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Financial Development and the Evolution of Property
Rights and Legal Institutions ??
Mihail Miletkov a and M. Babajide Wintoki b
a Whittemore
School of Business and Economics, University of New Hampshire
b University
of Kansas School of Business
Abstract
Using a panel of 129 countries over the period from 1965 to 2008, we examine the role of
financial development in the evolution of property rights and legal institutions. We postulate
that changes in the level of financial development change the costs and benefits of, and the
demand for property rights institutions. We predict, and find, a positive causal relationship
between the level of financial development and the subsequent quality of property rights
institutions, even after we control for country level heterogeneity and reverse causality.
Furthermore, our analysis suggests that this relationship is especially strong in emerging
market countries.
JEL Classification: F30; N20; O43
Key words: property rights, institutions, financial intermediaries, economic development
? We would like to thank Chris Anderson, Bob DeYoung, Harold Mulherin, Annette
Poulsen, two anonymous referees, seminar participants at the University of New Hampshire and the 2008 meetings of the Financial Management Association and the Southern
Finance Association, for many useful comments. Any errors of analysis and interpretation
are our own.
??Contact phone numbers: +1-(603)-862-3331 (Mihail Miletkov), +1-(785)-864-7515 (M.
Babajide Wintoki)
Email addresses: [email protected] (Mihail Miletkov), [email protected]
(M. Babajide Wintoki).
26 September 2012
1. Introduction
The literature on law and finance emphasizes the positive correlation between institutional and
financial development. The majority of the studies assert that the laws which protect property rights
and promote the enforcement of financial contracts foster higher levels of financial development
(La Porta, Lopez-de Silanes, Shleifer, and Vishny (1997, 1998), Levine (1999), Levine, Loayza,
and Beck (2000), Acemoglu, Johnson and Robinson(2002, 2005), Claessens and Laeven (2003),
Billmeier and Massa (2009)). In this paper we argue that the positive link between institutional and
financial development is due not only to the positive effects of institutions on financial development
but can be explained, at least in part, by the effect of exogenous changes in financial development
on the quality of institutions. Financial development itself has a positive causal effect on the quality
of property rights and legal institutions in a country.
The framework for our study is the model of institutional change advocated by Demsetz (1967,
2008) and North (1971, 1981, 2005). This model of institutional change asserts that institutional
innovations emerge when the social benefits of the innovations exceed the costs. Changes in the
environment, or technology shocks, change the benefit-cost possibilities of different institutional
arrangements and stimulate the demand for new institutions or changes to existing arrangements.
In this paper, we argue that exogenous changes in financial development change the costs and
benefits of particular institutional arrangements. In fact, our main hypothesis is that an increase in
the level of financial development will lead to a demand for, and emergence of higher quality legal
and property rights institutions.1
The intuition behind our argument is a simple one. On the benefits side, higher quality legal
institutions increase the value of existing or potential financial arrangements (e.g., by lowering
the risk and uncertainty). Property rights institutions generally require fixed costs to set up and
1 Throughout
this paper we define property rights institutions broadly construed as those legal, social and cultural
institutions which enforce and arbitrate contracts between private parties, restrain the predatory power of the government over individuals and the predatory power of some groups of individuals over other individuals.
1
maintain. However, once we have exceeded a threshold level of financial exchange within the
economy, the cost of these institutions will be exceeded by the benefits and the society will evolve
towards institutions that seek to capture these benefits. This process will lead to higher quality
institutions. A simple example of this is the emergence of securities law (and enforcement). It is
costly to set up and maintain institutions for enforcing the contracts and property rights involved
in securities transactions, but once the securities markets have reached a tipping point, the benefits
will exceed the costs. The institutions that emerge (with its supporting cast of lawyers, solicitors,
investigators etc.) will make it less costly to improve the general quality of the institutions in the
country as a whole. Exogenous change in the level of financial development could itself arise
from technology shocks, an influx of groups with well developed financial networks, demographic
shocks, etc. The underlying hypothesis of our paper is that if we control for the other historical,
geographical, political, and cultural determinants of the property rights institutions, there should
be a positive causal relationship between the exogenous changes in financial development and the
quality of a country’s property rights institutions.
We test our hypothesis using a panel of 129 countries over the period from 1965 to 2008. Our
primary measure of financial development is PRIVATE CREDIT from the World Bank database
and it equals financial intermediary credits to the private sector divided by the gross domestic
product. We measure the quality of a country’s property rights institutions using the Legal Structure
and Security of Property Rights Index from Economic Freedom of the World: 2010 Annual Report
(Gwartney, Hall, and Lawson, 2010).
We find strong evidence to support our hypothesis of a positive relation between PRIVATE
CREDIT and subsequent quality of property rights. For example, when we sort the countries into
quartiles based on the change in the level of PRIVATE CREDIT between 1970 and 1985, we find
that the quartile of countries that had the biggest increase in PRIVATE CREDIT subsequently had a
significantly bigger increase in the quality of property rights in the period between 1985 and 2000,
than the quartile of countries that had the biggest decrease in the level of PRIVATE CREDIT.
2
In our regressions we specifically address the two major endogeneity concerns that arise in our
analysis – country level heterogeneity and reverse causality. First, we control for several country characteristics, which may jointly influence the quality of the property rights institution and
financial development. These include the wealth of the country (GDP per capita), foreign aid, the
size and scope of government spending, trade openness, the magnitude of foreign direct investment (FDI), access to sound money (money growth, inflation, freedom to own foreign accounts),
regulation of credit, labor and business, legal origin, culture (dominant religion), politics (ethnic
fractionalization, membership in OPEC), and geography (latitude). Second, we include countryfixed effects to control for unobservable heterogeneity across countries. Finally, we control for
reverse causality using the dynamic panel GMM estimator of Arellano and Bond (1991), Arellano
and Bover (1995), and Blundell and Bond (1998). Across all our specifications we find a significantly positive relation between PRIVATE CREDIT at any point in time and the quality of property
rights institutions five years later. This association is economically significant. For example in our
fixed effects specifications, we find that, on average, a 1 standard deviation increase in PRIVATE
CREDIT is associated with a 0.1 standard deviation increase in PROPERTY RIGHTS. If the level
of PRIVATE CREDIT in Pakistan had increased at the same rate between 1965 and 2008 as it had
in Malaysia (a predominantly Muslim Asian country with which it had a similar level of PRIVATE
CREDIT in 1965), its quality of property rights would have been ranked 14 places higher than it
was at the end of 2008.
In additional analysis, we divide our sample into emerging and non-emerging market countries.
Our empirical results suggest that the relation between financial and institutional development is
stronger in emerging market countries. This is consistent with the results from Rathinam and Raja
(2010) who document a significant feedback effect from financial sector growth to institutional
development in India.
Our paper contributes to the literature in two crucial respects. First, we show that the level of
financial development is a determinant of the cross-sectional differences in institutional develop3
ment across countries at any particular point in time. More importantly, our results suggest that
change in the level of financial development is itself a causal determinant of change in the quality
of a country’s property rights and legal institutions.
Second, our findings contribute to the debate on how financial development contributes to
overall economic growth. While the prior literature emphasizes the direct channels through which
the level of financial development influences economic growth (e.g., Beck, Levine, and Loayza,
2000; Beck and Levine, 2004; Claessens and Laeven, 2003; Fung, 2009; Levine et al., 2000; Levine
and Zervos, 1998), our findings may indicate the presence of an indirect link between financial
development and economic growth. Specifically, financial development influences the subsequent
development of the property rights and legal institutions, which in turn foster economic growth.
This has strong implications for economic development policy. Policies which promote a wide
availability of credit to a large segment of the population (e.g. micro-credit financing schemes)
may have at least as positive an impact on a country’s long run economic performance as policies
which call simply for top-down “democratic” or “institutional” reforms.
The rest of the paper is structured as follows. In section 2 we review the literature and further develop our hypothesis. In section 3, we discuss our measures of financial development
and property rights institutions. In section 4, we discuss our variables and empirical estimation
methodologies. In section 5, we present and discuss our results. In section 6, we carry out a series of robustness tests in which we explore alternative measures of financial development and we
conclude in section 7.
2. Literature review and hypothesis development
In this paper we study the role of financial development in the evolution of property rights
institutions. The theoretical framework for our study is that provided by Demsetz (1967, 2008) and
North (1971, 1981, 2005). The basic premise of this framework is that property rights institutions
emerge, or change when the social benefits exceed the costs of setting up or maintaining these
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institutions.
Demsetz (1967, 2008) argues that changes in knowledge or the external environment lead to
changes in the costs and benefits of different economic activities to different individuals in the
society, and the emergence of new property rights takes place in response to the desires of the interacting persons for adjustment to new benefit-cost possibilities. He also suggests that the changes
in property rights do not have to come through conscious effort to resolve one particular issue, but
are more likely to come through changes in conventions, social mores, or legal and moral experiments.
North (1971, 1981, 2005) also develops the idea that institutional arrangements emerge or
change when the benefits of the institutional arrangements exceed the cost to individuals in a
society. North suggests that institutional innovations could emerge from at least two kinds of
exogenous events that could affect the relative costs and benefits of an institutional arrangement.
First, the potential income from an exogenous event (e.g., a change in market size for a product
or service) might increase the benefits from a higher quality institutional arrangement relative to
the costs. For example, the costs of institutions that police securities markets will not increase
proportionately with the number of transactions once a threshold number of transactions have
been reached. Second, scientific and technical innovation could dramatically reduce the costs
of institutional arrangements while generating increasing returns to scale for these institutional
arrangements.
The central thesis of our paper is that financial innovation, or exogenous changes in the level
of financial development, represents an event that changes the cost and benefits of institutional
arrangements, especially property rights institutional arrangements. In fact, we specifically argue
that an exogenous increase in the level of financial development increases the benefits (relative
to the costs) of having institutional arrangements that support more secure property rights and
higher quality legal institutions. This would lead to a demand for, and the eventual emergence or
evolution, of the property rights institutions in the society.
