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Ethnic Tensions and Social Infrastructure
Monica P. Escaleras* Charles A. Register**
October 31, 2007
Abstract: Nearly all countries face problems of ethnic tensions to one extent or another. And, beyond the obvious social costs, there is a growing literature that identifies such tensions as a significant limiting factor to economic growth. Further, prior research has identified some of the social infrastructure channels through which ethnic tensions exact their toll on growth. We extend this literature by 1) using a relatively more broad measure of ethnic tensions than is typical, 2) considering a comparatively large number of measures of social infrastructure development, 17 to be precise, for a rather broad panel of 87 countries across 16 years, and 3) explicitly addressing the endogenous nature of ethnic tensions empirically which has been previously pointed to but infrequently corrected for. Using an instrumental variables approach within the context of fixed effects modeling, we find that in each of the 17 cases, ethnic tensions significantly retard the formation of the particular element of social infrastructure development and, by extension, impose an unnecessary cap on economic growth and development. As such, government’s would well-serve the interests of their populaces by enacting policies, conducting politics, and carrying out their daily functions in ways that serve to dampen ethnic tensions, rather than the reverse, which too often seems the case. __________________________________ * Monica Escaleras: (Corresponding author) Florida Atlantic University, Department of Economics, 777 Glades Road, Boca Raton, FL 33431; E-mail: [email protected]; Phone: (561) 297-1312; FAX: (561) 297-2542. **Charles A. Register: Florida Atlantic University, Department of Economics, 777 Glades Road, Boca Raton, FL 33431; E-mail: [email protected]; Phone: (561) 297-3222; FAX: (561) 297-2542.
Ethnic Tensions and Social Infrastructure
I. Introduction
A number of recent studies have shown empirically the negative effects that ethnic tensions
within a country can have on economic growth. One of the earliest of these is Easterly and Levine
(1997) which argues that much of Sub-Saharan Africa’s poor growth performance through the
1980’s can be attributed to ethnic tensions. Even to this day, Sub-Saharan Africa remains both
one of the most ethnically divisive and poorest regions on Earth. Some studies have also offered
insight into the channels through which ethnic tensions exact their toll on growth. Specifically, it
has been found that such tensions can lead to reduced investments in a whole range of necessary
ingredients to economic development including, but not limited to, broad-based schooling
opportunities, access to adequate health and nutrition, construction of transportation networks,
and public utilities, all of which can be thought of, for simplicity, as social infrastructure1.
An overview of this empirical literature is offered in the following section. While this review
will show that quite a lot is known about the negative effects of ethnic tensions, at least in narrow
contexts, broad application of the results requires one to generalize from studies that typically
relate to one or a few elements of social infrastructure, based often on a single country or a
region(s) within a country, and often limited to either one year’s data or, at best, a brief
observational period2. We add to this literature in three important ways. First, while most of the
existing analyses have focused on language or racial differences between groups as the index of
ethnic fractionalization, we use the index of ethnic tensions provided by the International Country
Risk Guide which has two distinct advantages; 1) it offers a more comprehensive notion of ethnic
fractionalization by taking into account tensions resulting from racial, national, and language
1 Some have focused on pure public goods while others focus on goods and services which at most could be thought of as semi-public. Of course, when the focus is on growth, no such distinction is made. In this paper we use the term social infrastructure to include all goods and services for which there is a plausible potential social spillover or which is clearly part of a society’s economic infrastructure. 2 This latter point is, of course, not true for those studies that focus on general economic growth.
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divisions and 2) it allows for panel estimation in that the index varies over time, at least much
more so than does a simple language-based index. A second important difference in the current
analysis is that we consider the effect of ethnic tensions on a large array, 17 to be exact, of social
goods and services for a broad number of countries, 87, for the entire period of 1982-1997. By
applying the same definition of ethnic tensions across outcomes, countries, and time periods, we
hope that consistent and relatively more generalizable results can achieved. Finally, the results
that we offer come from models which explicitly address the potential endogeneity of ethnic
tensions. This is particularly important since it is abundantly clear that ethnic tensions can result
from as well as cause poor choices with respect to the various elements of social infrastructure
like education, health care, transportation networks, public utilities and the like. While this
endogeneity has been noted, it has rarely been explicitly addressed.
As intimated by previous research, we find rather consistent results across countries, across
elements of social infrastructure development, and across time periods. We take this to be very
strong evidence of the negative effects of ethnic tensions on social infrastructure development
and, by extension, on economic growth.
II. A Brief Review of the Empirical Literature
Before turning to a brief survey of the existing literature, insight into the empirical outcomes can
be had by considering the theoretical links presented in the literature between ethnic tensions and
the development of social infrastructure. While a number of models have been offered and no
attempt is made here to summarize them all, they can be brought together, for the present
purpose, into three groups. The first is best exemplified in Alesina and LaFerrara (2000) who
suggest that participation in community activities can be expected to be lower in areas with
higher levels of ethnic heterogeneity, especially when individuals have distaste for interacting
with those of ethnic groups different from their own. Such distaste naturally leads to local
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organizations forming along ethnically diverse lines so as to avoid the costs of mixing. Within
this framework, effective community action can be expected only to the extent that ethnic groups
can form homogeneous units that are economically strong enough to provide for investment in
social infrastructure for the members of their group. The problem of limited community action,
by contrast, is exacerbated whenever either the costs of ethnic segregation are extreme or when
the ethnically homogeneous groups lack the economic resources to provide their individual
groups with effective public investment in social goods and services.
A second way in which ethnic tensions can lead to limited investment in social
infrastructure draws on the observation that often ethnic groups place significantly different
values on a potential set of social goods and services. For example, should ten such projects be
offered to the general public for funding consideration, each unique group might willingly offer
to accept the taxes needed to fund their five most desired. This poses no problem, of course,
should each ethnic group favor the same five projects. To the contrary, gridlock is the outcome
when the groups’ ranking of projects does not coincide resulting in limited investment in any of
the projects.
