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Language skills and homophilous hiring discrimination: Evidence from gender and racially differentiated applications

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This paper investigates the importance of ethnic homophily in the hiring discrimination process. Our evidence comes from a correspondence test performed in France in which we use three different kinds of ethnic identification: French sounding names, North African sounding names, and “foreign” sounding names with no clear ethnic association. Within the groups of men and women, we show that all non-French applicants are equally discriminated against when compared to French applicants. Moreover, we find direct evidence of ethnic homophily: recruiters with European names are more likely to call back French named applicants. These results show the importance of favoritism for in-group members. To test for the effect of information about applicant’s skills, we also add a signal related to language ability in all resumes sent to half the job offers. The design allows to uniquely identify the effect of the language signal by gender. Although the signal inclusion significantly reduces the discrimination against non-French females, it is much weaker for male minorities.

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  1. We control for the potentially confounding effect of religion (as documented by Adida et al. 2010 by selecting foreign names that are not perceived as being Muslim.

  2. We refer to homophily as workers from one group being favored over others from employers belonging to the same group. Some authors label this phenomenon “in group bias” (see, for example Currarini and Mengel 2012), so as to distinguish unequal treatment behavior and self selection into groups. With some abuse of language, we only refer to homophily, since the two have similar empirical implications, at least in our setting.

  3. The present paper goes beyond the study by Jacquemet and Yannelis (2012) in four important ways. First, our experiment is implemented in France instead of Chicago. Second, we interact ethnic discrimination with gender discrimination by adding a male equivalent to each female applicant. Third, we consider three different levels of job occupations (in the same sector) to assess the sensitivity of our results to the kinds of skills required. Fourth, we directly observe a proxy for the ethnicity of recruiters, allowing us to identify homophily through recruiters favoring applicants of the same ethnicity. Our results generalize Jacquemet and Yannelis (2012) to the French context as well as to both males and females, and to all occupational categories under study. We take this as robust evidence that homophily has explanatory power on observed discrimination in hiring. Our main treatment of interest tries to disentangle the reasons behind this behavior.

  4. These two explanations refer to what sociologist call choice homophily (Kossinets and Watts 2009). A third one, formalized for instance by Jackson and Rogers (2007); Bramoullé and Rogers (2009) in the context of network formation, explains homophilous behavior by a higher probability of meeting an in-group partner—this is called induced homophily, because homophily results from the structural opportunities to interact. Because all applications are sent to each employer, correspondence testing is ill-equipped to provide reliable measures of such a phenomenon. It does not mean it cannot contribute to the observed differences between applicants—it will be the case, for instance, if employers’ decisions are correlated to the relative composition of the overall application pool, and the pool is not exogenous to the employers’ characteristics. In the empirical part, we will include covariates related to the employer location to provide some control over this channel.

  5. This result is supported by McPherson et al. (2001); Putnam (2007); Kets and Sandroni (2014) who suggest that individuals may cooperate with similar others for ease of communication, mutual trust and closed cultural features that smooth the coordination of activity. Ramachandran and Rauh (2013) moreover show that discrimination induced by such a mechanism can persist through coordination failures even once biased norms and beliefs about others have disappeared. Laboratory experiments evidence reported in Habyarimana et al. (2007) tend to support this belief-based explanation. The experiments take place in the neighborhood of Kampala, the Uganda’s capital, and use as a treatment variable the ethnic composition of the group of people playing together. The evidence relies on standard social preferences games and shows that (i) higher homogeneity inside groups is associated with higher voluntary contributions to public goods while (ii) there is no difference in the level of gift chosen in a dictator game. It is only when the tribe of both players is known that people treat others from their own tribe more favorably. The preferred interpretation of the authors is the existence of a norm within ethnic groups.

  6. The question about religion was not included in the first wave of the survey. We ran a second wave, with again 300 respondents, including only this question. We decided not to ask the origin again to this second sample in order to avoid that respondents mechanically relate religion to origin.

  7. The most frequent origins are Eastern Europe (5%) and Southern Europe (3%) for Jatrix Aldegi; and North African (9%) and Israeli (6%) for Alissa Hadav.

