4.8 Article

Climate variability and conflict risk in East Africa, 1990-2009

Publisher

NATL ACAD SCIENCES
DOI: 10.1073/pnas.1205130109

Keywords

social instability; standard precipitation index; generalized additive modeling; negative binomial modeling; disaggregated spatial analysis

Funding

  1. US National Science Foundation [0964687, 0964515]
  2. National Science Foundation
  3. Division Of Behavioral and Cognitive Sci
  4. Direct For Social, Behav & Economic Scie [0964515, 0964687] Funding Source: National Science Foundation

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Recent studies concerning the possible relationship between climate trends and the risks of violent conflict have yielded contradictory results, partly because of choices of conflict measures and modeling design. In this study, we examine climate-conflict relationships using a geographically disaggregated approach. We consider the effects of climate change to be both local and national in character, and we use a conflict database that contains 16,359 individual geo-located violent events for East Africa from 1990 to 2009. Unlike previous studies that relied exclusively on political and economic controls, we analyze the many geographical factors that have been shown to be important in understanding the distribution and causes of violence while also considering yearly and country fixed effects. For our main climate indicators at gridded 1 degrees resolution (similar to 100 km), wetter deviations from the precipitation norms decrease the risk of violence, whereas drier and normal periods show no effects. The relationship between temperature and conflict shows that much warmer than normal temperatures raise the risk of violence, whereas average and cooler temperatures have no effect. These precipitation and temperature effects are statistically significant but have modest influence in terms of predictive power in a model with political, economic, and physical geographic predictors. Large variations in the climate-conflict relationships are evident between the nine countries of the study region and across time periods.

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