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

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.

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 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 http://econ.worldbank.org _ *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 For example, Batra, Kaufmann, and Stone (2003), Dollar, Hallward-Driemeier, and Mengistae (2004) and Carlin, Fries, Schaffer, and Seabright (2001) (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 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 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) 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) 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 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 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 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 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 (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 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 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 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 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 leverage of firms that have borrowed foreign currency denominated debt, constraining their ability to obtain new equity or to adjust their capital structure 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 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 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 Firm Growth Firm Growth Firm Growth Firm Growth -0.046*** [0.011] 0.002* [0.001] -0.038*** [0.011] 0.001 [0.001] Firm Growth Political Instability*Upper Middle Political Instability*Lower Middle Political Instability*Low Income Street Crime Street Crime*Size Firm Growth -0.059** [0.026] -0.059** [0.024] -0.009 [0.025] Street Crime*Upper Middle Street Crime*Lower Middle Street Crime*Low Income N 6235 6133 NCountries 79 79 R2 (within) 0.0051 0.0023 R2 (between) 0.0583 0.0707 R2 (all) 0.0076 0.0043 F-Test of Interactions 0.0016 0.068 *, **, and *** indicate significance levels of 10, 5, and percent respectively 5964 79 0.0033 0.1091 0.0074 0.0889 5778 78 0.0089 0.1331 0.0141 0.0025 45 6235 79 0.0044 0.0461 0.0078 0.1898 6133 79 0.0038 0.0475 0.0068 0.0072 Firm Growth -0.009 [0.022] Firm Growth -0.042 [0.026] -0.03 [0.024] -0.011 [0.026] -0.02 [0.023] -0.023 [0.026] -0.053** [0.025] 0.025 [0.027] 5964 79 0.0064 0.0284 0.0066 0.0004 -0.005 [0.027] -0.027 [0.025] 0.035 [0.027] 5778 78 0.0115 0.0084 0.0011 0.0017 Table V: Robustness Test-Instrumental Variables Two stage instrumental variable regressions are used The first stage regression is Financing (or Political Instability or Street Crime) = α + γ1 GDP/capita + γ Firm Size + γ (Average value of) Financing (or Political Instability or Street Crime) The second stage regression equation estimated is: Firm Growth = α + β1 GDP/capita + β2 Firm Size+ β3 Financing (predicted value from first stage) + β4Political Instability (predicted value from first stage) + β5 Street Crime (predicted value from first stage) The variables are described as follows: Firm Growth is the percentage increase in firm sales over the past three years GDP/capita is log of real GDP per capita in US$ Firm Size is the Log of Sales Financing, Political Instability, and Street Crime are general obstacles as indicated in the firm questionnaire They take values to 4, with where indicates no obstacle and indicates major obstacle In specifications (1)-(3), the Average value of the general obstacle is calculated by averaging the obstacle across country and firm size categories and in specifications (4)-(6), the Average value of the general obstacle is calculated by averaging the obstacle across countries Specifications (1) and (4) instrument the Financing obstacle with its average value, specifications (2) and (5) instrument the Political Instability obstacle with its average value and specifications (3) and (6) instrument the Street Crime obstacle with its average value Each specification reports the adjusted R-squares from the first stage regression and the p-values of the F-test for the instruments used Detailed variable definitions and sources are given in the appendix Constant GDP/capita Firm Size Financing Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth 0.304*** 0.380*** 0.343*** 0.359*** 0.387*** 0.318*** [0.061] [0.070] [0.076] [0.072] [0.077] [0.081] 0.013*** 0.011** 0.002 0.011** 0.010* 0.004 [0.005] [0.005] [0.006] [0.005] [0.005] [0.007] -0.006*** -0.006*** -0.003*** -0.006*** -0.006*** -0.003*** [0.001] [0.001] [0.001] [0.001] [0.001] [0.001] -0.070*** -0.084*** [0.014] Political