Evidence from the World Bank’s Enterprise Survey … ANNUAL MEETINGS/2018... · 2018-03-15 ·...

Evidence from the World Bank’s Enterprise Survey

SME Credit Availability Around the World:

Evidence from the World Bank Enterprise Surveys

Rebel A. Cole

Dept. of Finance

Florida Atlantic University

Boca Raton, FL USA

E-mail: [email protected]

Phone: 561-297-4969

Andreas Dietrich

Institute of Financial Services IFZ

Lucerne University of Applied Sciences, Grafenauweg 10, 6304

Zug, Switzerland,

E-mail: [email protected]

DRAFT: Dec. 31, 2017

mailto:[email protected]

mailto:[email protected]



ABSTRACT:

In this study, we use data from the World Bank’s Enterprise Surveys of 133 countries

over the period from 2006 – 2014 to test the importance of governance to the availability of

credit. We model the credit-allocation process for SMEs into a sequence of three steps. Based

upon these three steps, we classify small businesses into four groups based upon their credit

needs. In a first step, we analyze which firms do, and do not, need credit. The No-Need firms

have received scant attention in the literature even though they typically account for more than

half of all small firms. We find that a No-Need firm is more likely to be owned by a foreigner;

and is more likely to be classified as a manufacturing or chemicals company, and that firms

located in developing, but not developed, countries with better governance are less likely to need

credit.

In a second step, we analyze Discouraged firms—those which reported a need for credit

but failed to apply because they feared being turned down or thought that interest rates and

collateral requirements were too unfavorable. Like No-Need firms, Discouraged firms have

received scant attention in the literature. Discouraged firms typically outnumber firms that apply

for and are denied credit. Among firms that need credit, we find that a Discouraged firm is both

younger and smaller; is much less likely to be organized as a corporation; and is less likely to run

by an experienced management team. We also find that firms located in countries with better

governance are less likely to report that they are Discouraged, both in developed and in

developing countries.

In our third step, we analyze firms that applied for credit and either were turned down

(Denied firms) or were extended credit (Approved firms). Among firms that apply for credit, we

find that a Denied firm is both younger and smaller; is less likely to be organized as a

corporation; and is less likely to be run by more experienced management team. We also find

that firms located in countries with better governance are less likely to report that they were

Denied credit, both in developed and in developing countries.

From this evidence, we conclude that country-level governance plays a critical role in the

availability of credit to SMEs in both developed and developing countries.

Key Words: availability of credit; denied credit; discrimination; discouraged firm;

entrepreneurship; governance; small business; WBES

JEL Classification: G21, G32, J71, L11, M13

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1 Introduction

Among small and medium enterprises (“SMEs”) around the world, which ones need

credit and which ones get credit? The answer to this query is of great interest not only to

SMEs, but also to SME creditors, customers and suppliers, as well as to policymakers and to

the economies in which these firms operate. Also important is how country-level governance

affects the availability of credit to SMEs, as policy-makers can implement changes that

improve governance, thereby affecting the availability of credit. In this study, we analyze

data from a series of surveys conducted by the World Bank in 133 countries during 2006-

2014 to provide new evidence on how to answer these questions.

The availability of credit is one of the most fundamental issues facing a small

business; therefore, it has received much attention in the academic literature (see, e.g., early

work by Petersen and Rajan, 1994; Berger and Udell, 1995; and Cole, 1998). Since the Great

Financial Crisis began in 2008, the issue of credit constraints faced by SMEs has been a

major concern of policymakers seeking to increase employment and improve economic

growth.

However, many small firms indicate that they do not need credit (“no-need” firms)

while others indicate that they needed credit but did not apply for credit (“discouraged”

firms). With a few notable exceptions, starting with Cole (2009), Brown et al. (2011), and

most recently, Cole and Sokolyk (2016), the existing literature largely has ignored “no-need”

firms. “Discouraged” firms—those that do not apply for credit because they expect to be

turned down—have received somewhat more attention in the literature (see, e.g., Han et al.

2009) than “no-need” firms, but not nearly as much as firms that actually apply for credit.

Many of the studies that have analyzed “discouraged” firms (see, e.g., Gropp et al 1997;

Berkowitz and White, 2004; and Berger et al., 2011) pool them with firms that actually

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applied for, but were denied credit, making it difficult to draw clear inferences from these

studies.

In this study, we analyze these four groups of firms to shed new light upon their

similarities and differences around the world. We utilize data from the World Bank’s

Enterprise Surveys (“WBES”) over the period from 2006 – 2014 to estimate a sequential set

of three logistic regression models. First, a firm first decides if it needs credit (“no-need”

firms versus “need” firms). Second, a “need” firm decides if it will apply for credit

(“discouraged” firms versus “applied” firms”). Finally, an “applied” firm applies for credit

and then learns from its prospective lender whether it is successful in obtaining credit

(“approved” firms versus “denied” firms).

Beginning in 2006, the World Bank implemented a “Global Methodology” for its

SME surveys, which was designed to ensure a consistent definition of the population, a

consistent methodology of implementation and a common core questionnaire. This

methodology enables researchers to compare the results of surveys across countries and

years.1 Hence, the results of our study provide politicians, policymakers, and regulators with

new insights on how to tailor macroeconomic policy and regulations to help small businesses

obtain credit when they need credit. An important caveat is that our results are descriptive—

based upon cross-sectional surveys—so our findings should not be interpreted as providing

the basis for causal inference.

Our main findings can be summarized as follows. Overall, we find that a “no-need”

firm is more likely to be owned by a foreigner; and is more likely to be classified as a

manufacturing or chemicals company, and that firms located in developing, but not

developed, countries with better governance are less likely to need credit.

1 In addition, we use a set of year dummies and a set of macro-economic variables to control

for differences across time and economic development.

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Among firms that need credit, we find that a “discouraged” firm is both younger and

smaller; is much less likely to be organized as a corporation; and is less likely to run by an

experienced management team. We also find that firms located in countries with better

governance are less likely to report that they are discouraged, both in developed and in


Among firms that apply for credit, we find that a denied firm is both younger and

smaller; is less likely to be organized as a corporation; and is less likely to be run by more


governance are less likely to report that they were denied credit, both in developed and in


Why are these issues of importance? Small businesses are critical to economic growth

and employment. In the U.S., for example, the government reports that small firms account

for over half of all private-sector employment and produce almost two-thirds of net job

growth.2 Outside the U.S., Ayyagari et al. (2011) analyze data from 99 countries surveyed

during 2006-2010 and find that small firms with less than 250 employees account for about

almost two-thirds of all employment and almost 90% of job creation.

The importance of small firms in less developed countries where publicly traded firms

are less prominent is all but certain to be even larger. Therefore, a better understanding of

who needs credit and who gets credit can help policymakers to take actions that will lead to

more jobs and faster economic growth.

We contribute to the literature in at least four important ways. First, we provide the

first rigorous analysis of the differences in our four types of firms—non-borrowers,

discouraged borrowers, denied borrowers and approved borrowers—for a large international

sample of countries around the world. So far, researchers only have analyzed SMEs in the

2 See, “Frequently Asked Questions,” Office of Advocacy, U.S. Small Business

Administration (2010). For research purposes, the SBA and Federal Reserve Board define

small businesses as independent firms with fewer than 500 employees.

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U.S. (Cole, 2009) and Europe (Brown et al., 2011). Moreover, our analysis spans the period

before, during and after the GFC, so we can shed new light on how the crisis impacted the

availability of SME credit.

Second, we provide an analysis of credit availability that properly accounts for the

inherent self-selection mechanisms involved in the credit application process: who needs

credit, who applies for credit conditional upon needing credit, and who gets credit,

conditional upon applying for credit. Most previous researchers except for Cole (2009) and

Brown et al. (2011) have ignored firms that do not need credit, and many have pooled

discouraged and denied firms. We find significant differences across these groups around the

world. Hence, our results shed new light upon the credit-allocation process.

Third, we provide evidence from the 2006 – 2014 WBES on the availability of credit

to small businesses using the World Bank’s “global methodology.” This contributes to the

growing literature on SME finance that has emerged from the cross-country World Bank

surveys of SMEs, including Beck et al. (2005, 2006, 2008); Chakravarty and Xiang (2009);

De la Torre et al. (2010 ); Ayyagari et al. (2011); Brown et al. (2011); Hanedar et al. (2014)

and Love and Peria (2015). Our study, however, focuses only on countries surveyed using the

global methodology.

Fourth, we contribute to the literature on how governance affects the availability of

credit to SMEs (Beck et al., 2005, 2006, 2008; Love and Peria, 2015). We find that firms

located in countries with better governance are less likely to need credit; are less likely to be

discouraged from applying for credit when they need credit; and are less likely to be denied

credit when they apply for credit.

In section 2, we briefly review the literature on the availability of credit, followed by a

description of our data in section 3 and methodology and our variables in section 4. Our

results appear in section 5 and we provide a summary and conclusions in section 6.

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2 Related Literature

The literature on availability of credit to SMEs dates back at least to Wendt (1947),

but really came into prominence following the release of a series of nationally representative

surveys of SMEs in the U.S. conducted by the Federal Reserve Board beginning in the late

1980s. More recently, international interest in this area has grown following the release of a

series of international surveys of SMEs conducted by the World Bank. In general, these

studies focus on how the opacity of SMEs affects their ability to obtain new financing when

they need credit to fund their existing operations or new projects.

