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The Small Entrepreneur in Fragile and Conflict-Affected Situations

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

The Small Entrepreneur in Fragile and Conflict-Affected Situations

John Speakman and Annoula Rysova

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The Small Entrepreneur in Fragile and Conflict-Affected Situationshttp://dx.doi.org/10.1596/978-1-4648-0018-4

© 2014 International Bank for Reconstruction and Development / The World Bank1818 H Street NW, Washington DC 20433Telephone: 202-473-1000; Internet: www.worldbank.org

Some rights reserved

1 2 3 4 17 16 15 14

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

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Attribution—Please cite the work as follows: Speakman, John and Annoula Rysova. 2014. The Small Entrepreneur in Fragile and Conflict-Affected Situations. Directions in Development. Washington, DC: World Bank. doi:10.1596/978-1-4648-0018-4. License: Creative Commons Attribution CC BY 3.0 IGO

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All queries on rights and licenses should be addressed to the Publishing and Knowledge Division, The World Bank, 1818 H Street NW, Washington, DC 20433, USA; fax: 202-522-2625; e-mail: [email protected].

ISBN (paper): 978-1-4648-0018-4ISBN (electronic): DOI: 10.1596/978-1-4648-0018-4

Cover photo: Cover design: Debra Naylor, Naylor Design

Library of Congress Cataloging-in-Publication Data[[CIP data]]

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v The Small Entrepreneur in Fragile and Conflict-Affected Situationshttp://dx.doi.org/10.1596/978-1-4648-0018-4

Acknowledgments ixAbout the Editors xiAbbreviations xiii

Introduction 1Observations of FCS Firms, Sectors, and Business

Environments 1Implications of Findings and Recommendations 2Notes 4Reference 4

Chapter 1 Overview of the Entrepreneur’s Challenges in FCS 5Overview 5Notes 7Reference 7

Chapter 2 Observations of FCS Firms, Sectors, and Business Environments 9FCS Firm-Level Characteristics 9FCS Sector-Level Characteristics 15FCS General Business Environment 21Notes 32References 33

Chapter 3 Implications of Findings 35Program Design 35World Bank Group Interventions 47Note 49References 49

Chapter 4 Conclusions and Recommendations 51Conclusions 51Recommendations 51References 54

Contents

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vi Contents


Appendix A User’s Guide to Data 55Eastern Europe and Central Asia (ECA) Region 55Sub-Saharan Africa 56South Asia and East Asia and the Pacific 56Issues and Constraints to Data Analysis 56Reference 57

Appendix B Additional World Bank Studies and Field Observations 59References 63

Appendix C Innovation (Correlation Tables) 65

Appendix D Survey Sample Statistics, ECA Region 67

Appendix E Survey Sample Statistics, Sub-Saharan Africa Region 71

Appendix F Basic Quantitative Indicators, ECA Region 73

Appendix G STATA Output Underlying Graphical Presentation 77

Boxes2.1 Trading in Niger 172.2 Challenge of Rebuilding a Sector: Carpet Weaving

in Afghanistan 203.1 Afghanistan Road Map for Improving the Business Enabling

Environment 363.2 Public-Private Dialogue in Investment Climate Interventions 373.3 Bulldozer Initiative in Bosnia and Herzegovina 383.4 Reforming Rwanda’s Tea Sector 393.5 West Bank and Gaza Facility for New Market Development

and Gaza Back-to-Work Programs 403.6 Lebanon Innovation and SME Growth Project 413.7 ILO’s TREE Program 423.8 Public-Private Partnerships in Somalia 433.9 Supporting Predictability: Examples from World Bank Projects 443.10 EU’s Trade Preferences to Western Balkans 453.11 Guinea-Bissau Cashew Nuts 453.12 Capacity Building for Employment in the Construction Sector 473.13 Timor-Leste: A Reflection 494.1 How Much Growth Does It Take to Generate Jobs? 52B.1 Hayel Saeed Anam Group: First Commercial Group

in the Republic of Yemen 59B.2 Success Story of Entrepreneurship in Republic of South Sudan:

Importance of Business Competition 59

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Contents vii


B.3 Cisco and the Importance of Corporate Social Responsibility in the Palestinian Territories 61

B.4 Importance of Household Enterprises (HE) in Rwanda 61B.5 Egypt’s First Revolution: A Cold for Large Businesses and

Pneumonia for SMEs (Importance of Clear Strategy Reform) 62

Figures1.1 Universe of Private Enterprises in FCS Countries in Which

Size of Shadow Economy Reaches 50 Percent of GDP 62.1 Appetite for Risk and Willingness to Innovate in FCS, 2011 102.2 Enterprise Capacity Utilization Lower in FCS Countries than

in Non-FCS Countries 122.3 Willingness to Take up Risk Less in FCS than in

Non-FCS Countries 132.4 Introduction of New Products Hindered by Long Power

Outages in ECA FCS 142.5 FCS Enterprises Start Smaller, Grow More Slowly, or Shrink

over Time Compared to Non-FCS Enterprises 142.6 Annual Export Revenue of FCS Firms in Sub-Saharan Africa

Trade Primarily with Neighboring Countries 162.7 Conflicts in Sub-Saharan Africa Result in Sudden Drops in

Manufactures’ Exports 182.8 Mobile Telecommunications Thrive in Even the Most

Difficult FCS Environments 202.9 Unpredictability in Private Markets and Public Governance

Commonplace in FCS Countries 222.10 Rent-Seeking More Common in FCS Countries than

in Non-FCS Countries 232.11 Biggest Obstacle to Business Environment by Fragility 242.12 Access to Formal Financial Services Appear More Limited

in FCS Countries 252.13 Why FCS Firms Should Not Apply for Loans 272.14 Credit Transactions Less Common in FCS Countries than

in Non-FCS Countries in Sub-Saharan Africa 282.15 Longer or More Frequent Power/Water Shortages Common

in FCS Countries (% of Firms that Experienced Power/Water Shortage the Past Year) 28

2.16 Power Supply Lacking or Very Expensive in FCS Countries 292.17 Access to General Purpose Technology Worse in FCS than

in Non-FCS Countries 292.18 FCS Enterprises in SSA and ECA Face High Losses due

to Crime, Theft, and Disorder 302.19 FCS Firms in Four Countries Experience Disruptions

in Their Product Markets with Uneven Recoveries 30

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viii Contents


2.20 31BB.5.1 Firm Sales Experience and Expectation, Weighted by Sales Value 63

TablesR2.1 Probability to Innovate in FCS Lower than

in Non-FCS Countries 112.1 Total Formal Trade as a Percentage of GDP (“Openness”)

Notably Lower for FCS Countries than for Non-FCS Countries 15

2.2 Weak Regulatory Systems in FCS Countries 22R2.2 Temporary Disruptions in Sales in Sub-Saharan Africa

due to Fragility 264.1 Strategy Matrix for Analysis and Intervention in FCS Areas 52

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ix The Small Entrepreneur in Fragile and Conflict-Affected Situationshttp://dx.doi.org/10.1596/978-1-4648-0018-4

Acknowledgments

This knowledge report was prepared by John Speakman (Lead Private Sector Development Specialist) and Annoula Rysova (Consultant, Finance and Private Sector Development). However, without the contribution of several colleagues, the report would not have reached its final form. Therefore, we are grateful for the help of Nabila Assaf, Michael Engman, Andres Garcia, Alan Gelb, Peter Mousley, and Vijaya Ramachandran (Center for Global Development). We also express our appreciation to our World Bank colleagues, Alvaro Gonzales, Benjamin Herzberg, Arvind Jain, Bertine Kamphuis, Suhail Kassim, Austin Kilroy, Jana Malinska, Alan Moody, Khadija Shaikh, and Andrew Stone.

The report uses data from the European Bank for Reconstruction and Development-World Bank Business Environment and Enterprise Performance Surveys (BEEPS). We wish to acknowledge the enterprise managers who have given their time to the Enterprise Survey effort over years. Alicia Hetzner edited the volume.

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xi The Small Entrepreneur in Fragile and Conflict-Affected Situationshttp://dx.doi.org/10.1596/978-1-4648-0018-4

About the Editors

John Speakman is a Lead Private Sector Specialist in the World Bank’s Africa Region. He has worked on Fragile and Conflict States (FCS) since joining the Bank in 1995. Initially, John worked in the Republic of Yemen, later in Iraq, Afghanistan, and the Khyber Pashtoonkhwa Province of Pakistan. Presently, he works on a number of FCS in Africa. He is academically qualified in Law, Finance, and Economics.

Annoula Rysova is a Consultant in Finance and Private Sector Development at the World Bank, Washington, DC. Before joining the World Bank, Annoula worked in the financial sector in Greece and the United States. She holds an MPA/International Development from Harvard University and an MSc in Economics from the Athens University of Economics and Business. Currently she works on a number of projects in Sub-Saharan Africa, the Middle East, and South Asia.

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xiii The Small Entrepreneur in Fragile and Conflict-Affected Situationshttp://dx.doi.org/10.1596/978-1-4648-0018-4

ADR Alternative Dispute Resolution

BEEPS Business Environment and Enterprise Performance Survey

CCSD Center on Conflict, Security and Development

CPIA Country Policy and Institutional Assessment

CSO civil society organization

DFID Department for International Development (UK)

ECA Eastern Europe and Central Asia

ES Enterprise Survey (World Bank)

EU European Union

FAO Food and Agriculture Organization of the United Nations

FATA Federally Administered Tribal Areas

FCS Fragile and Conflict States

FDI foreign direct investment

FNMD fund for new market development

FPD finance and private sector development

GDP gross domestic product

ha hectare

HE household enterprise

ICR implementation completion report

ICT information and communication technologies

IDA International Development Agency

IEG Independent Evaluation Group (World Bank)

ILO International Labour Organization (UN)

kg kilogram

M&E monitoring and evaluation

MCII Ministry of Commerce, Industry, and Investment (Republic of South Sudan)

MDTF Multi-Donor Trust Fund

NAEB National Agricultural Export Development Board (Rwanda)

Abbreviations

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xiv Abbreviations


NBF Nepal Business Forum Phase I

NGO nongovernmental organization

OECD Organisation for Economic Co-operation and Development

OLS ordinary least square

PCNA post-crisis needs assessment

PPD public-private dialogue

PPP public-private partnership

PSD private sector development

SDG South Sudanese Pound

SEED Sustainable Employment and Economic Development (UKAID)

SEZ special economic zone

SME small and medium-size enterprise

TREE Training for Rural Economic Empowerment (ILO)

UN United Nations

UNDP United Nations Development Programme

VC venture capitalist

WBG World Bank Group, West Bank Gaza

WDR World Development Report

WDI World Development Indicators

WTO World Trade Organization

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1 The Small Entrepreneur in Fragile and Conflict-Affected Situationshttp://dx.doi.org/10.1596/978-1-4648-0018-4

Introduction

This report is part of a broader effort by the World Bank Group to understand the motives and challenges of small entrepreneurs in fragile and conflict-affected situations (FCS).1 The report’s key finding is that, compared to entrepreneurs elsewhere, entrepreneurs in FCS have different characteristics, face significantly different challenges, and thus may be subject to different incentives and have different motives. Therefore, it is recommended that both the current analytical approach and the operational strategy of the World Bank be informed by the findings that follow.

Observations of FCS Firms, Sectors, and Business Environments

FCS Firm CharacteristicsThe report summarizes findings of recent World Bank Enterprise Surveys (ES) conducted across Sub-Saharan Africa (SSA), Asia, and the Eastern Europe and Central Asia (ECA) Region as well as Doing Business indicators and additional World Bank Group studies and field observations. The report finds that the majority of entrepreneurs in FCS countries are small, informal, and concentrated in the trade/services sectors. According to the ES, and after controlling for the level of development (that is, GDP per capita),

1. The average FCS firm in SSA and the ECA Region2 produces less output than non-FCS firms.

2. The average FCS firm in ECA is by 20 percent less likely to innovate (that is, to introduce/upgrade new products and services) than its non-FCS counterpart.

3. FCS firms start smaller and grow significantly more slowly, or even shrink (in the number of employees) over time, compared to non-FCS firms in the Regions analyzed.

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2 Introduction


FCS Sector CharacteristicsThe report also highlights the differences in sector characteristics between FCS and non-FCS business environments. Entrepreneurs in FCS are concentrated in the trade/services sector (and not manufacturing). However, the average FCS country is less open to formal trade across all the three Regions analyzed and trades mainly with its neighboring countries. Compared to the average non-FCS countries in the Region, the average FCS country also is subject to severe and immediate sales (exports) and production factor disruptions with uneven recov-ery. Despite these obstacles, new entrepreneurial opportunities can arise even in the most difficult FCS environments, such as mobile telephony industry that has been thriving in Afghanistan, Guinea-Bissau, Iraq, and Somalia.

FCS Business Environment CharacteristicsIn the majority of the FCS environments, to start a business is very difficult. The regulatory systems (particularly regarding construction permits, property registration, investors’ protection, and contracts enforcement) are very weak. Furthermore, the key trust-based relationships for public-private dialogue (PPD) and commerce may have broken down or even dissipated owing to heavy and widespread rent-seeking, severe political instability, inefficient courts, and lack of state-provided security. Moreover, the general business envi-ronment is suffering from political instability and very poor access to formal finance (Asia and Sub-Saharan Africa), as well as burdensome tax rates (ECA). In particular, formal financial services, such as the provision of loans or lines of credit, are very limited for FCS firms in ECA and SSA. Part of the reason is the high collateral requirements or complex loan application procedures. As a con-sequence, credit transactions are very limited. Last, serious basic infrastructure shortages (such as of power or water supply) elevate costs of doing business in FCS countries (especially in Kosovo, in which 97 percent of the respondent firms lack quality access to electricity). In addition, poor access to general pur-pose technology (that is, high-speed internet) makes business facilitation slower and even more costly.

Implications of Findings and Recommendations

Analytics

• Invest in data to be able to constantly evaluate FCS business environments. Enhance existing sources such as the ES in terms of data availability (make more frequent in all Regions), and data consistency (inflate all the relevant questionnaires with questions focused on the uniqueness of the FCS business environment and questions related to innovative activity). Improve local capacity in data analysis.

• Encourage information sharing and flows. Encourage (public-private) dialogues, ensure information sharing through internet wherever possible, and improve local capacity in knowledge management.


Introduction 3


Program DesignA typical private sector development (PSD) program in FCS countries would involve interventions aimed at encouraging investment and getting markets to work better and more competitively. Typical interventions would:

• Address public-private coordination failures. Launch effective PPDs at the overall economy level and at the sector level. Formal PPDs can help improve coordination and lead to encouragement of competitive industries, develop-ment corridors or economic zones, as well as local economic development. Furthermore, specific to FCS, PPDs can be particularly useful in building trust in such low-trust fragile environments and can avoid policy capture by estab-lished businesses with close political ties.

• Encourage innovation and entrepreneurship. Support business development services (professional training, high quality standards, or sound market con-nectivity); provide key factor markets such as access to finance; and target key groups including women, youth, and excombatants.

• Resolve market failures in the provision of public goods. Enhance regulatory sys-tems that support rather than obstruct markets, provide critical public goods (infrastructure, security), and ensure availability of serviced industrial land for purchase or lease.

• Provide a generally predictable business environment. Manage perceptions of political uncertainty, limit corruption, and ensure reliable judicial systems.

• Encourage demand and investment. Allow for trade preferences, encourage competitive industries, develop linkages with commodities and local sourcing of aid (construction, logistics, facilities management), and encourage and support remittance flows. In addition to the traditional foreign direct invest-ment (FDI), allow for measures that can support diaspora-based and collective investment.

Bank Group InterventionsAny FCS government is unlikely to have the capacity to manage and resource a program of this complexity and magnitude. Therefore, focus is required on:

• Ways that programs should be designed. Based on an assessment of past World Bank projects, the key success factors aligned with the Bank interventions are to (a) address the most severe growth constraints identified by businesses, (b) match the constraints with government priorities, and (c) target areas that have proven track records of the Bank’s success (based on IDA rating) (Leo, Ramachandran, and Thuotte 2012). In addition, review past projects to acquire additional sets of lessons in project design.

• Close coordination throughout the World Bank Group. Ensure small, on-the-ground presence; and deliver services through a joint strategy, leveraging one another’s comparative advantage (that is, make the Bank’s execution more flexible through partnering with IFC’s on-the-ground presence).


4 Introduction


• Design flexibility. To take into account the highly uneven recovery pattern and possible cycles of recurrent violence in FCS areas, at the beginning, start small; assess the response to the package; extend it in case of success; cut it otherwise.

• Rapid tactical intervention at moments of reduced violence and heightened political commitment to reform. At such moments, the Bank Group should be ready to immediately introduce a PSD response package. This package could include (a) a small matching grant component with a very flexible operational menu, (b) a challenge fund that can harness the delivery capabilities of nongovern-mental organizations (NGOs), and (c) immediate institutional support for regulatory reform and developing a coordinating capability. The package would be executed by a pre-procured firm.

Notes

1. This report is part of a series on finance and private sector development (PSD) in fragile and conflict-affected situations (FCS) prepared in a partnership of the World Bank Finance and Private Sector Development (FPD) Network, the International Finance Corporation, and the Center on Conflict, Security and Development (CCSD). The series comes under the auspices of the Bank’s Knowledge Project, “Private Sector Development (PSD) in FCS” (P125752), managed and facilitated by CCSD. The project’s objectives are, first, to examine the World Bank Group’s PSD practice in FCS in light of World Development Report 2011: Conflict, Security and Development. Second, the project will explore implications for future operations, analytics, and research with a focus on the process of inquiry, dialogue, and discussion to promote a community of practice around this nexus of FCS. The report’s primary aim is Bank Group learning; however, the report also is available for the benefit of development partners and clients. Other topics explored in the series include how firms cope with crime and violence, foreign direct investment (FDI), and investment climate reforms in FCS, as well as the potential contribution of financial institutions to resilience against violence and conflict.

2. Asia is not included in this finding because the surveys in these Regions either do not contain the same questions regarding innovation, or the number of firms that actually replied to the question was not enough to draw a general conclusion.

Reference

Leo, B., V. Ramachandran, and R. Thuotte. 2012. “Supporting Private Business Growth in African Fragile States.” Center for Global Development, Washington, DC. http://www.cgdev.org/files/1426061_file_Leo_Ramachandran_Thuotte_fragile_states _ FINAL.pdf.



Overview of the Entrepreneur’s Challenges in FCS

Overview

This investigation explores enterprises’ willingness to invest in fragile and conflict-affected situations (FCS), and how this investment can help economic recovery and employment creation. There is a specific focus on enterprises that have limited mobility and choices, that is, those that do not have the opportunity to operate and invest in other countries.1 In most cases, enterprises in FCS have lived through violent conflicts. Typically, these enterprises are small, not part of established business elites, more often than not informal, and sector biased. These businesses can range from household enterprises to small and medium-size enterprises (SMEs). They are the vast majority of enterprises in FCS—they are the “99 percent.”

Not only do FCS enterprises tend to be small. They also tend to be informal and engaged in sectors that are trade and service oriented with high returns on investment (figure 1.1). In addition to being small and informal, FCS enterprises tend to be dominated by household enterprises, especially in SSA. The average size of the shadow economy relative to gross domestic product (GDP) in the FCS states included in our analysis is 46 percent, with some over 60 percent.2 This size can be contrasted with the non-FCS states included in our analysis whose shadow economies relative to GDP average 36 percent.3 What also is typi-cal for the informal (and, to a lesser degree, the formal) FCS enterprises are the kinds of businesses. There are many trading/service enterprises, and any large manufacturing tends to be agribusiness related.4 In most FCS, the kind of insti-tutional framework that would allow for large-scale, job-intensive manufacturing simply does not exist.

These entrepreneurs face very specific challenges that are different and more extreme than challenges in non-FCS countries. To determine these challenges, the on-the-ground observations of finance and private sector devel-opment (FPD) experts working in FCS were collected.5 These observations include comments on the types of enterprises that are found, the sectors in

C H A P T E R 1


6 Overview of the Entrepreneur’s Challenges in FCS


which they are engaged, the challenges that they face, and the opportunities from which they can benefit. The data (appendix A, User’s Guide to Data) and evidence from the projects that support these more anecdotal observations are presented in our analysis.6

What can then be done by policymakers with the support of donors to help? The implications of these findings are reviewed using three lenses: (a) how can countries develop effective private sector development programs? (b) under cur-rent operating guidelines, how can the Bank Group best support private sector development? and (c) are there opportunities to improve operational processes and collaboration among the agencies across the Bank Group? A menu of typical program responses that could help FCS countries is discussed.

F inally, a consideration of what these challenges means for the overall recovery prospects of FCS is discussed. It is clear that generation of good jobs through the private sector is a key element of any recovery strategy. It is less clear that these jobs can be generated at the pace necessary. Therefore, it often is necessary to dovetail the private sector strategy with additional measures such as a public works program and agricultural revitalization.

Section II explores (a) the key characteristics of the FCS firms; (b) the FCS sector characteristics; and (c) the contrast between the FCS and non-FCS general business environment. Section III focuses on the findings of the analysis and their implications for program design and WBG interventions. Section IV draws concludes and makes recommendations.

100

80

20

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20

98 100

Large Medium Small Micro

Share of formal firms

Share of informal firms

Manufacturing Retail/services

Perc

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Figure 1.1 Universe of Private Enterprises in FCS Countries in Which Size of Shadow Economy Reaches 50 Percent of GDP

Source: Data from Schneider 2007.


Overview of the Entrepreneur’s Challenges in FCS 7


Notes

1. A parallel investigation looks at investors who have choices—typically, the foreign investors.

2. Authors’ calculations based on Schneider 2007. Schneider defines a shadow economy as that which that “includes all market-based legal production of goods and services that are deliberately concealed from public authorities for the following reasons: (a) to avoid payment of income, value added or other taxes, (b) to avoid payment of social security contributions, (c) to avoid having to meet certain legal labor market standards, such as minimum wages, maximum working hours, safety standards, etc., and (d) to avoid complying with certain administrative procedures, such as completing statistical questionnaires or other forms.” Schneider excludes the informal household economies from his calculation and does not focus on tax evasion or tax compliance.

3. According to Schneider, for OECD countries, the average size of the shadow economy relative to GDP is 15 percent.

4. According to the World Development Indicators (WDI) 2012, the average share of manufacturing in GDP of FCS states is 12 percent, whereas for the non-FCS states, the share reaches almost 17 percent.

5. As a knowledge product, surveys were conducted informally, and FCS practitioners (approximately 40 staff) in the FPD Network reported their core observations regard-ing FCS.

6. As with most areas of FCS, data describing initial conditions are hard to come by. It is only after some time that a reliable data set emerges. The main data sources are the World Bank’s Enterprise Surveys (appendix A).

Reference

Schneider, F. 2007. “Shadow Economies and Corruption All over the World: New Estimates for 145 Countries.” Economics e-journal 2007–9. http://www.economics-ejournal.org/.



Observations of FCS Firms, Sectors, and Business Environments

Through a consultative process with World Bank Finance and Private Sector Development (FPD) experts engaged in FCS, a number of common themes emerged. These features exist in most FCS countries to varying degrees depend-ing on endowments, demographics, and governance. In all cases, there is some degree of resilience of entrepreneurship in FCS environments, and observations in this regard conclude this section. These observations have been tested primar-ily by reviewing the World Bank’s Enterprise Survey for three Regions: Sub-Saharan Africa, Europe and Central Asia (ECA), and Asia (see appendix A for guide to data).1 Consequently, unless stated otherwise, the primary sources of data presented in this report are the World Bank’s Enterprise Surveys, which compare FCS with non-FCS countries within each of these three Regions.

FCS Firm-Level Characteristics

Entrepreneurship itself—enterprises’ appetite for risk and willingness to innovate—often is suppressed (figure 2.1). An average FCS firm in the ECA Region is 20 percent less likely to innovate, either by introducing new products or by upgrading existing product lines (regression table 2.1), than an average non-FCS firm in the same Region. The appetite for taking risk to expand opera-tions is suppressed in more fragile countries (figure 2.1a). However, once FCS firms decide to spend on new equipment, they tend to spend, on average, slightly more as a proportion of their sales compared to firms in less fragile countries (figure 2.1b).

Operating in an FCS country of the ECA Region results in an average of 21 percent less innovative activity than operating in a non-FCS ECA country.

Furthermore, the production capacity of the firms operating in the FCS coun-tries of Asia, the ECA Region, and Sub-Saharan Africa (SSA) is slightly more underutilized that those of non-FCS countries. In other words, firms operating in FCS countries seem to produce, on average, less of their potential output compared to non-FCS firms (figure 2.2). Therefore, not surprisingly, there is less

C H A P T E R 2


10 Observations of FCS Firms, Sectors, and Business Environments


investment in production capacity and innovation in FCS countries than in non-FCS countries (figure 2.3).

Moreover, FCS countries evidence very low levels of information and com-munication technologies (ICT) penetration, notably high-speed Internet (subsec-tion C), and poor access to basic production inputs such as electricity. The lack of new technologies and poor access to electric power that would enhance pro-ductivity suppresses the willingness to innovate (figure 2.4).

