Beck 2008 - Finance, Firm Size, And Growth

THORSTEN BECKASLI DEMIRGUC-KUNTLUC LAEVENROSS LEVINE

Finance, Firm Size, and Growth

Although research shows that financial development accelerates aggregateeconomic growth, economists have not resolved conflicting theoretical pre-dictions and ongoing policy disputes about the cross-firm distributional ef-fects of financial development. Using cross-industry, cross-country data, theresults are consistent with the view that financial development exerts a dispro-portionately positive effect on small firms. These results have implicationsfor understanding the political economy of financial sector reform.

JEL codes: G2, L11, L25, O1Keywords: firm size, financial development, economic growth.

ALTHOUGH RESEARCH SHOWS that financial development ac-celerates economic growth (Levine 2006), economists have not resolved conflictingtheoretical predictions about the distributional effects of financial development. Some

We thank Pok-Sang Lam (the editor) and two anonymous referees for comments that improved thepaper. We also thank Maria Carkovic, Stijn Claessens, Bill Easterly, Alan Gelb, Krishna Kumar, MichaelLemmon, Karl Lins, Alan Winters, and seminar participants at the World Bank, University of Minnesota,New York University, University of North Carolina, the University of Stockholm, Tufts University, and theUniversity of Utah for helpful comments on earlier drafts of the paper, and Ying Lin for excellent researchassistance. We also thank Lori Bowan at the U.S. Census Bureau for help with the U.S. Economic Censusdata on firm size distribution. This paper was partly written while the third author was at the World Bank.This papers findings, interpretations, and conclusions are entirely those of the authors and do not representthe views of the International Monetary Fund, the World Bank, their Executive Directors, or the countriesthey represent.

THORSTEN BECK is a Senior Economist, Development Research Group, World Bank(E-mail: [email protected]). ASLI DEMIRGUC-KUNT is a Senior Research Manager,Development Research Group, World Bank (E-mail: [email protected]). LUCLAEVEN is a Senior Economist, Research Department, International Monetary Fund, Centrefor Economic Policy Research (CEPR), European Corporate Governance Institute (ECGI)(E-mail: [email protected]). ROSS LEVINE is the James and Merryl Tisch Professor of Eco-nomics, Department of Economics, Brown University, National Bureau of Economic Research(NBER) (E-mail: Ross [email protected]).Received October 26, 2006; and accepted in revised form February 19, 2008.Journal of Money, Credit and Banking, Vol. 40, No. 7 (October 2008)C 2008 International Monetary Fund with Exclusive License to Printby The Ohio State University

1380 : MONEY, CREDIT AND BANKING

theories imply that financial development disproportionately helps small firms. Ifsmall firms find it more difficult to access financial services due to greater informa-tion and transaction costs, then financial development that ameliorates these frictionswill exert an especially positive impact on small firms (Cestone and White 2003,Galor and Zeira 1993). In contrast, if fixed costs prevent small firms from accessingfinancial services, then improvements in financial services will disproportionatelyhelp large firms (Greenwood and Jovanovic 1990, Haber, Razo, and Maurer 2003).In this paper, we seek to provide empirical evidence to help resolve this debate.

Besides assessing theoretical disputes, policy considerations motivate our study ofthe distributional effects of financial development. For example, if financial devel-opment helps small firms more than large ones, then even if financial developmenthelps all firms, large firms might oppose reforms that diminish their comparativepower.1 Rather than analyzing political lobbying by firms, we examine the more ba-sic question of whether financial development has distributional effects. In addition,governments and development agencies spend billions of dollars per year subsidizingsmall firms, with the expressed goals of stimulating growth, reducing poverty, andencouraging entrepreneurship. Research, however, suggests that (i) subsidizing smallfirms does not have these beneficial effects (Beck, Demirguc-Kunt, and Levine 2005),while (ii) improving the financial system accelerates growth and alleviates poverty(Levine 2006, Beck, Demirguc-Kunt, and Levine 2007). In this paper, we test whetherfinancial development exerts a disproportionately positive effect on small firms.

We examine whether industries that have a larger share of small firms for tech-nological reasons grow faster in economies with well-developed financial systems.As formulated by Coase (1937), firms should internalize some activities, but size en-hances complexity and coordination costs. Thus, an industrys technological firmsize depends on that industrys particular production processes, including capitalintensities and scale economies. After computing an estimate of each industrys tech-nological share of small firms, we use a sample of 44 countries and 36 industries inthe manufacturing sector to examine the growth rates of different industries acrosscountries with different levels of financial development. If small-firm industriesindustries naturally composed of small firms for technological reasonsgrow fasterthan large-firm industries in economies with more developed financial systems,this suggests that financial development boosts the growth of small-firm industriesmore than large-firm industries. In contrast, we might find that financial develop-ment disproportionately boosts the growth of large-firm industries or that financialdevelopment fosters balanced growth.2

1. A large literature examines the political economy of financial policies, e.g., Kroszner and Stratmann(1998), Kroszner and Strahan (1999), Rajan and Zingales (2003), Pagano and Volpin (2005), and Perottiand von Thadden (2006).

