How Important Are Financing Constraints? The role of finance in the business environment

WPS3820
How Important Are Financing Constraints?
The role of finance in the business environment
Meghana Ayyagari

Asli Demirgüç-Kunt

Vojislav Maksimovic*

Abstract: What role does the business environment play in promoting and restraining firm
growth? Recent literature points to a number of factors as obstacles to growth. Inefficient
functioning of financial markets, inadequate security and enforcement of property rights, poor
provision of infrastructure, inefficient regulation and taxation, and broader governance features
such as corruption and macroeconomic stability are discussed without any comparative evidence
on their ordering. In this paper, we use firm level survey data to present evidence on the relative
importance of different features of the business environment. We find that although firms report
many obstacles to growth, not all the obstacles are equally constraining. Some affect firm growth
only indirectly through their influence on other obstacles, or not at all. Using Directed Acyclic
Graph methodology as well as regressions, we find that only obstacles related to finance, crime
and political instability directly affect the growth rate of firms. Robustness tests further show that
the Finance result is the most robust of the three. These results have important policy implications
for the priority of reform efforts. Our results show that maintaining political stability, keeping
crime under control, and undertaking financial sector reforms to relax financing constraints are
likely to be the most effective routes to promote firm growth.
Keywords: Financing Constraints, Firm Growth, Business Environment
JEL Classification: D21, G30, O12
World Bank Policy Research Working Paper 3820, January 2006
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely

those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
.

_______________________________
*Ayyagari: School of Business, George Washington University; Demirgüç-Kunt: World Bank;
Maksimovic: Robert H. Smith School of Business at the University of Maryland. We would like to thank
Thorsten Beck, Daron Acemoglu, Gerard Caprio, Stijn Claessens, Patrick Honohan, Leora Klapper, Aart
Kraay, Norman Loayza, David Mckenzie, Dani Rodrik, L. Alan Winters and seminar participants at George
Washington University for their suggestions and comments.


I. Introduction
Understanding firm growth is at the heart of the development process, making it a
much researched area in finance and economics. More recently, the field has seen a
resurgence in interest from policymakers and researchers, with a new focus on the
broader business environment in which firms operate. Researchers have documented
through surveys that firms report many features of their business environment as
obstacles to their growth.

Firms report being affected by inadequate security and

enforcement of property rights, inefficient functioning of financial markets, poor
provision of infrastructure services, inefficient regulations and taxation, and broader
governance features such as corruption and macroeconomic instability.1 Many of these
perceived obstacles are correlated with low performance.
These findings can inform government policies that shape the opportunities and
incentives facing firms, by influencing their business environment. However, even if
firm performance is likely to benefit from improvements in all dimensions of the business
environment, addressing all of them at once would be challenging for any government.

Thus, understanding how these different obstacles interact and which ones influence firm
growth directly is important in prioritizing reform efforts. Further, since the relative
importance of obstacles may also vary according to the level of development of the
country and according to firm characteristics such as firm size, it is important to assess
whether the same obstacles affect all sub-populations of firms.
In this paper we examine which features of the business environment directly
affect firm growth. We use evidence from the World Business Environment Survey

1

For example, Batra, Kaufmann, and Stone (2003), Dollar, Hallward-Driemeier, and Mengistae (2004) and
Carlin, Fries, Schaffer, and Seabright (2001).

2


(WBES), a major firm level survey conducted in 1999 and 2000 in 80 developed and
developing countries around the world and led by the World Bank.2 We use this data to
assess (i) whether each feature of the business environment that firms report as an
obstacle affects their growth, (ii) the relative economic importance of the obstacles that
do constrain firm growth, (iii) whether an obstacle has a direct effect on firm growth or
whether the obstacle acts indirectly by reinforcing other obstacles which have a direct
effect, and (iv) whether these relationships vary by different levels of economic
development and for different firm characteristics.
We define an obstacle to be binding if it has a significant impact on firm growth.
Our regression results indicate that only Finance, Crime and Political Instability emerge
as the binding constraints with a direct impact on firm growth. In order to rule out
reported obstacles that are merely correlated with firm growth but are unlikely to be
causal we also use the Directed Acyclic Graph (DAG) methodology implemented by an
algorithm used in artificial intelligence and computer science (Sprites, Glymour, and

Scheines (2000)).3 This algorithm uses the correlation matrix of a set of variables to
determine whether a variable meets certain criteria, derived from probability and graph
theory, for it to be classified as a direct or indirect cause of another variable.
The DAG algorithm also confirms Finance, Crime and Political Instability to be
the binding constraints, with other obstacles having an indirect effect, if at all, on firm
growth through the binding constraints.
2

The World Bank created the steering committee of the WBES and several country agencies from
developed and developing countries were involved under the supervision of EBRD and Harvard Center for
International Development. For a detailed discussion of the survey see Batra, Kaufmann, and Stone (2003).
3
See Knill and Maksimovic (2005) for an application of this methodology to international finance. The
methodology has been used recently in economics and finance to analyze price discovery and
interconnectivity between separated commodity markets and the transportation markets linking them
(Haigh and Bessler, 2004), to model the US economy (Awokuse and Bessler, 2003) and to study the
interdependence between major international stock markets in the world (Bessler and Yang, 2003).

