'If You Only Have a Hammer': Optimal Dynamic Prevention Policy

Christopher Rauh, Ben Seimon, Alessandro Ruggieri and Hannes Mueller

No 1465, Working Papers from Barcelona School of Economics

Abstract: We study the gains of improving forecasting when a policymaker is facing a recurring risk and has the choice between a preventive early action and a de-escalating late action. We first introduce a simple two-stage Markov model to illustrate how prevention and de-escalation interact. We then study the role of forecasting for optimal armed conflict prevention in a 12-stage model which is calibrated using a large cross-country panel. Prevention benefits are substantial but critically depend on the systematic use of forecasting. The information rent of using a forecast is larger than 60% of GDP. In line with the theory we find that de-escalation policies reduce the incentives for prevention, whereas prevention increases incentives for de-escalation.

Keywords: dynamic optimization; machine learning; armed conflict; prevention; information gain (search for similar items in EconPapers)
JEL-codes: F1 F5 L8 O1 (search for similar items in EconPapers)
Date: 2024-11
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