Finally, a third summary link is well-developed in Vigdor (2004) who argues that it is not
differences in preferences between ethnic groups per se that lead to limited investment in social
infrastructure but, rather, the limits on such investment come about because each group simply
places very little value on the provision of a particular social good or service to those of another
ethnic group. That is, in an area with five distinct ethnic groups, all of which value basic
education, it might prove difficult to generate agreement on the distribution of the tax burden
necessary to fund basic education for the children of all groups since each group places a higher
value on educating their own children than on the children from any other group. This problem is
made noticeably worse when the ethnic groups differ in size and/or relative income.
Regardless of the actual underlying cause, a fairly large empirical literature has evolved over
the past two decades pointing to the negative effects of ethnic tensions on the development of
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social infrastructure and, by extension, economic growth. Table 1 summarizes the most
important of these studies by pointing out the population studied, the element(s) of social
infrastructure considered, and the year(s) under consideration. In each case, one can see how the
particular study draws on one or more of the theoretical sketches discussed above.
___________________Insert Table 1 About Here________________________________
Much of the prior empirical work on the economic and political affects of ethnic tensions has
focused on general economic growth, spending on and/or the production of social goods and
services as avenues through which growth is affected, quality of government institutions or
decision making, and/or on participation in community affairs. An early example of this
literature is Easterly and Levine (1997) who address the question of whether Sub-Saharan
Africa’s relatively poor economic performance can be related to that region’s unusually high
level of ethnic division, as measured by language differences. They report regressions pooled by
decade that suggest the region’s poor overall performance, relative to East Africa, results from
low educational attainment, political instability, poorly developed capital and foreign exchange
markets, burgeoning fiscal debts, and decaying social infrastructure. Most importantly for the
current study, the authors are able to relate some of these differences between the two regions, in
particular, limited schooling opportunities, weakly developed capital and foreign exchange
markets, and social infrastructure problems, directly to ethnic tensions existing in greater degree
in the Sub-Saharan region.
Ethno-linguistic fractionalization is also identified as a primary cause of Africa’s growth
problem in Collier and Gunning (1999). To be sure, Collier and Gunning also stress the
importance of other factors in explaining Africa’s poor development record since the early
1960’s, but, as with Easterly and Levine, a major growth-limiting factor for Africa has been the
lack of social capital investment resulting from the very high levels of ethnic tension existing on
the continent. Related, but more recent, Miguel and Gugerty (2004) examine the relation
between ethnic diversity and the provision of social goods and services in western Kenya.
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Consistent with Easterly and Levine and Collier and Gunning, they report that ethnic diversity is
significantly associated with poor primary schooling, decaying school infrastructures, and sub-
standard maintenance of water systems3. Finally, Montalvo and Reynal-Querol (2005) show that
economic development is slowed whether one uses the comparatively standard measures of ethnic
fragmentation or the newer indices of ethnic/religious polarization.
Perhaps the largest literature on the effects of ethnic division, however, comes from studies
which analyze the provision of social goods and services in ethnically differing regions of the
U.S. Persuasive evidence on the topic is offered by Goldin and Katz (1999), Cutler and Glaeser
(1997), Poterba (1997), Luttmer (2001), and Alesina et al. (1999). Goldin and Katz take the
unique approach of considering public support for universal primary education in the early part of
the 1990’s and find that, indeed, ethnic and religious heterogeneity within a community served to
slow adoption of policies providing for school expansion. By dividing metropolitan areas based
on their racial concentration, Cutler and Glaeser show that blacks fare worse on a variety of social
outcomes, including education, employment, and income, when located in relatively segregated
communities suggesting that such segregation leads to sub-optimal investment in or production of
social goods and services. Poterba, in an analysis that is later supported by Harris et al. (2001),
finds evidence of what has come to be known as the “Florida effect” of demographic diversity on
spending for public schooling. Specifically, two dimensions of ethnic or social difference are
found to negatively affect public support of schooling—demographic differences in age as well as
pure ethnic differences. Florida is an unfortunately good example of this outcome since while its
elderly (and commonly white) residents make-up a disproportionate part of the tax base,
Hispanics make-up a disproportionate share of school children with the expected outcome:
Relatively less support for public education is found in areas with greater degrees of age and
ethnic heterogeneity. Along the same lines, Luttmer shows that individual willingness to support
3 Supporting results can be found in Mauro (1995), La Porta et al. (1999), and Alesina et al. (2003) each of which points to the negative effects that ethnic fragmentation can have on both the extent and quality of government institutions and activities.
5
social welfare spending in California increases when a relatively large share of overall spending
on such programs accrues to those of the individual’s racial group. Finally, by focusing on cities
and regions around cities, Alesina et al. offer evidence indicating that the shares of public
spending devoted to such social goods and services as education, roads, and sanitation services
are negatively related to ethnic fragmentation.
From this brief review, it would appear that ethnic fractionalization and tensions should be
found to be negatively related to investment in social goods and services and participation in
community affairs. In the following section, we present the data and empirical methods that we
use to address the specific relationship between ethnic tensions and the development of social
infrastructure.
III. Data and Empirical Methods The unit of observation used is one of the 87 individual countries during one of the 16 years
between 1982 and 1997. Of these, 19 are from Africa, 24 are from the Americas, with the
remaining 18 being from Europe. Precise definitions and sources for the variables can be found
in Appendix 1 while descriptive statistics are presented in Table 2.
___________________Insert Table 2 About Here______________________________________
Given the rather large number of variables considered, here we only offer an overview, with the
exception of the key variables in the analysis. The most important variable in the analysis is that
for ethnic tensions, ETHNIC, provided by the International Country Risk Guide, published by
Political Risk Services Group, as assembled by the IRIS Center at the University of Maryland.
This source reports complete data on more than 100 countries between 1982 and 1997. (Our
sample is limited to 87 of these because we also required, for inclusion, that individual countries
have complete data for each of the independent variables of the primary equation, as described
below). ETHNIC ranges from 0-6 with lower values indicating countries where ethnic tensions
are high due to the intolerance and unwillingness to compromise of the various racial, national, or
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language based groups within the country. Most prior studies have used a Herfindahl-type index
of ethnic fractionalization based solely on linguistic differences within a country. This index was
created in the 1960s by Soviet researchers and measures the probability that two randomly drawn
people from a given country speak the same language. While no doubt indicative of the potential
for ethnic tensions, this type variable has two noticeable shortcomings for the analysis we wish to
conduct. First, given that the language make-up of a country varies extremely little over time,
panel models would likely prove problematic. Also, such an index completely misses any ethnic
tension that arises from non-language based conflicts, an example of which would be the black-
white conflict that exists in the U.S. or the sectarian conflict that has so damaged Northern Ireland
in the recent past. At the same time, by its nature, our measure of ethnic tensions can be
criticized for relying on surveys of opinions as to the extent of tensions within a country. With
that said, we opt for the index of ethnic tensions offered by the International Country Risk Guide
both due to its broad sensitivity to the broad number of factors potentially leading to ethnic
tensions and due to its relatively greater ability to vary over time.