Instability [0.017] -0.091*** -0.093*** [0.014] Street Crime [0.016] -0.073*** -0.066*** [0.013] First Stage Adj R2 (Financing) 0.191 First Stage Adj R2 (Political Instability) 0.2813 First Stage Adj R2 (Street Crime) F-Test of Instruments [0.015] 0.1559 0.2552 0.2955 0 N 6235 6133 5964 *, **, and *** indicate significance levels of 10, 5, and percent respectively 0.2703 0 6235 6133 5964 46 Table VI: Robustness Test-Controlling for Growth Opportunities The regression equation estimated is: Firm Growth = α + β1 GDP/capita + β2 Size+ β3 Financing + β4Political Instability + β5 Street Crime + β6 Average Sector Growth/External Finance The variables are described as follows: Firm Growth is the percentage increase in firm sales over the past three years GDP/capita is log of real GDP per capita in US$ Firm Size is the Log of firm sales Two proxies for growth opportunities are used: Average Sector Growth is the growth rate averaged across all firms in each sector in each industry and External Finance stands for the proportion of investment financed externally Financing, Political Instability, and Street are general obstacles as indicated in the firm questionnaire They take values to 4, with where indicates no obstacle and indicates major obstacle In specifications (1)-(3) and (5)-(7), Financing, Political Instability and Street Crime are entered individually In specification (4) and (8), they are entered together All regressions are estimated using country random effects Detailed variable definitions and sources are given in the appendix Constant GDP/Capita Firm Size Average Sector Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth 0.085* 0.052 0.100* 0.186*** 0.023 -0.022 0.058 0.239* [0.051] [0.052] [0.053] [0.061] [0.128] [0.131] [0.134] [0.141] -0.001 -0.005 -0.006 0.032** 0.033** 0.025 0.017 [0.006] [0.006] [0.006] [0.006] [0.016] [0.016] [0.017] [0.017] 0 0 -0.003 -0.002 -0.002 -0.002 [0.001] [0.001] [0.001] [0.001] [0.002] [0.002] [0.002] [0.002] 0.974*** 0.996*** 0.983*** 0.962*** [0.037] [0.038] [0.038] [0.039] -0.008 -0.013 -0.02 -0.01 [0.025] [0.025] [0.025] External Finance Financing -0.026*** [0.007] Political Instability NCountries R2 (within) R2 (between) R2 (all) -0.039*** [0.007] [0.009] [0.026] -0.035*** [0.009] -0.018** -0.007 -0.026*** -0.011 [0.007] [0.008] [0.010] [0.010] Street Crime N -0.022*** -0.025*** -0.020*** -0.038*** -0.033*** [0.007] [0.007] [0.009] [0.010] 4081 5814 5714 5546 5369 4251 4217 4150 79 79 79 78 58 58 58 58 0.0404 0.0385 0.0385 0.0403 0.0038 0.001 0.0039 0.0081 0.9709 0.9697 0.972 0.9644 0.1359 0.1327 0.0977 0.1158 0.1133 0.0142 0.011 0.0116 0.0172 0.1141 0.1176 0.114 *, **, and *** indicate significance levels of 10, 5, and percent respectively 47 Table VII: Robustness Test-Varying Samples The regression equation estimated is: Firm Growth = α + β1 GDP/capita + β2 Size+ β3 Financing + β4Political Instability + β5 Street Crime The variables are described as follows: Firm Growth is the percentage increase in firm sales over the past three years GDP/capita is log of real GDP per capita in US$ Firm Size is the Log of firm sales Financing, Political Instability, and Street are general obstacles as indicated in the firm questionnaire They take values to 4, with where indicates no obstacle and indicates major obstacle Specifications (1) to (4) exclude certain countries from the full sample of firms while specifications (5) to (9) exclude the countries from a reduced sample which does not include firms reporting very high(/low) growth rates (>+/-100%) All regressions are estimated using country random effects Standard errors reported are adjusted for clustering at the country level Detailed variable definitions and sources are given in the appendix High Growth Firms Included High Growth Firms Excluded Countries Excluded Countries Excluded Uzbekistan, African Bosnia & and Herzegovina, Transition Transition African Estonia Economies Economies economies Firm Firm Firm Firm Growth Growth Growth Growth Constant 0.350*** 0.177 0.148 0.225** [0.092] [0.118] [0.101] [0.092] GDP/Capita -0.012 0.016 0.013 0.001 [0.010] [0.014] [0.011] [0.011] Firm Size -0.001 -0.001 0.002 [0.002] [0.002] [0.002] [0.002] Financing -0.013** -0.027*** -0.020*** -0.019*** [0.006] [0.007] [0.006] [0.006] Political Instability -0.013* -0.009 -0.011 -0.009 [0.007] [0.008] [0.008] [0.007] Street Crime -0.017** -0.026*** -0.019*** -0.020*** [0.007] [0.007] [0.007] [0.007] N 3224 5230 2682 5532 NCountries 54 62 38 75 R2 (within) 0.0039 0.0066 0.0092 0.0044 R2 (between) 0.1953 0.1233 0.1921 0.0726 R2 (all) 0.0122 0.0147 0.0195 0.0055 *, **, and *** indicate significance levels of 10, 5, and percent respectively None Firm Growth 0.136* [0.072] 0.005 [0.009] 0.004*** [0.001] -0.018*** [0.005] -0.017*** [0.005] -0.008 [0.005] 5631 78 0.0065 0.1866 0.0226 48 Transition Economies Firm Growth 0.271*** [0.074] -0.004 [0.008] 0.001 [0.002] -0.017*** [0.006] -0.015** [0.006] -0.018*** [0.006] 3202 54 0.0083 0.1739 0.0195 African economies Firm Growth 0.01 [0.090] 0.022** [0.011] 0.002 [0.001] -0.021*** [0.005] -0.015*** [0.006] -0.007 [0.005] 5107 62 0.0076 0.2144 0.0248 African and Transition Economies Firm Growth 0.142 [0.101] 0.012 [0.011] [0.002] -0.022*** [0.006] -0.012* [0.007] -0.019*** [0.007] 2678 38 0.0114 0.1919 0.0233 Uzbekistan, Bosnia & Herzegovina, Estonia Firm Growth 0.108 [0.071] 0.006 [0.009] 0.004*** [0.001] -0.016*** [0.005] -0.017*** [0.005] -0.009* [0.005] 5421 75 0.0063 0.2475 0.0262 Table VIII: Robustness Test-Dealing with Perception Biases The regression equation estimated is: Firm Growth = α + β1 GDP/capita + β2 Size+ β3 Financing + β4Political Instability + β5 Street Crime + β6 Kvetch1/Kvetch2 The variables are described as follows: Firm Growth is the percentage increase in firm sales over the past three years GDP/capita is log of real GDP per capita in US$ Firm Size is the Log of firm sales Financing, Political Instability, and Street are general obstacles as indicated in the firm questionnaire They take values to 4, with where indicates no obstacle and indicates major obstacle Kvetch1 is the deviation of each firm’s response from the mean response for the country to the question “How helpful you find the central/national government today towards businesses like yours” Kvetch2 is the deviation of each firm’s response form the mean response for the country to the question “How predictable are changes in economic and financial policies” In specifications (1)-(3) and (5)-(7), Financing, Political Instability and Street Crime are entered individually In specification (4) and (8), they are entered together along with the other obstacles All regressions are estimated using country random effects Standard errors reported are adjusted for clustering at the country level Detailed variable definitions and sources are given in the appendix Constant GDP/Capita Firm Size Kvetch1 Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth 0.249** 0.210** 0.249** 0.383*** 0.112 0.067 0.11 0.313** [0.106] [0.104] [0.108] [0.111] [0.137] [0.141] [0.142] [0.147] -0.001 0.001 -0.003 -0.002 -0.001 -0.001 -0.001 [0.013] [0.013] [0.014] [0.002] [0.002] [0.002] [0.002] [0.002] 0 -0.006 0.02 0.02 0.016 0.007 [0.002] [0.002] [0.002] [0.014] [0.017] [0.018] [0.018] [0.018] -0.014** -0.012* -0.015** -0.012* [0.007] [0.007] [0.007] [0.007] -0.014** -0.011* -0.014** -0.01 [0.007] [0.007] [0.007] [0.007] Kvetch2 Financing -0.034*** -0.028*** -0.041*** [0.007] [0.007] [0.008] Political Instability [0.008] -0.028*** -0.016* -0.030*** -0.016 [0.008] [0.008] [0.009] [0.010] Street Crime N -0.036*** -0.032*** -0.025*** -0.038*** -0.031*** [0.007] [0.008] [0.008] [0.009] 4876 6030 5935 5777 5602 5241 5161 4992 79 79 79 78 60 60 60 59 0.0045 0.0024 0.0036 0.0071 0.0056 0.0026 0.0046 0.0098 0.0402 0.0876 0.0784 0.1027 R2 (all) 0.0062 0.0054 0.0068 0.0113 *, **, and *** indicate significance levels of 10, 5, and percent respectively 0.1101 0.0953 0.1159 0.1187 0.0132 0.0093 0.0117 0.0171 Ncountries R2 (within) R2 (between) Table IX: Firm Growth-Impact of Individual Financing Obstacles The regression equation estimated is: Firm Growth = α + β1 GDP/capita + β2 Size+ β3 Collateral + β4Paperwork + β5 High Interest Rates + β6 Special Connections+ β7 Lack money to lend+ β8 Access to foreign banks + β9 Access to non-bank equity+ β10 Export finance + β11 Lease finance + β12 Credit + β13 Long Term Loans + β14 (Residual) The variables are described as follows: Firm Growth is the percentage increase in firm sales over the past three years GDP/capita is log of real GDP per capita in US$ Firm Size is the Log of Sales Collateral, Paperwork, High Interest Rates, Special Connections, Lack money to lend, Access to foreign banks, Access to non-bank equity, Export finance, Lease finance, Credit, Long Term Loans are individual financing obstacles as indicated in the firm questionnaire They take values to 4, with where indicates no obstacle and indicates major obstacle In specifications (1) to (11), each of the obstacle variables is included individually Residual is the residual from a regression of the General Financing Obstacle on all the individual financing obstacles Specification 13 includes Collateral, Paperwork, High Interest Rates, Special Connections, Lack