percent of sales, the shares of foreign and
private ownership, and a variable indicating whether the ﬁrm was privatized) and country charac-
teristics (GDP per capita, measures of institutional development, inﬂation, GDP growth, popula-
Challenges to Enterprise Performance in the Face of the Financial Crisis 27

tion and population density). All of the results described in the chapter are derived holding all of
these factors constant.
27. Beck, Demirgüç-Kunt, and Maksimovic (2008).
28. Beck, Demirgüç-Kunt, and Maksimovic (2005).
29. A skeptic might argue that the changes in question format are driving these changes in reported
ﬁnancial constraints. However, since all respondents in each wave answered the same question,
changes in their reported obstacles measured relative to each other are meaningful, even if the ques-
tion format produced slightly higher (or lower) reported obstacles in a given BEEPS wave.
30. Many of the diﬀerences in reported ﬁnancing obstacles across years are statistically signiﬁcant
in Figure 2.5. For example, the share of large ﬁrms that reported access to ﬁnance was no obstacle
decreased signiﬁcantly from 2005 to 2008 (from 48.5 to 26.5 percent, t-statistic 2.65, p-value 0.01).
By contrast, the decline for small and medium ﬁrms was not signiﬁcant (from 38.8 to 33.5 percent,
t-statistic 1.34, p-value 0.18).
31. Beck, Demirgüç-Kunt, and Maksimovic (2008).
32. See Cull and Martinez Peria (2010) for an overview of that literature.
33. See Correa and Ioo y (2009, 2010).
34. These pa erns are again conﬁrmed in the regressions in Appendix 1. While all ﬁrms faced a
decline in demand due to the crisis, the hypothesis is that large ﬁrms were be er able to access
ﬁnance from external sources to make ends meet. This hypothesis is tested below in the analysis of
ﬁrm survival rates. Since interest rates on loans were increasing at the time of the crisis, they cannot
account for the steep increase in loan applications by large ﬁrms.
35. Based on the regression models in Table A1.3 of Appendix 1.
36. Low foreign bank participation is deﬁned as 0–20 percent of banking sector assets in the hands
of majority-foreign-owned banks, medium as 20–60 percent, and high above 60 percent.
37. See Corse i, Pesenti, and Roubini (1999) for a review of crises.
38. Since the crisis was triggered by banks in Europe and the United States, a series of regressions
was run to estimate separate coeﬃcients for the foreign participation variable for diﬀerent regions
of origin (Europe, North America, rest of developed world, developing world). The coeﬃcients for
European and North American foreign bank participation were signiﬁcant and very similar. Those
for the rest of the world were not. Therefore only those regressions with the aggregate foreign bank
participation variable are presented.
39. However, the evidence also suggests that these eﬀects were already present in 2002 in countries
with crawling pegs.
40. Regression models used for this analysis estimate the eﬀects of ﬁxed and crawling exchange
rate regimes relative to the omi ed reference category, countries with ﬂoating exchange rate regimes.
41. Again, see Kamil and Rai (2010) for comparisons across regions.
42. See Correa and Ioo y (2010) for comparisons of the severity of diﬀerent types of constraints.
43. Based upon the regression models in Table A1.2 of Appendix 1 evaluated at the sample means
for the explanatory variables.
44. The literature traces its origins back to the pecking-order theory of ﬁnancial services (Myers
and Majluf, 1984). This theory holds that internal funds are less costly than external funds, be-
cause borrowing ﬁrms have be er information about the quality of their investment opportunities
than lenders do. The lender, therefore, requires compensation in the form of a higher interest rate.
Because external funds are more costly, ﬁrm owners ﬁrst exhaust internal funds to support invest-
ment before turning to credit markets, leading to a pecking order with respect to the two sources of
ﬁnance. Informational asymmetries between borrower and lender tend to be more severe for small,
young ﬁrms, and lenders, therefore, charge them higher interest rates or do not extend credit to
them at all. As a result, small, young ﬁrms use less external credit than others. Analysis of the 2008
BEEPS shows, in ECA, 65 percent of large ﬁrms in the region had loans or lines of credit, while
only 38 percent of small ﬁrms did. The data are from www.enterprisesurveys.org based upon
BEEPS data. These are unweighted averages of the weighted country averages (i.e., all countries
are weighted equally but weighted averages are used for each country).
45. The regression that produces this estimate controls for other factors that could aﬀect ﬁrm sur-
vival.
28 World Bank Study

46. The regression model is:
Foreign Bank Asset Share (1999) = 0.453*** + 0.342**WGI Index (1996-1998)—0.0001**GDP Per Cap-
ita (1990–1998)—0.0018 GDP Growth + 0.248*** German Legal Origin, where ***, ** represent the
1 and 5 percent signiﬁcance levels, respectively. The WGI measure of institutional development is
positive and signiﬁcant, as expected. Controlling for that variable, the level of per capita GDP is
negative and signiﬁcant in the regression. The regression therefore indicates that, for a given level of
institutional development, foreign banks were more likely to enter lower income countries.
47. Cull and Martinez (2008).
48. Regressions conﬁrm that the diﬀerences in foreign bank participation across countries of Ger-
man and French legal origin are signiﬁcant. Additional factors including geographic proximity to
London and GDP growth were also included in the regressions, though none proved signiﬁcant.
CHAPTER 3

Infrastructure Bo lenecks

S ince the 1950’s there have been numerous examples of how infrastructure impacts
economic development in both OECD and developing countries. The construction
of highways, electricity, and telecommunications infrastructure has been linked to eco-
nomic growth and positive productivity gains.1 Recent studies show that in the ECA re-
gion, in contrast to the early transition years, infrastructure is becoming a bo leneck for
productivity.2 These studies show that quality rather than the quantity of transport and
infrastructure services is a binding constraint in ECA (for example, see Calderon, 2007)3
and this aﬀects both economic development4 and intra-regional trade.5
Since the beginning of the 2000’s, productivity has been increasingly identiﬁed as by
far the most important source of growth around the world.6 The role infrastructure plays
in productivity is not trivial. As ﬁrms move into the knowledge economy and use more
energy and ICT services such as email, websites, and the Internet as avenues for sales,
procurement, and communication, a reliable energy and telecommunications infrastruc-
ture is key to keeping these ﬁrms productive and growing.
This chapter analyzes the trends in infrastructure constraints that are covered in the
2008 BEEPS, including electricity and telecommunications, and provides a discussion
of the factors that may partially explain the increase in the number of ﬁrms indicating
electricity as an obstacle to doing business. It also discusses how these emerging infra-
structure bo lenecks constrain ﬁrm productivity in the ECA region.7
The results of the 2008 BEEPS analysis showed that infrastructure bo lenecks such
as power outages and limited access to information technology have a signiﬁcant impact
on ﬁrm productivity in ECA, in both the manufacturing and service sectors. The 2008
BEEPS also revealed:
■ Electricity became the 3rd most severe obstacle to doing business across the ECA
region. Only 39 percent of ﬁrms stated electricity posed no obstacle to doing
business in 2008, down from 66 percent in 2005.
■ Electricity bo lenecks have a signiﬁcant negative impact on productivity, par-
ticularly for lower-productivity ﬁrms, although results show these negative im-
pacts do not necessarily stem only from service outages.
■ Electricity bo lenecks primarily aﬀect small and medium-sized ﬁrms. Larger
ﬁrms seem to be able to use their capacity and resources to mitigate electricity
shortages with second best solutions, e.g. by acquiring power generators.
■ For service ﬁrms, telecommunications became a greater constraint. Telecommu-
nications became the 8th greatest obstacle overall in 2008, up from 14th in 2005.
Access to new information technologies such as email, high-speed Internet, and
websites has increased since 2005, and greater reliance on ICT may be causing
lagging infrastructure to be a greater constraint.
29
30 World Bank Study

The rest of this chapter is organized as follows. The next section provides a brief
overview of relevant developments in ECA over the last 10–15 years. The second section
discusses the sources of data used in the chapter. The third section explores changes in
perceptions of electricity as an obstacle using BEEPS data over time; examines the cost
of outages on ﬁrms in terms of lost sales, and the impact on productivity by focusing on
country and ﬁrm level factors; and discusses the means to mitigate the eﬀects of electric-
ity constraints. The fourth section presents a discussion on ICT bo lenecks and the im-
pacts on productivity. The ﬁfth section provides a discussion of complementary factors
highlighting the role of governance and regulation. The ﬁnal section provides the main
conclusions from the analyses.

Infrastructure in the Eastern Europe and Central Asia Region
The capacities of many infrastructure facilities, especially in the former Soviet Union
and Yugoslavia, were large as they were designed to meet the demand of subregions
rather than the demand of each republic. When these republics became independent
countries, they had to rely on trade with neighbors to meet their needs. In CIS countries
such as Russia, Kazakhstan, and others, key infrastructure facilities were constructed
during Soviet times to provide universal access. Prices were seldom related to the true
cost of infrastructure supply, which was generally considered an entitlement at li le or
no cost. As a result, artiﬁcially low energy prices induced an ineﬃciency of energy use
and promoted output with high-energy intensities.
When output contracted in the 1990s, the demand for infrastructure services de-
clined steeply, rendering the existing stocks excessive. Nonpayment for infrastructure
services became severe and shortages in government budgets could no longer ﬁnance
the deﬁcits of utilities. As funds for maintenance decreased, the deterioration in the qual-
ity of assets severely reduced the quality of service. Thus, while most other regions of the
world have to invest considerably to expand systems and increase access, the challenge
in ECA has been to ﬁnd resources to rehabilitate, operate, and maintain existing assets.
This situation was exacerbated further by tariﬀs set too low by the government or by the
regulator, thus distorting prices and consumption. The tariﬀs also constrain the utility
companies’ ability to maintain and invest in infrastructure.
Eﬀorts to modernize infrastructure across ECA countries have diﬀered over the last
decade. Countries investing in ICT8 over 2000–2006 were more often low-income coun-
tries: the largest increase took place in Moldova where 13.2 additional telephone lines
per 100 people were added over the period. Transmission and distribution losses (as a
percentage of output) were substantially reduced in poorer countries such as Armenia,
as well as in richer countries such as Latvia and Turkey. The countries where electric-
ity services did not improve or deteriorated were Russia, the Balkan countries and the
countries of Central Asia. It is important to notice, though, some countries in the la er
two groups were aﬀected by war or ethnic conﬂict.
Infrastructure is one area that also beneﬁts from private investment. In ECA, private
investment rates are lower than other regions but have increased in recent years. The
private investment in ECA9 infrastructure accumulated to $246 billion10 from 1990–2008
($39 billion speciﬁcally in electricity).11 This is lower than other regions. Private invest-
ment in LCR was $515 billion ($142 billion in electricity), while investment in EAP was
$294 billion ($92 billion in electricity) in the same period. However, yearly private in-
Challenges to Enterprise Performance in the Face of the Financial Crisis 31

vestment in ECA increased substantially in recent years: from $22 billion in 2006 to $46
billion in 2008, exceeding LCR ($40 billion). Over two-thirds of private infrastructure
investment in ECA was concentrated in three countries: Russia (33 percent of total in-
vestment), Turkey (21 percent), and Poland (16 percent).
There is evidence that these ﬁndings are still relevant in the aftermath of the ﬁnan-
cial crisis. Other studies show that infrastructure gaps in emerging markets may ex-
pand in times of macroeconomic crises due to scarce public investments associated with
budget deﬁcits (e.g. Latin America from 1980–2000).12 In addition, Mitra, Selowsky and
Zalduendo (2010) identify bo lenecks in electricity infrastructure as one of the two main
factors (together with education) that are most likely to distort the post-crisis recovery
in ECA.

Data and Methodology
The analysis in this chapter is primarily based on the 2008 BEEPS survey. The analysis
considers infrastructure services as an input to the production process. Firms producing
or distributing electricity and telecommunication services are not included. The compar-
isons of 2005 and 2008 values—such as regional changes in perception of infrastructure
constraints—are based on modiﬁed samples, maximizing comparability.
The following BEEPS infrastructure variables form the foundation for the analyses
presented in this chapter:
■ Power outage is a dummy variable representing a manager’s response to the sur-
vey question: “Over ﬁscal year 2007, did this establishment experience power
outages?”
■ Duration of power outages only considers a subset of ﬁrms that experienced pow-
er outages. It is a compound variable multiplying the responses to the following
questions: “In a typical month, over ﬁscal year 2007, how many power outages
did this establishment experience?” and “How long (hours) did these power
outages last on average?”
■ Cost of power outages when used in regressions, considers all ﬁrms and is equal
to zero if a ﬁrm did not experience power outages. When presented in terms of
regional or country averages, only ﬁrms that have experienced power outages
are considered. It is expressed as a percentage of total annual sales and refers
to the following survey question: “Please estimate the losses that resulted from
power outages?”
■ Generator is a dummy variable representing a manager’s response to the survey
question: “Over the course of ﬁscal year 2007, did this establishment own or
share a generator?”
■ Email is a dummy variable corresponding to the following question: “Does this
establishment use email to communicate with clients or suppliers?”
■ High-speed Internet is a dummy variable representing a manager’s response to
the survey question: “Does this establishment have a high-speed Internet con-
nection on its premises?”13
All regression analyses control for a variety of other ﬁrm and country characteris-
tics listed in Table A1.1 of Appendix 1. A detailed discussion of regression analyses and
model-speciﬁcations can also be found in Appendix 1.
32 World Bank Study

The survey provides ﬁrm-level information on power outages, the use of ICT infra-
structure, and managers’ perceptions of infrastructure constraints. The BEEPS data are
supplemented with infrastructure measures from the World Development Indicators,
data collected by the World Bank Doing Business project, data from the World Bank
Enterprise Surveys (for countries outside of ECA), selected World Bank country reports
and other sources. The review of trends observed in the data is complemented with re-
gression analysis where appropriate.

Electricity: A Growing Concern
Electricity Becomes a Top Business Constraint
One of the most noteworthy results regarding infrastructure was the emergence of elec-
tricity as a top constraint to doing business. Results of the 2008 BEEPS show that ﬁrms
report electricity as the third biggest obstacle to business operations (out of 14 measured
factors), behind only tax rates and corruption. Perceptions of electricity as a problem
increased in all 27 countries participating in both the 2005 and 2008 BEEPS.14 The great-
est drops in satisfaction with electricity are seen in the FSU countries, including Belarus,
Kazakhstan, the Kyrgyz Republic, and Russia. See Figure 3.1.
Some of the countries in Southeastern Europe also faced hefty electricity constraints.
For example, only 2 percent of ﬁrms in Kosovo and only 8 percent of ﬁrms in Alba-
nia stated that electricity was not a problem, yet the results for the la er country, may
have been aggravated by a spell of dry weather that seriously aﬀected hydropower pro-
duction, a signiﬁcant source for electricity generation in Albania and some other SEE
countries.
As expected, the perceptions of electricity as an obstacle are signiﬁcantly aﬀected by
the number of power outages experienced, percentage of sales lost as a result of power

Figure 3.1. Electricity as No Obstacle, 2005 and 2008

100
Percentage of Firms Indicating Electricity

Decrease between
as Not an Obstacle to Doing Business

2005 & 2008
80 Level in 2008

0
rg Al ovo
ec ep ia
ep lic

Uz jikis c
Ka ekis n
za tan
ian G stan
ov de ia
Re tion
Be blic
Uk rus
hu e
Po nia
Bu land

Mo rme ia
nte nia
Ro egro
R o ia
ce v a
Tu ia
Sl rkey
ia
ze bia
er na

La n
Cr a
Hu atia
Es ary
ia
Ta ubli
b ta

Lit rain

ija
tvi
Cz R n

Sl Fe org

A ar

FY M m a n

ton
h R ub

Ma ldo

Az vi
yz ba

er Ser
la

ng
pu

ba
s

ak ra

o
lg

go
kh

ov
n
Ko

dH
Ky

an
Ru

ia
sn
Bo

Source: BEEPS 2005, BEEPS 2008.
Challenges to Enterprise Performance in the Face of the Financial Crisis 33

outages, and generator ownership. As each of these values increases, the likelihood that
a ﬁrm will ﬁnd electricity to be a major or very severe obstacle to doing business rises.15
The analysis also shows that a similar relationship holds for ﬁrms expanding their labor
force in the past three years: these ﬁrms were less likely to indicate electricity as not an
obstacle to their business operations. Furthermore, the results point at the interdepen-
dence of infrastructure services; as access to high-speed Internet rises, electricity is more
likely to be perceived as a major or very severe obstacle.
The electricity constraints seen in ECA are not unique compared to other regions. In
fact, ECA ﬁrms have more positive perceptions of electricity as an obstacle than ﬁrms in
other regions (Figure 3.2). While on average, about 39 percent of ECA ﬁrms stated elec-
tricity is not an obstacle, only 15 percent ﬁrms in SAR, 20 percent in AFR, 29 percent in
LCR, and 30 percent in EAP have agreed with this statement. Though, this should not be
used to understate the gravity of electricity as an obstacle, as outages carry a substantial
cost to ﬁrms in terms of both sales and productivity.

Figure 3.2. Electricity as No Obstacle by Region

50
45
Percentage of Firms Indicating Electricity
as Not an Obstacle to Doing Business

40
10
35
29
30 7
25 7
4
20
11
15 6
16
10 39
6
5
0
SAR AFR LCR MNA EAP FSU-N FSU-S SEE ECA EU-10
Region

Source: BEEPS 2008, Enterprise Surveys 2006–2010.
* The number indicated within the bars represents the number of countries in each region.

Power Outages—Frequency and Cost to Business
The BEEPS results show a substantial variation in electricity bo lenecks across individ-
ual countries. On average, ﬁrms in poorer countries suﬀer more from electricity short-
ages. For example, 97 percent of ﬁrms in Kosovo and 90 percent of ﬁrms in Albania
experienced power outages in a typical month, although, as it was mentioned earlier,
results for Albania may have been aﬀected by a particularly dry year that exacerbated
hydro-power shortages in the SEE subregion. More than half of all ﬁrms suﬀered from
power outages in Tajikistan and Uzbekistan. In contrast, Hungary, Belarus, and Poland to-
gether averaged only around 20 percent of all ﬁrms reporting outages. Of these, Belarus is
34 World Bank Study

a noticeable outlier: its GDP per capita puts the country in the middle of the pack, while
at the time of survey, the country was beneﬁting (and still beneﬁts) from special prices
for energy resources and electricity deliveries from neighboring Russia. This favorable
arrangement is perhaps a reason Belarus has a signiﬁcantly lower than expected level of
power outages.
Figure 3.3 below illustrates the relationship between the percentage of ﬁrms experi-
encing power outages and GDP per capita. It shows that electricity constraints are most
severe in lower income countries. The reference line represents the linear relationship
between these two variables. Countries falling above the line experience greater elec-
tricity constraints than predicted by their income level. The trend is visible even when
extreme cases such as Kosovo and Albania16 are not taken into consideration.

Figure 3.3. Power Outages vs. GDP Per Capita

100
KSV
90 ALB

80
at Least One Power Outage in 2007
Percentage of Firms Experiencing

60 EST
TJK ROM
SRB
MNE TUR
UZB
50
KGZ LVA
40 GEO ARM SVN
MKD KAZ
BGR CZE
MDA AZE
BIH
UKR
30 RUS HRV
LTUSVK
POL
20 BLR
HUN
10

0
7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0
LN(GDP Per Capita) [PPP], 2007 in Constant 2005 International Dollars

Source: BEEPS 2008, World Bank World Development Indicators.

On average, electrical service outages were experienced by 43 percent of all ﬁrms in
ECA in 2007. In comparison to other Bank regions, ECA ﬁrms experienced the lowest
percentage of power outages. Data from Enterprise Surveys shows that over 50 percent
of ﬁrms experienced outages in the LCR, EAP, MNA, AFR, and SAR regions. Figure 3.4
below shows the percentage of ﬁrms experiencing outages over the year prior to the
survey.
What really makes ECA diﬀerent from other regions is the length and, consequent-
ly, costs of power outages to the ﬁrms. Power outages average 32 hours per month and
account for 5.4 percent of output losses relative to annual sales among ﬁrms in ECA
(while losses are 4.0 percent in EAP). The diﬀerence in losses is more pronounced when
lower income countries are compared. For example, according to Enterprise Surveys
and BEEPS respondents, power outages account for 6.7 percent and 9.7 percent of output
losses in low- and lower-middle-income countries in LCR and ECA, respectively. Losses
Challenges to Enterprise Performance in the Face of the Financial Crisis 35

Figure 3.4. Breadth of Power Outages

90
Percentage of Firms Experiencing at Least
One Power Outage in the Previous Year

80
70
6
60 39
50 6
11
40 7
16
30
7
20 29
10
4
10
0
FSU-N EU-10 ECA FSU-S LCR SEE EAP MNA AFR SAR
Region

Source: BEEPS 2008, Enterprise Surveys 2006-2010
* The number indicated within the bars represents the number of countries in each region.

are also greater for comparable higher income countries17—4.2 percent and 5.3 percent
in LCR and ECA, respectively. The impact of power outages is particularly high in the
three low-income Central Asian countries and in the countries of Southeastern Europe.
For example, the cost of power outages accounted for more than 10 percent of annual
sales in FYR Macedonia (11.8 percent), the Kyrgyz Republic (13.7 percent), Albania (13.8
percent18), Kosovo (17.7 percent), and Tajikistan (18.4 percent). See Figure 3.5.
Similarly, ﬁrms in higher income countries perceive electricity to not be an obstacle
more often than their counterparts in lower income countries (see Figure 3.6). This may
be partly due to be er quality infrastructure, the ability to invest in infrastructure main-
tenance and upgrades, and a be er governance and regulatory environment in higher

Figure 3.5. Losses Due to Power Outages

20
18
Percentage of Sales Lost

16
Due to Power Outages

14
12
10
8
6
4
2
0
hu c
Hu a n i a
Cr ary
ec Be tia
ep us
Es blic
d H Slo ia
ze nia
ian S vina
de ia
Po on
Ar land
nia
er ia
Mo ijan
Tu va
Bu key

Ka eo ia
za rgia

Mo oma n
nte nia
U gro
R e e
rg ce n
Re nia
Al blic
Ko nia
jik o
an
Lit ubli

R sta

FY Uzb krain
Ky Ma kista

Ta sov
ton

Fe erb

Az Latv

G r
oa
h R lar

ldo
ti

lga

ist
er ve

y z do

ba
ng

ne
u

pu
ba

r
ra
go

kh
p
Re
ak
ov

Cz
Sl

ss
an

Ru
ia
sn
Bo

Source: BEEPS 2008.
36 World Bank Study

Figure 3.6. Losses Due to Power Outages by Income Classiﬁcation

16
Percentage of Sales Lost Due to Power Outages

12 3
10

6 7

4
13
2
6
0
High income Upper middle income Lower middle income Low income
Income Classification

Source: BEEPS 2008.
* The number indicated within the bars represents the number of countries in each region.

income countries. Furthermore, certain ﬁrms19 in these countries may have a greater
ability to respond to outages by using generators as a secondary power source.20
Electricity Outages as a Constraint to Firm Productivity21
One important implication of infrastructure quality is its impact on ﬁrm productivity.
The analysis of the BEEPS data shows that electricity shortages are a productivity con-
straint primarily for small and medium-sized ﬁrms.22 The percentage of ﬁrms experi-
encing power outages is higher for small and medium-size ﬁrms than for large ﬁrms,
as are the losses incurred from those power outages as a share of annual sales. Further
analysis shows that the productivity costs incurred due to power outages are signiﬁcant
and comparable for small and medium-sized ﬁrms, whereas those of large ﬁrms are not
statistically diﬀerent from zero (after controlling for the eﬀect of various other ﬁrm and
country-speciﬁc factors).
The analysis shows that some ﬁrm-level factors appear to make certain ﬁrms more
vulnerable to electricity bo lenecks.23 For example, ﬁrms that innovate, low productiv-
ity ﬁrms, and ﬁrms with greater access to ﬁnance24 appear to face higher constraints in
terms of power outage costs’ eﬀects on labor productivity than their counterparts.
Large ﬁrms, ﬁrms that are part of a larger organization (i.e. subsidiaries), and high-
productivity ﬁrms, on the other hand, suﬀer less from electricity shortages in terms of
productivity. The analysis shows that labor productivity is not as constrained by power
outages for the la er types of ﬁrms since they have superior capacities to address these
shortages by themselves. For instance, buying or sharing a generator is an option, albeit
an expensive one—and the analysis shows that large ﬁrms are signiﬁcantly more likely
to own a generator (fully or partially).25
Challenges to Enterprise Performance in the Face of the Financial Crisis 37

Box 3.1. Cost of Power Outages in Kosovo

Managers in Kosovo consider the poor quality of electricity supply as the biggest obstacle
to their businesses. Thirty-four percent of all managers perceive electricity as the biggest
obstacle followed by corruption (21 percent). Eighty-three percent of all managers consider
electricity as a major or very severe obstacle, which is by far the highest fraction in ECA.
Kosovo is the country with the second highest reported cost due to power outages, account-
ing for 17.7 percent of annual sales. Moreover, 97 percent of all ﬁrms in Kosovo experience
power outages and, on average, face 105 hours per month without power.
The poor quality of electricity supply originates from capacity shortages, poor management,
low tariffs, and high distribution as well as commercial losses. The Kosovo Energy Corpora-
tion (KEK) is the sole producer and distributor of electric power in Kosovo. It operates the two
old and poorly maintained power plants, Kosovo A and Kosovo B. Only 60 percent of their
aggregate capacity is available, well below the peak capacity demand needed during the cold
season. The distribution network has also suffered from many years of underinvestment. As a
result, technical losses in the distribution system are very high, averaging 17 percent. More-
over, the operational and ﬁnancial performance of the power sector is poor due to low billing
and collection rates. In the last two years, commercial losses constituted about 35 percent of
energy entering the network (about €96 million per year).
While the rehabilitation of generating capacities and the distribution system require major
investments, reducing commercial losses requires institutional reforms. The investments re-
quired to rehabilitate the two power plants and the distribution network are estimated at about
€80 million per year from 2010 onwards. In addition, a new power plant will be constructed to
replace Kosovo A. Nevertheless, without policy measures improving the competence of KEK
to enforce collections and reduce theft, there is limited scope for improving electricity services.

Improving the performance of the power sector requires complementary institutional reforms
and good governance. In April 2010, the Parliament of Kosovo approved the Government’s
Energy Strategy 2009-2018 to address the problems. It is focused on rehabilitation and priva-
tization efforts. In particular, the government plans to privatize KEK Distribution to improve
payment discipline. This requires, however, the implementation of supporting measures; e.g.
(i) making electricity theft a criminal offense and prosecuting offenders vigorously, (ii) provid-
ing funding to the courts to deal expeditiously with cases of theft and nonpayment of bills,
and (iii) allowing a program of regular tariff increases as warranted, provided that customer
service and quality of supply improve hand-in-hand.
Source: BEEPS 2008 and Kosovo Country Economic Memorandum 2010.

Innovative ﬁrms, which have introduced a new product or service in the last three
years, experience slightly lower sales losses due to outages than non-innovative ﬁrms.
However, the productivity of these ﬁrms is more constrained by electrical-infrastructure
bo lenecks; and this occurs despite innovative ﬁrms being more likely to be larger ﬁrms
(they have, on average, 55 percent more employees than non-innovative ﬁrms).
Low-productivity ﬁrms face greater productivity costs due to electricity bo lenecks
than high-productivity ﬁrms. These ﬁndings are consistent with previous ones since
less productive ﬁrms are less likely to be large or subsidiaries. This suggests that low-
productivity ﬁrms might lack the capacity to address electricity shortages and are, as
a result, more vulnerable to low quality infrastructure services. Thus, poor electrical
38 World Bank Study

infrastructure services tend to contribute to the gap between low-productivity and high-
productivity ﬁrms.
Firms located in fast-growing low and medium income countries are particularly
vulnerable to electricity bo lenecks.26 One would expect to ﬁnd inadequate electricity
supplies in poorer countries. This conclusion is supported by the World Development
Indicators (WDI), which show that stocks of infrastructure services are highly correlated
with GDP per capita. In the survey year, productivity losses are particularly large in
Kosovo, Albania, and Tajikistan.
Firms in countries with rapid economic growth27 such as Azerbaijan, Belarus, Lat-
via, Russia, Tajikistan, and others appear to suﬀer more from electricity bo lenecks. This
is because rapidly increasing demand makes it diﬃcult to maintain a timely and ef-
fective supply of services.28 Thus, anticipating infrastructure bo lenecks and planning
ahead is particularly important for policy makers in fast-growing countries.
Mitigating the Effects in the Manufacturing Sector29
Some manufacturing ﬁrms are able to balance or oﬀset the eﬀects of power outages
through the use of power generators. Use of generators is naturally prevalent in lower
income countries with less reliable electricity systems. However, the use of generators
is less widespread in low-income ECA countries than in comparator countries outside
of the region. Partially and fully foreign owned ﬁrms and ﬁrms that innovate are more
likely to use and/or own generators. Figure 3.7 shows that foreign manufacturing ﬁrms
make extensive use of generators (24 percent) relative to domestic manufacturing ﬁrms
(14 percent). Likewise, 16 percent of ﬁrms that invest in R&D or innovate use generators
compared to 14 percent for those who invest in neither.30

Figure 3.7. Generator Use

25
Percentage of Firms Owning
or Sharing a Generator

0
No R&D R&D No Exports Exporter Domestic Foreign Not Innovative
Innovative

R&D Exports Ownership Innovation

Source: BEEPS 2008.