3


In further robustness tests, we find that the Finance result is the most robust, in
that Financing obstacles are binding regardless of which countries and firms are included
in the sample. Regression analysis also shows that Financing obstacles have the largest
direct effect on firm growth. These results are not due to influential observations, reverse
causality or perception biases likely to be found in survey responses. Political Instability
and Crime, the other two binding constraints in the full sample, are driven by the
inclusion of African and Transition economies where, arguably, they might be the most
problematic.
We also find that the relative importance of different factors varies according to

firm characteristics. Larger firms are affected by Financing obstacles to a significantly
lesser extent but being larger does not relax the obstacles related to Crime or Political
Instability to the same extent.
Examining the Financing obstacle in more detail, we find that although firms
perceive many specific financing obstacles, such as lack of access to long-term capital
and collateral requirements, only the cost of borrowing directly affects firm growth.
However, we find that the cost of borrowing itself is affected by imperfections in the
financial markets. Thus we find that the firms that face high interest rates are the ones
that perceive banks they have access to as being corrupt, under-funded, and requiring
excessive paperwork. We also find that difficulties with posting collateral and limited
access to long-term financing are also correlated with high interest rates. It is likely that
these latter obstacles are also aggravated by underdeveloped institutions.4

4

Fleisig (1996) highlights the problem with posting collateral in developing and transition countries with
the example of financing available to Uruguayan farmers raising cattle. While cattle are viewed as one of
the best forms of loan collateral by the US, a pledge on cattle is worthless in Uruguay. Uruguayan law
requires for specific description of the pledged property, in this case, an identification of the cows pledged.

4


The extensive literature on institutional obstacles to firm growth is reviewed in
the next section. Several papers have specifically pointed to the importance of financing
obstacles. Using firm level data, Demirguc-Kunt and Maksimovic (1998) and others
provide evidence on the importance of the financial system and legal enforcement in
relaxing firms’ external financing constraints and facilitating their growth. Rajan and
Zingales (1998) show that industries that are dependent on external finance grow faster in
countries with better developed financial systems.5 Although these papers investigate

different obstacles to firm growth and their impact, they generally focus on a small subset
of broadly characterized obstacles faced by firms.
More recently, Allen, Qian, and Qian (2005), argue that China is an important
counterexample to the findings in the law, finance and growth literature. China is one of
the fastest growing economies although neither its legal nor financial system is well
developed by existing standards. Thus, they argue that the role of different factors in
contributing to the growth process is not well understood. We investigate the impact of a
wider set of potential obstacles and evaluate their relative importance as well as
interactions between them in constraining firm growth.
Our work is most closely related to Beck, Demirguc-Kunt, and Maksimovic
(2005). They select, on a priori grounds, the financing, legal and corruption obstacles,
and examine, one at a time, the relation between these obstacles and growth rates of firms

The need to identify collateral so specifically undermines the secured transaction since the bank is not
allowed to repossess a different group of cows in the event of nonpayment.
5
There is a parallel literature on financial development and growth at the country level. Specifically, crosscountry studies (King and Levine 1993; Beck, Levine, and Loayza 2000; Levine, Loayza, and Beck 2000)
show that financial development fosters economic growth. Also see Levine (2005) for a review of the
finance and growth literature.

5


of different sizes. By contrast, we begin by examining a large set of business environment
obstacles and focus on empirically identifying the subset of binding ones.
Our paper also contributes to the policy debate generated by Hausman, Rodrik,
and Velasco (2004) who describe a theoretical framework for analyzing reform priorities
that focuses on identifying and targeting the most binding constraints in a particular
economic setting.
The paper is organized as follows. The next section presents the motivation for

the paper and describes the methodology. Section III discusses the data and summary
statistics. Section IV presents our main results. Section V presents the conclusions and
policy implications.

II. Motivation and Methodology
Numerous studies argue that differences in business environment can explain
much of the variation across countries in firms’ financial policies and performance.
While much of the early work relied on country-level indicators and firms’ financial
reports, more recent work has relied on surveys of firms which provide data on a wide
range of potential obstacles to growth.
The obstacles that have been investigated can be broadly divided into Financing
(such as problems with access to and cost of financing), Judicial Efficiency (security and
protection of property rights, effective functioning of the judiciary),

Taxes and

Regulation (taxes, regulations, anticompetitive practices), Infrastructure (quality and
availability of roads, electricity, water, telephone, postal service etc.), Corruption

