1

Measuring firms’ access to finance

By

Stijn Claessens and

Konstantinos Tzioumis World Bank

May 25, 2006

Abstract Firms across countries have different financing structures, determined by both firm-specific characteristics and countries’ institutional environment, including their financial development. Financing structures, particularly the mix between internal and external financing, reflect in part the ease of firms’ access to finance, in turn due to demand- and supply-side factors. We review methods used to date to measure individual firms’ (lack of) access to finance. For large firms with good financial data, econometric analyses of financial statements based on theoretical models are useful for measuring firms' access to finance. For small and medium sized firms, surveys are the preferred approach. Existing surveys in developing countries with a 'firm-finance' component, however, are not yet of genuine use for sound research and policy-making. Future firm survey efforts need to be more detailed and focused.

Paper prepared for Conference: Access to Finance: Building Inclusive Financial Systems, organized by the Brooking Institution and the World Bank in Washington, D.C., May 30-31, 2006.

2

1. Introduction

Firms across countries have different financing structures (see Figure 1). These structures

are determined not only by firm-specific characteristics that may vary across countries, but also

by the constraints posed by countries’ degree of financial development and their institutional

environment. From a policy perspective, it is important to distinguish between demand- and

supply-side effects in order to design policies that alleviate constraints on the supply of finance

and policies that address weaknesses in demand. Both the demand and supply side need to be

addressed to assure a sustainable financing of the corporate sector.

0 % 2 0 % 4 0 % 6 0 % 8 0 % 10 0 %

C hile

P hilippine s

P e ru

Tha ila nd

S . Ko re a

M e xic o

C hina

India

B ra zil

Common Equity Short-term Debt Long-term Debt Accounts Payable Other

Figure 1: Capital structure of listed manufacturing firms in emerging economies (2004)

3

The purpose of this paper is to discuss efforts used to date to measure firms’ access to

finance. Traditionally, two main methods have been used to examine firms’ ease of accessing

finance, namely econometric analysis of financial statements based on economic theory models

and surveys. The merits and shortcomings of each method are discussed and future avenues are

suggested.

The structure for the remainder of the paper is as follows: Section 2 presents the theoretical

and empirical literature on firm financing constraints. Sections 3 and 4, respectively, illustrate

the challenges in survey design, and provide an overview of existing surveys offering

information on firms’ access to finance. Section 5 summarizes the empirical evidence on factors

influencing finance availability, while Section 6 suggests some new avenues in measuring and

evaluating access to finance for firms. Section 7 concludes.

2. Theory and empirical evidence on financing constraints

The seminal work by Modigliani and Miller (1958) suggests that in perfect capital and

credit markets, the investment behavior of a firm is irrelevant to its financing decisions⎯and

vice-versa. However, in the presence of market imperfections, any financing constraints will

reflect on firms’ investment decisions. Empirically, financing constraints could be identified

through the sensitivity of investment with respect to internal funds. The basic premise of such

empirical design is that – due to information asymmetries – external funds are more costly than

internal funds. Higher sensitivity of investment to internal funds suggests the presence of

financing constraints.

There are two approaches to evaluating the sensitivity between investment and internal

funds, each with their own strengths and shortcomings. The first approach is based on the Q

4

theory of investment suggested by Tobin (1969) and has been widely used in the financial

literature after the influential paper of Fazzari et al. (1988). In the reduced form q-model, a

measure for internal funds (e.g. cash flows) is directly included as one of the independent

variables. In order to investigate the presence of financing constraints, the sample is divided

using a priori classifications of firms’ financing constraints, and the investment-cash-flow

sensitivities of the different sub-samples are then compared.1 Higher sensitivity for the samples

of a priori more constrained classified firms is interpreted as evidence of tighter financing

constraints.

Nevertheless, the empirical investigation based on the q-model has several problems. The a

priori classification of firms in different groups is often set arbitrarily and is not time-dependent

(Kaplan and Zingales, 1997). Also, average q may be an imprecise proxy for the unobservable

marginal q (Hayashi, 1982). In this case, internal funds could be a proxy for the profitability of

investment and the positive sensitivity cannot solely be interpreted as capital and credit market

imperfections but rather as firms with better liquidity also attaining superior investment

possibilities (Hoshi et al., 1991; Schiantarelli, 1996).

An alternative approach to examine sensitivity between investments and internal funds is

through estimating the Euler equation for the capital stock. The Euler equation uses a structural

model to capture the influence of current expectations of future profitability on current

investment decisions. Unlike the q-model, the Euler-equation approach measures how internal

funds indirectly affect investment via a Lagrange multiplier and does not use the market value of

q. The advantage of this is that future profitability, i.e. marginal q, does not need to be specified

or observed. The major shortcoming of the Euler-equation approach is that it incorporates

1 Several a priori criteria have been used: dividend policy (Fazzari et al., 1988), bond rating (Whited, 1992), age

(Devereux and Schiantarelli, 1990) and firm size (Audretsch and Elston, 2002). However, the empirical application of a singular criterion for classifying firms can be overly simplistic since financing constraints depend on many firm characteristics such as size, age, legal form and indebtness (Petersen and Rajan, 1994).