Acquiring or sharing generators has a positive impact on productivity.31 But genera-
tors are an ineﬃcient means of mitigating power outages. The use of power generators
indicates a poor quality or ineﬃciency of country-level institutional arrangements to
guarantee a reliable electricity supply rather than an eﬃcient solution. Thus, this costly
Challenges to Enterprise Performance in the Face of the Financial Crisis 39

ﬁrm-level intervention should be regarded as a second-best solution to address electric-
ity shortages.
Why do ﬁrms that operate conventional technologies choose not to acquire or share
generators? There are two factors that may potentially explain this: (i) limited access to
ﬁnance, and (ii) lack of pressure to improve competitiveness by acquiring generators.
Analysis of BEEPS data suggests that ﬁrms are more likely to use generators if they
have access to credit.32 This result suggests that ﬁrms in countries with be er access to
credit can be er protect their businesses against power outages by ﬁnancing generators.
However, the cost of funding these generators can oﬀset the reduction in productivity
losses from power outages. Therefore this is always a second-best solution as compared
to improving the quality of the public grid.

Telecommunications Usage on the Rise, but Access and Reliability
Remain Issues33
The BEEPS results also point to bo lenecks in information and communications technol-
ogy (ICT) infrastructure. Telecommunications became a greater obstacle to doing busi-
ness, ranking 8th overall in 2008, up from 14th (the last among obstacles being measured)
in 2005. Telecommunications became a bigger obstacle in 25 of 27 countries participating
in both the 2005 and 2008 BEEPS. Of these, the decline in ﬁrms indicating telecommuni-
cations is not an obstacle to doing business was statistically signiﬁcant in 22 countries.
The most dramatic declines are seen in Bulgaria, the Kyrgyz Republic, Russia, the Slovak
Republic, and Ukraine. See Figure 3.8 below.

Figure 3.8. Telecommunications as No Obstacle, 2005 and 2008
Percentage of Firms Indicating Telecommunications

Decrease between
100 2005 & 2008
Level in 2008
as Not an Obstacle to Doing Business

40
Increase between
2005 & 2008
20

0
ep on
Ko blic
rg Uk vo
Re ine
ov B blic
Re rus

Ka rme ic
kh a
Bu stan
Po ria
Mo land

Uz hua a
be nia
Tu an
Al k ey
ma a
Ge nia

La ia
R Cr a
ce tia
d H Sl onia

g a
jik a
E tan
er ia
Hu ijan

Mo Se ry
nte rbia
o
za ni

Lit dov

Ro ani

tvi

ze ni
Ta ovin

gr
A bl

Az ston

a
so

Ma oa
h R ati

lga

t
y z ra

er ove
ak ela

kis

ne
u

ba
b

d
ec er

l
Cz Fed
ian

FY
Ky

Sl
ss

an
Ru

ia
sn
Bo

Source: BEEPS 2005, BEEPS 2008.

The emergence of telecommunications as an obstacle coincides with a period of
greater access and use of email and other ICT technologies by ﬁrms. On average, 73
percent of ﬁrms across ECA in all sectors used email to communicate with customers or
suppliers in 2008. Firms that participated in the 2005 and 2008 rounds of BEEPS reported
40 World Bank Study

a signiﬁcant increase in the use of email communication.34 While use has increased, ac-
cess and reliability of services are still serious issues.
Usage of high-speed Internet is lower in ECA than other regions. Only 57 percent of
ﬁrms across ECA use high-speed Internet, compared to 61 percent of ﬁrms in LCR.35 The
gap is narrower (and comparison to LCR more positive) in the EU-10 countries, where
72 percent of ﬁrms were connected to high-speed Internet in 2008. Of those ﬁrms with
high-speed Internet, 37 percent of ﬁrms experienced Internet service interruptions, on
average 4.6 times per month lasting an average of 2.7 hours each.
As with electrical infrastructure, the gap in telecommunications access is partially
explained by country wealth. Poorer countries have less access to email and other ICT
technologies; only about 40 percent of ﬁrms use email communication in Tajikistan and
the Kyrgyz Republic compared to nearly 100 percent in Slovenia and the Czech Re-
public. Figure 3.9 below depicts the relationship between email use on the vertical axis
and GDP on the horizontal axis. As would be expected, countries with a higher GDP
per capita also have a greater percentage of ﬁrms using email. Countries falling above

Figure 3.9. Email Use vs. GDP Per Capita

100 SVN
EST CZE
HUN
LTUSVK
SRB TUR LVA HRV
RUS
Percentage of Firms Using Email

POL
80 ARM BIH BLR
MKD
BGR
KAZ
UKR ROM
60 ALB MNE
MDA
KSV
KGZ
TJK
40 GEO AZE

20 UZB

0
7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5 11.0
LN(GDP Per Capita) [PPP], 2007 in Constant 2005 International Dollars

Source: BEEPS 2008, World Bank World Development Indicators.

the reference line have a greater percentage of users than would be predicted based on
country income. Armenia seems to be the most distinguishable positive outlier36 having
signiﬁcantly more email users than would be expected for a country at its income level
(see Box 3.2). Firms in neighboring Azerbaijan and Georgia, however, report lower email
penetration than one would expect based on the country’s income level. Uzbekistan has
the lowest percentage of email users in the region.
Some ﬁrms have greater access to email communication and high-speed Internet.
Firm size, in particular, is a good predictor of broadband access.37, 38 Small and medium-
sized ﬁrms are less likely to use email communication (63 percent, 79 percent, and 93
percent for small, medium, and large ﬁrms, respectively) or have access to high-speed
Internet39 (49 percent, 68 percent and 84 percent, respectively) than their larger coun-
terparts. Even though large ﬁrms make more use of the ICT infrastructure (measured
as use of high-speed Internet), the regression analysis shows the gains to productivity
Challenges to Enterprise Performance in the Face of the Financial Crisis 41

Box 3.2. Progress in Armenia’s Telecom Sector

The telecom sector in Armenia has been improving rapidly after a long period of stagnation
caused by a monopoly. As a result, mobile penetration and broadband subscribers have in-
creased signiﬁcantly since 2008. Full liberalization triggered several large investment deals,
including the full acquisition of the former monopoly, Armentel, by Russian Vimpelcom; the
acquisition of local leading mobile operator, Viva Cell, by Russian MTS; and the entry of
France Telecom under the brand name Orange. Increased competition has resulted in new
investments in advanced technologies and in better telecom infrastructure overall. The In-
ternet services sector is also growing rapidly. iCON, a company established in 2007 with
Diaspora originated investment, and Cornet (a recent acquisition by a Russian company),
introduced WiMAX technologies in Armenia. Both companies provide broadband and high-
quality wireless services. Furthermore, ADC, an Armenian-Norwegian joint venture continues
to expand the ﬁber optic network in Yerevan and the Marzes.
Source: World Bank.

from the ICT usage are not much larger than those of small and medium-sized ﬁrms.40
Hence, ICT investment could induce across-the-board productivity gains and possibly
give small and medium-sized ﬁrms a be er competitive edge through higher email us-
age and be er access to high-speed Internet (i.e. since large ﬁrms are already more likely
to use email and Internet, such investment would implicitly favor small and medium-
sized ﬁrms and allow them to close the gap).
The country where a ﬁrm is located in also plays a signiﬁcant role in determining
the gains from ICT access. The results of the BEEPS show that the productivity eﬀect is
signiﬁcantly stronger in low-income countries and in fast-growing countries. Although
this does not allow for much in the way of policy recommendations, it may give ﬁrms in
these countries incentives to invest in their own ICT networks.
A similar pa ern holds when examining email use. Figure 3.10 below depicts the
relationship between email use and labor productivity. The graph shows that in general,
ﬁrms in countries with a greater percentage of email use are also more productive ﬁrms,
which is conﬁrmed by statistical analyses controlling for other factors.

Figure 3.10. Email Use vs. Labor Productivity

100 CZE SVN
EST
LTU SVK HUN
Percentage of Firms that Use Email

SRB HRV
TUR
RUS LVAPOL
80 ARM BLR BIH
MKD
BGR
KAZ
UKR ROM MNE
60 ALB
MDA
KSV

TJK KGZ
40 GEO AZE

20 UZB

0
7.0 8.0 9.0 10.0 11.0 12.0 13.0
LN(Labor Productivity)

Source: BEEPS 2008.
42 World Bank Study

The results of the analysis of individual ﬁrms show that in contrast to the electricity
constraint, ICT access is more strongly correlated to productivity for lower productiv-
ity ﬁrms. When considering ﬁrms below the 75th percentile of productivity, the results
show that the lower a ﬁrm’s productivity level, the stronger the positive correlation be-
tween the use of email and productivity (access to high-speed Internet yields compa-
rable results).40 This shows that low-productivity ﬁrms beneﬁt more from ICT in terms
of productivity, and that email and/or high-speed Internet access would result in greater
productivity gains for these ﬁrms.
As mentioned earlier, the ﬁndings show that access to ICT has a larger impact on
productivity in fast growing countries. For example, the percentage of ﬁrms using email
increased from 56 percent to 88 percent from 2002 to 2008 in Latvia and from 69 per-
cent to 94 percent in Lithuania while both countries grew annually by 8 percent from
2000–2008. The second fastest growing country in the sample was Armenia (with Azer-
baijan being the fastest), which experienced an annual growth rate of 11 percent from
2000–2008. At the same time, the percentage of ﬁrms using email to communicate with
clients and suppliers increased from 40 percent in 2002 to 81 percent in 2008. Reportedly,
the productivity in the service sector improved substantially in Armenia during this
period and improvements in access to ICT infrastructure are likely to have at least partly
contributed to this development.

The Role of Regulation and Governance
The Effect of Regulation
As shown in Figures 3.2 and 3.4, ECA is outperforming other regions in terms of percep-
tions of electricity as a problem and the breadth of power outage occurrences. Despite
this, accumulated outages average 32 hours per month and cost ﬁrms 5.4 percent in
annual sales (as opposed to 4.0 percent in EAP). Excessive regulation may be partly to
blame for lackluster performance in speciﬁc electricity-related services, and results from
the World Bank Doing Business Report oﬀers some evidence to that end.
The World Bank’s Doing Business Project, an annual exercise assessing business
regulations in countries around the world captures de jure measures of the business
environment. The methodology is constructed to provide comparable indicators of 10
aspects of the business environment. It uses a standard business case in order to en-
sure comparability across countries and regions. The BEEPS meanwhile, focuses on de
facto ﬁrm-level perceptions and interactions. The diﬀerence in methodologies can lead
to wide variation in values across indicators capturing similar aspects between the two
data sources (see for example, Hallward-Dreimer, Khun-Jush, and Pritche , 2009).
The Doing Business indicators on “Ge ing Electricity” suggest that the ECA region
is the most underperforming region with respect to the number of procedures required
to obtain an electricity connection and the median amount of time necessary to complete
these procedures. In 2010, eight ECA countries were among the 15 weakest performers
(out of 140 countries) in terms of number of procedures (Armenia, Azerbaijan, Russia,42
Tajikistan, Ukraine, Bosnia and Herzegovina, Uzbekistan, and the Kyrgyz Republic).
In terms of median duration of time to complete procedures, seven ECA countries (Russia,
Ukraine, Czech Republic, the Kyrgyz Republic, Belarus, Hungary, and Romania) are
also among the 15 weakest performers. An example of how the regulatory framework
may aﬀect access to electricity is shown in Box 3.3.
Challenges to Enterprise Performance in the Face of the Financial Crisis 43

The true costs of regulations, i.e. how far they diverge from the “on paper” costs,
and what relationship exists, if any, between them and the results from the BEEPS are,
however, outside the scope of this report.

Box 3.3. Weak Governance and Infrastructure Services: Getting Electricity in Ukraine

Governance indicators and electricity services are below average in Ukraine. Ukraine is
ranked in the lowest quartile among ECA countries in all three categories from the World Gov-
ernance Indicators. For example, it has the sixth lowest score on regulatory quality among the
29 ECA countries. Similarly, the Doing Business indicators for 2008 report the highest costs
to enforce contracts (42 percent of claim) in the region.
The Doing Business Report on Getting Electricity (2010) outlines how weak governance in
the form of poor regulatory quality restricts ﬁrms’ access to electricity services in Ukraine.
The report outlines the steps necessary for a ﬁrm to obtain electricity services, with the pro-
cedures taking 306 days to complete at a cost of $8,419 (or 262 percent of Ukraine’s income
per capita) (see Figure B3.3.1). The preliminary steps required include the submission of a
service application to the local distribution utility, securing a specialized project design from a
private ﬁrm, and the hiring of an electrical contractor. The documentation required is cumber-
some, as over 20 technical documents must be prepared for submission to different agencies
before inspections occur. Three rounds of external inspection follow by the State Energy
Inspectorate, the utility, and by the State Inspectorate for the Protection of Workers. Upon
signing a supply contract, the ﬁrm is subjected to another inspection from the supplying util-
ity. Once inspections have taken place, signatures from 14–15 different departments of the
distribution utility are needed in order to turn the electricity on.

Figure B3.3.1. Duration of Procedures for Getting an Electricity Connection in Ukraine

Source: Doing Business (2010).

Governance and Infrastructure Services
The eﬀectiveness of governance and quality of regulations have both been shown to
aﬀect infrastructure services. Thus, good governance is a complementary factor to in-
frastructure services.43 An approximation of a country’s institutional capability can be
made using data from the World Governance Indicators (WGI). Three variables from
this dataset are considered: government eﬀectiveness, regulatory quality, and control
of corruption.44 The ﬁrst indicator primarily captures the capacity and independence of
civil servants, the second the government’s ability to provide sound regulation, and the
third the extent to which public power is exercised for private gain as well as the capture
of the state by elites.45
44 World Bank Study

The results show that costs due to outages (measured by sales lost) is higher in coun-
tries with relatively weak governance. Figure 3.11 depicts the relationship between the
losses of sales due to power outages and the WGI indicator of government eﬀectiveness
categorized into low, medium, and high eﬀectiveness levels. The graph shows that the
value of losses is greater in countries with less eﬀective governments.46 Figure 3.12 tells
a similar story for outage costs compared to the WGI indicator on control of corruption.
In terms of productivity, the analysis shows47 that ﬁrms in countries with relatively
eﬀective or uncorrupt governments are less aﬀected by electrical outages than ﬁrms in
countries with governments that are ineﬀective or corrupt (when controlling for per
capita GDP and other country and ﬁrm characteristics).48 In addition, the decreases in
ﬁrm productivity from power outages are signiﬁcantly less in countries with good regu-
lation than in countries without.49

Figure 3.11. Outage Costs and Government Effectiveness

10
9
8
Cost of Power Outages as a
Percentage of Annual Sales

7
6
5
4
3
2
1
0
Low Medium High
Level of Government Effectiveness

Source: BEEPS 2008, World Governance Indicators.

Figure 3.12. Outage Costs and Control of Corruption

10
Cost of Power Outages as a
Percentage of Annual Sales

0
Low Medium High
Control of Government Corruption

Source: BEEPS 2008, World Governance Indicators.
Challenges to Enterprise Performance in the Face of the Financial Crisis 45

Weak governance also distorts the impact of ICT bo lenecks. Figures 3.13 and 3.14
below show that ﬁrms in countries with relatively non-eﬀective governments and gov-
ernments where the level of corruption is high are less likely to have access to high-
speed Internet.50 This lack of access to ICT constrains labor productivity.51 The results
are similar for the regulatory quality indicator. Thus, ﬁrms in countries with weak gov-
ernance are relatively more constrained by ICT bo lenecks.
These ﬁndings suggest that institutional capabilities that provide credible and eﬀec-
tive government policy, if enacted, may provide positive support to meet the need for
high-quality infrastructure services. Thus, cross-country variations in governance and
institutions (rather than variations in individual ﬁrms within a country) also inﬂuence
the eﬀectiveness of infrastructure services in ECA. The results are consistent with previ-
ous work (e.g. Fay and Estache, 2009). In other words, improving the quality of services
depends also on institutional reforms that go beyond the design of infrastructure projects.

Figure 3.13. Internet Access and Government Effectiveness

100
Percentage of Firms with Access to

80
High Speed Internet

0
Low Medium High
Level of Government Effectiveness

Source: BEEPS 2008, World Governance Indicators.

Figure 3.14. Internet Access and Control of Corruption

100
Percentage of Firms with Access to

80
High Speed Internet

0
Low Medium High
Control of Government Corruption

Source: BEEPS 2008, World Governance Indicators.
46 World Bank Study

Box 3.4. Transport Constraints Tighten Across ECA

The results of the 2008 BEEPS show a decline in positive perceptions of transport by respon-
dents. Overall, transport is ranked 12th of 14 obstacles doing business in 2008, however, the
country level analyses show steep declines in countries such as Belarus, Kazakhstan, Kyrgyz
Republic, Russia, Slovenia, and Ukraine among others. See Figure B3.4.1. These results
may signal that transport will become a greater obstacle to ﬁrms in the coming years.

Figure B3.4.1. Transport as No Obstacle, 2005 and 2008

100 Decrease between
2005 & 2008
Percentage of Firms Indicating Transport

Level in 2008
as Not an Obstacle to Doing Business

Increase between
20
2005 & 2008

0
kh c
ian B stan
rg de us
Re tion
Uk blic
ov Ar aine
Re nia

ma c
Sl nia

La ia
Al tvia
Mo ania
Ko ova
hu o
Po nia
Bu land
ria
R ajik ia
ce tan

Uz Turk a
be ey
d H G tan
a
Mo erba a
nte ijan
Es gro
Cr nia
Hu atia
y
za bli

Ro ubli

ar
Lit sov

ze gi
n
en

FY T Serb
Ky Fe elar

lga

Az govi
ak me

er eor

to
Ma is

kis

ng
ne
Ka epu

pu
yz ra

o
r

b
p
hR
ec
Cz

Sl
ss

an
Ru

ia
sn
Bo

Source: BEEPS 2005, BEEPS 2008.

Managers’ perceptions of transport constraints do not differ between countries with high or
low rates of trade growth since the start of this decade. Averages (from 2000-2008) of the
sum of export and imports relative to GDP (and FDI inﬂows relative to GDP) from the WDI are
used as a measure of changes in trade liberalization.a It is expected that ﬁrms in countries
with fast recent trade-growth are more constrained by transport bottlenecks. However, the
results suggest that higher growth in trade volumes do not inﬂuence managers’ perceptions
of transport constraints in ECA.b Yet, this might simply reﬂect that access to foreign markets
is limited to a few larger ﬁrms.
Comparing the data from the WEF Tourism and Transport Competitiveness Report to BEEPS,
results show no distinct correlation between perceptions of transport being no obstacle and
the quality of ground transport network indicator. One aspect of infrastructure that appears to
impact perceptions of transport is the cost of transport approximated by fuel costs. The EBRD
2010 Transition Report: Recovery and Reform notes that fuel costs are closely linked to ﬁrm’s
perceptions of the transport constraint. This ﬁnding is important as it shows that costs are a
greater driver of perceptions, however, it is also more difﬁcult for governments to respond with
policies other than tax reductions or subsidies that can reduce the cost of oil.
Sources: BEEPS 2005, BEEPS 2008, WEF Tourism and Transport Competitiveness Report 2009, EBRD Tran-
sition Report 2010, Recovery and Reform
Notes: a. There is no information available on Kosovo in the WDI.
b. This conclusion does not change if alternative measures of openness such as the share of FDI are used.
Challenges to Enterprise Performance in the Face of the Financial Crisis 47

Conclusion
The results of the 2008 BEEPS show a signiﬁcant increase in perceptions of infrastruc-
ture (electricity, telecommunications, and transport) as a problem doing business. The
greatest deterioration is seen in electricity, where across ECA, only 39 percent of ﬁrms
stated that electricity posed no obstacle doing business in 2008, down from 66 percent
in 2005. Overall, electricity rose to the third ranked problem doing business in 2008,
behind only tax rates and corruption. Similarly, deterioration is also seen in perceptions
of telecommunications and transport. These infrastructure issues, while becoming more
problematic, also have the potential to impact the growth and competitiveness of ECA
ﬁrms during the recovery from the recent ﬁnancial crisis.
At the ﬁrm level, electricity bo lenecks constrain the productivity of small and me-
dium-sized ﬁrms while ICT bo lenecks constrain ﬁrms regardless of size. Certain ﬁrms
with characteristics that are generally seen as desirable, e.g. innovative ﬁrms, end up
being more constrained by these bo lenecks relative to the ﬁrms that do not have these
characteristics. The ﬁrms that are able to avoid the negative productivity eﬀects of elec-
trical outages are either large, part of a larger ﬁrm, or highly productive. This indicates
that such ﬁrms have the capacity and resources to ameliorate infrastructure issues that
others cannot avoid, such as by taking full or partial ownership of a generator.
Results of the analysis show improving the quality of electricity services and in-
creasing access to ICT services have signiﬁcant productivity eﬀects in low and medium
income and fast-growing ECA countries. Thus, policy makers in these countries should
anticipate and remove electricity and ICT bo lenecks in order to catch up with richer
ECA countries. Further eﬀorts to remove electricity or ICT bo lenecks could substan-
tially boost ﬁrm productivity.
In order to improve infrastructure services, countries need not only invest in physi-
cal infrastructure projects but also in institutional capabilities that provide credibility
and eﬀectiveness. Findings show weak governance augments the eﬀects of electricity
constraints and access to ICT bo lenecks. Firms in countries with weaker government
eﬀectiveness experience higher losses as a share of sales due to service outages as do
those in countries with less eﬀective regulation.
Overall, be er governance at the country level is required to prevent productivity
slowdowns due to infrastructure bo lenecks. That is, improving the quality of infra-
structure services goes hand-in-hand with institutional reforms, including governance
reforms reaching beyond the design of individual projects in sectors.

Notes
1. Roeller and Waverman (2001) and Belaid (2004) ﬁnd large positive productivity eﬀects of tele-
communication infrastructure in OECD and developing countries, respectively. Fernald (1999)
shows that the construction of the interstate highway system in the U.S. from 1953-1973 induced
strong productivity growth. Calderon and Serven (2005) estimate positive causal eﬀects of diﬀer-
ent infrastructure measures on GDP-growth for a panel of 121 countries. Hulten et al. (2006) ﬁnd
that disparities in electricity-generating capacity across Indian states accounted for almost half the
productivity growth of registered manufacturing ﬁrms.
2. Kessides and Khan (2009).
3. The World Bank report on Infrastructure in the ECA region (2006) and Iimi (2008) also empha-
size the role of the quality of infrastructure services to reduce business costs.
4. Byrd and Raiser (2006).
48 World Bank Study

5. The World Bank EU-10 Report (2010) underlines the importance of energy and transport infra-
structure to promote productivity. In addition, Shepard and Wilson (2006), Grigoriou (2007), and
Molnar and Ojala (2003) show that transport infrastructure is crucial to promote intra-regional
trade in ECA.
6. See the World Bank report on Productivity Growth in ECA (2008).
7. The data are available for all ECA countries except for Turkmenistan. The country samples are
designed to be nationally representative. The data are limited, however, to the formal economy.
8. Investors in ICT services, measured by the diﬀerence in telephone lines between 2000 and 2006.
9. According to the World Bank Private Participation database.
10. All amounts are presented in US$.
11. The database covers commitments by private investors to investments associated with man-
agement, concession, greenﬁeld, and divestiture contracts that reached ﬁnancial closure. Data on
actual disbursements is not available.
12. See for example, Calderon, Easterly, and Serven (2003).
13. This question was included only on the Service Module of the BEEPS Survey.
14. It should be noted that Figure 3.1 does not capture how far countries have come along. For
example, Georgia’s power sector bo omed out in the 1990s and has improved substantially since
then, as communicated in most energy reports from the region.
15. See Table A1.12 in Appendix 1 for regression results.
16. Result based on data from the 2007 Enterprise Surveys. Due to the small universe of ﬁrms
coupled with “survey fatigue” in Albania, it was not possible to conduct the full BEEPS survey on
a large sample of ﬁrms in 2008.
17. These numbers are for counties at the same income level, i.e. the ECA average does not include
countries with incomes higher than those in Latin America.
18. Result based on data from the 2007 Enterprise Surveys. See note 16.
19. The questions on generator use and ownership were included only on the Manufacturing Mod-
ule of the BEEPS Survey.
20. However, these generators are typically very expensive and ineﬃcient. The power they gener-
ate is much more expensive than power from the grid. As a result, ﬁrms in higher-income countries
also suﬀer ﬁnancially from unreliable electricity supplies.
21. Conclusions in this section follow from the regression results presented in Tables A1.5 and A1.6
in Appendix 1.
22. Small ﬁrms are deﬁned as having 5–19 employees, medium size ﬁrms 20–99, and large ﬁrms
more than 100.
23. Electricity bo lenecks are the negative eﬀects of a rise in the costs from electrical outages on
labor productivity.
24. Level of access to ﬁnance is determined from Doing Business’ “Ge ing Credit” indicators, with
groups deﬁned as follows: good access to credit: ALB, AZE, BGR, CZE, HUN, KGZ, KSV, LVA,
MDA, MKD, MNE, POL, ROM, SRB, SVK, UKR; bad access to credit: ARM, BEL, BIH, EST, GEO,
HRV, KAZ, LTU, RUS, SVN, TJK, TUR, UZB.
25. The question on generator ownership was included only on the Manufacturing Module of the
BEEPS survey.
26. For the purposes of this analysis, the country income groups are as follows: high income: SVN,
CZE, SVK, EST, HUN, LTU, HRV, POL, LVA, RUS; middle income: TUR, ROM, BGR, BLR, KAZ,
SRB, MNE, MKD; low income: AZE, BIH, ALB, UKR, ARM, GEO, KSV, MDA, UZB, KGZ, TJK.
Thresholds are 8,000 and 13,000 GDP per capita at PPP in 2007 in constant 2005 international dollars.
27. A country is deﬁned as fast-growing if its average growth rate from 2000–2008 exceeded six percent.
Fast-growing are: AZE, ARM, KAZ, BLR, GEO, LVA, UKR, LTU, MDA, RUS, TJK, EST, BGR, ROM.
28. The fast-growing countries have an average GDP per capita at PPP in 2007 (in constant 2005
international dollars) of 9,500 relative to 12,500 in slow-growing.
29. Questions about generator ownership were primarily asked to ﬁrms within the manufacturing
sector, hence analysis in this section is limited to this set of ﬁrms.
30. Levels of generator use are based on simple, non-weighted averages of ﬁrm-level data. Both
diﬀerences are statistically signiﬁcant at p=0.10 or be er.
Challenges to Enterprise Performance in the Face of the Financial Crisis 49

31. See Table A1.5 for results.
32. The diﬀerence between generator usage in countries with good access to credit and those with-
out is statistically signiﬁcant at p=0.10.
33. Questions pertaining to access to high-speed Internet and telecommunications as an obstacle
were primarily asked to service-sector ﬁrms. Email usage questions were asked of all ﬁrms.
34. Based on the panel dataset.
35. The data for LCR is based on results from Enterprise Surveys. The countries used are listed in
Appendix 1. Firms in República Bolivariana de Venezuela were not asked this question; hence it is
not included in the average.
36. While Armenia lagged considerably in ICT reforms through the year 2000, recent reforms such
as liberalization of the telecommunications sector has led to signiﬁcant improvements since 2008.
The introduction of competition has resulted in new investments in advanced technologies and in
be er telecom infrastructure overall.
37. Arbore and Ordanini (2006).
38. Duﬀy-Deno (2003).
39. See notes 13 and 33.
40. See Table A1.7 in the Appendix 1 for regression results.
41. See Tables A1.8 and A1.9 in Appendix 1 for regression results.
42. The results of the Doing Business Project for Russia are somewhat misleading, as they measure
cost and duration in the capital of Moscow, which has a very particular set of rules. Other Russian
regions have signiﬁcantly lower costs and durations for this procedure.
43. Esfahani and Ramirez (2003), Cadot et al. (2006), and Fay and Estache (2009) reveal that the
impact of infrastructure investments depends on good governance and political economy con-
siderations. Basu and Fernald (2007) ﬁnd that ICT capital investments require complementary in-
vestments in human capital to generate productivity growth. Agenor and Moreno-Dodson (2006)
highlight the eﬀect of infrastructure on education and health outcomes (e.g. access to clean water,
hospitals, and schools).
44. The World Governance Indicators cover Montenegro only since 2006. Kosovo is covered from
2006, 2008, and 2003 for the government eﬀectiveness, regulatory quality, and control of corruption
indicators, respectively.
45. All three indicators are highly correlated among the 29 ECA countries. In particular, spli ing
the sample according to government eﬀectiveness or control of corruption yields almost identical
country groups.
46. Regression analyses that control for GDP and other characteristics show similar conclusions.
Regression results and estimated power outage costs are shown in Tables A1.13 and A1.14 in Ap-
pendix 1, respectively.
47. Shown in Table A1.10 in Appendix 1.
48. The coeﬃcient for government eﬀectiveness is nearly signiﬁcant at p=0.12 and the coeﬃcient for
control of corruption is nearly signiﬁcant at p=0.11.
49. The coeﬃcient for regulatory quality is signiﬁcant at p=0.06.
50. Regression results and estimates for the percentage of ﬁrms with Internet access (Tables A1.13
and A1.14 in Appendix 1, respectively) show similar results.
51. See Table A1.11 in Appendix 1 for regression results.
CHAPTER 4

