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Strategic Reporting: A Formal Model of Biases in Conflict Data

Gibilisco, Michael and Steinberg, Jessica (2022) Strategic Reporting: A Formal Model of Biases in Conflict Data. American Political Science Review . pp. 1-17. ISSN 0003-0554. doi:10.1017/s0003055422001162. (In Press)

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During violent conflict, governments may acknowledge their use of illegitimate violence (e.g., noncombatant casualties) even though such violence can depress civilian support. Why would they do so? We model the strategic incentives affecting government disclosures of illegitimate violence in the face of potential NGO investigations, where disclosures, investigations, and support are endogenous. We highlight implications for the analysis of conflict data generated from government and NGO reports and for the emergence of government transparency. Underreporting bias in government disclosures positively correlates with underreporting bias in NGO reports. Furthermore, governments exhibit greater underreporting bias relative to NGOs when NGOs face higher investigative costs. We also illustrate why it is difficult to estimate negative effects of illegitimate violence on support using government data: with large true effects, governments have incentives to conceal such violence, leading to strategic attenuation bias. Finally, there is a U-shaped relationship between NGO investigative costs and government payoffs.

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Gibilisco, Michael0000-0002-6484-1314
Steinberg, Jessica0000-0003-4312-8795
Additional Information:© The Author(s), 2022. Published by Cambridge University Press on behalf of the American Political Science Association. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (, which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. First Draft: April 2021. Thanks to Emiel Awad, Casey Crisman-Cox, Jacqueline DeMeritt, Alex Hirsch, Phil Hoffman, Kathy Ingram, Federica Izzo, Jin Yeub Kim, Cyanne Loyle, and Brad Smith for comments and discussions. This paper has benefited from audience feedback at MPSA 2021, APSA 2021, and ISA 2022. DATA AVAILABILITY STATEMENT. Replication code files for this study are available at the American Political Science Review Dataverse: The authors declare no ethical issues or conflicts of interest in this research. The authors affirm this research did not involve human subjects.
Record Number:CaltechAUTHORS:20230105-911538600.11
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ID Code:118748
Deposited By: Research Services Depository
Deposited On:08 Feb 2023 02:14
Last Modified:08 Feb 2023 02:14

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