169
September 2020

Linguistic Distance and Economic Prosperity:
A Cross-Country Analysis

Mariko Nakagawa(Center for Spatial Information Science, University of Tokyo & Tohoku University)

Shonosuke Sugasawa(Center for Spatial Information Science, University of Tokyo)

Linguistic Distance and Economic Prosperity:
A Cross-Country Analysis∗
Mariko Nakagawa†and Shonosuke Sugasawa‡
September 23, 2020

Abstract
We investigate the impacts of access to domestic and international communication on economic
development by constructing two indices of linguistic distance—domestic and international—, capturing language acquisition costs, which are higher when acquiring linguistically more distant languages. While the domestic linguistic distance index captures the constraints of communication
among speakers of diﬀerent mother tongues within a country, the international linguistic distanceindex captures the constraints of global communication via English. We find that domestic linguistic distance has a negative impact on GDP per capita, while international linguistic distance has
no significant impact. In addition, we conduct quantile regressions to see if the negative eﬀect ofthe domestic linguistic distance diﬀers across quantiles of GDP per capita. The analysis reveals
that the domestic linguistic distance index robustly has a negative impact on GDP per capita forcountries over various quantiles, but that there is no clear tendency of a heterogeneous impact on
economic prosperity across quantiles given by the domestic linguistic distance.
JEL code: O1, O5, F5Key words: domestic linguistic distance, international linguistic distance, mismatch in languageuse, economic success∗

We would like to thank Takatoshi Tabuchi for his thoughtful comments and suggestions. We also thank Ryo Ito,
Marcus Berliant, Takaaki Takahashi, Dan Sasaki, Hikaru Ogawa, and Shota Fujishima for their comments which have
improved this paper. We are grateful to the seminar participants at the Urban Economics Workshop at the University ofTokyo, the ARSC annual meeting at Ryukyu University, Asian Seminar in Regional Science at Tohoku University, andPolicy Modeling Workshop at National Graduate Institute for Policy Studies for their valuable comments. All remaining
errors are the authors’ responsibility. This study is supported by the Grants-in-Aid for Scientific Research (Research
project number: 13J10130) for the Japan Society for the Promotion of Science (JSPS) Fellows and Research Activity
Start-up (Research project number: 16H06703).
†
Center for Spatial Information Science, the University of Tokyo and the Graduate School of Information Sciences,
Tohoku University. Email: mnakagawa@csis.u-tokyo.ac.jp‡
Center for Spatial Information Science, the University of Tokyo. Email: sugasawa@csis.u-tokyo.ac.jp1

1

Introduction

In recent years, there has been increasing research interest on the impacts of ethno-linguistic heterogeneity on economic and political activities. With the realization that ethno-linguistic heterogeneity
is important when considering various social phenomena, a vast body of empirical and theoretical literature on ethno-linguistic diversity has developed. Easterly and Levine (1997), Alesina et al. (2003),
and Alesina and La Ferrara (2005) investigate the impacts of ethno-linguistic diversity on economic
development. Easterly and Levine (1997) show that ethnic diversity negatively aﬀects a country’s income level and explains the low incomes in African nations in particular. According to Alesina et al.’s(2003) cross-country analysis, ethnically and linguistically diverse structures decrease growth levels,
which implies that heterogeneous composition of ethno-linguistic characteristics negatively aﬀects economic development. Alesina and La Ferrara (2005) argue that the costs of heterogeneity come from
diﬃculty reaching agreement on productive public goods and policies common to all ethno-linguistic
groups. The diﬃculty of harmonization between groups could negatively aﬀect economically lagging
countries, such as some African countries, which are kept from catching up with developed countries.
What factors hinder harmonized conformity and communication among diﬀerent ethno-linguistic
groups? Admittedly, lack of trust toward or perceived reliability of other groups, or cultural diﬀerenceaﬀects the diﬃculty in arriving at agreement among various groups. In addition, one of the essential barriers to suﬃcient harmonization between various types of ethno-linguistic groups, especiallylinguistic diﬀerences, stems from diﬃculty in smooth communication caused by the lack of eﬀectively
common languages, or at least common languages that can be shared with little acquisition eﬀort.
It is typical of countries with colonized histories to have the colonizer’s language(s) as the oﬃcial
language(s). On the other hand, mother tongues of native residents in such post-colonial countries islikely to be a local ethnic language that is linguistically distant from colonizers’ languages.
To capture the cost required to attain the ability to use domestic oﬃcial languages diﬀerent fromone’s mother tongue when a society consists of diﬀerent linguistic groups, we use a measure of withincountry linguistic distance, which expresses how distant residents’ mother tongues is from the oﬃcial
language in terms of linguistic characteristics. This linguistic distance in the domain of domestic
communication is reflective of language acquisition costs (i.e., eﬀort made) when acquiring domestic
central languages. As mentioned in Chiswick and Miller (2005) and Isphording and Otten (2011,2013), it is more diﬃcult to acquire a language if the native language is linguistically distant from thelanguage to be learned (in this case, the oﬃcial language(s) of a country) and, hence, the measure of2

domestic linguistic distance to the oﬃcial language(s) expresses the language acquisition costs incurredby individuals whose mother tongues are diﬀerent from the oﬃcial language(s). We conduct analysisof the impact of domestic linguistic distance on cross-country diﬀerences in economic success.
Similar language acquisition costs necessary to accomplish smooth communication might be foundin the international context. Recent empirical studies suggest great importance of languages spoken
worldwide, such as English, for economic success in the modern era, when things and individuals
are densely connected globally. For example, in the realm of international migration, Adsera andPytlikova (2015) argue that English as a global language is one of the crucial factors determining
the configuration of migration decisions, by investigating whether potential immigrants prefer destinations where English is the local language. In addition, Ku and Zussman (2010) report that trade
partners sharing no common native languages mitigate the language barriers through communication
in a nonnative but highly influential language, English. They show that proficiency in English canpromote international trade and, thus, that acquired English capability can overcome diﬃculty incommunication. Similarly, Hutchinson (2005) argues that trade between the US and another country
will be less intense if languages used in that country are diﬀerent from English. Moreover, as pointed
out in the literature, such as Jones (2001, Chapter 6) and Ku and Zussman (2010), which focus on thecommunicative benefits of English as a lingua franca, acquired proficiency in English could promoteeconomic development by maintaining access to the world’s stock of technological knowledge that has
recently been accumulated in English. Given the substantive importance of English, acquisition cost ofEnglish, which can be captured by linguistic distance between residents’ mother tongues in a country
and English, may also impact economic outcomes of that country.
The impacts of distance to English on economic development are considered with a slightly diﬀerent concept from language acquisition in Spolaore and Wacziarg (2009), who provide an alternative
explanation for why distance to the US might matter. Their concept of distance is genetic distance, ameasure of the time passed since two populations shared ancestors. The authors show that variation in
genetic distance to the US, the most developed country, explains dispersion in economic development,
based on an idea that individuals genetically close to the US residents are more likely to follow or tomore easily catch up with the latest technology due to similarity of cultures or of methods of problem
solving.
Below, we briefly summarize the paper. First, we construct linguistic indices. As for the diﬃcultyin accessing eﬀective domestic communication—that is, acquisition costs of the oﬃcial language(s)3necessary to communicate with residents in a whole country—, we construct a domestic linguistic
distance, DLD, for each country. DLD is calculated as a population-weighted average of linguistic
distance between the oﬃcial language and residents’ mother tongues. Similarly, we construct an international linguistic distance, ILD, to capture the aspect of a cost to access the global communication
via English. As for ILD, we construct two types of indices, ILDP C and ILDCC , the former calculated as the population-weighted average of distances between English and residents’ mother tongues
in a country, and the latter of which is calculated as the linguistic distance between English and thecountry’s oﬃcial language.
The main results are as follows. We find a significantly negative impact of DLD on GDP per capita,
while no significant impact is found in the case of ILDs, which implies that diﬃculty in communication
within a country from a viewpoint of mismatch in language use hinders economic success. By contrast,
diﬃculty in English communication may not have a significant impact on a country’s economic success,at least at the country-aggregated level. We also conduct an analysis to see if the DLD’s impact diﬀers
between rich and poor countries by using quantile regression techniques. We find that DLD robustly
has a negative impact on GDP per capita for countries over various quantiles of GDP per capita, but
no clear tendency that DLD has a heterogeneous impact on economic prosperity across quantiles.
The remainder of this paper is organized as follows. Section 2 introduces notions of domestic andinternational linguistic centers and distances and provides indices for the linguistic distance. Section
3 presents an empirical framework of the baseline regression and a couple of comments on statisticalconcerns. We also address possible remedies for such concerns, although they are not a perfect way
to resolve them. Section 4 conducts baseline regressions, showing that DLD has a negative impact
on GDP per capita. Section 5 conducts quantile regressions to investigate possibility of heterogeneous
impacts of DLD on GDP per capita. In Section 6, robustness checks of the negative impacts oflinguistic distance indices are conducted, and Section 7 concludes the paper.

2

Indices of linguistic distance

2.1

Definition of linguistic distanceOur linguistic distance index data cover a wide cross-section of countries, where indices for eachcountry are based on weighted averages of linguistic distances. For the first step, we calculate thelinguistic distance for each pair of living languages listed in the 16th edition of Ethnologue (Lewis,

42009). In Ethnologue, the entire world’s known living languages are listed and are categorized basedon the similarities in their linguistic characteristics. If two arbitrarily chosen languages belong to thesame linguistic family, these are thought to be similar and, thus to exhibit shorter linguistic distance.
Before introducing the concept of linguistic distance, the notion of linguistic similarity is considered.
In order to provide a quantitative value for the abstract notion of linguistic similarity, linguistic
dendrograms, constructed from the language categorization provided in Ethnologue, are utilized. The
number of shared edges between languages i and j on a dendrogram are denoted by e(i, j). If e(i, j)
is large, it implies that languages i and j are categorized into a meta-group, such that they are closer
linguistically. When quantifying linguistic similarity, we employ the approach of Fearon (2003) and
Desmet et al. (2009). First, we define gmax as the maximum number of g(i) for all existing languages iin the world, where g(i) is the generation to which language i belongs, which is used to convert e(i, j)
into proportions of cognates between i and j (normalization into the interval [0, 1]). Put diﬀerently,
gmax is the maximum of the number of edges that can be shared by two languages.1 Then, linguistic
similarity is formulated as follows:

similarity(i, j) =e(i, j).
gmax

Now, we define linguistic distance between languages i and j, τ (i, j). Higher similarities show
shorter linguistic distances; hence, if similarity(i, j) increases, τ (i, j) decreases. Furthermore, we
assume that τ (i, j) = τ (j, i) for all languages i and j. τ (i, j) is a standardized metric (i.e., τ (i, j) ∈[0, 1]), and τ (i, i) = 0 for all i. Under these assumptions on metrics, τ (i, j), is defined as[]e(i, j) δτ (i, j) = 1 − [similarity(i, j)] = 1 −gmax
δ

for all i, j(i ̸= j),where δ ∈ (0, 1) is a parameter determining how fast the linguistic distance declines as the number ofshared edges increases. More intuitively, as mentioned in Desmet et al. (2009), δ captures how much
more distant two languages from diﬀerent linguistic families are, compared with languages that belong
to the same family.

