1. finance
2. personal finance
3. financial planning

Financial Development and Genetic Diversity
Eric Cardella, Ivalina Kalcheva, and Danjue Shang*
March 30, 2015
Abstract
It is well documented that there is substantial variation in the level of financial development
across countries, which research has been trying to explain. In this paper, we investigate how a
deep-rooted characteristic – a country’s degree of genetic diversity – impacts the level of
financial development. We hypothesize that a country’s degree of genetic diversity can impact its
level of financial development through two channels: (i) directly through its effect on innovation
in the financial sector, and (ii) indirectly through its effect on productivity and the subsequent
demand for financial development. Extending the argument put forth by Ashraf and Galor
(2013), we predict a hump-shaped relationship between a country’s degree of genetic diversity
and its level of financial development. Using data from almost 150 countries, our cross-sectional
analysis reveals results that are consistent with our prediction; namely, we observe a significant
and robust hump-shaped effect of a country’s degree of genetic diversity on its level of financial
development. Further, we show that both average years of schooling and the quality of social
infrastructure within a country are positively associated with the level of financial development.
Keywords: financial development, genetic diversity
JEL classification codes: G1, G2, O1, O4, 05
*
Cardella is from Texas Tech University. Kalcheva is from University of California, Riverside and Shang is
from University of Arizona. The emails for the authors are [email protected], [email protected], and
[email protected]. The authors thank Hank Bessembinder, Laura Cardella, Scott Cederburg, Daniel
Folkinshteyn, Rawley Z. Heimer and Feng Yang, as well as seminar participants at the University of Arizona, the
2015 Conference of the Midwest Finance Association, and the 2015 Conference of the Southwestern Finance
Association.
Abstract
It is well documented that there is substantial variation in the level of financial development
across countries, which research has been trying to explain. In this paper, we investigate how a
deep-rooted characteristic – a country’s degree of genetic diversity – impacts the level of
financial development. We hypothesize that a country’s degree of genetic diversity can impact its
level of financial development through two channels: (i) directly through its effect on innovation
in the financial sector, and (ii) indirectly through its effect on productivity and the subsequent
demand for financial development. Extending the argument put forth by Ashraf and Galor
(2013), we predict a hump-shaped relationship between a country’s degree of genetic diversity
and its level of financial development. Using data from almost 150 countries, our cross-sectional
analysis reveals results that are consistent with our prediction; namely, we observe a significant
and robust hump-shaped effect of a country’s degree of genetic diversity on its level of financial
development. Further, we show that both average years of schooling and the quality of social
infrastructure within a country are positively associated with the level of financial development.
1 Introduction
There exists a mature and wide-ranging body of literature suggesting, both theoretically and
empirically, that a country’s level of development in the financial sector is an important
component of its economic growth.1 In particular, a well-developed financial sector can serve
many integral functions including: reducing transaction and information costs, pooling and
managing risk, allocating resources, mobilizing savings, performing entrepreneurial screening,
and facilitating trade. Levine (1997), who provides a discussion of the importance of financial
development to growth and surveys the literature, notes: “a growing body of work would push
even most skeptics toward the belief that the development of financial markets and institutions is
a critical and inextricable part of the growth process.” (p. 689)
While the potential advantages of financial development have been identified and empirically
documented, there exists substantial heterogeneity in the level of financial development across
countries (Beck et al., 2003; Rajan and Zingales, 2003). This has spurred substantial research
attention aimed at identifying the possible factors that can affect financial development. The
result is a growing body of literature identifying many contributing factors to a country’s level of
financial development. These factors include: (i) a country’s level of economic development and
the subsequent demand for financial development2 (Rajan and Zingales, 2003; Ang and
1
Examples of papers documenting the importance of financial development include: Greenwood and Jovanovic
(1990), Roubini and Sala-i-Martin (1992), King and Levine (1993), Atje and Jovanovic (1993), Pagano (1993), De
Gregorio and Guidotti (1995), Jayaratne and Strahan (1996), Rajan and Zingales (1998), Demirgüç-Kunt and
Maksimovic (1998), Beck et al. (2000), Beck and Levine (2002), Carlin and Mayer (2003), Aghion et al. (2005),
Brown et al. (2009), and Hsu et al. (2014). See also Levine (1997), Rajan and Zingales (2001), Levine (2005), and
Zingales (2015) for additional survey-style discussions related to the link between finance development and growth.
The idea that financial development spurs economic growth is consistent with the supply side arguments suggested
by Schumpeter (1912) and Hicks (1969), where increases in the supply of financial development lead to increases in
economic growth.
2
This demand-side story for increased financial development is in line with Robinson’s (1952) argument that
“where enterprise leads, finance follows.”
3
McKibbin, 2007), (ii) a country’s level of trade and/or capital openness (Rajan and Zingales,
2003; Chinn and Ito, 2006), (iii) a country’s legal system origin, which is typically classified as
common law or civil law (La Porta et al., 1997, 1998; Beck et al., 2003), (iv) a country’s cultural
factors (Stulz and Williamson, 2003), and (v) a country’s natural endowment, which is typically
characterized by the geographical landscape and/or the disease environment (Acemoglu et al.,
2001; Beck et al., 2003). This extant literature is suggestive of the notion that many structural
sources can play an important role in shaping the cross-sectional variation in a country’s level of
financial development.
In this paper, we investigate how a more deep-rooted factor that might play a role in
explaining the heterogeneity in the level of financial development across countries – a country’s
degree of genetic diversity among its population. Using cross-sectional data from almost 150
countries, we explore if and to what extent a country’s aggregate degree of genetic diversity is
associated with its level of financial development. In our analysis, we consider several different
proxy measures of financial development, as well as control for other structural factors shown to
be associated with financial development as mentioned above.
The aim of this paper is to shed light on how country-level genetic diversity can possibly
affect an important country-level aggregate outcome – the country’s level of financial
development. Recently, a burgeoning field of research has emerged that lies at the intersection of
genetics and economics/finance, which focuses on identifying if and to what extent genetic
variation can account for differences in individual preferences, economic/financial decision
making, and outcomes (see Ebstein et al., 2010; Beauchamp et al., 2011; and Benjamin et al.,
2012 for thorough discussions and reviews of the literature). In general, this recent research
suggests that genetics can play a role in shaping economic/financial decision making and
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outcome. For example, genetic variations have been shown to impact individuals’: investment
biases (Cronqvist and Siegel, 2014), portfolio allocation choices (Barnea at al., 2010; Cesarini et
al., 2010), risk preferences (Cesarini et al., 2009; Zyphur et al., 2009; Kuhnen and Chiao, 2009;
Dreber et al., 2009), decision biases (Cesarini et al., 2012), income (Taubman, 1976; Benjamin et
al., 2012), cooperative tendencies and pro social behavior (Wallace et al., 2007; Cesarini et al.,
2008; Israel et al., 2009). While much of this previous literature has explored the effect of
genetic variation at the individual level, we explore the possible aggregate influence of genetic
variation at the country level. In doing so, our study complements the recent extant literature
related to genetics and economics/finance, as well as contributes more broadly to our
understanding of how genetic variation can play a role in shaping important financial outcomes.
In measuring a country’s degree of genetic diversity, we borrow the proxy recently
developed and used by Ashraf and Galor (2013) (A&G henceforth). Specifically, A&G build on
the work of population genetics by Ramachandran et al. (2005) and construct a country-level
measure of (predicted) genetic diversity that is based on migratory distance of the country from
East Africa. The basic idea behind this measure, as noted by A&G, is that “migratory distance
from the cradle of humankind in East Africa had an adverse effect on the degree of genetic
diversity within ancient indigenous settlements across the globe” (p. 2). We provide more
discussion of genetic diversity and how A&G construct their measure of predicted genetic
diversity in Section 3. A&G develop a stylized model where genetic diversity is predicted to
have a concave or “hump-shaped” effect on economic development. In particular, as the genetic
diversity of a country increases, they postulate that there is a beneficial effect of development
from expansion in the production possibilities frontier, which allows for the development and
implementation of new technologies. However, they also postulate that too high a degree of
5
genetic diversity can have a disadvantageous effect on development because of coordination
problems and mistrust, which lowers cooperation among the population and the ability to
produce at the production possibilities frontier. Using cross-sectional, country-level data the
authors find empirical support for their model of a hump-shaped effect of a country’s degree of
genetic diversity on its (proxied for) level of economic development.
Consistent with the arguments posited by A&G, we hypothesize that a country’s degree of
genetic diversity can impact its level of financial development. We posit that there are two ways
through which genetic diversity can influence financial development. The first is through the
direct effect on innovation in the financial sector. In particular, there will be a hump-shaped
relation between a country’s level of genetic diversity and innovation in the financial sector,
which in turn will impact the level of financial development via the supply of financial
development. The second is through its indirect effect on economic development. A&G
document a hump-shaped relation between genetic diversity and economic development. As a
result, this will lead to a corresponding change in the demand for and, subsequently, the level of
financial development in a country. Hence, the degree of genetic diversity in a country can
impact its level of financial development through two possible channels: (i) directly through its
effect on financial innovation, and (ii) indirectly through its effect on the demand for financial
development (working through the effect on economic development). Moreover, the effect of
genetic diversity on financial development is hump-shaped through both channels, and, hence,
we predict an overall hump-shaped relation between genetic diversity and financial development.
To empirically investigate our prediction, we use a cross-sectional analysis for around 150
countries. For each country, we gather data on its level of financial development from the World
Bank, a measure of its degree of genetic diversity developed by A&G, as well as other controls.
6
To measure a country’s level of financial development, we use several different proxies that have
been employed by previous research studies: (i) market capitalization, (ii) value of stocks traded,
(iii) number of stocks listed, and (iv) expropriation risk of foreign investment (Rajan and
Zingales, 2003; Beck et al., 2003; and Stulz and Williamson, 2003).
As predicted, our cross-sectional analysis in year 2000 CE yields a significant hump-shaped
relation between a country’s degree of genetic diversity and the proxy measures of financial
development we consider. This result is generally robust even after controlling for other possible
factors that may affect financial development including: a country’s openness, legal origin,
natural endowment, education level, and social infrastructure. To attempt to separate out the
effect of genetic diversity of financial development coming directly through financial innovation,
rather than through the demand for financial development, we include a proxy for the demand of
financial development (per capital GDP) as an independent control variable. Even after
controlling for the demand for financial development, a persistent significant hump-shaped
relation between genetic diversity and financial development exists. This is consistent with our
prediction that genetic diversity has a hump-shaped effect on financial innovation, which then
impacts the level of financial development. Again, these results are robust even after controlling
for other established factors that have been shown to impact financial development. Lastly, we
show that both the average years of schooling and the quality of social infrastructure are
positively associated with a country’s level of financial development. This result points to the
important and complex interplay between genetic and environmental factors, i.e., nature and
nurture (Coll et al., 2003), in influencing financial development at the county level.