Labor: Challenges
Ahead of the Crisis

D uring the last twenty years, labor markets across countries in Europe and Cen-
tral Asia (ECA) have undergone considerable changes. Global conditions boosted
growth and labor demand, and domestic policy and regulatory reforms have had an
impact on employment and wages. More recently (between 2004 and 2008), overall un-
employment rates fell across ECA with dramatic decreases seen in countries as diverse
as Croatia, Estonia, Poland, and Moldova. Higher growth has led to an overall increase
in demand for labor. However, structural transformations have also boosted demand
for workers with a speciﬁc set of skills, which appears to have led to a skills shortage in
many economies across the region. The BEEPS and other sources provide evidence that
ﬁrms are indeed having more diﬃculty locating workers with desired skill sets to ﬁll
vacancies.
This development is particularly important as the results of BEEPS show the skills
constraint is having a negative impact on the productivity of ﬁrms in ECA countries.
However, unbundling the factors that drive the rising perception of the skills constraint
is a challenging task, as there may be multiple explanations for this phenomenon. Firms
may perceive a rising skills constraint due to the general increase in labor demand and
rise in wages, which has increased competition for quality workers or because certain
types of skills shortages are indeed emerging. This chapter provides evidence of a rising
skills shortage and discusses related trends seen in ECA, including wage growth, char-
acteristics of the unemployed, and migration pa erns.
The analysis in this chapter also shows that:
■ Skills and education of labor became the 4th highest obstacle to ﬁrms behind
tax rates, corruption, and electricity. Only a third of ﬁrms in the 27 countries
covered by the BEEPS in both 2005 and 2008 indicated that skills and education
was not an obstacle to doing business in 2008. The declines are most severe in
Azerbaijan, Belarus, Russia and Uzbekistan.
■ Labor regulations are less of a constraint to enterprises, especially in countries
implementing labor market reforms. Recent labor market reforms have aimed
to reduce the burden on ﬁrms and usher in more ﬂexibility in labor manage-
ment. Also, in times of economic growth, such as the period before the ﬁnancial
crisis, labor regulations such as redundancy and severance are less of an ob-
stacle for ﬁrms.
■ Evidence shows that increasing the formal educational a ainment of the work-
force may not be the most eﬀective means to curb the skills constraint. Skills

50
Challenges to Enterprise Performance in the Face of the Financial Crisis 51

constraints are larger in countries with greater unemployment among people
with tertiary education, which may signal that the solution does not lie in a
simple increase in the quantity of educated workers, but in the development of
relevant skills.
■ Some ﬁrms, such as large ﬁrms, are able to mitigate the eﬀects of the skills con-
straint on productivity by providing training to their workers. Firms in higher
income countries, such as those in the EU-10, appear more able to train their
employees than those in lower-income countries.
This chapter presents key BEEPS results regarding features of the labor market in
the ECA region, including trends in ﬁrms’ perceptions regarding the characteristics of
the labor force in the business environment, in the period just before the global ﬁnancial
crisis. Where available, benchmark comparisons are made between ECA countries and
countries at similar levels of development outside the region using the World Bank En-
terprise Surveys data.
The chapter is organized as follows: ﬁrst, an overview of recent labor market trends
and a discussion of the BEEPS results on labor indicators are presented. A summary of
the data and methodology for analysis follows. Next, the results on skills and education
of labor are discussed in-depth, with an emphasis on the emerging skills shortage and
potential contributing factors, including the role of emigration, characteristics of unem-
ployed workers, and ﬁrm level factors that are shown to aﬀect perceptions of labor qual-
ity. Firms’ responses to the skills shortage are then discussed. A glimpse into the early
eﬀects of the ﬁnancial crisis on labor follows. The last section concludes.

Recent Labor Market Developments in the Eastern Europe and
Central Asia Region
Between 2004 and 2008, the region saw broad-based improvements in labor market con-
ditions alongside robust economic growth. For many countries in Central and Eastern
Europe, strong growth coincided with their EU accession. After a period of stagnant job
creation in the early 2000s following the 1998 Russian crisis—giving rise to the “jobless
growth” phenomenon particularly in the new EU member states and the Western Bal-
kans1—the region saw substantial net job creation starting in 2004, lasting until the eve
of the ﬁnancial crisis.
Stronger labor market conditions are evident in falling unemployment rates, grow-
ing employment ratios, and rising employment elasticity of growth. In the early 2000s,
employment growth associated with output growth (or the employment elasticity of
growth) was insigniﬁcant at 0.07, indicating that each percentage point expansion in out-
put was associated with close to zero employment growth. Between 2004 and 2008, the
employment elasticity of growth rose sharply to 0.24 (see Table 4.1). In addition, for the
region as a whole, unemployment rates fell, from close to 12 percent in the early 2000s
to 8.5 percent over the same period. Very sharp reductions in joblessness were seen in
the new EU member states. For example, between 2004 and 2008 the unemployment rate
in Poland fell from 19 percent to 7.1 percent, and in Lithuania, it dropped from 11.3 to
5.3 percent. Nonetheless, there remain important distinctions across country groups in
the region, with respect to employment and unemployment indicators that help explain
some of the peculiarities noted in the next sections.
52 World Bank Study

Table 4.1. Employment Elasticity of Growth 2004–2008
2000–04 2004–08
ECA 0.07 0.24
CIS 0.18 0.20
EU-10 0.08 0.23
SEE −0.35* 0.38
Source: KILM (6th Edition) and authors’ calculations.
* The negative employment elasticity of growth in the SEE subregion is driven by FYR Macedonia. Other
countries in the average such as Albania and Bosnia and Herzegovina have small but positive elasticities.
Although negative elasticities can indicate a higher level of productivity without an increase in employ-
ment, it appears that in FYR Macedonia, per the World Bank FYR Macedonia Poverty Assessment for
2002–2003, higher productivity sectors such as trade and manufacturing were shedding labor in this
period (World Bank, 2005).

The improvement in labor markets in ECA may be the result of rapid growth in labor
demand.2 Successful labor market reforms and improvements in the investment climate
appear to have contributed to the rise in labor demand. Further, the rise in labor demand
has been associated with rapid growth in wages. In the new EU member states, the aver-
age real wage growth in the period after EU accession and before the crisis was about
9 percent (based on the year-on-year four-quarter moving average).3 See also Box 4.3.

Data and Methodology
The primary data sources for this analysis are the results of the BEEPS for 2005, 2008,
and a panel of ﬁrms that participated in each of the 2002, 2005, and 2008 rounds of the
survey. As mentioned in the introductory chapter, in 2008, the BEEPS underwent a se-
ries of changes to both the sampling methodology and the questionnaire. Although the
changes in the survey and sample pose challenges to the cross-period comparability,
steps have been taken to ﬁnd an intersection between these two samples in terms of sec-
tor, ﬁrm size, age, and ownership, to make the samples from 2005 and 2008 as compa-
rable as possible. Analyses also include data from other sources such as Doing Business,
IMF, KILM, World Bank World Development Indicators, and others.
The two primary questions of interest in BEEPS regard labor-related obstacles to
doing business: one measuring the severity of labor regulations as an obstacle to doing
business, and the other measuring the severity of skills and education of labor as an ob-
stacle to doing business. Both questions were asked in the context of current operations:
“Are labor regulations no obstacle, a minor obstacle, a moderate obstacle, a major
obstacle or a very severe obstacle to the current operations of this establishment?”
“Is an inadequately educated workforce no obstacle, a minor obstacle, a moder-
ate obstacle, a major obstacle or a very severe obstacle to the current operations
of this establishment?”
Where it is possible to compare the 2008 results with those of 2005, the values are
based on the adjusted samples to maximize comparability. Other results discussed in the
context of the 2008 round are based on the full sample of ﬁrms. Similarly, the results of the
panel dataset are based solely on the panel of ﬁrms participating in each of the 2002, 2005,
and 2008 cycles. Readers will sometimes encounter diﬀerent country-level ﬁgures for the
same indicator for 2008. Both are correct, but for diﬀering purposes.
Challenges to Enterprise Performance in the Face of the Financial Crisis 53

In order to test hypotheses, econometric methods such as logistic regression analy-
sis and generalized ordered logit models are employed. The generalized ordered logit
models are based on the methodology of Pierre and Scarpe a (2004 and 2006). All mod-
els include control variables. More information on the methodology and the results can
be found in Appendix 1.

Labor Constraints in ECA
Firms’ responses to questions on labor regulations and skills and education of labor in
BEEPS 2008 compared to 2005 reveal two distinct and opposite trends. Region-wide,
ﬁrms’ perceptions of labor regulations have improved since 2005 while at the same time,
satisfaction with skills and education of labor has deteriorated. The improvements in
labor regulations are signiﬁcant in many countries (see Box 4.1). However, the declines
in the perceptions of skills are more widespread—decreasing in all but three countries:
Hungary, Latvia, and Slovenia.
The severity of these issues is not unique to the ECA region. Table 4.2 shows the
percentage of ﬁrms in ECA and other regions4 indicating that labor regulations and the
skills and education of labor are a major or very severe obstacle. ECA outperforms all
but Africa and the East Asia and Paciﬁc regions in terms of the severity of labor regula-
tions; however, the skills and education of labor constraint appears to be a problem for
all regions.

Table 4.2. Cross-Regional Comparison: Labor Regulations and Skills and Education
of Labor as a Major or Very Severe Obstacle, 2008 (percentage of respondents)
Labor Regulations as major or Skills and Education of Labor as a
Region very severe obstacle major or very severe obstacle
Eastern Europe and Central Asia 18.7 43.2
EU-10 21.8 42.3
FSU-N 18.8 62.8
FSU-S 17.2 45.0
SEE 16.3 32.6
Africa 17.3 32.6
East Asia & Paciﬁc 14.5 33.8
Latin America & Caribbean 29.5 42.5
South Asia 19.0 24.8
Middle East & North Africa 36.2 54.4
Sources: BEEPS 2008 and Enterprise Surveys 2006–2010

Skills and Education of Labor Becomes a Top Constraint
The results of BEEPS 2008 show skills and education of labor are a growing constraint
to doing business compared to 2005. The skill-level of workers ranks 4th out of 14 ob-
stacles to doing business in 2008, behind tax rates, corruption, and electricity, up from
7th in 2005. It is also among the top three obstacles in 11 countries in 2008, up from 4 in
2005 (see Table 4.3). The most dramatic changes in ranking (changing 5 rank positions
or more) are seen in Albania, Azerbaijan, Bosnia and Herzegovina, the Czech Republic,
Kazakhstan, Moldova, Poland, Tajikistan, Turkey, and Uzbekistan.
54 World Bank Study

Box 4.1. Strides in Labor Regulations

The results of BEEPS 2008 show labor regulations are less of an obstacle to ﬁrms in ECA:
the relative rank of labor regulations as a problem fell from 9th in 2005 to 13th in 2008. Only Es-
tonia and Slovenia ranked labor regulations as a top-three obstacle to doing business in 2008.
In general, labor regulations tend to be viewed less favorably by ﬁrms in EU-10 countries
which are more likely to enforce them (including through court challenges), and less of a
problem in CIS economies where the capacity for enforcement is weaker. In practice, for
many ﬁrms (especially the smallest ﬁrms), labor regulations tend to be bypassed, which then
translates to a labor market that is virtually unregulated.
The percentage changes in perceptions of labor regulations are small across many countries
(see Appendix 3), which points to labor regulations being a less important obstacle overall
when compared to other aspects of the business environment. The results indicate that in times
of greater prosperity, labor regulations such as redundancy or termination costs are not as
much of a hindrance to ﬁrms as they are in periods when economies and ﬁrms are contracting.
The results may also reﬂect recent labor market reforms designed to ease the burden on
ﬁrms. In recent years, new labor laws have been adopted in Estonia and Montenegro (2008), the
Czech Republic and Georgia (2006), FYR Macedonia and Serbia (2005), and Armenia and the
Kyrgyz Republic (2004). These reforms may have affected employers’ perceptions.
For example, Estonia launched a comprehensive effort to deregulate Employment Protection
Legislation (EPL), culminating in a new Employment Contract Act adopted by parliament in
2008 (implemented in 2009). The new legislation requires a shorter notice period for redun-
dancy and reduced severance payments, among other changes (Brixiova, 2009; O’Higgins,
2010).5 Although labor regulations remain a top obstacle, Estonian ﬁrms were already report-
ing more favorable views toward labor regulation in 2008. Meanwhile, in the Slovak Republic,
there have been important reversals in regulatory reform: the 2003 Labor Code was amend-
ed in 2007 to introduce more rigidity in employment regulation. Not surprisingly, Slovak ﬁrms
perceived labor regulations to be a greater obstacle in 2008 than in 2005.
The 2008 Doing Business Report shows Slovenia has the highest rigidity of employment index
value of ECA countries, ranking at the 54th percentile, while Georgia has the lowest (7th percen-
tile). Stricter EPL may minimize or smooth effects of economic shocks on the labor market at
the macro level (Cazes and Mesporova, 2003), while at the micro level, there are mixed effects.
Theoretically, stricter EPL beneﬁts ﬁrms6 through enhanced productivity (Scarpetta and Tressel,
2004)7, however, it also increases the cost of labor. The results of the BEEPS analysis support
those of previous studies (e.g. Pierre and Scarpetta, 2006) showing ﬁrms in countries with
more stringent employment regulations are more likely to report labor regulations as a major
or very severe obstacle when controlling for other factors such as GDP and unemployment.8*
Results also show:
• Income plays a role: Labor regulations are less of a problem in lower income countries,
which may be a function of less stringent regulations or weaker enforcement of labor regula-
tion. For example, Tajikistan, the poorest country in ECA, shows the greatest satisfaction
with labor regulations. See Figure B4.1.1.
• Certain ﬁrms are more vulnerable to labor regulations: Large ﬁrms and ﬁrms that in-
novate are less likely to report labor regulations are not an obstacle.9 For large ﬁrms, as
the number of workers employed by a ﬁrm rises, labor regulations reﬂected in the cost of
hiring would translate to higher overall costs. Likewise, innovative ﬁrms are more likely to
see labor regulations as a major problem because the hiring and ﬁring cost of workers may
inﬂuence the magnitude of innovative activity (Scarpetta and Tressel, 2004).
• Labor regulations affect innovation: The rigidity of employment regulations has a signiﬁcant
positive effect on ﬁrm innovation.10 See Figure B4.1.2. If labor regulations impose hiring costs,
ﬁrms may implement workﬂow innovations to increase productivity of existing staff.
(Box continues on next page)
Challenges to Enterprise Performance in the Face of the Financial Crisis 55

Box 4.1 (continued)

Figure B4.1.1. Labor Regulations vs. GDP Per Capita

90
Percentage of Firms Indicating Labor Regulations
as Not an Obstacle to Doing Business, 2008

80 TJK

70 AZE KAZ
GEO
MNE
60 KGZ ARM MKD
UZB
MDA BIH RUS
50 ALB LVA HRV
EST
UKR BLR
SRB
BGR HUN
40 SVN
SVK
30 ROM LTU
POL CZE
20
10
0
7.0 7.5 8.0 8.5 9.0 9.5 10.0 10.5
LN(GDP Per Capita) [PPP], 2007 in Constant 2005 International Dollars
Source: BEEPS 2008, World Development Indicators

Figure B4.1.2. EPL Rigidity vs. Innovation

60
SVN
50 EST
HRV
ROM TJK
EPL Rigidity (percentile)

MKDLVA
40 MDA
TUR LTU
RUS
BIH
UKR
UZB SRB
30 MNE
KGZ
ALB POL
HUN AZE SVK ARM
20 BGR
KAZ
BLR
10 CZE
GEO

0
20 30 40 50 60 70 80
Percentage of Firms Innovating
Source: BEEPS 2008, Doing Business 2008.

One of the most important implications of innovation is the impact on productivity. The results
of BEEPS 2008 support the results of previous research—innovation has a signiﬁcant and
positive effect on ﬁrm level productivity when controlling for other factors11 (see Ahn 2002;
Janz et al 2003; Grifﬁth et al 2006 as examples). The results of BEEPS also show that there
is a reciprocal relationship between productivity and innovation.12 This may be a function of
aggregate productivity increases with successive innovations—that ﬁrms reaping the beneﬁts
of product or process innovations may be more likely to invest in additional innovative activity.

Source: BEEPS 2005, BEEPS 2008, Doing Business (2005-2008).
* However, there may be a threshold level of regulation that reduces the burden on ﬁrms while protecting the
rights of workers. Moderate regulation may beneﬁt ﬁrms as a higher percentage of ﬁrms in countries with more
stringent EPL (from 45–60th percentiles) and those with less stringent EPL (ranging from the 0-14th percentile)
report labor regulations to be a major or very severe obstacle.
56 World Bank Study

Table 4.3. Skills and Education of Labor as an Obstacle to Doing Business
(relative ranking among 14 obstacles)
Top Ranked 2nd Ranked 3rd Ranked
2008 2005 2008 2005 2008 2005
Estonia None Belarus Estonia Albania Latvia`
Kazakhstan Latvia Lithuania Turkey
Russia Lithuania Ukraine
Moldova
Poland
Uzbekistan
Source: BEEPS 2005 and BEEPS 2008.

In all but three ECA countries, Hungary, FYR Macedonia, and Montenegro, more
than half the ﬁrms reported that the skills and education of workers have at least mar-
ginally constrained their operations. The magnitude of the deterioration in the quality of
skills is large: the percentage of ﬁrms reporting that skills and education of labor was not
a problem declined in 24 of 27 countries, 17 of which were statistically signiﬁcant (see
Figure 4.1 below). The sharpest declines can be seen in Azerbaijan and Uzbekistan, and
among the northern FSU countries of Belarus, Russia, and Kazakhstan.
The increase in the skills constraint appears to be a recent phenomenon. Looking at
ﬁrms that participated in the 2002, 2005, and 2008 rounds of BEEPS, the results show a
signiﬁcant decrease in the percentage of ﬁrms indicating skills and education of labor is
not an obstacle from 2005 to 2008. For the panel ﬁrms, 42 percent of ﬁrms stated skills and
education of labor was not an obstacle in 2005, compared to 37 percent in 2008. The results
for 2002 and 2005 show practically no change—44 percent of the panel ﬁrms stated skills
and education of labor was not an obstacle in 2002, compared to 42 percent in 2005.

Figure 4.1. Skills and Education of Labor as No Obstacle, 2005 and 2008
Percentage of Firms Indicating Skills and Educaiton

100
of Labor as Not an Obstacle to Doing Business

Level in 2008
Decrease between 2005
80 & 2008

20 Increase between 2005
& 2008
0
Ka era s
za tion

Po an
hu d
Mo ania
Ro dova
Uk nia
ec Ko ine
ov e vo
Re blic
Es blic
A nia
rg bek ia
Re tan

L ic
jik a
Tu an
Bu rkey
d H C aria
ze tia

Se na
Ar rbia
er ia
Sl ijan

r M eo a
Mo cedo ia
nte nia
Hu egro
y
d u

ar
Lit lan

Ta atvi

Fy G eni
bl
Ky Uz lban

Az men

a rg
Fe elar

Sl h R so

er roa
st

ist

vi
ma
ra

yz is

ng
ak pu
pu

ba
lg

go
kh

n
l
ian B

Cz
ss

an
Ru

ia
sn
Bo

Source: BEEPS 2005, BEEPS 2008.
Challenges to Enterprise Performance in the Face of the Financial Crisis 57

Box 4.2. Are the Trends Emerging from the BEEPS Data Consistent with Other
Sources?

The BEEPS is unique in the fact that no other enterprise surveys are conducted as consis-
tently in as many countries in the ECA region. However, there is a general consistency across
BEEPS and other data sources. For example:
In Poland, some 60 percent of all enterprises surveyed by the National Bank of Poland in
2007 reported difﬁculties in ﬁnding workers, both skilled and unskilled (Rutkowski 2007). This
was much higher than in 2006, when only about 40 percent reported such difﬁculties.
In Lithuania, some 12,000 unﬁlled vacancies were reported in 2005. The ﬁgures also sug-
gest large labor shortages in the manufacturing and trade sector (Kahanec et al 2009).
In Russia, the Large and Medium Enterprise (LME) survey of 2005 showed ﬁrms reported
that “the lack of skilled and qualiﬁed workforce” as one of the greatest constraints they face,
second only to taxation. For these ﬁrms, shortages were reported across all skill categories,
from management positions to skilled technical work. A longer time series suggests that the in-
cidence of (self-reported) understafﬁng among ﬁrms started growing in 1998, during the recov-
ery period, as output and labor use rates also grew (see Tan et al 2007a; and Tan et al 2007b).
In Moldova, a ﬁrm-level survey of 600 enterprises as part of a World Bank-assisted Com-
petitiveness Enhancement Project (World Bank 2008a) revealed about a quarter of all ﬁrms
felt that they do not have enough workers. There was a small increase in the number of en-
terprises reporting insufﬁcient workers in 2005, potentially foreshadowing the skills constraint
subsequently reported in the BEEPS.
Sources: Kahanec, et al 2009; Rutkowski, 2007; Tan et al (2007a); Tan et al (2007b);World Bank, 2008a.

Firms in ECA and other regions face serious skills constraints as shown in Table 4.2.
Likewise, skills are problematic across income groups. Figure 4.2 shows that, compara-
tively, skills are a bigger problem for middle-income countries; however, the majority of
ﬁrms in all income groups perceive skills and education to be at least a minor obstacle.

Figure 4.2. Skills and Education of Labor as No Obstacle by Income Classiﬁcation
Percentage of Firms Indicating Skills and Education

50
of Labor as Not an Obstacle to Doing Business

30 35
6
20 37
29
10

0
Low income Lower middle income Upper middle income High income
Income Classification

Source: BEEPS 2008, Enterprise Surveys 2006-2010.
* The number indicated within the bars represents the number of countries in each region.
58 World Bank Study

Some Firms Are Hit Harder by Skills Constraints
Certain ﬁrm-level characteristics appear to make ﬁrms more vulnerable to the skills constraint
including ﬁrm size, sector of the ﬁrm’s primary activity, innovative activity, and ICT use.
Small ﬁrms view quality of labor more favorably than medium-sized and large
ﬁrms.13 In 2008, 38 percent of small ﬁrms (5–19 employees) stated skills and education
was not an obstacle, compared to 29 percent of medium ﬁrms (20–99 employees) and 24
percent of large ﬁrms (100 employees or more). Secondly, ﬁrms in the manufacturing
sector perceive skills and education of labor to be a greater obstacle than those in the
service sector. Enterprises in the machine and equipment sector seem the least satisﬁed
with the qualiﬁcations of available workers, followed by ﬁrms in construction, basic
metals, and garments. Perhaps, these manufacturing sectors require more specialized
skills than what is required for the majority of the service sector jobs.
Similarly, innovative ﬁrms, i.e. those introducing a new product or service in the last
three years, are more likely to require workers with specialized skills, and thus are less
likely to perceive skills and education as no obstacle.14 Firms that use ICT, such as email
to communicate with customers or suppliers, also see skills and education as a greater
constraint. However, ﬁrms with these characteristics, at least in the manufacturing sec-
tor, may have found ways to mitigate the skills constraint via second-best alternatives,
e.g. by providing additional training.
The skills constraint has measurable and signiﬁcant negative impacts on ﬁrm produc-
tivity when controlling for other factors.15 The impact is still negative but is not signiﬁ-
cant at the country level (Figure 4.3). Firms with higher productivity are also signiﬁcantly
more likely to state that skills are not an obstacle.16 This oﬀers evidence that more produc-
tive ﬁrms may be be er able to overcome the productivity eﬀect than lower productivity
ﬁrms. Further, when controlling for the skills constraint, growing ﬁrms and innovating
ﬁrms continue to have higher productivity.17 This shows that more productive ﬁrms,
growing ﬁrms, and innovative ﬁrms may be be er able to locate and hire skilled work-
ers, or mitigate the negative impact by providing training. The provision of training as a
response to the skills constraint is discussed more fully later in the chapter.

Figure 4.3. Skills and Education of Labor as an Obstacle vs. Labor Productivity
Skills and Education of Labor as an Obstacle (mean)

3.0

BLR
2.5
RUS
KAZ
2.0 MDA UKR ROM
LTU LVA POL
TJK UZB ALB
KGZ SVKCZE
EST
1.5 TUR
HRV
KSV
GEO ARM BGR
AZE SRB
BIH
1.0 MKD SVN
MNE
0.5 HUN

0.0
7.5 8 8.5 9 9.5 10 10.5 11 11.5 12 12.5
LN(Labor Productivity)

Source: BEEPS 2008.
Challenges to Enterprise Performance in the Face of the Financial Crisis 59

Explaining Rising Skills Constraints: Evidence of a Regional Skills Shortage?
It is diﬃcult to discern whether the rise in perceptions of skills as a constraint to business
activity is the result of a general increase in labor demand and rising wages or because
certain types of skills shortages are indeed emerging. Answering this deﬁnitively is out-
side the scope of this report. This section examines the characteristics of the regional
skills shortage and assesses possible drivers of this trend to help explain why a skills
shortage may be emerging in ECA.
First, developments associated with the transition process itself may have contrib-
uted to the rising skills shortage. At the beginning of transition, indicators of educa-
tional a ainment suggested that these economies had higher stocks of human capital
than countries at comparable levels of development. During the transition, the structure
of employment shifted away from agriculture and industry into services, and jobs gen-
erally shifted from low to high skill content.18 Even among high skill jobs, some highly
specialized skills acquired during the central planning period quickly became obsolete,
while at the same time, demand for other skills in emerging sectors was rising.
Unemployment was on the decline across many countries in ECA from 2004 to 2007.
However, countries such as Georgia, Serbia, FYR Macedonia, the Czech Republic and
the Slovak Republic still experienced unemployment rates of 10 percent or higher. FYR
Macedonia had the highest unemployment rate of ECA countries (for which data are
available) with nearly 35 percent unemployment, although it was unevenly distributed
between two ethnic communities. The question becomes one of whether unemployment
is due to a lack of job vacancies, an aggregate labor shortage, or a deﬁciency in the skill level
of the available workforce, and it is a diﬃcult one to answer. Skills shortages and unemploy-
ment can coexist: meaning there are pools of available labor but skills shortages remain (due
to mismatches and the lack of regional mobility, among other reasons)—and this makes it
diﬃcult to explain labor market developments (Boswell, Stiller, and Straubhaar, 2004).
With many ﬁrms competing for quality workers with the desired skills mix, the is-
sue may be one of supply. Earlier analytical work (e.g. Rutkowski, 2007) suggests that
the former jobs shortage earlier in the transition period has been replaced by a skills
shortage, and that in times of economic growth (i.e. the pre-crisis environment), the lack
of skills in the available workforce becomes a bigger impediment to ﬁrms. During these
times, the long-term unemployed often suﬀer from a qualitative skills shortage— they
do not possess the skills or education to ﬁll available jobs.19 In 2007, the majority of un-
employed and inactive workers in ECA were low skilled: close to two-thirds of inactive
or long-term unemployed workers had, at best, completed only primary education. For
countries which data is available, between 2004 and 2007, the unemployment rate for
those with a tertiary education increased in Belarus, Georgia, and Ukraine, while over
the same period, Armenia, Bulgaria, Estonia, Lithuania, Russia, and Tajikistan experi-
enced a decrease in tertiary unemployment. Taken altogether, the evidence suggests that
unemployed workers in ECA are typically less skilled.20
Data on recent vacancies also suggest that higher educational a ainment is re-
quired. In Bosnia and Herzegovina, for example, between 2004 and 2008, the number
of new vacancies that required a university degree outstripped the number of available
workers with a tertiary education by several thousand.21 The results of a survey of ﬁrms
in Bosnia and Herzegovina also showed that over 80 percent of enterprises surveyed had
unﬁlled vacancies, of which close to half required a university education.22 Qualitative
interviews with representatives of enterprises indicate that the inability to ﬁll vacancies
60 World Bank Study

Box 4.3. Wages and Productivity Increases: Do They Reﬂect Rising Skills and
Labor Shortages?

