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Economic Shocks and Battle Deaths in Civil Wars
THORSTEN JANUS† and DANIEL RIERA-CRICHTON††
†Department of Economics, Dept. 3985, 1000 E. University Ave.
Laramie, WY 82071, United States. (email: [email protected])
††Department of Economics, Bates College, Pettengill Hall, Room 273
Lewiston, ME 04240, United States. (email: [email protected])
Abstract
This paper estimates the effects of exogenous income shocks - in the form of commodity terms
of trade (CTOT) shocks - on battle deaths in civil wars. We show that CTOT growth generally
decreases conflict, but that in fuel exporting economies with intermediate ethnic fractionalization
levels, dominant, or polarized ethnic groups, both negative and positive shocks increase conflict.
The positive effects come from fossil fuel windfall in fuel-exporters. The evidence is consistent
with opportunity cost and rent-seeking motives for conflict as well as the resource-curse
literature
JEL Classification: D74, O11, O17
Keywords: civil war, conflict, ethnicity, ethnic diversity, commodity prices, terms of trade
Wordcount: 12,073 +250 (Figure 1) = 12,323
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I. Introduction
During the course of civil wars, the death toll can vary immensely. For example, the dataset we
present below shows that the beginning and end phases of the El Salvadoran, Guatemalan, and
Nicaraguan civil wars generated less than a thousand fatalities per year. However, the death toll
in the intervening years sometimes exceeded 10,000.* In this paper, we estimate the effects of
exogenous income shocks in the form of commodity terms of trade (CTOT) shocks – the change
in national commodity export relative to import prices - on the annual deaths tolls in civil wars.
We argue that countries with intermediate ethnic fractionalization, a dominant ethnic group, and
a high level of ethnic polarization are more likely to respond adversely to commodity-income
shocks and that the net effect of the shocks depends on the balance between the rent-seeking and
peaceful economic opportunities they generate. We show that CTOT growth generally decreases
conflict, but that in fuel exporting economies with intermediate ethnic fractionalization levels,
dominant, or polarized ethnic groups, both negative and positive shocks increase conflict.
Further, the positive effects come from fossil fuel windfall in fuel-exporters. The evidence is
consistent with opportunity cost and rent-seeking motives for conflict as well as the resource-
curse literature. We estimate that a standard deviation increase in the growth rate of our 3-year
moving average CTOT index decreases the death toll per conflict year by 34-41%. However, a
standard deviation increase in the fossil-fuel-specific CTOT increases the death toll in fuel
exporters with adverse ethnic compositions by about 32%. The evidence is, therefore, consistent
with opportunity cost and rent-seeking motives for conflict as well as the resource-curse
literature.
* Bosnia and Herzegovina’s civil war from 1992-95 killed, respectively, 17,000, 23,000, 11,000, and 1,300 individuals. Appendix A depicts the time-series for the four countries.
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In order to estimate these effects, we address three identification challenges. First,
incomes can be endogenous to conflict. This is why we study the effects of CTOT as opposed to,
for example, per-capita-GDP growth. The focus on commodity prices allows us to isolate the
most volatile and plausibly-exogenous component of the general terms of trade. The
international-trade and macroeconomics generally predicts that terms of trade growth increases
national income growth in term of goods, that is, real GDP (Dixit and Norman 1980, Obstfeld
and Rogoff 1996, Agenor and Montiel 2008, Krugman et al. 2014, Feenstra 2015).
Second, wartime economies may respond differently than peacetime economies to
economic shocks. For example, the decline of the rule of law may change the production
structure away from contract, transport, and capital intensive sectors, such as formal
manufacturing, to informal, labor-intensive, and easier-to-protect sectors, such as the production
of primary resources for exports, mobile services, trade networks that procedure goods from
abroad, or non-seasonal agricultural crops that cannot be expropriated as easily (Mayshar et al.
2015). It can allow warlords, criminals, and even elements in the government and rebel armies to
pursue illegal activities, such as extortion, kidnapping, smuggling, trafficking, foreign-aid
appropriation, and illegal natural resource extraction (Keen 2000, Rubin 2000, Le Billon 2001,
Bannon and Collier 2003, Dube and Vargas 2013, Nunn and Qian 2014), and to create self-
governed (as well an ungoverned) regions, like in Afghanistan, Colombia, Somalia, and Syria in
recent decades. The fact that there can be many spillovers from the conflict and both the
government and the rebels can target civilians (Berman et al. 2011, Valentino et al. 2004) forces
households to protect themselves and their assets. The fact that most of these economic and
institutional changes only occur after the conflict begins suggests that the conflict can change the
economy’s structural equations and, therefore, its response to economic shocks. In order to
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address this concern, we purposely only estimate the death toll variation within the conflict years
and omit the peace years. Appendix B presents formal evidence that - in contrast to our finding
that positive fossil-fuel CTOT shocks predict more battle deaths in fuel exporters – they are
negatively (but insignificantly) related to the risk that civil wars begins in these countries in the
first place.
Finally, different income shocks could have different effects in different environments
(Dal Bó and Dal Bó 2011, Dube and Vargas 2013, Bazzi and Blattman 2014, Nunn and Qian
2014, Janus and Riera-Crichton 2015). Although we cannot test all possible non-linear
hypotheses, the literature suggests that ethnic dominance and polarization can increase conflict
(Horowitz 1985, Collier and Hoeeffler 2004, Esteban and Ray 2011). Moreover, the fact that
commodity windfalls can encourage rent-seeking (Tornell and Lane 1999, Ross 2012, Dube and
Vargas 2013) suggests that terms of trade growth should not necessarily decrease conflict. Even
if it increases the opportunity cost conflict inputs, perhaps by creating jobs, it can increase the
return to rent-seeking and the parties’ ability to finance the war effort (Bannon and Collier 2003).
We, particularly, hypothesize that the rent-seeking and conflict-finance effects are larger relative
to the opportunity cost effect after positive relative to negative terms of trade shock. The paper
shows that the data strongly supports this asymmetric-effects idea for economies with adverse
ethnic compositions.
The paper belongs to the literature that relates income shocks to civil war. We contribute
to this literature in three ways. First, we estimate the intensity of ongoing civil wars as opposed
to the onset and time-series incidence of the wars, which have often been studied (Hegre and
Sambanis 2006, Blattman and Miguel 2010). Second, in contrast to most previous studies that
relate internationally-determined commodity prices to conflict (Brückner and Ciccone 2010,
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Dube and Vargas 2013, Aguirre 2016) we estimate the effects of export prices in terms of import
goods rather than currency, that is, the effects of commodity terms of trade rather than
commodity export price shocks. Janus and Riera-Crichton (2015, 2017) show that that CTOT
declines predict a higher onset risk for civil wars; regressing the onsets separately on the log-
export and log-import price changes in the numerator and denominator of the CTOT-index yields
similar absolute coefficients; and failing to control for the change in import prices can changes
the export price estimates, which could, therefore, suffer omitted variables bias.
Third, we provide evidence that positive and negative income shocks can both increase
conflict. The fact the positive effects come from fossil fuel windfalls in fuel-exporter economies
with adverse ethnic compositions bridges the empirical conflict literature with the literature on
the natural resource curse (Smith 2004, Fearon 2005, Basedau and Lay 2009, Ross 2012) by
suggesting that fuel booms in fuel-dependent economies may only increase conflict in countries
with adverse ethnic compositions. A simple interpretation is that both fuel (mainly, oil)
dependence and certain ethnic compositions (having a dominant ethnic group or polarized
groups) encourage rent-seeking. The combination of the two during a boom in easily-
appropriable fossil fuel revenues crates a powerful rent-seeking effect.
In the remainder of the paper, Section 2 explains why we believe that commodity terms
of trade shocks and ethnic compositions can influence conflict. Section 3 presents our data and
econometric model. Section 4 presents the main results. In Section 5, we estimate the effects of
positive relative to negative and fossil fuel relative to other terms of trade shocks. Section 6
concludes the paper.
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II. Theoretical background
In this section, we detail the reasons we choose to relate terms of trade shocks and ethnicity to
conflict in the paper We also explain why positive and negative shocks could have different
conflict effects.
(1) The effects of terms of trade shocks
Although several studies have estimated the effects of commodity export price changes on
conflict (Brückner and Ciccone 2010, Dube and Vargas 2013, Bazzi and Blattman 2014, Aguirre
2016), the theory of international economics only relates changes in export prices relative to
import prices, that is, terms of trade changes, to national income (Matsuyama 1988, Easterly et
al. 1993, Turnovsky 1993, Mendoza 1995, Rodrik 1999, Agenor and Montiel 1999, Krugman et
al. 2014). For example, in a macroeconomic model a representative export good and a
representative import good, but, for simplicity, without a non-tradable good, real GDP in
international currency is Y C I G NX= + + + , where ( )x mNX p X p M= − in the standard
notation. If initially 20x mp X p M= = , a 10% decrease in export prices and a 10% increase in
import prices both decrease the trade balance and GDP by 2% on impact. A 10% price drop for
both export and import prices leave GDP unchanged; if the import prices fell 15%, income
increases on impact despite the decline in export prices. If export and import price changes have
these types of opposing, but symmetric income effects, then, regressions relating linking
economic outcomes to export prices also could suffer omitted variable bias (Janus and Riera-
Crichton 2015, 2017). Lederman and Porto (2016) discuss the impact of commodity prices on
household welfare based on survey data from African and Latin America as well as a literature
review. The conclude that households spend large budget fractions on commodities, often
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depend on commodities to earn income, and international price changes pass through to
households. Thus, households should be directly exposed to CTOT shocks.
(2) The effects of ethnic dominance and polarization
Although the empirical conflict literature has not established a linear effect of ethnic diversity or
fractionalization – that moving from a single to many small ethnic groups monotonically
increases the conflict risk (Fearon and Laitin 2003, Blattman and Miguel 2010) - there is
evidence that countries with either (a) a single large ethnic group that lives together with smaller
groups or (b) at least two significant ethnic groups may be conflict-prone. Our reading of the
literature suggests that the problem, frequently, is that one of the large ethnic groups can
dominate the political system and choose policies that, directly or indirectly, expropriate the
excluded groups. On the other hand, the groups that are currently excluded or fear they will be
excluded in the future can secede, rebel, and conduct military coups (Gellner 1983, Horowitz
1985, Smith 1986, Posen 1993, Gurr and Harff 1994, Collier and Hoeffler 2004, Ross 2005,
Østby 2008, Fearon and Laitin 2011, McGarry and O’Leary 2013, Weiner 2015). In Chad and
Sudan, the Arab population has historically dominated the smaller groups. The government’s
neglect of the countries’ peripheries has encouraged conflict. In Indonesia and Russia, the
traditional dominance of the Javanese and Russians have encouraged the ethnic minorities in
Aceh, East Timor, West Papua, and Chechnya to secede. Sri Lanka’s Tamil Tigers fought to
secede from the Sinhala-dominated central government. In Burundi, Iraq, and Syria, the Tutsi,
Sunni Muslim, and Alawite minorities (or powerful elements within these groups) conducted
military coups in the 1960s-1970. In these cases, the problem seems to be that a large group lives
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together with smaller groups. Following Collier and Hoeffler (2004), we call this situation ethnic
dominance.
Another potentially adverse ethnic configuration is ethnic polarization (Esteban and Ray
1994, 1999, 2011). The polarization concept relates the degree of polarity in the size distribution
of social groups and the social distance between individuals, to the level of conflict between
them. When the social distance is one between members of different groups and zero between
same-group members, as most empirical studies assume (Montalvo and Reynal-Querol2005,
Estbeban et al. 2012), then, the Esteban and Ray (1994) polarization index is maximized when
there are two equally large groups. More generally, societies with at least two significant ethnic
groups should experience more conflict. This could, potentially, explain the ethnic conflict
histories of Afghanistan, Angola, Bosnia, Croatia, Guatemala, Iraq, Israel, Lebanon, and Sri
Lanka. Montalvo and Reynal-Querol (2005) and Estbeban et al. (2012) show that the
polarization index predicts conflict incidence across countries.
(3) The asymmetric effects of positive and negative income shock
Surprisingly, the evidence suggests that not only negative (Miguel et al. 2004, Miguel 2005,
Brückner and Ciccone 2010, Bazzi and Blattman 2014, Blattman and Miguel 2010), but positive
income shocks as well can increase conflict. In the 1990s, US food aid may have increased
warlord conflict in Somalia as the warlords fought to control the scarce commodity (Dowden
2009, Albright 2013, Nunn and Qian 2014). Angrist and Kugler (2008) link increases in coca
production to violence in Colombia. During Sri Lanka’s civil war, the Tamil Tigers used
remittances to purchase military equipment; Angola’s UNITA rebels and Sierra’s Leone’s
Revolutionary United Front may have relied on diamonds. Timber and minerals may have
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financed the wars in Cambodia and the Eastern Congo in the 1990s and 2000s (Le Billon 2001,
Bannon and Collier 2003, Ross 2003, Janus 2012, Dube and Vargas 2013, Global Witness 2015).
