 The importance of social and government learning in ex ante policy evaluationGonzalo Castañeda and Omar A. Guerrero Journal of Policy Modeling, 2019, vol. 41, issue 2, 273-293 Abstract: We provide two methodological insights on ex ante policy evaluation for macro models of economic development. First, we show that the problems of parameter instability and lack of behavioral constancy can be overcome by considering learning dynamics. Hence, instead of defining social constructs as fixed exogenous parameters, we represent them through stable functional relationships such as social norms. Second, we demonstrate how agent computing can be used for this purpose. By deploying a model of policy prioritization with endogenous government behavior, we estimate the performance of different policy regimes. We find that, while strictly adhering to policy recommendations increases efficiency, the nature of such recipes has a bigger effect. In other words, while it is true that lack of discipline is detrimental to prescription outcomes (a common defense of failed recommendations), it is more important that such prescriptions consider the systemic and adaptive nature of the policymaking process (something neglected by traditional technocratic advice). Keywords: Policy evaluation; Development economics; Corruption; Agent-based model; Sustainable development goals (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:eee:jpolmo:v:41:y:2019:i:2:p:273-293 DOI: 10.1016/j.jpolmod.2019.01.001 Access Statistics for this article Journal of Policy Modeling is currently edited by A. M. Costa More articles in Journal of Policy Modeling from Elsevier |
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