WPS5805 Policy Research Working Paper 5805 The Evolving Importance of Banks and Securities Markets Asli Demirguc-Kunt Erik Feyen Ross Levine The World Bank Development Research Group Finance and Private Sector Development Team September 2011 Policy Research Working Paper 5805 Abstract This paper examines the evolving importance of banks economic development. Some exploratory evidence and securities markets during the process of economic further suggests that deviations of a country’s actual development. As economies develop, they increase their financial structure—the mixture of banks and markets demand for the services provided by securities markets operating in an economy—from the estimated optimal relative to those provided by banks, such that securities structure are associated with lower levels of economic markets become increasingly important for future activity. This paper is a product of the Finance and Private Sector Development Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at ademirguckunt@worldbank.org. The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent. Produced by the Research Support Team The Evolving Importance of Banks and Securities Markets Asli Demirguc-Kunt, Erik Feyen, and Ross Levine 1 JEL Classification: G1, G2, O16 Keywords: Financial development, economic development, banks, capital markets, financial structure 1 Demirguc-Kunt: World Bank; Feyen: World Bank; Levine: Brown University and the NBER. We received very helpful comments from Thorsten Beck, Norman Loayza, Yona Rubinstein, and seminar participants at the World Bank’s June 16, 2011 conference, “Financial Structure and Economic Development.� Mauricio Pinzon Latorre provided outstanding research assistance. Research finds that both the operation of banks and the functioning of securities markets influence economic development (Demirguc-Kunt and Maksimovic, 1998; Levine and Zervos, 1998), suggesting that banks provide different services to the economy from those provided by securities markets. For example, Acemoglu and Zilibotti (1997), Allen and Gale (1997, 1999), Boot and Thakor (1997, 2000), Dewatripont and Maskin (1995), Holmstrom and Tirole (1993), and Rajan (1992) argue that banks have a comparative advantage in reducing the market frictions associated with financing standardized, shorter-run, lower-risk, well-collateralized endeavors, while decentralized markets are relatively more effective in custom-designing arrangements to finance more novel, longer-run, higher-risk projects that rely more on intangible inputs. Economic theory also emphasizes the importance of financial structure—the mixture of financial institutions and markets operating in an economy. 1 For example, Allen and Gale’s (2000) theory of financial structure and their comparative analyses of Germany, Japan, the United Kingdom, and the United States suggest that (1) banks and markets provide different financial services; (2) economies at different stages of economic development require different mixtures of these financial services to operate effectively (Boyd and Smith, 1998); and (3) if an economy’s actual mixture of banks and markets differs from the “optimal� structure, the financial system will not provide the appropriate blend of financial services, with deleterious effects on economic activity. Empirical research, however, has been largely unsuccessful at clarifying the evolving importance of banks and markets during the process of economic development. Demirguc-Kunt and Levine (2001) show that banks and securities markets tend to become more developed as economies grow and that securities markets tend to develop more rapidly than banks. Thus, financial systems generally become more market-based during the process of economic development. But, this pattern could simply reflect supply side factors, such that securities markets grow more rapidly than banks as economies expand, with no implication that firms and households change their relative demand for the services provided by banks and markets respectively. Empirical research has not yet ascertained whether the relative demand for the types of financial services provided by banks and markets changes as economies grow, and 1 See Allen and Gale (1995), Goldsmith (1969), Morck and Nakamura (1999), Morck, Yeung, and Yu (2000), Weinstein and Yafeh (1998), and citations in Allen and Gale (2000). 2 hence whether impediments to changes in the mixture of banks and markets hamper economic development (Beck and Levine, 2002; Demirguc-Kunt and Maksimovic, 2002; Levine, 2002). In this paper, we evaluate empirically the changing importance of banks and securities markets as economies develop. In particular, we focus on assessing whether economies increase their demand for the types of services provided by securities markets relative to the services provided by banks as countries grow. We do this by testing whether the economic development “returns� to improvements to both bank and securities market development change as economies grow. At a more exploratory level, we also examine whether each level of economic development is associated with an “optimal� financial structure, such that deviations from this optimum are associated with lower levels of economic activity. We use data on 72 countries, over the period from 1980 through 2008, and we aggregate the data in 5-year averages (data permitting), so that we have a maximum of six observations per country. We use several measures of bank and securities market development, including standard indicators such as bank credit to the private sector as a share of gross domestic product (GDP), the value of stock market transactions relative to GDP, and the capitalization of equity and private domestic bond markets relative to GDP. The primary methodological contribution of this paper is using quantile regressions to assess how the sensitivities of economic activity to both bank and securities market development evolve as countries grow (Koenker and Basset, 1978). Ordinary least squares (OLS) regressions provide information on the association between, for example, economic development and bank development for the “average� country, the country at the average level of economic development. But, quantile regressions provide information on the relationship between economic activity and bank development at each percentile of the distribution of economic development. Thus, we assess how the associations between economic development and both bank and securities market development change during the process of economic development. Besides confirming that both banks and securities markets become larger relative to the size of the overall economy as countries grow, the paper’s major findings are (1) the sensitivity of economic development to changes in bank development decreases with economic development, and (2) the sensitivity of economic development to changes in securities market 3 development increases as countries grow. Put differently, as economies develop, the marginal increase in economic activity associated with an increase in bank development falls, while the marginal boost to economic activity associated with an increase in securities market development rises. These results suggest that the demand for the services provided by securities markets increases relative to the demand for those provided by banks as economies develop. We also conduct a preliminary examination of whether deviations of a country’s actual financial structure from our estimate of the country’s optimum are associated with lower levels of economic activity. To estimate the optimal mixture of