money to lend, and Lease Finance Specifications 12 and 14 include the Financing Residual In specifications 1-14, the first row represents the parameter estimate of the obstacle, the second row reports robust standard errors and the third row reports the economic impact of the obstacle All regressions are estimated using country random effects Standard errors reported are adjusted for clustering at the country level Detailed variable definitions and sources are given in the appendix Constant GDP/Capita Firm Size Collateral Paperwork High Interest Rates Special Connections Lack money to lend Lease Finance 10 11 12 13 14 Firm Growth 0.206* [0.108] 0.001 [0.014] 0.000 [0.002] -0.024*** [0.007] -0.060 Firm Growth 0.182* [0.104] 0.003 [0.013] 0.000 [0.002] Firm Growth 0.264** [0.108] -0.002 [0.014] 0.000 [0.002] Firm Growth 0.182* [0.100] 0.000 [0.013] 0.000 [0.002] Firm Growth 0.218** [0.104] -0.001 [0.013] -0.001 [0.002] Firm Growth 0.184 [0.112] 0.002 [0.014] -0.001 [0.002] Firm Growth 0.160 [0.107] 0.001 [0.014] 0.000 [0.002] Firm Growth 0.178 [0.113] 0.000 [0.014] -0.001 [0.002] Firm Growth 0.106 [0.112] 0.005 [0.014] 0.000 [0.002] Firm Growth 0.137 [0.116] 0.002 [0.015] 0.000 [0.002] Firm Growth -0.025 [0.139] 0.026 [0.018] -0.002 [0.002] Firm Growth -0.077 [0.148] 0.029* [0.016] -0.002 [0.002] Firm Growth 0.289*** [0.130] -0.002 [0.002] 0.001 [0.012] -0.003 [0.012] -0.007 -0.007 [0.009] -0.017 -0.020** [0.010] -0.065 -0.006 [0.009] -0.013 -0.006 [0.011] -0.013 -0.004 [0.013] -0.008 Firm Growth 0.08 [0.178] 0.024 [0.017] -0.002 [0.002] -0.009 [0.012] -0.022 -0.016 [0.010] -0.040 -0.015 [0.012] -0.048 -0.006 [0.013] -0.013 -0.009 [0.013] -0.019 0.014 [0.015] 0.029 -0.026*** [0.009] -0.065 -0.032*** [0.010] -0.103 -0.017** [0.007] -0.037 -0.026*** [0.008] -0.055 -0.015** [0.009] -0.031 -0.003 [0.007] -0.006 Access to foreign banks 50 10 11 12 13 14 Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth -0.006 [0.008] -0.013 Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth Firm Growth 4579 78 0.0027 0.0629 0.0089 -0.025** [0.011] 2988 58 0.0052 0.1364 0.0136 Access to non-bank equity 0.004 [0.008] 0.008 Export Finance 0.001 [0.007] 0.002 Credit -0.009 [0.008] -0.024 Long Term Loans Financing Residual N NCountries R2 (within) R2(between) R2 (all) 6024 79 0.0022 0.0119 0.0034 6133 79 0.0023 0.0140 0.0028 6298 79 0.0030 0.0056 0.0033 6002 79 0.0010 0.0289 0.0015 5808 79 0.0021 0.0796 0.0064 5076 78 0.0006 0.0161 0.0025 5093 78 0.0000 0.0078 0.0018 *, **, and *** indicate significance levels of 10, 5, and percent respectively 51 5037 78 0.0000 0.0331 0.0028 4440 78 0.0000 0.0010 0.0006 5332 78 0.0000 0.0010 0.0018 5030 60 0.0000 0.0983 0.0086 -0.024** [0.011] 2988 58 0.0010 0.1098 0.0075 Table X: High Interest Rates-Impact of Individual Financing Obstacles The regression equation estimated is: High Interest Rates = α + β1 GDP/capita + β2 Size+ β3 Collateral + β4Paperwork + β5 Special Connections+ β6 Lack money to lend+ β7 Access to foreign banks + β8 Access to non-bank equity+ β9 Export finance + β10 Lease finance + β11 Credit + β12Long Term Loans The variables are described as follows: GDP/capita is log of real GDP per capita in US$ Firm Size is the Log of Sales Collateral, Paperwork, High Interest Rates, Special Connections, Lack money to lend, Access to foreign banks, Access to non-bank equity, Export finance, Lease finance, Credit, Long Term Loans are individual financing obstacles as indicated in the firm questionnaire They take values to 4, with where indicates no obstacle and indicates major obstacle In specifications (1) to (10), each of the obstacle variables is included individually Specification (11) is the full model The regressions are estimated using ordered probit with clustering at the country level Detailed variable definitions and sources are given in the appendix GDP/Capita Firm Size Collateral Paperwork High Interest Rates -0.119* [0.065] -0.008 [0.007] 0.461*** [0.035] Special Connections Lack money to lend Lease finance Access to foreign banks High Interest Rates -0.140** [0.059] -0.007 [0.007] 0.514*** [0.036] High Interest Rates -0.108 [0.066] -0.01 [0.006] 0.478*** [0.037] High Interest Rates -0.068 [0.074] -0.005 [0.006] 0.352*** [0.031] High Interest Rates -0.151*** [0.048] -0.004 [0.007] 0.323*** [0.023] Access to non-bank equity High Interest Rates -0.155*** [0.047] -0.003 [0.006] 0.282*** [0.021] Export finance High Interest Rates -0.168*** [0.049] -0.003 [0.006] 0.286*** [0.024] Credit High Interest Rates -0.162*** [0.047] -0.006 [0.006] 0.303*** [0.026] Long term Loans N 5933 6036 5921 5727 5003 0.0943 0.099 0.0836 0.0595 0.0601 Pseudo R2 *, **, and *** indicate significance levels of 10, 5, and percent respectively 5024 0.054 4970 0.0543 52 4383 0.0598 High Interest Rates -0.156*** [0.048] -0.007 [0.006] 0.296*** [0.022] 5252 0.0555 10 High Interest Rates -0.036 [0.084] -0.005 [0.007] 0.442*** [0.040] 4958 0.0981 11 High Interest Rates -0.150*** [0.056] -0.008 [0.007] 0.215*** [0.038] 0.244*** [0.037] 0.117*** [0.030] 0.076** [0.035] 0.043 [0.034] -0.006 [0.033] -0.012 [0.039] 0.034 [0.040] 0.013 [0.033] 0.168*** [0.038] 2998 0.1681 Appendix A1: Variable Definitions and Sources Variable Definition Source Firm Growth Estimate of the firm's sales growth over the past three years World Business Environment Survey Firm Size Dummies A firm is defined as small if it has between and 50 employees, medium size if it has between 51 and 500 employees and large if it has more than 500 employees World Business Environment Survey GDP/Capita Real GDP per capita in US$, average 1995-99 World Development Indicators Financing How problematic is financing for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Political Instability How problematic is political instability for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Street Crime How problematic is street crime for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Inflation How problematic is inflation for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Exchange Rates How problematic is exchange rates for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Judicial Efficiency How problematic is functioning of the judiciary for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Corruption How problematic is corruption for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Business Environment Obstacles Anti-Competitive Behavior How problematic are taxes and regulation for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? How problematic is anti-competitive behavior by other enterprises or the government for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Infrastructure How problematic is infrastructure for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey How problematic are collateral requirements of banks and financial institutions for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? World Business Environment Survey Taxes and Regulation World Business Environment Survey World Business Environment Survey Individual Financing Obstacles Collateral 53 Variable Definition Paperwork How problematic is paperwork for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? High Interest Rates How problematic are high interest rates for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Special Connections How problematic are the need for special connections with banks, for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Lack money to lend How problematic is, banks lacking money to lend, for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Access to foreign banks How problematic is lack of access to foreign banks for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Access to non-bank equity How problematic is lack of access to equity partners for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Lease Finance How problematic is lack of access to lease finance for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Export Finance How problematic is lack of access to export finance for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Credit How problematic, is inadequate credit information on customers, for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Long Term Loans How problematic is lack of access to long term bank loans for the operation and growth of your business: no obstacle (1), a minor obstacle (2), a moderate obstacle (3) or a major obstacle (4)? Source World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey World Business Environment Survey Other Firm Level Variables External Finance Share of investment finance coming from external sources calculated as (1-proportion of firm’s fixed investment that has been financed from internal funds) This is the same as proportion of the firm’s fixed investment that has been financed from equity + local commercial banks + investment funds + foreign banks + family/friends + Money lenders and informal sources + Supplier Credit + Leasing Arrangement + State + Other Sources World Business Environment Survey World Business Environment Survey Average Sector Growth Kvetch1 Kvetch2 Growth rate of firms averaged across each sector in each country This is the deviation of each firm’s response from the country mean to the question “How helpful you find the central./national government today