Konferenzbeitrag
The impact of discrimination on redistributive preferences and productivity: experimental evidence from the United States
This study focuses on luck as a source of inequality, including (1) sheer luck and (2) luck that correlates with individual characteristics, such as gender and ethnicity. While the former is more random, the latter is more discriminatory. A strand of previous literature treats luck more generally, but this study distinguishes between different types of luck and investigates their distinct effects on redistributive preferences and productivity with the aid of experimental data. The study implements an online experiment on Amazon's Mechanical Turk with participants resident in the United States. Participants are informed about randomized payment schemes with or without discrimination on the grounds of gender or ethnicity. They earn money for performing a real-effort task and afterward have the chance to redistribute earnings. The key results of the study are: discrimination raises demand for redistribution more than sheer luck, especially when women are discriminated against, and ethnic discrimination reduces productivity marginally.
- Language
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Englisch
- Bibliographic citation
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Series: Beiträge zur Jahrestagung des Vereins für Socialpolitik 2019: 30 Jahre Mauerfall - Demokratie und Marktwirtschaft - Session: Behavioral Economics III ; No. D06-V2
- Classification
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Wirtschaft
Design of Experiments: General
- Event
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Geistige Schöpfung
- (who)
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Guenther, Isabel
Tetteh-Baah, Samuel Kofi
- Event
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Veröffentlichung
- (who)
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ZBW - Leibniz-Informationszentrum Wirtschaft
- (where)
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Kiel, Hamburg
- (when)
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2019
- Handle
- Last update
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10.03.2025, 11:44 AM CET
Data provider
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.
Object type
- Konferenzbeitrag
Associated
- Guenther, Isabel
- Tetteh-Baah, Samuel Kofi
- ZBW - Leibniz-Informationszentrum Wirtschaft
Time of origin
- 2019