5
There are at least four ways in which financial development could increase the benefits and
reduce the costs of quality property rights institutions and thus stimulate the demand for these
institutions:
1. There may be substantial fixed costs in setting up property rights institutions and in developing enforcement mechanisms. The level of financial development may provide a tipping
point beyond which the benefits just exceed these fixed costs. Consider, for example, the
process of setting up a land ownership registry in a developing society. In an environment
where most land-holders do not need to pledge their property to obtain credit, there is little
need to make the fixed investment in building a registry. However, once a critical mass of financial development is reached (and more land-holders are seeking more credit), it becomes
beneficial to build the registry and its supporting institutions. Recent examples can be found
in Mexico, where banks have lobbied for tougher financial supervision, and Tanzania, where
foreign investors have worked with local groups to establish a commercial court in the 1990s
(World Bank Development Report (2002)).
2. An increase in the quality of a society’s property rights institutions will increase the value
of the financial contracts that already exist and thus provide a huge increase in the income
of some individuals or groups in that society. This provides a strong political constituency
that can undertake the necessary political action to ensure institutional change. For example,
Greif (2006) describes how the increasing value of foreign trade led merchants to develop
community responsibility systems of contract enforcement for trade across cities and principalities. Eventually the wealth and power generated from trade gave these merchants the
power to challenge absolute rulers, especially in England and the Netherlands, culminating
in the establishment of the modern democratic system with checks on the predatory power
of the ruler.
3. A higher level of financial development could lead the norms and conventions in the society
to more readily accept the notions of clearly delineated and secure private property rights.
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This could lower the costs of institutional change or the costs of maintaining the institutional
arrangements once they are established.
4. The increase in the number of financial transactions provides incentives for individuals to invest in legal education, and in the acquisition of the skills involved in drafting, administering
and enforcing contracts. As noted by Dam (2006), the availability of skilled legal personnel
(lawyers, judges, clerks, paralegals, solicitors, detectives etc.) can significantly lower the
cost of setting up and maintaining high quality property rights institutions.
Our model does not assert that exogenous change in the level of financial development is the
only (or even the primary) cause of changes in the quality of property rights institutions. Indeed the literature has identified other cross-sectional determinants of property rights institutions
across countries. In addition, the complex interaction of financial institutions with other institutions
means that institutional change is path-dependent and will, to a large extent be country specific.
Nevertheless, the central hypothesis in our paper can be stated as follows: If we control for the
observable and unobservable country specific determinants of property rights institutions there is a
positive causal relationship between financial development and the quality of a country’s property
rights institutions.
Of course, the exact way by which the level of financial development will influence the institutions in a country is likely to be idiosyncratic and will vary across time and across countries. By
focusing, on a broad cross-section of countries, our study abstracts from some of these idiosyncratic country specific issues. For example, case studies on India (e.g., Pedersen, 2000; Armour
and Lele, 2009) suggest that political economy factors are particularly important and that reform
is very much path-dependent. To date, there has been little formal modeling of how the financial
sector could manifest itself through the political sector to affect the level of institutional development. A notable recent exception comes from the paper by Bebchuk and Neeman (2010) which
develops a formal model of how different interest groups in a society can influence the level of investor protection that politicians choose for the society. In their model, three groups of investors –
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insiders in existing companies, financial intermediaries (institutional investors), and entrepreneurs
planning to take new firms public – compete for influence over politicians setting the level of
investor protection.2 The groups direct resources towards politicians to influence their behavior
and will invest in political lobbying as long as the expected marginal benefits exceed the costs
they bear directly. Of these groups, two – financial intermediaries and new entrepreneurs – prefer
higher levels of investor protection, while insiders, who as incumbents can extract private benefits,
prefer lower levels of investor protection. One clear implication of this model is that change in the
level of investor protection is likely to occur if there is a change in the relative size distribution of
these three groups, the resources they control, or the expected benefits from institutional change
relative to the costs of lobbying. This could occur, if for example, growth opportunities increase
the number of entrepreneurs and their expected benefits or there is an increase in the number of
investors channeling their investments through financial intermediaries. While we do not explicitly
test Bebchuk and Neeman’s model, our findings are consistent with one of the main implications
from their model that a higher level of investor protection is, at least in part, the product – rather
than the cause – of a higher level of financial development.
A growing consensus regarding the close association between the quality of a country’s property rights institution and its level of financial development has spawned a growing empirical literature that explores the origins or determinants of property rights institutions themselves. The
prevailing literature on the determinants of property rights institutions can be classified into four
broad categories or viewpoints: legal origins, endowment (or geographic), cultural, and political
explanations. One theme that runs through most of these explanations is that property rights arise
from a confluence of historical or geographic factors which change very slowly (or in the case of
history, do not change at all). We refer to these explanations of the origins of legal institutions
as the static determinants of property rights institutions. The hypothesis we propose in this paper
2 In their model, they assume that individuals who invest in publicly traded firms either directly or indirectly through
institutional investors, are too dispersed to become an effective organized interest group
8
essentially explores the role of financial development as a dynamic determinant of property rights.
The legal origins viewpoint, first clearly articulated by La Porta, Lopez-de Silanes, Shleifer,
and Vishny (1997, 1998) suggests that legal traditions that emerged from Europe many centuries
ago, were spread round the world via colonization and imitation, and account for cross-sectional
differences in property rights and legal institutions. This viewpoint argues that along many dimensions, the quality of property rights institutions are higher in countries with the British (common
law) legal tradition than in countries with the French (civil law) legal tradition, with German and
Scandinavian law coming somewhere in between. More recently, Armour, Deakin, Sarkar, Siems,
and Singh (2009) challenge this traditional narrative and document a significant convergence in
investor protection laws between common law and civil law countries. The authors interpret this
convergence as evidence that legal origin is not the main driver of cross-sectional differences in
institutional development, and is not an exogenous determinant of financial and economic development. Regardless of the degree of exogeneity of legal origin it is still important to control for it
in our analysis of the evolution of property rights institutions.
The endowment viewpoint suggests that the continent in which a country is located, coupled
with its distance from the equator explains the quality of its property rights institutions. In one
variant of this viewpoint (popularized by Diamond, 1999), the shape of the continent in which a
country is located affected the spread of early institutions and technology that form the framework
for modern property rights institutions. More recently, Acemoglu, Johnson, and Robinson (2001)
argue that geography explains property rights through the fact that high levels of settler mortality
prevented effective transmission of European institutions to countries closer to the equator.
The cultural viewpoint suggests the differences in legal institutions are driven by the effect
of culture, especially the role of religion, on attitudes towards competition and private property.
Empirical evidence for the effect of religion on property rights and financial development is documented in La Porta, Lopez-de Silanes, Shleifer, and Vishny (1999) and Stulz and Williamson
(2003). These papers find evidence to suggest that Catholic and Muslim countries were more hos9
tile to private property and financial exchange and more likely to be dominated by clergy that was
resistant to institutional change.
The political viewpoint suggests that political competition between groups can encourage or
discourage the development of property rights institutions. Acemoglu and Robinson (2001), Beck,
Demirguc-Kunt, and Levine (2003), Rajan and Zingales (2003) and Acemoglu, Johnson, and
Robinson (2005) argue that entrenched elites may have enough power or influence to prevent the
adoption of higher quality institutions, or even to retard financial development, if they believe that
this will diminish their relative power or ability to extract rents from other groups. Easterly and
Levine (1997) suggest that this kind of entrenched behavior is likely to be found in ethnically
heterogenous countries where dominant ethnic groups resist institutional change to preserve their
power.
In summary, while the literature has identified a number of other determinants of property rights
institutions, we assert that if we control for the legal origins, endowment, cultural and political determinants of property rights – in other words, if we control for fixed, country-level heterogeneity
– there is a positive causal relationship between exogenous changes in the level of financial development and the quality of property rights institutions.
3. Measures of financial development and property rights
Our primary measure of financial development is PRIVATE CREDIT from the World Bank
database and equals financial intermediary credits to the private sector divided by the gross domestic product. The variable measures the claims on the private sector by financial intermediaries.
There are, of course, alternative measures of financial development such as stock market capitalization, trading volume, and ratios of the number of listed firms and the number of initial public
offerings to population. The main advantage of using the private credit measure is that it has the
longest time availability (1960–2008) for the largest cross-section of countries. The time dimension of the proxy for the level of financial development is especially important for our analysis,
10
because our goal is to explain the evolution of the property rights institutions in a dynamic setting.
Studies that use the ratio of private credit to GDP as a proxy for financial development include
Rajan and Zingales (1998), Levine et al. (2000), Beck (2002), Claessens and Laeven (2003), Beck
et al. (2003), and Bekaert, Harvey, and Lundblad (2005). However, in robustness tests we use
MARKET CAPITALIZATION (also from the World Bank database) as an alternative measure of
financial development.
We measure the quality of a country’s property rights institutions using the Legal Structure and
Security of Property Rights Index from Economic Freedom of the World: 2010 Annual Report
(Gwartney et al., 2010). An important feature of the index is that it does not simply reflect laws
on the books, but rather the overall legal environment as it relates to the protection of the property
rights and the overall quality of the legal institutions. The index is assessed on a scale of 0 to 10,
with 0 being the lowest and 10 being the highest. It is constructed from five key elements based on
data from the International Country Risk Guide (ICRG) and the World Economic Forum’s Global
Competitiveness Report (GCR), which are as follows:
• Judicial independence: The judiciary is independent and not subject to interference by the
government or parties in disputes. (Source: GCR)
• Impartial courts: A trusted legal framework exists for private businesses to challenge the
legality of government actions or regulations. (Source: GCR)
• Protection of intellectual property. (Source: GCR)
• Military interference in rule of law and the political process. (Source: ICRG)
• Integrity of the legal system. (Source: ICRG)
This index is an unbalanced panel of 129 countries over the period from 1970 – 2008. The
property rights index is part of the Economic Freedom of the World project. The Economic Freedom of the World index and/or its individual components have been used by La Porta et al. (1999),
11
La Porta, Lopez-de Silanes, and Shleifer (2002), Adkins, Moomaw, and Savvides (2002), Carlsson
and Lundström (2002), Dawson (2003), Heckelman and Knack (2008), and Fung (2009), among
others. There are alternative indices measuring the quality of property rights, for example, the index constructed by the Heritage Foundation. However, the benefit of using the index developed by
Gwartney et al. (2010) is that it goes back to 1970; in contrast the index developed by the Heritage
Foundation is available going back to 1995. Furthermore, de Haan and Sturm (2000) compare the
two indices and find a close correlation between the two (close to 0.8) for the year 1995 when both
indices are available.