Our primary objective is to relate ETHNIC to the development of social infrastructure
within a country. There are many examples of what can be considered elements of social
infrastructure, though most do not have data complete enough in terms of years and/or countries
or consistent enough in terms of definitions both within and across countries to allow for the type
analysis we conduct. Given these data constraints, we consider four classes of such measures. In
the first, Air, Rail, and Port Activities, we include 6 elements which are designed to capture the
extent to which a country has developed its transport infrastructures. The first two of these
indexes, AIRPP and AIRDP measure the extensiveness of airline transport opportunities with the
former measuring the number of airline passengers per capita and the latter capturing the number
of airline departures per capita. Each is taken from the World Bank’s World Development
Indicators. Similarly, we measure the extent of a country’s rail networks with FTONT and
PREVT which, respectively, measure the freight tons shipped by rail and the passenger revenues
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generated by rail traffic. Each is scaled by the total kilometers of the country’s rail network and
is taken from the World Bank’s Railways Dataset. To consider the extent of a country’s
development of port activities, we take from the U.N.’s Review of Maritime Transport the flow of
standardized shipping containers through a country’s ports, per kilometer of coast line, CONT.
Finally, we measure the extensiveness of a country’s tourism sector by taking TOUR, the total
expenditures of international inbound visitors, from the World Bank’s World Development
Indicators.
The second class of measures of social infrastructure is Public Utilities. Here we have
two indexes, ELCP, which measures electric power consumption per capita and TELLINES which
measures the number of telephone lines available within a country, per capita. Each of these is
taken from the World Bank’s World Development Indicators.
The third social infrastructure class relates to Education outcomes within a country. To
capture basic educational opportunities, we use the literacy rate, LIT, which indicates the
percentage of a country’s population aged 15 and above who are literate. For insight into more
advanced learning opportunities, we consider the proportion of a population that is enrolled in
post secondary education SCRTEG. Each of these is taken from the World Bank’s World
Development Indicators.
Finally, our fourth class of indexes of social infrastructure provides insight into a number
of elements of a country’s Health Care networks. Each of these measures is taken from the
World Bank’s World Development Indicators. Near the onset of life, we consider 3 indicators of
health care availability, DPT, MEAS, and MORT. The first two of these show the percentage of
children below age one who have received immunization against DPT and measles, respectively.
The third is the simple mortality rate, that is, the probability that a newborn will die prior to
reaching age five, expressed as the rate per 1,000 live births. Beyond the beginning of life, we
consider the nutritional access that a population has by taking into account both height and weight
for children under age five, MAL1 and MAL2. We also take a larger view of the availability of
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health care by considering the life expectancy, in years, of a newborn, LIFEXP. Lastly, as a
measure of the absence of health care, nutrition and the like, we use the gross death rate, DRATE,
which indicates the per capita number of deaths occurring within a country in a given year.
Finally, we selected a number of control variables, based on prior, related research, for
the models of social infrastructure development. The most obvious choice here is GDPP, the per
capita GDP of a country, in purchasing power parity terms. This is an obvious choice since, other
things equal, one would expect more fully developed social infrastructures in relatively wealthy
countries. Other factors likely to affect social infrastructures are the size of a country’s
population, POP, the total expenditures of a government, EXPTOT, and the share of the
population living in urban areas, URBPOP. Population is important in that there are clearly
economies of scale with respect to social infrastructure development, while the expenditures of
government are typically necessary for the provision of public or even semi-public goods. Of
course, an equally reasonable expectation for the total expenditures variable is that it may simply
reflect poor bureaucratic institutions and, as such, may prove negatively related to the
development of social infrastructures. The urban population is also somewhat unclear. While
one would like to believe that the daily interaction between those of differing ethnic groups that
urbanization brings would lead to greater inter-group understand and increased social
infrastructure development, just the opposite might result. Further, in many countries
urbanization simply does not lead to greater inter-group interaction but rather separate towns or
areas within towns dominated by individual ethnic groups. Regardless, it seems reasonable to
suppose that urbanization will affect the development of social infrastructure. Each of these is
taken from the World Bank’s World Development Indicators. And, in order to capture an
element of the country’s institutional make-up, we use a measure of the extent of democratic
institutions within the country, DEM, taken from Polity IV. This measure ranges from 0-10 with
higher values indicating relatively thoroughgoing democratic institutions. To the extent that
democratic institutions give voice to all ethnic groups, it may be expected that DEM will be
9
positively associated with the development of social infrastructure. Of course, when democracy
is not accompanied by clear and enforced rules protecting the rights of minorities, the power of
the majority might be used to the benefit (detriment) of the largest (smaller) ethnic groups. As
such, the expectation for the relation between DEM and social infrastructure development is also
a bit unclear.
With this data in hand, we can address the fundamental question of the paper: Do ethnic
tensions serve to limit a country’s development of social infrastructure? We take advantage of
the data’s time series/cross sectional nature by estimating panel models with fixed-effects at the
continent level.4 This method of estimation allows us to capture any unobserved continent
heterogeneity that is relatively fixed over time, such as attitudes, cultural norms and so forth.
Thus, since our interest is on the partial effects of time-varying covariates, fixed-effects
estimation is attractive because it allows any unobserved heterogeneity to be freely correlated
with the time-varying covariates.
