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Prediction, Proxies, and Power

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  • Robert J. Carroll
  • Brenton Kenkel

Abstract

Many enduring questions in international relations theory focus on power relations, so it is important that scholars have a good measure of relative power. The standard measure of relative military power, the capability ratio, is barely better than random guessing at predicting militarized dispute outcomes. We use machine learning to build a superior proxy, the Dispute Outcome Expectations (DOE) score, from the same underlying data. Our measure is an order of magnitude better than the capability ratio at predicting dispute outcomes. We replicate Reed et al. (2008) and find, contrary to the original conclusions, that the probability of conflict is always highest when the state with the least benefits has a preponderance of power. In replications of 18 other dyadic analyses that use power as a control, we find that replacing the standard measure with DOE scores usually improves both in‐sample and out‐of‐sample goodness of fit.

Suggested Citation

  • Robert J. Carroll & Brenton Kenkel, 2019. "Prediction, Proxies, and Power," American Journal of Political Science, John Wiley & Sons, vol. 63(3), pages 577-593, July.
    • Handle: RePEc:wly:amposc:v:63:y:2019:i:3:p:577-593
    DOI: 10.1111/ajps.12442
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    Cited by:

    1. Mark Souva, 2023. "Material military power: A country-year measure of military power, 1865–2019," Journal of Peace Research, Peace Research Institute Oslo, vol. 60(6), pages 1002-1009, November.

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