5

dynamics, which can complicate the estimation, and –like the q model of investment– it assumes

a geometric depreciation rate and convex adjustment costs.

Overall, the aforementioned approaches rely on strong theoretical assumptions, which in

the event they are not met, render the models misspecified. Econometric advances have provided

some solutions. For instance, Erickson and Whited (2000, 2002) proposed a class of GMM

estimators that alleviate the measurement problems associated with Tobin’s q, by utilizing the

information in the higher order moments of the regression variables. Also, in order to tackle the a

priori classification of firms, Hansen (1999) introduces a threshold investment model based on a

panel estimation method using a fixed-effects transformation where all parameters are

determined simultaneously with the determination of the threshold value of the uncertainty

measure. The main advantage of Hansen’s model is that the estimates of the thresholds are

conditional on the model specification as a whole.

Nevertheless, these advances do not overcome all limitations. These limitations, as

highlighted by recent findings by Alti (2003) and Gomes (2001), include that investment-cash-

flow sensitivities can be positive even in the absence of financial frictions. These findings

illustrate the need for alternative empirical methodologies in identifying financing constraints.

One alternative method is used by Demirgüç-Kunt and Maksimovic (1998) who estimate the

degree of financing constraints by using a financial planning model. They obtain the maximum

growth rate that firms could attain without access to long-term financing and then compare these

predicted rates to the actual growth rates. Another empirical method is to depart from the

standard model of reversible investment and combine the literature on financing constraints on

investment with the literature on investment uncertainty. In this way, one can take into account

irreversibility and the possibility to postpone the investment decision [see Bo et al. (2003),

Nilsen and Schiantarelli (2003) and Scarramozino (1997) for some applications].

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3. Importance and challenges of surveys on firms’ access to finance

The financing constraints literature which employs econometric modeling of firms’

financial statements, provides a rigorous method for empirical investigations. However, it is of

little usefulness in the context of developing countries, which are the focal point of World Bank

attention. In these countries, the majority of private sector activity originates from SMEs for

which financial data is limited. SMEs are typically not obliged to file detailed financial reports

nor raise equity or debt in public markets, thus not required to disclose the rich data needed for

empirically testing financing constraints. Also, in transition and developing countries, even the

listed or large non-listed firms’ financial statements are not as reliable as those in developed

countries. Illustrating this point, Figure 2 shows the relation between GDP per capita and an

index of corporate sector’s earnings management, a measure of credibility of accounting

reporting. It shows that listed firms in less developed countries engage more in earnings

manipulation in their financial statements. Moreover, for developing countries, there is a lack of

studies on all aspects of firm financing. Tests of corporate capital structure, including tests of

trade-off and pecking-order theories of debt, are few (Prasad et al., 2005). As a result, the general

role of taxation, bankruptcy costs and asymmetric information in financing decisions remains

unclear for corporations of developing countries.

One can assess the supply of financial services through aggregate measures of financial

development, which are broadly available. However, these measures do not provide the

distribution of financing among corporations. Since a well-developed financial system could

only provide access to a limited number of firms, financial depth indicators need not be good

measures of access. As a result, for developing countries, the only way to investigate firms’

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problems accessing finance is through tailored firm-level surveys directly addressing the issue of

financing constraints. Knowing the extent of financing obstacles as reported in surveys

inevitably does allow the research focus to switch from identifying constraints to examining the

determinants (and the business growth consequences) of financing constraints. For instance,

Beck et al. (2006), using the World Business Environment Survey, which covers firms from 80

countries, show the importance of country institutions as well as firm size, age and ownership in

the availability of external finance.

R2 = 0.35

1

18

35

0 10000 20000 30000 40000GDP per capita ($US)

Ear

ning

s man

agem

ent r

anki

ng (1

=Bes

t) .

Sample : 34 countries in 2004Source: FSDI

Figure 2: Economic development and credibility of accounting reporting

At the same time, when surveying firms themselves on their ease of access to finance, one

cannot avoid problems of endogeneity, definition ambiguity and lack of conceptual framework.

First, dependent and independent variables in empirical analyses using data from survey

responses often share a common parameter that is omitted in the survey and is usually the result

of self-selection in survey participation. Also, the cross-sectional nature of most surveys does not

allow tackling any simultaneity bias between survey responses that are used as dependent and

independent variables, respectively. Specifically, firms that are better may not complain about

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their access to finance while worse firms do. As a consequence, complaints about access to

finance can not be used directly as independent variables to predict in turn firms’ performance as

a dependent variable.

Second, defining access to finance is not an easy task. Generally, access to finance

commonly refers to the availability of supply of quality financial services at reasonable costs.

However, depending on what one considers ‘quality’ services and ‘reasonable’ costs, the

measurement of access to finance needs to be altered accordingly. Measurement of access to

finance is also influenced by the definition and priority of its various dimensions. For instance,

one can distinguish the dimensions of reliability, convenience, continuity and flexibility, with

each requiring a different measure. In a similar fashion, the notions of access and usage of

finance are often confused. Usage measures consumption of financial services and does not

reveal information about the unmet demand for financial services.