FCS enterprises tend to be small. The average size (in number of full-time permanent employees) of an enterprise in an FCS country is half the average size

[[AU: Was there footnote here? Please check.]]

Figure 2.1 Appetite for Risk and Willingness to Innovate in FCS, 2011

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: CPIA (Country Policy and Institutional Assessment) = diagnostic tool that focuses on the key elements that are within the country’s control. CPIA measures the extent to which a country’s policy and institutional framework supports sustainable growth and poverty reduction, and consequently the effective use of development assistance. http://web.worldbank.org / WBSITE/EXTERNAL/EXTABOUTUS/IDA/0,contentMDK:21378540~menuPK:2626968~pagePK:51236175~piPK:437394~theSitePK:73154,00.html.

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ZAR MLIMDG

CIV

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a. Fragility and appetite for risk

b. Fragility and appetite for innovation

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Asia Europe and Central Asia Sub-Saharan Africa Fitted values

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TJKUZB MDA

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ARMKGZ

Kosovo

AGOBFACMR

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Observations of FCS Firms, Sectors, and Business Environments 11


Regression Table 2.1 Probability to Innovate in FCS Lower than in Non-FCS Countries

Clustered at country levelDependent variable (= 1 if “yes”; = 0 if otherwise)

(1)Introduced

new products

(2)Upgraded existing

product line

(3)Introduced new

products(marginal effects)


product line(marginal effects)

Being fragile (d) −0.5324***(0.089)

−0.5131***(0.165)

−0.2073***(0.033)

−0.1868***(0.062)

Industry (c) −0.0046(0.004)

−0.0015(0.002)

−0.0018(0.099)

−0.0005(0.006)

Country (c) −0.0123(0.011)

0.0014(0.009)

−0.0049(0.004)

0.0005(0.003)

Micro-size (d) 0.0846(0.115)

−0.1463(0.148)

0.0338(0.046)

−0.0528(0.055)

Having more than 50% domestic ownership (d)

−0.1505 (0.133)

(0.107)0.1241

−0.0599(0.053)

0.0445(0.038)

Size of locality – city (250,000–1mil population) (d)

−0.2666**(0.128)

−0.1834(0.203)

−0.1052**(0.049)

−0.0658(0.076)

Total annual sales (ln) −0.3075***(0.012)

−0.0238**(0.015)

−0.0123***(0.005)

−0.0111**(0.005)

Having spent on R&D (d) 1.0931***(0.129)

1.1427***(0.170)

0.3964***(0.035)

0.3002***(0.022)

Having received government subsidy (d) 0.12866(0.2198)

0.1051(0.233)

0.0513(0.087)

0.0358(0.076)

Using own website to communicate with clients/suppliers (d)

0.4002***(0.096)

0.2336*(0.133)

0.1586***(0.038)

0.0799*(0.046)

Having paid for security (d) 0.2445***(0.066)

0.1894***(0.064)

0.0972***(0.026)

0.0662***(0.0206)

Dispose checking/savings account (d) 0.1866**(0.082)

0.2984*(0.182)

0.0737**(0.032)

0.1101*(0.069)

Dispose line of credit/loan (d) 0.1624(0.123)

0.0835(0.070)

0.0647(0.049)

0.0290(0.024)

Dispose internationally recognized quality certification (d)

0.0065(0.078)

0.0543(0.115)

0.0026(0.031)

0.0188(0.039)

Compulsory to have certification to produce/sell (d)

0.0955(0.070)

0.1367*(0.077)

0.0380(0.028)

0.0476*(0.027)

% of senior management’s time spent on gov. regulations

−0.0012(0.0017)

−0.0017(0.004)

−0.0005(0.001)

−0.0006(0.001)

Years of experience in the sector of Top Manager

0.0015(0.005)

0.0020(0.006)

0.0006(0.002)

0.0007(0.002)

table continues next page

of an enterprise in a non-FCS country of SSA or the ECA Region. Even more dramatically, in the ECA Region, the average size of an FCS enterprise shrinks over time (figure 2.5). Therefore, FCS enterprises tend to be small, either by choice or as a result of the main characteristics of the usual FCS business environ-ment. It is characterized by poor access to finance and infrastructure; and direct or indirect government-imposed barriers in the form of corruption, weak security, and/or policy biases toward large and politically connected firms (subsection C).


Regression Table 2.1 Probability to Innovate in FCS Lower than in Non-FCS Countries (continued)

Clustered at country levelDependent variable (= 1 if “yes”; = 0 if otherwise)

(1)Introduced

new products


product line

(3)Introduced new

products(marginal effects)


product line(marginal effects)

Constant 0.9613*(0.547)

0.3227(0.556)

Pseudo R-squared 0.1709 0.1317Number of observations 3,321 3,301 3,321 3,301

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: 1. Standard errors in parentheses. 2. The regression table outlines results of two probit regressions based on the WB’s Enterprise Surveys for the ECA Region (BEEPS). (A probit is a type of regression estimation with a binary outcome.) The svyset command was used in STATA to ensure that standard errors are calculated correctly. The dependent variable for models (1) and (3) is a binary variable equal to 1 if the establishment (explained farther down) introduced new products in the last three years. The dependent variable for models (2) and (4) is a binary variable equal to 1 if the establishment upgraded an existing line of products. The explanatory variables are sociodemographic characteristics of the establishments (country, industry, size, size of location, ownership, experience of top manager), economic indicators (total annual sales, government subsidy, security costs), indicators of innovative activity (expenditure on R&D), infrastructure indicators (access to finance, access to ICT), and regulatory indicators (time spent on dealing with regulations, compulsory certificates, quality certifications). For models (3) and (4), only marginal effects are shown. To control for possible income differences, the average gross domestic product (GDP) per capita (in 2000 constant US$ prices, per World Development Indicators 2012) of the group of fragile states included in the analysis (that is, US$1,523) is similar to the average GDP per capita of the group of non-fragile states included in the analysis (that is, US$1,505).Standard errors in parentheses: *** p<<0.01, ** p<<0.05, * p<<0.1.

Figure 2.2 Enterprise Capacity Utilization Lower in FCS Countries than in Non-FCS CountriesPercent

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Capacity utilization is defined as the ratio of output produced to maximum output. Results controlled for GDP per capita levels; p-value (ECA) = 0.558; p-value (SS Africa) = 0.947; p-value (Asia) = 0.001 (appendix G).

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Non-fragile Fragile



Figure 2.3 Willingness to Take up Risk Less in FCS than in Non-FCS Countries

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a. Comparison of firms’ purchases of fixed assets in FCS and non-FCS countries

Asia

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Results controlled for GDP per capita levels, p-value (ECA) = 0.176, p-value (SS Africa) = 0.282, p-value (Asia) = 0.755 (appendix G).

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Results controlled for GDP per capita levels (appendix G). p-value (new products) = 0.041, p-value (R&D) = 0.404, p-value (upgrade) = 0.275 (appendix G).

Non-fragile Fragile

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Introducing newproducts/services

Investing in R&D

b. Firm innovation in ECA greater in FCS than in non-FCS countries

Upgrading existingproduct lines




Figure 2.4 Introduction of New Products Hindered by Long Power Outages in ECA FCS

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Other Regions are not included in figure 2.4 due to the lack of specific questions on innovative activity in their questionnaires. Y-axis represents the correlation coefficient based on a correlation table (appendix C). Graph depicts top four aspects significantly (at 10% significance level) correlated with introduction of new products.

–0.1

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R&D

Own website

Security

Compulsory certificates

Duration of power outages

Figure 2.5 FCS Enterprises Start Smaller, Grow More Slowly, or Shrink over Time Compared to Non-FCS Enterprises

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Growth in employment = (no. of current employees – no. of employees at start-up)/firm age. Results controlled for GDP per capita levels. p-value (ECA) = 0.178, p-value (SS Africa) = 0.153, p-value (Asia) = 0.465 (appendix G).

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FCS enterprises tend to be informal. Work done as part of the Post-Crisis Needs Assessment (PCNA) for Federally Administered Tribal Areas (FATA) and crisis-affected areas of Khyber Pashtunkhwa (12 million people) found that only 1 firm with more than 200 employees was formally registered. All of the other firms (especially smaller ones) were informal. Khyber Pashtunkhwa is an extreme case because no large urban areas were included in the area under investigation. Nevertheless, the story of a few large and medium-size registered firms and many very small unregistered firms is repeated across almost all FCS areas.

FCS Sector-Level Characteristics

FCS enterprises specialize primarily in regional retail and services, rather than in manufacturing. In most FCS, the kind of institutional framework that would enable large-scale, job-intensive manufacturing simply does not exist. Any large manufacturing tends to be agribusiness related. Indeed, one often finds derelict factories that are the direct results of misguided industrial policies of prior years. According to the World Development Indicators (WDI) 2012, the average share of manufacturing in GDP (including commodities) is 17 percent for non-FCS countries, compared to 12 percent for FCS countries. The extreme contrasts are Mauritius with almost 21 percent compared to Angola with 5 percent in Sub-Saharan Africa; and Belarus with over 33 percent compared to Georgia with 11 percent in ECA.2

In addition, compared to non-FCS countries, FCS countries overall remain less open to international trade. In other words, the average net exports share of GDP is significantly smaller for FCS countries, compared to non-FCS countries (table 2.1), and formal trade is facilitated primarily among neighbors (figure 2.6).

Table 2.1 Total Formal Trade as a Percentage of GDP (“Openness”) Notably Lower for FCS Countries than for Non-FCS CountriesPercent

Year Region

Openness* (2005–09)

Current prices Constant prices

Fragile Non-fragile Fragile Non-fragile

2005 Sub-Saharan Africa 64 79 65 83ECA 99 105 93 102





Source: Heston, Summers, and Aten 2011, Penn World Table.*Openness = Total trade as % of GDP, i.e. Openness = (Exports+Imports)/real GDP per capita; for constant prices at 2005; simple average.




Thus, for international markets to function in FCS, trade relationships with neighbors play an important role (box 2.1). However, since conflicts or violent events often cause relationships with neighbors to deteriorate, FCS countries often reach the point at which trading becomes difficult and international mar-kets disappear (figure 2.7).3

The following sector motivations can be identified for FCS entrepreneurs:

• The opportunity to make high returns on investment through trading and provision of needed services. For example, working in a low-investment, high-return sector, telecommunications providers have succeeded in even the most difficult environments (figure 2.8). According to the Enterprise Surveys, 93 percent of FCS entrepreneurs in Sub-Saharan Africa use mainly mobile phones for their operations, compared to only 12 percent of the non-FCS entrepreneurs in the Region. In addition, entrepreneurs often emerge to take advantage of new opportunities (boxes B.1 and B.2, examples of Republic of South Sudan and the Republic of Yemen). Last, trading, easily transportable extractive industries, and basic food/agroindustry (such as soft drink manu-factures) are typical sectors that thrive. Corporate social responsibility invest-ments, although rarely significant, also can help. For example, Cisco’s investments in West Bank and Gaza began as part of its corporate social responsibility activities, but evolved on a low-key basis to a self-sustaining

Figure 2.6 Annual Export Revenue of FCS Firms in Sub-Saharan Africa Trade Primarily with Neighboring CountriesPercent

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Results controlled for GDP per capita levels (appendix G). p-value (neighbors) = 0.237, p-value (developed) = 0.129, p-value (other) = 0.217 (appendix G).

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Developedcountries

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Non-fragile Fragile




Box 2.1 Trading in Niger

Maradi is the trading and agricultural hub of Niger’s Hausa region. This large Sahelian country is one of the poorest in the world, and Maradi is home to a large malnourished population. Maradi also is a major livestock center and hosts 1 of the 4 abattoirs in the country. Breeding in Maradi generates 20 percent of the wealth of the local population.

Hassane Bola is a butcher who operates on the outskirts of Maradi. His business is twofold. Primarily, he sells meat, offal, and hides to local consumers and businesses. Occasionally, he also sells live animals to a nearby border market. These animals then make their way to Nigeria, which does not import cut meat from Niger. Hassane is justifiably proud of the quality of his meat. “Our [Niger’s] meat is famous in the region,” he says. “Everyone who tastes it says it is excellent.” Hassane, who operates a midsize business by Nigerian standards and employs two other men, is considered a reasonably successful butcher. “In a good month, I earn enough to support my wife and daughter,” he says. Zeinab is his only daughter, and he is proud of her: she goes to school and can read. In a country in which the value of meat often is measured in bags of cereal grains, Hassane says, “I am not rich but, compared to my friends, fate has been kind to me. We have enough millet to eat.”

Hassane is a member of a butchers’ association that works with Maradi’s abattoir. The association recently submitted a petition to the government requesting support for transporting their cattle across the border to Nigeria. “Right now, it takes so long, and the animals get tired. We use carts when we can, but the roads are not paved. Sometimes the authorities at the border also create problems,” explains Hassane. What is the solution? “Better roads, less red tape at the border. A nicer abattoir will also help convince Nigeria to buy our delicious cut meat. If we have proper veterinary services, our animals will be fatter and fall sick less often.”

Do local conflict and violence affect his business? By good fortune, not yet. “We are peace-loving people,” Hassane says, indicating his fellow butchers. “We ignore all the fighting that goes on. But one never knows when the tide will turn.” This uncertainty does not make him lose focus on what really matters to him: his livelihood. “My animals are my life. All I ask is a fair chance to keep my business running.”

Source: Author interview.

mainstream business (box 2.2). NGOs such as Fair Trade or Peace Dividend Trust support market connections.

• The vast majority of entrepreneurs stay in business because of a lack of alter-natives. Business people often have no choice. At the household enterprise level, enterprises have little capital at risk and, at the same time, have kept their know-how, which easily can be re-established (box B.4 on Rwanda). Thus, there is simply no other way to earn a living. These circumstances often are reinforced by tradition—in which a particular tribe or caste has engaged in a particular trade for many generations (common in many agricultural value chains). In several war-affected countries, the role of household survival imperatives,




Figure 2.7 Conflicts in Sub-Saharan Africa Result in Sudden Drops in Manufactures’ Exports

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Mugabe’s repression and violence response to popular protests

“Operationmurambatsvina”

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b. Côte d’Ivoire

Corrupt bedieoverthrown from his post

Massive popularprotests resulting frompresidential elections

France’s interventionto stop rebel groups

figure continues next page




Source: World Development Indicators, 1990–2010, World Bank.Note: Blue = left-hand side axis; Orange = right-hand side axis.

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d. Cameroon

Nigeria-Cameroondispute over Bakassi

Peninsula; nationwidetransport strike at high fuel

costs that turns into anti-government demonstrations

Figure 2.7 Conflicts in Sub-Saharan Africa Result in Sudden Drops in Manufactures’ Exports (continued)




Figure 2.8 Mobile Telecommunications Thrive in Even the Most Difficult FCS Environments

Source: ITU Development Index.

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AfghanistanGuinea-Bissau IraqSomalia

Box 2.2 Challenge of Rebuilding a Sector: Carpet Weaving in Afghanistan

The Afghan name is synonymous with hand-woven carpets. The tradition of carpet weaving dates back millennia. It is in part a function of the country’s location on one of the main “Silk Road” routes from China to the West (demand side). The tradition also is due to agricultural conditions well suited to the production of good-quality animal fibers and unique dyes from the orchard industry (supply side). At its peak, this industry employed as many as 6 million people. The carpets typically are woven at home, and 1 carpet trader could have as many as 10,000 “part-time” household-level suppliers.

Afghanistan’s sustained fragility and many events over many decades have severely disrupted its connection with markets for inputs and supplies. The result has been that some Afghan manufacturers have relocated to Pakistan, and some Pakistan entrepreneurs have established “Afghan” manufacturing businesses in Pakistan. This relocation has led to a breakdown in the Afghan brand. There are now three types of carpets on the market: Afghan carpets manufactured in Afghanistan (with Afghan and non-Afghan fibers), Afghan carpets manufactured in Pakistan using Afghan-sourced fibers, and Afghan carpets manufactured in Pakistan (and the Islamic Republic of) using non-Afghan fibers. The Afghan carpets manufactured in Afghanistan often are traded by Pakistanis. To add “salt to the wound,” in Afghanistan itself, less expensive Iranian machine-made carpets are displacing hand-woven carpets. Therefore, it is estimated that today only 1 million Afghans work in the carpet-weaving industry.

box continues next page




Not surprisingly, within Afghanistan, there have been strong calls to reestablish the Afghan brand and thereby retain the significant value added being lost ($200–300 million per annum). However, these are not easy tasks. Key aspects of the technology (such as know-how and designs), manufacturing capabilities (washing and scissoring), and connections to the market no longer are in Afghanistan, and there is little incentive for them to return.

Rebuilding these capabilities will require sustained coordination between the public and private sectors and significant investment to rebuild the brand. Despite many good intentions, these actions have not yet been taken.

Source: World Bank.

[[AU for John Speakman: Please provide the precise source. If this text is original with this book, then no source line is needed.]]

Box 2.2 Challenge of Rebuilding a Sector: Carpet Weaving in Afghanistan (continued)

financial constraints, remittances (and their possible “disincentive effects”), and informal and criminal economies have been identified as important factors in determining entrepreneurship. However, exactly how these factors influence entrepreneurs remains inconclusive. For example, silk weavers or furniture manufacturers in the Swat Valley in Pakistan will continue to try to work in their profession no matter what happens (KP-FATA and others 2010).

FCS General Business Environment

The weakness of the institutional environment for enterprises in FCS has a major impact on the predictability of interactions with the government and other firms. Because their regulatory systems may have dissipated, FCS countries are charac-terized by having no effective regulatory systems. The Doing Business indicators show FCS countries scoring poorly on almost all the regulatory measure rankings (table 2.2).

Key “trust”-based relationships essential for public private dialogue and commerce may have broken down. In general, social arrangements in FCS countries such as such as trust and clean government are absent. If a firm chose to move its operations from a non-FCS to an FCS area of Sub-Saharan Africa, corruption and/or political instability would become among its most severe obstacles (figure 2.9). By the same token, if a firm decided to start operations in an FCS country in Asia, informal competition would be a major obstacle to business. Finally extortive rent-seeking is the norm in FCS countries generally (figure 2.10). Informal payments/gifts frequently are required by officials for citizens to access basic infrastructure or to acquire essential business permits and licensing.

Despite widespread rent-seeking, entrepreneurs in FCS countries in SSA still do not perceive corruption as a primary obstacle to the general business envi-ronment. Instead, the primary obstacle reported is poor access to finance (figures 2.11 and 2.12). Specifically, formal financial services (such as the provi-sion of loans or lines of credit, figure 2.11 and figure 2.12) may have become limited or even ceased; and strong informal systems (such as the Hawala system)


Table 2.2 Weak Regulatory Systems in FCS Countries

Fragile states Ease of doing

businessStarting a business

Dealing with construction permits

Registering property

Protecting investors

Enforcing contracts

Afghanistan 160 30 162 172 183 161Angola 172 167 115 129 65 181Bosnia and Herzegovina 125 162 163 100 97 125Congo, Rep. 181 175 103 156 155 159Eritrea 180 182 183 178 111 47Kosovo 117 168 171 73 174 157Sierra Leone 141 72 167 169 29 141Timor-Leste 168 157 114 183 133 183

Non-fragile states Armenia 55 10 57 5 97 91Belarus 69 9 44 4 79 14Botswana 54 90 132 50 46 65Estonia 24 44 89 13 65 29Macedonia, FYR 22 6 61 49 17 60Mauritius 23 15 53 67 13 61Kyrgyz Republic 70 17 62 17 13 48Mongolia 86 97 119 26 29 33

Source: World Bank Doing Business, http://www.doingbusiness.org/reports/global-reports/doing-business-2014 (accessed in April 2012).Note: Rankings of 183 economies (1=most business-friendly regulation).

<100 100–150 >150

Figure 2.9 Unpredictability in Private Markets and Public Governance Commonplace in FCS Countries

Source: World Bank Enterprise Surveys 2009–11.Note: See appendix G for technical details. Percentage of firms reporting “yes” on the biggest obstacle to business. Aspects in the figures (political instability, informal competition, corruption, and crime) used as proxies to unpredictability.

ECA Sub-Saharan Africa Asia

a. Non-FCS countries (percent) b. FCS countries (percent)

010203040506070

Crime, Theft, anddisorder

Corruption

CourtsInformalcompetitors

Political instability

010203040506070

Crime, Theft, anddisorder

Corruption

CourtsInformal competitors



may have been established or become the only systems available (Wikipedia). Enterprise Surveys show that complex application procedures and high collat-eral requirements share the responsibility for the inability of small entrepre-neurs to acquire formal financing in the FCS countries of Sub-Saharan Africa and the ECA Region (figures 2.13 and 2.14). The cost of finance also can increase. The Arab Republic of provides a dramatic example: 50 percent of firms report increased financial costs after the revolution (World Bank 2011). Some sectors, such as tourism, can be hit particularly hard by violent conflict. For example, in Egypt, Pakistan, and Sri Lanka, the number of tourist arrivals declined substantially after conflict (UNWTO 2011).

Poor infrastructure elevates the cost of doing business in FCS countries. Frequent and/or long power outages and shortages in water supply are more common in FCS countries than in non-FCS countries (figure 2.15). Power ser-vices provided by the state prior to a violent conflict may have been destroyed and are no longer available. Kosovo is an extreme example in which a remarkable 97 percent of firms report experiencing power outages (figure 2.16). Moreover, 49 percent of FCS firms in Sub-Saharan Africa must rely on private, thus more

Figure 2.10 Rent-Seeking More Common in FCS Countries than in Non-FCS CountriesPercent

Source: World Bank Enterprise Surveys 2009–11.Note: SS Africa: p-value (electrical connection) = 0.587; p-value (water connection) = 0.871, p-value (phone connection) = 0.000; p-value (construction permit) = 0.039, p-value (tax officials) = 0.023, p-value (import license) = 0.037; p-value (business license) = 0.091. ECA: p-value (electrical connection) = 0.748, p-value (phone connection) = 0.617, p-value (water connection) = 0.498, p-value (construction permit) = 0.755, p-value (tax officials) = 0.015, p-value (import license) = 0.569, p-value (operating license) = 0.053. Asia: p-value (business license) = 0.752, p-value (import license) = 0.916, p-value (tax officials) = 0.397, p-value (construction permit) = 0.000, p-value (phone connection) = 0.214, p-value (water connection) = 0.045, p-value (electrical connection) = 0.319 (appendix G).

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Asia non-fragile Asia fragile ECA non-fragile ECA fragile Africa non-fragile Africa fragile




expensive, power supply (figure 2.16). Finally, access to general-purpose technol-ogy, such as high-speed internet connection, own website, or email, remains very limited in FCS countries. This fact also can explain the limited innovative activity and elevated costs of doing business in these countries (figure 2.17).

Security in FCS countries often has deteriorated to the point that, although enterprises survive, they must take extraordinary and sometimes informal measures. Losses in annual sales due to crime, theft, and disorder are com-monplace in the FCS countries of Sub-Saharan Africa and the ECA Region (figure 2.18). Moreover, the state cannot uphold security; thus, every second entrepreneur must self-provide security.

Figure 2.11 Biggest Obstacle to Business Environment by Fragility

Source: World Bank Enterprise Surveys 2009–11.

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40%

50%Access to finance

a. Biggest obstacle to non-FCS firms

Access to land

Business licensing and permits

Corruption

Courts

Crime, theft and disorder

Customs and trade regulations

ElectricityInadequately educated workforce

Labor regulations


Practices ofcompetitors in the

informal sector

Tax administration

Tax rates

Transport

b. Biggest obstacle to FCS firms

Asia ECA Sub-Saharan Africa

0%

10%

20%

30%

40%Access to finance

Access to land

Business licensing and permits

Corruption

Courts


Customs and trade regulations

ElectricityInadequately educated labor

Labor regulations


Informal competitors

Tax administration

Tax rates

Transport




FCS enterprises face disruptions (permanent or temporary) in their product and factor markets. Demand in internal markets may have collapsed as a result of an abrupt disruption. In Sub-Saharan Africa, being fragile has had no statisti-cally significant impact on sales either last year or three years ago (regression table 2.2). Nevertheless, firms in Egypt, especially small and medium size, reported significant temporary setbacks in sales and exports due to the recent revolution, which initially imposed a substantial shock to the economy (box B.2).

Figure 2.19 shows 4 different scenarios of GDP growth recovery in FCS coun-tries: before, during, and after a violent event (dashed line).

• During the Rwandan genocide in 1994, the country’s GDP growth collapsed by 32 percent but bounced back to higher than pre-conflict levels and has remained (on average) positive ever since (trend line).

• In Iraq, after the 2003 combat, GDP growth collapsed by 34 percent, bounced back to higher than pre-conflict levels, but then dropped again to even lower than pre-conflict levels. Since 2012, Iraq has not been able to reach its pre-invasion GDP growth levels.

• Similarly, the 2008 military conflict in Georgia sharply decreased the country’s GDP growth (by 10 percent), and the country has never regained its pre-conflict GDP growth levels. Instead, it has been steadily declining.

