Law, Property Rights, and Growth1 Stijn Claessens (University of Amsterdam and CEPR) and Luc Laeven (World Bank) May 2001 Abstract This paper investigates how different legal frameworks not only affect the amount of external financing available, but also the allocation of resources among different type of assets. Using a simple model, we show that a firm will get less financing, and thus invest less, in a weak law and order environment. We also show that weaker property rights can lead to an asset substitution effect with firms investing less in intangible assets. Empirically, these two effects appear to be equally important drivers of growth in sectoral value added for a large number of countries. Using individual firm data, we also show that weaker legal frameworks are associated with relatively more fixed assets, but less long-term financing for a given amount of fixed assets. JEL Classifications: G31, G32, K10, O34, O4 1 The views expressed do not necessarily represent those of the World Bank. Paper prepared for the Third Annual Conference on Financial Market Development in Emerging and Transition Economies, Hong Kong, June 28-30, 2001. 2 “Economic growth will occur if property rights make it worthwhile to undertake socially productive activity” Douglass C. North and Robert Paul Thomas2 1. Introduction Recently, a large number of papers have established that financial development fosters growth and that financial development is related to a country’s institutional characteristics, including a strong legal framework. This law and finance literature has found that firms in countries with well-developed financial markets and a strong legal framework find it easier to attract (long-term) financing for their investment needs (La Porta et al. 1998, Demirgü?-Kunt and Maksimovic 1998, Rajan and Zingales, 1998). Related work has established that debt structures of firms differ across institutional frameworks (Rajan and Zingales 1995, Demirgü?-Kunt and Maksimovic, 1999, Booth et al. 2000). In particular, it has been established that firms in developing countries have a smaller fraction of total debt in the form of long-term debt. Thus far, however, the literature has not paid much attention to differences in firms’ asset structure across countries. But these differences are large as well. Demirgü?- Kunt and Maksimovic (1999) find, for example, that firms in developing countries have higher proportions of fixed assets to total assets and less intangible assets. This is surprising as the literature on optimal capital structures (Harris and Raviv, 1991) would suggest that a lack of long-term financing would make it more difficult to finance fixed assets. So far, to our knowledge, no explanation of these findings has been provided. How come that firms in developing countries have more fixed assets while they find it more difficult to get long-term external financing? Is it that they need more nominal collateral to attract the same amount of financing? Or are the returns to fixed assets more secure from the firm’s point of view than the returns on intangible assets? In this paper, we explore the role on property rights in influencing the availability of external financing and the allocation of investable resources. Using a simple framework, we investigate the effects of legal framework on the amount of financing, 2 The Rise of the Western World: A New Economic History (Cambridge, MA: Cambridge University Press, 1973), 8. 3 confirming the well-established proposition in the law and finance literature that weaker legal frameworks diminish the availability of external resources. The model also shows that the allocation of investable resources between fixed and intangible assets is related to the protection of property rights. In particular, we show that it may be efficient for a firm, which operates in markets with weaker property rights, to choose more investment in fixed assets relative to intangible assets. The strength of this substitution effect will depend on the general protection of property rights, and maybe particularly on the strength of a country’s intellectual property rights.3 The model thus shows that two effects affect the choice of a firm’s asset structure in countries with imperfect financial markets and weaker property rights: a lack of finance and an asset substitution effect. The paper investigates empirically for a large number of countries the finance and asset substitution effects. We find that weaker property rights are associated with lower firm growth on account of both effects: firms get less financing, and thus underinvest overall; and they underinvest in intangible assets relative to fixed assets. Empirically, the two effects appear to be equally important drivers of growth in sectoral value added for a large number of countries. Using firm specific data, we furthermore show that firms in developing countries invest relatively more in fixed assets despite a legal framework that gives little collateral value to fixed assets. We confirm that this occurs because these countries’ property rights to protect intangible assets are even worse than those protecting fixed assets are. As a result, firms in these countries favor investments in fixed assets over investments in intangible assets. At the same time, firms in developing countries have less long-term financing for a given amount of fixed assets, as weaker creditor rights diminish the collateral value of their fixed assets. The paper is structured as follows. Section 2 reviews the related literature. Section 3 describes the finance and asset substitution effect using a simple framework and presents our methodology to disentangle the finance and asset substitution effect empirically. Section 4 presents the data used in the empirical work. Section 5 presents the empirical results. Section 6 concludes. 3 Intellectual property rights are monopoly rights and broadly include patents (property rights to inventions and other technical improvements), copyrights (property rights to authors, artists, and composers), and trademarks (property rights for distinctive commercial marks or symbols). 4 2. Related Literature Our work is related to several strands of literature. The starting point is the so-called law and finance literature initiated by La Porta et al. (1998) and Rajan and Zingales (1998). This literature focuses on the relationship between the institutional framework of a country and its financial development (see also La Porta et al. 1997, Demirgü?-Kunt and Maksimovic 1998, and Carlin and Mayer, 2000). This literature has established that financial sector development is higher in countries with better legal systems and creditor rights because such environments increase the ability of lenders to finance firms. Related is the work by King and Levine (1993), Levine and Zervos (1998), and Beck et al. (2000) that has established an empirical link between financial development and economic growth, with a focus on the role of legal systems. The second strand we draw on is the capital structure literature (Myers 1977, Titman and Wessels 1988, and Harris and Raviv 1991). This literature has established that real, tangible assets, such as plant and equipment, support more debt than intangible assets. In particular, fixed assets can support more long-term debt as they have more liquidation and collateralizable value. As intangibles have value only as part of a going concern, it follows that the debt-to-firm value ratio will be lower the larger the proportion of firm value represented by investment options (Myers 1977). Bradley et al. (1984) and Long and Malitz (1985) provide empirical support for the argument that a larger amount of intangible assets reduces the borrowing capacity of a firm. Rajan and Zingales (1995) and Demirgü?-Kunt and Maksimovic (1999) show for firms in a cross-section of countries that debt maturity and asset structures are related, with firms with more fixed assets being able to support more long-term debt. Demirgü?