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RESOURCE SCARCITY AND ARMED CONFLICT

A FUZZY-SET QUALITATIVE COMPARATIVE ANALYSIS EXPLAINING CONTRADICTORY RESULTS OF QUALITATIVE AND QUANTITATIVE STUDIES

Judith M. Bretthauer, PhD Candidate

Department of Political Science, VU University Amsterdam

j.m.bretthauer@vu.nl

Paper prepared for presentation at the ECPR Graduate Conference, Bremen 4 – 6 July 2012

ABSTRACT ‘The Darfur conflict began as an ecological crisis, arising at least in part from climate change’, Ban Ki Moon, the UN Secretary General, stated in the Washington Post in 2007. The assumption that environmental change and resource scarcity can lead to conflict has become wide-spread. Yet, academic studies on the issue provide contradictory conclusions: While there are strong theoretic arguments and qualitative case studies supporting the link between resource scarcity and armed intra-state conflict, quantitative studies contradict these findings, finding no or weak links. This study aims to solve this contradiction by arguing that the social, economic and political conditions play an important role in determining whether conflict erupts over scarce resources. Employing a fuzzy-set qualitative comparative analysis, I compare 15 resource-scarce cases with conflict like Burundi, Algeria and Haiti to 16 cases without conflict like Cape Verde, Jordan and Chile. I will focus on conditions drawn from an analysis of 20 qualitative case studies: quality of political institutions, corruption, ethnic exclusion, tertiary education, dependence on agriculture and poverty. My analysis shows that a combination of high dependence on agriculture, high poverty levels and low levels of tertiary education explain armed conflict in resource-scarce countries, suggesting an important role for economic diversification in preventing conflict.

INTRODUCTION

In 2007 the Norwegian Nobel Committee awarded the Nobel Peace Prize to Al Gore and the Intergovernmental Panel on Climate Change (IPCC) for their work in the area of climate change, as ‘global warming not only has negative consequences for "human security", but can also fuel violence and conflict within and between states’ (Mjøs, 2007), explicitly drawing a link between climate change and armed conflict. Conflicts in countries as diverse as Bangladesh (Friedmann, 2009), Kenya, Ethiopia, Somalia, Chad, Peru, Pakistan, Nepal and Liberia (Smith & Vivekananda,

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2007, 2009) have been linked to climate change or resource scarcity in academic studies as well as non-academic accounts.

While the idea that environmental change and resource scarcity can lead to conflict has become a wide-spread assumption, the picture drawn by academic research is more blurry. There are strong theoretic arguments and qualitative case studies supporting the link between resource scarcity and armed conflict but most quantitative studies contradict these findings. The aim of this study is to explain these contradictory results by focusing on the economic, political and social conditions in these countries. I argue that the causal mechanisms that most qualitative case studies focus on, only take place under very specific conditions; in particular, societies that are characterised by high levels of subsistence agriculture and low levels of education.

In the following, I will start by giving an overview over the academic literature and the empirical results on links between resource scarcity and armed conflict. I will then show how the set-theoretic approach of a qualitative comparative analysis can be employed to analyse the political, economic and social conditions in resource-scarce countries and shortly introduce the fuzzy-set qualitative comparative analysis as well as the measurements and data I used in my analysis. As a next step, I explain the selection of my cases and the theoretical relevance and operationalisation of the conditions for my first analysis. I then present my analysis and results, including a second analysis, in which agricultural dependence, poverty and tertiary education are replaced by economic development. The discussion provides two explanations for the main finding. The paper ends with some concluding remarks.

LITERATURE REVIEW

The debate on the links between resource scarcity and armed conflict was shaped in the 1990s by the research group around Thomas Homer-Dixon at the University of Toronto (1999) and the Environmental Change and Security Project at the Swiss Peace Foundation (Bächler, 1998a). Both research groups focused on exploring the causal mechanisms between resource scarcity and armed conflict through in-depth case studies. Their key argument has been put like this by Myers (1987, p. 16): ‘so great are the stresses generated by too many people making too many demands on their natural-resource stocks and their institutional support systems, that the pressures often create first-rate breeding grounds for conflict’. Much focus of resource scarcity theorists has been on the causal mechanisms that link resource scarcity to armed conflict. In summary, there are three key mechanisms: inter-group dynamics, where competition over scarce resources exacerbates existing cleavages in societies; migration with scarcity providing incentives for people to migrate to other areas, where this might result in tensions between those already living in the area and newcomers and exclusion where, elites abuse their power to secure their access to (potentially) scarce resources. By manipulating state policies in their favour, elites can limit access to resources, contributing to social unrest or sparking conflict. This view has drawn much criticism from political ecologists, neo-classical economists and from a cornucopian perspective.

Political ecologists (Matthew, 2002; Barnett, 2000) reject the strong causal link drawn by resource scarcity theorists and argue that links between the environment and violent conflict are manifold and complex and that taking the local context into account is paramount. In particular, the political and social nature of the land (such as the entitlements to land, its distribution and the actors involved) play an important role in mediating human-nature interrelations. The neo-Malthusian view of the resource scarcity theorists is as overly deterministic as the decision to take up arms by local can only be explained in very specific local contexts Barnett (2000, p. 283).

