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Genetic distance, trade, and the diffusion of development

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The determinants of countries' long‐term income differences feature prominently in the literature. Spolaore and Wacziarg (The diffusion of development, Quarterly Journal of Economics, 2009, 124, 469–529) argue that cultural differences, measured by countries' genetic distance, are an important barrier to the diffusion of development from the world's technological frontier. We revisit their findings in three ways. First, we successfully reproduce their results and confirm the robustness of their baseline findings. Second, we estimate their models for different time periods and find that the impact of genetic distance on income differences did not significantly change over time. Finally, we explore one of the underlying mechanisms of technology adoption and show that bilateral trade is one channel through which cultural differences retard the diffusion of development.
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Genetic Distance, Trade, and the Diusion of Development
Vincenzo Bove*
University of Warwick
Gunes Gokmen
New Economic School
November 2017
Abstract
The determinants of countries’ long-term income dierences feature prominently in the
literature. Spolaore and Wacziarg (The diusion of development, The Quarterly Journal of
Economics 2009; 124: 469-529) argue that cultural dierences, measured by countries’ ge-
netic distance, are an important barrier to the diusion of development from the world’s tech-
nological frontier. We revisit their findings in three ways. First, we successfully reproduce
their results and confirm the robustness of their baseline findings. Second, we estimate their
models for dierent time periods and find that the impact of genetic distance on income dier-
ences did not significantly change over time. Finally, we explore one of the underlying mech-
anisms of technology adoption and show that bilateral trade is one channel through which
cultural dierences retard the diusion of development.
Keywords: Genetic Distance; Culture; Bilateral Trade; Diusion of Development
JEL Code: F10, F40, O11, O57, Z10
*v.bove@warwick.ac.uk. Department of Politics and International Studies, University of Warwick.
ggokmen@nes.ru. New Economic School and the Center for the Study of Diversity and Social Interactions, 100A
Novaya Street, Skolkovo, Moscow, Russia, 143026. Gokmen acknowledges the support of the Ministry of Education
and Science of the Russian Federation, grant No. 14.U04.31.0002 administered through the NES CSDSI.
1
1 Introduction
What explains countries’ economic performances and the long-term dierences in per capita in-
come is one of the most fascinating and dicult questions in economics. For instance, Barro
(1991, 1996) convincingly argues that economic growth is enhanced by a number of factors such
as higher human capital, physical investment, rule of law and political stability. The influence of
human capital on development has since attracted a lot of attention (Bils & Klenow, 2000), and
several studies have explored how the composition of the population, particularly in terms of eth-
nic heterogeneity, helps explain cross-country dierences in growth rates (e.g., Easterly & Levine,
1997; Ottaviano & Peri, 2006; Bove & Elia, 2017). Establishing the very direction of the impact
of diversity on growth is not straightforward. Cultural diversity can erode trust among individuals
and social cohesion within societies at large. At the same time, however, a wider spectrum of
traits can nurture technological innovation, the diusion of new ideas, and thus the production of a
greater variety of goods and services (Alesina & Ferrara, 2005). Given the theoretical ambiguities
around the issues of cultural dierences, technology diusion and development, perhaps it comes
as no surprise that it has been very hard to detect empirically a robust association between culture
and development.1The seminal paper by Spolaore & Wacziarg (2009) directly tackles the question
of what hampers the diusion of technological and institutional innovations across societies. They
employ genetic distance to capture a wide array of cultural traits transmitted intergenerationally
within populations over the long run. They find that important dierences in societal norms, cus-
toms, and habits, proxied by genetic distance, act as barriers to the diusion of development from
the frontier country.2
We revisit Spolaore and Wacziarg’s (2009) findings in three ways. First, we successfully re-
produce their results in a narrow sense. Second, we check the robustness of their main findings in a
wide sense by adding a battery of classical gravity equation impediments to economic interaction
and exchange. Moreover, we estimate the baseline model for dierent time periods, and find that
the substantive impact of genetic distance on income dierences has not significantly increased
or decreased over time. Third, Spolaore & Wacziarg (2009, p.523) note that although their anal-
1Ashraf & Galor (2013) find that genetic diversity within a society has an inverse u-shaped relationship with income
per capita, reflecting the trade-obetween the beneficial and the detrimental eects of diversity on productivity.
