Property Rights Institutions and Firm Performance: A Cross-Country Analysis

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World Development Vol. 39, No. 4, pp. 648–661, 2011 Ó 2010 Elsevier Ltd. All rights reserved 0305-750X/$ - see front matter www.elsevier.com/locate/worlddev

doi:10.1016/j.worlddev.2010.09.009

Property Rights Institutions and Firm Performance: A Cross-Country Analysis MAHMUT YASAR The University of Texas at Arlington, Arlington, TX, USA CATHERINE J. MORRISON PAUL University of California, Davis, CA, USA

and MICHAEL R. WARD * The University of Texas at Arlington, TX, USA Summary. — This paper empirically examines the link between ﬁrms’ performance and institutional quality using data for ﬁrms in 52 countries. We control for potential endogeneity by instruments alternatively based on legal origin and “related-ﬁrm” institutions, and by a “bribery” variable. We also use two indicators of institutional quality: ﬁrms’ expectations that their contractual and property rights will be protected by the legal system, and a property rights index from The Heritage Foundation. We incorporate ﬁrm-speciﬁc characteristics including industry type and international linkages. We ﬁnd signiﬁcantly positive relationships between ﬁrms’ performance and perceived property rights protection independent of other observable ﬁrm characteristics. Ó 2010 Elsevier Ltd. All rights reserved. Key words — legal enforcement, property rights, institutions, ﬁrm performance, productivity, cross-country analysis

“Institutions are the humanly devised constraints that structure human interaction. They are made up of formal constraints (e.g., rules, laws, constitutions), informal constraints (e.g., norms of behavior, conventions, self-imposed codes of conduct), and their enforcement characteristics. Together they deﬁne the incentive structure of societies and speciﬁcally economies. Institutions and the technology employed determine the transaction and transformation costs that add up to the costs of production.” North (1994).

the security of property rights as one of the “three essential prerequisites of market economies.” In accordance with these predictions about the importance of institutions for economic development, recent macroeconomic studies have examined the role of institutional structure in international comparisons of economic performance using cross-country data. Some of these studies have explored this relationship by using clever instruments to control for reverse causality. For example, Hall and Jones (1999) use the distance from the equator as an instrument to show that diﬀerences in institutions and government policies are primary factors underlying countries’ income diﬀerences. La Porta, Lopez-deSilanes, Shleifer, and Vishny(1998, 1999), Acemoglu, Johnson, and Robinson (2001), Djankov, La Porta, Lopez-de-Silanes, and Shleifer (2002), Glaeser, La Porta, Lopez-de-Silanes, and Shleifer (2004), and Acemoglu and Johnson (2005) similarly use instruments including legal and colonial origins and human capital to identify country-level growth, investment, and ﬁnancial impacts of legal institutions. 1 These studies use country-level data to show that institutions matter for economic development. However, analyses

1. INTRODUCTION Over the past decade a sizable literature has emerged on the role of institutions in economic performance and growth. It has been hypothesized that institutional mechanisms create the “rules of the game” (North, 1990) that determine the strategies of both domestic and foreign ﬁrms (Wright, Filatotchev, Hoskisson, & Peng, 2005). Firms’ productivity and economic performance are thus expected to be augmented by higher quality institutions that are more supportive of ﬁrms’ business strategies and competitiveness. In particular, North (1990) deﬁnes an economic institution as, “. . . an arrangement between economic units that deﬁnes and speciﬁes the ways by which these units can co-operate or compete” (p. 5). He argues that higher quality institutions reduce transaction and transformation costs, allowing ﬁrms to increase productivity by planning and organizing (or strategizing) more eﬀectively, resulting in improved performance and competitiveness and thus higher industry- and country-level productivity growth. Further, Dixit (2009) emphasizes the “support” of economic activity and transactions provided by the “structure and functioning” of legal institutions, including

Catherine J. Morrison Paul passed away on June 30, 2010. She was a leading economist in applied production economics and econometrics, who made signiﬁcant contributions to the economic development ﬁeld. She ranks amongst the top in both the quantity and quality of her published work. She will be missed greatly, but many will continue the work in her spirit. * We would like to thank the World Bank Enterprise Surveys Staﬀ for very helpful discussions about the data. We also thank two anonymous referees for useful suggestions and comments. Final revision accepted: August 26, 2010. 648

PROPERTY RIGHTS INSTITUTIONS AND FIRM PERFORMANCE: A CROSS-COUNTRY ANALYSIS

at the country level, rather than ﬁrm level, often cannot identify the mechanisms through which better institutions aﬀect economic performance. For example, better institutions may increase investments if they allow entrepreneurs to appropriate a greater share of the returns from investment. Otherwise, governments may speciﬁcally target the successful projects for taxation, either legally or extra-legally. Better institutions may allow increased eﬃciency if ﬁrms know that supplier contracts are more enforceable. Otherwise, a ﬁrm may ineﬃciently integrate into the production of the input so as to assure sufﬁcient quality. Similarly, better institutions may make input markets thicker so that inputs (including labor inputs) become better suited to the specialized use; they may foster more competitive markets if ﬁrms are prevented from erecting legal entry barriers that potential entrants would have to overcome; and they may allow ﬁrms to oﬀer more appropriate product variety by equalizing tax and regulatory treatment across sectors. Otherwise, ﬁrms will avoid products that face the highest tax rates or most onerous restrictions. Our goal in this paper is thus to examine how ﬁrms’ performance is aﬀected by the institutional environment, using crosscountry ﬁrm-level data and speciﬁc indexes of institutional quality. Our ﬁrm-level treatment complements the macro-level studies; it allows us to empirically assess the economic performance eﬀects of property rights or contractual enforcement institutions for diﬀerent types of ﬁrms in diﬀerent countries. Our underlying hypothesis is that better property rights institutions facilitate contracting and trade, which improves ﬁrms’ abilities to make productivity- and economic performance-enhancing business decisions. Institutions could also impose constraints on ﬁrm strategies, which determine performance in combination with industry conditions and ﬁrm capabilities (Peng, 2002; Wright et al., 2005). Institutional quality in this context is expected to aﬀect ﬁrm performance through the domestic competitive environment as well as multinational investments and other international linkages. Our empirical framework explicitly models how ﬁrms’ performance is aﬀected by their expectations that their contractual and property rights will be protected by the legal system. We estimate our model by ordinary least squares (OLS), controlling for ﬁrm heterogeneity, and by an instrumental variable (IV) estimator that also controls for endogeneity. For our IV estimator (Generalized Method of Moments, GMM), we use two alternative sets of instruments—legal origin dummy variables and the average levels of our institutional variable for other ﬁrms in a ﬁrm’s industry and location category. To check the robustness of our results we also directly accommodate potential endogeneity from unobserved corruption (bribery) by including an indicator for the revenue share spent on bribes in our estimating model. Further, we use two indicators of institutional quality: ﬁrms’ expectations that their contractual and property rights will be protected by the legal system, and a property rights index from The Heritage Foundation. We also adapt our estimated standard errors for potential within-cluster (within-country and sector) correlation using a method that produces cluster-robust standard errors. We ﬁnd positive associations between two measures of ﬁrm performance, productivity, and proﬁts, and the perceived quality of the legal system from survey responses about ﬁrms’ conﬁdence in the legal protection of their property rights. The estimated performance impacts of legal system quality become even more pronounced after recognizing potential endogeneity of the institutional variable, via both instrumental variable estimation and directly controlling for impacts of “political connections.” We also ﬁnd that other external and internal sources of technology are positively related to ﬁrm perfor-