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Neo-classical and Cornucopian scholars argue that conflicts over resources can be prevented by overcoming resource scarcity. The basic neo-classical argument states that market mechanisms mitigate scarcity through prices and substitution. Therefore, even societies dependent on a particular resource can overcome this dependence by substitution or international trade. For instance, Lomborg (2001, p. 148) argues that scarcity of water can be prevented by pricing it adequately, increasing water efficiency and placing water-intensive productions in water-rich countries through international trade. There are, however, circumstances where neo-classical arguments do not hold. Local scarcities of livelihood resources such as freshwater or firewood in developing countries can be difficult to alleviate through price mechanisms or substitution (Barnett, 2000, p. 273). Cornucopians are named for the horn of plenty in Greek mythology which reflects the Cornucopian belief that human inventiveness and technological advances can overcome resource scarcities, for instance through the increase in agricultural production. This has been demonstrated by the impact of the ‘green revolution’ following the introduction of high-yield crops. Lomborg (2001, pp. 127 – 129) points to the doubling of the world’s agricultural production since 1961, coinciding with falling prices for agricultural produce.

Empirical evidence in the literature on the links between resource scarcity and armed conflict is mixed: While qualitative case studies show how resource scarcity is linked to armed conflict in many cases, quantitative studies find no support for the thesis that conflict is more likely in countries experiencing resource scarcity. Much of the early research has been based on illustrative case studies including Rwanda (André & Platteau, 1998), the Philippines (Kahl, 2008; Schwartz & Singh, 1999), Gaza (Kelly & Homer-Dixon, 1995), Chiapas in Mexico (Howard & Homer-Dixon, 1996), South Africa (Percival & Homer-Dixon, 1998), Pakistan (Schwartz & Singh, 1999), Bangladesh (Swain, 1996). Most of these case studies used process-tracing as the key methodology, aiming to map important variables and the causal pathway from resource scarcity to violent conflict. While many of these case studies are very convincing, in some cases different studies have reached divergent results. In a case study of Rwanda, Percival and Homer-Dixon (1996, p. 270) conclude that ‘environmental scarcity had at most a limited, aggravating role in the recent conflict’, whereas André and Platteau (1998) argue that land scarcity directly contributed to the 1994 violence. Bächler (1998b, p. 114) also finds environmental discrimination and population dynamics among the key factors contributing to the violence in Rwanda.

However, empirical evidence from large-scale quantitative studies to back up these findings is weak. The strongest statistical support for resource scarcity theories comes from an early study by Hauge and Ellingsen (1998) who established strong relationships between land degradation, freshwater scarcity, population density, deforestation and conflict. In a study using the same data, Theisen (2008) could not replicate their findings nor was he able to find a significant relationship using an updated dataset. In a second study finding a positive relationship, Raleigh and Urdal (2007) found a significant relationship between low freshwater availability, land degradation, population growth and conflict in some models. On the other hand, the State Failure Taskforce (Esty et al., 1998), found no correlation between indicators of environmental scarcity and state failure. In a study of developing and developed countries, Urdal (2005) could not link demographic pressure to the risk of conflict in either developed or developing countries. This result is corroborated by other studies finding no relation between population growth and violent conflict (Buhaug & Rød, 2006; Collier & Hoeffler, 2004; Hauge & Ellingsen, 1998). Yet, there are some studies that do find a positive relationship between population growth and violent conflict (Raleigh & Urdal, 2007).

In conclusion, most of the quantitative works in the field do not provide evidence for the theses of resource scarcity theorists and the generally inconclusive results point to a more complex relationship between scarcity and conflict than most resource scarcity theorists envision. In this

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study I follow the critique of political ecologists in arguing that the context-specific conditions matter. However, in contrast to most political ecologists, I aim to compare various cases to find patterns of conditions across cases. I am choosing this approach to shed light on the conflicting results of qualitative and quantitative studies. Therefore my focus is not on the causal mechanisms linking resource scarcity and armed conflict, but on the conditions under which conflict does or does not break out in resource-scarce countries.

FIGURE 1: CAUSAL MECHANISMS LINKING RESOURCE SCARCITY AND ARMED CONFLICT

METHODS AND CASE SELECTION

While most resource scarcity theorists acknowledge the importance of social, economic and political factors, there is little analysis of their impact in detail. A review of 20 qualitative case studies, focusing on the economic, social and political conditions mentioned found six main conditions playing a role in linking resource scarcity and armed conflict: the quality of political institutions, corruption, ethnic heterogeneity and tensions, dependence on agriculture, capacity to adapt to environmental change and poverty. The last three conditions are all linked closely to economic development. To see whether they simply act as proxies for economic development or whether other patterns appear, I am replacing these three conditions by economic development in a second analysis. An overview of the conditions included can be seen in Figure 2.

FIGURE 2: CONDITIONS

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To analyse these conditions, I employ a fuzzy-set qualitative comparative analysis (fsQCA) in 31 resource-scarce countries. I will first discuss the method and how it can help to bridge the gap between qualitative and quantitative results by including social, economic and political conditions and then discuss my case selection of 31 resource-scarce countries.

QUALITATIVE COMPARATIVE ANALYSIS

QCA is a set-theoretic approach that focuses on the presence and non-presence of certain conditions. If we are interested in the three conditions A, B and C for the outcome Y, a number of possible combinations exist: A can be present, but neither B nor C; A and B can be present, but not C; neither A, B or C can be present and so on. QCA employs a table (called truth table) of all possible sets of conditions and then allocates the empirical cases to these sets. It is within this process that the qualitative nature of QCA comes into play as a lot of in-depth knowledge of cases is needed to determine certain conditions as present or absent. In the next step, the sets of conditions are simplified to provide a solution formula with the help of a software programme (in this study: fsQCA, version 2.5).

