Financial Development and Intersectoral Investment

Financial Development and Intersectoral Investment: New Estimates and Evidence Elias Papaioannou∗ European Central Bank

September 2005 [PRELIMINARY]

Abstract Building on Wurgler [Wurgler, Jeffrey. "Financial Markets and the Allocation of Capital." Journal of Financial Economics, January 2000, 58 (1), pp. 187-214], we construct country-level indicators on the sectoral responsiveness of investment to shifts in the production structure for 65 countries. We then use these indicators to examine whether intersectoral investment responsiveness is related to the size of capital markets. Our data yields strong support for the hypothesis that investment in expanding industries is greater in countries with larger capital markets. This continues to be the case when we focus on increases in capital market size due to lower government bank ownership, stricter insider-trading legislation, and more efficient legal systems. However, when we employ the general measure of investment responsiveness, proposed by Wurgler, rather than the new measure that isolates the intersectoral responsiveness, we find much weaker correlations.

––––––––––––– * European Central Bank, Financial Research Division, Postfach 160319, D-60066, Frankfurt, Germany. Email: [email protected]. This paper had been originally circulated as "Investor’s Protection, Banking Characteristics, Financial Development and the Elasticity of Investment to Output: New Estimates and Evidence." I thank Markus Baltzer and Marco Lo Duca for superb research assistantship and Philipp Hartmann for motivation and feedback. This paper could not have been written without Antonio Ciccone’s valuable input. The opinions expressed herein are those of the author and do not necessarily represent those of the European Central Bank or the Eurosystem. All remaining errors are my responsibility.

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Introduction Financial reform is high on policy debate. For example, the Lisbon Agenda sees it as

necessary for the European Union (EU) to close the productivity gap with the United States (US). But how is financial reform linked to aggregate productivity growth? An old, still very influential hypothesis is that well-working capital markets contribute to aggregate productivity growth by making sure that investment is allocated to the sectors where it is most productive (Bagehot, 1873; Schumpeter, 1911; see Levine, 2005, for a recent review of the literature on finance and growth). In this paper we examine this hypothesis using international data on sectoral investment and production. Building on an earlier contribution by Wurgler (2000), our empirical analysis proceeds in two steps. First using manufacturing data in 65 non-socialist countries over the period 1963-2002, we construct country-level indicators of the responsiveness of sectoral investment to intersectoral shifts in production. Second we examine whether, conditional on various other country characteristics, countries with larger capital markets display greater intersectoral investment responsiveness to value added growth. To test whether large and liquid capital markets allocate investment quickly to expanding sectors, it is important to have indicators that identify the responsiveness of sectoral investment to changes in the production as cleanly as possible. Wurgler proposed a countrylevel measure of investment responsiveness (he refers to it as "capital efficiency"), obtained by regressing country-by-country gross fixed capital formation to value added at the industry level. Wurgler then shows that the elasticity of investment to output is significantly higher in countries with large capital markets. Wurgler’s elasticity, however, besides capturing how fast investment responds to output, confounds permanent cross-country differences in the production structure with inter-temporal differences in the volatility of output and investment. The high elasticity of investment to value added may be driven, for example, by international specialization, since financially developed countries may concentrate their production in capital intensive sectors that respond quickly to output shocks. The general investment output elasticity may also be driven by industry-specific trends, arising for example, by skilled-biased technical change. A strong correlation between investment and production may also be driven by common across industries shocks, arising from financial crises, for example. In this paper we start by implementing the (straightforward) changes necessary to obtain 1

country-level indicators of investment to value added that isolate the intersectoral responsiveness of investment (in a given year). We also reconstruct Wurgler’s original (general) investment-output elasticity using more recent data. Our results indicate that while we do find that financial development correlates significantly with our measure that isolates the intersectoral responsiveness of investment, when we employ the original general measure proposed by Wurgler the results are much weaker and in general insignificant. This suggests that properly measuring the interindustry responsiveness of investment is key to test the hypothesis that financial development helps align investment with growth. This continues to be the case when we control for other country-characteristics that may affect the intersectoral investment responsiveness, like the level of economic development for example. Second, we try to dig deeper on the mechanisms of the financial development - intersectoral responsiveness correlation. We examine whether the association retains significance when we focus on cross-country differences in the size of capital markets due to government ownership of banks (La Porta et al., 2002), insider-trading and anti-self-dealing legislation (Bhattacharya and Dauk, 2002; La Porta et al., 2005) that protect outside investors from looting and tunneling, and the efficiency of legal system (La Porta et al., 1998; Djankov et al., 2003). This is important because financial reform is likely to affect capital markets through these channels. Here our results are twofold. First, in line with previous work we show that lower levels of government ownership of banks, stricter protection of investor’s rights, and more generally an efficient legal system are associated with larger capital markets. These results emerge in various country samples. Second, we provide robust evidence that the intersectoral investment responsiveness continues to be increasing in capital market size when we focus on increases in capital market size due to lower government bank ownership, stricter insider-trading legislation, and more efficient legal systems. Third, we show that these results not only hold when we condition on economic development or extract the historically predetermined component of financial development using legal origin as an instrument (Levine, et al. 2000; La Porta et al, 1998), but also when we fully exclude low income countries from the estimation. This gives more confidence that the results are not driven by the large differences in intersectoral investment responsiveness and financial markets size among developed and Third World countries. Furthermore most of our results (regarding both the impact of state ownership of banks and legal efficiency on financial markets size and on the impact of financial development on the investment-output elasticity across sectors) survive even when we just concentrate on the more homogenous 2

group of high income countries. Our paper aims to contribute to the literature examining how financial development affects productivity growth (see Levine, 2005, for a thorough overview). Within this literature, our paper is most closely related to work using international sectoral data, pioneered by Rajan and Zingales (1998). Rajan and Zingales argue that if financial development fosters growth then it should matter most for sectors that for inherent technological reasons depend more on outside finance. To test this they construct industry-level measures of external-finance dependence using financial statement data for large U.S. firms and then show that in financially advanced countries external-finance-hungry sectors grow faster. A vast subsequent literature has followed Rajan and Zingales approach in constructing industry characteristics using US data and then examining the interaction of these characteristics with financial development (as well as other country level characteristics) in fostering sectoral value added growth.1 The popularity of this approach stems from enabling researchers to assuage (although not fully resolve) some of the limitations of the standard cross-country growth regressions, such as omitted variables and multi-collinearity (see Rajan and Zingales, 1998 for a discussion). Using this approach Fisman and Love (2004a,b) show that financial development speeds growth of industries that face good growth opportunities, as proxied by sales growth in the US. Ciccone and Papaioannou (2006) build a multi-industry world equilibrium model that gives a theoretical basis of this type of cross-country cross-industry analysis. They also show that the positive effect of finance for the growth of sectors with good growth opportunities, proxied by capital growth in the US, is robust to measurement error in the US-based indicators and alternative channels of sector dynamics. Our work also uses industry level data, but follows a different approach, first proposed by Wurgler (2000) and more recently also employed by Almeida and Wolfenzon (2005). In contrast to the cross-country cross-industry models proposed by Rajan and Zingales (1998), this method first constructs a country-level measure of investment responsiveness, using industry output and investment data in a large number of countries over the past decades and then estimates cross-country regressions trying to identify the significant correlates of the constructed investment responsiveness measure. Although this method is not structural 1

For example Claessens and Laeven (2003) and Braun (2003) show that countries with good property rights protection and well-developed financial markets experience faster growth in intangible-intensive sectors. Beck and Levine (2002) construct industry measures on R&D intensity and show that financial development fosters industry growth of these sectors. Fisman and Love (2003) construct industry proxies of sector dependence on trade-credit and then show that in financially underdeveloped economies these sectors grow on average faster, most likely because trade credit is a substitute of external finance.

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and requires many assumptions it has two main merits, which makes it attractive: First, it uses country-industry specific proxies of shocks in production (i.e. realized contemporaneous value added growth) rather than US (or other country)-based industry specific measures that may not be representative in all countries. Second, this approach is intuitive, transparent and easy to implement. Our work differs from the original contribution of Wurgler in a number of ways. The key difference is that our focus is on the link between financial development and the intersectoral allocation of investment (which is obtained by isolating the inter-industry response of investment to value added from unobserved heterogeneity stemming from general country-, industry-, and time-specific effects as well as country-industry effects and industry-specific time trends). Our intersectoral focus also distinguishes us from Bekaert, Harvey, Lundblad and Siegel (forthcoming) recent work, who, among other things, examine the link between financial development and country-level investment growth (in response to global growth opportunities). We also try to be more closely linked to the ongoing policy questions on the potential consequence of financial sector reforms in Europe. Thus from the very beginning we pay a special attention to financially developed countries. In addition we examine what would be the most likely consequences of reforms in the banking system and the protection of outside investors on the intersectoral investment responsiveness.2 The remainder of the paper is structured as follows. In Section 2 we first describe the sources and main features of our data. We then estimate the intersectoral investment responsiveness for 65 countries. We also discuss the main differences with Wurgler’s original (general) investment responsiveness measure. Section 3 contains the first set of our main empirical results. We start by showing that there is an economically sizable and statistically highly significant link between capital market size and the intersectoral investment responsiveness. Then we examine the robustness of this link to accounting for the overall level of economic development, other country characteristics and alternative estimation techniques. In Section 4 we investigate the link between the intersectoral investment responsiveness and cross-country differences in capital market size due to government bank ownership, insider-trading legislation and the general efficiency of the legal system. This is done in a two-stage least-squares framework. The first stage shows that lower government 2

In this aspect our analysis is similar to the policy-oriented study of Guiso, Jappeli, Padula and Pagano (2003), who employing, however, the Rajan and Zingales (1998) approach examine the effect of institutional reforms on industry growth through financial development.

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bank ownership, stricter insider-trading legislation (and to some lesser extent strict antiself-dealing practices), and more efficient legal systems are associated with larger capital markets. The second stage yields that intersectoral investment responsiveness continues to be increasing in capital market size when we focus on increases in capital market size due to lower government bank ownership, stricter insider-trading legislation, and more efficient legal systems. Section 5 summarizes and concludes.

