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Field Experimental Evidence on Gender Discrimination in Hiring: Biased as Heckman and Siegelman Predicted?

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Abstract

Correspondence studies are nowadays viewed as the most compelling avenue to test for hiring discrimination. However, these studies suffer from one fundamental methodological problem, as formulated by Heckman and Siegelman (The Urban Institute audit studies: Their methods and findings. In M. Fix, and R. Struyk (Eds.), Clear and convincing evidence: Measurement of discrimination in America, 1993), namely the bias in their results in case of group differences in the variance of unobserved determinants of hiring outcomes. In this study, the authors empirically investigate this bias in the context of gender discrimination. The authors do not find significant evidence for the feared bias.
Received May 18, 2015 Published as Economics Discussion Paper June 16, 2015
Revised August 12, 2015 Accepted August 13, 2015 Published August 20, 2015
© Author(s) 2015. Licensed under the Creative Commons License - Attribution 3.0
Vol. 9, 2015-25 | August 20, 2015 | http://dx.doi.org/10.5018/economics-ejournal.ja.2015-25
Field Experimental Evidence on Gender
Discrimination in Hiring: Biased as Heckman and
Siegelman Predicted?
Stijn Baert
Abstract
Correspondence studies are nowadays viewed as the most compelling avenue to test for hiring
discrimination. However, these studies suffer from one fundamental methodological problem,
as formulated by Heckman and Siegelman (The Urban Institute audit studies: Their methods
and findings. In M. Fix, and R. Struyk (Eds.), Clear and convincing evidence: Measurement of
discrimination in America, 1993), namely the bias in their results in case of group differences
in the variance of unobserved determinants of hiring outcomes. In this study, the authors
empirically investigate this bias in the context of gender discrimination. The authors do not
find significant evidence for the feared bias.
JEL J16 J71 M51 J41 C93
Keywords Correspondence experiments; Gender discrimination; Unobserved
heterogeneity
Authors
Stijn Baert, Ghent University – Department of Social Economics, Stijn.Baert@UGent.be
Citation Stijn Baert (2015). Field Experimental Evidence on Gender Discrimination in Hiring: Biased as
Heckman and Siegelman Predicted? Economics: The Open-Access, Open-Assessment E-Journal, 9 (2015-25): 1—
11. http://dx.doi.org/10.5018/economics-ejournal.ja.2015-25
www.economics-ejournal.org 1
1 Introduction
During the last decade, economists have attempted to estimate hiring
discrimination against women in the labour market by means of correspondence
experiments.1 Within these experiments, pairs of fictitious job applications, only
differing by the gender of the candidate, are sent to real job openings. By means of
standard probit regressions of the subsequent call-back from the employer on the
gender of the candidate, discrimination is identified. The correspondence testing
methodology is the golden standard to estimate hiring discrimination in the labour
market. It allows to disentangle employer discrimination from supply side
determinants of labour market outcomes. Selection on gender differences in (the
average level of) unobservable characteristics is not an issue as all the employees’
individual characteristics are under control of the researcher (Riach and Rich
2002).
However, a major critique on this methodology can be formulated based on
Heckman and Siegelman (1993). They show that not controlling for group
differences in the variance of unobservable productivity determinants (and ipso
facto of unobservable determinants of positive call-back) can lead to spurious
evidence of discrimination. The robustness of ethnic discrimination to the
Heckman and Siegelman critique (henceforth “HS critique”) is tested by three
former contributions to the empirical discrimination literature (Baert et al. 2015,
on Belgian data; Carlsson et al. 2014, on Swedish data; Neumark 2012, on US
data). These studies show that the HS critique is relevant. The bottom-line of their
results is that a higher (perceived) variance in unobservable determinants of
positive call-back among ethnic minorities (compared to the ethnic majority) leads
to an underestimation of the level of discrimination against them when not
controlling for ethnic group differentials in this variance.2
_________________________
1 See, e.g., Albert et al. 2011, for Spain; Petit 2007, for France; Riach and Rich 2006, for the UK. Besides its
application in studies identifying gender discrimination in hiring, economists have used the correspondence
testing framework to test for unequal treatment in the labour market on grounds such as ethnicity, sexual
orientation, former unemployment and former employment in the army (see, e.g., Baert 2014; Baert and Balcaen
2013; Baert et al. 2015; Bertrand and Mullainathan 2004; Drydakis 2009; Eriksson and Rooth 2014; Kroft et al.
2013).