• Eritrea’s GDP growth simply oscillates due to repeated cycles of violence. On average, Eritrea’s GDP growth is steady over time (note the almost horizontal dashed trend line in figure 2.19).

Figure 2.12 Access to Formal Financial Services Appear More Limited in FCS CountriesPercent

Source: World Bank Enterprise Surveys 2009–11.Note: Results controlled for GDP per capita levels (for technical details, see appendix G). p-value (ECA) = 0.439, p-value (SS Africa) = 0.006 (for technical details, see appendix G).

0 10 20 30 40 50 60

ECA

Sub-Saharan Africa

Fragile Non-fragile




Overall, disruptions in product markets caused by violent events may be either temporary or more permanent, depending on a country’s ability to recover. Across all FCS countries, recovery is uneven. Similarly, disruptions in factor mar-kets in which workers or firms have been displaced are commonplace in FCS Regions (box 2.2).

Existing biases toward disadvantaged groups are exacerbated; as a result, these groups are affected disproportionately. Groups who already are struggling find it even more difficult to thrive in FCS situations. For example, women’s participa-tion in production or business management processes in the FCS countries is more limited than in non-FCS countries (figure 2.20).

In a nutshell, despite all the aforementioned entrepreneurial challenges—changes in institutions, security, and product and factor markets—in many cases,

Regression Table 2.2 Temporary Disruptions in Sales in Sub-Saharan Africa due to Fragility

Dependent variable (log) Clustered at country levelDependent variable (log)

(1)Total Sales (last year)

(2)Total Sales

(3 years ago)

(3)Total Sales (last year)

(4)Total Sales

(3 years ago)

Being fragile (d) −0.2515***(0.102)

−0.2429**(0.129)

−0.2515( 1.056)

−0.2429(0.211)

Having more than 50% domestic ownership (d)

−0.7123***(0.119)

−0.7420***(0.151)

−0.7123 (0.446)

−0.7420**(0.312)

Small size (d) −1.1354***(0.113)

−1.0584***(0.146)

−1.1354** (0.489)

−1.0584***(0.327)

Size of locality−city (250,000−1mil population) (d)

0.5934***(0.173)

−0.0063(0.236)

0.5934 (0.497)

−0.0063(0.723)

Using own website to communicate with clients/suppliers (d)

0.7464***(0.137)

0.9272***(0.177)

0.7464** (0.316)

0.9272***(0.302)

Having paid for security (d) −0.2438***(0.100)

−0.1471(0.131)

−0.2438(0.500)

−0.1471(0.408)

Dispose checking/savings account (d) 0.6026***(0.129)

0.5654***(0.180)

0.6026(0.429)

0.5654(0.548)

Dispose overdraft facility (d) 1.4578***(0.108)

1.8441***(0.138)

1.4578***(0.333)

1.8441***(0.466)

Dispose line of credit/loan (d) 0.0640(0.116)

−0.3703**(0.146)

0.0640(0.336)

−0.3703(0.265)

Dispose internationally recognized quality certification (d)

0.5246***(0.157)

0.3868**(0.194)

0.5246(0.373)

0.3868(0.422)

% of senior management’s time spent on gov. regulations

0.0339***(0.002)

0.0276***(0.003)

0.0339***(0.006)

0.0276***(0.006)

Years of experience of Top Manager in the sector

0.0047(0.005)

0.0368*(0.007)

0.0047(0.016)

0.0368*(0.010)

Constant 16.631***(0.209)

15.946***(0.269)

16.631***(1.285)

15.946***(1.439)

R-squaredNumber of observations

0.24923,198

0.25612,204

0.2523,198

0.26012,204

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.




Figure 2.13 Why FCS Firms Should Not Apply for Loans

Source: World Bank Enterprise Surveys 2009–11.Note: For technical details, see appendix G.

Non-fragile Fragile

0

10

20

30

40

50

No need for lo

an

Perc

ent

b. FCS firms in SSA find interest rates unfavorable

a. FCS firms in ECA have no need for loans

Perc

ent

Unfavorable interest

rates

Complex applicatio

n

proce

dures

High colla

teral

requirements

166% of loan valuein fragile states, while130% of loan value in

non-fragile states

0

10

20

30

40

Complex applicatio

n

proce

dures

No need for lo

an

Unfavorable interest

rates

c. FCS firms in Asia have no need for loans

0

10

20

30

No need for lo

an

Complex applicatio

n

proce

dures

Unfavorable

interest rates

High colla

teral

requirements

existing enterprises have proved resilient, and new enterprises have emerged. These enterprises are either well-established larger firms with strong financial services (often partly offshore) that have secured highly attractive market posi-tions (key agro-industries, trading, and services roles); or entrepreneurs who effectively have no choice in an environment in which household survival impera-tives, financial constraints, remittances (and their possible “disincentive effects”), and informal and criminal economies have been important in determining their behavior.




Figure 2.14 Credit Transactions Less Common in FCS Countries than in Non-FCS Countries in Sub-Saharan Africa

Source: World Bank Enterprise Surveys 2009–11.Note: Purchases of material inputs or services: p-value (before delivery) = 0.313, p-value (on delivery) = 0.021, p-value (after delivery) = 0.676. Sales of goods and services: p-value (before delivery) = 0.749, p-value (on delivery) = 0.078, p-value (after delivery) = 0.099 (for technical details see appendix G).

Paid for beforedelivery

Valu

e of

ann

ual p

urch

ases

/sal

es, %

Paid for ondelivery

Paid for afterdelivery

0

10

a. Purchase of material inputs or services

20

30

40

50

60

b. Sales of goods and services

Valu

e of

ann

ual p

urch

ases

/sal

es, %

Paid for beforedelivery

Paid for ondelivery

Paid for afterdelivery

0

10

20

30

40

50

60

Non-fragile Fragile

Figure 2.15 Longer or More Frequent Power/Water Shortages Common in FCS Countries (% of Firms that Experienced Power/Water Shortage the Past Year)Percent

Source: Authors. World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Data points refer to average duration/length of the shortage (in hours). Power outages: p-value (SS Africa) = 0.921, p-value (ECA) = 0.044, p-value (Asia) = 0.056. Water shortages: p-value (SS Africa) = 0.496, p-value (ECA) = 0.000, p-value (Asia) = 0.000 (appendix G).

b. Water shortages

0

10

20

30

40

50

60

70

80

Sub-Saharan Africa

Asia ECA

Perc

ent o

f fir

ms

repo

rtin

g “y

es”

0

10

20

30

40

50

60

70

80a. Power outages

Asia ECA Sub-Saharan Africa

Perc

ent o

f fir

ms

repo

rtin

g “y

es”

Non-fragile Fragile




Figure 2.16 Power Supply Lacking or Very Expensive in FCS Countries

Source: World Bank Enterprise Surveys 2009–11.Note: p-value (sharing generators) = 0.011 (see appendix G for technical details).

No Yes

0

20

40

60

80

100

Perc

ent o

f fir

ms

repo

rtin

gon

poo

r acc

ess

to e

lect

rici

ty

a. 97 percent of firms in Kosovo lack quality access to electricity

Tajikist

an

Georgia

Uzbekist

an

Bosnia and H

erzegovina

Belarus

Turkey

Ukraine

Russian Federatio

n

Poland

Romania

Serbia

Kazakhsta

n

Moldova

Azerb

aijan

Macedonia, F

YR

Armenia

Kyrgyz R

epublic

Estonia

Czech

Republic

HungaryLatvia

Lithuania

Slovak Republic

Slovenia

Bulgaria

Croatia

Montenegro

Kosovo

Non-fragile Fragile0

1020304050607080

Pere

nt o

f fir

ms

shar

ing

gene

rato

rs in

SSA

b. 73 percent of firms in FCS SSA rely on private power supply

Figure 2.17 Access to General Purpose Technology Worse in FCS than in Non-FCS CountriesPercent

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: For technical details, see appendix G.

Use email in communication with clients and suppliers

Use its own website

Dispose high-speed internetconnection

Non-fragileFragile

0 20 40 60 80 100

ECA

Sub-Saharan Africa

Asia

ECA

Sub-Saharan Africa

Asia

ECA

Sub-Saharan Africa




Figure 2.18 FCS Enterprises in SSA and ECA Face High Losses due to Crime, Theft, and Disorder

Source: World Bank Enterprise Surveys 2009–11, http://datacatalog.worldbank.org/datacatalog-offline.aspx.Note: Annual sales paid for security: p-value (ECA) = 0.383, p-value (SS Africa) = 0.830, p-value (Asia) = 0.010. Annual sales lost due to crime: p-value (ECA) = 0.010, p-value (SS Africa) = 0.254, p-value (Asia) = 0.008 (appendix G).

0

2

4

6

8

b. Annual sales lost due to crime (percent)a. Annual sales paid for security (percent)

10

12

14

16

Asia ECA Sub-SaharanAfrica

Asia ECA Sub-SaharanAfrica

0

2

4

6

8

10

12

14

16

Non-fragile Fragile

Figure 2.19 FCS Firms in Four Countries Experience Disruptions in Their Product Markets with Uneven Recoveries

–50–40–30–20–10

0102030

Perc

enta

ge c

hang

e in

GD

P (c

onst

ant p

rice

s)

a. Rwanda

19911992

19931994

19951996

19971998

19992000

20012002

20032004

20052006

20072008

20092010

20112012

1990

–50–40–30–20–10

0

2010

504030

60

Perc

enta

ge c

hang

e in

GD

P (c

onst

ant p

rice

s)

b. Iraq

19992000

20012002

20032004

20052006

20072008

20092010

20112012

1998





Source: IMF 2012.Note: % change in GDP, constant prices; Dot = periods of disruptions; black = trend line.

Figure 2.19 FCS Firms in Four Countries Experience Disruptions in Their Product Markets with Uneven Recoveries (continued)

–6–4–2

0

42

1086

1214

Perc

enta

ge c

hang

e in

GD

P (c

onst

ant p

rice

s)

20032004

20052006

20072008

20092010

20112012

2002

c. Georgia

–15–10

–50

105

2015

25

Perc

enta

ge c

hang

e in

GD

P (c

onst

ant p

rice

s)

d. Eritrea

20032004

20052006

20072008

20092010

20112012

20021997

19981999

20002001

Source: World Bank Enterprise Surveys 2009–11.Note: p-value (other sectors) = 0.074, p-value (manufacturing-production) = 0.036, p-value (manufacturing-non-production) = 0.058, p-value (top manager) = 0.000, p-value (owner) = 0.340 (appendix G).

Figure 2.20

a. In FCS firms in ECA, women less likely to participate in production and management

0

5

10

15

20

30

25

Ave

rage

num

ber o

f fe

mal

e em

ploy

ees

051015202530354045

Full-time employee(all other sectors)

Full-time productionemployee

(manufacturing)

Full-time non- productionemployee

(manufacturing)

Top manager Owner of establishment

Non-fragile Fragile


[[AU: Please provide the caption for fi gure 2.20.]]




Notes

1. Other Regions were excluded due to data limitations or inconsistency. In particular, regarding Enterprise Surveys (“surveys” hereafter) of countries in Central and Latin America as well as in the Middle East and North Africa, the team either was unable to match the time periods of the surveys conducted in FCS and non-FCS countries, or surveys for specific countries of interest were nonexistent. Therefore, any analysis of these surveys conducted for this report would not be accurate due to data-time inconsistency. Furthermore, many surveys of the countries in Asia selected for the analysis do not have exactly the same questions on topics such as innovation as the questions in the surveys of Sub-Saharan Africa and the ECA Region. Finally, the majority of firms in Asia selected for the analysis often chose not to reply to specific



Figure 2.20 (continued)

b. In FCS firms of Sub-Saharan Africa, women more likely to be principal owners

0

5

10

15

20

25

30

Ave

rage

num

ber o

f fe

mal

e em

ploy

ees

0102030405060708090



(manufacturing)


(manufacturing)


c. In Asian FCS firms, women less likely to be top managers or principal owners

0

5

10

15

20

25

Ave

rage

num

ber o

f fe

mal

e em

ploy

ees

Non-fragile Fragile

051015202530354045



(manufacturing)


(manufacturing)





questions such as the frequency of informal payments to “get things done.” Thus, the results for the Region frequently are missing from the visual presentations in this report. Drawing overall conclusions would be difficult due to the large number of missing values.

2. Comparisons are made within the pool of countries created specifically for our analy-sis, not the entire universe.

3. In some countries, such as Zimbabwe, during or after a violent event, the merchandise exports to neighbors move in tandem with declining manufacturing exports. In other countries, including Cameroon, the Central African Republic, and Côte d’Ivoire, the decline in merchandise exports to neighbors usually follows a sharp contraction in manufacturers’ exports.

4. GDP per capita in 2000 constant US$ prices (World Bank 2009–12).

References

Heston, A., R. Summers, and B. Aten. 2011. Penn World Table 7.0. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania. http://pwt.econ.upenn.edu/.

IMF (International Monetary Fund). 2012. Global Economic Outlook. April.

ITU (International Telecommunications Union) Development Index. http://www.itu.int / en/ITU-D/Pages/About.aspx.

KP-FATA (Governments of Khyber Pakhtunkhwa and the Federally Administered Tribal Areas Secretariat), ADB (Asian Development Bank), EU (European Union), UN (United Nations), and World Bank. 2010. “Post Crisis Needs Assessment: PCNA Khyber-Pakhtunkhwa and FATA.” Pakistan. September. http://www. khyberpakhtunkhwa .gov.pk/Departments/PnD/mne/MnE/Download/7.%20PCNA%20Report.pdf.

UNWTO (United Nations World Tourism Organization). 2011. Yearbook of Tourism Statistics. Madrid. http://www.untwo.org.

Wikipedia. http://en.wikipedia.org/wiki/Hawala.

World Bank. 2009–11. Enterprise Surveys. http://datacatalog.worldbank.org/datacatalog -offline.aspx.

———. 2009–12. World Development Indicators. Washington, DC. http://datacatalog .worldbank.org/datacatalog-offline.aspx.

———. 2011. Arab Republic of Egypt Investment Climate Survey. Washington, DC.

———. 2012b. Rwanda Competitiveness and Enterprise Development Project Implementation Completion Report. http://imagebank.worldbank.org/servlet/WDSContentServer / IW3P /IB/2012/03/16/000333038_20120316013602/Rendered/PDF / ICR22110P057290C0disclosed030140120.pdf.

World Bank and International Finance Corporation (IFC). 2012. “Supporting Innovation in Small and Medium Enterprises in Lebanon.” Washington, DC, World Bank and IFC. http://siteresources.worldbank.org/FINANCIALSECTOR/Resources/Investment _ Funds_SME_Innovation_in_Lebanon.pdf.

[[AU: For John Speakman: Please confi rm year 2012 added to reference.]]



Implications of Findings

The findings have implications for (a) program design of private sector develop-ment (PSD) interventions in FCS, and (b) Bank Group interventions in particu-lar. The findings also uncover opportunities to improve operational processes and collaboration.

Program Design

Not surprisingly, a typical private sector program in FCS countries generally contains interventions similar to those in non-FCS countries that aim at encour-aging investment and getting markets to work better and more competitively. Typically, the program includes interventions that (a) address public private coordination failures, (b) encourage innovation and entrepreneurship, (c) resolve market failures in the provision of public goods, (d) provide a generally predict-able business environment, and (e) encourage demand. The Afghanistan Road Map is illustrative (box 3.1). It has most of these interventions with the excep-tion of efforts to encourage demand, in a context in which donors already had made strong efforts to involve local contractors in providing construction and facilities management services.

However, based on our empirical findings, the intervention in FCS to develop further the private sector and support entrepreneurship in cycles of violence needs to be tailored.

Interventions that Address Public-Private Coordination FailuresSuccessful interventions in public-private coordination are predicated on an effective public-private dialogue (PPD) at both the overall economy and sector levels (box 3.2). Although formal PPD mechanisms can improve coordination, for these systems to be fully effective, there are certain important requirements. They include (a) the existence of a strong public sector combined with political will and leadership; (b) a well-organized and -led private sector that does not fear governmental retribution for speaking out; (c) a “highly visible” champion that can attract the attention of participants and media; and (d) instruments to make

C H A P T E R 3


36 Implications of Findings


Box 3.1 Afghanistan Road Map for Improving the Business Enabling Environment

Addressing Public Private Coordination Failures

• Instill an active practice of social responsibility and philanthropy that leads to the institu-tionalization of private (business and individual) support for economic and social develop-ment through civil society.

• Establish a framework to strengthen the governance and operations of civil society organi-zations (CSOs) to enhance their contributions to social and economic development in Afghanistan through, among other measures, revising and clarifying laws governing civil society as well as establishing independent certification bodies for CSOs.

• Establish mechanisms to oversee the implementation of measures to create an enabling environment, initially focusing on the 2007 Enabling Environment Conference recommendations.a

• Establish programs (both private and public-led) to build capacity to alleviate the binding capacity constraints facing the private and public sectors.

Encouraging Innovation and Entrepreneurship• Strengthen the financial sector to increase access to credit and financial services, paying

special attention to alleviating capacity constraints.

Resolve Market Failures in the Provision of Public Goods

• Implement measures to facilitate access to land by clarifying property rights, simplifying procedures for the transfer of titles, and allowing for longer term leases.

• Involve the private sector in the provision of public services through public-private partner-ships (PPPs) and other modalities in areas such as power generation and distribution, water supply, transportation infrastructure, and social development.

Provide a Generally Predictable Business Environment

• Build the structures, systems, and capacity of mediation and arbitration tribunals to ensure the efficient, effective, and impartial resolution of disputes.

• Enact and implement key laws and amendments to establish the basic legal and regulatory framework that will encourage private sector involvement in social and economic develop-ment in Afghanistan. The laws and regulations should (a) be clearly specified and transpar-ent; (b) be further streamlined to include only the minimum necessary steps, bureaucratic processes, and institutions; (c) reduce discretionary decisionmaking; and (d) be predictably, consistently, competently, and impartially applied.

Source: Private Sector Enabling Council 2007.Note: Effective Private Sector Contribution to Development in Afghanistan Conference Statement and Road Map. Kabul, Afghanistan, June 5, 2007 (Private Sector Enabling Council 2007).

[[AU: Please provide the signifi cance of superscript indicator “a” given here.]]


Implications of Findings 37


coordination happen: seed funding, and logistical and personnel support. Meeting these conditions in fragile countries will be very difficult and will require extraor-dinary leadership and support (box 3.2). Nevertheless, there are critical success factors for two specific interventions that can make meaningful differences to fragile states:

1. Encouragement of competitive industries.2. Establishment of development corridors and/or economic zones, and last, the

facilitation of local economic development.

Box 3.2 Public-Private Dialogue in Investment Climate Interventions

Public-private dialogue (PPD) has proven an important mechanism in World Bank interven-tions to improve national and Regional investment climates in client countries. In particular, PPD provides a mechanism between the private sector and political parties in government to encourage a national discussion focused on the reforms needed to generate new investments and jobs.

In 2009 the monitoring and evaluation (M&E) review of 30 WBG-sponsored PPD’s found that they had been an effective instrument of choice to prioritize and promote reforms in International Development Agency (IDA) and post-conflict countries, both top WBG priorities.

In 2011 an additional review of investment climate programs in 16 countries was under-taken to catalog and assess alternative approaches to investment climate and private sector development (PSD) in fragile and conflicted-affected states. The review found that interven-tions were successful in implementing its investment climate projects, based on the necessity for “quick wins” and reaching important results established at the project design stage. According to lessons learned and staff interviews, quick wins with regulatory reforms are important both for the client government to restore confidence to country constituents and for the WBG team to establish credibility and win support for follow-on work in future. Across all country groups, PPDs have worked well as entry strategy programs to discuss regulatory reforms in progress and to create dialogue on additional reforms. In fact, the PPDs have been essential to the reforms’ success.

A post-completion evaluation of the Nepal Business Forum Phase I (NBF) undertaken by the Independent Evaluation Group (IEG) further confirms the usefulness of a PPD in a conflict-affected state. IEG found appropriate the objective to promote PPD around private sector reforms in the context of a country’s struggle to establish democracy. Under this project, the PPD objectives have been broadly met. In addition, by August 2012, during the second phase of this project, over 41 recommendations of 120 coming from the PPD had been implemented. Two of these reforms have delivered US$5.67 m in private sector cost savings, notably in an environment of significant political turmoil and formerly little public-private or even private-private dialogue.

Source: Excerpted and edited from Goldberg 2011.




At the sector level, in most FCS countries, opportunities for engagement exist in the extractive sectors and sectors in which the country already has existing capabilities. Therefore, the industries in FCS countries often are resource based, including agriculture, fishing, forestry, and mining. A quick tour of FCS countries in Africa shows that Cameroon has strong forestry potential; Guinea has huge mineral deposits; Guinea-Bissau grows some of the best cashew nuts in the world; Rwanda has tea (box 3.4); and Somalia has underdeveloped fisheries. Sometimes, the industries are service based, such as tourism.1 If peace is estab-lished, construction, and logistics are additional typical examples. However, industries also can be labor cost based, as in Haiti. Therefore, cross-sectoral proj-ects between finance and PSD and rural development or human development (especially for education and health services) would be an effective approach to PSD in FCS countries.

A sharp focus on these sectors that have underlying competitive advantage can provide quick returns. In Haiti in 2011, providing just-in-time production of ready-made garments using the low-cost labor advantage was identified as a mar-ket niche. After the earthquake, the government immediately focused—with the support of the donor community—on rehabilitating and developing industrial

Box 3.3 Bulldozer Initiative in Bosnia and Herzegovina

Bosnia and Herzegovina launched the Bulldozer Initiative in 2002 to involve the private sector in reforms. A reform coordination unit invited 30 local associations to help in proposing, evalu-ating, and refining reforms. Among them were regional business associations, municipal asso-ciations of entrepreneurs, the Employers’ Confederation, the Women’s Business Network, the Micro-Credit Network, and the Association of Honey and Bee Production—all members of the Bulldozer Plenary Committee.

A group of lawyers and economists evaluated proposals. Each proposal was subjected to a cost-benefit analysis, and industry experts were invited to comment on ideas before taking the reform to the next stage. In this way, no single firm could exploit the process to serve its own interests. The proposed reforms then were submitted to the government, opening an intensive dialogue between the Bulldozer Committee and the Council of Ministers and Regional Governments. Once the reform was designed, the committee became an implementation watchdog. A biannual publication informed the public of progress, including scores for each reform.

The initiative has helped to reduce significantly the burden of bureaucratic procedures on firms. It has halved the number of steps to register foreign direct investment (FDI), expedited customs clearance procedures, bridged the constituency gap by training and empowering local advocacy groups, and established mechanisms for civic participation in government. In June 2003, the initiative established regional bulldozer committees, all voluntary and self-financed.

Source: Herzberg 2004.

[[AU: for John Speakman: Please provide in-text callout for box 3.3.]]




space to maintain and expand this opportunity. However, some industries, par-ticularly large-scale mining, may take longer to develop. The time lapse from the initial geophysical survey to production could be a decade. The detailed geologi-cal analyses, building the necessary transportation (often rail), and developing a mine all are time-consuming activities. It is in this area of efficient use of time that the notion of competitive industries and development corridors can be com-bined by using a cross-sectoral approach to design an effective intervention that provides quick returns.

Moreover, strengthening competitive industries typically begins with the iden-tification of the constraints to development. As suggested, strengthening is best done in a consultative mode. There must be buy-in from both the public and the private sectors. Typically, the consultation process will identify three broad groups of sector-specific constraints that are over and above general enabling environment concerns. These sector-specific constraints can be regulatory, tech-nology, or capacity related, such as a public market or a cool store.

Spatial approaches, such as enclave economies created through foreign investment or development corridors, are a common intervention in FCS states.

Box 3.4 Reforming Rwanda’s Tea Sector

Tea was 1 of the 2 main export crops in Rwanda, contributing 30–40 percent of total exports at the turn of the millennium, when the country was classified as a fragile state. Tea also was one of the few crops that provided regular cash income to farmers. There were 10 tea factories in Rwanda, 9 of which were owned and managed by the state through OCIR-Thé. At that time, yields of the tea estates were low and costs high; the price levels were too low to attract grow-ers to increase production; and the industry lacked factory capacity to process increases in tea production. The government was committed to the liberalization of the industry and privatiza-tion of state-owned assets. To these ends, the government prepared and adopted a strategy that addressed three main actions. They were (1) the participation of nationals and key stake-holders (cooperatives, small-holders, factory employees) in the ownership of tea factories and estates through privatization; (2) accompanying measures, including the empowerment of farmers to take over management of all commercial and technical activities that were under the responsibility of the government-owned tea body; and (3) the establishment of an inde-pendent Tea Board.

The twin foci of the reforms were to complete the privatization of the tea factories and to establish the independent Tea Board. These reforms succeeded. The Tea Board was created. It later was incorporated under the National Agricultural Export Development Board (NAEB), and 7 of the 9 tea factories and estates were privatized. The selling price of tea per kg increased from US$1.49 in 2001 to US$2.59 in 2010—a 70 percent increase. The earnings from tea exports increased from US$22.71 million in 2001 to US$44.95 million in 2008, and to US$55.71 million in 2010—an overall increase of 250 percent in 9 years.

