Data and Code for: Politically feasible reforms of non-linear tax systems
Principal Investigator(s): View help for Principal Investigator(s) Felix Bierbrauer, University of Cologne; Pierre Boyer, Ecole Polytechnique; Andreas Peichl, LMU
Version: View help for Version V1
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Project Citation:
Bierbrauer, Felix, Boyer, Pierre, and Peichl, Andreas. Data and Code for: Politically feasible reforms of non-linear tax systems. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-09-09. https://doi.org/10.3886/E120126V1
Project Description
Summary:
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Data and Code for "Politically feasible reforms of non-linear tax systems" (AER)
We study reforms of non-linear income tax systems from a political economy perspective. We present a median voter theorem for monotonic tax reforms, reforms so that the change in the tax burden is a monotonic function of income. We also provide an empirical analysis of tax reforms, with a focus on the US. We show that past reforms have, by and large, been monotonic. We also show that support by the median voter was aligned with majority support in the population. Finally, we develop sufficient statistics that enable to test whether a given tax system admits a politically feasible reform.
Scope of Project
Subject Terms:
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Non-linear income taxation;
Tax reforms;
Political economy;
Optimal taxation
JEL Classification:
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C72 Noncooperative Games
D72 Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
D82 Asymmetric and Private Information; Mechanism Design
H21 Taxation and Subsidies: Efficiency; Optimal Taxation
C72 Noncooperative Games
D72 Political Processes: Rent-seeking, Lobbying, Elections, Legislatures, and Voting Behavior
D82 Asymmetric and Private Information; Mechanism Design
H21 Taxation and Subsidies: Efficiency; Optimal Taxation
Geographic Coverage:
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USA,
OECD
Time Period(s):
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1962 – 2018
Data Type(s):
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administrative records data;
aggregate data;
observational data;
other
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