6


(corruption of government officials, crime), and general Macroeconomic Environment
that makes financing costly (political instability, exchange rate instability and inflation).6
A large literature in law and finance has identified the importance of Financing
and Judicial Efficiency for firm growth. Many studies, starting with LaPorta, Lopez-deSilanes, Shleifer, and Vishny (1998), argue that differences in legal and financial systems
can explain much of the variation across countries in firms’ financial policies and
performance.7
There are, however, few systematic multi-country studies of how other general
business environment obstacles faced by firms affect their growth. Many of the extant

studies have a regional or single-country focus and concentrate on a single obstacle. For
instance, recent studies have focused on the importance of Infrastructure and
Regulations. Klapper, Laeven, and Rajan (2005) use firm level data from Western and
Eastern Europe and show that anti-competitive regulations such as entry barriers lead to
slower growth in established firms. Dollar, Hallward-Driemeier, and Mengistae (2004)
use firm-level survey data and show that the cost of different bottlenecks such as days to
clear goods through customs, days to get a telephone line, and sales lost due to power
outages affect firm performance in Bangladesh, China, India and Pakistan. Using similar
data for African countries, Eifert, Gelb, and Ramachandran (2005) show that business
6

Several other papers using the same survey have analyzed specific financing obstacles. Beck, DemirgucKunt, and Maksimovic (2005) focus on the role of country-level financial and institutional development in
overcoming the constraining effect of financing obstacles and Beck, Demirguc-Kunt, Laeven, and Levine
(2005) analyze firm characteristics that explain differences in reported financing obstacles. None of these
papers tries to prioritize the importance of specific obstacles for growth.
7
Related to judicial efficiency is the absence of secure property rights. Johnson, McMillan, and Woodruff
(2000) analyze employment and sales growth from 1994 to 1996 in five countries and find that insecure
property rights are more inhibiting to private sector growth than the lack of bank finance. In a study
centered on SMEs in Russia and Bulgaria, Pissarides, Singer, and Svejnar (2003) find the opposite result
that while constraints on external financing limit the ability of firms’ to expand production, insecurity of
property rights is not a major constraint. Using Chinese data Cull and Xu (2005) also show that protection
of property rights as well as access to finance plays an important role in explaining firm reinvestment rates.

7


environment variables also have an impact on firm productivity. Sleuwaegen and
Goedhuys (2002) use firm-level data from the Ivory Coast and find that inadequate
physical and financial infrastructure impairs the growth of small firms.

Several other papers focus on Corruption and compare it to Taxes. One of the
earliest papers in this area by Shleifer and Vishny (1993) argues that corruption may be
more damaging than taxation because of the uncertainty and secrecy that accompanies
bribery payments. Using a unique dataset of Ugandan firms, Fisman and Svensson (2004)
find that corruption, specifically bribe payments, retards firm growth more than taxation.
Gaviria (2002) also finds that corruption and crime substantially reduce firm
competitiveness amongst Latin American firms.8 While these studies are important
contributions in understanding the effects of business environment in different countries,
they each examine a narrow aspect of the business environment and hence have limited
policy prescriptions.
Firms in the WBES survey also report on the quality of macroeconomic
governance, where we define macroeconomic governance to be the extent to which
Political Instability, Exchange Rate instability and Inflation impede business. While the
effects of inflation on investment and firm growth have been extensively studied in the
finance literature and now controlled for in most firm growth regressions, there is little
micro evidence on the impacts of political and exchange rate instability on firm growth. It
is conceivable that political instability and exchange rate volatility have a more indirect
impact on sales growth by affecting the type of financing available to firms. For instance,
Desai, Foley and Forbes (2004) argue that exchange rate depreciations increase the
8

There are several papers in the macro-literature that study the impact of the various business environment
obstacles at the country level. For instance Mauro (1995), Wei (1997) and Friedman et al. (2000) look at
the effect of corruption, crime and taxation on GDP growth, size of the unofficial economy and investment.

8


leverage of firms that have borrowed foreign currency denominated debt, constraining
their ability to obtain new equity or to adjust their capital structure. 9

Identifying Binding Constraints
Given the large number of potential obstacles to growth that have been identified
in surveys, we face a number of difficulties in identifying the obstacles that are truly
constraining.

First, a potential problem with using survey data is that enterprise

managers may identify several operational issues while not all of them may be
constraining.

Therefore, as in Beck, Demirguc-Kunt and Maksimovic (2005), we

examine the extent to which reported obstacles affect growth rates of firms. An obstacle
is only considered to be a “constraint” or a “binding constraint” if it has a significant
impact on firm growth. Significant impact requires that the coefficient of the obstacle in
the firm growth regression is significant and the value of the obstacle is greater than one,
indicating that the enterprise managers identified the factor as an obstacle.10
Second, to the extent that the characteristics of a firm’s business environment are
correlated, it is likely that many perceived business environment characteristics will be
correlated with realized firm growth. It is important to sort these into obstacles that
directly affect growth and obstacles that may be correlated with firm growth but affect it
only indirectly.

9

Alesina et. al. (1996) and Alesina and Perotti (1996) find that political instability has a strong negative
association with growth and income distribution. However these papers use cross-country analyses and
have little information on the effect of political instability on individual firms.
10

In a cross-country setting or even at the individual country level, the significance of the coefficient is
actually sufficient to determine whether an obstacle is binding or not since the value of all obstacles exceed
one. However, in determining the relative impact, it is important to take into account the level of the
obstacles.