banks and markets for each level of economic development, we first regress a measure of financial structure (such as the ratio of bank to securities market development) on GDP per capita for the sample of high-income OECD countries, while controlling for key institutional, geographic, and structural traits. The maintained hypothesis is that conditional on these traits, the high-income OECD countries provide information on how the optimal financial structure varies with economic development. We then use the coefficients from this regression to compute the estimated optimal financial structure for each country-year observation for all countries. Next, we compute the Financial structure gap, which equals the natural logarithm of the absolute value of the difference between the actual and the estimated optimal financial structure, controlling for systematic variation in the prediction errors. The Financial structure gap measures deviations of actual financial structure from the estimated optimum, where larger values indicate bigger deviations, regardless of whether the deviations arise because the country is “too� bank-based or “too� market-based. We find that deviations of an economy’s actual financial structure from its estimated optimum—i.e., increases in the Financial structure gap—are associated with a reduction in economic output. Even when controlling for bank development, securities market development, country characteristics, and country fixed effects, there is a negative relationship between the Financial structure gap and economic activity. Although we do not identify the causal impact of financial structure on economic development, these results are consistent with the view that the mixture of banks and markets—and not just the level of bank and market development—is important for understanding economic development. 4 This research is policy relevant. First, if the mixture of financial institutions and markets matters—and not only the development of financial institutions and markets, then this advertises financial structure as an independent indicator of the ability of the financial system to provide growth-enhancing services to the economy. Second, if the optimal mixture changes as an economy develops, then this advertises the costs of policy and institutional impediments to the evolution of the financial system. Third, this work suggests that the sensitivity of economic activity to bank and securities market development changes with economic development. This implies that the estimated OLS elasticities from past research of the impact of changes in bank or stock market development on economic development will yield misleading information about countries incomes far from the sample average income. Past studies do not account for the evolving importance of banks and markets during the process of economic development. This paper builds on earlier studies of financial structure. Demirguc-Kunt and Levine (2001), for instance, find that financial structure is not robustly linked with economic growth. We do not reject this finding. Rather, we show that economies tend to increase their relative demand for the services provided by securities markets during the process of economic development and that deviations of actual financial structure from an economy’s optimal financial structure—financial structure gaps—are associated with lower levels of economic activity. Thus, while earlier work focused on actual financial structure, we focus on the financial structure gap and stress the evolving importance of banks and markets. This paper is one step in deriving a better understanding of the dynamic relationships among economic development, financial institutions, and securities markets. In this paper, we show that both the “supply� of securities market services and the economic development “returns� to securities market development increase as economies grow. This suggests that the relative demand for securities market services increases with economic development. But, we do not identify the causal impact of banks, markets, and financial structure on economic outcomes. Although this paper advertises the desirability of understanding the policy and institutional determinates of the Financial structure gap, much work remains. 5 1. Data and Summary Statistics 1.1 Financial system indicators We use several measures of bank and stock market development to analyze the relationship between economic activity and the structure of the financial system. We would like to have indicators of the degree to which banks and markets ameliorate market frictions and thereby (1) improve ex ante information about possible investments, (2) enhance the monitoring of investments after financing occurs, (3) facilitate the trading, diversification, and management of risk, (4) ease the mobilization and pooling of savings, and (5) foster the exchange of goods, services, and financial claims. We would also like information on how the mixture of banks and markets affect the provision of these services. But, such empirical proxies do not exist for a broad cross-section of countries over the last few decades. Instead, we rely on standard measures of the size and activity of banks and securities markets. These measures are constructed over the period from 1980 to 2008, and Table 1 provides the primary sources of these indicators. To measure “bank� development, we use Private credit, which equals deposit money bank credit to the private sector as a share of gross domestic product (GDP). Private credit isolates credit issued to the private sector and therefore excludes credit issued to governments, government agencies, and public enterprises. Private credit also excludes credits issued by central banks. Not surprisingly, there is enormous cross-country variation in Private credit. For example, averaging over the 1980-2008 period, Private credit was less than 10% of GDP in Angola, Cambodia, and Yemen, while it was greater than 85% of GDP in Austria, China, and United Kingdom. Table 2 indicates that the annual average value of Private credit across countries was 39% with a standard deviation of 36%. To measure “market� development, we primarily use Stock value traded, which equals the value of stock market transactions as a share of GDP. This market development indicator incorporates information on the size and activity of the stock market, not simply on the value of listed shares. Earlier work by Levine and Zervos (1998) indicates that the trading of ownership claims on firms in an economy is closely tied to the rate of economic development. There is substantial variation across counties. As shown in Table 2, while the mean value of Stock value traded is about 29 percent of GDP the standard deviation is about double this value. In Armenia, 6 Tanzania, and Uruguay, Stock value traded annually averaged less than 0.23% over the 1980- 2008 sample (10th percentile). In contrast, Stock value traded averaged over 75%% in Hong Kong SAR, China; Saudi Arabia; Switzerland; and Unites States (90th percentile).Also, we confirm this paper’s results using other market development indicators. In particular, we examine Stock market capitalization, which simply measures the value of listed shares on a country’s stock exchanges as a share of GDP and Securities market capitalization, which equals the capitalization of the stock market plus the capitalization of the private domestic bond markets, divided by GDP. To measure the mixture of banks and markets operating in an economy, we use the Financial structure ratio, which equals Private credit divided by Stock value