649

mance; ﬁrms that export, import, have a foreign direct investment share and invest in research and development have signiﬁcantly higher productivity levels than those that are not involved in these activities, independent of institutional eﬀects. 2. THE LITERATURE In a seminal paper, Baumol (1990) argued that institutions aﬀect economic growth because they help to determine whether entrepreneurship is directed toward productive or unproductive activities. Bowen and Clercq (2008) empirically tested this hypothesis, and found that institutions contribute to economic growth both by creating conditions that support entrepreneurial activity and by directing entrepreneurial eﬀort toward high growth activities. More speciﬁcally, institutions that facilitate entrepreneurial activity provide incentives for ﬁrms to engage in more productive activity, whereas corruption reduces such incentives. In fact, better institutions (or less corruption) have been shown empirically to be associated with greater investment, economic growth, income equality, and technical eﬃciency, as well as with economic growth drivers such as human capital, urbanization, ﬁnancial depth, and foreign trade (Delios & Henisz, 2003; Depken & LaFountain, 2006; Doh, Rodriguez, Uhlenbruck, Collins, & Eden, 2003; Friedman, Johnson, Kaufmann, & Zoido-Lobato´n, 2000; Henisz, 2004; Klein & Luu, 2003; Li, Xu, & Zou, 2000; Mauro, 1995; Murphy, Shleifer, & Vishny, 1993). 2 For example, Doh et al. (2003) show that corruption imposes both direct and indirect costs, which might be somewhat mitigated by strategies of multinational corporations. Klein and Luu (2003) ﬁnd that technical eﬃciency is enhanced by both current policies supporting business stability and by expectations that these policies will remain in place. Empirical macroeconomic studies have more recently focused on the (aggregate) productivity eﬀects of institutions (La Porta, Lopez-de-Silanes, Shleifer, & Vishny, 1997, 1998, 1999; Hall & Jones, 1999; Acemoglu et al., 2001; Djankov et al., 2002; Easterly & Levine, 2003; Glaeser et al., 2004; Rodrik, Subramanian, & Trebbi, 2004; Kwok & Tadesse, 2006). 3 For example, Hall and Jones (1999) ﬁnd that diﬀerences in institutions are primary factors underlying countries’ diﬀerences in capital accumulation, educational attainment, and productivity. Acemoglu et al. (2001) show signiﬁcant eﬀects of colonial origins and resulting institutional structure on economic performance. Rodrik et al. (2004) document a signiﬁcant impact on income levels of institutional quality, controlling for geography and openness. Loayza, Oviedo, and Serven (2005) argue that the mechanism underlying such eﬀects is that high quality institutions increase productive eﬃciency by encouraging ﬁrms to invest in technology (capital) and in the creation and transfer of knowledge. 4 Aron (2000) attributes the performance eﬀects of better institutions to property rights enforcement that mitigates bureaucratic red tape and rent-seeking activities, thus releasing ﬁrm resources for more eﬃcient investments. More generally, higher quality institutions will reduce two types of production costs faced by ﬁrms: transformation costs (the cost of production and processing), and transaction costs (such as the costs of establishing contracts and relations with other agents, of searching for appropriate trading partners and products, of negotiation, and of monitoring and enforcing contracts). The latter is likely to be aﬀected the most by institutional factors, as Aron (2000) states: “. . .Transaction costs, for example, are far higher when property rights or the rule of law are not reliable. In such situations private ﬁrms typically

650

WORLD DEVELOPMENT

operate on a small scale, perhaps illegally in an underground economy, and may rely on bribery and corruption to facilitate operations. Transformation costs, too, can be raised substantially because unenforceable contracts mean using inexpensive technology and operating less eﬃciently and competitively on a short-term horizon.” Such costs determine ﬁrm productivity and proﬁtability. In particular, strong property rights and contract institutions create a business environment that encourages ﬁrms to produce on a larger scale, use better technology, have longer-term horizons, and operate within the legal framework, which all facilitate economic performance and competitiveness (Aron, 2000). Poor quality institutions instead make contract enforcement diﬃcult or make the payment of bribes necessary, sometimes prohibitively increasing the cost of doing business. The implication from macro empirical studies that better institutions positively aﬀect aggregate economic performance by enhancing ﬁrm-level productivity and proﬁtability is also consistent with the international business perspective that the institutional “leg” of the “strategy tripod” (along with industry and ﬁrm characteristics), determines ﬁrms’ strategies and resulting economic performance and competitiveness. Our treatment, using ﬁrm-level data to directly evaluate the eﬀects of institutional quality on ﬁrms’ productive and accounting performance, controlling for industry type and other ﬁrm characteristics, thus can be thought of as combining these three components of ﬁrm behavior developed in Peng et al. (2008). Speciﬁcally, our hypothesis to be tested empirically is that a better institutional environment (represented by ﬁrms’ expectations about property rights protection by the legal system) results in better productive and accounting performance of ﬁrms, independent of other ﬁrm characteristics that might affect economic performance such as industry type and international linkages. 3. EMPIRICAL SPECIFICATION AND DATA DESCRIPTION Our empirical model relates ﬁrms’ performance to a measure of institutional quality (property rights enforcement, Legal, deﬁned below), controlling for a variety of ﬁrm characteristics. The regression equation representing this relationship for our ﬁrm-level data, which we estimate for 17,082 observations in 52 countries, is speciﬁed as: ln P j ¼ b0 þ b1 Legalj þ b2 Agej þ b3 Age2j þ b4 Expj þ b5 FDI j þ b6 Impj þ b7 CU j þ b8 SLj þ b9 ln RDj þ b10 SZDumj þ b11 IndDUM j þ b12 YRDumj þ uj :

ð1Þ

The dependent variable (ln P) represents ﬁrms’ performance, for which we use two proxies. The ﬁrst proxy is labor productivity (LP), deﬁned as output (Y) per unit of labor input (L), Y/L (or in log-form as ln LP = ln Y ln L), 5 which represents ﬁrms’ productive performance. The second is the log of gross proﬁt, deﬁned as total revenue minus the cost of goods sold, which represents ﬁrms’ accounting performance. The independent variables, in addition to Legal, are: the age of the ﬁrm (Age); whether or not the ﬁrm is an exporter (Exp); whether or not the ﬁrm has a foreign share (FDI); the ﬁrms’ share of imported material inputs and supplies (Imp); the ﬁrm’s capacity utilization (CU); the share of skilled production workers, professionals and managers in total employment (SL); the natural log of the ﬁrm’s R&D expenditures (ln RD) 6; and size (SZDum), industry, (IndDum) 7 and year (YRDum) dummy variables.

(a) Data The variables in Eqn. (1) are based on the “Investment Climate Survey,” a cross-section survey of ﬁrms conducted during 2000–05 8 by the World Bank in various countries grouped into six regions. 9 For the few countries in which surveys were conducted in two years, we used the data for the most recent year. 10 We also dropped observations that were clearly erroneous, such as negative values of age, output, and labor. 11 After dropping such observations, we had 17,802 observations for 52 countries. The data are based on face-to-face interviews with ﬁrms’ managers and bookkeepers or accountants. 12 The sampling method was designed to ensure a random sample, 13 with adequate representation of ﬁrms by industry, 14 size, 15 ownership, export orientation, and location. We made adjustments so that all the sales and cost variables were in US dollar (USD) currency units, to make the data comparable across the countries in our sample. 16 The industry value added data for the tradable sector come from the Centre D’etudes Prospectives et D’informations Internationales’ Trade, Production and Bilateral Protection Database database. The data were collected by the United Nations Industrial Development Organization (UNIDO), which is the most comprehensive source of industry-level production data across the countries. The data include information on 28 ISIC (Revision 2) manufacturing sectors at the three digit level for 180 countries for 1980–2004. The value added data for the non-tradable sector are from the United Nations’ Statistical Division. We weighted the observations by the average value added share of each industry in each country from 1980 to 2004 to adjust the data for unequal sampling probabilities, to obtain unbiased estimates of our model coeﬃcients. 17, 18 As we elaborate below, we also adjusted the standard errors for potential within-cluster (within-country and within sector) correlation using cluster-robust standard errors, as when there is similarity within countries and sectors (clusters), but heterogeneity between clusters, one would expect high estimated standard errors. Our institutional variable (Legal) was phrased in the survey as follows: “I am conﬁdent that the judicial system will enforce my contractual and property rights in business disputes. To what degree do you agree with this statement? Do you (read 1–6)? 1. Fully disagree; 2. Disagree in most cases; 3. Tend to disagree; 4. Tend to agree; 5. Agree in most cases; 6. Fully agree.” 19 We converted this variable to 10–60 to make its units more consistent with our alternative property rights index, which is measured from 0 to 100. The other explanatory variables are included to control for ﬁrm heterogeneity and for alternative productivity factors. In particular, the Exp, FD,I and Imp variables control for productivity eﬀects of international technology transfer found in various studies (including Buckley, Clegg, & Wang, 2002; Feinberg & Majumdar, 2001; Hejazi & Safarian, 1999; Liu, Siler, Wang, & Wei, 2000; Yasar & Paul, 2007). For example, studies such as Kraay (1999) and Castellani (2001) ﬁnd significant ﬁrm-level productivity eﬀects from exporting. Caves (1974), Globerman (1979), and Aitken and Harrison (1999) ﬁnd that both industries and ﬁrms with higher foreign shares (FDI) are more productive. Coe and Helpman (1995), Keller (2002), and Rodrigue and Kasahara (2004) ﬁnd signiﬁcant productivity eﬀects from importing technology at the country, industry, and ﬁrm levels, respectively. Variables for other ﬁrm characteristics include Age because more production experience would be expected to enhance

PROPERTY RIGHTS INSTITUTIONS AND FIRM PERFORMANCE: A CROSS-COUNTRY ANALYSIS

productivity. Capacity utilization (CU) controls for the average utilization of a ﬁrm’s ﬁxed inputs. Own-ﬁrm R&D (ln RD) captures internal knowledge creation. The share of skilled production workers, professionals, and managers in total employment (SL) represents ﬁrms’ labor skill heterogeneity. As explained below, we also tried using real GDP, openness and the share of investment in real GDP data from the Penn World Tables (version 6.2) as variables in our model, with little support. The remaining variables are ﬁrm-speciﬁc controls for ﬁrm size, industry, and year. Summary statistics for the variables are presented in Table 1. To check for potential inference problems from multicollinearity of the explanatory variables, we constructed a correlation matrix (presented in Table A1) that did not reveal problematically high correlations. Multicollinearity tests based on variance inﬂation factors (VIFs) and tolerance levels (presented in Table A2) further conﬁrmed that relying on the variables in (1) is justiﬁed. Speciﬁcally, there were no variables with VIFs greater than 10 and tolerance levels less than 0.1, which are generally accepted thresholds to identify multicollinearity problems.