We view our study as contributing broadly to the area of research aimed at identifying
possible factors that can affect a country’s level of financial development. While much of the
7
previous literature (cited earlier) has focused primarily on the role of structural, institutional,
cultural, or political factors in shaping financial development, we take a complementary
approach by investigating a more deep-rooted characteristic of a country – its degree of genetic
diversity. We document a robust link between a country’s degree of genetic diversity and its
level of financial development. Furthermore, we show that the hump-shaped relation between
genetic diversity and financial development persists even after controlling for GDP (a proxy for
the demand for financial development), which suggests a direct relation between genetic
diversity and the supply of financial development via financial innovation. Given the important
and inextricable role that financial development plays in economics growth, it is important to
understand the factors that affect financial development (Beck et al., 2003); the insights gleaned
from this study contribute to this understanding.
The remainder of the paper is organized as follows. Section 2 develops the main hypothesis
of the paper regarding the effect of genetic diversity on financial development; Section 3
explains the design of our empirical tests, the data, and the construction of the variables we use
in the analysis; Sections 4 and 5 present the results; and Section 6 concludes.
2 Hypothesis Development
We proceed by motivating the main hypothesis of our study; namely, that a country’s degree
of genetic diversity plays a role in shaping its level of financial development. We predict that this
effect of genetic diversity will come through two channels: (i) directly, by impacting the level of
financial innovation in a country and the subsequent supply of financial development, and (ii)
indirectly, by impacting the level of economic development in a country and the subsequent
8
demand for financial development. Our jumping off point is the predictions and empirical
findings documented in A&G.
In particular, A&G explore the effect of a country’s degree of genetic diversity on its level of
economic growth. In doing so, they posit a model where an increase in genetic diversity has both
a beneficial and a detrimental effect on economic productivity. With regard to the beneficial
effect, they argue that an increase in genetic diversity widens the spectrum of traits across the
population. This, in turn, will enable higher levels of productivity through specialization of the
labor force, complementarities across these different specialized tasks, and a larger concentration
of higher-cognitive ability individuals. As stated by A&G, “higher diversity therefore enhances
society’s capability to integrate advanced and more efficient production methods, expanding the
economy’s production possibility frontier and conferring the benefits of improved productivity”
(p. 3). However, there is a possible detrimental impact on economic development resulting from
too high a degree of genetic diversity that arises through decreases in trust and cooperation
among the population, which, consequently, decreases the production efficiency of a country
relative to its production possibilities frontier. A&G argue that the interplay between the
beneficial and detrimental effects of genetic diversity is predicted to result in a hump-shaped
relation of genetic diversity and a country’s level of economic development. A&G proceed by
documenting robust empirical evidence of this predicted hump-shaped effect of genetic diversity.
The first distinct channel through which genetic diversity can impact financial development
is financial innovation and the subsequent supply of financial development. We consider
financial innovation in a broad sense to represent any new technologies, advancements, and/or
improvements in all of the possible functions of the financial sector. As discussed in Frame and
White (2004), this includes but is not limited to: new products, new services, new processes, and
9
new organizational forms, each of which facilitate and/or improve the functioning of the
financial sector. Both the prevalence and significance of financial innovation, especially during
the 20th century, have been extensively recognized (Miller, 1986; Merton, 1992; Allen and Gale,
1994; Tufano, 2003; Frame and White, 2004; Lerner, 2006). Moreover, Lerner (2006) points to
the importance of financial innovation within the financial sector as well industries outside the
financial sector; similarly, Frame and White (2004) note the direct and indirect benefits of
financial innovation.
Laeven et al. (2015) explicitly model financial innovation as an important factor that affects
economic growth. In their model, financial innovation results from the outcome of profitmaximizing financiers. The authors assert that financial innovation, via advancements and
improvements in entrepreneurial screening technologies, plays an important role as it increases
the likelihood of investing in the most promising technologies. Furthermore, the authors note that
there must be financial innovation, concurrent with technological innovation, to avoid stagnation.
Relatedly, Tufano (1989) hypothesizes that there is a first-mover advantage with regard to
financial innovation, and documents empirical evidence consistent with this hypothesis. Cardella
et al. (2014) assert that the intention to innovate the trading process has been fierce in recent
years primarily through the computerization of the trading process. Hence, a benefit is conferred
to financial innovators, which serves as a motivation to innovate that is distinct from the abovementioned demand channel.
We contend that the argument put forth by A&G regarding the effect of genetic diversity on
productivity, applies more specifically to innovation in the financial sector; namely, that the
effect of genetic diversity on financial innovation will be hump-shaped. An increase in genetic
diversity will have a beneficial effect on financial innovation through the complementarities of
10
more specialized traits and higher concentrations of more innovative thinkers. At the same time,
this beneficial effect will be offset by the detrimental effects on financial innovation resulting
from mistrust and lower levels of cooperation at high enough levels of genetic diversity.
Assuming that the benefits of increased genetic diversity are diminishing (as is assumed in
A&G), then a hump-shaped pattern will emerge. As a result, the first channel through which
genetic diversity is predicted to impact financial development is through its direct effect on
financial innovation and the subsequent supply of financial development.
There also exists substantial research highlighting the important link between economic
development and financial development. The idea is that as a country becomes more productive,
the accompanying increased economic development increases the demand for more developed
and well-functioning financial markets/institutions; this increase in demand then fosters financial
development. The demand-driven justification for financial development has been proposed
conceptually (Robinson, 1952), documented empirically (Luintel and Khan, 1999; Rajan and
Zingales, 2003; Ang and McKibbin, 2007), and even suggested anecdotally, e.g., ‘‘the US has
also regained its primacy as the world’s leading stock market . . . Underlying these gains is a
powerful upsurge in productivity.”3 Combining the link between economic development and
financial development (based on increased demand) with the findings of A&G yields the
following hypothesis: the degree of genetic diversity impacts a country’s level of productivity
and economic development, which in turn will impact the demand for financial development. As
a result, the second channel through which genetic diversity is predicted to impact a country’s
level of financial development is indirectly through its effect on economic development and the
3
Business Week, October 9, 1995, “Riding high,” by Christopher Farrell, Michael J. Mandel and Joseph Weber.
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demand for financial development. To summarize, our predictions regarding the relation between
a country’s degree of genetic diversity and its level of financial development are as follows:

Genetic diversity impacts financial development through two separate channels: (i) directly
through its effect on financial innovation, and (ii) indirectly through its effect on the demand
for financial development.

Genetic diversity has a hump-shaped effect on the level of financial development; namely,
low and high levels of genetic diversity will be associated with relatively lower levels of
financial development, while intermediate levels of genetic diversity will be associated with
relatively higher levels of financial development.
3 Data and Methodology
In this section, we begin with a description of the overall empirical methodology, followed
by the data and the various financial development measures and other controls we consider, and
conclude with summary statistics.
3.1 Methodology
Our main hypothesis is that a country’s degree of genetic diversity impacts its level of
financial development. To investigate this hypothesis, we employ a country-level, cross-sectional
regression analysis. To establish an overall relation between a country’s level of genetic diversity
and its financial development (without attempting to separate out the effect coming though each
of the two proposed channels), we first regress a country’s level of financial development on its
degree of genetic diversity with the following specification:
𝐹𝐷𝑖 = 𝑎0 + 𝑎1 ∙ 𝑔𝑑𝑖 + 𝑎2 ∙ 𝑔𝑑𝑖 2 + 𝜷𝑿𝑖 + 𝜀𝑖
(1)
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where 𝐹𝐷𝑖 is a given measure of the level of financial development for country i, gdi is the
country’s degree of predicted genetic diversity measure, 𝑔𝑑𝑖 2 is the square of its genetic
diversity measure, and Xi is a vector of control variables. We include the 𝑔𝑑𝑖 2 term to enable us
to test for the possibility of a non-linear effect of genetic diversity on financial development. In
particular, if a country’s genetic diversity and its level of financial development has a humpshaped relation, as hypothesized, then we would expect 𝑎1 > 0 and 𝑎2 < 0, both significant.
Next, we attempt to examine the effect of the country’s genetic diversity on financial
development that is operating directly through financial innovation, as opposed to the composite
effect that includes the indirect effect that is operating through the demand for financial
development. To do so, we include as an independent control variable a measure of a country’s
level of demand for financial development. Specifically, we consider the following specification:
𝐹𝐷𝑖 = 𝑎0 + 𝑎1 ∙ 𝑔𝑑𝑖 + 𝑎2 ∙ 𝑔𝑑𝑖 2 + 𝑎3 ∙ 𝑑𝑖 + 𝜷𝑿𝑖 + 𝜀𝑖 ,
(2)
where 𝐹𝐷𝑖 , gdi, 𝑔𝑑𝑖 2 , and Xi are defined as they were in specification (1), and 𝑑𝑖 is a measure of
country i’s level of demand for financial development, which will be proxied for with a measure
of economic development.4 If it is the case that there is a direct relation between genetic diversity
and financial development coming through financial innovation, and the relationship is humpshaped, then we would expect to continue to see 𝑎1 > 0 and 𝑎2 < 0 and both significant.
3.2 Data
We proceed by first explaining what we mean by a country’s level of financial development
4
We acknowledge here that the demand for financial development 𝑑𝑖 , as proxied for by a measure of economic
development, is likely to be endogenous to financial development. That said, the motivation of our paper is not to
identify a causal link between economic development and financial development. Rather, the motivation for
including 𝑑𝑖 is to control for the demand for financial development at the country level in the cross-sectional
analysis, allowing us to separate out the relation between genetic diversity and financial development that is not
coming through the demand channel.
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and then by describing the measures we use to proxy for financial development. We then
describe the main independent variables we use in the regression analysis, which include: our
measure for a country’s degree of genetic diversity (which is adopted from A&G), our proxy for
a country’s level of demand for financial development, as well as other control variables that
have been shown in previous studies to explain cross-sectional differences in financial
development across countries. In total, we collect the requisite data for a cross-section of almost
150 countries. The data on financial and economic development are collected from World Bank
Open Data and the World Bank’s Global Financial Development Database. Data on genetic
diversity are gathered by way of A&G. Data on the various control measures are gathered by
way of A&G, Rajan and Zingales (2003), Acemoglu et al. (2001), and Chanda et al. (2014). A
full description of the variables we use and their source can be found in Table 10.
Financial Development (FD)
The level of financial development in a country is multifaceted and quite complex, which
makes it extremely hard to measure in practice (Rajan and Zingales, 2003). Financial
development includes financial assets and instruments, the breadth of markets that trade these
assets and instruments, and the financial institutions and intermediaries that connect the suppliers
and demanders of capital. In an attempt to robustly capture a country’s level of financial
development, we consider four different measures.5 These four measures are generally regarded
as standard proxies for financial development and have been used in prior studies exploring
5
One of the primary objectives in this paper is to investigate the relation between genetic diversity and financial
development coming directly through financial innovation. As such, the measures of financial development we
consider mainly encompass equity markets characteristics. The reason being is that in a recent survey article,
Cardella et al. (2014) discuss how financial innovation is likely to be more prevalent in areas of the financial sector
related to equities markets vs. corporate bond markets due to increased use and implementation of technology (e.g.,
computerization).
