Data and Code for: Inducing Cooperation through Weighted Voting and Veto Power
Principal Investigator(s): View help for Principal Investigator(s) Antonin Macé, CNRS, PSE and ENS; Rafael Treibich, University of Southern Denmark
Version: View help for Version V1
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Project Citation:
Macé, Antonin, and Treibich, Rafael. Data and Code for: Inducing Cooperation through Weighted Voting and Veto Power. Nashville, TN: American Economic Association [publisher], 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-07-22. https://doi.org/10.3886/E111882V1
Project Description
Summary:
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We study the design of voting rules for committees representing heterogeneous groups (countries, states, districts) when cooperation among groups is voluntary. While efficiency recommends
weighting groups proportionally to their stakes, we show that accounting for participation constraints entails overweighting some
groups, those for which the incentive to cooperate is the lowest.
When collective decisions are not enforceable, cooperation induces
more stringent constraints that may require granting veto power
to certain groups. In the benchmark case where groups differ
only in their population size (i.e, the apportionment problem), the
model provides a rationale for setting a minimum representation
for smaller groups.
Scope of Project
Subject Terms:
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international cooperation;
weighted voting;
veto power
JEL Classification:
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C61 Optimization Techniques • Programming Models • Dynamic Analysis
C73 Stochastic and Dynamic Games • Evolutionary Games • Repeated Games
D02 Institutions: Design, Formation, Operations, and Impact
F53 International Agreements and Observance • International Organizations
C73
C61 Optimization Techniques • Programming Models • Dynamic Analysis
C73 Stochastic and Dynamic Games • Evolutionary Games • Repeated Games
D02 Institutions: Design, Formation, Operations, and Impact
F53 International Agreements and Observance • International Organizations
C73
Geographic Coverage:
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United States
Time Period(s):
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2016 – 2017
Data Type(s):
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aggregate data;
program source code
Methodology
Data Source:
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US Census Bureau
Unit(s) of Observation:
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US state
Geographic Unit:
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US state
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