2. Besides the argument that financial development disproportionately helps large firms because smallfirms are cut off from financial development, Petersen and Rajan (1994, 1995) show that local bankingmonopolies foster close ties between banks and small firms that ease credit constraints. Therefore, financialdevelopment that intensifies competition and loosens these ties might hurt small firms. On a global scale,Gozzi, Levine, and Schmukler (2008) show that when financial development lowers barriers to firmsaccessing international capital markets, it has predominantly helped large firms.

THORSTEN BECK ET AL. : 1381

More specifically, we use a difference-in-differences approach to examine whetherfinancial development enhances economic growth by easing constraints on industriesthat are technologically more dependent on small firms. We first measure an in-dustrys technological composition of small firms relative to large firms as theshare of employment in firms with less than 20 employees in the United Statesin 1992. Assuming that financial markets are relatively frictionless in the UnitedStates, we therefore identify each industrys technological share of small firmsin a relatively frictionless financial system. Then, we extensively test the validityof this benchmark measure of technological Small Firm Share by (i) using datafrom the United States in 1958 to compute Small Firm Share, (ii) measuring SmallFirm Share at different stages of the U.S. business cycle, (iii) computing techno-logical Small Firm Share from different countries, and (iv) defining small firmsdifferently.

The results indicate that small-firm industries grow disproportionately faster ineconomies with well-developed financial systems. This does not imply that financialdevelopment slows the growth of large firms. Rather, financial development exerts aparticularly positive growth effect on small-firm industries. Furthermore, our analysessuggest that large-firm industries are not the same as industries that rely heavily on ex-ternal finance. Rajan and Zingales (1998) show that industries that are technologicallymore dependent on external finance grow disproportionately faster in economies withbetter developed financial systems. When controlling for cross-industry differencesin external dependence, we continue to find that financial development dispropor-tionately accelerates the growth of industries that are composed of small firms fortechnological reasons.

We also show that the level of financial development affects industrial composi-tion. In countries with greater financial development, small-firm industries represent agreater proportion of total manufacturing value added than in countries with lower lev-els of financial development. Thus, financial development disproportionately boostsboth the growth rate of small-firm industries and the level of value added contributedby small-firm industries to total value added.

The results also provide information regarding which particular characteristics ofsmall-firm industries account for their greater sensitivity to financial development.One possibility is that small firms are more informationally opaque than large firms,so that financial improvements that lower the marginal costs of acquiring informationdisproportionately facilitate the flow of capital to small firms. Another possibilityis that small firms rely more on intangible assets, so that financial innovations thatreduce the need for collateral ease credit constraints on small firms more than largeones. A different possibility is that the results are spurious and arise only becausesmall-firm industries enjoyed greater growth opportunities than large-firm industriesover the sample period. From this perspective, financially more developed economieswere simply better at exploiting these growth opportunities that happened to be con-centrated in small-firm industries. If these potential characteristics of small-firm in-dustries are driving the results, then our findings should vanish when we control forthem.


The results indicate that financial development still exerts a disproportionatelypositive impact on small-firm industries even when controlling for cross-industrydifferences in informational opacity, asset intangibility, industry concentration, andgrowth prospects. This suggests that financial development affects small-firm indus-tries beyond opacity, collateral, and growth prospects. Although we do not have directmeasures of firms access to financial services, these findings are consistent with theview that financial development makes it affordable for more small firms to purchasefinancial services. Accordingly, the results suggest that financial development influ-ences the extensive margin by allowing new small firms to access financial servicesas well as facilitating the intensive margin by improving financial services for thosealready using the financial system.3

Our paper complements recent empirical work on finance and firm size. Threeinfluential papers examine individual countries or regions. Guiso, Sapienza, andZingales (2004) find that financial development helps small firms more than largefirms in Italy. Cetorelli and Strahan (2006) find that uncompetitive local bankingmarkets in the United States represent a barrier to the entry of new firms becausethe new firms have difficulty accessing credit. Kumar, Rajan, and Zingales (2001)assess the impact of different country and industry characteristics on industry sizedistribution across 15 European countries. Our work builds on this research. Ratherthan focusing on one country or region, or one characteristic of financial developmentsuch as competition, we examine a broad cross section of countries and test whetheroverall financial development influences small-firm industries differently from large-firm industries. Thus, we do not examine whether financial reforms influence thedistribution of firms in a country because (i) there are very limited cross-country dataon the distribution of firm sizes and (ii) theory stresses the link between financialmarket imperfections and small firms, not necessarily the link between finance andentire distribution of firm sizes in an economy. We instead examine whether industriesthat are naturally composed of small firms for technological reasons perform betterin countries with well-developed financial systems. Our research also complementsthat work by Beck, Demirguc-Kunt, and Maksimovic (2005), who use survey datato assess the relationship between the financing obstacles that firms report they faceand firm growth. They find that the negative impact of reported obstacles on firmgrowth is stronger for small firms than large firms and stronger in countries withunderdeveloped financial systems.4 Their study has the advantage of using cross-country, firm-level data, but it has the disadvantage of relying on survey responses