-Kunt and Maksimovic (1999) also show that firms in developing countries have a large share of fixed assets out of total assets, although they do not provide an explanation for it. This difference in asset composition for firms in developing countries can have large implications for firm growth, in light of recent studies on the importance of different types of inputs in firm production. The growth literature has broadened the set of productive inputs from capital and labor (Solow 1956) to human capital and technology (Romer 1990, and Barro 1991, among others). In particular, Romer (1986, 1987) shows that technology exhibits increasing returns to scale 5 and therefore the current endowment of technology is important for future growth. Empirically, a link has been established between equipment investment, which incorporates technology, and economic growth, especially for developing countries (De Long and Summers, 1991, 1993). Investment in intangibles also appears to foster growth. Nickell and Nicolitsas (1996) find a link between increased R&D expenditure and subsequent increase in fixed capital investment. The relatively higher degree of investment in fixed assets by firms in developing countries could thus mean that growth is below optimal levels. The lower degree of investment in intangible assets in developing countries may relate to the weaker protection of property rights in these countries. Mansfield (1995) already hints that there may be a relationship between protection of property rights and the allocation of investable resources between fixed and intangible assets. Using a survey of firm managers, he states that “Most of the firms we contacted seemed to regard intellectual property rights protection to be an important factor” … “[influencing] investment decisions”. More generally, the institutional economics literature (North, 1990, and De Soto, 2000) can be interpreted to suggest that investment in different type of assets will tend to be higher the more protected is the property right of the particular asset. 3. Framework and Empirical Methodology This section develops the link between on one hand the protection of property rights and the other hand the amount of investable resources and its allocation between fixed and intangible assets. Using a simple framework, we show that it may be efficient for a firm in a country with weaker property rights to choose more investment in fixed assets relative to intangible assets compared to a firm functioning in an environment with strong property rights. The law and finance literature already established that firms in a country with stronger property rights would find it easier to attract external financing and more generally have the benefit of more developed financial markets. A firm’s asset size and structure will thus be affected by the strength of property rights in the country in two ways: an availability of external finance and an asset substitution effect. 6 Figure 1 develops the difference between the finance effect and the asset substitution effect. Take labor and human capital as fixed. Assume the optimal production point given world factor prices for fixed and intangible assets entails combinations of amounts of fixed and intangible assets that lay along the line from the origin through point A. Assume the firm wants to expand from initial point A as demand increases. If the firm has access to highly developed financial markets and is able to collateralize all types of assets, it would be able to raise enough external financing to invest in an optimal proportion of fixed assets and intangible assets to arrive at say point D. The investment of firms in countries with well-developed financial markets should exhibit such patterns. If the supply of external financing is limited, the firm may only be able to reach point C. The difference between point D and point C could then be ascribed to a more limited supply of external financing. The firm, however, could also deviate from the optimal production line, points A, C and D. It may, for example, find it more efficient to choose point B rather than point C. This would be if the firm finds it more difficult to secure for itself returns from intangible assets compared to returns from fixed assets. This could be the case in developing countries, where due to poor (intellectual) property rights it may hard for a firm to collect revenues from assets such as patents and other intangible assets. More generally, a preference for investments in fixed assets rather than intangible assets may arise in countries with poor protection of property rights when there are fixed costs to producing intangible assets. With poor protection of property rights, it will be less attractive for a firm to incur any fixed costs to produce intangible assets, like investing in research and development, marketing, networks, human resources, etc. This could make it more attractive for a firm to invest largely in fixed assets. The law and finance literature focuses on the difference between point D and C, as the supply of external financing is not assumed to be affected by the asset choice. If firms in countries with low financial development and poor property rights systematically were to choose point B rather than point C, then the law and finance approach would ascribe the whole difference between point D and B, and any impact on firm growth, to limited financial development only. In other words, the law and finance literature ignores any differences on the asset side of the firm’s balance sheet when studying the effects of legal frameworks on firm financing and growth patterns. But the difference between 7 point D and B is not only due to a lack of finance effect, but also due to an asset substitution effect. The two effects may not only be different, they may also have different and complementary effects on final firm growth. The empirical question is whether the asset substitution effect is present, how it can be differentiated from the law and finance effect, and what its quantitative importance might be. Note that in our example firms in countries with a well-developed financial sector but poor protection of property rights would choose point E in Figure 1. Thus, point E illustrates the case where the finance effect is absent and the deviation from the optimal allocation (point D) can be contributed fully to the asset substitution effect that arises from poor property rights. 8 Figure 1: Investment in intangible assets versus fixed assets: financial development and asset substitution effects Fixed Assets (FA) 0FA LowFA ,1 HighFA ,1 HighIA ,1 LowIA ,1 0IA Intangible Assets (IA) Asset substitution Finance A D B C E 9 Overall, the above discussion suggests that firms in countries with stronger legal frameworks will have more overall investment and better asset structures, which in turn will be reflected in higher growth rates. To empirically test whether firms in countries with better legal frameworks indeed experience higher growth rates, we use a setup to assess the relationship between financial development and growth similar to the one used by Rajan and Zingales (1998, RZ). In particular, we test whether industrial sectors that typically use a lot of intangible assets grow faster in countries with stronger property rights. In addition to testing the effects of property rights on firm growth, we directly test the asset substitution effect by investigating whether firms in countries with weaker property rights invest less in intangible assets. We also directly explore the supply of external financing effect by investigating the relationship between the strength of legal rights and the amount of long-term debt extended by lenders per unit of fixed assets. In what follows, we develop testable regression specification for these three hypotheses. Let there be m countries, each indicated by index k, and n industries, each indicated by index j. The first set of equations relate the growth in real value added of a firm in a particular country to a number of country and firm-specific variables. The law and finance effect