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Field
Abstract
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Before
We conduct a correspondence test in the market for shared housing to identify the magnitude of ethnic and gender discrimination and the role of personal information posted on a social media platform. About 4,900 fictitious applications with a randomly assigned Turkish-, or German-sounding female or male name will be sent to vacant room ads. In addition, we randomly add a link to a social media profile to determine the influence of such personal information on callback rates. We carefully constructed these fictional social media accounts on Instagram for over a year to make them as realistic as possible, conducting several surveys among young people to ensure that the profiles are close to reality with a sufficient number of followers. Unlike previous research, we can approximate whether landlords or roommates actually visit these profiles and exploit this information using statistics on profile visits and page impressions. In a second study, we vary the content on the profiles signaling religious beliefs, sexual identification or cultural orientation to identify determinants of ethnic discrimination and how discrimination could potentially be reduced by additional personal information. In addition, we study how serving stereotypes of the minority or majority candidate affects callback rates, as well as how intentionally breaking stereotypes, e.g., using majority stereotypes on a minority member's social media profile and/or vice versa, affects ethnic and gender discrimination.
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After
We conduct a correspondence test in the market for shared housing to identify the magnitude of ethnic and gender discrimination and the role of personal information posted on a social media platform. About 4,900 fictitious applications with a randomly assigned Turkish-, or German-sounding female or male name will be sent to vacant room ads. In addition, we randomly add a link to a social media profile to determine the effect of such personal information on callback rates. We carefully constructed these fictional social media accounts on Instagram for over the course of two years to make them as realistic as possible, conducting several surveys among young people to ensure that the profiles are close to reality with a sufficient number of followers. Unlike previous research, we can approximate whether landlords or roommates actually visit these profiles and exploit this information using statistics on profile visits and page impressions. In a second study, we vary the content on the profiles signaling religious beliefs and cultural orientation to identify determinants of ethnic discrimination and the role of social media information. In addition, we study how serving stereotypes of the minority or majority candidate affects callback rates, as well as how intentionally breaking stereotypes, e.g., using majority stereotypes on a minority member's social media profile and/or vice versa, affects ethnic and gender discrimination.
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Trial End Date
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Before
April 01, 2022
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After
December 31, 2023
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Last Published
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Before
January 20, 2022 09:33 AM
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After
January 20, 2023 12:40 PM
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Intervention Start Date
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Before
June 01, 2021
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After
July 15, 2021
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Intervention End Date
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Before
December 31, 2021
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After
September 30, 2023
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Experimental Design (Public)
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Before
We use a non-matched pair randomized controlled trial (correspondence test design) to test for ethnic and gender discrimination in the market for shared housing in Germany. To do this, we randomly vary the applicant’s name, which signals an ethnic minority or ethnic majority member, and we randomly vary gender. In addition, we vary the amount of information by randomly providing a link to a social media profile that contains additional information about the applicant. In a second study, we also vary the content of the social media accounts to test whether additional information signaling religious beliefs or sexual identification affects callback rates. We also want to find out how serving stereotypes of the minority or majority candidate affects callback rates, as well as how intentionally breaking stereotypes, e.g., using majority stereotypes on a minority member's social media profile and/or vice versa, affects ethnic and gender discrimination.
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After
We use a non-matched pair randomized controlled trial (correspondence test design) to test for ethnic and gender discrimination in the market for shared housing in Germany. To do this, we randomly vary the applicant’s name, which signals an ethnic minority or ethnic majority member, and we randomly vary gender. In addition, we vary the amount of information by randomly providing a link to a social media profile that contains additional information about the applicant. In a second study, we also vary the content of the social media accounts to test whether additional information signaling cultural identification and religious beliefs affect callback rates. We also want to find out how serving stereotypes of the minority or majority candidate affects callback rates, as well as how intentionally breaking stereotypes, e.g., using majority stereotypes on a minority member's social media profile and/or vice versa, affects ethnic and gender discrimination.
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Planned Number of Observations
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Before
Study I: 4,960; Study II: n/a
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After
Study I: 4,960; Study II: 2,700
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Sample size (or number of clusters) by treatment arms
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Before
Assuming a 44% percent overall callback rate (from pre-study) we will send out approximately 620 inquiries for each of the eight treatment groups (immigrant/native*female/male*with/without social media info), resulting in 1,240 applications per treatment arm. Number of treatment arms for study II has yet to be determined.
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After
Study I: Assuming a 44% percent overall callback rate (from pre-study) we will send out approximately 620 applications for each of the eight treatment groups (immigrant/native*female/male*with/without social media info), resulting in 1,240 applications per treatment arm.
Study II: Assuming a 40.3% percent overall callback rate (from Study I) we will send out approximately 677 applications for each of the four treatment groups (immigrant/native*with/without stereotypical social media profile), resulting in 1,354 applications per treatment arm.
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Pi as first author
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Before
No
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After
Yes
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