  8. Among the residual respondents about the religion question, Jatrix is identified as Jewish by 16% of the sample and as Muslim by 9% of the sample; Alissa is perceived as either Christian (26%) or Muslim (14%).

  9. According to the International Standard Classification of Education (ISCED), this level of education corresponds to the first stage of tertiary education. This educational attainment is the most requested for accounting jobs.

  10. In a few cases, applications were sent by postal mail.

  11. We use the 2008 wave of the French census (providing exhaustive information on the population living on the territory) to compute the share of immigrants in the population of each city. This is linked with the location variable based on the zip code. Ethnic diversity is assumed to be low (high) if the immigrant share is lower (higher) than 20%.

  12. Table 4 also shows a slight tendency towards a reduced discrimination from big firms against female minorities. The difference however remains significant. This tendency is however strongly reinforced when we classify firms according to whether they have more than 500 employees (which results in 40 observations out of the 504 employers in the sample): these very big firms do not discriminate against female minorities. The results are available from the authors upon request.

  13. The gender is deduced from first names and/or abbreviations before the last names like “M.” and “Mme”. Also, an employer is considered to be (i) French if his/her first and last names sound French, (ii) non-French European if his/her last name sound German, Spanish, Italian or Portuguese and (iii) non-European if his/her last name appears non-European. Most recruiters with non-European sounding names tend to be originated from North-African and Asian countries. The decomposition of callbacks by employer origin has to be interpreted with caution inasmuch the sample size of non-European sounding names of employers is low, around 15 % of all job offers. The crucial issues to assess the extent of the bias induced by the use of these measures are (i) the relative size of the noise captured when our correspondent is not the actual recruiter; (ii) whether this noise is likely to be correlated with callbacks. On the second issue, one important likely driving force is the size of the firm, as this may jointly affect the likelihood that collecting the application and making the final decision are separated, and the tendency to discriminate. We do not find such a pattern once the relationships between employer’s identity and callback rates are interacted with firm size.

  14. This result is partly explained by the fact that female recruiters are overrepresented in firms which tend to exhibit higher callbacks. Female recruiters mainly work in large firms (70%) located in Paris (56%) and offering short-term contracts (60%).

  15. At the end of high-school, students have to take a national exam called “baccalauréat”. According to the test scores, students can have academic honors (no honors, relatively good, good and very good honors).

  16. However, the effect of the signal on callbacks for the non-French applicants is not significant with a t-test statistic (p-value) equal to –1.50 (0.13). As explained below, the effect of the signal on callbacks is mainly driven by the sub-sample of female identities.

  17. Decomposing the effect of ethnic origin on elicited callbacks across types of job implies that we cannot use this latter dimension as an identifying variable when implementing our heteroskedastic probit regressions. In Table 8, we thus only run simple probit estimation.

  18. European employers favoring French applicants is consistent with homophily. We observe reduced discrimination for North African candidates from non-European employers. This would be consistent with North African employers favoring North African candidates and other non-European employers discriminating against these applicants. Non-European employers are not all North African, so we are measuring a heterogeneous effect.

  19. To ease exposition, we focus on the two ethnicities case with R = 1 for minority applicants, although the framework easily generalizes to more ethnic origins or to other specifications of the characteristics describing discriminated sub-populations, such as gender. It is also worth mentioning that we gather all sources of unobserved heterogeneity in Z. One can add an i.i.d. error term to the functionals without changing the main conclusions.

  20. In the following, we will impose the linearity assumptions that will be used to estimate the model. Note that Z is not only unobserved to the econometrician but also to the employer in a correspondence test (as opposed, e.g., to an audit study, where employers might gather additional information by meeting experimental applicants). As a result, employer’s decisions should be deterministic conditional on the observables. To ease exposition and save on notation, we assume that employers take their callback decisions based on a random draw in the relevant distribution of unobservables—which generates random variations in decisions across firms. As discussed in (Neumark 2012, p.1135, an alternative way to arrive at a statistical model would be to assume random productivity differences across firms that are multiplicative in the observed productivity of a worker.

  21. This is the case for two reasons. First, the resumes are calibrated in such a way that all observed productive characteristics are equally likely. There still remains differences from one resume to another, but systematic differences across ethnicities are ruled out through randomization of the names-resumes matching.