Another problem with measuring and evaluating firms’ access to finance is the absence of a

unified conceptual framework for data collection. Somewhat surprisingly, theoretical models and

empirical evidence on the topic of access to finance has not yet resulted in a commonly accepted

framework for data collection. Thus, currently collected data are often of an ad-hoc nature, with

varying definitions over time. As a result, the data are often not comparable across countries and

do not necessarily yield appropriate variables for model testing.

4. Surveys relevant to firms’ access to finance

Consistent with its emphasis on the link between finance and growth, the World Bank has

been collecting data on access to finance in developing countries. It has been involved with

several cross-country surveys with ‘access to finance’ components. For instance, the World

Business Environment Survey (WBES) is a cross-sectional survey on investment climate and

9

business environment that covers 10,000 firms in 80 countries during 1999-2000. In a similar

fashion, the Business Environment and Enterprise Performance Survey (BEEPS) - a joint effort

of the World Bank with the European Bank for Reconstruction and Development (EBRD) – has

collected data on ease of access to finance. Two survey rounds, conducted in 1999 and 2002,

covered firms in Eastern European countries, the former Soviet Union, and Turkey. More

recently, the Investment Climate Assessment surveys (ICAs) have reviewed the investment

climate in 58 countries, based on surveys of more than 32,000 firms. These surveys deal

primarily with business perceptions of the investment climate. They also contain some questions

regarding sources of funds for new investments and collateral requirements that reveal

substantial variation of financing practices across countries, as demonstrated in Table 1 (see

Appendix A for selective data on all countries in WBES and ICA surveys).

Table 1: Data on financing obstacles and collateral practices from WBES and ICA surveys in developing and transition countries

Financing is major obstacle (%) 2 Collateral needed (% of loan) Loans requiring collateral (%)

Tunisia 0.0 Pakistan 69.5 Slovenia 58.7 Namibia 1.7 China 80.8 South Africa 61.1 Egypt 10.8 Thailand 87.0 Cambodia 61.5 Slovenia 11.0 Bangladesh 92.5 Ethiopia 62.0

Top

5

Cambodia 13.0 India 94.0 China 66.9

Median 38.5 Median 141.9 Median 86.1

Belarus 62.5 Bosnia and Herzeg. 196.4 Georgia 93.6 Moldova 64.4 Nicaragua 204.0 Bosnia and Herzeg. 95.9 Kyrgyz Republic 66.7 Syrian Arab Rep. 206.7 Macedonia, FYR 96.6 Haiti 74.4 Morocco 226.2 Albania 96.8

Bot

tom

5

China 75.0 Zambia 311.3 Morocco 98.9

As mentioned, the supply-side of financing is easier to document, but does not necessarily

provide perfect measures of ease of access. The World Bank’s Financial Sector Development

Indicators (FSDI) project presents an ongoing effort to measure various aspects of financial

2 In this paper, for the calculations using WBES we excluded firms with state or foreign ownership since they

probably enjoy preferential access to finance.

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development (i.e., size, access, efficiency, and stability) and facilitate direct cross-country

comparisons in terms of capital market and banking system development. In particular, FSDI not

only offers detailed information on the banking sector and capital markets, but also summarizes

this information in rankings that allow tracking countries’ progress in terms of financial

development (see Appendix B). Similarly, the corporate sector data could be used in assessing

corporate vulnerability issues that could worsen firms’ access to finance and put strain on the

financial system. Figures 3 and 4 compare corporate liquidity and leverage across Indonesia,

Malaysia and Philippines, from 1994 to 2004. These three south-east Asian countries

experienced crises in 1997. Nevertheless, even in the years before the crises, there is a clear

pattern of rapidly declining interest rate coverage and steadily increasing debt to sales ratio.

Moreover, the time-series perspective provides information on how corporate sectors recover

after shocks or crises. For instance, after the crises the corporate sectors in these three countries –

especially in Malaysia– dramatically reduced their leverage and improved their liquidity. The

aforementioned examples illustrate the effectiveness of FSDI in offering a venue for regularly

updated information on both financial and corporate sectors.

20%

40%

60%

80%

100%

120%

1994 1996 1998 2000 2002 2004

Figure 4: Corporate sector leverage, 1994-2004Debt to sales, median

Philippines

Malaysia

Indonesia

0

2

4

6

8

1994 1996 1998 2000 2002 2004

Figure 3: Corporate sector liquidity, 1994-2004 Interest coverage ratio, median

Malaysia

Indonesia

Philippines

Source: FSDI Source: FSDI

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Monitoring the supply-side of financing is important since firms’ access to finance is more

dependent on financial development compared to household financing. This relation can be

illustrated using data collected in FSDI. Figures 5 and 6 show that countries’ corporate sector

financing constraints (as measured by the median Kaplan-Zingales index across non-financial

listed firms) and the share of informal financing (as reported by firms participating in the World

Bank ICAs) are negatively related to countries’ financial depth.3 This suggests that, while it is

not a perfect or sufficient measure, financial depth does shed some light on the ease of access to

finance.

