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Institutions and the external capital structure of countriesq

Andre Faria a, Paolo Mauro b,*

a Barclays Global Investors, London, United Kingdomb International Monetary Fund, United States

JEL classification:

F21

F34

F36

Keywords:

Foreign direct investment

Portfolio equity

External debt

External liabilities

a b s t r a c t

A widespread view holds that countries that finance themselves

through foreign direct investment and portfolio equity, rather than

bonds and loans, are less prone to crises. But what determines

countries external capital structures? In a cross-section of

advanced economies, emerging markets, and developing coun-

tries, we find that equity-like liabilities as a share of countries total

external liabilities are positively and significantly associated with

indicators of educational attainment, openness, natural resourceabundance and, especially, institutional quality. These relation-

ships are robust to attempts to control for possible endogeneity,

suggesting that better institutional quality may help improve

countries external capital structures.

2008 Andre Faria. Published by Elsevier Ltd. All rights reserved.

1. Introduction

A widespread view holds that the external capital structure of countries (that is, the relative shares

of items such as foreign direct investment, portfolio equity, and external debt in a countrys external

finance) is an important determinant of economic performance and propensity to crises. Indeed, this

view has been reinforced by a number of recent emerging market crises, and some authors have argued

that it would be desirable for emerging market countries to reduce their reliance on debt and increase

the role of equity in their external capital structures (Rogoff,1999). Equity finance makes it possible for

domestic producers to share risk with foreign investors, thereby helping stabilize domestic

consumption and improving domestic producers ability to undertake projects with high risk and high

q The paper was written while both authors were at the IMF.

* Corresponding author. Barclays Global Investors, United States.E-mail addresses: [email protected], [email protected] (A. Faria), [email protected] (P. Mauro).

Contents lists available at ScienceDirect

Journal of International Money

and Financej o u r n a l h o m e p a g e : w w w . e l s e v i e r . c o m / l o c a t e / j i m f

0261-5606/$ see front matter 2008 Andre Faria. Published by Elsevier Ltd. All rights reserved.

doi:10.1016/j.jimonfin.2008.08.014

Journal of International Money and Finance 28 (2009) 367391
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finance literature, which has extensively analyzed capital structures at the firm level.10 In this section,

we provide a brief review of full-fledged theories and less formal hypotheses regarding external capital

structures.

A first theory, by Albuquerque (2003), focuses on the problems of expropriation and imperfect

enforcement of international financial contracts.11 The theory assumes that FDI is less subject to

expropriation than are other liabilities, though the validity of this assumption may depend on thespecific economic sector in which FDI is undertaken. On the whole, Albuquerque (2003) suggests that

much of FDI is of an intangible nature (technology, brand names) and thus difficult to expropriate.

Under this view, the optimal contract between international investors and financially constrained

countries, which are unable to pre-commit not to expropriate, will usually take the form of FDI.

Therefore this theory predicts that countries with tighter financial constraints will finance themselves

primarily through FDI. The theory may also be interpreted to predict thatdfor given financial con-

straintsdworse institutions (greater ease of expropriation of FDI) will lead to a lower share of FDI in

total external liabilities.12

A second theory, by Razin et al. (1998) focuses on the role of informational asymmetries, and

foresees a pecking-orderin countries external capital structures, as in the corporate finance literature.

Firms would finance themselves first through FDI (a parallel to retained earnings and, therefore,internal equity), then debt, and then portfolio equity (external equity). In fact, to circumvent infor-

mational barriers, foreign multinationals would favor placing their own managers in the recipient

country and thus investing abroad through FDI. To the extent that weak institutional quality (such as

a poorly regulated stock market) may be taken to proxy for more severe informational asymmetries, it

would be associated with a larger share of FDI, and a lower share of portfolio equity, in total external

liabilities.13

As mentioned in Section 1, early empirical tests of the relationship between indicators of institu-

tional quality and variables related to countries capital structures have reached a variety of results. In

a cross-section of countries (including advanced economies), Hausmann and Fernandez-Arias (2000)

document no relationship or a negative relationship between the ratio of FDI inflows to total private

capital inflows and institutional quality. In contrast, Wei (2000a,b, 2001) and Wei and Wu (2002) findthat weak institutions tilt capital inflows toward bank loans and away from FDI, consistent with their

hypothesis that foreign direct investors are less likely to be bailed out than are foreign banks in the

event of a crisis.

Other studies have identified a number of additional factors that may affect countries capital

structures, with special attention to FDI.14 Such factors include human capital, natural resources,

economic size, and openness. Human capital may act as a stronger pull factor for FDI (Borensztein

et al., 1998) than other forms of capital such as portfolio equity or debt. Natural resources may also

attract FDI to a greater extent than they do other types of capital, as suggested by Hausmann and

10 While corporate finance reasoning cannot be trivially applied to the international setting, the literature on sovereign debt

(Eaton and Gersovitz, 1981, 1984; Cole and English, 1991, 1992; Cole and Kehoe, 1995; Bulow and Rogoff, 1989 ) may be inter-

preted as being in a broadly similar vein. Hart and Moores (1994) analysis of default and renegotiation when one of the sides to

a financial contract cannot commit to the contract has particular resonance in international finance. Attempts to extend

corporate finance reasoning to the international finance setting are reviewed in Borensztein et al. (2004). A broader survey of

theories of capital structures in the domestic corporate context is Myers (2001). Rajan and Zingales (1995) and Booth et al.

(2001) have analyzed the effects of government policies, laws, and regulations on the domestic capital structures of the G-7

countries, and developing countries, respectively. Using firm level evidence for a cross-section of 39 developed and developing

countries, Fan et al. (2006) find that firms operating in more corrupt countries tend to have capital structures with less equity.11 Albuquerques (2003) main interest is in why FDI flows are less volatile than other capital flows, and he focuses on financial

constraintsdempirically proxied by credit risk ratings. For an alternative theoretical analysis on related issues, see Schnitzer

(2002).12 This is our interpretation of Albuquerques (2003) model, though it is not emphasized by the author. It is based upon the

authors simulations in Table 2, p. 370, and interpreting the parameter q as the ease with which FDI may be expropriated. (Other

types of capital can always be fully expropriated in the model.)13 There is a conceptual difference between informational asymmetries and institutional weaknesses, and this interpretation

may not have been intended by the authors.14 Lim (2001) reviews the literature on the determinants of FDI.