towards businesses like yours-(1) Very helpful (2) Mildly helpful (3) Neutral (4) Mildly unhelpful (5) Very unhelpful (6) Don’t know?” This is the deviation of each firm’s response from the country mean to the question “How predictable are changes in economic and financial policies-(1) Completely predictable (2) Highly predictable (3) Fairly predictable (4) Fairly unpredictable (5) Highly unpredictable (6) Completely unpredictable (7) Don’t know” 54 World Business Environment Survey World Business Environment Survey Appendix A2 : Preliminaries on Directed Acyclic Graphs Directed Acyclic Graphs (DAGs) help us in model selection especially in the absence of well defined theory that can point to regressors that need to be included in a multivariate linear regression The model selected by DAGs can then be submitted to a standard regression analysis for parameter estimation The DAGs themselves provide a compact representation of joint probability distributions with the nodes of the graphs representing the random variables and the (lack of) edges connecting the nodes representing conditional independence assumptions We describe below the assumptions behind linking probability dependence/independence relations to causal inference and illustrate how the software program TETRAD produces a causal pattern from raw data and conclude with a specific example of how supplementing regression analysis with DAGs can be useful and provide more accurate results A directed acyclic graph (DAG) is a picture or a path diagram representing causal flow between or among a set of variables For example, given a set of three vertices: {X1, X2, X3}, and a set of two edges among these vertices: {X1 Æ X2, X2 Æ X3}, the corresponding DAG would be: X1Æ X2 Æ X3 For the above DAG to be ascribed causal inference, we need the Causal Markov Condition Formally, the Causal Markov Condition states that for a variable X and any set of variables Y that does not include the effects of X, X is probabilistically independent of Y conditional on the direct causes of X The intuition behind the Causal Markov assumption is that each variable is independent of all other variables that are not its effects, conditional on its immediate causes So the above DAG implies that X3 is independent of X1 conditional on X2 (In graph theory, the equivalent of the Causal Markov Condition is referred to as d-separation Pearl (1988)) The Causal Markov Condition would also assert that if X and Y are related only as effects of a common cause Z, then X and Y are probabilistically independent conditional on Z How TETRAD Works The intuition behind discovering a causal pattern from observational data is that, under the Causal Markov condition, observed patterns of statistical independence limit the number of possible causal graphs compatible with observed data Consider again the above example with variables X1, X2 and X3, where, say, we observe from the data that X1 and X3 are independent conditioning on X2 This observation implies that the causal graph X1ÆX2ÅX3 is incompatible with the data, since if X1 and X3 were both causes of X2, then conditioning on X2 would render X1 and X3 statistically dependent The causal graphs that are compatible with the observed independence pattern include the one we saw before X1ÆX2ÆX3 as well as X1ÅX2ÅX3 and X1ÅX2ÆX3 Software tools such as TETRAD take observed data, either in raw form or as correlations (and the independence conditions they embody) as input, and use algorithms to search for all compatible graphs The number of graphs are significantly reduced, (maybe to even one) with added assumptions based on prior theory or knowledge of temporal order of the variables (in our case, we assumed all the business environment variables cause growth) or axiomatic assumptions such as: (a) Faithfulness (or Stability): Assuming that a population is Faithful is to assume that whatever independencies occur in it arise not from incredible coincidence but rather from structure If there are any independence relations in the population that are not a consequence of the Causal Markov condition, then the population is unfaithful For instance, if in the above example we had {X1 Æ X2, X2 Æ X3 and X1 Æ X3}, applying the Causal Markov Condition gives no independence relations However, by coincidence X1 could be independent of X3 (X1 has a negative direct effect on X3 but X1 has a positive effect on X2 which has a positive effect on X3 If the direct and indirect effects of X1 on X3 exactly cancel each other, then there will be no association between X1 and X3) In such a case, the population is said to be unfaithful to the causal