financial intermediation and growth: causality and …...financial intermediation and growth:...

qWe thank seminar participants at the University of Illinois, the Federal Reserve Banks ofRichmond and Dallas, the University of Texas at Austin, the University of Minnesota, the CentralBank of Chile as well as Robert King, Lant Pritchett, Andrei Shleifer, Jonathan Wright, and ananonymous referee for helpful comments. This paper's "ndings, interpretations, and conclusions areentirely those of the authors and do not necessarily represent the views of the Central Bank of Chile,the World Bank, its Executive Directors, or the countries they represent.

*Corresponding author.E-mail address: [email protected] (R. Levine).

Journal of Monetary Economics 46 (2000) 31}77

Financial intermediation and growth:Causality and causesq

Ross Levine!,*, Norman Loayza", Thorsten Beck#

!Carlson School of Management, University of Minnesota, Minneapolis, MN 55455, USA"Central Bank of Chile, Santiago, Chile and The World Bank, Washington, DC 20433, USA

#The World Bank, Washington, DC 20433, USA

Received 13 October 1998; received in revised form 9 August 1999; accepted 24 August 1999

Abstract

This paper evaluates (1) whether the exogenous component of "nancial intermediarydevelopment in#uences economic growth and (2) whether cross-country di!erences in legaland accounting systems (e.g., creditor rights, contract enforcement, and accounting stan-dards) explain di!erences in the level of "nancial development. Using both traditionalcross-section, instrumental variable procedures and recent dynamic panel techniques, we"nd that the exogenous components of "nancial intermediary development is positivelyassociated with economic growth. Also, the data show that cross-country di!erences in legaland accounting systems help account for di!erences in "nancial development. Together,these "ndings suggest that legal and accounting reforms that strengthen creditor rights,contract enforcement, and accounting practices can boost "nancial development and acceler-ate economic growth. ( 2000 Published by Elsevier Science B.V. All rights reserved.

JEL classixcation: O16; O40; G28

Keywords: Financial development; Economic growth; Legal system

0304-3932/00/$ - see front matter ( 2000 Published by Elsevier Science B.V. All rights reserved.PII: S 0 3 0 4 - 3 9 3 2 ( 0 0 ) 0 0 0 1 7 - 9

1The quotations from Hamilton and Adams are taken from Hammond (1991). For an historicalperspective, also see Bagehot (1873) and Schumpeter (1934) on how intermediaries spur economicgrowth.

2Also, see Townsend (1979); Gale and Hellwig (1985); Diamond (1984); Boyd and Prescott (1986);Diamond and Dybvig (1983); and Greenwood and Jovanovic (1990). For reviews of this literaturesee Gertler (1988) and Levine (1997).

3For more on how economic activity in#uences the "nancial sector, see Patrick (1966) andGreenwood and Jovanovic (1990).

1. Introduction

Do better functioning "nancial intermediaries } "nancial intermediaries thatare better at ameliorating information asymmetries and facilitating transactions} exert a causal in#uence on economic growth? Providing evidence on causalityhas implications for policymakers and economists. For instance, Hamilton(1781) argued that &banks were the happiest engines that ever were invented' forspurring economic growth. Others, however, question whether "nance boostsgrowth. Adams (1819) asserted that banks harm the &morality, tranquility, andeven wealth' of nations.1 Economic theories mirror these divisions. Some mod-els show that economic agents create debt contracts and "nancial intermediariesto ameliorate the economic consequences of informational asymmetries, withbene"cial implications for resource allocation and economic activity.2 How-ever, other models note that higher returns from better resource allocation maydepress saving rates enough such that overall growth rates actually slowwith enhanced "nancial development (Bencivenga and Smith, 1991; King andLevine, 1993b). Furthermore, Robinson (1952) argues that "nancial develop-ment primarily follows economic growth and the engines of growth must besought elsewhere.3 In terms of policy, if "nancial intermediaries exert aneconomically large impact on growth, then this raises the degree of urgencyattached to legal, regulatory, and policy reforms designed to promote "nancialdevelopment.

This paper rigorously examines whether the exogenous component of "nan-cial intermediary development in#uences economic growth. We also presentevidence concerning the legal, regulatory, and policy determinants of "nancialdevelopment. While past work shows that the level of "nancial development isa good predictor of economic growth (King and Levine, 1993a, b; Levine andZervos, 1998; Neusser and Kugler, 1998; Rousseau and Wachtel, 1998), theseresults do not settle the issue of causality. Although this paper does not fullyresolve all concerns about causality, it uses new data and new econometricprocedures that directly confront the potential biases induced by simultaneity,

32 R. Levine et al. / Journal of Monetary Economics 46 (2000) 31}77

4This paper complements recent microeconomic e!orts aimed at reconciling whether "nancialdevelopment is simply a good predictor of economic growth. Rajan and Zingales (1998) show that, incountries with well-developed "nancial systems, industries that are naturally heavy users of external"nance grow relatively faster than other industries. DemirguK c7 -Kunt and Maksimovic (1998) showthat "rms in countries with better-developed "nancial systems grow faster than they could havegrown without this access. Jayaratne and Strahan (1996) show that when individual states of theUnited States relaxed intrastate branching restrictions the quality of bank loans rose and per capitaGDP growth accelerated.

omitted variables, and unobserved country-speci"c e!ects that have plaguedprevious empirical work on the "nance-growth link.4

Methodologically, the paper uses two econometric techniques: (1) generalizedmethod-of-moments (GMM) dynamic panel estimators and (2) a cross-sectionalinstrumental-variable estimator. Whereas the pure cross-sectional estimatorfollows directly from traditional growth studies, the panel estimator uses pooledcross-country and time-series data to exploit the additional information pro-vided by the over-time variation in the growth rate and its determinants. Thisadded information allows us to obtain more precise estimates and, most impor-tantly, correct for biases associated with existing studies of the "nance-growthrelationship.

Consider "rst the GMM dynamic panel estimators, which are speci"callydesigned to address the econometric problems induced by unobserved country-speci"c e!ects and joint endogeneity of the explanatory variables in lagged-dependent-variable models, such as growth regressions. We assemble a paneldataset of 74 countries, where the data are averaged over each of the seven5-year intervals composing the period 1960}1995. The dependent variable is thegrowth rate of the real per capita gross domestic product (GDP). The regressorsinclude the level of "nancial intermediary development, along with a broad setof variables that serve as conditioning information. We employ two GMMpanel estimators; both are based on the use of lagged observations of theexplanatory variables as instruments (thus labeled &internal' instruments). In the"rst GMM panel estimator, we (a) di!erence the regression equation to removeany omitted variable bias created by unobserved country-speci"c e!ects, andthen (b) instrument the right-hand-side variables (the di!erenced values of theoriginal regressors) using lagged values of the original regressors to eliminatepotential parameter inconsistency arising from simultaneity bias. This diwerencedynamic-panel estimator, developed by Arellano and Bond (1991) and Holtz-Eakin et al. (1990), has increasingly been used in studies of growth (Caselli et al.,1996; Easterly et al., 1997). We also use a second GMM dynamic panelestimator that improves upon the diwerence estimator in so far as the quality ofthe instruments is concerned. Speci"cally, lagged values of "nancial develop-ment frequently make weak instruments for forecasting changes in "nancialdevelopment. This weak instrument problem can induce biases in "nite samplesand poor precision even asymptotically (Alonso-Borrego and Arellano, 1996).

R. Levine et al. / Journal of Monetary Economics 46 (2000) 31}77 33

The second GMM panel estimator mitigates this problem by complementingthe diwerence speci"cation with the original regression speci"ed in levels. Thissystem estimator, developed by Arellano and Bover (1995), o!ers dramaticimprovements in both e$ciency and consistency in Monte Carlo simulations(Blundell and Bond, 1997). These GMM estimators have not been used before toexamine the relationship between "nancial intermediary development and eco-nomic growth.

Our second econometric method to examine the e!ect of "nancial intermedi-ary development on economic growth is a cross-sectional estimator. Data for 71countries are averaged over the period 1960}1995, so that there is one observa-tion per country. Although the cross-sectional estimator does not deal asrigorously as the panel estimators with the potential problems induced bysimultaneity, omitted variables, and unobserved country-speci"c e!ects, thecross-sectional results are direct descendants of the cross-country literature on"nance and growth (e.g., King and Levine, 1993a; Levine and Zervos, 1998).Also, the cross-sectional estimator serves as a consistency check on the panel"ndings. Unlike much of the cross-country growth literature, we use instrumen-tal variables to extract the exogenous component of "nancial intermediarydevelopment. For this purpose we use the insight provided by LaPorta et al.(1997, 1998; henceforth LLSV). They note that most countries can be dividedinto countries with predominantly English, French, German, or Scandinavianlegal origins and that countries typically obtained their legal systems throughoccupation or colonization. Moreover, LLSV (1998) show that national legalorigin strongly in#uences the legal and regulatory environment governing"nancial sector transactions. Since legal origin explains cross-country di!er-ences in "nancial intermediary development and since legal origin is (reason-ably) exogenous, we use legal origin as an instrumental variable to control forsimultaneity bias.

In conducting this research, we construct a new dataset and focus on threemeasures of "nancial intermediation. One measures the overall size of the"nancial intermediation sector. The second measures whether commercialbanking institutions, or the central bank, is conducting the intermediation. Thethird measures the extent to which "nancial institutions funnel credit to privatesector activities. Our "nancial development indicators improve on pastmeasures by (i) more accurately de#ating nominal measures of intermediaryliabilities and assets, (ii) more comprehensively measuring the banking sector,and (iii) more carefully distinguishing who is conducting the intermediation andto where the funds are #owing. While the "nancial intermediary indicators arestill imperfect measures of how well "nancial intermediaries research "rms,monitor managers, mobilize savings, pool risk, and ease transactions, these threemeasures provide more information about "nancial intermediary developmentthan past measures and together they provide a more accurate picture than if weused only a single measure. Moreover, they produce similar conclusions.


The GMM dynamic panel estimators and the pure cross-sectional regressionsproduce very consistent "ndings: the exogenous component of "nancial inter-mediary development is positively and robustly linked with economic growth.In interpreting the results, note that the "ndings do not reject the view thateconomic activity in#uences "nancial development. Rather, the results showthat the positive link between "nance and growth is not only due to growthin#uencing "nancial development; the strong positive relationship between"nancial intermediary development and long-run growth is at least partlyexplained by the e!ect of the exogenous component of "nancial development oneconomic growth. Economically, the impact is large. For example, the estimatedcoe$cients suggest that if Argentina had enjoyed the level of "nancial intermedi-ary development of the average developing country during the 1960}1995period it would have experienced about one percentage point faster real percapita GDP growth per annum over this period.

The regression results pass a battery of diagnostic and sensitivity tests. Theresults are robust to modi"cations in the conditioning information set andalterations in the sample period. Outliers are not producing the results. Speci-"cation tests support the appropriateness of the instrumental variables. Thisgives credence to the conclusion that the estimated positive link between "nanceand growth is not due to simultaneity bias or insu$cient control for otherdeterminants of growth.

The results favor the growth-enhancing view of "nancial intermediationespoused by Hamilton (1781), Bagehot (1873), and Schumpeter (1934). In turn,the results are less consistent with those that minimize the positive role of"nancial intermediaries in the growth process (Adams, 1819; Robinson, 1952;Lucas, 1988). Similarly, this paper's "ndings are consistent with theoreticalmodels that predict that better functioning "nancial intermediaries accelerateeconomic growth. Our results do not favor models that emphasize the poten-tially growth-retarding impact of "nancial development. Finally, this paper's"ndings highlight "nancial reform. If economists can identify legal, regulatory,and policy reforms that promote "nancial development, this may positivelyin#uence economic growth.

Consequently, we also examine whether cross-country di!erences in particu-lar legal and regulatory system characteristics help explain cross-country di!er-ences in the level of "nancial intermediary development. The degree to which"nancial intermediaries can acquire information about "rms, write contracts,and have those contracts enforced will fundamentally in#uence the ability ofthose intermediaries to identify worthy "rms, exert corporate control, managerisk, mobilize savings, and ease exchanges. Thus, as argued by LLSV (1997,1998), the legal and regulatory system will fundamentally in#uence the ability ofthe "nancial system to provide high-quality "nancial services. LLSV (1997)examine securities markets. In contrast, we combine their data on the legal andregulatory environment with our data on "nancial intermediation to study the


links between "nancial intermediary development and cross-country di!erencesin legal and accounting systems.

The results provide useful information to policymakers. The data suggest thatcountries with legal and regulatory systems that give a high priority to creditorsreceiving the full present value of their claims on corporations have betterfunctioning "nancial intermediaries than countries where the legal system pro-vides weaker support to creditors. Moreover, contract enforcement seems tomatter even more than the formal legal and regulatory codes. Countries thate$ciently impose compliance with laws tend to have better developed "nancialintermediaries than countries where enforcement is more lax. The paper alsoshows that information disclosure matters for "nancial development. Countrieswhere corporations publish relatively comprehensive and accurate "nancialstatements have better developed "nancial intermediaries than countries wherepublished information on corporations is less reliable. Finally, we con"rm these"ndings when using the legal origin dummy variables (English, French, German,Scandinavian) as instrumental variables to extract the exogenous component ofthe legal, enforcement, and accounting environment: the legal/regulatory systemexerts a powerful in#uence on "nancial sector development. While considerableresearch remains, taken together, this paper's "ndings provide support for theview that legal and regulatory changes that strengthen creditor rights, contractenforcement, and accounting practices boost "nancial intermediary develop-ment with positive repercussions on economic growth.