A. Faria, P. Mauro / Journal of International Money and Finance 28 (2009) 367391370


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Fernandez-Arias (2000) and Lane and Milesi-Ferretti (2001b). Indeed, in many cases natural resources

might lie unexploited or even undiscovered without the crucial expertise provided by multinationals

(Markusen, 1997). However, the tangible nature of FDI aimed at extracting natural resources might

make it especially vulnerable to expropriation once it is in place. Larger economic size (proxied by

measures such as total GDP) also attracts FDI, which provides an opportunity to better serve the local

market (possibly circumventing trade barriers). Finally, openness may reduce the need for tariff-hopping FDI, though the ease with which products can be exported increases a countrys appeal as

a destination for FDI.

With a variety of existing theoretical hypotheses, the relationship between countries external

capital structures and variables such as institutions is ultimately an empirical question. Early empirical

tests have not reached definitive conclusions, largely owing to data constraints. In the next section, we

provide new empirical evidence on this question, drawing on data sets which have become available

recently and which provide far greater country coverage and better cross-country consistency than was

the case in the past.

3. Empirical analysis

This section briefly describes the data, presents the empirical strategy, and reports the results.

Appendix 1 describes the data sources and variable definitions in greater detail.

3.1. Data sources and variables used

The data on our dependent variabledexternal liabilities and their subcomponentsdwere assem-

bled by Lane and Milesi-Ferretti (2006), updating and extending their initial exercise (Lane and Milesi-

Ferretti, 2001a,b) from 67 to 145 countries. In the Lane and Milesi-Ferretti (2006) classification,

external liabilities comprise FDI, portfolio equity, debt (consisting of portfolio debt, loans, currency, and

deposits), and financial derivatives. Lane and Milesi-Ferrettis new database improves on alternative

sources, notably the International Investment Position (IIP) reported in the International Monetary

Funds International Financial Statistics, in terms of both country coverage and appropriate correction

for valuation effects.15 In the robustness section, we show that our main results are similar using the IIP

data set. More generally, evidence of robustness to changes in data source is provided by the obser-

vation that the working paper version (Faria and Mauro, 2004), where we obtained very similar results,

was entirely based upon a previous vintage of the IIP.

Potential explanatory variables include the size of the economy (total GDP in U.S. trillions of dollars

at constant 2000 prices); the level of economic development (GDP per capita in U.S. thousands of

dollars at constant 2000 prices); openness (sum of imports and exports over GDP); the relative

importance of natural resources (share of exports of fuels, metals, and ores as a ratio of GDP); human

capital (percentage of population over 25 that has attended some secondary schooling); financial

development (private credit to GDP)16; a dummy variable for transition economies; and an index of

institutional quality. This last variable is the simple average of six institutional indicators drawn from

15 The maximum number of countries covered by IIP for the period 19962004 is 106 for some categories of external

liabilities. (A thorough description of the IIP data is provided in IMF, 2002a.) While the IIP reports FDI at market value for some

countries and at book value for others, Lane and Milesi-Ferrettis database provides portfolio equity at market value and FDI at

book value consistently for all countries. Unfortunately, neither data set distinguishes between public and private liabilities, or

between bank loans and non-bank finance. While a public/private decomposition would be interesting, in practice it may not be

too informative, because many loans originally extended to private entities are assumed by the sovereign borrower when

repayment difficulties emerge.16 Our baseline measure of financial development, private credit to GDP, is recommended by Levine et al. (2000) as the

preferred indicator of financial development, because it proxies for higher levels of financial services and therefore greater

financial intermediary development, even though it does not directly measure the amelioration of information and transaction

costs. Another advantage is the large country coverage in the data. In the robustness section, we look at alternative measures of

financial development.

A. Faria, P. Mauro / Journal of International Money and Finance 28 (2009) 367391 371


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Kaufmann et al. (2006)17: voice and accountability, political stability and absence of violence,

government effectiveness, regulatory quality, rule of law, and control of corruption.18 In the full country

sample of Kaufmann et al. (2006), each index ranges between 2 and 2 for the vast majority of

countries, with a mean of 0 and a standard deviation of 1.19

We focus our analysis on two groups of countries: the whole sample, including countries at all

levels of economic development, and a sample of developing and emerging market countries only.The reason for looking at both samples separately is twofold. First, while advanced economies have

substantial gross assets and liabilities, emerging markets and developing countries have tradi-

tionally had limited external gross assets.20 Second, while we do control for GDP per capita, we are

not only interested in the heterogeneity between advanced countries, on the one hand, and

developing and emerging market countries, on the other, but also in the heterogeneity within

developing and emerging market countries; indeed, as mentioned in motivating our study,

developing countries and emerging markets may deserve special attention because they are more

crisis-prone than are advanced economies.

Our baseline sample consists of the 94 countriesd22 advanced and 72 emerging/developing,

listed in Appendix 1dfor which all of our key explanatory variables are available (at least for 1

year of the years between 1996 and 2004 for each country).21 We regress the time-series mean ofthe dependent variable for the available years on the time-series means of the explanatory vari-

ables. For the typical country in the sample (that is, the cross-country average of time-series means

in the sample of 94 countries used in the baseline regressions), FDI is 27 percent of total liabilities,

portfolio equity 6 percent, and debt 67 percent. Throughout the paper we report the results for

total equity (defined as the sum of FDI and portfolio equity). 22 Table 1 reports the descriptive

statistics for the variables used in this study.