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Data and Methodology

We first introduce the international sectoral data on investment and production that we employ to construct the indicator of intersectoral investment responsiveness to shifts in the production structure. Second, we explain the empirical approaches used to estimate countryspecific investment responsiveness indicators and discuss our estimates. Third, we present country-level financial development data and show how this data can be used to learn about the determinants of the intersectoral investment responsiveness.

2.1

The international sectoral data on investment and production

Our international data on sectoral investment (gross fixed capital formation) and production (value added) comes from the Industrial Statistics Database of the United Nations Industrial Development Organization (UNIDO). The database reports such data for 28 manufacturing sectors from 1963 to 2002 (the sectoral detail corresponds to the 3-digit International Standard Industrial Classification). We deflate the originally expressed in US dollars investment and value added series using the US Capital Equipment and Finished Goods PPI indexes, respectively.3 To reduce the influence of erroneous values we drop observations where log investment or value added growth exceed one in absolute value. We also exclude data from sectors that represent less than 0.1% of total manufacturing in each country-year. We also ignore socialist economies and countries with less than 50 sector-year observations. This leaves us with sector data on 65 economies. Table I columns (1)-(3) report the data coverage. Our sample is quite similar to Wurgler’s, except that our data also cover the period 3 We note that ideally one would need country-industry specific deflators, which are unfortunately not available. However, in the estimation of the intersectoral investment responsiveness, we account for countryyear effects that capture overall inflation and for country-industry effects that capture global industry specific price differences.

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1996-2002. (Wurgler’s data coverage stops in 1995).

2.2

Estimating the intersectoral responsiveness of investment

Wurgler estimates the general responsiveness of sectoral investment to shifts in the production structure by running the following linear regression country by country:

(1)

ln

Is,c,t Is,c,t−1

= αc + η c ln

Vs,c,t + εs,c,t . Vs,c,t−1

I denotes gross fixed capital formation, V denotes value added, and s, c, and t are subscripts denoting sectors, countries, and time respectively. The main parameter of interest is the country-specific investment responsiveness parameter (η c ), which aims at capturing how quickly investment grows (falls) in expanding (declining) sectors in different countries. (Estimating (1) country by country yields of course identical estimates of η c with a procedure that pools all countries together, controls for country-specific intercepts, αc , and allows the elasticity to vary across countries). Given our focus on the intersectoral allocation of investment, Wurgler’s estimating approach has the disadvantage that estimates of η c may capture global sector-specific trends. To see this, suppose that the true responsiveness of investment to value added is the same in all sectors and countries. Suppose also that sectors are subject to different global investment trends (because technological change is raising the optimal capital output ratio faster in some industries than others). In this case Wurgler’s approach may lead us to find different country-level investment responses simply because some countries happen to be specialized in sectors that experience rapidly growing (global) capital-output ratios. To address this, estimation of the country investment responsiveness parameters should allow for different intercepts by sector and year (on top of the country fixed-effects that account for time-invariant country unobservables). In this case the intercept in (1) would be of the form αc + αs,t . In addition, permanent differences in the production structure may affect the estimate of the country-specific investment responsiveness parameter, because the sectoral composition within (the rather broad) 3-digit industries differs across countries. For example recent work has shown that financially developed countries specialize in capital intensive (and) external 6

finance-dependent sectors (Beck, 2003; Manova, 2005; Levchenko, 2005). These differences can be controlled for by allowing for sector-country specific effects when estimating the country investment responsiveness parameters.4 The intercept in (1) would then take the form αc + αs,c + αs,t . Finally, the (general) investment responsiveness also captures differences in the overall volatility of investment and production. This is also undesirable from our intersectoral perspective, since it confounds it with the intertemporal response. To see this, suppose that some countries are subject to a great amount of volatility regarding their overall economic prospects. As a result, investment and production drop steeply in those years where the economic outlook turns bad and rise strongly when the outlook starts looking more favorable. For example, almost all countries in our sample experienced financial crises (and recoveries), where all sectors in the economy were experiencing slower (faster) investment and output growth. The estimating equation in (1) may misinterpret the response of both investment and output. Thus one needs to also control country-year intercepts (αc,t ) to account for general business cycle fluctuations. Thus our estimating equation for the intersectoral investment responsiveness will account for all possible sources of unobserved heterogeneity:

(2)

ln

Is,c,t Is,c,t−1

= αc,s + αc,t + αs,t + η ∗c ln

Vs,c,t + εs,c Vs,c,t−1

where asterisk ∗ indicates the responsiveness measure that isolates the intersectoral re-

sponsiveness of investment to value added

Table I reports the two different elasticity measures (and the standard errors respectively) for the 65 countries. We label the general (Wurgler’s) investment responsiveness as elasticity 1 (estimated from equation (1)) and the intersectoral investment responsiveness measure as elasticity 2 (estimated from equation (2)). When one accounts only for country unobservables (elasticity (1)), then on average the elasticity of investment to value added is 0.41. Turning now to the preferred elasticity 2 measure (η ∗c in equation (2)), the intersectoral responsiveness of investment to value added when we account for all sources of variation is much smaller 0.22. In all countries the intersectoral investment responsiveness elasticity is smaller that 4

Although this possibility is not explicitly stated, Wurgler does present some estimates where he controls for country-industry specific effects.

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the general one. This suggests that the general measure, captures other not-related to the intersectoral dimension, features, and thus one has to properly isolate the cross-industry component. A couple of interesting patterns emerge from Table I. First, there is a wide dispersion of investment responsiveness across countries with both measures. The Table shows that both the general (elasticity 1) and the intersectoral investment responsiveness (elasticity 2) measures are significantly higher in the group of industrial countries. For example, according to Wurgler’s original measure, West Germany scores the highest, while Japan, New Zealand, France and Austria fill in the rest top five positions. At the other side, in some low income countries, such as Bolivia, Swaziland and Panama the correlation coefficient of investment to value added growth is zero (and even negative). Although the estimated equations are nonstructural, this descriptive reassures that these elasticities do capture a potentially relevant economic effect. Second, in spite of the high correlation between the two measures (0.76) there are nonnegligible differences when we isolate the intersectoral reallocation of capital. Although the general pattern of sizable differences between developed and underdeveloped countries prevail, there are now significant differences especially within high-income countries. For example, the Unites States rather than Germany appear now to be the most efficient country in allocating capital to fast growing sectors. Australia, South Korea, Hong Kong, and Japan add to the list of the top 5. A notable difference in the ranking is France, which when we did not account for country-year shocks to sector output was in the top 5 list, while when we isolate the intersectoral investment responsiveness, it drops to the 34th position. Also Greece appears to score quite high.5 The descriptive analysis thus hints that one needs to carefully account for different sources of variation to precisely isolate the correlation between investment and output growth. This is particularly important since the hypothesis we want to test is whether conditional on global (and maybe sector-specific) trends, country and sector general unobservable characteristics, as well as country-sector composition effects and country-year common sector shocks, investment responds faster to output growth if firms are less financially constrained. 5

Some difference may also be driven by measurement error, which is likely to be non-negligible with industry-level data on investment.

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2.3

Cross-country empirical model and main data on financial size

Having now obtained the investment responsiveness measures, we can examine whether financial market’s size explain part of the significant variation across countries. We will do so by estimating cross-country models of the following general form:

η ∗c (η c ) = a + βF Dc + δX 0 + ν c The dependent variable is the intersectoral and the general investment responsiveness measures. Our main focus is on the β coefficient in a proxy measure of financial development (F D). For F D we will use a broad measure of total finance, which equals the sum of private credit by deposit money banks and other financial institutions and equity market capitalization, both expressed as a share of GDP. The data are retrieved from World Bank’s Financial Structure Database (Beck, Demirgük-Kunt, and Levine, 2001). Analogous measures have been employed, among others, by Guiso et al. (2004), Wurgler (2000) and Beck et al. (2001). Following these studies we average the total finance measure over the period 1980-1995. Averaging maximizes country-coverage and smooths business cycle fluctuations. To fully minimize concerns of endogeneity (which in any case should be minimal, since when we estimate the elasticities we account for country fixed effects, while for elasticity 2 we even condition on country-year specific characteristics), in the end of the next Section we will also present instrumental variable estimates, where we will use legal origin to extract the (exogenous) historically predetermined part of financial development. The vector X includes control variables, such as real GDP per capita (GDP ), schooling (SCH) and a composite measure of institutional quality (IQL). In the Channel Analysis (Section 4) we will also use measures of legal efficiency, banking system characteristics, corporate governance, and investor’s protection. We will discuss these indicators as we move on. Appendix Table A reports the values of the country-level variables, while the Data Appendix provides detailed variable definitions and sources.