2More recently, Spolaore & Wacziarg (2016) use new information on human microsatellite variation and confirm
that relative ancestral distance from the technological frontier had a statistically and economically significant eect on
income dierences.
2
ysis “provides a general macroeconomic framework [...], the study of the specific microeconomic
mechanisms through which the eects operate is left for future research. As a first step in this
direction, we provide some evidence on the underlying mechanisms of technology adoption (and
barriers to such adoptions). We show that, by reducing the substantive eect of genetic distance on
income dierences by almost 30%, lower bilateral trade due to genetic distance is one of the chan-
nels through which cultural dierences retard technology adoption from the frontier, and hence,
the diusion of development.
2 Data and Empirical strategy
The variable of interest is genetic distance, a measure of distance to the most recent common
ancestors of two populations, i.e. their degree of genealogical relatedness, or equivalently, the
length of time since two populations split apart (Spolaore & Wacziarg, 2009).3As in Spolaore &
Wacziarg (2009), to better determine the expected genetic distance between two randomly selected
individuals, we use data on genetic distance weighted by the share of population belonging to
each distinct ancestral group in each country, rather than genetic distance based on dominant
groups only. By measuring the time since two populations shared common ancestors, genetic
distance provides an ideal summary of dierences in slowly changing genealogically transmitted
characteristics, including habits and customs (Spolaore & Wacziarg, 2009, p. 523).
Information on GDP per capita comes from the Penn World Tables (PWT), version 7.1.7.4
Trade data are from UN ComTrade dataset that includes aggregate yearly trade flows across dyads.
With the exception of Table 2, our analyses are for the year 2000.
We start oby replicating the main results of Spolaore & Wacziarg (2009), and present regres-
sions of income dierences on relative genetic distance from the technological frontier, the US.
We estimate the following regression:
|LogYiLogY j|=γGeneticDistancei j,US +αkτki j +i j
3This measure of genetic distance, also called FS T distance, is constructed using information on 128 alleles related
to 45 selectively neutral genes. It includes alleles coding for blood groups, immunoglobulin, hemoglobin, enzymes and
lymphocyte antigens. We refer the interested reader to Spolaore & Wacziarg (2009) for a more comprehensive overview
and a formal definition of genetic distance. See also Cavalli-Sforza et al. (1994).
4Spolaore & Wacziarg (2009) use income numbers from the Penn World Tables and from the World Bank (both for
the year 1995) and find that this makes little dierence in the results.
3
where |LogYiLogY j|is the absolute per capita income dierence between all pairs of coun-
tries for the year 2000; GeneticDistanceij,US is the absolute dierence in genetic distance of
countries iand jfrom the US, i.e. genetic distance relative to the US (|GeneticDistanceUS ,i
GeneticDi stanceUS,j|); τki j represents the kbilateral controls other than genetic distance; and i j
is the error term.
3 Results
In Table 1 we first present a univariate regression of income dierences on genetic distance, and
then, we gradually add various measures of geographic isolation, physical barriers and environ-
mental factors. In the baseline model of column (2), we control for geodesic distance and conti-
guity. Subsequently, we test the robustness of the results to additional geographic factors such as
latitudinal and longitudinal distances as well as the number of islands and landlocked countries in
the pair. We also use an array of measures of climatic similarity, as climate may also act as a barrier
to the diusion of development, see column (5). Moreover, given the potential endogeneity of ge-
netic distance with respect to income dierences, in column (4), we use Spolaore and Wacziarg’s
(2009) data on genetic distance in 1500 as an instrument for current genetic distance. Finally, in
columns (6) and (7), we check whether the eect of genetic distance is robust to the inclusion
of other markers of identity (and thus cultural similarities), in particular religious and linguistic
distance relative to the US, and a shared colonial history. Results from columns (1) to (7) sug-
gest that, conditional on various controls, a one-unit change in genetic distance is associate with
an expected increase in income dierences of 7 to 13 units (where income is log-transformed).
