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Tax policy and entrepreneurial entry with information asymmetry and learning


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 mech-
anisms 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 di erent set
of assumptions a ects 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 a subsidy-only policy can be optimal as the detri-
mental e ects of excessive entrepreneurial entry are (partly or totally)
compensated by surplus-increasing faster learning.