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The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia

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  • Bazzi, Samuel
  • Blair, Robert
  • Blattman, Chris
  • Dube, Oeindrila
  • Gudgeon, Matthew
  • Peck, Richard

Abstract

Policymakers can take actions to prevent local conflict before it begins, if such violence can be accurately predicted. We examine the two countries with the richest available sub-national data: Colombia and Indonesia. We assemble two decades one fine- grained violence data by type, alongside hundreds of annual risk factors. We predict violence one year ahead with a range of machine learning techniques. Models reliably identify persistent, high-violence hot spots. Violence is not simply autoregressive, as detailed histories of disaggregated violence perform best. Rich socio-economic data also substitute well for these histories. Even with such unusually rich data, however, the models poorly predict new outbreaks or escalations of violence. \Best case" scenarios with panel data fall short of workable early-warning systems.

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  • Bazzi, Samuel & Blair, Robert & Blattman, Chris & Dube, Oeindrila & Gudgeon, Matthew & Peck, Richard, 2019. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," SocArXiv bkrn8, Center for Open Science.
  • Handle: RePEc:osf:socarx:bkrn8
    DOI: 10.31219/osf.io/bkrn8
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    2. Marup Hossain & Conner Mullally, 2022. "Using evaluation data to predict loan performance among poor borrowers: The case of BRAC’s asset transfer and microcredit programmes," Development Policy Review, Overseas Development Institute, vol. 40(3), May.
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    5. Hannes Mueller & Christopher Rauh, 2022. "The Hard Problem of Prediction for Conflict Prevention," Journal of the European Economic Association, European Economic Association, vol. 20(6), pages 2440-2467.
    6. Nicolas Gatti & Kathy Baylis & Benjamin Crost, 2021. "Can Irrigation Infrastructure Mitigate the Effect of Rainfall Shocks on Conflict? Evidence from Indonesia," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 211-231, January.
    7. Racek, Daniel & Thurner, Paul W. & Davidson, Brittany I. & Zhu, Xiao Xiang & Kauermann, Göran, 2024. "Conflict forecasting using remote sensing data: An application to the Syrian civil war," International Journal of Forecasting, Elsevier, vol. 40(1), pages 373-391.
    8. Arslan, Aslihan & Cavatassi, Romina & Hossain, Marup, 2022. "Research Series 69: Structural and rural transformation and food systems: a quantitative synthesis for LMICs," IFAD Research Series 320669, International Fund for Agricultural Development (IFAD).
    9. Adhvaryu, Achyuta & Fenske, James & Khanna, Gaurav & Nyshadham, Anant, 2021. "Resources, conflict, and economic development in Africa," Journal of Development Economics, Elsevier, vol. 149(C).
    10. Verme, Paolo & Schuettler, Kirsten, 2021. "The impact of forced displacement on host communities: A review of the empirical literature in economics," Journal of Development Economics, Elsevier, vol. 150(C).
    11. Yujun Zhou & Erin Lentz & Hope Michelson & Chungmann Kim & Kathy Baylis, 2022. "Machine learning for food security: Principles for transparency and usability," Applied Economic Perspectives and Policy, John Wiley & Sons, vol. 44(2), pages 893-910, June.
    12. Tapsoba, Augustin, 2022. "Conflict Prediction using Kernel Density Estimation," TSE Working Papers 22-1295, Toulouse School of Economics (TSE).
    13. Mueller, H. & Rauh, C. & Seimon, B., 2024. "Introducing a Global Dataset on Conflict Forecasts and News Topics," Janeway Institute Working Papers 2402, Faculty of Economics, University of Cambridge.
    14. Robert A. Blair & Nicholas Sambanis, 2020. "Forecasting Civil Wars: Theory and Structure in an Age of “Big Data†and Machine Learning," Journal of Conflict Resolution, Peace Science Society (International), vol. 64(10), pages 1885-1915, November.
    15. Racek, Daniel & Thurner, Paul & Kauermann, Goeran, 2024. "Integrating Spatio-temporal Diffusion into Statistical Forecasting Models of Armed Conflict via Non-parametric Smoothing," OSF Preprints q59dr, Center for Open Science.
    16. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    17. Sidney Michelini & Barbora Šedová & Jacob Schewe & Katja Frieler, 2023. "Extreme weather impacts do not improve conflict predictions in Africa," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-10, December.
    18. Mueller,Hannes Felix & Techasunthornwat,Chanon, 2020. "Conflict and Poverty," Policy Research Working Paper Series 9455, The World Bank.
    19. Reilly Barry & Sam Hannah, 2022. "The distributional impact of the Sierra Leone conflict on household welfare," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 13(1), pages 1-41, January.

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    More about this item

    JEL classification:

    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions

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