2.1 Studies using the Federal Reserve’s Survey of Small Business Finances

In a seminal article, Petersen and Rajan (1994) analyzes data on loan rates from the

1987 iteration of the Survey of Small Business Finances (SSBF) for evidence on how

relationships influence the availability of credit to small firms. The authors find that the

length of a relationship between a borrower and her bank decreases the rate she is charged.

Berger and Udell (1995) also analyzes data on loan rates from the 1987 SSBF, but

focused on lines of credit at small business in order to provide more compelling evidence on

the importance of relationships. The authors also find that the length of a relationship

between a borrower and her bank lowers the spread charged by the bank on her credit line.

Cole (1998) is the first study to analyze data from the 1993 SSBF and is the first to

focus on determinants of the loan turndown decision rather than of loan rates. Cole finds that

a pre-existing relationship between a prospective borrower and her bank increases the

likelihood that her bank approves the loan application, but also finds that the length of that

relationship is not important.

Chakraborty and Hu (2006) also uses data from the 1993 SSBF to analyze how

relationships affect a lender’s decision to secure lines of credit and other types of loans with

collateral. These authors find that the length of relationship decreases the likelihood of

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collateral for a line of credit, but not for other types of loans. Previously, Berger and Udell

(1995) had shown that longer relationships reduced the likelihood of collateral being required

for lines of credit, using data from the 1987 SSBF.

Cole (2009) is the first study to focus on “no-need” firms, and to separate firms into

the four categories that we also use. He analyzes data from the 1993, 1998 and 2003 iteration

of the SSBFs and finds that “no-need” firms look very much like “approved” firms and that

“discouraged” firms differ from “denied” firms in a number of significant ways.

Han et al. (2009) analyzes discouraged SMEs in the U.S., using data only from the

1998 SSBF. These authors find that both the demographics of the entrepreneur, such as age

and personal wealth, and of the business, such as size and use of financial products, influence

discouragement. They also find that riskier borrowers are more likely to be discouraged,

which they interpret as an “efficient self-rationing mechanism.”

Chakravarty and Yilmazer (2009) uses data from the SSBFs to provide evidence on

the availability of credit to “discouraged,” “denied” and “approved” firms, but ignore “no-

need” firms that constitute more than half of all SSBF firms. These authors find that various

measures of the strength of the relationship between a firm and its prospective borrower are

associated with a higher likelihood of applying for credit and, conditional upon applying a

higher likelihood of obtaining credit.

Cole and Sokolyk (2016) extend Cole (2009) by focusing on “no-need” and

“discouraged” firms. They find that about half of all small firms report no need for credit

even in times of recession, and, in a counterfactual exercise, that one in three discouraged

borrowers would have received credit had they applied for credit.

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2.2 Studies using the World Bank’s SME Surveys

The World Bank has catalogued more than 350 studies that use data from its SME

surveys;3 consequently, we will review only some of the most prominent of published

studies.

Beck et al. (2005) uses data from World Bank surveys of more than 4,000 SMEs in 54

countries to analyze whether financial, legal and corruption obstacles affect firm growth

rates. These authors find that it is growth of the smallest of firms that are consistently most

affected by all three types of obstacles.

Beck et al. (2006) uses data on more than 10,000 firms in 80 countries to examine

financial obstacles faced by SMEs. This study finds that older, larger, and foreign-owned

firms report fewer financing obstacles; and that institutional development is the most

important factor in explaining cross-country differences in financing obstacles.

Beck et al. (2008) uses data on more than 3,000 firms from 48 countries to analyze

how financial and institutional development affects firm-level financing at SMEs. This study

finds that, in countries with poor institutions, firms use less finance, especially from banks;

and that small firms, in general, use less bank finance.

De la Torre et al. (2010) uses data from World Bank’s SME surveys to provide

evidence on bank involvement in SME finance. This study finds that all sizes of banks cater

to SMEs, and that large banks have comparative advantages in offering many products.

In the study closest to ours, Brown et al. (2011) follows the general methodology of

Cole (2009). Their firm-level data come from the 2004/2005 and 2008 waves of the Business

Environment and Enterprise Performance Survey (BEEPS). These authors look at 20

countries in Eastern and Western Europe prior to, whereas we look at 133 countries around

the world after, implementation of the “Global Methodology” that ensures consistency across

surveys. They find that small and financially opaque firms are less likely to apply for credit,

3 See http://www.enterprisesurveys.org//Research.

http://www.enterprisesurveys.org/Research

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while firms with more financing needs are more likely to apply for credit. Most interestingly,

they also find that firms applying for credit rarely are denied credit.

Chakravarty and Xiang (2013) use data on more than 8,000 firms from ten countries

to analyze discouraged firms. Their firm-level data come from the 2002/2003 waves of the

Business Environment and Enterprise Performance Survey (BEEPS). These authors find that

discouraged firms differ across developed and developing countries; and that larger firms,

more transparent firms, and firms with stronger banking relationships are less likely to be

discouraged.

Hanedar et al. (2014) use data from the World Bank Business Environment and

Enterprise Performance Survey (BEEPS) to investigate what determines collateral

requirements for SMEs in less-developed countries. Not surprisingly, they find that firm-level

variables, especially measures of risk, are more important than country-level variables in

determining collateral requirements.

La Porta and Shleifer (2014) use data from the WBES and other sources to establish a

set of stylized facts about the informal economies in developing countries. They find that the

informal economies: are large, accounting for as much as half of the total economy in the

poorest countries; have low productivity as compared to the formal economies; are largely

disconnected from formal economies; and shrink as countries grow and develop.

Love and Peria (2015) uses data from the WBES to analyze how bank competition

affects the access of SMEs to finance. They find that low levels of competition reduce such

access, and that the impact depends upon the existence and coverage of mechanisms for

sharing credit information, such as public credit registries.

3 Data

To conduct this study, we use data from the World Bank’s Enterprise Surveys. The

World Bank conducted these surveys in 135 countries between 2006 and 2014 and provides

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117,358 firm-year observations. In order to focus on SMEs and to be consistent with the

studies that analyze the SSBFs, we only use data from firms with less than 500 employees

according to the definition of SMEs in the U.S. We also delete data from two countries for

which we cannot obtain governance data. This reduces our final sample to 106,611 firm-year

observations from 133 countries over the 2006 – 2014 period. Appendix Table 1 identifies

the number of observations in our sample by country and shows the year of the WBES for

each country.

The WBESs collect information about the business environment, how it is perceived

by individual firms, how it changes over time, and about the various constraints to firm

performance and growth. In each country surveyed, the WBESs collect firm-level data on a

representative sample in the non-agricultural, formal private economy in the manufacturing,

services, transportation, and construction sectors; the surveys explicitly exclude firms in the

public-utilities, government-services, health-care, and the financial-services sectors.

Besides the consistent definition of the population, the methodology of

implementation and core questionnaire have served as foundation for the so-called “Global

Methodology” under which the various surveys have been conducted. Because the

standardized approach of the Global Methodology was implemented beginning in 2006, it is

possible to compare the surveys across countries and years.

The data collected through the surveys include quantitative as well as qualitative

information. World Bank representatives conducted face-to-face interviews with company

owners and managers in order to gather information on their firms and the business

environment in each country. The surveys address a broad range of topics, such as general

information on the company, infrastructure, services, crime, finance, and labor.

One key limitation of the WBESs is the fact that most of the data gathered in the

survey are based on subjective perceptions of the owners and managers of the firms, with the

exception of some company figures. Another is the fact that the WBESs do not provide some

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key information about firms that typically are required by banks when a company applies for

a loan—performance indicators, such as the profitability, debt-equity-ratio, margins, etc. are

not included in the data. Even so, the WBESs provide detailed information about each firm's

most recent borrowing experience. This includes whether or not the firm applied for credit

and, if the firm did not apply, did it fail to apply because it feared its application would be

rejected (discouraged borrowers).

We also use data from the World Bank’s Worldwide Governance Indicators, which

provides annual country-level indicators of governance for 1996-2014.4 There are six

governance indicators: (1) voice and accountability, (2) political stability and absence of

violence/terrorism, (3) government effectiveness, (4) regulatory quality, (5) rule of law, and

(6) control of corruption. These six aggregate indicators are based on World Bank staff’s

analysis of hundreds of underlying variables that come from a numerous sources. Each

indicator is expressed in standard normal units ranging from -2.5 to +2.5. These six indicators

also are highly correlated within each country, with correlation coefficients greater than 0.6.

Consequently, we calculate an annual country-level average of the six, which we label

GovIndex.

Finally, we would like to control for country-level differences in the supply of credit.

To accomplish this, we gather data from the World Bank’s World Development Indicators

database on bank credit to the private sector as a percentage of GDP, which we label

PrivCredit.5

4 Methodology and Model Specification

4.1 Model Specification

In order to analyze characteristics of firms that need credit, apply for credit and get

credit, we follow the methodology of Cole (2009) and Cole and Sokolyk (2016). First, we

4 These data are available at www.govindicators.org 5 These data are available at http://data.worldbank.org/data-catalog/world-development-indicators.

http://www.govindicators.org/

http://data.worldbank.org/data-catalog/world-development-indicators

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classify firms into one of four categories based upon their responses to questions regarding

their most recent loan request during the previous years:

(1) “No-Need” Firms: the firm did not apply for credit during the previous three years

because the firm did not need credit.

(2) “Discouraged” Firms: the firm did not apply for credit during the previous year

because the firm feared rejection, even though it needed credit.

