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Economic Shocks and Battle Deaths in Civil Wars

Thorsten Janus. University of Wyoming. Associate Professor of Economics, Department of

Economics and Finance, Dept. 3985, 1000 E. University Ave., Laramie, WY 82071, United

States. Email: tjanus@uwyo.edu. Ph.: 1-307-766-3384.

Daniel Riera-Crichton. Bates College. Associate Professor of Economics, Department of

Economics, Bates College, Pettengill Hall, Room 273, Lewiston, ME 04240, United States.

Email: drieracr@bates.edu. Ph.:1-207-786-6084.

Abstract

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 are a

critical determinant of the way countries react to commodity-induced income shocks and that

positive as well as negative income shocks can intensity ongoing conflicts. While negative income

shocks increase the death toll everywhere, positive shocks in countries with ethnic dominance and

polarization also increase the death toll. This positive relationship may appear because income

gains encourage rent-seeking and make it easier to finance conflicts. Finally, we show that the

positive effect of terms of trade gains is driven by the effects of fossil fuel terms of trade gains in

fuel exporting countries. These results are consistent with resource-curse theories predicting that

rent-seeking can destroy the benefits of commodity windfalls, but only for countries with adverse

ethnic compositions.

Keywords: Civil war, social conflict, ethnicity, ethnic diversity, commodity terms of trade

JEL Classification: D74, O11, O17

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1. Introduction

During the course of civil wars around the world, the size of the death toll can vary immensely.

The beginning and end phases of the guerrilla wars in El Salvador, Guatemala, and Nicaragua, for

example, generated less than a thousand fatalities per year. During the intervening years, however,

the death toll occasionally exceeded 10,000. Bosnia and Herzegovina’s civil war from 1992-95

killed 17,000 individuals already in the first year, followed by 23,000, 11,000, and 1,300 people

in the remaining years.1 In this paper, we ask to what extent exogenous national income shocks

can explain the death toll variations during the conflict years. If it turns out that the income shocks

are important, then economic growth and stabilization policies may be able to save a large number

of lives.

The effort to identify the effects of income changes on civil war intensity raises three

important identification concerns. First, income is likely to be endogenous to conflict. In order to

address this concern, we study the effects of commodity terms of trade-generated income changes,

that is, the income changes that happen when commodity export prices change relative to

commodity import prices. The focus on commodity prices allows us to isolate the most volatile

and plausibly-exogenous component in the overall terms of trade. On the other hand, the focus on

the relative price of exports – the price of exports in terms of imports – allows us to account for

the fact that, according to the large international economics literature, it is the relative rather than

the absolute export prices that determine the real national income level (Svensson and Razin 1983,

Matsuyama 1988, Easterly et al. 1993, Turnovsky 1993, Mendoza 1995).

1 Figure 1 illustrates this variance in death tolls with the time-series for the annual death toll for these four countries

during active conflict years in our dataset.

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The second empirical challenge is that wartime economies can, potentially, respond

differently to economic shocks than peacetime economies. Not only do wars increase uncertainty,

destroy the capital stock, and generate capital flight and population displacement, but they also

force the remaining individuals and firms to prioritize their survival and protect their assets instead

of pursuing conventional utility and profit maximization. The decline of the rule of law can shift

the country’s comparative advantage from contract, transport, and capital intensive sectors like the

manufacturing sector to the more informal and labor-intensive sectors. As the government forces

retreat from the rebel-held areas, the rebel forces can create “shadow” states that have their own

legal and financial systems, tax collections, and public goods provision. This has happened, for

instance, in Afghanistan, Colombia, and the ISIS-held portions of Iraq and Syria in the last few

decades. In addition, both the government and the rebels can target civilians in the areas that remain

contested, either to win their “hearts and minds” via security and public goods provision (Valentino

et al. 2004), or to intimidate and displace them so they will not support the opponent (Berman et

al. 2011). The lack of the rule of law can allow warlords and criminals – as well as elements within

the government and rebel forces - to pursue illegal activities like looting, extortion, kidnapping,

smuggling, drug production, piracy, the appropriation of foreign aid shipments, and the

unsustainable and untaxed harvest of natural resources (Keen 2000, Rubin 2000, Le Billon 2001,

Bannon and Collier 2003, Dube and Vargas 2013, Nunn and Qian 2014).2 The fact that most of

these structural changes only happen after the conflict begins - and not in the peace period leading

2 Additionally, the war can change the way the government responds to economic shocks and the way the private

sector responds to policy. For example, instead of using counter-cyclical monetary and fiscal policies to respond to a

positive terms of trade shocks (which is probably the most common policy recommendation). the policy response in

wartime may be tailored to meet strategic military goals like weakening the rebel forces.

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up to the war - at least if the government enforces peace and the rule of law - makes it unclear that

the economic model for wartime and peacetime economies is the same. In order to address this

concern, we depart from the implicit assumption used in most of the empirical civil war literature

that the same empirical model can explain both the onset of civil wars and the outcomes that only

occur conditional on the war onset, such as the incidence, intensity, and duration of the conflict

after the onset year. In particular, in order to estimate only the intensity variation during the conflict

years and not the onset of the conflict, we drop all the peace years from the sample.

The third empirical challenge is that different types of income shocks may have different

effects in different conflict environments. Based on our reading of the conflict literature, we test

two types of non-linear hypothesis. First, we test the idea that adverse ethnic compositions in the

form of ethnic dominance and polarization can distort the way countries respond to economic

shocks in the sense that they mismanage the windfall gains from terms of trade booms and fail to

adjust efficiently to terms of trade declines. Second, we note that the literature suggests that both

positive and negative income shocks can increase conflict. On the one hand, positive shocks

encourage rent-seeking over the growing income pool and make it easier to pay for conflict inputs.

On the other hand, negative shocks decrease the opportunity cost of fighting. If this is true, we

should expect positive and negative income shocks to have different conflict effects.

The results of this study support the idea that economies with adverse ethnic compositions

in the form of ethnic dominance and high ethnic polarization respond differently to economic

shocks. Moreover, we find powerful evidence that both positive and negative income shocks can

increase conflict. Particularly, we show that (1) negative income shocks increase the annual death

toll per conflict year in all countries, but (2) in countries with ethnic dominance and polarization,

positive income shocks increase the death toll by roughly the same percent.

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Finally, we also address a follow-up question based on the initial evidence: why do positive

income shocks only increase conflict intensity in countries with adverse ethnic compositions? In

order to address this question, we draw on the literature on the natural resource curse. This

literature argues that natural resource dependence, and particularly oil and fossil fuel dependence,

can encourage rent-seeking, corruption, internal armed conflict, and other governance problems

(Fearon 2005, Ross 2006). In addition, we note that a part of the empirical conflict literature has

argued that price gains for capital-intensive (Dube and Vargas 2013, Mahmud and Basher 2014,

Aguirre 2016) and geographically concentrated (Le Billon 2001, Ross 2004) natural resources can

increase conflict. The idea is that price gains in capital-intensive sectors can increase the rent pool

conflict participants can conquer by, for example, rebelling against the government and seceding

with geographically concentrated resources, such as oil endowments (Dal Bó and Dal Bó 2011,

Dube and Vargas 2013). We, therefore, test whether fossil-fuel generated terms of trade gains may

have different effects than other sources of terms of trade gains and whether the effects differ for

net-fuel exporting countries (which may be more likely to suffer from resource-cursed-institutions

problems). Our results show that fossil fuel-based terms of trade gains are associated with more

conflict in the net-fuel exporting countries with adverse ethnic compositions. In fact, the positive

effects of fuel terms of trade gains in these particular countries – net-fuel exporting countries with

adverse ethnic compositions – account for the positive effects of terms of trade-induced income

growth in the full sample. The results, thus, provide conditional support for resource-curse theories

and conflict theories linking fossil fuels to conflict: we can support the theories for countries with

adverse ethnic compositions if not for all countries. This observation suggests that the net-fuel

exporting countries with adverse ethnic compositions may be less able to manage fossil fuel based

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terms of trade shocks efficiently, reacting with more conflict in ongoing civil wars both when the

fuel terms of trade increase and when they decrease (Rodrik 1999).