Figure 5: Financial depth and financing constraints

R2 = 0.18

-0.5

0

0.5

1

1.5

0 50 100 150 200 250

M2 to GDP (%)

Kap

lan-

Zin

gale

s Ind

ex .

Sample : 30 countries during the period 2000-2004Source : FSDI

3 The Kaplan-Zingales (1997) index has been shown to have some value in predicting the ease of access in

developed countries. The index is higher for firms that are more financially constrained.

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Figure 6: Financial depth and informal finance

R2 = 0.100

5

10

15

0 50 100Private credit to GDP (%)

Info

rmal

fina

nce

for

inve

stm

ent (

%) .

Sample : 44 developing and transition countries in 2004Source: FSDI and ICAs

Furthermore, much effort has emphasized the importance of proper institutional

arrangements and low transactions costs to enhance financing opportunities, among other

economic outcomes. As such, besides considering the aggregate supply of finance, assessing

countries’ institutional environment is important. The World Bank’s Doing Business indicators

offer much institutional information for 55 countries regarding regulatory costs and processes

relevant to firm finance. Appendix C illustrates country rankings from Doing Business, in terms

of rules affecting the scope, access and quality of credit information.

In addition to the World Bank, data gathering efforts across countries have originated from

other inter-governmental organizations, such as the European Union. However, in these cases

only a limited number of countries are covered. For various individual countries, ministries (e.g.

the UK’s Department for International Development), central banks and statistical agencies have

collected data on firm finance, but these data are not always comparable or available for

researchers. Moreover, some surveys have been conducted by academics but these represent one-

off efforts often using proprietary data and focused on particular research topics.

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5. Empirical evidence on factors influencing firms’ access to finance

This section summarizes empirical findings on the determinants of financing constraints

using financial statements or survey data. As noted, research has tried to identify the

determinants of firms’ access to finance by explaining variation in either investment-cash-flow

sensitivities or survey responses, and controlling for firm- and country-specific characteristics

and policies. Utilizing the investment-cash-flow sensitivity approach, Laeven (2003) shows that

financial liberalization in developing countries relaxes financing constraints of firms, particularly

smaller ones. Love (2003), employing a sample of 36 countries, verifies that financial

development affects firms’ investment by increasing the availability of external finance. This

effect is stronger for financially constrained firms in countries with low levels of financial

development.4 Similarly, Rajan and Zingales (1998) illustrate that industries requiring more

external finance grow faster in more developed capital markets. Regarding firm-specific

characteristics, Shin and Park (1999) and Hoshi et al. (1991) find that business group affiliation

in Korea and Japan, respectively, enhances access to finance because these firms have access to

the group’s internal capital market and are more likely to have close financial ties to large banks.

Using data from surveys from developing and transition economies, Clarke et al. (2001)

find that foreign bank penetration in a country improves financing conditions of firms. Also

using survey data, Beck et al. (2004) show that, in terms of access to external finance, small

firms benefit disproportionally from higher levels of property rights protection. Also, Beck et al.

(2006), illustrate that larger, older firms and foreign-owned firms enjoy increased access to

4 The findings in Laeven (2003) and Love (2003), which are cross-country studies, have been confirmed by several

country-specific studies. For instance, Harris et al. (1994) and Jaramillo et al. (1996) show that financial liberalization alleviated financing constraints in Indonesian and Ecuadorian firms, respectively.

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finance. They also confirm earlier results by Demirgüç and Maksimovic (1998) regarding the

impact of institutional arrangements – particularly the quality of the legal system – in reducing

financing constraints. Specifically investigating creditor protection, Love and Mylenko (2003)

find that the presence of private credit registries in a country is associated with lower financing

constraints and a higher share of bank financing.

Important empirical insights are also provided by the literature studying finance as a barrier

to firm entry. Bertrand et al. (2004) suggest that the banking reform in France during the 1980s

influenced product market competitiveness by increasing entry and exit of firms and lowering

industry concentration, especially in bank-dependent industries. Guiso et al. (2004) analyze

variations in financial development across Italian provinces and find that financial development

enhances entrepreneurship. Cetorelli and Strahan (2004) show that increased competition among

banks in the United States helped the creation of new firms due to enhanced access to finance.

Similarly, Black and Strahan (2002) employ US data and find that that entry of new firms

increased following deregulation. Overall, these studies provide evidence that financial

development – through banking competition – increases credit availability and enhances entry

and efficiency in the corporate sector, thus confirming the importance of access to finance.

The literature presented in this section on empirical estimations regarding the determinants

of firms’ access to finance is by no means exhaustive. Nevertheless, it serves the purpose of

mapping current advances, which is a prerequisite for identifying opportunities for novel

approaches in measuring and evaluating firms’ access to finance.