In Iraq after 2003, at least two of the main rebel groups, - al-Qa’ida in Iraq and the Mahdi Army
- relied on extortion, theft, and black market sales (Bahney et al. 2010, Stanford Mapping
Militant Organizations Project 2015†). After al-Qa’ida in Iraq renamed itself the Islamic State of
Iraq and Syria (ISIS) and entered the Syrian civil war in 2013, it took control of oil fields that by
September 2014 may have earned it $1-2 million per day (Byman 2015).‡ Dube and Vargas
(2013) link price gains for capital-intensive goods, such as oil, to violence in Colombia.
Our reading of this literature suggests that income growth has ambiguous conflict effects.
On one hand, it can increase the opportunity cost of conflict inputs like labour (Hirshleifer 1999,
Miguel et al. 2004, Chassang and Padró-i-Miquel 2009). We call this the opportunity cost effect.
On the other hand, there is a conflict-finance effect: marginal income can be used to procure
more conflict inputs Bannon and Collier (Rustad and Binningsbø 2012, Berman et al. 2011).
Additionally, there is a rent-seeking effect: as income increases, so does the pool of contestable
wealth and the return to fighting (Bannon and Collier 2003, Parker and Vadheim 2017).§
Whether the rent-seeking and conflict-finance effects can dominate the opportunity cost effect is
an empirical question. Although we lack the data we would need to quantify and compare the
different effects, however, we think it is reasonable to hypothesize that the rent-seeking and
† See http://web.stanford.edu/group/mappingmilitants/cgi-bin/, accessed November 28, 2015 ‡ This estimate comes from an expert assessment cited in the New York Times, 16 September, 2014: “How ISIS Works.” Oil revenues may be important for the Iraqi Kurds currently fighting ISIS: “Strapped for cash and increasingly frustrated with Baghdad’s stingy disbursement of the federal budget… the Kurdistan Regional Government, which governs the Kurdish region in northern Iraq, has been ramping up independent oil sales. The KRG says it needs the oil revenue because it is weighed down by the costs of fighting Islamic State militants.” (Washington Post, August 16, 2015, “Iraq oil feud renewed as cash-strapped Kurds turn backs on deal with Baghdad.”). § During recessions, we should expect the same effects in reverse. Additionally, economic downturns can, potentially, increase psychological distress and cause individuals to look for “scapegoats” and violent social identities that can restore their sense of belonging and empowerment (Miguel 2005, Cramer 201, Zivin et al. 2011).
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conflict-finance effects may be larger relative to the opportunity cost effects after positive
relative to negative terms of trade shocks. If this is true, the coefficient estimate on positive terms
of trade shocks in the conflict regressions should exceed the coefficient on negative shocks,
which is easily testable.
Although we postpone a formal justification, there are several ways to motivate the idea
that positive and negative income shocks can have different conflict effects. For example, the
desire to avoid starvation and bankruptcy thresholds after negative shocks can encourage risk-
taking and make the opportunity cost of fighting close to zero the failure to fight could push the
individual below the threshold and cause a large utility drop with certainty. In a behavioral
framework as well, loss-aversion can encourage risk-taking (Thaler and Johnson 1990, Tversky
and Kahneman 1991, Jervis 1992, Thaler et al. 1997). Related, economic downturns could lead
to psychological distress and cause individuals to look for “scapegoats” and violent social
identities (Miguel 2005, Cramer 201, Zivin et al. 2011). This effect should keep the subjective
return to violence high during recessions even if the monetary return decreases. Alternatively,
positive CTOT shocks could generate windfall earnings that come are psychologically salient
(Chowdury et al. 2014) and encourage “irrationally exuberant” or other myopic behaviors
(Khwaja and Mian 2011, Shiller 2015). This should increase the subjective return to rent-seeking
during commodity-income booms. Finally, we believe it is possible that political elites
appropriate the income gains from commodities in boom times but share the losses with society
during busts. Particularly, we find below that it is only positive fossil fuel (mainly, oil) generated
income shocks in fuel exporting countries that increase conflict. The literature suggests that oil-
dependence increases corruption and weakens state capacity (Fearon 2005, Besley and Persson
2010, Ross 2012). This suggests that oil booms increase not only the maximum rent pool
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political elites can appropriate but the actual rents the appropriate. When the terms of trade
decline, however, the corruption level, which partly depends on the theft of fuel revenues from
the public coffers, can only fall to the zero lower bound, unless the leaders liquidate their
accumulated wealth (Acemoglu and Robinson 2001)
III. Data and Estimation
In this section, we, first, explain the data sources and variable definitions we use. We, then,
outline the estimation procedure. Tables 1 and 2 display the summary statistics for the data.
[Table 1 Goes Here]
[Table 2 Goes Here]
Armed Conflict: We use the conflict data for internal and internationalized internal armed
conflicts for the 1946-2008 period provided by the Uppsala Conflict Data Program and the Peace
Research Institute of Oslo (UCDP/PRIO). Focusing on internal wars allows us to exclude
interstate conflicts and extra-systemic conflicts, which involve a state fighting a non-state group
abroad. Since, in these cases, one of the conflict actors is based abroad and may be another
government, the commodity price shocks we study may have different effects than in internal
conflicts. The definitions of armed conflict and internal armed conflict are as follows (Gleditsch
et al. (2002) and Themnér & Wallensteen (2011), Codebook for the UCDP/PRIO Armed
Conflict Dataset, Version 4, 1, 9; Lacina and Gleditsch (2005), Battle Deaths Dataset, Codebook
for Version 3, 2009, 2)
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‘[An armed conflict is] a contested incompatibility that concerns government or territory or both where the
use of armed force between two parties results in at least 25 battle-related deaths. Of these two parties, at
least one is the government of a state.”
“Internal armed conflict occurs between the government of a state and one or more internal opposition
group(s) without intervention from other states…Internationalized internal armed conflict occurs between
the government of a state and one or more internal opposition group(s) with intervention from other states
(secondary parties) on one or both sides.’
Battle-related Fatalities: The battle-related fatalities data comes from Lacina and Gleditsch
(2005) and includes 1957 observations of battle-related fatalities from 1946-2008. We use the
version of the dataset that is compatible with the conflict dataset we described above. 1717 of the
battle death observations in this dataset are linked to internal or internationalized internal armed
conflicts rather than interstate and extra-systemic conflict. The definition of battle-related
fatalities is (Lacina and Gleditsch (2005), Battle Deaths Dataset, Codebook for Version 3, 2009,
2)
‘…those deaths caused by the warring parties that can be directly related to combat over the contested
incompatibility. This includes traditional battlefield fighting, guerrilla activities (e.g. hit-and-run
attacks/ambushes) and all kinds of bombardments of military bases, cities and villages etc. Urban warfare
(bombs, explosions, and assassinations) does not resemble what happens on a battlefield, but such deaths
are considered to be battle-related. The target for the attacks is either the military forces or representatives
for the parties, though there is often substantial collateral damage in the form of civilians being killed in the
crossfire, indiscriminate bombings, etc. All fatalities – military as well as civilian – incurred in such
situations are counted as battle-related deaths.’
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Due to the difficulty of establishing the exact number of battle-related fatalities per year, Lacina
and Gleditsch (2005) provide a “low” and a “high” estimate for all the observations as well as a
“best” estimate for about 70% of the observations. Since they provide the data at a country-year-
conflict level, we add the low, high and best estimates for every country and year to compute
country and year specific low, high and best estimates. Table 1 shows that the low estimates
range from 10 to 50,000 with a mean of 1,478. The high estimates range from 25 to 250,000 with
a mean of 7,319. The best estimates average 4,061 with a standard deviation of 9,132.
Our empirical battle deaths measure is either the “best” country-year estimate, provided it
exists, or, since it does not exist for 30-40% of the observations, an “imputed best” estimate. This
imputed best estimate is the sum across the ongoing conflicts within a country year of either the
best conflict-specific estimate or, if it does not exist for that conflict, the average of the low and
high estimates for the conflict. This methodology follows Bazzi and Blattman (2014). If we
alternatively dropped the country-year observations that are based on missing-best estimates, we
could be dropping a non-random sample of observations and get selection bias. For example,
countries that have multiple ongoing conflicts –making missing data more likely ceteris paribus
- may have many ethnic groups and poor data collection. Moreover, as we show below, our
qualitative results are robust to using either the “low” or the “high,” instead of the “best” and
“imputed best” estimates, and to focusing on the observations for which Lacina and Gleditsch
(2005) use year-specific sources, which automatically excludes most of the imputed
observations.
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Ethnicity: In order to study the effects of ethnic dominance and polarization, we first observe
that they are conceptually distinct. The ethnic dominance concept identifies countries where
there is a single large ethnic group that lives together with smaller group(s) that it can potentially
dominate. The polarization concept identifies countries where two or more large groups vie for
dominance. Unfortunately, Janus and Riera-Crichton (2015) show that it is difficult to
distinguish countries with dominant ethnic groups (defined as when the largest groups represents
about 50-85% of the population), countries with a higher-than-median Esteban and Ray (1994)
polarization index, and countries with an intermediate (25th-75th percentile) Herfindahl-
Hirschman ethnic fractionalization index. This is the case using the popular Fearon (2003) cross-
country ethnicity dataset as well as the equally-popular Alesina et al. (2003) data. In order to
illustrate the problem, Figure 2 copies Janus and Riera-Crichton’s (2015) Figure 1. Janus and
Riera-Crichton (2015, 25) explain that:
‘A linear regression [of ethnic fractionalization on the population share of the largest ethnic group, which is
used to define the ethnic dominance measure] yields an R2 of 0.96. On the other hand, there is also a close
quadratic relationship between ethnic fractionalization and polarization. Regressing polarization on
fractionalization and its square yields an R2 of 0.92. …regressing polarization on the ethnic plurality and its
square yields a similarly high R2 of 0.92.’
The fact that high polarization levels are correlated with ethnic dominance status and
intermediate fractionalization levels makes it difficult to separate the empirical effects. In order
to avoid favoring either the ethnic dominance or the polarization idea – given that we lack the
cross-country ethnicity variation we need to distinguish their effects convincingly - we prefer to
(a) mainly distinguish between countries in-and outside the 25th-75th Herfindahl-Hirschman
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ethnic fractionalization percentiles, but (b) show that distinguishing the countries based on
whether they have dominant or polarized ethnic groups gives similar results. Specifically, we
import the following three indicator variables from Janus and Riera-Crichton (2015): (1) The
intermediate-ethnic fractionalization indicator is a dummy that equals one when the Herfindahl-
Hirschman ethnic fractionalization index is in 25th-75th percentiles in Fearon’s (2003) ethnicity
dataset. For brevity, we call these the intermediately-fractionalized/diverse (ID) and non-
intermediately-fractionalized/diverse (NID) countries. (2) The ethnic dominance indicator is a
dummy that equals one when the largest ethnic group represents 50-85% of the population. (3)
The ethnic polarization indicator is a dummy that equals one when the polarization index in
Esteban and Ray (1994) - under the parameter assumption in Montalvo and Reynal-Querol
(2005) and Esteban et al. (2012) that the social distance between individuals from different
ethnic groups is one and the within-group distance between individuals is zero - exceeds the
sample median.
As Janus and Riera-Crichton (2015) explain, the Fearon (2003) ethnicity dataset relies on
seven criteria, including, particularly, common ancestry and a sense of community and self-
consciousness as a group, to define a prototypical ethnic group. The paper identifies 822 ethnic
groups in 160 countries after consulting the CIA World Factbook, Encyclopedia Britannica,
Library of Congress Country Studies, as well as country-specific sources. We doubt that the
ethnicity measures is endogenous to conflict intensity. First, the ethnicity data is not time-
varying and social identities are unlikely to change much over the relatively short time horizon
we study. Second, even if political elites can manipulate the ethnic boundaries, they are likely to
be constrained by preexisting social categories (Horowitz 1985, Smith 1986, Chandra 2007,
Eifert et al. 2010). Third, as we show below, our results are robust to using the race-based
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ethnicity data from Alesina et al. (2003). Finally, there is abundant evidence that ethnicity affects
developing-country politics (Bates 1981, Horowitz 1985, Chandra 2007). Kramon and Posner
(2012), Franck and Rainer (2012), and Burgess et al. (2015) link ethnic favoritism to education,
infant mortality and road construction (De Luca et al. 2015, Francois et al. 2015).