(3) “Denied” Firms: the firm did apply for credit during the previous three years but

was denied credit by its prospective lender(s).

(4) “Approved” Firms: the firm did apply for credit and was approved for credit by its

prospective lender.

Once we have classified our sample firms, we calculate descriptive statistics for each

group of firms and test for significant differences across categories, e.g. differences between

“no-need” firms and “need-credit” firms. We also conduct multivariate tests on the data,

estimating logistic regression models that explain the sequential selection of the loan

application and approval process. First, a firm decides whether it needs credit. We include

firms from all four groups in this analysis, and define Need Credit as equal to zero for “no-

need” firms and a value of one to all other firms (“discouraged”, “denied,” and “approved”

firms).

Second, a firm that needs credit decides whether to apply for credit. We exclude “no-

need” firms from this model and define Apply for Credit as equal to zero for “discouraged”

firms and equal to one for firms in one of the two groups that applied for credit (“denied” and

“approved” firms).

Third, a firm that decides to apply for credit is either approved or denied credit by its

prospective lender. In this stage of the model, we include only those firms that applied for

credit and define Get Credit as equal to zero for “denied” firms and equal to one for

“approved” firms.

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We estimate this three-step sequential model using a set of three weighted logistic

regression models. We use logistic regression because our dependent variables are binary

(i.e., they take on a value of zero or one), so that ordinary least squares is inappropriate. We

use the sampling weights provided by the World Bank because the samples are stratified

random samples rather than simple random samples, so that certain types of firms were

oversampled and have different selection probabilities. We cluster robust standard errors at

the country level.

In addition to analyzing our full sample, we also analyze separately firms from

developing and developed countries. We categorize countries in developing and developed

countries based upon the World Bank income classification. Developing countries include the

countries classified as low-income and lower middle-income, while developed countries

include those in the categories of upper middle-income and high-income. In line with the

results of Beck et al. (2008), we expect firms in developed countries to face fewer difficulties

in obtaining a credit, as the financial sector in these countries is usually further developed.

4.2 Dependent variables

In this section, we explain in detail our classification criteria for each borrower type

with reference to specific WBES questions.

No-Need: We classify a firm as No-Need if (i) it reported that it did not apply for

credit during the last complete fiscal year and (ii) answered “No need for a loan -

establishment has sufficient capital” when asked about the main reason of absence of a credit

application on question K.17 of the WBES (2006-2014) questionnaire. All other firms are

classified as Need-Credit firms.

Discouraged: We classify a firm as Discouraged if (i) it is first classified above as

Need-Credit firm and (ii) it reported that it did not apply for credit during the last complete

fiscal year and (ii) it answered with

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“Application procedures for loans or line of credit are complex,”

“Interest rates are not favorable,”

“Collateral requirements for loans or line of credit are unattainable,”

“Size of loan and maturity are insufficient,”

“Did not think it would be approved,” or

“Other,”

when asked about the main reason of absence of a credit application on question K.17 of the

WBES (2006-2014) questionnaire. We classify all other Need-Credit firms as Applied firms,

as they did apply for credit.

Denied: We classify a firm as Denied if (i) we classify it above as an Applied firm

because it reporting that it applied for credit during the last complete fiscal year and (ii) it did

not report having credit at the time of the interview. These firms are identified using

questions K.16 (application of credit during last complete fiscal year), K.8 (existence of

credit at this time), and K.10 (year of approval of existing credit) of the WBESs (2006 –

2014).

Approved: We classify a firm as Approved if (i) we classify it above as an Applied

firm because it reported that it applied for a loan during the last fiscal year, (ii) it reported that

it had credit at the time of the interview, and (iii) that it reported that a credit was granted to

the firm during the last complete fiscal year or in the current year by its prospective lender(s).

4.3 Independent variables

For explanatory variables, we generally follow the existing literature on the

availability of credit, which hypothesizes that a lender is more likely to extend credit to a firm

when that firm shares characteristics of other firms that historically have been most likely to

repay their credits. We expect that the same set of characteristics should explain no-need

firms relative to need firms; applied firms relative to discouraged firms; as well as approved

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firms relative to denied firms. For No-Need firms, we assume that they do not apply for

additional credit because they are operating at their optimal capital structure. Consequently,

we look to the pecking-order theory (Myers and Majluf, 1984; Myers, 1984) and trade-off

theory (Kraus and Litzenberger, 1973) of capital structure to explain differences in No-Need

and Need-Credit firms.

4.3.1 Firm Characteristics

Firm characteristics include public reputation as proxied by firm age; firm size as

measured by the total number of employees; the legal form; and firm industrial classification

as measured by a set of dummy variables.

We expect that the age of a firm, measured by the number of years since the firm

started its operations, is a positive influence on the availability of credit. Older firms are

thought to be more creditworthy because they have survived the high-risk start-up period in a

firm’s life cycle and, over time, have developed a public track record that can be scrutinized

by a prospective lender. Hence, firm age can be used as a proxy for the firm’s reputation.

Beck et al. (2006) argue that older firms report fewer financing issues.

Larger firms, as measured by number of total employment, are thought to be more

creditworthy because they tend to be better established and typically are more diversified

than are smaller firms. Beck et al. (2006) and Aterido et al. (2007) find that micro and small

firms face more obstacles in accessing finance than do large firms. We use the logarithm of

the number of employees.

We also use information on legal form of organization to classify firms as

corporations, proprietorships and partnerships.

We classify firms by industry using a set of dummy variables: one each for

construction,6 food, textiles,7manufacturing,8 retail_wholesale trading companies, chemicals,

6 Construction includes firms classified in the construction and transportation industries.

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metals, transport, services and other services. Firms in chemicals, construction,

manufacturing, metals and transport industries are thought to be more creditworthy because

they typically have more tangible assets that can be pledged as collateral than do firms in

more service-oriented industries.

4.3.2 Owner Characteristics

Our vector of owner characteristics includes variables such as the experience of the

top manager in this sector, the gender of the owner as measured by dummy variables for

female- or male-owned firms; and dummy variables for domestic-owned or foreign-owned

firms.

We include a variable measuring the experience of the top manager in this sector in

years. The more experienced is a top manager, the better is her track record and, thus, the

better is her creditworthiness expected to be.

The gender of the firm owner variable takes into account whether there are any

females amongst the owners of the firm. We have no expectations regarding indicators for

firms with female owners, even though Muravyev et al. (2009) argue that the probability that

female-managed firms obtain credit is lower than with male-managed firms. We include this

variable in an effort to ascertain whether minority-owned firms are experiencing disparate

outcomes in the credit markets relative to firms whose controlling owners are males.

Furthermore, we include a dummy variable regarding foreign and domestic

ownership. The company is “foreign-owned” if private foreign individuals, companies or

organizations own 50% or more of the firm. The results of this variable might heavily depend

on the country of the owner and the country in which the firm is acting. Generally, we expect

7 Textiles includes firms classified in the textiles, leather and garments industries.

8 Manufacturing includes firms classified in the metals & machinery, electronics, chemicals & pharmaceuticals,

wood & furniture, non-metallic & plastic materials, auto & auto components, and other manufacturing

industries.

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that a lender perceives a domestic owner to be more creditworthy because it should be easier

for a lender to gather information on domestic than on foreign owners; in addition, it should

be easier to collect unsecured debt from a domestic owner.

4.3.3 Market/Environmental Characteristics

We include two country-level: govindex and bankcred, which we defined earlier. 9 We

expect that firms are more likely to be discouraged when governance, such as the level of

corruption, is worse, and we expect that firms are more likely to be denied when governance,

such as the rule of law, is worse. We have no a priori expectations about how governance

would affect the need for credit. We expect that credit supply as measured by bank credit to

the private sector as a percentage of GDP should positively affect the need for credit, and

reduce both discouragement and denials of credit. Table 1 gives a brief overview and

description of the variables used in our analysis.

5 Results

5.1 Descriptive Statistics and Univariate Results

Table 2 presents descriptive statistics for the full sample, and, separately, for firms

that need credit and for firms that have no need for credit, along with t-tests for differences in

means of these two groups. We first describe the full-sample means and then discuss the

differences in the means of the t-tests.

5.1.1 Full Sample

The averages for our full sample appear in column 2 of Table 2. The average firm in

our sample has been in business for 16.6 years. By employment size, the average firm in our

sample (median) has fewer than 20 employees. By legal form of organization, 47 percent of

9 We do not include GDP per capita (or other country-level governance variables) in our regressions because

these variables are highly correlated. For example, in our sample of countries, the governance index and GDP

per capita have a highly significant correlation of 0.65.

- 17 -

the firms organize as corporations while 54 percent organize as proprietorships (35%),

partnerships (16%), or “other” (3%)10.

By industry, 24 percent of the firms are each in manufacturing and services, 4 percent

are in chemicals, 8 percent are each in food, textiles and metals, 18 percent are in retail and

wholesale trade, 1 percent in each construction and transport and 4 percent in other services.

Regarding the owner characteristics, the top manager has, on average, 17.6 years of

experience in this sector. By ownership, 92 percent of the firms are domestic, while the

remaining 8 percent are foreign-owned. Foreign ownership refers to the nationality of the

shareholders. If the primary owner is a foreign national resident in the country, it is still a

foreign owned firm. At least one of the owners is a female at 29 percent of the firms.

