The relationship between natural resource wealth and civil conflict remains unclear, despite prolonged scholarly attention. Conducting a meta-analysis—a quantitative literature review—can help synthesize this broad and disparate field to provide clearer directions for future research. Meta-analysis tools determine both the aggregate effect of natural resources on conflict and whether any particular ways in which variables are measured systematically bias the estimated effect. I conduct a meta-analysis using sixty-nine studies from sixty-two authors. I find that there is no aggregate relationship between natural resources and conflict. Most variation in variable measurement does not alter the estimated effect. However, measuring natural resource wealth using Primary Commodity Exports and including controls for mountainous terrain and ethnic fractionalization all do significantly impact the results. These findings suggest that it may be worth exploring more nuanced connections between natural resources and conflict instead of continuing to study the overall relationship.

Introduction Many theoretical mechanisms plausibly link natural resource wealth to civil conflict (Ross, 2006). Scholars have devoted considerable effort to understanding both the overall relationship between natural resources and conflict and the effect of any one kind of resource on different measures of conflict (Lujala et al., 2005). Recent research has striven to improve the measurement of natural resource wealth and to theoretically differentiate between different types of natural resources that may be more likely to lead to conflict. While both of these strategies do remove some underlying theoretical and empirical noise, a more systematic stocktaking of existing literature can illuminate robust and persistent regularities that have public policy implications. Practitioners want to prevent future conflict, and any broad themes that can be derived from decades of existing scholarship will help achieve this goal. Conducting a meta-analysis can help harness the empirical power of previous research and structure further inquiry into the natural resources and conflict relationship. Meta-analyses are quantitative literature reviews that combine the results of existing, similar scholarly work (Stanley, 2001). A meta-analysis allows for the estimate of the aggregate (or overall) effect of natural resources on conflict that can then be corrected for publication bias—a phenomenon wherein research counter to conventional wisdom is less likely to get published. The ensuing moderator analysis is specifically designed to accommodate substantial divergence regarding the best ways to theorize about and measure the independent and dependent variable, as is the case with natural resources and conflict (Stanley, 2008). These meta-analysis tools can, therefore, evaluate both the aggregate effect of natural resources on conflict and highlight any variable specifications that produce systematically different results. I use data from sixty-nine studies to conduct a meta-analysis of natural resources on conflict. I show that there is no aggregate effect between natural resources and conflict. Studies measuring natural resource wealth using oil, forest resources, and minerals and conflict using onset, incidence, duration, and intensity all produce similarly null effect estimates. However, effect estimates that measure natural resources using Primary Commodity Exports (PCE) and those using controls for mountainous terrain and ethnic fractionalization are fundamentally different.1 These findings suggest that future research would do well to focus on studying factors that may condition the relationship between natural resources and conflict instead of continuing to develop new techniques to more precisely estimate the presence of an aggregate relationship.

Natural resources, conflict, and meta-analysis A selective reading of the vast literature on the relationship between natural resources and conflict results in wildly different conclusions depending on which studies are included. Most researchers propose that natural resources and conflict are highly correlated, and both qualitative and quantitative studies of different varieties confirm this proposition (Ross, 2004). The typical theoretical argument provided to link natural resources and conflict is that “resources have ‘fueled’ a given conflict” (Ross, 2004: 340). This implies that increased natural resource wealth causes more conflict. An alternative hypothesis presented by Bodea (2012) and others is that natural resource wealth gives the government more money to spend on security, either by increasing the size of security forces or by redistributing resource wealth to citizens to reduce their interest in rebelling. This implies that increased natural resource wealth reduces the number and intensity of conflicts. Strong research designs find a clear positive (e.g., Urdal, 2005), negative (e.g., Bodea, 2012), or no relationship (e.g., Arezki et al., 2015) between natural resources and conflict. Adding to this complexity, scholars are interested in both the overall relationship and specific theoretical mechanisms that may explain why certain types of natural resources influence certain measures of conflict. Lootable resources, for example, may provoke conflict while non-lootable resources have no effect (Lujala et al., 2005). Many studies include either oil or PCE as the independent variable and civil war onset as the dependent variable. However, disparate findings using these specifications have led to broadening definitions of natural resources (including forest and mineral resources) and of conflict (including incidence, intensity, and duration). Some believe that this heterogeneity makes these studies inherently incomparable. Additionally, recent developments such as Geographic Information System (GIS) technology and more robust statistical methods have led some to suggest that only certain studies are high enough quality to inform the relationship between natural resources and conflict. Conducting a meta-analysis will help us better understand both the overall relationship between natural resources and conflict and whether any particular methods of measurement or ways in which analyses were conducted influence the estimated effect. Meta-analysis has often been suggested (Zigarell, 2011), but is rarely used in political science. Scholars are wary about including diverse studies that have heterogeneous conceptions of the independent and dependent variable, different units of analysis, and may be of suspect quality (see Supplemental Material (SI.1)). Those meta-analyses that have been conducted limit themselves to incorporating studies of a preferred type—whether published, of subjectively high quality, or that define variables in a certain way. The result is that these meta-analyses cherry pick studies to include. Lau et al. (1999: 854) concisely address this criticism saying, “diversity is not a problem in meta-analysis as long as such diversity can be coded and taken account in the analysis.” Leveraging diversity is the strength of meta-analysis. Djankov and Murrell (2002) suggest thinking of a meta-analysis as a statistical literature review: studies about similar topics are evaluated with proper consideration of the different ways authors approach a common question. In particular, meta-analysis is well suited to consider studies that differ in the ways they measure and specify their independent and dependent variables, as is the case with the relationship between natural resources and conflict (Doucouliagos, 1995).