2 The results presented by Carlsson et al. (2014) deviate to some extent from this empirical pattern.
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At the same time, as argued by Azmat and Petrongolo (2014) in their overview
of experimental advances in the study of gender differences in the labour market
“it should be stressed that existing [...] correspondence evidence on gender
discrimination is [...] still open to this criticism.” The only attempt to fill this gap
we are aware of, is Carlsson et al. (2014) who apply Neumark’s (2012)
econometric framework to a number of already published correspondence studies
among which one targeted at gender discrimination. In the present study, we
complement their evidence by an empirical investigation of the HS critique in the
context of gender discrimination using the same framework but another and in
our opinion theoretically more convincing – identifying assumption.
2 Methods
2.1 Heckman and Siegelman’s Critique
As argued above, correspondence studies adequately address concerns of
individual differences in unobservable determinants of productivity. Heckman and
Siegelman (1993) show, however, that group differences in the variance of these
unobservable determinants may still lead to spurious evidence of discrimination.
To see this more clearly for the case of gender discrimination in hiring, assume
that both the average observed and the average unobserved determinants of
productivity are the same for male and female candidates for an unfilled vacancy,
but that the variance of unobservable job-relevant characteristics is, at least in the
perception of the employer, higher for females than for males. In addition, suppose
that the employer considers the observed determinants of productivity, inferred
from the CV and the motivation letter, as relatively low compared to the job
requirement. In that case it is rational for the employer to invite the female and not
the male candidate, since it is more likely that the sum of observed and unobserved
productivity is higher for the female candidates. A correspondence test that detects
discrimination against females could therefore underestimate the extent of
discrimination against females.3
_________________________
3 With other assumptions the bias may be in the opposite direction.
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2.2 Neumark’s Empirical Framework
Neumark (2012) explicitly addresses this critique and provides a statistical
procedure to recover unbiased estimates of discrimination. In what follows, we
succinctly describe Neumark’s approach applied to gender discrimination.
It is well known that in a standard probit model only the ratio of the coefficients to
the standard deviation of the unobserved residual is identified. Usually, this
standard deviation is arbitrarily set to 1. In our case this means that the variance of
unobservable job-relevant characteristics is implicitly assumed to be equal for both
males and females, which, for reasons stated above, may bias the measures of
discrimination.
Neumark (2012) shows, however, that if the researcher observes job-relevant
characteristics that affect the male and female populations’ propensities of call-
back in the same way, one can identify the ratio of the standard deviation of the
unobserved productivity components of these groups.4 Implementing Neumark’s
(2012) idea in the context of gender discrimination boils down to the estimation of
a heteroskedastic probit model in which the variance of the error term is allowed to
vary with gender.
2.3 Identification Strategy
As mentioned in the previous subsection, identification of the group-specific
variance in observable determinants of positive call-back within the
heteroskedastic probit framework requires experimental data with variation in
observable job-relevant characteristics that affect the (in our case gender) groups’
propensities of call-back in the same way. Variables used by Baert et al. (2015),
Carlsson et al. (2014) and Neumark (2012) in their application of the Neumark
framework in the context of ethnic discrimination were education level,
personality traits, work experience, type of neighbourhood, sport activities and
application quality. In the context of gender discrimination, Carlsson et al. (2014)
assumed equal returns for both genders from variation in educational degree,
_________________________
4 The intuition is that if in a standard probit model the estimated coefficients of these job-relevant characteristics
differ by gender, then this must be a consequence of a differential standard deviation, since by assumption the
coefficient of these characteristics should be the same across groups (and since, as mentioned before, in a probit
model only the ratio of the coefficients to the standard deviation are identified).
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international mobility, work experience, employment status and job tenure. Their
choice can be criticised on theoretical grounds.5 All the aforementioned variables
used for identification of the Neumark procedure result from variation in choices
and outcomes at the employee side. Therefore, they may be correlated with
ethnicity or gender in reality.6
The alternative variable we assume to have the same return across groups is
the distance between the candidate’s living place and the workplace. On the one
hand, it is clear that this variable has the potential to the affect hiring decisions of
employers. This is the case as employers may prefer workers with a social network
in the neighbourhood of the firm. In addition, they may expect a higher
commitment from workers living close to the firm (and, therefore, wasting not too
much time by commuting). On the other hand, by using this variable we actually
exploit employer variation instead of employee variation as the living place of the
employee is constant. As a result, there is no reason why this variable would be
more rewarded for members of a particular sex.7 Both considerations are
confirmed empirically (see Section 4).