651

improvement in institutions (La Porta et al., 1997). For this method, in a ﬁrst stage we regressed Legal on dummy variables for British, 25 French, and German legal origins (summarized in Table 2, where the socialist legal origin is the base group) and the exogenous variables in Eqn. (1). In a second stage we regressed the performance variable ln Pj on the predicted value of Legal (from the ﬁrst stage regression) and the other variables in Eqn. (1). To evaluate the correlation between the instruments and the error term, we used the Sargan test of overidentifying restrictions. The null hypothesis is that the instruments and the error terms are independent, so rejecting this hypothesis would invalidate the use of the instruments. To assess whether our instruments are suﬃciently related to our Legal variable, we use an F-test of the joint signiﬁcance of the instruments in reduced form, which supports their use. As a ﬁnal adaptation for potential endogeneity, we include a bribery indicator as an argument of estimating Eqn. (1). 26 This approach directly recognizes that the extent of bribery is a key omitted variable that might generate endogeneity. Our bribery variable Br is deﬁned as the share of informal payments to public oﬃcials to “get things done” as a percent of annual sales value.

4. ESTIMATION METHOD (a) Fully standardized coeﬃcients We used both ordinary least squares (OLS) and an instrumental variable (IV) estimator to empirically estimate our model in Eqn. (1). The IV estimator was used to control for potential endogeneity of our primary variable of interest, Legal. 20 To brieﬂy reiterate the use of the instrumental variable estimator (Generalized Method of Moments, GMM), suppose we are interested in estimating y i ¼ x0i b þ ui . In the presence of endogeneity, one must control for potential estimation biases by specifying J instruments in an associated vector zi. For b to be a consistent estimator, the population moment condition E½ðy i x0i bÞzi ¼ 0 should hold. 21 The GMM estimator the corresponding sample moment condition Pn solves 1 0 i ðy i xi bÞzi ¼ 0 by choosing values of b that drive this N condition as close to zero as possible by minimizing: " #0 " # N N 1 X 1 X 0 0 ð2Þ ðy xi bÞzi XN ðy xi bÞzi ; QN ðbÞ ¼ N i i N i i where XN is a weighting matrix. Because GMM allows one to optimally choose the weighting matrix, it is more eﬃcient than a standard two-stage least squares estimator. We used two sets of instruments for Legal to estimate our model by the GMM method. First, we used the average levels of the Legal variable for other ﬁrms (j – i) in a ﬁrm’s industry 22 and location category 23 for each country and year as an instrument. Assuming the industry averages are independent of ﬁrm-speciﬁc eﬀects (i.e., they are unaﬀected by ﬁrm idiosyncratic productivity or proﬁtability shocks), these industry- and location-based instruments will be uncorrelated with the error term, as required for valid instruments. Further, since the industry-speciﬁc variables deﬁne the environment in which a ﬁrm operates (Jaﬀe, 1986), we can expect the endogeneous variable and the instruments to be related. To check the sensitivity of our results to the speciﬁcation of instruments, we alternatively used the legal origin indicators of La Porta et al. (1997, 1998, 1999) as instruments for Legal. La Porta et al. (1997, 1998, 1999) and Glaeser et al. (2004) argue that legal origin is a valid instrument because Europeans imposed their own legal systems on the countries they colonized, 24 and that the greater protections of property against the state embodied in common law systems represents an

To facilitate the interpretation of our estimated coeﬃcients, we compute “fully standardized coeﬃcients.” 27 Assuming that rp is the unconditional standard deviation of the independent variable Xp, the fully standardized coeﬃcient for Xp is calculated as bFS p ¼ rp bp =ry . This measure indicates that for a standard deviation increase in Xp, the dependent variable yi is expected to increase by bFS p standard deviations, holding all other variables constant. (b) Clustering It is expected that, in our cross-country ﬁrm-level data, ﬁrms in a single country (or cluster) will have similarities not shared by ﬁrms in other countries due to both observable and unobservable factors. For example, prices for material and labor and the eﬀects of macro factors such as a recession would be expected to be similar for ﬁrms in the same country. One might therefore expect correlations among the residuals of the estimating equation for diﬀerent ﬁrms in the same country. Failing to adjust for such dependencies may result in biased standard errors (the estimated standard errors will be too small), mis-speciﬁed test statistics (the test statistic will be too large), and ineﬃciency of OLS estimation. 28 We thus adapted our estimated standard errors for potential within-cluster (within-country and sector) correlation using a method that produces cluster-robust standard errors. P The ro 1 Nc 0 29 0 ðX X Þ bust cluster variance estimator is j¼1 uj uj ðX 0 X Þ1 (where Nc is the total number of clusters, PN j uj ¼ i¼1 ei X i , Nj is the number of observations in cluster j, ei is the residual for the ith observation and Xi is a vector of explanatory variables), whereas the OLS variance estimator P is s2 ðX 0X Þ1 (where s2 ¼ 1=N k Ni¼1 e2i ). 5. RESULTS AND DISCUSSION Our estimates for Eqn. (1), with ﬁrm performance alternatively characterized by labor productivity and gross proﬁts,

652

WORLD DEVELOPMENT Table 1. Summary statistics (No. of Obs. = 17,082)

Variable

Mean

Standard deviation

Log output (ln Y) Log labor (ln L) Log material (ln M) Log capital (ln K) Labor productivity (LP = log(output/labor)) Value added labor productivity (LPVA = log(value added/labor)) Log gross proﬁt (GP) Legal system will enforce contractual and property rights in business disputes (Legal)a Age of the ﬁrm (Age)b Exporting dummy (Exp)c Foreign direct investment dummy (FDI)d Import share (Imp)e Capacity utilization (CU)f Skilled labor share (SL)g Research and development expenditures dummy (ln RD)h Bribery (Br)i Natural log of real GDP per capita (ln GDP)j Openness (OP)k Investment share of real GDPl Property rights (PR)m

12.391 2.751 11.570 7.313 9.640 9.175 11.910 38.422 15.941 0.134 0.104 0.230 81.478 0.723 0.926 0.979 2.840 79.267 15.431 48.295

3.101 1.611 2.909 3.624 2.894 2.837 3.080 13.960 16.208 0.341 0.306 0.356 19.029 0.291 2.882 3.604 0.899 37.815 7.870 19.212

a

Legal: in the survey, managers were asked whether they are conﬁdent that the judicial system will enforce their contractual and property rights in business disputes. The level of conﬁdence is measured in six increasing categories (1. Fully disagree; 2. Disagree in most cases; 3. Tend to disagree; 4.Tend to agree; 5.Agree in most cases; 6. Fully agree). We converted this variable to 10–60 so that it is more comparable to the property right index. b Age: mangers were asked in what year their ﬁrm began operations. c Exp: a dummy variable that is equal to one if ﬁrm is an exporter, and zero otherwise. d FDI: a dummy variable that is equal to one if ﬁrm is owned by the foreign private sector, and zero otherwise. e Imp: managers were asked what percent of their establishment’s material inputs and supplies are imported. f CU: capacity utilization is the amount of output actually produced relative to the maximum amount that could be produced with existing machinery and equipment and regular shifts. g SL: the share of skilled production workers, professionals, and managers in total employment. h ln RD: managers were asked how much their establishment spent on design or R&D last year, where spending includes wages and salaries of R&D personnel, such as scientists and engineers; materials; education costs; and subcontracting costs. i Br: the survey question reads: “We have heard that establishments are sometimes required to make gifts or informal payments to public oﬃcials to “get things done” with regards to customs, taxes, licenses, regulations, services, etc. On average, what percent of annual sales value would such expenses cost a typical ﬁrm like yours?” This variable is available for 13,944 observations. j ln GDP: natural log real GDP per capita. Real GDP is obtained by adding up consumption, investment, government expenditures and exports, and subtracting imports. This variable is from the Penn World Tables, version 6.2. k OP: exports plus imports divided by real GDP. This variable is from the Penn World Tables, version 6.2. l INV: investment share of real GDP. This variable is from the Penn World Tables, version 6.2. m PR: property rights index from The Heritage Foundation. This variable is scaled from 10 to 100. Higher values imply better institutional quality and vice versa. We rescaled this variable to 10–60 to make it comparable to Legal.