QCA does not assume a symmetry in causation, so while the presence of certain conditions might explain the outcome, this does not mean that the absence of these conditions does necessarily explain the absence of the outcome. Therefore, separate analyses are needed for the conflict and non-conflict outcome. For reasons of space and coherence I will focus on the conflict outcome only in this study using the full dataset including countries that have not experienced conflicts.

QCA is particularly beneficial to the analysis of context conditions as it focuses on the pathways and combinations of conditions that lead to an outcome. In particular, the case study literature often uses conjunctural rather than additive causation. For instance, Bächler (1998a, p. 25) argues that environmental degradation leads to conflict if 'social fault lines can be manipulated in struggles over social, ethnic, political or international power', in fact arguing that environmental degradation leads to conflict only in conjunction with a number of other conditions. Using QCA in an effort to explain contradictory results has been suggested earlier (Theisen, 2008) but has not achieved any attention so far. As QCA is based on conjunctural causation, conditions are assumed to be interrelated. Therefore correlations between conditions are not problematic in QCA.

While there are many conditions one might find theoretically interesting, too large a number of conditions presents a problem of limited diversity in QCA. Using QCA the number of possible combinations of conditions grows exponentially with each condition added. With a large number of conditions and a small or - as in this case - intermediate number of cases only a small fraction of all possible combinations can be observed empirically. This can lead to an individualization of cases where each case illustrate an individual combination of conditions. Therefore limiting the number of conditions is necessary. Berg-Schlosser and De Meur (2008, p. 28) suggest selecting 4 to 7 conditions for an intermediate number of cases. I have settled on the six conditions that were most prominently discussed in the case studies in the main analysis and four in the second analysis where three of the original conditions (agricultural dependence, poverty and tertiary education) are replaced by economic development.

CASE SELECTION

When is an area resource-scarce? Various measures such as population density (Theisen, 2008; Buhaug & Rød, 2006; Hegre & Sambanis, 2006; de Soysa, 2002; Hauge & Ellingsen, 1998) or the degree of soil erosion (Hendrix & Glaser, 2007; Raleigh & Urdal, 2007; Theisen & Brandsegg, 2007; Esty et al., 1998; Hauge & Ellingsen, 1998) have been used to determine whether an area is resource scarce, but no standard operationalisation has been established. I define resource scarcity in terms of arable land and freshwater as the most relevant resources for sustaining

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human livelihoods. To determine land-scarce cases, I use an index of land resource potential and constraints published by the United Nation’s Food and Agricultural Organisation (2000). This ranking includes a number of indicators that have an impact on land potential (equivalent potential arable land, deserts and drylands, steeplands, land degradation severity, actual arable land, land balance and population increase). I operationalize countries1

I operationalize water scarcity by using the Falkenmark indicator, which is based on assessing the gap between the water needed to satisfy an individual’s needs and the water available to that person. Falkenmark et al (1989) establish a threshold of 1700 m3 of renewable water resources per capita per year, based on water needs in the household, the agricultural and energy sectors as well as the needs of the environment. A country with less than 1700 m3 of renewable water resources per capita per year is considered water stressed, one with less than 1000 m3 water scarce and if countries have less than 500 m3, they experience absolute water scarcity. I use data on total renewable freshwater resources per year and person from the FAO Aquastat main country database (Food and Agricultural Organization of the United Nations, 2011) and use the average value between 1990 and 2010 and a threshold of 1000 m3 per capita per year.

as resource scarce where the equivalent potential arable land is below 0.1 ha per capita. The threshold of 0.1 ha builds on a study by Smil (1993, p. 69) that establishes 0.07 ha per person as the necessary threshold for a person to sustain their livelihood.

Of the resulting 39 cases, 23 have experienced conflicts between 1990 and 2010 according to PRIO/UCDP’s (Uppsala Conflict Data Program (UPCD), 2011a) definition of conflict, using a thresholds of 25 battle deaths. I limit my research to intrastate conflict (with and without foreign involvement) and non-state conflicts, thus excluding conflicts between countries as most conflicts since World War II fall into the former categories and wars between states tend to exhibit different patterns from wars within states. I use the UCDP’s datasets on armed conflict (Uppsala Conflict Data Program (UPCD), 2011b) and on non-state conflict (Uppsala Conflict Data Program (UPCD), 2011c).

In order to reduce the impact of reverse causalities, such as conflict leading to environmental degradation and resource scarcity, I have dropped cases that experienced an armed conflict in the ten years before the starting date of the first armed conflict within the period 1990 – 2010. Cases were an armed conflict was ongoing in 1990 are excluded, except for cases that did not reach the battle death threshold between 1980 and 1990. For instance, conflict broke out in Burundi in 1965. After this, the country did not experience conflict episodes with more than 25 battle deaths until 1991. Yet, this is coded as one conflict, since the parties involved remained the same. Burundi is included in the sample as the country did not reach the battle death threshold between 1980 and 1990. Resource scarce cases that were dropped from the analysis because of recurring or ongoing conflicts are Bangladesh, El Salvador, Iran, Iraq, Israel, Lebanon, the Philippines and Peru. As the conditions I am interested in are rarely present as dichotomous data, I use a fuzzy-set (fs) QCA, where each condition is calibrated into membership scores between 0 and 1, where cases coded 0 are considered fully out of the set, cases coded 1 are fully in the set and cases in between are either more in than out of the set or more out than in the set. For the outcome to be in fuzzy-set form, I have opted to use the number of conflict years according the UPCD datasets. Where countries experience more than one conflict within the same year, I have coded this as several conflict years. I set the cross-over point from countries being in the set of countries experiencing no conflict to those experiencing conflict at one

1While resource scarcity and conflict afflict areas rather than entire countries, I opt to base this analysis on countries rather than areas as the social, economic and political conditions I am interested in are mostly available on an aggregated, national level. I have excluded non-state territories as well as microstates (≤ 500 000 inhabitants) from my analysis.