As has been pointed out in prior studies, ethnic tensions may well not be exogenous when
considering the development of social infrastructure. In other words, it seems at least equally
plausible that ethnic tensions result from poor social infrastructure decision-making rather than
simply driving those decisions. As such, we take an instrumental variables approach in which
ETHNIC is the dependent variable in an auxiliary regression estimated prior to the estimation of
the models of social infrastructure development. To properly identify this regression, we include
both a measure of religious fractionalization, REL, and the rule of law, RULE. REL is a measure
of religious fractionalization in the country calculated as 1 minus a Herfindahl index of the shares
of the population belonging to Catholic, Protestant, Muslim, and Other religious categories. This
is a common variable in the literature, as discussed above, where it is often used to generate an
index of ethnic fragmentation. This data is taken from La Porta et al. (1999) and has the potential
to range from 0-1 with higher values indicating greater degrees of religious fractionalization. 4 The choice of fixed effects was based on a Hausman test.
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RULE reflects the extent to which a population accepts established institutions which make and
implement laws and resolve disputes arising from conflicts of interest. RULE ranges from 0-6
with lower values indicating an environment in which the rule of law is relatively weak. This
variable is also taken from International Country Risk Guide. REL and RULE are selected
because each offers a very strong correlation with the ethnic tensions variable but comparatively
weaker associations with the indexes of social infrastructure development. More specifically,
each of these shows a very strong simple correlation with ETHNIC (simple correlations above
0.5) and comparatively much smaller correlations with the various dependent variables of social
infrastructure and other included independent variables of those models. Finally, it should be
noted that in each model, all variables are entered in natural logs so as to limit the affects of
outliers (with the necessary adjustments in the indexes which commence with a value of zero
where the value of one has been added to the index prior to taking the log). Thus, we estimate the
following two equation system for each of the 17 indexes of social infrastructure development:
iiitititit aRULERELXETHNIC ηββββ +++++= )()( 3210 (1)
itiitiitit aXETHNICINF εααα ++++= )(10 (2)
where INF is one of the 17 indexes of social infrastructure for country i in time t, ETHNIC is
ethnic tensions (instrumented in Equation (2)), and X represents the control variables in each
equation. Since the underlying ETHNIC regressions are only of interest as they allow us to take
into account the endogeneity of ethnic tensions, these results are reported in Appendix 2.
Discussion of these results is limited to noting the models’ good fit (within R-Squares which are
shown and highly significant F’s (beyond .01) for the tests of the continent-specific fixed effects
and the joint significance of the independent variables, which are not shown to save space) and
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consistently expected signs and levels of significance for all of the included variables, with
special attention given to the two key instruments, REL and RULE. These two variables
consistently have the expected negative (positive) signs and are consistently quite significant,
indicating that ethnic tensions are likely to be relatively worse in countries with fractionalized
religious groups and relatively mitigated in countries where the rule of law carries substantial
weight.
Of most importance, however, are the results presented in Table 3, from the regressions
of the instrumented ETHNIC and the other control variables on the various measures of social
infrastructure (the differences in sample sizes noted in Tables 2 and 3 result from countries
lacking data on a given dependent variable for some period of time).
___________________Insert Table 3 About Here________________________________
The first thing of importance to note is that, in each case, the models offer very strong diagnostics
(within R-Squares, which are shown, and both F-statistics, not shown, which test for the fixed
effects and the combined significance of the included independent variables—the latter two being
consistently significant beyond the .01 level) indicating that the selected independent variables do
a reasonably good job in explaining the differences that exist between countries and over time
with respect to the measures of social infrastructure development and pointing to the fact that
there is significant heterogeneity across continents with respect to social infrastructure which may
be missed if simple one-period cross-sections are analyzed or if the data’s panel nature is ignored.
Beyond this, however, it should be immediately noted that ethnic tensions do clearly seem to be
related, in a negative fashion, to all of the measures of social infrastructure. Whether the focus is
on a country’s development of Air, Rail, and Port Activities, its Public Utilities, its Health Care
Infrastructure, or its Education opportunities, increasing ethnic tensions are consistently found to
negatively affect the country’s social infrastructure development. In each case, the instrumented
ethnic tensions variable is of the expected sign and is highly significant.
12
Beyond the key finding of the importance of ethnic tensions to the development of social
infrastructure, also consistently important is the result for GDP per capita which is strongly and
positively correlated with social infrastructure, as would be expected, in all cases other than the
two immunization rates and the overall death rate. Social infrastructure development typically
requires public investment which is, of course, limited both by the preferences of the public and
by public income. To the extent that a country enjoys relatively high per capita incomes, it can be
expected to have relatively more completely developed social infrastructures. Similarly, a
positive affect on social infrastructure is found in countries where the footprint of government is
relatively large suggesting that the more optimistic view of government spending is, on average,
appropriate. Apparently, rather than simply reflecting run-away public sectors wastefully
spending public monies, the results suggest a strong pro-social infrastructure investment element
to much public spending. The notable exceptions here are for the two rail indexes and the literacy
model which, unexpectedly, have negative and significant coefficients. While clearly beyond the
scope of the present analysis, these results are consistent with the notion that government
spending, at least in some cases, may not occur in a way that achieves social goals, though it may
well achieve the goals of those in or closely associated with government. The relation between
urbanization and social infrastructure is also less than completely clear with the impact being
negative on air and rail activities and also on literacy, though the impact is generally positive
elsewhere. Perhaps the results for rail and air activities results simply from the fact that a more
concentrated society requires less of these services. The effect of the size of the population is
uneven with increases in population being positively related to electric power consumption,
advanced education, and life expectancy, but negatively related to air, rail, and port activities,
telephone infrastructure, and several of the health related variables. Finally, the institutional
variable, DEM, does show the expected positive relation with air activities, public utilities,
education, and several of the health care indexes which is consistent with the view that
democracy provides effective voice to all groups within society.