Overall, the results of this narrow replication exercise confirm the signs and the magnitudes of the
estimated coecients reported in Spolaore and Wacziarg’s (2009) original study.5
As a second step, we ask whether the coecient of interest, γ, is stable across various time
periods. In Table 2, we reproduce the baseline model of column (2) of Table 1 for dierent years
from 1950 to 2005. One might expect an accelerated speed of technological progress in more
recent times. If genetic distance acts as a barrier to technology adoption from the technological
frontier, then the greater is the technological dierence between the frontier and the laggards, the
5For example, the coecient of genetic distance in column (1) is 7.18, which is very close to 6.36 in the same
model of Spolaore & Wacziarg (2009). The small dierences throughout the specifications are due to the choice of the
reference year (we use 2000, they use 1995), and therefore a dierent sample size. However, when we use the same
year (column (6) of Table 2), we get virtually the same coecient.
4
larger is going to be the impeding eect of genetic distance. Since a potential concern is that
our dependent variable, income dierences, varies greatly over several decades, whereas genetic
distance is time invariant, we follow Spolaore & Wacziarg (2009) and report the standardized
beta coecients on genetic distance (in square brackets).6Spolaore & Wacziarg (2009) find a
slight decrease in the eect of genetic distance in recent times (although they use variation in
income only for 1960 and 1995). Yet, on the one hand, the top panel in Table 2 suggests that
the standardized eect does not rise monotonically, moving from 15% in 1950 to 26% in 1965,
then stabilizing around 26% until 2005. Perhaps more importantly, however, the magnitudes of the
corresponding standard errors suggest that the coecients are never statistically dierent from one
another; in other words, there is no statistically significant change in the positive impact of genetic
distance over time. On the other hand, given the lack of data on some of the variables for earlier
periods, resulting in an increasing number of observations over decades, in the bottom panel of
Table 2 we rely on a common sample, using the 1950 countries only. Despite a non-monotonic
increase over time, the size of the standard errors suggest again that the eect of genetic distance
on income dierences does not significantly evolve over time when we use the same number of
countries.
Building on Table 2, Table 3 adds dyadic trade controls that are standard in gravity equations à
la Anderson & van Wincoop (2003). In particular, we take into consideration institutional and his-
torical links across countries. To that end, we control for common ocial language, isolating the
impact of genetic distance from simple communication costs; same legal origin, which can lower
transaction costs due to legal and regulatory systems and improve mutual trust (Guiso et al. , 2009);
and the existence of a colonial relationship. We additionally account for a host of economic factors
such as free trade agreements (FTA), GATT/WTO membership, common currency and general-
ized system of preferences agreements (GSP).7Finally, we account for the so-called “multilateral
resistance terms” by including monodic country fixed eects, whose exclusion biases estimates
in gravity models of trade (Anderson & van Wincoop, 2003). Controlling for monodic country
fixed eects is a quite “demanding” test for the model, as fixed eects soak up the explanatory
power of many variables by explicitly taking into account country-specific characteristics such as
6This should provide a more relevant metric of changes in the explanatory power of genetic distance. We thank an
anonymous referee for suggesting the inclusion of these estimates.
7Control variables can be accessed on CEPII’s or Thierry Mayer’s webpage. http://econ.sciences-po.fr/
node/131.
5
the quality of the institutions or the level of human capital. Furthermore, we combine fixed eects
with two-way clustered standard errors, which should make it harder for a number of variables
to appear either substantively or statistically significant. Despite the very demanding specifica-
tion, adding country fixed eects and dyadic trade controls does not alter the results (with a lower
bound estimate of the genetic distance coecient of 6.0). The reported results further corroborate
Spolaore and Wacziarg’s (2009) original findings in terms of the estimated sign, magnitude, and
statistical significance.