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3 3.1

Main Results Financial development and investment responsiveness

Tables 2 and 3 report cross-country regression estimates (equation (3)) that explore the impact of financial development in explaining differences in the general responsiveness of investment to production (elasticity 1) and the intersectoral investment responsiveness (elasticity 2), respectively. 3.1.1

Financial development and general investment responsiveness

Table (2), columns (1)-(2) replicate Wurgler’s main result. Model (1) show that financial markets size (F D) is strongly correlated with the general elasticity of investment to output. (See Appendix Figure I for a graphical illustration of the unconditional correlation in various country samples). Column (2) repeats this model, but now instead of assuming (like previous work has done) that gaps in data on stock market capitalization (see Appendix Table A) represent the absence of equity markets, we fully ignore these observations. The point estimate in column (2) is quite similar in magnitude to (1) and remains highly significant. The descriptive analysis showed that there are sizable differences among industrial and under-developed (and emerging) economies in the elasticity of investment to value added. The specification in column (3) formally verifies this. The coefficient on the log or real per capita GDP is positive and statistically significant at the 1% level. In columns (3) and (4), we explore the conditional on income impact of financial development in the general investment responsiveness. The coefficient on financial markets size has dropped considerably, compared to models (1)-(2), and it is now only marginally significant (p-values: 0.08 and 0.12). This stands in contrast to Wurgler’s estimates that conditioning on income, finance remain significant at the 1% confidence level (Wurgler (2000) Table 3-column (5)). This a priori seems weird since elasticity 1 is identically estimated as in Wurgler using the same data (conditioning only on country fixed-effects and ignoring extreme observations of value added and investment growth). In fact the general investment responsiveness is almost perfectly correlated with Wurgler’s original elasticity (0.96). The main reason for this difference is that Wurgler uses GDP per capita in 1960 as income control, while we use GDP per capita in 1981 (which corresponds to the beginning of the measurement of financial development and is approximately in the middle of the 40 year period of our data). We indeed find that

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once we use the 1970 or 1960 value of GDP p.c. the point estimate on financial development increases in magnitude and turns significant. To further investigate whether financial or economic development is the most significant correlate of the general investment responsiveness, in model (6) we exclude from the estimation low income countries (as classified by the World Bank). Although by dropping observations we lose efficiency, the poor data coverage on sector output and investment in low income countries from the UNIDO database (see Table I columns (1)-(3)), and some gaps in financial development components (see Appendix Table A) call for sensitivity checks in this direction. The coefficient estimates in column (6) further suggest that economic (in terms of GDP) rather than financial development is the key correlate of the general elasticity of investment to value added. The last four columns of Table 2 add further skepticism to this. In columns (7)-(8) we explore the role of financial development, with and without controlling for country-level income differences, focusing solely on the group of high income countries. In line with the already weak evidence in the full sample, the coefficient on financial development is positive, yet far from being insignificant. Although the loss in efficiency due to the small number of observations is now non-negligible (we are estimating regressions with less than 30 observations), this casts further doubt on the robustness of the finance-general investment responsiveness link. 3.1.2

Financial development and intersectoral investment responsiveness

In Table 4 we repeat the previous estimations, but now the dependent variable is elasticity 2 that isolates the intersectoral responsiveness of investment. In contrast to the previous evidence, in all model permutations in Table III financial development enters with a highly significant coefficient (at the 1% level), both when we control for income level differences in the full sample of countries (columns (4)-(5)) and when we drop low income countries (column (6)). The point estimate retains significance, even when we just examine the financeintersectoral investment responsiveness in high income countries (columns (7)-(8)). This suggests that in spite of the high correlation between financial modernization and general economic development (the correlation of F D with the log of per capita GDP is 0.71), the estimates do reflect some of the functions of capital markets in channelling capital to growing sectors. Besides the strong statistical significance, the coefficient on capital market size implies an economically sizable effect. Take for example Spain (which had capital markets

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equal to 90% of GDP) and the Netherlands (the country with the largest capital market size in terms of GDP, of 169%). The most conservative estimate (0.24 in column (4)) implies that if Spain was to increase its capital market size to the level of the Netherlands intersectoral investment responsiveness would increase by an additional 8.6 percentage points per year.6 This is an economically significant improvement, since under this scenario Spain’s rank in the 65 economies considered in our analysis would increase from the 21st to the 14th. Besides offering support to the theoretical conjecture that financial development fosters overall productivity, by moving capital to sectors with high growth prospects, these results also illustrate the usefulness of our approach in isolating the intersectoral responsiveness of investment.

3.2 3.2.1

Sensitivity Analysis Controls

In Table IV we explore the sensitivity of our results to additional controls. Although we did account in our previous estimates for the level of economic development, there might be concerns that the financial size measure is capturing other features of economic well-being. We did experiment with various controls that have been found to be significant predictors of cross-country aggregate productivity. In Table IV we report (for brevity) models where besides GDP, we also control for schooling as a proxy of human capital differences and a composite index of institutional quality. Schooling statistics are retrieved form the recent update of the Barro and Lee (2001) international attainment database. The institutional quality variable is retrieved from World Bank’s “Governance Matters” database (Kaufman, Kraay and Mastuzzi (2005) and Kaufman, Kraay and Zoido-Lobaton (1999)) and is a composite index of bureaucratic efficiency, control of corruption and rule of law. Although both schooling and institutional quality are unconditionally positively and significantly correlated with both elasticity measures (results not shown), the evidence in Table V shows once we control for financial and economic development, the importance of human capital and institutions turns insignificant.7 Turning now to the two elasticity measures, in columns (1)-(3) we examine the correlation 6 To obtain this number, recall that capital market size is expressed in logs, specifically ln(1 + F D). We thus multiply the log difference in the capital market size in the two countries by 0.25. 7 We also experiment with the sub-components of the composite institutional quality index or with other proxies of human quality (namely labor force quality and attainment rates) and found quite similar results.

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of financial markets size with the general investment responsiveness (elasticity 1), while in columns (4)-(6) with the intersectoral investment responsiveness (elasticity 2). In line with our previous evidence, the correlation coefficient between F D and the general investment responsiveness is weak (columns (1)-(3)). In contrast when we focus on the intersectoral investment responsiveness, we continue to find a very robust and significant effect of financial markets size (column (4)). This also prevails when we exclude from the estimation low income countries (column (5)) or when we just focus on rich countries (column (6)). 3.2.2

Specification and Outliers

In Table V Panel A we further investigate the robustness of our main result so far that the intersectoral responsiveness of investment to output is strongly correlated with financial development (while the general investment responsiveness measure is not). As a first check, in columns (1)-(3) we express financial development in levels rather than in logs. The OLS estimates show that this alternative way in specifying financial development does not alter the main result. In all three models F D enters with a quite stable coefficient, which is at least three standard errors greater than zero. Second, we investigate the sensitivity of the estimates using two procedures that account for influential observations (outliers). Least square estimates can be quite sensitive to outliers, especially in small-sized samples, like ours (e.g. Greene, 2000). Since there are some extreme observations in financial markets size, it is useful to check how this affects our results. In columns (4)-(6) we thus report estimates using an iterative least squares method that assigns lower weights to observations with large residuals (Huber, 1964; Hamilton, 1992). In all three models the coefficient on F D is statistically significant at standard confidence levels. In columns (7)-(9) we report least absolute deviation (median) regression estimates. Median models are less sensitive to outliers since this method minimizes the absolute value rather than the square of the residuals. Non-parametric bootstrapped standard errors using 1, 000 replications are reported in parenthesis. In all samples financial development continues to enter with a median effect that is statistically significant at least at the 5% confidence level. Panel B of Table VI reports similar models with elasticity 1 that only controls for general (time-invariant) country fixed effects as the dependent variable. In line with our results so far, the estimates clearly show that income rather than financial modernization is the key explanatory variable of the general investment responsiveness.

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3.2.3

Instrumental variable estimates

As a final sensitivity check, we estimated instrumental variable models using legal origin dummy variables to extract the historically predetermined component of financial development. In a series of influential papers La Porta et al. (1997, 1998, 1999) have argued that an efficient legal system is a prerequisite for developing deep and liquid capital markets.8 La Porta et al. have further shown that colonization had a long-standing impact in legal efficiency, because colonies inherited the legal tradition-system of the colonizer. Although there has been some debate on what colonization related instruments exactly capture,9 it is interesting to examine the financial development - intersectoral investment responsiveness with this IV approach.10 Panel A Table VI reports the second-stage coefficient on capital market’s size on elasticity 2, while Panel B reports the first stage model. Starting from Panel B common law countries and to some lesser extent German and Scandinavian civil law countries have significantly larger capital markets than countries with a French civil law system. The first R-squared indicates a good fit, although only in model (2), where we drop low income countries, we can be (relatively) confident that our estimates do not suffer from weak instrument bias.11 Turning now to the impact of financial development on the intersectoral investment responsiveness, the 2nd-stage coefficients are statistically significant at least at the 5% level in all three samples. The estimates are also quite similar in magnitude to the analogous OLS coefficients (reported in Table III), albeit somewhat larger. The slight increase may be due to capital market size measure having a noisy, transitory part that affects the speed of capital reallocation less than the permanent part related to legal origin. To further explore the validity of the instrument we explored whether legal origin exerts a direct effect of the intersectoral investment responsiveness across countries. The evidence (not reported) shows that conditional of capital market size, legal origin has no direct effect in explaining cross-country variation in elasticity of investment to output. Together with legal origin being 8

Beck and Levine (2005) provide a comprehensive review of the literature on the impact of legal tradition on financial development. 9 For example Glaeser and Shleifer (2002), Djankov et al. (2002) and Shleifer (2005) argue that the legal origin has also influenced regulation policies in labor and product markets. 10 Beck et al. (2000a, b) have used this IV approach in a cross-country growth regression framework, finding that the historically predetermined by legal origin component of financial development is a significant correlated with average cross country per capital GDP and productivity growth, respectively. 11 The rule of thumb is that the first-stage squared needs to be no less than 0.25 − 0.30 (e.g. Staiger and Stock, 1997; Stock, Wright and Yogo, 2003).

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imposed in most countries more than a hundred of years before and the plausible theoretical link between legal efficiency and finance, the IV estimates suggest that the significant coefficient of financial development and the intersectoral investment responsiveness may reflect something more than a simple correlation.

4

Channel Analysis

An important policy question is whether reforms that aim to improve the size and efficiency of capital markets affect the intersectoral responsiveness of investment to value added. In this section we try to answer this employing a 2SLS approach. In the first-stage we link financial development to fundamentals, related to investor protection, legal efficiency and banking structure, while in the second-stage we explore the effect of the fundamental-predicted component of financial development in the intersectoral investment responsiveness. Table VII reports the estimates.

4.1

The determinants of financial markets size

Our first-stage specification builds on advances of the law and finance literature. Specifically we found the following indicators of legal enforcement and financial structure to be significant predictors of the size of capital markets in the three samples we consider: • Government ownership of the banking system (around 1970). This measure is taken from La Porta et al. (2002), who show that state bank ownership is associated with poor

performance of the banking system, illiquid capital markets, and slower productivity growth. This measure is ideal for our analysis, because not only theory and previous empirical work suggest that it impacts capital markets size, but also because it is predetermined to F D (which is averaged over the 1981-1995 period).12 The first-stage models show that state ownership of banks is both statistically and economically a very important predictor of subsequent levels of capital market size. In all samples (even in high income countries) the coefficient on state ownership of banks is at least two standard errors greater than zero. 12 For the negative impact of state ownership of the banks on credit, see, among others Dinc (2005), Sapienza (2004) and Papaioannou (2004).