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While Table 3 reports results of remarkable consistency, especially for the key variables,
a couple of additional issues warrant addressing. First, while improved by the instrumental
variables technique, the reported results are not driven by that technique alone. To determine
this, we re-estimated the regressions of Table 3 using the actual rather than instrumented value of
ethnic tensions and found that variable to be significant and of the expected sign in 12 of the 17
cases. Further, in the remaining five cases, while insignificant, the actual ethnic tensions variable
was of the expected sign. Equally important, for each of the five dependent variables where the
actual ethnic tensions proved to be insignificant, when the two-stage approach is applied to
correct for endogeneity, the variables REL and RULE are particularly significant in the auxiliary
regression on ETHNIC. This suggests that while the endogeneity of ethnic tensions does pose a
problem for estimating the relation between such tensions and social infrastructure, failure to
control for it does not lead one to an incorrect conclusion, at least when a wide range of indexes
of social infrastructure are considered. Of course, should one focus on one or perhaps just a few
measures of infrastructure development and fail to take into account the endogeneity of ethnic
tensions, then an erroneous conclusion as to the importance of such tensions may well be drawn.
A second, related issue concerns the 17 measures of social infrastructure development
included in the analysis. As discussed above, our notion of social infrastructure development is
admittedly rather broad, certainly not limited to public or even semi-public goods. Rather, since
our goal is to investigate the various avenues through which ethnic tensions might retard
economic growth, we cast a wide net in attempting to come up with what might plausibly be
called elements of social infrastructure development. The only limiting factors for inclusion in
the analysis were that we required consistent definitions of the activity across countries and time
with that data being available for the maximum number of countries on an annual basis. While
the use of 17 such measures is rather broad, it should be noted that we began the analysis with
five additional measures. These relate to air freight, energy use of all types, kilometers of
roadways, and two measures of educational attainment. In each case, an insignificant relation
14
existed between ethnic tensions and the measure of social infrastructure development. Stated
more positively, in no case did we find a significant outcome that was inconsistent with those
reported. These regressions are omitted to save space but, more importantly, because in each case
we have alternative measures of the same or similar elements of social infrastructure, which did
yield significant results5. Beyond this, in several of the cases of the alternative measures, limited
sample sizes may have been the cause of the models’ failure. Finally, these models may have
failed due to their not being precisely targeted. For example, while a regression on the kilometers
of roadway within a country might not be found to be related to ethnic tensions, the negative
relation reported here might be found if one was able to focus on kilometers of modern, highly
improved roadways. Similarly, a country might have several roadway networks which result
from ethnic divisions that effectively serve local, ethnically distinct areas, and that also
effectively limit interaction between such areas. Regardless, while we do not suggest that we
have uncovered all of the measures of social infrastructure development that meet our analysis
requirements—though we are unaware of others— we do conclude that, even with the rather
broad net that we cast yielding a potential of 22 measures of social infrastructure development, in
more than three-fourths of the cases ethnic tensions were found to be deleterious while in no case
was the opposite found.
IV.Conclusion The potentially negative affects of ethnic tension on economic performance are relatively well
understood and has been studied previously. When the focus is on general economic growth,
most have found that countries with relatively high levels of ethnic fractionalization tend to suffer
in terms of sub-par growth. The sources of this limitation on growth are, no doubt, wide-
reaching. A number of papers have attempted to identify the channels through which ethnic
tensions impact growth by considering factors such as poor government decision making, limited 5 Available upon request.
15
public interaction across ethnic groups, limited participation in community affairs, and public
unwillingness to share the burden necessary to fully fund and develop social infrastructures. In
this paper, we target the channels through which long term growth is facilitated, that is, we
examine the linkages between a country’s degree of ethnic tensions and its development of
various elements of social infrastructure. We consider 17 such elements which can be grouped
into four categories: 1) Air, Rail, and Port Activities; 2) Public Utilities; 3) Education; and 4)
Health Care.
Our analysis extends the literature in this area in three ways. Perhaps the most important
of these is that we use an index of ethnic tension that takes into account all possible sources of
such tensions. Most of the previous literature has a much narrower notion of ethnic
fractionalization, relying on indexes of language or racial differences. Second, unusual for
studies relating to the development of social infrastructure, we focus on a comparatively large
number, 17 to be exact, of elements of social infrastructure, within the context of panel models
that include 87 countries and 16 years. Finally, our study is rather unique in that we explicitly
control for the potentially misleading effects of the endogeneity of ethnic tensions. While this has
often been discussed, it rarely has been treated empirically.
From the analysis, we can easily conclude that, in fact, ethnic tensions are very strongly
and negatively related to a whole variety of elements of social infrastructure. Given the role that
factors such as Air, Rail, and Port Activities, Public Utilities, Education, and Health Care play in
contributing to a country’s long run growth, the implication for growth of ethnic tensions is
obvious. As such, a government that actively seeks to reduce the tensions that exist between the
various ethnic groups within its boarders is greatly increasing that country’s economic prospects.
This is, of course, easier said than done. Especially troubling are circumstances in which, rather
than acting to reduce tensions, governments find it in their own interest to stoke the flames of
ethnic rivalry. Perhaps the most deplorable current example of this is the Arab-dominated
Sudanese government’s tolerance of and, in many cases, outright support of the Janjaweed
16
militias in their campaign of rape, torture, and murder targeting hundreds of thousands of non-
Arab Sudanese in the Darfur region of the country. Such impulses must be resisted if countries
are to fully realize their growth potentials. On a more positive note, from the underlying
regressions on ethnic tensions, it is clear that such tensions can be reduced if governments pursue
two policy goals. The first results from the very strong and consistent positive relation that is
found between religious fractionalization and ethnic tensions. Given this, policies designed to
lessen religious intolerance should be found to reduce ethnic tensions, to the benefit of all. The
exact nature of these policies would differ from country to country due their unique religious
settings. The one thing that is universal, however, is that government based on religious
differences or government policies which benefit one sect over another, while in the narrow
interest of a particular group, are not in the interest of the general public. Finally, the underlying
regressions on ethnic tensions show clearly that adherence to the rule of law effectively mitigates
such tensions. A large part of the explanation here has, no doubt, to due with the fact that a
thoroughgoing rule of law gives a country’s various religious (ethnic) groups a forum in which to
adjudicate their disputes short of actual conflict and protection to practice their religious beliefs as
they see fit without need to denigrate anyone holding differing beliefs. It should be noted,
however, that in this case, the rule of law is, at least to some extent, serving as a proxy for other
measures of what might be generally called good government. As such, all factors likely to
improve the functioning and reliability of government should be found to reduce ethnic tensions
within a country.