traded. The goal is to gauge the degree to which the financial system is relatively bank-based or market-based. Financial structure differs markedly across economies. As shown in Table 2, the annual average value of the Financial structure ratio is 279, ranging from 2.35 (10th percentile) in Australia, India, Singapore, and Sweden to over 356 (90th percentile) in Bolivia, Bulgaria, Serbia, and Uganda over the 1980-2008 sample period. We also construct a measure of the Financial structure gap, which equals the natural logarithm of the absolute value of the difference between the actual Financial structure ratio and the estimated “expected� (or estimated “optimal�) financial structure ratio. We describe the estimation of the expected financial structure ratio below. The Financial structure gap is designed such that it becomes larger when a country’s Financial structure ratio deviates from the estimated expected ratio, regardless of whether the country becomes “too� bank-based or “too� market- based relative to the estimated optimal structure for an economy at that level of economic development. The Financial structure gap is computed for each country in each year. As reported in Table 2, there is enormous cross-country variation of the Financial structure gap (averaged over the sample period). The financial structure gap measures the degree to which an economy’s mixture of bank and market development deviates from our expected mixture, but it does not measure whether the financial system too “bank-based� or too “market-based� relative to the estimated mixture. Consequently, we also examine the degree to which a financial system is more bank-based or 7 market-based relative to our estimated financial structure for each economy. Tables 1, 2, and 3 provide information on the actual Financial structure ratio / Optimal financial structure ratio, where we discuss the construction of the Optimal financial structure ratio below. This variable indicates that the average financial structure of countries such as Guatemala, Namibia, and United Arab Emirates are highly distorted relative to their estimated optimum in that they are too bank-based (>4.91; 90th percentile), while the financial structure of countries such as Denmark, Japan, and United Kingdom, are highly distorted relative to their estimated optimum in that they are too market-based (<0.03; 10th percentile). Appendix 1 contains a list of countries and provides an overview of the period medians for GDP per capita, Private credit, Stock Value Traded, the Financial structure ratio, and the Financial structure ratio / Expected-Optimal financial structure. 1.2 Other data As a measure of economic activity, we use Log Real GDP per capita, which equals the logarithm of GDP per capita in constant 2000 U.S. dollars. And, to assess the independent link between finance and economic development, we control for many other country characteristics that have been employed in the development literature. In some specifications, we use “standard controls� to evaluate the independent relationship between finance and economic activity. These standard controls include: years of schooling, openness to trade, inflation, government size, the initial GDP per capita of the economy in 1980, and dummy variables for the 5-year periods of analysis. Table 1 provides the specific definitions. In other specifications, we use “exogenous controls,� which include dummy variables for the legal origin of the country along with the country’s distance from the equator. Table 1 gives the detailed definitions and sources of these data and Table 2 provides descriptive statistics 1.3 Correlations The correlations in Table 3 highlight key features about the financial system and economic development. First, bank and securities market development are positively correlated with economic development. Second, bank and securities market development are positively correlated, suggesting that financial development involves both bigger banks and bigger markets. Third, the Financial structure gap is negatively correlated with bank and securities development, 8 as well as with economic development. Though simple correlations, we will see that these basic patterns hold when controlling for many other national traits. 2. The Relationships among Banks, Markets, and Economic Development 2.1 Quantile regressions To assess how the relationships between economic activity and both bank development and stock market development evolve with economic development, we use quantile regressions with data averaged over non-overlapping 5-year periods. Ordinary least squares (OLS) provide information on the relationship between Log Real GDP per capita and financial development for the country at the average level of economic development. But, OLS does not provide information on how the relationship between economic activity and financial development differs for countries at different levels of economic activity. Quantile regressions model the relation between Log Real GDP per capita and financial development at the specific percentiles (or quantiles) of Log Real GDP per capita. Thus, in a quantile regression of Log Real GDP per capita on Private credit, the procedure is able to yield a different estimated coefficient on Private credit for each percentile (or quantile) of Log Real GDP per capita. For example, the estimated coefficient at the 50th percentile is a median regression, yielding the estimated relationship between Log Real GDP per capita and Private credit at the median level of economic activity. By computing the quantile regression for each of the 5th to the 95th quantiles, we assess how the relationship between economic activity and financial development differs across distinct levels of Log Real GDP per capita. In neither the OLS nor the quanitle regressions do we identify the causal impact of bank and securities market development on economic development. Rather, the goal here is to explore whether, and how, the relation between changes in economic activity and changes in both bank and market development varies with the level of economic development. 2.2 Illustrating the quantile regression results In Figure 1-Panel A, the graph on the upper-left-hand-side plots the coefficients from quantile regressions for each of the 5th to 95th percentiles of Log Real GDP per capita, where the dependent variable is Log Real GDP per capita and the main regressor is Private credit and we 9 also control for Stock value traded. A circle indicates each coefficient estimate. The left axis provides information on the values of the coefficient estimates. Thus, the estimated coefficient, indicated by a circle, depicts the “sensitivity� of Log Real GDP per capita associated with a change in Private credit at each percentile of economic development. The graph also plots the actual value of Private credit at each percentile. A triangle indicates these actual values, where the scale is provided on the right axis. The triangles provide the average “quantity� of Private credit at each percentile of economic development. The graphs in the lower part of Figure 1 provide similar information on the relationship between economic activity and Stock value traded. The lower-hand-side charts confirm the increasingly relevant role for securities markets by documenting similar upward trends for both Securities market capitalization and Stock market capitalization. Panel B of Figure 1 provides the same types of quantile analyses, while controlling for other characteristics of the national economies. We use “standard controls:� Log Real GDP per capita in 1980, Government