are presented in Tables 3 and 4, respectively. The OLS parameter estimates are reported in the ﬁrst columns of these tables, with one, two, and three asterisks indicating statistical signiﬁcance at the 10%, 5%, and 1% signiﬁcance levels. The cluster-robust standard errors (which are almost double the unadjusted OLS standard errors), are in parentheses. The estimates show that both productivity and proﬁtability are positively associated with our Legal variable representing a ﬁrm’s expectations that the legal or judicial system will enforce their contractual and property rights in business disputes. The fully standardized coeﬃcients for the independent variables of Eqn. (1) are reported in the second columns of Tables 3 and 4. These estimates indicate that a one standard deviation increase in Legal is associated with a 0.058 standard deviation increase in labor productivity and a 0.053 increase in proﬁt, controlling for the other variables. This supports the ﬁndings of macro studies that better institutions create an environment in which ﬁrms can organize their activities more eﬃciently, resulting in greater economic performance and competitiveness. For more interpretation of these values, consider the impact of Legal on productivity across speciﬁc countries in the data

set. For example, the average values for Legal for the Philippines and Germany are 37 and 47, respectively, so the diﬀerence in Legal is ten. These estimates thus imply that if the Philippines had a legal system equivalent to Germany, labor productivity would be about 12.07% higher ([100 (exp (0.012) 1) 10] = 12.07). That is, a one unit increase in Legal increases labor productivity by 0.012, so productivity would increase by 1.2% (100 (exp(0.012) 1)) as a result of a one unit change in Legal, or 12.07% for a ten unit change in Legal. 30 Our OLS estimates also show that ﬁrm productivity and proﬁts are positively and signiﬁcantly related to international connections through exports, imports, and foreign direct investment, conditional on the control variables. The fully standardized coeﬃcients show that a one standard deviation increase in the materials import share increases ﬁrm productivity and proﬁt by 0.019 and 0.034 standard deviations, respectively, although the impact on productivity is not statistically signiﬁcant. Having a foreign share is associated with increased productivity and proﬁt of 0.032 and 0.046 standard deviations, respectively. These results corroborate suggestions in the

PROPERTY RIGHTS INSTITUTIONS AND FIRM PERFORMANCE: A CROSS-COUNTRY ANALYSIS

653

Table 2. Summary statistics for the macro variables Country

Year

Obs.

British

French

German

Socialist

PR

ln GDP

OP

INV

Albania Armenia Bangladesh Belarus Benin Bosnia and Herzegovina Brazil Bulgaria Cambodia Croatia Czech Republic Ecuador El Salvador Estonia Georgia Germany Greece Guatemala Honduras Hungary Ireland Kazakhstan Korea, Rep. Kyrgyz Republic Latvia Lithuania Macedonia, FYR Madagascar Mali Mauritius Moldova Nicaragua Oman Philippines Poland Portugal Romania Russian Federation Senegal Slovak Republic Slovenia South Africa Spain Sri Lanka Tajikistan Tanzania Turkey Uganda Ukraine Uzbekistan Vietnam Zambia

2005 2005 2002 2005 2004 2005 2003 2005 2003 2005 2005 2003 2003 2005 2005 2005 2005 2003 2003 2005 2005 2005 2005 2005 2005 2005 2005 2005 2003 2005 2005 2003 2003 2003 2005 2005 2005 2005 2003 2005 2005 2003 2005 2004 2005 2003 2005 2003 2005 2005 2005 2002

141 293 818 179 155 99 1,569 201 203 152 224 343 453 151 101 1,045 373 429 393 453 422 400 419 149 140 160 100 208 107 127 214 428 47 571 715 366 491 358 164 133 194 511 523 367 173 98 298 172 414 212 455 171

0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 1 0 1 0 1 0 0 0 1

0 0 0 0 1 0 1 0 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 1 1 1 0 1 1 1 0 1 0 0 1 0 0 0 1 0 0 0 1 0 0 0 0 0

0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0

1 1 0 1 0 1 0 1 1 1 1 0 0 1 1 0 0 0 0 1 0 1 0 1 1 1 1 0 0 0 1 0 0 0 1 0 1 1 0 1 1 0 0 0 1 0 0 0 1 1 1 0

30 50 30 30 30 10 50 30 30 30 70 30 50 70 30 90 50 30 50 70 90 30 70 30 50 50 30 50 50 70 50 30 50 50 50 70 30 30 50 50 50 50 70 50 30 30 50 50 30 30 10 50

2.402 2.313 1.685 3.371 1.292 2.179 3.041 3.050 0.405 3.263 3.679 2.530 2.622 3.474 2.426 4.290 3.706 2.419 1.875 3.500 4.285 2.943 3.822 2.288 3.265 3.283 2.730 0.871 1.115 3.784 1.864 2.303 3.853 2.410 3.221 3.920 2.719 3.294 1.520 3.340 3.970 3.176 4.040 2.466 1.575 0.867 2.811 1.125 2.678 2.333 1.852 0.924

55.090 73.620 37.270 143.620 57.740 82.110 22.840 116.760 116.640 99.390 132.000 68.060 69.830 180.350 60.920 66.400 61.630 48.760 98.340 151.550 184.100 105.030 78.300 89.430 91.590 96.140 112.150 68.620 63.190 124.150 126.160 74.520 91.140 102.960 62.220 74.320 71.370 68.090 69.250 144.090 115.830 52.790 61.200 89.160 154.600 37.960 54.160 34.180 119.860 60.010 112.500 52.470

45.270 9.610 11.980 14.470 7.560 8.790 16.490 8.060 5.420 13.290 20.410 12.830 8.570 14.910 6.950 23.340 24.200 5.970 18.540 24.340 23.560 7.960 34.790 8.840 13.550 11.810 12.150 4.120 6.320 12.150 8.890 13.770 6.700 13.970 22.690 25.960 10.830 8.900 4.910 18.620 27.950 7.720 26.010 12.330 3.390 3.540 19.360 3.130 8.180 5.760 15.810 12.840

Notes: (1) data on the countries’ legal origin come from La Porta et al. (1997, 1998, 1999). (2) PR is from The Heritage Foundation. (3) ln GDP, OP and INV are from the Penn World Tables, version 6.2.

literature that multinational ﬁrms may be less vulnerable to institutional limitations (Henisz & Williamson, 1999). Productivity also tends to be signiﬁcantly related (positively) to exporting. Internal innovation through direct investment in R&D also appears to enhance performance. For the R&D performing sub-sample, a standard deviation increase in R&D increases ﬁrms’ productivity and proﬁt by 0.069 and 0.079 standard deviations, respectively, holding all other variables constant.

Domestic as well as foreign sources of technology are thus positively related to performance, as suggested by the endogenous growth literature (Coe & Helpman, 1995; Eaton & Kortum, 1996; Keller, 2002), although our capacity utilization and skilled labor share variables are not signiﬁcantly related to the performance indicators. Our ﬁndings for these technology transfer variables should be interpreted cautiously since our empirical analysis is restricted to cross-sectional data that precludes standard

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adaptations for correlation of these variables with the error term due to measurement error, omitted variables, simultaneity, or self-selection. That is, these ﬁndings cannot necessarily be interpreted as a causal relationship since the methodology used does not control for associated biases. However, comparable results for one country’s panel data using alternative methodologies that control for such issues does support a causal relationship (Yasar & Paul, 2007, 2008). (a) Endogeneity of “Legal” Our OLS estimates reveal a positive and signiﬁcant relationship between our institutional variable, Legal, and ﬁrm productive and accounting performance, controlling for other observed sources of economic performance. However, it is possible that variables not captured in our data, such as bribes to government oﬃcials, have an impact on both ﬁrm performance and the quality of property rights enforcement. It is also possible that recently successful ﬁrms due to idiosyncratic avoidance of legal complications unduly attribute their success to the legal environment—or in reverse, that ﬁrms that are unsuccessful due to innate managerial problems may blame their failure on the legal system. 31 The error term will then be correlated with the performance measures and our OLS estimates will be biased. To control for this potential endogeneity of our Legal variable, we alternatively estimated our model using GMM methods, as explained above. The results denoted IV1, in the ﬁfth columns of Tables 3 and 4, are based on the instruments computed for ﬁrms i as the average levels of Legal for other ﬁrms (j – i) in ﬁrm i’s industry and location category for each country and year. Using the Sargan test of overidentifying restrictions to test for correlation between the instruments and error term, the P-values are 0.275 and 0.285 for productivity and proﬁt, respectively, validating the use of these instruments.