The average Governance Index of the 133 countries in the sample is -0.394, while the

average Bank credit to GDP ratio is 38.9 percent. Looking at the distribution by survey year,

13 percent of the firm-year observations come from 2006, 8 percent from 2007, 3 percent

from 2008, 16 percent in 2009, 13 percent from 2010, 3 percent from 2011, 6 percent from

2012, 23 percent from 2013 and 15 percent from 2014. Because of the standardized approach

of the Global methodology implemented in 2006, it is possible to compare the various

indicator sets across various countries and different years.

10 The missing 3% answered “don’t know” and “other”

- 18 -

5.1.2 No-Need Firms versus Need Credit Firms

In columns 3 and 4 of Table 2 are the averages for our “No-Need” and “Need”

subsamples, respectively; the difference in mean appears in column 5, followed in column 6

with a t-statistic for a significance test on the difference in means shown in column 5. Most of

the firm characteristics are significantly different for the subsamples of firms that need credit

(“discouraged,” “denied,” and “approved”) and firms that have “no need” for credit.

Overall, 56 percent of all companies needed credit while 44 percent did not. This

result is very similar from the U.S., where Cole (2009) reports that 55 percent of all

companies needed credit.

When compared to a firm with no need for credit, a firm needing credit is less likely

to be organized as a corporation (46% vs. 47%) and a partnership (15% vs. 16%), and more

likely to organized as a proprietorship (34% vs. 36%). By industry, it is less likely to be in the

construction (10% vs. 11%), retail/wholesale trade (17% vs. 19%), services (23% vs. 26%)

and transport (1% vs. 2%) sectors, and more likely to be in the manufacturing (25% vs. 23%),

chemicals (5% vs. 4%), food (8% vs. 7%), metals (8.5% vs. 8%), and textiles sectors (8% vs.

6%).

By ownership characteristics, a firm in need of credit is more likely to be domestic-

owned (94% vs. 91%) and female-owned (30% vs. 27%), and has a less experienced

management (17.3 years vs. 17.9 years).

By market characteristics, a firm in need of credit is more likely to be located in a

country with a lower Governance Index (-0.41 vs. -0.38) and a lower ratio of Bank Credit to

GDP (37.8% vs. 40.3%).

5.1.3 Discouraged versus Applied Firms

Table 3 presents averages for all firms that need credit, and then, separately, for firms

that applied for a credit and for discouraged firms, i.e., for firms that needed credit but did not

- 19 -

apply because they feared rejection, along with a t-test for differences in means of these two

groups.

Overall, 53% of the 62,928 firms that needed credit were discouraged. This is

significantly more than in the U.S. (28%; Cole, 2009), Western Europe (17%; Brown et al.,

2011) and also Eastern Europe (40%; Brown et al., 2011).

Relative to a “discouraged” firm, an “applied” firm has significantly more employees,

is older (17.5 vs. 15.7 years), and more likely to be organized as a corporation (62% vs.

32%). By industry, an “applied” firm is more likely to be in the chemicals (5% vs. 4%), food

(9% vs. 8%), and services (24% vs. 23%) sectors, and less likely to be in the manufacturing

(24% vs. 25%), construction (0.6% vs. 1.3%), metals (7% vs. 9%), Retail_Wholesale (16%

vs. 17%), textiles (8% vs 9%) and transport (1% vs. 2%) sectors.

By owner characteristics, an “applied” firm has more experienced management (19 vs.

15.9 years), is more likely to be foreign-owned (7.2% vs. 5.7%), and is more likely to have a

female owner (35% vs. 26%).

By market characteristic, an “applied” firm is significantly more likely to be located in

a country with a higher Governance Index value (-0.26 vs. -0.53) and higher bank credit to

GDP ratio (42% vs. 35%).

5.1.4 Approved Firms versus Denied Firms

Table 4 presents descriptive statistics for firms that applied for a credit, and then,

separately, for firms that were turned down (“denied” firms), and for firms that received

credit (“approved” firms), along with the difference in these two means and a t-test for

significant differences in means.

Overall, 30% of the firms applying for credit were turned down by their prospective

lenders. By comparison, Cole (2009) reports that only 11% - 22% of the U.S. firms applying

for credit were turned down. Hence, credit around the world appears to be tighter than in the

U.S.

- 20 -

When compared with a “denied” firm, we find that an “approved” firm is significantly

older (18.1 vs. 16.3 years) and larger. Furthermore, approved firms are more likely to be

organized as corporations (66% vs. 51%) and less likely to be organized as Partner (11.4%

vs. 14.1%) and as Proprietorship (19% vs. 25%). By industry, firms that are granted a credit

are less likely to be in the manufacturing (23.5% vs. 25%), construction (0.5% vs. 0.9%),

services (23% vs. 26%), transports (0.7% vs. 0.9%) and retail/wholesale trade (16% vs. 17%)

sectors, and more likely to in the chemicals (6% vs. 4%), food (9% vs. 8%), textiles (8% vs.

7%) and metals (8% vs. 7%) sectors.

By owner characteristics, an “approved” firm has significantly more managerial

experience (19.7 vs. 17.5 years), is significantly more likely to be domestic-owned (9.3% vs.

9.2%) and is significantly more likely to have a female owner (36.8% vs. 31.0%).

By market characteristics, an “approved” firm is significantly more likely to be

located in a country with a higher Governance Index (-0.19 vs. -0.40), and a higher bank

credit to GDP ratio (43.6% vs. 36.6%).

5.2 Multivariate Results

5.2.1 Need-Credit Firms vs. Need-No-Credit Firms

In Table 5, we present our multivariate results from estimating the logistic regression

models explaining who needs credit, as described in section 4.2. The dependent variable No-

Need is equal to one if the firm indicated that it did not need credit and equal to zero for firms

indicating a need for credit (including “discouraged,” “denied,” and “approved” firms). In

column 2, we present results for the full sample, while in columns 3 and 4, we present results

for our developing and developed subsamples, respectively. Rather than present logit

coefficients, which are uninformative other than sign, we exponentiate these coefficients to

obtain the “odds ratios” associated with each variable. This enables us to talk about the

magnitude of effects. An odds ratio greater than 1.00 indicates that a firm with that

- 21 -

characteristic is more likely to need no credit, whereas an odds ratio less than 1.00 indicates

that a firm with that characteristic is less likely to need no credit. Alternatively, one divided

by the odds ratio for being a no-need firms gives the odds ratio of being a firm that needs

credit.

In Column 2, we show the results for firms that need credit versus firms that do not

need credit. Among the firm characteristics, only industry indicators for manufacturing and

chemical firms are statistically significant. Firms in these two industries are about 30% more

likely to need credit than are firms in other industries. Among the owner characteristics,

foreign-owned firms are about 35% less like to need credit than are domestic-owned firms.

Neither of our market characteristics is significantly different from zero at standard levels of

significance but the odds ratio of GovIndex is 1.21, indicating that firms located in countries

with better governance are less likely to need credit (p-value=0.12). Among our year

indicators, the odds ratios for 2008-2011 are in the range of 0.60-0.70 indicating that firms

were about 50% more likely to need credit during these crisis years, but only the coefficients

for 2008 and 2010 are statistically significant at the 10% level or better.

Column 3 presents the results for our developing country subsample. Among the firm

characteristics, the coefficient on the indicator for corporations is significant and its

associated odds ratio indicates that corporations are about 30% more likely to need credit

than firms with other legal forms of organization. This is consistent with the limited liability

provided by the corporate form of ownership. Six of the industry indicators are statistically

significant. Firms classified as manufacturing, chemicals, construction, metals, services and

textiles are more likely to need credit. For all but services, this result is consistent with the

greater amount of tangible assets in these industries; for services, this result is counter-

intuitive and suggests that factors other than tangibility of assets are driving the need for

credit at service firms. Among the owner characteristics, foreign-owned firms are much less

likely to need credit than are domestic-owned firms and female-owned firms are more likely

- 22 -

to need credit than are male-owned firms. Both of our market characteristics is significantly

different from zero at the 10% level of significance. The odds ratio of GovIndex is 1.36,

indicating that firms located in developing countries with better governance are less likely to

need credit (p-value=0.04). The odds ratio of BankCred indicates that firms located in

countries where banks provide more credit to the private sector are less likely to report a need

for credit, most probably because their credit needs already have been met.

Among our year indicators, the coefficients for 2008, 2013 and 2014 are highly

significant and the associated odds ratios are in the range of 1.60-3.1, indicating that firms

were much less likely to need credit during these years. This finding suggests that the

financial crisis had a very different impact on firms in developing countries as compared with

firms in developed countries.

Column 4 presents the results for our developed country subsample. Among the firm

characteristics, the coefficient on our firm size proxy is significant at the 10% level and its

associated odds ratio indicates that larger firms are significantly more likely to need credit

than are smaller firms. This finding is consistent with what Cole (2009) reports for SMEs in

the U.S. Only two of the industry indicators are statistically significant—the same as in the

full sample—manufacturing and chemicals. Among the owner characteristics, foreign-owned

firms are much less like to need credit than are domestic-owned firms and female-owned

firms are more likely to need credit than are male-owned firms.

Neither of our market characteristics is significantly different from zero at standard

levels of significance but the odds ratio of GovIndex is 1.15, indicating that firms located in

countries with better governance are less likely to need credit. Among our year indicators, the

odds ratios for 2008-2011 are in the range of 0.50-0.70 indicating that firms were about 50%

more likely to need credit during these crisis years, and the coefficients for these year

indicators are statistically significant at the 10% level or better. In combination with the

results for year dummies from the developing country subsample, this shows that the full-

- 23 -

sample results for the year dummies are driven almost entirely by the developed country sub-

sample.

