How Important Are Financing Constraints?

Transcription

1 How Important Are Financing Constraints? The role of finance in the business environment Meghana Ayyagari Asli Demirgüç-Kunt Vojislav Maksimovic* August, 2005 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 all 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 (DAG) 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,, Business Environment JEL Classification: D21, G30, O12 *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 Daron Acemoglu, Gerard Caprio, Stijn Claessens, Patrick Honohan, Leora Klapper, Aart Kraay, Norman Loayza, David Mckenzie, Lant Pritchett, Dani Rodrik, L. Alan Winters and seminar participants at George Washington University for their suggestions and comments. This paper s findings, interpretations, and conclusions are entirely those of the authors and do not necessarily represent the views of the World Bank, its Executive Directors, or the countries they represent. 1

2 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 an 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. s 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. 1 For example, Batra, Kaufmann, and Stone (2003), Dollar, Hallward-Driemeier, and Mengistae (2004) and Carlin, Fries, Schaffer, and Seabright (2001). 2

3 In this paper we examine which features of the business environment directly affect firm growth. We use evidence from the World Business Environment Survey (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. 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

4 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. 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 4

5 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 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 firm s 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. 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. 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

6 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 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 6

7 Regulation (taxes, regulations, anticompetitive practices), Infrastructure (quality and availability of roads, electricity, water, telephone, postal service etc.), Corruption (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-de- Silanes, 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 6 Several other papers using the same survey have analyzed specific financing obstacles. Beck, Demirguc- Kunt, 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

8 outages affect firm performance in Bangladesh, China, India and Pakistan. Using similar data for African countries, Eifert, Gelb, and Ramachandran (2005) show that business 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. s 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 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

9 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 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 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

10 directly affect growth and obstacles that may be correlated with firm growth but affect it only indirectly. 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 10

11 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 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

12 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 WBES 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. 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 information on firms is limited, but in addition to employment, the survey also gives information on sales growth, industry and ownership. 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

13 Insert Table I here 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. is the sales growth rate for individual firms averaged over all sampled firms in each country. 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. Insert Table II here s 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

14 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 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. and Reported Obstacles A. Obtaining the Binding Constraints 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

15 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: = α + β 1 Obstacle + β 2 GDP/capita + β 3 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 β 1 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. Insert Table III here Table III results show that when we analyze individual obstacles separately, most are significantly related to firm growth. The only exceptions are Exchange Rate, Anti- Competitive 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

16 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

17 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

18 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 4197 firms consisting of correlations between firm growth and the ten business environment obstacles 19 Insert Figure 1 here 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 safety 20. Crime is directly affected by the Corruption and Judicial Efficiency obstacles and Political Instability is affected by Inflation 21. Political Instability, Crime, Taxes and Regulation and Exchange rates all influence each other although the direction of causality is unknown 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 observations, we use a significance level of 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 18

19 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 3. 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 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, 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. 23 Note that our results remain the same if we do not control for size in the firm growth regressions in Table III. 19

20 although there is also some indication that large firms are also affected less by Crime and Political Instability, this evidence is much weaker. Insert Table IV here We also interact the three obstacles with country income dummies, Upper Middle Income, Lower Middle Income and Low Income as defined in the data Appendix. 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 20

21 instrumental variables for Financing, Crime and Political Instability, we use average 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. Insert Table V here 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. 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 21

22 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 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. Insert Table VI here 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 (2004) analyzes several firm level surveys on Africa, 22

23 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 fundamentally different from other countries in their perceptions of protection of property rights. Insert Table VII here 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 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

24 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 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 1340 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 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

25 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 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 Insert Table VIII here 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. 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