17
References
Alesina, Alberto, A. Devleeschauwer, W. Easterly, S. Kurlat and R. Wacziag (2003), “Fractionalization”, Journal of Economic Growth, 8, 155-194. _________, R. Baqir, and W. Easterly (1999), “Public Goods and Ethnic Divisions,” Quarterly Journal of Economics, 114, 1243-1284. _________and E. La Ferrara (2000), “Participation in Heterogeneous Communities,” Quarterly Journal of Economics, 115, 847-904. Collier Paul, J. Gunning (1999), “Explaining African Economic Performance”, Journal of Economic Literature, 37:1, 64-111. Costa, Dora and M. Kahn (2002), “Civic Engagement and Community Heterogeneity: An Economist’s Perspective”, working paper. Cutler, David, and Edward Glaeser (1997), “Are Ghettos Good or Bad?” Quarterly Journal of Economics, 111, 828-872. Easterly, William, and R. Levine (1997), “Africa’s Growth Tragedy: Policies and Ethnic Divisions,” Quarterly Journal of Economics, 111:4, 1203-1250. Glaeser, Edward, J. Scheinkman, and A. Shleifer, (1995), “Economic Growth in a Cross- Section of Cities,” Journal of Monetary Economics, 36, 117-143. Goldin, Claudia and L. Katz (1999), “Human Capital and Social Capital: The Rise of Secondary Schooling in America, 1910-1940,” Journal of Interdisciplinary History, 19:4, 683-723. Harris, Amy, William Evans, and Robert M. Schwab, (2001), “Education Spending in an Aging America,” Journal of Public Economics, 81 (2), 449-472. Knack, Stephen and P. Keefer (1997), “Does Social Capital have an Economic Payoff? A Cross-Country Investigation,” Quarterly Journal of Economics, 112, 1251-1275. Kurzman, C. Werum, and R. Burkhart (2002), “Democracy’s effect on economic Growth: A Pooled Time-Series Analysis, 1951-1980,” Studies in Comparative International Development, 37, 3-33. La Porta, Rafael, F. Lopez-de Silanes, A. Shleifer and R.Vishny (1999), “The Quality of Government,” Journal of Law, Economics and Organization, 15:1, 222-279. Luttmer, Erzo (2001), “Group Loyalty and the Taste for Redistribution,” Journal of Political Economy, 109 (3), 500-528. Mauro, Pablo (1995), “Corruption and Growth,” Quarterly Journal of Economics, 110,
681-712.
18
Miguel, Edward and M. Gugerty (2004), “Ethnic Diversity, Social Sanctions, and Public Goods in Kenya,” Working paper. Montalvo, Jose, and M. Reynal-Querol (2005), “Ethnic Diversity and Economic Development,” Journal of Development Economics, 76, 293-323. Paci, Paerella, S. Alva, and E. Murrugarra (2002), “The Hidden Costs of Ethnic Conflict: Decomposing Trends in Educational Outcomes of Young Kosovars,” The World Bank, Policy Research Working Paper Series: 2880 Poterba, James (1997), “Demographic Structure and the Political Economy of Public Education,” Journal of Policy Analysis and Management, 16:1, 48-66. Vigdor, Jacob (2004), “Community Composition and Collective Action: Analyzing Initial Mail Response to the 2000 Census,” Review of Economics and Statistics, 86, 303-312.
19
Table 1. Literature Review
Author(s) Population Studied
Dependent Variable(s) Year(s)
Alesina et. al (1999)
Entire US Expenditure on roads per capita, sewerage and trash pickups and education
1990
Alesina and La Ferrera (2000)
Entire US Group participation 1974-1994
Alesina et al. (2003)
Cross Country
Growth rate of per capita GDP, School attainment, Financial Depth, Telephones per worker, Black market premium, Mortality rate, Corruption, Illiteracy rate, Political freedom
1960-1995
Collier and Gunning (1999)
Africa Growth rate of per capita GDP 1960-1989
Cutler and Glaeser (1997)
Entire US Educational attainment, income and employment
1990
Easterly and Levine (1997)
Sub-Saharan Africa
Growth rate of per capita GDP, School attainment, Financial Depth, Telephones per worker, Black market premium, Electrical system losses and Percentage of unpaved roads.
1965-1990
Glaeser et al (1995)
Entire US Population growth and Urban growth 1960-1990
Goldin and Katz (1999)
Entire US High School and College attendance 1910-1940
Harris et. al (2001)
Entire US Total Revenue per pupil at the district level 1972, 1982, 1992
Luttmer (2001) Entire US Self-Reported Support for Welfare Spending 1973-1994 La Porta et al. (1999)
Cross country
Property rights, Business regulation, Corruption, Bureaucratic delays, Tax compliance, Infant Mortality, School attainment, Illiteracy rate, Infrastructure quality, Size of public sector, Political freedom
1990s
Mauro (1995) Cross country
Investment
1960-1985
Miguel and Gugerty (2000)
Kenya School Funding, School infrastructure quality, Threatened sanctions and Water well maintenance.