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conflict year. The threshold for being fully in the set of conflict countries is set at ten conflict years, which is reached by three countries: Algeria, Kenya and Pakistan. As a robustness check, I use an alternative fuzzy-set outcome in terms of total battle deaths (Uppsala Conflict Data Program (UPCD), 2011d), where I have set the cross-over threshold at 25 battle deaths and the fully-in threshold at 1000, which is the threshold for an armed conflict to be considered a civil war. While the first measure captures conflict duration, the second focuses on conflict intensity.

CONDITIONS

In the following the theoretical impact of all conditions as well as the operationalisations and data is discussed. In an effort to avoid reverse causalities, such as conflict leading to poverty or weak political institutions, data for cases that experienced conflict is taken from the year prior to the conflict. If this was not available, the closest available data was used. In particular, data on corruption is only available from 1996. For non-conflict cases, the data was averaged out over the 21-year period.

Fuzzy-set QCA data sets are calibrated with qualitative anchors at 1 (fully in), 0 (fully out) and 0.5 (crossover point). This setting of thresholds is of great importance. Two main criteria for the thresholds are that they need to make sense theoretically and the cases need to be distributed across the spectrum. If all or almost all cases are judged to be more in than out or fully in a condition, this condition will always be a necessary condition for both, the outcome and the non-outcome, not yielding any theoretic insights. Thresholds can be set quite differently depending on the set of cases. For instance, in a set of OECD countries, Chile might be fully in the set of countries with a low per capita income but in a set of Latin American countries it might be fully in the set of countries with a high per capita income.

DEPENDENCE ON AGRICULTURE (DEP)

In societies where a large percentage of the population depend on agriculture for their livelihoods, resource scarcity threatens the livelihoods of a larger number of people than in societies where only very few people live off their land. Therefore it seems reasonable to argue that a strong dependence on agriculture is a condition for conflict breaking out over scarce resources. In fact, this has been argued in the case of the Philippines (Schwartz & Singh, 1999), Peru (Schwartz & Singh, 1999), Chiapas in Mexico (Howard & Homer-Dixon, 1996), Somalia (Farah, Hussein, & Lind, 2002) and Burundi (Oketch & Polzer, 2002). For instance, in the case of Burundi, Oketch and Polzer (2002, p. 86) argue that ‘Burundi’s prima facie environmental problem is the extreme scarcity of land in this small country where the majority of the population lives off subsistence agriculture’. They (Oketch & Polzer, 2002, p. 90) go on to argue that it is the combination of environmental pressure and the lack of economic alternatives to subsistence agriculture that ultimately leads to violence. The expectation is for dependence on agriculture to be a condition for armed conflict.

To measure dependence on agriculture I use data on the agricultural population as percentage of the total population, defined as all persons depending for their livelihood on agriculture, hunting, fishing and forestry, provided by the Food and Agricultural Organization (2009). To establish thresholds, I follow Alexandratos (1999) who defines a high dependence on agriculture as 50 to 80% of the population depending on agriculture as the main source of living. I use 50% as the threshold for countries to be more in than out of the set of countries with high levels of dependence on agriculture and 80% as the threshold for countries to be fully in the set. As a threshold for fully out of the set, I use less than 5% of the population depending on agriculture. This captures the city states of Bahrain and Singapore as well as a number of states that are ill-suited for agriculture (Kuwait and Qatar) and two states where agriculture plays a very limited role in the economy (Netherlands and Japan).

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EDUCATION (EDU)

Education is included as a condition as a consensus showing a conflict-mitigating effect of education seems to emerge (Dixon, 2009, p. 716). Education is an interesting factor in resource-scarce countries for two theoretical reasons: Firstly, education provides opportunities for income and livelihood beyond living off subsistence agriculture in areas where this is not feasible. Secondly, a better-educated society will be more able to adapt to environmental change. These arguments, however, have rarely been discussed in case studies. A case study of Ethiopia (Flintan & Tamrat, 2002, p. 275), for instance, points out that 80% of the indigenous population in the Afar River Basin rely on subsistence farming and that the level of education is low but fail to discuss how this might have affected violence in the area. The hypothesis in this study is that low levels of education are an important condition for armed conflict.

I focus on the population holding tertiary degrees as university-level education seems to capture the essence of adaptability better than other measures like literacy rates. I use data by Lee and Barro (2010) on educational attainment and use the average percentage of people over the age of 25 with tertiary degrees between 1990 and 2010. This is one indicator where setting qualitative anchors is very difficult as existing thresholds are often policy goals, focused on young people. For instance, the EU aims at 40% of 30 to 34-year-olds having attained tertiary education in 2020 – a much higher percentage than even the highest rate in my dataset (21,8% in Singapore). Considering the data, I decided to set the threshold for fully in the set of countries with high levels of tertiary education at thirteen percent, capturing five countries (Singapore, Japan, Uzbekistan, South Korea and the Netherlands) and the threshold for countries to be fully out at 1%, capturing the following countries: Eritrea, Djibouti, Rwanda, Lesotho, Yemen, Burundi and Haiti. The crossover threshold is set at seven percent between Jordan (6.6%) and Mongolia (7.9%).

