Probabilistic social preference: how Machina’s Mom randomizes her choice

Miao, Bin; Zhong, Songfa

doi:10.1007/s00199-016-1015-y

Cited by 16 publications

(15 citation statements)

References 33 publications

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“…Models of social preferences have also recently incorporated a dislike for unequal chances, usually referred to as a concern for ex ante or procedural fairness (Karni and Safra, 2002;Bolton et al, 2005;Trautmann, 2009;Krawczyk, 2011;Saito, 2013). A number of laboratory experiments have lent empirical support to the notion that individuals take into consideration the fairness of chances when making distributive decisions (Bolton et al, 2005;Karni et al, 2008;Krawczyk and Le Lec, 2010;Krawczyk, 2010;Brock et al, 2013;Cappelen et al, 2013;Andreoni et al, 2016;Grimalda et al, 2016;Trautmann and van de Kuilen, 2016;Cettolin and Riedl, 2016;Miao and Zhong, 2018). However, how ex ante chances influence the labor decisions of workers has not been investigated.…”

Section: Iib Unfair Chancesmentioning

confidence: 99%

The Effect of Unfair Chances and Gender Discrimination on Labor Supply

2020

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People interested in the research are advised to contact the author for the final version of the publication, or visit the DOI to the publisher's website.• The final author version and the galley proof are versions of the publication after peer review.• The final published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User Agreement:

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Section: Iib Unfair Chancesmentioning

confidence: 99%

The Effect of Unfair Chances and Gender Discrimination on Labor Supply

2020

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Section: Iib Unfair Chancesmentioning

confidence: 99%

The Effect of Unfair Chances and Gender Discrimination on Labor Supply

2020

View full text Add to dashboard Cite

published version features the final layout of the paper including the volume, issue and page numbers. Link to publication General rightsCopyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.• Users may download and print one copy of any publication from the public portal for the purpose of private study or research. • You may not further distribute the material or use it for any profit-making activity or commercial gain • You may freely distribute the URL identifying the publication in the public portal.If the publication is distributed under the terms of Article 25fa of the Dutch Copyright Act, indicated by the "Taverne" license above, please follow below link for the End User

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“…In particular, randomly allocating the high and the low outcome can be interpreted as a fair process in our setting (Broome, 1990;Konow, 2003). Empirically, random allocation has been shown to be an important mechanism employed by decision makers (Bastek et al, 2018;Brock et al, 2013;Miao and Zhong, 2018;also Dwenger et al, 2016, in contexts other than fairness). In practical applications, an equal representation of candidates or recipients in terms of sex, race or political orientation in the absence of clear differences in any hard criteria can be interpreted as evidence of a fair process.…”

Section: Introductionmentioning

confidence: 97%

Implementing (Un)fair Procedures? Favoritism and Process Fairness when Inequality Is Inevitable

Schmidt

Trautmann

2019

SSRN Journal

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We study allocation behavior when outcome inequality is inevitable but a fair process is feasible, as in selecting one person from several candidates for a job or award. We show that allocators may be influenced by inappropriate criteria, impeding the implementation of a fair process. We study four interventions to induce process fairness without restricting the allocator's decisions: Increasing the transparency of the allocation process; providing a private randomization device; allowing the allocator to delegate to a public randomization device; and allowing the allocator to avoid information on inappropriate criteria. All interventions except transparency have positive effects, but differ substantially in their impact.

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Probabilistic social preference: how Machina’s Mom randomizes her choice

Cited by 16 publications

References 33 publications

The Effect of Unfair Chances and Gender Discrimination on Labor Supply

The Effect of Unfair Chances and Gender Discrimination on Labor Supply

The Effect of Unfair Chances and Gender Discrimination on Labor Supply

Implementing (Un)fair Procedures? Favoritism and Process Fairness when Inequality Is Inevitable

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