The DOI matches its OpenAlex record; 2 of the 2 references it cites were found in a registry or an index.
Improving Forecasts of State Failure
Abstract
This article offers the first independent scholarly evaluation of the claims, forecasts, and causal inferences of the State Failure Task Force and its efforts to forecast when states will fail. State failure refers to the collapse of the authority of the central government to impose order, as in civil wars, revolutionary wars, genocides, politicides, and adverse or disruptive regime transitions. States that sponsor terrorism or allow it to be organized within their borders are all failed states. This task force, set up at the behest of Vice President Gore in 1994, has been led by a group of distinguished academics working as consultants to the U.S. CIA. State Failure Task Force reports and publications have received attention in the media, in academia, and from public decision makers. The article identifies several methodological errors in the task force work that cause its reported forecast probabilities of conflict to be too large, its causal inferences to be biased in unpredictable directions, and its claims of forecasting performance to be exaggerated. However, the article also finds that the task force has amassed the best and most carefully collected data on state failure to date, and the required corrections provided in this article, although very large in effect, are easy to implement. The article also demonstrates how to improve forecasting performance to levels significantly greater than even corrected versions of its models. Although the matter is still a highly uncertain endeavor, the authors are nevertheless able to offer the first accurate forecasts of state failure, along with procedures and results that may be of practical use in informing foreign policy decision making. The article also describes a number of strong empirical regularities that may help in ascertaining the causes of state failure.
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Citations
Cites
Designing Social Inquiry: Scientific Inference in Qualitative Research
King, G., Keohane, R. O., & Verba, S. (1994) · Princeton University Press · doi:10.1515/9781400821211 · Cited by 7,225
Self-citationAnalyzing Incomplete Political Science Data: An Alternative Algorithm for Multiple Imputation
King, G., Honaker, J., Joseph, A., & Scheve, K. (2001) · American Political Science Review · doi:10.1017/s0003055401000235 · Cited by 1,834
Self-citation
Cited by
HARFF, B. (2003) · American Political Science Review · doi:10.1017/s0003055403000522 · Cited by 849
A Closer Look at Oil, Diamonds, and Civil War
Ross, M. (2006) · Annual Review of Political Science · doi:10.1146/annurev.polisci.9.081304.161338 · Cited by 759
Penalized regression, standard errors, and Bayesian lassos
Casella, G., Ghosh, M., Gill, J., & Kyung, M. (2010) · Bayesian Analysis · doi:10.1214/10-ba607 · Cited by 567
Armed conflict as a public health problem
Murray, C. J. L. (2002) · BMJ · doi:10.1136/bmj.324.7333.346 · Cited by 485
Explaining External Support for Insurgent Groups
Salehyan, I., Gleditsch, K. S., & Cunningham, D. E. (2011) · International Organization · doi:10.1017/s0020818311000233 · Cited by 391
Metadata
- Authors
- King, G., & Zeng, L.
- Venue
- World Politics
- Publisher
- Cambridge University Press
- Published
- 2001-07-01
- Volume
- 53
- Issue
- 04
- Pages
- 623-658
- ISSN
- 0043-8871 (ISSN-L)
- OpenAlex
- W3125026074
- Cited by
- 271
- References
- 2
- FWCI
- 40.80
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.