17 Instead of simple averaging of the six subcomponents, one could consider extracting a common component (for example,

the first principal component obtained by applying principal components analysis to the six series). This yields essentially the

same resultsdnot only in this paper but also in the broader literature on institutional quality.18 In our view, the indices compiled by Kaufmann et al. (2006), now known as World Bank Governance Indicators (WGI), are

the state of the art among indicators of institutional quality, in the sense that they are a summary measure of the largest set

available of such indicators. These indices are based on several hundred individual variables measuring perceptions of

governance, drawn from 31 separate data sources constructed by 25 different organizations, ranging from think-tanks to

governments, multilateral organizations and commercial firms (for example, Freedom House, Heritage Foundation, World

Economic Forum, U.S. State Department, European Bank for Reconstruction and Development, Economist Intelligence Unit, and

Political Risk Services). They report values every other year beginning in 1996, and annually beginning in 2002.19 The range for the institutional quality index is narrower because we exclude countries without adequate data coverage for

other variables, and because of the averaging of the six governance indicators.20 Blonigen and Wang (2005) argue against pooling advanced economies together with emerging markets and developing

countries in empirical studies of FDI, on the grounds that advanced economies experience large two-way FDI flows, whereas

emerging markets and developing countries have traditionally been almost exclusively recipients of FDI.21 Our baseline sample size is smaller than that of Lane and Milesi-Ferretti (2006) primarily owing to constraints on the

availability of data on educational attainment. We eliminate offshore financial centers from the sample (13 countries in total, of

which six are non-high income countries). However, the main result of the paperdthe positive and significant correlation

between institutional quality and the share of equity-like components in total liabilitiesdstill holds at the 1 percent signifi-

cance level when offshore financial centers are included; the only change vis-a -vis the baseline regressions refers to the

increase in the coefficient and statistical importance of economic size in explaining cross-country shares of total equity in total

liabilities for the sample of non-high income countries. Results not reported for the sake of brevity.22 Whether FDI and portfolio equity should be treated separatelydas emphasized in some of the literaturedis an open

question: FDIs conceptually distinctive feature compared with portfolio equity is the existence of a long-term relationship

between the direct investor and the enterprise, and a significant degree of influence on the management of the enterprise. At

a practical level, the balance-of-payments statistics usually define FDI on the basis of whether the direct investor has at least

10 percent of the ordinary shares or voting power (for an incorporated enterprise) or the equivalent (for an unincorporated

enterprise); but several countries have chosen to permit qualifications from that criterion when a direct investor owns less

than 10 percent of an enterprise but has an effective voice in management, or when the investor owns more than 10 percent

but does not have an effective voice in management. In this paper, we focus on the aggregate, FDI plus portfolio equity, total

equity. In the robustness section, we explore the differential impact of the independent variables on the subcomponents of

equity. In the working paper version, using a different data set and period coverage, we look into these disaggregated

components in more detail.



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The list of potential explanatory variables we consider in our baseline specifications is relatively

parsimoniousdnot an unnatural choice in light of the limited number of countries for which data are

available and the need to attain a sufficient number of degrees of freedom in the estimation. Severalof these potential explanatory variables are correlated with each other (Table 2), highlighting the

importance of using multivariate regressions. Nevertheless, as mentioned in Section 1 and explained in

detail in an extensive robustness tests section presented below, our results are confirmed when we

introduce additional explanatory variables.

3.2. Results

Considering the univariate correlations between the shares of equity in total liabilities and

factors potentially associated with liability composition, a number of significant correlations

emerge (Table 3). For the whole sample, total equity as a share of total liabilities is significantly

and positively correlated with institutional quality, financial development, openness, and humancapital; across the sample of non-high income countries, total equity is significantly correlated

with the variables mentioned above (though the correlations have a different order of magnitude),

as well as with country size and level of economic development. Not surprisingly (given that the

shares of the various components of liabilities need to sum to 1), the remaining component of total

Table 1

Descriptive statistics: averages 19962004.

Variable Minimum Maximum Mean Median Standard deviation Coefficient of variation

Whole sample

Total equity 0.03 0.84 0.33 0.30 0.16 0.50

Institutional quality index

1.56 1.84 0.11

0.16 0.87 8.17GDP (constant 2000 US$ trillions) 0.001 9.55 0.31 0.02 1.12 3.61

GDP per capita

(constant 2000 US$ thousands)

0.12 36.90 6.80 2.04 9.64 1.42

Financial development 0.04 2.04 0.48 0.30 0.43 0.90

Natural resources 0.00 0.97 0.21 0.10 0.24 1.16

Openness 0.21 2.08 0.75 0.66 0.38 0.51

Human capital 0.03 0.90 0.40 0.39 0.23 0.58

Transition 0 1 0.16 0 0.37 2.31

Non-high income countries

Total equity 0.04 0.84 0.33 0.30 0.17 0.52

Institutional quality index 1.56 1.09 0.28 0.32 0.54 n.a.

GDP (constant 2000 US$ trillions) 0.001 1.24 0.08 0.01 0.19 2.24

GDP per capita(constant 2000 US$ thousands)

0.12 10.92 2.01 1.42 2.04 1.01

Financial development 0.04 1.80 0.32 0.25 0.31 0.95

Natural resources 0.00 0.97 0.23 0.11 0.25 1.08

Openness 0.24 2.08 0.76 0.65 0.39 0.52

Human capital 0.03 0.77 0.33 0.25 0.21 0.63

Transition 0 1 0.19 0 0.40 2.05

Sources and notes: The whole sample consists of 94 observations and the non-high income countries sample consists of 72

observations; the classification of countries according to the income level follows the Global Development Network Growth