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6. Avenues for the future

Despite all these efforts, there is much room for improvement. This section outlines future

avenues that could considerably improve the research and policy potential of data collection

efforts. Although there are many ways to innovate, we argue that any future measurement effort

regarding firms’ access to finance would mostly benefit from a better conceptual framework,

data expansion and novel research focus.

(a) Conceptual framework

Any study of firms’ access to finance needs a good operational structure to organize the

theories and empirical evidence on the issue. Such a framework would provide a sound

foundation for benchmarking and coordination in data collection efforts in order to be effectively

utilized for comparison and research analysis. For instance, a framework based on the simple -

yet powerful - notion of ‘demand and supply’ would allow for improved modeling of the

dynamic interactions among the factors influencing the supply and demand for firm finance,

while addressing the role of institutions in financial development.5

The purpose of such an improved conceptual framework would be to provide: (i) indicators

that allow cross-country comparisons, (ii) relevant variables in order to identify the constraints

causing mismatches between supply and demand for financial services, and (iii) an analytical

basis for designing appropriate interventions to improve access to finance. Overall, the

framework would need to be based on theoretical and empirical findings in order to influence the

5 For instance, the development of the banking system, the depth and functioning of capital markets, and the

availability of credit bureaus and mature insurance markets determine supply. From a demand viewpoint, important factors are firm characteristics, such as size, age, performance, collateral size, previous credit history, and industry. Beck and de la Torre (2006) have recently suggested a conceptual framework for payment and savings services based on demand and supply of financial services.

16

design of future data collection efforts and to assess the importance and relevance of policies in

alleviating financing obstacles.

(b) Expanded and improved data

From a data collection point of view, available information should expand both in terms of

coverage and content. Additional efforts should likewise focus on regions like Africa and Middle

East with traditionally poor data coverage. In terms of data content, the surveys should address

supply sources such as non-bank financial institutions, insurance, leasing, factoring, institutional

investors, and e-finance penetration. Also, data have to cover a longer time period and be

consistently collected so as to allow comparisons over time.

Future surveys should take into account the quality of accounting reporting, thus clarifying

whether financing constraints are real or reflecting firms’ informational opacity. In other words,

if the level of firms’ transparency and disclosure is poor then financing opportunities are going to

be diminished because of the increased risk for the sources of finance. As Arthur Levitt, the

former Chairman of US Securities and Exchange Commission has noted: “If investors are not

confident with the level of disclosure, capital will flow elsewhere. If a country opts for lax

accounting and reporting standards, capital will flow elsewhere.”6 Figure 7 illustrates that in

countries where fewer firms report financing constraints there is higher occurrence of

independent auditing. Studying the relation between access to finance and accounting practices

would provide valuable insights concerning the importance of international financial reporting

standards and government regulation.

6 Speech by SEC Chairman Arthur Levitt, “Remarks before the conference on the rise and effectiveness of new

corporate governance standards”, Federal Reserve Bank of New York, December 12th 2000.

17

Figure 7: Independent auditing and financing obstacles

R2 = 0.30

0%

100%

20% 100%% of firms having independent auditor

% o

f fir

ms r

epor

ting

finan

cing

as a

maj

or o

bsta

cle

.

Sample : Large and medium firms in 35 developing and transition countries.Source : WBES

Moreover, better coordination of efforts among international organizations and

governments would increase the consistency across surveys and lower the overall costs.7 For

instance, a recent survey on SMEs’ access to finance in the European Union illustrated that

overdraft was the most popular type of financing in many EU countries.8 However, in World

Bank surveys on financing obstacles the distinction between loans and overdrafts is often not

made, thus complicating comparisons and policy inferences. Also, data collection at the firm-

level rather than the establishment-level would solve existing aggregation issues and help clarify

the issues of wider firm financial management. In a similar fashion, surveys should try to address

the problem of endogeneity by including likely instruments to identify endogenous variables of

interest, and the problem of unobserved firm heterogeneity by upgrading to panel data design,

i.e., adding a time dimension. 7 One way to increase efficiency would be to benchmark questions or replicate existing surveys for countries not yet

covered. For instance, in developing countries the World Bank could replicate well-established EU surveys, thus widening the base for fruitful comparisons.

8 SME Access to Finance, Flash Eurobarometer 174 – TNS Sofres/EOS Gallup Europe, European Commission, October 2005.

18

From a behavioral viewpoint, data from surveys should also address and evaluate the

presence of psychological biases or cultural attitudes in responses. For example, firms’ owners or

managers might report ‘access to finance’ problems to justify bad performance. The detection of

such biases would help to distinguish between ‘actual’ and ‘perceived’ access to finance. In this

direction, using more data from providers – rather than users – of finance would prove

advantageous since it would complete the picture of the financing process. Furthermore, in

addition to surveys covering ongoing firms, future surveys could also look into new

entrepreneurial and bankrupt firms in order to consider the effect of ‘access to finance’ on firm

entry and exit.

