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The Future is a Moving Target: Predicting Political Instability

Published online by Cambridge University Press:  20 February 2019

Drew Bowlsby Open the ORCID record for Drew Bowlsby [Opens in a new window] ,
Erica Chenoweth ,
Cullen Hendrix Open the ORCID record for Cullen Hendrix [Opens in a new window]  and
Jonathan D. Moyer Open the ORCID record for Jonathan D. Moyer [Opens in a new window]
Drew Bowlsby
Affiliation:
Josef Korbel School of International Studies, University of Denver
Erica Chenoweth
Affiliation:
John F. Kennedy School of Government, Harvard University
Cullen Hendrix
Affiliation:
Josef Korbel School of International Studies, University of Denver
Jonathan D. Moyer*
Affiliation:
Josef Korbel School of International Studies, University of Denver
*
*Corresponding author. Email: [email protected]
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Abstract

Previous research by Goldstone et al. (2010) generated a highly accurate predictive model of state-level political instability. Notably, this model identifies political institutions – and partial democracy with factionalism, specifically – as the most compelling factors explaining when and where instability events are likely to occur. This article reassesses the model’s explanatory power and makes three related points: (1) the model’s predictive power varies substantially over time; (2) its predictive power peaked in the period used for out-of-sample validation (1995–2004) in the original study and (3) the model performs relatively poorly in the more recent period. The authors find that this decline is not simply due to the Arab Uprisings, instability events that occurred in autocracies. Similar issues are found with attempts to predict nonviolent uprisings (Chenoweth and Ulfelder 2017) and armed conflict onset and continuation (Hegre et al. 2013). These results inform two conclusions: (1) the drivers of instability are not constant over time and (2) care must be exercised in interpreting prediction exercises as evidence in favor or dispositive of theoretical mechanisms.

Keywords

political instabilitypredictionquantitative modelingstate failure
Type
Articles
Information
British Journal of Political Science , Volume 50 , Issue 4 , October 2020 , pp. 1405 - 1417
Copyright
© Cambridge University Press 2019