3 Data
We use data from Baert et al. (Forthcoming), a correspondence study investigating
the importance of employer preferences in explaining Sticky Floors. Sticky Floors
are defined as the pattern that women are, compared to men, less likely to start to
_________________________
5 It should be noted, however, that based on the empirical tests the aforementioned authors present one cannot
reject that the chosen variables affect call-back probabilities with a different magnitude for the groups they study.
6 For instance, members of ethnic minorities may have a higher probability of living in more disadvantaged
neighbourhoods (Bertrand and Mullainathan 2004). As particular values for the aforementioned variables may
(not) square (and therefore enforce or disprove) prejudices about ethnic minorities or women, variation in these
variables may be expected to be valued differently for these groups.
7 One could argue that applications to employers living very far away from the residence of the applicant reflect a
willingness to be mobile which may be correlated with female sex. Women of child-bearing and rearing age
might be perceived as being less flexible when it comes to the distance between their workplace and residence.
However, the fictitious job candidates in the experimental data we use mentioned they were quite young (26 or
27), unmarried and not having children (see Section 4). Therefore, we assume that the return to living close to the
workplace is the same for the candidates of both genders within our data. Moreover, if we redo our estimations
using only observations with distances lower than 30 minutes of car driving, the results are very comparable to
the ones presented in the main text.
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climb the job ladder. To this end, these authors sent fictitious job applications to
real job openings in the labour market of Flanders between October 2013 and
March 2014. During this period, they randomly selected 288 vacancies for jobs
targeting Bachelors in business administration and 288 vacancies for jobs targeting
Masters in business economics in the private sector. They restricted themselves to
vacancies requiring at most five years of work experience. Two job applications of
individuals with five years of work experience (in a first and current job), identical
in terms of productivity-relevant characteristics, were sent to the selected
vacancies. These applicants were single individuals born, studying and living in
comparable suburbs of Ghent, the second largest city of Flanders. Within each pair
of applicants, a typically male sounding name was randomly assigned to one of
both applications and a typically female sounding name to the other one. Call-
backs were received via telephone voicemail and email.
Baert et al. (Forthcoming) sent applications both to vacancies implying a
promotion in terms of occupational level and/or job authority and to vacancies at
the same level. Thereby, they were able to test whether unequal treatment of young
men and women in hiring was heterogeneous by whether or not jobs implied a
promotion in comparison with employees’ current position. They found significant
evidence of hiring discrimination against females when they applied for jobs at a
higher occupational level. For these jobs, females got, compared to males, about
33% less invitations for a job interview and 19% less positive reactions in broad
sense. On the other hand, they found no significant heterogeneity in hiring
discrimination by the job authority level of the posted jobs.
In the present study, we will test whether the discrimination measures
presented by Baert et al. (Forthcoming) are biased by gender differences in the
variance of unobserved determinants of hiring outcomes. Therefore, the data from
Baert et al. (Forthcoming) are, in view of our mentioned identifying strategy,
extended with the distance between the workplace announced in the vacancy and
the candidate’s residence.8
_________________________
8 This distance, expressed in minutes when driving by car, is calculated using the online routing tool of Google
Maps.
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4 Results
Table 1 presents the results of our empirical analysis. In Panel A we report the
degree of gender discrimination that comes out of a standard analysis of the data of
Baert et al. (Forthcoming). We retake their main findings by conducting basic
probit estimations with positive call-back as an outcome variable. Positive call-
back is defined as getting an invitation for an interview concerning the announced
job in models (1) and (2) and defined as getting any positive reaction from the
employer side in models (3) and (4).
On the one hand (in models (1) and (3)), we regress positive call-back on a
dummy indicating female sex of the candidate and the distance between the
workplace and the residence of the applicant. On the other hand, for models (2)
and (4), the effect of female sex is broken down by whether the vacancy indicated
a job implying a promotion in occupational level compared with the current job of
the candidate. This is done by replacing the dummy indicating female sex of the
candidate by two dummies: one indicating female candidates who applied for a job
not implying a promotion in occupational level and one indicating female
candidates who applied for a job implying a promotion in occupational level.9
By doing that, we get results that are very similar to those presented in Table 4
and Table 5 of Baert et al. (Forthcoming). More concretely, the regression results
indicate that, overall, the tested employers did not discriminate based on sex.
However, if the effect of revealing female gender is broken down by the
occupational level of the posted job, we find that a female name lowers the
probability of positive call-back by four to five percentage points when one applies
for jobs implying a promotion in this respect.