is measured by the interaction term between the typical external dependence variable for the particular sector and the country’s level of financial development. The argument of RZ is that financially dependent firms grow more in countries with a higher level of financial development. This equation also allows us to disentangle the law and finance effect from the asset substitution effect. We do this by testing directly whether growth is higher for firms that typically use a lot of intangible assets in countries with good protection of property rights. Specifically, equation (1a) extends the basic model in RZ by adding a variable that is the interaction of the typical ratio of intangible to fixed assets and the property rights index. In line with RZ, we use US firm data to construct proxies for the typical external dependence for a particular industry and the typical ratio of intangible-to-fixed assets for a particular industry. The presumption here is that the US financial markets are well developed and that property rights are protected well in the US such that firms are at the optimal external financing and asset structure point for their respective industrial sector. Following RZ, we add in the regression the industry’s market share in total 10 manufacturing to control for differences in growth potential across industries. Industries with large market shares initially may have less growth potential than industries with small market shares initially. . )country rightsProperty industry USassets edassets/Fix Intangible( )country t developmen Financialindustry USdependence External( )1980in country in ingmanufactur of share Industry ( indicatorsIndustry indicatorsCountry ConstantGrowth , 3 2 1 ...1...1, kj nm nm nm nmmmkj kj kj kj e b b b bb + ??+ ??+ ?+ ?+?+= ++ ++ ++ ++ (1a) The law and finance literature asserts that financial markets are more developed in environments with better law and order (and creditor rights). In this view, financial development is the result of a good legal framework and the supply of external financing is not independent of the quality of the legal framework. We therefore also estimate a variation on the previous specification that uses the law and order index of a country rather than its financial development to measure the link between a sector’s financial dependence and growth (equation 1b).4 . )country rightsProperty industry USassets edassets/Fix Intangible( )country order and Law industry USdependence External( )1980in country in ingmanufactur of share s'Industry ( indicatorsIndustry indicatorsCountry ConstantGrowth , 3 2 1 ...1...1, kj nm nm nm nmmmkj kj kj kj e b b b bb + ??+ ??+ ?+ ?+?+= ++ ++ ++ ++ (1b) The next two sets of tests focus on firm’s actual choices of investment and financing patterns, providing the supporting evidence that investment and financing structures can both be affected by the quality of the legal framework in a country. The first set of tests relate the investment structure of firm j in county k to the quality of property rights in country k. Equation (2) specifically tests whether firms in countries with better protection of property have relatively less amounts of fixed assets and larger 11 amounts of intangible assets. To investigate robustness, different variations are used for the dependent variable, including the ratio of fixed assets to total assets, the ratio of intangible asset to total assets and the ratio of intangible assets to fixed assets. Furthermore, two flow variables, the ratio of capital expenditures to sales and the ratio of research and development expenditure to sales, are used as indicators of firms’ investment structures. kjn nkj k ,1 ...1, country of rightsProperty indicatorsIndustry Constantassets edassets/Fix Intangible eb b +?+ ?+= + (2) The next and last set of tests relate the relative amounts of long-term and short-term debt for industry j in country k to the quality of law and order in the country. If the hypothesis that firms in countries with a weaker legal framework have less long-term and more short-term debt is correct, then equation (3) should produce a positive coefficient (after correcting for industry-specific effects) for the law and order index. Again, different variations of the dependent variable are used, including the ratio of long-term and short-term debt to total assets, the ratio of total debt to assets and the ratio of long- term debt to fixed assets. Furthermore, both the index of the quality of property rights as well as the share of fixed assets out of total assets are used as independent variables to investigate the importance of collateralizable assets for firm financing patterns. kjn nkj k ,1 ...1, country oforder and Law indicatorsIndustry Constanttsdebt)/Asse term-Shortor term- (Long eb b +?+ +?+= + (3) 4. Data We use firm-level data from WorldScope, the dataset from RZ, and some country- specific data from a variety of sources. Table 1 presents an overview of the country- 4 We should note that we tend to use the term law and order when referring to the impact of the legal framework on the supply of external financing and the term property rights when referring to the impact of the legal framework on the asset composition choices. 12 specific and firm-specific variables used in the empirical analysis and their sources. Most of the variables are self-explanatory and have been used in other cross-country studies of firm financing structures and firm growth. Table 2 presents the summary statistics of the country-specific and some firm variables grouped by developing and developed countries (Annex 1 presents the same summary statistics, but by individual country). The country summary statistics in panel A show that developing countries as a group have weaker law and order systems, worse protection of property rights, less developed financial systems, and fewer patents per capita. All variables excepts for the private-credit-to-GDP ratio are statistically significant different between the two groups of countries. This result on the differences in the degree of law and order between developed and developing countries has been documented extensively in other work. This difference in legal framework partly relates to the differences in the credit-to-GDP between these two groups of countries, where low contract enforcement environments have hindered the development of financial systems in developing countries. The result for the relative levels of patents granted suggests that developing countries generate lower levels of research and development. The level of patents relates in part to the quality of property rights, which has a negative correlation with the number of patents of 0.557, suggesting that lower property rights deter the adoption of patents. Of course, other factors, such as the level of education, capital investment and general development, will also affect the outcome of the number of patents in a country. In general, good (poor) law and order and good (poor) property rights tend to go together. This would imply that only points D (high law and order and strong property rights) or B (low law and order and strong property rights) in Figure 1 are relevant. As such, analyzing the differential effects of the quality of law and order and property rights on the level of external financing available and the allocation of investment would be difficult. However, the correlation between the two concepts is not perfect and it is possible to be at either points C or E, i.e., there exist countries with good property rights and poor law and order systems, and vice versa. South Korea, for example, has a high score on protection of property rights (property rights index equals 1), but relatively poor law and order (the law and order index is only 5.35, which is well below the sample median of 7.8). France, on the other hand, has a good legal system (reflected by a law 13 and order index of 8.98), but the protection of its property rights is only average (with a property rights index of 2). Calculating the simple correlation between