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• Legal formalism. This is a 0-7 ranging variable, which measures the time it takes to resolve a relatively simple legal case, collection of a bounced check (Djankov et al.

(2003)).In line with previous work (La Porta et al., 1997; Acemoglu and Johnson, 2006) the first-stage evidence shows that legal formalism is associated with lower levels of stock market development and lower levels of private credit.13 • An index of insider trading legislation that aims to proxy information frictions in

equity markets. The measure comes from Bhatatcharya and Dauk (2002), who trace the introduction and the first year of enforcement of insider trading legislation in 103

countries. Their empirical analysis shows that the cost of equity falls and the size of stock markets increase significantly after the enforcement of such legislation laws. The first-stage estimates show that years since the execution of insider trading legislation is strongly correlated with the broad measure of financial markets size, even when we drop low income countries or even when we just examine high-income countries. As columns (4)-(6) show these measures continue to be statistically significant correlates of financial development, even when we control for legal origin, that has been found to also be explaining cross-country variation in capital market’s size (Table VI). This shows that colonial-inherited legal heritage influences financial development through its impact on contemporary legal and financial structure.14

4.2

2nd stage model: Financial markets size and the intersectoral investment responsiveness

We now turn to the second-stage estimates, reported in Panel A of Table VII. The coefficient on the predicted by state ownership of banks, legal formalism and anti-insider trading legislation component of capital market size is statistically significant at standard confidence levels in all model permutations. In our preferred models (1) and (4), estimated in the full sample of countries, the coefficient on financial development is at least three standard errors 13

We also experiment with a recently constructed indicator of actual (de facto) protection of investors’ rights against tunnelling, employing the anti-self-dealing index constructed by Djankov, et al. (2005). This 0-1 index is a composite index that measures the ex ante and ex post disclosure requirements, the ease of proving managerial wrong-doing, and approval requirements from disinterested shareholders. In line with the evidence of Djankov et al. (2005) the first stage results show that better investor protection is associated with significantly more developed capital markets (results not shown). 14 We also found that in the rich country sub-sample proxy measures of the concentration of the banking system are also associated with lower levels of capital markets size.

16

greater than zero. Quantitatively the estimates are quite similar with the OLS coefficients reported in Table III. The coefficients are also similar when we exclude from the estimation low income countries (columns (2) and (5)) to account for potentially poor data quality and unobserved heterogeneity. Although excluding observations leads to a loss in efficiency, this reassures that the estimates are not driven by the large differences in intersectoral investment responsiveness (documented in Table I) between industrial and underdeveloped countries. An open question with these estimates is whether the legal and banking sector variables also have a direct effect in explaining the intersectoral investment responsiveness. In this scenario the 2SLS approach would be inappropriate. We thus explored this possibility in greater detail. First, we run standard over-identification tests (OID) on the exclusion restriction. The test (reported in Table VII) is reassuring, since we are not able to reject the null hypothesis of instrument validity in all models. Second, we run models where we directly added each of the first-stage regressors in an OLS regression (of the form of (3)). Appendix Table B reports these estimates. The evidence shows that state ownership of banks, legal system quality (proxied both by legal formalism or the anti-self-dealing index), and insider legislation turn insignificant in explaining the intersectoral investment responsiveness, once we control for financial development. In line with our evidence so far, in all models in Appendix Table B, financial development enters with a statistically significant positive coefficient. This suggests that these variables affect the intersectoral responsiveness of investment indirectly, through the size of capital markets.

5

Conclusion

In this paper we examine whether greater levels of financial development lead to greater investment in growing sectors. To do so we build on an influential two-step approach developed by Wurgler (2000). In the first step, we estimate the country-specific responsiveness of sectoral investment to changes in the structure of production, using manufacturing sector data for a large number of countries over the last forty years. To isolate the intersectoral component of investment responsiveness indicator in the estimation we account for all possible sources of unobserved heterogeneity. Besides general country, sector, and time effects, we also control for country-industry specialization patterns, industry-specific global trends and common country-year shocks that affect uniformly all sectors. The estimated proxy of intersectoral investment responsiveness thus captures (as cleanly as possible) whether in17

vestment is allocated to the sectors where it is more likely to be needed. The second step is to relate the intersectoral investment responsiveness to various measures of financial development and other controls. The aim of this approach is to examine whether larger capital markets allocate investment to the sectors where it is more likely to be needed. Our results can be summarized as follows: First, we show that there exists robust link between capital market size and the intersectoral responsiveness of investment to production. This is supportive to the old theoretical hypothesis that countries with better-working capital markets tend to allocate investment where it is likely to be needed most, which suggests that capital markets affect aggregate productivity growth in part through the efficient allocation of investment. Second, our empirics show that the link between capital market size and investment responsiveness is only robust when we focus on intersectoral responsiveness. In contrast when we examine the association between financial development and the general responsiveness of investment to value added (as in Wurgler’s study) the correlation is weak. Third, we try to shed some light on the policy debate on the merits of financial sector reforms. We therefore employ a two-stage least squares framework to explore whether increases in the size of financial markets, emerging from banking sector and legal reforms also contribute to higher intersectoral investment responsiveness. We find that privatizing stateowned banks, penalizing insider trading activities, improving outside investor’s rights and fastening the resolution of legal disputes in courts tend to improve intersectoral investment responsiveness by increasing the depth and breadth of capital markets. These results appear robust to a number of sensitivity checks, such as alternative estimation techniques, accounting for influential observations, and more. We also find that in spite of the loss in the efficiency of the estimates, when excluding observations, the results prevail when we also drop from the estimation low income countries, where data issues might be non-negligible. These results, although non-structural, add to recent studies, which employing alternative approaches and datasets (e.g. Fisman and Love, 2004a,b; Bekaert et al. forthcoming; Ciccone and Papaioannou, 2006) also reveal that financial development fosters aggregate productivity by enabling the fast reallocation of capital to the sectors that is most needed.

18

A

Appendix: Variable Definitions and Sources

Capital market size - Total Finance (FD): Sum of private credit by deposit money banks and other financial institutions as a share of GDP and stock market capitalization as a share of GDP, averaged over the period 1980-1995. Source: World Bank Financial Structure Database (Beck, Demirgük-kunt, and Levine, 2001). Original source: International Monetary Fund IFS. Income level (GDP): Real per capita GDP in 1981 at constant 1995 international US dollars. The variable is expressed in logs. Source: World Bank World Development Indicators Database (2004). Schooling (SCH): Average years of schooling in the population aged 25 and over in 1980. Source: Barro and Lee (2001). Institutional Quality (IQL): Composite index of institutional quality, based on three sub-indicators of government effectiveness (which proxies mostly bureaucratic efficiency and functioning), rule of law (which proxies for contract enforcement, protection of intellectual property rights and judicial efficiency) and corruption (which proxies corruption among public officials, effectiveness of anticorruption initiatives, and mentality regarding corruption). Source: World Bank Aggregate Governance Indicators Database (Kaufman, Kraay and Mastuzzi (2005) and Kaufman, Kraay and Zoido-Lobaton (1999)). Legal origin (LEGOR): A dummy variable that identifies the legal origin of the Company law or Commercial Code of each country. There are five legal families: English (Common Law), French (Civil Law), German (Civil Law), Nordic (Civil Law) and Socialist (although socialist countries are excluded all-together from the analysis). Source: La Porta, Lopez-de-Silanes, Shleifer and Vishny (1999). Legal Formalism (LEXFORM): The index measures substantive and procedural statutory intervention in judicial cases at lower-level civil trial courts, for completing a simple legal case, collecting a bounced check. The index ranges from 0 to 7, where higher values indicate greater control or intervention in the judicial process. Source: Djankov, La Porta, Lopez-de-Silanes and Shleifer (2003). Anti self-dealing (ANTISELF): Index that measures the de facto ex-ante and expost private control of self-dealing transactions. The ex-post components are the disclosure

19

requirements in periodic filings and the ease of proving wrongdoing. The ex-ante components are approval requirements of disinterested shareholders and ex-ante disclosure. The index ranges from 0 to 1, with higher values indicating stricter-better protection against insiders’ self-dealing activities (i.e. higher de facto investors protection). Source: Djankov, La Porta, Lopez-de-Silanes and Shleifer (2005). Insider Trading Legislation (INSTR): Years in 1995 that the country had established and implemented legislation against insider trading. Source: Bhattacharya and Dauk (2002). Government Ownership of Banks (BGOV): Share of the assets of the ten largest banks in country controlled or owned by the government of this country in 1970 (before bank privatization policies). The percentage of the assets owned by the government in a given bank is calculated by multiplying the share of each shareholder by the share that the government owns in that shareholder, and then summing the resulting shares. Source: La Porta, Lopez-de-Silanes, and Shleifer (2002).