The rest of the paper is organized as follows. Section 2 presents the resultsusing purely cross-sectional data, while Section 3 discusses and presents thediwerence and system dynamic panel results. Section 4 provides information onhow the legal and accounting environment explain cross-country di!erences in"nancial development. Section 5 concludes.

2. Finance and growth: Cross-sectional analyses

This section examines the relationship between "nancial intermediation andgrowth using a pure cross-sectional estimator. We begin with the pure cross-sectional estimator because it more directly follows from the large cross-countrygrowth literature. The next section uses GMM dynamic panel procedures thatmore comprehensively confront problems induced by country-speci"c e!ects,endogeneity, and the routine use of lagged dependent variables in growthregressions.

2.1. Financial intermediary development

As discussed above, numerous theoretical models show that economic agentsmay form "nancial intermediaries to mitigate the economic consequences ofinformation and transaction costs. More speci"cally, "nancial intermediaries


5For example, see Greenwood and Jovanovic (1990), Bencivenga and Smith (1991), and King andLevine (1993b).

6One way this paper improves upon past measures of "nancial intermediary development is byaccurately de#ating nominal measures of "nancial intermediary liabilities and assets. Speci"cally,while "nancial intermediary balance sheet items are measured at the end of the year, GDP ismeasured over the year. Some authors try to correct for this problem by using an average of "nancialintermediary balance sheet items in year t and t!1 and dividing by GDP measured in year t (Kingand Levine, 1993a). This however does not fully resolve the distortion, especially in highly in#ation-ary environments. This paper de#ates end-of-year "nancial balance sheet items by end of yearconsumer price indices (CPI) and de#ates the GDP series by the annual CPI. Then, we compute theaverage of the real "nancial balance sheet item in year t and t!1 and divide this average by realGDP measured in year t. This is described more fully in the data appendix. Although we haveattempted to be as careful as possible in constructing the data, measurement errors undoubtedlyremain. We could not identify any reasons to believe, however, that this would systematicallyin#uence this paper's "ndings since we control for a variety of factors } including the level ofeconomic development } and use instrumental variable procedures.

emerge to lower the costs of researching potential investments, exerting corpo-rate control, managing risk, mobilizing savings, and conducting exchanges.Theory further suggests that, by providing these services to the economy,"nancial intermediaries in#uence savings and allocation decisions in ways thatmay alter long-run growth rates.5 Thus, modern economic theory provides anintellectual framework for understanding how "nancial intermediaries in#uencelong-run rates of economic growth.

To evaluate the empirical predictions advanced by a variety of theoreticalmodels regarding the relationship between "nance and growth, therefore, wewould ideally like to construct measures of the ability of di!erent "nancialsystems to research and identify pro"table ventures, monitor and controlmanagers, ease risk management and facilitate resource mobilization. It isimpossible, however, to construct accurate, comparable measures of these "nan-cial services for a broad cross-section of countries over the past 35 years.Consequently, to measure the provision of "nancial services, this paper con-structs three indicators of "nancial intermediary development. (We alsoconsider two additional measures in the sensitivity section.) While each hasparticular strengths and weaknesses, we improve upon past measures of"nancial intermediary development.6

LIQUID LIABILITIES equals liquid liabilities of the "nancial system (cur-rency plus demand and interest-bearing liabilities of banks and nonbank "nan-cial intermediaries) divided by GDP. This is a typical measure of &"nancialdepth' and thus of the overall size of the "nancial intermediary sector (King andLevine, 1993a). This commonly used measure of "nancial sector developmenthas shortcomings. It may not accurately gauge the e!ectiveness of the "nancialsector in ameliorating informational asymmetries and easing transactions costs.Also, LIQUID LIABILITIES includes deposits by one "nancial intermediary inanother, which may involve &double counting'. Under the assumption that the


7Levine and Zervos (1998) also examine whether equity markets substitute for credit issuingintermediaries. They "nd that the answer is no. Measures of banking sector development and stockmarket development both enter signi"cantly when included together in simple cross-country growthregressions. Evidently, banks provide di!erent "nancial services from those provided by securitiesmarkets. Speci"cally, theory suggests that securities markets are particularly good at augmentingliquidity and allowing agents to custom design risk management tools. Theory suggests that inter-mediaries have a comparative advantage in reducing informational asymmetries. This paper is verydi!erent from Levine and Zervos (1998) because we are trying to control formally for simultaneity andomitted variable biases, which they do not do. To do this, we rely on the GMM dynamic panelprocedures and use the pure cross-sectional estimator to con"rm our results. Unfortunately, there donot exist securities market data over a su$ciently long period and across a su$ciently large number ofcountries to conduct our analyses with securities market data from Levine and Zervos (1998).

size of the "nancial intermediary sector is positively correlated with the provis-ion and quality of "nancial services, many researchers use this measure of"nancial depth (Goldsmith, 1969; King and Levine, 1993a; and McKinnon,1973). Thus, we include it as one measure of "nancial intermediary development.

COMMERCIAL-CENTRAL BANK equals the ratio of commercial bankassets divided by commercial bank plus central bank assets. COMMERCIAL-CENTRAL BANK measures the degree to which commercial banks versus thecentral bank allocate society's savings. Again, this measure of "nancial inter-mediary development does not directly measure the e!ectiveness of banks inresearching "rms, exerting corporate control, mobilizing savings, easingtransactions, and providing risk management facilities to clients. Thus, COM-MERCIAL-CENTRAL BANK is not a direct measure of the quality andquantity of "nancial services provided by "nancial intermediaries. The intuitionunderlying this measure is that banks are more likely to identify pro"tableinvestments, monitor managers, facilitate risk management, and mobilize sav-ings than central banks. Thus, King and Levine (1993a, b) recommend includingCOMMERCIAL-CENTRAL BANK as an additional measure of "nancialintermediary development.

PRIVATE CREDIT equals the value of credits by "nancial intermediaries tothe private sector divided by GDP. This measure of "nancial development ismore than a simple measure of "nancial sector size. PRIVATE CREDIT isolatescredit issued to the private sector, as opposed to credit issued to governments,government agencies, and public enterprises. Furthermore, it excludes creditsissued by the central bank. PRIVATE CREDIT is our preferred indicatorbecause it improves on other measures of "nancial development used in theliterature. For example, King and Levine (1993a, b) use a measure of grossclaims on the private sector divided by GDP. But, this measure includes creditsissued by the monetary authority and government agencies, whereas PRIVATECREDIT includes only credits issued by banks and other "nancial intermedia-ries. Also, Levine and Zervos (1998) and Levine (1998) use a measure of depositmoney bank credits to the private sector divided by GDP over the period1976}1993.7 That measure, however, does not include credits to the private


Table 1Summary statistics: 1960}1995!

Financial Intermediary Development

Liquid liabilitiesCommercial-centralbank Private credit

Mean 43.44 78.16 38.29Median 37.48 83.89 27.01Maximum 143.43 98.99 141.30Minimum 9.73 23.72 4.08Std. Dev. 25.61 18.26 28.71

Observations 71 71 71

!LIQUID LIABILITIES " liquid liabilities of the "nancial system (currency plus demand andinterest-bearing liabilities of banks and non-bank "nancial intermediaries) divided by GDP, times100. COMMERCIAL-CENTRAL BANK " assets of deposit money banks divided by assets ofdeposit money banks plus central bank assets, times 100. PRIVATE CREDIT " credit by depositmoney banks and other "nancial institutions to the private sector divided by GDP, times 100.

sector by non-deposit money banks and it only covers the period 1976}1993.PRIVATE CREDIT is a broader measure of credit issuing "nancial inter-mediaries and its time dimension is twice as long, 1960}1995. We should alsoemphasize here that these "nancial intermediary measures are not simplypicking up the relative importance of state-owned enterprises and the overalllevel of nationalization. In the analysis below, we control for the role ofstate-owned enterprises and this does not a!ect the conclusions. While PRI-VATE CREDIT does not directly measure the amelioration of information andtransaction costs, we interpret higher levels of PRIVATE CREDIT as indicatinghigher levels of "nancial services and therefore greater "nancial intermediarydevelopment.

Table 1 provides summary statistics on the "nancial intermediary develop-ment indicators. The data are listed country-by-country in Appendix A, Table 8.(Summary statistics and correlations with other variables used in this paper areprovided in Tables 10 and 11.) There is considerable variation across countries.For example, PRIVATE CREDIT is less than 10% of GDP in Zaire, SierraLeone, Ghana, Haiti, and Syria. PRIVATE CREDIT, however, is greater than85 percent of GDP in Switzerland, Japan, the United States, Sweden, and theNetherlands. Real per capita GDP growth also exhibits considerable cross-country variation. For instance, Korea, Malta, Taiwan, and Cyprus all enjoyedgrowth rates over greater than 5% per annum over the 35 year period, whileZaire, Niger, Ghana, Venezuela, Haiti, and El Salvador all su!ered growth ratesof less than negative 0.5% per year from 1960 to 1995. Thus, the dataset o!ers


Fig. 1. Financial development across income groups, 1960}1995.

rich cross-country variation for exploring the link between growth and "nancialintermediary development.

The positive relationship between income per capita and "nancial develop-ment is illustrated in Fig. 1. Fig. 1 shows that all three "nancial intermediarydevelopment indicators tend to increase as we move from low- to high-incomecountries. Since conditional convergence is a feature of cross-country data setsover the post 1960 period (Barro and Sala-i-Martin, 1995), the positive correla-tion between income per capita and "nancial development may then suggesta negative relationship between "nancial development and economic growth.Indeed, four out of the "ve countries with the highest level of PRIVATECREDIT have slower than average growth rates (Japan is the lone exception).In any case, these summary statistics highlight the importance of controlling forthe level of real per capita GDP } as well as a host of other economic andpolitical factors } in assessing the independent relationship between "nancialintermediary development and economic growth.

Fig. 2 illustrates that countries with higher levels of PRIVATE CREDIT tendto enjoy faster growth rates over the 1960}1995 period than countries withlower levels of "nancial intermediary development. Indeed, of the ten fastestgrowing countries over this 35-year period, all of them had larger-than-averagevalues of PRIVATE CREDIT. Many well-known &Asian Miracles', such asMalaysia, Thailand, Japan, Taiwan, and Korea, were in the top quartile ofcountries as ranked by "nancial intermediary development. It is worth notingthat four European countries (Greece, Ireland, Portugal, and Cyprus) were alsoamong the ten fastest growing countries during this sample period. Each of these


Fig. 2. Economic growth and "nancial intermediary development, 1960}1995.

8Some countries have e!ectively improved their "nancial systems through a range of "nancialreforms, e.g., Ghana, as documented in Gelbard and Leite (1999). Thus, it is important to exploit thetime-series dimension of the data. We do this below.

countries also had comparatively well-developed "nancial systems. Certainly,many factors may account for these economic success stories. At the other end ofthe spectrum, seven of the ten countries with negative growth rates over the35-year period were in the lowest quartile of countries as de"ned by "nancialintermediary development (Zaire, Niger, Ghana, Haiti, Liberia, Sierra Leone,and Guyana). The banking systems of these countries have been in disarray formuch of the last 35 years (see, for example, Gelbard and Leite, 1999; Mehran,1998; Sheng, 1996; Caprio et al., 1994 for discussions of the individual countries).Government ownership of banks, massive o$cial intervention in credit alloca-tion, high levels of nonperforming loans, controls on interest rates, and numer-ous restrictions impede the ability of the "nancial systems in these countriesfrom mobilizing and allocating capital e$ciently.8 But, these countries su!ermany other economic policy and political maladies. Thus, we now turn toregression analyses where we control for an array of factors associated witheconomic growth (including country speci"c-factors) and also confront poten-tial biases induced by simultaneity.


2.2. Legal origin

To confront the issue of simultaneity, we identify instrumental variables for"nancial intermediary development. Here, we follow LLSV (1998) in looking tolegal origin. Comparative legal scholars place countries into four major legalfamilies, English, French, German, or Scandinavian, that descended fromRoman law (Reynolds and Flores, 1996). As described by Glendon et al. (1982),Roman law was compiled under the direction of Byzantine Emperor Justinian inthe sixth century. Over subsequent centuries, the Glossators and Commentatorsinterpreted, adapted, and amended the Law (Berman, 1997). In the 17th and18th centuries the Scandinavian countries formalized their own legal codes. TheScandinavian legal systems have remained relatively una!ected from the farreaching in#uences of the German and especially the French Civil Codes.

Napoleon directed the writing of the French Civil Code in 1804. He made ita priority to secure the adoption of the Code in France and all conqueredterritories, including Italy, Poland, the Low Countries, and the HabsburgEmpire. Also, France extended her legal in#uence to parts of the Near East,Northern and Sub-Saharan Africa, Indochina, Oceania, French Guyana, andthe French Caribbean islands during the colonial era. Furthermore, the FrenchCivil Code was a major in#uence on the Portuguese and Spanish legal systems,which helped spread the French legal tradition to Central and South America.