3. Although Beck, Demirguc-Kunt, and Maksimovic (Forthcoming) show that small firms finance ahigher percentage of investment with external finance in countries with stronger property rights protection,we do not have direct evidence on fixed costs or on whether a higher proportion of small firms accessesfinancial services in more financially developed economies. Thus, we can only draw the cautious conclusionthat the results are consistent with the view that financial development lowers the fixed costs of accessingfinancial services with disproportionately positive ramifications on small firms.

4. Beck, Demirguc-Kunt, and Maksimovic (2006) find that financial development reduces constraintson firms choosing their optimal sizes.


regarding the obstacles that firms encounter. We use a different methodology thatassesses whether industries that are naturally composed of small firms grow fasterin countries with better-developed financial systems. Our research provides comple-mentary information on whether financial development fosters aggregate growth bydisproportionately facilitating the growth of small-firm industries.

1. DATA

To assess whether (i) financial development boosts the growth of small-firm indus-tries more than large-firm industries and whether (ii) financial development booststhe level of output accounted for by small-firm industries, we construct a new cross-country, cross-industry database. We compile data on (i) the relative size and growthrates of each industry across countries, (ii) each industrys technological firm size,and (iii) country-level indicators of financial development. This section describesthese key variables. Furthermore, in robustness tests presented below, we construct,define, and use additional information on industry and country traits. The data cover44 countries and 36 industries in the manufacturing sector. Tables 1 and 2 presentdescriptive and summary statistics.

1.1 Industry Growth Rates and SharesGrowthi,k equals the average annual growth rate of real value added of industry k

in country i over the period 1980 to 1990. The data are from the Industrial StatisticsYearbook database. When we extend the measurement period to 2000, the sample isreduced by one-third because of missing observations for several countries and indus-tries. Nevertheless, we demonstrate the robustness of these findings to (i) expandingthe estimation period from 198090 to 19802000 and (ii) examining Growthi,k overthe period 19902000.

Industry Sharei,k is the share of industry k in total manufacturing value added ofcountry i. Thus, besides testing whether financial development has differential ef-fects on the growth rate of large- and small-firm industries by examining Growthi,k,we also examine whether financial development shapes the level of industrial out-put patterns by examining Industry Sharei,k. Specifically, we test whether a coun-trys level of financial development shapes the cross-sectional distribution of in-dustries by increasing the proportion of value added accounted for by small-firmindustries.

Although we examine Industry Sharei,k, we focus on Growthi,k for two reasons.First, building on Rajan and Zingales (1998), a large literature examines the rela-tionship between financial development and industry growth. This provides a naturalframework for our analyses and facilitates comparisons, so that we identify an inde-pendent relationship between financial development and the growth rates of small-firm


