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Integrating Conflict Event Data

Author

Listed:
  • Karsten Donnay
  • Eric T. Dunford
  • Erin C. McGrath
  • David Backer
  • David E. Cunningham

Abstract

The growing multitude of sophisticated event-level data collection enables novel analyses of conflict. Even when multiple event data sets are available, researchers tend to rely on only one. We instead advocate integrating information from multiple event data sets. The advantages include facilitating analysis of relationships between different types of conflict, providing more comprehensive empirical measurement, and evaluating the relative coverage and quality of data sets. Existing integration efforts have been performed manually, with significant limitations. Therefore, we introduce Matching Event Data by Location, Time and Type (MELTT)—an automated, transparent, reproducible methodology for integrating event data sets. For the cases of Nigeria 2011, South Sudan 2015, and Libya 2014, we show that using MELTT to integrate data from four leading conflict event data sets (Uppsala Conflict Data Project–Georeferenced Event Data, Armed Conflict Location and Event Data, Social Conflict Analysis Database, and Global Terrorism Database) provides a more complete picture of conflict. We also apply multiple systems estimation to show that each of these data sets has substantial missingness in coverage.

Suggested Citation

  • Karsten Donnay & Eric T. Dunford & Erin C. McGrath & David Backer & David E. Cunningham, 2019. "Integrating Conflict Event Data," Journal of Conflict Resolution, Peace Science Society (International), vol. 63(5), pages 1337-1364, May.
    • Handle: RePEc:sae:jocore:v:63:y:2019:i:5:p:1337-1364
    DOI: 10.1177/0022002718777050
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    References listed on IDEAS

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    1. Wood, Reed M., 2014. "From Loss to Looting? Battlefield Costs and Rebel Incentives for Violence," International Organization, Cambridge University Press, vol. 68(4), pages 979-999, October.
    2. Nils B. Weidmann, 2016. "A Closer Look at Reporting Bias in Conflict Event Data," American Journal of Political Science, John Wiley & Sons, vol. 60(1), pages 206-218, January.
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    Cited by:

    1. Sebastian Schutte & Claire Kelling, 2022. "A Monte Carlo analysis of false inference in spatial conflict event studies," PLOS ONE, Public Library of Science, vol. 17(4), pages 1-22, April.
    2. Virginia Page Fortna & Nicholas J. Lotito & Michael A. Rubin, 2022. "Terrorism in armed conflict: new data attributing terrorism to rebel organizations," Conflict Management and Peace Science, Peace Science Society (International), vol. 39(2), pages 214-236, March.
    3. Clionadh Raleigh & Roudabeh Kishi & Andrew Linke, 2023. "Political instability patterns are obscured by conflict dataset scope conditions, sources, and coding choices," Palgrave Communications, Palgrave Macmillan, vol. 10(1), pages 1-17, December.
    4. Toke S. Aidt & Facundo Albornoz & Esther Hauk, 2019. "Foreign in influence and domestic policy: A survey," Cambridge Working Papers in Economics 1928, Faculty of Economics, University of Cambridge.
    5. Toke S. Aidt & Facundo Albornoz & Esther Hauk, 2021. "Foreign Influence and Domestic Policy," Journal of Economic Literature, American Economic Association, vol. 59(2), pages 426-487, June.
    6. Christopher J Fariss & James Lo, 2020. "Innovations in concepts and measurement for the study of peace and conflict," Journal of Peace Research, Peace Research Institute Oslo, vol. 57(6), pages 669-678, November.

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