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References

Banerjee, MV and Duflo, E (2009) The experimental approach to development economics. Annual Review of Economics 1, 151–178.CrossRefGoogle Scholar
Boix, C (2011) Democracy, development, and the international system. American Political Science Review 105 (4):809–828.10.1017/S0003055411000402CrossRefGoogle Scholar
Bowlsby, D, Chenoweth, E, Hendrix, C and Moyer, J (2018) Replication Data for: The Future is a Moving Target: Predicting Political Instability, https://doi.org/10.7910/DVN/XMGVO2, Harvard Dataverse, V1, UNF:6:s2+Q3W+hPkdB/e24Vhx/IQ== [fileUNF].Google Scholar
Cederman, LE and Weidmann, NB (2017) Predicting armed conflict: time to adjust our expectations? Science 355 (6324):474–476.CrossRefGoogle ScholarPubMed
Cerra, V and Saxena, SC (2008) Growth dynamics: the myth of economic recovery. American Economic Review 98 (1):439–457.10.1257/aer.98.1.439CrossRefGoogle Scholar
Chenoweth, E (2015) Major Episodes of Contention Dataset, Vol. 1. Denver, CO: University of Denver.Google Scholar
Chenoweth, E and Ulfelder, J (2017) Can structural conditions explain the onset of nonviolent uprisings? Journal of Conflict Resolution 61 (2):298–324.10.1177/0022002715576574CrossRefGoogle Scholar
Chiba, D and Gleditsch, KS (2017) The shape of things to come? Expanding the inequality and grievance model for civil war forecasts with event data. Journal of Peace Research 54 (2):275–297.CrossRefGoogle Scholar
Collier, P and Hoeffler, A (2004) Greed and grievance in civil war. Oxford Economic Papers 56 (4):563–595.10.1093/oep/gpf064CrossRefGoogle Scholar
Dafoe, A (2014) Science deserves better: the imperative to share complete replication files. PS: Political Science & Politics 47 (1):60–66.Google Scholar
Evanschitzky, H and Armstrong, JA (2010) Replications of forecasting research. International Journal of Forecasting 26 (1):4–8.CrossRefGoogle Scholar
Fawcett, R (2006) An introduction to ROC analysis. Pattern Recognition Letters 27 (8):861–874.CrossRefGoogle Scholar
Friedman, M (1968) The role of monetary policy. American Economic Review 58 (1):1–17.Google Scholar
Gates, S et al. (2012) Development consequences of armed conflict. World Development 40 (9):1713–1722.CrossRefGoogle Scholar
Ghobarah, HA, Huth, P and Russett, B (2004) The post-war public health effects of civil conflict. Social Science & Medicine 59 (4):869–884.CrossRefGoogle ScholarPubMed
Giacomini, R and Rossi, B (2009) Detecting and predicting forecast breakdowns. The Review of Economic Studies 76 (2):669–705.CrossRefGoogle Scholar
Goldstone, JA et al. (2010) A global model for forecasting political instability. American Journal of Political Science 54 (1):190–208.CrossRefGoogle Scholar
Gurr, TR (1970) Why Men Rebel. Princeton, NJ: Princeton University Press.Google Scholar
Hegre, H and Sambanis, N (2006) Sensitivity analysis of empirical results on civil war onset. Journal of Conflict Resolution 50 (4):508–535.CrossRefGoogle Scholar
Hegre, H et al. (2013) Predicting armed conflict, 2010–2050. International Studies Quarterly 57 (2):250–270.10.1111/isqu.12007CrossRefGoogle Scholar
Hegre, H et al. (2017) Introduction: forecasting in peace research. Journal of Peace Research 54 (2):113–124.CrossRefGoogle Scholar
Honaker, J and King, G (2010) What to do about missing values in time‐series cross‐section data. American Journal of Political Science 54 (2):561–581.CrossRefGoogle Scholar
Litterman, L and Scheinkman, J (1991) Common factors affecting bond returns. Journal of Fixed Income 1 (1):54–61.CrossRefGoogle Scholar
Lucas, RE (1973) Some international evidence on output–inflation tradeoffs. American Economic Review 63 (3):326–334.Google Scholar
Marshall, MG, Gurr, TR and Harff, B (2016) PITF State Failure Problem Set. Vienna, VA: Center for Systemic Peace.Google Scholar
Nosek, BA et al. (2015) Promoting an open research culture. Science 348 (6242):1422–1425.CrossRefGoogle ScholarPubMed
Nyseth Brehm, H (2017) Re-examining risk factors of genocide. Journal of Genocide Research 19 (1):61–87.CrossRefGoogle Scholar
Pesaran, M, Pettenuzzo, D and Timmermann, A (2006) Forecasting time series subject to multiple structural breaks. Review of Economic Studies 73 (4):1057–1084.10.1111/j.1467-937X.2006.00408.xCrossRefGoogle Scholar
Reinhart, C and Rogoff, KS (2009) This Time is Different: Eight Centuries of Financial Folly. Princeton, NJ: Princeton University Press.Google Scholar
Stock, JH and Watson, MW (1996) Evidence on structural instability in macroeconomic time series relations. Journal of Business & Economic Statistics 14 (1):11–30.Google Scholar
Themnér, L and Wallensteen, P (2012) Armed conflicts, 1946–2011. Journal of Peace Research 49 (4):565–575.10.1177/0022343312452421CrossRefGoogle Scholar
Vreeland, JR (2008) The effect of political regime on civil war: unpacking anocracy. The Journal of Conflict Resolution 52 (3):401–425.CrossRefGoogle Scholar
Ward, MD and Beger, A (2017) Lessons from near real-time forecasting of irregular leadership changes. Journal of Peace Research 54 (2):141–156.CrossRefGoogle Scholar
Ward, MD, Greenhill, BD and Bakke, KM (2010) The perils of policy by p-value: predicting civil conflicts. Journal of Peace Research 47 (4):363–375.10.1177/0022343309356491CrossRefGoogle Scholar
Wendt, AE (1987) The agent-structure problem in international relations theory. International Organization 41 (3):335–370.CrossRefGoogle Scholar
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Bowlsby et al. Dataset

Dataset

https://doi.org/10.7910/DVN/XMGVO2
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