TABLE 1FIRM SIZE DISTRIBUTION IN THE UNITED STATES IN 1992

ISIC Industry name S5 S10 S20 S100

311 Food manufacturing 0.56 1.68 3.82 13.77313 Beverage industries 0.60 1.76 4.04 14.75314 Tobacco manufactures 0.09 0.20 0.30 1.49321 Manufacture of textiles 0.40 1.17 2.81 13.43322 Manufacture of wearing apparel, except footwear 1.30 3.60 8.18 31.74323 Manufacture of leather and products of leather 1.94 4.78 10.45 36.89324 Manufacture of footwear 0.31 0.81 1.61 7.40331 Manufacture of wood and wood and cork products 4.20 11.20 21.37 47.31332 Manufacture of furniture and fixtures 1.57 4.19 9.09 28.74341 Manufacture of paper and paper products 3.03 16.16342 Printing, publishing, and allied industries 3.64 9.16 16.32 35.80352 Manufacture of other chemical products 0.87 2.68 5.80 17.67353 Petroleum refineries 0.05 0.18 0.36 1.90354 Manufacture of miscellaneous products of petroleum and coal 1.26 3.93 9.26 29.80355 Manufacture of rubber products 0.38 1.21 3.15 13.23356 Manufacture of plastic products not elsewhere classified 0.69 2.24 6.09 27.19361 Manufacture of pottery, china, and earthenware 2.30 4.91 8.80 26.52362 Manufacture of glass and glass products 1.15 2.82 5.05 13.92369 Manufacture of other nonmetallic mineral products 1.87 5.88 14.17 40.78371 Iron and steel basic industries 0.20 0.59 1.62 8.05372 Nonferrous metal basic industries 0.50 1.78 4.76 18.65381 Manufacture of fabricated metal products 1.28 4.07 9.98 33.87382 Manufacture of machinery except electrical 2.15 6.37 13.68 34.60383 Manufacture of electrical machinery apparatus, and appliances 0.50 1.48 3.44 14.18384 Manufacture of transport equipment 0.18 0.54 1.21 4.20385 Manufacture of professional and scientific equipment 0.68 1.87 4.01 12.88390 Other Manufacturing Industries 3.54 8.72 16.95 43.483211 Spinning, weaving, and finishing textiles 0.26 0.73 1.91 9.143411 Manufacture of pulp, paper, and paperboard 0.14 1.293511 Manufacture of basic industrial chemicals except fertilizers 0.29 0.89 1.75 6.513513 Manufacture of synthetic resins, plastic materials, and fibers 0.11 0.31 0.66 3.173522 Manufacture of drugs and medicines 0.26 0.86 2.10 8.093825 Manufacture of office, computing, and accounting machinery 0.48 1.32 2.85 10.433832 Manufacture of radio, television, and communication equipment 0.57 1.40 3.09 11.673841 Ship building and repairing 1.73 3.58 6.56 16.353843 Manufacture of motor vehicles 0.32 1.00 2.28 8.04Average 1.07 2.88 5.85 18.42

NOTES: This table shows employment shares by firm size bin in the United States by ISIC Revision 2 industries. Sx is the industrys shareof employment by firms with less than x employees, and is calculated using data from the U.S. Census on all U.S. firms for the year 1992.Employment shares are expressed in percentages of total number of employees.

industries relative to large-firm industries above and beyond the effects established bypast work. Second, focusing on growth links helps link our paper to an extensive bodyof theoretical and empirical work on the finance-growth relationship. As reviewedby Levine (2006), many theoretical models predict that a higher level of financialdevelopment will induce a faster rate of economic growth, not just an increase in thelevel of economic development. Thus, a higher level of financial development mightexert a disproportionately positive effect on the growth rate of particular types ofindustries, such as industries naturally composed of small firms facing high informa-tional asymmetries. This further motivates our focus on Growthi,k. Moreover, all ofthe results are confirmed with Industry Sharei,k.


1.2 Measure of Small Firm ShareWe construct measures of each industrys natural or technological share of small

firms based on an extensive body of research on the theory of the firm. As discussed, forexample, by Coase (1937) and Sutton (1991), differences in productive technologiesinfluence an industrys technological firm size. To get a proxy measure of each indus-trys natural or technological share of small firms, therefore, we need a benchmark

TABLE 2SUMMARY STATISTICS

Variable Mean Median St. dev. Minimum Maximum

Panel A. Country-industry variablesGrowth in real value added 0.034 0.029 0.099 0.447 1.000Industry share in value added 0.016 0.009 0.021 0.000 0.224Panel B. Industry variablesSmall Firm Share (empl


TABLE 2CONTINUED

Small Firm External Sales RatingShare dependence growth Intangibility splits R2

Panel E. Cross-industry correlation of industry characteristicsExternal dependence 0.16

(0.38)Sales growth 0.16 0.76

(0.39) (0.00)Intangibility 0.41 0.12 0.34

(0.02) (0.51) (0.05)Rating splits 0.24 0.09 0.10 0.19

(0.21) (0.66) (0.61) (0.33)R2 0.03 0.14 0.20 0.21 0.06

(0.87) (0.44) (0.27) (0.25) (0.75)Concentration 0.57 0.06 0.18 0.18 0.13 0.28

(0.00) (0.75) (0.32) (0.32) (0.50) (0.13)NOTES: This table reports summary statistics and correlations for the main variables in our analysis. Country-industry variables: Growthin real value added is average growth in real value added over the period 198090 by country and ISIC industry. Industry share in valueadded is the industrys share in total value added of the countrys manufacturing sector in 1980. Industry variables: Small firm share is theindustrys share of employment by firms with less than 20 employees, and is calculated using data from the U.S. Census on all U.S. firmsfor the year 1992. Small firm share (empl


only has relatively frictionless financial markets; it has relatively frictionless marketsin general. Again, the United States is a reasonable initial benchmark. The UnitedStates has the full spectrum of human capital skills. Furthermore, comparative studiesof U.S. and European labor markets suggest that the United States has many fewerpolicy distortions. Moreover, the U.S. internal market is huge andgiven its sizeitis comparatively open to international trade. Many studies also point to the UnitedStates as having a superior contracting environment and well-developed institutions(Barth, Caprio, and Levine 2006).