The German Civil Code (Bu( rgerliches Gesetzbuch) was completed almosta century later in 1896. The German Code exerted a big in#uence on Austria andSwitzerland, as well as China (and hence Taiwan), Czechoslovakia, Greece,Hungary, Italy, and Yugoslavia. Also, the German Civil Code heavily in#uencedthe Japanese Civil Code, which helped spread the German legal tradition toKorea. Unlike these Civil Law countries, the English legal system is commonlaw, where the laws were primarily formed by judges trying to resolve particularcases.

This paper takes national legal origin as an exogenous &endowment' since theEnglish, French, and German systems were spread primarily through conquestand imperialism. It is critical to recognize, however, that exogeneity is nota su$cient condition for economically meaningful instrumental variables. Itmust also be the case that there are good reasons for believing that legal origin isclosely connected to factors that directly a!ect the behavior of "nancial inter-mediaries. LLSV (1998) trace di!erences in legal origin through to di!erences inthe legal rules covering secured creditors, the e$ciency of contract enforcement,and the quality of accounting standards. Thus, legal origin is connected to legaland regulatory characteristics de"ning "nancial intermediary activities.

Table 2 presents regressions of the "nancial intermediary development indi-cators on the dummy variables for English, French and German legal origin,relative to Scandinavian origin (which is captured in the constant). We extendthe LLSV (1998) data set from 44 countries (with "nancial intermediary data) to


Table 2Legal origin and "nancial intermediary development, 1960}1995!

Financial intermediary development

Liquid liabilities Commercial-central bank Private credit

C 3.829 0.958 4.506 3.063 4.027 !0.674(0.000) (0.081) (0.000) (0.000) (0.000) (0.386)

ENGLISH !0.134 0.249 !0.170 0.022 !0.717 !0.090(0.325) (0.038) (0.002) (0.716) (0.002) (0.646)

FRENCH !0.434 !0.052 !0.270 !0.078 !0.894 !0.268(0.001) (0.703) (0.000) (0.152) (0.000) (0.190)

GERMAN 0.477 0.683 0.048 0.152 0.401 0.738(0.016) (0.000) (0.100) (0.010) (0.076) (0.002)

INCOME 0.330 0.166 0.541(0.000) (0.000) (0.000)

Obs. 71 71 71 71 71 71Prob(F-test) 0.001 0.000 0.040 0.000 0.000 0.000R-square 0.23 0.44 0.12 0.30 0.26 0.55

!LIQUID LIABILITIES " liquid liabilities of the "nancial system (currency plus demand andinterest-bearing liabilities of banks and non-bank "nancial intermediaries) divided by GDP, times100. COMMERCIAL-CENTRAL BANK " assets of deposit money banks divided by assets ofdeposit money banks plus central bank assets, times 100. PRIVATE CREDIT " credit by depositmoney banks and other "nancial institutions to the private sector divided by GDP, times 100.Values for the "nancial intermediary development indicators are averages over the 1960}1995period. ENGLISH " English legal origin. FRENCH " Napoleonic legal origin. GERMAN "

German legal origin. Scandinavian legal origin is the omitted category. INCOME " Logarithm ofreal per capita GDP in 1960.

71 using Reynolds and Flores (1996). The data are listed in Appendix A Table 8.Some of the regressions also control for the level of real per capita GDP. Themajor message is that countries with a German legal origin have better de-veloped "nancial intermediaries. While countries with a French legal traditiontend to have less well-developed institutions than other countries on average,this result does not hold when controlling for the overall level of economicdevelopment. Also, as indicated by the P-values of the F-test, the legal originvariables explain a signi"cant fraction of the cross-country variation of the"nancial intermediary development indicators.

2.3. Legal origin and growth in a pure cross-section of countries

2.3.1. Cross-sectional estimatorThe pure cross-sectional analysis uses data averaged over 1960}1995, such

that there is one observation per country. The basic regression takes the form:

GROWTHi"a#bFINANCE

i#c@[CONDITIONING SET]

i#e

i,


9Due to the potential nonlinear relationship between economic growth and the assortment ofeconomic indicators, we use natural logarithms of the regressors.

10Two-stage instrumental variable procedures produce the same conclusions.

11 Intuitively, the fact that we have more moment conditions (instruments) than parameters to beestimated means that estimation could be done with fewer conditions. We can use this fact toestimate the error term under a set of moment conditions that excludes one instrumental variable ata time; we can then analyze if each estimated error term is uncorrelated with the instrumentalvariable excluded in the corresponding instrument set. The null hypothesis of Hansen's test is thatthe overidentifying restrictions are valid, that is, the instrumental variables are not correlated withthe error term. The test statistic is simply the sample size times the value attained for the objectivefunction at the GMM estimate (called the J-statistic). Hansen's test statistic is distributed as s2 withdegrees of freedom equal to the number of moment conditions minus the number of parameters tobe estimated. We report this statistic in the Tables.

where the dependent variable, GROWTH, equals real per capita GDP growth,FINANCE equals either LIQUID LIABILITIES, COMMERCIAL-CEN-TRAL BANK, or PRIVATE CREDIT, and CONDITIONING SET representsa vector of conditioning information that controls for other factors associatedwith economic growth.9

To examine whether cross-country variations in the exogenous component of"nancial intermediary development explain cross-country variations in the rateof economic growth, the legal origin indicators are used as instrumental vari-ables for FINANCE. Our method of estimation is the generalized method ofmoments (GMM).10 In estimation we have only used linear moment conditions,which amount to the requirement that the instrumental variables (Z) be uncor-related with the error term (e). The economic meaning of these conditions is thatthe instrumental variables can only a!ect the dependent variable through theexplanatory variables, that is, they cannot have an independent e!ect onthe dependent variable. In the context of the cross-sectional growth regressions,the moment conditions mean that legal origin may a!ect per capita GDPgrowth only through the "nancial development indicators and the variables inthe conditioning information set (that is, the other determinants of growth). Wetest this condition.

Testing the validity of the moment conditions is crucial to ascertaining theconsistency of GMM estimates. The speci"cation test we use is the test ofoveridentifying restrictions introduced in the context of GMM by Hansen(1982) and further explained in Newey and West (1987).11 If the regressionspeci"cation passes the test, then we can safely draw conclusions taking themoment conditions as given. That is, we cannot reject the statistical andeconomic signi"cance of the estimated coe$cient on "nancial intermediarydevelopment as indicating an e!ect running from "nancial development to percapita GDP growth. We can safely discard the possibility that the relationshipbetween "nancial intermediary development and growth is due to simultaneitybias or to omitted variables linked to legal origin.


12The black market exchange rate premium is frequently used as an overall index of trade,exchange rate, and price distortions (Easterly, 1994; Levine and Zervos, 1998). The in#ation rate andsize of the government serve as indicators of macroeconomic stability (Easterly and Rebelo, 1993;Fischer, 1993)

2.3.2. Conditioning information setTo examine the sensitivity of the results, we experiment with di!erent condi-

tioning information sets. We seek to reduce the chances that the cross-countrygrowth regression either omits an important variable or includes a select groupof regressors that yields a favored result. We report the results with threeconditioning information sets. The simple conditioning information set includesthe constant, the logarithm of initial per capita GDP and initial level ofeducational attainment. The initial income variable is used to capture theconvergence e!ect and school attainment is used to control for the level ofhuman capital. The policy conditioning information set includes the simpleconditioning information set plus measures of government size, in#ation, theblack market exchange rate premium, and openness to international trade.12The full conditioning information set includes the policy conditioning informa-tion set plus measures of political stability (the number of revolutions and coupsand the number of assassinations per thousand inhabitants (Banks, 1994)) andethnic diversity (Easterly and Levine, 1997). Thus, for each of the three "nancialintermediary development indicators, we present regression results for the (i)simple, (ii) policy, and (iii) full conditioning information sets.

2.3.3. Regression resultsThe results indicate a very strong connection between the exogenous com-

ponent of "nancial intermediary development and long-run economic growth.Table 3 summarizes the purely cross-sectional instrumental variable results fornine regressions, where the instrumental variables are the legal origin variables.For brevity, we report only the coe$cients on the "nancial developmentindicators. Each of the three "nancial intermediary development indica-tors (PRIVATE CREDIT, COMMERCIAL-CENTRAL BANK, LIQUIDLIABILITIES) is signi"cantly correlated with economic growth at the "ve per-cent signi"cance level in the simple, policy, and full conditioning information setregressions. The exogenous component of "nancial intermediary development isclosely tied to long-run rates of per capita GDP growth. Furthermore, the data donot reject the orthogonality conditions at the ten percent level in any of the nineregressions. The inability to reject the orthogonality conditions plus the result thatthe instruments are highly correlated with "nancial intermediary development(Table 2) suggest that the instruments are appropriate. These results indicate thatthe strong link between "nancial development and growth is not due to simulta-neity bias. The estimated coe$cient can be interpreted as the e!ect of theexogenous component of "nancial intermediary development on growth.


Tab

le3

Fin

anci

alin

term

edia

tion

and

grow

th:c

ross

-sec

tion

regr

essions,

1960}19

95.D

epen

den

tva

riab

le:R

ealp

erca

pita

GD

Pgr

ow

th,1

960}

1995

.Inst

rum

enta

lva

riab

les:

lega

lorigi

ndum

my

variab

les!

Exp

lana

tory

variab

leC

oe$

cien

tSt

andar

der

ror

t-st

atistic

P-v

alue

Num

ber

ofob

serv

a-tions

J-st

atistic

Han

sen-tes

tO

IR

Reg

ress

ion

Set

d1:

sim

ple

conditio

nin

gin

form

atio

nse

tPR

IVA

TE

CR

ED

IT2.

515

0.81

43.

090

0.00

371

0.00

189

0.13

CO

MM

ER

CIA

L-C

EN

TR

AL

BA

NK

10.8

613.

086

3.52

00.

001

710.

0162

61.

15LIQ

UID

LIA

BIL

ITIE

S1.

723

0.84

42.

041

0.04

571

0.03

491

2.48

Reg

ress

ion

Set

d2:

pol

icy

conditio

nin

gin

form

atio

nse

tPR

IVA

TE

CR

ED

IT3.

222

1.24

52.

589

0.01

263

0.00

799

0.50

CO

MM

ER

CIA

L-C

EN

TR

AL

BA

NK

9.64

14.

039

2.38

70.

021

630.

0373

2.35

LIQ

UID

LIA

BIL

ITIE

S2.

173

0.90

82.

394

0.02

063

0.03

7999

2.39

Reg

ress

ion

Set

d3:

full

conditio

nin

gin

form

atio

nse

tPR

IVA

TE

CR

ED

IT3.

356

1.15

02.

918

0.00

563

0.02

239

1.41

CO

MM

ER

CIA

L-C

EN

TR

AL

BA

NK

11.2

893.

258

3.46

50.

001

630.

0032

50.

20LIQ

UID

LIA

BIL

ITIE

S2.

788

0.90

33.

089

0.00

363

0.03

901

2.46

!Critica

lva

lues

for

Han

sen-

Tes

tO

ver

Iden

tify

ing

Res

tric

tions

(2d.

f.):1

0%4.

61;5%

"5.

99.

Sim

ple

conditio

nin

gin

form

atio

nse

t:lo

garith

mof

initia

lin

com

epe

rca

pita

and

school

ing.

Pol

icy

conditio

nin

gin

form

atio

nse

t:sim

ple

set,

plu

sgo

vern

men

tsize

,in#

atio

n,b

lack

mar

ketpre

miu

m,a

nd

open

nes

sto

trad

e.Full

conditio

nin

gin

form

atio

nse

t:pol

icy

set,

plusin

dica

tors

ofr

evolu

tion

san

dco

ups,

pol

itic

alas

sass

inat

ions,

and

ethni

cdiv

ersity

.L

IQU

IDL

IABIL

ITIE

S"

liquid

liabili

ties

of

the"nan

cial

syst

em(c

urr

ency

plu

sde

man

dan

din

tere

st-b

earing

liabi

litie

sofb

anks

and

nonban

k"na

nci

alin

term

edia

ries

)div

ided

byG

DP,t

imes

100.

CO

MM

ER

CIA

L-C

EN

TR

AL

BA

NK

"as

sets

of

depo

sitm

oney

ban

ksdiv

ided

by

asse

tsofd

epositm

oney

bank

splu

sce

ntra

lban

kas

sets

,tim

es10

0.P

RIV

AT

EC

RE

DIT

"cr

edit

bydep

ositm

oney

banks

and

oth

er"na

nci

alin

stitutions

toth

epr

ivat

ese

ctor

divi

ded

byG

DP

,tim

es10

0.


13To get this, recall that the regressors are in logs and note that the ln(25) - ln(19.5) "0.25.Then, use the smallest parameter on PRIVATE CREDIT from Table 3, which equals 2.5, so that2.5H(0.25) "0.63.

14These sensitivity results are available on request.

15The partial scatter plot involves the two-dimensional representation of the relationship be-tween growth and Private Credit controlling for the other regressors. Thus, we regress real per capitaGDP growth against the full conditioning information set and collect these growth residuals. Then,we regress Private Credit against the full conditioning information set and collect these PrivateCredit residuals. The "gures in the text plot the growth residuals against the Private Credit residualsalong with the regression line. Thus, this regression line is the two-dimensional projection in growth* Private Credit space of the multivariate OLS regression.