Interestingly, the estimation results for the variable “distance between the
workplace and the candidate’s residence” not presented in Table 1 but available
on request are, for all of the mentioned models, highly significantly different
from zero (p < 0.01) and have the expected (negative) sign. Moreover, based on a
Wald test applied to the estimation results of an alternative probit model with an
additional interaction variable between female sex of the candidate and the
distance between workplace and residence, we cannot reject that this distance
_________________________
9 This operation also implies the introduction of a dummy indicating promotion jobs in terms of occupational
level and a dummy indicating promotion jobs in terms of job authority without an interaction with female sex.
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variable is rewarded equally for males and females. The test results are
summarised in Table 1.
Panel B reports the results based on a re-estimation of models (1) to (4) by
means of a heteroskedastic probit model in the spirit of Neumark (2012) allowing
the variance of the error term to vary with the gender of candidate. By doing that,
we get unbiased results that are very comparable to those in Panel A. In other
words: we find no evidence for a bias in the sense of the HS critique. This finding
is related to the fact that the estimated male and female standard deviations
concerning the error term Male and σFemale) are very comparable. Therefore, our
results seem to indicate that the tested employers do not perceive a (gender) group
difference in the variance of unobserved determinants of productivity. These
results, therefore, corroborate with those of Carlsson et al. (2014) based on
correspondence testing data gathered in Sweden.
Last, we decompose, in the spirit of Neumark (2012), the unbiased estimates
in an effect through level (keeping group differences in the variance of the error
term constant) and an effect through variance (keeping differences in unbiased
parameters constant). Interestingly, but differing from the findings of Carlsson et
al. (2014), we find that the effects through level are, although not significantly
different from zero, more or less of the same magnitude as the total unbiased
effect. In addition, the effects through variance are rather close to zero.
Our result of no important perceived gender group difference in the variance of
unobserved variables deviates from the finding of the more substantial ethnic
group difference in this respect outlined in Baert et al. (2015), Carlsson et al.
(2014) and Neumark (2012). One explanation for this finding is that perceived
group differences in the variance of unobserved variables can be thought of as a
sort of statistical discrimination. Following Altonji and Blank (1999) employers
may believe that the same observable signal is more precise for one group
compared to another. This theory seems to be more applicable to ethnic groups
than to gender groups.
5 Conclusion
In this study, we investigated the research gap indicated by Azmat and Petrongolo
(2014). This gap boils down to the fact that standard analyses of correspondence
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testing data aimed at investigating hiring discrimination do not control for group
differences in the variance of unobservable productivity determinants and, as a
consequence of that, may be biased. While the robustness of ethnic discrimination
to the this critique, formulated first by Heckman and Siegelman (1993), is tested
by three former studies, Azmat and Petrongolo (2014) stress that correspondence
studies on gender discrimination are still open to this critique. Estimating the bias
feared by Heckman and Siegelman (1993) in the context of gender discrimination
was the aim (and the contribution) of this study.
We used Belgian correspondence data aimed at measuring hiring
discrimination against young females. We employed the empirical framework
introduced by Neumark (2012) and proposed an original identifying assumption.
By doing that, we found no significant evidence for the bias feared by Heckman
and Siegelman (1993) related to the fact that the estimated (perceived) variance of
unobservables is very comparable for male and female job candidates.
The issue of gender differences in heterogeneity with respect to productivity is
an important puzzle piece in the study of gender convergence in the labour market.
We contribute modestly to this literature by showing that, at least in the perception
of Belgian employers, there is no evidence for the hypothesis that women are
(perceived as) more heterogeneous than men in productivity related variables
unobservable to researchers.