POVERTY

Poverty is considered a major risk for the outbreak of civil war (Collier et al, 2003). A higher risk in less developed countries is one of the most consistent findings in large-n civil war literature (Dixon, 2009). Homer-Dixon (1994, 26), on the other hand, argues that there is ‘no clear correlation between poverty (or economic inequality) and social conflict’. In a comparative study of the impact of resource scarcity on violent conflict in Uganda, Rwanda, Ethiopia and Burundi, Ejigu (2009, p. 889) argues that the close link between the environment and poverty has an impact on the relationship between resource scarcity and conflict. Poverty is closely linked to environment in societies where the majority of the population is dependent on natural resources for their livelihoods. As the natural resource base (e.g. forest cover, grazing land, arable agricultural land, water resources) is shrinking, there are few routes to escape poverty and a lack of market infrastructure. The hypothesis is that poverty is a condition for armed conflict, in particular where it acts in conjuncture with largely agricultural-based economies.

In this study, poverty is measured in absolute terms and I used the World Bank’s poverty headcount ratio at $1.25 a day. It is in the nature of poverty indicators and threshold to be targeted towards policy goals, such as the Millennium Development Goal of halving the number of people living on less than $1.25 a day. Therefore the thresholds in this study are based natural gaps occurring in the data. Eight countries in the sample have no people living on less than $1.25 a day, so this is taken as the first threshold. The crossover point is set at 10% between Algeria (7.6%) and Armenia (11.3%). Finally countries are considered fully in the sample of countries with high poverty rates if the rate is more than 30%, dividing Mongolia (22.7%) and Uzbekistan (31.8%).

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POLITICAL CORRUPTION (COR)

Political corruption within systems of patronage or neo-patrimonialism plays an important role in many resource-scarce societies as it is often through corrupt systems that the access and distribution of scarce resources is decided. In corrupt systems, issues concerning mainly disadvantaged groups are unlikely to be addressed, leading to grievances. Many case studies of conflicts in resource-scarce societies mention corruption or patronage: Kenya (Kahl, 1998), Philippines (Kahl, 2008), Rwanda (André & Platteau, 1998), Sudan (Suliman, 2008), Indonesia (Barber, 1998), Ethiopia (Flintan & Tamrat, 2002) and Burundi (Farah et al., 2002). Oketch and Polzer (2002, pp. 110 – 111) point to three ways in which political corruption and the resulting predatory state were linked to violence in Burundi: firstly, elites used violence to gain and maintain control over the state and its resources. Secondly, economic crises put elites under pressure by weakening their position, which prompted violence. Thirdly, the resulting pressure for economic and political reforms led elites to defend their influence by resorting to violence. The hypothesis is that corruption is a condition for conflict, in particular where it acts in conjuncture with ethnic exclusion.

I employ the World Bank’s Control of Corruption indicator, which captures ‘perceptions of the extent to which public power is exercised for private gain’ (World Bank, 2012). Countries are scored between -2.5 and 2.5 with higher values representing better governance. I keep the crossover point of 0 as it is established in the dataset and then calibrate countries as fully within the set of countries experiencing corruption if the corruption index is -1.0 or lower and as fully out of the set of corrupt countries if it is 1.0 or higher.

ETHNIC EXCLUSION (EEX)

The role of ethnic exclusion, is debated in the quantitative literature. Most studies find no significant relationship (Collier & Hoeffler, 2004; Fearon & Laitin, 2003), but few (Ellingsen, 2000) argue that ethnic fragmentization can contribute to violence. Wimmer, Cederman and Min (2009, p. 317) argue that it is the ethnopolitical constellation rather than fractionalization that explains ethnic violence. I follow their argument that conflicts are more likely where ethnic groups are excluded from the political game. In case studies of resource-scarce societies ethnic heterogeneity is often emphasized, but rarely seen as a key factor. For instance, Kahl (1998) argues that in the case of Kenya, it is not so much the existence of different ethnic groups in itself, but the ties across different groups that made the difference. While violence broke out between different ethnic groups in rural areas where few ties between groups existed, this was prevented in urban settings where ties across ethnic groups and along the lines of class, religion or profession were established. The hypothesis is that conflict is more likely in countries with a high ethnic fractionalization.

One of the measures suggested by Wimmer et al (2009) is the percentage of the population excluded from power. While Wimmer et al (2009, p. 322) hypothesize that a ‘high degree of ethnic exclusion will increase the likelihood of rebellion’, no clear thresholds are given what should be considered a high degree of exclusion. Taking a closer look at my data, I concluded to use 0.2 as a threshold for fully-in the dataset of ethnically excluded groups as this is a naturally occurring gap in the data. The threshold for being more in than out of the sample of countries with excluded groups is set fairly low at 0.05. As even fairly small excluded groups can have quite an impact, this is theoretically logical. In addition, it utilizes another (smaller) gap in the data. Finally, the threshold for fully out of the set of countries that exclude parts of their population is set at 0, resulting in 13 countries where no groups of the population are excluded from the political game.

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POLITICAL INSTITUTION (PIN)

State weakness is an important intermediate factor in many accounts of resource scarcity theorists. Increasing problems due to mounting environmental issues lead to higher demands on the state. This include development projects to mitigate the social effects of the loss of freshwater, arable land and firewood as well as social demands resulting from rural-to-urban migration, such as housing, employment, transport and energy (Homer-Dixon, 1994, 25). In addition, declining productivity due to environmental issues leads to decreasing revenues of the state placing greater limits on state capacities. Homer-Dixon (1994, 25) argues that it this widening gap between demands placed on and revenues available to the state that lead to growing discontent. Similarly, Goldstone (1997, pp. 101 – 106) argues that the combination of rising population and limited development that takes a toll on state capacities.