We also used an F-test to examine whether the instruments are suﬃciently related to our Legal variable by estimating their joint signiﬁcance in reduced form. The F-statistics for this test reported in Tables 3 and 4 show that the instruments are highly related to Legal, and that the equation is not weakly identiﬁed. The instruments were also individually signiﬁcant in the ﬁrst stage. These results further support the use of these instruments to accommodate any endogeneity. Our GMM results based on these instruments, and the fully standardized coeﬃcients for the independent variables, are reported in the ﬁfth and sixth columns of Tables 3 and 4 for productivity and proﬁt, respectively. These estimates show that each standard deviation increase of the Legal variable increases ﬁrms’ productivity and proﬁt by 0.499 and 0.410 standard deviations, respectively, holding the other variables constant. Controlling for the potential endogeneity of Legal by IV estimation thus raises its estimated performance eﬀect, indicating that the OLS coeﬃcients are under-estimated. The coeﬃcients for the remaining independent variables are similar in terms of both their magnitude and signiﬁcance for the IV estimation. This greater impact can be clariﬁed by an example. The average values for Legal for Turkey and Germany are 42 and 47, respectively, so the diﬀerence in Legal is 5. This implies that if Turkey had the legal system of Germany, labor productivity would increase by 54.08% ([100 (exp(0.104) 1) 5] = 54.08). That is, a one unit increase in Legal increases productivity by 0.104, which indicates that productivity increases by 10.96% (100 (exp(0.104) 1)) as a result of a one unit increase in Legal, or 54.08% for a ﬁve unit increase in Legal. To check the robustness of these IV results, we alternatively used the legal origin indicators (illustrated in Table 2) of La Porta et al. (1997, 1998, 1999) as instruments for Legal. Our Sargan test of over-identifying restrictions for this model resulted in P-values of 0.762 and 0.525 for productivity and proﬁt, conﬁrming the exogeneity of the instruments. F-tests

Table 3. OLS and IV parameter estimates [dependent variable = natural log of labor productivity] Independent variables

Ordinary least squares (OLS) OLS

bFS p

OLSBR

Legal system (Legal) 0.012 (0.005)** 0.058 0.013 (0.005)*** Age of the ﬁrm (Age) 0.021 (0.004)*** 0.119 0.022 (0.004)*** 0.000 (0.000)*** 0.083 0.000 (0.000)*** Age squared (Age2) Exporting dummy (Exp) 0.457 (0.166)*** 0.054 0.371 (0.098)*** Foreign direct investment dummy (FDI) 0.303 (0.118)*** 0.032 0.270 (0.115)** Import share (Imp) 0.152 (0.146) 0.019 0.001 (0.162) Capacity utilization (CU) 0.007 (0.005) 0.048 0.007 (0.005) Skilled labor share (SL) 0.028 (0.208) 0.010 0.082 (0.145) Natural log of 1 + R&D (ln RD) 0.069 (0.026)*** 0.069 0.065 (0.025)*** Bribery (Br) 0.042 (0.020)** Observations R-squared F-statistic of instruments (P-value) Sargan statistic (P-value)

17,082 0.64 0.000

13,944 0.55 0.000

Instrumental variables (IV) bFS p 0.073 0.142 0.092 0.050 0.033 0.000 0.049 0.020 0.077 0.084

IV1

bFS p

IV2

bFS p

0.104 (0.022)*** 0.499 0.186 (0.030)*** 0.896 0.016 (0.004)*** 0.088 0.007 (0.007) 0.042 0.000 (0.000) 0.053 0.000 (0.000) 0.011 0.353 (0.109)*** 0.042 0.409 (0.104)*** 0.048 0.223 (0.146) 0.024 0.024 (0.132) 0.003 0.375 (0.129)*** 0.046 0.622 (0.134)*** 0.077 0.009 (0.004)** 0.058 0.001 (0.006) 0.005 0.219 (0.172)** 0.048 0.131 (0.107)* 0.029 0.076 (0.016)*** 0.075 0.046 (0.022)** 0.046 17,040 0.951 0.000 0.275

17,082 0.912 0.000 0.762

Notes: (1) the OLS regressions reported in this table include dummy variables that control for size, year, and industry characteristics. The IV regressions include dummy variables that control for size and year. The coeﬃcients for these dummy variables are not reported here in the interest of space, but are available from the authors upon request. (2) The standard errors are clustered by country and sector. The clustered standard errors are much larger, by up FS SY to a factor of two. (3) bFS p are the fully standardized coeﬃcients for Xp (bp ¼ rp bp =rY ¼ rp bp ). (4) IV1 denotes the instrumental variables results in which average levels of the Legal variable for other ﬁrms (j – i) in a ﬁrm’s industry and location category for each country and year are used as instruments. (5) IV2 denotes the instrumental variables results in which dummy variables for English, French, and German legal origin are used as instruments. Socialist legal origin is the omitted category. * Signiﬁcant at the 10% level. ** Signiﬁcant at the 5% level. *** Signiﬁcant at the 1% level.

PROPERTY RIGHTS INSTITUTIONS AND FIRM PERFORMANCE: A CROSS-COUNTRY ANALYSIS

655

Table 4. OLS and IV parameter estimates [dependent variable = natural log of proﬁt] Independent variables

Ordinary least squares (OLS) OLS

Legal system (Legal) Age of the ﬁrm (Age) Age squared (Age2) Exporting dummy (Exp) Foreign direct investment dummy (FDI) Import share (Imp) Capacity utilization (CU) Skilled labor share (SL) Natural log of research and development expenditures (ln RD) Bribery (Br) Observations R-squared F-statistic of instruments (P-value) Sargan statistic (P-value)

bFS p

OLSBR

Instrumental variables (IV) bFS p

IV1

bFS p

IV2

bFS p

0.012 (0.005)** 0.053 0.014 (0.004)*** 0.067 0.091 (0.020)*** 0.410 0.184 (0.029)*** 0.832 0.032 (0.003)*** 0.170 0.033 (0.004)*** 0.191 0.029 (0.005)*** 0.151 0.021 (0.007)*** 0.113 *** *** *** 0.000 (0.000) 0.107 0.000 (0.000) 0.114 0.000 (0.000) 0.086 0.000 (0.000) 0.052 0.568 (0.178)*** 0.063 0.495 (0.113)*** 0.059 0.495 (0.099)*** 0.055 0.572 (0.100)*** 0.063 *** *** ** 0.046 0.426 (0.115) 0.045 0.330 (0.134) 0.033 0.110 (0.128) 0.011 0.470 (0.126) 0.297 (0.126)** 0.007 (0.005) 0.134 (0.223) 0.085 (0.024)***

0.034 0.045 0.028 0.079

0.134 (0.124) 0.006 (0.005) 0.229 (0.168) 0.080 (0.022)***

0.017 0.039 0.050 0.084

0.036 (0.016)**

0.046

16,659 0.673

13,605 0.634

0.258

0.856

0.472 (0.118)*** 0.010 (0.003)*** 0.082 (0.152) 0.099 (0.014)***

0.054 0.061 0.017 0.092

0.668 (0.137)*** 0.002 (0.007) 0.034 (0.146)* 0.079 (0.025)***

0.077 0.013 0.010 0.074

0.127 16,618 0.969 0.000 0.285

0.315 0.074

16,659 0.941 0.000 0.525

Notes: (1) the OLS regressions reported in this table include dummy variables that control for size, year, and industry characteristics. The IV regressions include dummy variables that control for size and year. The coeﬃcients for these dummy variables are not reported here in the interest of space, but are available from the authors upon request. (2) The standard errors are clustered by country and sector. The clustered standard errors are much larger, by up FS SY to a factor of two. (3) bFS p are the fully standardized coeﬃcients for Xp (bp ¼ rp bp =rY ¼ rp bp ). (4) IV1 denotes the instrumental variables results in which average levels of the Legal variable for other ﬁrms (j – i) in a ﬁrm’s industry and location category for each country and year are used as instruments. (5) IV2 denotes the instrumental variables results in which dummy variables for English, French, and German legal origin are used as instruments. Socialist legal origin is the omitted category. * Signiﬁcant at the 10% level. ** Signiﬁcant at the 5% level. *** Signiﬁcant at the 1% level.