1

The linguistic dendrogram constructed from Ethnologue has 15 nested classifications, so that gmax = 15.

5

2.2

Indices of linguistic distanceOur linguistic distance indices are based on the population-weighted averages of linguistic distances.
Consider country i with a population of N (i) individuals, who are partitioned into K(i) distinct
language groups according to their language use. Nj (i) is a population of language group j in countryi. We assume that each individual belongs to only one language group, and no individual is assumed
completely multilingual, which leads to∑K(i)N (i) =Nj (i).

j=1

The population share of group j in country i, sj (i), is defined assj (i) =so that∑Nj (i)N (i)K(i)

sj (i) = 1

j=1

for all countries.
First, we define the domestic linguistic distance index, DLD(i), for each country i, which is interpreted as the cost incurred when accessing the “linguistic center” of country i or an oﬃcial language
in country i, and by accessing it, the residents can communicate with each other. In other words,
without acquiring the nationally widely spoken language(s), communication among the residents ofa nation is impossible. Language c(i) is the central language of country i, and τj,c(i) is the linguistic distance between languages j and c(i). DLD(i) is defined as the population-weighted average of
linguistic distances to the domestic central language:2∑

K(i)DLD(i) =sj (i)τj,c(i) .

(1)

j=1

Next, we define the international linguistic distance index, ILD(i). Here, English is adopted as the2

Ginsburgh et al. (2005) consider which language(s) should be considered appropriate as the oﬃcial language(s) of theEuropean Union by calculating the population-weighted average of linguistic distances from residents’ mother tongues tomajor European languages. In so doing, the authors search for languages whose population-weighted averages of linguistic
distances are the smallest, because linguistic distance is considered the cost of acquiring other languages. Constructed
linguistic indices in Ginsburgh et al. (2005) are similar to our DLD(i), but in their analysis, linguistic distance indices
are utilized to determine the central language(s), while our DLD(i) is constructed given the linguistic center.

6

central language of the world, symbolized as C (by acquiring English, smooth global communicationis possible). As for the choice of ILD, there are two possibilities, ILDP C and ILDCC :∑K(i)ILDP C (i) =sj (i)τj,C ,(2)

sj (i)τc(i),C = τc(i),C .

(3)

j=1

∑

K(i)

ILDCC (i) =j=1

ILDP C (i) refers to the linguistic distance cost that each person in group j in country i incurs to accessthe international center, besides the cost to access the domestic center in country i. ILDCC (i) is the
linguistic distance between the domestic and international central languages (the domestic oﬃcial
language and English).

2.3

Linguistic center

To define linguistic centers, we introduce the notion of “language status” proposed in Ethnologue
17th edition (Lewis et al., 2014) on the criteria of the Expand Graded Intergenerational Description
Scale (EGIDS), which ranges from status 0 to 10 according to importance or usage of the languages.
For example, languages labeled status 0 (international) are widely used between nations in trade,knowledge exchange, and international policy. Status-1 languages (oﬃcial languages) are used in
education, work, mass media, and government at the national level.3
As for the international linguistic center, English is considered the most appropriate, because
among languages with status 0, it has the biggest second-language user population.4 For the domestic linguistic center(s), oﬃcial language(s) (status-1 languages) are chosen. Given the definition ofdomestic linguistic center, some modifications to linguistic distance indices are necessary. First, weconsider revising DLD. If country i has several status-1 languages, DLD in (1) is modified as

DLD(i) =∑c(i)∈C(i)

3
4

∑

K(i)

γc(i)

sj (i)τj,c(i) ,j=1

For more details of language status definition, see www.ethnologue.com/about/language-status.
Arabic, Chinese, English, French, Russian, and Spanish are labeled as status-0 languages.

7(4)

where C(i) is a set of status-1 languages in country i, and γc(i) is defined asγc(i) = ∑Nc(i)l∈C(i) Nl.

In short, DLD for a country with multiple oﬃcial languages is the weighted average of the weighted
averages of the domestic linguistic distances to status-1 languages. Similarly, ILDCC in (3) index ismodified as
ILDCC (i) =

∑

∑

K(i)

γc(i)

c(i)∈C(i)sj (i)τc(i),C .

(5)

j=1

Because it is not necessary to apply any modification for ILDP C , the revised DLD and ILDCC givenin (4) and (5) respectively, as well as the original ILDP C given in (2), are exploited in our empiricalanalysis.5

3

Model specification

Our main interest lies in empirically investigating the relationship between domestic and international
distances and economic success; to measure the economic prosperity of a country, we adopt thecountry’s income (GDP per capita) at real PPP from the Penn World Tables 8.0 (Feenstra et al.,2013b), and our empirical model is specified as follows:

ln GDP/capitai = β0 + βD DLDi + βI ILDi + Xcontrol,i βcontrol + ϵi ,(6)where ILD is either ILDP C or ILDCC . In the PWT 8.0, expenditure- or output-side GDP is available,
and by following a user guide of PWT 8.0 (Feenstra et al., 2013a), the output-side measure is adopted.
Hereafter, we notate country indicator i as a subscript. In addition, because DLD and ILD depend
on a given value of linguistic distance parameter δD and δI , respectively, we denote DLD(δD ) andILD(δI ), respectively.
A vector of control variables (Xcontrol ) contains variables in terms of market size, education, trade
openness, institutional qualities, and geographic characteristics. The choice of controls essentiallyfollows the perspectives of those in Alesina et al. (2016). Since the use of a large number of control
5

In some countries with multiple oﬃcial languages, one (or more than one) oﬃcial language(s) has no population of
speakers of that language as their mother tongues. For example, Ethnologue reports that Cameroon, which has twonational languages, English and French, has no population of speakers of these two oﬃcial languages. For such countries,
special treatments are needed to calculate the linguistic distance indices. Notes for constructing linguistic distances forsuch cases are listed in Table A3 in Appendix B.

8variables under a moderate sample size (111 in this study) may lead to unstable fitting of quantile
regressions in low or high quantiles that we will conduct in Section 5, it would be better to omit
controls that are not necessarily recognized as having eﬀects on GDP per capita. Then, we choose thefollowing control variables for our baseline model. We adopt years of schooling (Barro and Lee, 2013)
for education covariates, and population sizes from the Penn World Tables 8.0 (Feenstra et al., 2013b)
for the market size control. Furthermore, we control for the standard trade volume measure, trade
openness6 (i.e., export + import share of GDP in real PPP prices) from PWT 8.0. As the vector of
geographical determinants,7 we adopt absolute latitude of the capital city and land area from CEPII(Head et al., 2010), and a ratio of population within 100 km of ice-free coast to total population from
Gallup et al. (1999). For institutional quality, we choose the revised combined polity2 score fromthe Polity IV database (Marshall and Jaggers, 2012), which measures the extent to which politicalparticipation is unrestricted, open, and fully competitive, executive recruitment is elective, and towhich constraints on the chief executive are substantial.8 Further, following La Porta et al. (1999),
legal origin dummy variables are included.9
Before running regressions, a couple of comments should be made. Occasionally, regional fixedeﬀects are adopted as covariates to explain the extent of economic development. However, DLD,
which is one of the primary points of interest in our context, has high correlation with the Sub-SaharanAfrican continental dummy variable in the full range of the values of parameter δD (correlation between
DLD(δD = 0.5) and the Sub-Saharan African dummy variable is 0.71 and they show high correlation
throughout δD ∈ {0.1, . . . , 0.9}); and thus, we are forced to drop regional dummy variable from ourregressions. Despite excluding regional fixed eﬀects from the baseline model, in Section 6.6 we consider
this aspect from a viewpoint of spatial econometrics.
Another noteworthy issue concerns endogeneity caused by reverse causality. As considered in
Section 6.3, the eﬀects of linguistic distance, especially in richer economies, might be aﬀected byimmigrants attracted to such countries. Although the following treatments are not perfect remedies,
they reduce the reverse causality concerns related to immigrant attraction of the rich countries. First,
6

The impacts of trade on a country’s income or growth are discussed in Frankel and Romer (1999), Rodriguez andRodrik (2001), and Yanikkaya (2003).
7
As for the impacts of geographical determinants of economic development, see Sachs (2003) and Putterman and Weil(2010).
8
Hall and Jones (1999), Acemoglu et al. (2001), Glaeser et al. (2004), and Rodrik et al. (2004) consider how institutional qualities aﬀect economic growth or income diﬀerence.
9
We only include socialist law and do not include British law, French law, and German law, in order not to haveexcessive the number of control variables. However, we also conducted a regression with all these legal origin variables,
which revealed to have an eﬀectively the same result as we obtain in the baseline estimation. A result is available uponrequest.

9

when constructing linguistic distance indices, we excluded the “immigrant languages” reported in
Ethnologue. Ethnologue separately reports immigrant languages, which are categorized as such if they
are spoken by relatively recently arrived or transient populations. As our linguistic distance indices
do not include such immigrant populations/languages, the reverse causality concern of immigrant
attraction may be mitigated. In addition, in order to tackle the possibility of rich economies’ attractionof migrants, Section 6.3 directly includes either a stock migrant population size or a stock migrant
population share in a set of explanatory variables. Moreover, in all specifications, independent variables
precede the dependent variable, GDP per capita. Treating the linguistic distance data, as well as othercontrol variables, as predetermined by having them preceding the dependent variable might mitigate
the problem of reverse causality.1011

4

Empirical Results

4.1

Results of the baseline modelThis study concerns the eﬀect of domestic and international linguistic costs, and our first prediction isthat both intra- and international linguistic distances have negative impacts on GDP per capita. If it isdiﬃcult to access the domestic linguistic center (oﬃcial language), it is diﬃcult to communicate withina country. Furthermore, acquiring languages other than one’s mother tongue is costly, which might
reduce economic activity performance. The same can be said for international linguistic distance. If
acquiring English is costly and individuals have diﬃculty with fluent English communication, thenthey are likely to lose opportunities to create global connections, which may prevent improvement toeconomic status of a country.
Table 1 displays the baseline results.
[Table 1 around here]
Column (1) shows the result based on DLD and ILDP C and column (3) is based on DLD and
ILDCC without control variables, and columns (2) and (4) are those with baseline control variables.
Throughout the manuscript, DLD, ILDP C , and ILDCC are mainly calculated under δD = 0.5 and

10
The GDP per capita is in 2010, while linguistic distance indices are calculated by using Ethnologue data publishedin 2009. Due to the updating scheme of the Ethnologue, not a small number of linguistic compositions are based on thedata before 2009. To coordinate the Ethnologue data, other independent variables are mainly from year 2005.
11
Tables A1 and A2 in Appendix A exhibit the data sources and the summary statistics, respectively.