Source: World Bank 2012b.




In Africa, virtually every FCS has looked at encouraging some kind of spatial development such as a growth pole, development corridor, or special economic zone (SEZ) to reap the benefits of agglomeration and clustering. These advan-tages are common infrastructure and accessibility to transportation systems, chain suppliers, knowledge and technology, and production inputs. The idea is that, in limited-capacity contexts, a management and investment focus has the best prospects for meaningful results.

Interventions that Encourage Entrepreneurship and Innovation at the Firm LevelAs was shown above, FCS firms tend not to be particularly innovative and competitive. Thus, many PSD interventions aim to address this weakness. In many FCS, a common intervention is to support business development services. This support typically involves upgrading firms’ managerial capabilities (such as accounting and governance), production skills (on-the-job training) systems (quality standards), and market connectivity (market development support). When this support works, the pay-offs can be very high (box 3.5).

Box 3.5 West Bank and Gaza Facility for New Market Development and Gaza Back-to-Work Programs

The West Bank Gaza Facility for New Market Development (FNMD) ($2.5 million) is a combined matching grant and challenge fund program that supports business development services for established funds and start-ups. Initiated in 2008 this program is jointly coordinated by the World Bank and DFID. It was modeled on successful programs implemented in Mauritius and Tunisia.

Results in WBG to date are impressive. A total of 560 firms was planned to be sup-ported; 603 actually were. An increase of exports of 40 percent was planned; exporters actually recorded an increase of 52 percent, creating remarkable success stories of new market entry. The volume of incremental sales reached US$100 million, exceeding the tar-get of US$75 million. These sales figures do not take account of the 132 firms that could not yet report sales figures attributable to FNMD support due to the lag between the delivery of support and effects taking place. These firms improved products, obtained quality certification, and developed new products. In a recent initiative, the facility also operates a challenge fund for start-ups and has supported 11 firms so far.

The Gaza Back-to-Work Program ($7.5 million) was designed by DFID and initiated in 2010 as an emergency response to assist firms whose operations had been severely disrupted. This matching grant program provides working capital for firms to rehire staff and repair equip-ment. The program has supported 219 firms, and an additional 1,700 staff have been hired (directly and indirectly). To date, 183 of the supported firms have reported increased sales and total new investment in excess of $10 m.

Source: TripleLine Consulting (Siegfried Jenders) 2012.




Many firms struggle to start up due to key factor markets such as finance—oftentimes due to banks’ reservations about providing loans or lines of credit to clients with a high perceived risk. Again, whether support to microfinance institutions or small and medium-size enterprise (SME) financing, these are com-mon areas of intervention. What is less common but, in some cases, more critical is the provision of equity. A recent project in Lebanon illustrates one approach (box 3.6).

Box 3.6 Lebanon Innovation and SME Growth Project

The Government of Lebanon has requested support from the World Bank to develop a project to promote innovation, competitiveness, and growth of private sector firms. The proposed project will consist of a funding facility that will provide two types of funding. The process will engage private sector investors, including national, regional, and international venture capital funds; and industry experts and entrepreneurs from among the diaspora. The activities are detailed below.1. Development Grant (approximately US$15,000 each)Potential entrepreneurs with new business ideas will be invited to apply for a small grant to help them develop their innovative concepts. The grants will fund collecting evidence and building a case for external investment in their businesses, along with assessing the key areas of risk. The outcomes will be an improvement in the ability of potential entrepreneurs to dem-onstrate the value of their proposed business ideas, and to convert the idea into businesses ready for investment. The grants will be provided in 2 phases: (a) a small grant of up to $5,000 during the first phase will enable the entrepreneur to begin working on a proof of concept; and (b) a larger second phase grant of up to $10,000 will enable the entrepreneur to work closely with venture capitalists (VCs) to prepare the enterprise for possible early stage.2. Equity Investment (up to US$1,500,000)Potential entrepreneurs, both those who are in the stages covered by the development grant above and others who seek growth financing, will be encouraged to meet with VCs. These meetings will ensure that the evidence that the entrepreneurs have assembled, or their busi-ness plans, will address the investors’ information needs and will truly demonstrate that the entrepreneur is investment ready. To qualify for the equity investment financing, the entrepre-neur first must have approached a potential investor with the completed business plan and received from the investor a commitment to invest in the new venture. Having acquired this commitment, the venture investor or the entrepreneur then can approach the project funding facility to seek a matching investment that will share the initial venture fund’s risk, and add to the capital available to the new business.

The scheme relies on the efforts of partner VC funds both to identify and promote new business projects and to provide the active mentoring and professional inputs that character-ize truly effective VC participation in successful investments. For these reasons, VCs will be screened in advance to ensure that they enjoy a reputation of active participation and have a proven track record of mentoring and support.

Source: http://siteresources.worldbank.org/FINANCIALSECTOR/Resources/Investment_Funds_SME_Innovation_in_Lebanon.pdf.




Special measures sometimes are required to target key groups such as women, youth, and ex-combatants. When compared with the overall private sector, disadvantaged groups often suffer disproportionately regarding employ-ment and employment benefits. Thus, extraordinary measures often are required. These can be a mix of (a) targeting with strong monitoring and evaluation (M&E) within overall PSD programs, or (b) specific programs targeted at these groups for which they are the sole beneficiaries. These programs can be imple-mented through government agencies, donors, or nongovernmental organizations (NGOs). A typical example is the ILO’s Training for Rural Economic Empowerment (TREE) program (box 3.7), which easily can be focused on any particular target.

Interventions that Resolve Market FailuresPPPs can mitigate inefficient markets in FCS countries. The inefficiency of FCS markets typically results from (a) regulatory systems that obstruct rather than support market efficiency; (b) lack of critical public goods, such as roads; and (c) limited availability of serviced industrial land for purchase or lease. There can be too much regulation, not enough, and/or a failure to implement. In such situ-ations, the public-private partnership (PPP) serves as the short-term vehicle to deliver essential infrastructure and services, rehabilitate hard-hit industries and businesses, and mitigate entrepreneurs’ lack of trust and risk-aversion (box 3.8, example on PPPs in Somalia).

Box 3.7 ILO’s TREE Program

The International Labour Organization’s (ILO’s) Training for Rural Economic Empowerment (TREE) Program is a proven platform that assists workers in largely informal economies to build the skills and abilities they need to generate additional income. Tested under recent technical cooperation projects in Bangladesh, Burkina Faso, Madagascar, Niger, Pakistan, the Philippines, and Sri Lanka, TREE builds on ILO’s long experience in promoting community-based training worldwide.

A TREE program starts with institutional arrangements and planning among partner orga-nizations at the national and local levels. The program aims to systematically identify employ-ment- and income-generating opportunities at the community level; design and deliver appropriate training programs employing local public and private training providers; and pro-vide the necessary post-training support, for example, facilitating access to markets and credit.

By linking training directly to community-determined economic opportunities, TREE pro-grams ensure that the skills delivered are relevant. In communities in which formal training institutions do not exist, for example, remote rural locations, arrangements for mobile training may bring in teachers and equipment to identify the appropriate levels of training, design cur-ricula, and deliver training locally. Programs such as TREE’s can strengthen training delivery by formal institutions through developing new training programs that meet local demands.

Source: www.ilo.org/skills/projects/WCMS_103528/lang–en/index/htm.




Interventions that Support the General Predictability of the Business EnvironmentSurvey after survey identifies predictability factors as the biggest constraints to doing business in FCS. These factors include (a) managers’ perceptions of politi-cal uncertainty; (b) general governance environment, particularly regarding cor-ruption; (c) randomness of implementing rules and unreliability of judicial systems; (d) security; (e) ensuring basic macroeconomic stability for trade and fiscal and monetary parameters; and (f) access to electricity. These matters largely are beyond the scope of typical PSD interventions. Nevertheless, a private-sector-focused intervention is possible in three of these areas: (a) providing investor guarantees, (b) developing alternate dispute resolution (ADR) systems, and (c) using public-private dialogue (PPD) mechanisms to manage predictability and other issues (box 3.9).

As in the case of the Arab Republic of Egypt (box B.5), demand often can be a constraining factor. If the demand for products is not there, busi-nesses simply will not invest. Five areas of intervention can address demand challenges: (a) trade preferences, (b) encouragement of competitive indus-tries, (c) development of linkages of local demand with commodities and

Box 3.8 Public-Private Partnerships in Somalia

“The Sustainable Employment and Economic Development” (SEED) program was inaugu-rated in late 2010 as a flagship project to improve economic and employment prospects for youth and women in Somalia. The UKAID-funded program brings together FAO-Somalia, UNDP-Somalia, ILO-Somalia, and Save-the-Children as implementing partners. The goal of the program is to improve stability in Somalia through economic growth and sustainable employment. All of these are to be achieved by developing markets and creating employ-ment with accompanying skills training focusing on agriculture, livestock, fisheries, and fod-der and honey production in Somaliland, Puntland, and south-central regions of Somalia. The program also works toward improving the investment climate and supporting strengthening the regulatory framework to enhance economic growth in the three regions (DFID 2010).

Program interventions entailed infrastructure development along the livestock and fisher-ies value chain. This included the rehabilitation of the meat market in Borama Somaliland, rehabilitation of the livestock market in Hargeisa Somaliland, construction of a slaughterhouse in Burco Somaliland, and rehabilitation of a fish market in Garowe, Puntland. Stakeholders including regional administrations, local authorities, and associations within the value chain show concern for the fact that there are challenges related to governance by stakeholders. It is generally agreed that the said infrastructure projects should be managed through public- private partnership (PPP). A PPP is viewed as a means through which local authorities can improve revenue collection and the private sector can realize profitability, while ensuring effective delivery of services to value chain actors and the general public.

Sources: DFID 2010; Mangéni and Nyawira 2012.




local sourcing of aid (construction, logistics, facilities management), and (d) encouraging and supporting remittance flows.

Trade preferences often are offered to FCS by donor countries. These prefer-ences can take many forms (box 3.10). Some decades ago, as part of a peace pro-cess, the United States created a Qualified Industrial Zone program in which new investments in the textile sector in the Arab Republic of Egypt and Jordan that had Israeli partners qualified for preferential entry of goods into the United States.

Encouraging industries that are truly competitive can help both through sub-stituting for imported products that, in non-FCS circumstances, would be pro-duced locally; and through increasing exports. As a result of high transport costs relative to its value, cement is a typical example of import substitution. To quote from a USAID report on the Iraq cement industry: “The domestic demand for cement has risen with the rehabilitation of the country. However, the domestic supply has not kept pace with the increases in demand and the price of cement has therefore been elastic with prices in 2003 quoted at US$20 per ton and reportedly rising to US$120.00 per ton in 2005” (USAID 2007).

Encouraging export industries is more challenging. Trying to identify indus-tries that can export occupies much of policymakers’ time. A good example is Guinea-Bissau’s cashew nut sector, which has tremendous potential to increase export sales by increasing value added in the country (box 3.11).

Box 3.9 Supporting Predictability: Examples from World Bank Projects

Predictability remains the biggest concern to entrepreneurs in FCS countries. Still, there are some success stories among the World Bank projects regarding managing low predictability:

• Bosnia Emergency Industrial Re-Start Project. To vouchsafe government commitment to maintain the policy environment and “to mitigate non-commercial political risks in order to promote a transparent business climate based on rule of law,” Political Risk Guarantee Facility was designed. It sold risks against political and related risks in the aftermath of war. A World Bank loan backstopped the guarantee fund. The project was rated in the Implementation Completion Report as satisfactory with high institutional development impact. The project issued 26 guarantees/insurance policies totaling Euro 20.3 million in value. (http://imagebank.worldbank.org/servlet/WDSContentServer/IW3P/IB/2005/05/10 /000090341_20050510110253/Rendered/PDF/31976.pdf)

• Albania Private Industry Recovery Project. In 2004 the Albanian Government developed a simi-lar Political Risk Guarantee Facility. It issued 24 guarantee contracts totaling $8.7 million. During the period in which guarantees were current, 680 jobs were created, and production levels increased 341 percent. (http://www.worldbank.org/projects/P051602/private-industry -recovery-project?lang=en&tab=overview)

• Related projects. Africa Regional Trade Facilitation Project, Afghanistan Investment Guarantee Facility, Moldova Pre-Export Guarantee Facility, Russia Coal and Forestry Guarantee Facility.

Source: World Bank (URLs above).




Box 3.10 EU’s Trade Preferences to Western Balkans

In 2000 the European Union (EU) granted autonomous trade preferences to all the Western Balkans. Renewed in 2005, and subsequently in 2011 through 2015, these preferences allow nearly all of the region’s exports to enter the EU without customs duties or limits on quantities. Only wine, baby beef, and certain fisheries products enter the EU under prefer-ential tariff quotas.

This preferential regime has contributed to an increase in the Western Balkans’ exports to the EU. In 2010 the EU was the region’s largest trading partner for both imports (61 percent) and exports (65 percent).

The EU strongly supports the Western Balkans’ membership in the World Trade Organization (WTO). Albania (2000), Croatia (2000), the former Yugoslav Republic of Macedonia (2003), and Montenegro (2011) already are members. The WTO accession negotiations with Bosnia and Herzegovina and Serbia are ongoing.

Source: European Commission 2013.

Box 3.11 Guinea-Bissau Cashew Nuts

The cashew sector is critical to Guinea-Bissau’s economic growth and poverty alleviation. Agriculture is the dominant sector of the economy (contributing approximately 50 percent of total GDP), and cashew is the country’s most important agricultural product. It is grown by close to 55 percent of all agricultural households (mostly smallholders) and equals approxi-mately one-third of the sector’s total output. Cashew is responsible for more than 90 percent of the country’s exports and offers employment, directly or indirectly, to more than two-thirds of the population. Cashew is by far the main source of revenue for agricultural households, which are the poorest in the country. The rural poverty level is close to 80 percent. Therefore, the development of the cashew sector directly benefits the majority of the population. Achieving economic and social development in Guinea-Bissau would be very difficult without a flourishing cashew sector.

Despite negligible state support, the development of Guinea-Bissau’s cashew sector has been remarkable. Cashew production increased from 30,000 tons in the early 1990s to 180,000 tons in 2011. Guinea-Bissau is the world’s fifth largest producer of cashew, following India, Côte d’Ivoire, Vietnam, and Brazil. Guinea-Bissau’s nuts are considered of relatively high quality. This quality has been achieved despite minimal government support and severe constraints in the institutional and business environment. Approximately 50 percent of the trees are fewer than 10 years old, so have not yet reached their peak production period. Thus, output will continue to expand and be a major source of growth for the foreseeable future.

On the other hand, the sector also faces serious challenges that should be addressed immediately. Plantations are established primarily with low-productivity planting material. Moreover, due to the absence of research programs and appropriate extension services since





Encouraging SME linkages to donor aid and FCS exports of agricultural or mineral commodities is another area of opportunity. For both commodity exports and donor procurement, certain inputs could be provided locally but, largely for reasons of local capacity, are not. FCS programs need to work with both the demand and the supply sides. The programs could include demand-side efforts such as encouraging donors and firms to design procurements to enable local firms to participate on their own or in partnership. On the supply side, the programs could emphasize building capacities to enable local firms to participate (box 3.12).

Finally, in many FCS countries, remittances are the main source of income so play an important role in domestic demand. Policies that encourage and support remittances flows, such as more efficient payments systems and assisting emi-grant workers with visas and marketable skills, can significantly increase the otherwise suppressed local demand and, thus, the willingness of investors to do business.

Interventions that Encourage InvestmentAlthough the question of investment promotion is the topic of a separate inves-tigation, two areas of investment in the FCS countries require comment: dias-pora and collective investment. In addition to traditional FDI, diaspora-based

the mid-1990s, agricultural practices are poor. There is little or no thinning or pruning of trees and no treatment against pests and diseases. Although yields appear relatively acceptable at 500–600 kg/ha, comparable to yields in India and Brazil, G-B’s yields can be significantly improved, thus freeing both land and labor and allowing for diversification of the country’s agricultural base. Harvest and post-harvest techniques also are inadequate. They are charac-terized by premature harvesting and inadequate drying, handling, and storing, which cause losses in quantity and quality of nuts.

Promoting processing of cashew can have major economic and social impacts. Currently, almost all of Guinea-Bissau’s cashew crop is exported as raw nuts, mainly to India and Vietnam. There is a small installed processing capacity for the production of cashew kernel (some 25,000 tons, or approximately 13 percent of total production). However, due to the low competitiveness of the domestic processing industry, all facilities currently are idle (a mere 60 tons were exported as kernel in 2010). By exporting only raw nuts, Guinea-Bissau (a) is putting itself in a position of dependence vis-à-vis its two main buyers, India and Vietnam and (b) is depriving itself of the value added and jobs created by the processing industry. Processing creates approximately 1 fulltime job for every 3 tons of processed raw nuts. Thus, processing 60,000 tons of nuts would create approximately 20,000 jobs, primarily in rural areas. Breaking this dependence will require the development of local processing and direct access to consumer countries.

Source: World Bank 2012a.

Box 3.11 Guinea-Bissau Cashew Nuts (continued)




investment is common to encourage investment activity in FCS countries. This investment can be either fully foreign, or, more commonly, in partnership with local friends and family. Enabling measures that can support these types of investments are an important consideration in the program design. Collective investment in the form of investment funds remains rare. Nevertheless, the prospects for such investment should form part of the development of any PSD program.

World Bank Group Interventions

Any FCS government is unlikely to have the capacity to manage and resource a program of this complexity and magnitude. Therefore, focus from the World Bank Group is required. Focus will be a function of a number of variables includ-ing the government’s capacity; quality of governance; business environment; general capabilities, of which labor is the most important; and specific opportuni-ties that exist for growth.

• Regarding how programs should be designed, a 2012 detailed study of the Bank’s investment climate interventions in FCS countries in Africa identified three key success factors for a Bank Group intervention (Leo, Ramachandran, and Thuotte 2012). The study found that when these three factors were aligned, the Bank’s interventions tended to achieve their purposes. These success factors (a) address the most severe growth constraints identified by businesses, (b) match government priorities, and (c) target areas in which the Bank has proven track records. These findings are applicable in all FCS situations.

Box 3.12 Capacity Building for Employment in the Construction Sector

Well-designed and well-implemented, labor-based infrastructure programs offer specific advantages to the social partners (governments, employers, and workers) in developing countries: access to public markets, increased employment, and better returns to investment. Moreover, these programs provide a good opportunity to each of these partners to incorpo-rate social policy objectives in infrastructure investment policies. Such programs also offer better prospects for small entrepreneurs to establish themselves in the domestic market for civil works. In most developing countries, this market so far has been dominated by large-scale and non-local firms. Finally, such programs are attractive to donors and governments alike because they respond to employment and poverty objectives, increase incomes and standards of living in rural and urban areas, reduce foreign exchange requirements, and strengthen the domestic construction sector.

Source: http://www.ilo.org/public/english/employment/recon/eiip/download/green_guide.pdf.




• A review of WBG projects reveals an additional set of six lessons regard-ing design. These lessons include the necessity for stronger Bank Group coordination, flexibility, the necessity to augment government capac-ity through strong supervision, use of third parties (NGOs and UN agencies), Bank Group execution, and the need to finance unconventional programs.

• FCS projects in particular experience strong benefits from close coordination across the World Bank Group. These benefits range from the operational reali-ties of a small on-the-ground presence to the differing modalities of how the agencies within the World Bank Group function. IFC Advisory staff them-selves tend to execute the work, whereas the Bank’s projects are, with the odd exception, executed by the recipients. These varying modalities allow for strong IFC technical assistance in partnership with Bank operations, which also can finance hard assets, such as information systems, that IFC cannot finance easily.

• Flexibility in design is important. Recovery is very difficult to predict. The recovery pattern is very uneven (figure 2.19 in chapter 2). The realities of on-the-ground security are ever changing. The willingness of market par-ticipants to provide services is variable, and counterparts can change quickly.

• Uniformly weak FCS government capacity makes the traditional project cycle very difficult to implement. This problem is common whether the project involves infrastructure or social service delivery. However, there is a particular problem with the private sector for three reasons. First, counterparts often are not clearly defined, and multiple agencies are involved. Second, much of the work is TA so differs from the more standard works-type contracts with which most counterparts are familiar. Third, in many cases, the notion of private sec-tor support is neither clear nor well understood so there is a lack of trust between the public and private sectors.

• Project implementation models that allow for more WBG execution of proj-ects can help. Simple and straightforward components can be executed by government while the Bank executes the more complex areas. In this regard, the Bank can use NGOs and UN agencies to help implement.

• Necessary interventions often take the Bank outside its comfort zone. This point is well established by the comment on Timor-Leste (box 3.13). Examples of such necessary, but somewhat controversial, interventions include matching grants that support rehabilitation of destroyed assets in Gaza, grant programs that provide subsidized equity as in Lebanon or the provision of generators to the private sector in Kosovo.




Note

1. An example is Sri Lanka’s tourism sector, which has doubled in a few years. Given the country’s remarkable touristic endowments, there is no reason that this growth would not continue (Sri Lanka Tourism Development Authority http://www.sltda.gov.lk / index.html).

References

DFID (UK Department for International Development). 2010. Case Study: Sustainable Employment and Economic Development (SEED) Project/Somalia. http://shuraako.org / sites/shuraako.org/files/casestudy-dfid-final.pdf.

European Commission. 2013. http://ec.europa.eu/trade/policy/countries-and-regions / regions/western-balkans/2013.

Goldberg, M. 2011. World Bank, Washington, DC, http://www.publicprivatedialogue.org.

Herzberg, B. 2004. “Investment Climate Reform—Going the Last Mile: The Bulldozer Initiative in Bosnia and Herzegovina.” In World Development Report 2005: A Better Investment Climate for Everyone. Washington, DC: World Bank.

ILO (International Labour Organization). Geneva. http://www.ilo.org/skills/projects / WCMS_103528/lang–en/index/htm.

Box 3.13 Timor-Leste: A Reflection

At the outset of the engagement in 2000, the World Bank Group recognized that, in the short term, the public sector had the larger role to play in generating growth and creating jobs. However, in the longer term, the WBG saw the private sector as the engine for growth and employment.a This latter assumption, which is reasonable for many economies, should have called for going beyond the standard Doing Business approach to identify and address what really was needed to create a flourishing private sector in an FCS country such as Timor-Leste. Given the absence of an entrepreneurial tradition and skills, the lack of any obvious areas of comparative advantage, the small agriculture-based subsistence economy, and the total destruction of all nonagricultural production facilities in 1999 during the conflict, much more than the right legal environment was necessary to bolster the meager private sector to become the engine of growth. Moreover, it was not clear from the WBG’s documents from whence nor how fast the private sector growth would come. Also not clear was how important the nonpetroleum private sector activity ever could be relative to the public sector, which over time would benefit from huge petroleum revenues.

The Bank’s private sector strategy might have been more realistic and more credible if, instead of looking solely at overall business regulatory reforms, it had addressed the above questions upfront and focused on identifying and removing binding constraints in specific areas in which the private sector could have a future, such as agribusiness and tourism.

Source: World Bank 2011.

[[AU: Please provide the signifi cance of superscript indicator “a” given here.]]




Leo, B., V. Ramachandran, and R. Thuotte. 2012. “Supporting Private Business Growth in African Fragile States, Center for Global Development.” Center for Global Development, Washington, DC. http://www.cgdev.org/files/1426061_file_Leo _ Ramachandran_Thuotte_fragile_states_FINAL.pdf.

Mangéni, B. E., and Nyawira, K. 2012. “Public-Private Partnerships in Fragile States: Reflection on the Practice, Challenges and Opportunities in Somaliland.” SEED Program. FAO (Food and Agriculture Organization of the United Nations), Rome, August.

Private Sector Enabling Council. 2007. “Effective Private Sector Contribution to Development in Afghanistan Conference Statement and Road Map.” Kabul, Afghanistan, June 5.

Sri Lanka Tourism Development Authority. http://www.sltda.gov.lk/index.html.

TripleLine Consulting (Siegfried Jenders). 2012. “Facility for New Market Development (FNMD) to Strengthen the Private Sector in the Occupied Palestinian Territories.” Final Evaluation. London. https://www.gov.uk/government/uploads/system/uploads / attachment_data/file/204627/Facility-new-market-development-strengthen-private -sector-palestine.pdf.

USAID (United States Agency for International Development). 2007. “An Overview of the Iraq Cement Industry.” IZDIHAR (Iraq Private Sector Growth and Employment Generation), Washington, DC, November. http://www.izdihar-iraq.com/resources / papers_pdfs/cement_industry_overview_rev3_acc_opt.pdf.

World Bank. 2005. Bosnia and Herzegovina—Emergency Industrial Restart Project. Washington, DC: World Bank. http://documents.worldbank.org/curated / en/2005/04/5790712/bosnia-herzegovina-emergency-industrial-restart-project.

———. 2011. IEG (Independent Evaluation Group) Evaluation of Timor-Leste. Washington, DC.

———. 2012a. Guinea-Bissau Private Sector Rehabilitation and Agribusiness Development Project Appraisal Document. http://documents.worldbank.org/curated /en /2012/02/15879759/guinea-bissau-private-sector-rehabilitation-agribusiness - development -project-environmental-assessment-vol-1-4-plano-e-enquadramento -de-gestao-ambiental-e-social.