(c) Novel research focus

With the help of a sound conceptual framework and improved data collection, new types of

research and analysis could assist policy makers and researchers. In the context of developing

countries, future studies should more rigorously investigate the competing trade-off and pecking-

order theories of financing decisions in order to examine the importance of agency conflicts,

bankruptcy costs and taxation in capital structure.9 Currently, most of the empirical evidence on

these issues covers US and UK firms and a few other developed countries. Any extrapolation of

any findings to transition and developing countries would be erroneous. Related to this issue, the

recent wave of corporate tax reforms in developed countries (e.g., dividend taxation in the

United States) and several transition economies (e.g. Croatia, Russia) could provide a natural

experiment to assess changes in financing decisions as a result of exogenous changes.

Utilizing data from providers of finance, one could analyze the process and timeline of firm

financing, and examine the effect of institutional aspects or firm characteristics on post- 9 See Frank and Goyal ( 2005) for a detailed literature review.

19

contractual behavior of firms receiving finance. Recent developments in the econometrics of

contracts are closing the gap between theoretical models and empirical testing. Advances have

already been made in the field of consumer finance using contract data from credit providers.

Karlan and Zinman (2005) test, for example, for the presence of adverse selection and moral

hazard problems on repayment using data from a South African firm specializing in high-

interest, unsecured term lending to poor workers, while Bertrand et al. (2005) –using the same

data– find evidence for psychological biases in consumers’ financing decisions. Finally, using

data from financial institutions rather than firms’ surveys on financing obstacles would help

overcome the problem of measurement error inherent in using subjective survey responses as

dependent variables.10

7. Conclusions

The importance of private sector development for economic growth has prompted

increased interest in enhancing firms' access to finance. Since financial sector development does

not necessarily alleviate all firms' financing constraints, there is a need to measure individual

firms' ease of access to finance. Such measures would help guide policy to improve the financial

sector development role in reducing financing constraints, as well as in enhancing the effects of

financing on firm growth.

Unlike developed economies, the majority of the private sector in transition and developing

countries consists of SMEs whose financing behavior and constraints cannot be easily inferred

due to the lack of detailed and reliable data. In this context, firm-level surveys become the

10 In terms of subjective measures, Bertrand and Mullainathan (2001) illustrate the presence of measurement error in

survey responses because the measurement error appears to correlate with a large set of characteristics and behaviors. These responses may be used as independent variables only when the measurement error is small.

20

preferred approach for measuring firms' access to finance and (perceived) barriers to access to

finance. Existing cross-country surveys with a 'firm-finance' component, however, fall short of

what is desired from research and policy perspective needs. More detailed and focused future

survey efforts could help improve the potential for sound research and policy-making in

addressing firm finance questions in developing countries.

21

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Appendix A: Data on financing obstacles and collateral practices from WBES and ICA surveys

Financing is major obstacle (%)

Financing is major obstacle (%) – continued

Collateral needed (% of loan)

Loans requiring collateral (%)

