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Hiring Discrimination: An Overview of (Almost) All Correspondence Experiments Since 2005



This chapter aims to provide an exhaustive list of all (i.e. 90) correspondence studies on hiring discrimination that were conducted between 2005 and 2016 (and could be found through a systematic search). For all these studies, the direction of the estimated treatment effects is tabulated. In addition, a discussion of the findings by discrimination ground is provided.
63© Springer International Publishing AG 2018
S. M. Gaddis (ed.), Audit Studies: Behind the Scenes with Theory, Method,
and Nuance, Methodos Series 14,
Chapter 3
Hiring Discrimination: AnOverview
of(Almost) All Correspondence Experiments
Since 2005
Abstract This chapter aims to provide an exhaustive list of all (i.e. 90) correspon-
dence studies on hiring discrimination that were conducted between 2005 and 2016
(and could be found through a systematic search). For all these studies, the direction
of the estimated treatment effects is tabulated. In addition, a discussion of the nd-
ings by discrimination ground is provided.
Keywords Hiring discrimination · Measurement · Correspondence experiments ·
Review · Ethnicity · Gender · Religion · Disability · Age · Military service · Wealth
· Marital status · Sexual orientation · Political orientation · Union afliation ·
Physical appearance
3.1 Triple Goal
The lack of labour market integration of vulnerable groups, such as refugees and
other individuals with a migration background, the elderly, and people with a men-
tal or physical health impairment, has received much attention in both policy and
academic circles in the past decade (OECD 2008a, 2010). For policymakers, it is
important to understand what factors cause this lack of integration in order to design
the appropriate integration policies. Academic scholars have suggested discrimina-
tion in hiring as one important factor contributing to the poor labour market integra-
tion of these individuals (Altonji and Blank 1999; OECD 2008b). However, it is
very challenging to measure discrimination in hiring, which makes it difcult to
distinguish the effect of discrimination on employment from the effect of other fac-
tors, such as differences in human capital and other skills.
Historically, scholars have measured hiring discrimination through statistical
analysis of non-experimental (survey or administrative) data. A commonly used
S. Baert (*)
Ghent University, Ghent, Belgium
approach has been to try to control for as many observed individual factors as pos-
sible, such as education, experience, and occupation, and then interpret any unex-
plained part in employment between groups as pointing in the direction of
discrimination (Blinder 1973; Oaxaca 1973). In general, these studies are likely to
suffer from an important endogeneity bias, because job applicants who appear simi-
lar to researchers (except for their discrimination ground), based on non- experimental
data, might in fact appear to be different to employers. For example, administrative
data seldom contain information about language skills of individuals with a migra-
tion background, but this is likely to be observed by the employer, perhaps at a job
interview. As long as not all relevant variables, taken into account by employers in
making their hiring decisions, are controlled by the researcher, no conclusive proof
of discrimination can be provided.
In response to this methodological problem, and inspired by the seminal work of
Bertrand and Mullainathan (2004), scholars in labour economics, sociology of
labour, and personnel psychology during the past decade have turned to so-called
correspondence experiments to measure hiring discrimination (Gaddis 2018). In
these experiments, ctitious job applications, differing only in a randomly assigned
discrimination ground, are sent in response to real job openings. By monitoring the
subsequent call-back from employers, unequal treatment based on this single char-
acteristic is identied and can be given a causal interpretation.
Not surprisingly, given the seminal status of the correspondence experimentation
framework1 and the numerous academic studies that have adopted this framework,
during the past years, scholars have written reviews and meta-analyses concerning
this literature. We are aware of four such meta-studies: Bertrand and Duo (2016),
Neumark (in press), Rich (2014), and Zschirnt and Ruedin (2016). While all are
inspiring high-quality syntheses, with excellent policy links and clever directions
for further research, they share two limitations. First, these studies focus on an in-
depth review of the eld experimental evidence on labour market discrimination
based on some grounds, while neglecting other grounds based on which unequal
treatment is also forbidden. Second, none of these studies attempt to provide the
reader with an exhaustive list of all experiments (conducted during a particular time
frame). They all seem to focus on the better known (i.e. from their own country or
highly cited) experiments while neglecting complementary work.
