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The Socioeconomic Determinants of Terrorism: A Bayesian Model Averaging Approach

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  • Marcos Sanso-Navarro
  • María Vera-Cabello

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

Suggested Citation

  • Marcos Sanso-Navarro & María Vera-Cabello, 2020. "The Socioeconomic Determinants of Terrorism: A Bayesian Model Averaging Approach," Defence and Peace Economics, Taylor & Francis Journals, vol. 31(3), pages 269-288, April.
    • Handle: RePEc:taf:defpea:v:31:y:2020:i:3:p:269-288
    DOI: 10.1080/10242694.2018.1525935
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    Cited by:

    1. Nimonka Bayale & Brigitte Kanga Kouassi, 2022. "The Devil is in the Details: On the Robust Determinants of Development Aid in G5 Sahel Countries," Comparative Economic Studies, Palgrave Macmillan;Association for Comparative Economic Studies, vol. 64(4), pages 646-680, December.

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