Horizontal Inequalities and Ethnonationalist Civil War: A Global Comparison
Summary (3 min read)
A Global Comparison
- LARS-ERIK CEDERMAN ETH Zürich NILS B. WEIDMANN Yale University KRISTIAN SKREDE GLEDITSCH.
- Indeed, inequality continues to occupy a prominent place in the qualitative literature on civil wars and has repeatedly been linked to conflict processes (Sambanis 2005, 323; Stewart 2008b; Wood 2003).
- Relative deprivation theory remains perhaps the most prominent explanation that connects grievances with conflict, but has a very mixed record as regards empirical evidence (Brush 1996; Oberschall 1978).
- Noting that there is a major discrepancy between the quantitative nonfinding and the repeated references to inequality in the case studies, Sambanis (2005, 324) considers a number of explanations, including problems relating to interpretation and sampling of case evidence, as well as the fundamental issue of aggregation level:.
THEORIZING HORIZONTAL INEQUALITIES
- The authors now turn to their own account of inequality and conflict.
- The authors follow Stewart in treating the distribution of power and of wealth as conceptually separate components.
- Building on their previous work, the authors view HIs as structural asymmetries that make ethnonationalist civil war more likely and adopt an indirect research strategy that explains the effect of inequality by postulating a set of causal mechanisms.
- Second, the authors argue that such grievances trigger violent collective action through a process of group mobilization.
From Horizontal Inequalities to Grievances
- As opposed to objective conditions such as horizontal inequalities, grievances are intersubjectively perceived phenomena.
- As the authors have noted earlier in the text, this makes them very hard to measure, but they can draw on an extensive experimentally supported literature 4.
- In agreement with Petersen (2002), the authors postulate that resentment based on intergroup comparisons involving HIs often 6.
- It should be noted that the presence of HIs presupposes the existence of well-defined groups (Stewart 2000), which is not a trivial precondition (e.g., Kalyvas 2006).
- Yet, because their goal is to evaluate the conflict-inducing effect of HIs, the authors join Horowitz (1985), Gurr (1993; 2000b) and others in adopting a self-consciously group-based framework, although restricting their substantive focus to groups defined through ethnic categorization rather than through other cleavages.
From Grievances to Collective Action
- Clearly, emotions do not automatically trigger violent behavior.
- Yet most governmental incumbents will only reluctantly abandon their advantaged positions by sharing power or letting minorities secede.
- As convincingly argued by Kalyvas and Kocher (2007), the existence of a dilemma hinges on the questionable assumption that participation in combat is costlier than nonparticipation.
- Moreover, a number of studies show that collective identities, such as those constituting ethnic groups, facilitate collective action (e.g., Gates 2002; Simpson and Macy 2004).
- Thus, rather than classifying inequality as a pure “grievance” factor, the authors view its impact as a mobilizational resource.
ON HORIZONTAL INEQUALITIES AND CIVIL WAR
- Having postulated their causal mechanisms, the authors now return to the macro-level to perform the actual empirical analysis.
- The authors second hypothesis summarizes these theoretical expectations: H2.
- Economic and political HIs both increase the likelihood of civil war.
- Poorer groups, especially those residing in backward and peripheral regions, often desire to break away from the cores of their countries regardless of the cost, because they perceive themselves to be systematically disadvantaged compared to their wealthier compatriots in terms of economic development and distribution of public goods.
GLOBAL DATA ON HORIZONTAL INEQUALITIES AND OTHER DIMENSIONS
- The authors theoretical expectations must now be confronted with empirical evidence.
- In sum, survey data may be helpful for many purposes, but the DHS data do not provide a plausible alternative for evaluating the role of horizontal inequalities on a global basis.
- In fact, the only broadly available cross-national data source on variation in wealth within countries is the G-Econ data, developed by Nordhaus (2006; see also Nordhaus and Chen 2009).
- The authors therefore rely on the recently completed GeoEPR dataset, a comprehensive geocoded version of the EPR groups (Wucherpfennig et al. n.d.).
DERIVING OPERATIONAL MEASURES
- The G-Econ data allow deriving ethnic group–specific measures of wealth by overlaying polygons indicating group settlement areas with the cells in the Nordhaus data.
- Compared to the other parts of the country, Serbia shows up as a generally poor region.
- Figure 2 shows horizontal inequality for Yugoslavia, measured as the ratio of the group’s GDP per capita estimate to the average value for the entire country, depicting wealthier groups in darker shades and poorer ones in brighter shades.
- In addition, the authors measure the group’s demographic power balance with the ethnic group(s) in power (EGIP) as its share of the dyadic population.
EMPIRICAL ANALYSIS
- Given the limited temporal availability of inequality data, the authors restrict the sample to group years after the Cold War, from 1991 through 2005.
- The result is both substantively and statistically significant, suggesting that groups with wealth levels far from the country average are indeed more likely to experience civil war.
- The authors spatial method becomes unreliable for small population sizes, primarily because of the low resolution of the G-Econ data and the limited precision of the population estimates for tiny groups.
- 26 Consequently, the group-size restriction almost triples the inequality coefficient reported in Model 2 without affecting the size of the standard error.