This chapter has a different ambition. It starts with identifying all discrimination
grounds based on which unequal treatment is prohibited in at least one state of the
United States and then provides the reader with a register of all correspondence
experiments conducted (later than Bertrand and Mullainathan 2004) to measure
these forms of discrimination. Given that the information provided for each study
(i.e. particular treatment, country, and sign of the effect) is kept very limited—no
effect size information is provided—this chapter has to be seen as a working instru-
ment rather than as a classical review.
The register we will present serves three goals. First, it serves as a reference table to
which later chapters of this book will refer. Second, and more broadly, it can be used
1 Some deciencies of the method were discussed in Chap. 2.
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by scholars in search of a catalogue of all correspondence experiments on hiring dis-
crimination based on a (cluster of) particular ground(s). Third, it implicitly indicates
potentially fruitful directions for future correspondence experiments, as it unambigu-
ously shows where the lacunae in this literature are, i.e. the discrimination grounds and
regions to which researchers have paid little attention.
3.2 Scope
The register discussed in the next section is the result of a systematic search for cor-
respondence experiments conducted after Bertrand and Mullainathan (2004) with
the aim of measuring forms of unequal treatment in hiring which are prohibited by
law in at least one state of the United States, i.e. the country in which the most cor-
respondence experiments have been conducted. So, correspondence experiments
included to assess the causal effect of, e.g., other cv characteristics such as juvenile
delinquency, student employment and (former) unemployment spells were not
included (Baert and Verhofstadt 2015; Baert etal. 2016d; Kroft etal. 2013; Eriksson
and Rooth 2014).
Under US federal law, unequal treatment is forbidden based on nine (clusters of)
discrimination grounds: (A) race and national origin, (B) gender and pregnancy, (C)
religion, (D) disability, (E) (older) age, (F) military service or afliation, (G) wealth,
(H) genetic information, and (I) citizenship status.2 With respect to (B), discrimina-
tion based on motherhood is also prohibited in Alaska3 and California.4 Finally,
discrimination based on (J) marital status,5 (K) sexual orientation and gender
identity,6 (L) political afliation,7 (M) union afliation,8 and (N) physical appear-
ance9 is forbidden in at least one state.
With this list of discrimination grounds at hand, a key word search (for the word
groups ‘correspondence test’, ‘correspondence experiment’, ‘correspondence
study’, ‘ctitious resume’, ‘ctitious cv’, ‘ctitious application’, and ‘eld experi-
ment’ in combination with ‘discrimination’) was conducted on three sources: Web
of Science, Google Scholar, and the IZA Discussion Paper Series. This exercise was
followed by the screening of all references in the relevant articles found and the
screening of the studies citing these relevant articles.
2 Source:
3 Source:
4 Source:
5 Source:
6 Source:
7 Source:
8 Source:
9 Source:les/downloads/LAW%20589%20Appearance%20
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
3.3 The Register
Table 3.1 provides the reader with an overview of all studies (after Bertrand and
Mullainathan 2004 of which we are aware that build on correspondence experi-
ments aimed at measuring discrimination based on one of the grounds mentioned in
the previous section. The unit of observation is the individual correspondence
experiment. For each such experiment, there is a cell in column (3) of Table3.1.
Some cells contain more than one study, meaning that the studies exploited the same
experimental data. Some studies focussed on more than one discrimination ground,
and are therefore mentioned in more than one cell: Agerström etal. (2012), Albert
etal. (2011), Arceo-Gomez and Campos-Vazquez (2014), Banerjee etal. (2009),
Berson (2012), Capéau etal. (2012), Patacchini etal. (2015), Pierné (2013), and
Stone and Wright (2013).
In total, we are aware of 90 correspondence experiments conducted between
2005 and 2016 with the aim of measuring discrimination based on prohibited
grounds in at least one state of the United States. For 37 of these experiments, the
focus (at least partly) was on measuring ethnic discrimination. Other commonly
investigated discrimination grounds were gender (14 eld experiments), age (11
experiments), and sexual orientation (12 experiments). In addition, at least ve
experiments focussed on religion, disability, and physical appearance as determi-
nants of employers’ hiring decisions. Only three experiments had a wealth-related
focus and only two were related to military experience. Only one experiment has
been conducted on hiring discrimination based on political afliation and union
membership. We are not aware of any experiments measuring unequal treatment
based on genetic information, nor have any experiments—somewhat surprisingly
given the massive migration ows to Europe in recent years—investigated citizen-
ship status as a discrimination ground.