Tunisia 0.0 Georgia 53.2 Pakistan 69.5 Greece 57.8 Namibia 1.7 Mexico 54.8 China 80.8 Slovenia 58.7 Portugal 4.8 Nicaragua 56.3 Thailand 87.0 Portugal 60.1 Sweden 8.5 Cameroon 56.3 Bangladesh 92.5 South Africa 61.1 Singapore 9.7 Slovakia 56.7 India 94.0 Cambodia 61.5 Egypt 10.8 Uganda 56.8 Turkey 100.5 Ireland 61.7 Slovenia 11.1 Bosnia 56.9 Mauritius 103.2 Ethiopia 62.0 Germany 11.1 Ecuador 57.3 Sri Lanka 104.7 China 66.9 UK 11.8 Romania 57.5 Oman 106.7 Brazil 67.1 Cambodia 13.0 Trinidad & Tobago 57.9 Chile 106.9 Pakistan 67.1 Poland 13.9 Bulgaria 60.5 Senegal 108.0 Philippines 67.6 South Africa 14.8 Ukraine 61.9 Tanzania 110.6 Spain 68.3 US 16.3 Belarus 62.5 Guatemala 110.7 Korea, Rep. 68.6 Panama 16.5 Moldova 64.4 Uganda 112.9 Peru 69.3 Canada 19.4 Kyrgyz Republic 66.7 Indonesia 116.3 Bangladesh 69.9 Costa Rica 20.3 Haiti 74.4 Mali 117.5 Ecuador 72.5 Honduras 21.1 China 75.0 El Salvador 118.6 Uzbekistan 72.5 Botswana 21.2 Benin 118.7 Armenia 73.5 Belize 22.9 Portugal 119.2 Oman 73.8 Estonia 22.9 Brazil 119.9 Guatemala 74.3 Italy 23.3 Uzbekistan 122.5 Syria, Arab Rep. 75.5 Spain 23.9 Spain 122.5 Croatia 76.6 Cote d'Ivoire 24.4 Czech Republic 123.2 Tajikistan 76.7 Malaysia 25.0 Egypt, Arab Rep. 123.6 Slovak Republic 77.6 West Bank-Gaza 26.7 South Africa 123.8 Lithuania 77.8 India 27.9 Germany 125.0 Azerbaijan 79.4 Chile 27.9 Ethiopia 128.6 Madagascar 80.3 Hungary 29.0 Belarus 129.8 Poland 80.6 Colombia 29.5 Madagascar 130.1 Ukraine 80.6 Senegal 29.6 Greece 131.0 Algeria 82.4 Venezuela 30.7 Lithuania 131.3 Turkey 83.1 Kenya 32.1 Korea, Rep. 132.9 Thailand 83.2 Armenia 32.2 Estonia 133.4 Czech Republic 83.6 Uzbekistan 34.3 Ireland 133.7 Eritrea 85.7 Ethiopia 34.5 Slovak Republic 135.0 El Salvador 86.0 Lithuania 34.7 Latvia 136.1 Kenya 86.1 Albania 36.1 Honduras 141.9 India 86.3 France 37.5 Romania 142.2 Bulgaria 86.9 Philippines 37.7 Vietnam 142.7 Indonesia 87.2 Azerbaijan 38.1 Kazakhstan 143.3 Belarus 87.7 Zimbabwe 38.5 Slovenia 145.6 Russian Federation 88.2 Uruguay 38.6 Russian Federation 147.0 Vietnam 88.7 Madagascar 38.8 Croatia 148.1 Estonia 89.2 Indonesia 40.3 Moldova 148.6 Honduras 89.2 El Salvador 40.5 Azerbaijan 149.1 Egypt 89.4 Czech Rep 41.1 Poland 151.5 Sri Lanka 89.4 Dominican Rep. 41.2 Albania 154.0 Kyrgyz Republic 89.9 Brazil 41.2 Bulgaria 156.5 Zambia 89.9 Thailand 42.7 Hungary 164.5 Moldova 90.3 Bangladesh 42.9 Ecuador 166.6 Latvia 90.4 Malawi 43.8 Eritrea 168.1 Hungary 90.7 Tanzania 44.4 Algeria 168.6 Germany 90.9 Zambia 44.9 Kenya 172.5 Serbia and Monten. 91.0 Nigeria 45.7 Armenia 177.7 Tanzania 91.2 Guatemala 45.8 Kyrgyz Republic 180.6 Nicaragua 92.6 Peru 46.8 Tajikistan 180.9 Kazakhstan 92.7 Pakistan 47.6 Serbia and Monten. 183.7 Uganda 93.2 Kazakhstan 47.8 Georgia 188.5 Romania 93.4 Croatia 48.3 Ukraine 189.3 Georgia 93.6 Ghana 50.0 Macedonia, FYR 191.4 Bosnia and Herzeg. 95.9 Turkey 51.4 Bosnia and Herzeg. 196.4 Macedonia, FYR 96.6 Russia 51.8 Nicaragua 204.0 Albania 96.8 Bolivia 52.1 Syrian Arab Rep. 206.7 Morocco 98.9 Argentina 52.2 Morocco 226.2

(Continued) Zambia 311.3

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Appendix B: Country rankings in terms of banking sector, equity markets and bond markets (2004)

Banking Banking (cont.) Equity markets Bond markets

United States 1 Costa Rica 58 Hong Kong, China 1 Denmark 1 Canada 2 India 59 United States 2 Japan 2 Portugal 3 Dominican Rep. 60 Switzerland 3 United States 3 Spain 4 Jordan 61 Canada 4 Iceland 4 Switzerland 5 Tanzania 62 United Kingdom 5 Sweden 5 Japan 6 Colombia 63 Pakistan 6 Netherlands 6 Germany 7 Kenya 64 Australia 7 Italy 7 United Kingdom 8 Turkey 65 Saudi Arabia 8 Austria 8 Netherlands 9 Romania 66 Spain 9 Belgium 9 New Zealand 10 Azerbaijan 67 Korea, Rep. 10 France 10 France 11 Brazil 68 France 11 Germany 11 Belgium 12 Russian Fed. 70 South Africa 12 Greece 12 Austria 13 Philippines 71 Netherlands 13 Spain 13 Malta 14 Ethiopia 72 Japan 14 Korea, Rep. 14 Singapore 15 Lithuania 73 Sweden 15 Portugal 15 Australia 16 Mexico 74 Malaysia 16 Switzerland 16 Denmark 17 Guatemala 75 Finland 17 Canada 17 Ireland 18 Georgia 76 Belgium 18 Colombia 18 Finland 19 Indonesia 77 Norway 19 Poland 19 Korea, Rep. 20 Ecuador 78 Singapore 20 Singapore 20 Malaysia 21 Peru 79 Germany 21 Slovak Republic 21 South Africa 22 Kyrgyz Republic 80 Chile 22 Finland 22 Sweden 23 Sri Lanka 81 Mexico 23 United Kingdom 23 Italy 24 Albania 82 India 24 Ireland 24 Norway 25 Papua New Guinea 83 Israel 25 Malaysia 25 Greece 26 Nepal 84 Italy 26 Norway 26 Thailand 27 Bangladesh 85 Denmark 27 Australia 27 Poland 28 El Salvador 86 Turkey 28 Czech Republic 28 Chile 29 Ghana 87 Thailand 29 Thailand 29 Kuwait 30 Zambia 88 Austria 30 Hong Kong, China 30 Croatia 31 Kazakhstan 89 Ireland 31 Chile 31 Armenia 32 Uruguay 90 Portugal 32 South Africa 32 Zimbabwe 33 Belarus 91 China 33 India 33 Estonia 34 Egypt, Arab Rep. 92 New Zealand 34 Argentina 34 China 35 Ukraine 93 Peru 35 Russian Fed. 35 Hungary 36 Nicaragua 94 Poland 36 New Zealand 36 Panama 37 Argentina 95 Luxembourg 37 Hungary 37 Saudi Arabia 38 Slovenia 38 Indonesia 38 Slovak Republic 39 Czech Republic 39 Mexico 39 Israel 40 Brazil 40 Turkey 40 Botswana 41 Hungary 41 Pakistan 41 Czech Republic 42 Russian Fed. 42 Brazil 42 Madagascar 43 Bulgaria 43 Philippines 43 Bahrain 44 Jordan 44 Peru 44 Mauritius 45 Morocco 45 Bosnia and Herz. 46 Cyprus 46 Bolivia 47 Greece 47 Slovenia 48 Kenya 48 Uganda 49 Latvia 49 Lebanon 50 Estonia 50 Bulgaria 51 Colombia 51 Venezuela, RB 52 Indonesia 52 Trinidad & Tobago 53 Sri Lanka 53 Honduras 54 Philippines 54 Pakistan 55 Ecuador 55 Iran, Islamic Rep. 56 Nigeria 56 Nigeria 57 Venezuela, RB 57

