Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination

By David B. Abrams, Victoria Bede, Simone Berkowitz, Hong Chung, Almudena Fernandez, Mary Anne Guediguian, Christine Jaw, R. Maheswari, Beverley Martis, Alison Tisza and 2 more. 2007

Subfields: Economics & Operations Research, Social Sciences & Humanities

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Citations in the corpus

Semantics derived automatically from language corpora contain human-like biases (2016, methodology): “-American names Pleasant vs unpleasant from (5) (7) Not applicable 16×2 25×2 1.”
On formalizing fairness in prediction with machine learning (2017, background): “However, several studies [7, 18, 40, 50, 63, 68] have conrmed discrimination on the basis of protected aributes.”
Measuring Social Biases in Grounded Vision and Language Embeddings (2020, background): “Social biases have negative implications for the most marginalized people, e.g., applicants perceived to be Black based on their names are less likely to receive job interview call-backs than their white counterparts (Bertrand and Mullainathan, 2004).”
Attesting Biases and Discrimination using Language Semantics (2019, unknown): “[12,1].”
Intersectional HCI: Engaging Identity through Gender, Race, and Class (2017, background): “Research has shown that the mere presence of names that belong to non-white ethnic groups [12] and white women [70] has resulted in discriminatory treatment of otherwise identical job application material.”
Long-term reduction in implicit race bias: A prejudice habit-breaking intervention (2012, unknown): “…mundane, such as poorer quality interactions (McConnell & Leibold, 2001), to the undeniably consequential, such as constrained employment opportunities (Bertrand & Mullainathan, 2004) and a decreased likelihood of receiving life-saving emergency medical treatments (Green et al., 2007).”
In the Name of the Son (and the Daughter): Intergenerational Mobility in the United States, 1850-1940 (2015, unknown): “Bertrand and Mullainathan (2004) show that distinctively black names decrease the likelihood that a job applicant is called for an interview, while Fryer and Levitt (2004) ﬁnd no negative causal impact of having distinctively black names on life outcomes.”
AN EXAMINATION OF WHETHER AND HOW RACIAL AND GENDER BIASES INFLUENCE CUSTOMER SATISFACTION (2010, background): “…for women and non-whites to demonstrate their competence and so will provide lower customer satisfaction judgments for such workers even if their performance is objectively equivalent to that of their male or white counterparts (Biernat & Kobrynowicz, 1997; Yarkin, Town & Wallston, 1982).”
Better Late than Nothing: Some Comments on Deaton (2009) and Heckman and Urzua (2009) (2010, methodology): “See for example, Björklund and Moffitt (1987). Unlike in the constant effect model, however, in this case the normality assumption is not innocuous.”
Tweetment Effects on the Tweeted: Experimentally Reducing Racist Harassment (2017, unknown): “When generating the bots, I chose handles that consisted of first and last names that were identifiably male and white or black, following Bertrand and Mullainathan (2003).”
Towards causal benchmarking of bias in face analysis algorithms (2020, methodology): “A powerful approach to discovering cause-effect relationships is the experimental method which involves artiﬁcially manipulating the variable of interest, while ﬁxing all the other inputs [8, 58].”
The Reverse Gender Gap in Ethnic Discrimination: Employer Stereotypes of Men and Women with Arabic Names1,2 (2016, background): “(1968) for the first audit study and Jowell and Prescott-Clarke (1970) for the first correspondence test as well as Frank Bovenkerk, Benjamin Kilborne, François Raveneau and David Smith (1979), Hubbuck and Carter (1980), Firth (1981), Esmail and Everington (2001a), Esmail and Everington (2001b), Ber”