The Socioeconomic Determinants of Terrorism: A Bayesian Model Averaging Approach

Abstract

This paper introduces model uncertainty into the empirical study of the determinants of terrorism at country level. This is done by adopting a Bayesian model averaging approach and accounting for the over-dispersed count data nature of terrorist attacks. Both a broad measure of terrorism and incidents per capita have been analyzed. Our results suggest that, among the set of regressors considered, those reflecting labor market conditions and economic prospects tend to receive high posterior inclusion probabilities. These findings are robust to changes in the model specification and sample composition and are not meaningfully affected by the generalized linear regression model applied. Evidence of a geographically heterogeneous relationship between terrorism and its determinants is also provided.
Abbreviation: BMA- Bayesian Model Averaging; GLM- Generalized Linear Models
KEYWORDS:

• Terrorism determinants
• model uncertainty
• Bayesian model averaging
• generalized linear models

Description