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financial development at the country level (e.g., King and Levine, 1993; Wurgler, 2000; Rajan
and Zingales, 2003; Beck et al., 2003; Stulz and Willianson, 2003; Hsu et al., 2014). The first
measure is stock market capitalization (Market Cap), where stock market capitalization is the
total market value of all listed shares. The second measure is total stocks traded (Stocks Traded),
where stocks traded is the total value of shares traded during a given year. The third measure is
total listed companies (Stocks Listed), which is the total number of domestically listed companies
on the country’s stock exchanges. These three measures are intended to proxy for the depth of
financial markets. Our fourth measure, which is intended to proxy for investment risk and the
protection of investors, is the risk of expropriation of investment (Expropriation Risk), where
expropriation risk is a scaled measure of the average protection against the expropriation of
private foreign investment by the government. This measure is borrowed from Acemoglu et al.
(2001). The scale of this measure ranges from 0 (low protection/high risk) to 10 (high
protection/low risk). For example, the United States (among the highest in our sample) has an
expropriation risk measure of 10, while Sudan (among the lowest in our sample) has an
expropriation risk of 4. Expropriation risk can be an important factor in financial development,
as higher expropriation risk can deter investment and thus hamper financial sector growth. 6
Because our proxy for a country’s degree of genetic diversity is for year 2000 CE (the details
of the construction of this variable are explained below), we collect data on the first three
financial development proxies (Market Cap, Stocks Traded, and Stocks Listed) for each of the
countries in our sample for each year from 1998 to 2002, i.e., a five-year window around the
year in which a country’s degree of genetic diversity is measured. We then take the average over
6
We refer readers to p.56 of this report for a discussion of expropriation risk, and how it can deter equity
investment:http://www.doingbusiness.org/~/media/FPDKM/Doing%20Business/Documents/AnnualReports/English/DB05-FullReport.pdf
15
these five years to generate a single value of financial development for each country for each of
these three measures. The data for Expropriation Risk is a 10-year average from 1985-1995. We
acknowledge that none of the proxy measures for financial development are all-encompassing.
However, by considering four different measures and taking the average of each measure over a
range of time, we hope to establish a more robust conclusion regarding the effect of genetic
diversity on financial development.
In our regression specification, the measures Market Cap, Stocks Traded, and Stocks Listed
are not scaled by GDP. Rather, we employ a logarithm transformation of these variables to
address the positive skewness that is present in these measures. These measures are intentionally
not scaled by GDP for the following reason: Our main hypothesis is that genetic diversity has a
hump-shaped effect on a country’s level of financial development; further, this effect comes
through two channels: (i) directly through financial innovation, and (ii) indirectly through the
demand for financial development. In order to investigate if genetic diversity does impact the
level of financial development apart from its effect through the latter demand channel, it will be
necessary to control for the demand for financial development (See specification 2 above). To
proxy for a country’s level of demand for financial development, we will use the country’s per
capita GDP (denoted as d in specification 2), which is used by Rajan and Zingales (2003) and
also corresponds with the proxy of contemporary economic development used in A&G. As with
the construction of our financial development measures, we average the yearly measure of per
capita GDP across the five-year window of 1998 through 2002 and denote it Per Capita GDP; in
all specifications, we also take the log transformation of Per Capita GDP to address the
skewness in this measure. Thus, because we will be explicitly controlling for GDP, it is not
necessary to scale the financial development measures by GDP. In addition, by not scaling our
16
financial development measures by GDP, we ensure that any observed relation between genetic
diversity and financial development is not coming solely through its effect on GDP (i.e.,
impacting only the denominator). However, in all specifications where Per Capita GDP is not
added as a control, we will include country population as a control to ensure that any relation
between genetic diversity and financial development is not coming through differences in the
size of the country.
Genetic Diversity (gd)
For the degree of a country’s genetic diversity, we adopt the measure developed and used by
A&G. In what follows, we provide a sketch of what is meant by genetic diversity, how it is
generally measured, and the method that A&G use in creating their measure of predicted genetic
diversity at the country level. We refer interested readers to A&G for an unabridged and more
thorough discussion of these topics.
Population geneticists typically measure the degree of genetic diversity across individuals
within a given population using an index called expected heterozygosity. This index can be
interpreted as the probability that two randomly selected individuals from the relevant population
are genetically different from one another; the higher this index, the more genetically diverse the
population. To construct this index of expected heterozygosity, geneticists collect sample data on
allelic frequencies (i.e., the frequency of a specific gene variant or allele) within the given
sample population. Given the allelic frequencies, it is possible to construct a gene-specific
measure of heterozygosity. Then, to construct an overall measure of expected heterozygosity,
one simply averages this gene-specific heterozygosity measure over multiple genes for which
17
there is data.7 The most reliable data for genetic diversity consists of 53 ethnic groups from the
Human Genome Diversity Cell Line Panel, compiled by the Human Genome Diversity ProjectCentre d’Etudes du Polymorphisme Humain (HGDP-CEPH) (Cann et al., 2002 and CavalliSforza, 2005).8 Anthropologists maintain that these 53 ethnic groups are not only native to their
current locations but also have been essentially isolated from genetic flows from other ethnic
groups. These 53 ethnic groups span a total of 21 countries, and based on the data from HGDPCEPH, it is possible to construct a measure of observed genetic diversity for these 53 ethnic
groups based on expected heterozygosity.
However, as noted by A&G, there are two main limitations with using this measure of
observed genetic diversity. First, given that this observed genetic diversity data is available for
only these 53 ethnic groups that span 21 countries, the sample using observed genetic diversity
would be restricted to those 21 countries, which is a much smaller subset of countries than that
for which we have data on economic and financial development. Second, and more important,
there may be endogeneity between a country’s level of observed genetic diversity and its level of
financial development. Specifically, genetic diversity within a country may be determined, in
part, by migration patterns, which could have been influenced by a country’s level of economic
and/or financial development. Furthermore, as argued by A&G, the direction of this possible
7
More formally, suppose there is a single gene, denoted as l, with a total of k observed variants or alleles in the
𝑙
given sample population. Then, the expected heterozygosity for that gene, denoted as 𝐻𝑒𝑥𝑝
, is given by:
𝑘
2
𝑙
𝐻𝑒𝑥𝑝
= 1 − ∑𝑖=1 𝑝𝑖
where 𝑝𝑖 denotes the probability of the ith allele. If there are m different genes, then the overall expected
heterozygosity, denoted as 𝐻𝑒𝑥𝑝 , averaged over these m genes can be expressed as follows:
𝑚
𝐻𝑒𝑥𝑝
8
𝑘𝑙
1
= 1 − ∑ ∑ 𝑝𝑖2
𝑚
𝑙=1 𝑖=1
For a list of these 53 ethnic groups and the countries in which they reside, see A&G Appendix E.
18
endogeneity bias is ambiguous; on the one hand, it could have been the case that more developed
countries were more attractive to migrants (increasing genetic diversity), while on the other
hand, more developed countries could have had more developed infrastructure to limit the inflow
of migrants (decreasing genetic diversity).
As a result of the limitations associated with using actual observed genetic diversity, A&G
propose a measure of predicted genetic diversity for each country in year 2000 CE that is based
on the ethnic composition of that country as well as migratory distance from East Africa. As
postulated by the serial-founder effect, as subgroups of the population migrated over the earth,
they carried with them only a subset of the overall genetic diversity of the parent colony; hence,
the further the migratory distance (out of East Africa), the less genetically diverse this sub-group.
In particular, A&G build on the work from Ramachandran et al. (2005), who document that
migratory distance from East Africa has a significant negative linear effect on observed genetic
diversity within the 53 ethnic groups in the HGDP-CEPH data; they find that the variation in
migratory distance explains 78 percent of the variation in genetic distance across the 1,378 ethnic
group pairs and 86 percent of the cross-group variation in within-group diversity. Given the
results of Ramachandran et al., A&G construct a measure of predicted genetic diversity for each
country as follows: First, they identify the ethnic composition of each country based on the
World Migration Matrix, 1500-2000 of Putterman and Weil (2010); this data compiles for each
country the fraction of the 2000 CE population that is descended from the population of every
other country in 1500 CE. Given this data on ancestral source countries, A&G construct a
measure of predicted heterozygosity for each country that accounts for within-group genetic
diversity and between-group genetic diversity.
For within-group genetic diversity, A&G calculate the predicted level of genetic diversity
19
based on the migratory distance of the ancestral source country from Addis Ababa, Ethiopia.
Specifically, they apply the coefficient of the effect of migratory distance on observed genetic
diversity obtained by Ramachandran et al. (2005) (from the 53 ethnic groups and 21 countries in
the HGDP-CEPH data). However, this alone does not account for the between-group genetic
diversity of the ethnic composition of each country that results from post-1500 CE population
flows. For the between-group component of genetic diversity, A&G incorporate a measure of
genetic diversity between two populations that is referred to by population geneticists as genetic
distance. Again, A&G appeal to the results from Ramachandran et al., who also show there is a
strong positive correlation between pairwise genetic distances and pairwise migratory distances
from East Africa. Using the coefficient estimate from Ramachandran et al., A&G calculate the
predicted level of between-group genetic diversity across all pairs of ethnic groups within a
country. Given the estimates of within and between-group genetic diversity, which are both
predicted from migratory distance, A&G construct an overall measure of genetic diversity that is
essentially a weighted average given the ethnic composition of each country in 2000 CE. A&G
refer to this measure as the ancestry adjusted measure of predicted genetic diversity. This
predicted measure of genetic diversity minimizes endogeneity concerns based on the assumption
that prehistoric migratory paths out of Africa had no direct effect on Common Era development.
It is this predicted measure of genetic diversity for each country in year 2000 CE, which we
denote as Diversity, which we adopt as our measure of genetic diversity in our analysis. For the
remainder of the paper, we will drop the predicted qualifier for brevity and simply refer to this as
the measure of genetic diversity; however, it is implied that this is a predicted measure rather
than the actual measure of observed genetic diversity.
Other Possible Factors Affecting Financial Development
20
In the existing literature, several factors have been shown to influence a country’s level of
financial development. The factors include: the type of legal origins (La Porta et al., 1998; Beck
et al., 2003); the trade and capital openness of a country (Rajan & Zingales, 2003); the country’s
endowment (Acemoglu et al., 2001; Beck et al., 2003); and its cultural factors (Stulz &
Williamson, 2003). Below, we describe the variables that we use to attempt to control for the
effect of these other possible factors on financial development.
With regard to legal origin, the idea, as argued by La Porta et al. (1999), is that the type of
legal origin can influence financial development through two possible channels: The first is the
priority placed on protecting property rights, and the second is the protection of private
contracting rights. The two main types of legal origins are the British Common Law system,
which evolved to protect private property rights, and the French Civil Law system, which was
designed to reinforce the power of the State. As a result, British Common Law systems are
regarded as being more conducive for financial development (Beck et al. 2003). To control for
the effect of legal origins, we use the data from La Porta et al. (1998) and A&G on the type of
legal origin for each country. In particular, in our regressions we construct the following dummy
variables for legal origin of a country: Legal Origin UK (British Common law), Legal Origin FR
(French Civil law), and Legal Origin Other (all other forms of legal origin), which is the
excluded category in the regression analysis.9
With regard to openness, Rajan and Zingales (2003) argue that when the country’s borders
are open to trade and capital flows, financial development is improved. Following Rajan and
Zingales (2003), we use the sum of exports and imports of goods divided by GDP as a proxy for
9
These other forms of legal origin include: German Law, Socialist or Communist Law, and Scandinavian Law.