10δI = 0.5. Below in this section, we check the robustness of the results by changing the values of δD
and δI within the range of 0 to 1.
As for the results without controlling for other covariates, both DLD and ILD are significantly
negative, as shown in column (1) for the specification with ILDP C and column (3) for that with
ILDCC . However, after controlling for the baseline control variables, ILD loses significance andis indistinguishable from zero for both specifications. This may be partly because, especially indeveloping countries, only a small number of elites are required to acquire English as well as thedomestic oﬃcial language, but the rest of the citizens may not. Thus, when investigating the eﬀectof access to English communication at the country level, the importance of English skills does notappear clear. By contrast, DLD keeps significantly negative at the 1% significance level, which is anindication of the negative eﬀect on the output-side GDP per capita given by the diﬃculty in domestic
communication in terms of acquisition costs of the oﬃcial language other than one’s mother tongue,
if the interpretation of the correlation as a causal inference is permitted.
Previous literature focuses on the negative eﬀect of ethno-linguistic diversity on economic performance, as reviewed in Section 1. Previous studies interpret that the negative eﬀect of ethno-linguistic
diversity is caused by the inability to agree on common public goods and policies. Individuals with
diﬀerent preferences have to share common policies, which would decrease the average utility level,leading to negative eﬀects on some economic aspects. Instead, we tackle this negative eﬀect from theviewpoint of a linguistic distance cost, which captures diﬃculty acquiring an oﬃcial language otherthan one’s mother tongue. We will turn to this point in relation with linguistic diversity in Section
6.4.
Since linguistic distance parameters can take various values in the range in which they are defined,
we vary the parameter values and rerun regressions. Table 2 reports the coeﬃcients of domestic andinternational linguistic distance indices with diﬀerent linguistic parameter values (δD , δI ), whose specifications are based on the regression including DLD and ILDP C with the baseline control variables.12
Values of δD changes in the vertical direction (δD increases from the top to the bottom in the rangeof 0 to 1) and those of δI varies in the horizontal direction (δI increases from left to right in the rangeof 0 to 1).
[Table 2 around here]
12

Table A4 in Appendix C shows an analogous result based on the specification with DLD and ILDCC . The result
under this specification is eﬀectively the same as that with DLD and ILDP C shown in Table 2.

11

The behavior of coeﬃcients on DLD listed in Table 2 shows strong robustness of negative significanceto a change in diﬀerent values of δD and δI . This stability of DLD negativity confirms the claim thatdiﬃculty in domestic communication from the viewpoint of language acquisition hinders economic
prosperity as assessed by the output-side GDP per capita.

5

Quantile regression

In this section, we investigate whether the negative eﬀect of DLD found in Section 4.1 diﬀers acrossquantiles of the country’s per-capita GDP. As discussed in Alesina et al. (2016), in which the authors
show that the eﬀect of birthplace diversity on GDP per capita diﬀers between relatively poor countries
and rich countries, we consider the possibility of heterogeneous impacts of DLD on GDP per capita.
Unlike in Alesina et al. (2016), in which the whole sample is divided into two subsamples, poor
countries are defined as countries with GDP per capita lower than the median GDP per capita andrich countries as those with GDP per capita higher than the median, and regressions run separately
for two subsamples, we conduct quantile regression.13 The advantage of quantile regression is that bydirectly modeling low or high quantiles of GDP per capita, it can avoid the dichotomized groupings
such as “poor” and “rich”, which are often sensitive to the method of grouping. Furthermore, it
can provide a wide range of information regarding the potentially heterogeneous eﬀects of DLD on
diﬀerent quantiles of GDP per capita.
The quantile regression estimator is obtained by solving the following optimization problem:

min
β

[ n
∑

]
ρθ (ln GDP/capitai − (β0 + βD DLDi + βI ILDi + Xcontrol,i βcontrol )) ,i=1

where ρθ (u) = u(θ − I(u < 0)) for the θ-th quantile (0 < θ < 1).
Table 3 displays the result of quantile regressions for diﬀerent quantiles, and Figure 1 is thecorresponding graphical illustrations, based on the ILDP C specification.14[Table 3 and Figure 1 around here]

13

Quantile regression methods are applied to the cross-country growth analysis. Foster (2008) examines the relationshipbetween trade liberalization and per-capita GDP growth. The author utilizes the quantile regression techniques toconsider the parameter heterogeneity of the impact of trade liberalization. Also, Dufrenot et al. (2010) employ quantile
regression methods to see how the impact on the growth rate of per-capita income given by trade openness diﬀers acrossdiﬀerent quantiles.
14
Table A5 and Figure A1 in Appendix C exhibit the results for the specification with ILDCC . The results do notchange eﬀectively.

12

The values of the goodness-of-fit (GOF) measure (Koenker and Machado, 1999) in Table 3 suggestthat all the models fit the data reasonably well. It also shows that ILD does not have significant eﬀects
on all the quantiles of GDP per capita, whereas DLD has always negative eﬀects on it. Although
the magnitude of the negative eﬀects of DLD can be heterogeneous over diﬀerent quantiles, no clear
tendency of the behavior of the coeﬃcient of DLD across quantiles is observed. For example, if thenegative eﬀect of DLD were more severe in poor countries with lower GDP per capita than richcountries with higher GDP per capita, the absolute value of the coeﬃcient of DLD might be larger for
lower quantiles, but such a clear and easily interpretable tendency is not corroborated in our quantile
regression results. In Figure 1, we report 81 paths of estimated regression coeﬃcients in 9 quantiles,
where each path corresponds to a quantile regression model using δD , δI ∈ {0.1, . . . , 0.9}. This showsthat the results in Table 3 are rather robust against alternative specifications of δD and δI .

6

Robustness checks

6.1

Natural characteristics

In this class of robustness checks, we consider the eﬀects of natural characteristics on GDP per capita,
in terms of climate and geographic aspects. First, we include the average annual temperature andprecipitation in the baseline model, as mild climate is considered to facilitate human activities, which
can lead to improved economic status of a country. Table 4 shows the result for this robustness check.
[Table 4 around here]
For the specification of ILDP C and ILDCC , column (1) and column (4), respectively, include theaverage annual temperature, column (2) and column (5), respectively, include the average annual precipitation, and column (3) and column (6), respectively, include both. In all columns, the significantly
negative coeﬃcient of DLD remains unchanged.
Next, we consider the eﬀect of elevation. The rough and ragged land or mountainous geographic
features may decrease eﬃciency of transportation within a country, which can aﬀect economic activity
in a negative way. To account for this geographical aspect, we add the average and standard deviationof elevation in the baseline model.15 Table 5 displays the results.
[Table 5 around here]
15

The elevation data are extracted from Michalopoulos (2012).

13

For the specification of ILDP C and ILDCC , column (1) and column (4), respectively, include theaverage elevation within a country, column (2) and column (5), respectively, include the standard
deviation of the elevation, and column (3) and column (6), respectively, simultaneously include bothvariables. In this estimation, we can certify the significantly negative impact of DLD on the outputside GDP per capita, as the coeﬃcients of DLD are negative at the 1% level in all columns.
Finally in this section, we take into account agricultural suitability, as countries with fertile land,
which is captured by better agricultural suitability, may be in an advantageous position than thosewith barren land. Table 6 adds the average and variation of agricultural suitability within a country.16[Table 6 around here]
For the specification of ILDP C and ILDCC , column (1) and column (4), respectively, include theaverage agricultural suitability within a country, column (2) and column (5), respectively, include thestandard deviation of the agricultural suitability, and column (3) and column (6), respectively, include
both agriculture variables. Although none of the agriculture variables are significant, possibly due
to the industrial shift from agriculture to manufacturing or service industries, the significance of thenegative coeﬃcient of DLD is mostly unaﬀected in all columns.

6.2

Infectious disease

As the extent of medical diﬀusion, such as suﬃcient vaccination, diﬀers across countries, and such
improvement of medical facilities may enhance economic activities. To consider this point, we include
variables expressing incidence of various infectious diseases. From the infectious diseases listed in
the WHO database, we choose diseases with fewer missing observations to keep a suﬃcient number ofobservations. Then, the incidence or the number of reported cases per million people of the following six
infectious diseases are taken into account: tuberculosis, malaria, measles, neonatal tetanus, pertussis,
and total rubella. Instead of including these six disease variables, we conduct a principal component
analysis (PCA), which is known as a variable-reduction method when there are multiple measures ofsimilar concepts. Following a conventional procedure of running a PCA, we choose the first and second
principal components with eigenvalues exceeding unity, as suggested by Kaiser’s rule. Table 7 reports
the result of the PCA for the first two principal components.17 Table 7 implies that considering only
16

The agricultural suitability data are extracted from Michalopoulos (2012).
Figure A2 in Appendix C displays a scree plot which shows the eigenvalues of each principal component. The firstand second principal components exceed one, but the others do not. In addition, by checking the diagnostic measures
for sampling adequacy, the the Kaiser-Meyer-Olkin (KMO) index, the overall KMO measure exceeds 0.5, indicating thatthe data set is suitable for the PCA (see Table A6 in Appendix C).
17

14

the first and second principal components will explain about 50% (48.3%) of the information provided
by all six infectious disease variables.18
By adding the predicted values of the first and second principal components of the infectious
disease variables to the baseline model, we run the regressions (results shown in Table 8).
[Tables 7 and 8 around here]
As the number of observations drops due to inclusion of infectious disease variables (from 111 in thebaseline estimation to 70 in the infectious disease estimation), we run the baseline estimation withthe same 70 countries in column (1) for the ILDP C specification and column (3) for the ILDCC
specification, simply for reference. In columns (2) and (4), the first principal component for the
infectious disease is significant at the 10% level, so that some aspect of infectious disease is negatively
associated with GDP per capita. More importantly in terms of our interest, even after controllingfor infectious diseases, DLD maintains a significantly negative coeﬃcient in both ILD specifications.
Thus, diﬃculty in domestic communication from a viewpoint of language acquisition costs decreases
a country’s GDP per capita when the infectious disease eﬀect is accounted for.