‘the immediate effects of population growth are political. Well before societies experience widespread absolute deprivation, the institutions that deal with the distribution of goods and power and the resolution of social conflicts may be overwhelmed and collapse in the face of persistent population pressure and limited resources.’

In most case studies state weakness is prominently discussed in many aspects linked to the quality of political institutions. For instance Flintan and Tamrat (2002, p. 253) argue that ‘one reason for recurring violent conflict in Ethiopia is the absence of democratic institutions to negotiate disputes and mediate competition’. In the case of the Philippines, Kahl (2008, p. 101) argues that the communist insurgency was fuelled as the capacities of the state, in particular the resources of the local government, did not keep up with the increasing pressure due to population growth. The theoretical expectation is for low quality of political institutions to be a condition for conflict. I am using the civil liberties index of Freedom House Index, which includes a measurement of the rule of law to measure the quality of political institutions. While the Freedom House Index has met a lot of criticism (Munck & Verkuilen, 2002), there are, unfortunately, very few indicators that span the entire time frame and a global sample. Freedom House categorizes countries as ‘free’ where their score is between 1 and 2.5, as partly free between 3 and 5 and as not free between 5.5 and 7. According to this I have used 5.5 as threshold for fully in the condition of state weakness and 2.5 as threshold for full in. While it would be preferable to use 4 as crossover point, I have opted for 4.1 as 7 countries score 4 and these would then be coded as neither in nor out, which is undesirable from a methodological standpoint. Therefore, the crossover point is set at 4.1.

ECONOMIC DEVELOPMENT (ECOD)

High-income nations are less likely to experience civil war. This is one of the most widely accepted relationships in the quantitative conflict literature (Buhaug, 2006; Elbadawi & Sambanis, 2002; Fearon & Laitin, 2003; Sambanis, 2001). Economic development and many of its faces, especially poverty and threatened livelihoods are also seen as relevant in many of the qualitative studies on resource scarcity and armed conflict; for instance in studies on Ethiopia (Flintan & Tamrat, 2002), the Philippines (Kahl, 2008; Schwartz & Singh, 1999) and Indonesia (Barber, 1998). The hypothesis is that conflict is more likely in countries with low levels of economic development.

I follow the World Bank’s categorization of countries into high-income upper middle-income, lower middle-income and low-income countries to measure economic development, which is based on the Gross National Income (GNI). I define countries as fully in the set of developed countries if their GNI per capita is above 12 276 USD, which is the World Bank threshold for high-income countries. The crossover point between developed and non-developed countries is

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set as 3 976 USD, which in World Bank terminology is the threshold between upper-middle income and lower-middle income. Finally the threshold for fully out of the set of developed countries is set 1 005 USD, in accordance with the World Bank definition of low-income economies.

ANALYSIS AND RESULTS

QCA differentiates between necessary and sufficient conditions. With a necessary condition, whenever the outcome is present, the condition is as well. Thus, it is possible for the condition to exist without the outcome. With a sufficient condition, whenever the condition is present, so is the outcome. It is possible for the outcome to exist without the condition being present (Berg-Schlosser, De Meur, Rihoux, & Ragin, 2008, p. 10).

FIGURE 3: NECESSARY AND SUFFICIENT CONDITION

The first step in a QCA is to look for necessary conditions. For a fsQCA a condition is considered necessary if it has a very high consistency of 0.9 or higher (Schneider & Wagemann, 2007, p. 213). Two measures – consistency and coverage – are given for each solution formula. Consistency indicates the degree to which cases share conditions, conveying the relationship between sub-sets of conditions and outcome2

The analysis of conditions gives two necessary conditions: the absence of economic development has a consistency score of 0.94 and the absence of education has a consistency score of 0.90. For a necessary condition, the membership values of the condition (x-values) exceed the outcome values (y-values). Therefore, if a condition is a necessary condition, no cases occur above the bisector. This is mostly the case for both, absence of economic development and absence of education as you can see in

. The coverage gives the degree to which the solution formula explains the cases in the sample. Raw coverage gives the coverage of a single pathway for the entire sample, while unique coverage gives the rate of the sample that is covered by this specific pathway only.

Figure 4 and Figure 5.

2The mathematical formula for consistency for sufficient condition is: . The formula for coverage for

sufficient conditions is: . For formula for consistency of necessary conditions is the same as the coverage formula for sufficient conditions and the coverage formula of necessary conditions is the same as the consistency formula for sufficient conditions.

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FIGURE 4: NECESSARY CONDITION: ABSENCE OF ECONOMIC DEVELOPMENT

FIGURE 5: NECESSARY CONDITION: ABSENCE OF EDUCATION

In substantive terms this means that there are no countries displaying a conflict outcome, that have high levels of economic development . For the absence of education, there are no conflict cases with high levels of education. The only exception to this is Uzbekistan, which has a high score in education (0.99) and has experienced conflicts.

In a second step, sufficient conditions are analysed, giving three separate solution formulas with different levels of complexity. These differences are due to the way the combinations of conditions that do not exist in the empirical reality (logical retainers), where it is impossible to determine the outcome, are dealt with. Excluding them gives the most complex solution, including them but making no assumptions about their outcome gives the most parsimonious

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solution. Making assumptions about the outcomes based on theoretical insights gives an intermediate solution.