Data collection To collect relevant work about natural resources and conflict for the meta-analysis, I searched Harzing’s “Publish or Perish” and Google Scholar for all papers containing the words “natural resource,” “conflict,” “regression,” and various synonyms published from 1998 to 2017. From the results of this search, I read all abstracts and selected potentially relevant articles. I cross-referenced these articles’ citations to develop the most complete and systematic search of the literature. Relevant studies needed to contain regression analysis with a natural resource measure as an independent variable and a conflict measure as the dependent variable; some studies used more sophisticated designs that had to be excluded because of non-comparability (see Supplemental Material (SI.2)). The unit of analysis had to be at either the country-year level or the geographic grid square-year level. The final sample includes sixty-nine studies from sixty-two different groups of authors. Even the most comprehensive computer assisted literature search will miss some important studies on any particular topic (Rosenthal and DiMatteo, 2001). However, “the central findings of meta-analysis are remarkably robust to marginal changes in the population of studies” so we should not be overly concerned with missing a few relevant studies (Stanley and Doucouliagos, 2012: 16, 31). Using these studies, I code effect sizes using the Fisher z value, which adjusts the t statistic of an estimate by the sample size. The standard measurement for the quality of each effect size estimate is the precision, P = 1 SE or the inverse of the standard error. I use these measures to determine the presence of an aggregate effect and to test for publication bias. I also code a series of moderator variables about each study’s regression specifications. Moderator variables include dummy variables accounting for different definitions of the independent and dependent variables and some controls (see Supplemental Material (SI.3)). Thus, there are dummy variables for whether a particular study measures conflict onset, incidence, intensity, or duration and for natural resource wealth measured using PCE, oil, timber, or minerals. I also code commonly used controls whose inclusion is believed to theoretically influence the estimated effect. Moderator variables are relatively broadly defined with the goal of capturing theoretical differences in the ways in which different scholars develop regression specifications. Subtle disagreements about how to measure a particular variable are ignored due to limited degrees of freedom and myriad possible minor differences in measurement (Stanley and Doucouliagos, 2012: 30). I test whether moderator variables systematically bias the estimated effect. This is an indicator of whether different measurement strategies are capturing the same underlying relationship between natural resources and conflict.

Discussion and conclusion This meta-analysis finds that there is no aggregate effect of natural resources on conflict. I introduce several econometric techniques to show that peer reviewed work tends to estimate a positive effect when none exists. Influence analysis assures that these results are not unduly biased by a small number of studies. The theoretical mechanisms linking different types of natural resources and kinds of conflict may lead to systematically biased estimates. In general, I show that this is not the case. PCE does emerge as a natural resource variable unlike other, more traditional methods of measuring resource quantity or production. The significance of the mountainous terrain and ethnic fractionalization moderator variables suggests the presence of a more nuanced relationship between natural resources and conflict. Humphreys (2005), Wegenast and Basedau (2014), Wimmer et al. (2009), and others have highlighted the conditioning effect that ethnic diversity and state presence (which could be proxied using mountainous terrain) can have on the relationship between natural resources and conflict. Many researchers are leveraging increased technological capacity to study the relationship between natural resources and conflict using more precise data and smaller units of analysis. The results of this meta-analysis suggest that a more productive avenue for future research is to return to theory and think carefully about conditioning effects. Further investment in estimating the aggregate effect is unlikely to change the overall non-relationship between different types of natural resource wealth and different measures of conflict. The meta-analysis techniques introduced here allow researchers to obtain a comprehensive picture of complex and heterogeneously studied relationships. Not only can we estimate an aggregate effect between two variables and correct that estimate for publication bias, but we can also utilize the full range of model specifications used by different scholars to determine if any one systematically biases the estimated effect. Topics prone to debates over statistical models and variable definitions are now prime targets for meta-analysis.

Acknowledgements I especially thank and am extremely appreciative of the guidance and support I received from T.D. Stanley. Guillermo Rosas and Santiago Olivella helpfully assisted with methodological advice. Editor-in-Chief Kristian Skrede Gleditsch, Matthew Gabel, and three anonymous reviewers provided valuable suggestions.

Declaration of conflicting interest

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article. ORCID iD

William O’Brochta https://orcid.org/0000-0002-9907-301X Supplemental materials

The supplemental files are available at http://journals.sagepub.com/doi/suppl/10.1177/2053168018818232. The replication files are available at https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/CJIZGS

Notes 1.

PCE refers to exports of natural resources and products made directly with natural resources. 2.

Replication data with the specific commands used in R is available online. 3.

Scholars do not report all models they run, but we can only take their reported models as representative.

Carnegie Corporation of New York Grant

This publication was made possible (in part) by a grant from the Carnegie Corporation of New York. The statements made and views expressed are solely the responsibility of the author.