Database. International liabilities and their components are from Lane and Milesi-Ferretti (2006). Total equity consists of

portfolio equity plus FDI, and is expressed as a share of total international liabilities. The institutional quality index is the simple

average of six governance indicators from Kaufmann et al. (2006), also known as World Bank Governance Indicators (WGI): voice

and accountability; political stability and absence of violence; government effectiveness; regulatory quality; rule of law; and

control of corruption. GDP (U.S. trillions of dollars at constant 2000 prices) and GDP per capita (U.S. thousands of dollars atconstant 2000 prices) are from the World Banks World Development Indicators (WDI). Financial development is measured by

private credit divided by GDP, from the WDI. Natural resources are the percentage of ore, metals and fuels in total exports, built

using data from the WDI. Openness is the sum of exports and imports divided by GDP, built using data from the WDI. Human

capital is the share of population over 25 that attended at least some level of secondary schooling, from the World Banks

Education Indicators, EDSTATS (Education Attainment in the Adult Populationdfollowing Barro and Lee, 2001). Transition is

a dummy variable that indicates whether a country belonged to the former Soviet Union, former Yugoslavia, or ex-communist

countries, from the Global Development Network Growth Database. Variables are time-series means for the available years

during the period 19962004. Appendix 1 provides further details on sources and variable definitions.



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liabilities (unreported) bears relationships with all these variables with opposite signs to those of

total equity.

Turning to multivariate regressions, we begin by focusing on the determinants of the share of

total equity in total liabilities in the whole sample (Table 4). Our main, and most robust, finding is

that institutional quality is positively and significantly associated with total equity in essentially

all specifications and samples, and controlling for a variety of other explanatory variables. Themagnitude of the coefficient is economically significant: for example, a one-digit improvement in

the institutional quality index is associated with an 18 percentage point increase in the ratio of

total equity to total liabilities controlling for economic size and economic development in the

whole sample (column 2).23 That magnitude is also reasonably robust to changes in the set of

controls and sample considered. Other variables seem to play a role, too, with a statistically and

economically significant impact in a number of specifications. Total equity is positively correlated

with economic size (perhaps because market size tends to attract foreign investors), butdwhen

institutional quality is included as a regressordnegatively with the level of economic develop-

ment (in the whole sample but not in the sample that excludes advanced economies). The ratio of

private credit to GDP, a proxy for domestic financial development, is positively associated with the

share of total equity in total liabilities in most specification, and occasionally significant. Openness,natural resource abundance, and indicators of educational attainment are positively, significantly,

and fairly robustly associated with the share of total equity in total liabilities. Transition countries

have a significantly lower share of total equity controlling for the other baseline explanatory

variables. The overall ability of these independent variables to fit the cross-sectional variation in

the total equity share is considerable: the adjusted R2 coefficient is 0.37 in the whole sample, and

0.46 in the non-high income sample; the adjusted R2 coefficient in the univariate regressions

using institutional quality alone is 0.07 in the whole sample, and 0.31 in the non-high income

sample.

3.3. Robustness tests

In this section, we outline a number of potential concerns regarding our main estimates, explain our

approach in seeking to address them, and report the related findings, as follows. Some results are not

shown to conserve space.

3.3.1. Changes in the sample

The results are robust to other alternative samples, including the following: an enlarged sample that

uses all available data for each specification (i.e., no longer restricted to those countries for which all

explanatory variables are available)dsee Table 5; and a narrower sample consisting of the countries

that have observations for all dependent and explanatory variables in all years between 1996 and

2004.24

23 In the institutional quality scale, one digit is approximately equal to one standard deviation within the full country sample

ofKaufmann et al. (2006): taking the index at face value, this would be equivalent, for example, to improving the institutions of

Jamaica to the level of those of Chile, or improving the institutions of Peru to the level of Slovenia. Of course, these comparisons

between pairs of countries are only for illustration purposes. Our view is that the institutional quality index is useful in

identifying broad cross-country correlations, but measurement error is often too large for comparisons between pairs of

countries to be taken too seriously (see Kaufmann and Kraay, 2004).24 Human capital and the institutional quality index only have information available for some of the years (for human capital

we only have information, at best, for 1990, 1995, and 2000). Given the sluggishness of these variables, wherever data for

a given year are not available, we use the data for the most recent available year. Narrowing the sample to those years for which

data on institutional quality are available (1996, 1998, 2000, 2002-04) does not change the results. Narrowing the sample to the

set of countries that have recent data for education (1995 and 2000) does not change the results either. We describe in detail

the construction of these variables in Appendix 1.



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3.3.2. Dropping potentially influential observations

To show that our results are robust to changes in the sample of countries, we run the key regressions

routinely dropping one country at a time, and find that no individual country has excessive influence

on the results.25

3.3.3. Robust regressionsFrom the partial correlation plots, we identify a potential outlier (Fiji), and four possible influential

observations (Botswana, Chile, Tajikistan, and Trinidad and Tobago, for the whole sample; and Bot-

swana, Chile, Namibia, Tajikistan, and South Korea, for the non-high income sample). 26 Therefore we

run robust regressions that take these cases into account by weighting the observations inversely to

their residuals so that observations with smaller residuals have more weight after dropping influential

observations (it is a form of weighted least squares regression). Results for the institutional quality

index are unchanged for our preferred specifications (columns 4 and 5, for the whole sample, and

columns 9 and 10 for the non-high income countries sample). One result that appears to be somewhat

fragile is the (conditional) relationship between openness and total equity for the non-high income

countries sample (Table 6).27

3.3.4. FamaMacBeth procedure

The results hold when we adopt the Fama and MacBeth (1973) approach, widely used in finance, as

an alternative estimation procedure to explore both time-series and cross-section information. In the

first step, this involves running cross-section regressions for each year. In the second step, the time-

series averages of the cross-sectional regression point estimates are used as point estimates for the

coefficients of interest, and the standard deviations of the cross-sectional estimates are used to

generate the standard errors for the estimates. The point estimates for all estimated coefficients are

very similar to those reported in our baseline regressions, and all main results retain their high degree

of statistical significance.

3.3.5. Bounded nature of the dependent variablesTo take into account that the shares of total equity by definition cannot lie outside the 01 range, we

run the key regressions using a quasi-maximum likelihood procedure, as proposed by Papke and

Wooldridge (1996), and obtain broadly similar results (not shown) to those reported above.