Table 1 shows a summary of PRIVATE CREDIT and PROPERTY RIGHTS. The data for property rights is available every five years between 1970 and 2005 and we complement our analysis by
adding data from 2008. The coverage of countries for which we have PROPERTY RIGHTS data
increases from 48 countries in 1970 to 129 in 2008. Overall, we see that the mean (median) value
of PROPERTY RIGHTS actually declines gradually over the sample period, owing to the fact that
countries with weaker property rights institutions were added to the sample over time.3 In contrast
there has been a slight increase in the mean (median) level of PRIVATE CREDIT over time.
Our time-varying control variables include FOREIGN DIRECT INVESTMENT, FOREIGN
AID, GDP (per capita), and GOVERNMENT SPENDING from the World Bank database, and
FREE TRADE, GOVERNMENT SIZE, SOUND MONEY, and REGULATION from Economic
Freedom of the World: 2010 Annual Report (Gwartney et al., 2010). The time-invariant country
characteristics LATITUDE and ETHNIC FRACTIONALIZATION are from Beck et al. (2003),
while legal origin and dominant religion are from Djankov, McLiesh, and Shleifer (2007).
Table 2 shows the correlation of PROPERTY RIGHTS and PRIVATE CREDIT with our key,
time-varying, control variables. The results show the strong and very similar correlation of both
property rights institutions and financial development with these variables. For example, we see
3 The
mean PROPERTY RIGHTS for just those countries for which PROPERTY RIGHTS is observed going all
the way back to 1970 is 6.08. For the full sample, it is 5.70, which is statistically significantly smaller.
12
that PROPERTY RIGHTS and PRIVATE CREDIT are both positively correlated with GDP per
capita, government spending, foreign direct investment (FDI), free trade, and regulation of credit,
labor, and business. In addition, both PROPERTY RIGHTS and PRIVATE CREDIT are negatively
correlated with foreign aid. Overall, these correlations underlie the reason for the inclusion of the
variables as control variables in our empirical specifications.
The most important correlation in Table 2 is probably the correlation between PROPERTY
RIGHTS and PRIVATE CREDIT – a significantly positive correlation of 0.64. This is consistent
with the conventional wisdom that institutional quality and the level of development of the financial
sector are closely associated. It also suggests that our key measures of PROPERTY RIGHTS
and PRIVATE CREDIT are reasonably good proxies for institutional development and financial
development respectively.
While we develop and estimate our full empirical model in the next section (section 4), we
start here by carrying out “univariate” analysis to determine if any preliminary evidence exists to
carry out a full blown investigation of the causal effect of financial development on property rights
institutions. We divide the forty-year period from 1965 to 2005 into two twenty-year periods,
and show in Figure 1, a scatter plot of changes in PROPERTY RIGHTS in the second period
(1985-2005) against changes in PRIVATE CREDIT in the first period (1965-1985). While there is
obviously plenty of cross-country heterogeneity (which we ultimately control for in our empirical
model in section 4), a simple regression line drawn through this plot suggests a positive slope of
0.0126, a number that is significant at the 10% level (t = 1.68).
Next, we sort the countries into quartiles based on the magnitude of the change in the level
of financial development in the first twenty-year period (1965–1985). We find (as shown in Table
3) that the quartile of countries that had the biggest increase in PRIVATE CREDIT subsequently
had a significantly bigger increase in the quality of PROPERTY RIGHTS in the period between
1985 and 2005 than the quartile of countries that had the biggest decrease in the level of PRIVATE
CREDIT. In fact there is a monotonic relationship across the quartiles between changes in PRI13
VATE CREDIT between 1965 and 1985, and the quality of PROPERTY RIGHTS institutions in
the subsequent fifteen-year period (1985 – 2005). On average, the countries that experienced the
largest change in the level of PRIVATE CREDIT from 1965 to 1985, subsequently experienced the
largest change in the quality of their PROPERTY RIGHTS institutions.
To get a clear perspective on the economic magnitude of these numbers, consider the countries
from Quartile 4 in Panel B of Table 3. Between 1970 and 1985, these countries experienced an
average increase in PRIVATE CREDIT that was about 45% of their GDP – a number that would
have moved their level of PRIVATE CREDIT from the median to the seventy-fifth percentile across
all countries in the sample in the year 2005.4 These countries also experienced an average increase
of 1.40 in the quality of their property rights institutions – a number that would have also moved the
quality of PROPERTY RIGHTS in these countries from the median of the sample to well above
the seventy-fifth percentile in 2005. So the causal effect of PRIVATE CREDIT on PROPERTY
RIGHTS is not only statistically significant but also of a significant economic magnitude.
While, these “univariate” tests are far from conclusive, since they do not control for heterogeneity across countries, they do offer intriguing preliminary evidence that gives us confidence to
move forward with a more carefully specified multivariate empirical model and analysis which we
do in section 4.
4. Empirical model and estimation
Our estimates are based on the empirical model which we define as:
yit = α + κyi,t−1 + β fi,t−1 + γXi,t−1 + θ Zi + ηi + µt + εit
(1)
The dependent variable, y is the quality of the property rights institutions in country i. The main
variable of interest is f , which measures the level of financial development and our hypothesis
4 See
the summary statistics in Table 1.
14
relates to the value of β . We include the lagged dependent variable (yi,t−1 ) to control for the
persistence of property rights institutions and to account for the convergence of property rights
across countries.5 ηi represents country specific fixed-effects. In our regressions we include year
dummies (µt ) to account for any time-specific shocks as well as trends that have been common
across countries.
We include two sets of control variables. The first set, X, which we include in various combinations across all our specifications, consists of time-varying variables which might affect the
level of private credit, but may simultaneously impact the quality of a country’s property rights
institutions. Thus, X includes the following:
• FOREIGN DIRECT INVESTMENT, foreign direct investment divided by gross domestic
product: Direct foreign investment could have a direct effect on the level of financial development, but foreign investors could demand a certain level of property rights protection as
precondition for the financial inflows.
• FOREIGN AID, gross foreign aid per capita: Foreign donors (just like foreign investors)
may have an impact on the level of financial development but may also demand property
rights reforms as a precondition for their aid. In addition, it is also possible that foreign aid
may have the effect of entrenching a rent-seeking elite and actually retard the development
of institutions.
• GDPPC, gross domestic product per capita: GDP per capita is closely associated with the
level of financial development (see for example, Levine et al. (2000)). However, wealthier countries can more easily afford the cost of setting up and maintaining higher quality
institutions.
• GOVERNMENT SPENDING, total government expenditure divided by gross domestic prod5 Thus,
we measure the effect of financial development on property rights institutions given the existing quality of
property rights institutions.
15
uct: La Porta et al. (1999) find that the quality of government is positively associated with the
size of government, however, it is possible that government’s dominance over the economy
could crowd out private financial exchange.
• FREE TRADE, Freedom to Trade Internationally Index:6 Recent theoretical and empirical
work suggests that the level of trade openness in a country is related to both its institutional
quality (Levchenko, 2007) and its level of financial development (Beck, 2002; Rajan and
Zingales, 2003; Do and Levchenko, 2007).7
• GOVERNMENT SIZE, Size of Government Index:8 The level of the government’s involvement in the economy reflects the extent to which the country relies on the political process
to allocate resources, and therefore, may be associated with both the development of the
financial sector and the quality of the institutional environment.
• SOUND MONEY, Access to Sound Money Index:9 Monetary policy affects the development of the financial sector and reflects the government’s commitment to the protection of
private property.
• REGULATION, Regulation of Credit, Labor, and Business Index:10 Regulation, especially
of the credit markets, is obviously related to our PRIVATE CREDIT measure and may also
reflect the broader institutional environment in the country. In some of our analysis we also
6 The
index is assessed on a scale of 0 to 10, with higher scores assigned to countries which have low tariffs, large
trade sectors, efficient administration of customs, freely convertible currencies, and few controls on the movement of
capital.
7 In robustness tests, we use the sum of imports and exports divided by gross domestic product as an alternative
proxy for trade openness. The results remain qualitatively unchanged.
8 The index is assessed on a scale of 0 to 10, with higher scores assigned to countries which have low levels of
government spending, small government enterprise sectors, and low marginal tax rates.
9 The index is assessed on a scale of 0 to 10, with higher scores assigned to countries which follow policies that
lead to low inflation rates, and to countries which do not limit the ability of their citizens to use alternative currencies.
10 The index is assessed on a scale of 0 to 10, with higher scores assigned to countries which impose less restrictions
on the functioning of their credit and labor markets, and to countries which promote competition and have lower
barriers to entry into business.
16
use the sub-components of this index that relate specifically to the regulation of the credit
markets: Bank Ownership, Credit Extension, and Interest Rate Control.11
The second set of control variables, Z, which we include in a number of our OLS specifications,
includes those variables that proxy for the static determinants of property rights (discussed in
Section 2). These factors may also simultaneously affect the level of financial development. Thus,
Z includes the following:
• Dummy variables (which take a value of 1 if true, 0 otherwise) for ENGLISH, FRENCH and
GERMAN law as proxies for legal origin.
• LATITUDE, a measure of distance from the equator as proxy for endowment. Countries
closer to the equator have a harsher climate and would have had higher levels of settler
mortality if they were colonies in colonial times.
• Dummy variables (which take a value of 1 if true, 0 otherwise) for BUDDHIST, CATHOLIC,
MUSLIM, ORTHODOX or PROTESTANT being the dominant religious group in the country as proxies for culture.
• ETHNIC FRACTIONALIZATION, the probability that two randomly selected individuals
from a country are from different ethnic or linguistic groups as a proxy for politics.