To sum up, this paper belongs to the literature that relates economic factors to civil war

outcomes (Blattman and Miguel 2010). We make three contributions to this literature. First, we

estimate the determinants of the intensity of civil conflicts rather than the onset and incidence,

which is the main focus in the existing literature. Due to the reasons we discussed earlier, it is far

from clear that civil war intensity can be explained with the same regression model as the onset

and incidence of war (Keen 2000, Rubin 2000, Dube and Vargas 2013, Nunn and Qian 2014).

Bazzi and Blattman (2014), similarly, conclude that the onset and continuation of conflict follow

different processes. Second, while several recent papers estimate the effects of commodity export

price shocks on conflict (Brückner and Ciccone 2010, Dube and Vargas 2013, Bazzi and Blattman

2014, Mahmud and Basher 2014, Aguirre 2016), the international economics literature only relates

the change in export prices relative to import prices, that is, the change in the terms of trade, to

national income (Agenor and Montiel 1999, Krugman, Obstfeld and Melitz 2014).3 Finally, we

study the role of adverse ethnic compositions in mediating the effects of terms of trade-generated

income shocks and the idea that has been widely suggested, but rarely tested in the conflict

literature, that positive and negative income shocks can have asymmetric effects.

In the remainder of the paper, Section 2 develops the theoretical motivation for studying

the effects of terms of trade-generated income shocks rather than export-price generated income

shocks, why we believe adverse ethnic compositions in the form of ethnic dominance and

polarization might mediate the effects of income shocks, and why we believe that both positive

3 In Appendix I, we show that regressions linking conflict to changes in export prices without accounting for the

effects of import price changes can, potentially, generate omitted variables bias.

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and negative income shocks can, potentially, increase conflict. Section 3 presents the dataset and

estimation framework. Section 4 presents the results. Section 5 studies the effects of fossil fuel

dependence and fossil fuel terms of trade shocks on conflict. Section 6 concludes the paper.

2. Theoretical background

In this section, we develop the informal theoretical and empirical bases that lead to us to expect

that terms of trade shocks may be a more precise measure of income shocks than just export price

changes; that adverse ethnic compositions in the form of ethnic dominance and polarization can

mediate the effects of income shocks; and that positive and negative income shocks can,

potentially, have asymmetric effects on conflict intensity.

(1) The effects of terms of trade shocks relative to (just) export price shocks

Although several recent conflict studies estimate the effects of commodity export price shocks on

conflict (Brückner and Ciccone 2010, Dube and Vargas 2013, Bazzi and Blattman 2014, Mahmud

and Basher 2014, Aguirre 2016), it is important to note that the traditional literature in economics

that studies the effects of international price shocks is the literature on terms of trade shocks. This

literature, however, only relates the change in export prices relative to import prices, that is, the

change in the terms of trade, to national income (Svensson and Razin 1983, Matsuyama 1988,

Easterly et al. 1993, Turnovsky 1993, Mendoza 1995, Rodrik 1999, Agenor and Montiel 1999,

Krugman et al. 2014). Intuitively, increases in import prices decrease the national trade balance

and the income left to spend on domestic goods just as much as an export price decline, or this is

at least what happens on impact before the economy reacts to the price shocks. For example, in a

basic macroeconomic framework with a representative export good and a representative import

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good (but for simplicity without a non-tradable good), a 10% decrease in export prices and a 10%

increase in import prices will both decrease the trade balance and national income by 2% on

impact. Foreign currency earnings fall by 2%, but the cost of living is just the import price, which

also falls 2%, so the real income level remains the same. In a two-sector Hechscher-Ohlin model

model with capital and labor inputs and an export and an import-competing sector, a proportional

decline in export and import prices leaves the relative return to capital and labor across the sectors

as well as the trade volumes unchanged. Thus, there is no factor reallocation or real income change.

The same is true in the specific-factors Ricardo-Viner model of trade, where one of the inputs is

immobile across sectors (Krugman et a. 2014).

Another interpretation of terms of trade effects is that export price declines decrease

nominal national income measured in terms of export goods, but import prices decrease the real

income level as the export goods buy fewer imports (Agenor and Montiel 1999, Krugman et al.

2014). Lederman and Porto (2016) study the effects of commodity prices on household welfare

using survey data from Africa and Latin America. They find that households devote a large fraction

of their budgets to commodities and that households often depend on commodities to earn income

as well. Thus, we should expect commodity prices to affect both nominal income and its

purchasing power. If households are net-exporters of produced goods and net-importers of

purchased goods (or their purchased goods are produced domestically with international

commodity inputs like oil (Lederman and Porto 2016)), their welfare should depend negatively on

the export-import price ratio, which is the terms of trade.

(2) The effects of adverse ethnic compositions in the form of ethnic dominance and polarization

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Although the empirical conflict literature has not established a linear effect of ethnic diversity or

fractionalization – that is, it is unclear that moving gradually from having a single to many small

ethnic groups increases conflict (Fearon and Laitin 2003, Blattman and Miguel 2010) - there is

evidence that countries with either a single large ethnic group and some smaller groups, or at least

two large ethnic groups, may be conflict-prone. Our reading of the case-study literature (Gellner

1983, Horowitz 1985, Smith 1986, Posen 1993, Gurr and Harff 1994, Østby 2008, McGarry and

O’Leary 2013) suggests that the problem is often that one of the large groups can try to dominate

the central government and implement policies that, effectively, expropriate the assets and tax the

income of the excluded groups (Collier and Hoeffler 2004, Ross 2005, Fearon and Laitin 2011,

Weiner 2015). The excluded groups can then resist the takeover attempt by seceding, rebelling, or

conducting military coups. In Chad and Sudan, for example, the Arab population has historically

dominated the many smaller groups in the country both politically and culturally. This dominance,

and the economic neglect of the non-Arab areas far from the capital, has led to protracted ethnic

conflict. In Indonesia and Russia, the dominance of the Javanese and Russians may have

contributed to the ethnic secessionist conflicts in Aceh, East Timor, West Papua, and Chechnya.

The Sri Lankan Tamil population fought for autonomy to escape the Sinhala-dominated central

government. In Burundi, Iraq, and Syria, the Tutsi, Sunni Muslim, and Alawite minorities

conducted military coups and established ethnic minority regimes in the 1960s-1970. Following

Collier and Hoeffler (2004), we call a situation where a large ethnic group lives together with other

groups that are either individually or jointly a significant population share ethnic dominance.

Another adverse ethnic configuration may be ethnic polarization. The ethnic polarization

idea (Esteban and Ray 1994, 1999, 2011, Estbeban et al. 2012, Montalvo and Reynal-Querol 2005)

focuses on situations where there are at least two large ethnic groups. As the histories of

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Afghanistan, Angola, Bosnia, Croatia, Guatemala, Iraq, Israel, Lebanon, and Sri Lanka illustrate,

one can think of many examples where the two or three largest ethnic groups have fought each

other. Esteban and Ray (1999) develop a theoretical conflict model that allows for an arbitrary

number of warring groups and show that economies where the groups are a larger share of the total

population and more symmetric in terms of their size expend the most total conflict effort. All else

constant, therefore, we should expect conflict intensity to increase with the Esteban and Ray’s

(1994) social polarization measure, which captures the degree of multi-polarity in the social group

size distribution and is maximized where there are precisely two equally large groups. Consistent

with this idea, Montalvo and Reynal-Querol (2005) and Esteban and Ray (2011) show that the

polarization index predicts the incidence of conflict across countries. Janus and Riera-Crichton

(2015) show that commodity terms of trade shocks predict the onset of civil wars in countries with

an intermediate Herfindahl-Hirschman ethnic diversity or fractionalization index, which is a proxy

for having either ethnic dominance or an above-median ethnic polarization level.

Since our main interest in this paper is the effects of income shocks rather than the direct

effects of ethnicity - and because we want to estimate a fixed effects country panel and the standard

cross-country ethnicity datasets do not have time-varying ethnicity data (Alesinsa et al. 2003,

Fearon 2003) –, we build ethnicity into the estimation by asking how adverse ethnic compositions

affect the way countries respond to exogenous income shocks. Given the evidence that both

negative income shocks (Miguel et al. 2004, Brückner and Ciccone 2010, Blattman and Miguel

2010) and adverse ethnic compositions can increase conflict, it is natural to ask whether part of

the problem is that adverse ethnic compositions distort the response to economic shocks. For

example, if there is a social consensus on spending and saving priorities, windfall gains from

commodity prices in good times should increase economic activity and job creation, and in bad

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times society can reach agreement on how should bear the economic adjustment burden (Rodrik

1999). In countries without a consensus, however, rapid income growth can, potentially, lead to

rent-seeking, such as ethnic conflicts over controlling the government and its budget and efforts to

secede with natural resources (Esteban and Ray 1999, Ross 2005). Downturns can lead to

distributional conflict over who should suffer the spending cuts, particularly given rising

unemployment rates and similar social stress factors.