20

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[34] La Porta, Rafael; Lopez-de-Silanes, Florencio; Shleifer, Andrei and Vishny, Robert. "Legal Determinants of External Finance." Journal of Finance, July 1997, 53(1), pp. 1131-1150. [35] La Porta, Rafael; Lopez-de-Silanes, Florencio; Shleifer, Andrei and Vishny, Robert. "Law and Finance." Journal of Political Economy, December 1998, 106 (6), pp. 1113-1155. [36] La Porta, Rafael; Lopez-de-Silanes, Florencio; Shleifer, Andrei and Vishny, Robert. "The Quality of Government." Journal of Law, Economics and Organization, January 1999, 15 (1), pp. 222-279. [37] La Porta, Rafael; Lopez-de-Silanes, Florencio; and Shleifer, Andrei. "Government Ownership of Banks." Journal of Finance, February 2002, 57 (1), pp. 265-302. [38] Levchenko, Andrei A. "Institutional Quality and International Trade." IMF Working paper 04/0231, December 2004. [39] Levine Ross. "Finance and Growth: Theory, Evidence, and Mechanisms." in Philippe Aghion and Steve Durlauf, eds. Handbook of Economic Growth, Amsterdam, NorthHolland, 2005. [40] Manova, Kalina. "Credit Constraints in Trade: Financial Development and Export Composition" mimeo Harvard University, November 2005. [41] Papaioannou, Elias. "What Drives International Bank Flows: Politics, Institutions and Other Determinants." ECB Working Paper (Frankfurt, Germany), 437, February 2004. [42] Sapienza, Paola. "The Effects of Government Ownership on Bank Lending." Journal of Financial Economics, May 2004, 72 (2), pp. 357-384. [43] Schumpeter, Joseph A. The Theory of Economic Development. translated by R. Opie. Harvard University Press, Cambridge, MA, 1911 [1934]. [44] Shleifer, Andrei. "Understanding Regulation." European Financial Management, September 2005, 11 (4), pp. 439-451. [45] Staiger, Douglas and Stock, James H. "Instrumental Variables Regression with Weak Instruments." Econometrica, May 1997, 65 (3), pp. 557-586. 24

[46] Stock, James H.; Wright Jonathan H. and Yogo, Motohiro. "A Survey of Weak Instruments and Weak Identification in Generalized Method of Moments." Journal of Business and Economic Statistics, October 2002, 20 (4), pp. 518-529. [47] Rajan, Raghuram G. and Zingales, Luigi. "Financial Dependence and Growth." American Economic Review, June 1998, 88 (3), pp. 559-586. [48] United Nations Industrial Development Organization. Industrial Statistics. New York, NY, 2005. [49] World Bank. World Development Indicators. Washington, DC. 2005. [50] Wurgler, Jeffrey. "Financial Markets and the Allocation of Capital." Journal of Financial Economics, January 2000, 58 (1), pp. 187-214.

25

Appendix Figures I: Financial Development and Investment Responsiveness across different country groups

DNK ITA BEL ZWE GRC MAR

CAN

FIN

VEN

PRT

IRN URY

LBY

NOR

JPN HKG

USA SWE NLD SGP

CYP

MYS

CHL MEX EGY ISR ECU PHL JOR TUR TUN CMR COL MLT LKA MWI TTO NGA SLVPAKIND TZAZMB BGD IDN KENKWT ETH FJI PAN BRB SWZ

DEUW DNK ITA BEL GTM PER

PRT

MEX EGY TURECU COL LKA SLV

ISR PHL TUN MLT TTO

HKG

SWE

CAN

FIN

VEN

JPN USA

NOR

IRN URY

LBY

GBR

NLD SGP

CYP

MYS

CHL JOR

KWT FJI PAN BRB SWZ

.5 1 Financial Development (FD)

0

1.5

Investment-Value Added Growth Elasticity (b4) 0 .5 1

DNK ITA BEL

GRC

MAR

AUS

JPN

HKG

1.5

AUT DNK GTM ZWE GRC ITA MYS SGP NZL PAK IRL PER URY EGY ESP NOR CAN GBRNLD ECU COL VEN ISR SWE LBY CYP FRA JOR CMR IRN PHL TUN BEL MLT TUR LKA MEX KWTTTOPRT FINCHL DEUW NGA BGD SLVIDN TZAZMB MWI FJI ETH IND KEN BRB PAN SWZ

GTM PER LBY

URY EGY ECU COL IRN TUR LKA MEX SLV FJI

AUS

JPN

HKG

AUT DNK GRC ITA MYS SGP NZL IRL ESP NOR CAN GBRNLD ISR SWE VEN PHL CYP FRA JOR TUN BEL MLT DEUW KWTTTO CHL PRT FIN

BRB PAN SWZ BOL

-.5

-.5

BOL

KOR

.5 1 Financial Development (FD)

Investment-Growth elasticity estimated: controlling for country, industry, year, country-industry, industry-year and country-year fixed-effects

1.5

JPN HKG

USA SWE NLD

CAN

FIN

SGP

PRT

CYP

ISR MLT KWT

BRB

.6

.8 1 Financial Development (FD)

1.2

1.4

Investment-Growth elasticity estimated: controlling for country fixed-effects

USA MAR

NOR

GBR

USA AUS KOR

JPN HKG

GRC

AUT

ITA DNK

SGP

NZL IRL ISR

ESP

BEL MLT

NOR

CAN

CYP FRA

GBR SWE NLD

DEUW

KWT PRT

FIN

BRB

-.2

KOR

.5 1 Financial Development (FD)

NZL AUT FRA ESP KOR AUS IRL

.4

Investment-Growth elasticity estimated: controlling for country fixed-effects

USA

0

DEUW

BOL

Investment-Growth elasticity estimated: controlling for country fixed-effects

Investment-Value Added Growth Elasticity (b4) 0 .5 1

GRC MAR

BOL

0

NZL AUT FRA ESP KOR AUS IRL

Investment-Value Added Growth Elasticity (b4) 0 .2 .4 .6 .8

GTM PER

GBR

0

DEUW NZL AUT FRA ESP KOR AUS IRL

High-Income Countries only Investment-Value Added Growth Elasticity (b1) .2 .4 .6 .8 1

Excluding Low income countries Investment-Value Added Growth Elasticity (b1) 0 .5 1

Investment-Value Added Growth Elasticity (b1) 0 .5 1

All Countries

0

.5 1 Financial Development (FD)

Investment-Growth elasticity estimated: controlling for country, industry, year, country-industry, industry-year and country-year fixed-effects

1.5

.4

.6

.8 1 Financial Development (FD)

1.2

Investment-Growth elasticity estimated: controlling for country, industry, year, country-industry, industry-year and country-year fixed-effects

1.4

1.5

Appendix Figures II: Fundamentals and Total Finance Size (excl. low income countries)

HKG

Financial Development (FD)

JPN SGP

1

MYS USA NLD GBR

SWE

AUS CAN NOR

.5

NZL

IRL

JOR DEUW FRA KOR CYP AUT

FIN ISR

DNK BEL BRB MLT

TTO

CHL

ESP

PRT TUN ITA KWT GRC

PAN VEN

PHL

COL SWZ EGY URY LKA

MEX MAR ECU SLV

BOL PER GTM

0

TUR

1

2

3 4 Legal Formalism Index (LEXFORM)

5

6

1.5

Estimation: Excluding Low Income Countries

HKG SGP

1

USA

MYS

NLD GBR

SWE DEUW AUT

FRA

KOR NOR

ESP

TUN PAN

ITA

FIN PRT DNK BEL

CHL

NZL

IRL ISR

PHL GRC

VEN

URY MEX

ECU BOL

AUS

CAN

JOR

.5

Financial Development (FD)

JPN

LKA

EGY

COL MAR SLV

0

TUR PER

0

.2

.4 .6 Anti Self Dealing Index (ANTISELF)

.8

1

1.5

Estimation: Excluding Low Income Countries

HKG JPN

Financial Development (FD) .5 1

SGP USA

MYS NLD

GBR

SWE AUS

CAN CYP IRL TTO DNK

JOR

DEUW KOR NOR

NZL ESP FIN PAN

BEL KWT URY

FRA AUT ISR

TUN

CHL PRT

ITA

PHL

VEN

GRC

MEX IRN

COL MAR SLV BOL

TUR

EGY LKA ECU

PER

0

GTM

LBY

0

.2

.4 .6 Government Ownership of Banks (BGOV)

Estimation: Excluding Low Income Countries

.8

1

Table I: Country-specific Elasticities of Investment with respect to Value Added

Sample Characteristics Observations

General Investment Responsiveness elasticity 1

Intersectoral Investment Responsiveness elasticity 2

Country

Obs.

Ind.

Years

Elasticity

se

rank

Elasticity

se

rank

Australia Austria Bangladesh Barbados Belgium Bolivia Cameroon Canada Chile Colombia Cyprus Denmark Ecuador Egypt, Arab Rep. El Salvador Ethiopia Fiji Finland France Germany, West Greece Guatemala Hong Kong, China India Indonesia Iran, Islamic Republic Ireland Israel Italy Japan Jordan Kenya Korea, Rep. Kuwait Libya Macao, China Malawi Malaysia Malta

638 774 177 211 625 223 161 704 590 715 614 647 678 472 147 152 240 914 678 692 771 194 471 607 513 539 601 590 816 1008 422 141 877 403 101 211 294 432 544

28 28 26 17 24 26 25 28 28 28 26 28 28 28 26 21 18 28 25 27 28 27 26 28 27 28 26 26 28 28 27 25 28 25 16 23 20 28 26

26 29 11 25 34 18 13 27 31 37 31 28 36 28 12 12 24 37 37 29 32 10 29 24 28 29 28 27 33 37 27 13 35 33 16 22 30 20 36

0.7011 0.8054 0.1285 0.0030 0.7561 -0.1294 0.2620 0.5469 0.3359 0.2476 0.4536 0.7935 0.2937 0.3194 0.1925 0.0949 0.0644 0.5427 0.8131 0.9272 0.5918 0.6298 0.7221 0.2049 0.1107 0.4446 0.6819 0.2982 0.7895 0.7977 0.2846 0.1457 0.7342 0.1367 0.3719 0.0783 0.2192 0.4230 0.2572

(0.130) (0.070) (0.121) (0.114) (0.085) (0.143) (0.092) (0.113) (0.081) (0.091) (0.105) (0.115) (0.062) (0.067) (0.120) (0.154) (0.125) (0.077) (0.061) (0.058) (0.089) (0.096) (0.123) (0.082) (0.070) (0.058) (0.111) (0.093) (0.054) (0.059) (0.079) (0.319) (0.064) (0.081) (0.123) (0.137) (0.074) (0.097) (0.091)

14 5 57 62 9 65 43 23 34 45 29 7 39 36 52 59 61 24 4 1 20 18 12 51 58 30 15 37 8 6 40 55 11 56 33 60 47 31 44

0.5943 0.4459 0.0944 -0.1061 0.1742 -0.2991 0.2026 0.2743 0.1101 0.2501 0.2088 0.4143 0.2610 0.2850 0.0781 0.0365 0.0742 0.0910 0.2095 0.1430 0.4070 0.4131 0.5097 0.0228 0.0690 0.2159 0.3369 0.2414 0.4138 0.5463 0.2083 -0.0026 0.5478 0.1336 0.2367 -0.0260 0.0851 0.3815 0.1700