The regression results reported later are robust if we include dummy variables for each legal origin.
21
a country’s level of trade openness. We collect data for each year for the periods of 1998 through
2002 and take the average over these five years. The resulting country-level proxy is denoted as
openness in our regression analyses.
The endowment theory emphasizes the roles of a country’s geography and its disease
environment in shaping migration and subsequent institutional development and growth
(Acemoglu et al., 2001). Beck et al. (2003) provide evidence that a country’s endowment, as
proxied for by settler mortality rate in the early nineteenth century, does impact a country’s level
of financial development.10 To control for possible different environmental endowment levels
across countries, we use the percentage of the population (in 1994) that was at risk of contracting
falciparum malaria, denoted as Malaria; this measure was obtained from A&G and originally
constructed by Gallup and Sachs (2001). Because of data constraints, we opt to use the Malaria
proxy for a county’s endowment in lieu of the settler mortality. Specifically, in our specification
with a full set of controls, our sample sizes for Market Cap, Stocks Traded, and Stocks Listed
would be reduced to 45, 45, and 48 country observations, respectively. We also note that the
Malaria measure to proxy for endowment is used in Acemoglu et al. (2001) and A&G, and is
highly correlated with settler mortality r = .6744 (p < .001).
Lastly, it has been shown that cultural differences across countries can play a role in shaping
differences in financial development (Stulz & Williamson, 2003). Specifically, Stulz &
Williamson documented a negative association between some proxies for financial development
10
Their study provides evidence for both the law and endowment theories. However, their results show that
initial endowments explain more of the cross-country variation in financial intermediary and stock market
development across countries.
22
they consider (e.g., measures of creditor rights) and counties that are predominately Catholic.11
In order to control for the possible effect of religion, we include as a control the percentages of a
country’s population that is Catholic, denoted as P_Catholic.12
3.3 Summary Statistics and Correlations
Table 1 reports summary statistics of all variables used in our analysis. We conduct a crosssectional regression analysis at the country level for each of the four proxies of financial
development we consider: Market Cap, Stocks Traded, Stocks Listed, and Expropriation Risk.
Depending on the measure of financial development that is used, the number of observations in
each corresponding regression specification is different based on availability of the data. Thus,
the summary statistics in Table 1 are broken down based on the corresponding measure of
financial development: Panel A for Market Cap, Panel B for Stocks Traded, Panel C for Stocks
Listed, and Panel D for Expropriation Risk.
With regard to the financial development measures, Panel A shows that the mean of Market
Cap is about $297 billion. Panel B reveals that the mean of Stocks Traded is about 349 billion.
Panel C reveals that mean Stocks Listed is 442, which means on average each country has 442
companies domestically listed. Lastly, Panel D reveals that the mean of Expropriation Risk is
7.0364 (on a 10-point scale). It is important to point out here that for each of the first three
measures of financial development, the mean is substantially higher than the median. This
11
However, Beck et al. (2003) show that religion of a country has very little effect on the measures of financial
development they consider after controlling for legal origin of the country.
12
We note that the results from our main specifications are generally robust if we include the fraction of the
population belonging to Protestant and Muslim. Furthermore, the results are generally robust if we include the ethnic
fractionalization (Beck et al., 2003) of each country as a control. That said, consistent with the finding in Beck et al.,
these variables seem to have little residual effect on financial development and are rarely significant in the
regression results. Given our small sample size and the relatively extensive set of control variables we consider, we
report our results without the inclusion of these additional controls. In addition, if we perform a stepwise iterative
approach for selecting control variables, neither Protestant, nor Muslim, nor ethnic fractionalization survives as
selected control variables.
23
indicates, as we would expect, the presence of substantial positive skewness and outliers at the
upper end of the distribution of financial development. In order to mitigate the possibility that
our results may be driven by such outliers, we take logarithmic transformations of each of the
first three financial development measures in our analyses that follow.
In terms of genetic diversity in the sample of countries, we see from Table 1 that the variable
Diversity of a country ranges from about 0.63 to 0.77 in our sample, with a standard deviation of
0.028. In our sample, the least genetically diverse country is Bolivia, while the most genetically
diverse country is Uganda. For comparison, the USA ranks 42nd with Diversity = 0.72.
Table 2 reports the pairwise correlations among the four measures of financial development
we consider. From Table 2, we can see that the correlations range from 0.43 to 0.96. That fact
that they are all strongly positively correlated with each other (p < .001 for each pairwise
correlation) suggests that each of the four measures is a reasonable proxy for level of financial
development. At the same time, the fact that the correlations are not very near to one suggests
that there is some variation in the components of development in the financial sector each these
measures is capturing. Hence, a consideration of all four different measures in our upcoming
analysis will provide a robust picture of how genetic diversity impacts financial development.
4 Results
This section reports the main results of our empirical investigation of the relation between a
country’s degree of genetic diversity and its level of financial development. The dependent
variable in our analysis is one of the four measures of financial development (described in
Section 3.2). For each specification, we report the results for all four measures of financial
development. Overall, the empirical findings indicate a significant hump-shaped relation
24
between a country’s degree of genetic diversity and its level of financial development. This
relation persists even after controlling for other country-level characteristics that have previously
been shown to impact financial development, as well as controlling for per capita GDP (i.e., the
demand for financial development).
4.1 Overall Relation of Genetic Diversity and Financial Development
We first look at the overall relation of genetic diversity and financial development. Table 3a
reports the results from the unconditional cross-sectional regressions of each of the four financial
development measures on Diversity and its square, which we denote Diversity Sqr.13 From Table
3a, we see that the coefficient on Diversity is positive and significant at the 1% level for all four
measures of financial development. In addition, the coefficient on Diversity Sqr is negative and
significant at the 1% level for all four measures. In Table 3b, we additionally control for county
population, and the linear coefficients on Diversity remain positive and significant, while the
quadratic coefficients on Diversity Sqr remain negative and significant. This provides initial
evidence consistent with our main hypothesis of the overall hump-shaped association between
genetic diversity and financial development.
Based on our coefficient estimates, one can calculate the estimated degree of genetic
diversity where financial development is maximized. The last row in each panel of Table 3
reveals that the predicted values of Diversity where financial development is maximized range
from 0.669 to 0.707 (depending on which of the four measures of financial development is used).
Examples of some countries close to this maximizing level of Diversity include: Japan, China,
13
In addition, we include continent dummy variables in all regression specifications to control for continent
fixed effects. Hence, we will not continue to reiterate that continent dummies are included in each specification, as
they are included in every specification. In addition, because we are using generated genetic diversity measures from
an implicit first-stage regression, in our regressions, we bootstrap all standard errors with 500 replications.
25
Mexico, Columbia, and Belize. Given that Diversity ranges from 0.63 to 0.77 in our sample, this
confirms the hump-shaped relation between genetic diversity and financial development, namely,
intermediate levels of genetic diversity are associated with the highest levels of financial
development.
To explore the robustness of the hump-shaped effect of genetic diversity on financial
development, we add in a full set of country-level control variables that have previously been
shown to impact a country’s level of financial development (these are described in detail in
Section 3.2). Table 4 reports the relevant results. From Table 4, we see that for all four measures
of financial development, the coefficient on Diversity remains positive and significant.
Moreover, the coefficient on Diversity Sqr also remains negative and significant. In terms of the
magnitude of the relation between genetic diversity and financial development, it is useful to
look at the elasticity of financial development with regard to Diversity. For brevity, we report
only on the Market Cap measure and calculate the elasticity at the level of Diversity one standard
deviation below the maximizing level of 0.710 (reported in the last row of Table 4), as well as
one standard deviation above. Given the coefficients from Column 1 of Table 4, a 1 percentage
point increase in Diversity at one standard deviation below the maximizing level implies an
approximately 18 percent increase in Market Cap. On the other hand, a 1 percentage point
decrease in Diversity at one standard deviation above the maximizing level implies an
approximately 19.5 percent increase in Market Cap. As a result, the overall hump-shaped
relation between genetic diversity and financial development remains significant even after
controlling for other factors that can possibly affect financial development.
In general, looking across the control variables, the signs of the coefficients are generally in
the direction to be expected, given the prior literature. Namely, Legal Origin FR has a smaller
26
coefficient than Legal Origin UK, indicating a negative effect of financial development
compared to Legal Origin UK. Malaria is negative and significant. While Openness is not
always significant, it is positive. Importantly, the magnitude of the coefficients on Diversity and
Diversity Sqr are generally consistent even after adding in the full set of control variables, and
remain statistically and economically significant. The last row of Table 4 reveals the predicted
level of Diversity, estimated from the regression coefficients, where each proxy of financial
development is maximized. From Table 4, we see that the values range from 0.700 to 0.710;
similar to the result from Table 3, this is consistent with the notion that intermediate degrees of
genetic diversity are associated with higher levels of financial development.
4.2 Direct Relation of Genetic Diversity and Financial Development
We hypothesized that there are two possible channels through which genetic diversity can
impact financial development. The first is directly through its effect on financial innovation and
the subsequent supply of financial development, while the second is indirectly through its effect
on economic development and the subsequent demand for financial development. To attempt to
separate these two effects, we a proxy for economic development to control for a country’s
demand for financial development. Table 5 replicates Table 3b, displaying the results of the
effect of genetic diversity on financial development with the inclusion of Per Capita GDP – our
proxy for a country’s level of economic productivity. Generally consistent with our hump-shaped
prediction, we observe that the coefficient associated with Diversity is still positive and
significant (for 3 of 4 measures), and the coefficient associated with Diversity Sqr is negative and
significant (for 3 of 4 measures). As expected, the coefficient on Per Capital GDP is positive
and highly significant for all four measures. Again, the last row of Table 5 indicates that the
estimated values of genetic diversity for which the financial development measures reach their
27
maximum are intermediate levels and range between 0.676 and 0.696.
In Table 6, we add in the full set of controls, and the results are robust. Namely, the
coefficients on Diversity continue to be positive and significant, while the coefficients on
Diversity Sqr are negative and significant. In terms of the magnitude of this hump-shaped
relation, we can do a similar elasticity-type calculation as reported above. In particular, based on
the coefficient estimates in Column 1 of Table 6, a 1 percentage point increase in Diversity (at
one standard deviation below the maximizing level of 0.669) implies an approximate increase of
17 percent in Market Cap. Conversely, a 1 percentage point decrease in Diversity (at one
standard deviation above the maximizing level of 0.669) implies an approximate increase of 18.5
percent in Market Cap.