In Table 4, we evaluate the idea that the eect of genetic distance on income dierences works
through barriers to technology adoption from the technological frontier, the US. We argue that
genetic distance delays the diusion of development partly by reducing trade, and hence, bilateral
exchange and interaction with the technological frontier, the US.8Lower bilateral exchange with
the US, due to genetic dierences, will then retard the adoption and the diusion of technology,
and as a result, will lead to greater income dierences.
To make our point, we need to estimate a number of auxiliary models. In column (1) of Table
4, we first show how genetic distance from the technological frontier, the US, aects a county’s
bilateral trade with the same technological frontier, the US. Cultural distance seems to have a
substantive influence on bilateral trade. A one percentage point increase in genetic distance leads
to a 21% decrease in imports from the US. Furthermore, column (2) of Table 4 shows that genetic
distance of country jfrom the US lowers its income per capita (in line with Table 1 of Spolaore &
Wacziarg (2009)). At the same time, as expected, we observe in column (3) that more trade with
the US is associated with higher income. Column (4) combines columns (2) and (3) to show that
when we include both genetic distance and trade with the US in an income regression, the eect
of genetic distance from the US on income is reduced by 25% compared to column (2).
Column (5), on the other hand, shows that relative trade of countries with respect to the US
increases with their genetic distance relative to the US. For example, country ithat is genetically
close to the US will trade more with the US than does country jthat is genetically distant from
the US. Hence, both their genetic distance relative to the US and their relative trade with the US
will be large. Column (6) reports the eect of relative genetic distance on dierences in income
replicating column (8) of Table 3. Similarly, column (7) reports that larger relative trade with the
8For studies on the negative impact of cultural distance on trade, see Guiso et al. (2009), Felbermayr & Toubal
(2010), Gokmen (2017).
6
US is associated with higher income dierences. Relative genetic distance aects relative trade
and income dierences, while trade has an independent eect on income. Therefore, in column
(8), by combining columns (6) and (7), we asses what part of the eect of relative genetic distance
on income dierence is intermediated through bilateral trade. When both relative genetic distance
and relative trade with the US are included in the specification, the impact of relative genetic
distance on income dierences is mitigated by 29% compared to column (6).9In sum, these
results from Table 4 suggest that a substantial part of the eect of genetic distance on the diusion
of development is mediated through its eect on trade and bilateral exchange.
A fair criticism would be to point out the endogeneity problems plaguing the trade to in-
come dierences dynamics. Although we mitigate the issue of endogeneity stemming from the
likely omission of important co-determinants of trade and income dierences with country iand j
fixed eects, the coecient of trade might be contaminated by other unobserved factors and from
causality running both ways. Yet, finding a suitable exogenous instrument for trade is challenging.
The remoteness variable is often used in the international trade literature as an instrument for trade,
yet a country with low remoteness has many close and large alternative sources of goods and this
could in turn directly aect its level of development. Virtually all factors aecting bilateral trade,
including geographic distance or the presence of a common language, are also likely to violate the
exclusion restrictions. As such, this result must be interpreted with caution.
4 Conclusions
The level of economic development varies enormously across countries, and a number of economic
studies have pursued the question of what factors determine the large observed income dierences.
This paper successfully reproduces the main findings of Spolaore & Wacziarg (2009): genetic
distance bears a statistically significant relation to income dierences, and as such it captures the
important barriers to the diusion of technology. We further show that the substantive eect of
genetic distance on income dierences has not significantly changed over time. Finally, as there
9It is easy to check that multiplying the coecient of genetic distance on trade in column (5) by the coecient of
trade on income dierences in column (7) gives approximately an idea of the size of the impact of genetic distance on
income dierences through its eect on trade. In fact, the product of the two coecients, 1.85, is roughly equal to the
amount of change in the coecient of genetic distance when we move from column (6) to column (8) and explicitly
include trade in the equation for income dierences. The two magnitudes are similar, which suggests that trade is
indeed capturing about one-third of the eect of genetic distance on income. We thank an anonymous reviewer for
highlighting this.