based on the ﬁrst stage also show that these instruments are suﬃciently correlated to Legal to be valid instruments. Our resulting GMM estimates of the fully standardized coeﬃcients, denoted IV2, are reported in the seventh and eighth columns of Tables 3 and 4 for productivity and proﬁt, respectively. These estimates show that each standard deviation increase in the Legal variable increases ﬁrms’ productivity and proﬁt by 0.896 and 0.832 standard deviations—an even greater estimated impact than using the industry and location instruments. For example, the diﬀerence in the average values for Legal for Turkey and Spain is 2. As a one unit increase in Legal increases labor productivity by 0.204, or by 20.04% (100 (exp(0.186) 1)), this implies that if Turkey’s legal system were equivalent to Spain’s, their productivity would increase by 40.89%. Finally, one speciﬁc endogeneity concern is that “political connections” or bribery may have impacts on both ﬁrm productivity and legal system quality. This is controlled for by our IV estimates that accommodate any unobserved or mismeasured variables that may result in a correlation between Legal and the error term. We also addressed this issue more directly, however, by including our “bribery” (Br) variable in the model to recognize that a ﬁrm with “political connections” may expect enforcement even with a poor quality legal system. The results of this experiment, presented in the fourth columns of Tables 3 and 4 as OLSBR, show larger and more signiﬁcant productivity and proﬁt impacts than with OLS, but smaller than with IV estimation. (b) Robustness To check the robustness of our results we also tried using an alternative institutional quality indicator—an Index of Economic Freedom (IEF) from The Heritage Foundation

(which is available annually from 1995; see Kane, Holmes, & O’Grady, 2007). In particular, we used the index most closely related to our Legal variable, their property rights index (with values from 0 to 100 and summary statistics presented in Tables 1 and 2) as a substitute for Legal. 32 GMM estimates of the resulting parameters and fully standardized coeﬃcients for labor productivity and gross proﬁts are presented in Panels A and B of Table 5, respectively, with cluster-robust standard errors in parentheses. The results are broadly consistent with those based on our Legal variable. For example, a one standard deviation increase in the property rights index is associated with 0.435 and 0.386 standard deviations higher ﬁrm labor productivity and gross proﬁt, respectively, when the industry and location instruments are used, and 0.325 and 0.308 standard deviations higher productivity and proﬁt when the legal origin instruments are used. For example, consider the impact of property rights (PR) on productivity between Hungary and Germany, with average PR values of 70 and 90, respectively. If Hungary had the legal system of Germany, this implies that labor productivity would be 115.195% higher ([100 (exp(0.056) 1) 20] = 115.195) for this 20 unit change in PR. The results obtained by using the legal origin indicators of La Porta et al. (1997, 1998, 1999) as instruments for PR can be interpreted similarly. For example, the average PR values for Tanzania and South Africa are 30 and 50, respectively, so if Tanzania had the legal system of South Africa labor productivity would be expected to increase by 85.79% ([100 (exp(0.042) 1) 20] = 85.79). In addition, we incorporated macro-level variables in our analysis to assess whether simultaneously including such information aﬀected our estimated performance impacts of property rights institutions. In particular, we tried including

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WORLD DEVELOPMENT Table 5. Parameter estimates using the Economic Freedom Index for property rights Panel A: labor productivity IV1

bFS p

IV2

Panel B: proﬁt bFS p

IV1

bFS p

IV2

bFS p

Property rights (PR) 0.056 (0.018)*** 0.435 0.042 (0.005)*** 0.325 0.053 (0.012)*** 0.386 0.042 (0.006)*** 0.308 Age of the ﬁrm (Age) 0.001 (0.005) 0.008 0.006 (0.005) 0.033 0.016 (0.004)*** 0.086 0.019 (0.006)*** 0.099 2 Age squared (Age ) 0.000 (0.000) 0.008 0.000 (0.000) 0.022 0.000 (0.000)*** 0.049 0.000 (0.000)*** 0.057 Exporting dummy (Exp) 0.190 (0.099)* 0.022 0.233 (0.082)*** 0.028 0.387 (0.092)*** 0.043 0.379 (0.103)*** 0.042 Foreign direct investment dummy (FDI) 0.152 (0.181) 0.016 0.204 (0.135) 0.022 0.334 (0.116)*** 0.033 0.330 (0.122)*** 0.032 Import share (Imp) 0.577 (0.188)*** 0.071 0.473 (0.125)*** 0.059 0.546 (0.166)*** 0.063 0.481 (0.111)*** 0.056 Capacity utilization (CU) 0.006 (0.003)* 0.042 0.007 (0.004)* 0.047 0.011 (0.003)*** 0.064 0.009 (0.004)** 0.054 Skilled labor share (SL) 0.181 (0.216) 0.040 0.058 (0.099) 0.013 0.132 (0.177)** 0.027 0.073 (0.154) 0.015 Natural log of 1 + R&D (ln RD) 0.041 (0.015)*** 0.041 0.047 (0.012)*** 0.047 0.079 (0.008)*** 0.073 0.074 (0.017)*** 0.068 Observations R-squared F-statistic of instruments (P-value) Sargan statistic (P-value)

17,040 0.967 0.000 0.509

17,587 0.972 0.000 0.790

16,618 0.979 0.028 0.433

17,053 0.981 0.000 0.516

Notes: (1) the OLS regressions reported in this table include dummy variables that control for size, year, and industry characteristics. The IV regressions include dummy variables that control for size and year. The coeﬃcients for these dummy variables are not reported here in the interest of space, but are available from the authors upon request. (2) The standard errors are clustered by country and sector. The clustered standard errors are much larger, by up FS SY to a factor of two. (3) bFS p are the fully standardized coeﬃcients for Xp (bp ¼ rp bp =rY ¼ rp bp ). (4) IV1 denotes the instrumental variables results in which average levels of the Legal variable for other ﬁrms (j – i) in a ﬁrm’s industry and location category for each country and year are used as instruments. (5) IV2 denotes the instrumental variables results in which dummy variables for English, French, and German legal origin are used as instruments. Socialist legal origin is the omitted category. (6) When we rescale the PR variable to make it comparable to the Legal variable, the coeﬃcients illustrated in the ﬁrst row of this table are 0.090, 0.067, 0.085, and 0.067, respectively. * Signiﬁcant at the 10% level. ** Signiﬁcant at the 5% level. *** Signiﬁcant at the 1% level.

country-level variables such as the natural log of real GDP per capita, openness, and investment share of real GDP as explanatory variables (see Table A3). As these variables and performance would be expected to be positively related, and so might involve reverse causality (for instance, countries with higher productivity levels have a higher per capita GDP), we used year 2000 values of these variables to mitigate this problem. The coeﬃcients on these variables are positive, but the estimate for real GDP is insigniﬁcant and this adaptation makes little diﬀerence to the overall results. 33 In preliminary investigation we also tried using as explanatory variables a British colony dummy variable, the percent of Protestants and the average years of schooling in the population, which the literature implies might be important. However, these variables would also be expected to be closely related to Legal. That is, past British colonization, the percent of Protestants and legal institutions all reﬂect a country’s cultural heritage, as shown by the close correlation between the legal origin dummy variable and the Legal variable documented above. In addition, these variables are available for such a limited number of countries that there are too few clusters (countries) to calculate a robust variance-covariance matrix. We thus omitted these variables from the ﬁnal empirical speciﬁcation. Finally, we checked the robustness of our results by using value-added labor productivity (LPVA, deﬁned as value added per unit of labor input, VA/L, or in log-form as ln LPVA = ln VA ln L), and total factor productivity (TFP, deﬁned as ln Y minus the sum of all inputs weighted by their Cobb Douglas production function coeﬃcients) as performance indicators (see Table A4). Although there are fewer observations, due to missing data, the results are consistent with those obtained using labor productivity deﬁned as sales per unit of labor. For instance one unit increase in Legal increases value-added labor productivity by 10.2% but output per unit of labor by 10.4% and total factor productivity by 12%.

