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Using Divide and Conquer to Improve Tax Collection: Theory and Laboratory Evidence

Author

Listed:
  • Sylvain Chassang
  • Lucia Del Carpio
  • Samuel Kapon

Abstract

We consider a government collecting taxes from a large number of tax-payers using limited enforcement capacity. Under random enforcement, limited capacity results in multiple equilibria: if most agents comply, limited enforcement is sufficient to dissuade individual misbehavior; if most agents do not comply, enforcement capacity is over-stretched and fails to dissuade misbehavior. In settings without behavioral frictions, prioritized enforcement strategies can implement high collection as the unique rationalizable outcome. We investigate both theoretically and experimentally the extent to which this insight extends to environments with incomplete information and bounded rationality.

Suggested Citation

  • Sylvain Chassang & Lucia Del Carpio & Samuel Kapon, 2020. "Using Divide and Conquer to Improve Tax Collection: Theory and Laboratory Evidence," NBER Working Papers 28042, National Bureau of Economic Research, Inc.
    • Handle: RePEc:nbr:nberwo:28042
    Note: DEV LE PE POL
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    More about this item

    JEL classification:

    • C72 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Noncooperative Games
    • C73 - Mathematical and Quantitative Methods - - Game Theory and Bargaining Theory - - - Stochastic and Dynamic Games; Evolutionary Games
    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • D73 - Microeconomics - - Analysis of Collective Decision-Making - - - Bureaucracy; Administrative Processes in Public Organizations; Corruption
    • D82 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Asymmetric and Private Information; Mechanism Design
    • D86 - Microeconomics - - Information, Knowledge, and Uncertainty - - - Economics of Contract Law
    • H26 - Public Economics - - Taxation, Subsidies, and Revenue - - - Tax Evasion and Avoidance

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