8 - Carbonnier and Wagner - Resource Dependence and Armed Violence

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Resource Dependence and ArmedViolence: Impact on Sustainability inDeveloping CountriesGilles Carbonniera & Natascha Wagnerba Development Studies Section, The Graduate Institute ofInternational Development Studies, Genve, Switzerlandb International Institute of Social Studies of Erasmus UniversityRotterdam, The Hague, The NetherlandsPublished online: 30 Oct 2013.

To cite this article: Gilles Carbonnier & Natascha Wagner (2015) Resource Dependence and ArmedViolence: Impact on Sustainability in Developing Countries, Defence and Peace Economics, 26:1,115-132, DOI: 10.1080/10242694.2013.848580

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RESOURCE DEPENDENCE AND ARMED VIOLENCE:IMPACT ON SUSTAINABILITY IN DEVELOPING

COUNTRIES

GILLES CARBONNIERa* AND NATASCHA WAGNERb

aDevelopment Studies Section, The Graduate Institute of International Development Studies, Genve,Switzerland; bInternational Institute of Social Studies of Erasmus University Rotterdam, The Hague,

The Netherlands

(Received 3 July 2012; in nal form 21 March 2013)

The dependence on oil, gas, and mineral exports arguably has a negative impact on economic growth inresource-rich, developing countries. This article looks at the impact of resource dependence on adjusted net savings(ANS) as an indicator of weak sustainability. Our results, based on a panel of 104 developing countries during therecent commodity price boom, conrm a negative relationship between resource extraction and sustainable devel-opment as measured by ANS. We further look at the specic role of armed conict and armed violence as capturedby the homicide rate. Armed conict, which is positively associated with resource dependence, negatively affectsANS per capita according to both our OLS and instrumental variables (IV) estimates. Similarly, armed violence hasa detrimental effect on sustainable development. Our IV estimate suggests that a one-point increase in the homiciderate decreases ANS per capita by $60. Since education expenditures are a critical ANS component, we furtherexamine the impact of resource dependence and violence on human capital. Consistent with previous ndings,resource-dependent countries underinvest in education but armed conict and violence do not affect the instanta-neous share of education expenditures, hinting at a detrimental effect working through physical and social capitalrather than education.

Keywords: Armed conict; Genuine savings; Homicide; Resource dependence; Weak sustainability

JEL codes: Q56, O13, O43, O44

1. INTRODUCTION

By relaxing budgetary and balance-of-payments constraints, extractive resources should bea blessing for producer states during price booms when they can draw substantial revenuesfrom oil, gas, and mineral exports to nance development. Empirical studies display amixed record, hinting to a negative impact of resource dependence on GDP growth andincome per capita, in particular in so-called fragile states. This is a big challengefor resource-rich developing countries. While GDP remains the benchmark of choice foreconomic development, it does not say much about sustainability, which is critical in thecase of exhaustible resources whose extraction is bound to peak and then shrink over time.

*Corresponding author: Development Studies Section, The Graduate Institute of International DevelopmentStudies, Genve, Switzerland. Email: [email protected]

2013 Taylor & Francis

Defence and Peace Economics, 2015Vol. 26, No. 1, 115132, http://dx.doi.org/10.1080/10242694.2013.848580

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This paper considers the impact of resource dependence on sustainable development asmeasured by adjusted net savings (ANS) in conjunction with armed conict and armed vio-lence.Rent-seeking dynamics associated with resource dependence also increase the risk of

armed conict, and recent studies show that extractive activities tend to fuel violent con-frontation between host communities and oil or mining industries in the absence of effectiveinstitutional mechanisms to manage conicts over land, water, and other environmental andsocial issues in a peaceful manner (Arellano-Yanguas, 2008; Vasquez, 2010). We thereforeexpand the analysis of resource dependence by assessing the impact of conict and armedviolence on ANS relying on a novel homicide database from the Geneva-based Small ArmsSurvey.We seek to uncover the relationship between resource dependence and genuine savings

during price boom periods in low-income and middle-income countries. Our analysis coversa panel of 104 developing countries over the ve years of the recent commodity priceboom, from 2003 to 2007 (see Figure 1). The analysis is limited to this period as homicidedata for the developing countries in our sample are not available neither for earlier nor laterperiods. The data-set does not span all developing countries due to missing information butallows to include 104 countries in the panel. By considering only developing countries, weavoid that our results are conated with those found for developed countries as the latterexperience different dynamics. Moreover, based on in-sample predictions, we can establishwhether the developing countries under study engage in sustainable economic activities andwhich factors foster and hinder these. We focus specically on extractive resources to high-light the impact of the oil, gas, and mineral boom on development as measured by ANS percapita. Our empirical analysis rests on two identication strategies. First, we employ axed-effects panel regression (OLS) controlling for country-xed effects and commonshocks. Second, we address the potential endogeneity of conict and armed violence withan instrumental variables approach. We instrument the homicide rate with urban populationgrowth as previous research has established a link between rapid societal transition andhomicides (Messner, 1982; Krahn et al., 1986; Dicristina, 2004). We further show thatarmed conict increases with agricultural land scarcity. Obviously, instrumental variables