(0.1782) (0.1211) (0.1215) (0.1249) (0.1402) (0.1463) (0.1025) (0.1384) (0.1054) (0.1139) (0.1624) (0.1854) (0.0754) (0.0745) (0.1455) (0.1770) (0.1344) (0.0883) (0.1248) (0.1066) (0.1165) (0.1113) (0.1482) (0.0866) (0.0788) (0.0862) (0.1566) (0.1134) (0.0842) (0.1317) (0.0791) (0.3784) (0.0961) (0.0989) (0.2175) (0.1698) (0.0729) (0.1219) (0.1013)

2 7 50 62 40 65 37 22 47 26 35 8 25 20 53 58 55 51 34 43 12 10 5 59 56 32 17 28 9 4 36 60 3 45 30 61 52 14 41

Mexico Morocco Netherlands New Zealand Nigeria Norway Pakistan Panama Peru Philippines Portugal Singapore Spain Sri Lanka Swaziland Sweden Tanzania Trinidad and Tobago Tunisia Turkey United Kingdom United States Uruguay Venezuela, RB Zambia Zimbabwe Mean Median Standard Deviation Stand. Dev. /Mean

520 308 712 464 493 909 151 427 235 586 703 811 799 258 81 610 315 146 594 751 754 1000 214 418 135 522

28 26 27 28 24 28 27 26 27 28 27 27 28 26 16 26 25 24 27 28 28 28 27 28 22 22

500.05 25.86 522 27 251.19 2.99

27 16 30 27 16 38 8 31 12 28 29 39 34 16 21 24 21 14 31 34 28 36 11 20 12 33

0.3293 0.5345 0.5840 0.8465 0.2091 0.5895 0.2064 0.0021 0.6148 0.2940 0.4752 0.5006 0.7513 0.2346 -0.0767 0.6650 0.1594 0.2119 0.2660 0.2730 0.8221 0.7149 0.3961 0.5059 0.1649 0.6494

25.69 28 8.54

0.4154 0.3719 0.2667 0.6421

(0.100) (0.114) (0.069) (0.124) (0.104) (0.092) (0.150) (0.101) (0.079) (0.071) (0.087) (0.074) (0.076) (0.097) (0.160) (0.118) (0.099) (0.182) (0.095) (0.063) (0.085) (0.065) (0.125) (0.077) (0.150) (0.102)

35 25 22 2 49 21 50 63 19 38 28 27 10 46 64 16 54 48 42 41 3 13 32 26 53 17

0.1198 0.5016 0.2479 0.3682 0.0985 0.2716 0.3431 -0.1145 0.3331 0.2099 0.0960 0.3942 0.2745 0.1945 -0.1744 0.2383 0.0750 0.1428 0.1798 0.1680 0.2685 0.6946 0.3040 0.2251 0.0611 0.4101

(0.1380) (0.1239) (0.1027) (0.1696) (0.1208) (0.1030) (0.1677) (0.1204) (0.1385) (0.0830) (0.1000) (0.0955) (0.1371) (0.1151) (0.1905) (0.1646) (0.1093) (0.2226) (0.1128) (0.0731) (0.1375) (0.0930) (0.1461) (0.1103) (0.1766) (0.1268)

46 6 27 15 48 23 16 63 18 33 49 13 21 38 64 29 54 44 39 42 24 1 19 31 57 11

0.2213 0.2099 0.1851 0.8364

The Table reports the general and the intersectoral (country-specific) responsiveness of investment growth to value added growth. Estimation is based on a three dimensional unbalanced panel that covers 65 countries, 28 manufacturing industries in the period 19632002 (32203 observations). The first three columns report panel descriptives. Elasticity 1 (general investment responsiveness) is based on regressing investment growth on value added growth, controlling for country (65) fixed-effects (equation (1) in the text). Elasticity 2 (intersectoral investment responsiveness) is based on regressing investment growth on value added growth, controlling for country (65), industry (28), year (39), country-industry (1681), industry-year (1091) and country-industry (1670) fixed-effects (equation (2) in the text). Standard errors for each elasticity measure are reported in parentheses. Country ranks are given in italics.

Table II -- Capital Markets Size and General Investment Responsiveness Dependent variable: Elasticity (1) General Investment Responsiveness

Capital Markets Size (FD) stand.error p-value

adjusted R-squared Countries

(5)

0.1663 (0.0939) 0.08

0.1354 (0.0862) 0.12

0.1236 (0.0952) 0.20

0.1213 (0.0130) 0.00

0.0984 (0.0192) 0.00

0.1155 (0.0187) 0.00

0.1365 (0.0272) 0.00

All countries (1)

(2)

0.5075 (0.0748) 0.00

0.5262 (0.0907) 0.00

Income-Real GDP p.c. (GDP ) stand.error p-value Intercept stand.error p-value

(4)

No LowIncome (6)

(3)

High-Income (7)

(8)

0.3194 (0.1640) 0.06

0.1877 (0.1389) 0.19 0.1965 (0.0888) 0.04

0.1737 (0.0463) 0.00

0.1667 (0.0610) 0.01

-0.5606 (0.1074) 0.00

-0.4577 (0.1206) 0.00

-0.5804 (0.1339) 0.00

-0.7771 (0.2131) 0.00

0.3860 (0.1459) 0.01

-1.3936 (0.8418) 0.11

0.343 64

0.328 54

0.483 62

0.500 62

0.533 54

0.474 51

0.122 28

0.311 28

The dependent variable is the country-specific estimated elasticity of investment growth to value added growth, where we control only for country fixed-effects (Elasticity 1). The set of explanatory variables includes a measure of financial markets size (FD) and income level (GDP), both expressed in logs. The models in columns (1)-(5) are estimated in the widest sample of countries; the model in column (6) excludes low income countries (LIC); the models in columns (7) and (8) are estimated only in high income countries. Table I and the Appendix Table report the elasticity and the values of the independent variables for each country. The Data Appendix provides detailed variable definitions and sources. Heteroskedasticity-adjusted standard errors are reported in parenthesis below the coefficients. p-values are reported in italics below the standard errors.

Table III -- Capital Markets Size and Intersectoral Investment Responsiveness Dependent variable: Elasticity (2) Intersectoral Investment Responsiveness

Capital Markets Size (FD) stand.error p-value

(1)

(2)

0.2935 (0.0597) 0.00

0.3179 (0.0730) 0.00

Income-Real GDP p.c. (GDP ) stand.error p-value Intercept stand.error p-value adjusted R-squared Countries

(4)

(5)

No LowIncome (6)

0.2363 (0.0817) 0.01

0.2534 (0.0852) 0.00

0.2465 (0.0812) 0.00

0.0520 (0.0124) 0.00

0.0195 (0.0148) 0.19

0.0191 (0.0175) 0.28

0.0195 (0.0238) 0.42

All countries (3)

High-Income (7) (8) 0.3625 (0.1118) 0.00

0.3500 (0.1125) 0.01 0.0186 (0.0719) 0.80

0.0823 (0.0379) 0.03

0.0666 (0.0511) 0.20

-0.1956 (0.1025) 0.06

-0.0494 (0.1014) 0.63

-0.0567 (0.1308) 0.67

-0.0585 (0.2010) 0.77

0.0444 (0.0915) 0.63

-0.1242 (0.6773) 0.86

0.239 64

0.238 54

0.184 62

0.256 62

0.249 54

0.218 51

0.252 28

0.255 28

The dependent variable is the country-specific elasticity of investment growth to value added growth, where we control for country, industry, time, country-industry, industry-time and country-year fixed-effects (Elasticity 2). The set of explanatory variables includes a measure of financial markets size (FD) and income level (GDP), both expressed in logs. The models in columns (1)-(5) are estimated in the widest sample of countries; the model in column (6) excludes low income countries (LIC); the models in columns (7) and (8) are estimated only in high income countries. Table I and the Appendix Table report the elasticity and the values of the independent variables for each country. The Data Appendix provides detailed variable definitions and sources. eteroskedasticity-adjusted standard errors are reported in parenthesis below the coefficients. p-values are reported in italics below the standard errors.

Table IV -- Sensitivity Analysis I: Additional Controls General Investment Responsiveness Elasticity (1)

Dependent variable

Intersectoral Investment Responsiveness Elasticity (2)

All countries (1)

No LowIncome (2)

High Income (3)

All countries (4)

No LowIncome (5)

High Income (6)

Capital Markets Size (FD) stand.error p-value

0.1282 (0.0971) 0.19

0.1193 (0.0938) 0.21

0.1743 (0.1207) 0.16

0.3187 (0.0874) 0.00

0.3173 (0.0884) 0.00

0.4128 (0.1082) 0.00

Income-Real GDP p.c. (GDP ) stand.error p-value

0.0860 (0.0367) 0.02

0.1398 (0.0535) 0.01

0.1677 (0.1201) 0.18

0.0328 (0.0235) 0.17

0.0457 (0.0357) 0.21

0.0316 (0.0755) 0.68

Schooling (SCH ) stand.error p-value

0.0031 (0.0157) 0.84

0.0008 (0.0170) 0.96

0.0112 (0.0220) 0.61

0.0127 (0.0121) 0.30

0.0142 (0.0124) 0.26

0.0313 (0.0145) 0.04

Institutional Quality (IQL ) stand.error p-value

0.0308 (0.0502) 0.54

0.0064 (0.0534) 0.91

0.0108 (0.0981) 0.91

-0.0660 (0.0412) 0.12

-0.0698 (0.0445) 0.12

-0.1330 (0.0672) 0.06

Intercept stand.error p-value

-0.3783 (0.2279) 0.10

-0.8203 (0.3656) 0.03

-1.2114 (0.9872) 0.23

-0.2384 (0.1574) 0.14

-0.3622 (0.2744) 0.19

-0.3243 (0.6511) 0.62

0.504 59

0.505 50

0.321 28

0.300 59

0.300 50

0.393 28

adjusted R-squared Countries

In columns (1)-(3), the dependent variable is the country-specific estimated elasticity of investment growth to value added growth, where we control for country fixed-effects (Elasticity 1; General Investment Responsiveness). In columns (4)-(6) the dependent variable is the estimated country-specific elasticity of investment growth to value added growth, where we control in the estimation for country, industry, time, country-industry, industry-time and country-year fixed-effects (Elasticity 2; Intersectoral Investment Responsiveness). The set of explanatory variables includes a financial markets size (FD), income (GDP), schooling (SCH) and a composite index of institutional quality (IQL). The models in columns (1) and (4) are estimated in the widest sample; the model in columns (2) and (5) exclude low income countries (LIC); the models in columns (3) and (6) are estimated only in high income countries. Table I and the Appendix Table report the elasticity and the values of the independent variables for each country. The Data Appendix provides detailed variable definitions and sources. Heteroskedasticity-adjusted standard errors are reported in parenthesis below the coefficients. p-values are reported in italics below the standard errors.