Commodity Terms of Trade: The dataset for commodity terms of trade (CTOT) covers the
period from 1970-2008. The CTOT index was originally developed by Ricci et al. (2008) and
Spatafora and Tytell (2009) and is defined as
( / ) / ( / )i ij jX M
jt it t it ti i
CTOT P MUV P MUV= Π Π (1)
where is the CTOT index for country in year ; is a common price index for each
of six commodity categories (food, fuels, agricultural raw materials, metals, gold, and
beverages); is the average share of exports of commodity in GDP from 1970 to 2006;
is the corresponding average share of imports; and the commodity prices are deflated by a
manufacturing unit value index (MUV). The fact that and are averaged over the sample
year ensures that the CTOT index is invariant to changes in export and import volumes in
response to conflict outcomes, thus isolating the effect of commodity price fluctuations. If we
compute the change in the log CTOT we can get the approximate CTOT growth rate per year
( 1)
( 1) 1 ( 1) 1
( / ) ( / )ln ln ln ln
( / ) ( / )
i ij j
i ij j
X M
it t it ti i
jt j t X M
i t t it ti i
P MUV P MUVCTOT CTOT
P MUV P MUV−
− − − −
Π Π − = − Π Π
(2)
jtCTOT j tjtP
i
jX i i
jM
i
jX i
jM
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Following Brückner and Ciccone (2010), Bazzi and Blattman (2014), and Janus and Riera-
Crichton (2015), we note that the annual commodity price shocks may be serially correlated and
have lagged conflict effects. In order to address this concern, we include the growth rate of the
three-year moving average of the terms of trade index,
1
2 3
ln / 3 ln / 3t t
jt js js
s t s t
CTOT CTOT CTOT−
= − = −
∆ = −∑ ∑ (3)
Since the growth rate of the three-year moving average CTOT index approximately equals the
average annual growth rate over the three-year period (see the appendix to Janus and Riera-
Crichton 2015), we can interpret every % (0.01) increase in the index as a mean increase of 1%
per year over three years. The standard deviation of CTOT∆ is 0.012. In order to formally test
whether it is appropriate to include the shock to the moving average in the regressions - instead
of include the three component annual CTOT shocks - we first include the annual shocks and
test whether the coefficients differ statistically. As we show below, we find no clear evidence
that this is the case. Nor is there a clear pattern suggesting that one of the terms is a better battle-
deaths predictor. Due to the fact that it is difficult to include the separate annual shocks together
with their interactions with our ethnic measures - it gives us six terms to interpret an potential
multi-collinearity problems - we prefer to only include the growth in moving-average, CTOT∆ ..
Estimation: The estimation regresses the logarithm of the number of battle-related fatalities on
the 3-year-movig-average CTOT growth rate in equation (3). in a linear specification with
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country and year fixed effects, country-specific time trends, and robust standard errors which we
cluster at the country level to control for serial correlation. Following Bazzi and Blattman
(2014), we also control for the duration of the conflict and add a dummy for the first conflict-
year. The regressions take the form
jt jt jt j jt jt j t j jtb CTOT CTOT ID d f z tα β γ µ ρ ε= + ∆ + ∆ × + + + + + + , (4)
where is the natural logarithm of the number of battle-related fatalities in country in year ,
jtCTOT∆ is the growth rate of the three-year moving-average CTOT index, jID is the time-
invariant dummy for the ID countries, andj t
d andjt
f represent the duration of the conflict since
the onset year and a dummy for the first conflict year (Bazzi and Blattman 2014) Finally, j
µ
and tz are the country and year fixed effects, jtρ is the country-specific time-trend, and
j tε is
the error term.**
IV. Results
Table 3, Column (1) presents the results of regressing the natural logarithm of the annual battle-
related death toll on the annual CTOT shocks for periods t, t-1, and t-2 as well as their
interactions with the ID dummy. The CTOT coefficients are insignificantly different and their
sum is significant and negative. Thus, terms of trade growth appears to decrease the death toll in
** Appendix C explains why we estimate a linear fixed-effects probability model instead of an interval regression model to account for the fact that some observations lack a point estimate for battle deaths (Bazzi and Blattman 2014). The reasons are that the interval estimator makes it difficult to include country fixed effects and non-normally-distributed errors can bias the estimates. Appendix C also explains why we believe that the country-level may be the best unit of analysis for civil war outcomes, despite the fact that there are several recent studies of subnational violence.
jtb j t
19
the NID economies. In the ID economies, however, the sum of the six CTOT terms (including
the interactions) is insignificant. Thus, we find no evidence that CTOT growth monotonically
decreases the death toll in ID countries.
In column (2), we use the growth rate of the three-year moving average CTOT, CTOT∆ ,
instead of the annual growth rates. Again, CTOT growth has negative effects in NID countries
but insignificant effects in ID countries. Column (3) replaces the ID dummy based on the
Fearon’s (2003) ethnicity data with the ID similarly-defined ID dummy based on the Alesina et
al.’s (2003) ethnicity dataset. Column (4) adds a lagged dependent variable to control for
persistence in the number of battle-related fatalities (Bazzi and Blattman 2014); to correct the
dynamic panel bias, we use the random-effects procedure developed in Hausman and Taylor
(1981) and Amemiya and MaCurdy (1986).†† In column (5), we restrict the sample to the
observations that are at least three years into the conflict in order to ensure that we do not
confound the effects of CTOT∆ shocks pre-and post-conflict initiation. In column (6), we again
follow Bazzi and Blattman (2014) and restrict attention to observations where Lacina and
Gleditsch (2005) observe year-specific deaths and, therefore, do not need to provide interpolated
or other noisy estimates (Lacina 2009, 5). Column (7) shows the results of imposing the two
restrictions jointly. Column (8) replaces the country fixed effects and country-specific time
trends with conflict-episode-fixed effects and a quadratic conflict-episode-specific time trend.
The idea is that either (i) the mean conflict-episode-specific death toll could vary within
countries or (ii) the conflict-specific death tolls have hump-shaped time-trends, as Figure 1 might
†† The Hausman-Taylor estimation declares the dynamic regressor endogenous. It is infeasible to use the alternative
General Methods of Moments (GMM) Arellano and Bond (1991, 1998) estimator because the number of time
periods in our panel does not exceed the number of panel units.
20
suggest, even after controlling for other factors. Through all of these robustness checks, the
results remain similar to the Column (2) results. Tables A2-A3 in Appendix D show that the
results also remain similar if we (a) replace the intermediate-diversity dummy with eight
alternative indicators for intermediate ethnic diversity, ethnic polarization, and ethnic dominance
(Table A1) and (b) control for the interaction between the CTOT shock and a range of
geographic, historical, and other, non-ethnic diversity measures (Table A2).
[Table 3 Goes Here]
In Table 4, we test whether negative terms of trade shocks have different effects than
positive shocks. In order to do so, we define a positive CTOT shock
{ ,0}jt jtCTOT Max CTOT+∆ = ∆ and a negative shock { ,0}jt jtCTOT Min CTOT−∆ = ∆ . The effect of a
positive shock is the coefficient on jtCTOT +∆ .. The effect of a negative shock, in contrast, is
minus the coefficient on jtCTOT −∆ . The full-sample results in Column (1) show that positive and
negative CTOT shocks have asymmetric effects: rather strikingly, both positive and negative
terms of trade shocks predict more an increased death toll.
In Columns (2)-(3), we try to explain the results in Column (1) by dividing the sample
into ID and NID countries. The results show that the positive full-sample effect of the positive
shocks come entirely from the ID countries. In other words, positive commodity-generated
income shocks only appear to increase the death toll in intermediately diverse countries. The
estimates for the NID countries in Column (3), in contrast, imply that positive and negative
CTOT shocks have symmetric effects: larger positive and less negative (closer-to-zero) shocks
21
both decrease the death toll. Since we cannot reject that the positive and negative shock
coefficients are the same in these countries, Column (4) reports the estimate with the original,
non-decomposed terms of trade shock. Although the estimate is negative, however, it is
insignificant at conventional levels (p=0.16).
In order to examine the robustness of these results, we inspected the partial residual plots
for the CTOT shocks and re-estimated the regressions after eliminating one country at a time.
For the ID countries, the magnitude of the positive shock estimate - the coefficient on
Pos∆ΤΟΤ(t) in Table 4 – is somewhat sensitive to removing a few of the oil-exporting ID
countries. In Tables 8-9 and Section 5 below, we show that the fact that the oil exporting ID
countries affect the results for the ID countries as a whole is not a coincidence. In fact, the fact
that positive terms of trade shocks increase the death toll in the ID countries as whole only
reflects that they increase he death toll in the fossil fuel (mainly, oil) exporters or about a quarter
of the ID countries. We explain these findings in more detail in Section 5.
The inspection of the partial residual plots and the re-estimation after eliminating one
country at a time in the case of the NID countries, on the other hand, shows that the results
remain stable unless when we omit Indonesia..‡‡ Excluding Indonesia nearly doubles the
coefficient estimate from -15.6 to -28.6 and it makes the t-statistic highly significant. In order to
better understand the Indonesia effect, Appendix E examines the case-study evidence for
Indonesia. The evidence suggests that a coalition of Indonesia’s largest two ethnic groups - the
Javanese and the Sundanese, which both live on Java - tend to dominate the central government
and that the government has historically fought three ethnic minorities that live on peripheral
‡‡ Dropping any of the 38 countries apart from Indonesia keeps the ∆TOT(t) coefficient in [-18.6, -12.2], closely
centered around the original -15.6.
22
islands; namely the Acehnese, East Timorese, and West Papuans. We believe that the conflict
problem in Indonesia is a good example of what an ethic dominance problem can look like in
practice. Although the single largest group in Indonesia, the Javanese, only represents 45% of the
population, Ross (2005) notes that several observers count the Javanese together with the 15%
Sundanese as a single group. If we followed that procedure (unlike Fearon (2003)), the
aggregated group would meet the 50%-population-share threshold we use to define ethnic
dominance an it would fall in the 25th-75th ethnic fractionalization percentile that defines the ID
countries. Due to Indonesia’s large effect on the NID estimate and its potentially-borderline
ethnicity classification, Table 4, Column (5) presents the NID estimates without Indonesia as
well. The -28.6 estimate implies that a standard deviation increase in the terms of trade shock
decreases the annual death toll in NID countries by 34%. This is comparable to the effect of
negative shocks in ID countries, which is about 41%. Nonetheless, we acknowledge that the
decision to exclude Indonesia from the NID sample is ultimately a judgment call.
[Table 4 Goes Here]
Figure 2 shows that the results remain similar when we divide the sample into countries
with low and high ethnic polarization and countries with and without a dominant ethnic group (a
group representing 50-85% of the population). Table 5 confirms that this is the case. In Tables 6-
7, we show that the results with alternative dependent variables look similar as well. In Table 6,
columns (1)-(3) re-estimate the Table 4, Column (2) regression but replace the dependent
variable with, respectively, the lowest estimates for the annual battle-related death tolls implied
by the Lacina and Gleditsch (2005) dataset; the corresponding highest annual estimates; and an
23
ordinal measure, which we coded to be equal to one when the best or imputed best estimate is at
most 1000 and two when the best or imputed best estimate exceeds 1000. In Table 7, similarly,
we replicate the Table 4, Columns (4)-(5) regressions for the NID countries with and without the
inclusion of Indonesia. The results again remain similar.
[Table 5 Goes Here]
[Table 6 Goes Here]
[Table 7 Goes Here]
V. The effects of fossil fuel dependence and fossil fuel terms of trade shocks on conflict
The results so far suggest that – consistent with the idea that income growth can increase
rent-seeking and help to finance civil wars (Angrist and Kugler 2008; Dube and Vargas 2013;
Aguirre 2016) - positive income shocks can increase conflict. The positive effects are, however,
restricted to intermediately ethnically fractionalized countries and countries with dominant and
polarized ethnic groups. In this section, ask whether the positive conflict effects in these
countries come from particular commodity groups and country types.
In order to address this question, we note that several studies have found that price
increases for capital-intensive natural resource sectors, such as the oil sector, increase conflict
(Dube and Vargas 2013; Aguirre 2016). The idea is that higher prices of capital-intensive goods
increase the return to rent-seeking effort relative to the opportunity cost because the conflict
sector is relatively labor intensive (Dal Bó and Dal Bó 2011). Additionally, fossil fuel resources
24
are often geographically concentrated, so ethnic groups that live in resource-rich areas may fight
to increase their autonomy and revenue shares (Le Billon 2001; Ross 2004). Alternatively, the
government can invade the regions preemptively (Ross 2005). The conflicts between the Iraqi
government and the Iraqi Kurds, Sudan’s Second Civil War, and Indonesia’s ethnic-secessionist
conflicts, for instance, pitted ethnic groups that were associated with the central government
against peripheral groups with access to oil and natural gas. Both the capital-intensity
geographic-concentration hypotheses suggest price increases for fossil fuels can increase
conflict.