In terms of the controls, Per Capita GDP remains positive and significant. After controlling
for Per Capital GDP, we see that the coefficient on Legal Origin FR has a smaller coefficient
than Legal Origin UK, indicating a negative effect of financial development compared to Legal
Origin UK. The coefficient on Malaria becomes insignificant, while the coefficient on Openness
becomes negative and significant.14
14
There is a strong negative correlation between Malaria and Per Capita GDP, which is the reason the
coefficient on Malaria becomes insignificant when Per Capita GDP was included in Table 6, as compared to when
Malaria was strongly positively significant in Table 4. With regards to Openness, the negative and significant
coefficient is surprising. Stulz and Williamson (2003), however, also find a negative and significant relationship
between shareholder rights index and openness, and indicate that this negative coefficient is due to a negative
relation between openness and the dummy variable for cumulative or proportional voting. This is in contrast to the
results reported in Rajan and Zingales (2003), who find a positive and significant coefficient on Openness. We take
the analysis of the data further to offer a better understanding of these discrepancies. It should be noted that both
Stulz and Williamson (2003) and Rajan and Zingales (2003) consider scaled measures of financial development
(e.g., Market Cap / GDP), while we consider unscaled measures of financial development (e.g., Market Cap). After
analyzing the correlations in the data, we find a strong negative correlation between Openness and GDP (the
denominator of their scaled measures). We note that we are by no means implying that this negative cross-sectional
correlation implies a causal relationship given the likely endogeneity between Openness and GDP. Rather, we
suggest that this negative correlation is likely driving the difference in the effect of Openness between our results
and those of Rajan and Zingales. Specifically, if we take, for example, our unscaled Market Cap measure, we find a
negative correlation between Openness and Market Cap; hence, it is not surprising that the coefficient on Openness
28
Taken together, the results from Tables 5 and 6 indicate that, even after controlling for the
level of demand for financial development as proxied by Per Capita GDP, we observe a
persistent hump-shaped relation between a country’s financial development and its degree of
genetic diversity. This result is consistent with our hypothesized prediction that genetic diversity
can impact financial development through two channels: (i) directly through its effect on
innovation in the financial sector, which is not captured through a country’s level of GDP and
the demand for financial development, and (ii) indirectly through its effect on productivity and
the subsequent demand for financial development.
4.3 Addition of Schooling and Social Infrastructure
In this section, we explore the role of a country’s degree of human capital and social capital in
influencing the level of financial development. The motivation for this is twofold: First, we want
to ensure that our results regarding the observed hump-shaped relation between genetic diversity
and financial development are not being driven by cross-section differences in these two
characteristics of the country. Second, by investigating how human capital and social capital can
impact financial development, we can shed light on possible feasible mechanisms through which
a country can increase its financial development. To proxy for human capital, we use average
years of schooling for the population in the country, which we get by way of A&G and denote as
is negative and significant for the Market Cap measure (column 1 of Table 6). However, the negative relation
between Openness and GDP dominates the negative relation between Openness and Market Cap. This pattern
generates an overall positive relation of Openness to the relation of Market Cap/GDP, which is what Rajan and
Zingales document. Thus, it is the difference in the way in which we measure financial development (scaled vs
unscaled by GDP), in combination with the observed negative relation between Openness and GDP, that is able to
reconcile the difference in the effect of Openness between our results and those of Rajan and Zingales. Furthermore,
consistent with Rajan and Zingales, we are able to generate a positive coefficient on Openness if we use Market Cap
/ GDP and Stocks Listed/Population as our dependent variables in specifications in Table 6. This discussion also
reinforces the fact that if we are interested in disentangling the two channels (the demand channel and the innovation
channel) through which financial development is affected by the genetic diversity of a country, we should not scale
our dependent variables.
29
School. Our proxy for social capital is the social infrastructure index originally developed by
Hall and Jones (1999), which we get by way of A&G and denote as Social Inf. This index is
from 0 to 1 with a higher value indicating better social infrastructure. Hall and Jones describe
social infrastructure as “the institutions and government policies that determine the economic
environment within which individuals accumulate skills, and firms accumulate capital and
produce output” (p. 84). Hence this Social Inf index, which is a weighted average over many
different components of social infrastructure, is intended to proxy for a country’s overall level of
social infrastructure.
Table 7 Panel A presents the results of our main specification of the regression of financial
development on genetic diversity, per capita GDP, controls, and the inclusion of the School
variable. For all four financial development measures, the coefficient on Diversity is positive and
significant, while the coefficient on Diversity Sqr is negative and significant, indicating the
persistent hump-shaped relation of genetic diversity and financial development. Importantly, the
School proxy is positive and enters in significantly for 3 of 4 financial development measures.
This suggests a positive association between the amount of schooling and the level of financial
development. In Panel B of Table 7, we add Social Inf as a control variable. From Panel B, we
see that for all four financial development measures, the coefficient on Diversity is, again,
positive and significant while the coefficient on Diversity Sqr is negative and significant; this
further indicates the persistent hump-shaped effect of genetic diversity on financial development.
Furthermore, Social Inf is positive and significant for 3 of 4 financial development measures,
which suggests that higher levels of social infrastructure within a country is associated with
higher levels of financial development.
In terms of the magnitude of the relation between schooling and financial development, the
30
coefficient on School for the Market Cap proxy (Table 7 Panel A) implies that an increase in
School by 1 year of the population within a country is associated with an approximately 19
percent increase in Market Cap. To put this into context, the coefficient estimates in the same
specification imply that a 1 percentage point increase in Diversity (at one standard deviation
below the maximizing level) is associated with an approximate increase of 17.5 percent in
Market Cap; conversely, a 1 percentage point decrease in Diversity (at one standard deviation
above the maximizing level) implies an approximate increase of 19 percent in Market Cap. In
terms of the magnitude of relation of social infrastructure to financial development, the
coefficient on Social Inf for the Market Cap proxy (Table 7 Panel B) implies that an increase of
10 percent in the Social Inf index is associated with an approximately 16 percent increase in
Market Cap. The coefficient estimates in the same specification imply that a 1 percentage point
increase in Diversity (at one standard deviation below the maximizing level) is associated with
an approximate increase of 20 percent in Market Cap; conversely, a 1 percentage point decrease
in Diversity (at one standard deviation above the maximizing level) implies an approximate
increase of 21.5 percent in Market Cap. Taken together, our results suggest that the magnitude of
the increase in financial development association with an increase in schooling by 1 year or an
increase in the social infrastructure index by 10% would comparable in magnitude to the increase
in financial development associated with a 1% change in genetic diversity.
5 Additional Robustness Analysis
Next, we address the possibility that the predicted measure of genetic diversity that we use in
the analysis is endogenous to financial development. Recall that the genetic diversity measure for
each country comprises two sources: (i) the within-ethnic group genetic diversity, and (ii) the
between-group genetic diversity between each pairwise ethnic group comparison. A predicted
31
measure of each of these sources was then calculated based on prehistoric migratory distance
from East Africa; thus, the predicted measure of each component or genetic diversity is likely
exogenous to current levels of financial development. That said, the overall measure of predicted
genetic diversity (Diversity) is determined, in part, by the number of different ethnic groups
within a country and the concentration of each ethnic group. Thus, there is a possibility for this
measure of predicted genetic diversity to be endogenous to financial development to the extent
that financial development may have impacted the post-1500 CE population flows and the
current ethnic composition of each country. Said differently, it is possible for there to have been
post-1500 CE migration away from less financially developed countries and toward more
financially developed counties, which would then increase the between-group source of genetic
diversity in these countries.15 We address this possible endogeneity issue in two ways: (i) with an
alternative measure of predicted genetic diversity, and (ii) with a sub-sample analysis.
5.1 Alternative Measure of Predicted Genetic Diversity
Instead of using the Diversity measure of genetic diversity, which was ancestry adjusted to
account for post-1500 migration, we use an alternative measure created by A&G that is based
strictly on migratory distance. The construction of this measure, denoted as Alt Diversity, is
solely based on the migratory distance of each country from East Africa and does not take into
account the ethnic composition of the country.16 Thus, there is no scope for post-1500 CE
migration flows to impact the genetic diversity measure. As a result, we contend that this
measure, predicted solely on prehistoric migratory distance, is exogenous to contemporary levels
15
A&G discuss this same possibility of endogeneity of this measure of genetic diversity to the level of
economic development.
16
A&G use this Alt Diversity distance-only based measure to investigate the effect of genetic diversity on
economic development in 1500 CE when, presumably, there was little migratory flows and insufficient data on
ethnic composition within a country.
32
of financial development. Furthermore, the Alt Diversity measure is strongly correlated with the
Diversity measure (correlation coefficient of 0.75 and p-value < .0001).
Table 8 presents the results with Alt Diversity serving as the independent variable for genetic
diversity. From Table 8, we can see that this measure of genetic diversity has a significant humpshaped relation to the level of financial development (in 3 of the 4 measures), even after
including the full set of control variables. Hence, our main results regarding the relation of
genetic diversity and financial development are generally robust to this more crude measure of a
country’s degree of genetic diversity, which is more likely to be exogenous to contemporary
levels of financial development. We note here that the distance-only based measure of genetic
diversity (Alt Diversity) is indeed a cruder and less accurate predicted measure of actual genetic
diversity than the ancestry-adjusted measure (Diversity) since it does not account for betweengroup diversity arising from migration flows.17 For this reason, combined with the fact that our
results are generally robust if using the less informative Alt Diversity measure (as indicated in
Table 8), the main analysis in our paper is done using the ancestry-adjusted Diversity measure.
5.2 Subsample Analysis
The second way we address the possibility that the Diversity measure we use in our main
analysis in Section 4 is endogenous to a country’s current level of financial development is
through subsample analysis. In particular, it is not clear or obvious whether this possible
endogeneity would lead to a positive or negative bias regarding the effect of genetic diversity on
financial development. In particular, extending the argument put forth by A&G, more
17
In fact, if we regress measures of financial development on Alt Diversity, Alt Diversity Sqr, Diversity, and
Diversity Sqr, then only Diversity, and Diversity Sqr are significant. That is, the ancestry-adjusted measures do
dominate the unadjusted distance-only measures, which is consistent with the findings reported in A&G with regard
to the effect of genetic diversity on economic development.
33
developed/advanced countries may have been more attractive to migrate to, thus increasing
genetic diversity; at the same time, these more developed/advanced countries could have been
more effective at minimizing immigration (if they chose to do so), thus decreasing genetic
diversity. That being said, we test the robustness of our results using various sub-samples where
we exclude from the sample various countries that may be more desirable to migrate away from
because of lower levels of financial development, as well as those countries that may be more
desirable to migrate to because of higher levels of financial development. Table 9 presents the
results of the effect of genetic diversity on each of the four financial development measures for
each different sub-sample (Panels A-E) with the inclusion of Per Capita GDP and other controls.
Consistent with Beck et al. (2003), we drop P_Catholic from the sub-sample analysis as it is
never significant at the 10% level for any of the previously reported specifications for any of the
four financial development measures.
In Panel A of Table 9, we exclude the 30 OECD countries from our sample (i.e., those
countries that may be more attractive to migrate to as suggested by A&G). From Panel A, we see
that for two of the four measures (Stocks Traded and Stocks Listed) the coefficient on Diversity is
positive and significant, while the coefficient on Diversity Sqr is negative and significant,
revealing a hump-shaped effect. For the other two measures (Market Cap and Expropriation
Risk), Diversity and Diversity Sqr are not significant, although the direction is hump-shaped, and
the magnitudes of the coefficients are very similar to the full sample reported in Table 6. Panel B
reports the results where we omit 48 Sub-Saharan African countries (i.e., those counties that may
have been less attractive to migrate to and generally have more genetic diversity as suggested by
A&G). Panel B reveals a positive and significant coefficient on Diversity, and a negative and
significant coefficient on Diversity Sqr for all four measures of financial development.