SENSITIVITY ANALYSIS
- Because the number of onsets is quite limited, their findings need to be treated with some caution.
- Extending the sample of Model 3 to the entire post-WWII period, Model 7 reveals that the main inequality result holds in this case as well.30 Because the Nordhaus data represent a temporal snapshot, there are also reasons to be concerned that endogeneity could have distorted the results.
- The results of this 29 A simple test (not shown) based on a dummy variable that indicates whether the group’s settlement area intersects with oil fields also fails to make any substantial difference in the effect of the inequality variables (see data at http://www.prio.no/CSCW/Datasets).
- The authors also ran models controlling for world regions, which further confirm the robustness of their findings.
CONCLUSION
- Building on the pioneering efforts of Gurr (1993; 2000b) and his team, who have also collected extensive data on social and cultural HIs, such information would help disentangle the process at lower levels of aggregation and help us establish whether the causal imputations remain robust to such scrutiny.
- Their spatial method is limited to territorially segregated groups, and therefore cannot measure nonspatial, economic HI, as in the case of the Hutu and Tutsi in Rwanda.
- One key implication is that the authors have not derived the falsifiable predictions that distinguish between material and nonmaterial theoretical accounts.
- Rejecting “messy” factors, such as grievances and inequalities, may lead to more elegant models that can be more easily tested, but the fact remains that some of the most intractable and damaging conflict processes in the contemporary world, such as the conflicts afflicting the Sudan or the former Yugoslavia, are to a large extent about political and economic injustice.
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...…in and importance of economic privileges between individuals and groups, thus raising levels of dissatisfaction and grievances to the extent that it could spur violent reactions (Brennan-Galvin, 2002; Gizewski and Homer-Dixon, 1995; see Cederman et al., 2011; Østby, 2008 for empirical research)....
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Frequently Asked Questions (13)
Q2. What are the future works in "A global comparison" ?
Although there is plenty of room for further data refinement in future research, the authors believe that the results presented in this article are both of considerable theoretical importance and of direct policy relevance. Although survey-based information has limited scope, it could be used to extend and validate their measurements ( see Baldwin and Huber 2010 ). Although such explanations have partly fallen out of favor in recent civil war research, this finding will hopefully contribute to convincing scholars of civil war that the frustrations driving ethnonationalist mobilization and violence can not be separated easily from economic factors. Although their proposed causal mechanisms are potentially capable of closing this explanatory gap, the authors can not provide direct evidence of their operation in this article.
Q3. What is the main reason for disaggregating the cases of civil war?
There may exist a relationship between inequality and popular revolutions or class conflict, which is another reason to consider disaggregating the cases of civil war.
Q4. What could be done to help develop an explicit account of HIs?
Fine-grained temporal measurements could also help developing an explicitly endogenous account of HIs, which have been kept exogenous in this study.
Q5. Why have scholars had to content themselves with selective case studies or statistical samples restricted to particular world?
Because formidable problems of data availability associated with the uneven coverage and comparability of surveys have stood in the way of assessing such “horizontal inequalities” (HIs), most scholars have had to content themselves with selective case studies or statistical samples restricted to particular world regions.
Q6. Why is the spatial method unreliable for small population sizes?
Their spatial method becomes unreliable for small population sizes, primarily because of the low resolution of the G-Econ data and the limited precision of the population estimates for tiny groups.
Q7. What data are used to identify the fighting organizations involved in civil wars?
For a full sample of rebel groups and their conflict involvement, the authors rely on the Non-State Actors dataset (Cunningham, Gleditsch, and Salehyan 2009) that identifies the fighting organizations involved in civil wars (according to the Uppsala/PRIO Armed Conflicts Data, see Gleditsch et al. 2002).
Q8. What is the method of wealth estimation used to test the hypotheses?
To test the hypotheses, the authors then introduce the datasets and describe their spatial method of wealth estimation in detail, including how to use the contours of the ethnic groups’ settlement areas as “cookie cutters,” which allows us to extract the relevant wealth estimates from the spatial wealth map.
Q9. What is the only widely available data source on variation in wealth within countries?
In fact, the only broadly available cross-national data source on variation in wealth within countries is the G-Econ data, developed by Nordhaus (2006; see also Nordhaus and Chen 2009).
Q10. What is the way to conclude that the civil war literature is misguided?
concluding at least tentatively that both economic and political inequality at the group level increase the risk of ethnonationalist civil war, the authors argue that the civil war literature’s tendency to downplay the importance of grievances as a source of internal conflict is both premature and misguided.
Q11. How do the authors link grievances to social identities?
6Before grievances can be acted upon, they need to be cognitively linked to social identities through selfcategorization (Hogg and Abrams 1988, 21).
Q12. What is the promising way to capture the link between uneven wealth distributions and conflict?
A more promising way to capture the link between uneven wealth distributions and conflict has been proposed by Stewart (2008b) and her colleagues, who1 In more recent research, Collier, Hoeffler, and Rohner (2009) maintain that civil wars are caused by factors associated with “feasibility” rather than by grievances and other types of motivations.
Q13. What is the effect of the group size restriction on the inequality coefficient?
26 Consequently, the group-size restriction almost triples the inequality coefficient reported in Model 2 without affecting the size of the standard error.