Protecting Minorities in Large Binary Elections. A Test of Storable Votes Using Field Data
Alessandra Casella,
Shuky Ehrenberg (),
Andrew Gelman and
Jie Shen
Additional contact information
Shuky Ehrenberg: Yale Law School - Yale University [New Haven]
Andrew Gelman: Applied Statistics Center Columbia University - Columbia University [New York]
Jie Shen: Department of Statistics University of California - UC Irvine - University of California [Irvine] - UC - University of California
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Abstract:
Democratic systems are built, with good reason, on majoritarian principles, but their legitimacy requires the protection of strongly held minority preferences. The challenge is to do so while treating every voter equally and preserving aggregate welfare. One possible solution is storable votes: granting each voter a budget of votes to cast as desired over multiple decisions. During the 2006 student elections at Columbia University, we tested a simple version of this idea: voters were asked to rank the importance of the different contests and to choose where to cast a single extra "bonus vote," had one been available. We used these responses to construct distributions of intensities and electoral outcomes, both without and with the bonus vote. Bootstrapping techniques provided estimates of the probable impact of the bonus vote. The bonus vote performs well: when minority preferences are particularly intense, the minority wins at least one of the contests with 15--30 percent probability; and, when the minority wins, aggregate welfare increases with 85--95 percent probability. When majority and minority preferences are equally intense, the effect of the bonus vote is smaller and more variable but on balance still positive.
Keywords: Storable Votes; Referendums; Minorities; Majority Voting; Experiments (search for similar items in EconPapers)
Date: 2008-12-22
Note: View the original document on HAL open archive server: https://shs.hal.science/halshs-00349037v1
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Journal Article: Protecting Minorities in Large Binary Elections: A Test of Storable Votes Using Field Data (2010) 
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