Our second idea comes from the resource-curse literature that relates natural-resource
dependence and, particularly, oil dependence to economic development. Oil dependence can,
potentially, encourage the growth of undemocratic, corrupt, and repressive “rentier states,”
where the political elite uses resource income, such as royalties, to finance high living standards.
Further, since the state is relatively independent on tax collections, it may have little incentive to
invest in economic development and state capacity through improving the legal system, the
quality of the bureaucracy, the ability to collect income taxes, and so forth. (Smith 2004; Fearon
2005; Basedau and Lay 2009). The most extreme example may be Equatorial Guinea, whose
2015 PPP-based GDP per capita of $30,000 was about the same as Portugal’s, but whose Human
Development Index – a broader development measure that is tracked by the United Nations
Development Program, and which responds to health and education as well as income – looks
like Zambia’s. Once the repressive status apparatus fails and civil war begins in such countries,
the lack of social cohesion and institutional capacity could encourage a lot of rent-seeking
violence on average and, particularly, increases in rent-seeking after positive terms of trade
shocks.
25
On the basis of these two ideas, we estimate whether fossil-fuel-generated terms of trade
shocks have different effects than non-fuel terms of trade shocks and whether fuel and non-fuel
shocks have different effects in fuel-dependent economies. Thus, we decompose the change in
the log three-year-moving average terms of trade index in equation (3) into the change in the
fossil fuel component – the fossil fuel category in the CTOT index includes coke, coal, and
briquettes; petroleum and petroleum products; and gas (natural and manufactured) – and the
change in the remaining, non-fossil fuel component. Additionally, we define fuel-dependent
economies as economies whose average export share of fuels in GDP from 1970-2006 - which is
the year range we used to construct the CTOT-index weights - exceeds its import share.
Moreover, the average export share of fuel has to exceed 2% of GDP. The last requirement
excludes Afghanistan and Argentina, which are rarely considered fuel-dependent economies.
In Table 8, Column (1) presents the estimates for positive and negative CTOT shocks
generally (not yet disaggregated by commodity category) in ID fuel exporters, NID fuel
exporters, ID non-fuel exporters, and NID non-fuel exporters. The estimates imply that positive
CTOT shocks increase conflict intensity in ID as well and NID fuel exporters. Although it is
possible to interpret these estimates as suggesting that fuel dependence alone and not a country’s
ethnic composition that creates a positive relationship between conflict and terms of trade gains,
we do not believe this is the best interpretation. The reason is that 91% (89/98) of the
observations for NID fuel-exporters in the regression sample come from just four countries –
Angola, Azerbaijan, Indonesia, and Sudan - that have a history of ethnic conflict involving large
or dominant ethnic groups. Moreover, although their ethnic fractionalization levels are outside
the 25th-75th percentiles we use to define the ID economies, they are in the 15th-85th percentiles.
In Appendix F, we study the individual conflicts that generated these 89 observations and
26
conclude that they were ethnic conflicts that were, at least in part, either motivated or financed
by natural resources. On this basis, we believe that
(a) the evidence suggests that positive CTOT shocks increase the number of battle-related
fatalities in intermediately diverse fuel exporters; but that
(b) our study lacks enough civil war observations to allow us to estimate the effects in
highly ethnically homogenous and fractionalized fuel exporters - given that we only have 9
observations for countries below the 15th or above the 85th ethnic-fractionalization percentiles.§§
In Column (2) we show that, if we widen the ID definition from the countries in the 25th-
75th to the countries in the15th-85th ethnic fractionalization percentile, the coefficient on the
positive CTOT shocks in NID fuel exporters turns negative. Since the coefficient is based on
very few observations, however, it is hard to interpret. More importantly, the effect in the ID
economies is almost unchanged.
[Table 8 Goes Here]
In Table 9 we restrict attention to the intermediately diverse fuel exporter sample but
decompose the CTOT shocks sand ask whether the positive fossil fuel shocks specifically, as
opposed to the positive non-fossil fuel shocks and the negative shocks cause the conflict
§§ The 9 observations include two for highly diverse Cameroon (1984) and Nigeria (2004), and seven for relatively
homogenous Egypt (1993-98) and Tunisia (1980). The fact that we have so few observations for these countries,
however, suggests that they may be unlikely to experience civil war in the first place (Fearon and Laitin 2003;
Collier and Hoeffler 2004). Even if they are unlikely to experience civil war, however, they may be relatively
corrupt (e.g., Cameroon, Gabon, Nigeria) and autocratic (e.g., Libya, Syria, Tunisia, Yemen, and the Arab Gulf
states historically).
27
increases. The estimates in Column (1) suggest that this is the case. A standard deviation (0.011)
increase in the fossil fuel CTOT shock increases the annual death toll in the ID fuel exporters by
about 32%. Columns (2)-(3) show that the results for the ethnic dominance and polarized
countries are similar.
The positive relationship between the positive fossil-fuel CTOT shocks and conflict raise
the potential identification concern that conflict upticks in the fuel exporters increase the global
prices of fossil fuels, that is. In that regard, however, note that the reverse-causality hypothesis
can only explain the positive sign on the positive CTOT shocks in Table 9 and not the negative
sign on the negative shocks: if increases in conflict caused global fuel prices to increase, then
decreases in conflict should cause decrease in fuel prices. This implies that the regression
coefficients for both the positive and the negative CTOT shocks should be positive.
Second, we can address the reverse causality concern by inspecting the sample countries
and removing the ones that could plausibly affect global oil prices. Reviewing the sample
countries for the regression in Table 9, Column (1), the country that we should be most
immediately concerned with is Saudi Arabia. However, Saudi Arabia only contributes a single
conflict observation and dropping this observation gives virtually identical results. Nonetheless,
global energy and particularly oil markets can be volatile, so potentially even small events that
do not cause major contemporaneous supply disruptions can affect oil prices by affecting
expectations about future supplies and changing investors’ precautionary demand (Kilian 2014).
Although it is impossible to rule out such mechanisms, we can drop the countries that we believe
could plausibly affect global fuel prices either via current production or expectations. These
countries include Iran, Iraq, Mexico Oman, Russia, Saudi Arabia, and Venezuela.
28
Second, we can inspect the sample countries and remove the ones we believe could affect
global oil prices. Reviewing the Table 9, Column (1) sample immediately suggests that we
should omit Saudi Arabia. Dropping the single Saudi Arabia observation, however, gives
virtually identical results. Nonetheless, we cannot rule out that some of the other producers can
influence global prices and even events that do not affect the contemporaneous oil-supply could
affect oil prices by affecting investor expectations about future supplies and precautionary
demand (Kilian 2014). Although it is difficult to rule out such mechanisms entirely –
particularly, we do not observe market expectations regarding future supplies - we can drop the
countries that we intuitively believe could affect global fuel prices. These countries include Iran,
Iraq, Mexico Oman, Russia, Saudi Arabia, and Venezuela. Unfortunately, omitting these
countries only leaves us with 3 of the original 10 ID fuel exporters - Colombia, Malaysia, and
Trinidad and Tobago – and only 40 battle deaths observations. We hesitate to try to estimate
such a small sample; the year effects alone would perfectly explain one of the time-series. In
order to address this problem, the Columns (4)-(5) regressions redefine the ID countries as the
countries in the 15th-85th rather than 25th-75th ethnic-fractionalization percentiles. This implies
that we add the four countries we discussed earlier - Angola, Azerbaijan, Indonesia, and Sudan –
to the fuel exporter sample. Column (4) reports the estimates for all the ID fuel exporters in 15th-
85th fractionalization percentile. Column (5) reports the results without Iran, Iraq, Mexico Oman,
Russia, Saudi Arabia, and Venezuela. The results in Column (4) are almost identical to the
results in Column (1). The Column (5) results show that omitting the potentially-influential fuel
producers actually increases the positive-shock (as well as the negative-shock) estimates. These
results suggest that reverse causality is unlikely to explain the paper’s findings. Finally, we note
that the results are consistent with Dube and Vargas (2013), who show that higher international
29
oil prices increase conflict in Colombian municipalities. As the authors note, Colombia supplies
less 1% of the world’s oil, so the causal arrow is more likely to run from fuel prices to conflict
than in the other direction.***
[Table 9 Goes Here]
VI. Conclusion
In this paper, we study the effects of economic factors on the intensity of internal armed
conflicts. We show that adverse ethnic compositions in the form of ethnic dominance and
polarization critically affect how countries respond to commodity-generated income shocks.
Although negative shocks increase the death toll everywhere, positive shocks in countries with
an intermediate ethnic fractionalization index, ethnic dominance, and ethnic polarization also
increase the death toll. Finally, we show that the conflict-increasing effect of commodity
windfalls in these countries reflect fact that fossil fuel terms of trade gains increase conflict in
fuel exporters.
The results may reflect that income growth increases the opportunity cost of conflict
inputs as well as the return to fighting and the ability to finance the wars. In fuel-exporters with
adverse ethnic compositions, the rent-seeking and conflict-finance effects can, potentially,
dominate the opportunity-cost effect during fuel-price booms. (Dal Bó and Dal Bó 2011; Dube
and Vargas 2013). The literatures on the resource curse (Le Billon 2001; Fearon 2005) and
ethnicity (Horowitz 1985; Esteban and Ray 1999) suggest that fuel dependence, ethnic
*** In order to ensure that outlier effects do not explain the results, Appendix G depicts the partial residuals for the
positive and negative fossil fuel terms of trade shocks in the Table 9, Column (1) and (5) specifications. None of the
four panels, indicate that outliers affect the estimates.
30
dominance, and polarization can increase rent-seeking. The fossil fuel results could also be
relevant to the resource-curse literature as they suggest that resource booms may only increase
conflict in economies with structural weaknesses (adverse ethnic compositions and fuel
dependence).
Finally, our study suggests that economic stabilization policies could diminish the
number of human causalities and other welfare losses that are generated by civil wars. Moreover,
for a subset of particularly vulnerable economies, there may be no such thing as a “good”
economic shock. Thus, better management of both positive and negative economic shocks could
save lives.
Plain Language Summary
In this paper, we study the effects of commodity terms of trade shocks – the change in countries’
commodity export prices relative to their import prices - on the number of fatalities in civil wars.
We find that terms of trade growth, which should normally increase national income and create
more economic opportunities for individuals, decreases the annual numbers of fatalities.
However, when the fossil fuel terms of trade – the price of fuel exports relative to imports –
increase the death toll in fuel- (typically, oil-) exporting economies with certain ethnic
compositions that have been linked to conflict increases. The findings suggest that, in most
cases, national income growth decreases conflict. In some cases, however, conflict between
ethnic groups over the distribution of resources can undermine the positive effects of income
growth on peace.