———. 2012b. Rwanda Competitiveness and Enterprise Development Project Implementation Completion Report. http://imagebank.worldbank.org/servlet/WDSContentServer / IW3P/IB/2012/03/16/000333038_20120316013602/Rendered/PDF / ICR22110P057290C0disclosed030140120.

———. n.d. “Supporting Innovation in Small and Medium Enterprises in Lebanon.” IFC / Financial and Private Sector Development. http://siteresources.worldbank .org / FINANCIALSECTOR/Resources/Investment_Funds_SME_Innovation_in _ Lebanon.pdf.



Conclusions and Recommendations

Conclusions

In the course of this investigation, the following key points emerged:

• From a private sector perspective, the list of challenges and constraints at first often is overwhelming.

• Unexploited private sector opportunities often coexist with even the direst circumstance.

• Unfortunately, in most FCS, the private sector is too small to make an immedi-ate difference (box 4.1). Therefore, in most FCS, a private sector program needs to work in close harmony with public sector solutions.

Recommendations

The Bank has substantial room to improve its private sector performance to support of FCS through better designed operations, stronger coordination, and more pragmatic implementation. These would take into account:

• The difference in timing of the conflict cycle (that is, continued fragility vs. periods immediately after political settlement, peace agreement, or other action that reduces violence). The heightened weakness and urgency of post-crisis areas call for immediate, small-scale, and more flexible interventions versus the more traditional approach applied throughout the cycle following the post-conflict period.

• The link between constant data review and continuous information flow (analytics) and innovative intervention (operations).

C H A P T E R 4


52 Conclusions and Recommendations


Table 4.1 Strategy Matrix for Analysis and Intervention in FCS Areas

Analytic strategy Operational strategy

All FCS areas Common approach(a) Invest in data to constantly evaluate business

environment in FCS through frequent surveys. Enterprise Surveys (ES) can be sporadic in terms of periods and FCS areas (especially regarding Iraq, West Bank and Gaza, and Yemen, Rep.). ES also lack the flexibility to analyze crucial topics (such as innovative activity) across Regions. Moreover, in general, ES contain very few questions that can serve as proxies to evaluate the uniqueness of the FCS business environments. For example, to rigorously assess the impact of fragility on innovative behavior using the ES was possible only for the ECA Region. Questions relevant to innovation were included in only a few questionnaires for countries, for instance, in Sub-Saharan Africa (CAR, Mauritius, and Zimbabwe). Thus, the availability of data specific to the topic in other Regions was very limited. ES also lack the ability to focus in detail on issues unique to FCS areas. These include security, disruptions in product and factor markets, chances for survival and factors that affect these chances, unique factors that influence the ability to grow and create profits and jobs despite severe essential constraints, and, most importantly, widespread informality.

Moreover, surveys are perception based. Therefore, to capture formal aspects of institutional and entrepreneurial settings in FCS, it also is crucial to

(b) Design other tools that can provide a detailed analysis of the longer term issues related to sustainability and risk.

(c) Encourage (public-private) dialogues and information flows (web space) to set measurable targets and reform in a coordinated manner. Data and dialogues are vital to define priorities, decide on concrete directions, and track results (M&E). Taken together, all of these actions can build trust and confidence among all stakeholders.

(a) Pursue a joint World Bank Group strategy to ensure accountability.

(b) Adopt a joint Bank-client project implementation approach (see, for example, project implementation in West Bank and Gaza as well as in Somalia). Move from recipient-executed mode to joint Bank-client project implementation whereby straightforward components can be executed by government, and the Bank can execute more complex areas.

(c) Fine-tune project design. Review project design. Address the most severe growth constraints identified by the business environment. Match government priorities with target areas for which the Bank has a proven track record of success.

Box 4.1 How Much Growth Does It Take to Generate Jobs?

Even with the most supportive and favorable of policy environments, it can take a decade or more of stellar growth (10 percent + per annum) before the private sector will be of sufficient size to generate the needed jobs. The Khyber Pashtunkhwa and Federally Administered Territories Post Crisis Needs Assessment (KP-FATA PCNA) determined the resources required to generate a sufficient number of private sector jobs to bring employment to a level at which peace could be sustained. This annual level of growth was calculated at 15 percent. However, no other country in the history of the world had achieved these heady heights in 10 years.

Source: World Bank. http://imagebank.worldbank.org/servlet/WDSContentServer/IW3P/IB/2012/07/03/000386194_20120703013107/Rendered/PDF/703300ESW0P11700PCNA0FINAL0REPORT01.pdf.

[[AU: For John Speakman: Please provide the publication year for this citation “World Bank.” Or if this box text is original to this book, the source line can be deleted.]]



Conclusions and Recommendations 53


Table 4.1 Strategy Matrix for Analysis and Intervention in FCS Areas (continued)


Post-crisis areas Innovative/highly flexible approach(a) Conduct updated surveys in the immediate aftermath

of the conflict/violent event to diagnose immediate shocks to the business and economic environment and prioritize short-term policy interventions (Arab Republic of Egypt 2011).

Due to weak capacity and urgency of post-crisis situations, (a) deliver services through a joint strategy. Make the Bank’s execution more flexible through partnering with IFC in on-the-ground presence as well as in general modalities of how WBG works. This close coordination would enable strong IFC TA in partnership with the Bank’s operations that finance/procure hardware or provide services such as cutting- edge business/corporate governance products (such as project execution in Pakistan). The joint strategy also would enable leveraging Bank staff, since the Bank is rigid regarding providing staff assistance. Joint strategy also would address IFC’s rigidity regarding purchasing hardware.

(b) Take into account cycles of violence and introduce very flexible package. In repeating cycles of violence and in post-crisis situations it is very difficult to predict the recovery. First, wait for the moment of reduced violence and heightened political commitment to reform. Then introduce a small matching grant with a very flexible operational menu. Grant will be executed by, for instance, a preprocured firm and will aim at business membership organizations (see Afghanistan), challenge funds for NGOs (since, in FCS areas, government-run projects are not feasible), business plan competitions for firms (see Nigeria), microfinance to provide quick (urgent) capital to stabilize and smooth firms’ operations, or programs that provide subsidized equity (see Lebanon), support rehabilitations of destroyed assets (see Gaza), and enhance access to electricity (such as provision of generators in Kosovo). Last, re-engage to assess the response to the package. If successful, expand it; if not, reduce/cut it.

Fragile areas More traditional approachMost FCS firms are small and informal.(a) Choose specific analytic tools that can reach these

small and informal enterprises systematically over time.(b) Aim to improve local capacity in data analysis and

information provision directly to business environment to restore confidence in the markets. (Re)build public sector institutions responsible for private sector development (PSD) (such as Government Services for Business Development project in West Bank and Gaza); or improve the information flow through enhanced general purpose technology such as access to high-speed internet, own website, or email.

In fragile situations, the urgency is limited so adopt a more traditional approach.

(a) If the public sector is strong enough, launch private-public dialogues (PPDs). Otherwise, (re)build the public sector institutions that have the primary responsibility for PSD and business reforms (see West Bank and Gaza “Government Services for Business Development” project).

(b) Intervene to encourage entrepreneurship and innovative activity at a firm level through, for example, business development services. Examples are training in SME loan applications, accumulation of managerial and production capabilities, and enhancement of market connectivity.

(c) Intervene to support general predictability of business environment through managing perceptions of political uncertainty (see Bosnia Emergency Industrial Re-Start Project), security (see Africa Regional Trade Facilitations Project), access to electricity, reliable judicial systems, and reduced corruption.



54 Conclusions and Recommendations


References

Arab Republic of Egypt. 2011. “Investment Climate Update: Private Enterprises in the Aftermath of the Revolution.” Cairo.

Herzberg, B. 2004. “Investment Climate Reform—Going the Last Mile: The Bulldozer Initiative in Bosnia and Herzegovina.” In World Development R eport 2005: A Better Investment Climate for Everyone. Washington, DC: World Bank.

Table 4.1 Strategy Matrix for Analysis and Intervention in FCS Areas (continued)


(d) Intervene to increase demand through encouraging competitive industries, developing linkages with commodities and local sourcing of aid, and supporting remittance flows or trade preferences.

(e) Intervene to attract nonconventional investment such as diaspora-based or collective investment.

[[AU: Note that Herzberg 2004 is not cited in the text. Please cite or delete it.]]



User’s Guide to Data

The assessment of the impact of fragility on entrepreneurship is based primarily on World Bank’s Enterprise Surveys (ES), which enable both perceptive and objective analyses of business environment using indicators standardized across different Regions and time. The following data sources also were used:

• Penn World Table 7.0 (2011). Provides purchasing power parity and national income accounts converted to international prices for 189 countries/territories for the years 1950–2010. In the current analysis, the Penn World Table is used to quantitatively assess the openness of an economy by fragility for selected countries of the ECA Region, Sub-Saharan Africa, South Asia, and East Asia and the Pacific.

• World Bank World Development Indicators 2012.• International Monetary Fund (IMF) World Economic Outlook 2012.

For each Region, the following approach to data analysis was adopted. First, within each Region, country-specific ES data were pooled and weighted using median weights. Next, based on the World Bank’s official Fragile and Conflict-Affected Situations (FCS) list, countries within each Region were divided in two groups: fragile and non-fragile. Last, to ensure data consistency, the analysis pre-sented in this report was conducted separately for each Region within the same (or close) time period and, wherever relevant, controlled for GDP per capital levels.

The Regions, countries, and periods covered in the report are:

Eastern Europe and Central Asia (ECA) Region

The Business Environment and Enterprise Performance Surveys (BEEPS) of for-mal enterprises for 2008 or 2009 were pooled for this series of countries:

• Fragile states: Bosnia and Herzegovina (2009), Georgia (2008), Kosovo (2009), Tajikistan (2008), and Uzbekistan (2008).

A P P E N D I X A


56 User’s Guide to Data


• Non-fragile states: Armenia (2009), Azerbaijan (2009), Belarus (2008), Bulgaria (2009), Croatia (2009), the Czech Republic (2009), Estonia (2009), the for-mer Yugoslav Republic of Macedonia (2009), Hungary (2009), Kazakhstan (2009), the Kyrgyz Republic (2009), Latvia (2009), Lithuania (2009), Moldova (2009), Montenegro (2009), Poland (2009), Romania (2009), the Russian Federation (2009), Serbia (2009), the Slovak Republic (2009), Slovenia (2009), Turkey (2008), and Ukraine (2008). Although the Albania 2009 Enterprise Survey is available, the country is excluded from the analysis due to inconsistent data weights compared to the rest of the countries.

Sub-Saharan Africa

The Enterprise Surveys of formal enterprises for 2009, 2010, or 2011 were pooled for the following countries:

• Fragile states: Angola (2010), Cameroon (2009), the Central African Republic (2011), Chad (2009), the Republic of Congo (2009), the Democratic Republic of Congo (2009), Eritrea (2009), Côte d’Ivoire (2009), Liberia (2009), Niger (2009), Sierra Leone (2009), Togo (2009), Tonga (2009), and Zimbabwe (2011). Although the Nigeria 2010 Enterprise Survey is available, the country is excluded from the analysis due to inconsistent data weights compared to the rest of the countries.

• Non-fragile states: Benin (2009), Botswana (2010), Burkina Faso (2009), Cape Verde (2009), Gabon (2009), Lesotho (2009), Madagascar (2009), Malawi (2009), Mali (2010), and Mauritius (2009).

South Asia and East Asia and the Pacific

The Enterprise Surveys of formal enterprises for 2009 or 2011 were pooled for the following countries:

• Fragile states: the Lao People’s Democratic Republic (2009), Nepal (2009), Sri Lanka (2011), and Timor-Leste (2009).

• Non-fragile states: Bhutan (2009), Indonesia (2009), Mongolia (2009), the Philippines (2009), and Vietnam (2009).

Issues and Constraints to Data Analysis

Enterprise Surveys of other Regions, such as the Middle East and North Africa or Central and South America, are not included in the current analysis due to either limited data or data inconsistency. For example, for the fragile and conflict states of the MENA Region (Iraq, West Bank and Gaza, and the Republic of Yemen), the surveys available do not match the specific periods chosen for this analysis. Thus, including them would have compromised the consistency of the analysis. The probit model presented in the analysis to assess innovative behavior was not applicable to the group of countries of


User’s Guide to Data 57


Sub-Saharan Africa due to a significant portion of missing data. In addition, the relevant-to- innovation survey questions are not standardized across all, or at least most, of the Sub-Saharan countries included in this analysis.

Additional limitations of using the Enterprise Surveys as a tool to broadly analyze the impact of fragility on entrepreneurship were:

1. Since the ambition of the current approach was to compare general issues of fragility and conflict across Regions, the focus was on the core questionnaires. This focus reduced the flexibility to analyze in detail specific topics within specific countries. For example, the results presented in the report referring to the primary use of mobile phones for operations in fragile countries of SSA are based on specific questions available for African countries only.

2. Can the findings be trusted? Most of the descriptive findings are statistically significant. However, obtaining accurate results when aiming for a causal anal-ysis requires care to ensure external validity, which would enable generalizing the results to population. In addition, internal validity (the degree to which a study produces a single, unambiguous explanation for the results), is difficult to achieve when analyzing perceptive surveys in general.

3. The Enterprise Surveys contain very little information on fragile environments. This lack sometimes makes it difficult to even descriptively assess what deter-mines successful entrepreneurship or failure in these violent and conflict-affected situations.

4. Moreover, the indices used in the report that measure institutional quality capture respondents’ perceptions, rather than any of the formal aspects of the institutional setting. An example of such indices would be one in which firms are asked whether they consider courts, access to land, business inspections, and other as the biggest obstacle to their operations. Rather, such indices mea-sure how these factors are perceived to operate and whether they positively or negatively contribute to the business environment at a given moment, but not what those institutional rules are (Rodrik 2007). Consequently, even though these indices are useful in discovering specific trends at the time, it would be misleading to generalize the respondents’ perceptions, especially across time.

5. Finally, most of the analysis presented in the report determines predictive, not causal, relationships. For example, significant correlations are found between innovative behavior and the fact of being fragile. However, one aspect might not necessarily cause the other. The approach of this report is mainly descrip-tive (with the exception of the two probit regressions that test the impact of fragility on innovative behavior and sales disruptions) and aims to compare two types of business environments: firms that operate in a fragile environ-ment and those that do not.

Reference

Rodrik, D. 2007. One Economics, Many Recipes: Globalization, Institutions, and Economic Growth. Princeton, NJ: Princeton University Press.



Additional World Bank Studies and Field Observations

Some of the outstanding entrepreneurship success stories from FCS countries are highlighted below.

A P P E N D I X B

Box B.1 Hayel Saeed Anam Group: First Commercial Group in the Republic of Yemen

Hayel Saeed Anam Group of companies is a commercial entity established in the Republic of Yemen in 1938. Its activities include various investment fields (industrial, trading, and services) located in many countries. The important countries company has a significant presence in the Arab Republic of Egypt, Indonesia, Malaysia, and Saudi Arabia. By reputation, Hayel Saeed Anam Group is considered the first commercial group in the Republic of Yemen. The reasons are the volume of its investment and its competitive position in the market; its internal structures; and its administrative, technical, and technological rules and regulations.

The company comprises six main trading divisions: Group Products, Consumer Products, Unilever Products, Kraft Products, Cigarettes, and Raw Materials.

Source: Wikipedia.

Box B.2 Success Story of Entrepreneurship in Republic of South Sudan: Importance of Business Competition

The decades of war that culminated in the independence of Republic of South Sudan on July 9, 2011 suppressed the development of the private sector to the extent that South Sudanese were resigned to think that the government was the only employer. Reminiscent of this mindset, on attaining a secondary school certificate, Manjok Elijah Dut took up a job as an assistant clerk with the Ministry of Finance, Economic Planning, Trade and Supply in



60 Additional World Bank Studies and Field Observations


the Government of Jonglei State. However, his skills in computer operations caused him to believe that there were other opportunities outside government offices. Through a Business Plan Competition run by the World Bank’s Multi-Donor Trust Fund (MDTF) Private Sector Development Project, Manjok obtained the capital to set up a printing and computer shop called Computer Supply, Ltd. in the new business area, Bor town. Within months, Manjok was registering monthly profits.

Exposure to computing provided an opportunity for Elijah to respond to an online call for proposals to participate in an innovative initiative by the World Bank and Ministry of Commerce, Industry, and Investment (MCII). At 27 years of age, he was 1 of the 1,600 South Sudanese entrepreneurs who took part in the Business Plan Competition and business training designed by MCII to promote entrepreneurship. This competition was sponsored by the Multi-Donor Trust Fund for South Sudan (MDTF-SS). Manjok was awarded a grant of US$20,000 that he accessed in March 2010 through KCB Sudan, Ltd. One year after setting up his business, the young entrepreneur applied the knowledge he acquired during business training to recordkeeping, customer care, cash flow, and the financial management of his business.

Manjok’s business received a boost when he won contracts to supply computers to the Government of Republic of South Sudan and to the Government of Jonglei State, and to train government employees in computer skills. With the expansion of his business, he abandoned his “zinc building” and rented a bigger and better building partitioned into rooms used for an internet café and storage for desktops, laptops, and computer accessories. His business also offers secretarial services including typing, printing, scanning, and photocopying. Earning a daily net profit of SDG 195 (US$70) from Internet and secretarial services and a monthly net profit of SDG 4,800 ($1,700), Manjok is contributing to the development of Jonglei State by employing 6 people.

Manjok was the first beneficiary of the business competition awards to repay his loan and has since taken another loan with KCB Sudan, Ltd. This entrepreneur has embraced the computer age with enthusiasm, as reflected in his business slogan: “We are your partner in the world of technology. We believe that ‘technology makes life better.’” He is pleased with his business growth, and is confident that online courses are now possible in Jonglei State. His confidence is evident in his plans to expand through acquisition of land to construct permanent structures and the establishment of a Business Center.

As the Managing Director of Computer Supply Company, Ltd, Manjok is not distanced from what goes around the world and affects his business. He does not hide the excitement of wit-nessing the birth of a new nation. He calls on entrepreneurs in Republic of South Sudan to seize the opportunity and turn their dreams into realities because the political atmosphere is now favorable for investment. His message to the youth is that business can thrive in a peace-ful and corruption-free environment!

Sources: Andres Garcia, AFTEE, World Bank.

Box B.2 Success Story of Entrepreneurship in Republic of South Sudan: Importance of Business Competition (continued)


Additional World Bank Studies and Field Observations 61


Box B.3 Cisco and the Importance of Corporate Social Responsibility in the Palestinian Territories

In September 2009, Cisco announced an investment of $10 million to seed a sustainable model of job-creation and economic development in the Palestinian Territories. Made in coop-eration with the president of the Palestinian Authority, Mahmoud Abbas, the three-year investment demonstrates Cisco’s continued commitment to build stronger and healthier global communities through strategic social investments.

“Education and the Internet are the great equalizers and vital to a sustainable, productive economy that increases the standard of living for all,” commented Cisco Chairman and CEO John Chambers. “It’s a core part of our culture to give back, but it’s also a core strategy of what drives our success in the regions of the world where we do business. We make all our business decisions with three to five years in mind and, given our strong business momentum in the region, it’s only appropriate to make this investment in its economy and its people.”

Palestinian President Abbas stated, “One of our key priorities is to build a market economy in close cooperation with the private sector. We commend Cisco for its commitment to help us accomplish this goal, and look forward to working together toward enhancing the social, economic and education opportunities for the people of Palestine. This contribution, we hope, will help prosperity and peace in the region.”

Key aspects of the commitment include a multimillion dollar venture capital investment targeted at high-potential small businesses throughout the region, as well as the develop-ment of training programs for critical information and communications technology (ICT) skills. In the future, Cisco’s intention is to engage in a multistakeholder collaboration to encourage additional investment in the Palestinian economy from local, regional, and global organizations.

Cisco Chairman Chambers added, “As we execute against this commitment over the com-ing years, our hope also is to create a catalyst of active engagement and a call to action for others across the public and private sector to contribute toward a diverse and sustainable economic model for the region.”

Note: For more information on Cisco’s global development and corporate social responsibility initiatives, see http://www . cisco.com/web/about/citizenship.

Box B.4 Importance of Household Enterprises (HE) in Rwanda

Rwanda no longer is classified as FCS, It was FCS in the 1990s, but today it is a success story of recovery from FCS. There are many reasons for Rwanda’s recovery, including remarkable leadership, but the resilience of the household enterprise (HE) is part of the story.

HEs are enterprises that engage in nonfarm activities, often operated by an individual with the support of family members. HEs do not hire labor. In Rwanda, 30 percent of households have HEs. The majority (86 percent) of these HEs labor in the service sector (trading and transport), with some artisanal-type manufacturing (crafts, food).



62 Additional World Bank Studies and Field Observations


HEs are driven by pull and push factors. The pull factors are the perceived opportunity to earn a better living, the ease of entry, and the desire to be one’s own boss. Push factors include the inability to find another job, lack of land, and lack of education/skills. The constraints to establish HEs are preliminary access to a working space and finance. The big constraints to operations are access to finance, markets, and infrastructure.

Policymakers can support HEs with a variety of measures. These include ensuring that there are public market spaces; ensuring that microfinance reaches and targets HEs, making avail-able very simple basic financial and management training, and encouraging collective action through the formation of associations.

Source: World Bank 2011.

Box B.4 Importance of Household Enterprises (HE) in Rwanda (continued)

Box B.5 Egypt’s First Revolution: A Cold for Large Businesses and Pneumonia for SMEs (Importance of Clear Strategy Reform)

Initially, the events of the 2011 Egyptian revolution and its aftermath imposed a substantial shock on the economy. Private firms reported significant setbacks in sales and exports. Firms’ own identification of constraints placed uncertainty at the top: uncertainty about the macroeconomic situation, including aggregate demand and prices; uncertainty about the unstable political situation; and uncertainty about the regulatory policies they face—including potential changes and arbitrary administration. By 2012, concern about corruption had risen to the top 3 constraints; and, for the first time, concern with crime and theft rose to the top 5, suggesting an overall decline in rule of law. Transport, which had not been a leading constraint before 2011, rose to the seventh most commonly identified serious constraint.

The “bad news” of the aftermath of the first revolution was a period of economic disruption in which private demand sharply contracted, causing the output of firms to fall substantially. Most firms tried to maintain their levels of capital and labor, resulting in a sharp decline in productivity. By 2012, sales and productivity had recovered part of the way to their former levels, but firms clearly had made labor force reductions and continued to confront lower demand. The decline in growth expectations and pervasive sense of uncertainty suggested a strong need for a new government to establish a clear reform strategy to encourage private-led growth and to take a few key actions to signal credible commitment to this strategy.



Additional World Bank Studies and Field Observations 63


References

Stone, A., H. Dabidian, and L. Badawy. 2012. “Egyptian Private Enterprises in the Aftermath of the Revolution: An Investment Climate Update.” No. 73009. World Bank, Washington, DC.

World Bank. 2011. “Resilience in the Face of Economic Diversity: Policies for Growth with a Focus on Household Enterprises.” Rwanda Economic Update 2011. Washington, DC.

Box B.5 Egypt’s First Revolution: A Cold for Large Businesses and Pneumonia for SMEs (Importance of Clear Strategy Reform) (continued)

Figure BB.5.1 Firm S ales Experience and Expectation, Weighted by Sales Value

Source: Stone, Dabidian, and Badawy 2012.

