Title: Tax policy and entrepreneurial entry with information asymmetry and learning
Authors: D'ANDRIA DIEGO
Citation: INTERNATIONAL TAX AND PUBLIC FINANCE vol. 26 no. 5 p. 1211-1229
Publisher: SPRINGER
Publication Year: 2019
JRC N°: JRC116411
ISSN: 0927-5940 (online)
URI: https://link.springer.com/article/10.1007%2Fs10797-019-09540-1
http://publications.jrc.ec.europa.eu/repository/handle/JRC116411
DOI: 10.1007/s10797-019-09540-1
Type: Articles in periodicals and books
Abstract: We study a market with entrepreneurial and workers 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 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 set of assumptions affects 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.
JRC Directorate:Growth and Innovation

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