FIGURE 1 Commodity price index 20022008 (2005 = 100).Source: IMF

116 G. CARBONNIER AND N. WAGNER

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specications are subject to discussion. Therefore, we consider the results of ourinstrumental variables specication as upper bound of the coefcient estimates sinceinstrumentation increases variance and thus reduces the precision of the estimates. The OLSspecication is likely to be biased towards zero and is taken as lower bound. This permitsto give coefcient intervals that indicate the possible impact range of armed violence.Instrumenting the endogenous violence indicators with time-varying variables allows us tofully exploit the panel dimension of the data-set and to reduce cross-country heterogeneityby imposing country- xed effects. Following Miguel and Roland (2011), we furthercontrol for a range of variables related to our IVs (agricultural land and population) in thestructural equation.As expected, we nd that resource dependence which equates with natural capital

depletion tends to diminish ANS per capita. An increase of one sample standard deviationin resource dependence, as measured by oil, gas, and mineral exports over all exports,depletes ANS per capita by more than $35. Second, both armed conict and an increase inthe homicide rate have a negative impact on ANS per capita. Our instrumental variablesestimate suggests that ANS per capita is reduced by almost $220 due to conict and a one-point increase in the homicide rate decreases ANS per capita by $60. Since investment inhuman capital is critical for sustainable development, we also consider education expendi-tures as another outcome variable. Consistent with earlier ndings by Gylfason (2001), weobserve that resource gains are not reinvested in education. Yet, the picture is more nuancedwhen turning to the impact of conict and armed violence on education expenditures: whilethere is a negative link, it does not turn out to be signicant, which is, however, consistentwith previous ndings (Buckland, 2006).The remainder of the paper is structured as follows: the next section briey presents the

capital approach to economic development and discusses the literature on conict anddevelopment. Section 3 presents the data and sample selection together with their proper-ties. The econometric specication and the choice of instrumental variables are discussed inSection 4. Section 5 analyzes the results and Section 6 concludes.

2. EXTRACTION, SUSTAINABLE DEVELOPMENT, AND ARMED VIOLENCE

Over the past decades, extractive booms have been more of a curse than a blessing forfragile, resource-rich states such as the Democratic Republic of the Congo, EquatorialGuinea, or Nigeria. The main dynamics at play involve Dutch disease, rentier states, rent-seeking, and armed conict (e.g. Mehlum et al., 2006; Carbonnier, 2007, 2011; Collier andGoderis, 2008). Despite a rich literature, the relationship between resource extraction anddevelopment remains a controversial issue, and there is little consensus on the transmissionchannels from the exploitation of non-renewable natural resources to sustainable develop-ment outcomes.Most of the empirical literature on the resource curse follows Sachs and Warner by

assessing the impact of resource export dependence on development outcomes in terms ofeconomic growth (Sachs and Warner, 1997). The exploitation of fossil fuels and mineralstranslates into immediate economic growth but also into the depletion of the natural capitalbase of a country. The outcome can be regarded as sustainable only if the extractive reve-nues are reinvested in other forms of capital in a way that increases, or at least maintains,the overall capital stock. In the case of exhaustible resources in low-income countries, thisis obviously all the more important as the extractive rent will die off at some point, depend-ing on the resource abundance and the extraction pace (Stevens, 2011). This paper captures

RESOURCE DEPENDENCE AND ARMED VIOLENCE 117

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the linkages between resource extraction and sustainability by incorporating natural capitalin calculating national income and wealth (Hamilton, 2004, p. 31). Genuine savings serveas a sustainability indicator (Hartwick, 1977; Perman et al., 2003, p. 89; World Bank,2006), whereby national wealth is based on a comprehensive list of assets which includesproduced, natural and intangible capital, i.e. human and social capital (Goodwin, 2007).Resource extraction corresponds to an instant decumulation of genuine savings as it reducesthe natural capital stock. Genuine savings can nonetheless be maintained or even grow withcompensatory capital accumulation in physical and human capital. But since the capitalapproach rests on the unrealistic assumption that natural capital can be fully substituted byproduced and human capital, it is referred to as an indicator of weak sustainability (Everettand Wilks, 1999; Thiry and Cassiers, 2010).The World Bank has calculated genuine savings or ANS for 209 countries from 1970

onwards using the following formula (Bolt et al., 2002):

ANSit GSit DEPCit|{z}

Net Savings

EEit RRDit CDit; (1)

where ANSit is the adjusted net savings of country i at time t. It is composed of grossnational savings GSit net of the depreciation of produced capital DEPCit, augmented by(non-xed capital) expenditure on education EEit and reduced by the rents from depletionof natural capital1 and damages from carbon dioxide emissions CDit. In words, adjusted netsavings measure the true rate of savings after taking into account investments in humancapital, depletion of natural resources, and damage caused by pollution. Thus, increases intotal wealth are measured by positive adjusted net savings rates indicating sustainability inthe economy. Therefore, ANS is a suitable policy indicator as it underpins the need fordomestic savings, and at the same time includes environment and resource management ina framework that nance and development planning ministries can understand. Moreover,growth at the cost of resource and environmental depletion is captured by decreasedadjusted net savings rates.ANS data have recently been used more often in the empirical literature on the resource

curse (e.g. Stoever, 2011; Barbier, 2010; Van der Ploeg and Poelhekke, 2010; Van der Ploeg2010; Dietz et al., 2007). Existing studies tend to consider ANS as an outcome variableexpressed as a percentage of national income. This raises an endogeneity issue, especiallywhen national income appears as an explanatory variable in the empirical model. Instead,we consider ANS per capita. We further follow Barbier (2010) in excluding CO2 damagefrom the ANS calculation since it suffers from important methodological aws.Our empirical model presented in section 4 seeks to assess the impact of resource

dependence, armed conict, and violence on ANS per capita, with a specic focus onhuman capital. The conceptual framework draws on a range of theoretical considerationsand empirical studies. The negative impact of resource dependence on economic growth infragile states draws on several prominent explanations including in particular rentier-stateand rent-seeking dynamics together with macroeconomic issues associated with Dutch dis-ease (see e.g. Sachs and Warner (1997), Ross (1999)). Commodity price volatility and theensuing voracity effect have further been singled out as additional variables explaining theso-called resource curse. While these transmission channels have been tested with respect toGDP growth, there is yet little evidence concerning the impact on genuine savings. We seek

1Natural capital includes gas, coal, forest, oil, metals, and minerals such as bauxite, copper, gold, iron ore, lead,nickel, phosphate rock, silver, tin, and zinc.