3.3.1 Treatment andTreatment Effects
As can be seen in column (1) of Table3.1, for many discrimination grounds studied,
a variety of particular treatments strategies have been used. For instance, ethnic
origin is mostly revealed by means of the names of the candidates. The various
minority groups studied are always groups that are substantially represented in the
country where the data gathering took place. Alternative designs have disclosed
ethnic origin by means of adding a resume picture or revealing one’s nationality.
Column (4) shows the average treatment effect for each experiment (averaged
across all vacancies and neglecting analyses by subsamples as presented in many
studies). Overall, an overwhelming majority of the studies report negative treatment
effects (i.e. discrimination of the group hypothesised to be discriminated against).
More concretely, 80 (i.e. 78.4%) treatment effects are signicantly negative, 17 (i.e.
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Table 3.1 Register of correspondence experiments conducted between 2005 and 2016 with the
aim of measuring discrimination based on prohibited grounds in US law
(1) Treatment
(2) Country of
analysis (3) Study
A.Discrimination ground: race and national origin
A.1. African (versus native)
France Cediey and Foroni (2008)
Edo etal. (2013)
US Nunley etal. (2015)
Gaddis (2015)
Jacquemet and Yannelis (2012)
Agan and Starr (2016)
A.2. African or Hispanic
(versus native) name
Sweden Bursell (2014)
US Darolia etal. (2016) 0
Decker etal. (2015) 0
A.3. African, Asian, or
German (versus native) name
Ireland McGinnity and Lunn (2011)
A.4. African, Caribbean,
Indian, or Pakistani (versus
native) name
UK Wood etal. (2009)
A.5. Albanian (versus native)
Greece Drydakis and Vlassis (2010) and
Drydakis (2012a)
A.6. Antillean, Moroccan,
Surinamese, or Turkish (versus
native) name
Netherlands Andriessen etal. (2012)
A.7. Arabian (versus native)
Netherlands Derous etal. (2012)
Blommaert etal. (2014)
Sweden Agerström etal. (2012)
US Widner and Chicoine (2011)
A.8. Asian or Roma (versus
native) name
Bartoš etal. (2014)
A.9. Chinese, Greek, Indian,
or Pakistani (versus native)
US Oreopoulos (2011)
A.10. Chinese, Indigenous,
Italian, or Middle-Eastern
(versus native) name
Australia Booth etal. (2012)
A.11. Chinese, Nigerian,
Serbian, or Turkish (versus
native) name and appearance
Austria Weichselbaumer (in press)
A.12. Congolese, Moroccan,
Italian, or Turkish (versus
native) name
Belgium Capéau etal. (2012)
A.13. Ghanaian, Moroccan,
Turkish, or Slovakian (versus
native) name
Belgium Baert etal. (2017)
A.14. Indigenous (versus
native) name
Peru Galarza and Yamada (2014)
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
Table 3.1 (continued)
(1) Treatment
(2) Country of
analysis (3) Study
A.15. Malaysian (versus
Chinese) name
Malaysia Lee and Khalid (2016)
A.16. Middle-Eastern (versus
native) name
Sweden Carlsson (2010), Carlsson and
Eriksson (in press), Carlsson and
Rooth (2007) and Carlsson and Rooth
Attström (2007)
A.17. Mixed-race or
Indigenous (versus white) skin
Mexico Arceo-Gomez and Campos-Vazquez
A.18. Mongolian, Tibetan, or
Uighur (versus native) name
China Maurer-Fazio (2012)
A.19. Moroccan (versus
native) name
France Pierné (2013)
Berson (2012)
Duguet etal. (2010)
A.20. Pakistani (versus native)
Norway Midtbøen (2013) and Midtbøen (2016)
A.21. Turkish (versus native)
Belgium Baert etal. (2015)
Baert and Vujić (2016)
Germany Kaas and Manger (2012)
A.22. Ukraine or Vietnamese
(versus native) name
Poland Wysienska-Di Carlo and Karpinski
B.Discrimination ground: gender and motherhood
B.1. Being a mother (versus a
childless woman)
US Correll etal. (2007)
B.2. Being pregnant (versus
revealing no pregnancy)
Belgium Capéau etal. (2012)
B.3. Female (versus male)