5.2.2. Discouraged Firms vs. Applied Firms


models explaining which firms reported being discouraged from applying for credit. The

dependent variable Discour is equal to one if the firm indicated that it needed credit but was

discouraged from applying and equal to zero if the firm indicated that it needed credit and

that it applied for credit (including “denied” and “approved” firms). In column 2, we present

results for the full sample, while in columns 3 and 4, we present results for our developing

and developed subsamples, respectively.

Among the firm characteristics, the coefficient on our firm size proxy is significant at

the 1% level. Its odds ratio of 0.55 indicates that larger firms are significantly less likely to be

discouraged from applying for a credit than smaller firms. Older firms are significantly more

likely to be discouraged to apply for a credit. This is in contrast to the opposite relation

reported by Cole (2009) for U.S. firms. The coefficient of our indicator for corporate legal

form of ownership is statistically significant and its associated odds ratio indicates that

corporations are about 40% less likely to be discouraged from applying for credit. Four of our

industry indicators are statistically significant. Firms classified as manufacturing,

construction, food and textiles are more likely to be discouraged from apply for credit.

Among our owner characteristics, firms with more experienced management are less likely to

be discouraged borrowers than firms with a management with less experience.

Among our market characteristics, GovIndex is statistically significant at better than

the 0.1% level. The associated odds ratio of 0.44 indicates that firms located in countries with

a better governance are much less likely to be discouraged from applying for a credit. A one

- 24 -

standard deviation improvement in governance reduces the odds of being discouraged by

about 55 percent.

Among our year indicators, the coefficients for 2007 and 2014 are highly significant,

and the odds ratios indicate that firms were much more likely to be discouraged from

applying for credit during these years.

Columns 3 and 4 of Table 6 present results for our regression model when we split

our sample into developed and developing countries, respectively. As in our full model, we

find a positive relation between firm size and “applying for a credit” in both subsamples, with

the odds ratio indicating a stronger relation in developed (0.54) than developing (0.63)

countries. Furthermore, the coefficient of the legal form of “corporations” is statistically

highly significant in the full model and in the separate estimations for developing and

developed countries with similar odds ratio between 0.56 and 0.61.

We find some differences between our subsample of developing and developed

countries for the industry indicators. In developing countries, firms classified as chemicals

and transport are less likely to be discouraged while firms classified as construction are more

likely to be discouraged to apply for a credit. In developed countries, industry indicators for

manufacturing, construction, food and textiles are statistically significant. Firms in these four

industries are more likely to be discouraged from applying for credit than are firms in other

industries.

Among the ownership characteristics, firms with a more experienced management are

less likely to be discouraged firms in developed countries. In developing countries, ownership

does not have an influence on whether a firm applies for a credit or if it was discouraged and

did not apply.

By market characteristics, we find a negative relation between the Governance index

variable and the discouraged firms in both developed and developing countries. The odds

ratios of 0.56 and 0.40 indicate that firms located in countries with better governance are

- 25 -

much less likely to be discouraged from applying for a credit. Furthermore, we see that the

results for the PrivCred variable are driven by developing countries as we find a statistically

highly significant negative relation between this variable and discouraged firms.

We find some differences between developed and developing countries in the year

dummies. For 2007, the relation between the year and discouraged borrowers is statistically

highly significant and negative for only developing countries. For 2008 and 2011, are

insignificant for developing countries but positive and significant for developing countries.

For 2012, the coefficients are negative and significant for developed countries. Only for 2014

are the coefficients negative and significant for both subsamples.

5.2.3. Approved firms vs. Denied Firms


models explaining which firms reported being denied credit when applying for credit. The

dependent variable Denied is equal to one if the firm indicated that it needed credit, applied

for credit, but was denied credit and equal to zero if the firm indicated that it needed credit,

applied for credit, and was approved for credit. In column 2, we present results for the full

sample, while in columns 3 and 4, we present results for our developing and developed

subsamples, respectively.

Overall, our analyses suggest that many firms in our sample were experiencing limited

access to credit. As mentioned above, 70% of the firms applying for credit have their

applications approved. Given this substantial share of firms that are a credit, it is worthwhile

looking closer at the determinants of loan rejection.

Among firm characteristics, we see that larger firms are significantly less likely to be

denied credit. This result confirms, among others, the findings of Beck et al. (2006) that

financing obstacles are higher for small firms than for large firms. The reason for this

relationship is that information asymmetries are expected to be greater for smaller firms, for

- 26 -

which there is less information available. Furthermore, larger firms are thought to be more

creditworthy because they tend to be better established and typically are more diversified

than are smaller firms. We also find that firms organized as corporations are less likely to be

denied credit.

By industry, firms in the metals sector are less likely to be denied credit than are firms

in the omitted category. Among the owner characteristics, only managerial experience has a

positive and statistically significant effect on the likelihood of approval.

Among the market characteristics, we find that firms in countries with a higher

governance index and a higher bank credit to GDP ratio are more likely to be granted a credit.

From this evidence, we conclude that country-level governance plays a critical role in the

availability of credit to SMEs. Good governance helps that SME have better access to loans.

Not surprising, another crucial determinant of credit availability is the economic

environment as proxied by our set of year dummies. Companies applying in times of turmoil

(2007) were significantly more likely to be turned down than in the years before or after the

crisis.

Columns 3 and 4 of Table 7 present results for our regression model when we split our

sample into developed and developing countries, respectively. As in our full model, we find a

negative relation between firm size and loan denial in both subsamples. The odds ratios

indicate a strong relation in both developed and developing countries. Similar results hold for

firms organized as corporations, for which we also find significantly positive relations of

similar magnitude in both subsamples.

Among ownership characteristics, a firm with a more experienced manager is

significantly less likely to be denied credit, and this relation also holds for developed, but not

for developing, countries. A firm with a foreign owner is much more likely to be denied

credit in developed countries (odds ratio = 3.69), but not in developing countries.

- 27 -

Among market characteristics, we find that a firm located in a country with a higher

governance index and a higher ratio of bank credit to GDP is significantly less likely to be

denied credit. Within our subsamples, these relations are significant only in developed

countries.

6. Conclusions

In this study, we use data on more than 100,000 SMEs in 133 countries surveyed by

the World Bank to analyze the availability of credit around the world. Following Cole (2009),

we classify firms into one of four mutually exclusive groups: no-need, discouraged, denied

and approved. As compared with results for the U.S. reported by Cole (2009), we find that

firms around the world are just about as likely to report a need for credit (55% vs. 56%).

However, among firms that need credit, international firms are much more likely to be

discouraged from applying for credit, even though they need credit; and are much more likely

to be denied credit when they need and apply for credit. In our sample, more than 50% of the

firms that reported a need for credit also reported that they did not apply because they were

discouraged, whereas in the U.S., the corresponding figure is about 30%. When we split our

sample into developed and developing countries, we find that 48% of the firms in developed

countries are discouraged, while 62% of the firms in developing countries are discouraged.

Firms are discouraged not only by subjective feelings, but also by unfavorable interest

rates and collateral conditions. However, the reason why a firm is discouraged from applying

for a loan is not fully clear. High interest rates and large collateral requirements may reflect

true impediments of promising firms and projects. On the other hand, high interest rates and

large collateral requirement might also be due to financial difficulties of the discouraged firm.

The subjective “feeling” of discouragement can be the result of the fact that a firm knows that

the probability of success of the relevant project is low, or because of non-economic reasons

such as discrimination.

- 28 -

In our sample, about 30% of the firms that applied were turned down, whereas in

U.S., the corresponding figure is less than 20%. In other words, credit is much less

“available” around the world than in the U.S., so that policies to improve the availability of

credit are even more important. Not surprisingly, the turndown rate was significantly higher

in developing countries (41%) than in developed countries (26%).

Overall, we find that a “no-need” firm is more likely to be owned by a foreigner; and

is more likely to be classified as a manufacturing or chemicals company, and that firms

located in developing, but not developed, countries with better governance are less likely to

need credit.

Among firms that need credit, we find that a “discouraged” firm is both younger and

smaller; is much less likely to be organized as a corporation; and is less likely to run by an


governance are less likely to report that they are discouraged, both in developed and in


Among firms that apply for credit, we find that a denied firm is both younger and

smaller; is less likely to be organized as a corporation; and is less likely to be run by more


governance are less likely to report that they were denied credit, both in developed and in


Our results suggest to policy-makers that the need for policies to foster credit access is

still one of the key issues in many countries, especially measures related to country-level

governance. Policies that improve governance should improve the availability of credit,

which, in turn, should improve economic growth and employment. Policy measures should

promote credit access, as many small firms (above all in certain industries) are discouraged

from applying for credit. These credit constraints might limit product development and

innovation by some firms, possibly harming long-term economic growth (see Levine, 2005,

- 29 -

for an overview of the literature supporting this relationship). As our results reveal, a policy

to increase information sharing and transparency (for example by external auditors) seem to

be an effective way to improve credit availability, as it reduces informational asymmetries

and significantly improves the probability of getting a credit. Still, the question remains

whether this large fraction of discouraged and denied borrowers reflects missed growth

opportunities, or if it is the result of a useful screening of weak applicants and, thus, points to

a more efficient allocation of credit.