The empirical methodology does not require that the United States has perfectfinancial markets, labor markets, contracting systems, or institutions. Rather, we re-quire that policy distortions and market imperfections in the United States do notdistort the ranking of industries in terms of the technological share of small firmswithin each industry. Thus, we begin with the following benchmark measure of eachindustrys technological share of small firms.

Small Firm Sharek equals industry ks share of employment in firms with lessthan 20 employees in the United States and is obtained from the 1992 Census. Wemeasure Small Firm Share in 1992 because the U.S. Census did not start collectingcomprehensive firm size distribution data at the firm level until 1992. For a less refinedcategorization of firms by employment size, the data extend back to 1958. We confirmthe findings with the 1958 data. In our baseline regressions, we use Small Firm Shareas the measure of each industrys natural or technological share of small firms.

Table 1 lists the Small Firm Share for each industry in the sample. The SmallFirm Share has a mean of 6% but varies widely from 0.1% in manufacturing of pulp,paper, and paperboard to 21% in wood manufacturing.5 We omit three industries withfewer than 10 firms for each size bucket (Tobacco (ISIC 314), Petroleum refineries(ISIC 353), and Pulp and paper (ISIC 3411)) because the low number of observationsmay impede an accurate estimate of the natural Small Firm Share. Nevertheless, thepapers findings hold when including these three industries.

Below, we present a large battery of sensitivity analyses of the benchmark mea-sure of Small Firm Share. We use different measures of Small Firm Share, differentbenchmark years from the United States, different benchmark countries, and differ-ent cutoffs for the definition of a small firm. We also control for numerous indus-try traits, including asset tangibility and opacity, sales growth, and dependence onexternal finance. We further condition on country characteristics, including the level ofeconomic development, labor market frictions, market size, and barriers to firm entry.

1.3 Indicator of Financial DevelopmentIdeally, one would like indicators of the degree to which the financial system

ameliorates information and transactions frictions and facilitates the mobilization

5. Note that the share of small firms among all firms in the United States is substantially higher thanfor the subset of listed enterprises. From the Compustat database, the share of small listed manufacturingfirms is only 0.009%. Thus, it is not useful to only consider listed firms to assess which firm characteristicsdrive the results.


and efficient allocation of capital. Specifically, we would like indicators that capturethe effectiveness with which financial systems research firms and identify profitableprojects, exert corporate control, facilitate risk management, mobilize savings, andease transactions. Unfortunately, no such measures are available across countries.Consequently, we rely on a traditional measure of financial development that existingwork shows are robustly related to economic growth.

Private Crediti equals the value of credits by financial intermediaries to the privatesector divided by GDP for country i. It captures the amount of credit channeled throughfinancial intermediaries to the private sector. Levine, Loayza, and Beck (2000) showthat Private Credit is a good predictor of economic growth. In our baseline regression,we measure Private Credit in the initial year of our estimation period, 1980 (or thefirst year in which data are available), to control for reverse causation. Since usinginitial values instead of average values implies an informational loss, we also confirmthe robustness of the results when using Private Credit averaged over the full period198089 and employing instrumental variables to control for endogeneity. Data forPrivate Credit are from Beck, Demirguc-Kunt, and Levine (2000). There is widevariation in Private Credit, ranging from 7% in Bangladesh to 117% in Japan. Below,we define and use several alternative indicators of financial development, includinga measure of stock market development.

2. METHODOLOGY

To examine whether industries that are naturally composed of small firms growfaster than large-firm industries in countries with higher levels of financial devel-opment, we interact an industry characteristiceach industrys technological SmallFirm Sharewith a country-characteristicthe level of financial development. Indescribing the econometrics, we only discuss the interaction between financial de-velopment and Small Firm Share. In the actual implementation, we control for manyinteractions between country and industry characteristics.

Consider the following regression:

Growthi,k =

ii Countryi +

kk Industryk + Industry Sharei,k

+ (Small Firm Sharek FDi) + i,k,where Growthi,k is the average annual growth rate of value added, in industry kand country i, over the period 1980 to 1990. Countryi and Industryk are countryand industry dummies, respectively, and Industry Sharei,k is the share of industry kin manufacturing in country i in 1980. Small Firm Sharek is the benchmark shareof small firms in industry k, which in our baseline specification equals the shareof employment in firms with less than 20 employees in the United States in 1992.FDi is an indicator of financial development for country i, which equals PrivateCredit in our baseline regression. We include the interaction between the share ofsmall firms in an industry and financial development. We do not include financial


development on its own, since we focus on within-country, within-industry growthrates. The dummy variables for industries and countries control for country- andindustry-specific characteristics that might determine industry growth patterns. Wethus isolate the effect that the interaction of Small Firm Share and Private Credithas on industry growth relative to country and industry means. By including theinitial share of an industry we control for a convergence effect: industries with alarge share might grow more slowly, suggesting a negative sign on . We includethe share in manufacturing rather than the level since we focus on within-country,within-industry growth rates. We exclude the United States (the benchmark country)from the regressions.