0

20

40

60

80

100

120

140

2010 2011 2012(expectation)

Micro Small LargeMediumOverall


Appendix C. Innovation (Correlation Tables)

Correlat ion Table

Total Non-Fragile Fragile Total Non-Fragile Fragile

R&D (Yes/No) ECAo3 0.2753* 0.2677* 0.3908* 0.2525* 0.2470* 0.3251*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Amount spent on R&D (LCU) ECAo4 -0.0462* -0.0478* 0.0546 -0.0255 -0.0272 0.1848*

[0.0853] [0.0903] [0.5342] [0.344] [0.3356] [0.0339]

Regular use of computers ECAo6 -0.1604* -0.1565* -0.2059* -0.1627* -0.1599* -0.1906*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

% of sales as national sales d3a 0.0331* 0.0241* 0.1665* 0.0467* 0.0393* 0.1498*

[0.0005] [0.0198] [0.0000] [0.0000] [0.0002] [0.0000]

% of sales as indirect exports d3b -0.0175* -0.013 -0.0855* -0.0144 -0.0113 -0.0499*

[0.0661] [0.2088] [0.0004] [0.1314] [0.2756] [0.0396]

% of sales as direct exports d3c -0.0291* -0.0212* -0.1489* -0.0456* -0.0388* -0.1442*

[0.0022] [0.0403] [0.0000] [0.0000] [0.0002] [0.0000]

Year of first export d8 -0.0907* -0.0962* 0.0862 -0.1372* -0.1447* 0.1293*

[0.0000] [0.0000] [0.1479] [0.0000] [0.0000] [0.0297]

Subsidies from government (Yes/No) ECAq53 0.1069* 0.1020* 0.1848* 0.1002* 0.0985* 0.1120*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Competing against informal/unregistered firms (Yes/No) e11 0.0528* 0.0552* 0.0197 0.0792* 0.0814* 0.0555*

[0.0000] [0.0000] [0.4345] [0.0000] [0.0000] [0.0286]

Informal gift to get things done (Yes/No) j7a -0.1370* -0.1353* -0.3057* -0.0973* -0.0815* -0.2966*

[0.0000] [0.0000] [0.0000] [0.0002] [0.005] [0.0000]

Technology from foreign firms (Yes/No) e6 0.0961* 0.0947* 0.1152* 0.1308* 0.1239* 0.2417*

[0.0000] [0.0000] [0.006] [0.0000] [0.0000] [0.0000]

Using own website (Yes/No) c22b 0.2011* 0.1837* 0.3505* 0.1685* 0.1523* 0.3254*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

% of sales paid for security i2a 0.0148 0.0196 -0.1417* 0.014 0.0223 -0.2222*

[0.398] [0.3002] [0.0023] [0.4266] [0.239] [0.0000]

Informal gift for electrical connection (Yes/No) c5 0.0201 0.0154 0.1190* -0.0319 -0.0407 0.1467*

[0.431] [0.5826] [0.0548] [0.212] [0.1478] [0.018]

Paid for security (Yes/No) i1 0.1786* 0.1693* 0.2453* 0.1536* 0.1455* 0.2119*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Losses due to theft, robbery etc. (Yes/No) i3 0.0927* 0.0829* 0.1885* 0.1297* 0.1227* 0.1954*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Cheking/savings account (Yes/No) k6 0.0546* 0.0600* 0.0470* 0.0817* 0.0886* 0.0213

[0.0000] [0.0000] [0.0524] [0.0000] [0.0000] [0.3809]

Overdraft facil ity (Yes/No) k7 0.1158* 0.1020* 0.2291* 0.0717* 0.0539* 0.2928*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Loan/credit l ine from fin. Institution (Yes/No) k8 0.0909* 0.0798* 0.1859* 0.0665* 0.0525* 0.2319*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Main market of main product sold e1 -0.0867* -0.0838* -0.1278* -0.1241* -0.1252* -0.0834*

[0.0000] [0.0000] [0.0021] [0.0000] [0.0000] [0.0454]

# of competitors e2 -0.0520* -0.0453* -0.1522* -0.0786* -0.0778* -0.0797*

[0.0014] [0.0093] [0.0007] [0.0000] [0.0000] [0.0794]

Change in monthly sales e3 -0.1019* 0.1019* 0.1102* 0.1256* 0.1250* 0.1662*

[0.0000] [0.0000] [0.0148] [0.0000] [0.0000] [0.0002]

Change in prices e4 -0.0477 0.0522* 0.0242 0.0757* 0.0777* 0.0985*

[0.0033] [0.0027] [0.5938] [0.0000] [0.0000] [0.0299]

* significant at 10%

Introduction of new products/services

(las t 3 years )

Upgraded products/services

(last year)

Source: Authors’ calculations based on World Bank Enterprise Surveys 2009–11 (accessed April 2012).

Correlat ion Table (selec t ive based on GDP level)

Total Non-Fragile Fragile Total Non-Fragile Fragile

R&D (Yes/No) ECAo3 0.3344* 0.3908* 0.2525* 0.2470* 0.3251*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Amount spent on R&D (LCU) ECAo4 0.0376 -0.0541 -0.0255 -0.0272 0.1848*

[0.0903] [0.6271] [0.344] [0.3356] [0.0339]

Regular use of computers (%) ECAo6 0.1728* 0.1842* -0.1627* -0.1599* -0.1906*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

% of sales as national sales d3a -0.0366* -0.1665* 0.0467* 0.0393* 0.1498*

[0.0279] [0.0000] [0.0000] [0.0002] [0.0000]

% of sales as indirect exports d3b -0.0373* 0.0855* -0.0144 -0.0113 -0.0499*

[0.0251] [0.0004] [0.1314] [0.2756] [0.0396]

% of sales as direct exports d3c 0.0644* 0.1489* -0.0456* -0.0388* -0.1442*

[0.0001] [0.0000] [0.0000] [0.0002] [0.0000]

Year of first export d8 -0.0112 0.0639 -0.1372* -0.1447* 0.1293*

[0.7790] [0.3402] [0.0000] [0.0000] [0.0297]

Subsidies from government (Yes/No) ECAq53 0.0593* 0.1848* 0.1002* 0.0985* 0.1120*

[0.0004] [0.0000] [0.0000] [0.0000] [0.0000]

0.0831* 0.0197 0.0792* 0.0814* 0.0555*

[0.0000] [0.4345] [0.0000] [0.0000] [0.0286]

Informal gift to get things done (%) j7a -0.0116 0.3057* -0.0973* -0.0815* -0.2966*

[0.8120] [0.0000] [0.0002] [0.005] [0.0000]

Technology from foreign firms (Yes/No) e6 0.1920* 0.1152* 0.1308* 0.1239* 0.2417*

[0.0000] [0.006] [0.0000] [0.0000] [0.0000]

Using own website (Yes/No) c22b 0.2271* 0.3505* 0.1685* 0.1523* 0.3254*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

% of sales paid for security i2a -0.0741* 0.1417* 0.014 0.0223 -0.2222*

[0.3002] [0.0023] [0.4266] [0.239] [0.0000]

Informal gift for electrical connection (Yes/No) c5 -0.027 0.1190* -0.0319 -0.0407 0.1467*

[0.5723] [0.0548] [0.212] [0.1478] [0.018]

Paid for security (Yes/No) i1 0.1600* 0.2453* 0.1536* 0.1455* 0.2119*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Losses due to theft, robbery etc. (Yes/No) i3 0.1567* 0.1885* 0.1297* 0.1227* 0.1954*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Cheking/savings account (Yes/No) k6 0.1331* 0.0470* 0.0817* 0.0886* 0.0213

[0.0000] [0.0524] [0.0000] [0.0000] [0.3809]

Overdraft facil ity (Yes/No) k7 0.1807* 0.2291* 0.0717* 0.0539* 0.2928*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Loan/credit l ine from fin. Institution (Yes/No) k8 0.1650* 0.1859* 0.0665* 0.0525* 0.2319*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0000]

Main market of main product sold e1 0.1250* 0.1822* -0.1241* -0.1252* -0.0834*

[0.0000] [0.0000] [0.0000] [0.0000] [0.0454]

# of competitors e2 0.0381 0.1989* -0.0786* -0.0778* -0.0797*

[0.2002] [0.0000] [0.0000] [0.0000] [0.0794]

Increase in sales 0.1687* 0.1280* 0.1256* 0.1250* 0.1662*

[0.0000] [0.0047] [0.0000] [0.0000] [0.0002]

Decrease in sales -0.1149* -0.0701

[0.0001] [0.1227]

Sales unchanged -0.0890* -0.0812*

[0.0024] [0.0736]

Increase in price of main product 0.0275 -0.0575

[0.3487] [0.2061]

Decrease in price of main product -0.0053 -0.1063*

[0.8569] [0.0191]

Price of main product unchanged -0.0259 0.1114*

[0.3764] [0.0140]

* significant at 10%

Competing against informal/unregistered firms (Yes/No) e11

Introduction of new products/services

(las t 3 years )

Upgraded products/services

(last year)

Upgraded products/servicesIntroduction of new products/services


Survey Sample Statistics, ECA Region

A P P E N D I X D

Non-fragile

n = 9,401 (%) Fragile

n = 1,730 (%)Total

n = 11,131 (%)

IndustryOther manufacturing 9 8.7 9Food 4.4 5.9 4.5Textiles 1.8 0.7 1.7Garments 3 1.6 2.9Chemicals 1 1.3 1.1Plastics & rubber 2 1.5 2Nonmetallic mineral products 1.2 3 1.3Basic metals 0.4 0.6 0.4Fabricate metal products 4.2 2.2 4.1Machinery and equipment 2.3 1 2.2Electronics 0.9 1.6 0.9Construction 10.6 12.6 10.7Other services 6.7 3.3 6.5Wholesale 14.2 13.5 14.2Retail 22.1 27 22.3Hotel and restaurants 6.1 8.6 6.2Transport 8 6.7 8IT 2.1 0 2

Size Micro 5.9 1.3 5.7Small 59.8 66.1 60.1Medium 25.5 23.7 25.4Large 8.8 8.8 8.8

Industry by sizeMicro Other manufacturing 1.6 0 1.6Food 2 5 2.5



68 Survey Sample Statistics, ECA Region


Non-fragile

n = 9,401 (%) Fragile

n = 1,730 (%)Total

n = 11,131 (%)

Textiles 0.3 0 0.3Garments 0.8 1.7 0.8Chemicals 0.3 0 0.3Plastics & rubber 0 0 0Nonmetallic mineral products 0 0 0Basic metals 0 0 0Fabricate metal products 4 8 4.1Machinery and equipment 1.1 2.6 1.1Electronics 0.3 0 0.3Construction section 12.1 0 12Other services 8.9 16.1 9Wholesale 19.2 21.6 19.2Retail 20.8 42.5 21.1Hotel and restaurants 11 2.3 10.9Transport section 15 0 14.8IT 1.9 0 1.9

SmallOther manufacturing 9 7 8.9Food 3.6 5.9 3.7Textiles 1.1 0.3 1.1Garments 2.2 1.5 2.1Chemicals 0.9 1.5 0.9Plastics & rubber 1.8 1.8 1.8Nonmetallic mineral products 0.9 3.1 1Basic metals 0.3 0.3 0.3Fabricate metal products 4.1 1.7 4Machinery and equipment 2.1 0.9 2Electronics 0.6 2.2 0.7Construction section 8.6 13.0 8.9Other services 7.9 3.8 7.6Wholesale 15.3 11.8 15.1Retail 26.8 29.2 27Hotel and restaurants 6.1 9.1 6.3Transport section 6.3 6.8 6.3IT 2.4 0.1 2.3

MediumOther manufacturing 9.7 13.1 9.9Food 5.2 5.8 5.3Textiles 2.8 1.3 2.7Garments 4.3 1.6 4.2Chemicals 1.4 0.7 1.4Plastics & rubber 2.9 0.8 2.8

Appendix D (continued)



Survey Sample Statistics, ECA Region 69


Non-fragile

n = 9,401 (%) Fragile

n = 1,730 (%)Total

n = 11,131 (%)

Nonmetallic mineral products 1.3 3.1 1.4Basic metals 0.4 1.2 0.5Fabricate metal products 4.9 3.3 4.8Machinery and equipment 2.6 0.3 2.5Electronics 1.1 0.6 1.0Construction section 14.4 9.7 14.2Other services 4.2 2 4.1Wholesale 13.2 19.1 13.5Retail 15.4 23.2 15.8Hotel and restaurants 5.9 8.8 6Transport section 9 4.2 8.7IT 1.3 1.2 1.3

LargeOther manufacturing 11.6 10.4 11.5Food 8.6 6.9 8.5Textiles 4.7 1.5 4.5Garments 5.7 1.6 5.5Chemicals 1.3 2.1 1.3Plastics & rubber 2.9 0.8 2.8Nonmetallic mineral products 3.8 2.2 3.7Basic metals 1.6 1.1 1.5Fabricate metal products 3.7 2 3.6Machinery and equipment 3.8 3 3.7Electronics 2.6 0.5 2.5Construction section 11.7 19.2 12.1Other services 4.3 1.2 4.2Wholesale 6.1 10 6.3Retail 9.9 19.3 10.4Hotel and restaurants 3.4 4.9 3.5Transport section 12.3 13.4 12.4IT 2 0 1.9

Size of localityCapital city 16.6 34 17.5City with population over 1 million 14.2 4.1 13.7City with population over 250,000 to

1 million 18.7 16.6 18.6City with population 50,000 to 250,000 25 31.7 25.4City with less than 50,000 population 25.4 13.7 24.8

ParentshipYes 9.7 4.3 9.4No, a firm on its own 90.3 95.7 90.6




70 Survey Sample Statistics, ECA Region


Non-fragile

n = 9,401 (%) Fragile

n = 1,730 (%)Total

n = 11,131 (%)

Legal statusPublicly listed company 7.3 8.7 7.3Privately held, limited liability

company 57.3 24.2 55.6Sole proprietorship 20.6 40.4 21.6Partnership 5.2 1.4 5Limited partnership 6.5 21.9 7.3Other 3.1 3.3 3.2

Average share of ownershipDomestic ownership share 92.2 92.7 92.3Foreign ownership share 5.2 4 5.2Government ownership share 1.1 1.6 1.1Other ownership share 1.3 1.7 1.3

AgePercentiles: 10 4 years 4 years 4 years25 8 years 7 years 8 years50 (median age) 13 years 11 years 12 years75 17 years 16 years 17 years90 30 years 38 years 32 yearsAverage age 16 years 16 years 16 yearsMinimum age 0 years 0 years 0 yearsMaximum age 183 years 140 years 183 yearsPercentage of establishments that: Started 2008 0.1 0.1 0.1Started 2007 1.2 1.2 1.2Started 2006 2.6 3.2 2.7Started before 2006 96.1 95.5 96

Average sales breakdownNational sales 91.2 95.5 91.5Indirect exports 2 0.8 2Direct exports 6.7 3.7 6.6

Source: World Bank Enterprise Surveys 2009–11, http://www.enterprisesurveys.org (accessed April 2012) .



Appendix E. Survey Sample Statistics, Sub-Saharan Africa Region

Non-fragile Fragile Total

Industry

Manufacturing 5.7% 3.2% 4.2%

Services 29.5% 22.7% 25.5%

Food 3.4% 3.1% 3.2%

Textiles 1.9% 0.5% 1%

Garments 2.4% 4.8% 3.9%

Transport services (informal) 0% 0.1% 0%

Cleaning and washing services (informal) 0% 0.2% 0.1%

Hairdressers and barber shops (informal) 0% 0.3% 0.2%

Professional services (informal) 0% 0.2% 0.1%

Chemicals 0.8% 1% 0.9%

Plastics and rubber 0.8% 0.8% 0.8%

Non-metallic mineral products 0.4% 0.6% 0.5%

Basic metals 1.7% 0.5% 1%

Fabricated metal products 2.5% 1.4% 1.8%

Machinery and equipment 0.3% 1.3% 0.9%

Electronics (31 and 32) 0.3% 0.3% 0.3%

Construction Section F 7.6% 2.7% 4.7%

Services of motor vehicles 12.7% 11.9% 12.2%

Wholesales 5.3% 7.3% 6.5%

Retail 18.4% 22.1% 20.6%

Hotel and restaurants 2.3% 9.9% 6.8%

Transport Section I (60–64) 2.9% 3.6% 3.3%

IT 1.3% 1.3% 1.3%

Total 100.0% 99.8% 99.9%

Zone


Size

Small 70.2% 76.5% 73.8%

Medium 21.8% 18.4% 19.8%

Large 8% 5.2% 6.3%

Total 100% 100% 100%

Zone


Size of locality

Capital city 54.1% 40.7% 46.4%

City with population over 1 million 10% 49.7% 32.9%

City with population over 250,000 to 1 million 8.5% 4.7% 6.3%

City with population 50,000 to 250,000 15.5% 4.1% 8.9%

City with less than 50,000 population 11.9% 0.9% 5.5%

Total 100% 100% 100%

Parentship

Yes 18.4% 16.3% 17.2%

No, a firm on its own 81.6% 83.7% 82.8%

Total 100% 100% 100%

Legal status

Publicly listed company 4.1% 2.3% 3.1%

Privately held, limited liability company 27.2% 11.1% 17.8%

Sole proprietorship 53.6% 64.3% 59.8%

Partnership 8.4% 7.5% 7.9%

Limited partnership 4.1% 7.8% 6.3%

Other 2.5% 7.0% 5.1%

Total 100% 100% 100%

Average share of ownership

Domestic ownership share 75.3% 79.8% 77.9%

Foreign ownership share 18.4% 11.5% 14.4%

Government ownership share 0.4% 0.4% 0.4%

Other ownership share 5.8% 8.3% 7.3%

Total 99.9% 100% 100%

Zone


Basic Quantitative Indicators, ECA Region

A P P E N D I X F


74

Non

-frag

ile st

ates

Doi

ng

Busin

ess

2008

ra

nkin

gs

(181

coun

-tr

ies)

Doi

ng

Busin

ess

2009

ra

nkin

gs

(181

coun

-tr

ies)

Dur

atio

n of

pow

er

outa

ges

(hou

rs)

Annu

al

cost

of

secu

rity

(% o

f sa

les)

% o

f co

ntra

ct

valu

e pa

id

info

rmal

ly

to se

cure

co

ntra

ct

% o

f tot

al

annu

al sa

les

paid

info

r-m

ally

to “g

et

thin

gs d

one”

% o

f tim

e sp

ent b

y se

nior

man

-ag

emen

t on

gove

rnm

ent

regu

latio

ns

% o

f new

in

vest

-m

ent

from

pr

ivat

e ba

nks

% o

f new

in

vest

men

t fro

m st

ate-

owne

d ba

nks

% o

f firm

s w

ith in

ter-

natio

nally

re

cogn

ized

qu

ality

cert

i-fic

atio

n

% o

f firm

s us

ing

tech

nolo

gy

licen

se fr

om

fore

ign

com

pani

es

% o

f fir

ms

usin

g th

eir o

wn

web

site

Bela

rus

110

853

31

615

1110

1418

36Tu

rkey

5759

53

41

2735

330

1675

Ukr

aine

139

145

64

47

1417

113

2736

Russ

ian

Fede

ratio

n10

612

06

45

622

75

1225

55Po

land

7476

42

24

1416

717

664

Rom

ania

4847

54

34

1119

226

1635

Serb

ia86

942

42

514

254

2215

65Ka

zakh

stan

7170

65

67

616

211

1527

Mol

dova

9210

33

42

48

201

915

32A

zerb

aija

n96

334

42

113

140

1824

22M

aced

onia

, FYR

7571

310

05

1725

421

4139

Arm

enia

3944

43

15

1318

427

4063

Kyrg

yz R

epub

lic94

687

75

86

131

1619

26Es

toni

a17

224

30

57

221

2126

67Cz

ech

Repu

blic

5675

42

23

1311

643

1281

Hun

gary

4541

32

07

1625

739

1370

Latv

ia22

295

75

711

226

1828

56Li

thua

nia

2628

22

16

1129

316

2562

Slov

ak R

epub

lic32

362

65

69

183

2930

73Sl

oven

ia55

542

30

49

1123

2815

80Bu

lgar

ia46

452

21

512

243

2012

48Cr

oatia

9710

64

81

1916

258

1915

63M

onte

negr

o81

903

32

313

403

1317

30To

tal n

on-fr

agile

st

ates

44

34

1618

521

1756

tabl

e co

ntin

ues n

ext p

age


75

App

endi

x F

(con

tinue

d)

Frag

ile st

ates

Doi

ng

Busin

ess

2008

ra

nkin

gs

(181

coun

-tr

ies)

Doi

ng

Busin

ess

2009

ra

nkin

gs

(181

coun

-tr

ies)

Dur

atio

n of

pow

er

outa

ges

(hou

rs)

Annu

al

cost

of

secu

rity

(% o

f sa

les)

% o

f co

ntra

ct

valu

e pa

id

info

rmal

ly

to se

cure

co

ntra

ct

% o

f tot

al

annu

al sa

les

paid

info

r-m

ally

to “g

et

thin

gs d

one”

% o

f tim

e sp

ent b

y se

nior

man

-ag

emen

t on

gove

rnm

ent

regu

latio

ns

% o

f new

in

vest

-m

ent

from

pri-

vate

ba

nks

% o

f new

in

vest

men

t fro

m st

ate-

owne

d ba

nks

% o

f firm

s w

ith in

ter-

natio

nally

re

cogn

ized

qu

ality

cert

i-fic

atio

n

% o

f firm

s us

ing

tech

nolo

gy

licen

se fr

om

fore

ign

com

pani

es

% o

f fir

ms

usin

g th

eir o

wn

web

site

Geo

rgia

1815

84

03

326

116

1731

Tajik

ista

n15

315

910

43

1314

56

1725

21U

zbek

ista

n13

813

815

34

613

35

111

7Bo

snia

and

H

erze

govi

na10

511

97

40

313

252

3025

56Ko

sovo

n/a

n/a

210

16

109

08

2036

Tota

l fra

gile

stat

es

12

52

612

163

1016

22

Dur

atio

n of

po

wer

ou

tage

s (h

ours

)

Annu

al co

st

of se

curit

y (%

of s

ales

)

% o

f co

ntra

ct

valu

e pa

id

info

rmal

ly

to se

cure

co

ntra

ct

% o

f tot

al

annu

al

sale

s pai

d in

for-

mal

ly to

“g

et

thin

gs

done

”

% o

f tim

e sp

ent b

y se

nior

m

anag

e-m

ent o

n go

vern

-m

ent r

egu-

latio

ns

% o

f new

in

vest

men

t fro

m p

rivat

e ba

nks

% o

f new

in

vest

men

t fro

m st

ate-

owne

d ba

nks

% o

f firm

s w

ith

inte

rna-

tiona

lly

reco

g-ni

zed

qual

ity

cert

ifica

-tio

n

% o

f firm

s us

ing

tech

nolo

gy

licen

se fr

om

fore

ign

com

pani

es

% o

f firm

s us

ing

thei

r ow

n w

ebsit

e

Non

-frag

ile st

ates

44

34

1618

521

1756

Frag

ile st

ates

125

26

1216

310

1622

Sour

ce: W

orld

Ban

k En

terp

rise

Surv

eys

2009

–11,

htt

p://

ww

w.e

nter

prise

surv

eys.o

rg (a

cces

sed

Apr

il 20

12).

Not

e: R

ed =

the

high

est v

alue

with

in th

at c

olum

n; g

reen

= th

e lo

wes

t val

ue w

ithin

that

col

umn.


Appendix G. STATA Output Underlying Graphical Presentation

<<A>> Capacity Utilization

<> ECA (mean GDP per capita = US$4073.735)

<> Sub-Saharan Africa (mean GDP per capita = US$1083.353)

<> Asia (mean GDP per capita = US$962.512):

<<A>> Purchase of Fixed Assets

_cons 71.4185 3.271089 21.83 0.000 64.70678 78.13022

gdppc .0011848 .0006346 1.87 0.073 -.0001173 .002487

fragile -2.289879 3.859401 -0.59 0.558 -10.20872 5.628958

f1 Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust

(Std. Err. adjusted for 28 clusters in a1)

Root MSE = 21.922

R-squared = 0.0199

Prob > F = 0.1057

F( 2, 27) = 2.45

Linear regression Number of obs = 4308

(sum of wgt is 1.4174e+05)

. reg f1 fragile gdppc [pw=wmedian], cluster(a1)

_cons 70.33577 2.618385 26.86 0.000 64.6791 75.99245

gdppc -.0002154 .0014583 -0.15 0.885 -.0033658 .002935

fragile -.2862403 4.218791 -0.07 0.947 -9.400385 8.827905


Robust


Root MSE = 22.795

R-squared = 0.0002

Prob > F = 0.9876

F( 2, 13) = 0.01



. reg f1 fragile gdppc [pw=wmedian], cluster(a1)

_cons 72.32358 7.0921 10.20 0.000 54.09276 90.55441

gdppc .008996 .0065748 1.37 0.230 -.007905 .025897

fragile -7.764435 1.123269 -6.91 0.001 -10.65189 -4.87698


Robust


Root MSE = 25.288

R-squared = 0.0048

Prob > F = .

F( 1, 5) = .



. reg f1 fragile gdppc [aw=wmedian], cluster(a1)


OR


(*) dy/dx is for discrete change of dummy variable from 0 to 1

gdppc .0000121 .00001 1.15 0.251 -8.5e-06 .000033 4071.28

fragile* -.1688796 .12482 -1.35 0.176 -.413522 .075762 .052205

variable dy/dx Std. Err. z P>|z| [ 95% C.I. ] X

= .55424437

y = Pr(k4) (predict)

Marginal effects after probit

. mfx

. do "C:\Users\wb417696\AppData\Local\Temp\STD02000000.tmp"

end of do-file

.

_cons .0343614 .1091231 0.31 0.753 -.1795159 .2482386

gdppc .0000305 .0000267 1.14 0.253 -.0000218 .0000829

fragile -.4271728 .3240945 -1.32 0.187 -1.062386 .2080407

k4 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust


Log pseudolikelihood = -405086.86 Pseudo R2 = 0.0082

Prob > chi2 = 0.1610

Wald chi2(2) = 3.65

Probit regression Number of obs = 11043

Iteration 3: log pseudolikelihood = -405086.86




. probit k4 fragile gdppc [pw=wmedian], cluster(a1)

_cons .5146554 .0425392 12.10 0.000 .4273721 .6019387

gdppc .0000118 .0000102 1.16 0.255 -9.05e-06 .0000327

fragile -.1677259 .1217537 -1.38 0.180 -.4175439 .082092

k4 Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = .49432

R-squared = 0.0113

Prob > F = 0.1581

F( 2, 27) = 1.98



. reg k4 fragile gdppc [pw=wmedian], cluster(a1)

OR

<> Asia (mean GDP per capita = US$962.512):


gdppc .0000178 .00001 1.52 0.129 -5.2e-06 .000041 1227.44

fragile* -.0743776 .06915 -1.08 0.282 -.209917 .061162 .608209


= .42814941



. mfx


end of do-file

.

