Determinants of terrorism in the MENA region: a Bayesian Model Averaging based approach


  • Zohra Aroussi Faculty of Economic Sciences and Management of Sousse, University of Sousse, Sousse, Tunisia
  • Mekki Hamdaoui Higher Institute of Informatics and Management of Kairouan (ISIGK), Modeling, Financing and Economic Development (MOFID-LR21ES28), University of Kairouan, Kairouan, Tunisia
  • Mounir Smida Faculty of Economic Sciences and Management of Sousse, University of Sousse, Sousse, Tunisia



Terrorism; MENA; Bayesian Model Averaging; Logit; Early warning system


In this work we aim to identify potential determinants and seek to predict terrorism attack. Thus, to eliminate uncertainty linked to explanatory variables we used the BMA method. We show that, contrary to expectations terrorism in MENA region is no longer purely of economic origin but mainly due to political problems, education, financial development and countries’ demographic characteristics. Likewise, we find that national, international and global terrorism are not of same origins even they present many common roots. In the end, we show that it is possible to predict majority of attacks based on a small number of indicators measuring political risk, financial development and income inequalities.


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How to Cite

Aroussi, Z., Hamdaoui, M., & Smida, M. (2023). Determinants of terrorism in the MENA region: a Bayesian Model Averaging based approach. Journal of Economic Analysis, 3(3), 1–24.