6. CONCLUDING REMARKS Using country-level data, macro studies show that institutions matter for economic development. Dunning (2004) also emphasizes the importance of institutions for international business, saying that: “There can be little doubt that institutionrelated assets have become a more important inﬂuence over the extent of ownership and patterns of international business activity over the last 30 years. . . Institutional assets need to be in place if the economic objectives of society, and its constituent parts, are to be tackled and advanced in a socially acceptable way.” In this article we used ﬁrm level data to explore these inﬂuences. Our empirical results shed light on the contribution of institutions to ﬁrm performance in various industries and countries, controlling for ﬁrm heterogeneity by recognizing ﬁrm-speciﬁc characteristics and for endogeneity by using an instrumental variables (GMM) estimator. In particular, we evaluate whether higher quality institutions, in the form of property rights enforcement, enhance ﬁrms’ and thus ultimately countries’ productive and accounting performance. We ﬁnd that better property rights institutions matter for ﬁrm performance and competitiveness. Speciﬁcally, we ﬁnd a signiﬁcantly positive association between ﬁrms’ performance, in terms of labor productivity or gross proﬁts, and their expectations that the legal system will enforce their contractual and property rights. Our ﬁndings are consistent with the hypothesis that high quality institutions create an environment in which ﬁrms can organize their activities more eﬃciently and invest more conﬁdently. One mechanism through which ﬁrms may become more eﬃcient is by transferring or creating knowledge. This is supported by our ﬁndings that both external and internal sources of technology are also positively related to ﬁrm performance. Firms that export, import, have a foreign (FDI) share and invest in research and development (R&D) have signiﬁcantly higher productivity and proﬁt levels than those that are not involved in these activities, independent of institutional eﬀects.

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657

NOTES 1. Note that the implication of diﬀerent legal origins have been challenged by, for example, Lamoreaux and Rosenthal (2005), who argue that during the relevant period of industrial development there was no signiﬁcant diﬀerence between the Anglo-Saxon and French legal traditions in terms of property rights. We appreciate this being pointed out by an anonymous referee.

materials, Other manufacturing, Other services, Paper, Real estate and rental services, Retail and wholesale trade, Telecommunications, Textiles, Transport, Wood and furniture.

2. See Hillman, Rodriguez, Siegel, and Eden (2006) for a discussion and review of the literature.

16. In particular, the sales and the cost variables for Albania, Armenia, Belarus, Bosnia and Herzegovina, Bulgaria, Croatia, Czech Republic, Estonia, Georgia, Hungary, Kazakhstan, Kyrgyz Republic, Latvia, Lithuania, Macedonia, Moldova, Poland, Romania, Russian Federation, Serbia Montenegro, Slovak Republic, Slovenia, Tajikistan, Turkey, Ukraine, Uzbekistan, and Kosovo were reported in thousands of USDs. We thus just multiplied these variables by 1,000. Those variables for Germany, Greece, Ireland, Korea, Portugal, Spain, and Vietnam were in thousands of Euros. We thus multiplied them by 1,000 and converted them to USD by using the corresponding exchange rates. The data for Cambodia, Ecuador, El Salvador, Guatemala, Honduras, and Nicaragua were in USD, so they did not require any changes. The variables for Bangladesh, Oman, Peru, Philippines, South Africa, Sri Lanka, and Zambia were in thousand domestic currency units and those for Benin, Brazil, Chile, Madagascar, Mali, Mauritius, Senegal, Tanzania, Uganda were in domestic currency units. We thus multiplied these variables by 1,000 and 1, respectively, and converted them to USD by using the appropriate exchange rates.

3. See Glaeser et al. (2004) for a useful review of this literature. 4. See Peng, Wang, and Jiang (2008) for a review of relevant studies in the international business literature and in particular the institutional third “leg” of the international business “strategy tripod”—the view that better institutions such as property rights enforcement combine with ﬁrm and industry conditions to enhance ﬁrms’ performance and competitiveness and thus countries’ aggregate productivity and income levels. 5. We also used value added per unit of labor and total factor productivity as performance indicators as alternative speciﬁcations to check robustness to the productivity deﬁnition. 6. Managers were asked how much their establishment spent on design or R&D last year, where spending includes wages and salaries of R&D personnel (such as scientists and engineers), materials, education costs and subcontracting costs. We used the natural log of (1 + R&D) to accommodate zeroes and missing observations. This approach has been used in the literature when there are missing observations in order not to lose observations (see, for example, Paul, Johnston, & Frengley, 2000). 7. Industry dummies are used only in the OLS regressions, as the industry is accommodated by our instruments for the GMM estimation. 8. The data are cross sections for each country but were collected for diﬀerent years during 2000–05. 9. The regions are Africa-Sub-Saharan (AFR), East Asia and Paciﬁc (EAP), Europe and Central Asia (ECA), Latin America and Caribbean (LCR), Middle East and North Africa (MNA), and South Asia (SAR). 10. We do not have information on which ﬁrms were surveyed, so we did not wish to double-count ﬁrms (or countries) by including two years for some countries. 11. The standardized data was obtained from the World Bank on February 6, 2006. There was a total of 51,169 observations in the data. 12. For an excellent discussion of these data see Lederman (2007).

15. The size dummies for estimation (SZDum) are created using the number of workers.

17. For the manufacturing sector the shares at three-digit industry-level were used, which is consistent with the industry classiﬁcation in the survey. For the domestically-oriented non-tradable sectors (services and construction) and mining and quarrying, we used the share at the aggregate level, that is, the shares for service, construction and mining and quarrying. We replaced missing value added shares with the industry averages. 18. See Deaton (1997) for further information about this methodology. We use Stata’s pw command to weight the observations, in which the elements that have a smaller probability of selection relative to other elements are weighted more heavily. 19. We alternatively created a dummy variable indicating whether or not ﬁrms agree with the statement “I am conﬁdent that the judicial system will enforce my contractual and property rights in business disputes,” deﬁned as equal to 1 if Legal = 4, 5 or 6, and equal to 0 if Legal = 1, 2, or 3. For this speciﬁcation we generated similar results to those found for our ﬁnal reported speciﬁcation. 20. One might also try to recognize potential endogeneity of our international linkage variables. However, they are control variables rather than the focus of the analysis, and our cross-sectional data does not permit much consideration of endogeneity. Further, Yasar and Paul (2007) show that measures controlling for endogeneity of these variables for other data are robust to estimation methods accommodating potential endogeneity. We therefore did not pursue this further.

13. The World Bank’s Enterprise Surveys use either simple random sampling or random stratiﬁed sampling to ensure randomness of their sample. They also state that they “use both standardized survey instruments and a uniform sampling methodology to minimize measurement error and to yield data that are comparable across the world’s economies.”

22. The 24 industries in the data are identiﬁed in footnote 12.

14. The industries, distinguished by dummy variables (IndDUM) for estimation, are: Advertising and marketing, Agro industry, Auto and auto components, Beverages, Chemicals and pharmaceutics, Construction, Electronics, Food, Garments, Hotels and restaurants, IT services, Leather, Metals and machinery, Mining and quarrying, Non-metallic and plastic

23. The location category is based on the survey question: “Where are this establishment and your headquarters located in this country? (Enumerator, Please code as follows: 1 = Capital City; 2 = Other city of over 1 million population; 3 = City of 250,000–1million; 4 = City of 50,000–250,000; 5 = Town or Location with less than 50,000 population).”

21. See Cameron and Trivedi (2005) for more detailed information.

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WORLD DEVELOPMENT

24. Acemoglu et al. (2001) use the mortality rate of European settlers in colonized countries as an instrument for institutional development during colonization in the post-1500 century. They argue that European colonizers settled in colonies where disease risk was lower, which was reﬂected in mortality rates. We do not have enough observations for settler mortality rates for the countries in our sample to use this approach.

29. We use Stata’s cluster command to estimate cluster-robust standard errors. See http://www.stata.com/support/faqs/stat/cluster.html. 30. Although this estimate is rather small, the impact is higher based on the IV results that comprise our “preferred model.” 31. We appreciate this suggestion from an anonymous referee.

25. The British legal origin dummy variable is equal to one if the country adopted their law from British common law and zero otherwise. Other legal origin dummy variables are deﬁned similarly. 26. The Br variable is not available for all countries in our sample, so we do not include it in all regressions. To deﬁne this variable we use the results of the survey question: “We have heard that establishments are sometimes required to make gifts or informal payments to public oﬃcials to “get things done” with regards to customs, taxes, licenses, regulations, services etc. On average, what percent of annual sales value would such expenses cost a typical ﬁrm like yours?”

32. For further information see http://www.heritage.org/index/. Note also that in preliminary investigation we included a combination of these indexes to determine their relative signiﬁcance. As some were too closely correlated to satisfy multicollinearity tests, we used a subset that was suﬃciently diﬀerent to include them in combination. We found the property rights index to be the most signiﬁcant (at the 1% level), supporting the use of this index as our primary institution quality indicator. We also rescaled this variable to 10–60 to make its units comparable to the Legal variable. As expected with rescaling, the coeﬃcients on the property rights index are similar to the coeﬃcients on our Legal variable.

27. See Long (1997) for an excellent discussion of this issue. 33. Without clustered standard errors, the coeﬃcient on the investment share of real GDP is statistically signiﬁcant.