The focus of our analyses is on the interaction between financial development andSmall Firm Share; i.e., we focus on the sign and significance of . If is positive andsignificant, this suggests financial development exerts a disproportionately positiveeffect on small-firm industries relative to large-firm industries. This would suggestthat financial development tends to ease growth constraints on small firms more thanon large firms.

We conduct the regression analyses under alternative assumptions to assess thevalidity of the results. In the baseline regressions, we use ordinary least squares (OLS),which assumes that the error term is uncorrelated across both industries and countries.We then relax these restrictions and allow first for correlation across observations fromthe same industry and second for correlations across observations from the samecountry. We thus present standard errors based on clustering at both the industry- andcountry-level in Table 3.6 For simplicity, we do not report standard errors based onindustry- and country-clustering in the rest of the paper. However, all of the papersfindings are robust to clustering at the industry and country level and these results areavailable on request.

3. RESULTS, EXTENSIONS, AND SENSITIVITY TESTS

3.1 Main ResultsThe results presented in Table 3 indicate that small-firm industries (industries

with technologically larger shares of small firms) grow faster in economies withbetter-developed financial intermediaries. The interaction of Private Credit with SmallFirm Share enters positively and significantly at the 5% level in column (1). We alsofind that the coefficient on Industry Share enters negatively and significantly, suggest-ing some convergence in industrial composition. The results indicate that industrieswhose organization is based more on small firms than on large firms grow faster incountries with better-developed financial intermediaries.

6. Two-way clustering, at the country-industry level, is infeasible in this papers econometric specifi-cation where (i) there are only country-industry observations, (ii) country and industry fixed effects areestimated, and (iii) the number of clusters is small, with 42 countries and 33 industries. Under these condi-tions, the asymptotic justification for the robustness of clustered standard errors does not hold (Cameron,Gelbach, and Miller 2006).

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Given the influential findings of Rajan and Zingales (1998), we were concernedthat there might be a large, negative correlation between industries that are naturallyheavy users of external finance and industries that are naturally composed of smallfirms. If this were the case, then it would be difficult to distinguish between the findingthat externally dependent industries grow faster in economies with well-developedfinancial systems and our result that small-firm industries grow faster in economieswith well-developed financial systems. While there is a negative correlation betweenSmall Firm Share and External Dependence, it is very small (0.16) and insignificantas shown in Table 2, Panel E. This suggests that the industry characteristics explainingfirm size distribution are not the same as the characteristics explaining technologicaldependence on external finance, and that the firm size channel we have identified isdifferent from the external financial dependence channel.

The column (2) regression of Table 3 demonstrates the robust link between financialdevelopment, Small Firm Share, and industry growth when controlling for externaldependence. As shown in column (2), the interaction between each industrys levelof external dependence and financial development (Private Credit External De-pendence) enters positively and significantly. This indicates that industries that arenaturally heavy users of external finance grow faster in economies with higher levelsof financial development.

Moreover, column (2) shows that the interaction between each industrys techno-logical Small Firm Share and financial development (Private Credit Small FirmShare) enters positively and significantly when controlling for external dependence.Thus, we find that industries with technologically larger shares of small firms growmore quickly in countries with higher levels of financial development even whencontrolling for cross-industry differences in external dependence. In unreported re-gressions, we also tested whether the interaction between Private Credit and SmallFirm Share varies across industries with different degrees of external dependence.The triple interaction term does not enter significantly and the interaction of PrivateCredit with Small Firm Share continues to enter significantly and positively. This re-sult suggests that small firms consistently face high financing constraints, irrespectiveof whether they are in an industry with a naturally high or low demand for externalfinance.

The relationship between financial development, an industrys Small Firm Share,and industry growth is not only statistically, but also economically large. To illustratethe effect, we compare the growth of an industry with a relatively large share ofsmall firms and an industry with a relatively low share of small firms across twocountries with different levels of financial development. The last row in Table 3 (andsubsequent tables) shows the growth difference between industries at the 25th and75th percentiles of the Small Firm Share and countries at the 25th and 75th percentilesof Private Credit. Take the example of column (2). The estimation suggests that thefurniture industry (75th percentile of Small Firm Share) should grow 1.4% per annumfaster than the spinning industry (25th percentile of Small Firm Share) in Canada (75thpercentile of Private Credit) than in India (25th percentile of Private Credit). Sincethe average growth rate in our sample is 3.4%, this is a relatively large effect.