(3) The asymmetric effects of positive and negative income shock

Although many studies have found that negative income shocks are associated with conflict

(Miguel et al. 2004, Miguel 2005, Brückner and Ciccone 2010, Bazzi and Blattman 2014, Blattman

and Miguel 2010), there is also plenty of evidence linking positive income shocks to conflict. In

the early 1990s, for example, US food aid to Somalia increased the inter-warlord conflict. This

increase conflict was likely due to the warlords perception that it would be valuable to control the

scarce commodity (Dowden 2009, Albright 2013, Nunn and Qian 2014). Angrist and Kugler

(2008) find that increases in coca production increased violence in Colombia and conclude that

the conflict is fueled by the financial opportunities that coca provides. During Sri Lanka’s civil

war, the Tamil Tigers relied on international remittances to purchase military equipment. Although

we are unaware of studies linking remittance changes to conflict-intensity changes during the war,

we should expect that higher remittance years should be more intensive conflict years. Angola’s

UNITA rebels and Sierra’s Leone’s Revolutionary United Front (RUF) used diamond revenues to

finance the war or at least diamonds were an important motivating factor (Bannon and Collier

2003). Timber and minerals exports may have 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,

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Dube and Vargas 2013, Global Witness 2015). In Iraq after the US-led invasion in 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, see http://web.stanford.edu/group/mappingmilitants/cgi-bin/, accessed November 28,

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 a number of oil fields, which by September 2014

may have earned it $1-2 million per day (Byman 2015).4 Dube and Vargas (2013) hypothesize that

when labor-intensive goods prices increase, the resulting income gains decrease conflict by

increasing the opportunity cost of fighting. However, when capital-intensive goods prices increase,

the income gains increase the contestable rent pool and the return to fighting. They find evidence

for both hypotheses in a panel of Colombian municipalities.

Our reading of these papers suggests that both positive and negative income shocks can

increase conflict. On the one hand, negative income shocks may mainly have an opportunity cost

effect, that is, more negative income changes (such as a fall in a negative growth rate from -2% to

-3%) should decreases the opportunity cost and increase the conflict intensity (Hirshleifer 1999,

Miguel et al. 2004, Chassang and Padró-i-Miquel 2009). Moreover, recessions might increase

4 This estimate comes from an expert assessment cited in the New York Times, 16 September, 2014: “How ISIS

Works.” Another recent article suggest that oil revenues are important for the Iraqi Kurds who 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.”).

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conflict by increasing bankruptcy rates and force individuals to switch from peaceful sectors to a

conflict sector. Additionally, economic downturns can bring conflict by increasing mental health

problems and the desire to look for “scapegoats” and violent social identifiers that can restore the

individual’s sense of belonging and empowerment (Miguel 2005, Cramer 201, Zivin et al. 2011).

On the other hand, positive shocks can have, first, a conflict-finance effect: as long as the warring

parties operate in imperfect financial markets, marginal income gains can be used to procure

additional labor and capital inputs in the conflict.5 Second, positive income shocks can have rent-

seeking effects as income growth increases the payoff to conquering and taxing (or looting) areas

during the war, as well as the payoff to winning the war and the control of the central government

(Bannon and Collier 2003, Angrist and Kugler 2008, Dube and Vargas 2013, Rustad and

Binningsbø 2012),

The assumption that negative and positive income shocks can have different (asymmetric)

effects on conflict intensity is testable: if we allow positive and negative shocks to have different

effects in the econometric analysis, we can let the data decide whether the coefficients are

statistically different. Dube and Vargas (2013) and Bazzi and Blattman (2014), similarly,

hypothesize and test whether the effects of income growth on conflict tends to be positive when it

comes from higher prices for capital-intensive commodities, but negative when it comes from

higher prices for labor-intensive commodities. Although they argue that income shocks can always

have opposing conflict effects due to a rent-seeking mechanism (which encourages conflict) and

an opportunity cost mechanism (that discourages conflict), they assume, partly based on the

5 Alternatively, they can be used to win the “hearts and minds” of civilians in guerrilla-warfare situations by providing

them with physical security and public goods (Berman et al. 2011) or they can be used for propaganda or to bribe

political leaders.

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theoretical model of Dal Bó and Dal Bó (2011) that the rent-seeking mechanism is more likely to

dominate when the income shocks comes from a capital-intensive good price increase (such as an

increase in oil prices) than when it comes from a rising price of a labor-intensive good like coffee.

We similarly, assume (and then test) that the rent-seeking and conflict-finance mechanisms are

more likely to dominate after a positive income shocks than after a negative shock.

3. Data and Estimation

In this section, we, first, explain the data sources and variable definitions used in the paper. We,

then, outline our estimation procedure. Tables 1 and 2 display the summary statistics for the data.

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, p. 1 and p. 9; Lacina and Gleditsch (2005), Battle Deaths Dataset, Codebook for Version 3,

2009, p. 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, p. 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

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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.”

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. The methodology follows Bazzi and Blattman (2014), who use it

to construct the lagged dependent variable, though not the dependence variable, in their interval

regressions for battle deaths. As we explain below, however, we prefer to estimate a conventional

linear panel model (that is, to use least-squares-dummy-variables or LSDV estimation) so we can

include country fixed effects and country-specific time-trends, and so we do not have to assume

that the regression errors are normally distributed.

Instead of imputing the 30-40% missing best estimates, we could just drop the observations

without a best country-year estimate, we doubt that these observations would constitute a random

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sample. Dropping them could, therefore, cause a severe selection bias problem. For example,

countries that have multiple ongoing conflicts (making missing data for at least one of them more

likely ceteris paribus) may be large countries with many ethnic groups and poor data collection,

which may correlate with vulnerability to commodity price shocks. In any case, as we show below,

our qualitative results are robust to using the either “low” of the “high,” rather than the “best” and

“imputed best,” country-year estimates for battle deaths. They are also robust to using the sample

where Lacina and Gleditsch (2005) were able to consult year-specific sources to identify the

number of battle deaths. This requirement restricts our attention to the observations with high data

quality and automatically excludes most of the imputed observations.

Ethnicity: In order to study the effects of ethnic dominance and polarization, we start by observing

that the two concepts are conceptually distinct. The ethnic dominance concept only requires the

presence of a single large ethnic group together with smaller groups in the country. In contrast, the

polarization measure is larger when the second-largest group is larger ceteris paribus. Countries

like Indonesia and Sudan arguably have a dominant ethnic group, Angola and Bosnia-Herzegovina

are polarized countries, and Rwanda and Sri Lanka are in-between because the second-largest

group is significant, but significantly smaller than the ethnic plurality group. Despite these

conceptual points, however, Janus and Riera-Crichton (2015) show that it is almost impossible to

distinguish countries with ethnic dominance from countries with high polarization in the two most

commonly used cross-country ethnicity datasets of Alesina et al. (2003) and Fearon et al. (2003).

Moreover, it is difficult to distinguish both types of countries from countries with an intermediate

Herfindahl-Hirschman ethnic fractionalization or diversity index. Figure 2, which is copied from

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Janus and Riera-Crichton (2015), shows the point graphically. As Janus and Riera-Crichton (2015,

p. 25) explain:

“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. Overall, Figure 1 suggests that it may be difficult to distinguish the effects of

having an intermediate ethnic diversity level, an intermediately large ethnic plurality or

high ethnic polarization.”