1995, 1996
Montalvo and Reynal-Querol(2005)
Cross Country
Growth rate of per capita GDP 1960-1989
Poterba (1997) Entire US Per child school spending 1960-1990
20
Table 2. Descriptive Statistics Variable Mean Standard
Deviation Minimum Maximum
Dependent Variables
ETHNIC 3.92 1.64 0.00 6AIRPP 40236.12 60871.56 0.00 490588.3AIRDP 682.33 1131.75 1.07 8000FTONT 0.39 0.48 0.0002 1PREVT 0.52 0.48 0.0002 1.82CONT 766755.60 1727537 18379 1.41e+07TOUR 4960469.00 9944118 2.00 23.5ELCPC 2914.35 4105.50 15.84 23799.54TELLINES 157.63 193.29 1.15 706.85LIT 71.92 19.53 22.38 98.26SCTERG 21.41 18.94 0.26 97.35DPT 71.83 23.43 1.00 99MEAS 68.89 23.36 1.00 99MORT 41.48 34.67 4.00 146MAL1 26.32 15.85 2.00 67.7MAL2 18.12 15.52 0.80 70.9LIFEXP 67.80 9.70 40.49 80.42DRATE 8.93 3.39 2.00 23.5Independent Variables
GDPPC 8053.00 7741.14 543 35792.31POP 5.03e+07 1.51e+08 23400 1.23e+09EXPTOT 6543.00 632.98 212.78 1212.09URBPOP 56.16 23.33 9.24 100.00DEM 5.21 4.26 0.00 10.00REL 54.87 19.65 0.00 100RULE 5.14 4.12 0.0 1.0
21
Table 3. Correlates of Social Infrastructure
Log (ETHNIC)
Log (GDPPC)
Log (EXPTOT)
Log (URBPOP)
Log (POP)
Log (DEM)
R2
Air/Rail/Port Activities: Log(AIRPP) N=990
0.276** (0.134)
1.107*** (0.052)
0.241*** (0.067)
0.0612 (0.081)
-0.172*** (0.016)
0.073*** (0.029)
0.72
Log (AIRDP) N=996
0.507*** (0.151)
1.012*** (0.057)
0.300*** (0.074)
-0.229*** (0.088)
-0.259*** (0.018)
0.166*** (0.032)
0.66
Log(FTONT) N=927
2.177*** (0.567)
0.546** (0.227)
-0.582** (0.288)
-1.160*** (0.342)
-0.678*** (0.070)
-0.152 (0.122)
0.10
Log(PREVT) N=767
1.658*** (0.625)
0.278 (0.225)
-0.628*** (0.245)
-0.803*** (0.295)
-0.520*** (0.059)
-0.082 (0.099)
0.13
Log(CONT) N=386
1.329*** (0.405)
0.213 (0.228)
-0.096 (0.205)
0.104 (0.286)
-0.393*** (0.047)
0.475 (0.080)
0.35
Log(TOUR) N=485
1.156*** (0.285)
0.874*** (0.107)
-0.085 (0.157)
-0.032 (0.177)
-0.360*** (0.034)
0.025 (0.078)
0.52
Public Utilities: Log(ELCPC) N=956
0.915*** (0.123)
1.073*** (0.044)
0.410*** (0.059)
0.259*** (0.082)
0.023* (0.014)
0.173*** (0.023)
0.82
Log(TELLINE) N=998
0.658*** (0.268)
0.727*** (0.102)
0.204 (0.132)
0.901*** (0.157)
-0.897*** (0.033)
0.214*** (0.057)
0.66
Education: Log(LIT) N=714
0.165*** (0.050)
0.166*** (0.021)
-0.045* (0.024)
-0.055* (0.034)
-0.023*** (0.006)
0.057*** (0.011)
0.30
Log(SCTERG) N=532
0.435*** (0.133)
0.542*** (0.050)
0.150** (0.071)
0.636*** (0.081)
0.070*** (0.016)
0.194*** (0.033)
0.73
Health Care: Log(DPT) N=960
0.888*** (0.108)
-0.082* (0.046)
0.351*** (0.055)
0.258*** (0.067)
0.014 (0.014)
0.101*** (0.024)
0.03
Log(MEAS) N=940
1.049*** (0.125)
-0.158*** (0.054)
0.304*** (0.064)
0.343*** (0.078)
-0.004 (0.017)
0.124*** (0.028)
0.02
Log(LIFEXP) N=540
0.109*** (0.018)
0.046*** (0.006)
0.037*** (0.009)
0.055*** (0.010)
0.004** (0.002)
0.016*** (0.004)
0.62
Log(MORT) N=121
-0.269* (0.165)
-0.850*** (0.080)
-0.080 (0.106)
0.215* (0.128)
0.054** (0.025)
0.054 (0.057)
0.72
Log(MAL1) N=111
-1.689*** (0.415)
-0.326* (0.175)
-0.332** (0.166)
-0.017 (0.219)
0.093** (0.045)
-0.182** (0.081)
0.37
Log(MAL2) N=128
-1.671*** (0.394)
-0.271* (0.173)
-0.354* (0.192)
-0.445* (0.246)
0.071 (0.050)
0.043 (0.082)
0.38
Log(DRATE) N=696
-0.415*** (0.069)
0.048* (0.026)
-0.146*** (0.038)
-0.150*** (0.041)
0.018** (0.008)
0.011 (0.016)
0.45
Notes: Standard errors in parentheses:***, ** and * denote significance at the 1%, 5% and 10%, respectively.
22
Appendix 1: Variable Definitions and Sources Name Definition Source ETHNIC Ethnic tensions index from ICRG, annual surveys from 1982-
1997: 6 (lowest tension), 0 (highest tension). ICRG dataset
AIRPP It includes both domestic and international aircraft passengers of air carriers registered in the country divided by population.
World Bank World Development Indicators 2004
AIRDP Aircraft departures are the number of domestic and international takeoffs of air carriers registered in the country per capita.
World Bank World Development Indicators 2004
FTONT Rail freight tons per kilometer of total rail routes. World Bank’s Railways Dataset (2001)
PREVT Rail passenger revenue per kilometer of total rail routes. World Bank’s Railways Dataset (2001)
CONT It measures the flow of containers from land to sea transport modes, and vice versa, in twenty-foot equivalent units (TEUs).
United Nations, Review of Maritime Transport
TOUR Expenditures by international inbound visitors, including payments to national carriers for international transport. These receipts should include any other prepayment made for goods or services received in the destination country.
World Bank World Development Indicators 2004
ELCPC Electric power consumption measures the production of power plants and combined heat and power plants, less distribution losses, and own use by heat and power plants per capita.
World Bank World Development Indicators 2004
TELLINES Telephone lines connecting a customer's equipment to the public switched telephone network. Data are presented per 1,000 people for the entire country.
World Bank World Development Indicators 2004
LIT Percentage of population ages 15 and above who can read and write.
World Bank World Development Indicators 2004
SCRTEG Percentage of the population that is enrolled in post secondary education.
World Bank World Development Indicators 2004
DPT Child immunization measures the percentage of children ages 12-23 months who received vaccinations before one year of age.
World Bank World Development Indicators 2004
MEAS Child immunization measures the percentage of children ages 12-23 months who received vaccinations before one year of age.
World Bank World Development Indicators 2004
MORT Probability that a newborn baby will die before reaching age five, if subject to current age-specific mortality rates. The probability is expressed as a rate per 1,000.