All else being equal, the growth of skills shortages should be reﬂected in wage increases that
exceed the growth of productivity. If the BEEPS results reﬂect broader labor market trends,
then wage and productivity trends should be consistent with these perceptions as ﬁrms offer
higher wages in search of suitably qualiﬁed workers.
In countries such as Bulgaria, FYR Macedonia, the Kyrgyz Republic, Latvia, Lithuania, Po-
land, Russia, and Ukraine, wage increases have outstripped productivity growth over the pe-
riod from 2000 to 2007, or more recently from 2004 onwards. See Figures B4.3.1 and B4.3.2
as examples. Some of these developments may reﬂect institutional wage-setting arrange-
ments. In Central and Eastern European countries, statutory minimum wages are a large
fraction of average wages, and their frequent adjustment in turn pushes up average wages.

Figure B4.3.1. Productivity and Wage Figure B4.3.2. Productivity and Wage
Growth in Poland 2004–2008 Growth in Ukraine 2004–2008

120 150
Productivity and Wage Index (2004=100)

Productivity and Wage Index (2004=100)

140
115
130
110
120
105
110

100 100
2004 2005 2006 2007 2004 2005 2006 2007
Year Year
Productivity Productivity
Real manufacturing wage indices Real manufacturing wage indices
Source: KILM 6th Edition, 2009. Source: KILM 6th Edition, 2009.

Such arrangements have been observed to hamper wage ﬂexibility and employment growth,
especially among workers at the lower end of the skills distribution. Meanwhile, in Slovenia,
where wage growth has lagged behind productivity growth, collective bargaining agreements
follow the principle of keeping wage increases just below growth in productivity.23
Most likely, these rapid wage increases reﬂect rising skills shortages, especially in recent
years, as wage growth exceeded productivity broadly across countries in different sub-groups
in the region.24 Although minimum wages and centralized wage-setting mechanisms are rela-
tively less common in the CIS, a number of CIS countries have also been experiencing very
rapid increases in wages, such as Ukraine.
In addition, recent rapid increases were noted in Eastern European countries just as workers
started migrating in large volumes to older EU member countries immediately following the
2004 EU enlargement, and as companies started complaining of labor shortages (see Wag-
styl et al 2008 and Rutkowski 2007).
Sources: ILO (2009) KILM 6th Edition.

was largely due to the lack of the relevant skills among applicants, rather than reasons
related to lack of mobility, labor market regulations, or wages.
Skills constraints are also known to be caused by regional mismatches characterized
by the immobility of the workforce, among other reasons (Boswell, Stiller, and Straub-
haar 2004). Internal geographic mobility is constrained in many of the countries in the
Challenges to Enterprise Performance in the Face of the Financial Crisis 61

region. In some cases, geographic immobility seems to reﬂect cultural preferences or ties
to local communities; in other cases, underdeveloped rental markets and administrative
controls help curb internal movements. For example, in Russia, an internal residence per-
mit is required to get a job and switch residence. The lack of inter-regional movement may
help explain the trends seen in BEEPS. Further, international emigration may also play a
role as higher skilled workers seek opportunities in higher-income countries. See Box 4.4.

Box 4.4. Emigration and Brain Drain in ECA

The transition to market economies has been accompanied by generally poor domestic labor
market outcomes in ECA (World Bank, 2008b). High levels of long-term unemployment, com-
bined with large wage differentials, created powerful incentives for emigration, evidenced by
the very large remittances to output ratios found in the low to lower middle-income economies
in the region (Figure B4.4.1). Tajikistan is the most remittance-dependent economy in the
world, and Moldova and Kyrgyz Republic are also in the top ﬁve countries with the highest
ratio of workers’ remittances to GDP.

Figure B4.4.1. Workers’ Remittances (% of GDP), 2008

30
Percent

0
To n
rg Mo nga
Re ova
Le blic
Sa tho
Le moa
Gu non
na
nd al
as
Jo iti
d H Sa an

Uz ama a
be ica
ca an

Gu lba a
ate nia

Ph lade a
ilip sh
Se es
ia
Ca ene o
Ve l
Th Arm de
am ia
Mo bia

an ietn o
Re am

Le c
e
go r

pe ga
ze do

ra li
ta

J vin

A u

ng al

S og

nic V occ

on
Ha
Ho Nep

e G en

er ub
ur

pin
ya

an El rd

Ni kist
g

r
Ba m
kis

er lva
pu

T
yz ld

Si p
r
ji
Ta

mi
Do
ia
sn
Bo

Source: IMF Balance of Payments, 2008.
Note: Remittances comprised of workers’ remittances and compensation of employees.

If ﬂows among developed economies are excluded, emigration among ECA countries and
between ECA and industrial economies constitutes one-third of global immigration and emi-
gration. Migration affects both the lower income ECA countries as well as higher income
countries such as Russia, Kazakhstan, and Ukraine. The importance of emigration is not
found solely in the CIS, but also in the EU as labor liberalization beginning in 2004 resulted in
large emigration ﬂows from new member states to Western Europe (World Bank, 2008c). For
example, over 100,000 Poles registered to work in UK during its ﬁrst year of EU membership.
Over 60 percent of these emigrants were between ages 24 and 35 while 40 percent had a
university degree.
Two mechanisms could link these high levels of migration with the emerging skills gap. First,
the supply of skilled workers may have simply shrunk due to the prospect of higher wages
abroad, particularly in the EU-10.a Second, demand for unskilled migrant labor may decrease
the incentive to invest in education at home. Evidence from Albania ﬁnds that the opportunity
costs of higher education may be too high when considering the signiﬁcantly higher wages
paid for unskilled work in Greece and Italy (Miluka and Dabalen, 2009). It is also possible
that complications in having educational credentials recognized abroad results in so-called
(Box continues on next page)
62 World Bank Study

Box 4.4 (continued)
‘brain waste’, where highly educated workers only qualify for low skilled jobs abroad, which
increases the costs of pursuing higher education for students who may know that they want
to emigrate (even if only on a temporary basis).
However, caution needs to be exercised in ﬁrmly drawing causal lines: the empirical relation-
ships between the BEEPS 2008 results on skills constraints and various proxies of emigration
intensity and ‘brain drain’ are mixed. The ﬁgures below reveal certain cases where there ap-
pears to be a relationship between emigration and the skills proﬁle of migrants with perceptions
of skills as no obstacle from BEEPS 2008, such as: Albania, Croatia, FYR Macedonia, the
Kyrgyz Republic, Moldova, Poland, and Tajikistan. However, these relationships are not robust
for the entire sample of ECA countries.

Figure B4.4.2. Skills and Education of Labor as No Obstacle vs. Remittances
Percentage of Firms Indicating Skills and Education of

80
Labor as Not an Obstacle to Doing Business, 2008

HUN
70
60
SVNMKD
50 GEO
AZEBGR ARM BIH
40 HRV
TUR TJK
30 LVA
CZE ALB KGZ
EST
SVK UKR
LTU
KAZPOL ROM
20 MDA
RUS
10 BLR
0
-5 5 15 25 35 45 55
Migrants' Remittances (% GDP) 2008
Source: BEEPS 2008, IMF Balance of Payments.

Figure B4.4.3. Skills and Education of Labor as No Obstacle and Emigration of the
Tertiary Educated
Percentage of Firms Indicating Skills and Education of

80
Labor as Not an Obstacle to Doing Business, 2008

HUN
70
60
SVN MKD
50 GEO ARM
AZE BGR BIH
40 HRV
30 TJK TUR
KGZ
UZB LVA
CZE ALB
SVK EST
20 KAZ MDA UKR LTU ROM POL
RUS
10 BLR
0
0 5 10 15 20 25 30
Emigration Rate of Tertiary Educated (%)

Sources: BEEPS 2008; World Bank (2008) Migration and Remittances Factbook.

Sources: BEEPS 2008; IMF Balance of Payments, 2008; World Bank Migration and Remittances Factbook, 2008.
Note: a. Kahanec et al (2009) summarizes recent papers on rising labor shortages in the new EU member
states, suggesting that the emigration of workers from the EU-10 to some of the older EU member countries
may worsen the weaknesses in labor markets. In the years immediately following the 2004 EU enlargement,
large volumes of unﬁlled vacancies were reported in Lithuania, for example.
Challenges to Enterprise Performance in the Face of the Financial Crisis 63

The Education Paradox
Without taking into account the educational a ainment of unemployed workers, higher
unemployment rates should have a positive eﬀect on skills and education perceptions,
as there is a larger pool of job applicants to ﬁll vacancies. Comparing the average percep-
tions of skills and education as a problem and the unemployment rate in ECA countries
(for which data are available), there is evidence that ﬁrms in countries experiencing high
unemployment do indeed report a lower dissatisfaction with the education and skill of
the labor force, on average. See Figure 4.4.

Figure 4.4. Skills and Education of Labor as an Obstacle vs. Unemployment

2.5
RUS
Skills and Education of Labor as an

2.0 MDA
Obstacle—Mean Score, 2008

ROM
LTU UKR POL
LVA
CZE
EST SVK
1.5 TUR
HRV
BGR GEO
AZE SRB BIH
1.0 SVN MKD

0.5 HUN

0.0
0 5 10 15 20 25 30 35 40
Unemployment (%) 2007

Source: BEEPS 2008, World Bank.

On the surface, this aggregate pa ern may appear to contradict the discussion in
the previous section on the links between unemployment and the skills constraint, i.e.,
how skills shortages may persist, despite growing pools of excess labor. However, the
data on unemployment for the tertiary educated and secondary educated show very dif-
ferent pa erns. Firms in countries with greater tertiary unemployment (a greater share
of unemployed workers with tertiary education) report a higher level of dissatisfaction
with the skills and education of labor than do those with unemployed workers consist-
ing mostly of individuals with secondary school education. See Figures 4.5 and 4.6. One
explanation is that at lower skill levels, unemployed workers can substitute for workers
who do not have specialized skills. On the other hand, workers with tertiary education
are not considered substitutes as they have specialized skills (e.g., engineers and law-
yers). Thus, a larger pool of tertiary unemployed does not necessarily mean a larger pool
of workers to draw from.
The graphs below suggest that the remedy to the skills constraint may not lie in for-
mal education, but in relevant skills development. The BEEPS results follow this trend
at the ﬁrm level: ﬁrms with a greater percentage of workers with university degrees are
more likely to report that skills and education of labor is a major obstacle.25 One expla-
nation for this result is the degree of “over-education” among workers. In some cases,
workers may possess a higher level of education or skill than the job requires, which
64 World Bank Study

Figure 4.5. Skills and Education of Labor as an Obstacle vs. Unemployment of
Tertiary Educated

3.0
Skills and Education of Labor as an Obstacle—Mean Score, 2008

BLR
2.5
RUS

2.0 ROM
POL UKR
LTU
LVA
TJK
SVK
CZE EST
1.5 TUR
HRV
BGR ARM GEO
BIH
AZE
1.0 SVN

0.5 HUN

0.0
0 10 20 30 40 50 60
Unemployment of Tertiary Educated 2007 (%)

Source: BEEPS 2008, World Bank.

Figure 4.6. Skills and Education of Labor as an Obstacle vs. Unemployment of
Secondary Educated

3.0
Skills and Education of Labor as an Obstacle—Mean Score, 2008

BLR
2.5
RUS

2.0 ROM
UKR LTU POL
LVA
TJK
EST SVK CZE
1.5 TUR
HRV
BGRGEO ARM
AZE
1.0 SVN

0.5 HUN

0.0
20 30 40 50 60 70 80 90
Unemployment of Secondary Educated 2007 (%)

Source: BEEPS 2008, World Bank.
Challenges to Enterprise Performance in the Face of the Financial Crisis 65

may lead to decreased productivity, the underutilization of skills, or skills mismatches
within ﬁrms.26 This relationship may also reﬂect an inability of formal training institu-
tions to adapt to changing needs of the labor market, which is well documented.27 This
means that deﬁciencies in the educational system, which have been around for some
time, may have become a greater obstacle as labor demand has grown stronger.

Box 4.5. Information Technology Use and Skills

ICT is becoming more pervasive in the business world globally, not just in the ECA region.
Across ECA, the use of ICT has increased signiﬁcantly from 2002–2008. The investment
in ICT is best viewed using the panel dataset. Comparing the results from 2002, 2005, and
2008, the use of email communication increased signiﬁcantly for panel ﬁrms between 2002
and 2005, increasing from 56 percent to 69 percent of all ﬁrms in the sample. In 2008, email
use continued to increase, reaching an average of 74 percent.
ICT needs skills, or skills need ICT?
The increased use of ICT, and changes in organizational processes and products increase
the need for workers to have a higher level of skills. Hence, labor demand has shifted toward
skilled labor as ﬁrms become more innovative and adopt new technologies, the phenomenon
referred to as skills-biased technical change (Bresnahan, Brynjolfsson and Hitt, 2002; Juhn,
Murphy and Pierce, 1993).
Answering the question of causality, whether the use of ICT increases ﬁrm-need for skills,
or vice versa is outside the scope of this report. However, the results of BEEPS provide evi-
dence that they are complementary factors. Results of the multivariate regression support the
hypothesis that ﬁrms that use ICT will use more highly skilled labor.28 Similarly, ﬁrms with a
higher percentage of workers with university degrees are more likely to use ICT, even when
controlling for skills mix.29
In turn, the analysis shows that ﬁrms that use email are also more likely to provide training for
their full-time workers.30 Firms investing in ICT may complement this investment with other
human capital investments. Results show that this practice pays off, as training has a signiﬁ-
cant positive effect on labor productivity at the ﬁrm level when controlling for other factors.31
More on the role of ICT in productivity is found in Chapter 3.
Sources: BEEPS 2002, 2005, and 2008.

Responding to the Rising Skills Constraints
One way ﬁrms are responding to the rising skills constraint is by providing training to
enhance the skills of those they hire. The provision of training is one means to increase
the capacity of labor and develop a more functional and eﬀective workforce. These ﬁrms
that provide training are also more productive ﬁrms: training has a signiﬁcant and posi-
tive relationship with productivity at the ﬁrm level when controlling for other factors.32
However, despite the productivity boost, only 35 percent of ﬁrms provided training
in ECA, which places it in the middle of the pack compared to other regions. Based on
comparable data from Enterprise Surveys, 29 percent of ﬁrms in AFR provided training
to their full-time employees, as compared to 42 percent of EAP ﬁrms and 43 percent of
LCR ﬁrms (see Figure 4.7). However, there is a wide variation across the countries within
ECA—50 percent or more of the ﬁrms in Russia, Poland, Estonia, Bosnia and Herze-
govina, and the Czech Republic oﬀered training to their full time employees.33 This is in
sharp contrast to Azerbaijan, Georgia, and Uzbekistan, where 15 percent or less of ﬁrms
66 World Bank Study

oﬀered training. There is not a discernible pa ern across countries; however, there are
signiﬁcant diﬀerences across subregional averages.
Firms in the EU-10 train more than ﬁrms located in other ECA subregions. One
explanation is that these countries tend to have higher incomes. Examining the provi-
sion of training by income classiﬁcation conﬁrms that more ﬁrms in high-income coun-
tries provide training than in lower income countries (Figure 4.8). Only 25 percent of
ﬁrms in low-income countries on average provided training to their full time employees,
compared to 44 percent of ﬁrms in high-income countries. The relationship is further
conﬁrmed by the results of statistical analyses that show GDP as a positive and signiﬁ-

Figure 4.7. Provision of Training by Region

50
45
Percentage of Firms Providing Formal

40
Training to Full-Time Employees

35 10

30 16
11
25 4
29
20
7
15
39
10 6
7
5 6
0
SAR FSU-S MNA AFR SEE ECA FSU-N EAP LCR EU-10
Region

Source: BEEPS 2008, Enterprise Surveys 2006–2010.
* The number indicated within the bars represents the number of countries in each region.

Figure 4.8. Provision of Training by Income Classiﬁcation

50
45
Percentage of Firms Providing Formal
Training to Full-Time Employees

40
35
30
6
25
29
20
37
15
10 35
5
0
Low income Lower middle income Upper middle income High income
Income Classification

Source: BEEPS 2008, Enterprise Surveys 2006–2010.
* The number indicated within the bars represents the number of countries in each region.
Challenges to Enterprise Performance in the Face of the Financial Crisis 67

cant predictor for provision of training, controlling for other factors.34 One important
implication of this ﬁnding is that although skills constraints are apparent across income
groups, ﬁrms in richer countries appear be er able to respond to the skills constraint.35 Be-
cause ﬁrms in richer countries can provide more training and thus oﬀset skills shortages,
the gap in enterprise performance between rich and poor countries may widen further.
Diﬀerences in training pa erns are also partially explained by ﬁrm characteristics,
such as ﬁrm size. Small ﬁrms historically are less likely to provide formal training,36
as they have fewer resources (Freel, 1999). This is visible in the BEEPS results: only 24
percent of small ﬁrms oﬀered formal training to their employees, versus 39 percent of
medium ﬁrms and 55 percent of large ﬁrms. The results of the analysis conﬁrm that large
ﬁrms are more likely to provide training than other ﬁrms (controlling for other factors).37
Firms that export or are partially foreign-owned are also more likely to provide
training. In turn, these ﬁrms are also more likely to be innovative ﬁrms. Innovative ﬁrms
may require a more specialized skill set of their employees, and therefore are more like-
ly to provide training than their non-innovating counterparts. (See also Box 4.5). The
results of BEEPS reﬂect this: 42 percent of innovative ﬁrms oﬀered training programs
compared to 26 percent of ﬁrms that did not innovate. Firms that increased their work-
force between 2004 and 2007 were also more likely to provide training to their full-time
workers. This may be a signal that new hires are lacking fundamental skills needed by
ﬁrms. Thus, the training provided by growing ﬁrms may be aimed at equipping new
hires with requisite skills to complete on-the-job tasks, rather than enhancing the exist-
ing skill sets of employees.
In summary, ﬁrms that are advancing in terms of adoption of technology, and in-
novating via the development of new products and services are more likely to provide
training to their workers. However, this should not imply that ﬁrms that train are more
satisﬁed with the quality of labor: in fact the opposite is true. Only 24 percent of ﬁrms
that provided training reported that skills and education of labor is not an obstacle,
compared to 33 percent of ﬁrms that did not train, and the diﬀerence is signiﬁcant. See
Figure 4.9 below.

Figure 4.9. Skills and Education of Labor as an Obstacle vs. Provision of Training

3.0

BLR
2.5
an Obstacle—Mean Score, 2008
Skills and Education of Labor as

RUS
KAZ
2.0 ROM
UKR MDA POL
ALB LVALTU
UZB TJK
KGZ SVK CZE
EST
1.5 TUR
HRV
KSV
GEO ARM
BGR SRB
AZE BIH
1.0 MKD SVN
MNE
0.5 HUN

0.0
0 10 20 30 40 50 60 70 80
Percentage of Firms Providing Training

Source: BEEPS 2008.
68 World Bank Study

The characteristics that make ﬁrms more likely to provide training are the same
characteristics associated with a higher level of dissatisfaction with labor quality, par-
ticularly innovative activity and ICT use. These ﬁrms may be providing training to equip
their workers with basic, requisite skills, instead of enhancing the skills of high-skilled
workers. The negative relationship between training and labor satisfaction may be a
function of the cost to the ﬁrm to provide these programs. If ﬁrms cannot ﬁnd qualiﬁed
labor, investing in training to meet internal needs may place an undue burden on these
ﬁrms to stay competitive.

Box 4.6. Early Effects of the Financial Crisis

The ﬁnancial crisis had a distinct effect on ﬁrms’ management of human capital. The
2009 Financial Crisis Survey conducted by the Enterprise Surveys unit of the World Bank,
revisited ﬁrms participating in the 2008 BEEPS in six countries and asked about the effects
of the crisis and their responses to it. The results show approximately 60 percent of ﬁrms that
participated in the 2008 BEEPS had reduced their workforce by 2009.
Of ﬁrms participating in both surveys, many reported that they are planning to reduce their
full-time permanent workforce (see Figure B4.6.1). The highest value is seen in Lithuania,
where 42 percent of ﬁrms speculated they will reduce their workforce in response to the crisis.

Figure B4.6.1. Firms Planning to Figure B4.6.2. Skills and Education of Labor
Downsize as No Obstacle and Plans to Downsize
Percentage of Firms Indicating Skills and Educaiton

50 70
of Labor as Not an Obstacle to Doing Business

60
40 Planning to Downsize
50 Not Planning to Downsize
30 40
Percent

30
20
20
10 10

0 0
ia

nia
y

ria

a
ia

nia

ria

tvi
rke
tvi

lga
an

ma
ng
lga

La
ma

hu
La

Tu
hu

Bu
Hu

Ro
Bu

Lit
Ro
Lit

Source: Financial Crisis Survey, 2009. Source: BEEPS 2008, Financial Crisis Survey, 2009.

Firms that planned to downsize in 2009 were more dissatisﬁed with their quality of
labor in 2008.38 Across all ﬁrms participating in the survey, only 22 percent of ﬁrms planning
to downsize stated skills and education of labor was not an obstacle in 2008, compared to 33
percent of ﬁrms that were not planning to downsize.39 However, the differences across coun-
tries are dramatic. See Figure B4.6.2. The difference in perceptions of labor quality between
ﬁrms that are planning to downsize versus those that are not planning to downsize does not
vary for Hungary, and although there are visible differences across the other 5 countries,
the difference is only statistically signiﬁcant in Latvia. Latvian ﬁrms that are not planning to
downsize are signiﬁcantly more satisﬁed with the quality of their labor. For those ﬁrms planning
to downsize, the crisis may provide an opportunity to shed ineffective workers and capitalize
on the increased pool of available workers resulting from the rise in unemployment due to the
crisis.
Source: Financial Crisis Survey, 2009.
Challenges to Enterprise Performance in the Face of the Financial Crisis 69

Effects of the Crisis on Labor
The data for the 2008 BEEPS describes the period before the ﬁnancial crisis, and the ef-
fects of the crisis will undoubtedly have implications for labor in ECA. Firms may shed
workers and cut capacity and/or production, which may reverse the positive trend seen
in perceptions of labor regulations as redundancy and termination costs rise. The ﬁnan-
cial crisis may also aﬀect the perceptions of skills and education of labor. As the pool of
candidates grows, due to some ﬁrms contracting, other ﬁrms may be able to capitalize
on the crisis by hiring be er-skilled workers. However, even if there is more skilled labor
on the market, dissatisfaction may persist either because ﬁrms become more selective or
because in the post-crisis environment, less skilled labor is produced by the education
system due to mismatches between the skills needs of ﬁrms and the existing curricula
in the educational systems.40 The early eﬀects of the crisis on BEEPS ﬁrms in selected
countries are discussed in Box 4.6.
Though the crisis may provide temporary relief to ﬁrms hampered by the skills
constraint, the shortage of relevant skills may eventually prove to be a drag on growth
and economic recovery if left unaddressed. Other regional reports41 have recommended
a greater role for lifelong learning through various training schemes. At the same time,
where reforms to labor market institutions remain incomplete, labor rigidity may con-
strain job creation.

Conclusion
In the three years since the previous BEEPS was conducted in 2005, two distinct trends
have emerged. First, skills and education of labor has become a top-ﬁve obstacle to ﬁrms
across the ECA region, while at the same time, ﬁrms’ relative perceptions of labor regu-
lations have improved. The trend in labor regulations suggests greater ﬂexibility in the
employment decisions of the private sector made possible by recent reforms to labor
market institutions and employment regulation.42 Despite this ﬂexibility, ﬁrms report
greater dissatisfaction with the educational and skills qualiﬁcations of their workforce.
This increase in the skills constraint is a more recent phenomenon, but one that is wide-
spread and signiﬁcant and is consistent with other data sources and what is known more
broadly of labor markets in the ECA region. Although satisfaction with labor quality has
been on the decline for the last decade, the skills constraint is becoming an urgent issue
for ﬁrms in ECA.
There is evidence of a skills shortage driven in part by features of the transition pro-
cess, including the structural transformation that shifted the demand for labor from low-
skilled to high-skilled, international emigration, and the inability of educational systems
in the region to respond adequately to the changing needs of enterprises. These drivers
predate the recent rise in skills constraints, but there is evidence that they have become
more important drivers of skills shortages in recent years.
The rising skills constraint has a measurable impact on labor productivity. While
some ﬁrms are able to provide training in response to the skills constraint, others cannot.
In addition, ﬁrms that do provide training are also more likely to report that skills and
education of labor is a major obstacle, which may suggest that the added burden of cul-
tivating an eﬀective workforce using their own resources is hindering enterprise perfor-
mance. Further, ﬁrms in high-income countries provide more training to their workers.
If ﬁrms in poorer countries cannot provide adequate training to complement the skills
70 World Bank Study

acquired by their workers through formal education, the gaps in enterprise performance
and productivity between rich and poor countries may widen further.
One important ﬁnding is that the solution to the problem of the rising skills con-
straint does not appear to reside in formal education, but in developing relevant skills.
Although BEEPS does not capture perceptions of a range of employer-desired skills,
such as the ability of individuals to solve problems, work independently, and use IT-
based applications, it is clear that there is a lack of fundamental skills in these countries.
Other recent regional reports identify early education as an area for countries to aim
policies and resources, as well as enhancing job-relevant skills of the current and future
workforce. However, this approach is a long-term solution, and does not address the
immediate problem of the long-term unemployed.
In the aftermath of the ﬁnancial crisis, reducing long-term unemployment is a ris-
ing concern. In the short term, enhancing the skills of existing workers and unemployed
workers can be achieved through policy measures such as re-training programs. While
some argue that these government programs may be unnecessary, they may provide
relief for stressed state budgets and employers in the long-term by reducing the amount
spent on social welfare programs and by reducing labor costs to ﬁrms. In addition,
retraining these workers could be an important step toward replenishing the stock of
qualiﬁed workers, which in turn will enhance competitiveness. However, longer-term
sustainable measures that bring together the private sector and educational institutions
should be considered to achieve be er congruence between ﬁrms’ needs in the changing
economy and fundamental skills taught in secondary and post-secondary institutions.