the property rights index and the law and order index, 0.77, confirms that the relationship between the two concepts is close, but not perfect. Panel B of Table 2 presents the medians of the firm-specific WorldScope data. The medians are taken across countries, across firms, and over the period 1995-1999. The raw statistics show that for our sample of firms, the median size is about the same in developing countries as in developed countries. This reflects in part that Worldscope only covers publicly listed firms, which are larger than the average firm in developing countries. But it does mean that our results are probably not due to any size differences. In terms of total financial leverage, firms in developing countries are slightly more leveraged, but this is not a significant difference between the two groups. And, although firms in developing countries have higher internal cash flow, this is not statistically significant different (at the 5% level) either from firm cash flow in developed countries. We do find many differences, however, in terms of assets and debt structures. Firms in developed countries use statistically significant less fixed assets and more intangible assets. This greater emphasis on intangibles in confirmed in the flow ratios, such as the ratio of capital expenditures to sales, which is statistically significant lower for developed countries, and the ratio of R&D expenditures to sales, which is statistically significant higher for developed countries. In terms of debt structures, firms in developed countries have statistically significantly more long-term and less long-term debt per dollar of total assets compared to firms in developing countries. Firms in developed countries also have more long-term debt per unit of fixed assets. [Insert Table 2 here] The above firm-specific statistics were obtained for the whole sample of countries for which Worldscope has coverage, 51. Since we for some of our tests we use data from RZ, and more generally to allow for a consistent comparison with their results, we want to use the same set of countries. For four countries out of the 43 countries RZ uses, data is not available from WorldScope. Merging then the WorldScope dataset and the dataset 14 of RZ, we arrive at a dataset that includes 39 countries.5 For the three basic regressions described in section 3, we therefore use data on these 39 countries.6 For robustness, however, we also estimate equations 2 and 3 using the larger dataset that include all WorldScope countries, i.e., including the four countries outside the RZ sample. Similarly to RZ, we construct benchmark data on an industry basis. We use the benchmark data for most variables from RZ, but for our regression specifications we need to add the intangible-to-fixed assets ratio. Analogue to RZ, we assume that the intangible- to-fixed assets ratio for each industry in the U.S. forms a good benchmark (similar to RZ that the US financial dependence ratio forms a good benchmark). We calculate this benchmark using data on US firms for the years 1995-99. This creates a lack of overlap with the growth data that are for the period 1980-89, but unfortunately we do not have the data to construct US intangible-to-fixed assets ratios for the period 1980-89. We therefore assume that the benchmark intangible-to-fixed assets ratios are stable across time, at least across the periods 1980-89 and 1995-99. 5. Empirical Results In this section, we present the results of the regression models of section 3. We start with the analysis as in RZ, expanded to include a term to capture the potential effect of differences in property rights on firm growth across countries (equation (1) of section 3). The dependent variable is the real value added in a particular sector in a particular country over the 1980-90 period. The results are in Table 3. We find that industrial sectors that rely relatively more on external finance develop disproportionately faster in countries with more-developed financial markets as the coefficient for the interactive variable Credit-to-GDP times External Financial Dependence is statistically significant (at the 1% level, column 1). Hence, financial development facilitates economic growth 5 The 39 countries studies here include Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, India, Indonesia, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, Singapore, South Africa, Spain, Sri Lanka, Sweden, Turkey, United Kingdom, Venezuela, Zimbabwe and United States. Compared to the RZ data, we miss four countries (Bangladesh, Costa Rica, Kenya and Morocco) for which we do not have data from WorldScope. 15 through greater availability of funds, consistent with the findings of RZ. This effect is related to the quality of the legal system in the country as the statistically significant coefficient for the interactive variable Law and Order times External Financial Dependence indicates (column 2). While, as noted by Beck et al. (2000) and others, the quality of the legal system not only influences financial sector development, but also has a separate, additional effect on economic growth, the two regression results together confirm the law and finance view that increased availability of external financing and better legal systems enhance firm growth. In terms of the asset substitution effect, we find that industrial sectors that use relatively more intangible assets develop faster in countries with better protection of property rights as the coefficient for the interactive variable Property times Intangible-to- Fixed Assets is statistically significant and positive (column 3). Hence, better property rights can facilitate economic growth as it favors growth in firms that would naturally choose a higher share of investment in intangible assets. This effect appears to be in addition to the increase in firm growth due to greater external financing as in the regression where both the external financing dependence and the assets composition variables are included (column 4) both interactive variables remain statistically significant. The coefficients are also of similar magnitude as in the regression where they are included separately (columns 1 and 3), suggesting that the two variables measure complementary effects. The effects of financial development (or quality of law and order) and property protection on firm growth are both economically important. The average effect on real value added growth is 0.8 percentage points for the financial development variable, 0.9 percentage points for the law and order variable, and 1.3 percentage points for the property rights variable.7 It follows that the impact of good property rights on firm growth is economically equally and perhaps more important than the impact of a well- developed financial system (or a good legal system). In other words, the results indicate that the asset substitution effect due to differences in property rights is at least as 6 For the growth regressions (equation 1a and 1b in section 3) we need to drop the benchmark country, the United States, as in RZ, and therefore we use 38 countries for the growth regressions. 7 These figures are calculated by multiplying the regression coefficients with the sample average values for the regression variables. In other words, 0.1224*0.0780 = 0.008 for the credit-to-gdp*external financial dependence, 2.2012*0.00460 = 0.009 for law*external financial dependence, and 0.2639*0.0499 = 0.013 for property*intangible-to-fixed assets. The sample averages can be found in Table 2 of Annex 2. 