6.3

Immigrant

While one of the primary interests of this study is the impact of linguistic distance indices on GDP
per capita, reverse causality could be an issue, because of rich countries’ attraction of migrants, which
can aﬀect linguistic distance indices. Controlling for a migrant stock population size or its share to
the total population, although not a perfect remedy to this problem, might mitigate this issue. Table
9 reports the result for inclusion of the immigrant variables.
[Table 9 around here]
Under ILDP C specification and ILDCC specification, column (1) and column (2), respectively, show
the results with a migrant stock population size variable, and column (3) and column (4), respectively,
show results with the migrant stock population share. In all specifications, the coeﬃcient of DLD issignificantly negative after immigrant eﬀects are controlled. Although this is not an ideal solution tothis endogeneity issue, the strong negative eﬀect of the linguistic distance to the oﬃcial language isrobustly corroborated.
18

We also conducted including the third principal component as well as the first and second components, which explains
63% of the infectious disease information. Including the predicted values of the first three principal components in the
regression does not change the negative significance of DLD. Results are provided upon request.

15

6.4

Linguistic aspects

This section deals with robustness checks in relation with linguistic aspects. Specifically, we conduct
two types of robustness checks, the first being about multiple oﬃcial languages, and the second about
linguistic fractionalization/polarization.
First, we consider the aspect of multiple oﬃcial languages. Countries consisting of heterogeneous
linguistic groups tend to have more than one oﬃcial language (status-1 languages defined in Ethnologue) and, hence, some countries have a single oﬃcial language, but others have several oﬃcial
languages. To shed a light on this part, we construct “multiple oﬃcial language variable” which takes
one if there are several status-1 languages in a country and zero otherwise. Table 10 displays theresult including multiple oﬃcial language dummy variable.
[Table 10 around here]
Both specifications (column (1) under ILDP C and column (2) under ILDCC ) show the significantly
negative coeﬃcient on DLD. In addition, as for the multiple oﬃcial language variable, the coeﬃcientis negative at the 10% level. This result coordinates with the finding that DLD has a negative impact
on GDP per capita, as the multiple oﬃcial language may also capture linguistic heterogeneity. Even
after this eﬀect of multiple oﬃcial languages is separated out, the significantly negative eﬀect of thedomestic linguistic distance remains.
Next, we consider linguistic fractionalization and polarization. As both the linguistic distance andlinguistic fractionalization/polarization stem from linguistic heterogeneity within a society, it is worth
trying to separate out these eﬀects. To do so, we add linguistic fractionalization/polalization indices,such as GI, ELF , ER, RQ, and P H,19 all of which come from Desmet et al. (2009). GI and ELF
are categorized in a family of fractionalization indices, while ER and RQ are classified in a family ofpolarization indices. Peripheral heterogeneity, P H, is an intermediate index between fractionalizationand polarization. Among these variables, GI, ER, and P H account for linguistic distance between
languages, while ELF and RQ do not. As these linguistic diversity variables are based on Ethnologue,
utilizing the variables is adequate and fits well to our linguistic distance data. Table 11 shows the estimation result both including linguistic distance indices and a linguistic fractionalization/polarizationindex.
∑K(i) ∑K(i)19With our notation, these indices are formally defined as follows: GI(i) =k=1 sk sj τjk , ELF (i) = 1 −
j=1
∑K(i)
∑
∑K(i) ∑K(i)∑K(i) 2K(i) 22
k=1 sk sj τjk , RQ(i) =j=1 sj sc(i) τj,c(i) .
j=1 sj (1 − sj ), and P H(i) = 2
j=1
j=1 sj , ER(i) =16

[Table 11 around here]
The first five columns are under the specification with ILDP C , and the latter five are under ILDCC .
Columns (1) and (6) add GI, columns (2) and (7) add ELF , columns (3) and (8) add ER, columns (4)and (9) add RQ, and columns (5) and (10) add P H to the baseline control variables. All specifications
other than those with ELF show a significantly negative coeﬃcient of DLD when the linguistic
fractionalization/polarization eﬀects are controlled for. By contrast, for the inclusion of ELF as incolumns (2) and (7), DLD loses its significance. In column (2), both DLD and ELF are insignificant,and in column (7), DLD is insignificant and ELF is severely significant at the 10% level. Suchinsignificance (or weak significance) of DLD and ELF may be due to high correlation between thesetwo variables (correlation between them is 0.76). However, it can be asserted that DLD tends to givea negative impact on the output-side GDP per capita after linguistic fractionalization/polarization
aspects are considered.

6.5

Genetic distance to the US

This section features ILD instead of DLD rather than checking the robustness of the negative eﬀectof DLD. International linguistic distance in this paper is calculated based on the linguistic distance
between English and each language. The idea behind the choice of English as the international
linguistic center is simply that English is the most widely used language in the world. A similar
measure to ILD in the context of genetic distance is the genetic distance to the US, proposed in
Spolaore and Wacziarg (2009). Their concept of distance, genetic distance, measures the time passed
since two populations shared ancestors. They calculate two genetic distance indices to the US from eachcountry for cross-country regression, the first of which is a genetic distance between dominant groups
and the second of which is a population weighted genetic distance based on the genetic composition
in a country. By using these genetic distance indices, they find that shorter genetic distance to the
US leads to higher income per capita. Their interpretation of this result is that individuals geneticallyclose to the US residents are more likely to follow or more easily catch up with the latest complex
technological and institutional innovations invented in the US. In addition, when the genetic distanceto the US and the linguistic distance to the US are put together in the estimation model, the former
is significantly negative (the closer to the US in terms of genetic composition, the more significantly
higher the per capita income) while the latter is insignificant. They view the genetic distance indicesas such, capturing a broad set of characteristics including the language aspect.
17

To see whether their results can be consistently observed in our framework, we add indices of thegenetic distance to the US employed in Spolaore and Wacziarg (2009) to our baseline model. Table
12 shows the result.
[Table 12 around here]
Columns (1) and (2) include the index of genetic distance between genetically dominant groups ina country in the baseline ILDP C and ILDCC specifications, respectively, and columns (3) and (4),respectively, include the index of population-weighted genetic distance based on genetic compositions.
Unlike in Spolaore and Wacziarg (2009), indices of the genetic distance to the US in our estimation
are only significant at the 10% level (in columns (1)–(3)) or insignificant (in column (4)). However,in comparison with our ILD variables that are insignificant in all specifications, the finding that thegenetic distance to the US more explains a country’s GDP per capita than ILD is consistent withthat in Spolaore and Wacziarg (2009). As for the coeﬃcient of DLD, it is significantly negative in all
specification, so the robustness of the negative impact of DLD is confirmed.

6.6

Spatial dependence

As mentioned in Section 3, due to high correlation between DLD and the Sub-Saharan African dummy
variable, we excluded regional fixed eﬀects from the baseline model in order to circumvent the multicollinearity problem. To deal with the possibility of spatial dependence among error terms due toomitting regional fixed eﬀects, we conducted an empirical analysis in a spatial econometrics framework, which resolves spatial dependences among observations. This resettlement to the empirical fieldof spatial econometrics is natural, since regional dummy variables are considered the simplest way to
account for the factors related to spatial characteristics of observations. For instance, Attfield et al.
(2000) shed light on the relationship between the continent dummy variable and spatial econometrics
model (based on distance among countries) in a cross-country analysis of economic growth. Moreno
and Trehan (1997) and Maurseth (2003) also conduct empirical analysis of economic growth at thecross-country level using spatial econometrics techniques, and both conclude the eﬀectiveness of employing those techniques to find the clustered economic growth. In addition, Romero and Burkey(2011) analyze the impact of debt/GDP ratio on GDP levels with spatial empirics, the scope of whichis restricted to the Eurozone. Furthermore, cross-country diﬀerences and spillover eﬀects of the qualityof governance and institution are inspected using spatial econometrics models (Seldadyo et al., 2010;18

Kelejian et al., 2013).
To check the possible spatial correlation in the data, we first computed Moran’s I statistic of theresiduals in the regression model (6), calculated under two types of spatial weight matrices: the firstorder contiguity matrix and an inverse distance matrix based on the distance between capital cities.
The obtained value is not statistically significant under the 5% nominal level, as shown in the last row
in Table 13, implying that there is no significant spatial dependence among error terms even when themodel does not include regional dummy variables.
[Table 13 around here]
Despite the insignificance of the Moran’s I, we check if the significantly negative coeﬃcient of DLD
is unaﬀected even when the spatial correlation is considered and run a spatial regression specified asfollows:

ln GDP/capitai = ρn∑wij ln GDP/capitaj + β0 + βD DLDi + βI ILDi + Xcontrol,i βcontrol + ϵi ,

(7)

j=1

where ρ is an unknown correlation parameter, and wij is the (i, j)-th entry of a spatial weight matrix
with the principal diagonal elements as zeros. The spatial weight matrix is either the contiguity∑
matrix or inverse distance matrix, both of which are row-standardized (namely, nj=1 wij = 1 for alli). Results of the estimation based on (7) are given in Table 13. The first two columns are the results
under the contiguity spatial weight matrix, and the last two columns are those under the inverse
distance matrix. Columns (1) and (3) show the results with the ILDP C specification, and columns(2) and (4) are those with ILDCC . From Table 13, we can confirm that the estimated results of theparameters are not changed from those obtained in Section 4.1. That is, DLD is significantly negativein all specifications, which is natural because of the insignificance of ρ.20

7

Conclusion

In this study, we investigated the impacts of domestic and international linguistic distances on outputside GDP per capita, based on the idea that linguistic distance expresses language acquisition costs,
which may hinder eﬀective economic activities. First, we constructed pair-wise linguistic distances
for all living languages in the world using a linguistic dendrogram. Then, we constructed two types
20

We fit the spatial model with all the possible choices of δD and δI , but none of them detects significance of ρ evenunder the 5% nominal level.