graph that generated it (b) Causal Sufficiency: Causal Sufficiency is satisfied if we have measured all the common causes of the measured variables An Example To illustrate the algorithm and the link between graphs and probability conditions, consider the possible interrelations between Firm Growth and three reported obstacles, Financing, Taxes and Regulation, and Judicial Efficiency The undirected graph below shows all possible relations between these variables An edge or path between two variables indicates that the two variables may be dependent Taxes and Regulation Financing Growth Judicial Efficiency The TETRAD search algorithm begins by assuming that all variables in the model are dependent, corresponding to the graph shown above It then checks for conditional independence relations between the variables and depending on the relations found in the data, the edges between the variables are oriented Knowledge of temporal precedence (for example that the three obstacles affect growth and not the other way around) allows for limiting the number of tests for conditional independence For the purpose of this example, we will not impose any temporal ordering Suppose the following independence relations (and no other) are found in the data at a certain significance level: {Taxes & Regulation} and {Judicial Efficiency} are independent {Growth}and{Taxes & Regulation} are independent conditional on the value of Financing {Growth} and {Judicial Efficiency} are independent conditional on the value of Financing Note that these are the only exhaustive set of independence relations found in the data and all other dependencies assumed originally remain Under the assumption of the Causal Markov Condition and Faithfulness, the above independence relations imply the following: 56 (1) (2) (3) Equation (1) implies that there is no edge connecting {Taxes & Regulation} and {Judicial Efficiency} Equation (2) implies that there is no edge between Growth and Taxes and any dependence between them is only through Financing The following patterns would be consistent with equation (2): (i) Taxes & RegulationÅFinancingÆGrowth OR (ii) Taxes & RegulationÅFinancingÅGrowth OR (iii) Taxes & RegulationÆFinancingÆ Growth The pattern that is inconsistent with equation (2) is Taxes & RegulationÆFinancingÅ Growth since it violates the Causal Markov Condition Similarly Equation (3) implies that there is no edge between Growth and Judicial Efficiency and any dependence between them is only through Financing The following patterns are consistent with Equation (3): (iv) Judicial EfficiencyÅFinancingÆGrowth OR (v) Judicial EfficiencyÅFinancingÅGrowth OR (vi) Judicial EfficiencyÆFinancingÆ Growth Again, the pattern that is inconsistent with equation (3) is Judicial EfficiencyÆFinancingÅ Growth since it violates the Causal Markov Condition Further, since (1)-(3) are the only set of independence relations found in the data, we know that none of the following patterns are possible between {Taxes & Regulation} and {Judicial Efficiency} with respect to a third variable since they all imply that {Taxes & Regulation} and {Judicial Efficiency} are independent conditional on Financing (or Growth), which is not one of the independence relations found in the data (vii) Taxes& RegulationÅFinancingÅJudicial Efficiency OR (viii) Taxes& RegulationÅFinancingÆJudicial Efficiency OR (ix) Taxes& RegulationÆFinancingÆJudicial Efficiency OR (x) Taxes& RegulationÅGrowthÅJudicial Efficiency OR (xi) Taxes& RegulationÅGrowthÆJudicial Efficiency OR (xii) Taxes& RegulationÆGrowthÆJudicial Efficiency Working only with the set of compatible patterns implied by the independence relations ((i) to (vi), we have the following candidate graphs: 57 Case (a): Combination of (i) and (iv) Taxes & Regulation Financing Judicial Efficiency Growth Case (a) is not possible since it encompasses the incompatible case of (viii) Case (b): Combination of (i) and (vi) Taxes & Regulation Financing Growth Judicial Efficiency Case (b) is not possible since it encompasses the incompatible case of (vii) Case (c): Combination of (ii) and (v) Taxes & Regulation Financing Growth Judicial Efficiency Case (c) is not possible since it encompasses the incompatible case of (viii) Case (d): Combination of (iii) and (iv) Taxes & Regulation Financing Growth Judicial Efficiency Case (d) is not possible since it encompasses the incompatible case of (ix) Case (e): Combination of (iii) and (vi) Taxes & Regulation Financing Growth Judicial Efficiency Case (e) is the only combination that works and doesn’t include any of the incompatible cases in (vii) to (xii) Thus from the conditional