16Speci"cally, Private Credit enters with a coe$cient of 2.98 and a t-statistic of 2.10 and theregression passes all the diagnostic tests discussed above. Furthermore, removing Switzerland,Japan, and Portugal in addition to Niger, South Africa, and Korea did not alter the conclusioneither, i.e., Private Credit enters with a coe$cient of 4.27 and a t-statistic of 2.64.

The regression results also indicate an economically large impact of "nancialdevelopment on growth. For example, India's value of PRIVATE CREDIT overthe 1960}1995 period was 19.5% of GDP, while the mean value for developingcountries was 25% of GDP. The results suggest that an exogenous improvementin PRIVATE CREDIT in India that had pushed it to the sample mean fordeveloping countries would have accelerated real per capita GDP growth by anadditional 0.6 of a percentage point per year.13 Similarly, if Argentina hadmoved from its value of PRIVATE CREDIT (16) to the developing countrysample mean, it would have grown more than one percentage point faster peryear. This is large considering that growth only averaged about 1.8% per yearover this period. These types of conceptual experiments, however, must betreated as illustrative only; they do not account for how to increase xnancialintermediary development.

2.4. Sensitivity analyses

We have conducted a wide array of sensitivity analyses to gauge the robust-ness of these "ndings.14 First, consider the partial scatter plot of the growthregressions involving Private Credit.15 Fig. 3 illustrates the relationship betweengrowth and "nancial intermediary development after controlling for the fullconditioning information set. Since Korea, South Africa, and Niger fall parti-cularly far from the regression line, we removed these countries and re-did theestimation. The new GMM results are not substantially di!erent from the Table3 results.16 To further check for the potential in#uence of outliers, we examinedthe residuals from the GMM estimator. We removed all countries with residualsmore than three-standard deviations away from zero (South Africa and Switzer-land) and re-ran the regressions. This did not alter the results. Then, we removed


Fig. 3. Partial scatter plot of growth vs. private credit.

17Speci"cally, when we remove South Africa and Switzerland the coe$cient on Private Creditrises to 4.72 and the t-statistics equals 3.65 while the GMM estimate satis"es the litany of diagnostictests. Similarly, when the seven additional countries are removed, the Private Credit enters witha value of 4.53 and a t-statistic of 3.91, while passing the diagnostic tests.

18For the COMMERCIAL-CENTRAL BANK regressions, Haiti's level of "nancial develop-ment is much less than predicted by its country characteristics. Nonetheless, removing Haitiincreases the estimated coe$cient on COMMERCIAL-CENTRAL BANK to 13.4 (with a t-statisticof 3.35). Moreover, when removing other potential outliers such as Korea, Niger, and Peru, theresults are unchanged (coe$cient estimate of 9.6 on Commercial-Central Bank and a t-statistic of2.44). When examining the GMM residuals, Niger, Honduras, Jamaica, Korea, Mauritius, Pakistan,Senegal, and Taiwan are more than two-standard deviations from zero. Removing these countriesproduces an estimated coe$cient of 7.71 on COMMERCIAL-CENTRAL BANK, with a t-statisticof 2.92, and the regression passes the battery of diagnostic tests discussed in the text. In terms ofLIQUID LIABILITIES, the robustness checks produce similar results. The partial scatter plotspoint to Niger and Korea as potential outliers. Removing these countries does not a!ect the results(The estimated coe$cient becomes 2.24 with a t-statistic of 2.71). Similarly, when using the GMMresidual criteria, Korea, Jamaica, Switzerland, Taiwan, and Zaire fall more than two-standarddeviations away from zero. Removing these countries produces a coe$cient estimate of 2.63 onLIQUID LIABILITIES, with a t-statistic of 4.24, and a regression that passes the various diagnostictests used in this paper.

seven additional countries with residuals more than two-standard deviationsaway from zero (Belgium, El Salvador, Guyana, Jamaica, Mauritius, Niger,and Senegal.) This did not change the conclusions either.17 We followed thesame procedures in checking for the e!ect of outliers for COMMERCIAL-CENTRAL BANK and LIQUID LIABILITIES. In no case did removingoutliers alter the results.18 The strong positive connection between the


19This result is consistent with the fact that legal origin is a proper instrument for "nancialdevelopment in a growth regression, insofar as the judicial and accounting environment depends onlegal origin.

20While we make the results on the relationship between growth over 1960}1995 period and"nancial intermediary development measured in 1960 available to readers, there are econometricreasons for using values of the "nancial development indicators averaged over the entire sampleperiod as we do in the body of the paper. First, the speci"cation tests support the validity of theinstruments. This supports the interpretation of the estimated coe$cients as being free fromendogeneity bias. Second, the instrumental variables procedures address the issue of endogeneity.Thus, there is no reason to discard the informational gain provided by using observations over theentire sample period. Theory stresses the potential connection between growth and the contempor-aneous provision of "nancial services. Third, by using initial values of the explanatory variables,there is not only an e$ciency (informational) loss but also a potential consistency loss. Theorysuggests that what matters for current growth is the contemporaneous behavior of the explanatoryvariables. By using initial values, we run the risk of grossly mis-measuring the &true' explanatoryvariables, which could bias the coe$cient estimates.

exogenous component of "nancial intermediary development and economicgrowth does not seem to be driven by outliers.

Second, in assessing the independent link between "nancial development andeconomic growth, we considered a broad collection of additional control vari-ables. We included measures of the e$ciency of the bureaucracy, the level ofcorruption, the role of the state-owned enterprises in the economy, an index ofthe strength of property rights, an index of the costs of business regulation,a measure of the risk of expropriation, a measure of the degree to which thecountry follows the rule of law, and a measure of the accounting standardsemployed in the country (Knack and Keefer, 1995; Mauro, 1995; LLSV, 1998,1999). These did not alter our "ndings.

Third, we considered as instrumental variables measures of the religiouscomposition of each country and the distance of the country from the equator,which have been used in a recent study of the quality of government by LLSV(1999). This did not alter our results. Furthermore, if we use the LLSV (1998)indicators of creditor rights, contract enforcement e$ciency, and accountingstandards as instrumental variables, we again "nd that the exogenous compon-ent of "nancial development is positively associated with faster economicgrowth. These alternative instrumental variable estimations pass the test of theoveridentifying restrictions, which implies that these variables, measuring thequality of the legal and accounting environment, a!ect growth through "nancialdevelopment and the other regressors.19

Fourth, as in King and Levine (1993a), we use the measures of "nancialintermediary development at the beginning of the period (1960) to forecastgrowth. We "nd that "nancial intermediary development in 1960 signi"cantlypredicts economic growth over the next 35 years after controlling for an array ofcountry characteristics.20 We have also restricted the sample to those countriesfor which LLSV (1998) collect legal data. This did not alter the results.


Furthermore, we conduct the estimation over the 1980}1995 period. We "nd thesame results: the exogenous component of "nancial development is positively,signi"cantly, and robustly linked with economic growth.

Fifth, we experimented with two additional measures of "nancial intermedi-ary development. One measure equals deposit money bank credit to the privatesector divided by GDP. This is smaller than PRIVATE CREDIT, which alsoincludes other "nancial intermediaries. The second additional measure equalsthe ratio of deposit money bank domestic assets to GDP (and so does notdistinguish between credits issued to the private sector and those issued to thepublic sector). These two additional measures also suggest that the exogenouspart of "nancial intermediary development is positively and robustly associatedwith economic growth.

3. Finance and growth: Panel procedures

3.1. GMM estimators for dynamic panel models

3.1.1. MotivationEstimation using panel data, that is pooled cross-section and time-series data,

has several advantages over purely cross-sectional estimation. First, besidesconsidering the cross-country relationship between "nancial development andgrowth, we also would like to take into account how "nancial development overtime within a country may have an e!ect on the country's growth performance.Working with a panel, we gain degrees of freedom by adding the variability ofthe time-series dimension. Speci"cally, the within-country standard deviation ofPRIVATE CREDIT in our panel data set is 15%, which in the panel estimationis added to the between-country standard deviation of 28%. Similarly, thewithin-country standard deviation for growth is 2.4% and the between-countrystandard deviation is 1.7%. Thus, adding the time-series dimension of the datasubstantially augments the variability of the data.

Second, in a pure cross-sectional regression, any unobserved country-speci"ce!ect would be part of the error term, potentially leading to biased coe$cientestimates. This problem plagues previous studies of the growth-"nance relation-ship. However, in a panel context, we are able to control for unobservedcountry-speci"c e!ects and thereby reduce biases in the estimated coe$cients.

Third, our panel estimator controls for the potential endogeneity of allexplanatory variables, while the cross-sectional estimator presented previouslyonly controls for the endogeneity of "nancial development. The way our panelestimator controls for endogeneity is by using &internal instruments', that is,instruments based on lagged values of the explanatory variables. This methoddoes not allow us to control for full endogeneity but for a weak type of it. To beprecise, we assume that the explanatory variables are only &weakly exogenous',


21We also include time dummies to account for time-speci"c e!ects.

which means that they can be a!ected by current and past realizations of thegrowth rate but must be uncorrelated with future realizations of the error term.Thus, the weak exogeneity assumption implies that future innovations of thegrowth rate do not a!ect current "nancial development. This assumption is notparticularly stringent conceptually and we can examine its validity statistically.Weak exogeneity does not mean that economic agents do not take into accountexpected future growth in their decision to develop the "nancial system; it justmeans that future (unanticipated) shocks to growth do not in#uence current"nancial development. It is the innovation in growth that must not a!ect"nancial development. Finally, we statistically assess the validity of the weakexogeneity assumption below.

3.1.2. MethodologyWe use the generalized-method-of-moments (GMM) estimators developed

for dynamic models of panel data that were introduced by Holtz-Eakin et al.(1990), Arellano and Bond (1991), and Arellano and Bover (1995). Our panelconsists of data for 74 countries over the period 1961}1995. We average dataover non-overlapping, "ve-year periods, so that data permitting there are sevenobservations per country (1961}1965; 1966}1970; 1971}1975; etc.). Thus, thesubscript &t' designates one of these "ve-year averages. Consider the followingregression equation;

yi,t!y

i,t~1"(a!1)y

i,t~1#b@X

i,t#g

i#e

i,t, (1)

where y is the logarithm of real per capita GDP, X represents the set ofexplanatory variables (other than lagged per capita GDP), g is an unobservedcountry-speci"c e!ect, e is the error term, and the subscripts i and t representcountry and time period, respectively.21 We can rewrite Eq. (1) as

yi,t"ay

i,t~1#b@X

i,t#g

i#e

i,t, (2)

Now, to eliminate the country-speci"c e!ect, take "rst-di!erences of Eq. (2),

yi,t!y

i,t~1"a(y

i,t~1!y

i,t~2)#b@(X

i,t!X

i,t~1)#(e

i,t!e

i,t~1). (3)

The use of instruments is required to deal with (1) the likely endogeneity ofthe explanatory variables, and, (2) the problem that by construction the newerror term, e

i,t!e

i,t~1is correlated with the lagged dependent variable,

yi,t~1

!yi,t~2

. Under the assumptions that (a) the error term, e, is not seriallycorrelated, and (b) the explanatory variables, X, are weakly exogenous (i.e., theexplanatory variables are assumed to be uncorrelated with future realizations ofthe error term), the GMM dynamic panel estimator uses the following moment


22An additional problem with the simple diwerence estimator relates to measurement error:di!erencing may exacerbate the bias due to errors in variables by decreasing the signal-to-noise ratio(see Griliches and Hausman, 1986).

23Given that lagged levels are used as instruments in the di!erences speci"cation, only the mostrecent di!erence is used as instrument in the levels speci"cation. Using the other lagged di!erenceswould results in redundant moment conditions. (see Arellano and Bover, 1995).

conditions:

E[yi,t~s

(ei,t!e

i,t~1)]"0 for s52; t"3,2,¹, (4)

E[Xi,t~s

(ei,t!e

i,t~1)]"0 for s52; t"3,2,¹. (5)

We refer to the GMM estimator based on these conditions as the diwerenceestimator.