As Table 1 shows, the complex solution formula consists of two separate pathways. Both pathways combine low levels of education, high poverty levels and high dependence on agriculture. In pathway one this is combined with corruption and in pathway two with the combination of low quality levels of political institutions and low levels of ethnic exclusion.

Using battle deaths rather than conflict years as outcome measure yields the same pathways and similar consistency and coverage measures.

Complex Solution3 outcome: conflict years frequency cutoff4 consistency cutoff: 1.00 5

Causal Pathways : 0.90

edu*POV*DEP*COR6 edu*POV*DEP*pin*eex consistency 0.92 0.86 raw coverage 0.59 0.32 unique coverage 0.30 0.04 cases covered by pathways7

Burundi (0.96,0.99), Kenya (0.95,1), Rwanda (0.94,0.99), Haiti (0.86,0.58), Djibouti (0.79,0.79), Yemen (0.6,0.66), Pakistan (0.55,1), Nepal (0.51,0.98)

Burundi (0.95,0.99), Eritrea (0.87,0.66), Haiti (0.86,0.58)

Solution formula edu*POV*DEP (COR + pin*eex) -> CONFLICT Solution consistency 0.90 Solution coverage 0.63 TABLE 1: COMPLEX SOLUTION, CONFLICT-YEAR OUTCOME

The overall solution formula states that a lack of education combined with high levels of poverty and high dependence on agriculture and either high levels of corruption or the combination of low quality of political institutions and low levels of ethnic exclusion leads to conflict. The overall consistency of 0.9 approximates a perfect subset relationship. The coverage rates show that the first path has much more empirical weight with a raw coverage of 0.59 as opposed to 0.32 for the second pathway.

As both pathways share the term edu*POV*DEP, I will discuss this separately, and first focus on the individual terms. Both pathways support hypotheses regarding the impact of corruption and the quality of political institutions, but surprisingly, undermines the hypothesis regarding ethnic exclusion The first pathway supports the hypothesis that high corruption levels are a condition for conflict. However, it does not support the hypothesis that corruption would act in conjuncture with ethnic exclusion, since ethnic exclusion does not appear in this pathway. In fact, ethnic exclusion only appears in the second pathway, and it is the absence of ethnic exclusion rather than the presence that is a condition for conflict. This is explained by the

3 The intermediate solution is: edu*POV*DEP (pin + COR) -> CONFLICT, the most parsimonious solution is edu*DEP -> CONFLICT 4 The frequency threshold gives the minimum number of cases a line in the truth table needs to represent to be included in the analysis. Lines with less cases are treated as logical remainders. 5 The consistency threshold gives the minimum consistency above which cases are coded as having experienced the outcome. 6 Please note: condition in upper case denote the presence of that condition, while lower case denotes the absence of a condition. * denotes an AND combination of conditions, + denotes and OR relationship 7 The first number gives the membership in the pathway and the second the membership in the conflict set.

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empirical cases in the pathway as Burundi, Haiti and Eritrea are all judged to not have ethnically excluded groups. However, the conflicts in both, Burundi and Eritrea, had an ethnic dimension.

The combination of high dependence on agriculture, poverty and low levels of education raises questions regarding the role of economic development. Interestingly, this combination is shared between both pathways, meaning that it applies to all the cases covered by the solution formula. The hypothesis that poverty and dependence on agriculture might act in conjuncture is supported by this analysis. In addition, all three conditions are closely linked to economic development and it raises the question whether the role of economic development in conflicts can be explained by these conditions. To test this, I include economic development as a condition to replace the conditions of poverty, dependence on agriculture and poverty.

Complex Solution outcome: conflict years

frequency cutoff: 1.00 consistency cutoff: 0.80

Causal Pathways

ecod*pin*COR ecod*pin*eex ECOD*PIN*COR*eex

consistency 0.78 0.80 0.80 raw cov. 0.67 0.34 0.09 unique cov. 0.36 0.04 0.05 cases covered by pathways

Burundi (0.98,0.99), Kenya (0.96,1), Uzbekistan (0.96,0.66), Turkmenistan (0.95,0.05), Rwanda (0.94,0.99), Haiti (0.87,0.58), Djibouti (0.81,0.79), Egypt (0.55,0.84)

Burundi (0.95,0.99), Eritrea (0.87,0.66), Haiti (0.87,0.58)

Jamaica (0.54,0.5)

Solution formula

ecod*pin (eex + COR) + ECOD*PIN*COR*eex -> CONFLICT

Solution cons.

0.77

Solution cov. 0.76 TABLE 2: COMPLEX SOLUTION, CONFLICT-YEAR OUTCOME

Replacing dependence on agriculture, poverty and education by economic development shows that these conditions are not simply acting as proxies for economic development. If this combination was nothing more than a proxy for economic development, one would expect the same pathways to emerge in this second analysis. However, this is not the case: Including economic development yields three pathways. These are similar (as could be expected) but show some interesting aberrations. The first path is similar, but includes weak political institutions in addition to corruption. The second path is the same, but this version of the analysis includes a third path, that has not occurred in the previous analysis and covers only one case: Jamaica. It combines high levels of economic development, high quality of political institutions, corruption and no ethnic exclusion. The countries covered are also quite different, with the first path covering Uzbekistan and Turkmenistan and Egypt, which are not covered by the first solution formula. The first solution formula, however, covers Yemen, Pakistan and Nepal, which are not covered by the second formula. This shows that economic development not only acts in conjuncture with different conditions than the combination of poverty, agricultural dependence and low levels of education but also the resulting formulas cover different cases. Therefore including three separate conditions of poverty, agricultural dependence and education yields interesting insights different to those won by the inclusion of economic development.