19

of linguistic distance indices—domestic and international. A domestic linguistic distance index is
calculated as a population-weighted average of linguistic distances between mother tongues of residents
in a country and the oﬃcial language. International linguistic distance indices are calculated in two
ways: (i) population-weighted average of linguistic distances between a mother tongue of residents ina country and English, and (ii) linguistic distance between oﬃcial languages and English.
This study found that the eﬀects of domestic linguistic distance on output-side GDP per capita aresignificantly negative. As many African countries are likely to have longer domestic linguistic distance,
Africa’s poor economic status can be explained partly by its lesser access to the domestic linguistic
center, causing more diﬃcult nationwide communication due to mismatch of oﬃcial languages andmother tongues. As for international linguistic distance, we did not find significant impact on GDP
per capita. This result aligns with what was found in Spolaore and Wacziarg (2009), showing thatthe genetic distance to the US rather than the linguistic distance to the US explains a country’s
lower income per capita. We also conducted the quantile regression analysis to determine whether thenegative impact of the domestic linguistic distance to GDP per capita is heterogeneous over diﬀerent
quantiles of GDP per capita. Unfortunately, we did not find a clear tendency of heterogeneous impactsof the domestic linguistic distance on GDP per capita across quantiles.
Despite this, our results are distinct from those in the previous literature, mostly featuring ethnolinguistic diversity, in that our linguistic indices capture language acquisition costs that are indispensable
in communicating, even within a country, if the society is heterogeneous in terms of language use.

20

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24

Table 1: Baseline result
(1)

(2)

Dependent variable (log)DLD(δD = 0.5)
ILDP C (δI = 0.5)

(4)

GDP per capita-1.591***(0.29)-2.049***(0.40)-0.599***(0.21)
0.042(0.22)ILDCC (δI = 0.5)
Years of schooling

0.231***(0.03)-0.002(0.04)
0.092**(0.04)
0.396**(0.16)
0.640***(0.23)
0.018***(0.00)-0.728***(0.17)-0.024*(0.01)
111
0.82

Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared

(3)

111
0.49

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.125-2.404***(0.24)-0.576***(0.22)-0.900***(0.31)

0.043(0.18)
0.232***(0.03)-0.004(0.04)
0.092**(0.04)
0.396**(0.17)
0.642***(0.23)
0.018***(0.00)-0.728***(0.17)-0.024*(0.01)
111
0.82

111
0.43

Table 2: Linguistic distance indices and GDP per capita (full range of (δD , δI )): Baseline model with ILDP CδD \δI
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

0.1
-0.530**(0.21)-0.545**(0.22)-0.551**(0.22)-0.549**(0.22)-0.542**(0.22)-0.533**(0.23)-0.523**(0.23)-0.512**(0.22)-0.501**(0.22)

0.2-0.545**(0.21)-0.562**(0.22)-0.567**(0.22)-0.566**(0.22)-0.560**(0.22)-0.551**(0.22)-0.541**(0.22)-0.530**(0.22)-0.520**(0.22)

0.3-0.561***(0.21)-0.578***(0.21)-0.584***(0.22)-0.583***(0.22)-0.578**(0.22)-0.570**(0.22)-0.560**(0.22)-0.550**(0.22)-0.539**(0.22)

0.4-0.572***(0.20)-0.590***(0.21)-0.597***(0.21)-0.596***(0.22)-0.591***(0.22)-0.584***(0.22)-0.574***(0.22)-0.564**(0.22)-0.554**(0.22)

0.5-0.578***(0.20)-0.596***(0.21)-0.604***(0.21)-0.604***(0.21)-0.599***(0.21)-0.591***(0.22)-0.582***(0.22)-0.573***(0.22)-0.563**(0.22)

0.6-0.580***(0.20)-0.598***(0.21)-0.606***(0.21)-0.606***(0.21)-0.602***(0.21)-0.594***(0.21)-0.585***(0.21)-0.576***(0.21)-0.566***(0.21)

0.7-0.580***(0.20)-0.598***(0.21)-0.606***(0.21)-0.606***(0.21)-0.601***(0.21)-0.594***(0.21)-0.586***(0.21)-0.576***(0.21)-0.567***(0.21)

0.8-0.579***(0.20)-0.597***(0.20)-0.604***(0.21)-0.605***(0.21)-0.600***(0.21)-0.593***(0.21)-0.584***(0.21)-0.575***(0.21)-0.566***(0.21)

0.9-0.577***(0.20)-0.595***(0.20)-0.603***(0.21)-0.603***(0.21)-0.599***(0.21)-0.591***(0.21)-0.583***(0.21)-0.574***(0.21)-0.564***(0.21)

This table shows coeﬃcients of DLD on a full range of linguistic distance index parameters, δ (δD and δI ). ILDP C as
the international linguistic distance index. GDP/capita is the dependent variable. All results include the vector of baseline
control variables. Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

26

27

10%
-1.077***(0.30)-0.163(0.31)
0.195***(0.04)
0.048(0.05)
0.189***(0.06)
0.672***(0.23)
0.460*(0.24)
0.017***(0.01)-0.671***(0.23)-0.016(0.01)
111
0.63

20%
-0.735**(0.33)
0.074(0.34)
0.205***(0.04)
0.047(0.06)
0.166**(0.06)
0.573**(0.25)
0.609**(0.27)
0.020***(0.01)-0.753***(0.27)-0.003(0.02)
111
0.66

(2)

30%
-0.642*(0.33)
0.187(0.35)
0.242***(0.05)
0.029(0.06)
0.131**(0.06)
0.459*(0.25)
0.556**(0.28)
0.018**(0.01)-0.804***(0.28)-0.012(0.02)
111
0.67

(3)

(5)

50%
-0.935***(0.29)
0.076(0.35)
0.214***(0.04)
0.038(0.07)
0.082(0.07)
0.445*(0.23)
0.530*(0.30)
0.011(0.01)-0.429*(0.23)-0.008(0.02)
111
0.66

(6)

60%
-0.919***(0.29)
0.135(0.34)
0.195***(0.04)-0.003(0.07)
0.108(0.07)
0.399*(0.23)
0.475(0.30)
0.013**(0.01)-0.496**(0.23)
0.002(0.02)
111
0.65

GDP per capita
40%-0.718**(0.30)
0.186(0.33)
0.219***(0.04)
0.024(0.06)
0.114*(0.06)
0.445*(0.23)
0.668**(0.26)
0.016**(0.01)-0.489**(0.23)-0.001(0.02)
111
0.67

(4)

Standard errors based on 10,000 bootstrap replications are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

Observations
GOF

Polity2Socialist law

Absolute latitude

Coastal population

Trade openness

Area size

Population size

Years of schooling

ILDP C (δI = 0.5)

Quantile
DLD(δD = 0.5)

Dependent variable (log)

(1)

(7)

70%
-0.817**(0.33)
0.008(0.37)
0.218***(0.05)
0.025(0.07)
0.038(0.07)
0.540*(0.28)
0.310(0.32)
0.011*(0.01)-0.698***(0.24)-0.025(0.02)
111
0.64

Table 3: Quantile regression result (baseline control with ILDP C )80%-0.833**(0.35)
0.037(0.40)
0.210***(0.05)
0.023(0.07)
0.009(0.07)
0.514*(0.30)
0.282(0.32)
0.015**(0.01)-0.823***(0.24)-0.028(0.02)
111
0.61

(8)

90%
-0.748**(0.34)-0.023(0.42)
0.224***(0.05)
0.063(0.07)-0.080(0.08)
0.300(0.29)
0.197(0.31)
0.020***(0.01)-1.078***(0.24)-0.050**(0.02)
111
0.56

(9)

50

70

50

70

30

50

70

Area size

Absolute latitude

90

10

30

50

70

0.015

90

10

30

50

70

quantile (%)

Years of schooling

Trade openness

Socialist law

Estimate

−1.0

0.2
70

90

−0.6

0.8
0.6
0.4

Estimate

0.22

50

90

−0.2

quantile (%)

quantile (%)

30

10

30

50

70

90

10

quantile (%)

30

50

70

90

quantile (%)

Figure 1: Quantile regression (Baseline model with DLD and ILDP C )28

50quantile (%)

quantile (%)

30

10

0.005

−0.05

Estimate

Estimate
70

90

0.025

ILD_PC50−0.02

Estimate−0.06
10quantile (%)

0.18
10

0.6

90

quantile (%)

−0.2

30

0.4

Estimate
30

quantile (%)−0.6

Estimate

0.2
10

0.26

10

Estimate0.05

900.10 0.20

30

0.2

10

Polity2

Coastal population−0.05 0.00

Estimate−0.6

Population size

−1.0

Estimate

DLD

70

90

Table 4: Eﬀect of temperature and precipitation
(1)

(2)

(3)

Dependent variable (log)DLD(δD = 0.5)
ILDP C (δI = 0.5)

-0.593***(0.21)
0.030(0.23)-0.603***(0.22)
0.024(0.20)

0.022*(0.01)

Precipitation
Years of schooling
Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared

(5)

(6)

-0.572***(0.21)-0.586**(0.24)-0.561**(0.23)

0.040(0.18)
0.022*(0.01)

0.032(0.18)

0.052(0.18)
0.023*(0.01)
0.000(0.00)
0.251***(0.03)-0.011(0.05)
0.095**(0.05)
0.349**(0.16)
0.524**(0.25)
0.027***(0.01)-0.729***(0.18)-0.023**(0.01)
111
0.82

GDP per capita-0.589***(0.22)
0.044(0.22)ILDCC (δI = 0.5)
Temperature(4)0.250***(0.03)-0.007(0.05)
0.092**(0.05)
0.360**(0.16)
0.540**(0.21)
0.025***(0.01)-0.716***(0.16)-0.023*(0.01)
111
0.82-0.000(0.00)
0.232***(0.03)
0.000(0.04)
0.090*(0.05)
0.405**(0.16)
0.655**(0.27)
0.017***(0.01)-0.715***(0.17)-0.023*(0.01)
111
0.82

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

290.023*(0.01)
0.000(0.00)
0.250***(0.03)-0.009(0.04)
0.094*(0.05)
0.352**(0.16)
0.526**(0.26)
0.026***(0.01)-0.726***(0.17)-0.024**(0.01)
111
0.82

0.250***(0.03)-0.008(0.05)
0.092**(0.05)
0.359**(0.17)
0.541**(0.21)
0.025***(0.01)-0.718***(0.17)-0.023*(0.01)
111
0.82-0.000(0.00)
0.232***(0.03)-0.001(0.04)
0.090**(0.04)
0.403**(0.17)
0.654**(0.27)
0.017***(0.01)-0.718***(0.18)-0.023*(0.01)
111
0.82

Table 5: Eﬀect of elevation
(1)

(2)

(3)

-0.661***(0.20)
0.085(0.22)-0.623***(0.22)
0.038(0.23)-0.642***(0.20)
0.123(0.22)

Dependent variable (log)DLD(δD = 0.5)
ILDP C (δI = 0.5)

-0.230*(0.13)