9


Since there is no theoretical basis for classifying the obstacles, we must proceed
empirically. However, if some of the obstacles share a common unmeasured cause with
firm growth, then the estimates of these obstacles as well as other obstacles will be biased
and inconsistent. This could cause obstacles having no influence on growth whatsoever,
not even a common cause with growth, to have significant regression coefficients leading
to an incorrect estimation of the binding constraints.
As a robustness test of our multiple regression analysis, we use the Directed
Acyclic Graph (DAG) methodology. The DAG algorithm begins with a set of potentially
related variables and uses the conditional correlations between them to rule out possible
causal relations among these variables. The final output of the algorithm is a listing of
potential causal relations between the variables that have not been ruled out and shows
(a) variables that have direct effects on the dependent variable or other variables, (b)
variables that only have indirect effects on the dependent variable through other
variables, and (c) variables that lack a consistent statistical relation with the other
variables.
The DAG algorithm imposes a stricter criterion than regression analysis to
identify the variables with direct effects. In OLS regression the variables that are
identified as significantly predicting dependent variable Y are the ones that have
significant partial correlations conditional on the full set of regressor matrix (X’X). By
contrast, in the algorithm used to construct the pattern of directed acyclic graphs, a
variable is identified as having a direct effect on dependent variable Y only if it has a
significant partial correlation conditional on the full set of regressors and all subsets of

the regressor matrix (X’X). Thus, if DAG identifies a particular obstacle as having a

10


direct effect on firm growth, that obstacle would also have a significant coefficient in all
OLS regressions regardless of which subset of other obstacles are entered as control
variables in the regression equation.
The criteria for causality in DAG are derived from the application of Bayes rule
and reasonable assumptions on the probability distributions of the variables, most
importantly, the Causal Markov Condition. The Causal Markov Condition amounts to
assuming that every variable X is independent of all variables that are not its direct
causes.11 It also implies that if two variables, X and Y, are related only as effects of a
common cause Z, then X and Y are probabilistically independent conditional on Z.
Appendix A2 includes a detailed description of the DAG methodology and how it
compares to regression analysis. Ayyagari, Demirguc-Kunt and Maksimovic (2005b)
further illustrate the use of this methodology.
DAG is useful as a data simplification device and identifying indirect effects
among different obstacles which the regression analysis does not show. But we use
regression analysis to do further robustness tests, such as testing for possible endogeneity
bias via instrumental variable methods.

We also perform other robustness tests,

controlling for additional variables at the firm and country level, growth opportunities,
influential observations and potential perception biases in survey responses using
regression analysis.

11

A common example used to illustrate this is that applying a flame to a piece of cotton will cause it to
burn, irrespective of whether the flame (direct cause of fire) came from a lighter or a match or some other
spark (indirect causes).

11


The obstacles a firm faces depend on the institutions in each country, but are not
likely to be the same for each firm in each country.12 Thus, our unit of analysis is the
firm. As described below, the regressions have country-level random effects.

III. Data and Summary Statistics
The main purpose of the World Business Environment Survey is to identify
obstacles to firm performance and growth around the world. Thus, the survey contains a
large number of questions on the nature and severity of different obstacles. Specifically,
firms are asked to rate the extent to which Finance, Corruption, Infrastructure, Taxes and
Regulations, Judicial Efficiency, Crime, Anti-Competitive Practices, Political Instability,
and macro issues such as Inflation and Exchange Rate constitute obstacles to their
growth.13
In addition to the detail on the obstacles, one of the greatest values of this survey
is its wide coverage of smaller firms. The survey is size-stratified, with 40 percent of
observations on small firms, another 40 percent on medium-sized firms, and the
remainder from large firms. Firm size is defined based on employment. Small firms are
those that employ five to 50 employees. Medium firms are those that employ 51 to 500
employees. And large firms are those that employ more than 500 employees. General

12

For example, Johnson and Mitton (2003) find that the effect of the 1997 Asian financial crisis on firms in
Malaysia depended on whether or not the firms were connected to specific politicians who gained power

over the period of the crisis. Similarly, the effect of crime might depend on factors such as the firm’s
location, the ethnic group to which the business owner belongs or his political affiliations. Beck,
Demirguc-Kunt, and Maksimovic (2005) find that the correlation between firm growth and financing, legal
and corruption obstacles varies by firm size.
13
The survey provides two obstacles on crime, one capturing street crime and the other organized crime.
Since the correlation between the two obstacles is higher than 70 percent, we use only street crime in our
analysis, which is also more strongly correlated with firm growth among the two.