• A dummy variable (which takes a value of 1 if true, 0 otherwise) for OPEC if the country
is a member of the organization of Petroleum Exporting Countries. This is an additional
proxy for politics since the existence of a valuable mineral resource could have the effect of
entrenching a rent-seeking elite (see, for example, Sala-i-Martin and Subramanian, 2003).
11 Bank
Ownership reflects the percentage of bank deposits that are held in privately owned banks, Credit Extension
measures the extent to which government borrowing crowds out private borrowing, and Interest Rate Control reflects
the extent to which interest rates are set by the government instead of by market forces. See, Economic Freedom of
the World: 2010 Annual Report (Gwartney et al., 2010) for further details.
17
Although we start our analysis with OLS estimates of (4.1), OLS estimates may be of limited value because they generally ignore the effect of unobserved heterogeneity. Thus, a pooled
OLS regression will not account for the effect of long-term unobservable country-specific factors
that may affect both the quality of property rights institutions and the level of financial development. Standard fixed-effects (“within”) estimation will be biased because (i) the lagged dependent
variable yi,t−1 is mechanically correlated with εis for s < t; (ii) there is dynamic endogeneity in
the sense that values of financial development at time t will probably be related to realizations of
property rights at time s < t.
Thus, in addition to OLS estimates, we estimate (4.1) using the system GMM estimator of
Arellano and Bover (1995) and Blundell and Bond (1998). This procedure enables us to control
for the unobservable heterogeneity, simultaneity and reverse causality (Beck et al. (2000), Beck
(2002), and Beck and Levine (2004)).
The basic GMM panel estimator, originally conceived as a difference GMM estimator by
Arellano and Bond (1991), carries out the estimation in first-differences using the levels of the
lagged values of our time-varying variables as instruments for the equations in differences. Firstdifferencing effectively eliminates unobservable heterogeneity and enables us to include country
fixed effects. The other key assumptions we make are that (i) the time-varying variables (X) are
predetermined (ii) the static variables (Z) are strictly exogenous (iii) there is no serial correlation
in the errors. These assumptions can be summarized as follows:
E(εit |yi0 , . . . , yi,t−2 , Xi0 , . . . , Xi,t−2 , Zi ) = 0
E(εit εis ) = 0,
∀t 6= s
(2)
(3)
Following Arellano and Bond (1991), (4.2) suggests that level values of y and X, lagged two
periods or more can be used to carry out GMM estimation of (4.1) in first differences.
18
However, it is possible that lagged levels of our variables may be weak instruments for the
equation in first differences. To obtain more efficient estimates, the system GMM estimation procedure of Blundell and Bond (1998) stacks the equations in levels with those in first-differences
and estimates the system with lagged differences of the time-varying variables as additional instruments for the equations in levels. Of course, leaving the equations in levels means that we still
have to deal with possible correlation between the unobserved country-specific effects. However,
if the potential correlation between the endogenous variables and the unobserved country-level
heterogeneity is constant over the sample period, we can use lagged differences as instruments for
the GMM estimation of (4.1). The additional orthogonality condition is:
E(εit |∆yi,t−1 , ∆Xi,t−1 ) = 0
(4)
The system GMM estimator thus enables us to control for simultaneity, reverse causality and unobservable heterogeneity. Indeed, system GMM can be described as estimating the following system:








 yit 
 yit−p 
 fit 
 Xit 

 = α +κ 
+β 
+γ 
 + εit
∆yit
∆yit−p
∆ fit
∆Xit
(5)
As part of our analysis in Section 5, we carry out specification tests of the instrument set suggested
by each of the orthogonality conditions in (4.2), (4.3) and (4.4).
In summary, it is worth restating how our empirical methodology enables us to identify exogenous changes to financial development. First, we note that all our specifications include controls
for observable time-varying country-specific factors such as economic growth (GDP per capita),
foreign aid, foreign direct investment, government spending etc. We also include country fixed effects to account for unobservable (fixed) country-specific factors. Finally, the instrumental-variable
(IV) procedure of the dynamic GMM estimator described above enables us to extract the exogenous component of financial development in a way similar to that in Levine et al’s (2000) analysis
19
of the effect of exogenous changes in the financial sector on economic growth.
One potential pitfall of the system GMM estimator, since it uses the lags of the dependent and
explanatory variables as instruments, is the problem of instrument proliferation (Roodman, 2009).
As the number of instruments approaches the number of observations, the large instrument set
“over-fits” endogenous variables while weakening the test of the instruments’ validity. To reduce
the possibility of a bias from instrument proliferation we limit the instrument set for our dynamic
panel GMM regressions to just two lags of the dependent and explanatory variables.
5. Results
Table 4 shows the results of OLS regressions of PROPERTY RIGHTS on lagged values of
PRIVATE CREDIT and other control variables. The results show a strong relation between PRIVATE CREDIT and subsequent levels of PROPERTY RIGHTS. In columns (1) – (3) of Table 4,
when we control for just the time-varying regressors (GDP per capita, Foreign Aid, Government
Spending, FDI, Free Trade, Government Size, Sound Money, and Regulation), the coefficient estimate on (lagged) PRIVATE credit falls within a narrow range between 0.0024 (t = 1.83) and
0.0030 (t = 2.33), with the coefficient estimates significant at the 10% level or lower. Even after
we control for all other static determinants of PROPERTY RIGHTS (membership in OPEC, legal
origin, dominant religion, ethnic fractionalization, and latitude), as we do in column (4) of Table 4,
the coefficient estimate on PRIVATE CREDIT remains a statistically significant 0.0031 (t = 2.34).
The level of a country’s financial development as proxied by PRIVATE CREDIT is a significant determinant of the subsequent quality of the country’s PROPERTY RIGHTS institutions. Although
unrelated to our main hypothesis, we find, as expected, a strong relation between a country’s wealth
(as measured by its GDP per capita) and PROPERTY RIGHTS. We also observe some evidence
that Government Size is negatively related to PROPERTY RIGHTS, and that the private ownership
of bank deposits (Bank Ownership) is positively related to PROPERTY RIGHTS.
However, as we noted in section 4, OLS results do not account for the unobservable hetero20
geneity across countries. The complex, path-dependent nature of institutional change across countries means that we have to account for unobservable factors which will affect the way in which
changes in financial development will influence institutional arrangements. To address these issues
we estimate (4.1) using the dynamic panel GMM estimator as described in section 4.
The results of the GMM estimates are shown in Table 5. As we saw with the OLS estimates, there is a strong relation between PRIVATE CREDIT and subsequent levels of PROPERTY
RIGHTS. Across all our GMM estimates, we find that the effects of PRIVATE CREDIT on PROPERTY RIGHTS are slightly higher but of similar magnitudes to those from our OLS estimates;
they range from 0.0044 (t = 1.67) to 0.0055 (t = 2.13). Thus the inference from the Dynamic
GMM fixed effects regression is not significantly different from that from our OLS regressions.
Overall, across the different specifications reported in Table 5, we find that a 1% increase in the
level of PRIVATE CREDIT (relative to GDP) is associated with an increase ranging from about
0.0044 to 0.0059 points in the quality of PROPERTY RIGHTS institutions. In addition, we find,
as we did in our OLS regressions, a positive relation between GDP per capita and PROPERTY
RIGHTS, a negative relation between Government Size and PROPERTY RIGHTS and a positive
relation between Bank Ownership and PROPERTY RIGHTS.
To get an idea of the economic significance of our point estimates of the effect of PRIVATE
CREDIT on PROPERTY RIGHTS in Table 5, we note the standard deviation of PRIVATE CREDIT
across our sample is about 41% while that of PROPERTY RIGHTS is about 1.9. Thus, the point
estimates from Table 5 suggest that a 1 standard deviation increase in PRIVATE CREDIT is associated with about a 0.1 standard deviation increase in PROPERTY RIGHTS. This is an economically
significant result. If Pakistan had increased its level of financial development over the period between 1965 and 2008 at the same rate as Malaysia (a Muslim, Asian country, with a similar British
colonial heritage, with which it had similar levels of private credit in 1965), the quality of its property rights institutions would have ranked it 14 places higher among 129 countries at the end of
2008. Similarly, if Guatemala had the same increase in its level of private credit as Panama, a
21
similar central American country, the quality of its property rights institutions would have ranked
it 7 places higher than it was in 2008.12
Overall, our empirical results, which are similar across our different OLS and GMM estimates,
suggest a strong positive causal relationship between financial innovation and the quality of property rights institutions. The results provide evidence that financial development is a determinant of
the cross-sectional differences in property rights institution across countries.
Table 5 also shows the results of a number of post-estimation tests of the validity of the dynamic
GMM specification as well as the validity of the instrument set. The first test is a test of serial
correlation. Table 5 shows the results of an AR(2) test of the null hypothesis of no second order
serial correlation. For the GMM estimates, if the assumptions of our specification are valid, by
construction the residuals in first differences should be correlated, but there should be no serial
correlation in second differences (AR(2)). Results of these tests confirm that this is the case: the
AR(2) test yields p-values that range from 0.22 to 0.55 across our different specifications.
The second test is a Hansen test of over-identification. The dynamic panel GMM estimator uses
multiple lags as instruments. This means that our system is over-identified and provides us with
an opportunity to carry out the test of over-identification. Table 5 shows the results of the Hansen
test for our GMM estimates. The Hansen test yields a J-statistic which is distributed χ 2 under
the null hypothesis of the validity of our instruments. The results in Table 5 reveal a J-statistics
with p-values that range from 0.98 to 1.00, and as such, we cannot reject the hypothesis that our
instruments are valid.
In Table 5, we also report the results from a test of the exogeneity of a subset of our instruments. As we discussed in section 4, the system GMM estimator makes an additional exogeneity
assumption: the assumption that any correlation between our endogenous variables and the unobserved (fixed) effect is constant over time (equation (4.4)). This is the assumption that enables us to
12 Pakistan
would have ranked 94 instead of 108 and Guatemala would have ranked 80 instead of 87.