16 important as the finance effect due to differences in availability of external financing (or legal system). It is worth noting that the variables external financing dependence and intangible assets usage interacted respectively with the law and order index and property rights index measure different concepts. Annex 2 Table 1 reports the correlation between the three variables. The correlation between the external financing dependence variable interacted with law and order index and the intangible usage variable interacted with the property rights index is very low (0.123). So is the correlation between the external financing dependence variable interacted with the financial development measure and the intangible usage variable interacted with the property rights index (0.085). The correlation between the external financing dependence variable interacted with the financial development measure and with the law and order index respectively, is very high, 0.9, exactly as the law and finance literature has documented. [Insert Table 3 here] We next analyze whether we can explain the differences in growth patterns more specifically by variations in firm investment and financing patterns. We start with analyzing whether firms in countries with weaker property rights invest less in intangible assets, i.e., we estimate equation (2) in section 3. The results are presented in Table 4.8 We find that firms in countries with weaker property rights have less intangible assets relative to fixed assets (row 1), a relatively larger share of assets in fixed assets (row 2) and less investment in intangible assets relative to fixed assets (row 3). Also, firms in countries with weaker property rights have higher ratios of spending on plant and equipment relative to sales (row 4) and less research and development spending (row 5). These findings are in line with those of Demirgü?-Kunt and Maksimovic (1999) who find that firms in developing countries have higher proportions of net fixed assets to total 8 Note that the number of observations for each regression in Table 3 is larger than in Tables 4 and 5 (although the number of countries is the same in each Table) because in Tables 4 and 5 we use 2-digit SIC codes to create 20 industry dummies, while for Table 3 we follow RZ and use 3 or 4-digit SIC codes to create 36 industry dummies. Hence, the number of different industries distinguished in Table 3 is almost twice as large as the number of industries distinguished in Tables 4 and 5. 17 assets. This result implies that greater protection of property rights supports higher firm investment in intangible assets. [Insert Table 4 here] We next analyze firm financing structures. We start with investigating the amount of debt used by a firm across countries in relationship to the quality of law and order, i.e., we estimate equation (2) in section 3. The results are presented in Table 5. We find that long-term debt as a share of assets is lower and short-term debt as a share of assets is higher in countries with weaker law and order (rows 1 and 2). Given that countries with weaker law and order are typically developing countries, these results are similar to those of Demirgü?-Kunt and Maksimovic (1999) who report that firms in developing countries use less long-term debt. We also find, however, that total debt as a share of assets is higher in countries with weaker law and order (row 3). Our interpretation is that the lack of alternative sources of external financing (other than debt) in countries with weaker law and order leads to more extensive use of debt, and that this debt is mostly of a short-term nature as weaker creditor rights reduce the attractiveness of extending long-term financing. [Insert Table 5 here] We also find that firms in countries with weaker law and order have lower ratios of long-term debt to fixed capital (row 4), reflecting that they invest more in fixed capital and are less able to attract long-term debt. The inability to attract more long-term financing per unit of fixed assets must be because the collateralizable value of fixed assets is lower in countries with weaker law and order. Put differently, from the perspective of a lender, the collateral value of the fixed assets of a firm that operates in an environment with weaker law and order is likely less than the book value of fixed assets. Indeed, if we interact the fixed assets-to-total assets ratio with the law and order index and use this variable as independent variable to proxy for the collateralizable value of the fixed assets, we find that it is positively related to the ratio of long-term debt to total assets (row 5). In other words, in developing countries due to weak law and order, the 18 collateral of fixed assets is reduced and therefore lenders provide less long-term debt. We also find that both collateral (as measured by the amount of fixed assets) and an index of law and order are important explanatory variables for the relative amount of long-term firm debt (rows 6 and 7). This confirms again that it is the collateralizable value of fixed assets, which influences the ability of firms to attract long-term financing. For consistency, the results in Tables 3-5 are based on the same set of countries as in RZ, totaling 39 countries. This is a subset, however, of all the countries for which we have data. For robustness, we repeat the above analysis using a larger dataset that includes all countries in WorldScope for which we have the law and order index or the property rights index. Table 6 shows the results of estimating model 2 in section 3 using an expanded dataset that includes all countries in WorldScope, 51, for which we have the property rights index (and otherwise replicates Table 4). The results of Table 6 are very similar to those of Table 4. Again, we find that firms in countries with weaker property rights have more (investment in) fixed capital and less (investment in) intangible assets and research and development than firms in countries with good property rights. Table 7 replicates Table 5 by using an expanded dataset that includes all countries for which we have the law and order index. This sample amounts to 45 countries. The results of Table 7 are very similar to those of Table 5. Again, we find that long-term debt is lower and short-term debt higher in countries with weaker law and order, and that total debt is higher in countries with weaker law and order due to higher amounts of short-term debt. Again, firms in countries with weaker law and order have much lower ratios of long-term debt to fixed capital because the collateralizable value of fixed assets is lower. Together, these two Tables suggest that our results are robust to the use of different datasets. 19 6. Conclusions Developing countries differ from developed countries in many ways. One distinct feature of developing countries is their poorer property rights and weaker (enforcement of) laws. This paper shows that the existence of such an environment has two effects: first, it reduces the value of collateral, which in turns leads to lower external financing; and second, it leads to lower investment in intangible assets. The first effect has already been put forward in the so-called law and finance literature. This paper goes beyond the law and finance literature by showing that the second consequence of weaker property rights, a result we call the asset substitution effect, is economically as important as the lack of financing as a result of weaker law and order. Specifically, the paper shows that both effects impede the growth of firms in the same quantitative magnitude. To the extent that the emergence of the “new economy” has increased the returns to intangibles assets going forward, our results could even underestimate the overall costs of weak property rights for developing countries. If indeed new economy assets and future growth opportunities are related to investment in intangible assets, while the old economy is related to investment in fixed assets, an over-allocation of investable resources towards tangible assets is likely to impede the future growth of firms, and the economy more generally, even more. Our results thus has the important policy implication that, equally important as the development of a proper functioning legal system to help develop a good financial system, is the presence of a strong protection of intangible assets. 