To assess the robustness of the results, we relax assumptions concerning the dis-tribution of the error term in the estimation equation. First, industry-specific shocksacross all countries would invalidate the standard OLS assumption of independenterrors. Thus, in column (3), we cluster at the industry level; i.e., we allow error termsto be correlated within industries but not across industries. As shown, this does notchange the results. Second, country-specific shocks across all industries within acountry would also invalidate the standard OLS assumption of independent errors.Thus, column (4) presents a regression with clustering at the country-level; i.e., weallow errors to be correlated within countries but not across countries. While thesignificance of the Small Firm Share-Private Credit interaction term decreases, thecoefficient remains significant at the 7% level.7

We were also concerned that including industries that provide very little valueadded in countries could bias the results. Consequently, we excluded industries belowthe median share of value added for each country. These results are presented incolumn (5) of Table 3. With this subsample, financial development continues to exerta particularly large impact on small-firm industries.

3.2 Controlling for Different Country and Industry CharacteristicsIn this subsection, we control for additional country and industry traits. If finan-

cial development simply proxies for other country characteristics that interact withindustry firm size to shape cross-industry growth rates, we might draw inappropriateinferences about the independent impact of the financial system on cross-industrygrowth rates unless we control for these other country characteristics. Similarly, byomitting key industry traits from the analyses, we might inappropriately interpret theresults as relating to the natural firm size of industries rather than to other industrytraits correlated with firm size. Thus, we control for numerous country and industrytraits to gauge the robustness of the findings.

Based on a large and growing literature, we control for an array of country traits.First, we control for the interaction of Small Firm Share with GDP per capita sincefinancial development might simply reflect overall development, as measured by GDPper capita, and not something particular about the financial system. If this is the caseand overall development exerts a particularly beneficial effect on small firms, thenwe will draw inappropriate inferences about the impact of financial development onthe growth of small-firm industries if we do not control for GDP per capita.8 Second,industries that depend on relatively large firms may grow faster in economies with

7. The relationship between financial development and industry growth is robust to controlling forreverse causality by using legal origin and other historic and geographic characteristics of each countryas instrumental variables, and when correcting the standard errors for clustering at the industry or countrylevels.

8. We also included a proxy for educational attainment and its interaction with Small Firm Share.A more educated population might be more conducive to the growth of industries composed of smaller(or larger) firms since technical, entrepreneurial, and managerial skills influence industrial organizationand growth. Adding this additional term did not change the results on the interaction between financialdevelopment and Small Firm Share and did not enter independently significantly.


larger markets that allow them to exploit economies of scale more fully (Braun andRaddatz 2008). To test this, we include a proxy for market size: openness to interna-tional trade, which is measured as exports plus imports divided by GDP. Furthermore,using the size of the economy (GDP) as a proxy for market size rather than the tradeyields the same results. Third, financial market frictions might be highly correlatedwith regulatory impediments to labor mobility and new firm formation. If this is thecase, we might inappropriately interpret the results as applying to finance when theyreally apply to other frictions. For instance, Klapper, Laeven, and Rajan (2006) findthat new firms are disproportionately hurt by regulatory impediments to labor mo-bility and high entry barriers. We therefore control for an interaction of Small FirmShare with Entry Regulation, which is the cost of registering an enterprise relativeto GDP in 1999 (Djankov et al. 2002). Table 2 provides summary statistics on thesecountry indicators.

Since a parallel literature examines how different industry traits affect cross-industry growth rates across countries, we also use this research to inform our robust-ness tests. First, if (i) financial development has a disproportionately positive effecton industries with good growth opportunities (Fisman and Love 2007) and (ii) small-firm industries just happened to enjoy good growth opportunities over the sampleperiod, then we might erroneously infer that financial development exerts an espe-cially positive impact on small firms. We therefore control for Sales Growth, whichis calculated as real annual growth in net sales of U.S. firms over the period 1980 to1989 using data from Compustat. Second, if (i) small firms rely heavily on intangibleassets and (ii) strong private property rights are closely associated with financial de-velopment, then our findings may simply be confirming the results in Claessens andLaeven (2003). We therefore control for the interaction of Property Rights with thepercentage of intangible assets in each industry, computed as the ratio of intangibleassets to fixed assets of U.S. firms over the period 1980 to 1989 using data fromCompustat. Third, differences in informational asymmetries might account for finan-cial developments disproportionate influence on small-firm industries. To test this,we use two measures of the informational opacity of industries. First, Rating Splitsmeasures disagreement between the two major bond rating agenciesMoodys andS&Pabout the risk of U.S. firms, based on the bond ratings of almost 8,000 firmsduring the period 198393 (Morgan 2002). Greater disagreement suggests greateropacity. The second measure of informational opacity comes from Durnev, Morck,and Yeung (2004), who compute the degree to which individual stock prices movewith average stock prices in an industry based on an R2 measure of synchronic-ity, with higher R2greater synchronicityas an indication that investors have amore difficult time discerning firm-specific differences. Fourth, the Small Firm Sharemight simply proxy for the degree of industry concentration and we therefore con-trol for the interaction of Private Credit with the four-firm concentration ratio basedon U.S. Census data. Table 2 provides summary statistics and partial correlationsamong these industry characteristics. Small Firm Share is significantly correlatedonly with the share of intangible assets (positively) and with industry concentration(negatively).