22
include levels equations in our GMM estimates and use lagged differences as instruments for these
levels. Bond, Hoeffler, and Temple (2001) suggest that this assumption can be tested directly using
a difference-in-Hansen test of exogeneity. This test also yields a J-statistic which is distributed
χ 2 under the null hypothesis that the subset of instruments that we use in the levels equations are
exogenous. The results in Table 5 show p-values that range from 0.93 to 1.00 for the J-statistics
produced by the difference-in-Hansen test across our system GMM estimates. This implies that
we cannot reject the hypothesis that the additional subset of instruments used in the system GMM
estimates is indeed exogenous.13
6. Additional Analysis and Robustness Tests
6.1. An alternative measure of financial development
As a robustness check we use MARKET CAPITALIZATION (defined as the total value of outstanding shares as a fraction of GDP) as an alternative measure of financial development. Levine
and Zervos (1998), Beck et al. (2003), and Claessens and Laeven (2003), among others, also
use this measure as a proxy for financial development. This data is taken from the World Bank
database. Unfortunately, the data only goes back to the late 1980s for most countries, severely
limiting the time series we have available for some of our tests (such as dynamic panel GMM
estimation). Nevertheless, estimates from OLS regressions can provide a robustness check for our
analysis.
Columns (1) – (3) of Table 6 illustrate the results of OLS regressions of PROPERTY RIGHTS
on lagged values of MARKET CAPITALIZATION and other control variables. The results are
13 We
offer an important caveat to the interpretation of our results. There are undoubtedly many factors that may
jointly affect both institutional quality and financial development some of which we may not capture in our empirical
models. This means that even our carefully developed dynamic empirical model of financial development and institutional quality may still be misspecified. While the dynamic panel GMM estimator includes a number of specification
tests that test the validity of our instruments and the lag length selected for our dynamic specification, these tests do
not completely eliminate the possibility of mis-specification. While dynamic panel data estimation accounts for timeinvariant (fixed) unobservable heterogeneity, our inference could still be biased if there are unobserved time-varying
factors that affect both private credit and institutional quality.
23
very similar to those we obtained in Table 4 where we used PRIVATE CREDIT as a measure of
financial development. We document a strong positive relation between MARKET CAPITALIZATION and subsequent levels of PROPERTY RIGHTS. Taken together with our prior analysis,
these results provide additional evidence of the positive causal relationship between exogenous
changes in financial development and the quality of property rights and legal institutions.
6.2. Financial and institutional development in emerging and non-emerging market countries
Our hypothesis development in Section 2 suggests that the effect of financial development on
institutional development may be biggest in countries that are on the cusp of shifting from low
quality institutions to relatively high quality institutions. Small changes in the relative costs and
benefits of property rights institutions can lead to a rapid change in institutional arrangements in
countries that are at a tipping point. In contrast, countries with already relatively good institutions
will experience little change in institutional development even when the level of financial development increases. Similarly (at the opposite end of the spectrum), in countries with very low quality
institutions, slight increases in financial development may not increase the potential benefits of
higher quality institutions enough to lead to the demand and creation of these institutions. As such
we predict that the effect of financial development on institutional development will be biggest in
countries typically classified as “emerging market” countries.
To test this hypothesis, we identify, from our sample, a subset of countries that are conventionally defined as emerging market economies. The list of these countries is shown in Table 7.
Countries were added to the list if they were considered part of an emerging market index (as
of December 2010) by one of the following: the FTSE group, MSCI Barra, Standard and Poor’s
(S&P), Dow Jones, Frontier Strategy Group, Banco Bilbao Vizcaya Argentaria (BBVA) Research,
and Mastercard Emerging Market Index. We then replicate our analysis from column (3) of Table
5 on the subsets of emerging and non-emerging market countries. The result of the analysis is
shown in Table 8.
24
The results show that the effect of private credit on subsequent property rights institutions is
larger for emerging market countries than for other countries. The estimated coefficient of the
effect of private credit on property rights is 0.0092 (t = 2.12) in the sample of emerging market
countries, while it is 0.0032 (t = 1.47) in the sample of non-emerging market countries. The
difference between these two estimated coefficients is significant at the 10% level. While the
effect of financial development on the quality of institutional development is generally positive, it
is somewhat more positive in emerging market or transitional economies.
6.3. Which Components of Property Rights and Legal Institutions are Driven by Changes in Private Credit?
Our analysis thus far has focused on the effect of financial development on a broad property
rights index. However, while we carefully control for other general country specific characteristics
like GDP per capita, government spending, foreign direct investment and others, an argument can
be made that financial development should have the greatest effect on legal arrangements pertaining
specifically to the financial sector. While we maintain our hypothesis that financial development
has a broad impact on the quality of property rights in general, we explore the effect of financial
development on two specific areas directly related to private credit markets: judicial efficiency and
creditor rights.14
As we discussed in section 3, the PROPERTY RIGHTS index is made up of five indicators: Judicial independence; Impartial courts; Protection of intellectual property (IP Protection); Military
interference in rule of law and the political process; and Integrity of the legal system. In Table 9,
we examine which of these components is determined by changes in the level of financial development. We regress each of the components of the PROPERTY RIGHTS index on lagged PRIVATE
CREDIT and the other time-varying control variables described in section 4. Unfortunately, the
Economic Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)) has the split into
14 We
thank the referee for suggesting this additional analysis.
25
the five indicators going only as far back as 1990 which limits the length of the panel we can use
for our analysis and also limits us to OLS regressions. However, we believe even this length is long
enough to give us some indication of which factors are affected by financial market development.
The results suggest that four (Judicial independence, Impartial courts, IP Protection, and Integrity of the legal system) of the components of PROPERTY RIGHTS are significantly related
to past values of PRIVATE CREDIT. The magnitudes of the point estimates range from 0.0040
(t = 1.70) in the case of Judicial Independence to 0.0065 (t = 3.45) in the case of IP Protection;
these values fall within the range of those obtained for the full PROPERTY RIGHTS index and
longer time series reported in Tables 4 and 5. Only one of the components – Military interference – is not driven by changes in PRIVATE CREDIT. This supports our conjecture that judicial
efficiency (or the integrity of the legal system), as opposed to the general political environment
(military interference), is most likely to be affected by financial development.
We also examine the effect of financial development on changes in a specific set of creditor
rights identified by Djankov et al. (2007). As described in their paper, the creditor rights index
measures four powers of secured lenders in bankruptcy: (1) whether there are restrictions, such
as creditor consent, when a debtor files for reorganization; (2) whether secured creditors are able
to seize their collateral after the petition for reorganization is approved, that is, whether there is
no automatic stay or asset freeze imposed by the court; (3) whether secured creditors are paid
first out of the proceeds of liquidating a bankrupt firm; and (4) whether an administrator, and not
management, is responsible for running the business during the reorganization. A value of one is
added to the index when a country’s laws and regulations provide each of these powers to secured
lenders. The creditor rights index aggregates the scores and varies between 0 (poor creditor rights)
and 4 (strong creditor rights).
Djankov et al. (2007) identify 32 changes in the creditor rights index over the period between
1978 and 2003, and in Table 10, we examine the relation between changes in financial development
and changes in the creditor rights index. We regress changes in the creditor rights index on changes
26
in private credit (while controlling for changes in other factors including GDP per capita, foreign
aid, government spending and foreign direct investment). Our results show a positive relation
between changes in financial development and changes in creditor rights. While we maintain that
financial development has an effect on general level of institutional development in a country, the
results in Table 9 and 10 suggest that the effect is especially strong in those factors – judicial
efficiency and creditor rights – that are most easily identified with financial contracts.
6.4. Bi-directional causality and panel vector auto regressions
Our primary focus in this paper, and our theoretical and empirical development has been on
the effect of changes in financial development on subsequent changes in the quality of institutions.
However, our analysis does not preclude bi-directional causality. Financial sector development
could lead to improvement in the quality of legal institutions which could in turn lead to further
financial development.
We explore this potential bi-directional causality further using simultaneous panel vector auto
regressions (VARs) and formal Granger causality tests to further assess the direction of causality.
A variable x is said to Granger cause another variable y if we can reject the hypothesis that the
coefficients on the lags of variable x in the vector auto regression (VAR) equation of variable y are
zero (Granger, 1969).
Our tests are based on estimating panel VARs of the form:
p=k
yit = α +
fit = α +
p=k
∑ κ pyi,t−p + ∑ β p fi,t−p + γXi,t−1 + θ Zi + ηi + µt + εit
p=1
p=1
p=k
p=k
∑ β p fi,t−p + ∑ κ pyi,t−p + γXi,t−1 + θ Zi + ηi + µt + εit
p=1
(6)
(7)
p=1
where the variables are as described in sections 3 and 4. We conduct the tests for k = 1 to 2 lags.
The VARs are estimated using the dynamic panel data estimation estimator described in section 4.
27
The results of our Granger causality tests are presented in Table 11. Columns (1) and (2) show
the F-statistics and p-values of the results of the hypotheses tests that the lags of private credit in
the property rights VAR of equation (6) are zero. Columns (3) and (4) show the F-statistics and pvalues of the results of the hypotheses tests that the lags of property rights in the private credit VAR
of equation (7) are zero. The results show bi-directional causality: private credit Granger causes
property rights quality, and vice-versa. We are able to reject (at a ten percent level of significance)
the null that lags of private credit are not significant in predicting current property rights quality at
lag horizons up to two lags. We are also able to reject (at a ten percent level of significance) the
null that lags of property rights quality are not significant in predicting the level of private credit at
lag horizons up to two lags.
7. Conclusion
In this paper we have examined the role of financial development in the evolution of property
rights institutions. Our basic premise is that exogenous increases in the level of financial development increase the benefits of higher quality property rights institutions relative to the costs of
setting up and maintaining these institutions, and would lead to the demand for these institutions.
We predict that exogenous changes in the level of financial development would be positively related to subsequent changes in the quality of property rights institutions.
In a panel of 129 countries over the 43 year period between 1965 and 2008, we find strong
evidence to support this hypothesis. We document that the exogenous component of financial development is positively associated with the quality of property rights institutions. This finding is
robust to alternative measures of financial development, reverse causality, country fixed-effects,
and controls for other legal origins, historical, geographical and cultural determinants of institutional quality that have been identified in the literature. We also find some evidence that the positive
relation between financial development and institutional development is somewhat stronger in the
sample of emerging market countries.
