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Does the Bomb Really Embolden? Revisiting the Statistical Evidence for the Nuclear Emboldenment Thesis

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  • Kyungwon Suh

Abstract

This paper revisits the latest statistical evidence for the nuclear emboldenment thesis—nuclear-armed states are more likely to initiate military aggression than non-nuclear states—from ( Bell and Miller 2015 ). If correct, their findings have important theoretical and policy implications regarding the effect of nuclear proliferation on international conflict. This paper shows, however, that Bell and Miller’s findings heavily rely on two important components of their statistical analysis: (1) using all state dyad observations, and (2) employing pooled regression models to analyze time-series-cross-sectional (TSCS) data. I argue that those components are based on questionable assumptions on heterogeneity in their dataset. Based on alternative strategies dealing with heterogeneity in dyadic data, my reanalysis shows that the emboldening effect of nuclear weapons is not as robust as originally claimed. Instead, I find the robust deterrent effect of nuclear weapons: nuclear-armed states are less likely to be targeted in military disputes. These findings highlight the need for careful application of quantitative methods to produce a more robust understanding of nuclear issues.

Suggested Citation

  • Kyungwon Suh, 2023. "Does the Bomb Really Embolden? Revisiting the Statistical Evidence for the Nuclear Emboldenment Thesis," Journal of Conflict Resolution, Peace Science Society (International), vol. 67(6), pages 1067-1094, July.
  • Handle: RePEc:sae:jocore:v:67:y:2023:i:6:p:1067-1094
    DOI: 10.1177/00220027221132474
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    References listed on IDEAS

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    1. Andrew Bell & Malcolm Fairbrother & Kelvyn Jones, 2019. "Fixed and random effects models: making an informed choice," Quality & Quantity: International Journal of Methodology, Springer, vol. 53(2), pages 1051-1074, March.
    2. Beck, Nathaniel, 2020. "Estimating Grouped Data Models with a Binary-Dependent Variable and Fixed Effects via a Logit versus a Linear Probability Model: The Impact of Dropped Units," Political Analysis, Cambridge University Press, vol. 28(1), pages 139-145, January.
    3. King, Gary, 2001. "Proper Nouns and Methodological Propriety: Pooling Dyads in International Relations Data," International Organization, Cambridge University Press, vol. 55(2), pages 497-507, April.
    4. Bell, Andrew & Jones, Kelvyn, 2015. "Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data," Political Science Research and Methods, Cambridge University Press, vol. 3(1), pages 133-153, January.
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