20 Table 1: The Variables This table describes the variables collected for the countries included in our study. The first column gives the names of the variable. The second column describes the variable and provides the source from which it was collected. Variable Description Developing Takes value of 1 if the country is defined by the World Bank as a developing country; and 0 otherwise. Source: World Bank Population The population of the country in millions at the end of 1997. Source: World Bank Development Indicators 1999. Patents The number of patents granted during the year 1997 to (non)-residents of the country. Source: World Intellectual Property Organization. Law and Order Assessment of the law and order tradition in the country. Average of the months of April and October of the monthly index between 1982 and 1995. Scale from 0 to 10, with lower scores for less tradition for law and order. Source: International Country Risk Guide and La Porta et al. (1998) (“Legal determinants of external finance”) Property A rating of property rights in each country (on a scale from 1 to 5). The more protection private property receives, the lower the score. The score is based, broadly, on the degree of legal protection of private property, the probability that the government will expropriate private property, and the country’s legal protection to private property. We use the rating during 1997. Source: The 1997 Index of Economic Freedom from the Heritage Foundation. Private Credit-to-GDP Private Credit divided by GDP in 1980. Source: International Financial Statistics, IMF Assets Total assets. Source: WorldScope May 1999 CD-Rom. Assets (US$) Total assets converted to US dollars using the fiscal year end exchange rate. Source: WorldScope May 1999 CD-Rom. Fixed assets Net fixed assets or Net property, plant and equipment (represents gross property, plant and equipment less accumulated reserves for depreciation, depletion and amortization. It includes gross land, net buildings, net machinery, net equipment, construction work in progress, net minerals, net oil, net autos and trucks, net timberland and timber rights, net leasehold improvements, net rented equipment, net furniture and fixture, property, and plant and equipment under capitalized lease). Source: WorldScope May 1999 CD-Rom. Intangible assets Intangible assets (represents assets not having a physical existence). The value of these assets lies in their expected future return. It includes goodwill, patents, copyrights, trademarks, formulae, franchises of no specific duration, software, organizational costs, customer lists, licenses of no specific duration, capitalized advertising cost, capitalized servicing rights, purchased servicing rights). Source: WorldScope May 1999 CD-Rom. Long-term debt Long-term debt (represents all interest bearing financial obligations, excluding amounts due within one year). Source: WorldScope May 1999 CD-Rom. Short-term debt Short-term debt and current portion of long term debt (represents that portion of debt payable within one year including current portion of long term debt and sinking fund requirements of preferred stock or debentures). Source: WorldScope May 1999 CD-Rom. Total debt Short-term debt plus long-term debt. Source: WorldScope May 1999 CD-Rom. Sales Net sales. Source: WorldScope May 1999 CD-Rom. Cash flow Cash flow. Source: WorldScope May 1999 CD-Rom. CAPEX Capital expenditures (represents the funds used to acquire fixed assets other than those associated with acquisitions. It includes additions to property, plant and equipment, and investments in machinery and equipment). Source: WorldScope May 1999 CD-Rom. R&D Research and development (represents all direct and indirect costs related to the creation and development of new processes, techniques, applications and products with commercial possibilities). Source: WorldScope May 1999 CD-Rom. Growth in Value Added Real annual growth in value added by ISIC sector over the period 1980-89. Source: UN. Taken from Rajan and Zingales (1998). Fraction of value added Fraction of ISIC sector in value added of total manufacturing sector in 1980. Source: UN. Taken from Rajan and Zingales (1998). External financial dependence (US) External financial dependence of US firms by ISIC sector over the period 1980-89. Source: Compustat. Taken from Rajan and Zingales (1998). Intangible-to-fixed assets (US) Median ratio of intangible assets to fixed assets of US firms by ISIC sector over the period 1995-99. Source: WorldScope May 1999 CD-Rom. 21 Table 2: Summary Statistics This table reports summary statistics of the variables used in our study. For each variable, we report the mean (or median) across all sampled countries, across developing countries and across developed countries. For comparison purposes, we also present t- or z-statistics of tests of differences in the means (or medians) of the variables across developing and across developed countries. Panel A reports summary statistics of country variables and panel B presents summary statistics of firm-based country variables. The firm-based variables are calculated in two stages. First, we calculate for each firm the median value of the firm-specific variable during the period 1995-1999. Second, the median is taken over these median-firm values across all firms in the country. See Annex 1 for the median values of the country-specific variables for each country and the selection of countries. The sources of the data are reported in Table 1. Means across countries: t-or z-Tests of difference in means or medians Developed countries Developing countries All countries Developed versus Developing countries (t- or z-statistics) Panel A: Country variables Law 9.19 4.74 7.01 10.45 Property 1.26 2.39 1.88 -6.45 Private Credit-to-GDP in 1980 0.41 0.38 0.39 0.37 Patents (per mln pop) 1076.44 112.73 629.84 6.12 Panel B: Firm-based variables Assets (in US$) 377,597 304,508 337,469 0.36 Total debt/Total assets 0.23 0.25 0.24 -0.82 Cash flow/sales (%) 8.84 11.56 10.33 -1.83 Net fixed assets/Total assets 0.33 0.44 0.39 -5.00 Intangible assets/Total assets 0.02 0.01 0.01 2.32 Intangible assets/Net fixed assets 0.06 0.01 0.04 2.68 CAPEX/Sales (%) 5.40 7.94 6.80 -3.42 R&D/Sales (%) 2.04 0.76 1.42 3.05 Long-term debt/Total assets 0.12 0.09 0.10 2.10 Short-term debt/Total assets 0.08 0.12 0.10 -2.76 Long-term debt/Net fixed assets 0.36 0.19 0.27 4.04 Number of countries 23 28 51 22 Table 3 Growth, financial dependence, property rights and intangible assets The table presents the OLS regression results from Rajan and Zingales (1998), which are extended to include the ratio of Intangible assets to total assets times a measure for property rights in the country. Table 1 describes all variables in detail. As measure for property rights we use inverse of the property rights index from the 1997 Index of Economic Freedom from the Heritage Foundation. This index provides a rating of property rights in each country (on a scale from 1 to 5). The more protection private property receives, the lower the score. The score is based, broadly, on the degree of legal protection of private property, the probability that the government will expropriate private property, and the country’s legal protection to private property. We use the rating during 1997. Since we take the inverse, our index ranges from 0.2 to 1, with a higher score indicating more protection of private property. The dependent variable in all regressions is the real growth in value added of a particular sector in a particular country. We use the dataset of Rajan and Zingales (1998) plus WorldScope for the industry median of the intangible to fixed assets ratio in the US during 1995-99 and the property rights index as of 1997. External financial dependence is the fraction of capital expenditures not financed with internal funds for US firms in the same industry between 1980-1990. We refer to Rajan and Zingales (1998) for a detailed description of the variables. All regressions include industry dummies and country dummies but these are not reported. Robust standard errors are shown below the coefficients. From the set of countries that Rajan and Zingales (1998) use we drop Nigeria, because we do not have