28
The results of this paper have some interesting empirical implications for further research. The
focus of our study has been to aggregate private credit and property rights institutions across countries. Thus, we have abstracted from the potentially rich detail of institutional change within each
country. Future research may shed some light on the process by which exogenous changes in financial development occur within individual countries and the detailed mechanisms by which this
results in institutional evolution. A recent study by Boubakri, Cosset, and Smaoui (2009) provides
useful insights about these issues. The authors document that the sale of state-owned enterprises
through share issue privatizations is associated with improvements in the overall institutional environment in developing countries. This evidence is consistent with our results since share issue
privatizations foster financial development (Boutchkova and Megginson (2000); Megginson, Nash,
Netter, and Poulsen (2004)) which in turn promotes institutional changes.
29
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33
Table 1
Summary Statistics of PRIVATE CREDIT and PROPERTY RIGHTS
This table shows the summary statistics for our measures of financial development and property
rights. The proxy for financial development is PRIVATE CREDIT from the World Bank database.
PRIVATE CREDIT is the volume of private credit divided by the country’s GDP. PROPERTY
RIGHTS is the Legal Structure and Security of Property Rights Index from Economic Freedom of
the World: 2010 Annual Report (Gwartney et al. (2010)).
Variable
Year = 1970
Property Rights
Private Credit
Year = 1975
Property Rights
Private Credit
Year = 1980
Property Rights
Private Credit
Year = 1985
Property Rights
Private Credit
Year = 1990
Property Rights
Private Credit
Year = 1995
Property Rights
Private Credit
Year = 2000
Property Rights
Private Credit
Year = 2005
Property Rights
Private Credit
Year = 2008
Property Rights
Private Credit
N
Mean
Median
Min
Max
25 Pctl
75 Pctl
49
88
5.86
24.85
6.20
18.06
1.10
1.12
8.30
106.09
4.40
11.27
8.00
30.31
49
98
4.76
29.77
4.90
22.11
1.10
1.01
8.30
130.17
3.70
14.86
6.20
41.97
89
104
4.98
33.94
4.90
27.39
1.80
1.98
8.30
130.47
3.20
17.33
6.60
44.75
109
106
5.08
39.37
5.30
31.14
1.70
1.98
8.30
144.25
3.50
18.51
6.30
54.70
110
107
5.28
41.66
5.30
32.40
2.00
2.25
8.30
180.33
3.50
17.63
6.80
54.06
122
128
5.86
41.82
5.55
28.85
2.20
1.00
9.30
200.94
4.80
13.59
6.80
61.36
122
127
5.82
47.45
5.70
31.96
2.00
2.53
9.60
219.16
4.40
12.54
6.90
67.15
129
127
5.69
56.06
5.63
34.56
2.05
1.06
9.02
247.66
4.35
16.58
6.91
87.11
129
123
5.70
64.82
5.71
44.74
2.14
2.16
8.98
252.64
4.47
21.70
6.81
94.99
34
35
0.66a
−0.12a
0.29a
0.19a
0.49a
0.05
0.46a
0.49a
0.42a
0.30a
0.40a
−0.21a
0.35a
0.16a
0.60a
−0.05b
0.48a
0.49a
0.45a
0.23a
0.43a
Foreign Aid
Government Spending
FDI
Free Trade
Government Size
Regulation
Sound Money
Bank Ownership
Credit Extension
Interest Rate Control
1.00
0.74a
0.64a
Private Credit
Private
Credit
GDPPC
1.00
Property Rights
Property
Rights
0.38a
0.21a
0.53a
0.46a
0.46a
−0.02
0.60a
0.16a
0.36a
−0.16a
1.00
GDPPC
−0.06c
−0.01
−0.01
−0.09a
−0.01
−0.05
−0.01
0.09a
0.23a
1.00
Foreign
Aid
0.13a
−0.06c
0.19a
0.15a
0.10a
−0.53a
0.26a
0.02
1.00
Government
Spending
0.12a
0.12a
0.14a
0.15a
0.28a
0.03
0.18a
1.00
FDI
0.53a
0.44a
0.54a
0.54a
0.53a
0.11a
1.00
Free
Trade
0.24a
0.31a
0.34a
0.22a
0.36a
1.00
Government
Size
0.68a
0.55a
0.71a
0.47a
1.00
Regulation
0.68a
0.34a
0.46a
1.00
Sound
Money
0.48a
0.39a
1.00
Bank
Ownership
0.40a
1.00
Credit
Extension
1.00
Interest
Rate Control
The variables are defined as follows: PROPERTY RIGHTS is the Legal Structure and Security of Property Rights Index from Economic Freedom of the
World: 2010 Annual Report (Gwartney et al. (2010)); PRIVATE CREDIT is from the World Bank database and equals financial intermediary credits to
the private sector divided by the gross domestic product. The control variables are GDPPC (log of GDP per capita); FOREIGN AID (gross foreign aid
per capita); GOVERNMENT SPENDING (government expenditure divided by gross domestic product); FDI (foreign direct investment divided by gross
domestic product); FREE TRADE is the Freedom to Trade Internationally Index; GOVERNMENT SIZE is the Size of Government: Expenditures, Taxes,
and Enterprises Index; REGULATION is the Regulation of Credit, Labor, and Business Index; SOUND MONEY is Access to Sound Money Index; Bank
Ownership, Credit Extension and Interest Rate Control are Credit Market Regulation components of the REGULATION index. Indices are from Economic
Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). a, b and c represents significance at the 1%, 5% and 10% levels respectively.
Table 2
Correlation Table of Key Variables
Table 3
Property Rights and Financial Development: Univariate Analysis
This table compares changes in financial development to subsequent changes in property rights
across countries around the world. The proxy for financial development is PRIVATE CREDIT
from the World Bank database. PRIVATE CREDIT is the volume of private credit divided by the
country’s GDP. PROPERTY RIGHTS is the Legal Structure and Security of Property Rights Index
from Economic Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). Countries
are ranked and sorted into quartiles based on the change in the use of PRIVATE CREDIT in the
country between 1965 and 1985. Quartile 1 contains the countries with the least change in PRIVATE CREDIT between 1965 and 1985; Quartile 4 contains the countries with the most change.
The significance of the difference between Quartiles 4 and 1 is based on a t-test of the difference
in means. a represents significance at the 10% level or higher.
Panel B: Relationship between changes in private credit and subsequent changes in property rights
Quartile
Mean Change in Private Credit
between 1965 and 1985
Mean Change in Property Rights
between 1985 and 2005
N
1
−2.18
0.69
20
2
8.72
0.70
20
3
20.45
0.80
20
4
44.89
1.40
20
Difference (4−1)
47.07
0.71a
[t = 1.80]
36
Table 4
Property Rights and Financial Development: OLS Regression Estimates
The dependent variable is PROPERTY RIGHTS at time t which is the Legal Structure and Security of Property Rights Index from Economic
Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). The key explanatory variable is PRIVATE CREDIT from the World Bank
database and equals financial intermediary credits to the private sector divided by the gross domestic product. The control variables are GDPPC
(log of GDP per capita); FOREIGN AID (gross foreign aid per capita); GOVERNMENT SPENDING (government expenditure divided by gross
domestic product); FDI (foreign direct investment divided by gross domestic product); FREE TRADE is the Freedom to Trade Internationally
Index; GOVERNMENT SIZE is the Size of Government: Expenditures, Taxes, and Enterprises Index; REGULATION is the Regulation of Credit,
Labor, and Business Index; SOUND MONEY is Access to Sound Money Index; Bank Ownership, Credit Extension and Interest Rate Control are
Credit Market Regulation components of the REGULATION index; a dummy variable for OPEC if the country is a member of the organization of
Petroleum Exporting Countries; dummy variables for ENGLISH, FRENCH and GERMAN law; dummy variables for BUDDHIST, CATHOLIC,
MUSLIM, or PROTESTANT being the dominant religious group in the country; ETHNIC FRACTIONALIZATION, the probability that two
randomly selected individuals from a country are from different ethnic or linguistic groups; and LATITUDE. The base sample is an unbalanced
panel sampled every five years from 1965 – 2005 and in 2008 (such that if t = 1970, t − 1 = 1965 etc., except for t = 2008 where t − 1 = 2005).
t-statistics (in parentheses) are based on robust standard errors that are clustered by country. a, b and c represents significance at the 1%, 5% and
10% levels respectively. Year dummies are included in all specifications.