There are, however, conceptual and statistical shortcomings with this di!er-ence estimator. Conceptually, we would also like to study the cross-countryrelationship between "nancial development and per capita GDP growth, whichis eliminated in the diwerence estimator. Statistically, Alonso-Borrego andArellano (1996) and Blundell and Bond (1997) show that when the explanatoryvariables are persistent over time, lagged levels of these variables are weakinstruments for the regression equation in di!erences. Instrument weaknessin#uences the asymptotic and small-sample performance of the di!erence es-timator. Asymptotically, the variance of the coe$cients rises. In small samples,Monte Carlo experiments show that the weakness of the instruments canproduce biased coe$cients.22

To reduce the potential biases and imprecision associated with the usual di!er-ence estimator, we use a new estimator that combines in a system the regression indi!erences with the regression in levels (Arellano and Bover, 1995; Blundell andBond, 1997). The instruments for the regression in di!erences are the same asabove. The instruments for the regression in levels are the lagged diwerences of thecorresponding variables. These are appropriate instruments under the followingadditional assumption: although there may be correlation between the levels of theright-hand side variables and the country-speci"c e!ect in Eq. (2), there is nocorrelation between the diwerences of these variables and the country-speci"ce!ect. This assumption results from the following stationarity property,

E[yi,t`p

gi]"E[y

i,t`qgi] and E[X

i,t`pgi]"E[X

i,t`qgi]

for all p and q. (6)

The additional moment conditions for the second part of the system (theregression in levels) are23

E[yi,t~s

!yi,t~s~1

)(gi#e

i,t)]"0 for s"1, (7)

E[(Xi,t~s

!Xi,t~s~1

)(gi#e

i,t)]"0 for s"1. (8)


24 In addition, we used the &di!erence-Sargan test', presented in Blundell and Bond (1997), toexamine the null hypothesis that the lagged di!erences of the explanatory variables are uncorrelatedwith the residuals (which are the additional restrictions imposed in the system estimator with respectto the di!erence estimator). Giving further support to the system estimator, we could not reject thisnull hypothesis at usual levels of signi"cance.

25We do not use the full conditioning information set with data on political and institutionalvariables in the panel estimates. These variables frequently have very limited, if any, time-dimension.

Thus, we use the moment conditions presented in Eqs. (4), (5), (7), and (8) andemploy a GMM procedure to generate consistent and e$cient parameterestimates.

Consistency of the GMM estimator depends on the validity of the instru-ments. To address this issue we consider two speci"cation tests suggested byArellano and Bond (1991), Arellano and Bover (1995), and Blundell and Bond(1997). The "rst is a Sargan test of over-identifying restrictions, which teststhe overall validity of the instruments by analyzing the sample analog of themoment conditions used in the estimation process. The second test examines thehypothesis that the error term e

i,tis not serially correlated. In both the di!erence

regression and the system di!erence-level regression we test whether thedi!erenced error term is second-order serially correlated (by construction,the di!erenced error term is probably "rst-order serially correlated even if theoriginal error term is not).24

3.2. Results

The dynamic panel estimates suggest that the exogenous component of"nancial intermediary development exerts a large, positive impact on economicgrowth. Table 4 presents the results using the diwerence and system estimatorsdescribed above. We also present the results when the panel estimation isperformed purely in levels for comparative purposes. In Table 4, only the resultson the "nancial indicators are given. Table 5 gives the full results from systemdynamic-panel estimation. The analysis was conducted with two conditioninginformation sets. The "rst uses the simple conditioning information set, whichincludes initial income and educational attainment. The second uses the policyconditioning information set, and includes initial income, educational attain-ment, government size, openness to trade, in#ation, and the black marketexchange rate premium.25 Table 5 also presents (1) the Sargan test, where thenull hypothesis is that the instrumental variables are uncorrelated withthe residuals and (2) the serial correlation test, where the null hypothesis is thatthe errors in the di!erenced equation exhibit no second-order serial correlation.

The three "nancial intermediary development indicators (LIQUID LIABILI-TIES, COMMERCIAL-CENTRAL BANK, and PRIVATE CREDIT) are sig-ni"cant at the 0.05 signi"cance level in the levels, diwerence, and system dynamicpanel growth regressions, with one exception. The coe$cient on LIQUID


Table 4Financial intermediation and growth: dynamic panel regressions, summary!

EstimatorConditioninginformation set

LIQUIDLIABILITIES

COMMERCIAL-CENTRAL BANK

PRIVATECREDIT

Observa-tions

System estimator Simple 2.163 4.642 2.185 359(0.001) (0.001) (0.001)[0.313] [0.278] [0.183]

Policy 2.952 2.437 1.522 359(0.001) (0.001) (0.001)[0.713] [0.626] [0.581]

First di!erences Simple 1.135 2.007 1.699 285(0.035) (0.002) (0.001)[0.319] [0.184] [0.192]

Policy 1.446 2.065 0.663 285(0.249) (0.010) (0.001)[0.080] [0.330] [0.315]

Levels Simple 1.848 4.813 1.838 359(0.012) (0.011) (0.001)[0.472] [0.445] [0.345]

Policy 2.958 3.267 2.073 359(0.001) (0.001) (0.001)[0.346] [0.155] [0.180]

!Numbers in parentheses are p-values for the coe$cient and numbers in brackets are p-values forthe Sargan-test.

Simple conditioning information set:logarithm of initial income per capita, average years ofsecondary schooling. Policy conditioning information set: simple set plus government size, opennessto trade, in#ation, black market premium LIQUID LIABILITIES: liquid liabilities of the "nancialsystem (currency plus demand and interest-bearing liabilities of banks and nonbank "nancialintermediaries) divided by GDP. COMMERCIAL -CENTRAL BANK: assets of deposit moneybanks divided by assets of deposit money banks plus central bank assets PRIVATE CREDIT: creditby deposit money banks and other "nancial institutions to the private sector divided by GDP.

LIABILITIES is insigni"cant in the diwerence dynamic panel growth regressionwith the policy conditioning information set. While this may indicate a some-what less robust link when using a purely &size' measure of "nancial intermedi-ary development, LIQUID LIABILITIES enters the levels and system dynamicpanel growth regressions signi"cantly in all speci"cations. Put di!erently, aftercontrolling for country-speci"c e!ects, endogeneity, and potential problemsassociated with lagged dependent variables and weak instruments, the datasuggest a strong, positive, link between "nancial intermediary development andeconomic growth.

The regressions satisfy the speci"cation tests. There is no evidence of secondorder serial correlation and the regressions pass the Sargan speci"cation test. It


Table 5Financial intermediation and growth: dynamic panel regressions, system estimator

Regressors (1) (2) (3)

Constant 0.06 !5.677 4.239(0.954) (0.001) (0.001)

Logarithm of initial income per capita !0.742 !0.117 !0.364(0.001) (0.223) (0.001)

Government size! !1.341 !1.13 !1.987(0.001) (0.001) (0.001)

Openness to trade! 0.325 0.497 0.442(0.169) (0.002) (0.010)

In#ation" 1.748 !1.772 !0.178(0.001) (0.001) (0.543)

Average years of secondary schooling 0.78 0.638 0.639(0.001) (0.001) (0.001)

Black market premium" !2.076 !1.044 !1.027(0.001) (0.001) (0.001)

Liquid Liabilities! 2.952(0.001)

Comm. vs. Central Bank! 2.437(0.001)

Private Credit! 1.522(0.001)

Dummy 71}75 !1.074 !0.792 !0.959(0.001) (0.001) (0.001)

Dummy 76}80 !1.298 !0.825 !1.177(0.001) (0.001) (0.001)

Dummy 81}85 !3.328 !2.616 !3.179(0.001) (0.001) (0.001)

Dummy 86}90 !2.614 !1.894 !2.434(0.001) (0.001) (0.001)

Dummy 91}95 !3.631 !2.77 !3.308(0.001) (0.001) (0.001)

Sargan test# (p-value) 0.713 0.626 0.581Serial correlation test$ (p-value) 0.588 0.957 0.764

p-values in parentheses

!In the regression, this variable is included as log(variable)."In the regression, this variable is included as log(1# variable).#The null hypothesis is that the instruments used are not correlated with the residuals.$The null hypothesis is that the errors in the "rst-di!erence regression exhibit no second-order

serial correlation.

is also worth noting that many of the other regressors enter signi"cantly with theexpected signs (Table 5).

The regression estimates are also economically large. As shown the coe$-cients that emerge from the dynamic panel estimation are very close to thosethat we obtain from the purely cross-section, instrumental-variable estimation.


26There is some divergence in coe$cient estimates between the system dynamic panel estimates,Table 4, and the cross-sectional IV regressions, Table 3, when the conditioning information set isexpanded to include the policy conditioning information set. The cross-section regression producesa coe$cient estimate of 3.2 on PRIVATE CREDIT, while the panel yields a coe$cient of 1.4. Besidesexploiting the time-series dimension of the data, the dynamic-panel also recognizes the endogeneityof the other regressors, which may help account for the di!erent coe$cient estimates.

27Note, that in the system dynamic panel regressions with the policy conditioning information set,the number of instrumental variables is larger than the number of cross-sectional observations (i.e.,countries). This &over-"tting' of the data can bias the t-statistics upwards. This arises when thevariance-covariance matrix is constructed from the "rst-stage residuals in order to allow fornon-spherical distributions of the error term } and thereby get more e$cient estimates in the secondstage. (However, this &over-"tting' problem does not plague (a) the simple conditioning informationset regressions, or (b) the level or diwerence estimators because there are many more countries thaninstruments in these speci"cations.) More generally, the two-step GMM estimator sometimesconverges to its asymptotic distribution only slowly. Thus, we also considered the "rst-stage results,which assume homoskedasticity and independence of the error terms. These "rst-stage results alsoindicate that "nancial intermediary development exerts a causal impact on economic growth.

28These results are available on request.

For example, PRIVATE CREDIT has a coe$cient of 2.5 in the cross-sectionresults (the simple conditioning information set regression in Table 3), whilePRIVATE CREDIT has a coe$cient of 2.2 in the system dynamic-panel resultsreported in Table 4.26 As noted earlier, these coe$cients suggest that exogenouschanges in "nancial intermediary development imply large changes in economicgrowth.

3.3. Sensitivity analyses and discussion

The dynamic panel results are also robust to a variety of sensitivity analyses.For instance, when we use alternative measures of "nancial intermediary devel-opment (deposit money bank credit to the private sector divided by GDP; andthe ratio of total deposit money bank domestic assets to GDP), we still "nda strong of "nance on growth. Furthermore, if we control for terms of tradechanges and population growth, we obtain virtually identical results to thosereported in Tables 4 and 5.27 Finally, when we include the legal origin variablesas instruments in the dynamic panel estimates, we still "nd a strong, positiverelationship between the exogenous component of "nancial intermediary devel-opment and economic growth.28

4. Searching for determinants of 5nancial intermediary development

This section undertakes a limited search of potential legal and accountingdeterminants of "nancial intermediary development. We use LLSV's (1998)data. Instead of examining the links between the legal/regulatory environment


29Here it is important to highlight a substantive weakness with AUTOSTAY and MANAGES.They do not measure the e$ciency of the legal and regulatory system in coping with bankruptcy.For instance, two countries could have very similar legal codes, such that management stays in placepending the resolution of a bankruptcy hearing and there is an automatic stay on the assets of a "rmuntil the bankruptcy courts process the reorganization petition. However, the two countries legaland regulatory systems may process bankruptcy and reorganization very di!erently. One country'ssystem may take a long time and be subject to great uncertainty. The other may be very rapid,e$cient, and transparent. Thus, a major di!erence across countries may be the quality of thebankruptcy system, not the laws themselves. Currently, there do not exist cross-country measures ofthe speed, transparency, and fairness of bankruptcy systems.

and measures of bond market and equity market development as in LLSV(1997, 1998), we study the ties between the legal environment and measures of"nancial intermediary development. Moreover, unlike earlier studies, we useinstrumental variables to assess whether the positive association betweenlegal/regulatory indicators and "nancial development is due to simultaneitybias.

4.1. The legal and accounting environment

We use three LLSV's (1998) indicators of national legal and regulatorysystems: the legal rights of creditors, the soundness of contract enforcement, andthe level of corporate accounting standards.

4.1.1. Creditor rightsThe degree to which the legal system supports the rights of creditors will

fundamentally in#uence "nancial contracting and the functioning of "nancialintermediaries. Speci"cally, legal systems di!er in terms of the rights of creditorsto (i) repossess collateral or liquidate "rms in the case of default, (ii) removemanagers in corporate reorganizations, and (iii) have a high priority relative toother claimants in corporate bankruptcy.

AUTOSTAY equals one if a country's laws impose an automatic stay on theassets of "rms upon "ling a reorganization petition. AUTOSTAY equals 0 if thisrestriction does not appear in the nation's legal codes. The restriction wouldprevent creditors from gaining possession of collateral or liquidating a "rm tomeet a loan obligation. Thus, all else equal, AUTOSTAY should be negativelycorrelated with the activities of credit issuing intermediaries.

MANAGES equals one if "rm managers continue to administer the "rmsa!airs pending the resolution of reorganization processes, and zero otherwise. Insome countries, management stays in place until a "nal decision is made aboutthe resolution of claims. In other countries, a team selected by the creditorsreplaces management. If management stays pending resolution, this reducespressure on management to pay creditors. Thus, MANAGES should be nega-tively correlated with the activities of credit issuing intermediaries.29


30We could have rede"ned AUTOSTAY and MANAGES such that values of one indicatedstronger (instead of weaker) creditor rights. This would have produced values of CREDITORbetween 0 and 3 and would not have altered the results. We did not do this for consistency: thevariables in this paper are de"ned the same as the variables in LLSV (1997, 1998).

The third measure of the legal rights of creditors is SECURED1, which equalsone if secured creditors are ranked "rst in the distribution of the proceeds thatresult from the disposition of the assets of a bankrupt "rm. SECURED1 equalszero if non-secured creditors, such as the government or workers get paid beforesecured creditors. In cases where SECURED1 equals zero, this certainly reducesthe attractiveness of lending secured credit. SECURED1 should be positivelycorrelated with activities of intermediaries engaged in secured transactions,holding everything else constant.