Due to these empirical difficulties, we mainly present the results for intermediately ethnically

diverse countries. However, as we show below, the results look almost identical when we focus

on countries with ethnic dominance and above-median polarization. The three ethnicity dummies

are coded using Fearon’s (2003) ethnicity dataset, which relies on seven criteria to define a

prototypical ethnic group. The main criteria include common ancestry and a sense of community

and self-consciousness as a group. Fearon (2003), thus, codes 822 ethnic groups in 160 countries

using the CIA World Factbook, Encyclopedia Britannica, Library of Congress Country Studies,

and country-specific sources (Janus and Riera-Crichton 2015). However, as we show below, using

the alternative Alesina et al. (2003) ethnicity dataset gives similar results. The intermediate

diversity measure is a dummy with a value of one when the Herfindahl-Hirschman ethnic

fractionalization or diversity index is in the second to third quartiles and zero otherwise. Ethnic

19

dominance is a dummy with a value of one when the largest ethnic group constitutes 50-85 percent

of the population and zero otherwise. High polarization is a dummy 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.

We doubt that the resulting ethnicity measures will lead to endogeneity problems for

several reasons. First, the ethnicity data is not time-varying and social identities like ethnic

belonging seem unlikely to change much over the relatively short time horizon we study. Second,

we show that terms of trade shocks have different effects on battle deaths in intermediately

ethnically diverse countries and that positive and negative shocks have asymmetric effects in those

countries. In order for endogenous ethnicity to explain this, societies that respond differently to

economic shocks for reasons that are not related to their ethnic composition must be more likely

to have an intermediate ethnic diversity level. While we cannot rule this out, the country fixed

effects included in almost all the regressions ensure that we only link deviations from the country

mean terms of trade growth rates to the country mean battle deaths during conflict years. The

omitted variable bias must, therefore, be less than fully captured in the mean death toll. Third, 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).

Fourth, our results are robust to using the alternative and entirely race-based ethnicity data from

Alesina et al. (2003) instead of Fearon’s (2003) partly culture based ethnicity dataset. Finally, there

is also abundant evidence that ethnicity affects the politics of developing countries (Bates 1981,

Horowitz 1985, Chandra 2007). Kramon and Posner (2012), Franck and Rainer (2012) and

Burgess et al. (2015), for example, link ethnic favoritism to education, infant mortality and road

20

construction in Africa (De Luca et al. 2015, Francois et al. 2015). We, therefore, follow the large

literature on ethnicity and social outcomes (Easterly and Levine 1997, Fearon and Laitin 2003,

Collier and Hoeffley 2004, Alesina and La Ferrara 2005, Hegre and Sambanis 2006) in treating

our intermediate ethnic diversity dummy as an exogenous variable.

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)6 and is defined as

(4)

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

6 Rodrik (1998, p. 1014, 1999) and Aizenman et al. (2012) use similar terms of trade measures to predict

macroeconomic performance.

,)/(/)/(ij

ij M

titi

X

titi

jt MUVPMUVPCTOT

jtCTOT j t jtP

i

jX i i

jM

i

jX i

jM

21

. (5)

In the paper’s empirical section, 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. Our empirical specifications therefore

include the growth rate of the three-year moving average of the terms of trade index, which is

computed as follows:

1

2 3

ln / 3 ln / 3t t

jt js js

s t s t

CTOT CTOT CTOT

(6)

Since the growth rate of the three-year moving average is approximately equal to the average

annual growth rate over the three-year period, 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 the CTOT is 0.016.7

In order to formally test whether it was appropriate to include the shock to the moving average

CTOT in the regressions instead of either (i) just the annual CTOT shock in year (t) or (ii) each of

the shocks in years (t-2), (t-1), and (t), we re-estimated the core regressions with the separate

7 If we include the annual terms of trade shocks separately, we can only occasionally reject that the effects are equal.

Moreover, there is no clear pattern in which of the lagged effects are most important and the separate inclusion would

force us to include the three terms of trade shocks plus their respective interactions with a time-invariant dummy for

having an intermediate ethnic diversity level. This makes the results more difficult to interpret and gives us potential

multi-collinearity problems. Thus, we simply include the growth rate of the moving average.

ij

ij

ij

ij

M

titi

M

titi

X

titi

X

titi

tjjt

MUVP

MUVP

MUVP

MUVPCTOTCTOT

)/(

)/(ln

)/(

)/(lnlnln

1)1(1)1(

)1(

22

shocks in the three years. The results showed that we could rarely reject that the effects were equal

and there was no clear pattern in which of the lagged effects were most important. Thus, we found

no econometric justification for either including only the year (t) economic shock or including the

separate annual shocks instead of the change in the log of the three-year moving average CTOT

change.8

Estimation: We regress the logarithm of the number of battle-related fatalities on the relevant set

of commodity price shocks in a linear specification with country and year fixed effects, country-

specific time trends, and robust standard errors clustered at the country level in order to control for

potential serial correlation. Following Bazzi and Blattman (2014), we also control for the duration

of the conflict and a dummy for the first conflict-year. Finally, the initial regressions formally test

whether intermediately ethnically diverse countries respond differently to relative commodity

price shocks by interacting the CTOT shocks with our dummy for these countries. The regressions,

thus, take the form

jt jt jt j jt jt j t j jtb CTOT CTOT D d f z t , (7)

8Another problem with trying to separate the effects of the annual terms of trade shocks is that we use a number of

interaction specifications in the paper. For example, if we replace the regressions in Table 3 with regressions

containing the three annual terms of trade shocks plus their interactions with the time-invariant dummy for having an

intermediate ethnic diversity level, we get six different terms of trade coefficients. This fact not only makes the results

difficult to interpret, but since the ethnicity dummy is not time-varying, we can get multi-collinearity problems.

23

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, jD is the time-

invariant dummy for having an intermediate ethnic diversity level, andjtd and

jtf represent the

duration of the conflict since the onset year and a dummy for the first conflict year (Bazzi and

Blattman 2014) Finally, and are the country and year fixed effects, is the country-

specific time-trend, and is an i.i.d. error term.

Instead of using a linear regression model, we could alternative follow Bazzi and Blattman

(2014), who estimate the effects of export price changes on battle-related fatalities in civil wars

using a non-linear interval regression. This approach has the advantage that it is 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. On the other

hand, 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,

, (8)

jtb j t

j tz tj

jt

itlitithit

hitititlit

xyxyyxypr )(

24

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 errors do not bias

the linear estimates (Arabmazar and Schmidt 1982, Lewbel and Linton 2002).9

4. 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 dummy variable that equals one when the country is intermediately ethnically diverse

(henceforth, ID) and, therefore, tends to exhibit ethnic dominance or high polarization. All the

three CTOT shocks have a negative sign, their coefficients are insignificantly different, and the

sum of the shocks is significantly negative. Thus, terms of trade increases appear to decrease the

death toll outside the ID countries. However, we also find that each of the three interaction terms

is positive, the coefficients are again insignificantly different, and the sum of the six CTOT terms

is actually positive. Thus, on average, CTOT increases are associated with more battle deaths in

the intermediately ethnically diverse countries. The effect is, therefore, the opposite of what we

found for the non-ID countries.

In column (2), we use the growth rate of the three-year moving average CTOT instead of

the three annual growth rates in order to increase the precision of the estimation. Again, we find

9Greene (2004) finds that adding fixed effects in the Tobit model might not bias the estimates, unlike what happens in

the discrete-choice logit and probit models. However, the error variance and the standard errors are still incorrectly

estimated. It is also possible to test whether the maximum likelihood estimated residuals for Tobit models are normally

distributed (Drukker 2002), but we could not find a comparable test for interval regressions.

lity hity i t .

25

that CTOT growth predicts more battle deaths in the ID sample and fewer battle deaths elsewhere.

In the remaining part of the table, we focus on testing the robustness of the column (2) estimates.

First, Column (3) replaces the intermediate ethnic diversity dummy based on the Fearon (2003)

ethnicity data with the corresponding dummy based on the Alesina et al. (2003) ethnicity data. In

column (4), we include a lagged dependent variable to control for potential persistence in the

battle-related fatalities (Bazzi and Blattman 2014). In order 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 focus on the observations that are at least three years into the

conflict in order to ensure that we do not confound the effects of the pre-and post-conflict initiation

terms of trade shocks in the three-year moving average shock. In column (6), again following

Bazzi and Blattman (2014), we restrict attention to the observations for which Lacina and

Gleditsch (2005) are able to report year-specific battle-related deaths. The observations without

year-specific fatalities reflect that Lacina and Gleditsch (2005) had to distribute a multi-year deaths

estimate uniformly over the relevant years or base the estimates only on knowing whether there

were 25-1000 or more than 1000 battle deaths (Lacina 2009, p. 5). Column (7) shows the results

of imposing the two restrictions jointly. In column (8), we replace the country fixed effects and

country-specific time trends with conflict episode fixed effects and a quadratic conflict episode-

specific time trend in case either (i) the conflict-mean death toll varies within countries over time

or (ii) the conflict-specific death tolls have a hump-shaped time-trend (as Figure 1 might suggest)

even after controlling for other factors. The fact that the interaction term remains significant

through all these robustness checks implies that the CTOT shocks continue to have significantly

different effects in the ID countries compared to the remaining countries.