31
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41
Tables and Figures
TABLE 1
Summary Statistics
Variable Obs Mean SD Min Max
Total Battle Deaths Low Estimate 906 1,196 2,899 14 37,000 Total Battle Deaths High Estimate 906 5,820 12,437 26 200,000 Total Battle Deaths Best Estimate 655 3,603 8,320 25 80,000 Commodity Terms of Trade Shock 906 0.001 0.016 -0.099 0.132 3 Year Moving Ave. CTOT Shock 906 0.001 0.012 -0.071 0.111 3 Year Mov. Ave. Fuel CTOT Shock 906 0.001 0.011 -0.076 0.097 Conflict Duration Up to Present Year 906 10.62 11.03 1 60 First Year of Conflict Dummy 906 0.16 0.37 0 1 Intermediate Ethnic Diversity Dummy 900 0.45 0.50 0 1 Ethnic Dominance Dummy 900 0.45 0.50 0 1 High Ethnic Polarization Dummy 900 0.45 0.50 0 1
TABLE 2
Sample Countries
Afghanistan Cuba Haiti Mauretania* Philippines Trin &Tob* Angola DR Congo India Mexico* Rep of Congo Tunisia Argentina Djibouti* Indonesia Morocco* Rwanda Turkey* Azerbaijan Dom Rep* Iran* Mozambique Saudi Arabia* Uganda Bangladesh Egypt Iraq* Nepal* Senegal Uruguay Bolivia El Salvador Ivory Coast Nicaragua* Sierra Leone Venezuela* Burkina Faso Eritrea* Kenya Niger* Somalia Vietnam Burundi* Ethiopia Laos* Nigeria South Africa Zimbabwe* Cambodia Gabon Lebanon Oman* Sri Lanka*
Cameroon Gambia Lesotho* Pakistan* Sudan Central African
Ghana Liberia Panama* Syria*
Chad Guatemala* Madagascar Papua New G Tajikistan*
Chile* Guinea* Malaysia* Paraguay Thailand*
Colombia* Guin.-Bissau Mali Peru* Togo Note: * indicates intermediately ethnically diverse countries
42
TABLE 3
The effects of commodity terms of trade shocks on battle-related deaths in civil wars (1) (2) (3) (4) (5) (6) (7) (8)
Estimation Method LSDV LSDV LSDV HTaylor LSDV LSDV LSDV LSDV
Dep. Variable: Ln (battle deaths)
dCTOT(t) -7.295
[5.910]
dCTOT(t-1) 0.283
[5.319]
dCTOT(t-2) -11.708**
[5.616]
dCTOT*ID(t) 11.349*
[6.603]
dCTOT*ID(t-1) 2.102
[6.127]
dCTOT*ID(t-2) 7.335
[6.046]
∆TOT(t) -17.107* -16.737* -26.620*** -16.534 -14.701 -32.917** -42.044***
[9.583] [9.687] [9.748] [12.422] [10.084] [16.089] [12.924]
∆TOT*ID(t) 20.116* 20.690* 23.843** 17.554 18.639 35.267** 38.146***
[10.995] [10.731] [10.490] [13.111] [11.871] [15.862] [13.236]
Duration -0.014 -0.013 -0.007 -0.027** -0.019 -0.028 -0.055* 6.991***
[0.023] [0.022] [0.021] [0.011] [0.032] [0.025] [0.031] [1.921]
First Year Dummy -0.950*** -0.927*** -0.893*** -0.937*** -0.155
[0.166] [0.165] [0.168] [0.193] [0.202]
AR(1) term 0.463***
[0.034]
Observations 900 900 885 756 664 742 536 900
R-squared 0.523 0.519 0.537 0.587 0.514 0.615 0.677
# countries/conflicts 79 79 78 59 50 76 45 154
p-val sum of shocks 0.06 0.08 0.09 0.01 0.19 0.15 0.05 0.00
p-val sum of interac. 0.08 0.07 0.06 0.02 0.19 0.12 0.03 0.00
p-val shocks+interac. 0.67 0.43 0.23 0.42 0.74 0.47 0.46 0.03
p-val shocks equal 0.35
p-val interac equal 0.6
Year dummies Y Y Y Y Y Y Y Y
Cntry/conf time trnds Y Y Y Y Y Y Y Y
Note: Robust standard errors clustered at the country-level (except in Column (1)) in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. Columns (1)-(2) estimate the effects of the current and two preceding years’ commodity terms of trade shocks on battle-related fatalities in countries with and without an intermediate ethnic diversity level. Column (2) estimates the same effects of the growth rate of the three-year moving average terms of trade shock. Column (3) replaces the intermediate diversity dummy based on the Fearon (2003) ethnicity dataset with the corresponding dummy based on the Alesina et al. (2003) ethnicity dataset. Column (4) includes a lagged dependent variable and uses Hausman and Taylor (1981) and Amemiya and MaCurdy (1986) to correct the dynamic panel bias. Column (5) restricts sample to observations which are at least three years into the conflict. Column (6) restricts it to observations for which Lacina and Gleditsch (2005) report year-specific battle-related fatalities. Column (7) imposes the two restrictions simultaneously. Column (8) estimates the column (2) specification with conflict fixed effects and a quadratic conflict-specific time trends rather than country fixed effects and country-specific time trends.
43
TABLE 4
The effects of positive and negative commodity terms of trade shocks in non-intermediately
diverse and intermediately diverse countries
(1) (2) (3) (4) (5)
Estimation Method LSDV LSDV LSDV LSDV LSDV
Sample Full ID NID NID NID
Pos∆ΤΟΤ(t) 19.171*** 20.118** -5.723
[7.208] [8.393] [36.590]
Neg∆ΤΟΤ(t) -24.477*** -34.393*** -25.754
[5.475] [7.495] [24.485]
∆ΤΟΤ(t) -15.605 -28.630***
[10.863] [9.895]
Duration -0.012 -0.050 0.000 0.000 0.007
[0.021] [0.045] [0.012] [0.012] [0.009]
First Year -0.928*** -1.023*** -0.926*** -0.920*** -0.863***
[0.159] [0.242] [0.219] [0.216] [0.216]
Observations 906 409 491 491 464
R-squared 0.524 0.486 0.614 0.614 0.614
p-val (Pos∆ΤΟΤ=-Neg∆ΤΟΤ ) 0.00 0.00 0.73
Number of countries 81 40 39 39 38
Year dummies Y Y Y Y Y
Country time trends Y Y Y Y Y
Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. Column (1) estimates the effects of positive and negative commodity terms of trade shocks in the full sample. Columns (2)-(3) estimate the effects in, respectively, the intermediately and non-ethnically diverse countries. Column (4) replaces the positive and negative shocks in the non- intermediately diverse countries with the original terms shock measure. Column (5) excludes Indonesia from the non-intermediately diverse sample.
44
TABLE 5
Results with polarization and ethnic dominance-based sample divisions
(1) (2) (3) (4) (5)
Estimation Method LSDV LSDV LSDV LSDV LSDV
Sample High
Polarization
Low
Polarization
Ethnic
Dominance
Non-Ethnic
Dominance
Non-Ethnic
Dominance
∆ΤΟΤ(t) -19.460*** -14.701 -26.134***
[6.011] [10.108] [9.639]
Pos∆ΤΟΤ(t) 23.783*** 18.726**
[4.716] [8.558]
Neg∆ΤΟΤ(t) -24.703*** -34.278***
[5.177] [8.287]
Duration 0.016 -0.065** -0.052 -0.002 0.005
[0.019] [0.032] [0.046] [0.013] [0.010]
First Year -0.711*** -1.235*** -0.976*** -0.989*** -0.923***
[0.161] [0.253] [0.251] [0.210] [0.209]
Observations 549 355 383 510 483
R-squared 0.579 0.554 0.468 0.624 0.625
Number of countries 48 32 37 41 40
Year dummies Y Y Y Y Y
Country time trends Y Y Y Y Y
Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. Columns (1)-(2) divide the sample into countries with high and low ethnic polarization, defined as a polarization index below and above the sample median. Columns (3)-(4) divide the sample into countries with and without a dominant ethnic group, defined as a group that represents 50-85% of the population. Column (5) reports the results without Indonesia.
45
TABLE 6
Robustness to alternative dependent variables (intermediately diverse sample)
(1) (2) (3)
Estimation Method LSDV LSDV LSDV
Dep. Var. Measure of Battle Deaths Low High Ordinal
Sample ID ID ID
Pos∆ΤΟΤ(t) 23.560** 21.119*** 3.669
[9.090] [7.123] [2.990]
Neg∆ΤΟΤ -19.120 -33.631*** -7.725**
[13.801] [7.579] [3.199]
Duration -0.057 -0.054 -0.018
[0.042] [0.043] [0.012]
First Year -0.920*** -0.848** -0.207**
[0.246] [0.317] [0.085]
Observations 409 409 409
R-squared 0.440 0.473 0.389
Number of countries 40 40 40
Year dummies Y Y Y Country time trends Y Y Y Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. The regression estimates apply to the intermediately ethnically diverse sample countries. Column (1) report the effects of positive and negative commodity terms of trade shocks on the best and imputed best battle deaths measure. In columns (2)-(4) we estimate the effects on the low and high estimates for the annual battle-related deaths in Lacina and Gleditisch (2005) and the effects on an ordinal measure which equals one when the best or imputed best estimate is at most 1000 and two when the best or imputed best estimate exceeds 1000.
TABLE 7
Robustness to alternative dependent variables (non-intermediately diverse sample)
(1) (2) (3) (4) (5) (6)
Estimation Method LSDV LSDV LSDV LSDV LSDV LSDV
Dep. Var. Measure of
Battle Deaths Low High Ordinal Low High Ordinal
Sample NID NID NID NID NID NID
∆ΤΟΤ(t) -13.940 -12.571 -3.310 -38.462** -28.558** -5.787**
[19.514] [12.778] [2.524] [15.174] [11.706] [2.495]
Duration 0.050 0.015 0.002 0.069*** 0.025** 0.001
[0.031] [0.016] [0.005] [0.021] [0.012] [0.005]
First Year -0.785*** -0.767*** -0.157** -0.664*** -0.671*** -0.158**
[0.269] [0.207] [0.074] [0.240] [0.192] [0.075]
Observations 491 491 491 464 464 464
R-squared 0.515 0.517 0.478 0.541 0.532 0.453
Number of countries 39 39 39 38 38 38
Year dummies Y Y Y Y Y Y Country time trends Y Y Y Y Y Y Note: Robust standard errors clustered at the country-level in brackets.* significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. The regression estimates apply to the non-intermediately ethnically diverse sample countries. Columns (2)-(4) report the effects of commodity terms of trade shocks using the low and high estimates for the annual battle-related deaths in Lacina and Gleditisch (2005) as well as an ordinal measure which equals one when the best or imputed best estimate is at most 1000 and two when the best or imputed best estimate exceeds 1000. Columns (3)-(6) reports the estimates without Indonesia.
46
TABLE 8
The effects of positive and negative shocks in intermediately and non-intermediately diverse net
fuel exporters and net fuel importers Estimation Method LSDV LSDV
Sample Full Full
Intermediate ethnic diversity definition Main (25th-75th percentile of
ethnic fractionalization index)
Extended (15th-85th percentile of
ethnic fractionalization index)
Pos∆ΤΟΤ(t) in ID net fuel exporter 23.070*** 24.710***
[6.183] [6.041]
Pos∆ΤΟΤ(t) in NID net fuel exporter 63.238* -5,440.035***
[33.034] [966.792]
Pos∆ΤΟΤ(t) in ID non-fuel exporter -13.219 -17.001
[25.606] [24.494]
Pos∆ΤΟΤ(t) in NID non-fuel exporter -41.426 -58.627*
[29.689] [34.689]
Neg∆ΤΟΤ(t) in ID net fuel exporter -17.170** -20.989***
[7.732] [6.678]
Neg∆ΤΟΤ(t) in NID net fuel exporter -78.935*** 262.301
[18.691] [178.779]
Neg∆ΤΟΤ(t) in ID non-fuel exporter -21.232 -18.187
[15.118] [13.170]
Neg∆ΤΟΤ(t) in NID non-fuel exporter -12.685 -11.021
[34.261] [59.105]
Duration -0.009 -0.009
[0.021] [0.021]
First year -0.924*** -0.904***
[0.160] [0.155]
Observations 900 900
R-squared 0.53 0.53
Number of countries 79 79
Year dummies Y Y
Country time trends Y Y
Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. ID and NID denote intermediately diverse and non-intermediately diverse countries. Column (1) reports the effects of positive and negative commodity terms of trade shocks in intermediately ethnically diverse (ID) and non-intermediately ethnically diverse (NID) net fossil fuel exporters and importers. Columns (1) and (2) define the intermediate ethnically diverse countries as countries with an ethnic fractionalization index in, respectively, the 25th -75th percentile and the 15th-85th percentile.
47
TABLE 9
The effects of fuel and non-fuel terms of trade shocks in fuel exporters
(1) (2) (3) (4) (5)
Estimation Method LSDV LSDV LSDV LSDV LSDV
Sample definition
Fuel exporters in
25th-75th percentile of ethnic fractionalization
Ethnic
dominance
High
polarization
Fuel exporters in 15th-
85th percentile of fractionalization
Small fuel
exporters in
15-85th
percentile of
fractionalization
Sample countries
Colombia Iran Iraq
Malaysia Mexico Oman Russia
Saudi Arabia Trinidad & Tobago
Venezuela
Colombia Iran Iraq
Malaysia Mexico Oman Russia
Saudi Arabia Venezuela
Angola Colombia
Iran Iraq
Malaysia Mexico Oman
Saudi Arabia Sudan
Trinidad & Tob Venezuela
Angola Azerbaijan Colombia Indonesia
Iran Iraq
Malaysia Mexico Oman Russia
Saudi Arabia Sudan
Trinidad & Tob Venezuela
Angola Azerbaijan Colombia Indonesia Malaysia
Sudan Trinidad & Tob
Pos fuel ∆ΤΟΤ(t) 28.69* 28.69* 37.34*** 32.42*** 119.32**
[13.605] [13.733] [6.319] [7.891] [37.996]
Pos non-fuel ∆ΤΟΤ(t) -24.66 -24.66 -17.06** -47.60 -173.12
[35.419] [35.752] [7.092] [28.210] [119.387]
Neg fuel ∆ΤΟΤ(t) -38.47*** -38.47*** -32.27*** -30.97*** -109.38***
[2.649] [2.674] [5.453] [7.001] [28.709]
Neg non-fuel ΤΟΤ(t) 16.80 16.80 -43.19 4.64 -76.12
[34.334] [34.657] [32.347] [40.085] [126.880]
Duration -0.09 -0.09 -0.01 -0.04 -0.02
[0.074] [0.075] [0.037] [0.036] [0.038]
First year -1.16* -1.16* -0.73*** -1.03*** -0.70
[0.547] [0.552] [0.185] [0.218] [0.518]
Observations 116 115 157 205 129
R-squared 0.723 0.72 0.71 0.644 0.740
No. countries 10 9 11 14 7
Year dumm. Y Y Y Y Y
Cntry time tr. Y Y Y Y Y
Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; ***
significant at 1%. ∆ denotes the change in the three-year moving average. ID denote intermediately diverse. Column (1) reports the estimates for positive and negative fuel and non-fuel commodity terms of trade shocks in intermediately ethnically diverse net fuel exporters. Columns (2)-(3) repeat the analysis for net fuel exporters with ethnic dominance and high ethnic polarization. The Column (4) regression uses the countries with Herfindahl-Hirschman ethnic fractionalization index in the 15th-85th percentile instead of the 25th-75th percentile in the Column (1) regression. Column (5) omits fuel producers that could potentially influence global fossil fuel prices.