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Table 1: Estimation results
Model (1) Model (2) Model (3) Model (4)
A. Estimates from basic probit model
Female candidate
-0.010 (0.013)
-0.010 (0.017)
Female candidate x No promotion in occupational level
0.022 (0.018)
0.042 (0.026)
Female candidate x Promotion in occupational level
-0.040** (0.019)
-0.050** (0.022)
B. Estimates f rom heterosk edastic probit mo del
Female candidate
-0.012 (0.020)
-0.011 (0.017)
Female candidate x No promotion in occupational level
0.022 (0.021)
0.041 (0.029)
Female candidate x Promotion in occupational level
-0.037** (0.017)
-0.050** (0.020)
C. Effect through level
Female candidate -0.003 (0.049) 0.010 (0.051)
Female candidate x No promotion in occupational level 0.033 (0.053) 0.063 (0.059)
D. Effect through variance
Female candidate x No promotion in occupational level
-0.011 (0.046)
-0.022 (0.036)
Female candidate x Promotion in occupational level
-0.010 (0.041)
-0.023 (0.045)
Log (σFemale/σMale)
-0.052
-0.041
-0.110
-0.105
Wald test statistic, null hypothesis that σ
Female
/σ
Male
= 1 (p-
value)
0.852 0.811 0.612 0.596
Wald test statistic, null hypothesis that ratio of coefficients
for distance between workplace and residence = 1 (p-value)
0.910 0.885 0.732 0.721
Dependent variable: invitation to a job interview
Yes
Yes
No
No
Dependent variable: any positive reaction
No
No
Yes
Yes
Observations
1152
1152
1152
1152
Notes: Additional controls included in the basic probit and heteroskedastic probit
models are: distance between the workplace and the candidate’s
residence and, for models (2) and (4) a dummy that indicates
jobs implying a promotion. The presented statistics are marginal effects and standard
errors, corrected for clustering at the vacancy level, in parentheses. *** (**) ((*)) indicates significance at the 1% (5%) ((10%)) level.
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... Although it provides evidence of unexplained inequality, it cannot provide reliable evidence of discrimination as such, since it depends on a rather unrealistic assumption that all other relevant characteristics are observed in the data. These studies are likely to suffer from endogeneity bias, as individuals who appear similar to researchers might in fact exhibit substantial heterogeneity and appear very different to employers (see Baert 2018). However, the residual gap approach nonetheless constitutes the implicit approach taken to the conceptualization of discrimination in comparative family policy studies (see Mandel 2012;Mandel and Semyonov 2006;Mandel and Shalev 2009;Shalev 2008). ...
... Our results are also in line with the most common finding reported in the rapidly expanding literature that is using correspondence audits to test for gender hiring discrimination in different institutional contexts (see Baert 2018). The rule seems to be an absence of statistically significant gender differences in employer callbacks (Baert 2015;Baert, Pauw, and Deschacht 2016;Capéau et al. 2012 [Belgium]; Zhou, Zhang, and Song 2013 [China]; Albert, Escot, and Fernández-Cornejo 2011 [Spain]). ...
... Our results are also in line with the most common finding reported in the rapidly expanding literature that is using correspondence audits to test for gender hiring discrimination in different institutional contexts (see Baert 2018). The rule seems to be an absence of statistically significant gender differences in employer callbacks (Baert 2015;Baert, Pauw, and Deschacht 2016;Capéau et al. 2012 [Belgium]; Zhou, Zhang, and Song 2013 [China]; Albert, Escot, and Fernández-Cornejo 2011 [Spain]). In some studies, female applicants have been found to be subject to positive discrimination (Booth and Two facts are notable here: (i) Sweden seems to be a typical rather than deviant case with regard to the degree of gender hiring discrimination practiced by employers. ...
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... This ranged from 0.050 in the district of Maaseik to 0.361 in the district of Roeselare. 19 As in Baert et al. (2015), we hypothesised that employers would be less selective (in terms of degree class and extra-curricular activities) when filling temporary and part-time jobs and in times of high labour market tightness. ...
... Second, candidates from a caring or technical programme received more invitations than candidates from a business programme. This might be explained by the relatively high numbers of bottleneck vacancies (with a high labour market tightness) in these occupations (Baert et al., 2015). Third, and not surprisingly, given the small differences between the CV template types, invitation rates do not substantially vary across these types. ...
... In particular, we did not know to what extent other candidates with a high degree class or extra-curricular activities also had candidated. The treatment effects measured for our candidates may depend on this (Baert, 2015;Heckman, 1998). However, to the extent that the vacancies tested were representative, which we believe was the case based on the random selection, our experiment shows that degree class and extra-curricular activities increase the chance of being invited to a job interview. ...
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This study investigates the impact on first hiring outcomes of two main curriculum vitae (CV) characteristics by which graduates with a tertiary education degree distinguish themselves from their peers: degree class and extra‐curricular activities. These characteristics were randomly assigned to 2,800 fictitious job applications that were sent to real vacancies in Belgium. Academic performance and extra‐curricular engagement enhance job interview rates by 7.0% (CI 95% [0.3%, 13.7%]) and 6.5% (CI 95% [−0.5%, 13.4%]), respectively. We did not find evidence for these CV characteristics to reinforce or reduce their effect.