26

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 measure

{ ,0}jt jtCTOT Max CTOT and a negative shock measure { ,0}jt jtCTOT Min CTOT . The

effect of a positive shock is the regression coefficient on jtCTOT .. The effect of a negative

shock, in contrast, is minus the coefficient on jtCTOT . The full-sample results in Column (1) not

only show that positive and negative CTOT shocks have different effects, but that both positive

and negative shocks are associated with a higher battle-related death toll.

In columns (2)-(5), we divide the sample into the non-intermediately and intermediately

ethnically diverse countries. Comparing the results in columns (2)-(4) for the non-ID countries to

the results in Column (5) for the ID countries suggests that the increase in conflict after a positive

economic shock found in the full-sample estimation reflects solely the positive effects stemming

from the ID economies. Particularly, the estimates in column (2) suggest that positive and negative

economic shocks have largely symmetric effects in the non-ID countries: larger positive terms of

trade changes and larger negative (that is, closer-to-zero) changes both decrease the death toll.

Since the positive and negative economic shock coefficients are insignificantly different, column

(3) further reports the estimates with the original CTOT measure. Although the CTOT estimate

in column (3) is only borderline significant with a p-value of 0.15, it turns out that Indonesia,

which is just one of the 38 sample countries, exerts a vastly disproportional influence on the

estimates.10 Column (4) shows that, once we drop Indonesia from the non-ID sample, the CTOT

estimate for the remaining 37 sample countries doubles in magnitude and the p-value falls to 0.004.

10 In Appendix II, we show that that there is both an ethnic-group coding argument and an empirical argument for

dropping it from the non-ID sample.

27

The estimate in Column (4) implies that, in the non-ID sample minus Indonesia, a standard

deviation (0.016) decline in the terms of trade measure increases the number of battle-related

fatalities by about 52%.

In contrast to these findings for the non-ID countries, the estimates in column (4) indicate

that positive and negative terms of trade shocks both increase the death toll in the ID sample. A

standard deviation (0.016) positive change in the CTOT growth rate increases the number of battle-

related fatalities by 48%. A standard deviation negative change increases the number of fatalities

by 64%. We can easily reject the null hypothesis that the coefficients on the positive and negative

shocks are the same. Thus, it appears that, while the opportunity cost effects of economic shocks

always dominate the potential rent-seeking and conflict-finance effects in the non-ID countries,

the rent-seeking and conflict-finance effects dominate after positive shocks in the ID countries.

In Table 5, we confirm that – as we should expect based on Figure 2 - distinguishing the

countries according to whether they have low or high ethnic polarization and whether they have a

dominant ethnic group (rather than whether they are intermediately ethnically diverse) gives us

very similar results.

In Tables 6-7, we show that the results are robust to using alternative dependent variables.

In Table 6, columns (1)-(3) replicate the Table 4, Column (3) regression for the non-ID countries

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 ordinal measure, which we coded to be equal to one when the best or

imputed best estimate was at most 1000 and two when the best or imputed best estimate exceeded

1000. Columns (4)-(5) present the estimates without Indonesia. The results look very similar to

28

the results in Table 4. In Table 7, similarly, we replicate the Table 4, Column (5) regressions for

the ID countries. The results again remain similar.

5. The effects of fossil fuel dependence and fossil fuel terms of trade shocks on conflict

The results in Tables 4-5 imply that positive economic shocks increase the conflict intensity

in intermediately ethnically diverse countries and countries with a high ethnic polarization level

and ethnic dominance. These findings are, on one hand, consistent with the idea that income

growth can increase rent-seeking incentives and the ability to finance the war effort (Angrist and

Kugler 2008, Dube and Vargas 2013, Mahmud and Basher 2014, Aguirre 2016) and that these

effects can dominate the potential increase in the individual opportunity cost of fighting. On the

other hand, the results are also consistent with the idea that ethnic configurations allowing a large

group to threaten the remaining groups in the country due to ethnic polarization and dominance

(Esteban and Ray 1999, Collier and Hoeffler 2004, Montalvo and Raynal-Querol 2005, Østby

2008) can increase redistributive conflict. At the same time, however, we find no evidence that

negative economic shocks decrease conflict - as we should expect if the rent-seeking and conflict-

finance effects always dominated the opportunity cost effect of income changes - or that positive

income shocks increase conflict intensity in the non-intermediately diverse sample countries.

These findings suggest that the rent-seeking and finance mechanisms may only prevail under

particular circumstances, including when the income shock is positive and the country has a

problematic ethnic configuration that could give it a high “latent” conflict potential (Rodrik 1999).

In this section, we attempt to further refine the analysis by narrowing down the source of the

positive income effects on conflict within the intermediately diverse sample countries.

In order to pursue this goal, we draw on two different political economy literatures. First,

as we noted earlier, several recent conflict studies find that commodity price increases for capital-

29

intensive natural resource sectors, such as oil price increases, are empirically associated with

greater conflict intensity (Dube and Vargas 2013, Mahmud and Basher 2014, Aguirre 2016). The

main theoretical idea is that when the prices of capital-intensive goods increase, the return to rent-

seeking discussed above increases. Meanwhile, relatively few job opportunities are created to

discourage individuals from becoming conflict participants. Another reason for why increasing oil

and other fossil fuel prices can increase conflict is that fossil fuel resources are often

geographically concentrated. As a result, ethnic groups that live in the resource-rich areas can fight

for greater autonomy in order to increase their revenue shares (Le Billon 2001, Ross 2004) - or the

government can choose to invade the area preemptively (Ross 2005). The struggle to control such

geographically concentrated resources may have contributed to the conflicts between the Iraqi

government and the Iraqi Kurds in the north of the country, Sudan’s decades-long north-south civil

war, and Indonesia’s ethnic-secessionist conflicts. Both the capital-intensity idea and the

geographic concentration idea suggest that international price increases for oil and other fossil fuel

resources, such as coal and natural gas, may increase conflict.

The second idea we build on comes from the literature linking natural resource dependence

and, particularly, oil dependence to economic development. Even if natural resource dependence

does not slow long-run economic growth (James 2015), it may encourage the rise of institutionally

underdeveloped, undemocratic, corrupt, and repressive “rentier states” where the ruling elite uses

the resource income, such as royalties, to finance a high living standard. Furthermore, since the

state is relatively independent on regular tax collections, it may have little incentive to invest in

economic development and state capacity, such as building a well-functioning legal system,

bureaucracy, and tax agency (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

30

the same as Portugal’s, but whose Human Development Index – a broader development measure

that responds to health and education as well as income – looks like Zambia’s. Other countries,

like Angola, Bahrain, Cameroon, Indonesia, Iran, Iraq, Libya, Nigeria, Oman, and Saudi Arabia

also suffer from elite-rule and democratic deficits. While countries like Bahrain and Saudi Arabia

have historically used repression to remain stable, others (like Angola, Iran, Iraq, and Nigeria)

have been plagued by continued unrest. Moreover, once the repressive states fall, as Iraq and Libya

illustrate, they may descend into protracted conflict. If the rentier-state theory is correct, the lack

of social cohesion and institutional capacity in such states could mean that terms of trade gains

generally and fuel terms of trade gains particularly have larger rent-seeking effects in such places.