CREDITOR is a cumulative index of these creditor rights indicatorsand equals CREDITOR " SECURED1 ! AUTOSTAY ! MANAGES.CREDITOR takes on values between 1 (best) and !2 (worst).30 One wouldexpect countries with higher values of CREDITOR to have stronger creditorrights and better-developed "nancial intermediaries, all else equal.

Table 6 gives summary statistics on CREDITOR, and the data are listed inTable 8. As shown there is substantial cross-country variation in CREDITOR,where the maximum value is 1, the minimum value is !2, and the standarddeviation is about 1. Brazil, Colombia, France, Mexico, Peru, and the Philip-pines (all countries with a French legal origin) are countries where CREDI-TOR"!2, indicating that their legal systems do not stress the rights ofcreditors. In contrast, the legal codes of Egypt, Hong Kong, India, Indonesia,Israel, Korea, Malaysia, Nigeria, Pakistan, Singapore, Thailand, United King-dom, and Zimbabwe stress the rights of creditors, such that CREDITOR"1.CREDITOR is an indicator of legal codes, however, it does not incorporateinformation regarding enforcement.

4.1.2. EnforcementThe e!ectiveness of the legal system in enforcing contracts will materially

in#uence "nancial sector activities. RULELAW, from LLSV (1998), is an assess-ment of the law and order tradition of the country that ranges from 10, stronglaw and order tradition, to 1, weak law and order tradition. This measure wasconstructed by International Country Risk Guide (ICRG) and is an averageover the period 1982}1995. Given the contractual nature of banking, highervalues of RULELAW are likely to positively in#uence banking development.CONRISK, also from LLSV (1998), is an assessment of the risk that a govern-ment will } and therefore can } modify a contract after it has been signed.CONRISK ranges from 10, low risk of contract modi"cation, to 1, high risk ofcontract modi"cation. Speci"cally, &modi"cation'means repudiation, postpone-ment, or reducing the governments "nancial obligation. This measure was


Table 6Summary statistics on the legal and accounting environment!

Legal and accounting environment

CREDITOR ENFORCE ACCOUNT

Mean !0.3 7.5 61.2Median 0 8.2 64.0Maximum 1 10.0 83.0Minimum !2 3.6 24.0Std. Dev. 1.1 2.0 13.5

Observations 44 44 40

!CREDITOR " index of secured creditor rights. ENFORCE " index of law and contractenforcement. ACCOUNT " index of the comprehensiveness and quality of company reports.

Values for the legal environment indicators are averages over the 1982}1995 period. Values ofaccounting quality are assessments of company reports in 1990.

constructed by ICRG and is an average over the period 1982}1995. Legalsystems that e!ectively enforce contracts will tend to support banking activities.

ENFORCE equals the average of RULELAW and CONRISK. The empiricalanalyses focus on this aggregate index of the e$ciency of the legal system inenforcing contracts. Summary statistics on ENFORCE are given in Table 6, andthe data are listed in Appendix Table 8. As shown, there is substantial cross-country variation in ENFORCE, where the maximum value is 9.99, the min-imum value is 3.55, and the standard deviation is 2.0. The countries with veryhigh values of enforcement, values of ENFORCE greater that 9, are Australia,Austria, Belgium, Canada, Denmark, Finland, France, Germany, Japan, Neth-erlands, New Zealand, Norway, Sweden, and Switzerland. In contrast, countrieswhere contract enforcement is poor, values of ENFORCE less than 5, includeColombia, Nigeria, Pakistan, Philippines, Peru, and Zimbabwe.

4.1.3. Accounting standardsInformation about corporations is critical for exerting corporate governance

and identifying the best investments. Accounting standards that simplify theinterpretability and comparability of information across corporations will sim-plify "nancial contracting. Furthermore, "nancial contracts that use accountingmeasures to trigger particular actions can only be enforced if accountingmeasures are su$ciently clear. ACCOUNT, from LLSV (1998), is an index ofthe comprehensiveness of company reports. The maximum possible value is 90and the minimum is 0. The Center for International Financial Analysis andResearch assessed general accounting information, income statements, balancesheets, funds #ow statement, accounting standards, and stock data in company


31This is not necessarily true and raises the need for a general conceptual quali"cation. Aneconomy with perfect information, perfect contract enforcement and perfect legal codes (i.e., aneconomy with essentially zero transaction and information costs) would have little reason for"nancial intermediaries. Put di!erently, market frictions motivate the emergence of "nancialintermediaries, e.g., Boyd and Prescott (1986). Conceptually, this implies that at very high levels oflegal system development and information dissemination, a marginal increase in legal e$ciency orinformation quality may cause a reduction in the role and importance of "nancial intermediaries.However, quadratic expressions for ACCOUNT and CONRISK never entered signi"cantly.

32Since CREDITOR, ENFORCE, and ACCOUNT are measured over the 1980s and 1990s, weuse "nancial intermediary measures over the same period. However, we get very similar results whenthe analysis uses "nancial intermediary measures over the period 1960}1995. These results areavailable on request.

33First, note that the legal origin variables help explain cross-country di!erences in creditorrights, enforcement quality, and accounting standards. As shown by LLSV (1998), English legaltradition countries have laws that emphasize the rights of creditors to a greater degree than theFrench, German, and Scandinavian countries. French civil law countries protect creditors the least,with German and Scandinavian civil law countries falling in the middle. In terms of enforcementquality, countries with a French legal heritage have the lowest quality of law enforcement, whilecountries with German and Scandinavian legal traditions tend to be the best at enforcing contracts.Finally, LLSV (1998) show that countries with an English legal tradition tend to have much betteraccounting standards than French or German civil law countries.

reports in 1990. We expect ACCOUNT to be positively correlated with "nancialintermediary development.31 As shown in Table 6, ACCOUNT exhibits sub-stantial cross-country variation. The data are listed in Appendix Table 8. Themaximum value is 83, Sweden, while the minimum value in our sample is Egypt(24). The United States has a value of 71, which is well above the mean valueof 61.

4.2. Determinants of xnancial intermediary development

Table 7 shows that cross-country di!erences in creditor rights, enforcementquality, and accounting standards help explain cross-country di!erences in"nancial intermediary development, even after controlling for the level ofincome per capita. Jointly, the variables CREDITOR, ENFORCE, andACCOUNT explain a signi"cant amount of the cross-country variation in thethree "nancial intermediary indicators (PRIVATE CREDIT, LIQUIDLIABILITIES, and COMMERCIAL-CENTRAL BANK). Each of the legal/accounting indicators, however, is not signi"cantly correlated with all of theintermediary measures. For instance LIQUID LIABILITIES is most closelyassociated with ENFORCE and CREDITOR. In turn, PRIVATE CREDIT isvery strongly linked with ENFORCE and ACCOUNT.32

Simultaneity bias does not seem to be driving these results. As instrumentalvariables, we use the legal origin dummy variables for countries with French,English, and German legal origins.33 Since we only have three instruments, we


Tab

le7

Leg

alen

viro

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d"na

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alin

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(198

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95)!

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4.40

23.

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3.64

04.

200

4.40

31.

480

2.55

74.

368

4.32

2(0

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216

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125

0.08

8(0

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178

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412

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0.68

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0.66

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34None of these "ndings changes when the instrumental variable set is expanded to include theLLSV (1999) exogenous variables, i.e., religious composition of the population, ethnic diversity, anddistance from the equator. These results are available on request.

35The evidence we provide suggests that the strong link between "nancial sector development onthe one hand and the legal/regulatory environment on the other is not due to simultaneity bias.These results do not suggest that the legal/regulatory environment only impacts growth through"nancial intermediary development. Rather, these results imply that the legal/regulatory environ-ment has an important impact on "nancial intermediary development.

36Put di!erently, when countries are endowed with a certain legal heritage, this produces a prob-ability distribution regarding the laws, regulations, and enforcement mechanisms that they are likely toadopt. Thus, for example, the data suggest that countries with a French Civil Code have a lowerprobability of selecting laws that give a higher priority to secured credits, selecting accountingstandards that produce high-quality corporate "nancial statements, and enforcing contracts thancountries with English, German, and Scandinavian legal systems. The resultant laws, regulations, andenforcement mechanisms then a!ect the ability of the "nancial system to research "rms, exertcorporate control, mobilize savings, and provide risk management and transactions services.

construct a general index of the legal and regulatory environment. We computethe "rst standardized principal component of CREDITOR, ENFORCE, andACCOUNT and use this as the index of the legal/regulatory environmentgoverning "nancial transactions. We call this index LEGAL. In the simple OLSregression, LEGAL explains a substantial amount of the cross-country vari-ation in all of the "nancial intermediary development indicators, even aftercontrolling for the level of real per capita GDP. When we use an instrumentalvariables estimator to control for simultaneity bias, the results are unchanged.34The basic message that emerges from Table 7 is that countries with (i) laws thatgive a high priority to secured creditors, (ii) legal systems that rigorously enforcecontracts, and (iii) accounting standards that produce comprehensive and com-parable corporate "nancial statements tend to have better developed "nancialintermediaries.35

4.3. Discussion of causes

These "ndings } in conjunction with those in LLSV (1998) } are consistentwith the view that countries with particular legal origins tend to create parti-cular types of laws, regulations, and enforcement mechanisms. These laws,regulations, and enforcement mechanisms directly in#uence the functioning of"nancial intermediaries. Financial intermediaries that are better at amelioratinginformation and transactions costs induce a more e$cient allocation of re-sources and faster growth.36 While it is di$cult to change legal origin, the resultso!er a strategy for boosting "nancial development and accelerating long-rungrowth. Countries can target reforms that ensure that lenders have con"dencethat the legal system will quickly, transparently, and e!ectively enforce theirclaims against borrowers and that outside investors have easy access to high-quality, comprehensive, and comparable information about "rms.


5. Conclusions

This paper "rst examined the nature of the e!ect of "nancial intermediarydevelopment on economic growth. We used two econometric approaches. The"rst, GMM dynamic panel estimators, are speci"cally designed to deal with keyproblems plaguing past studies of the "nance-growth nexus: simultaneity biasand omitted variable bias, including that derived from unobserved country-speci"c e!ects. As a consistency check, we also used a pure cross-sectional,instrumental variable. The panel and cross-sectional results tell the same story:the exogenous component of "nancial intermediary development is positivelyassociated with economic growth; speci"cally, the large, positive link between"nancial intermediary development and economic growth is not due to poten-tial biases induced by omitted variables, simultaneity or reverse causation. Ina sequel to this paper, Beck et al. (2000) examine the channels through which"nancial intermediary development is associated with growth. In that paper, weargue that the "nance-growth nexus runs primarily through total factor produc-tivity growth and not through savings and physical capital accumulation.

Next, we investigated whether cross-country di!erences in the legal rights ofcreditors, the e$ciency of contract enforcement, and accounting system standardshelp explain cross-country di!erences in the level of "nancial intermediary devel-opment. The results are clear: countries with (1) laws that give a high priority tosecured creditors getting the full present value of their claims against "rms, (2)legal systems that rigorously enforce contracts, including government contracts,and (3) accounting standards that produce high-quality, comprehensive andcomparable corporate "nancial statements tend to have better developed "nan-cial intermediaries. The paper's "ndings are consistent with the view that legal andaccounting reforms that strengthen creditor rights, contract enforcement, andaccounting practices can boost "nancial intermediary development and therebyaccelerate economic growth. Due to data limitations, however, we do not conducta comprehensive evaluation of the regulatory determinants of "nancial intermedi-ary development (e.g., see Calomiris, 1989; Kane, 1985, 1989; Barth et al., 1997;BIS, 1997; Calomiris and Gorton, 1991; Kroszner and Rajan, 1994; Kroszner andStrahan, 1996; Barth et al., 2000). Future work should substantially broaden anddeepen our understanding of the determinants of "nancial intermediary develop-ment by obtaining additional measures of the legal, supervisory, and regulatoryfactors that determine the level of "nancial intermediary development.

Appendix A

The data are listed country by country in Table 8 and the countries in thesample in Table 9. The summary statistics and correlations with other variablesused in the paper are provided in Tables 10 and 11.


Tab

le8

Eco

nom

icgr

ow

th,"na

nci

aldev

elopm

ent

and

polic

ies

acro

ssco

untr

ies

Fin

anci

alD

evel

opm

ent

Indi

cato

rsPolic

yva

riab

les

Coun

try

code

Coun

try

nam

e

Ave

rage

annu

algr

owth

rate

1960}19

95LIQ

UID

LIA

BIL

ITIE

S

CO

MM

ER

CIA

L-

CEN

TR

AL

BA

NK

PR

IVA

TE

CR

ED

ITLeg

alor

igin

CR

ED

I-TO

REN

-FO

RC

EA

C-

CO

UN

T

AR

GA

rgen

tina

0.62

18.3

474

.16

15.6

9F

!1

5.13

45A

US

Aus

tral

ia1.

9851

.73

92.6

754

.82

E!

19.

3675

AU

TA

ustr

ia2.

8967

.50

98.4

465

.30

G0

9.80

54BG

DBan

glad

esh!

0.71

24.6

986

.38

13.5

5E

BR

BBar

bados

!2.

6551

.59

91.8

540

.79

EBE

LBel

gium

2.65

49.0

292

.01

25.6

5F

09.

