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The author would like to thank Salvador Barrios, Ivan Savin, participants to the IIPF Congress 2017 in Tokyo and two anonymous referees of for useful comments and suggestions which greatly improved the paper. The findings, interpretations, and conclusions expressed in this paper are entirely those of the author. They should not be attributed to the European Commission. Any mistake and all interpretations are his and his only.
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We study a market with entrepreneurial and worker entry where both entrepreneurs’ abilities and workers’ qualities are private information. We develop an agent-based computable model to mimic the mechanisms described in a previous analytical model (Boadway and Sato in Int Tax Public Finance 18(2):166–192, 2011). Then, we introduce the possibility that agents may learn over time about abilities and qualities of other agents, by means of Bayesian inference over informative signals. We show how such different assumptions affect the optimality of second-best tax and subsidy policies. While with no information, it is optimal to have a subsidy to labour and a simultaneous tax on entrepreneurs to curb excessive entry, with learning the detrimental effects of excessive entry are partly compensated by surplus-increasing faster learning.
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- Tax policy and entrepreneurial entry with information asymmetry and learning
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International Tax and Public Finance
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