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to address this gap and focus on a period of rising commodity prices, which is expected toincrease natural capital depletion, i.e. to reduce genuine savings, ceteris paribus. This canbe (partly) offset when extractive revenues are reinvested in physical and human capital.A series of studies from the mid-1990s onward show that armed conict associated with

resource dependence are important factors in explaining poor economic growth in resource-rich and yet weak states. Often based on extraction theory, they posit that primary commod-ities can be either taxed or looted. Rulers and rebels both seek to extract maximal revenueunder a set of constraints including transaction costs. Rebels seek to capture the state orsecede from it in an attempt to rule over the portion of territory where the resources arelocated. The incentive to rebel diminishes with the states capacity to defend itself and theopportunity cost of joining the rebellion. It increases with the probability of successfulrebellion and the prize associated with seizing power. Taking GDP per capita, GDP growth,and primary commodity dependence as proxies, the link between natural resources, andcivil war in developing countries has notably been established by Collier (1999) and Collierand Hoefer (2002, 2004). Following Collier, we consider four channels through whicharmed conict may negatively affect ANS to an even greater extent than GDP. Civil warcauses the destruction of physical and human capital such as infrastructure and the laborforce through killing and displacement. It further disrupts social capital and causes politicalinstability that results in less private investment in physical capital. Civil war may alsodivert public expenditures away from education and investment in xed capital. Moreover,dissaving further destroys the capital stock, for instance when the elite put their capital in asafer place outside the country. There are thus convincing arguments to establish a negativelink between armed conict and genuine savings.The linkage between armed conict and resource dependence remains more contested 15

years after Collier and Hoefers initial work on the economic causes of civil war. ForBrunnschweiler and Bulte (2008), there is no evidence of a causal relationship betweenresource dependence and armed conict. Resource abundance per se is even associated witha weaker probability of civil war. Van der Ploeg and Rohner (2012) draw another conclu-sion by treating resource extraction as endogenous on the basis that ghting affects theextraction pace. Other authors nd that resource dependence tends to prolong rather thancause armed conicts (Di John, 2007), and underline that the location and type of resourcesmatter a great deal (see e.g. Lujala, 2009). There is general agreement that countriesendowed with strong institutions and good governance face a lower risk of armed conictassociated with resource dependence than weak states, notwithstanding issues of circularcausality (Carbonnier et al., 2011). This is echoed by Teorell (2009) who nds evidencesupporting the hypothesis that civil war is associated with the lack of impartial institutions.Relative deprivation remains an important driver of armed conict associated with resourcedependence, pointing to the link between resource dependence, exclusion, and conict(Billon, 2001). We posit ANS per capita to be affected not solely by natural capitaldepletion but also by a lack of investment in human and physical capital associated witharmed conict. We consequently explore the impact of resource dependence and armedconict on education expenditure to assess to what extent the overall negative impact onANS can be explained by human capital.Finally, recent research on the local dynamics of resource extraction in Latin America

has highlighted the prevalence of so-called socio-environmental conicts pitching hostcommunities against extractive rms. Disputes often revolve over land property issues,water quality and quantity, waste water treatment, and job opportunities. Such disputes tendto turn violent in institutional settings where appropriate mechanisms for peaceful conictmanagement are missing (Vasquez, 2010; Arellano-Yanguas, 2008). Yet, these conicts


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often do not qualify as armed conict in the dataset used in most of the relevant empiricalstudies to date, i.e. in the Uppsala Conict Data Program. The latter codes for episodes ofconict when there are more than 25 battle-related deaths in a given year and when the stateis a party to the conict. This conict indicator misses out armed violence episodes such asthose related to socio-environmental conicts opposing extractive rms and local communi-ties, which are often characterized by the lack of ofcial state intervention and/or less than25 battle-related deaths.High crime and homicide rates have been singled out as major concerns for development,

in particular with regard to Latin America and Southern Africa (World Bank, 1997;Fajnzylber et al., 1998). The prevalence of armed violence (below the threshold of armedconicts) associated with oil and gas projects has been highlighted by reports of16 unresolved conicts in Peru and 23 in Ecuador by mid-2007 (Killeen, 2007). In anattempt to quantify the impact of armed conict and armed violence on ANS, we use anovel indicator of armed violence from a database that captures the overall homicide rateincluding both war-related and nonwar-related violence. The data are provided by theGeneva Declaration on Armed Violence and Development (Small Arms Survey). We expectthe homicide rate to have a negative impact on genuine savings, as does armed conict,even if we are aware that in some instances the homicide rate might actually go down as aresult of authorities harshly repressing public expression of discontent around the strategiclocations where extractive industries operate (Lowi, 2005).

3. DATA DESCRIPTION

We consider sustainable development, resource dependence, and armed violence data across104 developing countries over a ve-year period, from 2003 to 2007. The sample isrestricted to developing countries because we aim at analyzing whether there are systemati-cally different development outcomes for resource-rich developing countries vs. resource-poor ones in low- and middle-income countries, and to what extent the lack of sustainabilitycan be explained by armed conict and violence.The period under review is rather short and might seem arbitrary. This choice has rst

been dictated by data availability. Our analysis includes armed violence as measured by thehomicide rate. These data exist in relatively comprehensive fashion as of 2003, with 331country-year pairs available over this ve-year period (whereas armed conict informationis available for 525 country-year pairs). Because of an increasing prevalence of armed vio-lence associated with extractive projects in weak states, we consider it worthwhile to usethese novel data for the rst time in a quantitative study on the relationship betweenresource dependence and sustainability outcomes despite the limiting factor imposed bymissing information. Second, the period under review corresponds to a boom in commodityprices that reversed the downward trend observed during the two previous decades. Study-ing a period in which commodity markets boomed presents new insights into violencedynamics and extraction against the background of a global rush on commodities and thesurge of Asian state-owned companies as major new actors in the market.A comprehensive list of countries and the number of observations per country can be

found in Table I. Among these 104 developing countries, roughly one-third are low-incomecountries according to the current World Bank classication, the remaining two-thirds arelower-middle-income and upper-middle-income countries. Thus, low-income countries arenot systematically underrepresented, as may happen with novel and incomplete databases.