Based on these considerations, we proceed to estimate whether fuel-related terms of trade

shocks have different effects than other terms of trade shocks in our country panel and whether the

fuel and non-fuel terms of trade shocks have different effects in fuel-dependent economies. In

order to do this, we separate the change in the log three-year-moving average terms of trade index

in equation (6) into the log change due to fossil fuel price changes - our fuel category includes

coke, coal, and briquettes; petroleum and petroleum products; and gas (natural and manufactured)

– and the log change due to price changes in the remaining commodity categories. In addition, we

define a net fuel exporter as a country’s whose average export share of fuels in GDP from 1970-

2006 (which is the year range we used to construct the weights in the CTOT index) exceeded its

import share of fuels in GDP. Finally, since we only find that positive CTOT shocks increase

conflict in intermediately ethnically diverse countries, we continue to distinguish these countries

from the remaining countries. This methodology identifies the following ID net fuel exporters:

Trinidad and Tobago, Colombia, Iran, Iraq, Malaysia, Mexico, Oman, Saudi Arabia, and

Venezuela. The methodology, similarly, identifies the following non-ID fuel exporters:

31

Afghanistan, Angola, Argentina, Cameroon, Egypt, Indonesia, Nigeria, Sudan, and Tunisia. The

remaining countries are net fuel importers and contain both ID and non-ID countries.

In Table 8, Column (1) presents the estimates for positive and negative aggregate CTOT

shocks in the 4 country “types”, that is, when we separate the countries into net fuel exporters and

importers and well as the previous distinction between ID and non-ID countries. The results show

that the significant relationship between the positive CTOT shocks and conflict intensity found for

the ID countries earlier in the paper comes from the nine net-fuel exporting countries. In order to

see whether the result is driven by fuel or other CTOT gains, Column (2) replaces the positive

CTOT shocks for the ID fuel exporters with their positive fuel and positive non-fuel CTOT shocks.

We find that the positive conflict intensity effect comes entirely from the fuel-terms of trade gains,

while other types of terms of trade gains have negative effects.

In Table 9, Column (1) we report the effects of estimating the positive fuel and non-fuel

terms of trade shocks in just the subsample of intermediately diverse net fuel exporters. Columns

(2)-(3) report the results for countries with ethnic dominance and above-median polarization index.

As expected, we find that fuel-terms of trade gains increase conflict in all three samples.

A potential identification concern is that some of the net fuel exporting countries may be

large enough to influence the global prices of fossil fuels, such as international oil prices. However,

the most immediate concern in that case may be Saudi Arabia, which only contributes a single

conflict observation in the dataset. Dropping this one observation gives virtually identical results.

Nonetheless, global oil markets are notoriously volatile and oil investors might react, not only to

current production levels, but to expectations and news about future production prospects (Kilian

2006). In order to address this concern, columns (4)-(6) replicate columns (1)-(3) regressions but

dropping all the countries that we believe could plausibly affect the global commodity prices of

32

fossil fuels, including Iran, Iraq, Mexico Oman, Saudi Arabia, and Venezuela. Unfortunately, this

omission leaves us with a very small number of countries compared to the original Column (1)-

(3) samples. For example, we only retain three of the originally nine intermediately diverse

countries used in the Column (1) regressions, namely Colombia, Malaysia, and Trinidad and

Tobago. Moreover, the sample size becomes very small, which gives us almost zero degrees of

freedom to estimate the terms of trade effects. In order to address these problems, we expand the

country samples as follows. First, in the column (4) regression, we expand the ethnic

fractionalization range used to define the intermediately diverse countries from the 25th-75th

percentile of the Herfindahl-Hirschman ethnic fractionalization index to the 15th-85th percentile.

In the Column (5) regression, similarly, we expand the band that defines countries with ethnic

dominance from the countries where the largest ethnic group represents 50-85 percent of the

population to the countries where the largest group represents 40-90 percent of the population.

Finally, dropping the large oil producers from the original high-polarization sample estimated in

Column (3) still leaves us with a reasonable number of countries and observations. We, therefore,

do not add any countries to this sample. Altogether, the modified samples of intermediately diverse

countries and countries with ethnic dominance and polarization include some or all of the

following countries: Afghanistan, Angola, Argentina, Colombia, Indonesia, Malaysia, Sudan, and

Trinidad and Tobago. All of these countries are very small producers in global fossil fuel markets

and we do not believe that changes in their conflict intensity are likely to influence fossil fuel

prices. Despite the absence of reverse causality potential, we find that the results in in the new

country samples in Column (4)-(6) look qualitatively similar to the original results in columns (1)-

(3). This observation suggests that reverse causality is unlikely to be the explanation for the

original column (1)-(3) results: if it were, the results if columns (4)-(6) should have been

33

insignificant. Moreover, we note that Dube and Vargas’ (2012) study of the effects of oil prices on

conflict within Colombian municipalities also finds a robust positive relationship with conflict

intensity. As they explain, it is unlikely that their results are driven by reverse causality since

Colombia supplies less than 1% of the world’s oil.

While we leave in-depth discussion of why fossil fuels terms of trade booms might only

have “perverse” effects on conflict intensity in fuel dependent countries with certain ethnic

compositions for future research, we advance several plausible explanations. First, one possible

explanation may be that several of the non-intermediately diverse fuel exporters are relatively

ethnically diverse sub-Saharan African countries (including Angola, Cameroon, and Nigeria)

where three or more ethnic groups play an important political role and which may have traditions

emphasizing redistributive policies. Even if the government favors its own supporters, such as the

ethnic group of the head of state (Burgess et al. 2015), it may share enough of the income gain

during commodity booms to avoid intensifying ongoing conflicts. Francois et al. (2015) show that

ministerial posts in sub-Saharan Africa are roughly allocated in proportion to ethnic population

shares rather than monopolized by the ethnic group(s) that currently control the government. The

competing ethnic groups in Nigeria and Kenya, at least, may also engage in a dynamic

redistributive game that enforces relatively peaceful behaviors in normal years by letting the

groups take turns to control the government (Rothchild 1995, 1997, Dowden 2009). Even so, it

may be possible that these redistributive arrangements mainly financially benefit the political elites

rather than the ordinary citizens. Alternatively, both governments and rebel armies in sub-Saharan

Africa may be relatively weak and when fuel price booms increase the borrowing-constrained

government’s budget, the rebels may choose to retreat strategically and the conflict intensity could

fall. Finally, another possibility comes from the fact that international diplomacy in sub-Saharan

34

Africa has historically emphasized territorial unity and discouraged the break-up of countries. A

region or ethnic group that secedes with an oil source, for example, may find it difficult to survive

politically. Relatedly, small ethnic groups fighting governments together may be prone to fighting

each other after the conflict ends, as when South Sudan broke from Sudan in 2011. This should

moderate the increase in conflict returns when commodity prices rise. In contrast, the Kurds in

Iraq may represent a reasonably unified group (despite some historical infighting) and they already

control a large and relatively well-functioning self-governing territory that could be supported by

Kurds in neighboring countries if they seceded with the oil they live on.

6. 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 are a

critical determinant of the way countries react to the commodity-induced income shocks and that

positive as well as negative income shocks can intensify ongoing conflicts. While negative income

shocks increase the death toll everywhere, positive shocks in countries with ethnic dominance and

polarization also increase the death toll. Finally, we show that the positive effect of terms of trade

gains in death tolls are accounted for by the effects of fossil fuel terms of trade gains in the roughly

half of the sample countries with ethnic dominance and polarization that are also net fuel exporters.

A possible explanation for income gains increasing the intensity of conflict is that, while

income growth may increase the opportunity cost of fighting, it can also increase the return to rent-

seeking and make it easier to finance wars. Under particular circumstances, the rent-seeking and

financing effects can dominate the opportunity-cost effect and create a positive association

between income growth and conflict (Angrist and Kugler 2008, (Dal Bó, and Dal Bó 2011, Dube

35

and Vargas 2013, Nunn and Qian 2014). Although we cannot directly estimate the relative

importance of the rent-seeking, financing, and opportunity-cost mechanisms, we can take

advantage of certain indirect evidence. For example, if the rent-seeking and financing effects can

ever dominate the opportunity cost effect during a commodity windfall –thus creating a positive

correlation between income growth and conflict-, it should only happen in countries that we, a-

priori, believe to have (a) a high potential to engage in redistributive conflict, such as countries

with ethnic dominance and polarization (Horowitz 1995, Esteban and Ray 1999, 2011, Fearon and

Laitin 2011) and (b) low state capacity and a history of rent-seeking due to historical fossil fuel

dependence (Smith 2004, Fearon 2005, Basedau and Lay 2009). In addition, the rent-seeking and

financing effects should be more likely to dominate when the source of the windfall earnings is

capital intensive sectors and geographically concentrated commodities (Le Billon 2001, Ross

2004, Mahmud and Basher 2014, Aguirre 2016). The fact that it is the interaction of these three

characteristics that generates the positive relationship between income gains and conflict intensity

in our sample of countries can, therefore, be interpreted as indirect evidence for the importance of

the rent-seeking and conflict-finance mechanisms. The fossil fuel results may also be relevant to

the literature on the natural resource curse. While empirical evidence in previous literature suggest

that natural resource earnings do not necessarily increase redistributive conflict and decrease

economic development (Cotet and Tsui 2013, James 2015), our results indicate that windfalls from

types of commodities that are easy to appropriate in the countries with the lowest capacity to

manage them can be problematic (Aslaksen and Torvik 2006).