  22. This condition is more likely to be met as the set of observable characteristics is wider. Still, the relative share of taste-based and statistical discrimination in the aggregate effect of ethnicity remains a matter of interpretation and cannot be empirically tested even in this kind of framework.

  23. The general principle that heteroskedasticity causes the coefficient estimates in discrete choice models to be inconsistent draws back to Yatchew and Griliches (1985).


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We gratefully acknowledge David Neumark for sharing his data and estimation programs with us. We also thank Francis Bloch, Nick Bloom, Emmanuel Duguet, Christelle Dumas, Raquel Fernandez, Stéphane Gauthier, James Heckman, Shelly Lundberg, Muriel Niederle, Phillip Oreopoulos, Paolo Pin, Chris Taber, Marie-Anne Valfort as well as participants to various conferences and seminars for their thoughtful comments on the paper. We are grateful to the CEPREMAP for financial support. Nicolas Jacquemet acknowledges the Institut Universitaire de France for its support. Constantine Yannelis thanks the Alexander S. Onassis foundation for generous support.

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1.1 Hiring Discrimination with heteroskedastic Unobserved Heterogeneity

The data generating process of a correspondence study stems from employers treatment of the content of the applications. We denote \(P_i^*(J,X,Z)\) the productivity of an application i in a given position. This productivity depends on the job’s characteristics (measured mainly through firm specific variables) J. At the individual level, productivity results from two components: a set of individual characteristics that are observable to both the econometrician and the employer, Xi; and a component which is unobservable to them both, Zi. We assume that ethnicity, denoted Ri = 0 for non-minorities (e.g., whites) and Ri = 1 for minorities (e.g., North Africans), do not enter productivity directly.Footnote 19 This allows focus on discrimination, which in this framework might occur for two reasons. First, let the employer callback decision be described by the latent variable \(T_i^*\), which determines the treatment applied to a given application (it will drive a dichotomous choice variable below, but it can be thought of as any continuous outcome, such as e.g., wage offers). Taste-based discrimination implies that such treatment depends not only on the productivity of the applicant but also on unproductive observable characteristics such as ethnicity:Footnote 20

$$T_i^* = T^*[P_i^*,R_i] = P_i^* + \gamma R_i = \delta J + \beta X_i + Z_i + \gamma R_i.$$

Second, the invitation decision relies on employer’s perceived productivity of applicant i, to whom Zi is not observed while Ri is:

$${\Bbb E}[P_i^*(J,X,Z)|X,J,R_i] = \delta J + \beta X_i + {\Bbb E}(Z|R_i),$$

which implies statistical discrimination as long as \({\Bbb E}(Z|R_i) \ne {\Bbb E}(Z_i)\)—the unobserved productivity of an applicant is thought of as being dependent on ethnicity. When each employer receives two applications, one from a non-minority applicant and another from a minority one, discrimination shows up in the average differential treatment reserved to applications,

$${\Bbb E}[T^*|R = 0] - {\Bbb E}[T^*|R = 1] = \beta [{\Bbb E}(X|R = 0) - {\Bbb E}(X|R = 1)] + {\Bbb E}(Z|R = 0) - {\Bbb E}(Z|R = 1) + \gamma .$$

By construction, this difference in callback cannot be driven by employer’s characteristic. We thus omit the term δJ below, implicitly including it in the deterministic part of the model, βX.

1.1.1 The content of correspondence test data

By way of construction, a correspondence test controls the observables X in such a way that they do not systematically differ across sub-groups: the difference in observed characteristics must balance over job applications, hence \({\Bbb E}(X|R = 0) = {\Bbb E}(X|R = 1)\).Footnote 21 The difference in treatment over all job applications thus arises due to two parameters: taste based discrimination, γ, and statistical discrimination which induces a gap in the (perceived or actual) means of the unobserved productive characteristics \({\Bbb E}(Z|R = 0) - {\Bbb E}(Z|R = 1)\). Note that the two parameters can only be identified together unless the study provides enough control to guarantee that the distribution means are exactly equal, so that only taste based discrimination occurs.Footnote 22 However, all components of this aggregate effect arise as the result of an unequal treatment based on unproductive observable characteristics. As such, they correspond to the economic definition of discriminatory behavior: in the remaining, we thus focus on the identification of the gross handicap experienced by minority applicants, μ—standing for the sum of these two parameters: \(\mu = {\Bbb E}(Z|R = 0) - {\Bbb E}(Z|R = 1) + \gamma\).