We contribute to the literature in at least four important ways. First, we provide the

first rigorous analysis of the differences in our four types of firms: non-borrowers,

discouraged borrowers, denied borrowers and approved borrowers, for a large international

sample of countries around the world. So far, researchers only have analyzed SMEs in the

U.S. (Cole, 2009) and Europe (Brown et al., 2011). We also separately consider developing

and developed countries. Even though our study reveals some differences between

developing and developed countries, it also shows a number of interesting similarities and

patterns between these two groups.

Second, we provide an analysis of credit availability that properly accounts for the

inherent self-selection mechanisms involved in the credit application process: who needs

credit, who applies for credit conditional upon needing credit, and who gets credit,

conditional upon applying for credit. Most previous researchers, except for Cole (2009) and

Brown et al. (2011), have ignored firms that do not need credit, and many have pooled

discouraged and denied firms. We find differences across these groups. Hence, our results

shed new light upon the credit-allocation process.

Third, we provide evidence from the 2006 – 2014 WBES on the availability of credit

to small businesses using the World Bank’s “global methodology.” This contributes to the

growing literature on SME finance that has emerged from these surveys, including

Beck et al. (2005, 2006, 2008); De la Torre et al. (2010); Brown et al, (2011); Chakravarty

- 30 -

and Xiang (2013); and Love and Peria (2015). Our study, however, analyzes only those

surveys conducted under the “Global Methodology” implemented by the World Bank in 2006

to ensure comparability of surveys across years and countries.

Fourth, we contribute to the literature on how country-level governance affects the

availability of credit to SMEs (Beck et al., 2005, 2006, 2008; Love and Peria, 2015). We find

that firms located in countries with better governance are less likely to need credit; are less

likely to be discouraged from applying for credit when they need credit; and are less likely to

be denied credit when they apply for credit.

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Table 1: Definitions of variables used to explain who needs credit and who gets credit around the

world

Variables Description

Dependent variables

NoNeed Binary variable which takes on a value of 0 if the firm indicated that it

needed credit, and 1 if the firm needs a credit.

Discouraged Binary variable which takes on a value of 0 if the firm applied for credit

and was extended or denied credit and a value of 1 if the firm needs a

credit but was discouraged and did not apply for credit.

Denied Binary variable which takes on a value of 0 if the firm applied for and

was extended credit and a value of 1 if the firm applied for and was

denied credit.

Independent variables

Firm characteristics

Total Employment Firm’s size measured by the ln of the total employment.

Age Number of years since the firm started its operations.

Corp Dummy variable for firms that are organized as corporations.

Partner Dummy variable for firms that are organized as partnerships.

Proprietorship Dummy variable for firms that are organized as proprietorships.

Economic Sector Dummy variables for the sectors in which a firm is operating (chemicals,

construction, food, manufacturing, metals, textiles, transport,

retail-wholesale, services, and other services).

Owner characteristics

Female Owner Dummy variable for female-managed firms.

Male Owner Dummy variable for male-managed firms.

Domestic Owner Dummy variable with a domestic owner.

Foreign Owner Dummy variable with a foreign owner.

Experience Mgt Experience of the top manager in this sector in years.

Market/Environmental characteristics

Gov Index

The Governance Indicators as reported by the World Bank and

aggregated for six dimensions of governance (Voice and Accountability,

Political Stability and Absence of Violence, Government Effectiveness,

Regulatory Quality, Rule of Law, Control of Corruption).

Bank Credit The World Bank development indicator variable for bank credit to the

private sector as percentage of GDP (%).

Year Dummy variable for the year in which the survey was conducted.

- 34 -

Table 2: Descriptive statistics for the full sample and, separately, for Need and No-Need firms

No-Need is a binary variable that takes on a value of 0 if the firm indicated that it needed credit

during the previous year (applied for credit and was extended or denied credit; or was

discouraged and did not apply for credit) and a value of 1 if the firm did not apply for credit

because it reported that it did not need additional credit during the previous year. For each

variable in column 1, column 2 presents the mean for the full sample, while columns 3 and 4

present the means for No-Need and Need firms, respectively. Column 5 presents the difference in

the means of Need and No-Need firms, and column 6 presents the results of t-tests for

significance of the differences in means of these two groups of firms. Variables are defined in

Table 1. Data are from the World Bank Enterprise Surveys, and include 106,611 firm-year

observations from 133 countries over the 2006 – 2014 period.

*, **, *** indicate statistical significance at the 0.10, 0.05 and 0.01 levels, respectively.

(1)

Variable

(2)

All

(3)

No Need

(4)

Need

(5)

Difference

(6)

t-Statistic

Observations 111,647 48,719 62,928

Firm Characteristics

Log Total Employment 3.174 3.174 3.173 0.001 0.12

Age 16.585 16.600 16.575 0.025 0.33

Corporation 0.462 0.465 0.459 0.006 1.90 **

Partner 0.157 0.162 0.153 0.009 4.11 ***

Proprietorship 0.353 0.344 0.360 -0.016 -5.49 ***

Manufacturing 0.240 0.231 0.246 -0.015 -5.83 ***

Chemicals 0.047 0.046 0.048 -0.002 -1.55 *

Construction 0.010 0.011 0.010 0.001 2.21 **

Food 0.079 0.074 0.083 -0.009 -5.87 ***

Metals 0.082 0.080 0.083 -0.003 -1.87 **

Retail-Wholesale 0.179 0.192 0.169 0.023 9.71 ***

Services 0.246 0.263 0.233 0.030 11.47 ***

Textiles 0.075 0.065 0.083 -0.018 -11.70 ***

Transport 0.016 0.020 0.013 0.007 9.21 ***

Other 0.037 0.032 0.042 -0.010 -8.62 ***

Owner Characteristics

Female Owner 0.288 0.271 0.301 -0.030 -10.88 ***

Male Owner 0.712 0.729 0.699 0.030 10.88 ***

Foreign Owner 0.076 0.092 0.064 0.028 17.15 ***

Domestic Owner 0.924 0.908 0.936 -0.028 -17.15 ***

Experience Mgmt 17.610 17.917 17.372 0.545 7.59 ***

Market Characteristics

Gov Index -0.394 -0.377 -0.406 0.029 7.59 ***

Bank Credit 38.948 40.339 37.871 2.468 15.14 ***

Year2006 0.126 0.108 0.140 -0.032 -16.25 ***

Year2007 0.078 0.065 0.087 -0.022 -14.01 ***

Year2008 0.034 0.029 0.037 -0.008 -7.18 ***

Year2009 0.167 0.151 0.179 -0.028 -12.68 ***

Year2010 0.126 0.113 0.136 -0.023 -11.84 ***

Year2011 0.026 0.019 0.032 -0.013 -13.70 ***

Year2012 0.062 0.064 0.061 0.003 2.19 **

Year2013 0.231 0.285 0.190 0.095 36.77 ***

Year2014 0.148 0.163 0.136 0.027 12.50 ***

- 35 -

Table 3: Descriptive statistics for Need-Credit firms, Applied firms and Discouraged firms

Applied is a binary variable that takes on a value of 1 if the firm applied for credit and was

extended or denied credit and a value of 0 if the firm needs a credit but was discouraged and did

not apply for credit. Discouraged is a binary variable that takes on a value of 0 if the firm applied

for credit and was extended or denied credit and a value of 1 if the firm needs a credit but was

discouraged and did not apply for credit. Need firms include all Applied and Discouraged firms.

For each variable in column 1, column 2 presents the mean for Need firms, and columns 3 and 4

present the means for Applied firms and Discouraged firms, respectively. Column 5 presents the

difference in the means of Discouraged and Applied firms, and column 6 presents the results of

t-tests for significance of the differences in means. Variables are defined in Table 1. Data are

from the World Bank Enterprise Surveys, and include 106,611 firm-year observations from 133

countries over the 2006 – 2014 period, of which 62,928 indicated a need for credit.


(1)

Variable

(2)

Need

(3)

Applied

(4)

Discouraged

(5)

Difference

(6)

t-Statistic

Observations 62,928 29,413 33,515


Log total employment 3.174 3.510 2.878 -0.631 -70.01 ***

Age 16.600 17.552 15.716 -1.836 -18.06 ***

Corporation 0.465 0.615 0.323 -0.292 -76.53 ***

Partner 0.162 0.122 0.180 0.058 20.55 ***

Proprietorship 0.344 0.229 0.475 0.246 67.17 ***

Manufacturing 0.231 0.240 0.252 0.012 3.54 ***

Chemicals 0.046 0.054 0.042 -0.012 -6.86 ***

Construction 0.011 0.006 0.013 0.007 9.70 ***

Food 0.074 0.088 0.079 -0.008 -3.76 ***

Metals 0.080 0.072 0.093 0.021 9.38 ***

Retail-Wholesale 0.192 0.166 0.172 0.006 1.91 **

Services 0.263 0.237 0.230 -0.007 -2.12 **

Textiles 0.065 0.081 0.085 0.004 1.74 **

Transport 0.020 0.008 0.017 0.009 10.93 ***

Other 0.032 0.055 0.029 -0.026 -15.78 ***


Female Owner 0.271 0.350 0.257 -0.093 -25.37 ***

Male Owner 0.729 0.650 0.743 0.093 25.37 ***

Foreign Owner 0.092 0.072 0.057 -0.015 -7.73 ***

Domestic Owner 0.908 0.928 0.943 0.015 7.73 ***

Experience Mgmt 17.917 19.036 15.913 -3.123 -34.50 ***


Gov Index -0.377 -0.257 -0.537 -0.279 -58.82 ***

Bank Credit 40.339 41.541 34.614 -6.928 -32.73 ***

Year2006 0.108 0.164 0.119 -0.045 -16.05 ***

Year2007 0.065 0.060 0.112 0.052 23.53 ***

Year2008 0.029 0.049 0.027 -0.022 -14.45 ***

Year2009 0.151 0.223 0.141 -0.082 -26.51 ***

Year2010 0.113 0.183 0.095 -0.088 -31.67 ***

Year2011 0.019 0.023 0.040 0.017 12.16 ***

Year2012 0.064 0.060 0.061 0.001 0.36

Year2013 0.285 0.182 0.198 0.016 5.05 ***

Year2014 0.163 0.056 0.207 0.151 58.54 ***

- 36 -

Table 4: Descriptive statistics for Applied firms and, separately, for Approved and Denied firms