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Descriptive statistics are presented in Table II. Unless specied otherwise, our data comefrom the World Banks World Development Indicators (WDI). We use the adjusted netsavings as provided by the WDI database and construct ANS per capita. As ANS is denotedin current US dollar, we also control for ination in our analysis. On average ANS percapita amounts to $207.70 but varies greatly across countries and time-periods, as doesGDP per capita. ANS per capita is only 10% of GDP per capita on average, indicating thatdepletion of natural capital and low levels of reinvestment in physical or human capital canbe observed for many countries. A prominent example is Angola with an average ANS per

TABLE I Alphabetical List of the 109 LICs, LMICs, and UMICs that are Considered in the Regression Analysis

Country Obs. Country Obs. Country Obs.

Albania 3 Ethiopia 5 Panama 5

Algeria 5 Fiji 5 Papua New Guinea 5

Angola 5 Gabon 5 Paraguay 5

Antigua and Barbuda 5 Gambia, The 5 Peru 5

Argentina 5 Georgia 5 Philippines 5

Armenia 5 Grenada 5 Romania 5

Azerbaijan 5 Guatemala 5 Russian Federation 5

Bangladesh 5 Guinea 5 Rwanda 5

Belarus 5 Guyana 5 Samoa 5

Belize 5 Honduras 5 Seychelles 5

Benin 3 India 5 Solomon Islands 4

Bhutan 5 Indonesia 5 South Africa 5

Bolivia 5 Iran, Islamic Rep. 5 Sri Lanka 5

Botswana 5 Jamaica 5 St. Kitts and Nevis 5

Brazil 5 Jordan 5 St. Lucia 5

Bulgaria 5 Kazakhstan 5 Vincent and the Grenad. 5

Burkina Faso 4 Kenya 5 Suriname 5

Burundi 3 Kyrgyz Republic 5 Swaziland 5

Cambodia 5 Lao PDR 5 Syrian Arab Republic 5

Cameroon 5 Lesotho 5 Tajikistan 5

Cape Verde 5 Lithuania 5 Tanzania 4

Central African Rep. 4 Macedonia, FYR 5 Thailand 5

Chad 2 Madagascar 5 Togo 3

Chile 5 Malawi 5 Tonga 5

China 5 Malaysia 5 Tunisia 5

Colombia 5 Mali 5 Turkey 5

Comoros 5 Mauritania 5 Uganda 5

Congo, Dem. Rep. 5 Mauritius 5 Ukraine 5

Costa Rica 5 Mexico 5 Uruguay 5

Cote dIvoire 5 Moldova 5 Uzbekistan 5

Djibouti 4 Mongolia 5 Vanuatu 5

Dominica 5 Morocco 5 Venezuela, RB 5

Dominican Republic 5 Mozambique 5 Vietnam 5

Ecuador 5 Namibia 5 Zambia 5

Egypt, Arab Rep. 5 Nepal 5 Zimbabwe 3

El Salvador 3 Nicaragua 5

Eritrea 5 Pakistan 5

Note: For each country the maximum number of available observations is also presented. On average 4.81 observations areavailable per country.


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capita over the period of $-427.97. Similarly, Bolivia, which is highly resource dependent,has an average ANS per capita of only 3% of GDP per capita. Resource dependentKazakhstan and Mauritania are even doing worse in terms of ANS per capita vs. GDP.Interestingly, the country with the highest share is Botswana with an average ANS percapita of roughly 60% of GDP per capita.We dene resource dependence as the share of fuel, ores, and mineral exports over total

merchandise exports. On average, these extractive resources make up for 19.2% ofmerchandise exports. It is noteworthy that 14.3% of the observations in our samplecorrespond to country-time pairs with no resource exports while 31% of the country-timepairs represent observations with an export ratio equal or above 20%. The remaining 54.7%of observations correspond to intermediate levels of resource dependence.Population dynamics show the expected patterns. Mean national population growth is

almost three times as high as rural population growth. The former is driven by growingcities with an average urban growth higher than 2%. At the same time, the rural populationstill accounts for more than half the population in most countries. Gylfason (2001) arguesthat natural capital crowds out human capital, which is a major channel through which resource dependence slows down the pace of economic growth. Therefore, we also considereducation expenditures as a specic outcome variable. Our descriptive statistics show that,on average, education expenditures amount to $105.90 (which corresponds to roughly 50%of ANS, again with important variations across countries).The WDI data are complemented by other data sources on armed conict and violence.

We use the standard conict indicator from the UCDP/PRIO Armed Conict Database

TABLE II Descriptive Statistics

Variable Mean Std. Dev. Min Max

ANS pc 207.696 350.516 1219.94 2638.03

ANS pc (Lag) 183.145 314.293 557.317 2638.03

Education expenditure pc 105.879 110.93 0.933 638.957

Education expenditure pc (Lag) 94.35 102.336 0.922 638.957

GDP pc (Lag) 1948.27 1950.97 80.943 10,917.81

GDP growth (Lag, %) 3.616 4.292 15.156 33.03

Ination (%) 10.437 26.164 3.557 381.265

Population growth (%) 1.445 1.068 1.854 4.229

Rural population (%) 52.899 20.945 7.02 90.98

Rural population growth (%) 0.516 1.367 4.827 3.836

Land area (km2) 792,624 2022,978 260 16,400,000

Forest area (km2) 261,777 932,997 36 8089,814

Agricultural value pw 2018.63 2033.77 69.85 12,049.52

Agricultural value growth (%) 3.271 7.893 41.239 69.631

Resource dependence 19.253 25.872 0 98.631

Conict dummy 0.116 0.321 0 1

Homicide rate 8.833 11.245 0.2 64.55

Agricultural area 106 km2 0.295 0.684 0 5.528

Urban population growth (%) 2.135 1.542 0.742 7.075

Note: Statistics are based on the maximum available number of observations per category, in most cases this is 525 country-yearpairs. Per capita (per worker) is abbreviated with pc (pw). The homicide rate is measured per 100,000 inhabitants.