34
In Panels C through E of Table 9, we attempt to control for the possible increased
international flow of capital over the last several decades (Laeven, 2014). Specifically, Laeven
documents “the capitalization of stock markets (relative to GDP) saw an increase of about 50
percent globally but a more than twofold increase in upper middle income countries over this
period [1994-2010]” (p. 6). He goes on to document that the capitalization of stock markets and
the number of listed companies has actually decreased over that period for low income countries.
To ensure that our results are not being driven by this somewhat recent increase in international
capital flows recently discussed by Laeven (2014), we consider three additional sub-samples
where we omit the 10% of highest GDP per capita countries (Panel C), the 10% of lowest GDP
per capita countries (Panel D), and both the 10% of highest and the 10% of lowest GDP per
capita countries (Panel E). From Panels C-E, we see that the significant hump-shaped effect of
genetic diversity generally persists across the various subsamples and measures of financial
development. Namely, in 11 of the 12 total specifications, the coefficient on Diversity is positive
and significant, while the coefficient on Diversity Sqr is negative and significant.
These results from Table 9 indicate that the hump-shaped relation of genetic diversity on
financial development remains robustly intact when we look at sub-samples of countries where
we omit countries that may be more or less prone to migrations, as well as omit the relatively
high and low income counties. Taken together, this suggests that the strong hump-shaped effect
we document between a country’s degree of genetic diversity and the measures of financial
development in our main analysis (Table 6) is not strictly an artifact of migration flows for more
financially developed countries or capital flows toward higher income countries.
6 Conclusion
It is well established in the literature that development in a country’s financial sector is an
35
important component of its growth process. At the same time, ample empirical evidence
documents considerable heterogeneity in financial development across countries. This has
spurred much research aimed at identifying possible factors that can influence financial
development and thus accounts for differences across countries. Subsequently, many structural,
institutional, cultural, and political factors have been shown to influence financial development.
In this paper, we take a complementary approach by investigating if and to what extent a
country’s level of genetic diversity impacts its level of financial development. Building on the
recent work from Ashraf and Galor (2013), we hypothesize that a country’s level of financial
development is determined based on two components: (i) the country’s level of financial
innovation (the supply of financial development), and (ii) the country’s level of economic
development (demand for financial development). We assert that a country’s degree of genetic
diversity can impact the total level of financial development through each of these two channels.
Paralleling the argument put forth by Ashraf and Galor, we hypothesize that genetic diversity
will have a hump-shaped relation with financial development; namely, when genetic diversity is
low, we will observe a positive relation between financial development and genetic diversity,
while at high enough levels of genetic diversity we will observe a negative relation between
genetic diversity and financial development. We test this hypothesis empirically using a cross
section of almost 150 countries. In doing so, we gather data on a country’s level of financial
development, its degree of genetic diversity, its demand for financial development, as well as
other controls that have received attention in the literature. The measure of genetic diversity we
use is adopted from Ashraf and Galor, and is determined based on the ethnic composition of a
country combined with prehistoric migratory distances of ethnic groups out of East Africa.
As predicted, our results indicate a significant hump-shaped relation between a country’s
36
degree of genetic diversity and its level of financial development. These results are robust across
four different proxy measures of financial development that we consider. Furthermore, the
results are robust after controlling for a country’s demand for financial development, which is
consistent with our hypothesis that genetic diversity impacts financial development through its
effect on financial innovation. Our results remain robust even after controlling for other factors
that have been shown in the extant literature to impact financial development; these include the
country’s type of legal origin, its degree of openness, its initial endowment, and its religion.
Furthermore, the hump-shaped effect of genetic diversity is persistent even when we consider
subsamples where we omit countries that may be more or less desirable to migrate to, as well as
omit those countries with relatively low and high income levels.
Given the close link between the functioning of a country’s financial sector and its economic
development and growth, it is important to understand factors that can shape financial
development. Similar to the view of Rajan and Zingales (2003), we assert that the existing
theories regarding factors that can influence financial development are not wrong; rather, they
are incomplete. Our results suggest that some of the variation in financial development across
country’s may be the result of a more deep rooted factor – the degree of genetic diversity within
its population. That said, we also find a strong and positive relation between average years of
schooling of the country’s population and its financial development, as well between the
country’s overall level of social infrastructure and its financial development. This suggests that
investment in both human capital and social capital may serve as feasible and effective
mechanisms through which a country can increase its financial development in addition to
increases in the demand for financial development through economic growth.
37
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41
Table 1 – Summary Statistics
This table reports summary statistics. Panel A reports the statistics for the subsample used when
Market Cap is the measure of financial development. Market Cap is the stock market capitalization
in billion dollars averaged across 1998 – 2002. Panel B reports the statistics for the subsample used
when Stocks Traded is the measure of financial development. Stocks Traded is the total value of
shares traded in billion dollars averaged across 1998 – 2002. Panel C reports the statistics for the
subsample used when Stocks Listed is the measure of financial development. Stocks Listed is the total
number of domestically listed companies on the country’s stock exchanges averaged across 1998 –
2002. Panel D reports the statistics for the subsample used when Expropriation Risk is the measure of
financial development. Expropriation Risk is the risk of expropriation of investment averaged across
1985 – 1995. Diversity is the ancestry adjusted predicted genetic diversity in 2000 CE from Ashraf
and Galor (2013). Per Capita GDP is the per capita GDP averaged across 1998 - 2002. Openness is
the sum of import and export divided by GDP averaged across 1998 – 2002. Legal Origin UK and
Legal Origin FR are dummy variables for a country’s legal origin of English Common Law and
French Civil Law, respectively. P_Catholic is the percentage of a country’s population belonging to
Roman Catholic. Malaria is the percentage of the population at risk of contracting malaria.
Population is the population of the country in millions. School is the average years of schooling
amongst the population. Social Inf is an index (ranging from 0 to 1) capturing the overall quality of
institutions and government policies with a country.
Panel A – Analysis with Market Cap
Variable
Mean
Market Cap
297
Diversity
0.7225
Per Capita GDP
12039.0
Openness
0.85
Legal Origin UK
0.3535
Legal Origin FR
0.3434
P_Catholic
32.88
Population
53.34
Malaria
0.1517
School
5.82
Social Inf
0.5584
Panel B – Analysis with Stocks Traded
Variable
Mean
Stocks Traded
349
Diversity
0.7226
Per Capita GDP
12152.8
Openness
0.85
Legal Origin UK
0.3469
Legal Origin FR
0.3469
P_Catholic
33.22
Population
53.88
Malaria
0.1484
School
5.8280
Social Inf
0.5584
Median
7
0.7313
4544.9
0.77
0
0
13.10
10.24
0
5.85
0.5037
Std
1520
0.0278
15091.6
0.52
0.4805
0.4773
37.29
166.46
0.2958
2.63
0.2475
Min
0
0.6279
270.9
0.20
0
0
0
0.39
0
0.69
0.1563
Max
14680
0.7653
70438.6
3.45
1
1
97.30
1261.90
1
10.86
1
N
99
99
98
98
99
99
99
99
96
84
79
Median
1
0.7315
4642.1
0.76
0
0
13.75
10.25
0
5.8450
0.5037
Std
2336
0.0280
15127.7
0.52
0.4784
0.4784
37.34
167.23
0.2957
2.6347
0.2475
Min
0
0.6279
270.9
0.20
0
0
0
0.39
0
0.6922
0.1563
Max
23050
0.7653
70438.6
3.45
1
1
97.30
1261.90
1
10.8622
1.0000
N
98
98
97
97
98
98
98
98
95
84
79
42
Panel C – Analysis with Stocks Listed
Variable
Mean
Stocks Listed
442
Diversity
0.7221
Per Capita GDP
12227.9
Openness
0.8402
Legal Origin UK
0.3364
Legal Origin FR
0.3645
P_Catholic
33.33
Population
50.15
Malaria
0.1411
School
5.7441
Social Inf
0.5549
Median
116
0.7313
4646.6
0.7700
0
0
13.10
10.21
0
5.7306
0.5033
Panel D – Analysis with Expropriation Risk
Variable
Mean
Median
Expropriation Risk
7.0364
7.0455
Diversity
0.7246
0.7326
Per Capita GDP
10707.25
2711.90
Openness
0.7806
0.6494
Legal Origin UK
0.3217
0.0000
Legal Origin FR
0.5217
1.0000
P_Catholic
33.2235
13.1000
Population
49.17
11.32
Malaria
0.3370
0.0281
School
4.6183
4.2550
Social Inf
0.4681
0.3896
43
Std
1072
0.0281
15503.7
0.5000
0.4747
0.4836
37.71
160.47
0.2866
2.6188
0.2438
Min
2
0.6279
270.9
0.2003
0
0
0
0.39
0
0.6922
0.1563
Max
7133
0.7653
70438.6
3.4491
1.0000
1.0000
97.30
1261.90
1
10.8622
1
N
107
107
106
106
107
107
107
107
104
88
82
Std
1.8029
0.0290
15401.45
0.5123
0.4692
0.5017
37.4440
154.75
0.4285
2.7357
0.2514
Min
1.6364
0.6279
135.23
0.0104
0.0000
0.0000
0.0000
0.39
0.0000
0.4089
0.1127
Max
10.0000
0.7743
70438.59
3.4491
1.0000
1.0000
97.3000
1261.90
1.0000
10.8622
1.0000
N
115
115
112
112
115
115
115
115
113
98
105
Table 2 – Pearson Correlation Coefficients of Transformed Financial Development
Measures
This table reports the correlation matrix of four measures of financial development. Log(Market
Cap) is the logarithm of the average stock market capitalization over 1998 - 2002. Log(Stocks
Traded) is the logarithm of the average total value of shares traded over 1998 - 2002. Log(Stocks
Listed) is the logarithm of the average number of domestically listed companies over 1998 –
2002. Expropriation Risk is the average risk of expropriation of investment over 1985 - 1995.
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Traded)
0.9629
(p <.0001)
Log(Stocks Listed)
0.7022
(p <.0001)
0.7671
(p <.0001)
Expropriation Risk
0.6737
(p <.0001)
0.6707
(p <.0001)
44
Log(Stocks Listed)
0.4322
(p <.0001)
Table 3 – Financial Development and Genetic Diversity
This table reports the unconditional relationship between financial development and genetic
diversity. Panel A reports the regression of the four measures of financial development on genetic
diversity and its square. Panel B reports the regression of the four measures of financial development
on genetic diversity, its square and logarithm of population. Log(Market Cap) is the logarithm of the
average stock market capitalization over 1998 - 2002. Log(Stocks Traded) is the logarithm of the
average total value of shares traded over 1998 - 2002. Log(Stocks Listed) is the logarithm of the
average number of domestically listed companies over 1998 – 2002. Expropriation Risk is the risk of
expropriation of investment averaged across 1985 – 1995. Diversity is the ancestry adjusted predicted
genetic diversity in 2000 CE from Ashraf and Galor (2013), and Diversity Sqr is its square.
Log(Population) is the country’s population. Bootstrapped standard errors are reported in the
parentheses. (*), (**) and (***) indicate the coefficients are statistically significant at the 10%, 5%,
and 1% levels, respectively.