3.3.6. Shares of GDP

Although we are mostly interested in the determinants of the composition of external liabilities, we

also checked whether the same explanatory variables are associated with the size of total equity (and

its subcomponents, FDI and portfolio equity) expressed as a share of GDP. The findings are similar:

institutional quality, openness, and, for the non-high income countries sample, natural resources are

25 By dropping one country at a time for specifications (4) and (5) for the whole sample, and (9) and (10) for the non-high

income countries sample (Table 4), the institutional quality index coefficient remains significant at the 1 percent level, the only

exception being when we drop South Korea from the non-high income countries sample for specification (9), which makes the

coefficient significant only at the 5 percent level and slightly smaller (0.11). By dropping Fiji, the results are strengthened;

however, we keep Fiji in the sample as its inclusion goes against the main result of the paper.26 An extreme version of this procedure would be to drop all observations we think are outliers or influential observations.

First, we adopt a conservative approach and drop only the influential observations. For the regressions in the whole sample, the

significance level for the institutional quality index remains at the 1 percent level, though the magnitude of the coefficient is

lower (1213 percentage points). For the regressions in the non-high income countries, for specification (10), the magnitude of

the coefficient falls to 9 percentage points, and is significant only at the 10 percent level (for specification 9, the coefficient is

only 4 percent and we cannot reject it is different from zero). By dropping Fiji (the outlier), institutional quality is again

significant in all specifications, even when the influential observations are dropped.27 For specifications (7) and (8), Fiji obtains zero weight and China is dropped from the regressions (technically, its Cooks D

value is larger than one, so an influential observation). The institutional quality index coefficient remains significant at the 5

percent level but its magnitude is smaller. For specification (5), the United States is dropped from the regressions because its

Cooks D value is larger than one.



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3.3.10. Controlling for international financial constraints

We now turn to changes in the list of explanatory variables. To relate our results to one of thepropositions put forward by Albuquerque (2003), we add a control for international financial

constraints. As a proxy, we use the number of years a country was in default between 1970 and 2001.32

We find that institutional quality is still positively and significantly associated with the share of equity

in total liabilities and the ratio of FDI to total liabilities (Table 9). Financing constraints are positively

and significantly associated with the share of equity in total liabilities, in particular FDI, as predicted by

Albuquerque (2003).

3.3.11. Domestic stock market development instead of domestic credit

The baseline regressions reported in the previous section have included the ratio of domestic credit

to GDP as an explanatory variable. The results are robust to using instead alternative measures of

domestic financial development, notably proxies for domestic stock market development, which mayhelp attract not only portfolio equity, but also foreign direct investment, because multinational firms

often tap domestic markets to finance local investments. Although domestic stock market capitaliza-

tion is significant in a number of specifications, institutional quality remains highly significant in most

specifications (Table 10). These results are robust to using the number of listed firms as an alternative

proxy for domestic stock market development.

3.3.12. Controlling for international equity liberalization

This is of course potentially most important for portfolio equity, and is also related to the previous

point, as both portfolio equity flows and domestic stock market capitalization have become gradually

more important in recent years. We run the regressions controlling for a dummy variable indicating

whether a country had liberalized international access to its equity markets by 1995 (Bekaert et al.,

Table 8

Robustness tests: role of individual subcomponents of institutional quality.

Whole sample Non-high income countries

Institutional quality index 0.17*** (0.04) 0.18*** (0.05)

Voice and accountability 0.08*** (0.03) 0.07** (0.03)

Government effectiveness 0.14*** (0.04) 0.13*** (0.05)Political stability 0.12*** (0.03) 0.11*** (0.03)

Regulatory quality 0.11*** (0.04) 0.08 (0.05)

Rule of law 0.09** (0.04) 0.08 (0.05)

Control of corruption 0.13*** (0.03) 0.17*** (0.04)

Sources and notes: robust standard errors in parentheses. Ordinary least squares regressions. ***Significant at 1%; **significant at

5%;*significant at 10%.The dependentvariable is total equity as a share of liabilities. Each line of the table represents a regression.

In each regression, the controls are GDP, GDP per capita, financial development, natural resources, openness, human capital, and

a transition dummy. The whole sample includes 94 observations and the non-high income countries sample include 72

observations; the classification of countries according to the income level follows the Global Development Network Growth

Database. International liabilities and their components are from Lane and Milesi-Ferretti (2006). Total equity consists of

portfolio equity plus FDI. The institutional quality index is the simple average of six governance indicators from Kaufmann et al.

(2006), also known as World Bank Governance Indicators (WGI): voice and accountability; political stability and absence of

violence; government effectiveness; regulatory quality; rule of law; and control of corruption. GDP (U.S. trillions of dollars atconstant 2000 prices) and GDP per capita (U.S. thousands of dollars at constant 2000 prices) are from the World Banks World

Development Indicators (WDI). Financial development is measured by private credit divided by GDP from the WDI. Natural

resources are the percentage of ore, metals and fuels in total exports, built using data from the WDI. Openness is the sum of

exports and imports divided by GDP, built using data from the WDI. Human capital is the share of population over 25 that

attended at least some level of secondary schooling, from the World Banks Education Indicators, EDSTATS (Education Attain-

ment in the Adult Populationdfollowing Barro and Lee, 2001). Transition is a dummy variable that indicates whether a country

belonged to the former Soviet Union, former Yugoslavia, or ex-communist countries, from the Global Development Network

Growth Database. Dependent and independent variables are time-series means for the available years during the period 1996

2004. Appendix 1 provides further details on sources and variable definitions.

32 We thank a referee for suggesting this measure, whichdas suggested by Reinhart et al. (2003)dmay be viewed as more

exogenous than credit ratings.