private credit-to-GDP figures for 1980; Bangladesh, Costa Rica, Jamaica and Morocco because we do not have rule of law figures; and Kenya because we do not have WorldScope data. The remaining countries are a subset of the our WorldScope countries. Dataset includes 38 countries: Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, India, Indonesia, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, Singapore, South Africa, Spain, Sri Lanka, Sweden, Turkey, United Kingdom, Venezuela, and Zimbabwe. United States is dropped as it is the benchmark. (1) (2) (3) (4) (5) Constant .0428a .0438a .0448a .0357b .0373b (.0156) (.0163) (.0163) (.0162) (.0170) Fraction of sector in value added of total manufacturing in 1980 -.5613a -.5450a -.5473a -.6044a -.5801a (.1533) (.1484) (.1670) (.1737) (.1683) Private credit-to-GDP * External Financial Dependence (US) .0780a .0777a (.0255) (.0260) Law and order * External financial dependence (US) .0046b .0043c (.0022) (.0022) Property * Intangible-to-fixed assets (US) .0499b .0518b .0510b (.0231) (.0231) (.0232) R2 .3442 .3419 .3109 .3412 .3385 N 1129 1129 1093 1063 1063 Number of countries 38 38 38 38 38 Note: a Significant at 1%; b Significant at 5%; c Significant at 10%. 23 Table 4 Regressions: Property rights and investment in intangible assets The table presents the results of OLS regressions using the following 5 dependent variables: (1) Intangible assets as a fraction of fixed assets; (2) Intangible assets as a fraction of assets; (3) Fixed assets as a fraction of assets; (4) Capital expenditure as a fraction of sales; (5) Research and development as a fraction of sales. Table 1 describes all variables in detail. Median across years 1995-99 for each firm, and then median across sector for each country. Only manufacturing firms (SIC four-digit codes 2000-3999). We use the property rights rating during 1997, and do not take the inverse. All regressions include industry dummies on a two-digit industry level (20 dummies, 19 included). The industry dummies are not reported. Robust standard errors are shown below the coefficients. Dataset includes 39 countries: Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, India, Indonesia, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, Singapore, South Africa, Spain, Sri Lanka, Sweden, Turkey, United Kingdom, Venezuela, Zimbabwe, and United States. Dependent Variable Cash flow/ Sales Property Constant R2 [N] 1 Intangible assets/Fixed assets -.0507a .1717a .1029 (.0151) (.0358) [523] 2 Fixed assets/Assets .0170b .3602a .3716 (.0072) (.0205) [535] 3 Intangible assets/Assets -.0118a .0498a .1216 (.0027) (.0094) [523] 4 CAPEX/Sales .0260 .8618b .0526a .1050 (.0328) (.4265) (.0118) [527] 5 R&D/Sales -.0427a -.7151a .0208a .3744 (.0134) (.1459) (.0034) [340] Note: a Significant at 1%; b Significant at 5%; c Significant at 10%. 24 Table 5 Regressions: Rule of law, collateral and debt financing The table presents the results of OLS regressions using the following 4 dependent variables: (1) Long-term debt as a fraction of fixed assets; (2) Long-term debt as a fraction of total assets; (3) Short-term debt as a fraction of assets; (4) Debt as a fraction of assets. Median across years 1995-99 for each firm, and then median across sector for each country. Only manufacturing firms (SIC four-digit codes 2000-3999). All regressions include industry dummies on a two-digit industry level (20 dummies, 19 included). The industry dummies are not reported. Table 1 describes all variables in detail. Robust standard errors are shown below the coefficients. Dataset includes 39 countries: Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, India, Indonesia, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, Singapore, South Africa, Spain, Sri Lanka, Sweden, Turkey, United Kingdom, Venezuela, Zimbabwe, and United States. Dependent Variable Cash flow/ Sales Law and Order Fixed assets/Assets * Law and Order Fixed assets/ Assets Constant R2 [N] 1 Long-term debt/Assets -.0008 .0069a .0748a .1013 (.0007) (.0018) (.0182) [534] 2 Short-term debt/Assets -.0040b -.0124a .2310a .3781 (.0017) (.0018) (.0221) [534] 3 Debt/Assets -.0040b -.0071a .3359a .2048 (.0017) (.0026) (.0300) [534] 4 Long-term debt/Fixed assets -.0018 .0698c -.1799 .0341 (.0022) (.0388) (.2728) [543] 5 Long-term debt/Assets -.0008 .0235a .0579a .1489 (.0007) (.0039) (.0166) [534] 6 Long-term debt/Assets -.0008 .1883a .0494b .1249 (.0008) (.0425) (.0210) [534] 7 Long-term debt/Assets -.0008 .0085a .2151a -.0213 .1674 (.0007) (.0016) (.0413) (.0225) [534] Note: a Significant at 1%; b Significant at 5%; c Significant at 10%. 25 Table 6 Robustness of results presented in Table 4 The table presents the results of OLS regressions using the following 5 dependent variables: (1) Intangible assets as a fraction of fixed assets; (2) Intangible assets as a fraction of assets; (3) Fixed assets as a fraction of assets; (4) Capital expenditure as a fraction of sales; (5) Research and development as a fraction of sales. Median across years 1995-99 for each firm, and then median across sector for each country. Only manufacturing firms (SIC four-digit codes 2000-3999). All regressions include industry dummies on a two-digit industry level (20 dummies, 19 included). The industry dummies are not reported. Table I describes all variables in detail. Robust standard errors are shown below the coefficients. Sample of 51 countries including Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, China, Colombia, Czech Republic, Denmark, Egypt, Finland, France, Germany, Greece, Hong Kong, Hungary, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Poland, Portugal, Russian Federation, Singapore, Slovakia, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Kingdom, Venezuela, Zimbabwe, and United States. Dependent Variable Cash flow to Sales Property Constant R2 [N] 1 Intangible assets/Fixed assets -.0394a .1416a .0930 (.0116) (.0280) [663] 2 Fixed assets/Assets .0149b .3781a .3477 (.0060) (.0176) [680] 3 Intangible assets/Assets -.0084a .0406a .1194 (.0020) (.0075) [663] 4 CAPEX/Sales .0567 1.0594a .0459a .1184 (.0410) (.3774) (.0103) [663] 5 R&D/Sales -.0314b -.6470a .0182a .3348 (.0131) (.1278) (.0030) [405] Note: a Significant at 1%; b Significant at 5%; c Significant at 10%. 26 Table 7 Robustness of results presented in Table 5 The table presents the results of OLS regressions using the following 4 dependent variables: (1) Long-term debt as a fraction of fixed assets; (2) Long-term debt as a fraction of total assets; (3) Short-term debt as a fraction of assets; (4) Debt as a fraction of assets. Median across years 1995-99 for each firm, and then median across sector for each country. Only manufacturing firms (SIC four-digit codes 2000-3999). All regressions include industry dummies on a two-digit industry level (20 dummies, 19 included). The industry dummies are not reported. Table I describes all variables in detail. Robust standard errors are shown below the coefficients. Sample of 45 countries including Argentina, Australia, Austria, Belgium, Brazil, Canada, Chile, Colombia, Denmark, Egypt, Finland, France, Germany, Greece, Hong Kong, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Korea, Malaysia, Mexico, Netherlands, New Zealand, Norway, Pakistan, Peru, Philippines, Portugal, Singapore, South Africa, Spain, Sri Lanka, Sweden, Switzerland, Taiwan, Thailand, Turkey, United Kingdom, Venezuela, Zimbabwe, and United States. Dependent Variable Cash flow/ Sales Law and Order Fixed assets/Assets * Law and Order Fixed assets/ Assets Constant R2 [N] 1 Long-term debt/Assets -.0008 .0069a .0744a .1018 (.0007) (.0017) (.0175) [618] 2 Short-term debt/Assets -.0039b -.0134a .2432a .3347 (.0017) (.0018) (.0212) [618] 3 Debt/Assets -.0038b -.0083a .3485a .1849 (.0017) (.0025) (.0286) [618] 4 Long-term debt/Fixed assets -.0020 .0678b -.1677 .0308 (.0021) (.0356) (.2509) [627] 5 Long-term debt/Assets -.0008 .0212a .0630a .1374 (.0007) (.0037) (.0160) [618] 6 -.0010 .1520a .0548a .1160 (.0007) (.0359) (.0189) [677] 7 -.0008 .0085a .1890a -.0120 .1551 (.0007) (.0016) (.0376) (.0216) [618] Note: a Significant at 1%; b Significant at 5%; c Significant at 10%. 