7
are no empirical works directly exploring the specific underlying mechanisms, we oer a first step
in this direction and show that bilateral trade is potentially an important channel linking cultural
distance to the diusion of development.
8
Table 1: Genetic Distance Relative to the US and Income Dierences
(1) (2) (3) (4) (5) (6) (7)
Genetic Distance Relative to the US, Weighted 7.186∗∗∗ 6.819∗∗∗ 8.547∗∗∗ 12.892∗∗∗ 8.959∗∗∗ 8.638∗∗∗ 8.604∗∗∗
(1.113 (1.148) (1.427) (0.570) (1.368) (1.459) (1.463)
Log Distance X X X X X X
Contiguity X X X X X X
Absolute Dierence in Latitude X X X X X
Absolute Dierence in Longitude X X X X X
Number of Islands X X X X X
Number of Landlocked Countries X X X X X
Log Absolute Dierence in Elevation X X X X X
Log Absolute Dierence in Distance to Coast X X X X X
Abs. Dif. in Polar Land Percentage X
Abs. Dif. in Boreal Land Percentage X
Abs. Dif. in Temperate Desert Percentage X
Abs. Dif. in Tropical Desert Percentage X
Abs. Dif. in Dry Land Percentage X
Abs. Dif. in Wet Land Percentage X
Abs. Dif. in Subtropical Land Percentage X
Abs. Dif. in Tropical Land Percentage X
Religious Distance Relative to the US, Weighted X X
Linguistic Distance Relative to the US, Weighted X X
Colonial Link X
N23944 23944 11693 11693 10492 10845 10845
Regressand: |LogYiLogYj|: Absolute income per capita dierence in 2000.
Genetic Distance Relative to the US: |GeneticDistanceU S,iGeneticDi stanceUS,j|.
In column (4), genetic distance is instrumented with genetic distance in 1500.
Two-way clustered standard errors are in parentheses.
p<0.10,∗ ∗ p<0.05,∗∗∗p<0.01
9
Table 2: Genetic Distance Relative to the US and Income Dierences over Time
(1) (2) (3) (4) (5) (6) (7)
1950 1955 1965 1975 1985 1995 2005
Genetic Distance Relative to the US, Weighted 3.903∗∗ 4.187∗∗ 5.519∗∗∗ 6.391∗∗∗ 6.381∗∗∗ 6.927∗∗∗ 7.952∗∗∗
(1.990) (1.981) (0.936) (0.992) (0.972) (1.149) (1.235)
[0.144] [0.147] [0.264] [0.261] [0.259] [0.237] [0.266]
N3780 4420 14012 16714 19370 23328 22720
Fixing the sample to 1950 countries only:
Genetic Distance Relative to the US, Weighted 3.903∗∗ 4.692∗∗ 7.043∗∗ 8.959∗∗∗ 7.024∗∗ 9.820∗∗ 10.187∗∗∗
(1.990) (2.170) (2.733) (3.166) (2.741) (3.804) (3.732)
[0.144] [0.164] [0.227] [0.266] [0.216] [0.240] [0.248]
N3780 3785 3833 3842 3850 3507 3622
Log Distance X X X X X X X
Contiguity X X X X X X X
Regressand: |LogYiLogYj|: Absolute income per capita dierence in specified years.
Genetic Distance Relative to the US: |GeneticDistanceU S,iGeneticDi stanceUS,j|.
Standardized beta coecients are in brackets.