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2005). About one-third of the countries in our sample had not liberalized by that time, whereas several

emerging markets had already liberalized by the early 1990s.33 The impact of institutional quality on

external capital structures is essentially the same as in the main tables. We also run these same

regressions using a financial liberalization reform index created by Detragiache et al. (unpublished):

the institutional quality index remains significant at the 1 percent level and the financial development

index is only significant (at the 10 percent level) when the only regressors are financial development,institutional quality, size, and level economic development.

3.3.13. Adding capital controls to the list of independent variables

Our main results hold when we introduce standard measures of capital controls as an additional

regressor.34 In all of our main regressions, the coefficient on institutional quality is essentially

unchanged and always remains significant, whereas capital controls are never statistically significant.

Capital controls are always positively (and significantly, in some specifications) associated with the

share of total equity in total liabilities. The impact of capital controls on total equity comes from its

positive (and sometimes significant) association with the share of FDI in total liabilities. Specifically, to

proxy for capital controls, we use the sum of four dummy variables that take the value of one if thecountry has (a) multiple exchange rates; (b) current account restrictions; (c) capital account restric-

tions; and (d) export proceeds surrender requirements. (Thus, in each year, the summary measure

takes integer values between 0 and 4.) For each country, we use the 19901995 average of this sum. The

dummy variables are all drawn from the International Monetary Funds Annual Report on Exchange

Arrangements and Exchange Restrictions (AREAR).35

3.3.14. Possible endogeneity of the institutional quality index

We run regressions of liability components (as a share of total liabilities) on institutional quality

using a variety of instruments such as settler mortality. The F-statistic in the first-stage regression

confirms that the instruments are not weak, and that 2SLS estimation is thus warranted. The

identifying assumption is that settler mortality (and/or the other instruments) affects institutional

quality, and institutional quality in turn affects the composition of countries external liabilities, with

no other links between liability structures and the instruments. In particular, for the identifying

assumption to hold, there must be no direct channel from the instruments to liability structures. This

leads us to use univariate regressions combined with a broad interpretation of institutions (Table 11).

For example, column (2) reports the results of the share of total equity in total liabilities on an index of

institutional quality, using settler mortality and population density in the 1500s as instruments (as in

Acemoglu et al., 2001). This specification may be of interest to those who believe that settler mortality

and population density in the 1500s affected institutions in the broad sense (the institutional quality

index would then proxy for many aspects of institutions, perhaps even including educational attain-

ment); and institutions in turn affected our dependent variable, with no direct channel from the

instruments to liability structures. Column (4) reports the results using instead ethnolinguistic frac-tionalization (as in Mauro,1995) and British legal origin (as in La Porta et al.,1998) as instruments. In all

33 The samples for which the data are available consist of 2544 countries (depending on the specification). Institutional

quality is significant at the 1 percent level in all specifications (except one in which it is significant at the 5 percent level); in

such limited samples, the equity market liberalization dummy is never significant. Moreover, Bekaert et al. (2005) use an index

of institutional quality as an instrument for equity market liberalization, suggesting that they view equity market liberalization

as endogenous to institutional quality. Equity market liberalization appears with the expected (positive) sign and significant

when the dependent variable is the share of portfolio equity in total liabilities: a country that liberalized its equity market

before 1995 has a share of portfolio equity in total liabilities 23 percentage points larger than a country that did not liberalize.

(For comparison, the sample mean share of portfolio equity in total liabilities is 7 percent.)34 Ideally, in our set up, one would wish to control for restrictions on certain types of capital flows (such as FDI, or short-term

flows). Unfortunately, reliable cross-country measures of capital controls by type of flow are not yet available.35 In 1996, the format of the AREAR changed to more detailed dummies with no simple mapping to the previous system. In our

view,and in lightof the persistence of many aspects of capital controls, this is thebestcompromisemeasure in terms of precision of

the capital controls measure, relevance for the questions we address, and availability for a broad cross-section of countries.



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cases, the coefficient on institutional quality rises compared with the ordinary least squares estimation.

(To emphasize this point, we report the OLS results obtained with the same sample of countries as is

available for instrumental variable estimation.)

4. Conclusion

Previous studies have emphasized the importance of countries external capital structures for

economic performance: reliance on equity-like instruments (FDI and portfolio equity) improves an

economys ability to share risks with international investors; moreover, FDI is usually considered to be

a vehicle for technological transfer. This study has shown that equity-like components in countries

external capital structures are significantly associated with indicators of institutional quality, as well as

educational attainment, and natural resources. This finding may help shed light on the mechanism

underlying the observed correlation between weak institutional quality and severe crises (Acemoglu

et al., 2004): weak institutions may tend to increase countries reliance on crisis-prone forms of

financing, thereby increasing the frequency and severity of crises.

Our interpretation of the results is that improving institutionsdobviously no easy task, and typi-

cally requiring a long timedmay help to promote a shift toward more desirable external liability

structures. Moreover, measures aimed at improving countries external capital structures in a more

direct manner should be evaluated carefully, because their effectiveness might be undermined by

countries weak institutional quality.

Acknowledgements

We thank Geert Bekaert, Eduardo Borensztein, Simon Johnson, Gian Maria Milesi-Ferretti, Elias

Papaioannou, Alan Taylor, Shang-Jin Wei, participants in seminars at the International Monetary Fund

and the InterAmerican Development Bank, the CEPR conference on Institutions, Policies, and EconomicGrowth, the Annual Congress of the European Economic Association, and the Annual Meeting of the

Latin American and Caribbean Economic Association, and two anonymous referees for insightful

suggestions; and Priyanka Malhotra and Martin Minnoni for able research assistance. The views

expressed are those of the authors and do not necessarily represent those of the IMF or IMF policy.

Table 11

Robustness tests: two stage least squares regressions.

(1) (2) (3) (4)

OLS IV OLS IV

Institutional quality index 0.12*** (0.02) 0.13*** (0.03) 0.06*** (0.02) 0.08*** (0.03)

Constant 0.35*** (0.02) 0.35*** (0.02) 0.30*** (0.02) 0.29*** (0.02)

Observations 51 51 85 85

R-squared in OLS 0.35 n.a. 0.15 n.a.