(1)
(2)
(3)
(4)
Property Rights (t − 1)
0.6142a
0.5665a
0.6027a
0.5052a
(14.32)
(10.46)
(13.51)
(10.82)
Private Credit (t − 1)
0.0030b
0.0024c
0.0027b
0.0031b
(2.33)
(1.83)
(2.31)
(2.34)
GDPPC (t − 1)
0.2533a
0.2662a
0.2090a
0.1664c
(5.63)
(5.74)
(4.30)
(2.88)
Foreign Aid (t − 1)
−0.0014
−0.0021c
−0.0014
−0.0014
(−1.42)
(−1.97)
(−1.27)
(−0.97)
Government Spending (t − 1)
0.0205a
0.0194b
0.0316a
0.0204
(2.98)
(2.07)
(3.08)
(1.53)
FDI (t − 1)
−0.0107b
−0.0152a
−0.0109b
−0.0081
(−2.26)
(−2.97)
(−2.10)
(−1.97)
Free Trade (t − 1)
0.0388
0.0441
0.0388
0.0928c
(1.04)
(1.03)
(1.04)
(1.76)
Government Size (t − 1)
−0.0434
−0.0186
−0.0317
(−0.91)
(−0.43)
(−0.75)
Sound Money (t − 1)
0.0245
0.0283
0.0576b
(1.29)
(1.43)
(2.29)
Regulation (t − 1)
0.0667
(1.46)
Bank Ownership (t − 1)
0.0076
0.0338c
(0.53)
(2.20)
Credit Extension (t − 1)
−0.0241
−0.0446c
(−1.15)
(−1.81)
Interest Rate Control (t − 1)
0.0148
−0.0063
(0.84)
(−0.34)
OPEC
−0.3334
(−1.53)
English
−0.0287
(−0.15)
French
−0.4352b
(−1.99)
German
−0.0371
(−0.18)
Buddhist
−0.3850c
(−1.81)
Catholic
−0.1942
(−1.28)
Muslim
−0.0841
(−0.46)
Protestant
−0.1344
(−0.70)
Ethnic Fractionalization
−0.3169
(−1.41)
Latitude
0.4729
(1.16)
Observations
624
518
507
411
Number of countries
124
116
119
85
R2
0.786
0.789
0.810
0.846
37
Table 5
Property Rights and Financial Development: Fixed Effects (Dynamic GMM) Estimates
The dependent variable is PROPERTY RIGHTS at time t which is the Legal Structure and Security of Property Rights
Index from Economic Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). The key explanatory
variable is PRIVATE CREDIT from the World Bank database and equals financial intermediary credits to the private
sector divided by the gross domestic product. The control variables are GDPPC (log of GDP per capita); FOREIGN
AID (gross foreign aid per capita); GOVERNMENT SPENDING (government expenditure divided by gross domestic
product); FDI (foreign direct investment divided by gross domestic product); FREE TRADE is the Freedom to Trade
Internationally Index; GOVERNMENT SIZE is the Size of Government: Expenditures, Taxes, and Enterprises Index;
REGULATION is the Regulation of Credit, Labor, and Business Index; SOUND MONEY is Access to Sound Money
Index; Bank Ownership, Credit Extension and Interest Rate Control are Credit Market Regulation components of the
REGULATION index. The base sample is an unbalanced panel sampled every five years from 1965 – 2005 and in
2008 (such that if t = 1970, t − 1 = 1965 etc., except for t = 2008 where t − 1 = 2005). All regressions are carried
out using Dynamic GMM Panel Estimation. t-statistics (in parentheses) are based on robust standard errors. a, b and
c represents significance at the 1%, 5% and 10% levels respectively. Year dummies are included in all specifications.
AR(1) and AR(2) are tests of first-order and second-order serial correlation in the first-differenced residuals, under the
null of no serial correlation. Hansen test of over-identification is under the null that all instruments are valid. Diffin-Hansen tests of exogeneity is under the null that instruments used for the equations in levels are exogenous. The
instruments used in the GMM estimation are: differenced equations: yit−2 , yit−3 , Xit−2 , Xit−3 , ∆Zit ; level equations:
∆yit−1 , ∆Xit−1 , Zit .
(1)
(2)
(3)
Property Rights (t − 1)
0.3781a
0.4536a
0.4815a
(4.39)
(5.56)
(5.42)
Private Credit (t − 1)
0.0044c
0.0055b
0.0039b
(1.67)
(2.13)
(2.18)
GDPPC (t − 1)
0.5375a
0.4050a
0.3003a
(5.39)
(4.38)
(3.28)
Foreign Aid (t − 1)
−0.0015
−0.0022c
−0.0015
(−1.34)
(−1.95)
(−1.32)
Government Spending (t − 1)
−0.0046
−0.0062
−0.0005
(−0.45)
(−0.56)
(−1.32)
FDI (t − 1)
−0.0170
−0.0226b
−0.0203
(−3.07)
(−2.53)
(−3.07)
Free Trade (t − 1)
0.1352b
0.0831
0.0747
(2.04)
(1.30)
(1.09)
Government Size (t − 1)
−0.0749
−0.1152b
(−1.25)
(−1.98)
Sound Money (t − 1)
0.0116
0.0284
(0.42)
(0.85)
Regulation (t − 1)
0.0691
(0.80)
Bank Ownership (t − 1)
0.0395c
(1.67)
Credit Extension (t − 1)
−0.0360
(−1.03)
Interest Rate Control (t − 1)
0.0263
(1.08)
AR(1) test [p-value]
[0.00]
[0.00]
[0.00]
AR(2) test [p-value]
[0.29]
[0.55]
[0.22]
Hansen test of over-identification [p-value]
[0.98]
[1.00]
[1.00]
Diff-in-Hansen tests of exogeneity [p-value]
[1.00]
[1.00]
[1.00]
No. of observations
624
518
507
No. of countries
124
116
119
38
Table 6
Property Rights and Financial Development: Using Market Capitalization As a Measure of Financial Development
The dependent variable is PROPERTY RIGHTS at time t which is the Legal Structure and Security of Property Rights
Index from Economic Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). The key explanatory
variable is MARKET CAPITALIZATION from the World Bank database and equals the total market value of the publicly traded companies in the country divided by the gross domestic product. Other explanatory variable are PRIVATE
CREDIT from the World Bank database which equals financial intermediary credits to the private sector divided by
the gross domestic product; GDPPC (log of GDP per capita); FOREIGN AID (gross foreign aid per capita); GOVERNMENT SPENDING (government expenditure divided by gross domestic product); FDI (foreign direct investment
divided by gross domestic product); FREE TRADE is the Freedom to Trade Internationally Index; GOVERNMENT
SIZE is the Size of Government: Expenditures, Taxes, and Enterprises Index; REGULATION is the Regulation of
Credit, Labor, and Business Index; SOUND MONEY is Access to Sound Money Index; Bank Ownership, Credit
Extension and Interest Rate Control are Credit Market Regulation components of the REGULATION index. The
base sample is an unbalanced panel sampled every five years from 1990 – 2005 and in 2008 (such that if t = 1990,
t − 1 = 1985 etc., except for t = 2008 where t − 1 = 2005). Results are from OLS regressions. t-statistics (in parentheses) are based on robust standard errors. a, b and c represents significance at the 1%, 5% and 10% levels respectively.
Year dummies are included in all specifications.
(1)
(2)
(3)
a
a
Property Rights (t − 1)
0.6624
0.5381
0.6157a
(14.08)
(8.94)
(11.94)
Market Capitalization (t − 1)
0.0020b
0.0023c
0.0021b
(2.49)
(1.93)
(2.29)
GDPPC (t − 1)
0.1571b
0.2304a
0.1689b
(2.45)
(2.94)
(2.62)
Foreign Aid (t − 1)
0.0003
0.0004
0.0009
(0.35)
(0.35)
(0.79)
Government Spending (t − 1)
0.0383a
0.0292b
0.0344a
(4.16)
(2.18)
(2.94)
a
a
FDI (t − 1)
−0.0161
−0.0173
−0.0139a
(−4.02)
(−3.94)
(−3.27)
Free Trade (t − 1)
0.0737
0.0462
0.0661
(1.31)
(0.77)
(1.24)
Government Size (t − 1)
−0.0849
−0.0249
(−1.54)
(−0.51)
Sound Money (t − 1)
0.0564c
0.0608b
(1.82)
(2.05)
Regulation (t − 1)
0.0845
(1.00)
Bank Ownership (t − 1)
−0.0209
(−1.09)
Credit Extension (t − 1)
−0.0144
(−0.48)
Interest Rate Control (t − 1)
0.0251
(1.00)
Observations
271
216
260
No. of countries
98
88
95
R2
0.826
0.819
0.837
39
Table 7
Countries classified as emerging market economies
The following countries were classified as emerging market economies over the sample period of
1965 – 2008. Countries were added to the list if they were considered part of an emerging market
index (as of December 2010) by one of the following: the FTSE group, MSCI Barra, Standard and
Poor’s (S&P), Dow Jones, Frontier Strategy Group, Banco Bilbao Vizcaya Argentaria (BBVA)
Research, and Mastercard Emerging Market Index.
Argentina
Bangladesh
Brazil
Bulgaria
Chile
China
Colombia
Czech Republic
Dominican Republic
Ecuador
Egypt
Estonia
Hong Kong
Hungary
India
Indonesia
Iran
Jordan
Kenya
Kuwait
Latvia
Lithuania
Malaysia
Mauritius
Mexico
Morocco
Nigeria
Oman
Pakistan
Peru
Philippines
Poland
Romania
Russia
Senegal
Singapore
Slovak Republic
South Africa
South Korea
Sri Lanka
Thailand
Tunisia
Turkey
Ukraine
United Arab Em.
Uruguay
Venezuela
Vietnam
40
Table 8
Property Rights and Financial Development: Emerging vs. Non-emerging markets
The dependent variable is PROPERTY RIGHTS at time t which is the Legal Structure and Security of Property Rights
Index from Economic Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). The key explanatory
variable is PRIVATE CREDIT from the World Bank database and equals financial intermediary credits to the private
sector divided by the gross domestic product. The control variables are GDPPC (log of GDP per capita); FOREIGN
AID (gross foreign aid per capita); GOVERNMENT SPENDING (government expenditure divided by gross domestic
product); FDI (foreign direct investment divided by gross domestic product); FREE TRADE is the Freedom to Trade
Internationally Index; GOVERNMENT SIZE is the Size of Government: Expenditures, Taxes, and Enterprises Index;
REGULATION is the Regulation of Credit, Labor, and Business Index; SOUND MONEY is Access to Sound Money
Index; Bank Ownership, Credit Extension and Interest Rate Control are Credit Market Regulation components of
the REGULATION index. Emerging market countries are as defined in Table 7. The base sample is an unbalanced
panel sampled every five years from 1965 – 2005 and in 2008 (such that if t = 1970, t − 1 = 1965 etc., except for
t = 2008 where t − 1 = 2005). All regressions are carried out using Dynamic GMM Panel Estimation. t-statistics
(in parentheses) are based on robust standard errors. a, b and c represents significance at the 1%, 5% and 10%
levels respectively. Year dummies are included in all specifications. AR(1) and AR(2) are tests of first-order and
second-order serial correlation in the first-differenced residuals, under the null of no serial correlation. Hansen test of
over-identification is under the null that all instruments are valid. Diff-in-Hansen tests of exogeneity is under the null
that instruments used for the equations in levels are exogenous. The instruments used in the GMM estimation are:
differenced equations: yit−2 , yit−3 , Xit−2 , Xit−3 , ∆Zit ; level equations: ∆yit−1 , ∆Xit−1 , Zit .