7461

BO

LBoliv

ia!

0.36

16.3

931

.60

13.1

4F

BR

ABra

zil

2.93

19.1

661

.68

22.1

0F

!2

6.31

54C

AN

Can

ada

2.39

56.5

089

.00

60.8

6E

!1

9.48

74C

HL

Chi

le1.

4522

.96

52.8

127

.81

F!

16.

9152

CO

LC

olom

bia

2.23

22.4

180

.16

22.0

8F

!2

4.55

50C

RI

Cos

taR

ica!

1.61

29.3

872

.82

21.7

7F

CY

PC

ypru

s!5.

3874

.49

92.7

262

.39

ED

NK

Den

mar

k2.

1849

.48

88.1

042

.45

S0

9.66

62D

OM

Dom

inic

anR

epub

lic!

2.50

20.5

873

.34

19.1

1F

EC

UEcu

ador

2.39

20.0

562

.16

17.9

9F

15.

93SL

VElSa

lvad

or!

!0.

6126

.94

72.0

422

.85

FFJI

Fiji!

1.85

37.4

896

.95

23.6

5E

FIN

Fin

land

2.80

45.3

597

.22

51.7

8S

!1

9.58

77FR

AFra

nce

2.43

63.3

796

.54

75.4

7F

!2

9.09

69D

EU

Ger

man

y2.

4557

.46

97.5

776

.46

G0

9.50

62G

HA

Ghan

a!!

0.96

17.5

839

.78

5.07

EG

RC

Gre

ece

3.22

53.3

474

.36

36.7

2F

!1

6.40

55


GT

MG

uat

emal

a!0.

9320

.22

75.1

613

.32

FG

UY

Guya

na!

!0.

2852

.96

58.0

220

.52

EH

TI

Hai

ti!

!0.

6622

.60

23.7

27.

71F

HN

DH

ond

ura

s!0.

6023

.04

76.5

423

.86

FIS

LIc

elan

d!3.

0131

.76

88.9

434

.79

SIN

DIn

dia

1.92

32.9

563

.68

19.5

3E

15.

1457

IRL

Irel

and

3.25

54.7

494

.73

49.1

4E

!1

8.38

ISR

Isra

el2.

8151

.95

84.2

837

.43

E1

6.18

64IT

AIt

aly

2.93

77.4

887

.77

59.0

3F

!1

8.75

62JA

MJa

mai

ca!

0.42

36.8

578

.09

24.5

5E

JPN

Japa

n4.

3012

5.94

96.7

212

8.38

G0

9.34

65K

EN

Ken

ya1.

9635

.74

81.2

723

.93

E1

5.54

KO

RK

ore

a,R

epub

licof

7.16

41.0

283

.95

66.5

2G

16.

9762

LB

RLib

eria

!!

0.47

9.73

37.9

010

.16

EM

YS

Mal

aysia

4.11

63.7

496

.45

47.2

0E

17.

1176

MLT

Mal

ta!

6.65

143.

4392

.57

43.9

7E

MU

SM

auritius!

3.02

46.8

782

.21

24.3

6F

MEX

Mex

ico

1.97

25.5

769

.38

22.8

9F

!2

5.95

60N

PL

Nep

al!

0.77

20.2

757

.86

7.72

EN

LD

Net

herlan

ds

2.20

71.4

198

.10

86.6

9F

!1

9.68

64N

ZL

New

Zea

land

1.12

49.6

382

.43

37.5

9E

09.

6570

NE

RN

iger

!!

2.75

14.4

383

.89

13.0

5F

NO

RN

orw

ay3.

1854

.04

90.0

281

.62

S!

19.

8674

PA

KPak

ista

n2.

7038

.68

67.8

920

.77

E1

3.95

PA

NPan

ama!

2.03

33.3

771

.97

40.2

2F

PN

GPap

uaN

ewG

uin

ea!

1.12

31.0

589

.12

20.8

4E

PR

YPar

agua

y!2.

3817

.62

65.2

914

.52

FPER

Per

u0.

0618

.52

86.0

413

.32

F!

23.

5938

PH

LPhili

ppin

es1.

1627

.50

81.4

027

.01

F!

23.

7765

PR

TPort

ugal

3.65

78.0

290

.35

55.0

1F

!1

8.63

36SE

NSen

egal

!!

0.44

22.8

084

.54

27.5

1F

SL

ESie

rra

Leo

ne!

!0.

3416

.83

49.5

55.

07E


Tab

le8

(continue

d)

Fin

anci

alD

evel

opm

ent

Indi

cato

rsPolic

yva

riab

les

Coun

try

code

Coun

try

nam

e

Ave

rage

annu

algr

owth

rate

1960}19

95LIQ

UID

LIA

BIL

ITIE

S

CO

MM

ER

CIA

L-

CEN

TR

AL

BA

NK

PR

IVA

TE

CR

ED

ITLeg

alor

igin

CR

ED

I-TO

REN

-FO

RC

EA

C-

CO

UN

T

ZA

FSo

uth

Afric

a0.

3951

.44

94.7

771

.94

E0

5.85

70ESP

Spai

n2.

8870

.31

92.7

465

.05

F0

8.10

64LK

ASr

iLan

ka

2.70

30.3

457

.80

17.1

1E

03.

58SW

ESw

eden

1.89

53.4

988

.94

89.1

1S

!1

9.79

83C

HE

Switze

rlan

d1.

4212

3.41

98.9

914

1.30

G!

19.

9968

SYR

Syrian

Ara

bR

ep.!

2.51

43.3

246

.93

8.83

FTW

NTai

wan

,C

hin

a6.

6266

.97

95.7

257

.32

G0

8.84

65TH

AThai

land

4.88

47.7

984

.66

48.5

2E

16.

9164

TG

OTogo

!0.

4632

.90

82.0

521

.88

FTT

OTrinid

adan

dToba

go!

1.12

37.4

691

.72

31.2

0E

GB

RU

nited

Kin

gdom

1.96

48.6

383

.55

46.3

1E

19.

1078

USA

United

Stat

es1.

7162

.12

93.1

111

3.07

E!

19.

5071

UR

YU

rugu

ay1.

0329

.47

59.2

421

.21

F0

6.15

31V

EN

Ven

ezue

la!

0.88

36.8

490

.90

33.1

2F

6.34

40ZA

RZai

re!

!2.

8116

.03

28.5

84.

08F

ZW

EZim

bab

we

0.84

46.9

275

.80

23.0

4E

14.

36

!Countr

ies

adde

dto

the

LLSV

(199

8)sa

mpl

e.LIQ

UID

LIA

BIL

ITIE

S"

liquid

liabili

ties

ofth

e"nan

cial

syst

em(c

urre

ncy

plu

sdem

and

and

inte

rest

-bea

ring

liabi

litie

sofb

anks

and

nonban

k"na

nci

alin

term

edia

ries

)div

ided

byG

DP,t

imes

100.

CO

MM

ER

CIA

L-C

EN

TR

AL

BA

NK

"as

sets

of

depo

sitm

oney

ban

ksdiv

ided

by

asse

tsofd

epositm

oney

bank

splu

sce

ntra

lban

kas

sets

,tim

es10

0.P

RIV

AT

EC

RE

DIT

"cr

edit

bydep

ositm

oney

banks

and

oth

er"nan

cial

inst

itut

ionsto

thepriva

tese

ctordiv

ided

byG

DP,t

imes

100.

Val

ues

forth

e"nan

cial

inte

rmed

iary

dev

elopm

entin

dic

ators

areav

erag

esov

erth

e19

60}19

95pe

riod

.L

egal

orig

in:E"

Engl

ish,F"

Fre

nch

,G"

Ger

man

,S"

Scan

din

ivia

nC

RED

ITO

R"

index

ofse

cure

dcr

editor

righ

ts.

EN

FO

RC

E"

inde

xofla

wan

dco

ntra

cten

forc

emen

t.A

CC

OU

NT

"in

dex

ofth

eco

mpre

hen

sive

ness

and

qual

ity

ofco

mpa

ny

repor

ts.


Table 9Countries in the sample

Algeria! Greece NorwayArgentina Guatemala PakistanAustralia Guyana" PanamaAustria Haiti Papua New GuineaBangladesh" Honduras ParaguayBarbados" Iceland" PeruBelgium India PhilippinesBolivia Indonesia! PortugalBrazil Iran! Rwanda!

Cameroon! Ireland SenegalCanada Israel Sierra LeoneCentral African Republic! Italy South AfricaChile Jamaica SpainColombia Japan Sri LankaCosta Rica Kenya Sudan!

Cyprus Korea SwedenDenmark Lesotho! SwitzerlandDominican Republic Liberia" SyriaEcuador Malawi! Taiwan"

Egypt! Malaysia ThailandEl Salvador Malta" TogoFiji" Mauritius Trinidad and TobagoFinland Mexico United States of AmericaFrance Nepal" UruguayGambia! Netherlands VenezuelaGermany New Zealand ZaireGhana Nicaragua! ZimbabweGreat Britain Niger

!Not in the 71 country pure cross-sectional data set."Not in the 74 country panel data set.


Tab

le10

Des

crip

tive

stat

istics

,cr

oss-

sect

ion

1960}19

95

GD

Pgr

ow

th

Initia

lin

com

epe

rca

pita

Ave

rage

year

sof

schoo

l-in

gPriva

tecr

edit

Com

-m

erci

al-

centr

alLiq

uid

liabilt

ies

Gove

rn-

men

tsize

Open

-ne

ssto

trad

eIn#at

ion

rate

Bla

ckm

arket

pre-

miu

m

Rev

olu-

tion

and

Cou

ps

Ass

ass-

inat

ions

Eth

nic

frac

tiona

l-iz

atio

n

Mea

n1.

9531

204.

0640

.86

79.2

645

.21

14.7

559

.46

15.5

623

.34

0.16

0.29

0.29

Med

ian

1.98

2019

3.65

27.8

183

.89

41.0

213

.16

54.3

39.

085.

360.

070.

100.

19M

axim

um7.

1698

9510

.07

141.

3098

.99

143.

4331

.37

231.

6990

.78

277.

420.

972.

470.

87M

inim

um

!2.

8136

70.

204.

0823

.72

14.4

36.

6814

.05

3.63

0.00

0.00

0.00

0.00

Stan

dard

dev

iation

1.92

2519

2.50

29.1

617

.37

26.2

65.

2336

.43

18.2

549

.31

0.22

0.50

0.27

Obse

rvat

ions

6363

6363

6363

6363

6363

6363

63

Cor

rela

tion

sG

DP

grow

th1

Initia

lin

com

eper

capi

ta0.

041.

00A

vera

geye

ars

of

school

ing

0.30

0.82

1.00

Priva

teC

redi

t0.

430.

630.

571.

00C

om

mer

cial

-C

entr

alBan

k0.

460.

480.

430.

641.

00

Liq

uid

Lia

bilt

ies

0.56

0.39

0.45

0.77

0.59

1.00

Gove

rnm

entsize

0.21

0.36

0.46

0.30

0.38

0.30

1.00

Open

ness

totr

ade

0.19

!0.

18!

0.04

!0.

090.

080.

300.

311.

00In#at

ion

rate

!0.

28!

0.15

!0.

10!

0.38

!0.

48!

0.42

!0.

24!

0.28

1.00

Bla

ckm

arke

tpr

emiu

m!

0.38

!0.

28!

0.30

!0.

37!

0.54

!0.

26!

0.11

0.36

0.27

1.00

Rev

olution

and

Coups

!0.

24!

0.35

!0.

30!

0.40

!0.

46!

0.44

!0.

42!

0.23

0.41

0.20

1.00

Ass

assinat

ions

!0.

15!

0.09

!0.

14!

0.14

!0.

07!

0.17

!0.

31!

0.27

0.24

0.02

0.51

1.00

Eth

nic

frac

tiona

lizat

ion

!0.

35!

0.43

!0.

47!

0.34

!0.

20!

0.29

!0.

060.

070.

030.

200.

160.

01


Tab

le11

Des

crip

tive

stat

istics

,pan

el19

60}19

95

Var

iable

GD

Pgr

ow

th

Initia

lin

com

epe

rca

pita

Ave

rage

year

sof

sec.

schoo

ling

Priva

tecr

edit

Com

-m

erci

al-

centr

alba

nkLiq

uid

liabilt

ies

Gove

rn-

men

tsize

Open

nes

sto

trad

eIn#at

ion

rate

Bla

ckm

arke

tpre

miu

m

Mea

n1.

5647

101.

3042

.71

77.6

745

.14

14.8

354

.44

17.8

074

.31

Max

imum

9.86

2013

55.

1520

5.95

99.9

819

1.44

38.0

218

0.09

344.

4010

990.

70M

inim

um

!10

.02

188

0.04

1.56

14.0

26.

724.

899.

29!

3.06

!3.

68St

anda

rddev

iation

2.76

5229

0.95

35.1

620

.07

27.0

75.

3627

.57

33.0

360

8.65

Obse

rvat

ions

359

359

359

359

359

359

359

359

359

359

Cor

rela

tion

sG

DP

grow

th1

Initia

lin

com

eper

capi

ta0.