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(Harbom et al., 2009) that takes on the value of one when there are at least 25 battle-relateddeaths in a given country and year. In 11.6% of our observations, armed violence amountsto an armed conict. As motivated earlier, in order to get a ner measure of the intensity ofarmed violence, we include homicide rate information from the Small Arms Survey.However, this indicator is available only for a subsample of 78 countries. The averagehomicide rate in our sample amounts to 8.83 homicides per 100,000 inhabitants (whichcompares to an average homicide rate of 7.67 for OECD countries over the same period).Table I provides the list of all the variables and indicators used and their matrix acronymsemployed in the empirical analysis.

4. ECONOMETRIC SPECIFICATION AND IDENTIFICATION

Our econometric model is based on the neoclassical, long-term growth framework, wherebycapital accumulation occurs though savings which are constituted by the fraction of outputthat is not consumed. Savings in turn depend on GDP as well as on the exogenous popula-tion growth (Solow, 1956, 1974). We further follow Dietz et al. (2007) who, in a rstattempt to identify the determinants of the level of gross savings (and therefore also genuinesavings), provide an overview of the major empirical studies on savings, and conclude thatthe most signicant and robust explanatory variables for genuine savings include GDP percapita and growth. Based on such basic assumptions underlying the neoclassical model andthe determinants of savings, we build our initial model as follows:

ANSt a1ANSt1 a2GDPt1 a3POPt (2)

where ANSt is adjusted net savings at time t that depends on adjusted net savings in theprevious period ANSt1 as well as on GDP and population variables. We then extend thismodel to include resource dependence, armed conict, armed violence, and other country-specic variables.Our analysis is faced with a variety of endogeneity issues. First, descriptive statistics

indicate that the countries under study differ greatly, even within the same income category.Heterogeneity across countries is dealt with by including country-xed effects. Second,since the data-generating process is dynamic, current observations of the dependent variabledepend on past realizations. Therefore, we include the lagged value of the dependent vari-able in the set of covariates. Third, some explanatory variables such as GDP are clearly cor-related with ANS per capita. We follow the standard approach in the literature and tacklethe potential endogeneity of the GDP measures by including only the lagged values of percapita GDP and GDP growth, which further accounts for the process of income transforma-tion into capital. Fourth, we address shocks that are time-varying but common across coun-tries such as the increase in commodity prices by including a binary variable for every year.Our basic econometric model then looks as follows:

ANSit b1ANSi;t1 b2GDPi;t1 b3POPit b4COUNit b5RDit b6V IOLit kt mi it;(3)

where our dependent variable ANSit is per capita genuine savings of country i at time t.The list of control variables includes the lagged dependent variable ANSi,t1, and the laggedlevel of per capita GDP, lagged GDP growth, and ination as denoted by the matrixGDPi,t1. As ANS is denoted in current US dollar we also control for ination in our


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analysis. We further include population growth, the percentage of the population living inrural areas, and rural population growth; these variables are all collected in the matrixPOPit. The last set of control variables is found in COUNit and contains the surface and theforest area as well as the agricultural value per worker and growth in agricultural value.Our measure of resource dependence is represented by RDit. Armed conict and homicidesare denoted by VIOLit. We decompose the disturbance term as follows: time-xed effectsare captured by t and country-xed effects by i, the idiosyncratic disturbance componentis it. We control for the rst two elements of the disturbance term and cluster it at thecountry level as we do not expect country-level disturbances to be independent from eachother.This specication controls for a wide range of observable country characteristics that are

very likely to be correlated with ANS and deals with time-invariant country-specic endo-geneity by including xed effects. However, we cannot rule out that there are hiddendynamics that jointly affect investment in sustainable development and a countrys exposureto violence, nor can we rule out reverse causality. We thus compare the results from theOLS estimation of equation (3) with the results from a two-step procedure in which weinstrument the potentially endogenous variables armed conict and the homicide rate. In thereduced form regression of the two-step procedure, we regress the endogenous violencevariable on the set of covariates of equation (3) and the exogenous instrument:

VIOLit d1ANSi;t1 d2GDPi;t1 d3POPit d3COUNit d4RDit cINSTit ct ni git;(4)

Finding a time-varying country-specic instrument is a hard task. Prominent and accreditedinstruments such as settler mortality or ethnolinguistic fractionalization (Easterly andLevine, 1997; Acemoglu et al., 2001) cannot be employed here: the rst instrument is time-xed and the second is only considered valid if it relies on past data. Ethnolinguistic com-position does not change quickly and thus cannot be exploited here. Therefore, we have torely on different instruments that identify conict and violence, and vary over time for anygiven country. We propose to instrument a countrys propensity for conict with its totalagricultural surface. Agricultural output is a non-negligible component of national incomein developing countries. Obviously, this income component has a direct impact on invest-ment in sustainable development and will be immediately controlled for in the structuralequation. However, the availability of agricultural land per se does not directly translate intocapital accumulation and agricultural yield varies depending on the intensity of agriculture.At the same time, it is is well documented in the literature on land titling and on the rela-tionship between access to land and household income that the availability of agriculturalland plays a signicant role in developing countries (Feder and Noronha, 1987; Shiptonand Goheen, 1992; Jayne et al., 2003). In addition, the extractive resource and food priceindices have both followed a similar boom pattern throughout the period under review,making the lack of agricultural land a potential source of conict as our reduced formresults clearly conrm.The instrumentation strategy for the homicide rate differs substantially from the one

employed for armed conict. It has been argued that a high-population density may berelated to an increased risk of homicides (Gillis, 1974; Krahn et al., 1986). However, this isnot veried in reality: low homicide rates can be found in societies with very differentpopulation density (Neuman and Berger, 1988; Cole and Gramajo, 2009). In turn, a linkhas been established between rapid population growth and homicides (Messner, 1982;Krahn et al., 1986). Obviously, population growth per se cannot be considered as