First stage for institutional quality index

Settler mortality 0.26*** (0.08)

Population density in 1500 0.23*** (0.04)

Ethnolinguistic fractionalization 0.02*** (0.003)

British legal origin 0.45** (0.19)

Constant 1.16*** (0.37) 0.72*** (0.16)

R-squared in first stage 0.58 0.22

F-statistic 38.05 18.62

Hansens J statistic (p-value) 0.19 0.14

Sourcesand notes: robust standard errors in parentheses. ***Significantat 1%; **significant at 5%; *significantat 10%. Thedependentvariable (in the second stage) is total equityas a share of liabilities. International liabilities and their components are fromLane and

Milesi-Ferretti (2006). Total equity consists of portfolio equity plus FDI. The institutional quality index is the simple average of six

governance indicators from Kaufmann et al. (2006), also known as World Bank Governance Indicators (WGI): voice and

accountability; political stability and absence of violence; government effectiveness; regulatory quality; rule of law; and control of

corruption. Settler mortality is the logarithm of settler mortality for former colonies; and population density in the 1500s is the

logarithm of population density in the 1500s for former colonies; both from Acemoglu et al. (2001). Ethnolinguistic fractional-

ization is the probability that two randomly selected persons from a given country will not belong to the same ethnolinguistic

group fromMauro (1995). British legal originis a dummyvariable that attributes oneto countries with English law or former British

colonies or protectorates, from La Porta et al. (1998). Appendix 1 provides further details on sources and variable definitions.



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Appendix 1. Sources and description of the variables

Table A1

FamaMacBeth regressions.

Whole sample Non-high income countries

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)

Institutional

quality index

0.05*** (0.01) 0.16*** (0.01) 0.14*** (0.01) 0.16*** (0.01) 0.14*** (0.01) 0.17*** (0.01) 0.14*** (0.01) 0.11*** (0.01) 0.14*** (0.01) 0.15*** (0.01)

GDP 0 .03** * (0.0 01) 0.02* ** (0.0 01) 0 .03** * (0.0 01) 0.03 *** (0.0 02) 0.16** * (0.01) 0.11*** (0.01) 0 .21* ** (0 .01) 0.22* ** (0.01)

GDP per capita 0.01***

(0.0003)

0.01***

(0.0003)

0.01*** (0.0005) 0.02***

(0.0004)

0.01*** (0.002) 0.01*** (0.002) 0.003***

(0.002)

0.002***

(0.002)

Financial

development

0.07*** (0.01) 0 .07*** (0.01) 0.03* (0.01) 0.11*** (0.01) 0.09*** (0.01) 0.03* (0.002)

Natural

resources

0.17*** (0.01) 0.13*** (0.01) 0.19*** (0.01) 0.18*** (0.01)

Openness 0.07** (0.02) 0.08*** (0.02) 0.08*** (0.02) 0.10*** (0.01)

Human capital 0.31*** (0.03) 0.12*** (0.02)

Transition 0.13*** (0.01) 0.09*** (0.02)

Constant 0.32*** (0.02) 0.39*** (0.02) 0.36*** (0.02) 0.27*** (0.01) 0.22*** (0.01) 0.37*** (0.02) 0.33*** (0.01) 0.29*** (0.01) 0.21*** (0.01) 0.20*** (0.01)

Observations 737 737 737 737 737 545 545 545 545 545

Number

of time periods

9 9 9 9 9 9 9 9 9 9

Adjusted

R-squared

0.09 0.24 0.26 0.34 0.40 0.30 0.37 0.41 0.49 0.51

Sources and notes: robust standard errors in parentheses. ***Significant at 1%; **significant at 5%; *significant at 10%. The classification of countries according tothe income level follows the

Global Development Network Growth Database. The dependent variable (in the second stage) is total equity as a share of liabilities. International liabilities and their components are from

Lane and Milesi-Ferretti (2006). Total equity consists of portfolio equity plus FDI. The institutional quality index is the simple average of six governance indicators from Kaufmann et al.

(2006), also known as World Bank Governance Indicators (WGI): voice and accountability; political stability and absence of violence; government effectiveness; regulatory quality; rule of

law; and control of corruption. Settler mortality is the logarithm of settler mortality for former colonies; and population density in the 1500s is the logarithm of population density in the

1500s for former colonies; both from Acemoglu et al. (2001). Ethnolinguistic fractionalization is the probability that two randomly selected persons from a given country will not belong to

the same ethnolinguistic group from Mauro (1995). British legal origin is a dummy variable that attributesone to countries with English law or former British colonies or protectorates from

La Porta et al. (1998). Appendix 1 provides further details on sources and variable definitions.

A.Faria,P.Mauro/JournalofInternationalMoneyandFinance28(2009)3

67391

387


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Dependent variables

The source for countries total external liabilities and their components in the baseline regressions

(FDI, portfolio equity and debt) is the data set developed by Lane and Milesi-Ferretti (2006). All vari-

ables are in millions of U.S. dollars. Data are available at http://www.imf.org/external/pubs/ft/wp/

2006/data/wp0669.zip.An alternative source for countries total external liabilities and their components (FDI, portfolio

equity, portfolio debt, and other instruments) is the International Investment Position reported in the

IMFs International Financial Statistics. All variables are in millions of U.S. dollars. A thorough description

of the methodology is available at http://www.imf.org/external/np/sta/iip/guide/index.htm .

The dependent variables are expressed as ratios to total liabilities. The dependent variables used in

the baseline regressions and in most specifications are, unless otherwise noted, a time-series mean of

the variables of interest between 1996 and 2004, whenever available.