The dependent variable Get Credit (getcredit) is a binary variable that takes on a value of 1 if the

firm applied for and was extended credit and a value of 0 if the firm applied for and was denied

credit. For each variable in column 1, column 2 presents the mean for firms indicating that they

applied for credit, and columns 3 and 4 present the means for “approved” firms and “denied”

firms, respectively. Column 5 presents the difference in the means of “approved” firms and

“denied” firms, and column 6 presents the results of t-tests for significance of the differences in

means. Variables are defined in Table 1. Data are from the World Bank Enterprise Surveys, and

include 106,611 firm-year observations from 133 countries over the 2006 – 2014 period.


(1)

Variable

(2)

Applied

(3)

Denied

(4)

Approved

(5)

Difference

(6)

t-Statistic

Observations 29,413 8,848 20,565


Log total employment 3.510 3.148 3.665 0.517 35.56 ***

Age 17.552 16.288 18.096 1.807 11.08 ***

Corporation 0.615 0.511 0.660 0.149 23.78 ***

Partner 0.122 0.141 0.114 -0.027 -6.33 ***

Proprietorship 0.229 0.321 0.190 -0.131 -23.13 ***

Manufacturing 0.240 0.250 0.235 -0.014 -2.58 ***

Chemicals 0.054 0.043 0.058 0.016 5.89 ***

Construction 0.006 0.009 0.005 -0.004 -3.90 ***

Food 0.088 0.076 0.093 0.017 4.80 ***

Metals 0.072 0.067 0.075 0.008 2.56 ***

Retail-Wholesale 0.166 0.173 0.163 -0.010 -2.08 **

Services 0.237 0.255 0.229 -0.026 -4.82 ***

Textiles 0.081 0.075 0.084 0.008 2.43 ***

Transport 0.008 0.009 0.007 -0.001 -1.30 *

Other 0.055 0.050 0.057 0.007 2.47 ***


Female Owner 0.350 0.310 0.368 0.058 9.71 ***

Male Owner 0.650 0.690 0.632 -0.058 -9.71 ***

Foreign Owned 0.072 0.076 0.071 -0.005 -1.40 *

Domestic Owned 0.928 0.924 0.929 0.005 1.40 *

Experience Mgmt 19.036 17.521 19.687 2.166 14.83 ***


Gov Index -0.257 -0.405 -0.194 0.211 26.73 ***

Bank Credit 41.541 36.659 43.600 6.941 20.86 ***

Year2006 0.164 0.153 0.169 0.015 3.32 ***

Year2007 0.060 0.086 0.048 -0.038 -11.32 ***

Year2008 0.049 0.040 0.053 0.013 4.97 ***

Year2009 0.223 0.167 0.246 0.079 15.85 ***

Year2010 0.183 0.158 0.193 0.035 7.38 ***

Year2011 0.023 0.037 0.017 -0.020 -9.28 ***

Year2012 0.060 0.064 0.059 -0.005 -1.77 **

Year2013 0.182 0.199 0.174 -0.024 -4.88 ***

Year2014 0.056 0.094 0.039 -0.054 -16.10 ***

- 37 -

Table 5: Regressions results explaining who needs credit?

Full sample, developing and developed countries

The dependent variable No-Need is a binary variable that takes on a value of 0 if the firm

indicated that it needed credit (applied for credit and was extended or denied credit, or was

discouraged and did not apply for credit) and a value of 1 if the firm did not apply for credit

because it reported that it did not need credit. Explanatory variables are defined in Table 1. Data

are from the World Bank Enterprise Surveys, and include 106,611 firm-year observations from

133 countries over the 2006 – 2014 period. We report odds ratios over robust z-statistics (in

parentheses).


(1) (2)

All

(3)

Developing

(4)

Developed Variables


Log Total Employment 0.943 1.051 0.937 *

(-1.609) (1.003) (-1.732)

Age 1.000 0.999 1.000

(0.0603) (-0.386) (0.0186) Corporation 0.928 0.757 ** 0.964

(-0.770) (-2.076) (-0.353)

Partner 1.002 1.006 0.990

(0.0185) (0.0758) (-0.120) Manufacturing 0.783 ** 0.690 ** 0.801 * (-2.116) (-2.566) (-1.898) Chemicals 0.701 *** 0.505 ** 0.752 **

(-2.858) (-2.381) (-2.387) Construction 0.984 0.428 *** 1.018

(-0.0641) (-3.824) (0.0683)

Food 0.861 0.727 0.911

(-1.040) (-1.516) (-0.645)

Metals 0.792 0.644 *** 0.857

(-1.575) (-3.382) (-1.075) Retail-Wholesale 1.206 0.987 1.247

(1.360) (-0.105) (1.574)

Services 1.138 0.746 ** 1.231

(0.829) (-1.994) (1.315)

Textiles 0.763 0.429 *** 0.833

(-1.179) (-4.286) (-0.787)

Transport 1.214 1.245 1.257

(0.749) (1.147) (0.879) Owner Characteristics

Female Owner 1.048 0.879* 1.083

(0.547) (-1.708) (0.910) Foreign Owner 1.531 *** 1.688 *** 1.540 ***

(3.419) (8.144) (2.813) Experience Mgmt 0.999 1.004 0.997

(-0.477) (1.435) (-0.829)


Gov Index 1.207 1.356 ** 1.148

(1.562) (2.064) (1.159)

- 38 -

Bank Credit 1.000 1.010 * 0.999

(-0.108) (1.878) (-0.365) Year2007 0.944 0.971 0.918 (-0.202) (-0.117) (-0.271) Year2008 0.568 * 2.452 *** 0.484 **

(-1.845) (2.602) (-2.193) Year2009 0.619 1.164 0.510 **

(-1.616) (0.689) (-2.150) Year2010 0.721 ** 0.911 0.660 ***

(-2.163) (-0.272) (-2.685) Year2011 0.594 1.132 0.501 *

(-1.428) (0.646) (-1.930) Year2012 0.988 0.897

(-0.0382) (-0.289) Year2013 1.377 1.650 ** 1.256

(1.051) (2.389) (0.679) Year2014 1.173 3.075 *** 1.022

(0.572) (4.115) (0.0726)

Developed 1.375

(1.569)

Constant 0.960 0.494 *** 1.487

(-0.119) (-3.275) (1.050)

Observations 106,611 33,644 72,967

- 39 -

Table 6: Regressions results explaining who applies for credit?


The dependent variable Discouraged is a binary variable that takes on a value of 0 if the firm

applied for credit and was extended or denied credit and a value of 1 if the firm was discouraged

and did not apply for credit. Explanatory variables are defined in Table 1. Data are from the

World Bank Enterprise Surveys, and include 106,611 firm-year observations from 133 countries

over the 2006 – 2014 period. We report odds ratios over robust z-statistics (in parentheses).


(1)

All

(2)

Developing

(3)

Developed Variables


Log Total Employment 0.553 *** 0.634 *** 0.540 ***

(-9.562) (-8.168) (-9.521)

Age 1.007 ** 1.001 1.008 **

(2.358) (0.338) (2.104) Corporation 0.596 *** 0.565 *** 0.609 ***

(-3.526) (-7.837) (-2.928)

Partner 0.694 0.747 0.685

(-1.611) (-1.165) (-1.537)

Manufacturing 1.506 *** 1.292 1.541 ***

(3.071) (1.144) (3.031) Chemicals 0.983 0.540 * 0.974

(-0.0984) (-1.934) (-0.156) Construction 1.776 *** 4.360 *** 1.783 ***

(3.204) (4.651) (3.050)

Food 1.655 *** 0.997 1.751 ***

(3.271) (-0.00974) (3.696)

Metals 1.129 1.034 1.125

(1.013) (0.0991) (1.068) Retail-Wholesale 1.308 1.303 1.313

(1.384) (0.858) (1.299)

Services 1.067 1.042 1.058

(0.410) (0.159) (0.329)

Textiles 1.682 ** 1.381 1.705 **

(2.445) (0.901) (2.434)

Transport 0.736 0.307 ** 0.726

(-1.127) (-2.238) (-1.125) Owner Characteristics

Female Owner 0.896 0.917 0.888

(-0.587) (-1.553) (-0.563) Foreign Owner 0.967 1.228 0.915

(-0.164) (1.509) (-0.353) Experience Mgmt 0.986 *** 0.998 0.985 ***

(-4.202) (-0.404) (-4.549) Market Characteristics

Gov Index 0.441 *** 0.563 *** 0.400 *** (-5.242) (-6.758) (-4.366) Bank Credit 0.992 0.980 *** 0.993

(-1.469) (-4.321) (-1.191) 2.778 *** 1.371 3.390 ***

- 40 -

Year2007

(3.212) (1.565) (3.068)

Year2008 0.573 0.433 ** 0.620

(-1.422) (-2.529) (-1.055)

Year2009 0.956 0.871 1.076

(-0.143) (-0.514) (0.187)

Year2010 0.798 1.318 0.720

(-1.270) (1.202) (-1.534) Year2011 0.843 0.438 *** 0.900

(-0.389) (-2.943) (-0.207) Year2012 3.367 3.438 *

(2.015) (1.881) Year2013 1.250 1.266 1.396

(0.679) (1.045) (0.734) Year2014 8.335 *** 1.819 * 10.32 ***

(6.621) (1.878) (6.343)

Developed 0.909

(-0.504)

Constant 6.584 *** 6.856 *** 5.534 ***

(6.137) (5.532) (4.425)

Observations 60,069 20,188 39,881

- 41 -

Table 7: Regressions results explaining who gets credit?