Elevation (std. dev.)
Years of schooling
Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared(6)-0.622***(0.20)-0.601***(0.22)-0.589***(0.21)

0.073(0.18)-0.231*(0.13)

0.041(0.18)

0.097(0.18)-0.380*(0.21)
0.255(0.22)
0.245***(0.03)-0.022(0.05)
0.073(0.05)
0.296*(0.17)
0.408*(0.21)
0.017***(0.00)-0.800***(0.17)-0.026**(0.01)
111
0.83

GDP per capita

ILDCC (δI = 0.5)
Elevation (average)

(5)

(4)

0.242***(0.03)-0.003(0.05)
0.082*(0.05)
0.306*(0.17)
0.452**(0.21)
0.018***(0.00)-0.790***(0.16)-0.027**(0.01)
111
0.82-0.105(0.12)
0.233***(0.03)
0.004(0.04)
0.093**(0.04)
0.377**(0.16)
0.608**(0.23)
0.018***(0.00)-0.739***(0.17)-0.025*(0.01)
111
0.82

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.130-0.375*(0.21)
0.251(0.21)
0.245***(0.03)-0.019(0.05)
0.074(0.05)
0.295*(0.17)
0.409*(0.21)
0.017***(0.00)-0.803***(0.16)-0.026*(0.01)
111
0.83

0.242***(0.03)-0.005(0.05)
0.082*(0.04)
0.306*(0.17)
0.452**(0.21)
0.017***(0.00)-0.789***(0.17)-0.027**(0.01)
111
0.83-0.105(0.12)
0.233***(0.03)
0.003(0.05)
0.093**(0.04)
0.376**(0.17)
0.610***(0.23)
0.018***(0.00)-0.740***(0.17)-0.025*(0.01)
111
0.82

Table 6: Eﬀect of agriculture
(1)

(2)

Dependent variable (log)DLD(δD = 0.5)
ILDP C (δI = 0.5)

(3)-0.606***(0.22)
0.041(0.23)

Agricultural suitability (average)-0.575***(0.22)
0.014(0.22)-0.074(0.32)

Agricultural suitability (std. dev.)

Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared

(5)

(6)

-0.586***(0.22)-0.552**(0.22)-0.558**(0.22)

0.036(0.18)-0.066(0.32)

0.048(0.18)

0.044(0.18)-0.039(0.31)-0.528(0.63)
0.237***(0.03)
0.017(0.05)
0.079(0.06)
0.367**(0.18)
0.594***(0.22)
0.018***(0.00)-0.742***(0.17)-0.023**(0.01)
111
0.82

GDP per capita-0.580***(0.22)
0.014(0.22)ILDCC (δI = 0.5)

Years of schooling(4)0.232***(0.03)
0.004(0.05)
0.084(0.06)
0.390**(0.17)
0.636***(0.23)
0.018***(0.00)-0.718***(0.16)-0.023**(0.01)
111
0.82-0.528(0.62)
0.236***(0.03)
0.015(0.05)
0.082*(0.05)
0.372**(0.17)
0.594***(0.22)
0.019***(0.00)-0.742***(0.17)-0.024*(0.01)
111
0.82

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

31-0.050(0.32)-0.516(0.61)
0.236***(0.03)
0.019(0.05)
0.077(0.06)
0.369**(0.17)
0.592***(0.22)
0.018***(0.00)-0.735***(0.17)-0.023**(0.01)
111
0.82

0.232***(0.03)
0.002(0.05)
0.085(0.06)
0.391**(0.17)
0.637***(0.23)
0.018***(0.00)-0.719***(0.17)-0.023**(0.01)
111
0.82-0.538(0.64)
0.237***(0.03)
0.014(0.05)
0.083*(0.05)
0.369**(0.17)
0.596***(0.22)
0.019***(0.00)-0.748***(0.18)-0.023*(0.01)
111
0.82

Table 7: Principal component analysis (Infectious disease)
First principal component

Second principal component

1.686
0.475
0.281
0.281

1.211
0.299
0.202
0.483

0.614
0.409
0.024
0.602
-0.293-0.0850.032-0.1620.665-0.003-0.302
0.663

Eigenvalue
Diﬀerence
Proportion
Cumulative
Loadings:
Tuberculosis
Malaria
MeaslesNeonatal Tetanus
Pertussis
Total Rubella

Principal component analysis based on a correlation matrix (unrotated). 70 observations
are in the data. Only lists two principal components which show eigenvalues larger thanone, based on the Kaiser’s rule.

Table 8: Eﬀect of infectious disease(1)
Dependent variable (log)

(2)

(3)

(4)

GDP per capita

DLD(δD = 0.5)

-0.895***(0.26)
0.155(0.25)

ILDP C (δI = 0.5)

-0.659***(0.24)
0.188(0.25)ILDCC (δI = 0.5)
First principal component (infectious disease)
Second principal component (infectious disease)
Years of schooling

0.189***(0.04)
0.005(0.06)
0.071(0.06)
0.279(0.27)
0.559**(0.27)
0.024***(0.01)-0.730***(0.21)-0.023(0.02)
70
0.81

Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared
Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

32-0.195*(0.10)-0.104(0.07)
0.183***(0.04)-0.005(0.06)
0.066(0.05)
0.337(0.25)
0.307(0.23)
0.019***(0.01)-0.688***(0.20)-0.034(0.02)
70
0.83-0.802***(0.25)-0.603**(0.23)

0.212(0.20)

0.144(0.18)-0.186*(0.10)-0.105(0.07)
0.179***(0.03)-0.009(0.06)
0.066(0.05)
0.366(0.24)
0.303(0.22)
0.020***(0.01)-0.688***(0.20)-0.034(0.02)
70
0.83

0.187***(0.04)-0.002(0.07)
0.072(0.06)
0.307(0.26)
0.558**(0.26)
0.024***(0.01)-0.748***(0.22)-0.022(0.02)
70
0.81

Table 9: Eﬀect of immigrant population and share(1)
Dependent variable (log)DLD(δD = 0.5)
ILDP C (δI = 0.5)

(2)-0.613***(0.22)
0.106(0.26)-0.698***(0.22)
0.213(0.27)

0.000(0.00)

Immigrant population share
Years of schooling
Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared(4)

GDP per capita

ILDCC (δI = 0.5)
Immigrant population(3)0.228***(0.03)-0.010(0.05)
0.090**(0.04)
0.400**(0.17)
0.656***(0.24)
0.018***(0.00)-0.718***(0.17)-0.023*(0.01)
111
0.82

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

33

0.024*(0.01)
0.200***(0.03)
0.030(0.04)
0.102**(0.04)
0.369**(0.15)
0.611***(0.21)
0.016***(0.00)-0.605***(0.16)-0.010(0.01)
111
0.84-0.571**(0.22)-0.677***(0.21)

0.073(0.20)
0.000(0.00)

0.005(0.18)

0.228***(0.03)-0.012(0.05)
0.089**(0.04)
0.402**(0.17)
0.656***(0.24)
0.018***(0.00)-0.714***(0.17)-0.023*(0.01)
111
0.82

0.023*(0.01)
0.195***(0.03)
0.031(0.04)
0.098**(0.04)
0.383**(0.15)
0.604***(0.21)
0.015***(0.00)-0.575***(0.15)-0.011(0.01)
111
0.84

Table 10: Eﬀect of multiple oﬃcial languages(1)
Dependent variable (log)DLD(δD = 0.5)

GDP per capita-0.562**(0.22)
0.103(0.23)

ILDP C (δI = 0.5)
ILDCC (δI = 0.5)
Multiple oﬃcial languages
Years of schooling
Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared-0.239*(0.14)
0.244***(0.03)
0.005(0.04)
0.084*(0.04)
0.415***(0.16)
0.565**(0.22)
0.017***(0.00)-0.796***(0.17)-0.025*(0.01)
111
0.82

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

34

(2)

-0.506**(0.22)

0.104(0.18)-0.248*(0.14)
0.244***(0.03)
0.002(0.04)
0.084*(0.04)
0.416**(0.16)
0.565**(0.22)
0.017***(0.00)-0.799***(0.18)-0.025*(0.01)
111
0.82

35

0.237***(0.03)-0.008(0.05)
0.102**(0.04)
0.398**(0.16)
0.629***(0.24)
0.017***(0.00)-0.720***(0.17)-0.024*(0.01)
110
0.81-0.318(0.35)-0.558**(0.23)
0.079(0.23)

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

Observations
Adjusted R-squared

Polity2Socialist law

Absolute latitude

Coastal population

Trade openness

Area size

Population size

Years of schooling

PH

RQ

ER

ELF

GI

ILDCC (δI = 0.5)

ILDP C (δI = 0.5)

DLD(δD = 0.5)

Dependent variable (log)(1)

0.233***(0.03)
0.008(0.05)
0.097**(0.04)
0.425***(0.16)
0.624**(0.24)
0.018***(0.00)-0.713***(0.17)-0.024*(0.01)
110
0.82-0.403(0.24)-0.330(0.27)
0.071(0.23)(2)

0.236***(0.03)-0.011(0.05)
0.101**(0.05)
0.391**(0.16)
0.645***(0.24)
0.018***(0.00)-0.718***(0.17)-0.025*(0.01)
110
0.81-0.895(1.20)-0.595***(0.22)
0.081(0.24)(3)0.230***(0.03)-0.003(0.05)
0.092**(0.04)
0.389**(0.17)
0.658***(0.24)
0.017***(0.00)-0.731***(0.17)-0.023*(0.01)
110
0.81

0.820
(1.01)-0.648***(0.23)
0.032(0.22)(4)(6)-0.249(0.48)
0.235***(0.03)-0.008(0.05)
0.099**(0.05)
0.397**(0.16)
0.642***(0.24)
0.018***(0.00)-0.722***(0.17)-0.025*(0.01)
110
0.81-0.588***(0.22)
0.065(0.24)

0.237***(0.03)-0.010(0.05)
0.102**(0.04)
0.399**(0.17)
0.630***(0.24)
0.017***(0.00)-0.719***(0.17)-0.024*(0.01)
110
0.81

0.078
(0.19)-0.336(0.36)-0.514**(0.23)

GDP per capita(5)

Table 11: Eﬀect of linguistic diversity

0.234***(0.03)
0.006(0.05)
0.097**(0.04)
0.424**(0.16)
0.627***(0.24)
0.018***(0.00)-0.715***(0.17)-0.024*(0.01)
110
0.82-0.419*(0.24)

0.089(0.17)-0.273(0.26)(7)