Panel A
Diversity
Diversity Sqr
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
1412.8***
(480.7)
-1010.4***
(341.0)
2139.9***
(613.3)
-1527.1***
(433.8)
771.7***
(269.7)
-552.0***
(190.2)
Expropriation
Risk
596.7***
(178.4)
-425.2***
(126.8)
Yes
Yes
Yes
Yes
99
0.101
98
0.216
107
0.137
115
0.492
0.699
0.701
0.699
0.702
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
1178.1***
(432.6)
-836.0***
(309.0)
1.043***
(0.163)
1778.8***
(548.0)
-1260.2***
(389.2)
1.332***
(0.189)
580.0**
(232.7)
-410.2**
(165.1)
0.606***
(0.0689)
Expropriation
Risk
597.4***
(178.6)
-425.4***
(126.9)
0.0308
(0.0880)
Yes
Yes
Yes
Yes
99
0.392
98
0.484
107
0.467
115
0.488
0.705
0.706
0.707
0.702
Panel B
Diversity
Diversity Sqr
Log(Population)
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
45
Table 4 – Financial Development and Genetic Diversity with Controls
This table reports the regression of financial development on genetic diversity, its square, and
controls. Log(Market Cap) is the logarithm of the average stock market capitalization over 1998 2002. Log(Stocks Traded) is the logarithm of the average total value of shares traded over 1998 2002. Log(Stocks Listed) is the logarithm of the average number of domestically listed companies
over 1998 – 2002. Expropriation Risk is the risk of expropriation of investment averaged across 1985
– 1995. Diversity is the ancestry adjusted predicted genetic diversity in 2000 CE from Ashraf and
Galor (2013), and Diversity Sqr is its square. Log(Population) is the logarithm of population. Legal
Origin UK and Legal Origin FR are dummy variables for a country’s legal origin of English
Common Law and French Civil Law, respectively. Malaria is the percentage of the population at risk
of contracting malaria. Openness is the sum of import and export divided by GDP averaged over
1998 – 2002. P_Catholic is the percentage of a country’s population belonging to Roman Catholic.
Bootstrapped standard errors are reported in the parentheses. (*), (**) and (***) indicate the
coefficients are statistically significant at the 10%, 5%, and 1% levels, respectively.
Diversity
Diversity Sqr
Log(Pop)
Legal Origin FR
Legal Origin UK
Malaria
Openness
P_Catholic
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Stocks Traded)
694.5*
(386.2)
-489.3*
(277.2)
1.379***
(0.190)
2.378***
(0.697)
2.398***
(0.828)
-3.200***
(1.148)
1.313*
(0.679)
-0.00301
(0.00888)
1209.5**
(470.0)
-856.5**
(337.4)
1.636***
(0.269)
2.684***
(0.788)
3.057***
(1.044)
-4.555***
(1.445)
0.904
(0.972)
-0.00751
(0.0114)
442.3**
(210.5)
-315.7**
(150.0)
0.667***
(0.0855)
0.642
(0.395)
0.904**
(0.436)
-1.649**
(0.662)
0.291
(0.259)
-0.00774*
(0.00434)
468.0***
(162.3)
-333.6***
(116.8)
0.0428
(0.121)
-0.471
(0.366)
0.274
(0.394)
-1.359***
(0.386)
0.152
(0.373)
0.00220
(0.00498)
Yes
Yes
Yes
Yes
95
0.551
94
0.596
103
0.527
110
0.559
0.710
0.706
0.700
0.701
46
Log(Stocks Listed)
Expropriation
Risk
Log(Market Cap)
Table 5 – Financial Development and Genetic Diversity with Demand Proxy
This table reports the regression of financial development on genetic diversity, its square, and the
demand for financing. Log(Market Cap) is the logarithm of the average stock market capitalization
over 1998 - 2002. Log(Stocks Traded) is the logarithm of the average total value of shares traded
over 1998 - 2002. Log(Stocks Listed) is the logarithm of the average number of domestically listed
companies over 1998 – 2002. Expropriation Risk is the risk of expropriation of investment averaged
across 1985 – 1995. Diversity is the ancestry adjusted predicted genetic diversity in 2000 CE from
Ashraf and Galor (2013), and Diversity Sqr is its square. Per Capita GDP is the per capita GDP
averaged across 1998 - 2002. Bootstrapped standard errors are reported in the parentheses. (*), (**)
and (***) indicate the coefficients are statistically significant at the 10%, 5%, and 1% levels,
respectively.
Diversity
Diversity Sqr
Log(Per Capita
GDP)
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Market Cap)
Log(Stocks traded)
Log(Stocks Listed)
496.0
(394.1)
-366.8
(279.7)
1084.9**
(529.0)
-784.5**
(374.4)
643.5**
(260.7)
-462.5**
(184.1)
Expropriation
Risk
353.9**
(161.1)
-257.2**
(115.3)
1.623***
1.804***
0.272**
0.632***
(0.192)
(0.256)
(0.137)
(0.0902)
Yes
Yes
Yes
Yes
98
0.474
97
0.485
106
0.166
112
0.662
0.676
0.691
0.696
0.687
47
Table 6 – Financial Development and Genetic Diversity with Demand Proxy and Controls
This table reports the regression of financial development on genetic diversity, its square, and
controls. Log(Market Cap) is the logarithm of the average stock market capitalization over 1998 2002. Log(Stocks Traded) is the logarithm of the average total value of shares traded over 1998 2002. Log(Stocks Listed) is the logarithm of the average number of domestically listed companies
over 1998 – 2002. Expropriation Risk is the risk of expropriation of investment averaged across 1985
– 1995. Diversity is the ancestry adjusted predicted genetic diversity in 2000 CE from Ashraf and
Galor (2013), and Diversity Sqr is its square. Per Capita GDP is the per capita GDP averaged across
1998 - 2002. Openness is the sum of import and export divided by GDP averaged over 1998 – 2002.
Legal Origin UK and Legal Origin FR are dummy variables for a country’s legal origin of English
Common Law and French Civil Law, respectively. P_Catholic is the percentage of a country’s
population belonging to Roman Catholic. Malaria is the percentage of the population at risk of
contracting malaria. Bootstrapped standard errors are reported in the parentheses. (*), (**) and (***)
indicate the coefficients are statistically significant at the 10%, 5%, and 1% levels, respectively.
Diversity
Diversity Sqr
Log(Per Capita
GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
P_Catholic
Continnent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
Expropriation
Risk
667.3*
(399.8)
-499.0*
(284.8)
1288.4***
(445.3)
-946.9***
(316.6)
714.3***
(248.1)
-522.1***
(176.8)
301.6*
(166.4)
-220.9*
(119.4)
1.724***
1.951***
0.335**
0.648***
(0.214)
1.413**
(0.631)
1.979***
(0.768)
0.308
(1.311)
-2.023***
(0.591)
-0.00345
(0.00805)
(0.268)
1.682**
(0.804)
2.931***
(0.972)
-0.253
(1.572)
-3.078***
(0.793)
-0.00998
(0.00963)
(0.139)
0.387
(0.427)
1.001**
(0.467)
-0.682
(0.735)
-1.169***
(0.377)
-0.00726
(0.00522)
(0.117)
-0.708**
(0.308)
0.0242
(0.338)
0.104
(0.469)
-0.230
(0.242)
0.000315
(0.00381)
Yes
Yes
Yes
Yes
94
0.584
93
0.638
102
0.304
108
0.690
0.669
0.680
0.684
0.682
48
Table 7 – Financial Development, Genetic Diversity, and Human and Social Capitals
This table reports the regression of financial development on genetic diversity, its square, and
controls, inclusion of proxies for human capital (Panel A) and social capital (Panel B). Log(Market
Cap) is the logarithm of the average stock market capitalization over 1998 - 2002. Log(Stocks
Traded) is the logarithm of the average total value of shares traded over 1998 - 2002. Log(Stocks
Listed) is the logarithm of the average number of domestically listed companies over 1998 – 2002.
Expropriation Risk is the risk of expropriation of investment averaged across 1985 – 1995. Diversity
is the ancestry adjusted predicted genetic diversity in 2000 CE from Ashraf and Galor (2013), and
Diversity Sqr is its square. Per Capita GDP is the per capita GDP averaged across 1998 - 2002.
Openness is the sum of import and export divided by GDP averaged over 1998 – 2002. Legal Origin
UK and Legal Origin FR are dummy variables for a country’s legal origin of English Common Law
and French Civil Law, respectively. Malaria is the percentage of the population at risk of contracting
malaria. School is the average years of schooling amongst the population. Social Inf is an index
(ranging from 0 to 1) capturing the overall quality of institutions and government policies with a
country. Bootstrapped standard errors are reported in the parentheses. (*), (**) and (***) indicate the
coefficients are statistically significant at the 10%, 5%, and 1% levels, respectively.
Panel A
Diversity
Diversity Sqr
Log(Per Capita
GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
School
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
682.2*
(397.6)
-504.2*
(283.0)
1092.4**
(541.5)
-800.4**
(384.2)
527.8**
(231.1)
-386.7**
(164.6)
Expropriation
Risk
316.8*
(172.1)
-233.0*
(123.9)
1.560***
1.857***
0.102
0.470***
(0.241)
1.404**
(0.700)
1.624**
(0.797)
1.344
(1.302)
-1.922***
(0.662)
0.191
(0.164)
(0.371)
2.112**
(0.940)
2.592**
(1.072)
0.872
(1.739)
-2.847***
(0.795)
0.350
(0.226)
(0.185)
1.184***
(0.421)
1.660***
(0.473)
-1.155
(0.967)
-1.365***
(0.449)
0.303**
(0.119)
(0.149)
-0.127
(0.369)
0.421
(0.355)
0.110
(0.562)
-0.296
(0.301)
0.202**
(0.0972)
Yes
Yes
Yes
Yes
79
0.624
79
0.652
83
0.456
92
0.705
0.677
0.682
0.682
0.680
49
Panel B
Diversity
Diversity Sqr
Log(Per Capita
GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
Social Inf
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
Expropriation
Risk
768.9**
(363.1)
-555.2**
(256.4)
1292.5***
(452.2)
-929.1***
(320.0)
588.9**
(237.0)
-420.6**
(168.5)
319.0**
(156.1)
-228.3**
(112.0)
0.912**
1.035**
0.0296
0.359**
(0.394)
0.232
(0.564)
0.630
(0.787)
-0.510
(1.212)
-1.757***
(0.516)
2.875
(1.842)
(0.505)
0.191
(0.729)
1.183
(0.916)
-1.578
(1.568)
-2.996***
(0.723)
4.385*
(2.434)
(0.239)
-0.0332
(0.380)
0.492
(0.491)
-1.271
(0.782)
-1.264***
(0.339)
2.074*
(1.187)
(0.146)
-0.642**
(0.295)
-0.0448
(0.322)
-0.199
(0.394)
-0.327
(0.202)
3.009***
(0.860)
Yes
Yes
Yes
Yes
75
0.597
75
0.663
78
0.497
99
0.760
0.692
0.696
0.700
0.699
50
Table 8 – Financial Development and Alternative Measure of Genetic Diversity with
Demand Proxy and Controls
This table reports the regression of financial development on genetic diversity, its square, and
controls, where genetic diversity is proxied by the 1500 CE-measure. Log(Market Cap) is the
logarithm of the average stock market capitalization over 1998 - 2002. Log(Stocks Traded) is the
logarithm of the average total value of shares traded over 1998 - 2002. Log(Stocks Listed) is the
logarithm of the average number of domestically listed companies over 1998 – 2002. Expropriation
Risk is the risk of expropriation of investment averaged across 1985 – 1995. Alt Diversity is the
distance only based measure of predicted genetic diversity from Ashraf and Galor (2013), and Alt
Diversity Sqr is its square. Per Capita GDP is the per capita GDP averaged across 1998 - 2002.