Notes
1. See Alam et al. (2005).
2. See, for example, Rutkowski (2007). Rutkowski rules out other explanations such as emigration
from the new EU member states to the older member states, arguing that although such an outﬂow
may explain the fall in unemployment rate, it does not explain rising job creation.
3. See, for example, Schreiner (2008). This refers to year-on-year, four-quarter moving average, for
the new EU member states as a group.
4. The regional averages are calculated so that each country has an equal weight.
5. O’Higgins (2010) traces a longer history, spanning the last 15 years or so, behind the eﬀort to
deregulate EPL.
6. Stricter EPL theoretically can beneﬁt the ﬁrm (Cazes and Mesporova 2003), as it can lead to en-
hanced productivity though results of prior studies on the relationship between EPL and produc-
tivity are mixed (e.g. Scarpe a and Tressel 2004). What is clear is that stricter EPL can also increase
the cost of labor for individual ﬁrms and may reduce a ﬁrm’s propensity to hire, particularly for
full-time positions, favoring short term or temporary arrangements which may be less costly, un-
less EPL also has strict regulations governing contractual employment.
7. Scarpe a and Tressel (2004) found that stringent EPL has a signiﬁcant negative impact on ﬁrm
productivity, particularly for innovative ﬁrms.
8. This ﬁnding is based on a generalized ordered logit model that uses perceptions of labor regula-
tions as the dependent variable as informed by Pierre and Scarpe a (2006). Results are signiﬁcant
at p= 0.10 or be er. See Table A1.15 in Appendix 1 for results.
9. See Table A1.15 in Appendix 1 for results.
10. See Table A1.17 in Appendix 1 for results.
11. See Table A1.19 in Appendix 1 for results.
12. See Table A1.17 in Appendix 1 for results.
13. This is somewhat surprising. Smaller ﬁrms are expected to face higher barriers in the business
environment, due to resource and access constraints and their limited ability to provide employ-
Challenges to Enterprise Performance in the Face of the Financial Crisis 71

ment beneﬁts that larger ﬁrms do, but instead larger ﬁrms appear to be more constrained. See Table
A1.16 in Appendix 1.
14. See Table A1.16 in Appendix 1 for results.
15. Results of the multivariate regression analysis show the skills constraint to have a negative
relationship with labor productivity, signiﬁcant at the 0.08 level. See Table A1.19 in Appendix 1.
16. See Table A1.16 in Appendix 1 for results.
17. See Table A1.19 in Appendix 1 for results.
18. There is a large literature documenting the rising demand for skilled labor as a result of the
structural transformation and the transition process, more generally. The relevant studies include
Brixiova et al (2009), Tan et al (2007) and Commander and Kollo (2003). In the Western Balkans, the
relevant literature is a comprehensive review by Fetsi et al (2007). Further, preliminary evidence
on Poland also shows enterprise restructuring has been associated with a pronounced shift of labor
demand away from less skilled blue collar labor toward highly skilled white collar labor. Respec-
tively, newly created jobs tend to diﬀer in skill content from the old jobs they replace. Consequent-
ly, workers who lose their jobs due to enterprise restructuring often lack skills that are required in
the newly created jobs. This gives rise to an excess supply of some skills, which often coexists with
the shortage of other skills—those needed in new and expanding activities. Educational systems
and labor markets in general have not been able to catch up with this phenomenon. This is reﬂected
in employers’ perceptions on skills.
19. Rutkowski (2007).
20. One exception is Belarus, where the employment rate is 51 percent for those with a tertiary edu-
cation. A similar picture is seen in Georgia and Ukraine, which had tertiary unemployment rates
of 42 percent and 39 percent respectively. In all three countries, the unemployment rate of workers
with tertiary education rose between 2004 and 2007.
21. See World Bank (2009) for details.
22. See World Bank (2009).
23. See Rutkowski, Scarpe a, et al (2005: 239).
24. See, for example, Alam, Asad et al (2005) on wage increases from 1997 to 2003.
25. See Table A1.16 in the Appendix for results.
26. Research has shown that workers who possess a higher level of education than what the job
requires may be more prone to report job dissatisfaction and reduce their work eﬀort, resulting in
lower productivity of the ﬁrm overall. See Buchel (2000) and Tsang and Levin (1985).
27. See O’Higgins (2010).
28. See Table A1.20 in Appendix 1 for results.
29. See Table A1.17 in Appendix 1 for results.
30. See Table A1.18 in Appendix 1 for results.
31. See Table A1.19 in Appendix 1 for results.
32. See Table A1.19 in Appendix 1 for results.
33. The question on provision of formal training was included only on the Manufacturing Module
of the BEEPS survey.
34. See Table A1.18 in Appendix 1 for results.
35. However, these ﬁrms in higher income countries may require more skilled labor as they pro-
duce higher value-added goods. This hypothesis cannot be tested with available data.
36. See Storey (2004).
37. See Table A1.18 in Appendix 1 for regression results on ﬁrm characteristics and provision of training.
38. T-test signiﬁcant at p=0.000.
39. Based on the full weighted sample of the Enterprise Surveys Financial Crisis Survey 2009.
40. See for example, World Bank (2010); Mitra, Selowsky and Zalduendo (2010).
41. World Bank (2010); Mitra, Selowsky and Zalduendo (2010).
42. Nonetheless, some caution is warranted in interpreting these developments. Although it is possible
to demonstrate the links between reforms to labor market institutions and the changing perception of
labor regulation, it is extremely diﬃcult to isolate reforms to employment protection legislation from
other reforms. In particular, because labor and product markets are interrelated, the impact of re-
forms in one market is likely to be aﬀected by how heavily other markets continue to be regulated.
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Appendixes

81
Challenges to Enterprise Performance in the Face of the Financial Crisis 83

Appendix 1. Technical Notes and Data Tables

BEEPS 2008 Survey Questions

Problems Doing Business: Access to Finance, Electricity, Telecommunications,
Transport, Labor Regulations and Skills and Education of Labor

Access to Finance
Q.K30: Is access to ﬁnance, which includes availability and cost, interest rates, fees and
collateral requirements, No Obstacle, a Minor Obstacle, a Moderate Obstacle, a Major
Obstacle, or a Very Severe Obstacle to the current operations of this establishment?
(No obstacle=0 Minor obstacle=1 Moderate obstacle=2 Major obstacle=3 Very severe
obstacle=4)

Electricity
Q.C30a: Is electricity No Obstacle, a Minor Obstacle, a Moderate Obstacle, a Major
Obstacle, or a Very Severe Obstacle to the current operations of this establishment?
(No obstacle=0 Minor obstacle=1 Moderate obstacle=2 Major obstacle=3 Very severe
obstacle=4)

Telecommunications
Q.C30b: Is telecommunications No Obstacle, a Minor Obstacle, a Moderate Obstacle,
a Major Obstacle, or a Very Severe Obstacle to the current operations of this establish-
ment? (No obstacle=0 Minor obstacle=1 Moderate obstacle=2 Major obstacle=3 Very
severe obstacle=4)

Transport
Q.D30a: Is transport No Obstacle, a Minor Obstacle, a Moderate Obstacle, a Major
Obstacle, or a Very Severe Obstacle to the current operations of this establishment?
(No obstacle=0 Minor obstacle=1 Moderate obstacle=2 Major obstacle=3 Very severe
obstacle=4)

Labor Regulations
Q.L30a: Are labor regulations No Obstacle, a Minor Obstacle, a Moderate Obstacle, a
Major Obstacle, or a Very Severe Obstacle to the current operations of this establish-
ment? (No obstacle=0 Minor obstacle=1 Moderate obstacle=2 Major obstacle=3 Very
severe obstacle=4)

Inadequately Educated Workforce
Q.L30b: Is an inadequately educated workforce No Obstacle, a Minor Obstacle, a
Moderate Obstacle, a Major Obstacle, or a Very Severe Obstacle to the current opera-
tions of this establishment? (No obstacle=0 Minor obstacle=1 Moderate obstacle=2
Major obstacle=3 Very severe obstacle=4)

Overdraft Facility
Q.K7: At this time, does this establishment have an overdraft facility? (Yes=1 No=2)

Credit Line
Q.K8: At this time, does this establishment have a line of credit or a loan from a ﬁnan-
cial institution? (Yes=1 No=2)
84 World Bank Study

Innovation
Q.O.1: In the last three years, has this establishment introduced new products or ser-
vices? (Yes=1 No=2)

Power Outages
Q.C7: In a typical month, over ﬁscal year 2007, how many power outages did this es-
tablishment experience? Average number of power outages per month:

Duration of Power Outages
Q.C8: How long did these power outages last on average? Average duration of power
outages (hours)___. Less than 1 hour=1.

Losses due to Power Outages
Q.C9: Please estimate the losses that resulted from power outages either as a percent-
age of total annual sales or as total annual losses.
- Loss as percent of total annual sales due to power outages ___% (Q.C9a)
- Annual losses due to power outages ___ (LCU) (Q.C9b)

Email Use
Q.C22a: At the present time, does this establishment use any of the following: Email to
communicate with clients or suppliers? (Yes=1 No=2)

High-speed Internet
Q.C23: Does this establishment have a high-speed Internet connection on its premises?
(Yes=1 No=2)

Training
Q.L10: Over ﬁscal year 2007, did this establishment have formal training programs for
its permanent, full-time employees? (Yes=1 No=2)

Professionalism of Labor
Q.69: What percent of this establishment’s labor force employed at the end of ﬁscal
year 2007 had a university degree? ___%

Foreign and Domestic Competition
Q.63: How important are each of the following factors in aﬀecting decisions to develop
new products or services and markets? (Not at all important=1 Slightly important=2
Fairly important=3 Very important=4)
- Pressure from domestic competitors (Q.63a)
- Pressure from foreign competitors (Q.63b)

Notes on Regional and Subregional Averages
For many graphs and tables, the regional and subregional averages are presented. The
following notes describe the composition of the ECA region and subregional averages
(unless otherwise stated in individual table and ﬁgure notes).
■ The ECA average for BEEPS 2008 indicators includes 29 countries: Albania, Ar-
menia, Azerbaijan, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech
Republic, Estonia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz Republic,
Latvia, Lithuania, FYR Macedonia, Moldova, Montenegro, Poland, Roma-
Challenges to Enterprise Performance in the Face of the Financial Crisis 85

nia, Russian Federation, Serbia, Slovak Republic, Slovenia, Tajikistan, Turkey,
Ukraine and Uzbekistan.
■ Northern FSU (FSU-N) averages include Belarus, Kazakhstan, Russia, and
Ukraine.
■ Southern FSU (FSU-S) averages include Armenia, Azerbaijan, Georgia, Kyrgyz
Republic, Moldova, Tajikistan, and Uzbekistan.
■ South Eastern Europe (SEE) averages include Albania, Bosnia and Herzegovi-
na, Croatia, FYR Macedonia, Kosovo, Montenegro, and Serbia.
■ European Union-10 (EU-10) averages include Bulgaria, Czech Republic, Esto-
nia, Hungary, Latvia, Lithuania, Poland, Romania, Slovak Republic, and Slo-
venia.
■ Turkey is included in the ECA average, but is not included in any subregional
category.

Enterprise Surveys Data-Based Regional Averages
For many graphs and tables, data from Enterprise Surveys is used to show the regional
comparison between the ECA region and other regions covered by Enterprise Surveys.
The following notes describe the composition of the individual regions (unless other-
wise stated in individual table and ﬁgure notes).
■ East Asia & Paciﬁc: Fiji, Indonesia, Lao PDR, Micronesia, Mongolia, Philippines,
Samoa, Timor Leste, Tonga, Vanuatu, Vietnam.
■ South Asia: Afghanistan, Bangladesh, Bhutan, India, Nepal, Pakistan.
■ Latin America & Caribbean: Argentina, Bolivia, Brazil, Chile, Colombia, Ecua-
dor, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Paraguay,
Peru, Uruguay, República Bolivariana de Venezuela.
■ Africa: Angola, Benin, Botswana, Burkina Faso, Burundi, Cameroon, Cape
Verde, Chad, Republic of Congo, Democratic Republic of the Congo, Eritrea,
Ethiopia, Gabon, The Gambia, Ghana, Guinea, Guinea Bissau, Côte d’Ivoire,
Kenya, Lesotho, Liberia, Madagascar, Malawi, Mali, Mauritania, Mauritius,
Mozambique, Namibia, Niger, Nigeria, Rwanda, Senegal, Sierra Leone, South
Africa, Swaziland, Tanzania, Togo, Uganda, Zambia.
■ Eastern Europe and Central Asia: Albania, Armenia, Azerbaijan, Belarus, Bos-
nia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, FYR Macedo-
nia, Georgia, Hungary, Kazakhstan, Kosovo, Kyrgyz Republic, Latvia, Lithu-
ania, Moldova, Montenegro, Poland, Romania, Russia, Serbia, Slovak Republic,
Slovenia, Tajikistan, Turkey, Ukraine, Uzbekistan.
■ Middle East and North Africa: Algeria, the Arab Republic of Egypt, Jordan, the
Syrian Arab Republic, West Bank and Gaza, the Republic of Yemen.
And the years that the surveys for each country were conducted in are as follows.
■ 2006: Angola, Argentina, Bolivia, Botswana, Burundi, Chile, Colombia, Demo-
cratic Republic of the Congo, Ecuador, El Salvador, Ethiopia, The Gambia, Gua-
temala, Guinea, Guinea Bissau, Honduras, India, Jordan, Mauritania, Mexico,
Namibia, Nicaragua, Panama, Paraguay, Peru, Rwanda, Swaziland, Tanzania,
86 World Bank Study

Uganda, Uruguay, República Bolivariana de Venezuela, and the West Bank and
Gaza.
■ 2007: Albania, Algeria, Bangladesh, Croatia, Ghana, Kenya, Mali, Mozambique,
Nigeria, Pakistan, Senegal, South Africa, and Zambia.
■ 2008: Afghanistan, Belarus, Egypt, Georgia, Tajikistan, Turkey, Ukraine, and
Uzbekistan.
■ 2009: Armenia, Azerbaijan, Benin, Bhutan, Bosnia and Herzegovina, Brazil, Bul-
garia, Burkina Faso, Cameroon, Cape Verde, Chad, Congo, Cote d’Ivoire, Czech
Republic, Eritrea, Estonia, Fiji, FYR Macedonia, Gabon, Hungary, Indonesia,
Kazakhstan, Kosovo, Kyrgyz Republic, Lao PDR, Latvia, Lesotho, Liberia,
Lithuania, Madagascar, Malawi, Mauritius, Micronesia, Moldova, Mongolia,
Montenegro, Nepal, Niger, Philippines, Poland, Romania, Russian Federation,
Samoa, Serbia, Sierra Leone, Slovak Republic, Slovenia, the Syrian Arab Repub-
lic, Timor Leste, Togo, Tonga, Vanuatu, and Vietnam.
■ 2010: the Republic of Yemen.

Table A1.1. Regression Controls: Summary of BEEPS-Based Control Variables
Firm Size: Small dummy Assigned a value of 1 if the ﬁrm is a small ﬁrm (between 5 and 19 employees,
inclusive), zero otherwise.
Firm Size: Medium dummy Assigned a value of 1 if the ﬁrm is a medium ﬁrm (between 20 and 99 employees,
inclusive), zero otherwise.
Firm Size: Large dummy Assigned a value of 1 if the ﬁrm is a large ﬁrm (100 or more employees), zero otherwise.
Manufacturing sector dummy A dummy variable indicating the ﬁrm operates in the manufacturing sector. The variable
is assigned a value of 1 for yes, zero otherwise.
Service sector dummy A dummy variable indicating the ﬁrm operates in the service sector. The variable is
assigned a value of 1 for yes, zero otherwise.
Foreign-Owned dummy Assigned a value of 1 if the ﬁrm is 10% foreign-owned or higher, zero otherwise.
Government-Owned dummy Assigned a value of 1 if the ﬁrm is 10% state-owned or higher, zero otherwise.
Exporting Firm dummy Assigned a value of 1 if the ﬁrm has any direct exports, zero otherwise.
Domestic competition Importance of pressure from domestic competition in developing new products or
services, based on BEEPS Q.63a.
Foreign competition Importance of pressure from foreign competition in developing new products or
services, based on BEEPS Q.63b.
Innovation dummy Assigned a value of 1 if the responding ﬁrm answered yes to the question of “In the last
three years, has this establishment introduced new products or services?” zero otherwise.
IT Use/Email Assigned a value of 1 if the responding ﬁrm indicated that it uses email to communicate
with customers or suppliers, zero otherwise.
Labor Growth dummy A dummy variable indicating the ﬁrm expanded the number of full-time permanent
employees from 2004 to 2007. The variable is assigned a value of 1 for yes, zero otherwise.
Education constraint Firm perception of an inadequately educated labor force as a problem.
Training Assigned a value of 1 if the responding ﬁrm provided training to its permanent full-time
employees, zero otherwise.
Professionalism of Labor The percentage of workers with a university degree (see Q.69).
Percentage of Skilled Labor The percentage of skilled workers within a ﬁrm.
Percentage of Unskilled Labor The percentage of unskilled workers within a ﬁrm
LN(GDP) The natural log of GDP per capita at PPP in 2007 (in constant 2005 international dollars).
LN(age) Natural log of the ﬁrm’s age.
Challenges to Enterprise Performance in the Face of the Financial Crisis 87

EU-10 dummy Assigned a value of 1 if the ﬁrm is located in an EU-10 country, zero otherwise.
FSU-N dummy Assigned a value of 1 if the ﬁrm is located in an FSU-N country, zero otherwise.
FSU-S dummy Assigned a value of 1 if the ﬁrm is located in an FSU-S country, zero otherwise.
SEE dummy Assigned a value of 1 if the ﬁrm is located in an SEE country, zero otherwise.
Country-speciﬁc dummies A dummy for each country in the BEEPS 2008, based on the country in which the ﬁrm
is located.
City-size dummies A set of dummies for the locality size the ﬁrm is in, based on question a3 in the BEEPS
2008.

Chapter 2: Access to Finance

Figure Notes
Figure 2.1 presents the change in liquid liabilities as a share of GDP for various regions
from 1996 to 2008. Source: World Bank Database of Financial Development and Struc-
ture. h p://go.worldbank.org/X23UD9QUX0. Regions include the following countries:
■ Europe and Central Asia: Albania, Armenia, Bulgaria, Croatia, Czech Republic,
Estonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania,
Moldova, FYR Macedonia, Poland, Romania, Russian Federation, Serbia, Slo-
vak Republic, Slovenia, and Turkey.
■ East Asia and the Paciﬁc: Cambodia, Fiji, Indonesia, Lao PDR, Malaysia, Mon-
golia, Myanmar, Papua New Guinea, Philippines, Samoa, Solomon Islands,
Thailand, Timor-Leste, Tonga, Vanuatu, and Vietnam.
■ Latin America and the Caribbean: Argentina, Belize, Bolivia, Brazil, Chile, Co-
lombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Gre-
nada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Panama, Para-
guay, Peru, St. Ki s and Nevis, St. Lucia, St. Vincent and Grenadines, Suriname,
and Uruguay.
■ Middle East and North Africa: Algeria, the Arab Republic of Egypt, the Islamic
Republic of Iran, Jordan, Libya, Morocco, the Syrian Arab Republic, Tunisia,
and the Republic of Yemen.
■ South Asia: Bangladesh, Bhutan, India, Nepal, Pakistan, and Sri Lanka.
■ Sub-Saharan Africa: Angola, Benin, Botswana, Burkina Faso, Burundi, Camer-
oon, Cape Verde, the Central African Republic, Chad, the Democratic Republic
of Congo, the Republic of Congo, Côte d’Ivoire, Ethiopia, Gabon, the The Gam-
bia, Ghana, Guinea-Bissau, Kenya, Lesotho, Madagascar, Malawi, Mali, Mau-
ritania, Mauritius, Mozambique, Niger, Nigeria, Rwanda, Senegal, Seychelles,
Sierra Leone, South Africa, Sudan, Swaziland, Tanzania, Togo, Uganda, and
Zambia.
Figure 2.2 displays the percent of total banking sector assets that were held by foreign
banks from 1996 to 2005 for various regions. Source: Claessens, Van Horen, Gurcanlar
and Mercado, 2008. “Foreign Bank Presence in Developing Countries. 1995–2006: Data
and Trends,” mimeo, Washington, DC: The World Bank.
Regions include the following countries:
88 World Bank Study

■ Europe and Central Asia: Albania, Armenia, Azerbaijan, Belarus, Bosnia and
Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary,
Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, FYR Macedonia, Moldova, Po-
land, Romania, Russian Federation, Serbia, Slovak Republic, Slovenia, Turkey,
Ukraine, and Uzbekistan.
■ East Asia and the Paciﬁc: Cambodia, China, Indonesia, Republic of Korea, Ma-
laysia, Mongolia, Philippines, Thailand, and Vietnam.
■ Latin America and the Caribbean: Argentina, Bolivia, Brazil, Chile, Colombia,
Costa Rica, Cuba, Dominican Republic, Ecuador, El Salvador, Guatemala, Haiti,
Honduras, Jamaica, Mexico, Nicaragua, Panama, Paraguay, Peru, Trinidad and
Tobago, Uruguay, and República Bolivariana de Venezuela.
■ Middle East and North Africa: Algeria, the Arab Republic of Egypt, the Islamic
Republic of Iran, Jordan, Lebanon, Libya, Morocco, Oman, Tunisia, and the Re-
public of Yemen.
■ South Asia: Bangladesh, India, Nepal, Pakistan, and Sri Lanka.
■ Sub-Saharan Africa: Angola, Benin, Botswana, Burkina Faso, Burundi, Camer-
oon, the Democratic Republic of Congo, Côte d’Ivoire, Ethiopia, Ghana, Ke-
nya, Madagascar, Malawi, Mali, Mauritania, Mauritius, Mozambique, Namibia,
Niger, Nigeria, Rwanda, Senegal, Seychelles, South Africa, Sudan, Swaziland,
Tanzania, Togo, Uganda, Zambia, and Zimbabwe.
Figure 2.3 presents regional averages from 1996 to 2008 of total credit provided to the
private sector by banks and other ﬁnancial institutions divided by GDP. Source: World
Bank Database of Financial Development and Structure. h p://go.worldbank.org/
X23UD9QUX0. The country groupings match those of Figure 2.1.

Figure 2.4 presents regional averages from 1996 to 2008 of total foreign claims as a share
of GDP on an immediate risk basis. The data was obtained from the Bank of Interna-
tional Se lements. Regional groupings are set up as follows:
■ Europe and Central Asia: Albania, Armenia, Azerbaijan, Belarus, Bosnia and
Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary,
Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, FYR Macedonia, Moldova,
Montenegro, Poland, Romania, Russian Federation, Serbia, Slovak Republic,
Slovenia, Tajikistan, Turkey, Turkmenistan, Ukraine, and Uzbekistan.
■ East Asia and the Paciﬁc: Cambodia, China, Fiji, Indonesia, Kiribati, Lao PDR,
Malaysia, Micronesia, Mongolia, Palau, Papua New Guinea, Philippines, Sa-
moa, Solomon Islands, Thailand, Timor Leste, Tonga, Vanuatu, and Vietnam.
■ Latin America and the Caribbean: Argentina, Belize, Bolivia, Brazil, Chile, Co-
lombia, Costa Rica, Dominica, Dominican Republic, Ecuador, El Salvador, Gre-
nada, Guatemala, Guyana, Haiti, Honduras, Jamaica, Mexico, Nicaragua, Pan-
ama, Paraguay, Peru, St. Lucia, St. Vincent, Surinam, Uruguay, and República
Bolivariana de Venezuela.
■ Middle East and North Africa: Algeria, Djibouti, the Arab Republic of Egypt,
the Islamic Republic of Iran, Iraq, Jordan, Lebanon, Libya, Morocco, the Syrian
Arab Republic, Tunisia, and the Republic of Yemen.
■ South Asia: Afghanistan, Bangladesh, Bhutan, India, Maldives, Nepal, Pakistan,
and Sri Lanka.
Challenges to Enterprise Performance in the Face of the Financial Crisis 89

■ Sub-Saharan Africa: Angola, Benin, Botswana, Burkina Faso, Burundi, Cam-
eroon, Cape Verde, Central African Republic, Chad, Comoros Islands, Congo,
the Democratic Republic of Congo, Côte d’Ivoire, Eritrea, Ethiopia, Gabon, The
Gambia, Ghana, Guinea, Guinea Bissau, Kenya, Lesotho, Madagascar, Malawi,
Mali, Mauritania, Mauritius, Mozambique, Namibia, Niger, Nigeria, Rwanda,
São Tomé and Príncipe, Senegal, Seychelles, Sierra Leone, South Africa, Sudan,
Swaziland, Tanzania, Togo, Uganda, Zambia, and Zimbabwe.

Figure B2.1.1 displays the correlation between the percentage change in credit to the pri-
vate sector from 2004 to 2007, obtained from the World Bank Database of Financial De-
velopment and Structure. h p://go.worldbank.org/X23UD9QUX0, and the GDP growth
for each corresponding country in 2009 from the World Development Indicators data
set. The correlation coeﬃcient is −0.4831 and is signiﬁcant at 0.027.

Figure B2.1.2 plots the relationship between the percentage change in foreign bank claims
as a percent of GDP from 2004 to 2007, obtained from the Bank of International Se lements
and the GDP growth for each corresponding country in 2009 from the World Develop-
ment Indicators data set. The correlation coeﬃcient is −0.43 and is signiﬁcant at 0.025.

Figure 2.5 presents the percentage of ﬁrms indicating that access to ﬁnance was not an
obstacle to doing business, by ﬁrm size. The ﬁgure is based on the panel data set cover-
ing the BEEPS 2002, 2005, and 2008.1 The ﬁrm sizes are determined by the number of
employees the ﬁrm has in the survey year, with small ﬁrms having 5–19 employees,
medium ﬁrms having 20–99 employees, and large ﬁrms having 100 or more employees.
The percentages are calculated as a simple division of the number of ﬁrms (of speciﬁc
size) answering “no obstacle” to the access to ﬁnance as a problem question by the total
number of ﬁrms of the same size answering that question (for each survey year).

Figure 2.6 presents the percentage of ﬁrms indicating that they applied for a loan or line
of credit in the previous ﬁscal year, by ﬁrm size. The ﬁgure is based on the panel data set
covering the BEEPS 2002, 2005, and 2008. The ﬁrm sizes are determined by the number
of employees the ﬁrm has in the survey year, with small ﬁrms having 5–19 employees,
medium ﬁrms having 20–99 employees, and large ﬁrms having 100 or more employees.
The percentages are calculated as a simple division of the number of ﬁrms (of speciﬁc
size) stating that they applied for a loan or line of credit in the last ﬁscal year by the total
number of ﬁrms of the same size answering that question (for each survey year).

Figure 2.7 compares the predicted probabilities that a ﬁrm would indicate that access to
ﬁnance was not an obstacle to doing business, by level of foreign ownership of banking
sector assets across the BEEPS survey years. The ﬁgure is based on the panel data set
covering the BEEPS 2002, 2005 and 2008. The level of foreign ownership categories are
deﬁned as low if the level of foreign ownership in 1999 was less than 20 percent, medium

1. The question wording for access to ﬁnance as an obstacle in 2002 and 2005 is as follows: Can you tell me
how problematic are these diﬀerent factors for the operation and growth of your business? Access to ﬁ-
nancing (e.g., collateral required or ﬁnancing not available from banks) (No obstacle=1 Minor obstacle=2
Moderate obstacle=3 Major obstacle=4).
90 World Bank Study

if the level was greater than or equal to 20 percent and less than 60 percent, and large if the
level was 60 percent or higher. The probabilities are calculated based on Table A1.3, model
2, with all variables assigned their mean values except for the level of foreign ownership.

Figure 2.8 compares the predicted probabilities that a ﬁrm will apply for a loan, by level
of foreign ownership of banking sector assets across the BEEPS survey years. The ﬁgure
is based on the panel data set covering the BEEPS 2002, 2005, and 2008. The level of for-
eign ownership categories are deﬁned in the same way as in Figure 2.7. The probabilities
are calculated based on Table A1.3, model 3.

Figure 2.9 presents the predicted probability that a ﬁrm will indicate access to ﬁnance
is not an obstacle to doing business, by the level of the current account deﬁcit of the
country that the ﬁrm is located in for countries with a ﬁxed exchange rate regime, across
BEEPS survey years. The ﬁgure is based on the panel data set covering the BEEPS 2002,
2005 and 2008. The probability calculations are based on Table A1.3, model 2 and current
account deﬁcit levels are assigned such that low current account deﬁcits correspond to
0–4 percent of GDP, medium to 4–8 percent, and high to above 8 percent. All other vari-
ables are assigned their mean values.

Figure 2.10 presents the same relationship as Figure 2.9, but for countries that use a
crawling exchange rate regime. The ﬁgure is based on the panel data set covering the
BEEPS 2002, 2005, and 2008. The probability calculations are based on Table A1.3, model
2 and current account deﬁcit levels are assigned as in Figure 2.9. All other variables are
assigned their mean values.

Figure 2.11 presents the predicted probability that a ﬁrm will apply for a loan, by the
level of the current account deﬁcit of the country that the ﬁrm is located, across BEEPS
survey years. The ﬁgure is based on the panel data set covering the BEEPS 2002, 2005,
and 2008. The probability calculations are based on Table A1.3, model 3 and current ac-
count deﬁcit levels are assigned as in Figure 2.9.

Figure 2.12 shows the relationship between ﬁrm size and the predicted probability of
ﬁrm survival. Shorter bars correspond to a lower likelihood that a ﬁrm will cease opera-
tions. Data is based on the regression model discussed in the ﬁrm survival regression
results section.

Figure 2.13 shows the relation between ﬁrm age and the predicted probability of ﬁrm
survival. Shorter bars correspond to a lower likelihood that a ﬁrm will cease operations. Data
is based on the regression model discussed in the ﬁrm survival regression results section.