size, Openness to trade, Inflation, Average years of schooling, and period-fixed effects. In each of the eight graphs in Panels A and B of Figure 1, we provide two additional pieces of information. First, the horizontal dotted line is the OLS estimate of the coefficient on the financial development indicator. Thus, in the graph on the upper-left-hand-side of Figure 1- Panel A, the horizontal dashed line is simply the coefficient on Private credit from an OLS regression of Log Real GDP per capita on Private credit for the full sample of country-year observations. Second, the solid line shows the estimated linear relationship between each estimated coefficient of the financial development indicator and the GDP per capita percentile associated with the coefficient. As a specific example, consider the graph in the upper-right quadrant of Figure 1-Panel B. We first collect the estimated coefficients on Stock value traded after conditioning on the standard controls and period-fixed effects. We then regress these estimated coefficients on the GDP per capita percentile associated with the estimates. Table 4, column (4) provides the results from this regression. The estimated coefficient on GDP per capita percentile provides the trend line graphed in Figure 1. 10 2.3 Quantile results In terms of bank development, Figure 1 shows that as Log Real GDP per capita rises, two things happen: (1) Private credit rises (triangles) and (2) the marginal increase in Log Real GDP per capita associated with an increase in Private credit falls (circles). Put differently, quantities rise and sensitivities fall. As reported in Table 4, this relationship is statistically significant: as economic increases, there is a significant reduction in the sensitivity of Log Real GDP per capital to an increase in Private credit. The results are different for securities market development. As Log Real GDP per capita rises, (1) Stock value traded rises and (2) the marginal increase in Log Real GDP per capita associated with an increase in Stock value traded also rises. That is, quantities and sensitivities rise. Table 4 shows that this effect is statistically significant: the sensitivity of economic activity to Stock value traded increases as Log Real GDP per capita rises. These results suggest that the relationship between bank development and economic activity differs from that between securities market development and economic activity. 2.4 Broader implications of quantile analyses The evidence is consistent with insights from Allen and Gale (2000) and Boyd and Smith (1998), who argue that economic development increases the demand for the services provided by securities markets relative to the services provided by banks, such that the optimal financial structure becomes more market-based at higher levels of economic development. As economies grow, both bank and stock market development increase, but the sensitivity of economic activity to changes in bank development falls while the sensitivity of economic activity to changes in market development increases. While one could argue that the “supply� of Private credit increases with economic development, reducing its marginal sensitivity with economic activity, the same argument cannot be made about Stock value traded, where the “supply� and “return� increase. Thus, the quantile regressions suggest both that the demand for the services provided by securities markets increases as countries develop and that the optimal financial structure changes—becoming more market-oriented—as economies develop. These quantile regressions provide information on the evolving importance of banks and markets during the process of economic development. This evidence is inconsistent with the 11 view that economic development is simply associated with an increase in bank and stock market development with no effect on the relative demand of the services provided by these two components of the financial system. This evidence is also inconsistent with the view that banks and markets provide perfectly substitutable services to individuals and firms. Rather, the evidence suggests that the relative demand for the distinct services provided by securities markets increases with economic development. 3. The Financial Structure Gap 3.1 Computing the financial structure gap While the quantile analyses are the major contribution of this paper, we provide additional information on the relationship between economic activity and the mixture of banks and markets in economies at different stages of economic development. We examine whether deviations of an economy’s actual financial structure from its estimated “optimal� mixture of banks and markets are associated with less economic activity. To accomplish this, we need to construct a measure of each economy’s estimated “optimal� financial structure. This is challenging. Many factors influence the operation of banks, markets, and the mixture of banks and markets. Fortunately, existing research provides guidance on constructing an acceptable proxy of optimal financial structure (Rajan and Zingales, 1998). We do not need a perfect estimate of each country’s optimal financial structure in each year. Rather, we require that the country-year estimates are positively correlated with the true optimal financial structure and that our estimates are not systematically biased in such a manner that drives the results. We proceed in four steps: First, we select benchmark countries that, arguably, have few impediments to their financial systems achieving an optimal financial structure. We use the high-income OECD countries from 1980 through 2008. 2 This approach is similar to that employed by Rajan and Zingales (1998), who use the United States (and other highly developed countries such as Canada) as a benchmark financial system. 2 The OECD countries in this sample for which we have sufficient data are: Australia, Austria, Belgium, Canada, Czech Republic, Denmark, Finland, France, Germany, Greece, Hungary, Ireland, Italy, Japan, Korea, Rep., Netherlands, New Zealand, Norway, Portugal, Slovak Republic, Spain, Sweden, Switzerland, United Kingdom, and United States. 12 Second, for these benchmark countries, we regress the Financial structure ratio on key national traits that might affect each country’s optimal financial structure. Table 5 provides the results for our core analyses. We use six regressors. First and foremost, Real GDP capita captures the insights mentioned above that the optimal mixture of banks and markets changes as economies develop. We also include dummy variables for the legal origin of the country (English, French, Scandinavian, with German as the omitted category). Considerable research suggests that the common law is more conducive to securities market development (La Porta et al., 1998), suggesting that the optimal financial structure of such countries will be more market- based. Further, to condition on the geographic characteristics and the economic structure of the countries, we control for the country’s distance to the equator, population size and density, along with the role of natural resources in the economy as discussed in Beck (2010) and Haber and Menaldo (2011a, b). The major finding from this second stage of the process is that the estimated Financial structure ratio falls—financial systems become more market-based—as economies grow (Table 5-Panel A). Third, we compute the estimated financial structure for each country-year observation using the parameter estimates from the benchmark regression in Table 5. That is, for each country-year observation, including both OECD and