28. See Deaton (1997) for an excellent discussion of clustering.

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APPENDIX See Tables A1–A4.

Table A1. Correlation matrix of independent variables considered for the empirical model speciﬁcation

Legal Age Exp FDI Imp CU SL ln RD ln GDP OP INV PR Br

Legal

Age

Exp

FDI

Imp

CU

SL

ln RD

ln GDP

OP

INV

PR

1 0.026 0.037 0.015 0.076 0.102 0.023 0.010 0.203 0.058 0.192 0.225 0.115

1 0.087 0.006 0.005 0.013 0.091 0.134 0.099 0.015 0.035 0.080 0.052

1 0.248 0.195 0.038 0.039 0.184 0.088 0.004 0.063 0.048 0.014

1 0.224 0.012 0.049 0.096 0.074 0.020 0.060 0.017 0.003

1 0.040 0.033 0.078 0.106 0.087 0.079 0.142 0.029

1 0.063 0.004 0.277 0.043 0.202 0.100 0.123

1 0.014 0.098 0.181 0.056 0.007 0.024

1 0.044 0.049 0.013 0.022 0.011

1 0.091 0.711 0.706 0.231

1 0.021 0.037 0.018

1 0.622 0.173

1 0.143

660

WORLD DEVELOPMENT Table A2. Tests of multicollinearity: variance inﬂation factors (VIF) and tolerance

Variable

Legal Age Age2 Exp FDI Imp CU SL ln RD

VIF

Tolerance

Panel A Model with ﬁrm-level variables 1.040 5.230 4.960 1.200 1.170 1.210 1.140 1.140 1.130

0.958 0.191 0.202 0.833 0.854 0.829 0.878 0.879 0.885

Mean VIF

3.860 Panel B Model speciﬁcation with both micro- and macro-level variables 1.060 5.280 4.990 1.250 1.180 1.160 1.130 1.180 1.180 2.220 1.370 1.760

Legal Age Age2 Exp FDI Imp CU SL ln RD ln GDP OP INV Mean VIF

0.941 0.189 0.201 0.799 0.848 0.865 0.888 0.850 0.851 0.451 0.731 0.576

2.470

Note: the “rule of thumb” in the econometric literature is that a VIF > 10 or a tolerance level < 0.1 are signs of severe multicollinearity problems.

Table A3. Parameter estimates with macro variables [dependent variable = natural log of labor productivity] Independent variables

Ordinary least squares (OLS) bFS p

OLS Legal system (Legal) Age of the ﬁrm (Age) Age squared (Age2) Exporting dummy (Exp) Foreign direct investment dummy (FDI) Import share (Imp) Capacity utilization (CU) Skilled labor share (SL) Natural log of 1 + R&D (ln RD) Natural log of real GDP per capita (ln GDP) Openness (OP) Investment share of real GDP per capita (INV) Observations R-squared F-statistic of instruments (P-value) Sargan statistic (P-value)

***

0.013 (0.003) 0.010 (0.004)*** 0.000 (0.000)*** 0.166 (0.057)*** 0.567 (0.157)*** 0.148 (0.148) 0.010 (0.002)*** 0.451 (0.159)*** 0.076 (0.023)*** 0.903 (0.299)*** 0.017 (0.008)*** 0.039 (0.023)* 17,082 0.616

0.057 0.055 0.036 0.020 0.054 0.017 0.057 0.095 0.078 0.241 0.195 0.090

Instrumental variables (IV) bFS p

IV1 ***

bFS p

IV2 **

0.113 (0.029) 0.007 (0.004)* 0.000 (0.000)*** 0.263 (0.066)*** 0.350 (0.098)*** 0.307 (0.137)** 0.007 (0.003)*** 0.498 (0.137)*** 0.071 (0.021)*** 0.738 (0.296)** 0.019 (0.009)** 0.031 (0.024)

0.488 0.034 0.021 0.032 0.033 0.034 0.041 0.105 0.073 0.197 0.215 0.072

0.182 (0.093) 0.003 (0.004) 0.000 (0.000) 0.328 (0.098)*** 0.195 (0.154) 0.411 (0.155)*** 0.005 (0.003)* 0.529 (0.144)*** 0.068 (0.020)*** 0.629 (0.335)* 0.020 (0.010)** 0.026 (0.032)

17,040 0.923 0.000 0.053

0.315 0.074

17,082 0.878 0.000 0.582

0.786 0.018 0.009 0.040 0.019 0.046 0.031 0.111 0.070 0.168 0.229 0.060

Notes: (1) the OLS regressions reported in this table include dummy variables that control for size, year, and industry characteristics. The IV regressions include dummy variables that control for size and year. The coeﬃcients for these dummy variables are not reported here in the interest of space, but are available from the authors upon request. (2) The standard errors are clustered by country and sector. The clustered standard errors are much larger, by up FS SY to a factor of two. (3) bFS p are the fully standardized coeﬃcients for Xp (bp ¼ rp bp =rY ¼ rp bp ). (4) IV1 denotes the instrumental variables results in which average levels of the Legal variable for other ﬁrms (j – i) in a ﬁrm’s industry and location category for each country and year are used as instruments. (5) IV2 denotes the instrumental variables results in which dummy variables for English, French, and German legal origin are used as instruments. Socialist legal origin is the omitted category. (6) When we rescale the PR variable to make it comparable to the Legal variable, the coeﬃcients illustrated in the ﬁrst row of this table are 0.090, 0.067, 0.085, and 0.067, respectively. * Signiﬁcant at the 10% level. ** Signiﬁcant at the 5% level. *** Signiﬁcant at the 1% level.

PROPERTY RIGHTS INSTITUTIONS AND FIRM PERFORMANCE: A CROSS-COUNTRY ANALYSIS

661

Table A4. Parameter estimates using value added based labor productivity and total factor productivity Panel A: value-added labor productivity

Legal system (Legal) Age of the ﬁrm (Age) Age squared (Age2) Exporting dummy (Exp) Foreign direct investment dummy (FDI) Import share (Imp) Capacity utilization (CU) Skilled labor share (SL) Natural log of 1 + R&D (ln RD) ln M ln L ln K Observations R-squared F-statistic of instruments (P-value) Sargan statistic (P-value)

Panel B: TFP

IV1

bFS p

IV2

bFS p

IV1

bFS p

0.102 (0.023)*** 0.019 (0.005)*** 0.000 (0.000)*** 0.362 (0.107)*** 0.176 (0.133) 0.326 (0.135)** 0.009 (0.004)** 0.196 (0.144) 0.075 (0.013)***

0.503 0.109 0.064 0.043 0.019 0.041 0.061 0.044 0.076

0.199 (0.030)*** 0.010 (0.008) 0.000 (0.000) 0.441 (0.100)*** 0.009 (0.121) 0.542 (0.144)*** 0.001 (0.007) 0.179 (0.097)* 0.049 (0.022)**

0.979 0.057 0.017 0.053 0.001 0.068 0.007 0.041 0.050

0.120 (0.029)*** 0.006 (0.009) 0.000 (0.000) 0.289 (0.114)*** 0.064 (0.096) 0.233 (0.182) 0.007 (0.004)* 0.196 (0.042)*** 0.011 (0.016) 0.633 (0.059)*** 0.177 (0.050)*** 0.096 (0.035)***

0.508 0.030 0.007 0.039 0.007 0.027 0.038 0.036 0.013 0.660 0.070 0.095

16,917 0.967 0.000 0.509

16,959 0.972 0.000 0.790

1,946 0.981 0.000 0.291

Notes: (1) the OLS regressions reported in this table include dummy variables that control for size, year, and industry characteristics. The IV regressions include dummy variables that control for size and year. The coeﬃcients for these dummy variables are not reported here in the interest of space, but are available from the authors upon request. (2) The standard errors are clustered by country and sector. The clustered standard errors are much larger, by up FS SY to a factor of two. (3) bFS p are the fully standardized coeﬃcients for Xp (bp ¼ rp bp =rY ¼ rp bp ). (4) IV1 denotes the instrumental variables results in which average levels of the Legal variable for other ﬁrms (j – i) in a ﬁrm’s industry and location category for each country and year are used as instruments. (5) IV2 denotes the instrumental variables results in which dummy variables for English, French, and German legal origin are used as instruments. Socialist legal origin is the omitted category. * Signiﬁcant at the 10% level. ** Signiﬁcant at the 5% level. *** Signiﬁcant at the 1% level.

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Property Rights Institutions and Firm Performance: A Cross-Country Analysis

Property Rights Institutions and Firm Performance: A Cross-Country Analysis

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