All
Emerging
Non-Emerging
Countries
Markets
Markets
Property Rights (t − 1)
0.4815a
0.6628a
0.5343a
(5.42)
(3.79)
(6.80)
Private Credit (t − 1)
0.0039b
0.0092b
0.0032
(2.18)
(2.12)
(1.47)
GDPPC (t − 1)
0.3003a
0.0841
0.2785a
(3.28)
(0.42)
(3.03)
Foreign Aid (t − 1)
-0.0015
0.0010
-0.0021
(-1.32)
(0.53)
(-1.38)
Government Spending (t − 1)
-0.0005
0.0404
0.0155
(-0.03)
(1.03)
(0.58)
FDI (t − 1)
-0.0203a
-0.0134
-0.0151a
(-3.07)
(-0.22)
(-2.94)
Free Trade (t − 1)
0.0747
0.0312
0.0393
(1.09)
(0.14)
(0.44)
Government Size (t − 1)
-0.1152b
0.0744
-0.1185
(-1.98)
(0.52)
(-1.40)
Sound Money (t − 1)
0.0284
-0.0389
-0.0039
(0.85)
(-0.38)
(-0.06)
Bank Ownership (t − 1)
0.0396c
-0.0479
0.0421c
(1.67)
(-0.69)
(1.70)
Credit Extension (t − 1)
-0.0360
-0.0054
-0.0175
(-1.03)
(-0.06)
(-0.43)
Interest Rate Control (t − 1)
0.0263
-0.0233
0.0341
(1.08)
(-0.27)
(0.60)
AR(1) test [p-value]
[0.00]
[0.00]
[0.00]
AR(2) test [p-value]
[0.22]
[0.31]
[0.68]
Hansen test of over-identification [p-value]
[1.00]
[1.00]
[1.00]
Diff-in-Hansen tests of exogeneity [p-value]
[1.00]
[1.00]
[1.00]
No. of observations
507
204
303
No. of countries
119
46
73
41
Table 9
Property Rights and Financial Development: Which Components of Property Rights and
Legal Institutions are Driven by Changes in Private Credit?
The dependent variables are Judicial Independence, Impartial Courts, Intellectual Property (IP) Protection, Military Interference and Legal System
Integrity all of which are components of the PROPERTY RIGHTS at time t which is the Legal Structure and Security of Property Rights Index from
Economic Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). The key explanatory variable is PRIVATE CREDIT from the World
Bank database which equals financial intermediary credits to the private sector divided by the gross domestic product. Other explanatory variables
are GDPPC (log of GDP per capita); FOREIGN AID (gross foreign aid per capita); GOVERNMENT SPENDING (government expenditure
divided by gross domestic product); FDI (foreign direct investment divided by gross domestic product); FREE TRADE is the Freedom to Trade
Internationally Index; GOVERNMENT SIZE is the Size of Government: Expenditures, Taxes, and Enterprises Index; REGULATION is the
Regulation of Credit, Labor, and Business Index; SOUND MONEY is Access to Sound Money Index. The base sample is an unbalanced panel
sampled every five years from 1990 – 2005 and in 2008 (such that if t = 1990, t − 1 = 1985 etc., except for t = 2008 where t − 1 = 2005). Results
are from OLS regressions. t-statistics (in parentheses) are based on robust standard errors. a, b and c represents significance at the 1%, 5% and 10%
levels respectively. Year dummies are included in all specifications.
Judicial
Impartial
IP
Military
Legal System
Independence
Courts
Protection
Interference
Integrity
Judicial Independence (t − 1)
0.7709a
(8.31)
0.7395a
(13.46)
Impartial Courts (t − 1)
0.5150a
(5.95)
IP Protection (t − 1)
0.8828a
(17.33)
Military Interference (t − 1)
Legal System Integrity (t − 1)
Private Credit (t − 1)
GDPPC (t − 1)
Foreign Aid (t − 1)
Government Spending (t − 1)
FDI (t − 1)
Free Trade (t − 1)
Government Size (t − 1)
Sound Money (t − 1)
Regulation (t − 1)
Observations
R-squared
0.0040c
(1.71)
−0.0199
(−0.16)
−0.0006
(−0.19)
0.0198
(0.79)
−0.0020
(−0.35)
0.2711
(1.45)
−0.0475
(−0.49)
0.0292
(0.44)
0.1549
(0.72)
209
0.844
0.0048b
(2.58)
−0.0143
(−0.23)
−0.0020
(−1.05)
0.0018
(0.11)
−0.0161a
(−2.91)
0.0695
(0.76)
−0.1654a
(−2.92)
0.0235
(0.63)
0.1910b
(2.16)
216
0.809
42
0.0065a
(3.45)
0.2215c
(1.98)
−0.0015
(−0.74)
0.0300
(1.54)
−0.0084
(−1.56)
0.1623
(1.37)
−0.1643b
(−2.33)
0.0489
(1.23)
0.2100a
(1.69)
126
0.865
0.0004
(0.16)
0.0722
(0.60)
−0.0012
(−0.28)
0.0551b
(2.56)
−0.0015
(−0.26)
0.1909
(1.53)
0.0548
(0.72)
−0.0506
(−1.02)
−0.0446
(−0.38)
211
0.822
0.4217a
(6.13)
0.0060c
(1.87)
0.1866
(1.35)
−0.0020
(−0.46)
0.0081
(0.27)
0.0013
(0.16)
0.0722
(0.46)
−0.3330a
(−3.22)
0.1176
(1.42)
0.1068
(0.58)
202
0.651
Table 10
The effect of changes in financial development on changes in creditor rights
The dependent variable is the change in creditor rights from time t − 1 to t, where the index of creditor rights comes
from Djankov et al. (2007). The key explanatory variable is the change in PRIVATE CREDIT from the World Bank
database and equals financial intermediary credits to the private sector divided by the gross domestic product. The
control variables are changes in GDPPC (log of GDP per capita); FOREIGN AID (gross foreign aid per capita);
GOVERNMENT SPENDING (government expenditure divided by gross domestic product); and FDI (foreign direct
investment divided by gross domestic product). Emerging market countries are as defined in Table 7. The base sample
is an unbalanced panel sampled every five years from 1978 – 2003. All results are based on OLS regression. t-statistics
(in parentheses) are based on robust standard errors. a, b and c represents significance at the 1%, 5% and 10% levels
respectively.
All
Emerging
Non-Emerging
Countries
Markets
Markets
∆Private Credit
0.0017b
(2.20)
0.0018b
(2.25)
0.0016c
(1.66)
∆GDPPC
0.0544
(0.67)
0.0436
(0.50)
0.0820
(0.70)
∆Foreign Aid
0.0004
(1.56)
-0.0001
(−0.09)
0.0005
(1.48)
−0.0004
(−0.13)
0.0014
(0.38)
−0.0013
(−0.28)
∆FDI
0.0004
(0.17)
0.0111b
(2.19)
0.0000
(0.00)
R2
0.494
0.1097
0.0538
374
119
133
46
241
73
∆Government Spending
No. of observations
No. of countries
43
Table 11
Bi-directional causality and panel vector auto regressions
The table presents the results of Granger causality tests applied to the panel vector auto regression (VAR) of the
following equations:
p=k
yit = α +
p=k
∑ κ p yi,t−p + ∑ β p fi,t−p + γXi,t−1 + ηi + µt + εit
p=1
p=k
fit = α +
p=1
p=k
∑ β p fi,t−p + ∑ κ p yi,t−p + γXi,t−1 + ηi + µt + εit
p=1
p=1
y is PROPERTY RIGHTS at time t which is the Legal Structure and Security of Property Rights Index from Economic
Freedom of the World: 2010 Annual Report (Gwartney et al. (2010)). f is PRIVATE CREDIT from the World
Bank database and equals financial intermediary credits to the private sector divided by the gross domestic product.
X consists of GDPPC (log of GDP per capita); FOREIGN AID (gross foreign aid per capita); GOVERNMENT
SPENDING (government expenditure divided by gross domestic product); FDI (foreign direct investment divided by
gross domestic product); FREE TRADE is the Freedom to Trade Internationally Index; GOVERNMENT SIZE is the
Size of Government: Expenditures, Taxes, and Enterprises Index; REGULATION is the Regulation of Credit, Labor,
and Business Index; SOUND MONEY is Access to Sound Money Index; Bank Ownership, Credit Extension and
Interest Rate Control are Credit Market Regulation components of the REGULATION index. The base sample is an
unbalanced panel sampled every five years from 1965 – 2005 and in 2008 (such that if t = 1970, t − 1 = 1965 etc.,
except for t = 2008 where t − 1 = 2005). Columns (1) and (2) show the F-statistics and p-value of the results of the
hypotheses tests that the lags of PRIVATE CREDIT ( f ) in the PROPERTY RIGHTS (y) equation are zero. Columns
(3) and (4) show the F-statistics and p-value of the results of the hypotheses tests that the lags of PROPERTY RIGHTS
(y) in the PRIVATE CREDIT ( f ) equation are zero.
H0 : PRIVATE CREDIT does not cause
PROPERTY RIGHTS
H0 : PROPERTY RIGHTS do not cause
PRIVATE CREDIT
(1)
(2)
(3)
(4)
k
F-statistic
(p-value)
F-statistic
(p-value)
1
3.62
(0.06)
3.35
(0.07)
2
5.14
(0.08)
3.20
(0.10)
44
Fig. 1: Property Rights and Financial Development. This scatter plot compares changes in financial development to subsequent changes in the quality of property rights institutions across
countries around the world. The proxy for financial development is PRIVATE CREDIT from the
World Bank database. PRIVATE CREDIT is the volume of private credit divided by the country’s
GDP (expressed in percentage terms). PROPERTY RIGHTS is the Legal Structure and Security
of Property Rights Index from Economic Freedom of the World: 2010 Annual Report (Gwartney
et al. (2010)).
Change in Property Rights (1985 – 2005)
5
4
3
2
y = 0.0126x + 0.6689
1
0
‐20
‐10
0
10
20
30
40
50
60
70
80
‐1
‐2
‐3
Change in Private Credit (1965 – 1985)
45