(Continued) Argentina 58

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Appendix C: Country rankings in terms of institutional environment for credit information

Drawing from the Doing Business database (2005), the following table provides country rankings in terms of a Credit Information Index. This index measures rules affecting the scope, access and quality of credit information. It ranges from 6 to 0, with higher values representing better institutional environment.

Country Index Country Index Country Index

Argentina 6 Greece 4 Mali 1 Austria 6 Honduras 4 Mauritania 1 Canada 6 Kuwait 4 Morocco 1 Chile 6 Lebanon 4 Niger 1 Costa Rica 6 Mozambique 4 Senegal 1 Germany 6 Nicaragua 4 Serbia and Monten. 1 Italy 6 Norway 4 Togo 1 Japan 6 Pakistan 4 Afghanistan 0 Lithuania 6 Poland 4 Albania 0 Malaysia 6 Portugal 4 Algeria 0 Mexico 6 Romania 4 Bhutan 0 Panama 6 Singapore 4 Cambodia 0 Paraguay 6 Thailand 4 Congo, Dem. Rep. 0 Peru 6 Venezuela 4 Croatia 0 Spain 6 Armenia 3 Eritrea 0 United Kingdom 6 Azerbaijan 3 Ethiopia 0 United States 6 Belarus 3 Georgia 0 Australia 5 Bulgaria 3 Ghana 0 Bosnia and Herzeg. 5 Burundi 3 Guyana 0 Botswana 5 China 3 Iraq 0 Brazil 5 Indonesia 3 Jamaica 0 Czech Republic 5 Iran 3 Kazakhstan 0 Dominican Rep. 5 Latvia 3 Kiribati 0 El Salvador 5 Macedonia, FYR 3 Lao PDR 0 Estonia 5 Mongolia 3 Lesotho 0 Finland 5 Nepal 3 Malawi 0 Guatemala 5 Nigeria 3 Maldives 0 Hong Kong, China 5 Slovenia 3 Marshall Islands 0 Hungary 5 Sri Lanka 3 Mauritius 0 Iceland 5 Vietnam 3 Micronesia 0 Ireland 5 Bangladesh 2 Moldova 0 Israel 5 Cameroon 2 Oman 0 Kenya 5 Central African Rep. 2 Palau 0 Korea, Rep. 5 Chad 2 Papua New Guinea 0 Namibia 5 Congo, Rep. 2 Russian Federation 0 Netherlands 5 Egypt 2 Samoa 0 New Zealand 5 France 2 Sierra Leone 0 Puerto Rico 5 Haiti 2 Solomon Islands 0 Saudi Arabia 5 India 2 Sudan 0 South Africa 5 Jordan 2 Syria, Arab Rep. 0 Sweden 5 Kyrgyz Republic 2 Tanzania 0 Switzerland 5 Madagascar 2 Timor-Leste 0 Taiwan, China 5 Philippines 2 Tonga 0 Turkey 5 Rwanda 2 Uganda 0 Uruguay 5 Slovak Republic 2 Ukraine 0 Angola 4 Tunisia 2 Uzbekistan 0 Belgium 4 United Arab Emirates 2 Vanuatu 0 Bolivia 4 Yemen 2 West Bank and Gaza 0 Colombia 4 Benin 1 Zambia 0 Denmark 4 Burkina Faso 1 Zimbabwe 0 Ecuador 4 Cote d'Ivoire 1 Fiji 4 Guinea 1

(Continued) (Continued)