non-OECD countries, we compute the projected financial structure and call this the expected financial structure—or the estimated optimal financial structure—since it is based on the parameter estimates from the high-income OECD countries. Besides this core measure of the estimated financial structure, we also construct an estimate of financial structure based on a version of the Table 5 regression that excludes Log Real GDP per capita for a robustness check. Fourth, we compute the Financial structure gap for every country-year observation as the logarithm of the absolute value of actual financial structure minus the estimated optimal financial structure, standardizing by the prediction error, i.e., Financial structure gap = Ln( | [financial structure – estimated financial structure] | / prediction error ). Intuitively, the prediction error is the sum of the variance of the error term in the high-income OECD benchmark regression and the uncertainty of the parameter estimation. While the former 13 is constant across countries, the latter increases for countries which are more distant from the high-income OECD sample in terms of their independent variables. 3 As a result, out-of-sample countries might exhibit higher Financial structure gaps by construction which can lead to spurious results. We therefore standardize the absolute value of the financial structure deviation by the prediction error because we were concerned that countries that the high-income OECD countries might be relatively weak benchmarks for much poorer countries. If this is the case, the estimated financial structure will be measured poorly, yielding a large estimated gap. Thus, we normalize to reduce this potential bias. For similar reasons, we also conduct the analyses omitting Log Real GDP from the variables used to construct the estimated financial structure ratio, obtaining similar results as we show below. The Financial structure gap is our estimate of deviations of financial structure from the estimated or optimal level for a country at a particular stage of economic development. The Financial structure gap can potentially take on values between negative and positive infinity, where smaller values signify smaller deviations of actual financial structure from the estimated optimum. While the Financial structure gap is clearly measured with error, there seems little reason for believing that these errors bias the results in a particular manner. Table 5-Panel B provides descriptive statistics on the Financial structure gap for different groups of countries. The average Financial structure gap is smallest among the OECD countries, largest for the group of low-income economies, and the group of high-income, non-OECD countries falls in the middle. 3.2 Relationship between the financial structure gap and economic development Figure 2 presents the estimated coefficients from quantile regressions of Log Real GDP per capita on the Financial structure gap. We again graph the coefficients from the 5th through the 95th percentile of Log Real GDP per capita. We provide results both from quantile regressions without controls and from quantile analyses using the “standard controls� defined above. The true structure ratio 𝑦 0 we estimate for a particular country-year in the (out-of-sample) non-high-income OECD sample is: 𝑦 0 = 𝑥 0 𝛽 + 𝜀 0 , where 𝑥 0 represents the vector of independent variables associated with the out-of- 3 sample observation. Our estimate, based on the high-income OECD countries regression, is:𝑦 0 = 𝑥 0 �. Therefore, � the variance of the prediction error is: 𝑉𝑎𝑟[𝑒 0 |𝑋, 𝑥 0 ] = 𝜎 2 (1 + 𝑥 0 [(𝑋 ′ 𝑋)−1 ]𝑥 0 ). ′ 14 There are two noteworthy results. First, the estimated coefficients are negative for each Log Real GDP per capita percentile. That is, an increase in the Financial structure gap is associated with a reduction in economic activity at each level of Log Real GDP per capita. Second, the reduction in economic activity associated with an increase in the Financial structure gap diminishes at higher levels of Log Real GDP per capita. The upwards sloping linear fit of the estimated coefficients formally confirms that the sensitivity of output to marginal increases in deviation of financial structure from the estimated optimum is largest in lower income economies. Next, we examine the relationship between Log Real GDP per capita and financial structure gap while also conditioning on the level of bank and securities market development. Table 6 presents panel OLS regressions for 5-year non-overlapping periods over the period 1980-2008 of Log Real GDP per capita on the Financial structure gap, Private credit, and Stock value traded, while controlling for several country characteristics and adjusting the errors for country clustering. Specifically, we control for country fixed effects in all of the regressions; thus, we control for all time-invariant national characteristics. Regressions 3 and 4 also control for period-fixed effects to account for common time-varying factors associated with economic activity in all countries such as the global cycle. Finally, we also add time-varying, nation- specific characteristics, i.e., the standard controls, in Regressions 3 and 4. Regression 4 differs from regression 3 in that we use a different measure of the financial structure gap in regression 4. Specifically, we use the measure the financial structure gap that excludes Log Real GDP from the equation used to constructed the estimated optimal financial structure ratio (in Table 5). We find that Log Real GDP per capita is negatively associated with the Financial structure gap across all of the specifications in Table 6. The economic magnitude of the relationship between economic activity and the Financial structure gap is large. From columns (2) – (4), a one-standard deviation increase in the Financial structure gap (1.6) is associated with a drop in Log Real GDP per capita of 0.03 (=1.6*(-0.02)), i.e., a three percent reduction in economic activity. 4 While these results use Stock value traded as the main dependent variable, in 4 We extend the analyses by assessing whether the nature of the Financial structure gap matters; that is, does it matter whether an economy is too bank-based or too market-based? Recall that the Financial structure gap measures is constructed to be larger when the deviations of actual financial structure from the estimated optimal structure are larger, regardless of whether actual financial structure is bigger or smaller than the estimated optimum. In 15 unreported regressions we confirm that they are qualitatively similar when Securities market capitalization and Stock market capitalization are used as dependent variables and the financial structure ratios are redefined accordingly. 4. Conclusions This paper provides an empirical exploration of the evolving importance of banks and markets during the process of economic development. As economies grow, both the banking system and financial markets become more developed, but the sensitivity of economic output to bank development tends to fall while the sensitivity of economic output to securities market development tends to increase. These results suggest that the services provided by financial markets become comparatively more important as countries grow. This paper’s results are consistent with the view that (a) financial institutions provide different financial services from those provided by financial markets; (b) as economies grow, they require different mixtures of these financial services to operate efficiently, so that the optimal mixture of financial institutions and markets evolves, with an increasing relative role for markets; and (c) policies and institutions that impede an economy from optimally adapting its financial structure will hinder economic activity. unreported tests, we also examined whether the sign of the deviation matters. We found that the direction of the deviation from the optimum did not matter and it is the Financial structure gap that matters, not whether the country is too bank- or too market-based. 