Table 2.1 data comes from the following: Percentage of assets held by foreign banks
comes from Claessens et al. (2008). WGI indicators are from the World Bank Governance
Indicators database. GDP per capita is from the World Development Indicators (World
Bank). Banking and crisis data were taken from Caprio and Klingebiel (2003), “Data-
base on Episodes of Systemic and Broderline Financial Crises” at h p://go.worldbank.
org/5DYGICS7B0. Legal origin variables are taken from La Porta et al. (1999).
Challenges to Enterprise Performance in the Face of the Financial Crisis 91

Empirical Analysis
Two types of empirical analyses were conducted to explain the relative severity of the re-
cent global economic crisis across ﬁrms in the ECA region. As described above, the ﬁrst
exploits data on whether ﬁrms survived from the follow-ups to the 2008 BEEPS surveys
conducted in June and July 2009 in six countries. The second exploits variation over time
in ﬁrms’ reported ﬁnancial constraints for a balanced panel of respondents to the 2002,
2005, and 2008 BEEPS surveys. For both types of analysis, the basic regression is:

Yit = α + βXit + γFit + δCit + λSit + μTi + εit

Where Y is a dummy variable = 1 if a ﬁrm died for the survival analysis,2 or a dummy
variable = 1 if the ﬁrm owner reported that access to ﬁnance was not an obstacle to
its operations for the ﬁnancial constraints analysis. Note that the survival analysis is
cross-sectional because information on whether a ﬁrm survived was available at only
one point in time (mid-2009). The subscript “t” therefore is relevant only for the analysis
of perceived ﬁnancial constraints.
X represents a matrix of ﬁrm characteristics that includes the number of employ-
ees, the age of the ﬁrm, exports as a percent of sales, the shares of foreign and private
ownership, and a dummy variable indicating whether the ﬁrm was privatized. In some
speciﬁcations, a control variable capturing ISO certiﬁcation was added as an additional
measure of ﬁrm quality. It was expected that all of these variables would help to relax
ﬁrms’ ﬁnancial constraints and improve their prospects for surviving the crisis.
In the ﬁnancial constraints regressions, ﬁrm size, as measured by the number of
employees, was interacted with the year of the survey to test whether large ﬁrms were
diﬀerentially aﬀected by the crisis relative to small and medium-sized ﬁrms. For the rea-
sons described above, it was expected that larger ﬁrms would have be er access to exter-
nal ﬁnance in non-crisis periods (2002 and 2005) and thus report less severe constraints,
and become more constrained in the crisis (2008) due to their reliance on dwindling
sources of external funding.
The X matrix also includes measures of ﬁrm performance including employment
growth and a dummy variable indicating whether the ﬁrm made investments in the pri-
or year. Unfortunately, only the employment growth variable is available in a consistent
format across the three rounds of the BEEPS. It is therefore the only ﬁrm performance
variable that enters the ﬁnancial constraints regressions. The full set of performance vari-
ables enters the survival regressions.

2. The 2,501 ﬁrms from the third round of the BEEPS survey can be divided into four groups: (i) those that were still
operating and were interviewed in the follow-up survey; (ii) those that were still operating but were not interviewed
during the follow-up survey because enough ﬁrms had been interviewed, an interview could not be scheduled, or the
ﬁrm manager refused to participate in the survey; (iii) ﬁrms that could not be located; and (iv) ﬁrms that had closed.
The approach of Ramalho et al. (2010) is employed it is assumed that ﬁrms were no longer operating if they fell into
categories (iii) or (iv), or if they were in category (i) but reported that they had ﬁled for insolvency or bankruptcy.
Firms that could not be located because they had temporarily shut down, rather than closed permanently, are implic-
itly included in the category of ﬁrms that are no longer operating. This classiﬁcation carries some limitations—some
ﬁrms that could not be located might have had contact information recorded incorrectly during the BEEPS survey
and some ﬁrms that were still operating but were not interviewed might have ﬁled for insolvency or bankruptcy.
However, it is probably the most reasonable breakdown possible given the available information.
92 World Bank Study

To ensure that ﬁrms that were no longer operating at the time of the second survey
can be included in the survival regressions, the ﬁrm characteristics are all taken from the
original BEEPS survey (2008). An additional advantage of this approach is that it reduces
the likelihood of reverse causality. One particular concern is that ﬁrm performance is
likely to have been aﬀected by the crisis and those aﬀected most seriously are also most
likely to be forced to close during the crisis. To reduce these concerns, where possible,
variables such as ﬁrm size and growth are measured in ﬁscal year 2007 or earlier.
F represents of matrix of variables that summarize ﬁrms’ use of ﬁnancial services.
These are particularly relevant for the ﬁrm survival regressions. All else equal, ﬁrms that
had readier access to ﬁnance are expected to be more likely to survive. Although hav-
ing access to ﬁnance would not aﬀect ﬁrms that were basically insolvent, it might allow
ﬁrms that have temporary liquidity problems due to the drop in demand to survive the
crisis. To reduce concerns about reverse causation, measures of access to ﬁnance prior to
the crisis would be ideal. Unfortunately, the two best measures in the survey—whether
the ﬁrm has a loan and whether the ﬁrm has a line of credit or overdraft—were asked
‘at the current time’ (i.e., at the time of the survey). Since the surveys were mostly con-
ducted between September and December of 2008, these questions were asked after the
onset of the crisis. To the extent that banks either extended loans or overdrafts to ﬁrms
that they believed had long-term potential if they could survive temporary liquidity
problems or ended lines of credit or loans for ﬁrms that they believed were the most
likely to fail during the crisis, this could aﬀect estimates. Still, it is likely that a large share
of the repercussions of the crisis in terms of access to credit for ﬁrms in ECA were yet to
be felt at the time of the last round of the BEEPS survey.
Because the ﬁnancial constraints regressions are designed to summarize ﬁrms’ ac-
cess to ﬁnancial services, this set of variables represented by F is obviously less relevant,
though in some speciﬁcations a dummy variable for whether the ﬁrm applied for a loan
in the past year was included to control for its demand for ﬁnance.
C represents a matrix of country characteristics. Because the survival analysis is
based on a cross-section and because the FCS was done in only six countries, it is not
possible to include a full set of country-level controls in those regressions, and so coun-
try dummy variables were included instead. Because the BEEPS data oﬀer time series
variation and cover a much broader set of countries, a rich set of country-level variables
in the ﬁnancial constraints regressions are able to be included. These include GDP per
capita, the WGI index of institutional development,3 inﬂation, and GDP growth. All ex-
cept inﬂation are likely to be associated with less severe ﬁnancial constraints. Population
and population-density were included in the constraints regressions, though it is less
clear how those variables are expected to aﬀect ﬁnancial constraints. The GDP growth
variable was interacted with dummy variables for the year of the survey to test whether
that variable had diﬀerential eﬀects on ﬁnancial constraints in crisis and non-crisis years,
in line with some predictions from the literature.

3. This is the measure of broad institutional development created by Kaufmann, Kraay, and Maztruzzi
(2007).
Challenges to Enterprise Performance in the Face of the Financial Crisis 93

The country variables also include the share of banking sector assets in state-owned
and foreign owned banks. For the reasons described above, and elaborated on in Chapter
2, the foreign bank participation variable is measured in 1999. That variable was then in-
teracted with year dummies to test whether well-established foreign banks helped ﬁrms
to weather the crisis as reﬂected in less severe ﬁnancial constraints compared with those
of ﬁrms in countries that did not have well-established foreign presence in banking.
Finally, controls were introduced for each country’s exchange rate regime with dum-
my variables for pegged and crawling exchange rates. The coeﬃcients on those variables
reﬂect diﬀerences in constraints relative to the omi ed category, ﬁrms in countries with
ﬂoating exchange rates. For the reasons described above, the combination of large cur-
rent account deﬁcits and exchange rates based on ﬁxed or crawling pegs coincided with
explosive credit growth in the run-up to the crisis. Therefore, interaction of the exchange
rate regime dummy variables with the current account deﬁcit were introduced to test
whether the coeﬃcients on those variables indicate looser ﬁnancial constraints prior to
the crisis (2002 and 2005) and steep increases in constraints in 2008.
S is a matrix of sector dummy variables because ﬁnancial constraints and the prob-
ability of survival could vary across industries. T represents variables that control for the
timing of surveys. In the case of the ﬁnancial constraints, these are simple dummy vari-
ables corresponding to the round of the survey. For the survival analysis, time dummies
indicating the month that the ﬁrm was surveyed in the 2008 BEEPS survey were also
used. Although the FCS were conducted within a tight two-month window (June and
July 2009), most of the 2008 BEEPS were conducted over a three to six month period in
individual countries. The dummies indicating the month of the 2008 BEEPS are included
to partially control for diﬀerences in survival possibilities for ﬁrms interviewed before
the global crisis and those interviewed in the early part of the crisis.
The timing of the 2008 BEEPS survey was introduced in a simple cross-sectional
regression of the 2008 ﬁnancial constraints, though it was not signiﬁcant. This could be
because perceptions of ﬁnancial constraints are forward looking and thus all respon-
dents had a good sense of the coming crisis, regardless of the month in which they were
interviewed. In any event, the month of 2008 survey was not controlled for in the ﬁnan-
cial constraints regressions presented below. Both types of regressions are estimated via
a standard Probit model.
Regression Results

FIRM SURVIVAL
Base Results. Table A1.2 shows the results from the base regressions. The results in col-
umn 1 omit the ﬁnancial sector variables. The coeﬃcients on the variables indicating
ﬁrm size at the end of 2007 and the age of the ﬁrm are both statistically signiﬁcant and
negative. This suggests that larger ﬁrms and older ﬁrms were more likely to be operat-
ing at the time of the second survey than other ﬁrms. Based upon the coeﬃcient esti-
mates and estimating the eﬀects at sample means of all variables, increasing the number
of workers by 10 reduces the likelihood that the ﬁrm is no longer operating by about
94 World Bank Study

0.1 percentage points. Similarly, increasing age by 1 year reduces the likelihood by 0.4
percentage points. Firms that had ISO or other international certiﬁcations were about 4
percentage points less likely to be no longer operating—suggesting that ﬁrms a uned to
international standards might have been more likely to survive.

Inclusion of ﬁnancial variables. Including a dummy variable (model 2, Table A1.2) indicat-
ing that the ﬁrm had either a loan or an overdraft at the time of the ﬁrst interview (i.e.,
in 2008 or early 2009) does not have a notable eﬀect on most of the other variables. In
particular, the coeﬃcients on the variables representing the age of the ﬁrm and whether
the ﬁrm has ISO certiﬁcation remain negative and statistically signiﬁcant. In contrast, the
coeﬃcient on the variable representing the size of the ﬁrm becomes smaller and becomes
statistically insigniﬁcant. This suggests that one reason why large ﬁrms might have fared
be er in terms of surviving is that they had be er access to ﬁnance than smaller ﬁrms
did, in line with the literature described above.
The coeﬃcient on the dummy variable representing whether the ﬁrm has a loan,
line of credit or overdraft is negative and statistically signiﬁcant. The coeﬃcient suggests
that ﬁrms with loans or overdrafts were about 4 percentage points more likely to still be
operating in mid-2009 than ﬁrms without. This suggests that retaining access to ﬁnanc-
ing was important for survival. Given that the most common concern about the impact
of the crisis on ﬁrm performance was the eﬀect of a drop in demand, one explanation is
that ﬁrms with access to ﬁnance were be er able to maintain access to external sources
for replenishment of working capital and to manage the drop in demand than ﬁrms that
were not.
Given that overdrafts and lines of credit might be be er for ﬁnancing working capi-
tal and other expenses during a temporary drop in demand, it is worthwhile to separate
the variable into two components: whether the ﬁrm has a loan and whether it has an
overdraft or line of credit. Although there is considerable overlap between these catego-
ries—about 73 percent of ﬁrms in the sample with overdrafts had loans compared to 40
percent of ﬁrms without overdrafts—potentially leading to multicollinearity, the diﬀer-
ences in the estimated coeﬃcients could be instructive.
The coeﬃcients in model 3 (Table A1.2) conﬁrm the intuition in that the marginal
eﬀect of the dummy variable indicating that the ﬁrm has an overdraft is larger and more
highly statistically signiﬁcant than on the dummy variable indicating that the ﬁrm has a
loan. However, the coeﬃcient is smaller and less statistically signiﬁcant than that on the
joint variable. Results for the overdraft variable by itself, moreover, tend to be less robust
than for the combined variable.

Performance Variables. One concern is that be er performing ﬁrms might be more likely
to survive the crisis and to have bank credit. Because of this, included is a set of vari-
ables intended to measure ﬁrm performance: a dummy variable indicating that the ﬁrm
invested in ﬁxed assets in 2007, and employment growth between 2005 and 2007. As
noted above, these performance metrics are measured before the start of the crisis. The
Challenges to Enterprise Performance in the Face of the Financial Crisis 95

coeﬃcients on these variables are mostly statistically insigniﬁcant (model 4, Table A1.2)
and do not appear to have a signiﬁcant impact on the other results. The main exception
is that the coeﬃcient on ﬁrm size becomes smaller and even less statistically signiﬁcant.
In contrast, the coeﬃcient on the dummy variable indicating that the ﬁrm had access to
credit becomes slightly larger (in absolute value) and remains statistically signiﬁcant.
FINANCIAL CONSTRAINTS
Firm Characteristics. In an unreported regression that does not include interactions (for
ﬁrm size, GDP growth, foreign bank participation in 1999, and exchange rate regimes),
the likelihood of reporting ﬁnance is no obstacle is highest in 2005 (i.e., there are nega-
tive coeﬃcients for 2002 and 2008). This is encouraging in that the dependent variable is
picking up both the easing of ﬁnancial constraints from 2002 to 2005 and the eﬀects of the
crisis from 2005 to 2008. When the interaction terms are included, the coeﬃcients for the
simple year dummies (2002, 2008) are no longer signiﬁcant (model 1, Table A1.3). In that
sense, the interaction terms are isolating the types of ﬁrms responsible for the negative
coeﬃcients on the year dummies in the simpler unreported models.
The key ﬁrm characteristic in the ﬁnancial constraints analysis is size. As suggested
by Figure 2.1 and for the reasons described above, it is expected that the eﬀect of ﬁrm
size will be discontinuous—since large ﬁrms had much be er access to external sources
of funding, they were more vulnerable to a credit crunch. Rather than use the continu-
ous size variable based on number of workers (as was done in the analysis of ﬁrm sur-
vival), dummy variables corresponding the BEEPS deﬁnitions of small, medium, and
large ﬁrms: <=19 workers, 20–99 workers, and >=100 workers are used, respectively. The
omi ed category in the regressions is small ﬁrms, and thus the insigniﬁcant coeﬃcient
for medium-sized ﬁrms indicates no signiﬁcant diﬀerence in ﬁnancial constraints across
those two groups (Table A1.3, model 1). The model interacts the dummy variable for
large ﬁrms with dummies for the 2005 and 2008 BEEPS. Thus the positive signiﬁcant
coeﬃcient for the large ﬁrms dummy indicates that they were much more likely than
smaller ﬁrms to report ﬁnance as being no obstacle in 2002. Large ﬁrms were still more
likely to report ﬁnance as no obstacle in 2005 as reﬂected in the insigniﬁcant coeﬃcient on
Large*2005. However, the coeﬃcient for large ﬁrms in 2008 is negative and of a magnitude
similar to that for the large ﬁrm dummy. To calculate the eﬀects of large size on “ﬁnance
as an obstacle” in 2008 the ‘Large size’ and ‘Large*2008’ coeﬃcients must be summed.
Doing so wipes out the advantage of being a large ﬁrm that was evident for 2002 and
2005, that is, the hypothesis that sum of the two coeﬃcients is zero for large ﬁrms in 2009
cannot be rejected. This conﬁrms Figure 2.1, where large ﬁrms come to look a lot more
like the others in terms of reported ﬁnancial constraints in 2008. The external ﬁnancing
pa erns of large ﬁrms, therefore, could be seen as being more aﬀected by the crisis.
This pa ern only becomes stronger when a dummy indicating that the ﬁrm had
applied for a loan in the past year to control for its demand for credit is included (Table
A1.3, model 2). In fact, the coeﬃcient for Large*2008 is negative and signiﬁcant. The ap-
96 World Bank Study

plied for a loan variable is itself negative and highly signiﬁcant and improves the overall
ﬁt of the regression (pseudo r-squared improves from 0.07 to 0.10). In model 3 (in Table
A1.3), the dependent variable is the ‘applied for a loan’ dummy, to get a sense of which
ﬁrms came to demand more credit during the crisis. The results indicate that large ﬁrms
were much more likely to apply for credit in 2002 than others, a gap that was maintained
in 2005. That gap grew signiﬁcantly larger in 2008 as reﬂected in the positive signiﬁcant coef-
ﬁcient on Large*2008, indicating that large ﬁrms were much more apt than others to apply
for external sources of funding in response to the dramatic drop in demand documented in
Correa and Ioo y (2009, 2010). Taken together, the steep decline in the share of large ﬁrms
that reported ﬁnance was not an obstacle to their ﬁrms’ operations in 2008 and the steep
increase in the share of large ﬁrms applying for loans shows that they were the ﬁrms most af-
fected by crisis. None of the other ﬁrm characteristics is signiﬁcant in the ﬁnancial constraints
regressions (though some of them are in the loan application regression).

Country Characteristics. Based on the literature on multinational banking, it is conjec-
tured that the crisis might have compelled foreign banks to pull back from countries
where growth prospects were weak if the substitution motive dominated (Morgan et al.,
2004). To test this hypothesis GDP growth is interacted with the dummy variables for
the BEEPS rounds. If this conjecture were true, it is expected that the coeﬃcient for GDP
growth*2008 will be positive and signiﬁcant, but it is not. Insigniﬁcance could arise for a
number of diﬀerent reasons. As described above, multinational banks have both substi-
tution and support motives in assigning funds across aﬃliates in developing countries.
Insigniﬁcance could arise because those motives cancel each other out, which could also
account for the insigniﬁcant coeﬃcients in the non-crisis years (2002, 2005). Another pos-
sible explanation may be that information about future growth prospects at a country
level is already subsumed within the foreign bank participation variable or that current
growth is not a strong predictor of future growth, especially during crisis (although
other papers have found links between host country growth and foreign bank partici-
pation). If anything, the negative, nearly signiﬁcant (p-value, 0.188) coeﬃcient for GDP
growth in 1999 provides some weak evidence for the support motive, in line with other
ﬁndings on the stabilizing eﬀects of foreign bank participation during crises (de Haas
and Van Lelyveld, 2006, 2010).
Population density is the only other variable among the country characteristics that
is signiﬁcant in the ﬁnancial constraints regressions (though again, there are some sig-
niﬁcant country variables in the loan application regression). The negative, signiﬁcant
coeﬃcient for population density in model 1 (Table A1.3) goes away when the loan ap-
plication dummy is included in the regression in model 2 (Table A1.3), suggesting that
greater reported ﬁnancial obstacles in densely populated countries are due to greater
demand for external sources of funds. The general insigniﬁcance of the country level
variables in the ﬁnancial constraints regressions might also arise because correlation be-
tween errors at the country level is allowed (i.e., clustered standard errors are used).
Challenges to Enterprise Performance in the Face of the Financial Crisis 97

Banking Sector Variables. State ownership of banking sector assets is not signiﬁcantly as-
sociated with either reported ﬁnancial constraints or loan applications in Table A1.3.
However, the key banking sector variable is the share of banking sector assets held by
foreign-owned banks in 1999, which we interact with the dummy variables for the lat-
ter two survey rounds of the BEEPS (2005, 2008). The negative, signiﬁcant coeﬃcient
for foreign participation level in 1999 therefore represents the eﬀects of that variable on
the 2002 responses, indicating that ﬁnancial constraints were more severe, at that time,
in countries with greater foreign participation. The positive, signiﬁcant coeﬃcient for
1999 foreign bank participation share multiplied by the BEEPS 2005 dummy is of similar
magnitude to the negative association for 2002, indicating that by 2005 constraints were
similar across countries regardless of their level of foreign bank participation in 1999.
However, the interaction term for the 2008 BEEPS is positive, signiﬁcant, and twice as
large (in absolute value) as the negative association for 2005, which indicates that ﬁnan-
cial constraints were substantially less severe during the crisis in countries with a high-
level foreign bank participation in 1999. The pa ern of results is robust to using diﬀerent
measures of foreign bank participation and diﬀerent years between 1999 and 2002.44
For the reasons described above, and analyzed in greater detail in the next section,
foreign bank participation level in 1999 is viewed as an indicator of well-established
presence and commitment to a local market. The pa ern of results is therefore support-
ive of the hypothesis that reported obstacles were much less severe during the early
stages of the economic crisis in countries with high foreign bank participation levels.
The results also do not support the hypothesis that the increase in reported ﬁnancial
constraints would be less severe in countries with well-established foreign bank partici-
pation. But this is because reported constraints are less severe in 2008 than they were in
2005 and, especially, 2002 in countries with such participation. This suggests that foreign
banks had entered relatively credit-starved countries seeking proﬁt opportunities and
were trying to meet that demand in 2002. Demand was being be er met by 2005 in that
constraints were on par with other countries in the region, and by 2008, constraints were
less severe than ever despite the crisis. The loan application regression (model 3 in Table
A1.3) also indicates that demand was highest in countries with a high level of foreign
participation in 2002, and then dropped to levels prevalent in the region in 2005 and
2008, consistent with demand for credit being be er met over time in countries with a
well-established foreign bank presence.
It must be acknowledged that, although the signs of the coeﬃcients for the foreign
participation variables are identical when these regressions are run on the full BEEPS
sample, magnitudes and signiﬁcance levels are smaller than for the balanced panel of
BEEPS respondents. It should therefore be kept in mind that these results hold for a
sample that diﬀers slightly from the full one in that the ﬁrms come from smaller coun-
tries on average with a greater tendency toward ﬁxed rather than ﬂoating exchange rate
regimes, and have a slightly higher degree of foreign ownership and greater likelihood
of being in the trade industry.

4. We use the 1999 share of total banking assets held by foreign-owned banks from Claessens and Van
Horen (2008). We derive similar results when we use the foreign claims on the countries in the ECA re-
gion (as a share of GDP) as reported by the Bank for International Se lements.
98 World Bank Study

Table A1.2. Results from Probit Regression of Firm Exit on Enterprise Characteristics
Firm no longer operating at time of the FCS survey
Model number (1) (2) (3) (4)
Observations 2203 2174 2138 1699
Country Dummies Yes Yes Yes Yes
Sector Dummies Yes Yes Yes Yes
Month of Interview Dummies Yes Yes Yes Yes
Bank Credit
Firm has loan, line of credit or overdraft −0.038** −0.048***
(dummy) (−2.49) (−2.66)
Firm has line of credit or overdraft −0.030**
(dummy) (−2.01)
Firm has loan −0.004
(dummy) (−0.30)
Firm Characteristics
Number of workers −0.011** −0.009 −0.009* −0.007
(natural log) (−2.05) (−1.57) (−1.71) (−1.03)
Age of ﬁrm −0.041*** −0.041*** −0.040*** −0.028*
(natural log) (−4.35) (−4.26) (−4.13) (−1.85)
Exports 0 0 0 0
(% of sales) (−0.52) (−0.68) (−0.85) (−0.27)
Foreign-owned 0.009 0.005 0.012 −0.034
(dummy) −0.34 −0.2 −0.46 (−1.23)
Firm has ISO certiﬁcation −0.039*** −0.037** −0.037** −0.043**
(dummy) (−2.58) (−2.48) (−2.47) (−2.52)
Firm Performance
Firm Growth 2005-2007 0
(employment growth, %) (−0.8)
Firm invested in 2007 −0.004
(dummy) (−0.22)
Joint test for signiﬁcance of overdraft and 5
loan variables
P-value 0.08
Pseudo R-Squared 0.07 0.07 0.07 0.06
Source: Authors’ calculations based upon World Bank Enterprise Surveys
Note: ***,**, * Signiﬁcant at 1, 5, and 10 percent level. t-statistics in parentheses. All regressions include
dummy variables indicating sector, country, and month of ﬁrst interview (i.e., in 2008 or early 2009).
Marginal eﬀects calculated at sample means for each variable are shown in place of coeﬃcients. The de-
pendent variable is dummy variable indicating a ﬁrm in existence in late 2008/early 2009 was no longer
operating at the time of the FCS second survey.
Challenges to Enterprise Performance in the Face of the Financial Crisis 99

Table A1.3. Probit Models, Financial Constraints Analysis
Dep. Var = 1 if applied
Dependent Var =1 if ﬁnance is no obstacle for loan
Model number (1) (2) (3)
Applied for Loan -0.384***
(0.066)
2002 0.117 -0.096 -0.088
(0.199) (0.306) (0.441)
2008 -0.142 -0.066 0.165
(0.320) (0.271) (0.288)
Firm Characteristics
Medium size 0.071 0.172 0.655***
(0.110) (0.156) (0.119)
Large size 0.390* 0.628** 0.801***
(0.233) (0.251) (0.260)
Large*2005 0.047 0.002 -0.032
(0.244) (0.307) (0.289)
Large*2008 -0.421 -0.484* 0.617*
(0.258) (0.247) (0.342)
Firm Age -0.062 0.003 -0.054
(natural log) (0.092) (0.126) (0.085)
% Employment Growth -0.008 -0.001 0.114*
(0.049) (0.047) (0.069)
% Private Owned -0.001 -0.001 0.010***
(0.002) (0.003) (0.002)
% Foreign Owned 0.001 0.0004 0.004
(0.002) (0.003) (0.004)
Privatized 0.063 0.086 -0.206**
(0.157) (0.179) (0.097)
Export Share -0.001 0.0001 0.003
(0.003) (0.003) (0.003)
Country Characteristics
GDP Growth*2002 -0.035 0.004 -0.053
(0.036) (0.055) (0.054)
GDP Growth*2005 0.023 -0.017 -0.045
(0.016) (0.020) (0.034)
GDP Growth*2008 -0.030 -0.030 0.008
(0.024) (0.023) (0.023)

(Table continues on next page)
100 World Bank Study

Table A1.3 (continued)

Dep. Var = 1 if applied
Dependent Var =1 if ﬁnance is no obstacle for loan
Model number (1) (2) (3)
GDP Per Capita 0.007 0.026 0.145***
(1000s) (0.032) (0.026) (0.036)
WGI Instit. Develop. 0.207 0.152 -0.686***
(0.217) (0.189) (0.216)
Population Density -0.005*** -0.003 0.004**
(0.002) (0.002) (0.002)
Population (millions) -0.0003 -0.0003 0.002
(0.014) (0.017) (0.002)
Inﬂation 0.001 -0.004 -0.021**
(0.005) (0.006) (0.009)
Banking Sector
% State Bank Assets 0.001 0.001 0.0001
(0.003) (0.004) (0.003)
% Foreign in 1999 -0.006* -0.007** 0.013***
(0.003) (0.003) (0.004)
% Foreign1999*2005 0.007** 0.006 -0.014**
(0.003) (0.004) (0.006)
% Foreign1999*2008 0.013*** 0.012*** -0.016***
(0.003) (0.003) (0.005)
Exchange Rate Regime
Fixed Peg*current account 0.023 0.023 -0.003
deﬁcit*2002 (0.017) (0.027) (0.036)
Fixed Peg*current account 0.068*** 0.082*** 0.087***
deﬁcit*2005 (0.014) (0.020) (0.029)
Fixed Peg*current account 0.006 0.010* 0.026***
deﬁcit*2008 (0.005) (0.005) (0.008)
Crawling*current account 0.062** 0.053* -0.066
deﬁcit*2002 (0.029) (0.029) (0.047)
Crawling*current account 0.044** 0.064** 0.093**
deﬁcit*2005 (0.018) (0.031) (0.038)
Crawling*current account 0.003 0.006 0.024**
deﬁcit*2008 (0.011) (0.010) (0.010)
Sector Dummies Yes Yes Yes
Observations 920 731 748
Pseudo R-squared 0.07 0.10 0.16
Note: Robust standard errors in parentheses. Errors clustered at country level.
Note: ***,**, * Signiﬁcant at 1, 5, and 10 percent level.
Challenges to Enterprise Performance in the Face of the Financial Crisis 101

Table A1.4. Comparison of Balanced BEEPS Panel and Full Sample, BEEPS 2002,
2005, and 2008
Balanced Panel Full BEEPS Sample
Variable Obs. Mean St. Dev. Min Max Obs. Mean St. Dev.
Finance No Obstacle 920 0.35 0.48 0 1 22340 0.34 0.47
(dummy)
Small Firms (<=19 workers) 920 0.50 0.50 0 1 22340 0.47 0.50
Medium Firms (20-99 920 0.32 0.47 0 1 22340 0.32 0.46
workers)
Large Firms (>=100 workers) 920 0.18 0.38 0 1 22340 0.22 0.41
Firm Age (Years) 920 16.6 16.8 1 141 22340 15.6 16.4
Employment Growth (%) 920 24.0 96.7 -96.7 1400 22340 33.9 220.8
Private ownership share (%) 920 79.0 38.1 0 100 22340 82.5 35.7
Foreign ownership share (%) 920 12.8 30.9 0 100 22340 9.2 26.6
Privatized (dummy) 920 0.23 0.42 0 1 22340 0.14 0.35
Exports as a % of sales 920 10.1 24.1 0 100 22340 10.5 24.8
GDP Growth % 920 5.8 4.6 -4.6 26.4 22340 5.8 4.0
GDP per capita 920 3.2 3.1 0.3 13.7 22340 3.4 2.5
(1000s $US 2002)
% of banking assets in state 920 14.7 20.6 0.0 75.2 22340 18.6 20.4
banks
% of banking assets, foreign 920 31.6 30.1 0.0 92.3 22340 31.4 28.5
banks 1999
WGI, Institutional 920 -0.02 0.66 -1.06 1.04 22340 -0.001 0.61
development
Population density, residents 920 76.3 32.2 5.5 135.0 22340 77.1 38.3
per sq .mile
Population (millions) 920 12.4 21.5 1.3 145.0 22340 28.4 40.0
Inﬂation (%) 920 9.1 9.6 0.2 53.4 22340 9.4 8.5
Hotel, Services Industry 920 0.13 0.34 0 1 22340 0.14 0.35
(dummy)
Construction Industry 920 0.13 0.33 0 1 22340 0.09 0.29
(dummy)
Trade Industry (dummy) 920 0.37 0.48 0 1 22340 0.26 0.44
Transport Industry (dummy) 920 0.06 0.24 0 1 22340 0.06 0.23
Manufacturing Industry 920 0.30 0.46 0 1 22340 0.40 0.50
(dummy)
Pegged Exchanged Rate 920 0.36 0.48 0 1 22340 0.22 0.42
(dummy)
Crawling Peg Exch. Rate 920 0.53 0.50 0 1 22340 0.54 0.50
(dummy)
Band, Managed ﬂoating Ex. 920 0.09 0.29 0 1 22340 0.16 0.37
Rate (dummy)
Current Account Deﬁcit as 920 7.0 6.2 0.0 25.2 22340 6.6 6.5
% GDP
102 World Bank Study

Chapter 3: Infrastructure Bottlenecks

Data Notes

CALCULATED VARIABLES
Power outage costs. There are two methods to calculate the costs incurred from power
outages, which are presented throughout the paper and used in the regression analyses.
Both methods rely on answers to the questions c9a (losses from power outages as a
percentage of annual sales) and c9b (annual losses from power outages in terms of lo-
cal currency units (LCU)), but they diﬀer in which ﬁrms are included. The ﬁrst method,
which is used to calculate regional averages, considers only ﬁrms that experienced a
power outage in the previous year. The value for the costs of power outages is set to c9a,
if this is unavailable then the cost is set to c9b as a share of the ﬁrm’s sales (in LCU) for
the previous year. The second method, whose resulting values are used in regressions, is
nearly identical except that it considers all ﬁrms. It uses the same methodology to calcu-
late outage costs from ﬁrm responses to c9a and c9b, but it assigns zero as the outage cost
to those ﬁrms that did not experience a power outage in the previous year (in contrast to
being left a missing value as in the ﬁrst method).