12


information on firms is limited, but in addition to employment, the survey also gives
information on sales growth, industry and ownership.
In Table I we summarize relevant facts about the level of economic development,
firm growth and the different obstacles that firms report, averaged over all firms in each
country. We provide details on our sources in Appendix A1. The countries in the sample
show considerable variation in per capita income. They range from Ethiopia, with an
average GDP per capita of $109 to developed countries like U.S. and Germany, with per
capita incomes of around $30,000. Firm Growth is the sales growth rate for individual
firms averaged over all sampled firms in each country. Firm growth also shows a wide
dispersion, from negative rates of 20 percent for Armenia and Azerbaijan to 64 percent
for Malawi and Uzbekistan.14
Table I also reports firm level obstacles. The WBES asked enterprise managers to
rate the extent to which each factor presented an obstacle to the operation and growth of
their business. A rating of one denotes no obstacle; two, a minor obstacle; three, a
moderate obstacle; and four, a major obstacle.
Looking at average obstacles across countries (Table II, panel A) we see that
firms report Taxes and Regulations to be the greatest obstacle.

Inflation, Political

Instability and Financing obstacles are also reported to be highly constraining.

By

contrast, factors associated with Judicial Efficiency and Infrastructure are ranked as the
lowest obstacles faced by entrepreneurs.
Firms in higher income countries tend to face lower obstacles in all areas. Table I
also highlights regional differences: When it comes to Corruption and Infrastructure,
14

Note that some of the countries with very high firm growth rates are also countries with high inflation
rates. For instance inflation rate in Malawi was over 80% in 1995 and between 26-30% during the period of
the WBES Survey.

13


African firms report the highest obstacles; Latin American Crime and Judicial Efficiency
obstacles are the highest in the world; and Financing obstacles in Asian countries are
lowest in the developing world. Finally, smaller firms face higher obstacles than larger
firms in all areas, except in those related to Judicial Efficiency and Infrastructure, where
the ranking is reversed. 15
Table II, panel B contains the correlation matrix of the variables. As our cursory
examination of Table I suggests, all obstacles are significantly lower in more developed
countries. Larger firms also face significantly lower obstacles in most cases except
Crime, Corruption and Infrastructure.
The correlations among the obstacles reported by firms are significant but fairly
low, with few above 0.5. As expected, the two macro obstacles, Inflation and Exchange

Rate, are highly correlated at 0.58.

The correlation of Corruption with Crime and

Judicial Efficiency is also relatively high at 0.55, indicating that in environments where
corruption and crime are wide-spread, judicial efficiency is adversely affected. It is also
interesting that the correlation between the Financing obstacle and all other obstacles is
among the lowest, indicating that the Financing obstacle may capture different effects
than those captured by other reported obstacles.

Table II also shows that all obstacles

are negatively and significantly correlated with firm growth. We explore these relations
further in the next section.

IV. Firm Growth and Reported Obstacles

15

Note that the Judicial Efficiency obstacle captures those obstacles related to working of the court system
in resolving business disputes, whereas legal issues related to collateral, credit information, paperwork etc.
are captured within the Financing obstacle as further discussed in section IV.H below.

14


A. Obtaining the Binding Constraints
In Table III we regress firm growth rates on the different obstacles they report.
All regressions are estimated with firm-level data using country-level random effects. To
maximize the number of observations and country coverage, we limit the control

variables to firm size and GDP per capita.16 Specifically, the regression equations we
estimate take the form:
Firm Growth = α + β1 Obstacle + β2 GDP/capita + β3 Firm Size + ε

(1)

To test the hypothesis that a reported obstacle is a binding constraint, that is, it has
a significant impact on firm growth, we test whether β1 is significantly different from
zero. Significant impact also requires that the obstacle has a value higher than one, which
is true for all obstacles. We also report an estimate of the economic impact of the
obstacle at the sample mean by multiplying its coefficient by the sample mean of the
obstacle.17 This allows us to compare the relative impact of different obstacles on
growth.
Table III results show that when we analyze individual obstacles separately, most
are significantly related to firm growth. The only exceptions are Exchange Rate, AntiCompetitive behavior, and Infrastructure obstacles which are not significantly related to
firm growth. These regressions explain up to 11 percent of the variation in firm growth

16

The random effects control for country-level differences in economic growth and other policy variables.
In unreported regressions we also checked the robustness of our results to including additional control
variables in the regression. Specifically, controlling for inflation and GDP per capita growth at the country
level, and adding variables at the firm level capturing a firm’s industry, number of competitors,
organizational structure, and whether it is government or foreign owned, an exporter, or a subsidy receiver
reduces country coverage from 80 to 56, but does not affect the results significantly for individual
obstacles. Of the three binding constraints identified above, only Political Instability obstacle loses
significance.
17

In estimating the economic impact of the obstacles we make the assumption that a one unit change in the

obstacle has a similar value regardless of the initial value of the obstacle.