27 Annex 1: Country-specific data This table reports several variables for the countries studied. Countries are sorted in ascending alphabetical order. The sample of countries includes all 51 countries represented in WorldScope. We have data on the protection of property rights for all 51 countries, but only for 45 countries we have data on the rule of law. Of these 45 countries, 39 countries are part of the Rajan & Zingales (1998) dataset. Panel A reports country data that is invariant across firms in the country. Panel B reports aggregate firm-level data for each country, calculated as the median value across all firms in the country of the median values of the firm-specific variables during the period 1995-1999. As source for the data in panel B we use WorldScope. Panel A: Country data Developing Population 1997 (in mln) Law Property Patents (per mln pop) Private Credit-to-GDP in 1980 Argentina 1 35.7 5.35 2.00 34.40 - Australia 0 18.5 10.00 1.00 511.57 0.28 Austria 0 8.1 10.00 1.00 1978.40 0.77 Belgium 0 10.2 10.00 1.00 1732.65 0.29 Brazil 1 163.7 6.32 3.00 - 0.23 Canada 0 30.3 10.00 1.00 240.36 0.45 Chile 1 14.6 7.02 1.00 - 0.36 China 1 1227.2 - 4.00 2.85 - Colombia 1 40 2.08 3.00 - 0.14 Czech Republic 1 10.3 - 2.00 143.40 - Denmark 0 5.3 10.00 1.00 2283.58 0.42 Egypt 1 60.3 4.17 3.00 - 0.21 Finland 0 5.1 10.00 1.00 453.92 0.48 France 0 58.6 8.98 2.00 860.89 0.54 Germany 0 82.1 9.23 1.00 670.56 0.78 Greece 0 10.5 6.18 2.00 814.76 0.44 Hong Kong 0 6.5 8.22 1.00 228.77 - Hungary 1 10.2 - 2.00 116.57 - India 1 962.4 4.17 3.00 - 0.24 Indonesia 1 200.4 3.98 3.00 - 0.20 Ireland 0 3.7 7.80 1.00 1861.89 - Israel 1 5.8 4.82 2.00 371.03 0.67 Italy 0 57.5 8.33 2.00 488.63 0.42 Japan 0 126.1 8.98 1.00 1171.18 0.86 Jordan 1 4.4 4.35 2.00 13.18 0.54 Korea (South) 1 46 5.35 1.00 534.33 0.50 Malaysia 1 21.7 6.78 2.00 36.22 0.48 Mexico 1 94.3 5.35 3.00 41.82 0.16 Netherlands 0 15.6 10.00 1.00 1525.26 0.60 New Zealand 0 3.8 10.00 1.00 1006.05 0.19 Norway 0 4.4 10.00 1.00 668.64 0.34 Pakistan 1 128.5 3.03 2.00 - 0.25 Peru 1 24.4 2.50 3.00 7.38 0.11 Philippines 1 73.5 2.73 2.00 12.46 0.28 Poland 1 38.7 - 2.00 60.21 - Portugal 0 9.9 8.68 2.00 730.20 0.52 Russian Federation 1 147.3 - 3.00 201.58 - Singapore 0 3.1 8.57 1.00 - 0.57 Slovakia 1 5.4 - 3.00 104.07 - South Africa 1 40.6 4.42 3.00 - 0.26 28 Developing Population 1997 (in mln) Law Property Patents (per mln pop) Private Credit-to-GDP in 1980 Spain 0 39.3 7.80 2.00 524.50 0.76 Sri Lanka 1 18.6 1.90 3.00 8.60 0.21 Sweden 0 8.8 10.00 2.00 2205.91 0.42 Switzerland 0 7.1 10.00 1.00 2546.90 - Taiwan 1 - 8.52 1.00 - - Thailand 1 60.6 6.25 1.00 12.03 - Turkey 1 63.7 5.18 2.00 7.19 0.14 United Kingdom 0 59 8.57 1.00 758.54 0.25 United States 0 267.6 10.00 1.00 418.48 - Venezuela 1 22.8 6.37 3.00 431.75 0.30 Zimbabwe 1 11.5 3.68 3.00 2.87 0.30 Panel B: WorldScope data Country Number of firms Number of observations Assets (in US$) Cash flow/ Sales (%) R&D/ Sales (%) CAPEX/ Sales (%) Net fixed assets/ Total assets Intangible assets/ Total assets Intangible assets/ Net fixed assets Total debt/ Total assets Long-term debt/ Total assets Long-term debt/ Total assets Long-term debt/ Net fixed assets Argentina 23 98 396,201 10.65 - 6.33 0.49 0.01 0.01 0.20 0.07 0.09 0.16 Australia 69 308 316,959 9.36 0.57 5.05 0.36 0.04 0.10 0.24 0.21 0.03 0.47 Austria 63 237 157,535 8.81 2.84 5.46 0.35 0.01 0.04 0.28 0.12 0.13 0.32 Belgium 62 230 146,743 10.57 2.92 6.44 0.25 0.03 0.13 0.24 0.09 0.12 0.36 Brazil 94 369 596,747 10.27 1.45 7.87 0.50 0.00 0.00 0.29 0.13 0.14 0.26 Canada 183 745 234,349 8.33 0.79 6.04 0.34 0.02 0.07 0.24 0.16 0.03 0.42 Chile 35 136 336,828 19.85 0.10 15.79 0.52 0.01 0.02 0.20 0.07 0.05 0.13 China 79 290 178,798 11.86 0.17 9.62 0.33 0.01 0.04 0.25 0.03 0.19 0.13 Colombia 15 55 472,517 10.20 - 4.01 0.49 0.00 0.00 0.10 0.04 0.05 0.21 Czech Rep 35 98 122,018 7.98 0.99 11.18 0.52 0.01 0.01 0.31 0.13 0.17 0.20 Denmark 93 400 74,849 8.68 4.13 5.40 0.35 0.00 0.00 0.22 0.13 0.07 0.36 Egypt 7 10 200,352 28.88 - 12.64 0.33 0.00 0.00 0.26 0.03 0.18 0.23 Finland 81 337 216,690 9.89 1.52 6.42 0.33 0.03 0.09 0.29 0.20 0.07 0.56 France 390 1,568 90,692 7.60 3.20 4.17 0.19 0.04 0.23 0.21 0.09 0.09 0.51 Germany 426 1,685 145,511 6.96 3.23 4.72 0.26 0.01 0.05 0.19 0.09 0.07 0.33 Greece 86 299 57,766 8.89 0.54 5.61 0.33 0.00 0.00 0.23 0.01 0.16 0.06 Hong Kong 138 585 165,196 7.21 0.49 5.92 0.38 0.00 0.00 0.23 0.06 0.14 0.16 Hungary 17 53 76,474 15.96 4.05 13.11 0.42 0.01 0.02 0.08 0.01 0.05 0.03 India 269 1229 99,063 8.67 0.26 7.32 0.42 0.00 0.00 0.37 0.20 0.14 0.50 Indonesia 82 313 135,727 14.63 0.06 9.73 0.38 0.00 0.00 0.45 0.08 0.25 0.24 Ireland 23 99 370,779 10.20 0.23 4.96 0.39 0.00 0.00 0.30 0.18 0.05 0.60 Israel 30 83 332,020 9.09 4.33 4.71 0.29 0.01 0.05 0.19 0.08 0.08 0.35 Italy 120 452 343,792 9.17 2.47 5.04 0.25 0.02 0.08 0.23 0.08 0.11 0.36 Japan 1,300 6,335 417,846 5.57 1.51 4.44 0.30 0.00 0.00 0.26 0.11 0.14 0.35 Jordan 2 4 390,344 32.13 0.27 16.99 0.61 0.00 0.00 0.24 0.18 0.06 0.29 Korea (South) 183 689 506,444 6.76 0.28 8.02 0.39 0.00 0.00 0.48 0.20 0.25 0.51 Malaysia 179 807 123,037 10.44 0.10 7.73 0.43 0.00 0.00 0.27 0.04 0.14 0.09 Mexico 48 188 661,168 13.26 0.45 7.26 0.63 0.02 0.03 0.31 0.17 0.09 0.28 Netherlands 101 441 273,268 8.96 4.08 4.55 0.34 0.00 0.00 0.23 0.12 0.07 0.32 New Zealand 16 73 140,723 8.18 0.38 5.13 0.36 0.00 0.01 0.26 0.21 0.02 0.53 30 Country Number of firms Number of observations Assets (in US$) Cash flow/ Sales (%) R&D/ Sales (%) CAPEX/ Sales (%) Net fixed assets/ Total assets Intangible assets/ Total assets Intangible assets/ Net fixed assets Total debt/ Total assets Long-term debt/ Total assets Long-term debt/ Total assets Long-term debt/ Net fixed assets Norway 72 286 85,554 7.38 5.15 7.15 0.29 0.03 0.09 0.20 0.14 0.02 0.56 Pakistan 79 324 32,379 5.23 0.10 3.35 0.45 0.00 0.00 0.43 0.11 0.22 0.24 Peru 16 47 52,257 13.36 - 5.48 0.44 0.00 0.00 0.27 0.08 0.13 0.16 Philippines 36 147 144,882 13.22 0.00 13.07 0.43 0.04 0.08 0.26 0.09 0.11 0.14 Poland 38 117 56,946 8.72 - 6.77 0.48 0.01 0.01 0.13 0.01 0.07 0.03 Portugal 40 137 98,977 7.49 - 4.96 0.40 0.01 0.02 0.27 0.11 0.12 0.26 Russian Fed. 4 7 2,487,186 -9.03 1.79 8.48 0.68 0.00 0.00 0.16 0.02 0.10 0.04 Singapore 86 355 119,063 9.55 0.50 7.69 0.40 0.00 0.00 0.18 0.05 0.10 0.10 Slovakia 14 29 111,192 3.60 - 6.17 0.46 0.00 0.01 0.27 0.16 0.12 0.35 South Africa 129 368 80,497 8.48 0.19 4.18 0.28 0.00 0.00 0.11 0.04 0.03 0.18 Spain 68 262 290,463 12.01 0.40 4.70 0.38 0.01 0.02 0.15 0.03 0.08 0.08 Sri Lanka 8 36 42,144 10.48 0.00 4.37 0.37 0.00 0.01 0.29 0.03 0.18 0.06 Sweden 117 478 147,589 8.28 2.08 5.99 0.30 0.03 0.12 0.21 0.14 0.03 0.41 Switzerland 111 458 273,202 9.92 3.71 4.69 0.37 0.01 0.02 0.25 0.16 0.08 0.46 Taiwan 165 639 325,758 9.38 1.32 8.09 0.35 0.00 0.01 0.25 0.09 0.12 0.23 Thailand 123 515 75,971 9.02 0.00 5.59 0.45 0.00 0.01 0.48 0.10 0.30 0.23 Turkey 54 166 111,852 21.24 0.16 8.69 0.30 0.00 0.00 0.13 0.10 0.03 0.02 UK 674 2,853 72,998 7.96 1.39 4.23 0.33 0.00 0.00 0.18 0.08 0.05 0.22 US 229 1,135 4,444,191 12.33 2.84 5.54 0.29 0.11 0.38 0.24 0.17 0.04 0.50 Venezuela 10 43 339,556 14.52 - 5.32 0.62 0.02 0.04 0.19 0.10 0.09 0.15 Zimbabwe 1 5 37,859 4.94 0.00 0.60 0.32 0.00 0.00 0.05 0.00 0.04 0.01 Total number 6,323 26,623 Annex 2 Table 1 Correlation matrix of explanatory variables in Table 3 Private Credit-to-GDP * External Dependency Law and order* External Dependency Property* Intangible-to-fixed assets Private Credit-to-GDP * External Dependency 1.0000 Law and order* External Dependency 0.8733 1.0000 Property* Intangible-to-fixed assets 0.0852 0.1233 1.0000 Table 2 Summary statistics of explanatory variables in Table 3 Variable Mean Standard deviation Observations Private Credit-to-GDP * External Dependency 0.1224 0.1850 1,322 Law and order* External Dependency 2.2012 3.1003 1,322 Property* Intangible-to-fixed assets 0.2639 0.2268 1,283 32 Literature Barro, Robert J. 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