Integrating fundamental model uncertainty in policy analysis

Johannes Ziesmer, Ding Jin, Askar Mukashov and Christian Henning

Socio-Economic Planning Sciences, 2023, vol. 87, issue PB

Abstract: Sustainable economic development in the future is driven by public policy on regional, national and global levels. Therefore a comprehensive policy analysis is needed that provides consistent and effective policy support. However, a general problem facing classical policy analysis is model uncertainty. All actors, those involved in the policy choice and those in the policy analysis, are fundamentally uncertain which of the different models corresponds to the true generative mechanism that represents the natural, economic, or social phenomena on which policy analysis is focused. In this paper, we propose a general framework that explicitly incorporates model uncertainty into the derivation of a policy choice. Incorporating model uncertainty into the analysis is limited by the very high required computational effort. In this regard, we apply metamodeling techniques as a way to reduce computational complexity. We demonstrate the effect of different metamodel types using a reduced model for the case of CAADP in Senegal. Furthermore, we explicitly show that ignoring model uncertainty leads to inefficient policy choices and results in a large waste of public resources.

Keywords: Quantitative policy analysis; Model uncertainty; Bayesian approach; Metamodeling (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:soceps:v:87:y:2023:i:pb:s0038012123000915

DOI: 10.1016/j.seps.2023.101591

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