All in all, our study suggests that economic stabilization policies could substantially

diminish the negative impacts of civil wars beyond deterring the onset of conflict itself. We show

that for a subset of particularly vulnerable economies, there is no such thing as a “good” economic

36

shock. Thus, strong policies directed at stimulating income growth and management of external

economic shocks in conflict-riddled countries could save lives.

37

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44

Tables and Figures

Table 1: Summary Statistics

Variable Obs Mean SD Min Max

Total Battle Deaths Low Estimate 1,030 1,478 4,137 10 50,000

Total Battle Deaths High Estimate 1,030 7,319 18,355 25 250,000

Total Battle Deaths Best Estimate 637 4,061 9,132 13 80,000

3 Year Moving Ave. of Ex Price Shock 1009 -0.02 0.36 -1.59 2.90

Commodity Terms of Trade Shock 811 0.001 0.016 -0.10 0.13

3 Year Moving Ave. CTOT Shock 783 0.000 0.012 -0.07 0.11

Conflict Duration Up to Present Year 1,030 9.14 9.25 1 47

First Year of Conflict Dummy 1,030 0.18 0.38 0 1

Intermediate Ethnic Diversity Dummy 959 0.39 0.49 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

Republic n

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

45

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) -4.841

[5.185]

dCTOT(t-1) -0.923

[5.126]

dCTOT(t-2) -13.894**

[6.109]

dCTOT*ID(t) 10.067*

[6.005]

dCTOT*ID(t-1) 4.972

[5.719]

dCTOT*ID(t-2) 11.685*

[6.682]

TOT(t) -18.211* -17.383 -22.636** -17.501 -14.384 -33.809** -43.000***

[10.678] [10.686] [9.794] [12.291] [11.457] [16.055] [12.525]

TOT*ID(t) 25.079** 23.686** 21.375** 18.924 25.191* 36.897** 39.574***

[11.947] [11.627] [10.572] [13.090] [12.697] [15.785] [12.911]

Duration -0.005 -0.004 -0.004 -0.024** -0.018 -0.010 -0.052 -0.109

[0.022] [0.021] [0.021] [0.011] [0.031] [0.023] [0.031] [1.224]

First Year Dummy -1.026*** -1.001*** -0.996*** -1.015*** -0.192

[0.161] [0.163] [0.165] [0.191] [0.207]

AR(1) term 0.454***

[0.036]

Observations 827 827 828 704 624 672 498 827

R-squared 0.55 0.54 0.54 0.58 0.55 0.60 0.54

# countries/conflicts 68 68 68 50 43 65 39 132

p-val sum of shocks 0.07 0.09 0.11 0.02 0.16 0.21 0.04 0

p-val sum of interac. 0.03 0.04 0.05 0.04 0.16 0.05 0.02 0

p-val shocks+interac. 0.05 0.01 0.02 0.72 0.66 0.01 0.34 0.04

p-val shocks equal 0.19

p-val interac equal 0.78 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 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.

46

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 NID NID

NID

(omitting

Indonesia)

ID

Post 23.885*** -19.446 29.560***

[4.548] [36.234] [7.646]

Negt -25.312*** -14.747 -40.000***

[5.497] [24.074] [9.607]

t -17.184 -32.304***

[11.668] [10.577]

Duration -0.003 0.001 0.001 0.008 -0.028

[0.019] [0.013] [0.012] [0.009] [0.054]

First Year -0.975*** -0.945*** -0.947*** -0.894*** -1.097***

[0.160] [0.217] [0.213] [0.213] [0.243]

Observations 833 486 486 459 341

R-squared 0.55 0.62 0.62 0.62 0.53

p-val (Pos=-Neg ) 0.00 0.94 0.00

Number of countries 70 38 38 37 30

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 three-year moving average. Column (1) estimates the effects of positive and negative commodity terms of

trade shocks in the full sample. Columns (2) estimates the effects in the non-intermediately ethnically diverse countries. Column

(3) drops the distinction between positive and negative shocks in the non- intermediately diverse countries. Column (4) drops

Indonesia from the non-intermediately diverse sample. Column (5) reports the positive and negative shock estimates for the

intermediately diverse countries.

47

Table 5: Results with polarization and ethnic dominance-based sample divisions

(1) (2) (3) (4) (5)

Estimation Method LSDV LSDV LSDV LSDV LSDV

Sample

Low

Polarization

countries.

High

Polarization

countries

Non-Ethnic

Dominance

countries

Non-Ethnic

Dominance

% Indonesia

Ethnic

Dominance

countries

t -25.991** -16.633 -30.143***

[11.706] [10.838] [10.095]

Post 29.010*** 28.223***

[4.638] [8.214]

Negt -27.829*** -40.881***

[6.665] [10.798]

Duration 0.008 -0.004 -0.002 0.005 -0.033

[0.020] [0.026] [0.013] [0.010] [0.056]

First Year -0.974*** -0.943*** -1.012*** -0.945*** -1.052***

[0.294] [0.195] [0.209] [0.207] [0.254]

Observations 315 511 501 474 319

R-squared 0.62 0.56 0.63 0.63 0.51

Number of countries 31 37 39 38 28

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 three-year moving average. Columns (1)-(2) divide the sample into countries with

low ethnic polarization - defined as polarization below and above the sample median - instead of the original division

into non-intermediately ethnically diverse and intermediately ethnically diverse countries. Columns (3)-(4) divide the

sample into countries without and with ethnic dominance, where a dominant ethnic group is a group that represents

50-85% of the population.

48

Table 6: 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

%Indonesia

NID

%Indonesia

NID

%Indonesia

t -15.658 -17.260 -3.933 -43.695** -37.241*** -7.037**

[20.887] [13.659] [2.767] [16.329] [11.427] [2.605]

Duration 0.051 0.016 0.002 0.070*** 0.025* 0.001

[0.031] [0.017] [0.005] [0.021] [0.013] [0.005]

First Year -0.811*** -0.819*** -0.165** -0.701*** -0.727*** -0.167**

[0.264] [0.199] [0.074] [0.234] [0.183] [0.074]

Observations 486 486 486 459 459 459

R-squared 0.52 0.52 0.48 0.54 0.54 0.45

Number of countries 38 38 38 37 37 37

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 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 when

we omit Indonesia from the sample.

Table 7: 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

Post 23.751** 27.100*** 6.279*

[9.256] [7.314] [3.088]

Neg -13.136 -35.556*** -10.941***

[14.471] [10.831] [2.906]

Duration -0.056 -0.035 -0.015

[0.052] [0.050] [0.013]

First Year -1.115*** -0.859** -0.202**

[0.262] [0.389] [0.091]

Observations 341 341 341

R-squared 0.450 0.49 0.42

Number of countries 30 30 30

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 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.

49

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

Post in ID net fuel exporter 27.845***

[4.339]

Post in NID net fuel exporter -7.056 -4.654

[22.903] [20.524]

Post in ID net fuel importer -0.842 0.966

[41.106] [40.480]

Post in NID net fuel importer -42.383 -41.726

[32.254] [32.199]

Negt in ID net fuel exporter -20.399*** -26.205***

[6.087] [7.730]

Negt in NID net fuel exporter -61.134*** -62.984***

[18.903] [18.672]

Negt in ID net fuel importer -20.076 -20.574

[31.705] [32.619]

Negt in NID net fuel importer -11.565 -13.678

[35.278] [35.344]

Pos fuel t in ID net fuel exprtr 34.388***

[3.248]

Pos non-fuel t inID fuel exprtr -45.715***

[13.351]

Pos t * Indonesia 70.383***

[13.808]

Pos fuel(t) * Indonesia 73.530***

[12.531]

Duration -0.001 0.003

[0.020] [0.017]

First year -0.955*** -0.933***

[0.170] [0.169]

Observations 827 827

R-squared 0.76 0.77

Number of countries 68 68

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 three-year moving average. ID and NID denote intermediately diverse and non-intermediately

diverse countries.

