Arbeitspapier

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 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 a subsidy-only policy can be optimal as the detrimental effects of excessive entrepreneurial entry are (partly or totally) compensated by surplus-increasing faster learning.

Sprache
Englisch

Erschienen in
Series: JRC Working Papers on Taxation and Structural Reforms ; No. 01/2017

Klassifikation
Wirtschaft
Asymmetric and Private Information; Mechanism Design
Search; Learning; Information and Knowledge; Communication; Belief; Unawareness
Information and Market Efficiency; Event Studies; Insider Trading
Business Taxes and Subsidies including sales and value-added (VAT)
Thema
Entrepreneurship
Taxation
Asymmetric Information
Learning
Adverse Selection
Agent-Based Computational Model

Ereignis
Geistige Schöpfung
(wer)
d'Andria, Diego
Ereignis
Veröffentlichung
(wer)
European Commission, Joint Research Centre (JRC)
(wo)
Seville
(wann)
2017

Handle
Letzte Aktualisierung
10.03.2025, 11:42 MEZ

Datenpartner

Dieses Objekt wird bereitgestellt von:
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.

Objekttyp

  • Arbeitspapier

Beteiligte

  • d'Andria, Diego
  • European Commission, Joint Research Centre (JRC)

Entstanden

  • 2017

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