16 References Acemoglu, D., Zilibotti, F. 1997. “Was Prometheus unbound by chance? Risk, diversification, and growth�, Journal of Political Economy 105, 709-775. Allen, F., Gale, D. 1995. “A welfare comparison of the German and U.S. financial systems�. European Economic Review 39, 179-209 Allen, F., Gale, D. 1997. “Financial markets, intermediaries, and intertemporal smoothing�. Journal of Political Economy 105, 523-546. Allen, F., Gale, D. 1999. “Diversity of opinion and financing of new technologies�. European Economic Review 39, 179-209. Allen, F., Gale, D. 2000. Comparing Financial Systems. MIT Press, Cambridge, MA. Barth, J.R., Caprio, G. Jr., Levine, R. 2005. Rethinking Bank Regulation: Till Angles Govern. Cambridge University Press, Cambridge, UK. Beck, T. (2010). “Finance and Oil: Is there a resource curse in financial development?� European Banking Center Discussion Paper No. 2011-004. Beck, T. Demirguc-Kunt, A., Levine, R. 2003. “Law, endowments, and finance�. Journal of Financial Economics 70, 137-181. Beck, T., Levine, R. 2002. “Industry growth and capital accumulation: Does having a market- or bank-based system matter?�. Journal of Financial Economics 64, 147-180. Beck, T., Levine, R. 2004. “Stock markets, banks, and growth: Panel evidence�. Journal of Banking and Finance 28, 423-442. Boot, A.W.A., Thakor, A. 1997. “Financial system architecture�. Review of Financial Studies 10, 693-733. Boot, A.W.A., Thakor, A. 2000. “Can relationship banking survive competition?� Journal of Finance 55, 679-713. Boyd, J. H., Levine, R., Smith, B.D. 2001. “The impact of inflation on financial sector performance�, Journal of Monetary Economics 47, 221-248. Boyd, J.H., Smith, B.D. 1998. “The evolution of debt and equity markets in economic development�. Economic Theory 12, 519-560. Demirguc-Kunt, A., Levine, R. 2001. Financial Structures and Economic Growth: A Cross- Country Comparison of Banks, Markets, and Development. MIT Press, Cambridge, MA. Demirguc-Kunt, A., Maksimovic, V. 1998. “Law, finance, and firm growth�. Journal of Finance 53, 2107-2137. 17 Demirguc-Kunt, A., Maksimovic, V. 2002. “Funding growth in bank-based and market-based financial systems: Evidence from firm level data�. Journal of Financial Economics 65, 337-363. Dewatripont, M., Maskin, E. 1995. “Credit efficiency in centralized and decentralized economies�. Review of Economic Studies 62, 541-555. Goldsmith, R.W. 1969. Financial Structure and Development. Yale University Press, New Haven, CT. Haber, S., Menaldo, V. 2011. “Do natural resources fuel authoritarianism? A reappraisal of the resource curse�. American Political Science Review 105, 1-26. Haber, S., Menaldo, V. 2011. “Rainfall and democracy�. Stanford Center for International Development Working Paper. Holmstrom, B., Tirole, J. 1993). "Market liquidity and performance monitoring�, Journal of Political Economy 101, 678-709. Huybens, E., Smith B.D. 1999. “Inflation, financial markets, and long-run real activity�. Journal of Monetary Economics 43, 283-315. Koenker, T., and G. Basset, 1978. “Quantile regressions�. Econometrica 46:1, 33-50. La Porta, R., Lopez-de-Silanes, F., Shleifer, A., Vishny, R. 1998. “Law and finance�. Journal of Political Economy 106, 1113-1155. Levine, R., 2002. “Bank-based or market-based financial systems: Which is better?�. Journal of Financial Intermediation 11, 398-428. Levine, R., Zervos, S. 1998. “Stock markets, banks, and economic growth�. American Economic Review 88, 537-558. Morck, R., Nakamura, M. 1999. “Banks and corporate control in Japan�, Journal of Finance 54, 319-340. Morck, R., Yeung, B., Yu, W. 2000. “The information content of stock markets: Why do emerging markets have synchronous stock price movements�, Journal of Financial Economics 58, 215-260. Rajan, R.G. 1992. “Insiders and outsiders: The choice between informed and arms length debt�. Journal of Finance 47, 1367-1400. Rajan, R., Zingales, L. 1998. “Financial dependence and growth�. American Economic Review 88, 559-586. StataCorp. 2007. “Base Reference Manual.� Stata Press, College Station, TX. 18 Sylla, R.E. 1998. “U.S. Securities markets and the banking system, 1790-1840�. Federal Reserve Bank of St. Louis Review 80, 83-104. Weinstein, D.E., Yafeh, Y. 1998. “On the costs of a bank-centered financial system: Evidence from the changing main bank relations in Japan�. Journal of Finance 53, 635-672. 19 Table 1: Variable definitions and sources Name Source Definition Dependent variable and baseline financial sector controls Log Real GDP per World Development Logarithm of real GDP per capita (constant 2000 USD). capita Indicators (WDI) International Financial Private credit Deposit money bank credit to the private sector as % of GDP. Statistics (IFS) Stock value traded Standard & Poor’s Value of stock market transactions as % of GDP. Stock market The value of listed shares on a country’s stock exchanges as a Standard & Poor’s capitalization share of GDP as % of GDP. Standard & Poor’s; Bank Securities market Stock market capitalization plus Domestic private bond market of International capitalization capitalization as % of GDP. Settlements Financial structure Financial structure ratio Authors’ calculations Bank private credit / Stock value traded. The expected ratio is derived by regressing the Financial structure ratio on log real GDP per capita, legal origin, distance Expected financial Authors’ calculations to the equator, population size and density, and natural structure ratio resource as % of exports using OLS regression on annual OECD data. Log absolute value of the difference between the expected and Financial structure gap Authors’ calculations the actual Financial structure ratio. The residuals were first deflated by the prediction error. Fin. structure Actual Financial structure ratio / Expected Financial structure ratio/Optimal fin. Authors’ calculations ratio structure ratio Standard controls Initial GDP per capita WDI Log Initial real GDP per capita (constant 2000 USD). Avg. years of Barro and Lee (2010) Log (1 + Average years of schooling). schooling Openness to trade WDI Log Sum ex- and imports of goods and services as % of GDP. Government size WDI Log General government consumption as % of GDP. Other controls Set of five dummy variables that refers to the legal origin of Global Development Legal origin each country: British, French, Socialist, German and Network Growth Database Scandinavian. Distance from equator Shleifer (2002) Latitude. Natural resources as % Value of fuel exports plus ores and metals exports as a fraction WDI of total exports of total merchandise exports. Population size WDI Population size (millions). Population density WDI Number of people per square km. 20 Table 2: Descriptive statistics Descriptive statistics are calculated on all available annual data in the period 1980-2008. Standard Variable Mean Maximum Minimum deviation Dependent variable and baseline controls Log Real GDP per capita 7.58 1.57 10.94 4.13 Private credit 39.28 35.90 319.71 0.00 Stock value traded 28.80 57.44 632.34 0.00 Stock market capitalization 47.7 58.39 561.44 0.00 Securities market capitalization 59.08 71.19 588.27 0.00 Financial