Independent variables

Institutional quality index

Simple average of six institutional indicators (voice and accountability, political stability and

absence of violence, government effectiveness, regulatory quality, rule of law, control of corruption),

drawn from Kaufmann et al. (2006), for all available years between 1996 and 2004 (available for 1996,

1998, 2000, and annually from 2002 on). The institutional quality index in a given year is formed only

for countries that have information for all governance indicators in that year. Each institutional indi-

cator is modeled by the authors as a standard normal distribution (zero mean, and standard deviation

one), http://info.worldbank.org/governance/wgi2007/resources.htm .

Gross domestic product

Constant 2000 U.S. dollars for all available years between 1996 and 2004. Rescaled to trillions in the

regressions to make results more legible. Source: World Development Indicators, World Bank, http://

devdata.worldbank.org/dataonline/ .

GDP per capita

Constant U.S. dollars in 2000 for all available years between 1996 and 2004. Rescaled to thousands

in the regressions to make results more legible. Source: World Development Indicators, World Bank.

Financial development

Private credit divided by total GDP for all available years between 1996 and 2004. Source: World

Development Indicators, World Bank.

Natural resources

Percentage of ore, metals and fuels in total exports for all available years between 1996 and 2004.

Source: World Development Indicators, World Bank.

Openness

Sum of imports and exports divided by total GDP for all available years between 1996 and 2004.

Source: World Development Indicators, World Bank.

http://www.imf.org/external/pubs/ft/wp/2006/data/wp0669.ziphttp://www.imf.org/external/pubs/ft/wp/2006/data/wp0669.ziphttp://www.imf.org/external/np/sta/iip/guide/index.htmhttp://info.worldbank.org/governance/wgi2007/resources.htmhttp://devdata.worldbank.org/dataonlinehttp://devdata.worldbank.org/dataonlinehttp://info.worldbank.org/governance/wgi2007/resources.htmhttp://www.imf.org/external/np/sta/iip/guide/index.htmhttp://www.imf.org/external/pubs/ft/wp/2006/data/wp0669.ziphttp://www.imf.org/external/pubs/ft/wp/2006/data/wp0669.ziphttp://devdata.worldbank.org/dataonlinehttp://devdata.worldbank.org/dataonline


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Human capital

Percentage of total population over 25 that attended at least some secondary schooling.

Sources: Barro and Lee (2001) available from World Bank Education Indicators (EDSTATS),

http://devdata.worldbank.org/edstats/td10.asp. Data refer to 1995 and 2000 for a vast majority

of countries and to 1990 for a smaller set of countries (values for the U.S.S.R. attributed toRussia).

Transition

Countries that belonged to the former Soviet Union, former Yugoslavia, or ex-communist

countries. Source: Global Development Network Growth Database, http://www.nyu.edu/fas/

institute/dri/dataset/Social%20Indicators%20Fixed%20Factors_7_2005.xls .

Market capitalization as a share to GDP

All available years between 1996 and 2004. Source: World Development Indicators, World Bank.

Financing constraints

Number of years in default between 1970 and 2001. A country is in default if any of the following

sources considers it in default. Detragiache and Spilimbergo (2001), Manasse and Roubini (2005), and

Reinhart et al. (2003).

The independent variables used in the baseline regressions and in most specifications are,

unless otherwise noted, a time-series mean of the variables of interest between 1996 and

2004, whenever available. Time-series means of variables are only formed using data for the

years for which observations for the dependent variable are available. To enlarge the number of

years available, for the years 1997, 1999, and 2001 we attribute to a given countrys institu-tional quality index the value for that same country in the previous year (1996, 1998, and

2000, respectively). In the baseline regressions, we restrict the sample to the set of countries

for which we have information for all key variables in at least one year between 1996 and

2004.

Instruments

Logarithm of settler mortality: for former colonies. Source: Acemoglu et al. (2001).

Logarithm of population density in the 1500s: for former colonies. Source: Acemoglu et al. (2001).

Ethnolinguistic fractionalization: Probability that two randomly selected persons from a given

country will not belong to the same ethnolinguistic group. Source: Mauro (1995).

Countries

The baseline sample used in the regressions consists of the following 94 countries: Algeria,

Argentina, Australia*, Austria*, Bangladesh, Belgium*, Benin, Bolivia, Botswana, Brazil, BulgariaT,

Cameroon, Canada*, Chile, China, Colombia, CroatiaT, Czech RepublicT, Denmark*, Dominican

Republic, Ecuador, Egypt, El Salvador, EstoniaT, Ethiopia, Fiji, Finland*, France*, Germany*,

Ghana, Greece*, Guatemala, Haiti, Honduras, HungaryT, Iceland*, India, Indonesia, Iran, Ireland*,

Italy*, Jamaica, Japan*, Jordan, KazakhstanT, Kenya, Kuwait*, LatviaT, LithuaniaT, Malawi,

Malaysia, Mali, Mexico, MoldovaT, Mozambique, Namibia, Nepal, New Zealand*, Nicaragua,

Niger, Norway*, Pakistan, Papua New Guinea, Paraguay, Peru, Poland

T

, Portugal*, Romania,Russia, Rwanda, Senegal, Slovak RepublicT, SloveniaT*, South Africa, South Korea*, Spain*, Sri

Lanka, Sudan, Swaziland, Sweden*, Syria, TajikistanT, Thailand, Togo, Trinidad and Tobago,

Tunisia, Turkey, Uganda, United Kingdom*, United States*, Venezuela, Vietnam, Zambia,

Zimbabwe.

http://devdata.worldbank.org/edstats/td10.asphttp://www.nyu.edu/fas/institute/dri/dataset/Social%20Indicators%20Fixed%20Factors_7_2005.xlshttp://www.nyu.edu/fas/institute/dri/dataset/Social%20Indicators%20Fixed%20Factors_7_2005.xlshttp://www.nyu.edu/fas/institute/dri/dataset/Social%20Indicators%20Fixed%20Factors_7_2005.xlshttp://www.nyu.edu/fas/institute/dri/dataset/Social%20Indicators%20Fixed%20Factors_7_2005.xlshttp://devdata.worldbank.org/edstats/td10.asp


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