World Bank World Development Indicators 2004
MAL1 Percentage of children under five whose height for age is more than two standard deviations below the median for the international reference population ages 0 to 59 months.
World Bank World Development Indicators 2004
MAL2 Percentage of children under five whose weight for age is more than two standard deviations below the median reference standard for their age as established by the World Health Organization.
World Bank World Development Indicators 2004
LIFEXP Number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.
World Bank World Development Indicators 2004
23
DRATE Crude death rate indicates the number of deaths occurring during the year, per 1,000 population estimated at midyear.
World Bank World Development Indicators 2004
Independent Variables
GDPPC GDP per capita based on purchasing power parity (PPP). World Bank World Development Indicators 2004
POP The de facto total of population, which counts all residents regardless of legal status or citizenship.
World Bank World Development Indicators 2004
EXPTOT Cash payments for operating activities of the government in providing goods and services. It includes compensation of employees, interest and subsidies, grants, social benefits, and other expenses such as rent and dividends.
World Bank World Development Indicators 2004
URBPOP The share of the total population living in areas defined as urban. World Bank World Development Indicators 2004
DEM Scales 0-10 with higher values indicating more thoroughgoing democratic institutions.
Polity IV database www.cidcm.umd.edu//inscr/polity
REL Percent of population that belonged to the three most widely spread religions of the world in 1980 (or for 1990-95 for countries formed more recently). The four classifications are: Catholic, Protestant, Muslim and “other”.
La Porta et. al (1999)
RULE Higher scores indicate sound political institutions, a strong court system, and provisions for an orderly succession of power.
ICRG dataset
24
25
Appendix 2. Correlates of Ethnic Tensions Log
(GDPPC) Log
(EXPTOT) Log
(URBPOP) Log
(POP) Log
(DEM) Log
(REL) Log
(RULE)
Air/Rail/Port Activities: Log(AIRPP) N=990, R2=0.41
0.059*** (0.021)
-0.164*** (0.026)
0.078** (0.034)
-0.039*** (0.006)
-0.073*** (0.012)
-0.041*** (0.008)
0.522*** (0.029)
Log (AIRDP) N=996, R2=0.40
0.068*** (0.020)
-0.166** (0.025)
0.064** (0.033)
-0.040*** (0.006)
-0.077*** (0.011)
-0.040*** (0.008)
0.509*** (0.029)
Log(FTONT) N=927, R2= 0.41
0.094*** (0.021)
-0.158*** (0.027)
0.022 (0.035)
-0.038*** (0.007)
-0.083*** (0.012)
-0.040*** (0.008)
0.515*** (0.030)
Log(PREVT) N=767, R2=0.40
0.125*** (0.023)
-0.129*** (0.028)
0.020 (0.037)
-0.034*** (0.007)
-0.044*** (0.012)
-0.024*** (0.008)
0.442*** (0.034)
Log(CONT) N=386, R2=0.50
0.142*** (0.45)
-0.198*** (0.042)
0.089 (0.063)
-0.026** (0.011)
-0.602*** (0.017)
-0.027** (0.014)
0.469*** (0.042)
Log(TOUR) N=485, R2=0.46
0.031 (0.025)
-0.114*** (0.025)
0.075* (0.043)
-0.026*** (0.008)
-0.071*** (0.017)
-0.040*** (0.009)
0.597*** (0.042)
Public Utilities: Log(ELCPC) N=956, R2=0.39
0.076*** (0.021)
-0.172*** (0.027)
0.088** (0.040)
-0.037*** (0.007)
-0.084*** (0.012)
-0.040*** (0.008)
0.479*** (0.030)
Log(TELLINE) N=998, R2=0.40
0.069*** (0.020)
-0.165*** (0.025)
0.064** 0.033
-0.039*** (0.006)
-0.077*** (0.012)
-0.040*** (0.008)
0.509*** (0.029)
Education: Log(LIT) N=714, R2=0.38
0.079*** (0.027)
-0.121*** (0.039)
0.056 (0.009)
-0.025*** (0.009)
-0.106*** (0.132)
-0.037*** (0.010)
0.484*** (0.332)
Log(SCTERG) N=532, R2=0.47
0.052** (0.024)
-0.144*** (0.034)
0.064 (0.040)
-0.031*** (0.008)
-0.074*** (0.015)
-0.039*** (0.009)
0.573*** (0.039)
Health Care: Log(DPT) N=960, R2=0.42
0.095*** (0.021)
-0.159*** (0.025)
0.026 (0.034)
-0.032*** (0.007)
-0.086*** (0.012)
-0.034*** (0.008)
0.525*** (0.029)
Log(MEAS) N=940, R2=0.42
0.092*** (0.021)
-0.156*** (0.026)
0.026 (0.034)
-0.032*** (0.007)
-0.083*** (0.011)
-0.033*** (0.008)
0.533*** (0.029)
Log(LIFEXP) N=540, R2=0.40
0.078*** (0.027)
-0.169*** (0.036)
0.049 (0.045)
-0.041*** (0.104)
-0.077*** (0.010)
-0.041*** (0.407)
0.486***
26
(0.040) Log(MORT) N=121, R2=0.56
0.041 (0.053)
-0.081 (0.070)
0.061 (0.088)
-0.034** (0.016)
-0.079** (0.036)
-0.061*** (0.019)
0.626** (0.488)
Log(MAL1) N=111, R2=0.41
0.120** (0.065)
-0.152** (0.070)
-0.065 (0.097)
-0.036* (0.197)
-0.106*** (0.026)
0.009 (0.022)
0.408*** (0.073)
Log(MAL2) N=128, R2=0.40
0.071 (0.064)
-0.177** (0.076)
-0.038 (0.101)
-0.039** (0.020)
-0.088*** (0.028)
-0.017 (0.023)
0.472*** (0.075)
Log(DRATE) N=696, R2=0.45
0.069*** (0.024)
-0.223*** (0.031)
0.064* (0.039)
-0.047*** (0.007)
-0.083*** (0.015)
-0.037*** (0.009)
0.527*** (0.035)
Note: *, **, *** denotes significance at the 10%, 5% and 1% respectively.