50

Table 9: The effects of fuel and non-fuel shocks

(1) (2) (3) (4) (5) (6)

Estimation Method LSDV LSDV LSDV LSDV LSDV LSDV

Sample ID net fuel

exporters

Net fuel

exporters

with ethnic

dominance

Polarized

net fuel

exporters

ID net fuel

Exporters

(sample

w/o large

producers)

Net fuel

exporters with

ethnic dom.

(sample

w/o large

producers)

Polarized net

fuel exporters

(sample

w/o large

producers)

Pos fuel t 35.718** 34.083** 36.021*** 84.012** 59.183** 35.718**

[11.832] [10.306] [6.834] [28.133] [18.617] [11.832]

Pos non-fuel -23.526* -29.272** -41.459* -182.015 -101.554 -23.526*

[12.015] [11.436] [20.269] [97.740] [101.988] [12.015]

Neg fuel t in -39.248*** -39.067*** -27.067*** -64.547 -151.185*** -39.248***

[2.982] [3.745] [7.949] [53.940] [21.417] [2.982]

Neg non-fuel -8.997 -8.283 20.323 -53.744 21.163 -8.997

[16.692] [13.466] [24.631] [130.538] [90.679] [16.692]

Duration -0.039 -0.042 -0.019 -0.011 -0.115*** -0.039

[0.068] [0.063] [0.031] [0.016] [0.015] [0.068]

First year -0.667 -0.699 -0.861** -1.071** -1.117*** -0.667

[0.655] [0.551] [0.310] [0.366] [0.189] [0.655]

Observations 100 103 183 158 98 124

R-squared 0.882 0.876 0.876 0.853 0.880 0.901

Number of countries 9 9 11 8 6 6

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 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 ethnic

polarization above the median in the (4)-(6) regressions omit Saudi Arabia, Iraq, Iran, Mexico, Oman, and Venezuela

from the samples. The Column (4) sample replaces the omitted countries via expanding the ethnic fractionalization

range used to define the intermediately diverse countries from the 25th-75th percentile of the Herfindahl-Hirschman

ethnic fractionalization index to the 15th-85th percentile. The Column (5) sample expands the band that defines ethnic

dominance from countries where the largest ethnic group represent 50-85 percent of the population to countries where

it represents 40-90 percent of the population. The column (6) regression adds no replacement countries.

51

Figure 1: Time-series Death Toll Estimates for Guatemala, El Salvador, Nicaragua, and Bosnia and Herzegovina

0

20

00

40

00

60

00

80

00

10

00

0

De

ath

to

ll e

stim

ate

1950 1960 1970 1980 1990 2000Year

bandwidth = .4

Guatemala

-50

00

0

50

00

10

00

015

00

0

De

ath

to

ll e

stim

ate

1970 1975 1980 1985 1990Year

bandwidth = .4

El Salvador

0

20

00

40

00

60

00

80

00

De

ath

to

ll e

stim

ate

1975 1980 1985 1990Year

bandwidth = .4

Nicaragua

0

50

00

10

00

015

00

020

00

025

00

0

De

ath

to

ll e

stim

ate

1992 1993 1994 1995Year

bandwidth = .8

Bosnia and Herzegovina

52

Figure 2 The Close Relationship Between Ethnic Fractionalization, Ethnic Polarization and the Population Share of the Ethnic Plurality

Source: Janus and Riera-Crichton (2015)

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

53

Appendix 1:

Regressions linking conflict to the change in commodity export prices rather than terms of

trade changes can potentially generate omitted variables bias and decrease precision

In the main paper, we argue that regressions linking conflict to the change in commodity export

prices rather than terms of trade changes can generate omitted variables bias. In this appendix, we

formally present the problem and derive an analytical expression for the size of the bias. We also

show that, even in the special case where the export and import price shocks are uncorrelated, the

failure to control for the import price innovation typically increases the standard error of the export

price estimate. In order to see these points, consider the following regression

x x

jt jt jty p , (a1)

where the left hand side records outcome y in country j in year t , x

jtp is the log-change in

export prices from the last period. For simplicity, we abstract from country and time effects. The

error term jt , however, includes the effects of import price changes, that is,

m m

jr jt jtp , ` (a2)

where jt is an i.i.d. error term. If the change in export prices is correlated with the change in

import prices, assume that

m x

jt jt jtp p , 0 , . (a3)

54

where jt is i.i.d. and 0jt jtE . Substituting (a2-a3) into (a1) shows that

x x m x

jt jt jt jt jty p p . (a4)

such that [ ]x x m xE and the export price coefficient is biased unless 0.m In

principle, if import and export price changes are positively related but the import prices vary more,

we can get that ,x m x

jt jt jtCov p p Var p 2

, ,

1x x m m x x

jt jt jt

j t j t

p p p p p p .

In that case, if the export and import price shocks have the symmetric effects predicted in the most

international economics textbooks, such thatx m , we get that

sgn [ ] sgn (1 ) sgnx x xE . That is, the coefficient on the export price innovation

can, potentially, obtain the wrong sign relative to the true effect.

Even if the export and import price shocks are uncorrelated, such that 0 , models that

only estimate the effects of export-price shocks do not exploit all of the international-price shock

variation countries experience in order to identify the effects of these shocks. Intuitively, including

the import price shocks in the error term ignores the information these shocks provide. The limited

variation in the explanatory variable can increase the standard error of the estimator and make it

easier to commit type 2 errors (such as failing to reject a null-hypothesis of zero effect when the

null is false). Particularly, if we assume that the shocks are uncorrelated and have symmetric

effects on the dependent variable, the true equation is

55

tot x m

jt jt jt jty p p , (a5)

where tot x m is the terms of trade effect as well as the export price effects and (negative)

import price effect. In a sample with N observations, however,

ˆ ˆjt jt jt jtx tot

x x x mx mjt jt jt jtjt jt

Var Var Var VarVar Var

NVar p NVar p NVar p pN Var p Var p

so the export price estimate in the regression without the import price changes has higher variance.

If the export and import price shocks have the same variance in the data, particularly,

x m

jt jtVar p Var p , then

ˆ2 2

jt jt tot

x x mjt jt jt

Var VarVar

NVar p N Var p Var p

.

Thus, the export price estimator in equation (a1) has over twice the variance of the export price or

terms of trade estimator in eq. (a5), which exploits the information from import price movements.

Appendix 2:

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

56

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, p. 37):

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.

57

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 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 A1, 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, once we allow terms of trade shocks to have different effects in Indonesia, the

magnitude of the non-interacted terms of trade coefficient almost doubles compared to what we

found in Table 4, column (3), from -17.2 to -31.4.

58

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. Finally,

in columns (3)-(4), we repeat the analysis when we split the sample according to the presence or

absence of ethnic dominance. The results support the hypotheses that Indonesia responds

differently to terms of trade shocks compared to the other countries without a dominant group.

Controlling for the differential Indonesia response almost doubles the coefficient for the remaining

non-dominance countries and we find that both positive and negative terms of trade shocks

increase conflict intensity in Indonesia. Thus, 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 regressions in the main paper or to omit it

from the non-intermediately diverse and non-dominance samples as we also do some places.

59

Table A1: 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

t -31.380*** -29.150***

[10.537] [10.050]

t*Indonesia 48.131*** 43.113***

[14.324] [14.069]

Post 27.603*** 26.290***

[6.942] [7.298]

Negt -40.425*** -40.928***

[8.889] [10.069]

Post*Indonesia 40.468** 37.990**

[14.939] [16.185]

Negt*Indonesia -81.036*** -81.357***

[17.769] [20.601]

Duration 0.003 -0.036 -0.0004 -0.0388

[0.011] [0.043] [0.0127] [0.0445]

First year -0.931*** -1.213*** -0.993*** -1.167***

[0.212] [0.260] [0.209] [0.270]

Observations 486 368 501 346

R-squared 0.62 0.56 0.63 0.55

Number of countries 38 31 39 29

p-val (Pos+Pos*Indon.) 0.00 0.01

p-val (Neg+Neg*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 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.