Structure Financial structure ratio 279.24 5,070.42 207,726.7 0.09 Expected financial structure ratio 53.81 37.00 127.82 -27.95 Financial structure gap -1.41 1.59 7.63 -7.76 Fin. structure ratio/Optimal fin. structure ratio 3.52 57.33 2,111.86 -207.65 Standard controls Avg. years of schooling 6.18 2.99 13.08 0.03 Openness to trade 83.48 48.66 456.65 0.31 Government size 16.52 7.00 83.16 1.38 Controls Natural resources as a % of total exports 22.16 28.59 100 0.00 Distance from equator 0.28 0.19 0.72 0.00 Population size (mln.) 29.94 114.02 1,325.64 0.03 Population density (1,000 per square km) 252.39 1191.74 18,658.80 1.06 21 Table 3: Correlations Correlations are calculated on all available annual data in the period 1980-2008. * indicates a significant correlation coefficient at the 5% level or better. Panel A: Average years of schooling Log Real GDP per capita Financial structure gap Optimal Fin. Structure Fin. Structure Ratio / Stock value traded Openness to trade Private Credit Inflation rate Ratio Correlations Private Credit 0.67* 1 Stock value traded 0.41* 0.51* 1 Financial structure gap -0.61* -0.41* -0.28* 1 Fin. structure ratio / Optimal fin. structure ratio -0.03 -0.01 -0.04 0.24* 1 Average years of schooling 0.71* 0.52* 0.27* -0.36* 0.01 1 Openness to trade 0.27* 0.25* 0.14* -0.00 0.00 0.25* 1 Inflation rate -0.06* -0.05* -0.04 -0.00 0.00 -0.00 -0.03* 1 Government size 0.22* 0.16* 0.04 -0.41* -0.04* 0.25* 0.18* -0.02 22 Table 3 (continued): Correlations Correlations are calculated on all available annual data in the period 1980-2008. * indicates a significant correlation coefficient at the 5% level or better. Panel B: Optimal Fin. Struct. Financial structure Log Real GDP per Stock value traded Fin. Struct. Ratio / Population size Private Credit capita Ratio gap Correlations Private Credit 0.67* 1 Stock value traded 0.41* 0.51* 1 Financial structure gap -0.61* -0.41* -0.28* 1 Fin. St. Ratio / Optimal Fin. St. Ratio -0.03 -0.01 -0.04* 0.24* 1 Population size -0.07* 0.10* 0.08 0.12* -0.02 1 Population density 0.19* 0.18* 0.28* -0.06* -0.01 -0.02 23 Figure 1: Quantile coefficients for Private credit and Securities Market Activity The dependent variable is Log real GDP per capita. The figures depict the coefficients of quantile regressions of Private credit, Stock value traded, Securities market capitalization and Stock market capitalization for each of the 5th to 95th percentiles of the GDP per capita distribution on the left axis. Private credit is defined as deposit money bank credit to the private sector as % of GDP. Stock value traded is the value of stock market transactions as % of GDP. Stock market capitalization is the value of listed shares on a country’s stock exchanges as % of GDP. Securities market capitalization is defined as Stock market capitalization + Domestic private bond market capitalization as % of GDP. Percentile values are reported on the right axis. Data are 5-year non-overlapping country averages. Panel A does not control for additional variables. Panel B controls for Standard controls: Initial GDP per capita, Government size, Openness to trade, Inflation, Average years of schooling, and time-fixed effects. The horizontal dotted line depicts the OLS estimate. The solid lines represent linear fits. Panel A: No controls Private credit Stock value traded (controlling for market value traded) (controlling for Private credit) .008 .025 100 80 80 .006 .02 60 60 .004 .015 40 40 .002 .01 20 20 .005 0 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Quantiles of Log GDP per capita Quantiles of Log GDP per capita Priv. Cred. Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Stock Val. Traded Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Fitted values Private Credit / GDP (RHS) Fitted values Stock Value Traded / GDP (RHS) Securities market capitalization Stock market capitalization (controlling for Private credit) (controlling for Private credit) 150 .01 .01 150 .008 .008 100 100 .006 .006 50 50 .004 .004 .002 .002 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Quantiles of Log GDP per capita Quantiles of Log GDP per capita Sec. Mkt. Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Stock Mkt. Cap. Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Fitted values Securities Markets / GDP (RHS) Fitted values Stock Mkt. Cap. / GDP (RHS) 24 Panel B: Accounting for Standard Controls Private credit Stock value traded (controlling for market value traded) (controlling for Private credit) .003 .005 100 80 .0045 80 .002 60 60 .004 .001 40 40 .0035 0 20 20 .003 -.001 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Quantiles of Log GDP per capita Quantiles of Log GDP per capita Priv. Cred. Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Stock Val. Traded Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Fitted values Private Credit / GDP (RHS) Fitted values Stock Value Traded / GDP (RHS) Securities market capitalization Stock market capitalization (controlling for Private credit) (controlling for Private credit) .003 .0015 150 150 .001 .002 100 100 .0005 .001 0 50 50 -.0005 0 -.001 -.001 0 0 0 20 40 60 80 100 0 20 40 60 80 100 Quantiles of Log GDP per capita Quantiles of Log GDP per capita Sec. Mkt. Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Stock Mkt. Cap. Coeff. Quant. Reg.(LHS) OLS estimate (LHS) Fitted values Securities Markets / GDP (RHS) Fitted values Stock Mkt. Cap. / GDP (RHS) 25 Table 4: Robust regression results of linear regression fits of Figure 1 The table displays robust regressions results of the linear fits in Panels A and B of Figure 1. The dependent variables are coefficients of quantile regressions of Private credit and Stock value traded for each of the 5th to 95th percentiles of the GDP per capita distribution, respectively, on 5-year non-overlapping country averages. The independent variables are a constant and the percentile associated with the coefficient. Columns 1 and 3 use coefficients of quantile regressions without additional controls (Panel A of Figure 1). Columns 2 and 4 use coefficients of quantile regressions that include standard controls: Initial GDP per capita in 1980, Government size, Openness to trade, Inflation, Average years of schooling, and time-fixed effects (Panel B of Figure 1). The p-values in brackets are based on robust country-level clustered standard errors. *, **, *** denote significance on the 10, 5, and 1-percent level, respectively. Dep. Var.: Percentile regression coefficient Private Dep. Var.: Percentile regression coefficient Stock Value credit Traded 1 (No controls) 2 (With controls) 3 (No controls) 4 (With controls) Percentile -1.24E-04*** -1.02E-05*** 4.18E-05*** 3.79E-05*** [0.00] [0.00] [0.00] [0.00] Constant 2.51E-02*** 4.45E-03*** 2.05E-03*** -1.34E-03*** [0.00] [0.00] [0.00] [0.00] Standard controls No Yes No Yes Observations 91 91 91 91 26 Table 5: Constructing the Financial Structure Ratio and Gap Panel A shows regression results. The dependent variable is the financial structure ratio (the ratio of private credit to stock value traded). The financial structure gap is based on the expected relationship between the financial structure ratio and GDP per capita controlling for legal origin dummies, population size and density, distance to equator and exports of natural resources. This relationship is estimated on annual high-income OECD data using OLS regression. The expected ratios for non-OECD countries are estimated out-of-sample using the OECD model. The financial structure gap is defined as the log absolute value of the residual. The residuals were first corrected for the Standard Error of the