The dependent variable Denied is a binary variable that takes on a value of 0 if the firm applied

for and was extended credit and a value of 1 if the firm applied for and was denied credit.

Explanatory variables are defined in Table 1. Data are from the World Bank Enterprise Surveys,

and include 106,611 firm-year observations from 133 countries over the 2006 – 2014 period. We

report odds ratios over robust z-statistics (in parentheses).


(1) (2)

All

(3)

Developing

(4)

Developed Variables


Log Total Employment 0.675 *** 0.771 *** 0.658 ***

(-7.828) (-4.002) (-7.737)

Age 0.999 1.009 *** 0.996

(-0.228) (4.653) (-0.753) Corporation 0.766 ** 0.608 *** 0.789 *

(-2.244) (-3.529) (-1.858)

Partner 0.812 0.724 *** 0.819

(-0.878) (-3.958) (-0.753)

Manufacturing 0.969 1.553 0.894

(-0.173) (1.254) (-0.583) Chemicals 1.470 0.741 1.479

(0.811) (-0.797) (0.805) Construction 0.922 0.723 0.868

(-0.143) (-0.734) (-0.250)

Food 0.895 0.696 0.907

(-0.759) (-1.173) (-0.589)

Metals 0.598 *** 1.008 0.574 ***

(-2.970) (0.0212) (-3.016) Retail-Wholesale 0.871 1.265 0.829

(-0.832) (0.594) (-1.024)

Services 0.876 1.119 0.866

(-0.617) (0.332) (-0.596)

Textiles 1.188 1.617 1.155

(0.759) (1.251) (0.556)

Transport 1.066 41.69 *** 1.016

(0.474) (2.766) (0.120) Owner Characteristics

Female Owner 0.944 1.099 0.913

(-0.385) (0.881) (-0.556) Foreign Owner 2.940 0.934 3.688 *

(1.642) (-0.431) (1.774) Experience Mgmt 0.988 ** 0.994 0.988 **

(-2.389) (-0.899) (-2.131) Market Characteristics

Gov Index 0.698 ** 0.886 0.625 ** (-2.070) (-0.917) (-2.163) Bank Credit 0.991 *** 0.996 0.991 ***

(-3.319) (-0.759) (-3.245) 2.637 ** 1.022 3.468 ***

- 42 -

Year2007

(2.332) (0.101) (2.578)

Year2008 0.705 0.695 0.765

(-1.179) (-1.079) (-0.752)

Year2009 0.944 0.361 ** 1.091

(-0.206) (-2.514) (0.248)

Year2010 1.181 0.476 *** 1.404

(0.663) (-2.737) (1.489) Year2011 1.379 1.246 1.442

(0.345) (1.263) (0.394) Year2012 1.564 1.764

(1.417) (1.579) Year2013 1.429 1.074 1.383

(1.324) (0.343) (0.971) Year2014 4.798 *** 1.172 6.340 ***

(5.311) (0.598) (5.748)

Developed 0.980

(-0.121)

Constant 2.707 *** 1.821 * 2.714 **

(2.847) (1.775) (2.014)

Observations 28,245 7,742 20,503

- 43 -

Appendix Table 1: Number of observations and survey year(s) per country

# Country Obs. Survey year(s) # Country Obs. Survey year(s)

1 Afghanistan 945 2008, 2014 40 Eritrea 179 2009

2 Albania 661 2007, 2013 41 Estonia 546 2009, 2013

3 Angola 784 2006, 2010 42 Ethiopia 644 2011

4 Argentina 2,116 2006, 2011 43 Fiji 163 2009

5 Armenia 732 2009, 2013 44 Macedonia 725 2009, 2013

6 Antigua 148 2010 45 Gabon 179 2009

7 Azerbaijan 770 2009, 2013 46 Gambia 174 2006

8 Bahamas 150 2010 47 Georgia 731 2008, 2013

9 Bangladesh 1,438 2013 48 Ghana 1,213 2007, 2013

10 Barbados 150 2010 49 Grenada 151 2010

11 Benin 150 2009 50 Guatemala 1,111 2006, 2010

12 Belarus 631 2008, 2013 51 Guinea 223 2006

13 Bhutan 503 2009, 2015 52 Guinea Bissau 159 2006

14 Bolivia 974 2006, 2010 53 Guyana 164 2010

15 Bosnia/Herzeg. 721 2009, 2013 54 Honduras 792 2006, 2010

16 Botswana 610 2006, 2010 55 Hungary 598 2009, 2013

17 Brazil 1,800 2009 56 India 9,278 2014

18 Bulgaria 1,595 2007, 2009,

2013 57 Indonesia 1,442 2009

19 Burkina Faso 394 2009 58 Iraq 753 2011

20 Burundi 427 2006, 2014 59 Israel 483 2013

21 Cambodia 472 2013 60 Jamaica 368 2010

22 Cameroon 363 2009 61 Jordan 568 2013

23 Cape Verde 155 2009 62 Kazakhstan 1,143 2009, 2013

24 Chad 150 2009 63 Kenya 1,436 2007, 2013

25 Chile 2,046 2006, 2011 64 Kosovo 468 2009, 2013

26 China 2,690 2012 65 Kyrgyz Rep. 504 2009, 2013

27 Colombia 1,941 2006, 2010 66 Lao PDR 630 2009, 2012

28 Congo 148 2009 67 Latvia 607 2009, 2013

29 Costa Rica 538 2010 68 Lebanon 561 2013

30 Cote d’Ivoire 525 2009 69 Lesotho 151 2009

31 Croatia 993 2007, 2013 70 Liberia 150 2009

32 Czech Rep. 502 2009, 2013 71 Lithuania 545 2009, 2013

33 Djibouti 265 2013 72 Madagascar 515 2013

34 Dominica 150 2010 73 Malawi 150 523

35 Dominican

Rep 359 2010 74 Mali 845 2007, 2010

36 DRC 1,223 2006, 2010,

2013 75 Mauritania 386 2006, 2014

37 Ecuador 1,022 2006, 2010 76 Mauritius 398 2009

38 Egypt 2,896 2013 77 Mexico 2,955 2006, 2010

39 El Salvador 1,051 2006, 2010 78 Micronesia 68 2009

- 44 -

79 Moldova 723 2009, 2013 125 Uruguay 1,228 2006, 2010

80 Mongolia 722 2009, 2013 126 Uzbekistan 756 2008, 2013

81 Montenegro 248 2009, 2013 127 Vanuatu 128 2009

82 Morocco 405 2013 128 Venezuela 318 2010

83 Mozambique 479 2007 129 Vietnam 1052 2009

84 Myanmar 632 2014 130 West Bank/Gaza 431 2013

85 Namibia 886 2006, 2014 131 Yemen 829 2010, 2013

86 Nepal 848 2009, 2013 132 Zambia 1,203 2007, 2013

87 Nicaragua 813 2006, 2010 133 Zimbabwe 596 2011

88 Niger 150 2009

89 Nigeria 4,541 2007, 2014

90 Pakistan 2,167 2007, 2013

91 Panama 966 2006, 2010

92 Paraguay 971 2006, 2010

93 Peru 1,632 2006, 2010

94 Philippines 1,322 2009

95 Poland 982 2009, 2013

96 Romania 1,542 2009, 2013

97 Russia 5,218 2009, 2012

98 Rwanda 450 2006, 2011

99 Samoa 108 2009

100 Senegal 1,107 2007, 2014

101 Serbia 746 2009, 2013

102 Sierra Leone 150 2009

103 Slovak Rep 540 2009, 2013

104 Slovenia 545 2009, 2013

105 South Africa 937 2007

106 South Sudan 738 2014

107 Sri Lanka 610 2011

108 St.Kitts & Nev 149 2010

109 St. Lucia 150 2010

110 St. Vincent 153 2010

111 Sudan 662 2014

112 Suriname 152 2010

113 Swaziland 304 2006

114 Sweden 598 2014

115 Tajikistan 718 2008, 2013

116 Tanzania 1,232 2006, 2013

117 Timor Leste 150 2009

118 Togo 155 2009

119 Tonga 150 2009

120 Tunisia 592 2013

121 Turkey 2,490 2008, 2013

122 Trinidad/Tobago 364 2010

123 Uganda 1,320 2006, 2013

- 45 -

124 Ukraine 1,849 2008, 2013

Evidence from the World Bank’s Enterprise Survey … ANNUAL MEETINGS/2018... · 2018-03-15 ·...

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