15


across countries. The economic impact of four obstacles- Finance, Crime, Tax and
Regulations, and Political Instability- are the largest, ranging from 7.9 percent for
Political Instability to 9.6 percent for the Finance obstacles. This result implies that a one
unit increase in one of these obstacles leads to 8 to 10 percent reduction in firm growth,
which is substantial given that the mean firm growth in the sample is 15 percent. In
column 11, we include all obstacles in the regression equation. In this specification, only
Financing, Political Instability and Crime obstacles have a significant constraining effect
on growth. Dropping the remaining obstacles from the regression as in specification 12
does not alter the significance levels.18 The economic impact of the Financing obstacle is
the highest followed by that of Crime and then Political Instability. None of these
differences are statistically significant, however.
It is also possible to do such impact evaluation at the regional level, at the country
level or even at the firm level, instead of the sample mean we have used above. Looking
at the mean obstacles for individual countries reported in Table I, it is clear that the
binding obstacles are not equally important in every country. For example, in Singapore,
where the mean value of the binding obstacles are all closer to one, the economic impact
of the obstacles is much smaller compared to their impact in a country such as Nigeria,
where the mean value of all three obstacles are over three, indicating severe constraints.
Thus, it is possible to use these cross-country results to do growth diagnostics at the
country level as discussed in Hausmann et al. (2004). Going further down, there may
18

In unreported regressions we first regressed the three binding constraints individually on all other
obstacles. In the second stage we replaced the Financing, Political Instability and Crime obstacles by their
residuals from the first stage regressions, that is, the components of the three obstacles that are not

explained by any other obstacle. Consistent with specification 11, all three residuals have negative and
significant coefficients, indicating that they constrain firm growth independently of the other obstacles.
Replicating this process only for the Financing variable- since it is the most likely obstacle to act as a
sufficient statistic for the firm’s business environment- also results in a negative and significant coefficient
of the finance residual in the second stage.

16


also be some firms in Nigeria for which the constraints are not binding (depending on the
value of the obstacles they report) and others in Singapore for which they are. In fact,
average values of obstacles by firm size suggests that the three obstacles will always be
more binding for smaller firms compared to larger firms.
Overall, these results suggest that the three obstacles- Financing, Crime and
Political Instability – are the only true constraints, in that they are the only obstacles that
affect firm growth directly at the margin. The other obstacles may also affect firm
growth through their impact on each other and on the three binding constraints; however
they have no direct effect on firm growth.

B. Directed Acyclic Graphs Methodology
In this section we use the DAG methodology implemented by the software
program TETRAD III (Scheines, et al 1994) to check the robustness of our regression
findings. As described in Ayyagari, Demirguc-Kunt and Maksimovic (2005b), and in
Appendix A2, DAG is useful in simplifying the set of independent variables and
illustrating the causal structure among them.
In keeping with common practice, we impose the following assumptions that are
regularly used in the regression setting- the business environment obstacles cause firm
growth, not the other way around, and the model contains all common causes of the
variables in the model. However, this being a partial equilibrium model of the causation
of growth, it is to be expected that some of the obstacles may be jointly determined by

macroeconomic factors, all of which may not be in the model. We perform robustness

17


tests on our assumptions later in the paper using instrumental variable estimation and by
including additional control variables.
Figure 1 illustrates the application of this algorithm to our full sample. The input
to the algorithm is the correlation matrix from the sample of 4,197 firms consisting of
correlations between firm growth and the ten business environment obstacles19
Figure 1 shows that the only business environment obstacles that have a direct
effect on firm growth are Financing, Crime and Political Instability. Financing in turn is
directly affected by the Taxes and Regulation obstacle which includes factors such as
taxes and tax administration, as well as regulations in the areas of business licensing,
labor, foreign exchange, environment, fire and safety20. Crime is directly affected by the
Corruption and Judicial Efficiency obstacles and Political Instability is affected by
Inflation21. Political Instability, Crime, Taxes and Regulation and Exchange rates all
influence each other although the direction of causality is unknown22.

19

In addition, we select the significance level for tests of conditional independence performed by
TETRAD. Because the algorithm performs a complex sequence of statistical tests, each at the given
significance level, the significance level is not an indication of error probabilities of the entire procedure.
Spirtes, Glymour, and Sheines (1993) after exploring several versions of the algorithm on simulated data
conclude that “in order for the method to converge to correct decisions with probability 1, the significance
level used in making decisions should decrease as the sample size increases, and the use of higher
significance levels may improve performance at small sample sizes.” For the results in this paper obtained
from samples ranging from 2659-4197 observations, we use a significance level of 0.10.
20

Figure 1 also shows that Taxes and Regulation are in turn determined by Judicial Efficiency, which is
consistent with Desai, Dyck, and Zingales (2004), who find a strong interaction between a country’s legal
system, its corporate governance mechanisms and tax revenue. They find that across a panel of countries,
the level of corporate governance in the country determines the amount of tax revenue.
21
We find the DAG analysis and the set of causal structures determined by the algorithm as being useful
for an objective selection of variables, with the heuristic interpretation that that if DAG analysis shows that
obstacle X causes obstacle Y, then firms’ reports of X as an obstacle is also likely to affect the probability
that they report of Y as an obstacle). For details refer to formal definitions.
22
We also reproduced this figure after removing country averages from each firm response which roughly
corresponds to including fixed effects in a regression setting. We still get the same split among first order
and second order effects, i.e., only Financing, Political Instability and Crime have a direct effect on firm
growth. Including GDP per capita in the DAG analysis as an additional factor does not change the main
results. Financing, Political Instability and Crime remain the only obstacles with direct effects on growth.