48
FIGURE 1
The Relationship Between Ethnic Fractionalization, Ethnic Polarization,
and the Population Share of the Ethnic Plurality
Source: Janus and Riera-Crichton (2015). Note: Fearon (2003) ethnicity data
0.5
1
0 .2 .4 .6 .8 1ethnic fractionalization
ethnic plurality
polarization
quadratic prediction of polarization,Rsq=0.91
linear prediction of ethnic plurality, Rsq=0.96
0.2
.4.6
.81
.2 .4 .6 .8 1ethnic plurality
polarization
quadratic prediction of polarization,Rsq=0.92
49
Appendix A
Death Toll Estimates for Guatemala, El Salvador, Nicaragua, and Bosnia and Herzegovina in the dataset used in the paper
FIGURE A1 Death Toll Estimates for Guatemala, El Salvador, Nicaragua, and Bosnia and Herzegovina
Note: Locally-weighted-regression (lowess) fits, varying bandwidths
02000
4000
6000
8000
10000
Death toll estimate
1950 1960 1970 1980 1990 2000Year
bandwidth = .4
Guatemala
-5000
05000
10000
15000
Death toll estimate
1970 1975 1980 1985 1990Year
bandwidth = .4
El Salvador0
2000
4000
6000
8000
Death toll estimate
1975 1980 1985 1990Year
bandwidth = .4
Nicaragua
05000
10000
15000
20000
25000
Death toll estimate
1992 1993 1994 1995Year
bandwidth = .8
Bosnia and Herzegovina
50
Appendix B
Do Positive Terms of Trade Shocks Increase the Onset Risk for Civil War?
In the main paper, we argue that the determinants of the onset and intensity of civil wars can
differ. In order to test this idea, we combine our dataset with the dataset for the onset of civil
wars in Janus and Riera-Crichton (2015). The latter paper shows that commodity terms of trade
declines predict the onset of civil wars in countries with intermediate ethnic diversity, ethnic
dominance, and high polarization. Civil war onset is measured with a dummy that equals zero in
peace years, one in the onset year, and missing otherwise. Since the ongoing-conflict
observations are excluded except for the onset years, there is almost no overlap in the country-
year coverage in the two datasets. The onset and intensity data both come from the UCDP/PRIO
(v. 4) civil conflict coding project, so in principle every onset in the onset data (Gleditsch et al.
2002; Themnér & Wallensteen 2011) has a corresponding battle-death time series in the intensity
dataset (Lacina and Gleditsch 2005) and vice versa. The onset dummy switches to one when the
annual battle-related death toll reaches a threshold of either 25 (for a minor conflict onset) or
1000 (a civil war onset).
In the main paper, we find that positive fossil fuel terms of trade shocks increase the
battle-related death toll in intermediately diverse net fuel exporters. In order to test whether these
shocks also increase the onset risk for civil conflict, we estimate equation (1’) in Janus and
Riera-Crichton (2015) for the intermediately ethnically diverse countries. Additionally, however,
we (a) decompose the commodity terms of trade growth rate into its positive and negative fossil
fuel and non-fuel components and (b) add an interaction between positive fossil fuel terms of
trade shocks and the dummy for fossil fuel exporters we defined above.
In contrast to what we found when we estimated battle-related fatalities conditional on a
prior war onset, the results in Table A1, Column (1), do not support the idea that positive fossil
51
fuel terms of trade shocks increase the onset risk for civil wars in the first place. Columns (2)-(4)
show that using the three alternative onset dummies in Janus and Riera-Crichton (2015) gives
similar results. This evidence is consistent with the idea that the determinants of the intensity and
onset of civil wars can differ. Nonetheless, Ross (2003) reviews both systematic and case-study
evidence and concludes that oil can increase the onset risk for civil wars and particularly
separatist conflict. It may be possible that one should distinguish between separatist and non-
separatist conflicts or for instance between off-and onshore oil (Andersen et al. 2017).
Nonetheless, the fact that estimating battle deaths conditional on an onset rather than the war
onsets gives different regression coefficients at least in the UCDP data and conditional on our
regression specifications suggest that, at least in our dataset, we should not estimate onsets and
battle deaths during the conflict years with the same regression model.
52
TABLE A1
Commodity terms of trade shocks and civil war onsets
(1) (2) (3) (4) (5) (6) (7) (8)
Estimation Method LSDV LSDV LSDV LSDV LSDV LSDV LSDV LSDV
Sample ID ID ID ID ID ID ID ID
Onset measures UCDP War
UCDP War
UCDP Conflict
UCDP Conflict
COW War
COW War
FL War
FL War
∆ΤΟΤ(t-1) -0.604* -0.715** -0.427** -0.375*
[0.306] [0.310] [0.189] [0.200]
Pos fuel ∆ΤΟΤ(t-1) -0.945 -1.473 -1.549 -1.637
[1.618] [1.779] [1.269] [1.352]
Pos fuel ∆ΤΟΤ(t-1)*nfe 0.351 -0.130 0.694 1.201
[1.645] [1.917] [1.229] [1.403]
Pos non-fuel ∆ΤΟΤ(t-1) 1.100* 0.360 -0.085 0.671
[0.589] [0.640] [0.429] [1.096]
Neg fuel ∆ΤΟΤ(t-1) -0.169 0.382 0.587 0.164
[0.688] [0.865] [0.590] [0.333]
Neg non-fuel∆ΤΟΤ(t-1) -6.552** -1.244 -2.276*** -2.919**
[2.746] [1.296] [0.608] [1.361]
Observations 2,149 2,149 2,238 2,238 2,454 2,454 1,504 1,504
R-squared 0.088 0.100 0.061 0.063 0.047 0.051 0.115 0.117
No. of countries 70 70 70 70 70 70 68 68
Year dummies Y Y Y Y Y Y Y Y
Country time tr. Y Y Y Y Y Y Y Y
p-val(Posfuel+Pf*nfe) 0.14 0.13 0.20 0.40
Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; ***
significant at 1%. ∆ denotes the change in the three-year moving average. ID denote intermediately diverse. on civil war onsets. Columns (1) estimates the effects of the growth rate of the three-year moving average commodity terms of trade shock on the onset of civil wars in the Janus and Riera-Crichton (2015) civil war onset dataset. The onset variable equals one in a civil-war onset year and zero in peace years. The dummy is based on the Uppsala Conflict Data Program(UCDP). Column (2) separately estimates the effects of the positive and negative fuel and non-fuel terms of trade shocks as and allows the effects of positive fuel terms of trade shocks to differ for net fuel exporting countries.. Columns (3)-(4), (5)-(6), and (7)-(8) report the corresponding estimates for Janus and Riera-Crichton’s (2015) three alternative conflict onset dummies based on the Correlates of War dataset, Fearon and Laitin (2003), and the UCDP onset measure when the battle deaths threshold required for an onset is 25 instead of 1000 battle-related fatalities per year. We refer to Janus and Riera-Crichton (2015) for the detailed variable definitions and further discussion.
53
Appendix C
The estimation of linear compared to interval regressions and national vs. subnational data
Instead of using the linear fixed-effects model in the paper, we could alternatively follow Bazzi
and Blattman (2014), who estimate the effects of export price changes on battle-related fatalities
with an interval regression. This methodology, which is a non-linear estimation procedure, has
the advantage that it is precisely designed for situations where the researcher observes either an
interval or a specific value for the dependent variable. In our context, about a third of the battle
deaths observations are intervals. However, the interval regression model shares the potential
limitations of many other non-linear models. In particular, it does not allow us to include country
fixed effects, which are usually considered to be important in cross-country estimation.
Moreover, compared to the linear model it is more important, but it appears to be harder, to test
whether the errors are normally distributed. The reason why the error distribution is more
important than in the linear model is that the likelihood contribution of each interval observation
is the probability that the realized error term puts the dependent variable in the observed interval,
, (a1)
where and are the low and high estimates for battle deaths in country in year and
is the standard normal cdf. Therefore, the likelihood contribution and the likelihood
maximizing value of depend on assuming normality. In contrast, non-normally distributed
−Φ−
−Φ=<+<
σβ
σβ
εβ itlitithit
hitititlit
xyxyyxypr )(
lity hity i t
( ).Φ
β
54
errors do not bias the linear estimates (Arabmazar and Schmidt 1982; Lewbel and Linton
2002).†††
The fact that we estimate conflict intensity at the country level contrasts with the growing
study of subnational conflict outcome in the empirical conflict literature. data. Nonetheless, there
are three reasons why we doubt that the country-level approach will bias the estimates. First, the
main concern with cross-country studies is probably that countries have highly persistent but
unobservable cultural, historical, institutional, geographic, etc. conflict characteristics (Blattman
and Miguel 2010; Djankov and Reynal-Querol 2010; Cotet and Tsui 2013). In this paper,
however, we estimate a fixed-effects panel and not a cross-section of countries. The fixed effects
control for the mean effect of time-invariant, country-specific conflict determinants. Since the
fixed-effects estimator only uses the within-country deviations from the means to identify the
coefficients, ceteris paribus, it should only increase the bias compared to subnational panels if
the within-country deviations from the means are, loosely speaking, more highly correlated with
the error term than the subnational deviations from the means.
Second, due to the fact that subnational units are part of the national economy, they can
be exposed to correlated shocks, general equilibrium effects, and externalities. In that case, the
panel units lose their independence and the errors terms can become spatially correlated. The
creation of refugee flows and infrastructure destruction as well as supply and demand changes on
capita, labor, and goods markets can generate spillovers between neighboring municipalities or
provinces. Finally, it is important to note that the standard empirical civil war definitions in the
literature define a civil war as an armed conflict between the central government and a non-
governmental organization that kills a significant number of individuals (Lacina and Gleditsch
†††Greene (2004) finds that adding fixed effects in the Tobit model might not bias the estimates, but the error
variance and standard errors are incorrectly estimated.
55
2002; Fearon and Laitin 2003; Blattman and Miguel 2010). The fact that the central government
is involved suggests that there is a country-level unified actor that plays a strategic war-game in
which it engages strategically in several subnational battle theaters, and that operates under an
integrated national budget constraint. If a civil war is a strategic game that is played in the
country as a whole, the subnational units may be as inter-dependent as the battle theaters of
World War II.‡‡‡
References (uncited in the main paper)
Arabmazar, A., and Schmidt, P. (1982). ‘An investigation of the robustness of the Tobit
estimator to non-normality.’ Econometrica 50 (4): 1055-1063.
Ashraf, Q, and Galor, O. (2013). ‘The ‘Out of Africa’ hypothesis, human genetic diversity, and
comparative economic development.’ American Economic Review 103 (1): 1-46.
Greene, W. (2004). ‘Fixed effects and bias due to the incidental parameters problem in the Tobit
model.’ Econometric Reviews 23 (2): 125-147.
Lewbel, A., and Linton, O. (2002). ‘Nonparametric censored and truncated regression.’
Econometrica 70 (2): 765-779.
‡‡‡However, there are also examples of subnational violence where the central government is not obviously an
important decision maker (Dube and Vargas 2013; Bazzi and Gudgeon 2017).
56
Appendix D
Robustness of the Table 3 estimates to alternative ethnicity measures and control variables
In Table A2, we re-estimate the Table 3, Column (2) specification in the main paper with
alterative ethnicity measures: a dummy for ethnic diversity in the 15-85th instead of 25-75th
percentile; a dummy for high ethnic polarization with value one when the polarization measure
proposed in Esteban and Ray (1994) exceeds the sample median, under the assumption made in
Montalvo ad Reynal-Querol (2005) and Esteban et al. (2012) that the social distance between the
ethnic groups is one and zero otherwise; dummies for defining a dominant ethnic group as a
group that covers 50-85, 40-90, 45-90, ad 45-85 percent of the population; an above-median
polarization dummy using the alternative Alesina et al. (2003) ethnicity dataset; and a dummy
for ethnic diversity in the 25th-76th percentile using the alternative Soviet Atlas Narodov Mira
dataset. The results remain robust.