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instrument as it should be part of the structural equation. However, growth of the urbanpopulation can be employed when controlling for other population-based measures.Moreover, existing evidence demonstrates that crime grows dramatically with the level ofurbanization giving further support to our choice of instrument (Van Kesteren et al., 2000;Andrienko, 2002). Additional support for homicide rates being larger in cities, especially inrecent years comes from Cubbin et al. (2000)). The homicide data from the Small ArmsSurvey exhibit an urban bias that may be explained by several factors. Public health datafrom health posts and hospitals have a potential urban bias as even those who get woundedin rural areas go to urban centers to get treated, and then get recorded there. In addition,rural hinterlands in poor countries often have neither registry nor coverage at all. At thesame time, urban areas do not grow solely through birth but also experience considerablein-migration, which might have a destabilizing social effect. Since homicide data tend toshow an urban bias, with a majority of the information related to or recorded as urbanviolence, we instrument the homicide rate with urban population growth.By analogy, we follow Miguel and Roland (2011) who show that their use of a distance-

based instrument is valid only when controlling for other distance variables in thestructural equation. Hence, our urban population growth instrument calls for controlling forother demographic variables. Therefore, we control for population dynamics in the struc-tural equation. As for the instrument retained for armed conict, we control for surfaceareas and agricultural value added in the second stage equation. We are aware that ourinstruments are by no means perfect, as previous research shows how difcult it is to ndgood instruments. However, the coefcient estimates associated with the instruments arehighly signicant and economically meaningful, hinting to the relevance of our instrumentalvariables for identifying conict and armed violence.

5. PRESENTATION AND DISCUSSION OF RESULTS

Our xed effects (OLS) and instrumental-variable results all point in the same directionwith regard to four main ndings. First, and as expected, resource dependence decreasesANS per capita. Second, both armed conict and homicides have a negative impact onANS per capita. Third, education expenditures are far more path dependent than ANS, arelower in resource dependent countries, and do not seem to be inuenced by violencedynamics. The latter comes as a surprise but is consistent with previous empirical ndings.Fourth, our instruments are highly relevant and explain the endogenous variables well.The results are presented in Tables IV and V. Table IV presents the impact of armed

conict and resource dependence on ANS and education expenditure per capita, whileTable V presents the impact of homicides and resources dependence on the same outcomevariables. In each table, the rst pair of columns shows the ndings for ANS per capita asoutcome variable, and the second for education expenditures per capita. The rst column ofeach pair refers to the instrumental variables specication, with the coefcient associatedwith the instrument reported at the bottom of the table. The second column presents resultsstemming from the OLS specication. We include all covariates in the reduced formequation of the two-stage procedure but do not report them for the sake of readability.In Table IV, the reduced-form result shows that the more agricultural land available, the

less the propensity for conict. According to our estimates, an increase of 10,000 squarekilometers in agricultural land reduces a countrys propensity for conict by roughly 6% (or18.2% of the sample standard deviation). Conict itself is shown to have a considerablynegative impact on sustainable development, as it decreases ANS per capita by almost


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$220. Column 2 of the same table shows that the OLS coefcient associated with armedconict accounts for only 15.3% of the instrumental variables coefcient. Put differently,conict reduces ANS per capita by $33. While the impact of conict estimated in the OLSspecication is substantially lower, it points in the same direction as the IV specication.The coefcient of the instrumental variables estimate is larger because of the loss in preci-sion and its analogy to a local average treatment effect (LATE): We only identify the effectof conict on ANS for the sub-sample of countries whose propensity for conict dependson agricultural land availability. As a result, the point estimate associated with conict inthe instrumental variables specication has to be considered as an upper bound. Given thelimitations of our data, we agree with Imbens (2009) that it is better to have a LATE resultthan no result. Moreover, when comparing the IV and the OLS results, the latter appear tobe biased towards zero pointing to an inherent requirement for instrumenting armed conictto get a more complete picture of the magnitude of the negative impact conict can have onANS per capita.Resource dependence also has a signicant and negative impact on ANS per capita. In

the IV specication, the effect is signicant and the coefcient estimate amounts to -1.41.In the OLS specication, the magnitude and sign of the coefcient remain similar but theestimate is no longer signicant. Apparently, the endogeneity of armed conict also biasesthe other coefcient estimates towards zero in the OLS framework. When instrumentingconict, however, the negative relationship between resource dependence and ANS percapita amounts to more than 10% based on the sample standard deviations.The other control variables remain virtually unchanged across the two specications.

Sustainable development as measured by ANS per capita shows a moderate level ofpersistence with a coefcient estimate of roughly 0.42. Richer countries in terms of GDPper capita tend to have higher levels of ANS per capita. However, faster growing economiesinvest less in genuine savings. Consistent with the argument presented earlier, resource-dependent, fast growing economies tend to deplete their natural capital base faster. Thesame logic applies to the agricultural sector, where the agricultural value-added per workeris positively associated with ANS while growth in the agricultural sector decreases ANS.

TABLE III List of Variables and Their Matrix Acronyms

Variable Content

Acronym

ANS Adjusted Net Savings per capita replaced by Education expenditures per capita as dependent variable in

the human capital equations

GDP GDP per capita

GDP per capita growth (annual %), GDP deator (annual %)

POP Population growth (annual %)

Rural population (% of total population), Rural population growth (annual %)

COUN Land area (km2)

Forest area (km2)

Agricultural value added per worker

Agricultural value added (annual % growth)

RD Sum of fuel, ores, and minerals exports over all merchandise exports

VIOL Conict indicator coding for at least 25 battle-related deaths in a given year with 1

Homicides per 100,000 inhabitants

INST Agricultural area (km2)

Urban population growth (annual %)


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Turning to education expenditures as outcome variable, it appears that investment inhuman capital is more persistent than ANS (with a coefcient of 0.62 associated withlagged education expenditures, see Table IV, Column 3). As highlighted earlier, thecoefcient associated with conict is negative across specications but not signicant.As Stewart et al. (1997) have shown, education expenditures do not necessarily drop inthe context of armed conict. The early literature on military expenditures argues thatnancing the military may be done at the expense of other expenditure classes such aseducation (Rothschild, 1973; Deger, 1985). But high military expenditures may prevailfor prolonged periods, well before and long after conicts. Therefore, we are not ableto detect a direct impact of conict on instantaneous education expenditures. Thisnotion is also supported by micro-level evidence from Timor Leste showing that not allgroups in society experience a reduction in education due to conict. Justino et al.(2011) nd that conict has a negative impact on school attendance of boys, but canhave a positive impact on girls school attendance. School systems have further beenfound to be surprisingly resilient even during episodes of conict where the educationsystem keeps nonetheless functioning, at least in parts of the war-torn country (Buck-land, 2006).