121

Avg

.yea

rsof

sec.

schoo

ling

0.13

0.69

1Priva

teC

redi

t0.

20.

760.

621

Com

mer

cial

-Cen

tral

Ban

k0.

330.

540.

340.

61

Liq

uid

Lia

bilt

ies

0.22

0.61

0.47

0.84

0.51

1G

ove

rnm

entsize

!0.

040.

450.

270.

240.

270.

211

Open

ness

totr

ade

0.13

0.05

0.09

0.09

0.22

0.13

0.2

1In#at

ion

rate

!0.

29!

0.18

!0.

08!

0.26

!0.

26!

0.26

!0.

05!

0.22

1Bla

ckm

arke

tpre

miu

m!

0.2

!0.

08!

0.06

!0.

09!

0.11

!0.

030.

1!

0.1

0.54

1


Tab

le12

Var

iable

san

dso

urc

es

Var

iable

De"

nition

Origi

nal

sour

ceSe

condar

yso

urce

Lev

elan

dgr

owth

rate

ofG

DP

Rea

lper

capi

taG

DP

Wor

ldD

evel

opm

ent

Indic

ator

sLoay

zaet

al.(1

998)

Rea

lper

capita

GD

P(fo

rin

itia

lG

DP

incr

oss-

sect

ion

regr

ession

s)Pen

nW

orld

Tab

les

Gove

rnm

entsize

Gove

rnm

entex

pen

ditu

reas

shar

eofG

DP

Wor

ldD

evel

opm

ent

Indi

cato

rsLoay

zaet

al.(1

998)

Open

nes

sto

trad

eSum

ofre

alex

por

tsan

dim

port

sas

shar

eof

real

GD

PW

orld

Dev

elop

men

tIn

dica

tors

Loay

zaet

al.(1

998)

Gro

wth

rate

ofte

rms

oftr

ade

Log

di!er

ence

ofth

ete

rms

oftr

ade,

div

ided

by"ve

Wor

ldD

evel

opm

ent

Indi

cato

rsLoay

zaet

al.(1

998)

Popul

atio

ngr

owth

rate

Log

di!

eren

ceof

the

tota

lpopu

lation

,div

ided

by"ve

Wor

ldBan

k,In

tern

atio

nal

Eco

nom

ics

Dep

artm

ent

Loay

zaet

al.(1

998)

In#at

ion

rate

Log

di!er

ence

ofC

onsu

mer

Price

Index

Inte

rnat

iona

lF

inan

cial

Sta

tist

ics

(IF

S),lin

e64

Ave

rage

year

sofsc

hool

ing

Ave

rage

year

sofsc

hool

ing

inth

epop

ula

tion

over

25Bar

roan

dLee

(199

6)

Ave

rage

year

sofse

condar

ysc

hool

ing

Ave

rage

year

sofse

condar

ysc

hoolin

gin

the

popula

tion

over

15Bar

roan

dLee

(199

6)

Bla

ckm

arke

tpre

miu

mR

atio

ofbla

ckm

arke

tex

chan

gera

tean

do$

cial

exch

ange

rate

min

us

one

Pic

k's

Curr

ency

Yea

rbook

thro

ugh

(198

9)an

dW

orld

Cur

rency

Yea

rboo

k.

Liq

uid

liabi

litie

sM(

0.5)

*[F

(t)/P

e(t)#

F(t!

1)/P

e(t!

1)]N

/[G

DP(t)/

Pa(t

)],w

her

eF

isliqu

idliab

ilities

(line

55l),

GD

Pis

line

99b,P

eis

end-o

fpe

riod

CP

I(lin

e64

)an

dPa

isth

eav

erag

ean

nual

CP

I.

IFS

Com

mer

cial

-cen

tral

bank

DB

A(t)/(D

BA

(t)#

CBA

(t)),w

here

DB

Ais

asse

tsofde

positm

oney

ban

ks

(line

s22

a-d)

and

CBA

isce

ntr

alba

nkas

sets

(line

s12

a-d)

.

IFS


Priva

tecr

edit

M(0.

5)*[

F(t)/P

e(t)#

F(t!

1)/P

e(t!

1)]N

/[G

DP(t)/

Pa(t

)],w

her

eF

iscr

edit

by

depo

sitm

one

yban

ksan

dot

her"nan

cial

inst

itut

ions

toth

epriva

tese

ctor

(line

s22

d#

42d),

GD

Pis

line

99b,P

eis

end-o

fper

iod

CPI

(line

64)an

dP

ais

the

aver

age

CPI

for

the

year

IFS

Ban

kas

sets

M(0.

5)*[

F(t)/P

e(t)#

F(t!

1)/P

e(t!

1)]N

/[G

DP(t)/

Pa(t

)],w

her

eF

isdom

estic

asse

tsof

depo

sit

mone

yban

ks(li

nes22

a}d),

GD

Pis

line

99b,P

eis

end-o

fper

iod

CPI

(line

64)an

dP

ais

the

aver

age

CPI

for

the

year

.

IFS

Ban

kcr

edit

M(0.

5)*[

F(t)/P

e(t)#

F(t!

1)/P

e(t!

1)]N

/[G

DP(t)/

Pa(t

)],w

her

eF

iscr

edit

by

depo

sitm

one

yban

ksto

the

priv

ate

sect

or

(line

s22

d),

GD

Pis

line

99b,

Pe

isen

d-of

period

CP

I(li

ne64

)an

dP

ais

the

aver

age

CPI

for

the

year

.

IFS

Leg

alorigi

nD

um

my

variab

les

for

British

,Fre

nch

,G

erm

anan

dSc

andin

avia

nle

galorigi

nR

eyno

lds

and

Flo

res

(199

6)LLSV

(199

8)an

dow

nco

ding

Acc

ounting

Index

crea

ted

by

exam

inin

gan

dra

ting

com

pani

es'19

90an

nual

report

son

thei

rin

clus

ion

or

om

ission

of90

item

sin

bala

nce

shee

tsan

din

com

est

atem

ents

.T

hem

axim

um

is90

,th

em

inim

um0.

Cen

ter

ofIn

tern

atio

nal

Fin

anci

alA

nal

ysis

&R

esea

rch

Inc.

LLSV

(199

8)

Rul

elaw

Mea

sure

ofth

ela

wan

dord

ertr

aditio

nof

aco

unt

ry.I

tis

anav

erag

eov

er19

82}19

95.It

range

sfrom

10,s

tron

gla

wan

dor

dertr

aditio

n,t

o1,

wea

kla

wan

dord

ertr

aditio

n.

Inte

rnat

iona

lC

ountr

yR

isk

Guid

e(IC

RG

).LLSV

(199

8)

Con

risk

Mea

sure

oft

herisk

that

ago

vern

men

tw

illm

odify

aco

ntra

ctaf

ter

ithas

bee

nsign

ed.I

tra

nge

sfrom

10,lo

wrisk

,to

1,hig

hrisk

and

isav

erag

edove

r19

82}19

95.

ICR

GK

nac

kan

dK

eefe

r(1

995)


Tab

le12

(continued

)

Var

iable

De"

nition

Origi

nal

sour

ceSe

conda

ryso

urc

e

Auto

stay

,M

anag

es,S

ecure

d1

AU

TO

STA

Yeq

uals

oneif

aco

untr

y'sla

wsim

pose

anau

tom

atic

stay

on

the

asse

tsof"rm

supo

n"lin

ga

reor

gani

zation

pet

itio

n,an

dze

rooth

erw

ise.

MA

NA

GES

equal

sone

if"rm

man

ager

sco

ntin

ueto

adm

iniste

rth

e"rm's

a!ai

rspen

ding

the

reso

lution

ofre

orga

niza

tion

pro

ces-

ses,

and

zero

othe

rwise.

SEC

UR

ED

1eq

ual

son

eif

secu

red

cred

itor

sar

era

nked"rs

tin

the

dist

ribu

tion

ofth

epr

ocee

ds

that

resu

ltfrom

the

dispos

itio

nof

the

asse

sts

ofa

bank

rupt"rm

,an

dze

rooth

erw

ise.

Nat

ional

ban

kru

ptc

yan

dre

org

aniz

atio

nla

ws

LL

SV

(199

8)

Rev

olutions

and

coups

Are

volu

tion

isde"ned

asan

yille

galor

forc

edch

ange

inth

eto

pgo

vern

men

tale

lite,

any

atte

mpt

atsu

cha

chan

ge,o

ran

ysu

cces

sful

orunsu

cces

sful

arm

edre

belli

on

who

seai

mis

inde

pen

den

cefrom

cent

ralgo

vern

men

t.C

oup

d'E

tatis

de"

ned

asan

extr

acon

stitut

ional

or

forc

edch

ange

inth

eto

pgo

vern

men

tel

ite

and/o

ritse!

ective

contr

olo

fthe

nat

ion'

spow

erst

ruct

ure

ina

give

nye

ar.

Unsu

cces

sfulco

ups

are

not

counte

d.D

ata

are

aver

aged

over

1960}19

90

Ban

ks(1

994)

Ass

assina

tions

Num

ber

ofas

sass

inat

ions

per

thousa

nd

inhab

itan

ts.D

ata

are

aver

aged

ove

r19

60}19

90Ban

ks(1

994)

Eth

nic

frac

tion

aliz

atio

nA

vera

geva

lue

of"ve

indi

ces

ofet

hnolin

guistic

frac

tiona

lizat

ion,

with

valu

esra

ngi

ng

from

0to

1,w

her

ehi

gher

valu

esde

note

hig

her

leve

lsof

frac

tiona

lizat

ion.

Atlas

Nar

odov

Mira

(196

4);

Mul

ler

(196

4);R

ober

ts(1

962)

;G

unn

emar

k(1

991)

Eas

terly

and

Lev

ine

(199

7)

Bure

aucr

atic

e$ci

ency

Ave

rage

ofth

ree

indic

espub

lishe

dby

Busines

sIn

tern

atio

nalC

orpo

ration

(198

4):e$

cien

cyof

the

judic

iary

syst

em,re

dta

pean

dco

rrup

tion.T

heda

taar

eav

erag

esove

rth

epe

riod

1980}19

83.

Busines

sIn

tern

atio

nal

Corp

orat

ion

(198

4)M

auro

(199

5)


Corr

upt

ion

Mea

sure

ofco

rruption,

with

the

scal

ere

adju

sted

to0

(hig

hle

velof

corr

upt

ion)

to10

(low

leve

l).D

ata

are

aver

aged

ove

r19

82}19

95.

ICR

GK

nac

kan

dK

eefe

r(1

995)

Index

ofst

ate-

ow

ned

ente

rprise

sM

easu

res

the

role

ofSO

Es

inth

eec

ono

my,

rangi

ngfrom

0to

10,w

ith

data

aver

aged

ove

r19

75}19

95.H

ighe

rsc

ores

denote

coun

trie

sw

ith

less

gove

rnm

ent

owne

den

terp

rise

s,w

hic

har

ees

tim

ated

topro

duce

less

oft

he

count

ry's

outp

ut.

Gw

artn

eyet

al.(

1996

)LLSV

(199

9)

Pro

per

tyrigh

tsR

atin

gof

prop

erty

righ

tson

asc

ale

from

1to

5.The

mor

epro

tect

ion

priva

tepro

pert

yre

ceiv

es,

the

high

erth

esc

ore

.

Holm

eset

al.(1

997)

LLSV

(199

9)

Cost

ofbus

ines

sre

gula

tion

Rat

ing

ofre

gula

tion

polic

ies

rela

ted

toope

nin

gan

dke

epin

gopen

abusine

ss.The

scal

eis

from

0to

5,w

ith

hig

her

score

sm

eani

ngth

atre

gula

tions

are

stra

ightforw

ard

and

appl

ied

unifo

rmly

toal

lbus

ines

ses

and

that

regu

lation

sar

ele

ssofa

burd

ento

bus

ines

s.

Holm

eset

al.(1

997)

LLSV

(199

9)

Risk

ofex

pro

priat

ion

Ass

essm

ent

ofrisk

of`o

utr

ight

con"sc

atio

na

or

`forc

ednat

ional

izat

iona.

Itra

nges

from

0to

10,

with

low

ersc

ore

sin

dica

ting

ahig

her

risk

and

data

are

aver

aged

over

1982}19

95.

ICR

GK

nac

kan

dK

eefe

r(1

995)

Rel

igio

us

Com

position

Per

cent

ageoft

hepo

pula

tion

that

wer

e(1

)Rom

anC

atho

lic,(2

)Pro

test

ant,

and

(3)M

uslim

in19

80.

Bar

rett

(198

2),W

orldm

ark

Ency

clop

edia

ofN

atio

ns(1

996)

,Sta

tist

ical

Abs

trac

tofth

eW

orld

(199

5),U

nited

Nat

ions

Dem

ogra

phic

Yea

rbook

(199

5)an

dC

IAW

orld

Fac

tboo

k(1

996)

.

LL

SV

(199

9)

Dista

nce

from

equa

tor

The

dista

nce

ofth

eco

unt

ryfrom

the

equat

or,

scal

edbet

wee

n0

and

1.C

IAFac

tboo

k(1

996)

LLSV

(199

9)


Appendix B

The variables and sources are tabulated in Table 12.

For further reading

The following reference is also of interest to the reader: Political Risk Services,various issues.

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