18


The output also shows that relations between the obstacles themselves is quite
complex and there are multiple causal relations in the DAG between the various business
environment obstacles. Since the main focus of this paper is to determine the direct
causes of growth, we do not dwell on the interactions and common causes of the different
variables and leave it for future work. Most importantly, the DAG analysis also identifies
only Financing, Crime and Political Stability as having direct effects on firm growth, as
suggested by specification 12 of Table III. As discussed in Section II, the analysis
identifies direct effects after conditioning on all subsets of the other variables. This
suggests that in regression analysis, Financing, Crime and Political Instability will always
have significant coefficients irrespective of the subsets of other obstacles included in the

regression. Thus, these are binding constraints, and policies that relax these constraints
can be expected to directly increase firm growth.

C. Binding Constraints and Firm Size and Level of Development
Next we explore if these relationships are different for firms of different sizes and
at different levels of development.23

The first three columns of Table IV include

specifications that interact the three obstacles with firm size, given by the Logarithm of
sales. The interaction term with the Financing obstacle is positive and significant at one
percent, suggesting that larger firms are less financially constrained, confirming the
findings of Beck, Demirguc-Kunt and Maksimovic (2005). The interaction terms with
Political Instability and Crime are also positive, but only significant at ten percent. Thus,

23

Note that our results remain the same if we do not control for size in the firm growth regressions in Table
III.

19


although there is also some indication that large firms are also affected less by Crime and
Political Instability, this evidence is much weaker.
We also interact the three obstacles with country income dummies, Upper Middle
Income, Lower Middle Income and Low Income as defined in Appendix A1. The
excluded category is High Income. The results indicate that all three obstacles tend to be
more constraining for middle income countries. However, the F-tests for the hypotheses
that all the entered interactions are jointly equal to zero, are rejected at the one percent

level of significance for Crime and Political Instability obstacles, but not for the
Financing obstacle. This suggests that firms in countries in all income groups are
similarly affected by the Financing obstacle.

D. Checking for Reverse Causality
So far we have identified Financing, Crime, and Political Instability as first order
constraints, significantly affecting firm growth. However, as already noted above, the
relations we observe may also be due to reverse causality if underperforming firms
systematically blame Financing, Crime and Political Instability instead of taking
responsibility for their poor performance. This is most likely to bias the Financing
obstacle results since it is easy to imagine that entrepreneurs might complain about
restricted access to external finance even in cases where access is restricted due to their
own deficiencies.
To assess the robustness of our results, we use instrumental variable (IV)
regressions to extract the exogenous component of the three obstacles. In selecting
instrumental variables for Financing, Crime and Political Instability, we use average

20


value of these obstacles for the three size groups in each country. While it is likely that
individual firms may blame the different obstacles for their poor performance, it is less
likely for all firms in a given size group to engage in such blame shifting. By
instrumenting the obstacles with the average obstacle for each size group in the country,
we are isolating the exogenous part of the possibly endogenous obstacle reports and using
that to predict growth. When we consider the obstacles at the country-size level of
aggregation, the causality is likely to run from the average obstacles to individual firms,
not vice versa.
Table V provides the results of the IV estimation. Instrumenting all three
obstacles individually, we see that their coefficients remain negative and significant. As

an additional robustness test, we also use the average value of each obstacle at the
country level as instruments. It is even less likely for the growth of individual firms to
affect obstacles at the country level.

The results remain unchanged with this

specification. Overall, these results suggest that there are exogenous components of
financing, crime and political instability obstacles that predict firm growth and the results
we obtain are not due to reverse causality.

E. Growth Opportunities
The observed association between obstacles and firm growth might occur because
firms that face higher obstacles are also those that face limited growth opportunities. This
is a form of reverse causality, and does not explain our results since we find (in
subsection D above) that our binding obstacles affect firm growth when we control for
reverse causality using instrumental variables. We cannot directly control for growth

21


opportunities at the firm level because the WBES database does not contain appropriate
firm level control variables. However, following Fisman and Love (2004), we construct
two variables to proxy growth opportunities using average industry growth and firm level
dependence on external finance. First, we include average industry growth, which is the
growth rate averaged across all firms in each industry in each country. Second, we
include the proportion of investment financed externally by each firm as an indicator of
growth opportunities.
In the regressions reported in Table VI, our specification is augmented by these
proxies for growth opportunities. Average industry growth is significant and positive in
all specifications as expected, and leaves the results unchanged. External financing of

investment does not enter the regressions significantly. When we use this proxy for
growth opportunities, only Political Instability loses significance in the last specification
where all obstacles are included.