TABLE IV The Impact of Conict on Sustainable Development and Education

ANS Education

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

IV OLS IV OLS

ANS pc (Lag) 0.421***(0.000) 0.419*** (0.000)

Education expenditure pc (Lag) 0.620*** (0.000) 0.619*** (0.000)

GDP pc (Lag) 0.134*** (0.004) 0.123*** (0.009) 0.001 (0.972) 0.001 (0.974)

GDP growth (Lag, %) 3.820* (0.063) 2.819 (0.114) 0.585* (0.078) 0.575* (0.090)

Ination (%) 0.185 (0.830) 0.116 (0.903) 0.137** (0.038) 0.133** (0.037)

Population growth (%) 20.935 (0.750) 25.741 (0.701) 30.055*** (0.007) 30.096*** (0.009)

Rural population (%) 12.466 (0.419) 16.532 (0.247) 0.419 (0.834) 0.466 (0.819)

Rural population growth (%) 94.020* (0.094) 90.536 (0.113) 4.279 (0.651) 4.248 (0.662)

Land area (km2) 0.047** (0.001) 0.023** (0.014) 0.013*** (0.000) 0.014*** (0.000)

Forest area (km2) 0.001 (0.126) 0.001 (0.227) 0.000 (0.311) 0.000 (0.332)

Agricultural value pw 0.105*** (0.006) 0.106*** (0.009) 0.018** (0.043) 0.018* (0.050)

Agricultural value growth (%) 3.115*** (0.005) 2.651** (0.027) 0.264** (0.033) 0.259** (0.031)

Resource dependence 1.412* (0.064) 1.053 (0.135) 0.211*** (0.006) 0.206*** (0.009)

Conict dummy 216.809*** (0.000) 33.157 (0.143) 3.213 (0.842) 0.862 (0.739)

Observations 496 496 525 525

Countries 104 104 109 109

Country FE Yes Yes Yes Yes

Time FE Yes Yes Yes Yes

Instrument Yes No Yes No

First stage:

Instrument

(Agricultural area 106 km2)5.997*** (0.000) 5.929*** (0.000)

Note: Sustainable development is measured as ANS per capita, education is measured as education expenditures per capita. Resultswith instrumental variable and OLS are presented. Country-level xed-effects are included in all specications and standard errorsare clustered at the country level. p-values are shown in parentheses.***Signicance at the 1% level.**Signicance at the 5% level.*Signicance at the 10% level.


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Contrary to armed conict and violence, resource dependence has a negative impact oneducation expenditures: an increase of one sample standard deviation decreases educationexpenditures by $5.3 to $6, which echoes similar ndings by Gylfason (2001). These resultsare worrisome. If governments in developing countries do not invest in education during acommodity price boom as experienced in the period 20032007, it is uncertain when theywould do so. Interestingly, we nd that GDP per capita is unrelated to education expendi-tures. The coefcient estimate is almost zero and statistically insignicant to the extent thateven richer developing countries do not seem to invest a greater share of their income inhuman capital development.Our second block of results replaces the conict variable by homicides and is presented

in Table V. The number of observations in our dataset drops substantially with 78 develop-ing countries that can be considered in this analysis compared to the 104 for armed conict.With urban population growth as instrument, the reduced form result shows that theinstrument is valid and identies homicides well. An increase in urban population growth isassociated with an increase in the homicide rate with a highly signicant coefcientestimate of 1.14 (Table V, Column 1). Homicides cause ANS per capita to fall: a one-point

TABLE V The Impact of Homicide on Sustainable Development and Education

ANS Education

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

IV OLS IV OLS

ANS pc (Lag) 0.280* (0.000) 0.446*** (0.086)

Education expenditure pc

(Lag)

0.705*** (0.000) 0.610*** (0.000)

GDP pc (Lag) 0.259** (0.037) 0.150*** (0.002) 0.027 (0.550) 0.005 (0.843)

GDP growth (Lag, %) 13.074*(0.052) 5.039* (0.082) 0.056 (0.953) 0.663 (0.270)

Ination (%) 3.911 (0.390) 3.376 (0.431) 0.328 (0.126) 0.363 (0.110)

Population growth (%) 126.940 (0.197) 112.650 (0.137) 34.844** (0.022) 42.636*** (0.000)

Rural population (%) 89.866** (0.036) 40.505** (0.039) 0.091 (0.983) 2.558 (0.413)

Rural population growth (%) 216.414** (0.014) 144.556** (0.016) 3.665 (0.805) 11.508 (0.201)

Land area (km2) 0.037** (0.040) 0.025*** (0.004) 0.013*** (0.000) 0.013*** (0.000)

Forest area (km2) 0.002** (0.033) 0.001 (0.264) 0.000 (0.370) 0.000 (0.277)

Agricultural value pw 0.089 (0.144) 0.103*** (0.006) 0.023** (0.046) 0.020** (0.040)

Agricultural value growth (%) 5.721 (0.107) 3.591* (0.091) 0.350* (0.059) 0.388** (0.025)

Resource dependence 2.112** (0.061) 1.027 (0.176) 0.137 (0.324) 0.232** (0.037)

Homicide rate 60.049* (0.059) 1.333 (0.606) 2.636 (0.525) 1.205 (0.264)

Observations 316 316 331 331

Countries 75 75 78 78

Country FE Yes Yes Yes Yes

Time FE Yes Yes Yes Yes

Instrument Yes No Yes No

First stage:

Instrument (Urban population

growth, %)

1.140*** (0.000) 1.660*** (0.000)

Note: Sustainable development is measured as ANS per capita, education is measured as education expenditures per capita. Resultswith instrumental variable and OLS are presented. Country-level xed- effects are included in all specications and standard errorsare clustered at the country level. p-values are shown in parentheses.***Signicance at the 1% level.**Signicance at the 5% level.*Signicance at the 10% level.