Labor Productivity is the ratio of a ﬁrm’s total annual sales (as given by question d2
and converted to U.S. dollars) to its number of employees. The number of employees is
determined as a sum of the answers to questions l1 (the number of permanent full-time
employees) and l6 (the number of temporary full-time employees).
Figure Notes
For ﬁgures using Enterprise Surveys data, refer to the Note on Regional and Subregional
Averages at the beginning of Appendix 1.

Figure 3.1 presents the diﬀerences in electricity as not an obstacle to doing business be-
tween 2005 and 2008. Upward arrows reﬂect positive change as they indicate an increase
in the percentage of ﬁrms stating electricity is not a problem. The downward arrows in-
dicate negative change. The base of the arrow indicates the value in 2005, while the black
lines indicate the 2008 value. Both values are based on the truncated samples.

Figure 3.2 compares electricity not an obstacle across the World Bank regions and ECA
subregions. The ﬁgure uses BEEPS 2008 data for ECA countries and data from Enter-
prise Surveys covering 2006–2010 for non-ECA countries to present the average number
of ﬁrms, by region, that indicate electricity is not an obstacle to doing business. Within
each region, country-level averages are calculated and then the regional average is cal-
culated as the simple mean of these country-level values. Higher values indicate that
electricity is less of an obstacle within the region.
Challenges to Enterprise Performance in the Face of the Financial Crisis 103

Figure 3.3 presents the correlation between power outages and the natural log of GDP
per capita (2007). The vertical axis is the percentage of ﬁrms that experienced a power
outage in 2007 based on the BEEPS 2008. The underlying GDP per capita values are on
a purchasing power parity basis (in constant 2005 international dollars), from the World
Bank’s World Development Indicators data set. The coeﬃcient of correlation is -0.397
and is signiﬁcant at 0.033.

Figure 3.4 compares the percentage of ﬁrms that experienced electrical outages in the
previous year across regions. The regional means are based on data from the BEEPS 2008
for ECA regions and subregions, and Enterprise Surveys data for non-ECA countries
that covers the period 2006–2010. Within each region, country-level averages are calcu-
lated and then the regional average is calculated as the simple mean of these country-
level values.

Figure 3.5 displays the average percentage of sales lost due to power outages by country
for ECA, based on the BEEPS 2008 data.

Figure 3.6 shows country-level averages for power outage losses within ECA by income
classiﬁcation. Countries are assigned to an income classiﬁcation based on the 2009 World
Bank Income Classiﬁcations.

Figure 3.7 presents the percentage of ﬁrms owning or sharing a generator by various
ﬁrm characteristics for manufacturing ﬁrms. Using the non-weighted BEEPS 2008 data
for each of the categories (R&D, Exports, Ownership, and Innovation), a simple mean is
taken of the generator ownership dummy.

Figure 3.8 presents the diﬀerences in telecommunications as not an obstacle to doing
business between 2005 and 2008. Upward arrows reﬂect positive change as they indicate
an increase in the percentage of ﬁrms stating telecommunications is not a problem. The
downward arrows indicate negative change. The base of the arrow indicates the value
in 2005, while the black lines indicate the 2008 value. Both values are based on the trun-
cated samples.

Figure 3.9 presents the correlation between email use and GDP. The vertical axis is the
percentage of ﬁrms that use email as calculated from the BEEPS 2008. The horizontal axis
shows the natural log of 2007 GDP per capita values on a purchasing power parity basis
(in 2005 constant international dollars), as taken from the World Bank’s World Develop-
ment Indicators data set. The coeﬃcient of correlation is 0.8469 and is signiﬁcant at 0.000.
104 World Bank Study

Figure 3.10 presents the correlation between email use and labor productivity. The verti-
cal axis is the percentage of ﬁrms that use email and the horizontal axis is the natural log
of labor productivity (see above note on labor productivity). Each is based on the BEEPS
2008. The coeﬃcient of correlation is 0.8489 and is signiﬁcant at 0.000.

Figure 3.11 depicts the relation between power outage costs from the BEEPS 2008 and
the government eﬀectiveness indicator as determined by WGI (2000–2008). The levels
for these values were calculated such that a “medium” level is any score within one stan-
dard deviation of the mean score. Scores below this range were classiﬁed as “low” and
scores above this range were classiﬁed as “high.” Higher values represent larger costs to
ﬁrms due to power outages.

Figure 3.12 depicts the relation between power outage costs from the BEEPS 2008 and
the government corruption indicator as determined by WGI (2000–2008). The levels for
these values were calculated such that a “medium” level is any score within one stan-
dard deviation of the mean score. Scores below this range were classiﬁed as “low” and
scores above this range were classiﬁed as “high.” Higher values represent larger costs to
ﬁrms due to power outages.

Figure 3.13 depicts the relation between access to high-speed Internet from the BEEPS
2008 and the government eﬀectiveness indicator as determined by WGI (2000–2008). The
levels for these values were calculated such that a “medium” level is any score within
one standard deviation of the mean score. Scores below this range were classiﬁed as
“low” and scores above this range were classiﬁed as “high.” Higher values (longer bars)
represent a higher percentage of ﬁrms with access to high-speed Internet.

Figure 3.14 depicts the relation between access to high-speed Internet from the BEEPS
2008 and the government corruption indicator as determined by WGI. The levels for
these values were calculated such that a “medium” level is any score within one stan-
dard deviation of the mean score. Scores below this range were classiﬁed as “low” and
scores above this range were classiﬁed as “high.” Higher values (longer bars) represent
a higher percentage of ﬁrms with access to high-speed Internet.

Figure B3.3.1 presents the diﬀerences in transport as not an obstacle to doing business
between 2005 and 2008. Upward arrows reﬂect positive change as they indicate an in-
crease in the percentage of ﬁrms stating transport is not a problem. The downward ar-
rows indicate negative change. The base of the arrow indicates the value in 2005, while
the black lines indicate the 2008 value. Both values are based on the truncated samples.
Challenges to Enterprise Performance in the Face of the Financial Crisis 105

Regression Results

COMPLEMENTARY REGRESSIONS
Tables A1.5, A1.7, A1.10, and A1.11 are based on results from complementary regres-
sions on labor productivity. Complementary regressions are set up such that the costs
of electrical outages (or other variable of interest, such as government eﬀectiveness) are
segregated into mutually exclusive groups, which are based on the ﬁrm-level character-
istic of interest (e.g. foreign owned, ﬁrm size, etc.). For example, the regression to deter-
mine the eﬀect of foreign ownership on power-outage costs is set up as follows:
Yi = β1DiCi + β2FiCi + β3Fi + …
Whereby Yi is labor productivity of ﬁrm i, F a dummy which is equal to 1 for domestic
ﬁrms and equal to zero for foreign ﬁrms, D = (1 – F) a dummy for domestic ﬁrms, and β1,
β2 parameters to be estimated.
β1 measures the impact of the costs of power outages on labor productivity among do-
mestic ﬁrms. β2 measures the impact of the costs of power outages on labor productivity
among foreign ﬁrms. The estimates of speciﬁcation show directly if the impact of costs of
power outages on labor productivity are signiﬁcantly diﬀerent from zero between the two
ﬁrm groups.
REGRESSION CONTROLS
All complementary regressions included a base set of controls including ﬁrm size, own-
ership, export activity, innovation activity and others. See Table A1.1. For complemen-
tary regressions, the segregating dummy variable (e.g. foreign ownership in the above
example) was added as a control if it was not already included in the standard control
set. For example, in the regression that determined the diﬀerence in the productivity
impact between low-growth and high-growth countries, the high-growth dummy was
included as a control since it was not in the standard control set.
For the marginal eﬀects regressions, the same set of controls was used with the ex-
ception of country-speciﬁc dummies, which were not included.
WGI-indicators were converted to dummy variables that represented low govern-
ment eﬀectiveness, low control of corruption, etc. (0) and high government eﬀectiveness,
high control of corruption, etc. (1), and used as regressors. The median value of each
indicator was used to determine the low and high classiﬁcations.
106 World Bank Study

Table A1.5. Regressions on the Effects of Power Outage Costs on Labor Productivity
Based on Varying Firm Characteristics
Firm Characteristics LN(Labor Productivity)
Small size –1.735***
(–4.272)
Medium size –1.791***
(–3.515)
Large size –0.0324
(–0.0574)
Panel size firms –3.991
(–1.337)
Domestically owned –1.267***
(–4.302)
Foreign Owned 1 –2.766**
(–2.411)
Non-innovating –0.859**
(–2.118)
Innovating –1.772***
(–4.658)
Non–exporting –1.336***
(–4.253)
Exporting –1.396**
(–2.146)
Low Access to Credit –1.469***
(–3.866)
Good Access to Credit –1.709***
(–3.078)
Low Country Growth2 –0.786*
(–1.932)
High Country Growth 2 –2.008***
(–4.535)
Subsidiary –1.154
(–1.339)
Independent Firm –1.365***
(–4.537)
Low-Income Country3 –1.576***
(–4.337)
Medium-Income Country3 –3.736**
(–1.965)
High-Income Country 3 –0.781
(–1.607)
Ownership of a Generator 0.218***
(3.633)
Constant –3.139*** –3.168*** –3.168*** –3.173*** –4.571*** –3.242*** –3.168*** –1.899*** 7.825***
(–5.570) (–5.626) (–5.626) (–5.632) (–9.076) (–5.748) (–5.627) (–3.220) (6.214)

Observations 7,160 7,160 7,160 7,160 6,836 7,072 7,160 7,160 3,593
R–squared 0.417 0.417 0.417 0.417 0.426 0.420 0.417 0.417 0.425

Notes: t-statistics in parentheses.
*** p<0.01, ** p<0.05, * p<0.1
1. Firms are considered foreign owned if they are at least 10% foreign owned.
2. Fast growing countries are those that grew with an average growth rate of more than 6% from 2000 to 2008.
3. Income thresholds are 8,000 and 13,000 GDP per capita at PPP in 2007.
Challenges to Enterprise Performance in the Face of the Financial Crisis 107

Table A1.6. Regressions on the Effects of Power Outage Costs on Productivity by
Firm Productivity Quintile
Quintiles of Labor Productivity
10% 25% 50% 75% 90%
Power Outage Costs −1.834*** −1.868*** −1.196*** −1.210*** −0.964**
(−4.583) (−6.130) (−3.791) (−2.729) (−1.969)
Constant −6.586*** −5.632*** −3.954*** −1.402* 0.130
(−7.087) (−8.528) (−6.181) (−1.772) (0.140)
Observations 7,160 7,160 7,160 7,160 7,160
Pseudo R-squared 0.2951 0.2987 0.2704 0.2243 0.1793
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table A1.7. Regressions on the Effects of High-Speed Internet Access on Firm
Productivity Based on Varying Firm Characteristics
Firm Characteristics LN(Labor Productivity)
Low-Income Country 0.783***
(8.750)
Medium-Income Country 0.239**
(2.023)
High-Income Country 0.524***
(5.282)
Small size 0.559***
(7.022)
Medium size 0.492***
(5.081)
Large size 0.703***
(4.211)
Panel size 0.781***
(3.713)
Fast-Growth Country 0.628***
(8.351)
Low-Growth Country 0.440***
(4.506)
Constant −4.106*** −2.678*** 2.071*
(−4.035) (−2.619) (1.856)

Observations 2,637 2,637 2,586
R-squared 0.418 0.421 0.421
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1
108 World Bank Study

Table A1.8. Regressions on the Effects of Email Use on Productivity by Firm
Productivity Quintile
Quintiles of Labor Productivity
Variables 10% 25% 50% 75% 90%
Email 0.610*** 0.557*** 0.475*** 0.458*** 0.584***
(10.72) (13.37) (14.12) (8.925) (9.442)
Constant −4.936*** −4.694*** −3.381*** −1.588** 0.592
(−6.314) (−8.301) (−7.483) (−2.297) (0.718)
Observations 8,437 8,437 8,437 8,437 8,437
Pseudo R-squared 0.3003 0.3017 0.2751 0.2275 0.1796
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table A1.9. Regressions on the Effects of High-Speed Internet Access on Productivity
by Firm Productivity Quintile
Quintiles of Labor Productivity
Variables 10% 25% 50% 75% 90%
Internet access 0.574*** 0.518*** 0.441*** 0.502*** 0.526***
(5.637) (7.551) (5.590) (6.922) (5.988)
Constant −5.237*** −6.222*** −4.955*** −2.580** −1.303
(−3.043) (−5.277) (−3.756) (−2.207) (−0.954)
Observations 2,637 2,637 2,637 2,637 2,637
Pseudo R-squared 0.3077 0.2963 0.2715 0.2314 0.1999
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table A1.10. Regressions on the Effects of Power Outage Costs in Countries with
Good Governance, Good Control of Corruption, and Good Government Regulation on
Labor Productivity
Government Characteristics LN(Labor Productivity)
Effective Governance −0.945
(−1.550)
Non-effective Governance −1.462***
(−4.479)
Good Control of Corruption −0.977
(−1.603)
Bad Control of Corruption −1.453***
(−4.451)
Good Government Regulation −0.973*
(−1.847)
Bad Government Regulation −1.505***
(−4.383)
Constant 4.232*** −2.023** 0.0223
(4.971) (−2.421) (0.0342)
Observations 7,160 7,160 7,160
R-squared 0.417 0.417 0.417
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1
Challenges to Enterprise Performance in the Face of the Financial Crisis 109

Table A1.11. Regressions on the Effects of High-Speed Internet Access in Countries
with Good Governance, Good Control of Corruption, and Good Government
Regulation on Labor Productivity
Government Characteristics LN(Labor Productivity)
Effective Governance 0.353***
(3.714)
Non-effective Governance 0.691***
(9.309)
Good Control of Corruption 0.250***
(2.597)
Bad Control of Corruption 0.744***
(10.10)
Good Government Regulation 0.413***
(4.407)
Bad Government Regulation 0.661***
(8.812)
Constant 3.344 −2.531* −2.238**
(1.292) (−1.726) (−2.161)
Observations 2,637 2,637 2,637
R-squared 0.420 0.422 0.419
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table A1.12. Marginal Effects of Generalized Ordered Logit Models of the Effects of
Various Firm Characteristics on Electricity Perceptions
Electricity Perception
Major or Very Severe Obstacle Not an Obstacle
Number of power outages 0.007***
(8.14)
Losses from outages as a percent of sales 1.884***
(12.34)
Generator ownership 0.130***
(5.70)
Access to high-speed Internet 0.069***
(3.58)
Labor growth −0.024**
(−2.13)
Observations 3,916 8,385 4,258 3,191 8,942
Pseudo R-squared 0.077 0.078 0.056 0.070 0.053
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1
110 World Bank Study

Table A1.13. Regression Results for World Governance Indicators on Power Outage
Costs and Access to High-Speed Internet
Power Outage Costs High−Speed Internet Access
Control of Control of
Government Government Government Government
Effectiveness Corruption Effectiveness Corruption
Power Outage Costs −0.0898*** −0.1108***
(−11.37) (−19.23)
High-Speed Internet Access 0.2817*** 0.1611
(6.40) (1.44)
Constant 0.3418*** −.2733*** −1.442** −0.5033
(10.36) (−8.77) (−2.04) (−0.90)
Observations 8470 8470 3220 3220
R-squared 0.172 0.172 0.308 0.308
Notes: t-statistics in parentheses
*** p<0.01, ** p<0.05, * p<0.1

Table A1.14. Estimated Power Outage Costs and Access to High-Speed Internet
Estimated Power Outage Costs Estimated Percent of Firms with
(Percent of Sales) Access to High-Speed Internet
Low Government Effectiveness 12.29% 33.82%
High Government Effectiveness 0.52% 70.74%
Low Control of Corruption 16.50% 43.44%
High Control of Corruption 3.01% 63.04%

Estimates are based on regression results from Table A1.13, the survey-population means
for the controls, and assumed low and high levels of the WGI indicator (deﬁned as the
mean indicator-value across ECA countries plus or minus one standard deviation).

Chapter 4: Labor: Challenges Ahead of the Crisis

Data Notes

BEEPS-BASED VARIABLES OF INTEREST:
Skills as No Obstacle comes from the 2005 and 2008 BEEPS datasets, using the ques-
tion, “Is an inadequately educated workforce no obstacle, a minor obstacle, a moder-
ate obstacle, a major obstacle or a very severe obstacle to the current operations of this
establishment?” The variable is assigned a value of 1 if the ﬁrm reported “no obstacle,”
zero otherwise.

Skills as a Major Obstacle comes from the 2008 BEEPS dataset, using the question, “Is
an inadequately educated workforce no obstacle, a minor obstacle, a moderate obstacle,
a major obstacle or a very severe obstacle to the current operations of this establish-
ment?” The variable is assigned a value of 1 if the ﬁrm reported “major obstacle” or
“very severe obstacle,” zero otherwise.
Challenges to Enterprise Performance in the Face of the Financial Crisis 111

Labor Regulations as No Obstacle comes from the 2005 and 2008 BEEPS datasets, using
the question, “Are labor regulations no obstacle, a minor obstacle, a moderate obstacle, a
major obstacle or a very severe obstacle to the current operations of this establishment?”
The variable is assigned a value of 1 if the ﬁrm reported “no obstacle,” zero otherwise.

Labor Regulations as a Major Obstacle comes from the 2008 BEEPS dataset, using the
question, “Are labor regulations no obstacle, a minor obstacle, a moderate obstacle, a
major obstacle or a very severe obstacle to the current operations of this establishment?”
The variable is assigned a value of 1 if the ﬁrm reported “major obstacle” or “very severe
obstacle,” zero otherwise.

Labor Productivity is the ratio of a ﬁrm’s total annual sales (as given by question d2
and converted to U.S. dollars) to its’ number of employees. The number of employees is
determined as a sum of the answers to questions l1 (the number of permanent full-time
employees) and l6 (the number of temporary full-time employees).

OTHER VARIABLES:
EPL Rigidity is deﬁned as the value of the Rigidity of Employment Index for 2008 from
Doing Business. Values range from 0–100. Higher values reﬂect more stringent employ-
ment regulations.

Unemployment 2007 data comes from the World Bank, and is the percentage of total
unemployment for 2007.

Figures and Tables
When using Enterprise Surveys data, regions are comprised as shown in the Note on
Regional and Sub-regional Averages at the beginning of Appendix 1.

Table 4.1 data comes from the KILM (6th Edition) and authors’ calculations. The group-
ings show unweighted averages.

Table 4.2 shows the percentage of ﬁrms stating that labor regulations and skills and edu-
cation of labor respectively are major or very severe obstacles to doing business. Each
subregion is calculated so that each country has an equal weight.

Figure B4.1.1 is based on a simple correlation between labor regulations as no obstacle
and the log of GDP 2007 on a purchasing power parity basis (in constant 2005 interna-
tional dollars). The correlation between the two variables graphed is -0.65, signiﬁcant
at the 0.001 level. This relationship is signiﬁcant at the ﬁrm level when controlling for other
factors in generalized logit models. See Table A1.15 in the Regression Results section below.

Figure B4.1.2 is based on a simple correlation between EPL Rigidity deﬁned as the Rigid-
ity of Employment Index from Doing Business 2008 and the percentage of ﬁrms innovat-
ing (introducing a new product or service in the last three years) from BEEPS 2008. The
correlation between the two variables graphed is 0.36, signiﬁcant at the 0.06 level.
112 World Bank Study

Figure 4.1 presents the diﬀerences in skills and education of labor as not an obstacle to
doing business between 2005 and 2008. Upward arrows reﬂect positive change as they
indicate an increase in the percentage of ﬁrms stating skills and education of labor are
not a problem. The downward arrows indicate negative change. The base of the arrow
indicates the value in 2005, while the black lines indicate the 2008 value. Both values are
based on the truncated samples.

Figure 4.2 shows the averages for skills and education of labor as no obstacle by income
classiﬁcation including both ECA and countries in other regions using BEEPS 2008 and
Enterprise Surveys 2006–2010. Countries are assigned to an income classiﬁcation based
on the 2009 World Bank Income Classiﬁcations. Averages are calculated so that each
country has an equal weight.

Figure 4.3 is based on a simple correlation between the country-level mean value of skills
and education of labor as an obstacle from the 2008 BEEPS and the log of labor produc-
tivity. The correlation between the two variables graphed is -0.24, but is not signiﬁcant
at the country level (signiﬁcant at the 0.20 level).

Figure B4.3.1 and Figure B4.3.2 data comes from KILM 6th Edition, 2009

Figure B4.4.1 data comes from the IMF Balance of Payments 2008.

Figure B4.4.2 is based on a simple correlation between skills and education of labor as no
obstacle from BEEPS 2008 and migrants remi ances as a percent of GDP from the IMF
Balance of Payments, 2008. The correlation between the two variables graphed is -0.05,
and is not signiﬁcant (signiﬁcant at the 0.81 level).

Figure B4.4.3 is based on a simple correlation between skills and education of labor as
no obstacle from BEEPS 2008 and the emigration rate of the tertiary educated from the
World Bank Migration and Remi ances Factbook, 2008. The correlation between the two
variables graphed is 0.29, and is not signiﬁcant (signiﬁcant at the 0.15 level).

Figure 4.4 is based on a simple correlation between the country-level mean value of skills
and education of labor as an obstacle from BEEPS 2008 and the unemployment rate for
2007. The correlation between the two variables graphed is -0.43, and is signiﬁcant at the
0.06 level. This relationship is signiﬁcant at the ﬁrm level when controlling for other fac-
tors in generalized logit models.

Figure 4.5 is based on a simple correlation between the country-level mean value of skills
and education of labor as an obstacle from BEEPS 2008 and the percentage rate of unem-
ployment of those with a tertiary education. The correlation between the two variables
graphed is 0.45, and is signiﬁcant at the 0.05 level.
Challenges to Enterprise Performance in the Face of the Financial Crisis 113

Figure 4.6 is based on a simple correlation between the country-level mean value of skills
and education of labor as an obstacle from BEEPS 2008 and the percentage rate of unem-
ployment of those with a secondary education. The correlation between the two variables
graphed is -0.22, but is not signiﬁcant (signiﬁcant at the 0.36 level).

Figure 4.7 shows the regional and subregional averages for the percentage of ﬁrms pro-
viding formal training to permanent full-time employees for ECA and countries in other
regions using BEEPS 2008 and Enterprise Surveys 2006–2010. Averages are calculated so
that each country has an equal weight.

Figure 4.8 shows the averages for the percentage of ﬁrms providing formal training to
permanent full-time employees including both ECA and countries in other regions using
BEEPS 2008 and Enterprise Surveys 2006–2010 by Income Classiﬁcation. Countries are
assigned to an income classiﬁcation based on the 2009 World Bank Income Classiﬁca-
tions. Averages are calculated so that each country has an equal weight.

Figure 4.9 is based on a simple correlation between the country-level mean value of skills
and education of labor as an obstacle and the percentage of ﬁrms providing formal train-
ing to permanent full-time employees from BEEPS 2008. The correlation between the
two variables graphed is 0.31 and is signiﬁcant at the 0.10 level.

Figures B4.6.1 and B4.6.2 data come from the Enterprise Surveys Financial Crisis Survey 2009.