In Table A3, we re-estimate the Table 3, Column (2) specification in the main paper but
control for other factors - other than ethnicity - that could mediate the effects of commodity
terms of trade shocks. Column (1) includes an interaction between the terms of trade shocks and
a dummy for above-median mountainous terrain land cover, Column (2) adds an interaction with
a dummy for a non-contiguous state. Columns (3)-(4) interact the shock with dummies for
above-median and intermediate (1st-3rd quartile) religious fractionalization (Fearon and Laitin
2003). Columns (5)-(6) interact the shock with dummies for above-median and intermediate (1st-
3rd quartile) predicted genetic diversity (Ashraf and Galor 2013). Column (7) interact the shock
with dummies for British and French colonies (Fearon and Laitin 2003). Column (8) includes the
controls jointly except for including just one of the two religious diversity and one of the two
predicted genetic diversity measures. Except for the predicted genetic diversity measure, which
comes from Ashraf and Galor (2013), all the controls come from Fearon and Laitin (2003).
57
TABLE A2
Robustness to alternative ethnicity measures (1) (2) (3) (4) (5) (6) (7) (8)
Estimation Method LSDV LSDV LSDV LSDV LSDV LSDV LSDV LSDV
Dep. Variable: Ln (battle deaths)
∆TOT(t) -31.302 -15.914* -15.975* -32.470*** -17.872* -15.975* -34.180*** -17.107*
[19.082] [8.755] [9.300] [11.296] [10.277] [9.300] [9.222] [9.583]
∆TOT*EF1585 33.252
[20.016]
∆TOT*High polar 18.024*
[9.799]
∆TOT*Plural5085 18.812*
[10.770]
∆TOT*Plural4090 35.404***
[12.291]
∆TOT*Plural4590 20.515*
[11.428]
∆TOT*Plural4585 18.812*
[10.770]
∆TOT*Hi polar Ales 39.112***
[9.749]
∆TOT*ID(Sovi.dta) 20.116*
[10.995]
Duration -0.013 -0.013 -0.013 -0.013 -0.013 -0.013 -0.013 -0.013
[0.022] [0.022] [0.023] [0.022] [0.023] [0.023] [0.021] [0.022]
First Year Dummy -0.952*** -0.926*** -0.939*** -0.952*** -0.945*** -0.939*** -0.919*** -0.927***
[0.158] [0.160] [0.167] [0.162] [0.166] [0.167] [0.162] [0.165]
Observations 900 904 893 893 893 893 899 900
R-squared 0.518 0.518 0.519 0.521 0.519 0.519 0.523 0.519
# countries 79 80 78 78 78 78 79 79
p-val shocks 0.11 0.07 0.09 0.01 0.09 0.09 0 0.08
p-val interac. 0.10 0.07 0.08 0.01 0.08 0.08 0 0.07
p-val shocks+interac. 0.62 0.57 0.47 0.42 0.48 0.47 0.09 0.43
Year dummies Y Y Y Y Y Y Y Y
Cntry/conf time trnds Y Y Y Y Y Y Y Y
Note: Robust standard errors clustered at the country-level (except in Column (1)) in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. Columns (1)-(8) estimate the Table 3, Column (2) specification in the main paper with alterative ethnicity measures: a dummy for ethnic diversity in the 15-85th instead of 25-75th percentile; a dummy for high ethnic polarization with value one when the polarization measure proposed in Esteban and Ray (1994) exceeds the sample median, under the assumption made in Montalvo ad Reynal-Querol (2005) and Esteban et al. (2012) that the social distance between the ethnic groups is one and zero otherwise; dummies for defining a dominant ethnic group as a group that covers 50-85, 40-90, 45-90, ad 45-85 percent of the population; an above-median polarization dummy using the alternative Alesina et al. (2003) ethnicity dataset; and a dummy for ethnic diversity in the 25th-76th percentile using the alternative Soviet Atlas Narodov Mira dataset.
58
TABLE A3
Robustness to geographical, historical, and non-ethnic diversity measures (1) (2) (3) (4) (5) (6) (7) (8)
Estimation Method LSDV LSDV LSDV LSDV LSDV LSDV LSDV LSDV
Dep. Variable: Ln (battle deaths)
∆TOT(t) -34.110*** -56.613*** -18.351* -20.896** -21.045* -15.603 -37.573*** -38.500
[9.669] [14.136] [10.292] [10.268] [12.048] [12.164] [11.466] [25.998]
∆TOT*ID(t) 31.104*** 52.565*** 19.688* 14.819 17.204 21.311 31.975** 67.057***
[10.332] [14.800] [11.557] [10.998] [12.986] [14.340] [12.244] [22.844]
∆TOT*(Hi%Mnts) -2.541 -31.785
[6.199] [26.642]
∆TOT*(non-contig) 41.859* 44.609
[22.827] [28.664]
∆TOT*(Hi rel.frac) 2.432
[7.198]
∆TOT*(Int. rel.frac). 9.367 18.627
[11.020] [20.914]
∆TOT*(Hi gen.div) 8.003
[10.686]
∆TOT*(Int. gen.div). -2.826 -25.697
[17.521] [19.638]
∆TOT*Brit. colony 2.294 -24.350
[6.566] [26.385]
∆TOT*Fr. colony 15.652 33.512
[22.677] [26.074]
Duration -0.036 -0.035 -0.013 -0.012 -0.012 -0.013 -0.035 -0.031
[0.030] [0.030] [0.022] [0.022] [0.022] [0.022] [0.030] [0.029]
First Year Dummy -0.542*** -0.549*** -0.927*** -0.922*** -0.933*** -0.928*** -0.540*** -0.568***
[0.147] [0.144] [0.165] [0.166] [0.165] [0.164] [0.147] [0.147]
Observations 702 702 899 899 900 900 702 701
R-squared 0.637 0.641 0.519 0.519 0.519 0.519 0.638 0.646
# countries 75 75 78 78 79 79 75 74
p-val shock 0 0 0.08 0.05 0.08 0.20 0 0.14
p-val interac. 0 0 0.09 0.18 0.19 0.14 0.01 0
p-val shock+interac. 0.16 0.17 0.85 0.58 0.72 0.76 0.41 0.36
Year dummies Y Y Y Y Y Y Y Y
Cntry/conf time trnds Y Y Y Y Y Y Y Y
Note: Robust standard errors clustered at the country-level (except in Column (1)) in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. Columns (1)-(8) estimate the Table 3, Column (2) specification in the main but paper but controls for other factors than ethnicity that could mediate the effects of commodity terms of trade shocks. Column (1) includes an interaction between the terms of trade shocks and a dummy for above-median mountainous terrain land cover, Column (2) adds an interaction with a dummy for a non-contiguous state. Columns (3)-(4) interact the shock with dummies for above-median and intermediate (1st-3rd quartile) religious fractionalization. Columns (5)-(6) interact the shock with dummies for above-median and intermediate (1st-3rd quartile) predicted genetic diversity. Column (7) interact the shock with dummies for British and French colonies. Column (8) includes the controls jointly except for including just one of the two religious diversity and one of the two predicted genetic diversity measures. Except for the predicted genetic diversity measure, which comes from Ashraf and Galor (2013), all the controls come from Fearon and Laitin (2003).
59
Appendix E
Evidence that Indonesia could be considered an intermediately diverse country
In the main paper, we argue that it may be appropriate to categorize Indonesia as an
intermediately diverse country and include it in the intermediately diverse country group. In this
appendix, we explain why we believe this classification may be appropriate. We first provide an
argument based on the coding of the ethnic groups. We then provide an empirical argument that
shows that conflict intensity in Indonesia is poorly explained by the linear specification for the
non-intermediately diverse countries and well explained by the specification for the
intermediately diverse countries, that is, in Indonesia, both positive and negative income shocks
increase the conflict intensity.
In terms of the coding procedure, Indonesia does not quite match our empirical definition
of an intermediately ethnically diverse country: in order to be intermediately diverse, the ethnic
fractionalization index must be in the second to third quartiles or between 0.25-0.68. Indonesia’s
fractionalization index, however, is 0.77. Indonesia, similarly, does not quite satisfy our
definition of ethnic dominance: in order to have a dominant ethnic group, we require that the
largest ethnic group represents 50-85 percent of the population. The largest group in Indonesia,
however (the Javanese) only represent 45% of the population. Nonetheless, Indonesia is clearly
close to our somewhat arbitrary cut-off points for being intermediately diverse and having ethnic
dominance.
More importantly, the case-study of Indonesia’s historical civil conflicts in Ross (2005)
explains that (a) ethnic dominance is an important source of conflict in Indonesia. Moreover, (b)
the Javanese are often grouped with the second-largest group in the country, which is the
Sundanese (Ross 2005, 37):
60
‘Indonesia’s ethnic composition poses a civil war risk, however, because of the dominance of the
largest “ethnic” group, the Javanese. In 1976, the ethnic Javanese constituted 45 percent of the
population; the Sundanese, who are often grouped with the Javanese because they, like the
Javanese, are concentrated on the Island of Java, constituted another 15 percent of the population.
Whether they are treated as 45 percent or 60 percent of the population, the size of this group has
often contributed to antagonism between Indonesians who are indigenous to Java, and those from
other islands. Non-Javanese people see Indonesia’s government and military as Javanese-
controlled.’
If we group the Javanese and Sundanese together instead of separating the groups like in
Fearon’s original (2003) classification, Indonesia’s ethnic fractionalization index falls to 0.63.
This index puts it well within the 0.25-0.68 range which defines our intermediately diverse
countries. Since the largest group ethnic group, which now contains both the Javanese and the
Sundanese, now contains 60% of the population, Indonesia also satisfies the ethnic dominance
definition.
On the empirical side, if we believe that Indonesia resembles a country with intermediate
diversity and ethnic dominance, we should also expect its conflict intensity to follow the model
for the intermediately diverse rather than the non-intermediately diverse countries. Another
reason we might expect such a response is that the conflicts in Indonesia in our dataset pitted the
central government in Java against ethnic secessionists in East Timor, Aceh, and West Papua.
All three areas have natural resources that may be relatively easy to appropriate due to their
geographic concentration, and whose extraction process is likely to be capital-intensive and may
create relatively few employment opportunities that raise the opportunity cost of rebelling. They
include oil in East Timor (Dubois 2000; Le Billon 2007), oil and natural gas in Aceh (Robinson
1998; Dubois, 2000), and timber and minerals in West Papua (Heidbüchel 2007). As a result, we
61
should again expect that positive terms of trade shocks increase conflict in Indonesia rather than
decrease it like in the other non-intermediately diverse countries.
In order to test this idea, Table A4, Column (1) reports the regressions results for the non-
intermediately diverse countries when we allow terms of trade shocks in Indonesia to have
different effects than in the other countries. The results show that we can reject that terms of
trade growth has the same effect in Indonesia as in the other non-intermediately diverse
countries. Moreover, the -24.3 coefficient for the remaining non-intermediately diverse countries
is substantially larger in magnitude than the original Table 4, Column (4) estimate of -15.6.
In Column (2) we add Indonesia to the intermediately diverse sample and test whether, as
we should expect if Indonesia is effectively intermediately diverse, both positive and negative
terms of trade shocks increase its conflict intensity. The results support both hypotheses. In
Columns (3)-(4), we repeat the analysis when we split the sample according to the presence or
absence of ethnic dominance. The results in Colum (3) support that Indonesia responds
differently to terms of trade shocks than the other countries without a dominant group.
Controlling for the differential Indonesia response increases the coefficient magnitude for the
remaining non-dominance countries to -24.8 from -14.7 in Table 5, Column (4). Column (4)
suggests that both positive and negative terms of trade shocks increase conflict intensity in
Indonesia, so its shock responses resemble the responses of the original ethnic dominance
countries. On this basis, we believe that it may be reasonable to either control for the differential
Indonesia response in the main paper or to omit it from the non-intermediately diverse and non-
dominance samples.
