Field Experiments on Discrimination

By Marianne Bertrand, Esther Duflo. 2016

672 citations recorded by the source corpus.

Subfields: Gender Studies, Safety Research, Sociology and Political Science

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Citations in the corpus

The Econometrics of Randomized Experiments (2016, background): “Bloom (1984), Zelen (1979, 1990), Cuzick, Edwards, and Segnan (1997), and Baker (2000) discuss the instrumental variables approach in the special case of one-sided non-compliance, and Imbens and Angrist (1994), Angrist, Imbens and Rubin (1996) develop the general set up.”
The Ties That Double Bind: Social Roles and Women's Underrepresentation in Politics (2018, neutral): “…the use of different metrics to evaluate candidates from different groups, presumably because unconscious biases influence the criteria, the formula, or the weights given to particular attributes in an assessment of an individual’s productivity (Bertrand and Duflo 2017, 313; Paris and Feder 2016).”
Teachers’ Bias Against the Mathematical Ability of Female, Black, and Hispanic Students (2019, methodology): “These findings support theories of bias and discrimination that posit that biases are apparent in low-information situations (e.g., Aigner & Cain 1977; Arrow, 1973; Bertrand et al., 2005; Bertrand & Duflo, 2016; Dovidio et al., 1998; Greenwald & Banaji, 1995; Phelps, 1972).”
Gender in the Twenty-First Century (2020, background): “The regressions also control for country fixed effects, year fixed effects, a quadratic in age, dummies for the usual number of days at work, and education dummies. candidates and employees, such as individuation training or perspective-taking training (for an overview, see Bertrand and Duflo 2017).”
Inaccurate Statistical Discrimination: An Identification Problem (2019, neutral): “In addition to observational data analysis, a variety of empirical techniques have been used to document discrimination, including audit studies, own-group bias, and correspondence studies (for recent reviews of the discrimination literature, see Bertrand and Duflo 2017 and Charles and Guryan 2011).”
Ethnic discrimination in hiring: comparing groups across contexts. Results from a cross-national field experiment (2019, background): “To explain variation in discrimination, one could broadly identify three sets of explanations: cultural/psychological, economic and contextual/institutionalist (for reviews, see Bertrand and Duflo 2017; National Research Council 2004; Neumark 2018).”
Bias in Bios: A Case Study of Semantic Representation Bias in a High-Stakes Setting (2019, background): “esult, occupation classifiers compound injustices when existing gender imbalances are attributable to historical discrimination. Our paper is also closely related to research on gender bias in hiring [5, 23, 35, 36]. In particular, Bertrand and Mullainathan [6] conducted an experiment in which they ”
Racial Bias in Bail Decisions ⇤ (2018, neutral): “[22] Bertrand, Marianne, and Esther Duflo.”
Algorithmic bias? An empirical study into apparent gender-based discrimination in the display of STEM career ads (2019, background): “The ad (1)This means we focus on disparity of access to information, rather than echoing the majority of the economics literature which has focused on disparities in wages (Oaxaca, 1973; Gunderson, 1989; Brown and Corcoran, 1997; Goldin, 2014; Altonji et al., 2015; Bertrand and Duflo, 2017).”
Implicit Stereotypes: Evidence from Teachers’ Gender Bias* (2019, result): “This study adds to the recent literature in economics that has underlined the benefits from interacting with social psychologists and considering implicit bias in studying discrimination (Guryan and Charles, 2013; Bertrand and Duflo, 2017).”