F. Outlier Tests
We next investigate whether our results are driven by a few countries or firms. In
particular, we investigate two sets of countries: African and Transition economies.
Chandra et al. (2001) suggest that firms in African countries may exhibit different
responses than the other firms in the sample. A report by the United States General
Accounting Office, GAO-04-506 (2004) analyzes several firm level surveys on Africa,
including the WBES, and concludes that perceptions of corruption levels vary greatly for
African countries, proving a challenge for broad-based US Anticorruption Programs.
Ayyagari, Demirguc-Kunt and Maksimovic (2005) argue that Transition economies are

22


fundamentally different from other countries in their perceptions of protection of property
rights.
In the first four columns of Table VII we run our preferred specification on
different samples eliminating Transition and African countries. We find that while
Financing and Crime are binding constraints as before, Political Instability loses
significance if we do not include these countries in the sample. These results suggest that
the type of Political Instability present in Transition and African economies is particularly
damaging to firm expansion.
We also noted that high inflation rates may be responsible for the very high firm
growth rates we observe in some countries, particularly in Uzbekistan, Estonia and
Bosnia-Herzegovina. 24 However, constructing real firm growth rates and replicating all
the analyses in this paper does not change the main results.
To check whether our results are driven by specific outlier firms, we re-run

specifications 1-4 of Table VII after eliminating all firms with very high growth rates.25
The fastest growing firms, reporting growth rates in excess of 100%, are typically from
the Transition and African countries and it is conceivable that these firms achieve these
high growth rates because of political connections and are not impacted by general
business environment obstacles. Thus, the experience of these firms may differ from that
of the typical firm. We find that Financing remains the most binding constraint to firm
growth in our reduced sample, confirming that our results are not driven by the fastest
growing firms in the sample. The impact of Crime on firm growth is less robust to

24

Uzbekistan, Estonia and Bosnia-Herzegovina appear to be outlier countries in that they have average
firm growth rates above 60%, but their inflation rates are also in excess of 100%.
25
We eliminate 184 firms which have growth rates outside the range of +/- 100% (only 2 firms have
growth rates <-100%).

23


eliminating high growth rate firms, however. It is also possible that young firms in the
sample are affected differently by business environment obstacles. Excluding all firms
younger than five years old, from the sample, leaves Financing and Crime results
unchanged, while Political Instability loses significance. This suggests that political
stability is quite important to ensure growth of younger firms. 26
In unreported regressions, we also investigated whether firm ownership drives our
results. The sample includes 203 firms with government ownership. Excluding these
firms does not change our results. The sample also includes 1,340 firms with over 50
percent foreign ownership. Excluding these foreign firms from the analysis reduces the
significance of the Political Instability obstacle in some specifications. This suggests that

foreign owned firms are particularly sensitive to Political Instability. Including dummy
variables to control for government and foreign firms also leads to similar results in that
Political Instability loses significance.

G. Perception Biases
Survey responses are subject to several types of perception biases which may
affect our results. For example, some respondents may be more likely to exaggerate or
underestimate all obstacles and respond accordingly. Kaufman and Wei (1999) discuss
this possibility and construct “kvetch” variables to control for such differences in
perceptions across respondents. Following their work, we construct two kvetch variables,
Kvetch1 and Kvetch2, which are deviations of each firm’s response from the mean

26

Financing is still the main binding constraint to growth when we use robust regression analysis to control
for the presence of possible influential outliers. Robust regressions use iteratively re-weighted least squares
to estimate regression coefficients and the standard errors by underweighting influential outliers. We do not
report these results because robust regression does not country random effects.

24


country response to two general survey questions. Kvetch1 uses the responses to the
question “How helpful do you find the central government today towards businesses like
yours?” and Kvetch2 is constructed using the responses to “How predictable are changes
in economic and financial policies?” Since higher values correspond to unfavorable
responses, positive deviations from the country mean indicate pessimism whereas
negative deviations indicate optimism. As reported in Table VIII, controlling for such
differences in perceptions leave the results unchanged.27
Throughout the analysis we use ordinal variables as the obstacles take the value

one to four and make the assumption that a one unit change in the variable has a similar
value regardless of the initial value of the obstacle. Finally, for the Financing variable we
relax that assumption and construct a dummy variable for each value of the obstacle,
entering the three that correspond to values 2-4 instead of the ordinal Financing variable
in our preferred specification. The results indicate that those firms that complain the
most are the ones that grow significantly slower.

H. Individual Financing Obstacles
Our results indicate that Financing is one of the most important obstacles that
directly constrain firm growth. We would like to get a better understanding of exactly
what type of obstacles related to financing are constraining firm growth. Fortunately, our
survey data also includes more detailed questions regarding the Financing obstacles.
27

Another type of perception bias can be due to the “Halo effect” which occurs when survey respondents
respond more favorably to questions about richer countries, as explained in Glaeser, La Porta, Lopez de
Silanes and Shleifer (2004). This type of perception bias is likely to be more problematic in the case of
country level expert surveys rather than firm level surveys. However, even at the country level, Kaufmann,
Kraay and Mastruzzi (2005) argue that for this to be a significant issue, the correlation between the
perception error and income should be very high and the variance of the error should also be large relative
to the variance of the indicator being measured.

25