... Such beliefs, we argue, are likely to correspond with the politicians' (dis)taste for various sociodemographic groups and, hence, to affect their assessment of otherwise identical candidates in recruitment situations. Third, we employ insights from labor economics on discrimination in hiring processes (e.g., Baert 2015; to understand underrepresentation in public organizations and discuss the relevance of discriminatory perspectives explicitly in relation to top administrative positions. ...
... In comparison, evidence of gender discrimination is mixed and heterogeneous across different occupations, with effects indicating both discrimination of men and women as well as no discrimination at all (Baert 2017). Among studies in the Flemish context, two show no differences between callbacks for men and women (Baert 2015;Baert, De Pauw, and Deschacht 2016), while a third reveals a negative impact of pregnancy among women (Capeau et al. 2012). This may suggest that discrimination against women is primarily relevant among young women. ...
... While a study of labor market ethnic discrimination in Sweden (Carlsson and Rooth 2007) finds similar levels of discrimination in the public and private sectors, a Norwegian study documents significant discrimination in the private sector but not in the public sector (Midtbøen 2016). Furthermore, some studies have documented how the impact of minority group status on callbacks disappears when ethnic minorities mention volunteer work for organizations in their application or when they have had extensive work experience (Baert andVujic 2016, Baert et al. 2017). This suggests that discrimination is often based on statistical conclusions, where certain attributes (here, lack of appropriate experience) are erroneously ascribed to individuals from minority groups (Altonji and Blank 1999). ...
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While a voluminous literature on representative bureaucracy and minority discrimination suggests that characteristics other than qualifications influence hiring decisions, little is known about whether this also pertains to the top positions in political-administrative organizations. To shed light on this question, we ask how candidate ethnicity, gender, and age affect the recruitment preferences among politicians regarding the candidates for top administrative positions. Our study uses a survey experiment with random assignment of 1,688 Flemish local politicians to one of eight different descriptions of applicants to the leading managerial position of their local authority. We find that ethnic minorities, women, and younger candidates are generally considered more qualified for the job. Moreover, the impact of ethnicity and gender on recruitment preferences is conditional on politicians’ ideological predispositions: Left-wing politicians consider ethnic minority candidates more competent, whereas right-wing politicians consider them less representative and are less inclined to invite them for job interviews than candidates from the ethnic majority. Furthermore, politicians furthest to the left are more inclined than right-wing politicians to recognize women as representative of the public at large and support inviting them for job interviews.
... Endnotes 1 For information on 'Growth, Equal Opportunities, Migration and Markets' (GEMM) project, financed by Horizon2020, see http://gemm2020.eu/. 2 If employers act upon a perceived group difference in the variance of unobserved expected productivity, field experimental evidence of discrimination may not be very informative (Heckman and Siegelman, 1993). Using the method proposed by Neumark (2012), Baert (2015) found no evidence of this bias related to gender heterogeneity. 3 Several concepts have been introduced to differentiate so-called error discrimination (England, 1994) and stereotype-based discrimination (Bobbitt-Zeher, 2011) from the economic-rational model, but the theory of statistical discrimination (albeit with bounded rationality) can easily accommodate the notion of stereotypes affecting employers' hiring decisions. 4 See Di Stasio and Larsen (2020) for a study of the combined effects of ethnicity and gender on employers callbacks, based on the GEMM occupations. ...
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Gender discrimination is often regarded as an important driver of women’s disadvantage in the labour market, yet earlier studies show mixed results. However, because different studies employ different research designs, the estimates of discrimination cannot be compared across countries. By utilizing data from the first harmonized comparative field experiment on gender discrimination in hiring in six countries, we can directly compare employers’ callbacks to fictitious male and female applicants. The countries included vary in a number of key institutional, economic, and cultural dimensions, yet we found no sign of discrimination against women. This cross-national finding constitutes an important and robust piece of evidence. Second, we found discrimination against men in Germany, the Netherlands, Spain, and the UK, and no discrimination against men in Norway and the United States. However, in the pooled data the gender gradient hardly differs across countries. Our findings suggest that although employers operate in quite different institutional contexts, they regard female applicants as more suitable for jobs in female-dominated occupations, ceteris paribus, while we find no evidence that they regard male applicants as more suitable anywhere.
... CTs have characteristics that address these weaknesses (Bertrand and Duflo 2016) and, therefore, are now considered to be the gold standard for studying discrimination (Baert 2015). Most importantly, since CTs do not match actors, but instead written messages, they directly circumvent problems related to the presence of unobservable characteristics and to the experimenter effect (Bertrand and Duflo 2016;Pager 2007). ...
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