Table V -- Sensitivity Analysis II - Alternative Specifications and Estimation Techniques Panel A - Capital Markets Size and Intersectoral Capital Allocation

Dependent variable: Elasticity 2

All countries (1)

OLS No LowIncome (2)

High Income (3)

"Robust" Regression All No LowHigh countries Income Income (4) (5) (6)

All countries (7)

Capital Markets Size (FD) stand.error p-value

0.1243 (0.0333) 0.00

0.1279 (0.0325) 0.00

0.1501 (0.0424) 0.00

0.1075 (0.0464) 0.02

0.1157 (0.0502) 0.03

0.1401 (0.0614) 0.03

0.1174 (0.0587) 0.05

0.1287 (0.0514) 0.02

0.1569 (0.0726) 0.04

Income-Real GDP p.c. (GDP ) stand.error p-value

0.0215 (0.0133) 0.11

0.0214 (0.0235) 0.37

0.0219 (0.0728) 0.77

0.0227 (0.0177) 0.20

0.0111 (0.0258) 0.67

0.0220 (0.0675) 0.75

0.0164 (0.0219) 0.46

-0.0075 (0.0273) 0.78

0.0214 (0.1037) 0.84

Intercept stand.error p-value

-0.0385 (0.1000) 0.70

-0.0430 (0.2038) 0.83

-0.0713 0.92

-0.0340 (0.1263) 0.79

0.0620 (0.2020) 0.76

-0.0662 (0.6284) 0.92

-0.0149 (0.1389) 0.92

0.1996 (0.2100) 0.35

-0.1058 (0.9756) 0.91

0.273 64

0.239 51

0.260 28

0.214 64

0.141 51

0.137 28

0.152 64

0.105 51

0.144 28

adjusted R-squared Countries

LAD (Median) No LowHigh Income Income (8) (9)

Table V -- Sensitivity Analysis II - Alternative Specifications and Estimation Techniques (cont.) Panel B - Capital Markets Size and General Capital Allocation

Dependent variable: Elasticity 1

All countries (1)

OLS No LowIncome (2)

High Income (3)

"Robust" Regression All No LowHigh countries Income Income (4) (5) (6)

All countries (7)

Capital Markets Size (FD) stand.error p-value

0.0807 (0.0405) 0.05

0.0614 (0.0399) 0.13

0.0666 (0.0521) 0.21

0.0532 (0.0557) 0.34

0.0454 (0.0609) 0.46

0.0480 (0.0694) 0.50

0.1047 (0.0604) 0.09

0.0459 (0.0516) 0.38

-0.0075 (0.0779) 0.92

Income-Real GDP p.c. (GDP ) stand.error p-value

0.1015 (0.0176) 0.00

0.1383 (0.0265) 0.00

0.2027 (0.0894) 0.03

0.1114 (0.0212) 0.00

0.1457 (0.0313) 0.00

0.2030 (0.0763) 0.01

0.0862 (0.0268) 0.00

0.1585 (0.0387) 0.00

0.2369 (0.1118) 0.04

Intercept stand.error p-value

-0.4586 (0.1203) 0.00

-0.7743 (0.2163) 0.00

-1.3923 (0.8599) 0.12

-0.5042 (0.1518) 0.00

-0.8157 (0.2451) 0.00

-1.3665 (0.7104) 0.07

-0.3666 (0.1651) 0.03

-0.9414 (0.3011) 0.00

-1.6171 (1.0637) 0.14

0.501 64

0.475 51

0.300 28

0.485 64

0.434 51

0.216 28

0.385 64

0.337 51

0.162 28

adjusted R-squared Countries

LAD (Median) No LowHigh Income Income (8) (9)

In Panel A the dependent variable is the country-specific elasticity of investment growth to value added growth, where we control for country, industry, time, country-industry, industry-time and country-year fixed-effects (Elasticity 2; Intersectoral Investment Responsiveness). In Panel B, the dependent variable is the country-specific estimated elasticity of investment growth to value added growth, where we control only for country fixed-effects (Elasticity 1; General Investment Responsiveness). Models (1)-(3) are estimated with OLS. Models (4)-(6) are estimated with a robust regression procedure that assigns lower weights to influential observations (outliers). Models (7)-(9) report least absolute deviation (LAD) – median regression models. The set of explanatory variables includes a measure of financial markets size (FD) and income level (GDP), both expressed in logs. The models in columns (1), (4) and (7) are estimated in the widest sample of countries; the model in columns (2), (5) and (8) exclude low income countries (LIC); the models in columns (3), (6), and (9) are estimated only in high income countries. Table I and the Appendix Table report the elasticity and the values of the independent variables for each country. The Data Appendix provides detailed variable definitions and sources. Heteroskedasticity-adjusted standard errors are reported in parenthesis below the coefficients. In LAD-median regression models bootstrapped standard errors are report below the coefficients (1000 replications). p-values are reported in italics below the standard errors.

Table VI -- Capital Markets Size and Intersectoral Investment Responsiveness Instrumental variable estimates

Dependent variable: Sample

Intersectoral Investment Responsiveness Elasticity (2) All countries (1)

No LowIncome (2)

High Income (3)

Panel A: Second Stage Estimates Capital Markets Size (FD) stand.error p-value

0.3713 (0.1647) 0.03

0.2999 (0.1408) 0.04

0.5266 (0.2578) 0.05

Panel B: First Stage Estimates Common Law stand.error p-value

0.1489 (0.0766) 0.06

0.3178 (0.0799) 0.00

0.2518 (0.1023) 0.02

German Law stand.error p-value

0.4591 (0.1513) 0.00

0.4420 (0.1363) 0.00

0.2666 (0.1368) 0.06

Scandinavian Law stand.error p-value

0.3009 (0.1513) 0.05

0.2838 (0.1363) 0.04

0.1083 (0.1368) 0.44

0.177 64

0.281 52

0.233 28

1st Stage R2 Countries

The Table reports second-stage (in panel A) and first-stage (in Panel B) estimates of Instrumental Variable models. The dependent variable in the second-stage is the estimated country-specific elasticity of investment growth to valu added growth, where we control in the estimation for country, industry, time, country-industry, industry-time and country-year fixed-effects (Elasticity 2). The explanatory variable is a measure of financial markets size (FD), specified in logs. Financial markets size is instrumented in the first-stage model with legal origin dummy variables. The model in column (1) is estimated in the widest sample of countries; the model in columns (2) exclude low income countries (LIC); the model in columns (3) is estimated only in high income counttries group. Table I and th Appendix Table report the elasticity and the values of the independent variables for each country, respectively. The Data Appendix provides detailed variable definitions and sources. Heteroskedasticity-adjusted standard errors are reported in parentheses below the coefficients. p-values are reported in italics below the standard errors.

Table VII -- Determinants of Capital Market Size and Intersectoral Investment Responsiveness

Dependent variable: Elasticity (2) Intersectoral Investment Responsiveness

All countries (1)

No Low- High Income Income (2)

(3)

All No Lowcountries Income (4)

(5)

High Income (6)

Panel A: Second Stage Estimates Capital Markets Size (FD) stand.error

0.3412 (0.0951)

0.3286 0.3670 (0.1100) (0.1900)

0.3593 (0.0879)

0.3505 0.4211 (0.1046) (0.1624)

Panel B: First Stage Estimates Gov. Ownership of Banks stand.error

-0.3807 (0.0888)

-0.3285 -0.3083 (0.0988) (0.1356)

-0.3937 (0.0836)

-0.3431 -0.3634 (0.1004) (0.1384)

Legal Formalism stand.error

-0.0580 (0.0276)

-0.0787 -0.0656 (0.0280) (0.0463)

-0.0845 (0.0322)

-0.0755 -0.0854 (0.0326) (0.0510)

Insider Trading Legisl. stand.error

0.0130 (0.0030)

0.0105 0.0078 (0.0031) (0.0034)

0.0125 (0.0029)

0.0103 0.0082 (0.0030) (0.0032)

Common Law stand.error

-0.1065 (0.0719)

0.0018 -0.0531 (0.0932) (0.1152)

German Law stand.error

0.2082 (0.1015)

0.2316 0.1716 (0.1026) (0.1044)

Scandinavian Law stand.error

-0.0830 0.1070

-0.0419 -0.1051 (0.1106) (0.1145)

OID test; J-statistic p-value

0.279 0.86

1.344 0.51

1.7220 0.42

1.2470 0.94

2.5050 0.78

2.3670 0.80

1st Stage R-squred Countries

0.606 56

0.616 48

0.475 26

0.655 53

0.646 46

0.536 26

The Table reports second-stage (in panel A) and first-stage (in Panel B) estimates of two-stage least squares (2SLS) models. The dependent variable in the second-stage is the country-specific elasticity of investment growth to value added growth, where we control for country, industry, time, country-industry, industry-time and country-year fixed-effects (Elasticity 2; Intersectoral Investment Responsiveness). The explanatory variable is a measure of financial markets size (FD), specified in logs. Financial markets size is instrumented in the first-stage model with (1) State-ownership of banks around 1970. (2) An index of legal formalism (3) Years since the enforcement of insider trading legislation. (4) Legal origin dummy variables (in models (4)-(6)). The models in columns (1) and (4) are estimated in the widest sample of countries; the models in columns (2) and (5) exclude low income countries the models in columns (3) and (6) are estimated only in high income countries. The Table also reports the first-stage R-squared and a test of overidentifying restrictions, where under the null hypothesis the instruments are valid. Table I and the Appendix Table report the elasticity and the values of the independent variables for each country, respectively. The Data Appendix provides detailed variable definitions and sources. Heteroskedasticity-adjusted standard errors are reported in parentheses below the coefficients.