0.237***(0.03)-0.014(0.05)
0.101**(0.04)
0.392**(0.16)
0.646***(0.24)
0.018***(0.00)-0.717***(0.17)-0.024*(0.01)
110
0.81-0.930(1.22)

0.071(0.19)-0.556**(0.22)(8)0.230***(0.03)-0.004(0.05)
0.092**(0.04)
0.388**(0.17)
0.659***(0.24)
0.017***(0.00)-0.732***(0.17)-0.022*(0.01)
110
0.81

0.817
(1.01)

0.037(0.18)-0.629***(0.23)(9)-0.265(0.49)
0.235***(0.03)-0.010(0.05)
0.099**(0.04)
0.397**(0.17)
0.644***(0.24)
0.018***(0.00)-0.722***(0.17)-0.024*(0.01)
110
0.81

0.063
(0.19)-0.553**(0.22)(10)

Table 12: Eﬀect of genetic distance to the US(1)
Dependent variable (log)DLD(δD = 0.5)
ILDP C (δI = 0.5)

(2)

-0.544**(0.21)
0.259(0.23)-0.542**(0.21)
0.281(0.25)-0.000**(0.00)

Genetic distance to the US (weighted)
Years of schooling
Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2
Observations
Adjusted R-squared(4)

GDP per capita

ILDCC (δI = 0.5)
Genetic distance to the US (dominant)(3)0.227***(0.03)-0.022(0.05)
0.102**(0.04)
0.438***(0.16)
0.567**(0.22)
0.015***(0.00)-0.748***(0.17)-0.023*(0.01)
110
0.83

Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

36-0.000*(0.00)
0.225***(0.03)-0.026(0.05)
0.104**(0.04)
0.439***(0.16)
0.575***(0.21)
0.015***(0.00)-0.741***(0.17)-0.023*(0.01)
108
0.82-0.505**(0.22)-0.503**(0.22)

0.063(0.19)-0.000*(0.00)

0.056(0.20)

0.223***(0.03)-0.020(0.05)
0.099**(0.04)
0.445***(0.16)
0.573**(0.22)
0.015***(0.00)-0.718***(0.16)-0.024*(0.01)
110
0.83-0.000(0.00)
0.221***(0.03)-0.022(0.05)
0.100**(0.04)
0.444***(0.17)
0.579***(0.22)
0.015***(0.00)-0.710***(0.17)-0.024*(0.01)
108
0.82

Table 13: SAR result
(1)

(2)

Dependent variable (log)
Spatial weight matrix
DLD(δD = 0.5)
ILDP C (δI = 0.5)

Population size
Area size
Trade openness
Coastal population
Absolute latitude
Socialist law
Polity2ρObservations
Moran’s I (p-value)(4)

GDP per capita
Contiguity-0.589***(0.19)
0.003(0.29)ILDCC (δI = 0.5)
Years of schooling(3)

0.231***(0.03)-0.014(0.05)
0.097**(0.04)
0.372**(0.16)
0.693***(0.17)
0.018***(0.00)-0.725***(0.16)-0.027***(0.01)
0.030(0.02)
111
0.43-0.569***(0.20)

0.044(0.17)
0.232***(0.03)-0.015(0.05)
0.097**(0.04)
0.369**(0.16)
0.696***(0.17)
0.018***(0.00)-0.732***(0.15)-0.026***(0.01)
0.030(0.02)
111
0.39

Inverse distance-0.530***(0.19)
0.054(0.29)

0.230***(0.03)-0.015(0.05)
0.095**(0.04)
0.314*(0.17)
0.625***(0.17)
0.015***(0.00)-0.735***(0.16)-0.025**(0.01)
0.296(0.17)
111
0.88-0.520**(0.21)

0.014(0.17)
0.229***(0.03)-0.015(0.05)
0.094**(0.04)
0.317*(0.17)
0.624***(0.17)
0.015***(0.00)-0.729***(0.15)-0.025***(0.01)
0.294(0.17)
111
0.86Standard errors are in parentheses. Moran’s I statistics are calculated based on
the residuals of the OLS regressions of the baseline model.
*** p<0.01, ** p<0.05, * p<0.137Appendix A

Data sources and summary statistics
Table A1: Data sources

Variable name
Linguistic distanceDomestic linguistic distance DLD
International linguistic distance ILDP CInternational linguistic distance ILDCC
Language status
Multiple oﬃcial languages
Language status
IncomeGDP per capita
Population
Population size
Education
Years of schooling
Trade
Trade openness
Institutions
Quality of institutions
Legal origin

Geography & climate
Absolute latitude

Definition

SourceWeighted average of linguistic distances to the domestic linguistic center as definedin the main text
Weighted average of linguistic distances to the international linguistic center as
defined in the main text
Linguistic distances from the domestic linguistic center(s) to the international linguistic center as defined in the main text

Own calculation from Ethnologue 16thedition (Lewis, 2009)
Own calculation from Ethnologue 16thedition (Lewis, 2009)
Own calculation from Ethnologue 16thedition (Lewis, 2009)

Dummy variable taking 1 if a country has multiple status-1 languages
Status labeled to each language based on its intra- and international usages andimportanceOwn calculation from Ethnologue 17thedition (Lewis et al., 2014)
Ethnologue 17th edition (Lewis et al.,2014)

log of GDP/capita in year 2010 (Output-side real GDP at current PPPs in mil.
2005US$)

Penn World Tables 8.0, Feenstra, Inklaar,
and Timmer (2013b)

log of population size (in mil.) in year 2005

Penn World Tables 8.0, Feenstra, Inklaar,
and Timmer (2013b)

Years of schooling, population aged over 25 in year 2005

Barro and Lee (2013)

Merchandise exports + imports in share in GDP, at PPP in year 2005

Penn World Tables 8.0, Feenstra, Inklaar,
and Timmer (2013b)

Combined Polity2 score in year 2005 (-10 for most repressive, 10 for most democratic)

PolityIV database, Marshall and Jaggers(2012)
La Porta, Lopez-de Silanes, Shleifer, and
Vishny (1999)

British law (Dummy takes 1 if country’s legal origin is British law), socialist law(dummy takes 1 if socialist law), French law (dummy takes 1 if French law), German law (dummy takes 1 if German law), Scandinavian law (dummy takes 1 if
Scandinavian law)
Absolute latitude of capital city2

Land area sizelog of country land area size in km

Coastal population

Share in total population within 100 km of ice-free coast to total population in %in year 1995
Average elevation across regions within a country
Standard deviation of elevations across regions within a country
Average land quality across regions within a country
Standard deviation of land quality across regions within a country

Average elevation
Standard deviation of elevation
Average agricultural suitability
Standard deviation of agricultural suitability
Average temperature
Average precipitation
Regional dummy
Infectious disease
Tuberculosis
Malaria
MeaslesNeonatal Tetanus
Pertussis
Total Rubella
Diversity/polarization in language
GI index
ELF index
ER index
RQ indexP H index
Immigrants
Immigrant population
Immigrant population share in %Genetic distance
Genetic distance to the US (dominant)Genetic distance to the US (weighted)

CEPII (2010), Head, Mayer, and Ries
(2010)
CEPII (2010), Head, Mayer, and Ries
(2010)
Gallup, Sachs, and Mellinger (1999)
Michalopoulos (2012)
Michalopoulos (2012)
Michalopoulos (2012)
Michalopoulos (2012)

Average annual temperatures in deg. C for the period 1961-1999
Average annual precipitation in mm for the period 1961-1999
Sub-Saharan Africa

World Bank (2011)
World Bank (2011)
World Bank (2014)

Incidence of tuberculosis (per mil. people) in year 2005
Reported cases of malaria in the area at risk (per mil. people) in year 2005
Reported cases of measles (per mil. people) in year 2005
Reported cases of neonatal tetanus (per mil. people) in year 2005
Reported cases of pertussis (per mil. people) in year 2005
Reported cases of the total rubella (per mil. people) in year 2005

WHO (2020)
WHO (2020)
WHO (2020)
WHO (2020)
WHO (2020)
WHO (2020)

Herfindahl index of language group shares (linguistic distance considered)
Herfindahl index of language group shares
Polarization index of language group shares (linguistic distance considered)
Polarization index of language group shares
Peripheral heterogeneity index of linguistic groups (linguistic distance considered)

Desmet et al. (2009)
Desmet et al. (2009)
Desmet et al. (2009)
Desmet et al. (2009)
Desmet et al. (2009)

International migrant stock in year 2005
International migrant stock as a percentage of the total population in year 2005

United Nations (2015)
United Nations (2015)

FST genetic distance index to the US (between dominant groups)
FST population weighted genetic distance index to the US

Spolaore and Wacziarg (2009)
Spolaore and Wacziarg (2009)38

Table A2: Summary statistics
Variable

Obs

Mean

Std.Dev.

Min

Max

GDP per capita (log)DLD(δD = 0.1)
DLD(δD = 0.2)
DLD(δD = 0.3)
DLD(δD = 0.4)
DLD(δD = 0.5)
DLD(δD = 0.6)
DLD(δD = 0.7)
DLD(δD = 0.8)
DLD(δD = 0.9)
ILDP C (δI = 0.1)
ILDP C (δI = 0.2)
ILDP C (δI = 0.3)
ILDP C (δI = 0.4)
ILDP C (δI = 0.5)
ILDP C (δI = 0.6)
ILDP C (δI = 0.7)
ILDP C (δI = 0.8)
ILDP C (δI = 0.9)
ILDCC (δI = 0.1)
ILDCC (δI = 0.2)
ILDCC (δI = 0.3)
ILDCC (δI = 0.4)
ILDCC (δI = 0.5)
ILDCC (δI = 0.6)
ILDCC (δI = 0.7)
ILDCC (δI = 0.8)
ILDCC (δI = 0.9)
Years of schooling
Population size
Area size
Trade openness
Coastal population
Absolute latitude
Polity2Temperature
Precipitation
Agricultural suitability (average)
Agricultural suitability (std. dev.)
Elevation (average)
Elevation (std. dev.)
Multiple oﬃcial language
Immigrant population
Immigrant population share
British law
French law
German law
Socialist law
Scandinavian law
GI
ELF
ER
RQ
PH
Genetic distance to the US (dominant)Genetic distance to the US (weighted)
Tuberculosis
Malaria
MeaslesNeonatal Tetanus
Pertussis
Total Rubella

111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
111
110
110
110
110
110
110
108
70
70
70
70
70
70