Analytic Results

ANALYSIS OF OBSTACLES TO DOING BUSINESS
The primary method of analysis used to examine the obstacles doing business is a general-
ized ordered logit model, based on the model proposed by Pierre and Scarpe a (2004, 2006).
The dependent variable can take on values from 0 to 4; 0 if the aspect of the business envi-
ronment is not an obstacle, and a value of 4 if it is a very severe obstacle. For the purposes of
this analysis, the values for major and very severe obstacle were combined to capture diﬀer-
ences in perceptions between no obstacle and severe obstacle. This transformation was
completed to be consistent with the approach used by Pierre and Scarpe a (2004, 2006).
Pierre and Scarpe a (2004 and 2006) argue the use of ordinal regression models
often results in a violation of assumptions, and this method allows ﬂexibility in the pro-
portional odds assumption on the data. The generalized ordered logit models relaxes
these assumptions and “allows the eﬀects of explanatory values to vary with the point at
which the categories of the dependent variables are dichotomized” (Maddala, 1983 cited
in Pierre and Scarpe a, 2006, p. 330).
A totally unconstrained general ordered logit model is notated as:
exp(αj + Χiβj)
P(Yi > j) = g(Χβ) = , j = 1, 2, … m – 1
1 + [exp(αj + Χiβj)]
114 World Bank Study

The generalized ordered logit model estimates a set of coeﬃcients for each of the
m – 1 points at which the dependent variable can be dichotomized. From this set of k co-
eﬃcients (Bk), using the logistic cumulative distribution, it is possible to derive formulas
for the probabilities that y will take on each of the values 0, 1, …, m:
P(y = 0) = F(–Xβ1) P(y = 0 ) = F(–Xβ1)
P(y = j) = F(–Xβ(j+1) ) – F(–Xβj) j = 1, …, m – 1
P(y = m) = 1 – F(–Xβm)
The variables of interest including EPL rigidity, innovation, and a set of control vari-
ables (see Table A1.1) are regressed on the perception of labor regulations and skills and
education of labor as an obstacle, respectively. Using this method, the coeﬃcients of the
explanatory variables can be determined and the marginal eﬀects on the dependent vari-
able can be interpreted. These marginal eﬀects allow for more careful interpretation of
the eﬀects of the variables on a discrete change in the dependent variable, for example,
the change in value from 0 to 1 or from 2 to 3. Only the results for no obstacle and major
or very severe obstacle are presented for consistency with the results discussed in the
chapter. See Tables A1.15 and A1.16.
All models include control variables including those for ﬁrm size, sector, location,
ownership, export activity, ﬁrm age, and innovation, subregion (FSU N, FSU S, SEE, EU-
10), and others. The set of control variables were informed by previous literature (see for
example, Pierre and Scarpe a 2004, 2006). Additional control variables were inserted for
consistency with the models employed in Chapter 3.
In addition to the general ordered logit models, standard logit models are used to
examine the probability of provision of training and the propensity to innovate. Multi-
variate regression models are used to examine the eﬀects of variables of interest on labor
productivity and the professionalism of labor. These models also included the control
variables listed above.
Challenges to Enterprise Performance in the Face of the Financial Crisis 115

Table A1.15. Marginal Effects of Primary Variables on Labor Regulations as No
Obstacle and Major Obstacle (General Ordered Logit Models)

Labor Regulations Labor Regulations Labor Regulations Labor Regulations
Firm Characteristic No Obstacle Major Obstacle No Obstacle Major Obstacle
EPL Rigidity −0.002* 0.001*
(0.001) (0.001)
Unemployment (2007) 0.008*** −0.004*** 0.006*** −0.003**
(0.002) (0.001) (0.002) (0.001)
Log of Firm Age 0.004 0.000 0.004 0.000
(0.010) (0.007) (0.010) (0.007)
Innovation dummy −0.028** 0.023*** −0.027** 0.022***
(0.014) (0.009) (0.014) (0.009)
Small ﬁrm dummy −0.053 −0.005 −0.056 −0.003
(0.039) (0.026) (0.039) (0.026)
Medium ﬁrm dummy −0.073* 0.018 −0.076* 0.019
(0.039) (0.027) (0.039) (0.027)
Large ﬁrm dummy −0.110*** 0.023 −0.114*** 0.024
(0.039) (0.028) (0.039) (0.029)
Manufacturing ﬁrm dummy −0.023 0.009 −0.024 0.010
(0.017) (0.011) (0.017) (0.011)
Service ﬁrm dummy −0.027 0.017 −0.027 0.017
(0.018) (0.012) (0.018) (0.012)
Log of GDP 2007 0.100*** −0.122*** 0.021 −0.076**
(0.033) (0.019) (0.053) (0.031)
Observations 6175 6175 6175 6175
Pseudo R-squared 0.0414 0.0414 0.0417 0.0417

Notes: Values presented are marginal eﬀects with standard errors in parentheses
*** p <0.01, **p<0.05, * p<0.1
Table A1.16. Marginal Effects of Primary Variables on Skills and Education of Labor as No Obstacle and Major Obstacle

116
(General Ordered Logit Models)

Skills Not an Skills Major Skills Not an Skills Major Skills Not an Skills Major Skills Not an Skills Major Skills Not an Skills Major

World Bank Study
Firm Characteristics Obstacle Obstacle Obstacle Obstacle Obstacle Obstacle Obstacle Obstacle Obstacle Obstacle
Innovation −0.075*** 0.096***
(0.009) (0.010)
Training −0.045*** 0.022
(0.014) (0.016)
Email Use −0.039*** 0.071***
(0.013) (0.013)
Professionalism of Labor 0.000 0.001***
(0.000) (0.000)
Log of Labor Productivity 0.007** −0.006
(0.004) (0.004)
Firm Size: Small −0.111*** 0.073*
(0.025) (0.039)
Firm Size: Medium −0.160*** 0.104***
(0.024) (0.039)
Firm Size: Large −0.020*** 0.139***
(0.021 (0.041)
Log of GDP 0.111*** −0.093***
(0.011) (0.013)
Service Sector −0.017 −0.001
(0.012) (0.013)
Manufacturing Sector −0.042*** −0.005
(0.011) (0.012)
Observations 10,248 10,248 4,330 4,330 10,230 10,230 9,847 9,847 8,445 8,445
Pseudo R-Squared 0.0476 0.0476 0.0495 0.0495 0.0488 0.0488 0.048 0.048 0.0471 0.0471

Notes: Values presented are marginal eﬀects with standard errors in parentheses
*** p <0.01, **p<0.05, * p<0.1
Challenges to Enterprise Performance in the Face of the Financial Crisis 117

Table A1.17. Results of the Innovation and ICT Use Logit Models

Firm Characteristics Innovation Activity ICT Use (Email)
EPL Stringency 0.013***
(0.002)
Log of Labor Productivity 0.160*** 0.363***
(0.018) (0.027)
Professionalism of labor 0.022***
(0.001)
Skilled Labor −0.011***
(0.003)
Unskilled Labor −0.006
(0.004)
Firm Size: Small 0.220 0.214 0.521*** 0.621*
(0.139) (0.151) (0.183) (0.386)
Firm Size: Medium 0.387*** 0.331** 1.296*** 1.688***
(0.139) (0.152) (0.187) (0.391)
Firm Size: Large 0.449*** 0.385** 2.381*** 3.102***
(0.142) (0.156) (0.206) (0.419)
Exporting Firm 0.696*** 0.606*** 1.095*** 1.366***
(0.058) (0.063) (0.129) (0.155)
Foreign-Owned 0.294*** 0.188** 0.599*** 0.394*
(0.075) (0.082) (0.152) (0.205)
Log of GDP 0.549** 0.164*** 0.594*** 0.882***
(0.070) (0.061) (0.077) (0.121)
Constant −7.650*** −5.178*** −9.193*** −8.593***
(0.719) (0.596) (0.787) (1.311)
Observations 10189 8582 8573 4094
Pseudo R-Squared 0.0560 0.0644 0.3480 0.3332

Notes: Values presented are coeﬃcients with standard errors in parentheses
*** p <0.01, **p<0.05, * p<0.1
118 World Bank Study

Table A1.18. Regression Results of the Training Logit Models

Firm Characteristics Provision of Training
Innovation 0.910***
(0.072)
Labor Growth 0.365***
(0.073)
Professionalism of Labor 0.005***
(0.002)
Email Use 0.756***
(0.117)
Firm Size: Small 0.314
(0.401)
Firm Size: Medium 1.065***
(0.400)
Firm Size: Large 1.679***
(0.403)
Exporting Firm 0.257***
(0.079)
Foreign-Owned 0.282***
(0.110)
Log of GDP 0.227**
(0.010)
Constant −3.690*** −3.159*** −3.251*** −3.284***
(1.044) (1.087) (1.066) (1.063)
Observations 4404 3884 4221 4396
Pseudo R-Squared 0.1280 0.1306 0.1272 0.1356

Notes: Values presented are coeﬃcients with standard errors in parentheses.
*** p <0.01, **p<0.05, * p<0.1
Challenges to Enterprise Performance in the Face of the Financial Crisis 119

Table A1.19. Regression Results of Labor Productivity Models

Firm Characteristics Ln(Labor Productivity)
Skills Constraint −0.020*
(0.012)
Labor Growth 0.203***
(0.030)
Innovation 0.252***
(0.030)
Provision of Training 0.301***
(0.045)
Email Use 0.756***
(0.117)
Firm Size: Small 0.175*
(0.092)
Firm Size: Medium 0.273**
(0.093)
Firm Size: Large 0.263***
(0.095)
Log of GDP 0.885***
(0.036)
Constant 3.062*** 3.160*** −3.284***
(0.359) (0.590) (1.063)
Observations 8445 3612 4396
R-Squared 0.3778 0.3862 0.1356

Notes: Values presented are coeﬃcients with standard errors in parentheses
*** p <0.01, **p<0.05, * p<0.1

Table A1.20. Regression Results of the Professionalism of Labor Model

Firm Characteristics Professionalism of Labor
Email use 10.897***
(0.632)
Constant 2.383
(5.937)
Observations 10000
R-Squared 0.2488

Notes: Values presented are coeﬃcients with standard errors in parentheses
*** p <0.01, **p<0.05, * p<0.1
120 World Bank Study

Appendix 2. Notes on Sampling and the Survey Methodology

Over 11,000 ﬁrms were interviewed for the 2008 round of BEEPS in 29 ECA countries.
The number of ﬁrms surveyed varied from a low of 116 in Montenegro to more than
1,000 in Russia and Turkey. Most surveys were conducted between April 2008 and
March 2009, and most quantitative questions (e.g. sales, employment, etc.) refer to the
ﬁrm’s operations in the calendar year 2007.
The ﬁrms vary by size, sector of operation, and ownership, and were selected to
be representative of the non-agricultural private sector in each nation. The ﬁrms were
chosen using stratiﬁed random sampling (ﬁrms were stratiﬁed by size, sector of opera-
tions, and geographical location). Datasets include weights in order to extrapolate to the
overall population of ﬁrms in each country.
The sampling methodology used in 2008 diﬀers from that of prior rounds in several
ways:
■ The 2008 round of BEEPS utilized stratiﬁed random sampling, moving away
from the use of simple random sampling supplemented by elements of quota
sampling used in 2005 and earlier rounds of BEEPS.
■ In order to extrapolate the stratiﬁed sample to the targeted population of ﬁrms,
the BEEPS 2008 utilized weights, while the BEEPS 2005 sample was designed to
be self-weighted.
■ The self-weighted sample for BEEPS 2005 was designed to be “as representative
as possible” to the population of ﬁrms within the industry and service sectors
subject to the various minimum quotas for the total sample (e.g. x% of state-
owned enterprises, y% of large enterprise, z% from the capital city, etc.).
■ The sectoral composition of the sample changed from 2005 to 2008. For example,
a number of sectors were excluded from the 2008 sampling frame: mining and
quarrying, advertising and other business services, welfare services, and others.
■ While the 2005 sampling frame included ﬁrms with two or more employees
(including the owner), in 2008 the ﬁrm size strata changed to include only ﬁrms
with ﬁve or more employees (including the owner), although in both cycles, a
panel component included ﬁrms with less than ﬁve employees.
■ The 2005 sampling frame included ﬁrms that were 100 percent state owned,
while in 2008, 100 percent state owned ﬁrms were excluded.
■ The 2005 sampling frame was restricted to include only ﬁrms that had been op-
erating for three years or more, while the 2008 frame included ﬁrms of all ages.
For analyses that focus on 2008, i.e. do not require a cross-period comparison, all
ﬁrms from the 2008 BEEPS are included in the weighted averages. In comparisons of
2005 and 2008 results—such as changes in perceptions of obstacles over time—an inter-
section of two sample populations was sought, i.e. the ﬁrm samples were modiﬁed as
follows to maximize comparability:
■ Sector: Restricted to sectors present in both 2005 and 2008 samples. Firms operat-
ing in a number of manufacturing and service sectors were dropped from the 2008
database, while ﬁrms in certain other sectors (e.g. mining and quarrying, business
services and welfare services among others) were dropped from the 2005 data.
Challenges to Enterprise Performance in the Face of the Financial Crisis 121

■ Size: Firms in the 2005 data with fewer than ﬁve employees were dropped to
match the 2008 approach.
■ Ownership: Firms that were 100 percent state owned were dropped from the
2005 sample to match the 2008 approach.
■ Age: Firms that were established after 2006 were dropped from the 2008 sample
to match the 2005 approach.
These modiﬁcations result in dropping approximately 35 percent of ﬁrms surveyed
in 2005 and approximately 6 percent of those surveyed in 2008. In analyses comparing
2005 to 2008, therefore, the 2008 estimates exclude data from 6 percent of ﬁrms region-
wide, varying from less than 1 percent for several countries to as high as 14 percent in
Armenia. In contrast, analyses based purely on 2008 data do not require comparability
with the 2005 sample, so no ﬁrms are excluded. This means that readers will sometimes
encounter two diﬀerent country-level ﬁgures for the same indicator for 2008. Both are
correct, but for diﬀering purposes.
Results for two countries in the BEEPS 2008—Albania and Croatia—are based on
two diﬀerent surveys: (i) from BEEPS 2008 for ECA-speciﬁc questions not included in
the Enterprise Surveys, and (ii) from the 2007 Enterprise Surveys for all other questions.
Due to the small universe of ﬁrms coupled with “survey fatigue”1 in Albania and Croatia,
it was not possible to conduct the full BEEPS survey on a large sample of ﬁrms in 2008.

1. Survey fatigue results from over-surveying. When someone who recently completed a survey from
a particular organization is inundated with invitations to complete other surveys, they feel tired or “fa-
tigued” when it comes to taking surveys. Once a respondent forms an opinion that a survey organization
doesn’t respect him/her because of over-surveying, it is very diﬃcult to restore the organization’s image.
Other eﬀects of survey fatigue can include lower response rates and lower-quality data.
122 World Bank Study

Table A2.1. Sample Summary 2005 and 2008
2005 Sample for All Countries 2008 Sample for All Countries
Total Firms
Total Excluded Total Total Firms
Firms (Size, Percent Reduced Firms Excluded Percent Reduced Dates of Data
Surveyed Sector, and of Firms Sample Surveyed (Size and of Firms Sample Collection
Country in 2005 Ownership) Excluded Size in 2008 Age) Excluded Size 2008/2009
ALB1 204 50 24.5 154 304 1 0.3 303 12/2007–3/2008
ARM 351 79 22.5 272 374 52 13.9 322 10/2008–2/2009
AZE 350 63 18.0 287 380 25 6.6 355 9/2008–2/2009
BLR 325 116 35.7 209 273 9 3.3 264 5/2008–8/2008
BIH 200 56 28.0 144 361 26 7.2 335 9/2008–3/2009
BGR 300 138 46.0 162 288 37 12.9 251 9/2008–12/2008
HRV1 236 88 37.3 148 633 3 0.5 630 1/2007–12/2007
CZE 343 176 51.3 167 250 20 8.0 230 9/2008–3/2009
EST 219 91 41.6 128 273 7 2.6 266 4/2008–10/2008
MKD 200 88 44.0 112 366 40 10.9 326 9/2008–1/2009
GEO 200 90 45.0 110 373 19 5.1 354 4/2008–8/2008
HUN 610 170 27.9 440 291 22 7.6 269 8/2008–2/2009
KAZ 585 150 25.6 435 544 44 8.1 500 9/2008–1/2009
KGZ 202 60 29.7 142 235 16 6.8 219 9/2008–3/2009
LVA 205 100 48.8 105 271 19 7.0 252 9/2008–10/2008
LTU 205 76 37.1 129 276 23 8.3 253 9/2008–3/2009
MDA 350 71 20.3 279 363 39 10.7 324 9/2008–2/2009
POL 975 447 45.9 528 455 36 7.9 419 8/2008–3/2009
ROM 600 145 24.2 455 541 38 7.0 503 9/2008–12/2008
RUS 601 209 34.8 392 1004 35 3.5 969 9/2008–3/2009
SRB 282 139 49.3 148 388 35 9.0 353 9/2008–12/2008
SVK 220 110 50.0 110 275 27 9.8 248 9/2008–3/2009
SVN 223 110 49.3 113 276 17 6.2 259 9/2008–3/2009
TJK 200 66 33.0 134 360 38 10.6 322 5/2008–8/2008
TUR 557 183 32.9 374 1152 27 2.3 1125 4/2008–1/2009
UKR 594 238 40.1 356 851 46 5.4 805 6/2008–8–2008
UZB 300 98 32.7 202 366 3 0.8 363 4/2008–8/2008
KSV* 270 12 4.4 258 10/2008–2/2009
MNE* 116 5 4.3 111 9/2008–2/2009
Total 9,637 3,407 35.4 6,235 11,909 721 6.1 11,188
Notes: *Kosovo and Montenegro were included in the 2008 cycle.
1. Results for two countries in the BEEPS 2008—Albania and Croatia—are based on two diﬀerent sur-
veys: (i) from the 2007 Enterprise Surveys and (ii) from BEEPS 2008 for ECA-speciﬁc questions not in-
cluded in the Enterprise Surveys. Due to a small universe of ﬁrms coupled with survey fatigue in Albania
and Croatia, it was not possible to conduct the full BEEPS survey on a large sample of ﬁrms in 2008.
The numbers in the table above correspond to the Enterprise Surveys 2007 data sets. The 2008 BEEPS
dataset for Albania consisted of a total of 175 ﬁrms. In order to best match the 2008 sample to that of 2005,
the sample was adjusted. Seven ﬁrms (4.0 percent of the sample) were excluded on the basis of size and
age. The reduced sample size was 168 ﬁrms for 2008. The dates of data collection for the 2008 BEEPS in
Albania were from 10/2008 to 2/2009.
For Croatia, the BEEPS 2008 dataset consisted of a total of 159 ﬁrms. To adjust the 2008 sample to that
of 2005, 17 ﬁrms (10.7 percent of the sample) were excluded on the basis of size and age. The reduced
sample size was 142 ﬁrms for 2008. The dates of data collection for the 2008 BEEPS in Croatia were from
9/2008 to 3/2009.
Table A3.1. Problems Doing Business: Ranking of Problems 2008

Tax Rates
Corruption
Electricity
Skills and
Education of
Workers
Access to
Financing
Crime, Theft,
and Disorder
Tax
Administration
Telecom
Courts
Access to Land
Business
Licensing and
Permits
Transport
Labor
Regulations
Customs and
Trade
Regulations

ALB 4 2 1 3 9 8 5 10 14 7 12 11 13 6
ARM 1 3 9 10 2 4 7 5 12 11 14 6 13 8
AZE 3 1 9 6 4 7 5 13 11 2 8 12 14 10
BLR 1 10 5 2 8 3 9 6 13 7 4 11 14 12
BIH 1 2 9 7 3 8 4 13 5 14 6 12 10 11
BGR 2 1 9 6 8 3 5 4 7 13 12 11 10 14
HRV 1 5 7 4 6 11 3 9 2 12 10 13 8 14
CZE 3 9 1 4 8 10 7 2 6 12 13 5 11 14
EST 2 10 5 1 8 6 11 4 13 9 12 7 3 14
MKD 3 4 7 10 1 5 6 12 2 9 8 13 14 11
GEO 3 9 1 5 2 4 10 7 11 6 13 8 14 12
HUN 1 3 6 11 7 12 2 8 9 14 4 10 5 13
KAZ 2 3 4 1 6 5 8 9 12 11 10 7 14 13
KSV 11 2 1 5 4 3 12 6 7 8 13 10 14 9
KGZ 3 2 1 8 7 5 6 4 10 11 12 9 14 13
LVA 1 3 10 2 5 6 4 13 8 9 11 7 12 14
LTU 1 5 4 2 7 3 6 9 12 13 10 11 8 14
MDA 3 5 8 2 4 6 10 7 9 1 13 12 14 11
MNE 2 6 1 5 3 11 4 14 10 13 8 7 9 11
POL 1 8 3 2 7 6 5 9 11 12 10 13 4 14
ROM 1 3 10 4 5 9 2 14 7 11 6 12 8 13
RUS 2 3 4 1 8 6 10 7 12 5 11 9 14 13
SRB 3 1 6 5 2 10 9 14 4 13 12 11 8 7
SVK 2 3 1 5 8 4 9 7 6 13 10 11 12 14
SVN 1 13 4 7 2 9 11 5 10 8 12 6 3 14
TJK 1 3 2 4 5 6 9 12 13 7 10 11 14 8
TUR 1 2 7 3 9 13 5 6 8 14 4 11 10 12
UKR 1 2 10 6 8 7 4 9 5 3 11 12 14 13
UZB 4 7 3 2 5 1 6 8 11 9 10 13 12 14
Appendix 3. Summary of Obstacles Doing Business

Total Countries
Where the Rank
<=3 26 17 10 11 7 5 3 1 2 3 0 0 2 0
Challenges to Enterprise Performance in the Face of the Financial Crisis

Total Countries
Where the Rank
<=7 28 23 21 25 21 19 18 12 10 7 5 7 4 2
Source: BEEPS 2008.
123
Table A3.2. Problems Doing Business: Ranking of Problems 2005

124
Administration

Access to Land

Licensing and
Crime, Theft,

Customs and
and Disorder
Education of

Regulations

Regulations
Corruption

Electricity

Transport
Financing
Tax Rates

Access to
Skills and

Business
Workers

Telecom

Permits
Courts

World Bank Study
Trade
Labor
Tax
ALB 1 2 4 9 7 13 3 14 5 12 8 10 11 6
ARM 2 5 13 12 3 14 1 10 8 7 6 9 11 4
AZE 2 3 7 13 4 8 1 14 11 10 6 12 9 5
BLR 2 9 14 5 1 11 4 13 10 7 3 12 8 6
BIH 3 4 11 12 1 7 6 13 2 14 8 9 10 5
BGR 1 3 11 5 6 8 4 12 2 14 7 13 9 10
HRV 2 3 13 6 4 9 10 12 1 14 5 11 7 8
CZE 1 3 14 9 4 8 2 13 5 11 10 12 6 7
EST 7 3 13 2 4 12 8 14 9 11 5 10 1 6
MKD 3 1 10 12 4 8 5 11 2 14 6 13 9 7
GEO 1 4 2 8 3 5 10 14 7 13 11 9 12 6
HUN 1 8 14 4 2 11 3 12 9 13 7 10 5 6
KAZ 2 5 13 6 4 10 1 14 8 9 3 12 11 7
KSV
KGZ 1 3 12 5 7 4 2 14 6 10 8 13 11 9
LVA 1 4 14 3 10 8 2 12 9 13 6 11 5 7
LTU 1 4 12 2 11 8 6 14 7 3 9 13 5 10
MDA 1 7 13 9 5 10 2 14 4 11 6 12 8 3
MNE
POL 1 6 14 8 3 9 2 13 4 11 10 12 5 7
ROM 2 3 13 8 6 10 1 14 4 11 5 12 7 9
RUS 2 3 13 4 6 8 1 14 7 10 5 12 11 9
SRB 1 4 14 9 2 10 5 12 3 11 8 13 7 6
SVK 2 3 12 4 9 5 6 14 1 13 8 11 7 10
SVN 2 9 13 5 6 11 1 14 3 7 8 12 4 10
TJK 1 4 5 9 6 11 2 14 8 10 3 12 13 7
TUR 1 4 10 8 3 6 2 13 7 14 9 12 5 11
UKR 1 3 12 2 5 10 4 14 6 8 7 13 11 9
UZB 1 9 6 8 3 7 2 14 10 13 4 11 12 5
Total Countries
Where the Rank
<=3 26 13 1 4 9 0 16 0 8 1 2 0 1 0
Total Countries
Where the Rank
<=7 27 24 4 13 25 7 24 0 18 4 16 0 11 17

The problems are presented in the table in the order that they rank in severity ECA wide for 2008. The most severe problem, Tax Rates, is presented first in the table. The least severe
problem, Customs and Trade Regulations, is presented last. No data is presented for Kosovo or Montenegro.

Source: BEEPS 2005.
Table A3.3. Factors that are Not a Problem Doing Business, Percentage Point Changes and Statistical Signiﬁcance

Administra on

Access to Land

Licensing and

Sig. Nega ve
Customs and
and Disorder
Educ on of

Sig. Posi ve
Regula ons

Regula ons
Corrup on

Electricity

Transport
Financing
Tax Rates

Access to
Skills and

Business
Telecom
Workers

Changes

Changes
Permits
Crime,

Courts

Trade
Labor
Tax
ALB 17.5 7.9 -12.5 -13.0 6.1 -4.6 19.5 -2.1 32.7 -11.5 22.7 -5.8 4.7 13.8 6 3
ARM 4.3 6.8 -15.6 -3.2 10.2 -10.4 28.1 -11.5 24.4 25.9 31.1 -4.2 11.4 11.3 9 3
AZE 5.7 -11.6 -4.8 -36.0 5.0 -26.6 21.0 -10.8 -21.9 -42.6 7.9 -15.9 4.3 26.1 5 7
BLR -28.2 -30.4 -55.2 -31.8 -1.9 -59.8 -23.0 -50.6 -32.1 -26.3 -15.4 -45.9 -9.3 -17.6 0 13
BIH -4.7 1.0 -8.5 -13.2 8.1 -0.7 -7.5 -2.0 7.8 3.3 -3.7 9.9 8.5 28.8 5 3
BGR -8.0 -5.7 -38.1 -3.6 -13.0 -16.9 -6.0 -38.9 5.4 -14.8 -4.5 -25.3 -9.2 6.7 1 9

Challenges to Enterprise Performance in the Face of the Financial Crisis
HRV -6.4 4.0 -20.6 -10.5 -3.9 -4.5 -32.7 -19.6 0.8 -11.9 16.5 -9.6 -11.9 10.8 2 8
CZE 6.0 15.2 -38.3 -5.6 2.9 -0.4 14.5 -40.4 7.1 5.1 6.2 -24.4 3.2 29.5 5 4
EST -24.6 10.0 -11.4 -14.2 8.5 -21.8 6.3 -19.9 18.9 1.7 16.5 -9.5 26.9 21.1 6 6
MKD -0.2 3.5 -9.6 -7.6 -4.5 2.0 -2.9 0.4 7.1 -4.5 1.5 -4.2 9.4 11.6 2 1
GEO 21.2 27.4 -1.8 -5.1 -1.6 15.0 5.7 -15.5 12.3 -12.7 18.0 4.6 13.1 28.4 7 2
HUN -7.2 -33.3 -20.3 36.3 32.0 3.1 -17.8 -8.8 6.1 11.3 -20.5 3.2 2.8 24.4 5 6
KAZ -4.0 -22.8 -46.3 -26.4 -19.4 -26.7 8.2 -39.0 -9.6 -16.6 -12.3 -37.8 3.8 -0.6 1 11
KGZ -8.1 -6.2 -60.9 -19.0 -23.4 -11.5 4.6 -52.4 -8.7 -18.2 8.5 -39.4 -5.0 3.1 1 10
LVA -5.7 -23.3 -19.1 15.0 -28.8 -18.3 12.7 -16.1 -22.8 -19.9 -1.7 -24.3 2.0 3.8 2 9
LTU -3.1 -3.2 -38.5 -4.8 -35.4 -8.8 -6.3 -36.3 20.2 30.5 -9.8 -25.8 4.0 25.8 3 7
MDA 15.7 7.0 -21.1 -5.1 4.0 -1.9 18.5 -27.8 20.0 -24.1 20.3 -15.5 35.7 23.1 7 5
POL -3.2 0.3 -33.9 -15.5 4.2 -10.9 9.5 -23.9 13.0 -19.4 -9.7 -14.8 -1.9 22.0 4 8
ROM -9.1 -2.1 -19.6 -8.8 -3.4 -3.6 -5.4 -10.6 6.2 -14.7 -3.2 -15.6 0.9 9.1 2 9
RUS -9.0 -16.4 -45.6 -27.9 -19.0 -21.4 5.7 -62.9 -8.6 -27.1 -9.1 -35.5 -0.1 8.1 2 11
SRB 10.6 4.0 -16.2 -12.9 9.1 -2.4 13.6 8.8 18.9 -0.4 7.7 -7.9 8.2 9.6 8 3
SVK -32.5 -31.4 -45.2 -19.2 -41.5 -36.4 -38.3 -48.2 -32.5 -31.5 -33.3 -30.5 -22.0 -4.6 0 13
SVN -5.4 6.2 -32.2 5.5 -9.7 -30.9 30.2 -26.7 19.1 -8.3 2.7 -35.3 -4.5 13.3 3 6
TJK -2.2 -6.2 -10.1 -19.5 -10.4 -7.0 25.7 -0.6 18.0 -4.0 14.4 -5.4 13.2 4.0 4 3
TUR -4.6 -20.1 -4.0 -19.7 11.2 16.9 11.1 -11.8 0.8 -0.5 -15.3 -1.2 0.1 1.5 3 5
UKR -2.6 -18.5 -42.7 -7.9 -12.4 -18.9 -15.2 -50.6 -16.7 -18.6 -8.8 -37.6 -5.1 0.8 0 12
UZB 1.3 -19.4 -35.5 -32.3 -8.4 -46.9 12.4 -34.2 -5.8 -22.0 3.4 -7.4 -11.7 20.0 2 9
ANALYSIS OF CHANGES

Change 8 12 0 3 11 4 17 2 18 6 14 3 17 24
Sig. Pos.
Change 7 6 0 2 8 2 14 1 14 3 10 1 8 19

Change 19 15 27 24 16 23 10 25 9 21 13 24 10 3
Sig. Neg.
Change 13 11 24 19 12 15 9 22 8 17 10 19 6 1
1
Shaded cells indicate changes that are at p=0.10 or above.
Source: BEEPS 2005, BEEPS 2008.

125
Note: The table above shows the changes in the percentage of ﬁrms indicating a certain factor is not an obstacle to doing business from 2005 to 2008.
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BEEPS Data Portal: Information at Your Fingertips

The BEEPS Data Portal is
a new interactive tool that
allows users to analyze
and display data, design
custom charts and create
dynamic reports using
standard templates.

The Portal allows users
to manipulate data
from the 2005 and 2008
BEEPS surveys and other
supplementary sources.
Users can view results
by country and year and
calculate custom indicators
in seconds. All reports,
charts, and tables can be
downloaded into multiple
formats for ease of use.

The BEEPS Data Portal
is publicly available. For
more information on the
BEEPS Data Portal and the
BEEPS Project, enter the
link below in your browser.

Access the BEEPS Data Portal at: http://beeps.prognoz.com
C hallenges to Enterprise Performance in the Face of the Financial Crisis is part of the World Bank
Studies series. These papers are published to communicate the results of the Bank’s ongoing
research and to stimulate public discussion.
This study uses the results of the most recent Business Environment and Enterprise Performance
(BEEPS) Survey to examine key drivers of ﬁrm performance: access to ﬁnance, infrastructure, and
labor in 29 Eastern European and Central Asian countries.

World Bank Studies are available individually or on standing order. This World Bank Studies series is
also available online through the World Bank e-library (www.worldbank.org/elibrary).

ISBN 978-0-8213-8800-6

SKU 18800