_cons -.1217025 .1307542 -0.93 0.352 -.377976 .134571

gdppc .0000453 .00003 1.51 0.131 -.0000135 .0001042

fragile -.1891318 .1771196 -1.07 0.286 -.5362797 .1580162


Robust



Prob > chi2 = 0.0286

Wald chi2(2) = 7.11







_cons .451553 .0518291 8.71 0.000 .344066 .55904

gdppc .0000181 .0000119 1.52 0.144 -6.63e-06 .0000427

fragile -.0740307 .0688549 -1.08 0.294 -.2168271 .0687657


Robust


Root MSE = .49176

R-squared = 0.0131

Prob > F = 0.0417

F( 2, 22) = 3.68

OR

<<A head??>> Innovative Activity in ECA, ECA (mean GDP per capita = US$4073.735)


gdppc -.0003785 .00021 -1.84 0.066 -.000781 .000024 1040.71

fragile* -.0163108 .0522 -0.31 0.755 -.118619 .085998 .053431


= .28863321



. mfx


end of do-file

.

_cons .5985919 .6340042 0.94 0.345 -.6440335 1.841217

gdppc -.0011083 .0005922 -1.87 0.061 -.002269 .0000524

fragile -.0483486 .1543412 -0.31 0.754 -.3508517 .2541545


Robust



Prob > chi2 = 0.1059

Wald chi2(2) = 4.49







_cons .722492 .2447215 2.95 0.018 .1581634 1.286821

gdppc -.0004127 .0002263 -1.82 0.106 -.0009345 .0001091

fragile -.0098382 .0539035 -0.18 0.860 -.1341399 .1144636


Robust


Root MSE = .44697

R-squared = 0.0351

Prob > F = 0.1857

F( 2, 8) = 2.09

<> Introducing new products/services

OR


gdppc -2.44e-06 .00001 -0.31 0.757 -.000018 .000013 4074.11

fragile* -.2031131 .09925 -2.05 0.041 -.397647 -.008579 .052255


= .51611306

y = Pr(ECAo1) (predict)


. mfx


end of do-file

.

_cons .0927338 .130368 0.71 0.477 -.1627828 .3482504

gdppc -6.12e-06 .0000198 -0.31 0.757 -.0000449 .0000326

fragile -.5245789 .2735136 -1.92 0.055 -1.060656 .0114979

ECAo1 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.1473

Wald chi2(2) = 3.83






. probit ECAo1 fragile gdppc [pw=wmedian], cluster(a1)

_cons .5370233 .0518303 10.36 0.000 .4306762 .6433704

gdppc -2.45e-06 7.86e-06 -0.31 0.757 -.0000186 .0000137

fragile -.2038618 .1008653 -2.02 0.053 -.4108203 .0030968

ECAo1 Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = .49785

R-squared = 0.0078

Prob > F = 0.1341

F( 2, 27) = 2.17



. reg ECAo1 fragile gdppc [pw=wmedian], cluster(a1)

<> Investment in R&D

OR


gdppc -6.08e-07 .00001 -0.09 0.932 -.000014 .000013 4074.93

fragile* -.0827478 .09924 -0.83 0.404 -.277253 .111757 .05218


= .20696099



. mfx


end of do-file

.

_cons -.7910882 .1123867 -7.04 0.000 -1.011362 -.5708143

gdppc -2.13e-06 .0000248 -0.09 0.932 -.0000507 .0000465

fragile -.330531 .4683909 -0.71 0.480 -1.24856 .5874983


Robust



Prob > chi2 = 0.7794

Wald chi2(2) = 0.50







_cons .2147329 .0327917 6.55 0.000 .1474499 .2820158

gdppc -6.85e-07 7.20e-06 -0.10 0.925 -.0000155 .0000141

fragile -.083481 .1013652 -0.82 0.417 -.2914652 .1245032


Robust


Root MSE = .40522

R-squared = 0.0020

Prob > F = 0.7125

F( 2, 27) = 0.34

<> Upgrading existing lines

OR


gdppc -4.79e-06 .00001 -0.36 0.719 -.000031 .000021 4068.35

fragile* -.1605464 .14693 -1.09 0.275 -.448524 .127431 .051884


= .67844518



. mfx

_cons .5395683 .2338194 2.31 0.021 .0812907 .997846

gdppc -.0000134 .0000375 -0.36 0.721 -.0000868 .0000601

fragile -.4207678 .3749604 -1.12 0.262 -1.155677 .3141411


Robust



Prob > chi2 = 0.5282

Wald chi2(2) = 1.28







_cons .7052307 .0816439 8.64 0.000 .5377113 .8727502

gdppc -4.70e-06 .0000132 -0.36 0.725 -.0000318 .0000224

fragile -.1586606 .1441343 -1.10 0.281 -.4543998 .1370785


Robust


Root MSE = .46612

R-squared = 0.0052

Prob > F = 0.5517

F( 2, 27) = 0.61

<<A>> Growth in Employment

“growthempl” = (current number of employees – number of employees upon start-up)/firm age



<> Asia (mean GDP per capita = US$962.512)

_cons 4.547478 1.023738 4.44 0.000 2.446941 6.648014

gdppc .0001607 .000268 0.60 0.554 -.0003892 .0007105

fragile -6.528735 4.718425 -1.38 0.178 -16.21014 3.152674

growthempl Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 83.292

R-squared = 0.0009

Prob > F = 0.3399

F( 2, 27) = 1.12


. reg growthempl fragile gdppc, cluster(a1)

_cons 4.070451 1.946647 2.09 0.048 .0333533 8.10755

gdppc -.0008981 .0004815 -1.87 0.076 -.0018966 .0001005

fragile -2.601048 1.755854 -1.48 0.153 -6.242465 1.04037


Robust


Root MSE = 46.627

R-squared = 0.0009

Prob > F = 0.1974

F( 2, 22) = 1.75



_cons 7.803868 5.234201 1.49 0.174 -4.266223 19.87396

gdppc -.0027269 .0040509 -0.67 0.520 -.0120684 .0066146

fragile -1.465518 1.908247 -0.77 0.465 -5.865943 2.934906


Robust


Root MSE = 38.982

R-squared = 0.0007

Prob > F = 0.7494

F( 2, 8) = 0.30

<<A>> Trade Destination, Sub-Saharan Africa


(A) <<C>> Neighboring countries within Sub-Saharan Africa

(B) <<C>> Developed countries

(C) <<C>> Other

<<A>> Unpredictability

_cons 51.07685 18.75959 2.72 0.042 2.853777 99.29992

gdppc -.0321273 .028995 -1.11 0.318 -.1066613 .0424068

fragile 24.35791 18.12839 1.34 0.237 -22.24261 70.95844

afd3f Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 40.276

R-squared = 0.0470

Prob > F = 0.3937

F( 2, 5) = 1.13



. reg afd3f fragile gdppc [pw=wmedian], cluster(a1)

_cons 51.40556 18.99618 2.71 0.042 2.574322 100.2368

gdppc -.001651 .0292056 -0.06 0.957 -.0767263 .0734244

fragile -34.07735 18.75567 -1.82 0.129 -82.29034 14.13563

afd3g Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 38.987

R-squared = 0.1662

Prob > F = 0.2404

F( 2, 5) = 1.92



. reg afd3g fragile gdppc [pw=wmedian], cluster(a1)

_cons -2.48241 7.462948 -0.33 0.753 -21.66653 16.70171

gdppc .0337782 .0281172 1.20 0.283 -.0384993 .1060557

fragile 9.719436 6.886858 1.41 0.217 -7.983797 27.42267

afd3h Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 28.371

R-squared = 0.1215

Prob > F = 0.1684

F( 2, 5) = 2.60



. reg afd3h fragile gdppc [pw=wmedian], cluster(a1)

<> ECA

<<C>> Crime, theft, and disorder


gdppc -.0000302 .00001 -3.49 0.000 -.000047 -.000013 3290.67

fragile* .0406057 .07906 0.51 0.608 -.114349 .19556 .157998


= .26296235

y = Pr(msocrime) (predict)


. mfx


end of do-file

.

_cons -.3490376 .1153365 -3.03 0.002 -.5750931 -.1229822

gdppc -.0000925 .000027 -3.42 0.001 -.0001454 -.0000396

fragile .1213778 .2298931 0.53 0.598 -.3292043 .5719599

msocrime Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0004

Wald chi2(2) = 15.67






. probit msocrime fragile gdppc, cluster(a1)

Labelled to here Corruption


gdppc -.0000295 .00001 -4.06 0.000 -.000044 -.000015 3306.64

fragile* -.0291753 .07954 -0.37 0.714 -.18508 .126729 .156561


= .35779431

y = Pr(msocorr) (predict)


. mfx


end of do-file

.

_cons -.091111 .0978134 -0.93 0.352 -.2828217 .1005997

gdppc -.0000789 .0000193 -4.09 0.000 -.0001167 -.0000411

fragile -.0789734 .218153 -0.36 0.717 -.5065454 .3485986

msocorr Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0001

Wald chi2(2) = 17.93






. probit msocorr fragile gdppc, cluster(a1)

Courts


gdppc -.0000151 .00001 -2.40 0.017 -.000027 -2.8e-06 3371.62

fragile* -.0633163 .02603 -2.43 0.015 -.114327 -.012306 .156767


= .20806218

y = Pr(msocourts) (predict)


. mfx

_cons -.5983602 .1072057 -5.58 0.000 -.8084795 -.3882409

gdppc -.0000527 .0000216 -2.44 0.015 -.000095 -.0000103

fragile -.2368886 .0973221 -2.43 0.015 -.4276364 -.0461408

msocourts Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0250

Wald chi2(2) = 7.38






. probit msocourts fragile gdppc, cluster(a1)

Informal competition



gdppc -.0000222 .00000 -4.90 0.000 -.000031 -.000013 3323.92

fragile* -.095201 .01899 -5.01 0.000 -.13243 -.057973 .161168


= .28328491

y = Pr(msoinfcomp) (predict)


. mfx

_cons -.3068337 .0538887 -5.69 0.000 -.4124537 -.2012137

gdppc -.0000655 .0000138 -4.75 0.000 -.0000926 -.0000385

fragile -.3002718 .0611207 -4.91 0.000 -.4200662 -.1804775

msoinfcomp Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0000

Wald chi2(2) = 36.29






. probit msoinfcomp fragile gdppc, cluster(a1)

Sub-Saharan Africa

Crime, theft, and disorder


gdppc -.0000196 .00001 -1.62 0.105 -.000043 4.1e-06 3333.95

fragile* -.0334405 .08403 -0.40 0.691 -.19813 .131249 .15281


= .39899776

y = Pr(msopolinst) (predict)


. mfx

_cons -.0730396 .1415955 -0.52 0.606 -.3505617 .2044826

gdppc -.0000509 .0000314 -1.62 0.105 -.0001123 .0000106

fragile -.0873545 .2214749 -0.39 0.693 -.5214374 .3467283

msopolinst Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.2675

Wald chi2(2) = 2.64






. probit msopolinst fragile gdppc, cluster(a1)


gdppc -3.87e-06 .00002 -0.21 0.835 -.00004 .000032 1082.63

fragile* -.0560162 .0842 -0.67 0.506 -.221039 .109006 .588372


= .34219976



. mfx

_cons -.3057461 .1664697 -1.84 0.066 -.6320206 .0205285

gdppc -.0000105 .0000503 -0.21 0.834 -.000109 .000088

fragile -.1518108 .2309321 -0.66 0.511 -.6044295 .3008079


Robust



Prob > chi2 = 0.8056

Wald chi2(2) = 0.43







Corruption


gdppc -.0000141 .00002 -0.60 0.547 -.00006 .000032 1046.34

fragile* .0808102 .10186 0.79 0.428 -.118832 .280452 .589863


= .50220173



. mfx

_cons -.0771367 .2284712 -0.34 0.736 -.524932 .3706587

gdppc -.0000354 .0000588 -0.60 0.547 -.0001507 .0000799

fragile .202925 .2566793 0.79 0.429 -.3001572 .7060072


Robust



Prob > chi2 = 0.3030

Wald chi2(2) = 2.39







Courts


gdppc -.0000229 .00001 -2.14 0.032 -.000044 -1.9e-06 1019.74

fragile* -.001971 .04382 -0.04 0.964 -.087856 .083914 .595998


= .20526843



. mfx

_cons -.736712 .1014973 -7.26 0.000 -.9356431 -.5377808

gdppc -.0000805 .0000364 -2.21 0.027 -.0001518 -9.24e-06

fragile -.0069278 .1542201 -0.04 0.964 -.3091937 .295338


Robust



Prob > chi2 = 0.0723

Wald chi2(2) = 5.25









gdppc -.0000247 .00001 -1.70 0.088 -.000053 3.7e-06 1065.08

fragile* -.0176209 .06689 -0.26 0.792 -.148716 .113474 .590855


= .4328145



. mfx

_cons -.0759556 .1382114 -0.55 0.583 -.346845 .1949339

gdppc -.0000627 .0000365 -1.72 0.086 -.0001342 8.78e-06

fragile -.0447791 .1700872 -0.26 0.792 -.378144 .2885858


Robust



Prob > chi2 = 0.1827

Wald chi2(2) = 3.40







Political Instability


gdppc -.0000575 .00004 -1.60 0.110 -.000128 .000013 1070.79

fragile* .1892112 .1099 1.72 0.085 -.026186 .404608 .590547


= .44905409



. mfx

_cons -.2593948 .2304874 -1.13 0.260 -.7111417 .1923521

gdppc -.0001453 .000091 -1.60 0.110 -.0003236 .0000329

fragile .4859548 .2859247 1.70 0.089 -.0744472 1.046357


Robust



Prob > chi2 = 0.0161

Wald chi2(2) = 8.26







Asia



gdppc 7.22e-06 .00006 0.13 0.897 -.000102 .000116 960.795

fragile* .0076196 .05527 0.14 0.890 -.100714 .115953 .190762


= .08906026



. mfx


end of do-file

.

_cons -1.398478 .4166762 -3.36 0.001 -2.215148 -.5818075

gdppc .0000448 .0003508 0.13 0.898 -.0006426 .0007323

fragile .0463858 .3295981 0.14 0.888 -.5996146 .6923861


Robust



Prob > chi2 = 0.9748

Wald chi2(2) = 0.05






Corruption


gdppc .0000115 .00011 0.11 0.913 -.000195 .000219 967.711

fragile* -.0268556 .0586 -0.46 0.647 -.141705 .087994 .183954


= .17526264



. mfx

_cons -.9570817 .4694272 -2.04 0.041 -1.877142 -.0370213

gdppc .0000447 .0004105 0.11 0.913 -.0007599 .0008494

fragile -.1075026 .2338658 -0.46 0.646 -.5658713 .350866


Robust



Prob > chi2 = 0.8612

Wald chi2(2) = 0.30







Courts


gdppc .0000128 .00005 0.25 0.799 -.000086 .000112 980.959

fragile* .0090328 .04644 0.19 0.846 -.081984 .100049 .180707


= .06680597



. mfx

_cons -1.609464 .5090007 -3.16 0.002 -2.607087 -.6118408

gdppc .0000991 .0004011 0.25 0.805 -.0006869 .0008852

fragile .0675236 .3373227 0.20 0.841 -.5936168 .728664


Robust



Prob > chi2 = 0.9646

Wald chi2(2) = 0.07









gdppc .0000583 .00004 1.56 0.120 -.000015 .000132 963.921

fragile* .0452807 .03883 1.17 0.244 -.030828 .12139 .195169


= .17899353



. mfx

_cons -1.166585 .149909 -7.78 0.000 -1.460401 -.8727683

gdppc .0002231 .000143 1.56 0.119 -.0000571 .0005033

fragile .1655793 .1362494 1.22 0.224 -.1014645 .4326232


Robust



Prob > chi2 = 0.1206

Wald chi2(2) = 4.23









gdppc -.0002198 .00023 -0.95 0.343 -.000674 .000235 959.029

fragile* -.1056923 .1054 -1.00 0.316 -.31228 .100896 .19322


= .16148579



. mfx

_cons -.0288962 .9949836 -0.03 0.977 -1.979028 1.921236

gdppc -.0008981 .0008274 -1.09 0.278 -.0025197 .0007235

fragile -.5082382 .5621058 -0.90 0.366 -1.609945 .5934689


Robust



Prob > chi2 = 0.5258

Wald chi2(2) = 1.29







Rent Seeking

ECA

Electrical connections


fragile* .0110541 .03443 0.32 0.748 -.056433 .078542 .044105


= .09187484

y = Pr(c5) (predict)


. mfx

_cons -1.332142 .1353715 -9.84 0.000 -1.597465 -1.066819

fragile .0644884 .1999592 0.32 0.747 -.3274244 .4564013

c5 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.7471

Wald chi2(1) = 0.10





. probit c5 fragile [pw=wmedian], cluster(a1)

Water connection


fragile* .0145964 .02153 0.68 0.498 -.027603 .056796 .052836


= .06267433



. mfx

_cons -1.538511 .1344805 -11.44 0.000 -1.802088 -1.274934

fragile .1099015 .1656919 0.66 0.507 -.2148485 .4346516


Robust



Prob > chi2 = 0.5071

Wald chi2(1) = 0.44







Phone connection


fragile* .0077812 .01558 0.50 0.617 -.022749 .038311 .034316


= .03321667



. mfx

_cons -1.83881 .1078104 -17.06 0.000 -2.050115 -1.627506

fragile .0967929 .1873622 0.52 0.605 -.2704303 .4640161


Robust



Prob > chi2 = 0.6054

Wald chi2(1) = 0.27







Construction-related permit


fragile* .0350246 .11202 0.31 0.755 -.184537 .254586 .041085


= .20196764

y = Pr(g4) (predict)


. mfx

_cons -.8395041 .164901 -5.09 0.000 -1.162704 -.516304

fragile .119034 .3702867 0.32 0.748 -.6067146 .8447826

g4 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.7479

Wald chi2(1) = 0.10






. probit g4 fragile [pw=wmedian], cluster(a1)

Import license


fragile* .0243319 .04277 0.57 0.569 -.059505 .108168 .07612


= .08920154

y = Pr(j12) (predict)


. mfx

_cons -1.356304 .2205779 -6.15 0.000 -1.788629 -.9239798

fragile .139467 .2553577 0.55 0.585 -.361025 .639959

j12 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.5850

Wald chi2(1) = 0.30






. probit j12 fragile [pw=wmedian], cluster(a1)

Business license


fragile* .1514066 .0783 1.93 0.053 -.002059 .304872 .043975


= .12635789



. mfx

_cons -1.168575 .1349949 -8.66 0.000 -1.43316 -.9039898

fragile .5638896 .2589318 2.18 0.029 .0563926 1.071387


Robust



Prob > chi2 = 0.0294

Wald chi2(1) = 4.74







Inspections by tax officials


fragile* .0617788 .07295 0.85 0.397 -.081199 .204757 .038651


= .07327704



. mfx

_cons -1.465493 .14636 -10.01 0.000 -1.752353 -1.178632

fragile .3539714 .3575471 0.99 0.322 -.3468081 1.054751


Robust



Prob > chi2 = 0.3222

Wald chi2(1) = 0.98







Africa

Electrical connection


fragile* .0407867 .07511 0.54 0.587 -.106421 .187994 .680066


= .20146224



. mfx

_cons -.9373433 .2520194 -3.72 0.000 -1.431292 -.4433943

fragile .1484174 .2835528 0.52 0.601 -.4073358 .7041706


Robust



Prob > chi2 = 0.6007

Wald chi2(1) = 0.27







Phone connection


fragile* .0637112 .07632 0.83 0.404 -.085877 .2133 .561578


= .12885362



. mfx

_cons -1.305517 .1596301 -8.18 0.000 -1.618386 -.9926474

fragile .3092888 .3342452 0.93 0.355 -.3458198 .9643975


Robust



Prob > chi2 = 0.3548

Wald chi2(1) = 0.86









fragile* .1736227 .084 2.07 0.039 .008987 .338259 .499141


= .23724042



. mfx

_cons -.9974871 .1745298 -5.72 0.000 -1.339559 -.6554149

fragile .5655313 .2651715 2.13 0.033 .0458048 1.085258


Robust



Prob > chi2 = 0.0329

Wald chi2(1) = 4.55









fragile* .1387549 .06123 2.27 0.023 .018744 .258765 .632214


= .17475343



. mfx

_cons -1.302509 .1475199 -8.83 0.000 -1.591643 -1.013375

fragile .5804403 .2339253 2.48 0.013 .1219551 1.038926


Robust



Prob > chi2 = 0.0131

Wald chi2(1) = 6.16







Import license


fragile* .1455259 .06995 2.08 0.037 .008423 .282629 .59556


= .15801035



. mfx

_cons -1.3845 .2742818 -5.05 0.000 -1.922082 -.8469171

fragile .6411289 .3308039 1.94 0.053 -.0072349 1.289493


Robust



Prob > chi2 = 0.0526

Wald chi2(1) = 3.76







Business license


fragile* .171794 .10152 1.69 0.091 -.02719 .370778 .630776


= .12356487



. mfx

_cons -1.76185 .1322818 -13.32 0.000 -2.021118 -1.502583

fragile .9583449 .3735148 2.57 0.010 .2262694 1.69042


Robust



Prob > chi2 = 0.0103

Wald chi2(1) = 6.58








Water connection


fragile* -.0204772 .12574 -0.16 0.871 -.266927 .225973 .669645


= .18606863



. mfx

_cons -.8418648 .3256235 -2.59 0.010 -1.480075 -.2036544

fragile -.0755808 .4641448 -0.16 0.871 -.9852879 .8341262


Robust



Prob > chi2 = 0.8706

Wald chi2(1) = 0.03






Asia

Electrical connection


fragile* -.0332923 .03339 -1.00 0.319 -.098727 .032143 .020227


= .27303337



. mfx

_cons -.6015745 .098414 -6.11 0.000 -.7944624 -.4086866

fragile -.1033251 .1005526 -1.03 0.304 -.3004044 .0937543


Robust



Prob > chi2 = 0.3042

Wald chi2(1) = 1.06






Water connection


fragile* -.1478524 .07369 -2.01 0.045 -.292277 -.003428 .046015


= .1460811



. mfx

_cons -.9952567 .2953351 -3.37 0.001 -1.574103 -.4164105

fragile -1.263371 .6086217 -2.08 0.038 -2.456248 -.0704947


Robust



Prob > chi2 = 0.0379

Wald chi2(1) = 4.31








Phone connection


fragile* .0647568 .05213 1.24 0.214 -.037413 .166927 .002027


= .06370198



. mfx

_cons -1.525212 .2222962 -6.86 0.000 -1.960904 -1.089519

fragile .3910337 .3061832 1.28 0.202 -.2090743 .9911417


Robust



Prob > chi2 = 0.2016

Wald chi2(1) = 1.63









fragile* -.2408774 .04339 -5.55 0.000 -.325923 -.155832 .071794


= .39277624



. mfx

_cons -.2199646 .0629986 -3.49 0.000 -.3434397 -.0964896

fragile -.7260444 .1538357 -4.72 0.000 -1.027557 -.424532


Robust



Prob > chi2 = 0.0000

Wald chi2(1) = 22.27









fragile* -.1066884 .05286 -2.02 0.044 -.210299 -.003078 .112852


= .18805251



. mfx

_cons -.8317792 .1428713 -5.82 0.000 -1.111802 -.5517567

fragile -.4724458 .2462283 -1.92 0.055 -.9550444 .0101529


Robust



Prob > chi2 = 0.0550

Wald chi2(1) = 3.68







Import license


fragile* -.0160457 .15183 -0.11 0.916 -.313635 .281543 .091409


= .15106268



. mfx

_cons -1.025434 .1017354 -10.08 0.000 -1.224831 -.8260358

fragile -.0705927 .6927179 -0.10 0.919 -1.428295 1.287109


Robust



Prob > chi2 = 0.9188

Wald chi2(1) = 0.01






Business license


fragile* .0457166 .14441 0.32 0.752 -.237313 .328746 .040851


= .17857807



. mfx

_cons -.9274926 .1953639 -4.75 0.000 -1.310399 -.5445863

fragile .16389 .4939378 0.33 0.740 -.8042102 1.13199


Robust



Prob > chi2 = 0.7400

Wald chi2(1) = 0.11







Phone connection


fragile* .0647568 .05213 1.24 0.214 -.037413 .166927 .002027


= .06370198



. mfx

_cons -1.525212 .2222962 -6.86 0.000 -1.960904 -1.089519

fragile .3910337 .3061832 1.28 0.202 -.2090743 .9911417


Robust



Prob > chi2 = 0.2016

Wald chi2(1) = 1.63







Major obstacle to business

ECA

Design-based F(12.74, 1.3e+05)= 4.5659 P = 0.0000

Uncorrected chi2(14) = 104.2085

Pearson:

Key: column proportions

Total 1 1 1

transpor .0109 .0078 .0108

tax rate .1971 .2017 .1974

tax admi .0374 .0614 .0386

practice .1022 .0871 .1015

politica .1147 .0909 .1134

labor re .0384 .0182 .0373

inadequa .12 .0783 .1179

electric .0243 .0886 .0275

customs .0202 .0236 .0204

crime, t .0267 .0283 .0268

courts .0189 .0105 .0185

corrupti .0652 .0839 .0661

business .044 .0498 .0443

access t .0282 .0147 .0275

access t .1518 .1552 .152

ment 0 1 Total

establish fragile

of this

operation

the

affecting

obstacle

serious

most

Design df = 9827

Subpop. size = 530480.12

Subpop. no. of obs = 9930

Number of PSUs = 9930 Population size = 530480.12

Number of strata = 103 Number of obs = 9930

(running tabulate on estimation sample)

subpop() != 0 indicates subpopulation

Note: subpop() takes on values other than 0 and 1

. svy, subpop(a1): tab m1a fragile if m1a>0, col

Sub-Saharan Africa

Design-based F(10.82, 53173.33)= 10.4999 P = 0.0000


Pearson:


Total 1 1 1

transpor .0686 .0183 .039

tax rate .0758 .0357 .0523

tax admi .031 .028 .0292

practice .1287 .0921 .1072

politica .0368 .2046 .1354

labor re .0149 .0161 .0156

inadequa .0485 .0132 .0278

electric .1208 .0828 .0985

customs .0417 .0201 .029

crime, t .0709 .0247 .0438

courts .0044 .0065 .0056

corrupti .0583 .08 .071

business .0164 .0088 .0119

access t .0298 .0305 .0302

access t .2533 .3387 .3035

ment 0 1 Total

establish fragile

of this

operation

the

affecting

obstacle

serious

most

Design df = 4913









Asia

Note: missing test statistics because of stratum with single sampling unit.

