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

Author

Listed:
  • Blattman, Christopher
  • Dube, Oeindrila
  • Bazzi, Samuel
  • Gudgeon, Matthew
  • Peck, Richard
  • Blair, Robert
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 of finegrained 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.

Suggested Citation

  • Blattman, Christopher & Dube, Oeindrila & Bazzi, Samuel & Gudgeon, Matthew & Peck, Richard & Blair, Robert, 2019. "The Promise and Pitfalls of Conflict Prediction: Evidence from Colombia and Indonesia," CEPR Discussion Papers 13829, C.E.P.R. Discussion Papers.
    • Handle: RePEc:cpr:ceprdp:13829
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    3. 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.
    4. 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.
    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. 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.
    7. Galdo Virgilio & Acevedo Gladys Lopez & Rama Martin, 2021. "Conflict and the composition of economic activity in Afghanistan," IZA Journal of Development and Migration, Sciendo & Forschungsinstitut zur Zukunft der Arbeit GmbH (IZA), vol. 12(1), pages 1-23, January.
    8. Adhvaryu, Achyuta & Fenske, James & Khanna, Gaurav & Nyshadham, Anant, 2021. "Resources, conflict, and economic development in Africa," Journal of Development Economics, Elsevier, vol. 149(C).
    9. 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.
    10. Daisuke Miyakawa & Kohei Shintani, 2020. "Disagreement between Human and Machine Predictions," IMES Discussion Paper Series 20-E-11, Institute for Monetary and Economic Studies, Bank of Japan.
    11. Yu-Chin Hsu & Robert P. Lieli, 2021. "Inference for ROC Curves Based on Estimated Predictive Indices," Papers 2112.01772, arXiv.org.
    12. 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.
    13. 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.
    14. 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.
    15. Mueller,Hannes Felix & Techasunthornwat,Chanon, 2020. "Conflict and Poverty," Policy Research Working Paper Series 9455, The World Bank.
    16. Aslihan Arslan, Romina Cavatassi, Marup Hossain, 2022. "Research Series 69: Structural and rural transformation and food systems: a quantitative synthesis for LMICs," IFAD Research Series 320720, International Fund for Agricultural Development (IFAD).
    17. Augustin TAPSOBA, 2022. "Conflict prediction using Kernel density estimation," Working Paper 258fc89a-4ec3-4eef-a0ff-7, Agence française de développement.
    18. 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.
    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

    Keywords

    Conflict; Prediction; Indonesia; Colombia; Civil war; Machine learning; Forecasting;
    All these keywords.

    JEL classification:

    • D74 - Microeconomics - - Analysis of Collective Decision-Making - - - Conflict; Conflict Resolution; Alliances; Revolutions
    • 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

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