Two-way clustered standard errors are in parentheses.
p<0.10,∗ ∗ p<0.05,∗∗∗p<0.01
Table 3: Genetic Distance Relative to the US and Income Dierences, Adding Dyadic Trade
Controls
(1) (2) (3) (4) (5) (6) (7) (8)
Genetic Distance Relative to the US, Weighted 6.812∗∗∗ 6.794∗∗∗ 6.744∗∗∗ 6.392∗∗∗ 6.275∗∗∗ 6.253∗∗∗ 6.004∗∗∗ 6.000∗∗∗
(1.148) (1.150) (1.148) (1.127) (1.127) (1.123) (1.107) (1.286)
Log Distance XXXXXXXX
Contiguity XXXXXXXX
Common Ocial Language XXXXXXXX
Common Legal Origin XXXXXXX
Colonial Link XXXXXX
Free Trade Agreements XXXXX
GATT/WTO Membership XXXX
Common Currency XXX
Generalized System of Preferences X X
Country iFE X
Country jFE X
N23944 23944 23944 23798 23798 23798 23798 23798
Regressand: |LogYiLogYj|: Absolute income per capita dierence in 2000.
Genetic Distance Relative to the US: |GeneticDistanceUS ,iGeneticDist anceUS,j|.
Two-way clustered standard errors are in parentheses.
p<0.10,∗ ∗ p<0.05,∗∗∗p<0.01
10
Table 4: Genetic Distance, Trade and Income Dierences
(1) (2) (3) (4) (5) (6) (7) (8)
LogIm portsUS,jLogY jLogY jLogYj|LogImportsU S,iLogImport sUS,j| | LogYiLogYj| |LogYiLogYj| |LogYiLogYj|
Genetic Distance from the US, Weighted -21.947∗∗∗ -17.096∗∗∗ -12.773∗∗∗
(4.252) (2.474) (2.377)
LogIm portsUS,j0.273∗∗∗ 0.196∗∗∗
(0.035) (0.035)
Genetic Distance Relative to the US, Weighted 9.651∗∗∗ 6.000∗∗∗ 4.277∗∗∗
(1.978) (1.286) (1.162)
|LogIm portsUS,iLogImportsUS ,j|0.192∗∗∗ 0.178∗∗∗
(0.027) (0.026)
Log Distance X X X X X X X X
Contiguity X X X X X X X X
Common Ocial Language X X X X X X X X
Common Legal Origin X X X X X X X X
Colonial Link X X X X X X X X
Free Trade Agreements X X X X X X X X
GATT/WTO Membership X X X X X X X X
Common Currency X X X X X X X X
Generalized System of Preferences X X X X X X X X
Country iFE X X X X
Country jFE X X X X
N164 164 164 164 23798 23798 23798 23798
LogYj: Log Income per capita. |LogYiLogY j|: Absolute income per capita dierence.
Genetic Distance from the US: GeneticDistanceUS,j. Genetic Distance Relative to the US: |GeneticDi stanceUS,iG eneticDistanceUS ,j|.
Robust standard errors and two-way clustered standard errors (in columns (5)-(8)) are given in parentheses.
p<0.10,∗ ∗ p<0.05,∗∗∗p<0.01
11
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... Economists have long argued that culture affects important economic outcomes, in particular patterns of international trade and economic development. For example, important differences in societal norms, customs, and habits, proxied by genetic distance, can act as barriers to the diffusion of development from the frontier country (Spolaore and Wacziarg 2009;Bove and Gokmen 2018). Similarly, language barriers represent a major hurdle to trade between countries through its effect on transaction costs (see e.g., Melitz and Toubal 2014). ...
... Similarly, language barriers represent a major hurdle to trade between countries through its effect on transaction costs (see e.g., Melitz and Toubal 2014). Particularly in the post-Cold War period the leading source of conflict seems to be cultural, and therefore cultural differences cause clashes over several issues including trade (see Gokmen 2017;Bove andGokmen 2017, who empirically validate Huntington's (1993) Clash of Civilizations hypothesis). This literature has also pinpointed some of the channels through which immigration, and cultural differences, affect public spending. ...
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... Focusing on a cultural interpretation, they find that both of them impact trade flows whether trust measures are controlled for or not. Bove and Gokmen (2017) also use gravity models and genetics to revisit Spolaore and Wacziarg (2009), suggesting that trade is a possible channel through which cultural differences delay the diffusion of development. Gokmen (2017) also demonstrates the deterring effect of cultural gaps on trade and how they have progressively replaced other barriers such as geopolitical divides. ...
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