Openness is the sum of import and export divided by GDP averaged over 1998 – 2002. Legal Origin
UK and Legal Origin FR are dummy variables for a country’s legal origin of English Common Law
and French Civil Law, respectively. P_Catholic is the percentage of a country’s population belonging
to Roman Catholic. Malaria is the percentage of the population at risk of contracting malaria.
Bootstrapped standard errors are reported in the parentheses. (*), (**) and (***) indicate the
coefficients are statistically significant at the 10%, 5%, and 1% levels, respectively.
Alt Diversity
Alt Diversity Sqr
Log(Per Capita
GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
P_catholic
Continent
Dummies
N
Adj R2
Maximizing Level
of Diversity
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
Expropriation
Risk
427.3*
(228.5)
-338.9**
(163.0)
653.3**
(309.5)
-514.7**
(219.8)
282.3**
(126.7)
-226.3**
(91.73)
45.27
(75.86)
-45.27
(56.44)
1.561***
1.779***
0.281**
0.645***
(0.195)
1.731***
(0.579)
2.069***
(0.774)
-0.151
(1.104)
-2.022***
(0.594)
0.00129
(0.00762)
(0.259)
2.102***
(0.739)
3.089***
(0.941)
-0.973
(1.365)
-3.105***
(0.689)
-0.00472
(0.00884)
(0.131)
0.550
(0.406)
1.073**
(0.437)
-1.021
(0.699)
-1.186***
(0.350)
-0.00541
(0.00513)
(0.106)
-0.659**
(0.293)
0.114
(0.301)
-0.00229
(0.449)
-0.262
(0.227)
0.00220
(0.00389)
Yes
Yes
Yes
Yes
95
0.584
94
0.640
103
0.308
110
0.695
0.630
0.635
0.624
0.500
51
Table 9 – Financial Development and Genetic Diversity: Sub-sample Analysis
This table presents the results of the effect of genetic diversity on each of the four financial
development measures for each different sub-sample (Panels A-E) with the inclusion of Per
Capita GDP and other controls. In Panel A of Table 8 we exclude the 30 OECD countries from
our sample. Panel B reports the results where we omit 48 Sub-Saharan African countries. Panel
C, D, and E reports the sub-samples where we omit the 10% of highest GDP per capital countries
(Panel C), the 10% of lowest GDP per capital countries (Panel D), and both the 10% of highest
and the 10% of lowest GDP per capita countries (Panel E). Log(Market Cap) is the logarithm of
the average stock market capitalization over 1998 - 2002. Log(Stocks Traded) is the logarithm of
the average total value of shares traded over 1998 - 2002. Log(Stocks Listed) is the logarithm of
the average number of domestically listed companies over 1998 – 2002. Expropriation Risk is
the risk of expropriation of investment averaged across 1985 – 1995. Diversity is the ancestry
adjusted predicted genetic diversity in 2000 CE from Ashraf and Galor (2013), and Diversity Sqr
is its square. Per Capita GDP is the per capita GDP averaged across 1998 - 2002. Openness is
the sum of import and export divided by GDP averaged over 1998 – 2002. Legal Origin UK and
Legal Origin FR are dummy variables for a country’s legal origin of English Common Law and
French Civil Law, respectively. Malaria is the percentage of the population at risk of contracting
malaria. Bootstrapped standard errors are reported in the parentheses. (*), (**) and (***) indicate
the coefficients are statistically significant at the 10%, 5%, and 1% levels, respectively.
Panel A
Diversity
Diversity Sqr
Log(Per Cap GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
Continent Dummy
N
Adj R2
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
667.1
(521.1)
-495.7
(370.0)
1210.9**
(574.6)
-890.9**
(410.4)
617.4**
(260.3)
-452.4**
(185.9)
Expropriation
Risk
248.4
(177.9)
-183.3
(128.2)
1.262***
1.189**
-0.0355
0.586***
(0.394)
2.450
(1.610)
2.509
(1.585)
-0.291
(1.243)
-1.416
(0.996)
Yes
65
0.286
(0.472)
3.333*
(1.899)
3.957**
(1.725)
-0.948
(1.587)
-2.001*
(1.166)
Yes
64
0.411
(0.200)
1.196
(0.898)
1.387*
(0.817)
-1.005
(0.839)
-0.585
(0.532)
Yes
73
0.183
(0.195)
-1.285**
(0.612)
-0.564
(0.553)
0.0820
(0.556)
-0.177
(0.396)
Yes
81
0.354
52
Panel B
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
Continent Dummy
N
857.9**
(430.8)
-638.1**
(308.8)
1.790***
(0.210)
1.305**
(0.575)
1.994**
(0.780)
-0.636
(2.641)
-1.899***
(0.575)
Yes
81
1479.1**
(599.7)
-1084.9**
(427.6)
2.048***
(0.306)
1.258*
(0.750)
2.916***
(1.016)
-0.907
(3.194)
-2.903***
(0.726)
Yes
80
670.7**
(282.0)
-489.7**
(201.4)
0.362***
(0.136)
0.0286
(0.433)
1.043**
(0.519)
-0.546
(1.115)
-1.092***
(0.388)
Yes
89
Expropriation
Risk
449.5**
(177.1)
-328.1**
(127.5)
0.671***
(0.123)
-0.675***
(0.262)
0.159
(0.351)
0.0891
(0.682)
-0.283
(0.260)
Yes
79
Adj R2
0.597
0.632
0.213
0.721
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
628.9*
(377.1)
-472.6*
(269.2)
1.673***
(0.230)
1.447**
(0.681)
1.939**
(0.842)
0.190
(1.177)
-1.907***
(0.705)
Yes
85
0.545
1221.3**
(488.7)
-898.6***
(347.8)
1.866***
(0.309)
1.728**
(0.847)
2.892***
(1.031)
-0.532
(1.465)
-2.780***
(0.882)
Yes
84
0.607
591.9**
(256.9)
-434.1**
(182.9)
0.412***
(0.148)
0.376
(0.470)
1.038**
(0.511)
-0.860
(0.776)
-1.083***
(0.410)
Yes
92
0.326
Diversity
Diversity Sqr
Log(Per Cap GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
Panel C
Diversity
Diversity Sqr
Log(Per Cap GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
Continent Dummy
N
Adj R2
53
Expropriation
Risk
265.9
(176.1)
-195.7
(126.6)
0.680***
(0.128)
-0.857***
(0.317)
-0.0908
(0.317)
0.147
(0.469)
-0.275
(0.256)
Yes
98
0.637
Panel D
Diversity
Diversity Sqr
Log(Per Cap GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
Continent Dummy
N
Adj R2
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
964.4*
(502.1)
-712.6**
(358.5)
1.595***
(0.251)
1.514***
(0.549)
2.023**
(0.809)
-1.259
(1.654)
-2.108***
(0.597)
Yes
85
0.550
1711.0***
(548.5)
-1250.8***
(392.0)
1.728***
(0.296)
1.922***
(0.709)
3.521***
(0.937)
-2.273
(1.958)
-3.370***
(0.772)
Yes
84
0.617
833.5***
(287.4)
-606.0***
(205.4)
0.208
(0.147)
0.241
(0.408)
1.082*
(0.561)
-1.346
(0.898)
-1.320***
(0.420)
Yes
92
0.259
Log(Market Cap)
Log(Stocks Traded)
Log(Stocks Listed)
934.6*
(478.9)
-693.1**
(342.1)
1.520***
(0.285)
1.690**
(0.696)
2.029**
(0.968)
-1.399
(1.642)
-1.974***
(0.744)
Yes
76
0.507
1660.1***
(637.2)
-1216.0***
(454.8)
1.608***
(0.360)
2.318**
(0.942)
3.623***
(1.194)
-2.505
(2.073)
-3.033***
(0.844)
Yes
75
0.589
705.5**
(285.6)
-515.3**
(203.9)
0.278
(0.172)
0.408
(0.468)
1.136*
(0.582)
-1.463
(0.957)
-1.226***
(0.444)
Yes
82
0.283
Expropriation
Risk
449.7***
(174.3)
-328.5***
(125.3)
0.694***
(0.117)
-0.688***
(0.249)
0.147
(0.298)
0.215
(0.474)
-0.286
(0.232)
Yes
98
0.720
Panel E
Diversity
Diversity Sqr
Log(Per Cap GDP)
Legal Origin FR
Legal Origin UK
Malaria
Openness
Continent Dummy
N
Adj R2
54
Expropriation
Risk
412.5**
(184.2)
-302.1**
(132.8)
0.738***
(0.117)
-0.867***
(0.303)
0.0450
(0.350)
0.269
(0.500)
-0.342
(0.263)
Yes
88
0.674
Table 10 – Variable Description and Data Source
Market Cap
Stocks Traded
Stocks Listed
Expropriation
Risk
Diversity
Alt Diversity
Legal FR
Legal UK
P_catholic
Openness
Per Capita
GDP
Malaria
School
Social Inf
Stock market capitalization in billion dollars averaged across 1998 –
2002; Source: World Bank Global Financial Development Database
Total value of shares traded in billion dollars averaged across 1998 –
2002; Source: World Bank Global Financial Development Database
Total number of domestically listed companies on the country’s stock
Exchanges averaged across 1998 – 2002; Source: World Bank Global
Financial Development Database
Average Protection against expropriation risk, average of 1985-1995
- ranges from 0 to 10 with higher numbers meaning more protection
and less risk; Source: Acemoglu et. al. (2001)
Ancestry adjusted measure of predicted genetic diversity in 2000 CE;
Source: Ashraf and Galor (2013)
Migratory distance only predicted measure of genetic diversity;
Source: Ashraf and Galor (2013)
Dummy variable for a country’s legal origin of French Civil Law;
Source: Ashraf and Galor (2013)
Dummy variable for a country’s legal origin of English Common
Law; Source: Ashraf and Galor (2013)
Percentage of a country’s population belonging to Roman Catholic;
Source: Ashraf and Galor (2013)
Sum of import and export divided by GDP averaged across 1998 –
2002; Source: World Bank Open Data
Per capita GDP averaged across 1998 – 2002; Source: World Bank
Global Financial Development Database
Percentage of the population at risk of contracting malaria; Source:
Ashraf and Galor (2013)
Average years of schooling amongst the adult population from
reports of Barro and Lee (2001); Source: Ashraf and Galor (2013)
An index between 0 and 1 that measures the quality of the social
institutions and Government policies, calculated by Hall and Jones
(1999); Source: Ashraf and Galor (2013)
55