After controlling for all of these country and industry characteristics, we con-tinue to find that financial development disproportionately boosts the growth rate ofsmall-firm industries. The interaction of Private Credit with Small Firm Share enterspositively and significantly at the 1% level in column (6) of Table 3. The interac-tion between Private Credit and External dependence also enters significantly.9 Onlytwo other interaction terms enter significantly. The interaction between Small FirmShare and Entry Regulation enters negatively and significantly. This suggests thatregulations that impede entry are particularly harmful to industries that are naturallycomposed of small firms for technological reasons. Furthermore, the interaction be-tween Intangibility and Property Rights enters positively and significantly, indicatingthat industries that are naturally characterized by a high proportion of intangible as-sets grow relatively faster in countries with comparatively well-functioning propertyrights systems.

These results indicate that (i) Small Firm Share does not only reflect other industrycharacteristics and (ii) Private Credit does not simply reflect other national traits.Rather, we find an independent relationship between financial development and therelative growth rates of industries that are naturally composed of smaller firms fortechnological reasons. The robustness of Small Firm Share indirectly suggests thatfinancial development operates at the extensive margin by allowing new small firmsto access growth-enhancing financial services.

3.3 Alternative Definitions of a Small FirmTable 4 indicates that the results are robust to using alternative definitions of a

small firm below 20 employees. We use four different cutoffs to define a small firm:5, 10, and 100 employees, respectively.10 Table 1 lists Small Firm Share for thedifferent definitions of a small firm, where Sx in the table indicates the industrysshare of employment by firms with less than x workers. There is a high correlationamong the different measures of Small Firm Share, and the average correlation is 91%(Table 2, Panel D). Not surprisingly, the correlation decreases with higher thresholdmeasures of firm size. The correlation between S5 and S10 is 99%, but 87% betweenS5 and S100. Nevertheless, using different cutoffs provides additional robustnesstests and more fully characterizes the relationship between cross-industry firm size,

9. As shown, the size of the coefficient on the interaction between Small Firm Share and PrivateCredit does not change much when including all of the industry and country control variables. Rather thanincluding all of the control variables simultaneously as reported in regression (6), we also included themone at a time. When we only include the two interaction terms of Private Credit with External dependenceand Entry barriers with Small Firm Share, we drive the coefficient on the interaction between Private Creditand Small Firm Share from 0.54 in regression 6 to 0.27, but it remains significant at the 5% level. Theabsolute value of the coefficient on the interaction term between Entry barriers and Small Firm Share alsofalls, from 0.76 in regression (6) to 0.63. While this suggests a relation between entry barriers andfinancial development, the regression results demonstrate that both financial impediments on firms andnonfinancial barriers to firm entry have independent, negative effects on small-firm industries.

10. Two industries (Manufacture of paper and paper products, and Manufacture of pulp, paper, andpaperboard) drop from the sample due to missing U.S. Census data when using 5 or 10 employees as thecutoff.


TABL

E4

ALT

ERN

ATI

VE

MEA

SURE

SO

FFI

RM

SIZE

DIS

TRIB

UTI

ON

(1)(2)

(3)(4)

(5)(6)

(7)(8)

Indu

stry

shar

ein

val

uead

ded

1.1

59

1.1

64

1.1

48

1.1

28

1.1

06

1.1

73

1.1

06

1.1

27

(0.29

2)(0.

293)

(0.28

2)(0.

281)

(0.27

9)(0.

303)

(0.27

4)(0.

278)

Priv

ate

Cred

it

Exte

rnal

finan

cial

depe

nden

ce0.

174

0.

174

0.

166

0.

164

0.

161

0.

182

0.

152

0.

159

(0.

044)

(0.04

4)(0.

043)

(0.04

3)(0.

043)

(0.04

7)(0.

042)

(0.04

3)Pr

ivat

eCr

edit

Sm

allfi

rmsh

are

(empl

Beck 2008 - Finance, Firm Size, And Growth

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Transcript of Beck 2008 - Finance, Firm Size, And Growth