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increase in the homicide rate decreases ANS per capita by $60 in our IV estimation. As forthe case of conict, this estimate is rather an upper bound being restricted from below bythe xed effects estimate of 1.33. The relationship between resource dependence and ANSper capita is similar to the one established in the conict specication. The relationshipbetween education expenditures and homicides is negative, similar to the one between edu-cation and conict. Yet, here we obtain a statistically signicant coefcient associated withthe homicide rate in the OLS specication. It might thus be the case that an analysis of con-ict data understates the impact of overall armed violence on education, but this would de-nitely deserve to be complemented by additional research and country case studies. IVspecications tend to be sensitive to small changes. Therefore, we test the robustness of ourresults by jointly including conict and the homicide rate in one model and carrying outtwo reduced form estimations for one structural regression. The two instruments remainhighly relevant. As in the specications above, the agricultural area of a country instrumentsconict and urban population growth qualies as instrument for the homicide rate whenestimating the combined model. The other results remain virtually unchanged with thiscombined specication, and conrm the negative impact of resource dependence on sustain-ability. Similarly, conict and armed violence reduce ANS. The results further reafrm thateducation expenditures show persistence over the years and that neither conict nor homi-cides signicantly reduce education investments. Thus, the combined specication, includ-ing simultaneously a conict measure and the homicide rate, tend to conrm the robustnessof the results presented in Tables IV and V (we do not report the detailed results for thesake of brevity, but they are available from the authors upon request).

6. CONCLUSION

The commodity boom that started at the turn of the Millennium may not usher a completereversal of the SingerPrebisch thesis. Yet, in a new era of relative resource scarcity, theterms of trade of resource-rich developing countries have started to improve in the face ofbooming commodity prices and cheaper manufactured goods from emerging economies.The rush on extractive resources increased the number of resource-dependent countries inthe developing world, offering an exceptional opportunity to mobilize extractive revenuesfor development in new oil and mining eldorados, but also raising resource-curse risks.In this paper, we have examined the dynamic relationship between extractive resource

dependence, armed violence, and sustainable development in a panel covering 104 develop-ing countries over the recent commodity price boom period. Our results clearly support theresource-curse argument when looking at sustainable development outcomes as measuredby ANS per capita. This is not surprising to the extent that, ceteris paribus, natural capitaldepletion reduces genuine savings. Nonetheless, we further nd that resource dependencenegatively affects human capital as well: resource-dependent countries tend to have lowereducation expenditures per capita during the short period under review.This is aggravated by the fact that resource dependence tends to be associated with a

higher probability of armed conicts and higher homicide rates, both of which have a sig-nicant negative impact on ANS per capita. In a rst attempt to quantify the impact ofarmed violence, beyond armed conict, on development outcomes, we show that higherhomicide rates do not appear to affect education expenditures. Hence, future research on thetransmission channels from increased violence to reduced ANS could explore the impact ofarmed conict and violence on physical capital and institutions, which has not beenconsidered here. Moreover, governments who fail to appropriately re-invest the windfalls


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from a commodity price boom in ANS and education may be more prone to armed chal-lenges.The results conrm our initial assumption that resource dependence may be associated

with higher homicide rates. Yet, the reverse can also happen in cases where the authoritiesclamp down on popular unrest with more vigor in areas where extractive industries operatewith a view to maintaining tight security in and around strategic locations (e.g. Algeria inthe 1990s). To capture such dynamics, it would be necessary to add variables on staterepression including arrest rates for example. In addition, homicides can obviously resultfrom many factors that are not related to extractive projects. To get a more rened pictureof the dynamics at play, further research should include geo-referenced data on homicidespaired with the presence or absence of extractive sites. The increasing availability of dataon homicides worldwide, including geo-coded data, offers an opportunity to look beyondarmed conict. Today, nine out of ten deaths happen outside of armed conict. The 2011edition of the Global Burden of Armed Violence estimates that out of the 526,000 peoplewho die violently every year, only 55,000 of them lose their lives in conicts or as a resultof terrorism (Geneva Declaration on Armed Violence and Development, 2011). Detaileddata on homicides worldwide allow to factor into the analysis the socio-environmental con-icts associated with oil, gas, and mining projects. More research on the micro-level con-ict dynamics associated with extractive activities will help identify entry points tostrengthen the capacity of host states, communities, and industries to prevent and peacefullymanage conicts over socio-environmental and economic grievances in resource-dependentcountries.While the results presented here offer macro-level insights into conict dynamics during

the recent commodity price boom, they are indicative of changing conict patterns includ-ing socio-environmental violence. Yet, this research needs to be complemented by micro-level case-studies to better understand local and regional patterns. Similarly, micro-dataoffer further opportunities to tackle endogeneity and trace more precisely the channelsthrough which rent seeking and relative deprivation associated with extractive activitiestranslate into armed violence.

ACKNOWLEDGMENTS

The authors thank the participants of the 16th Annual International Conference onEconomics and Security as well as those of the 19th Annual Conference of the EuropeanAssociation of Environmental and Resource Economists, and two anonymous referees, fortheir remarks and suggestions.

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Abstract1. INTRODUCTION2. EXTRACTION, SUSTAINABLE DEVELOPMENT, AND ARMED VIOLENCE3. DATA DESCRIPTION4. ECONOMETRIC SPECIFICATION AND IDENTIFICATION5. PRESENTATION AND DISCUSSION OF RESULTS6. CONCLUSIONACKNOWLEDGMENTSReferences

8 - Carbonnier and Wagner - Resource Dependence and Armed Violence

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