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DISCUSSION

My analysis sheds light on three factors that do not play a prominent role in the literature on resource scarcity and armed conflict: poverty, agricultural dependence and education. Of these three, poverty is the most-widely discussed, in both case studies and the wider conflict literature. For instance, in the case of Rwanda, Bigagaza, Abong and Mukarubuga (2002, p. 52) argue that ‘deepening rural poverty, in effect, led to violent conflict’. While lack of economic development has often been discussed as a root cause for conflict, the importance of the dependence on agriculture and how this affects resource-scarce countries in particular has not been analysed systematically so far. While agricultural dependence is mentioned as an important factor in a number of qualitative studies (Farah et al., 2002; Oketch & Polzer, 2002; Schwartz & Singh, 1999; Howard & Homer-Dixon, 1996), is has not – to my knowledge – been discussed in quantitative studies. Putting a focus on the role of subsistence agriculture research on conflict in environmentally vulnerable areas might help to give a clearer picture. My results also show an important role for tertiary education. This supports the general mitigating effect of tertiary education that has been emerging in the wider conflict literature. However, tertiary education – university graduates – have not received a lot of focus in this debate, which often centers around indicators such as primary or secondary enrolment (Thyne, 2006), the secondary enrolment of males (focusing opportunity costs of joining rebel groups and foregoing an education) (Collier & Hoeffler, 2004).

There are two theoretical explanations for the effect of tertiary education on armed conflict, one focusing on economic diversification and one on adaptive capacities. Firstly, higher levels of education allow for gainful employment outside agriculture. As less people are dependent on agriculture, conflict over scarce resources is less likely. In this explanation tertiary education would be an indicator of economic diversification. Secondly, societies with high levels of tertiary education also have more adaptive capacities, including not only technical knowledge and experts to find solution to adapt to environmental change, but also general access to information that allow people to find solutions on a small scale. Adaptive capacities are of particular importance in environmentally vulnerable societies such as resource-scarce countries. This notion of adaptive capacity links to Homer-Dixon’s argument of the ingenuity gap. He (Homer-Dixon, 1999, p. 109) defines ingenuity as ideas applied to solve practical technical and social problems. He differentiates between social and technical ingenuity, arguing that resource-scarce countries need both, but that social ingenuity is a precursor for technical ingenuity as resource-scarce countries first need

sophisticated and stable systems of markets, legal regimes, financial agencies and educational and research institutions to promote the development and distribution of new grains adapted for dry climates and eroded soils, of alternative cooking technologies to compensate for the loss of firewood, and of water conservation technologies. (Homer-Dixon, 1999, p. 110)

Which of these two explanations provides the better explanation for the impact of tertiary education remains an interesting question for further research. However, the conjunctural impact of poverty, dependence on agriculture and tertiary education suggest more explanatory power of the economic diversification hypothesis since dependence on agriculture can also be an indicator for economic diversification.

While this analysis has achieved its goal of taking a closer look at a number of conditions and in particular the role of agricultural dependence, poverty and tertiary education in resource-scarce countries and their risk for conflict, the downside of this focus is the limited number of other conditions that could be explored in depth. A number of conditions might also yield interesting insights, for instance, unpacking the role of political institutions and focusing on aspects such as conflict-resolution mechanisms. Similarly, inequality in society and the access to and

16

distribution off resources should be included in future research. Taking a closer look at these issues in conjunction with ethnic exclusion might also give more weight to this issue, which so far has not turned out to be of great explanatory power.

CONCLUDING REMARKS

The increasing focus on the social consequences of climate change has brought back an old debate on the links between resource scarcity and armed conflict. While the literature on resource scarcity provides some strong theoretical arguments on how resource scarcity leads to armed conflict, the empirical evidence on the relationship between resource scarcity and armed conflict is mixed. While qualitative studies find that resource scarcity contributes to the outbreak of armed conflict and civil war, quantitative studies find no strong link. I have argued that taking the political, social and economic conditions into account can overcome this contradiction. Based on this logic of conjunctural causation I have chosen to employ a fuzzy-set qualitative comparative analysis (fsQCA) to explain these contradictory arguments. After identifying 31 resource-scarce cases (15 of which experienced conflict between 1990 and 2010), I have analysed the role of six conditions, which are based on a review of qualitative case studies in these countries: quality of political institutions, corruption, ethnic exclusion, poverty, agricultural dependence and tertiary education. My analysis shows that it is under conditions of poverty, high agricultural dependence and a low levels of tertiary education that resource-scarce countries experience conflicts. This can explain the contradictory results as the qualitative case studies generally focus on developing countries with high levels of subsistence agriculture, whereas quantitative studies generally do not restrict the sample in this way. Replacing agricultural dependence, poverty and tertiary education with economic development showed that the explanation including agricultural dependence, poverty and education gives more insights than a mere focus on economic development. Two possible explanations for the impact of this conjuncture were offered, one focusing on economic diversification and one focusing on capacities to adapt. While both explanations give theoretically viable answers if focusing on the impact of tertiary education alone, the conjuncture of tertiary education with dependence on agriculture and poverty suggests that a lack economic diversification explains why resource-scarce countries experience conflicts better than the focus on adaptive capacities or on economic development.

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