As noted by Heckman and Siegelman (1993); Heckman (1998) the observational context of a correspondence study may lead to biased estimates if the variance of the error term is ethnicity-specific.Footnote 23 This is the case because the observed outcome is non-linearly related to the underlying discriminatory decisions. To see this explicitly, denote c the perceived quality threshold an applicant has to reach to be invited for an interview: the outcome variable of the experiment is \(T = {\mathbf{1}}[T^ * >c]\). Further assume that the unobserved productivity Z is i.i.d normally distributed in each ethnic sub-group, but with a (perceived or actual) variance that varies across groups. To ease of exposition, we denote \(\sigma _0^2 = Var(Z|R = 0)\) and \(\sigma _1^2 = Var(Z|R = 1)\). The statistical model generating the observed outcome thus gives the following specifications for the probability of obtaining a callback:

$${\Bbb P}[T = 1|R = 1,X] = 1 - \Phi [(c - {\Bbb E}(Z|R = 1) - \beta X + \gamma )/\sigma _1] = \Phi [(\beta X + {\Bbb E}(Z|R = 1) + \gamma - c){\mathrm{/}}\sigma _1]$$
$${\Bbb P}[T = 1|R = 0,X] = 1 - \Phi [(c - {\Bbb E}(Z|R = 0) - \beta X){\mathrm{/}}\sigma _0] = \Phi [(\beta X + {\Bbb E}(Z|R = 0) - c){\mathrm{/}}\sigma _0],$$

where Ф denotes the standard normal distribution. The difference between these two expressions is the empirical source of identification for discrimination. However, even in the extreme case with neither statistical nor taste-based discrimination—i.e., γ = 0 and \({\Bbb E}(Z|R) = {\Bbb E}(Z)\), so that μ = 0—the difference in probabilities still depends on the comparison between σ1 and σ0. For instance, if the X have been chosen at the lower tail of the skills distribution, so that βX < c, and σ0 < σ1 then it has to be that \(\Phi [(\beta X - c){\mathrm{/}}\sigma _1] < \Phi [(\beta X - c){\mathrm{/}}\sigma _0]\), ∀X: the experiment produces (spurious) evidence of discrimination against people from R = 1 origin. In the more general case with both discrimination and differences in the variance of unobservables, this framework highlights the identification problem faced by correspondence test data.

1.1.2 Unbiased measures of discrimination

The identification problem arises because the average treatment effect \(\Delta = \overline T _{\left| {R = 0} \right.} - \overline T _{\left| {R = 1} \right.}\) provides an estimate of \(\frac{{{\Bbb E}(Z|R = 0) - c}}{{\sigma _0}} - \frac{{{\Bbb E}(Z|R = 1) + \gamma - c}}{{\sigma _1}}\) (up to the functional form of the distribution), while what one seeks to measure is \(\mu = {\Bbb E}(Z|R = 0) - {\Bbb E}(Z|R = 1) + \gamma\): the systematic disadvantage experienced by minority applicants due to their belonging to an observable sub-population. As a result, the confounding effect of the dispersion in the observables shows up in the comparison of callback probabilities, thus affecting the mean comparisons between outcomes from the study.

As noted by Neumark (2012), one can however use restrictions on β to restore identification. If the study provides enough variation on relevant applications characteristics, grouped in X in the model above, one can estimate β/σ0 and β/σ1 from the (heteroskedastic) Probit model derived in the previous section. Under the assumption that the effect of X on the callback is homogeneous across ethnicity (i.e., the true value of β is the same), the ratio of the two point estimations identifies σ0/σ1, which in turn allows us to estimate μ after the usual normalization setting one of the variance terms equal to 1. Since only one such identifying regressor is needed to achieve identification, any additional productivity control provides the usual specification tests of an over-identified model.

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Edo, A., Jacquemet, N. & Yannelis, C. Language skills and homophilous hiring discrimination: Evidence from gender and racially differentiated applications. Rev Econ Household 17, 349–376 (2019).

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