prediction. Panel B reports the descriptive statistics. The p-values in brackets are based on robust country-level clustered standard errors. *, **, *** denote significance on the 10, 5, and 1-percent level, respectively. Panel A: Financial Structure Ratio regression results (estimated on High-Income OECD sample) Dep. Var.: Financial Structure Ratio OLS 1 Log GDP per capita -26.64** [0.01] English Legal Origin 7.27 [0.69] French Legal Origin 5.30 [0.80] German Legal Origin 27.24 [0.22] Distance to equator 46.45 [0.44] Log Population Size -4.50 [0.35] Log Population Density -0.91 [0.86] Natural Resources Exports 0.10 [0.81] Constant 254.23** [0.01] Observations 493 R-squared (Root mean squared error) 0.05 (98.06) Panel B: Descriptive statistics of the Financial Structure Gap Mean Financial Structure Gap Mean Country Group Differences and 2-sided T-tests (log absolute value of the residual) for Financial Structure Gap Linear fit, OLS Linear fit, OLS Low inc., non-OECD -0.503 Low vs. high, non-OECD 0.618* (1.038) [0.000] High-inc., non-OECD -1.121 Low, non-OECD vs. OECD 2.111* (1.431) [0.000] High-Income OECD -2.614 High, non-OECD vs. OECD 1.493* (1.406) [0.000] Standard deviation in parentheses. P-value of t-tests in brackets. T-tests allows for unequal variance. * and ** denote significance at the 1 and 10-percent levels, respectively. 27 Figure 2: Quantile coefficients for the Financial Structure Gap The figures depict the coefficients of quantile regressions of the financial structure gaps for each of the 5th to 95th percentiles of the GDP per capita distribution. Data are 5-year non-overlapping country averages. Panel A only controls for Private credit and Stock value traded. Panel B also accounts for a set of standard controls: Initial GDP per capita in 1980, Government size, Openness to trade, Inflation, Average years of schooling, and period-fixed effects. The financial structure gap is based on the expected relationship between the financial structure ratio (the ratio of Private credit to Stock value traded) and GDP per capita. In addition, the regression controls for legal origin dummies, population size and density, distance to equator and exports of natural resources. This relationship is estimated on annual high-income OECD data according to a linear fit using robust regression. The expected ratios for non-OECD countries are estimated out-of-sample using the OECD model. The financial structure gap is defined as the log absolute value of the residual. The horizontal dotted line depicts the OLS estimate. The finer-dotted lines represent linear fits. Panel A: No controls -.2 -.3 -.4 -.5 -.6 -.7 0 20 40 60 80 100 Quantiles of Log GDP per capita Lgap Coeff. Quant. Reg. OLS estimate Fitted values Panel B: Standard controls -.02 -.03 -.04 -.05 -.06 -.07 0 20 40 60 80 100 Quantiles of Log GDP per capita Lgap Coeff. Quant. Reg. OLS estimate Fitted values 28 Table 6: Economic development and the Financial Structure Gap OLS panel estimates. The dependent variable is Log Real GDP per capita. OECD high-income countries are excluded from the regression. Data are 5-year non-overlapping country averages. The main independent variable is the Financial structure gap defined as the log of the absolute difference between the actual and the expected financial structure ratio (Private credit to Stock value traded) deflated by the prediction error. The expected financial structure ratio used to construct the Financial structure gap in columns 1-3 is estimated on annual OECD high-income data with OLS using as controls log real GDP per capita, legal origin, population size and density, distance to equator and exports of natural resources. The expected ratio in column 4 is estimated on annual high-income OECD data with OLS using the same set of controls in columns 1-3, but excludes log real GDP per capita. Standard controls are Average years of schooling, Openness to trade, Annual inflation, Government size and period-fixed effects. The p- values in brackets are based on robust country-level clustered standard errors. *, **, *** denote significance on the 10, 5, and 1-percent level, respectively. Dep. var.: Log real GDP per capita 1 2 3 4 (excl. GDP per capita in fin. structure regression) Financial Structure Gap -0.05*** -0.02** -0.02*** -0.02** [0.00] [0.02] [0.01] [0.01] Private Credit 8.11*** 4.44*** 3.87*** 3.96*** [0.00] [0.00] [0.00] [0.00] Stock Value Traded 2.24** 0.18 -0.22 -0.26 [0.03] [0.78] [0.70] [0.63] Standard controls No No Yes Yes Country-fixed effects Yes Yes Yes Yes Time-fixed effects No No Yes Yes Observations 253 253 229 229 Adjusted R-squared 0.45 0.77 0.78 0.78 Countries 69 69 63 63 29 Appendix 1: Countries and medians for selected indicators The table provides country medians for the period 1980-2008 of Private credit, Stock value traded, Financial structure ratio, and Actual Financial structure ratio / Estimated Optimal financial structure ratio. Median Fin. Median Real Median Median Median Stock structure constant Financial Country Private value traded ratio/Optimal GDP per structure credit (%) (%) fin. structure capita ratio ratio Argentina 7,169 19.9 2.7 4.9 0.3 Armenia 683 7.4 0.0 232.4 2.9 Bangladesh 277 16.7 1.4 23.5 0.3 Bolivia 987 35.2 0.0 1,974.7 25.8 Botswana 2,595 14.7 0.7 20.0 0.4 Brazil 3,586 37.8 13.4 2.6 0.1 Bulgaria 1,564 41.7 0.8 27.4 0.3 Chile 3,917 55.8 8.8 7.3 0.2 China 600 93.0 29.5 3.5 0.0 Colombia 2,333 30.1 1.3 23.6 0.6 Costa Rica 3,549 19.0 0.2 105.9 2.8 Croatia 4,823 36.5 1.0 36.5 0.5 Côte d'Ivoire 635 20.0 0.2 105.6 0.7 Ecuador 1,335 21.0 0.3 66.5 1.1 Egypt, Arab Rep. 1,182 29.2 4.0 13.6 0.2 El Salvador 1,877 34.9 0.2 236.4 4.9 Georgia 1,075 7.8 0.2 63.7 0.8 Ghana 234 5.2 0.5 27.5 0.3 Guatemala 1,599 19.1 0.0 517.9 9.2 Hong Kong SAR, China 23,345 148.0 123.4 1.2 -0.1 India 352 25.9 38.5 0.8 0.0 Indonesia 773 24.7 7.1 3.4 0.1 Iran, Islamic Rep. 1,486 22.8 1.9 14.3 0.2 Israel 16,920 68.6 22.3 3.5 1.0 Jamaica 3,469 24.0 2.3 12.2 0.3 Jordan 1,901 66.0 10.4 6.0 0.1 Kazakhstan 1,397 21.2 0.7 24.9 0.3 Kenya 421 24.2 0.6 38.7 0.5 Kuwait 16,929 56.5 36.0 1.3 0.1 Kyrgyz Republic 321 5.3 1.6 3.1 0.0 Latvia 3,588 22.8 0.8 31.0 0.3 Lebanon 4,459 73.5 1.4 59.2 1.5 30 Appendix 1 (continued): Countries and medians for selected indicators Real Fin. structure Financial constant Private Stock value ratio/Optimal Country structure GDP per credit (%) traded (%) fin. structure ratio capita ratio Lithuania 3,506 16.8 1.9 13.2 0.2 Macedonia, FYR 1,752 23.9 1.4 23.5 0.2 Malawi 144 8.9 0.3 19.7 0.2 Malaysia 3,366 105.7 43.7 2.5 0.1 Mexico 5,277 17.2 8.1 2.3 0.1 Moldova 512 13.3 1.9 11.7 0.1 Mongolia 464 11.1 0.3 51.9 0.5 Morocco 1,234 29.0 2.6 15.1 0.2 Namibia 2,052 46.6 0.3 209.3 4.0 Nepal 199 18.3 0.5 57.3 0.5 Nigeria 368 13.2 0.4 28.9 0.2 Oman 7,537 24.7 3.4 13.2 0.4 Pakistan 503 24.6 17.2 1.5 0.0 Panama 3,480 60.4 0.5 170.6 5.1 Papua New Guinea 630 18.1 0.1 171.2 2.4 Paraguay 1,399 20.1 0.1 296.1 4.2 Peru 2,049 13.3 2.9 7.5 0.2 Philippines 941 29.3 9.6 3.3 0.1 Poland 4,251 27.5 5.1 4.3 0.1 Romania 1,896 37.5 0.9 11.3 0.2 Russian Federation 2,037 16.2 7.8 1.7 0.0 Saudi Arabia 9,402 22.7 9.7 2.4 0.1 Singapore 18,451 90.0 74.0 1.4 -0.1 Slovenia 9,595 35.5 2.6 14.6 0.3 South Africa 3,181 58.0 43.4 1.4 0.0 Sri Lanka 676 21.8 1.8 14.3 0.2 Tanzania 264 5.4 0.1 63.9 0.7 Thailand 1,827 95.6 34.0 2.9 0.1 Tunisia 1,639 53.8 1.4 39.8 0.7 Turkey 3,580 17.8 30.3 0.7 0.0 Uganda 215 4.2 0.0 1,334.7 14.0 Ukraine 944 11.1 0.6 41.1 0.5 United Arab Emirates 22,586 47.4 1.1 46.8 27.0 Uruguay 6,068 35.3 0.0 2,730.4 87.8 Venezuela, RB 5,030 16.5 0.7 14.3 0.5 Vietnam 328 37.3 1.0 217.8 2.8 Zambia 369 8.2 0.2 37.7 0.3 31