independence relations found in the data, Tetrad has identified that Financing has a direct effect on Growth while Taxes and Regulation and Judicial Efficiency are indirect effects in that they affect Growth only through Financing 58 DAGs versus Regression Analysis The strengths of Directed Acyclic Graph (DAG) methodology over regression analysis lies in its ability (i) to distinguish genuine from spurious correlations in a set of data (ii) to identify which variables need to be included in a model to accurately measure one variable’s effect on another and (iii) to differentiate between direct and indirect effects of different variables When two variables are correlated, it could mean one of four things: random variation or one variable causes the other or they have a prior common cause or that we have conditioned on a common effect of the two variables (which induces a correlation between the two causes) While identifying the source of correlation is not possible in an ordinary least squares regression analysis, it is possible through the Directed Acyclic Graph methodology that relies on the concept of the Markov Condition (or d-separation) to identify which variable is a true cause or predictor of the outcome variable To illustrate this, consider the following example from Spirtes et al (2000) where we have the following linear structure (unknown to us) between the outcome variable Y and regressors X1, X2, X3, X4, and X5 where X3 and Y share a common unmeasured cause Z : X1 X2 X3 Z X4 Y X5 The same can be represented by the following set of equations: Y = a1X1 + a2X5 + a3Z + eY (A1) X1 = a4X2 + a5X4 + e1 (A2) X3 = a6X2 + a7Z + e3 (A3) A linear multiple regression of Y on the X variables will give all variables in the set {X1, X2, X3, X5} non-zero coefficients, even though X2 has no direct influence on Y and X3 has no direct/indirect influence on Y Linear regression takes a variable Xi to influence Y provided the partial correlation of Xi and Y controlling for all of the other X variables does not vanish However, this is a sufficient condition only when there is no X variable that has a common unmeasured cause with Y (such as X3 in the example above) or is an effect of Y Indeed, regressing Y on all subsets of the X’X matrix would show that X2 and X4 will not be significant for some combination(s) of other X variables entered as regressors However, application of the PC algorithm correctly selects {X1, X5} as the variables that directly influence Y Again, comparing to the regression analysis, these are the only two variables that will have significant coefficients in regressions of Y on X, regardless of which X variables are entered as regressors Further the Directed Acyclic Graph is also able to point that X3 and Y may be driven by a common cause and that X2 and X4 indirectly affect Y though their effect on X1 Thus, the Directed Acyclic Graph points to a possible pattern of causal influences between the different variables and hence can be very useful as a starting point in model selection 59 [...]... of one to four, increasing in the severity of obstacles Table IX reports the regressions that parallel those in Table III, but this time focusing on specific financing obstacles In addition to the individual financing obstacles, we also include a residual, the component of the general financing obstacle not explained by the individual obstacles The results indicate that not all financing obstacles reported... 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. .. 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... 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... 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... 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... 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... 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... 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... biases in survey responses do not alter our main results Further investigation of the Financing obstacles reveals the importance of high interest rates in constraining firm growth This result highlights the importance of macroeconomic policies in influencing growth at the firm level as indicated by the correlation between high interest rates and banks’ lack of money to lend variables Furthermore, the ... Yang, 2003) 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... 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. .. component of the general financing obstacle not explained by the individual obstacles The results indicate that not all financing obstacles reported by firms are constraining Only the coefficients of