Appendix Table A: Main Variables

Country Australia Austria Belgium Bangladesh Bolivia Barbados Canada Chile Cameroon Colombia Cyprus Germany Denmark Ecuador Egypt, Arab Rep. Spain Ethiopia Finland Fiji France United Kingdom Greece Guatemala Hong Kong, China Indonesia India

Country Abbrev. wbcode

Capital Markets Size FD

Priv Credit / GDP PRIVC

Market Cap. / GDP MCAP

Real GDP p.c. GDP

Legal Formalism LEXFORM

Anti-self Dealing ANTISELF

Insider Trading INSTR

Gov. Own. Banks BGOV

AUS AUT BEL BGD BOL BRB CAN CHL CMR COL CYP DEU DNK ECU EGY ESP ETH FIN FJI FRA GBR GRC GTM HKG IDN IND

1.2433 0.9413 0.6280 0.1792 0.2095 0.6146 1.2217 0.9349 0.2281 0.3361 0.9625 1.1090 0.6383 0.2860 0.3311 0.9010 0.2031 0.8542 0.3262 1.1066 1.5070 0.4830 0.1512 2.6404 0.3152 0.4002

0.8123 0.8699 0.3699 0.1648 0.2006 0.4005 0.7663 0.5023 0.2281 0.2719 0.7748 0.9226 0.4153 0.1886 0.2799 0.7202 0.2031 0.6699 0.3009 0.9089 0.7441 0.4020 0.1512 1.3582 0.2623 0.2681

0.4310 0.0713 0.2580 0.0144 0.0089 0.2141 0.4554 0.4326 . 0.0642 0.1877 0.1864 0.2231 0.0974 0.0512 0.1808 . 0.1844 0.0254 0.1977 0.7629 0.0810 . 1.2822 0.0529 0.1321

16329.8 22184.1 21212.7 251.6 996.1 6514.8 16842.7 2749.3 821.3 1867.5 6438.3 22593.3 26753.2 1826.1 739.7 10742.2 114.4 21023.1 2475.6 21558.3 13976.3 10442.2 1568.2 12417.4 533.2 237.5

1.8026 3.5216 2.7259 3.2412 5.7522 2.3706 2.0855 4.5658 . 4.1118 3.6754 3.5066 2.5482 4.9232 3.7939 5.2478 . 3.1382 . 3.2346 2.5849 3.9912 5.6798 0.7303 3.9013 3.3421

0.790 0.210 0.540 . 0.080 . 0.650 0.630 . 0.580 . 0.280 0.470 0.080 0.490 0.370 . 0.460 . 0.380 0.930 0.230 . 0.960 0.680 0.550

1996 2005 1994 1998 2005 2005 1976 1996 . 2005 2005 1995 1996 2005 2005 1998 . 1993 . 1975 1981 1996 2005 1994 1996 1998

0.209 0.708 0.399 1.000 0.531 . 0.110 0.915 . 0.577 0.000 0.519 0.098 1.000 1.000 0.326 . 0.321 . 0.744 0.000 0.927 0.321 0.000 0.749 1.000

Ireland Iran, Islamic Rep. Israel Italy Jordan Japan Kenya Korea, Rep. Kuwait Libya Sri Lanka Macao, China Morocco Mexico Malta Malawi Malaysia Nigeria Netherlands Norway New Zealand Pakistan Panama Peru Philippines Portugal Singapore El Salvador Sweden Swaziland Trinidad and Tobago Tunisia Turkey

IRL IRN ISR ITA JOR JPN KEN KOR KWT LBY LKA MAC MAR MEX MLT MWI MYS NGA NLD NOR NZL PAK PAN PER PHL PRT SGP SLV SWE SWZ TTO TUN TUR

0.8936 0.3415 0.7943 0.6238 1.1463 2.4227 0.4084 1.0545 0.5053 0.0000 0.3208 . 0.3062 0.3216 0.6044 0.1462 1.8685 0.1876 1.6889 1.0370 0.9464 0.3216 0.5790 0.1582 0.5003 0.7097 2.1787 0.2360 1.4703 0.3306 0.6087 0.6416 0.1996

0.6280 0.3002 0.5054 0.5050 0.6212 1.6926 0.2914 0.8090 . . 0.1912 . 0.2580 0.1761 0.6044 0.1462 0.7969 0.1466 1.2797 0.8851 0.5417 0.2339 0.5080 0.0977 0.2941 0.6321 0.9480 0.2360 1.0894 0.2004 0.4982 0.5648 0.1382

0.2656 0.0413 0.2889 0.1187 0.5251 0.7301 0.1169 0.2455 0.5053 . 0.1296 . 0.0482 0.1455 . . 1.0716 0.0410 0.4092 0.1519 0.4047 0.0876 0.0710 0.0604 0.2062 0.0775 1.2306 . 0.3809 0.1302 0.1105 0.0768 0.0614

11119.5 1291.7 12201.0 14688.2 1811.1 28806.8 337.5 4098.7 12043.3 . 474.6 . 1059.2 3486.3 4813.5 147.5 2397.0 264.6 20873.2 23384.8 14235.4 333.7 2893.3 2685.5 1183.9 7382.2 11601.7 1412.7 23250.7 1103.6 4710.4 1686.2 2002.8

2.6263 . 3.3048 4.0414 3.5219 2.9759 3.0877 3.3684 3.8772 . 3.7760 . 4.7105 4.7083 2.4386 2.9539 2.3421 3.1930 3.0658 2.9539 1.5789 3.7610 5.8377 5.6009 5.0000 3.9254 2.4978 4.6009 2.9825 3.6996 1.8048 4.0482 2.5285

0.790 . 0.710 0.390 0.160 0.480 0.220 0.460 . . 0.410 . 0.570 0.180 . . 0.950 0.520 0.210 0.440 0.950 0.410 0.150 0.410 0.240 0.490 1.000 0.570 0.340 . . 0.170 0.430

2005 2005 1989 1996 2005 1990 2005 1988 2005 . 1996 . 2005 2005 2005 2005 1996 2005 1994 1990 2005 2005 2005 1994 2005 2005 1978 2005 1990 2005 2005 2005 1996

0.038 0.894 0.676 0.757 0.281 0.069 0.451 0.566 0.360 1.000 1.000 . 0.591 0.827 . . 0.200 0.575 0.078 0.546 0.335 0.735 0.179 0.874 0.522 1.000 0.129 0.531 0.208 . 0.036 0.529 0.818

Tanzania Uruguay United States Venezuela, RB Zambia Zimbabwe

Mean Median Standard Deviation

TZA URY USA VEN ZMB ZWE

0.0000 0.3199 1.8892 0.4680 0.0609 0.3531

. 0.3123 1.3074 0.3869 0.0609 0.2192

. 0.0076 0.5818 0.0811 . 0.1338

. 5525.1 21305.6 3865.8 602.9 662.5

3.8221 4.0548 2.6206 6.0088 2.1306 3.1053

. 0.170 0.650 0.090 . .

0.7093 0.5421 0.5891

0.5125 0.4020 0.3552

0.2569 0.1455 0.2900

7950.8 3676.0 8475.0

3.5090 3.3684 1.1270

0.4664 0.4600 0.2531

2005 2005 1961 2005 2005 2005

1.000 0.423 0.000 0.829 . 0.000

0.5090 0.5300 0.3404

The Table reports the values of the main variables used in the empirical analysis for each country. It also reports the country average, median value and standard deviation. The Data Appendix provides detailed variable definitions and sources.

Appendix Table B State Bank Ownership Dependent variable: Elasticity (2)

Capital Markets Size p-value

Legal System Efficiency

All No Low- High All No Lowcountries Income Income countries Income (1) (2) (3) (4) (5) 0.1371 0.01

0.1471 0.01

0.1333 0.01

State Bank Ownership -0.0090 p-value 0.92

0.0519 0.58

0.0112 0.92

Legal Formalism p-value

High Income (6)

0.1562 0.00

0.1359 0.00

0.1552 0.01

-0.0037 0.89

-0.1212 0.67

-0.0010 0.98

Anti self-dealing p-value

Investor's Protection

Insider Trading Legisl.

All No Low- High countries Income Income (7) (8) (9)

All No Low- High countries Income Income (10) (11) (12)

0.1249 0.00

0.1019 0.01

0.1066 0.03

0.1424 0.16

0.1782 0.08

0.0619 0.59

Insider Trading Legisl. p-value

0.1265 0.00

0.1088 0.00

0.1278 0.00

0.0033 0.21

0.0041 0.13

0.0029 0.37

Intercept p-value

0.1506 0.08

0.1201 0.21

0.1686 0.06

0.1270 0.21

0.1797 0.15

0.1351 0.41

0.0860 0.14

0.1008 0.10

0.1790 0.04

0.1446 0.00

0.1661 0.00

0.1550 0.02

adjusted R-squared Countries

0.231 56

0.211 48

0.218 26

0.253 59

0.224 49

0.256 28

0.289 47

0.279 42

0.190 24

0.269 60

0.249 50

0.280 28

The Table reports OLS estimates. The dependent variable is the estimated country-specific elasticity of investment to value added, where we control for country, industry, time, countryindustry, industry-time and country-year fixed-effects (Elasticity 2; Intersectoral Investment Responsiveness). The independent variables are: (1) A measure of financial markets size. (2) State-ownership of banks around 1970. (3) An index of legal formalism. (4) An anti-self dealing index. (5) Years since the enforcement of insider trading legislation. The models in columns (1), (4), (7) and (10) are estimated in the widest sample of countries; the models in columns (2), (5), (8) and (11) exclude low income countries (LIC); the models in columns (3), (6), (9) and (12) are estimated only in high income countries.Table I and the Appendix Table report the elasticity and the values of the independent variables f each country. The Data Appendix provides detailed variable definitions and sources. Heteroskedasticity-adjusted standard errors are reported in parenthesis below the coefficients. pvalues are reported in italics below the standard errors.