8.80
0.29
0.31
0.33
0.34
0.36
0.37
0.38
0.39
0.39
0.62
0.70
0.75
0.80
0.83
0.86
0.88
0.89
0.90
0.44
0.53
0.60
0.66
0.70
0.73
0.76
0.78
0.79
7.56
2.67
12.52
0.57
0.40
28.64
4.72
16.74
1034.10
0.46
0.19
0.60
0.37
0.21
1525180
6.58
0.28
0.43
0.05
0.21
0.04
0.16
0.45
0.04
0.11
0.12
843.13957.321381.113554.34
40.02
0.58
52.11
67.94

1.25
0.36
0.36
0.36
0.36
0.36
0.36
0.36
0.37
0.37
0.37
0.31
0.27
0.24
0.23
0.22
0.21
0.21
0.21
0.38
0.34
0.32
0.31
0.31
0.32
0.32
0.33
0.33
3.27
1.41
1.59
0.39
0.35
17.68
6.05
8.71
709.60
0.24
0.09
0.51
0.38
0.41
4139164
8.85
0.45
0.50
0.21
0.41
0.19
0.17
0.31
0.05
0.06
0.13
822.41565.622280.9113198.79
158.481.18
172.80319.39

6.14
0
0
0
0
0
0
0
0
0
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0.01
0
0
0
0
0
0
0
0
0
1.11
0.10
9.33
0.10
0
0.23
-10
-7.14
32.91
0
0
0.03
0.01
0
6290
0.05
0
0
0
0
0
0
0
0
0
0
0
314.48
53.90
0
0
0
0
0

11.02
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
13.13
7.16
16.65
2.41
1
60.13
10
28.30
3268.27
0.96
0.41
2.52
1.91
1
39300000
58.90
1
1
1
1
1
0.65
0.96
0.21
0.24
0.50
2288.00
2088.01
11538.73
74308.77
1062.37
4.98
1197.412598.0939

Appendix B

Note on construction of linguistic distance indices
Table A3: Notes on multiple status-1 language countries

Cameroon
IsraelLesotho
Malaysia

Pakistan
Somalia

Sudan

Switzerland

Double status-1 language (English and French), but no L1 speakers either of English
or French, so weights 0.5 are assigned to English and French
Double status-1 language (Hebrew and Standard Arabic), but no L1 speakers ofStandard Arabic, so no weight is assigned to Standard Arabic
Double status-1 language (Southern Sotho and English), but no L1 speakers ofEnglish, so no weight is assigned to English
Triple status-1 language (Mandarin Chinese, English, and Standard Malay), but noL1 speakers of Mandarin Chinese and Standard Malay, so no weights are assigned
to Mandarin Chinese and Standard Malay
Double status-1 language (English and Urdu), but no L1 speakers of English, so noweight is assigned to English
Quadruple status-1 language (Somali, Standard Arabic, Italian, and English), but
no L1 speakers of Standard Arabic, Italian, and English, so no weights are assigned
for Standard Arabic, Italian, and English.
Double status-1 language (English and Standard Arabic), but no L1 speakers ofEnglish and Standard Arabic, so weights 0.5 are assigned to English and Standard
ArabicQuadruple status-1 language (French, Standard German, and Italian), but no L1
speakers of Standard German, so no weight is assigned for Standard German

This table lists the notes on calculations of linguistic distance indices for countries with multiple status-1 languages
which need special treatments. Basically, calculation of linguistic distance indices is conducted as in Section 2.
However, for some of the countries with multiple Status-1 languages, Ethnologue reports there are no status-1
language speaker as their L1 languages (mother tongues). For example, Ethnologue reports that no one speaks
English or French as L1 language in Cameroon, both of which are labeled as status-1 languages there. In thiscase, we simply calculate the linguistic distance indices based on the same weights on English and French, thatis, γEnglish = γFrench = 0.5. Such all special treatments implemented in linguistic distance index calculation arelisted in this table.

40Appendix C

Other estimation results

Table A4: Linguistic distance indices and GDP per capita (full range of (δD , δI )): Baseline model with ILDCCδD \δI
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9

0.1
-0.685***(0.19)-0.691***(0.19)-0.683***(0.20)-0.669***(0.20)-0.651***(0.20)-0.631***(0.20)-0.613***(0.21)-0.595***(0.21)-0.579***(0.21)

0.2-0.668***(0.19)-0.674***(0.20)-0.667***(0.20)-0.652***(0.20)-0.635***(0.21)-0.616***(0.21)-0.598***(0.21)-0.581***(0.21)-0.566***(0.21)

0.3-0.639***(0.19)-0.647***(0.20)-0.641***(0.20)-0.629***(0.21)-0.613***(0.21)-0.596***(0.21)-0.579***(0.21)-0.564***(0.21)-0.549**(0.21)

0.4-0.610***(0.20)-0.619***(0.20)-0.616***(0.21)-0.606***(0.21)-0.592***(0.21)-0.577***(0.21)-0.562**(0.21)-0.548**(0.21)-0.535**(0.21)

0.5-0.587***(0.20)-0.598***(0.21)-0.597***(0.21)-0.588***(0.21)-0.576***(0.22)-0.563**(0.22)-0.549**(0.22)-0.536**(0.22)-0.524**(0.22)

0.6-0.570***(0.20)-0.582***(0.21)-0.583***(0.21)-0.576***(0.22)-0.565**(0.22)-0.553**(0.22)-0.540**(0.22)-0.528**(0.22)-0.516**(0.22)

0.7-0.559***(0.20)-0.572***(0.21)-0.573***(0.21)-0.568**(0.22)-0.558**(0.22)-0.546**(0.22)-0.534**(0.22)-0.522**(0.22)-0.511**(0.22)

0.8-0.551***(0.20)-0.565***(0.21)-0.567***(0.21)-0.562**(0.22)-0.553**(0.22)-0.542**(0.22)-0.530**(0.22)-0.519**(0.22)-0.508**(0.22)

0.9-0.546***(0.20)-0.560***(0.21)-0.563**(0.22)-0.558**(0.22)-0.549**(0.22)-0.539**(0.22)-0.528**(0.22)-0.516**(0.22)-0.506**(0.22)

This table shows coeﬃcients of DLD on a full range of linguistic distance index parameters, δ (δD and δI ). ILDCC as theinternational linguistic distance index. GDP/capita is the dependent variable. All results include the vector of baseline control
variables. Robust standard errors are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

41

42

10%
-1.071***(0.29)-0.165(0.22)
0.200***(0.04)
0.045(0.05)
0.190***(0.05)
0.676***(0.22)
0.464**(0.23)
0.017***(0.01)-0.667***(0.22)-0.017(0.01)
111
0.63

20%
-0.721**(0.36)-0.037(0.27)
0.199***(0.04)
0.061(0.06)
0.155**(0.06)
0.549**(0.25)
0.619**(0.28)
0.020***(0.01)-0.724***(0.27)-0.003(0.02)
111
0.66

(2)

30%
-0.621*(0.36)
0.051(0.27)
0.235***(0.05)
0.039(0.06)
0.112*(0.06)
0.414*(0.25)
0.579**(0.27)
0.018**(0.01)-0.745***(0.26)-0.006(0.02)
111
0.67

(3)

(5)

50%
-0.953***(0.30)-0.016(0.23)
0.215***(0.04)
0.043(0.07)
0.066(0.07)
0.427*(0.23)
0.503*(0.30)
0.010(0.01)-0.447*(0.23)-0.011(0.02)
111
0.66

(6)

60%
-0.852***(0.29)
0.046(0.24)
0.193***(0.04)-0.012(0.07)
0.102(0.07)
0.429*(0.23)
0.553*(0.30)
0.012*(0.01)-0.466**(0.23)
0.001(0.02)
111
0.65

GDP per capita
40%-0.833***(0.30)-0.063(0.23)
0.238***(0.04)
0.038(0.06)
0.072(0.06)
0.445*(0.23)
0.533*(0.27)
0.011*(0.01)-0.496**(0.23)-0.012(0.02)
111
0.67

(4)

Standard errors based on 10,000 bootstrap replications are in parentheses.
*** p<0.01, ** p<0.05, * p<0.1

Observations
GOF

Polity2Socialist law

Absolute latitude

Coastal population

Trade openness

Area size

Population size

Years of schooling

ILDCC (δI = 0.5)

Quantile
DLD(δD = 0.5)

Dependent variable (log)

(1)

(7)

70%
-0.817**(0.38)
0.003(0.29)
0.218***(0.05)
0.027(0.07)
0.036(0.07)
0.543*(0.28)
0.301(0.32)
0.011*(0.01)-0.699***(0.24)-0.025(0.02)
111
0.64

Table A5: Quantile regression result (baseline control with ILDCC )80%-0.723*(0.37)
0.223(0.28)
0.233***(0.05)
0.006(0.07)
0.035(0.07)
0.475*(0.28)
0.274(0.32)
0.011(0.01)-0.824***(0.24)-0.026(0.02)
111
0.61

(8)

90%
-0.761*(0.42)
0.024(0.37)
0.225***(0.05)
0.054(0.07)-0.073(0.08)
0.312(0.30)
0.207(0.31)
0.020***(0.01)-1.073***(0.23)-0.047**(0.02)
111
0.56

(9)

70

30

50

70

90

30

50

70

ILD_CCArea size

Absolute latitude

Estimate

−0.10
90

10

30

50

70

10

30

90

10

30

50

70

quantile (%)

quantile (%)

quantile (%)

Years of schooling

Trade openness

Socialist law

90

Estimate

−1.0

0.2
30

50
quantile (%)

70

90

−0.6

0.8
0.6
0.4

Estimate

0.22
0.18
10

10

30

50

70

90

10

quantile (%)

30

50

70

90

quantile (%)

Figure A1: Quantile regression (Baseline model with DLD and ILDCC )43

50
quantile (%)

0.010

Estimate
70

90

0.020

quantile (%)

0.05 0.15 0.25

quantile (%)

50

−0.02

Estimate−0.06
10quantile (%)

−0.2

30

0.6
0.2

10

0.26

10

0.4

Estimate

0.05

90

−0.6

Estimate

Polity20.10
50−0.05 0.00

Estimate

−0.4
−0.8

Estimate

−1.2

30

0.2

10

Estimate

Coastal population

Population size

DLD

70

90

Table A6: Kaiser-Meyer-Olkin measure ofsampling adequacy
Infectious disease variable

KMO measure

Tuberculosis
Malaria
MeaslesNeonatal Tetanus
Pertussis
Total Rubella
Overall

0.57
0.73
0.51
0.57
0.72
0.52
0.59

.6

.8

Eigenvalues
1
1.2

1.4

1.6

Scree plot of eigenvalues after PCA

1

2

3
4
Number of components

5

6

Figure A2: Scree plot of eigenvalues after PCA

44