62
TABLE A4
The effects of terms of trade growth in Indonesia
(1) (2) (3) (4)
Estimation Method LSDV LSDV LSDV LSDV
Sample NID ID
+Indonesia No ethnic dominance
Ethnic dom. +Indonesia
∆ΤΟΤ(t) -27.268** -24.768**
[10.172] [9.934]
∆ΤΟΤ(t)*Indonesia 42.678*** 37.392***
[13.622] [13.702]
Pos∆ΤΟΤ(t) 19.267** 17.891**
[7.684] [7.801]
Neg∆ΤΟΤ(t) -34.975*** -34.720***
[7.302] [8.062]
Pos∆ΤΟΤ(t))*Indonesia 30.218* 27.834*
[14.975] [15.967]
Neg∆ΤΟΤ(t))*Indonesia -76.787*** -75.869***
[14.575] [16.661]
Duration 0.002 -0.050 -0.001 -0.050
[0.011] [0.038] [0.013] [0.039]
First year -0.899*** -1.101*** -0.967*** -1.054***
[0.215] [0.242] [0.210] [0.250]
Observations 491 436 510 410
R-squared 0.617 0.515 0.626 0.502
Number of countries 39 41 41 38
p-val (Pos∆ΤΟΤ+Pos∆ΤΟΤ*Indon.) 0.02 0.01
p-val (Neg∆ΤΟΤ+Neg∆ΤΟΤ*Indon.) 0.00 0.00
Year dummies Y Y Y Y
Country time trends Y Y Y Y
Note: Robust standard errors clustered at the country-level in brackets. * significant at 10%; ** significant at 5%; *** significant at 1%. ∆ denotes the change in the three-year moving average. Column (1) replicates the Table 4, Column (3) regression model but allows the effect of terms of trade growth to differ in Indonesia compared to the remaining non-intermediately diverse countries. Column (2) reports the estimates when we add Indonesia to the intermediately diverse sample and estimate the separate effects of positive and negative terms of trade shocks. Columns (3) repeat the analysis but divide the sample into the countries without and with ethnic dominance.
63
References (uncited in the main paper)
Dubois, Brian. 2000. ‘The Timor Gap Treaty – where to now?’ Briefing Paper no. 25. Oxford,
UK: Oxfam Community Aid Abroad. Based on initial research by Monique Hanley and Kirsty
Miller.
Heidbüchel, E. (2007). The West Papua Conflict in Indonesia: Actors, Issues and Approaches.
Wettenberg: Johannes Herrmann Verlag.
Le Billon, P. (2007). ‘Geographies of war: perspectives on ‘resource wars’. Geography Compass
1 (2): 163-182.
Robinson, G. (1998). ‘Rawan is as Rawan Does: The Origins of Disorder in New Order Aceh.’
Indonesia 66: 126-57.
64
Appendix F
The role of ethnicity in the conflict observations for NID fuel exporters
In Section 5 in the main paper, we argue that most of the conflict observations for the fuel
exporters outside the intermediate-ethnic-fractionalization range (the 25th-75th percentile of the
Herfindahl-Hirschman ethnic fractionalization index) nonetheless included large or dominant
ethnic groups. Particularly, 91% or 89 of the total of 98 observations for NID fuel-exporters in
the Table 8, Column (1) regression come from just four countries - Angola (1975-2002, 2004,
2007), Azerbaijan (1992-95, 2005), Indonesia (1975-92, 1997-2005) and Sudan (1976, 1983-
2008) - that have a history of ethnic conflict involving large or dominant ethnic groups. The
Angola observations reflect the 1975-2005 postcolonial war to control the government and, from
1991, the Cabinda secession war. The Azerbaijan observations come from the Ngorno-Karabakh
conflict and two aborted coups in 1993 and 1995. The Indonesia observations come from a
mixture of the Acehnese, East Timorese, and West Papuan conflicts. The Sudan observations
come from the Islamic Charter Front coup in 1976, the Second Sudanese Civil War (for 1983-
2008), and the Western Darfur rebellion (2003-08). Below, we argue that most of these conflict
episodes can be characterized as ethnic conflicts and were either partly motivated or partly
financed by fossil fuels.
The main Angolan civil war from 1975-2002 started as a conflict between the largest
three ethnic groups over controlling the central government upon independence from Portugal.
During the war, the country’s oil revenues helped to finance the Angolan government’s war
effort against the UNITA rebels (Le Billon 2000; Bannon and Collier 2003; Le Billon 2000). In
addition, oil is an important factor in the ongoing Cabinda conflict, which we observe from 1991.
This conflict represents the attempt of the mainly ethnic Bakongo-inhabited, oil-rich, and
65
geographically separated Cabinda province in the north to secede (Porto 2003; Minorieties at
Risk 2017).
Although the historical roots of Sudan’s conflict go beyond the discovery of oil, the
discovery of oil reserves in the south and the perception that the northern government displaced
thousands of people to get access to the oil fields contributed to the Second Civil War from
1983-2008 (Johnson 2003; Collins 2005). Oil also helped to finance the Sudanese government
during the Western Darfur rebellion from 2003-08 (Patey 2010).
Indonesia’s conflict years in the sample are a combination of three distinct ethnic
conflicts that all reflected that the Javanese-dominated central government tried to establish
greater control of resource-rich peripheral areas where the ethnic minorities wanted to secede
(see Appendix D).
Azerbaijan’s 1992-94 and 2005 observations reflected that the mainly Armenian-
inhabited Ngorno-Karabakh region attempted to secede to Armenia. Although Ngorno-Karabakh
is not known to possess fossil fuels, oil extraction appears to have helped to finance Azerbaijan’s
war effort (Kaldor 2007, 163):
‘Because of the collapse of the official [Soviet] economy and because, in any case, taxation had
been centralised in the Soviet era, there was almost no official funding. On the Armenian side,
funding was almost entirely war related – diaspora support, Russian military assistance, loot and
pillage, contraband trade (especially petroleum products) and hostagetaking…On the Azeri side,
the government was able to commandeer crude oil from the Azerbaijan State Oil Company
(SOCAR) either for use at the front or for sale...’
66
Related, our 1995 observation for Azerbaijan is a coup attempt that Cornell (1999) argues can
most convincingly be explained be Russia’s desire to control the country’s oil (Cornell 1999,
57):
‘Moscow saw its control over Azerbaijan slipping away with the oil deal [that the
Azerbaijan’s state oil company had just re-negotiated with a western oil companies] and
therefore triggered a crisis that would bring its ally to power.’
References (uncited in the main paper)
Collins, R.O. (2005). ‘Civil Wars and Revolution in Sudan.’ Hollywood, CA: Tsehai Publishers
and Distributors.
Cornell, S. E. (1999). The Nagorno-Karabakh Conflict. Inst. för Östeuropastudier.
Johnson, D. H. (2003). The root causes of Sudan's civil wars. Vol. 601. Bloomington, IN:
Indiana University Press.
Kaldor, M. (2007). ‘Oil and conflict: the case of Nagorno Karabakh.’ In Mary Kaldor, Terry
Lynn Karl and Yahia Said (eds.) Oil Wars. London: Pluto Press,157-182.
Le Billon, P. (2000). ‘Angola's political economy of war: The role of oil and diamonds, 1975–
2000.’ African Affairs 100 (398): 55-80.
67
Minorities at Risk. (2017). Assessment for Cabinda in Angola. Accessed March 25, 2017,
http://www.mar.umd.edu/assessment.asp?groupId=54003
Patey, L. A. (2010). ‘Crude days ahead? Oil and the resource curse in Sudan.’ African Affairs
109 (437): 617-636.
Porto, J. G. (2003). Cabinda: Notes on a Soon-to-be-forgotten War. Institute for Security Studies
ISS Paper 77, August
Appendix G
Partial residual plots for the Table 9, Column (1) and (5) regressions.
In order to ensure that outlier effects do not explain the paper’s Table 9, Columns (1) and (5)
results, Figures A2-A3 plot the partial residuals for the positive and negative fossil fuel terms of
trade shocks in the two regressions.
68
FIGURE A2
Partial residual plots for the Table 9, Column (1) regression: the effects of positive and negative
fuel terms of trade shocks in intermediately ethnically diverse fuel exporters. Herfindahl-
Hirschman ethnic fractionalization index in the 25-75th percentile.
76Colombia
75Colombia
05Colombia
79Iraq
73Iraq78Iraq74Colombia
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92Iran
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-2-1
01
23
e( lNewtotbdeadbes_impute | X )
-.02 -.01 0 .01 .02 .03e( posdy3mactotfuel | X )
coef = 28.687887, (robust) se = 14.622514, t = 1.96
88Iraq
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00Iran95Iraq
82Iraq
79Iraq
83Iraq
74Colombia75Colombia90Iraq96Iraq
77Iraq
91Iraq
92Iraq
86Colombia
87Colombia
88Colombia
-2-1
01
23
e( lNewtotbdeadbes_impute | X )
-.02 -.01 0 .01 .02 .03e( negdy3mactotfuel | X )
coef = -38.466011, (robust) se = 2.8476158, t = -13.51
69
FIGURE A3 Partial residual plots for the Table 9, Column (5) regression: the effects of positive and negative
fuel terms of trade shocks in intermediately ethnically diverse fuel exporters. Herfindahl-
Hirschman ethnic fractionalization index is in the 15th - 85
th percentile and large producers
omitted.
95Azerbaijan
76Sudan
75Colombia
05Sudan
76Colombia
98Indonesia
05Colombia
01Sudan
83Indonesia
99Indonesia
03Indonesia
79Indonesia
04Angola
78Indonesia
92Indonesia
01Colombia
84Indonesia85Indonesia
93Angola
89Indonesia
07Angola93Colombia
81Colombia88Sudan02Colombia
02Sudan92Angola
80Indonesia
93Sudan
82Indonesia75Angola
00Colombia
77Indonesia
91Indonesia
00Sudan
97Colombia
83Angola
06Sudan
94Angola
85Angola
96Colombia
97Indonesia
87Sudan
87Colombia
91Angola86Indonesia
86Angola81Angola
88Colombia
89Angola
97Sudan
77Angola84Angola
80Angola08Sudan
90Indonesia
90Angola
87Angola75Malaysia74Colombia94Colombia
90Trinidad &Tobago73Colombia81Malaysia90Colombia
74Malaysia
90Sudan08Colombia00Indonesia
94Sudan
86Colombia
82Angola
96Angola
91Colombia
78Angola
04Colombia
96Sudan
79Angola
04Sudan
94Azerbaijan
88Angola
86Sudan
06Colombia
99Angola
87Indonesia
92Colombia
07Sudan
98Angola82Colombia
91Sudan
02Indonesia
89Sudan
07Colombia
77Colombia
85Colombia
99Colombia
88Indonesia80Colombia
97Angola
89Colombia
76Angola
84Sudan
02Angola
00Angola
03Colombia
05Azerbaijan
98Colombia84Colombia85Sudan
92Azerbaijan
81Indonesia92Sudan
83Colombia
95Angola
99Sudan
01Angola98Sudan
03Sudan
83Sudan
04Indonesia
95Colombia
78Colombia95Sudan
79Colombia93Azerbaijan
01Indonesia
75Indonesia
05Indonesia76Indonesia
-3-2
-10
12
e( lNewtotbdeadbes_impute | X )
-.005 0 .005 .01e( posdy3mactotfuel | X )
coef = 119.31958, (robust) se = 39.467298, t = 3.02
93Azerbaijan
88Indonesia
87Indonesia
94Azerbaijan86Indonesia
92Sudan
83Sudan
92Colombia
95Sudan
79Colombia
95Colombia
05Indonesia91Sudan90Sudan
92Angola
80Colombia
75Indonesia82Colombia77Colombia
07Angola
88Angola
03Sudan
04Angola
95Angola98Sudan
87Angola91Colombia
96Sudan
90Colombia
76Indonesia
78Colombia
03Colombia
81Colombia
76Sudan83Colombia01Indonesia
86Angola
97Sudan
98Colombia
00Sudan85Indonesia02Sudan
00Colombia
84Sudan
75Angola
02Colombia74Malaysia
06Colombia77Angola79Angola
89Sudan
82Angola
89Colombia
96Colombia
78Angola
99Sudan
97Indonesia
99Colombia
08Colombia85Sudan81Malaysia
97Colombia
01Angola80Angola73Colombia90Trinidad &Tobago
81Angola85Angola84Colombia
04Sudan
84Angola
08Sudan
99Indonesia
99Angola
89Angola
04Colombia
01Colombia
00Angola
89Indonesia
76Angola
06Sudan84Indonesia
02Angola74Colombia98Angola75Malaysia
05Azerbaijan90Angola91Angola05Colombia07Colombia85Colombia
02Indonesia
01Sudan
00Indonesia
97Angola
92Indonesia
07Sudan81Indonesia
04Indonesia
83Angola
78Indonesia
05Sudan
96Angola
98Indonesia
76Colombia
75Colombia80Indonesia
94Angola
82Indonesia
77Indonesia03Indonesia
90Indonesia
93Angola
79Indonesia
94Colombia
91Indonesia
86Colombia
94Sudan
86Sudan
93Sudan
93Colombia
87Colombia
83Indonesia
88Colombia
87Sudan
88Sudan
95Azerbaijan
92Azerbaijan
-3-2
-10
12
e( lNewtotbdeadbes_impute | X )
-.005 0 .005 .01e( negdy3mactotfuel | X )
coef = -109.38375, (robust) se = 29.820848, t = -3.67