Design-based F(., .) = . P = .


Pearson:


Total 1 1 1

15 .0502 .0304 .0491

14 .0236 .1428 .0304

13 .0121 .0548 .0146

12 .1407 .1467 .141

11 .0768 .0099 .0729

10 .0234 .0589 .0254

9 .0661 .0743 .0665

8 .0669 .1132 .0696

7 .0178 .026 .0183

6 .0238 .0195 .0235

5 .0016 .0047 .0018

4 .0187 .0186 .0187

3 .0235 .0578 .0254

2 .0469 .0945 .0496

1 .4081 .148 .3931

obstacle 0 1 Total

biggest fragile

Design df = 5041









Disposal of loan or line of credit

ECA (mean GDP per capita = US$4073.735)


gdppc .0000246 .00001 2.88 0.004 7.8e-06 .000041 4069.51

fragile* -.1120079 .14489 -0.77 0.439 -.395982 .171966 .052362


= .43464353



. mfx

_cons -.4032404 .122858 -3.28 0.001 -.6440378 -.1624431

gdppc .0000624 .0000218 2.86 0.004 .0000196 .0001053

fragile -.2943291 .3995865 -0.74 0.461 -1.077504 .4888461


Robust



Prob > chi2 = 0.0061

Wald chi2(2) = 10.19







Sub-Saharan Africa (mean GDP per capita = US$1083.353)


gdppc .0000297 .00001 2.61 0.009 7.4e-06 .000052 1242.69

fragile* -.1338436 .04846 -2.76 0.006 -.228831 -.038856 .607041


= .22346204



. mfx

_cons -.6200937 .1265223 -4.90 0.000 -.8680728 -.3721145

gdppc .0000993 .000037 2.68 0.007 .0000266 .0001719

fragile -.4345861 .1595001 -2.72 0.006 -.7472006 -.1219717


Robust



Prob > chi2 = 0.0000

Wald chi2(2) = 32.97







Reasons not to apply for a loan

ECA

No need of a loan


fragile* -.0470761 .03379 -1.39 0.164 -.113296 .019144 .051888


= .40969517

y = Pr(k17noneed) (predict)


. mfx

_cons -.2219517 .0423567 -5.24 0.000 -.3049693 -.1389341

fragile -.1229088 .0891557 -1.38 0.168 -.2976508 .0518333

k17noneed Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.1680

Wald chi2(1) = 1.90





. probit k17noneed fragile [pw=wmedian], cluster(a1)

Application procedures


fragile* .0705845 .03717 1.90 0.058 -.002271 .14344 .051888


= .03416896

y = Pr(k17complex) (predict)


. mfx

_cons -1.853169 .0820723 -22.58 0.000 -2.014027 -1.69231

fragile .5858104 .2211834 2.65 0.008 .1522989 1.019322

k17complex Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0081

Wald chi2(1) = 7.01







. probit k17complex fragile [pw=wmedian], cluster(a1)

Interest rates and loan maturity


fragile* .0331415 .01087 3.05 0.002 .011844 .054439 .051888


= .07704986

y = Pr(k17intrate) (predict)


. mfx

_cons -1.435688 .0724007 -19.83 0.000 -1.577591 -1.293785

fragile .2021474 .0747226 2.71 0.007 .0556938 .3486011

k17intrate Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0068

Wald chi2(1) = 7.32






. probit k17intrate fragile [pw=wmedian], cluster(a1)

Collateral requirements


fragile* .0131304 .01203 1.09 0.275 -.010454 .036715 .051888


= .0304404

y = Pr(k17collat) (predict)


. mfx

_cons -1.882968 .0625574 -30.10 0.000 -2.005578 -1.760358

fragile .1658927 .1382505 1.20 0.230 -.1050732 .4368587

k17collat Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.2302

Wald chi2(1) = 1.44






. probit k17collat fragile [pw=wmedian], cluster(a1)

Sub-Saharan Africa

No need for loan


fragile* -.1675433 .07921 -2.12 0.034 -.322793 -.012294 .578049


= .29118863



. mfx

_cons -.2711343 .1780001 -1.52 0.128 -.6200081 .0777394

fragile -.4822797 .222029 -2.17 0.030 -.9174486 -.0471108


Robust



Prob > chi2 = 0.0298

Wald chi2(1) = 4.72







Application procedures


fragile* .1681089 .05438 3.09 0.002 .06152 .274698 .578049


= .14925766



. mfx

_cons -1.483399 .1555017 -9.54 0.000 -1.788177 -1.178622

fragile .7677147 .2252221 3.41 0.001 .3262876 1.209142


Robust



Prob > chi2 = 0.0007

Wald chi2(1) = 11.62







Interest rate


fragile* .002171 .02752 0.08 0.937 -.051761 .056103 .578049


= .09288883



. mfx

_cons -1.330733 .0858113 -15.51 0.000 -1.49892 -1.162546

fragile .0130771 .1650862 0.08 0.937 -.3104859 .33664


Robust



Prob > chi2 = 0.9369

Wald chi2(1) = 0.01








fragile* .0515886 .02628 1.96 0.050 .000074 .103103 .578049


= .08383774



. mfx

_cons -1.579797 .1352849 -11.68 0.000 -1.844951 -1.314644

fragile .3461394 .1768468 1.96 0.050 -.000474 .6927529


Robust



Prob > chi2 = 0.0503

Wald chi2(1) = 3.83







Asia

No need for loan


fragile* .0129436 .05345 0.24 0.809 -.091808 .117695 .053543


= .25479712



. mfx

_cons -.661604 .0830985 -7.96 0.000 -.824474 -.498734

fragile .0398631 .1635899 0.24 0.807 -.2807673 .3604935


Robust



Prob > chi2 = 0.8075

Wald chi2(1) = 0.06






Application process


fragile* .0667949 .01401 4.77 0.000 .03933 .09426 .053543


= .08502077



. mfx

_cons -1.3908 .0834729 -16.66 0.000 -1.554404 -1.227196

fragile .3498005 .0873962 4.00 0.000 .178507 .521094


Robust



Prob > chi2 = 0.0001

Wald chi2(1) = 16.02







Interest rates


fragile* .0006516 .02217 0.03 0.977 -.042808 .044111 .053543


= .10712966



. mfx

_cons -1.242127 .0978541 -12.69 0.000 -1.433918 -1.050337

fragile .0035249 .120038 0.03 0.977 -.2317454 .2387951


Robust



Prob > chi2 = 0.9766

Wald chi2(1) = 0.00








fragile* -.0428498 .02602 -1.65 0.100 -.093842 .008143 .053543


= .11364021



. mfx

_cons -1.193641 .1247558 -9.57 0.000 -1.438158 -.9491241

fragile -.2568603 .1404851 -1.83 0.067 -.532206 .0184853


Robust



Prob > chi2 = 0.0675

Wald chi2(1) = 3.34







Transactions on credit

Sub-Saharan Africa

Material inputs or services paid for before delivery

Material inputs or services paid for on delivery

Material inputs or services paid for after delivery

_cons 33.17218 4.425939 7.49 0.000 23.73852 42.60585

gdppc -.0015459 .0009363 -1.65 0.120 -.0035416 .0004498

fragile -5.467889 5.231642 -1.05 0.313 -16.61887 5.683092

k1a Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 37.557

R-squared = 0.0050

Prob > F = 0.2809

F( 2, 15) = 1.38



. reg k1a fragile gdppc [aw=wmedian], cluster(a1)

_cons 35.89287 4.025609 8.92 0.000 27.31249 44.47325

gdppc -.0007168 .0009191 -0.78 0.448 -.0026758 .0012423

fragile 17.20316 6.642286 2.59 0.021 3.045459 31.36085

k1b Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 42.985

R-squared = 0.0436

Prob > F = 0.0165

F( 2, 15) = 5.46



. reg k1b fragile gdppc [aw=wmedian], cluster(a1)

_cons 29.69211 6.45445 4.60 0.000 16.26935 43.11487

gdppc .003612 .0020382 1.77 0.091 -.0006267 .0078507

fragile -3.867669 9.132063 -0.42 0.676 -22.85883 15.1235

k1c Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 38.772

R-squared = 0.0325

Prob > F = 0.0496

F( 2, 21) = 3.48



. reg k1c fragile gdppc [aw=wmedian], cluster(a1)

Goods and services paid before delivery

Goods and services paid on delivery

Goods and services paid after delivery

_cons 29.16608 5.021884 5.81 0.000 18.69061 39.64154

gdppc -.0018651 .0012447 -1.50 0.150 -.0044615 .0007312

fragile -1.955167 6.016054 -0.32 0.749 -14.50443 10.5941

k2a Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 36.15

R-squared = 0.0055

Prob > F = 0.2689

F( 2, 20) = 1.40



. reg k2a fragile gdppc [aw=wmedian], cluster(a1)

_cons 36.20965 3.401131 10.65 0.000 29.11502 43.30429

gdppc .0004247 .0008522 0.50 0.624 -.001353 .0022023

fragile 16.22754 8.722239 1.86 0.078 -1.966737 34.42181

k2b Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 42.272

R-squared = 0.0318

Prob > F = 0.2024

F( 2, 20) = 1.73



. reg k2b fragile gdppc [aw=wmedian], cluster(a1)

_cons 34.60672 6.533126 5.30 0.000 21.05784 48.15559

gdppc .001383 .0015735 0.88 0.389 -.0018803 .0046463

fragile -12.76353 7.409498 -1.72 0.099 -28.12989 2.602825

k2c Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 35.931

R-squared = 0.0426

Prob > F = 0.0151

F( 2, 22) = 5.10



. reg k2c fragile gdppc [aw=wmedian], cluster(a1)

Power and water shortages

ECA

Power outages


fragile* .1307661 .06478 2.02 0.044 .003803 .25773 .052538


= .367603



. mfx

_cons -.3558165 .1045436 -3.40 0.001 -.5607181 -.1509148

fragile .3351481 .1663511 2.01 0.044 .009106 .6611903


Robust



Prob > chi2 = 0.0439

Wald chi2(1) = 4.06







Water supply shortages


fragile* .0715702 .01536 4.66 0.000 .041474 .101667 .056004


= .07480809



. mfx

_cons -1.463148 .0784614 -18.65 0.000 -1.61693 -1.309367

fragile .3974656 .0922798 4.31 0.000 .2166006 .5783307


Robust



Prob > chi2 = 0.0000

Wald chi2(1) = 18.55







Sub-Saharan Africa

Power outages


fragile* .0116085 .11754 0.10 0.921 -.21876 .241977 .582239


= .69396977



. mfx

_cons .4878933 .2555706 1.91 0.056 -.0130159 .9888025

fragile .0330469 .3341406 0.10 0.921 -.6218567 .6879505


Robust



Prob > chi2 = 0.9212

Wald chi2(1) = 0.01








fragile* -.0724254 .10647 -0.68 0.496 -.281094 .136243 .594446


= .18333916



. mfx

_cons -.7440062 .2920059 -2.55 0.011 -1.316327 -.1716852

fragile -.2669823 .3833502 -0.70 0.486 -1.018335 .4843702


Robust



Prob > chi2 = 0.4862

Wald chi2(1) = 0.49







Asia

Power outages


fragile* .1961503 .10264 1.91 0.056 -.005022 .397322 .052689


= .50466832



. mfx

_cons -.0152376 .067231 -0.23 0.821 -.1470079 .1165328

fragile .5112909 .2887935 1.77 0.077 -.054734 1.077316


Robust



Prob > chi2 = 0.0767

Wald chi2(1) = 3.13









fragile* .1019981 .00513 19.89 0.000 .091948 .112048 .035185


= .04051565



. mfx

_cons -1.769036 .061458 -28.78 0.000 -1.889491 -1.64858

fragile .6907004 .061458 11.24 0.000 .570245 .8111558


Robust



Prob > chi2 = .

Wald chi2(0) = .








Access to electricity

Sub-Saharan Africa


fragile* .2772607 .10872 2.55 0.011 .064174 .490347 .630572


= .45079663



. mfx

_cons -.5819059 .0576226 -10.10 0.000 -.6948442 -.4689676

fragile .7267316 .2770282 2.62 0.009 .1837663 1.269697


Robust



Prob > chi2 = 0.0087

Wald chi2(1) = 6.88







Access to general purpose technology

ECA

Communication with clients/suppliers via email


fragile* -.4507686 .15425 -2.92 0.003 -.753099 -.148439 .051973


= .80095088

y = Pr(c22a) (predict)


. mfx

_cons .9098639 .1191029 7.64 0.000 .6764264 1.143301

fragile -1.247591 .4180935 -2.98 0.003 -2.06704 -.4281433

c22a Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0028

Wald chi2(1) = 8.90






. probit c22a fragile [pw=wmedian], cluster(a1)

Own website


fragile* -.3405246 .12901 -2.64 0.008 -.59337 -.087679 .052148


= .5412328

y = Pr(c22b) (predict)


. mfx

_cons .1517594 .1094478 1.39 0.166 -.0627543 .3662731

fragile -.9246721 .4251374 -2.17 0.030 -1.757926 -.0914181

c22b Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0296

Wald chi2(1) = 4.73






. probit c22b fragile [pw=wmedian], cluster(a1)

High-speed Internet on premises


fragile* -.497724 .07803 -6.38 0.000 -.650653 -.344795 .061698


= .63187249



. mfx

_cons .422744 .088907 4.75 0.000 .2484894 .5969986

fragile -1.392697 .2982823 -4.67 0.000 -1.97732 -.8080746


Robust



Prob > chi2 = 0.0000

Wald chi2(1) = 21.80







Sub-Saharan Africa



fragile* -.1873402 .09993 -1.87 0.061 -.3832 .00852 .581967


= .49400276



. mfx

_cons .2608926 .1145184 2.28 0.023 .0364406 .4853446

fragile -.4741269 .2568193 -1.85 0.065 -.9774836 .0292298


Robust



Prob > chi2 = 0.0649

Wald chi2(1) = 3.41







Own website


fragile* -.0880111 .04776 -1.84 0.065 -.181623 .005601 .580441


= .19582478



. mfx

_cons -.6754651 .1149863 -5.87 0.000 -.9008341 -.450096

fragile -.3121154 .1703391 -1.83 0.067 -.6459739 .0217431


Robust



Prob > chi2 = 0.0669

Wald chi2(1) = 3.36







High-speed Internet on premises


fragile* -.1129652 .13312 -0.85 0.396 -.373884 .147953 .611331


= .37185022



. mfx

_cons -.1456041 .1935968 -0.75 0.452 -.5250468 .2338386

fragile -.2966525 .3581412 -0.83 0.407 -.9985963 .4052913


Robust



Prob > chi2 = 0.4075

Wald chi2(1) = 0.69







Asia



fragile* .0390175 .14215 0.27 0.784 -.239595 .31763 .054395


= .26827641



. mfx

_cons -.6242836 .4329415 -1.44 0.149 -1.472833 .2242662

fragile .11489 .4329787 0.27 0.791 -.7337326 .9635127


Robust



Prob > chi2 = 0.7907

Wald chi2(1) = 0.07






Own website


fragile* .0504549 .08432 0.60 0.550 -.114809 .215718 .054468


= .14110631



. mfx

_cons -1.08654 .3809544 -2.85 0.004 -1.833197 -.3398836

fragile .2052297 .3812494 0.54 0.590 -.5420054 .9524649


Robust



Prob > chi2 = 0.5904

Wald chi2(1) = 0.29







Security

ECA

Sub-Saharan Africa

Asia

_cons 3.524432 .289584 12.17 0.000 2.930255 4.11861

fragile 1.016677 1.147383 0.89 0.383 -1.337558 3.370912

i2a Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 6.3545

R-squared = 0.0010

Prob > F = 0.3834

F( 1, 27) = 0.79



. reg i2a fragile [aw=wmedian], cluster(a1)

_cons 6.701966 1.068797 6.27 0.000 4.485417 8.918515

fragile -.4077894 1.871493 -0.22 0.830 -4.289028 3.47345


Robust


Root MSE = 9.2767

R-squared = 0.0005

Prob > F = 0.8295

F( 1, 22) = 0.05




_cons 4.461478 .3116388 14.32 0.000 3.742838 5.180118

fragile -1.709061 .5066049 -3.37 0.010 -2.877294 -.5408283


Robust


Root MSE = 8.4303

R-squared = 0.0043

Prob > F = 0.0097

F( 1, 8) = 11.38




ECA

Sub-Saharan Africa

Asia

_cons 3.532645 .2144435 16.47 0.000 3.092643 3.972647

fragile 10.1916 3.68681 2.76 0.010 2.62689 17.75631


Robust


Root MSE = 5.6589

R-squared = 0.0491

Prob > F = 0.0102

F( 1, 27) = 7.64




_cons 9.728118 1.353562 7.19 0.000 6.921003 12.53523

fragile 4.819766 4.112298 1.17 0.254 -3.708617 13.34815


Robust


Root MSE = 16.666

R-squared = 0.0200

Prob > F = 0.2537

F( 1, 22) = 1.37




_cons 12.54239 2.779559 4.51 0.002 6.132719 18.95207

fragile -9.909536 2.809668 -3.53 0.008 -16.38864 -3.43043


Robust


Root MSE = 17.058

R-squared = 0.0240

Prob > F = 0.0078

F( 1, 8) = 12.44




Role of women

ECA

All other sectors

Manufacturing: production employee

Manufacturing: non-production employee

_cons 20.02959 5.347821 3.75 0.001 9.056769 31.00241

fragile -11.04925 5.933726 -1.86 0.074 -23.22426 1.125747

l5 Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 123.52

R-squared = 0.0004

Prob > F = 0.0735

F( 1, 27) = 3.47



. reg l5 fragile [aw=wmedian], cluster(a1)

_cons 24.21054 4.084549 5.93 0.000 15.82974 32.59135

fragile -10.88405 4.93811 -2.20 0.036 -21.01621 -.7518842

l5a Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 97.923

R-squared = 0.0007

Prob > F = 0.0362

F( 1, 27) = 4.86



. reg l5a fragile [aw=wmedian], cluster(a1)

_cons 7.939076 1.462294 5.43 0.000 4.938696 10.93946

fragile -3.775553 1.910538 -1.98 0.058 -7.695652 .1445468

l5b Coef. Std. Err. t P>|t| [95% Conf. Interval]

Robust


Root MSE = 28.632

R-squared = 0.0010

Prob > F = 0.0584

F( 1, 27) = 3.91



. reg l5b fragile [aw=wmedian], cluster(a1)

Female as owner


fragile* -.032038 .03355 -0.95 0.340 -.0978 .033724 .052591


= .39854299

y = Pr(b4) (predict)


. mfx


end of do-file

.

_cons -.2527082 .0603825 -4.19 0.000 -.3710557 -.1343607

fragile -.0838928 .0879614 -0.95 0.340 -.2562939 .0885083

b4 Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.3402

Wald chi2(1) = 0.91





. probit b4 fragile [pw=wmedian], cluster(a1)

Top manager


fragile* -.0917377 .02371 -3.87 0.000 -.138209 -.045266 .051878


= .20857468

y = Pr(ECAb7a) (predict)


. mfx

_cons -.7921339 .0710512 -11.15 0.000 -.9313918 -.6528761

fragile -.3709212 .0910218 -4.08 0.000 -.5493206 -.1925217

ECAb7a Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0000

Wald chi2(1) = 16.61






. probit ECAb7a fragile [pw=wmedian], cluster(a1)

Sub-Saharan Africa

All other sectors

Manufacturing: Production employee

Manufacturing: Non-production employee

_cons 15.98477 7.402937 2.16 0.042 .6706322 31.29892

fragile -11.61689 7.436407 -1.56 0.132 -27.00027 3.76649


Robust


Root MSE = 313.02

R-squared = 0.0003

Prob > F = 0.1319

F( 1, 23) = 2.44




_cons 24.26389 10.12763 2.40 0.040 1.353605 47.17418

fragile -20.26315 10.24511 -1.98 0.079 -43.4392 2.912888


Robust


Root MSE = 69.672

R-squared = 0.0195

Prob > F = 0.0793

F( 1, 9) = 3.91




_cons 4.664696 .9430354 4.95 0.001 2.531402 6.79799

fragile -1.96577 1.698779 -1.16 0.277 -5.808674 1.877135


Robust


Root MSE = 12.632

R-squared = 0.0056

Prob > F = 0.2770

F( 1, 9) = 1.34




Owner


fragile* .3197753 .06356 5.03 0.000 .195203 .444348 .760595


= .45546914



. mfx

_cons -.7758502 .1379622 -5.62 0.000 -1.046251 -.5054492

fragile .8729943 .1847484 4.73 0.000 .5108942 1.235094


Robust



Prob > chi2 = 0.0000

Wald chi2(1) = 22.33







Top manager


fragile* -.0506948 .02997 -1.69 0.091 -.109433 .008044 .53474


= .15281543

y = Pr(b7a) (predict)


. mfx

_cons -.9104672 .1012781 -8.99 0.000 -1.108969 -.7119659

fragile -.2131236 .1199067 -1.78 0.076 -.4481365 .0218893

b7a Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0755

Wald chi2(1) = 3.16






. probit b7a fragile [pw=wmedian], cluster(a1)

Asia

Other sectors

Manufacturing: Production employee

Manufacturing: Non-production employee

_cons 9.168064 3.516592 2.61 0.031 1.058787 17.27734

fragile -3.567259 3.900031 -0.91 0.387 -12.56075 5.426229


Robust


Root MSE = 35.231

R-squared = 0.0008

Prob > F = 0.3871

F( 1, 8) = 0.84




_cons 12.8743 7.085205 1.82 0.129 -5.338803 31.0874

fragile 7.833397 7.085205 1.11 0.319 -10.3797 26.0465


Robust


Root MSE = 156.12

R-squared = 0.0001

Prob > F = .

F( 0, 5) = .




_cons 2.183231 1.474348 1.48 0.199 -1.606702 5.973164

fragile .3631945 1.474348 0.25 0.815 -3.426738 4.153127


Robust


Root MSE = 22.633

R-squared = 0.0000

Prob > F = .

F( 0, 5) = .




Owner


fragile* -.1534754 .05351 -2.87 0.004 -.258343 -.048607 .053217


= .41984824



. mfx

_cons -.1800654 .1280206 -1.41 0.160 -.4309812 .0708504

fragile -.4174693 .1393574 -3.00 0.003 -.6906048 -.1443338


Robust



Prob > chi2 = 0.0027

Wald chi2(1) = 8.97







Top manager

Source: Authors’ calculations based on World Bank Enterprise Surveys 2009–11 (accessed in April, 2012).


fragile* -.1941858 .00766 -25.34 0.000 -.209208 -.179164 .048694


= .26940319

y = Pr(b7a) (predict)


. mfx

_cons -.5767708 .0226889 -25.42 0.000 -.6212403 -.5323014

fragile -.7772722 .0226889 -34.26 0.000 -.8217417 -.7328028

b7a Coef. Std. Err. z P>|z| [95% Conf. Interval]

Robust



Prob > chi2 = 0.0000

Wald chi2(1) = 1173.60






. probit b7a fragile [pw=wmedian], cluster(a1)

The Small Entrepreneur in Fragile Public Disclosure ... Hayel Saeed Anam Group: ... The Small...

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