Australia Booth and Leigh (2010) +
Belgium Capéau etal. (2012) 0
Baert (2015) and Baert etal. (2016a) 0
China Zhou etal. (2013) +
France Petit (2007)
Berson (2012) +
Spain Albert etal. (2011) 0
Sweden Agerström etal. (2012) 0
Carlsson (2011) 0
UK Jackson (2009) +
Riach and Rich (2006b)
B.4. Transgender sexual
US Make the Road NewYork (2010)
C.Discrimination ground: religion
C.1. Muslim (versus majority
France Adida etal. (2010)
Pierné (2013)
India Banerjee etal. (2009) 0
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Table 3.1 (continued)
(1) Treatment
(2) Country of
analysis (3) Study
C.2. Pentecostal, Evangelical,
or Jehovah’s Witness (versus
majority religion)
Greece Drydakis (2010b)
C.3. Religious group
US Wright etal. (2013)
C.4. Wearing headscarves Germany Weichselbaumer (2016)
D.Discrimination ground: disability
D.1. Blindness, deafness, or
Belgium Baert (2016)
D.2. Former depression Belgium Baert etal. (2016b)
D.3. Former mental illness
(versus physical injury)
US Hipes etal. (2016)
D.4. HIV Greece Drydakis (2010a)
D.5. Obesity Sweden Agerström and Rooth (2011) and
Rooth (2009)
D.6. Spinal cord injury or
Asperger’s Syndrome
US Ameri etal. (2015)
D.7. Unspecied physical
Belgium Capéau etal. (2012)
D.8. Wheelchair user UK Stone and Wright (2013)
E.Discrimination ground: age
E.1. Age 21 or age 27 (versus
age 39 or age 47)
UK Riach and Rich (2010)
E.2. Age 24 or age 25 (versus
age 50 or age 51)
UK Tinsley (2012)
E.3. Age 24 or age 28 (versus
age 38)
Spain Albert etal. (2011)
E.4. Age 27 (versus age 57) France Riach and Rich (2006a)
Spain Riach and Rich (2007)
E.5. Age 29, age 30, or age 31
(versus age 64, age 65, or age
US Neumark etal. (2015) and Neumark
etal. (2016)
E.6. Age 35 or age 45 (versus
age 50, age 55, or age 62)
US Lahey (2008)
E.7. Age 35, age 47, or age 53
(versus age 23, age 35, or age
Belgium Capéau etal. (2012)
E.8. Age 46 (versus age 31) Sweden Ahmed etal. (2012)
E.9. Age 50 or age 44 (versus
age 44 or age 38)
Belgium Baert etal. (2016c)
E.10. Age 50 or older (versus
US Farber etal. (2016)
F.Discrimination ground: military service or afliation
F.1. Military work experience Belgium Baert and Balcaen (2013) 0
F.2. Military service US Kleykamp (2009) +
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
Table 3.1 (continued)
(1) Treatment
(2) Country of
analysis (3) Study
G.Discrimination ground: wealth
G.1. Residence in
neighbourhood with poor
(versus bland) reputation
UK Tunstall etal. (2014) 0
G.2. Non-upper-caste (versus
India Banerjee etal. (2009) 0
Siddique (2011)
H.Discrimination ground: genetic information
No related correspondence experiments found.
I.Discrimination ground: citizenship status
No related correspondence experiments found.
J.Discrimination ground: marital status
J.1. Married (versus
Mexico Arceo-Gomez and Campos-Vazquez
K.Discrimination ground: sexual orientation
K.1. LGBT organisation
Cyprus Drydakis (2014)
Germany Weichselbaumer (2015)
Greece Drydakis (2009)
Drydakis (2011)
Drydakis (2012b)
Italy Patacchini etal. (2015) 0
Sweden Ahmed etal. (2013)
Bailey etal. (2013) 0
UK Drydakis (2015)
US Tilcsik (2011)
Mishel (2016)
K.2. Same-sex marriage
Belgium Baert (2014) 0
L.Discrimination ground: political orientation
L.1. Orientation of mentioned
youth political organisation
Belgium Baert etal. (2014) 0
M.Discrimination ground: union afliation
M.1. Youth union membership Belgium Baert and Omey (2015)
N.Discrimination ground: physical appearance
N.1. Lower attractiveness of
resume picture
Argentina Lopez Bóo etal. (2013)
Belgium Baert (in press)
China Maurer-Fazio and Lei (2015)
Israel Rufe and Shtudiner (2015)
Italy Patacchini etal. (2015) 0
N.2. Facial disgurement (in
resume picture)
UK Stone and Wright (2013)
+ (0) (()) indicates an overall signicantly positive (neutral) ((negative)) effect of the treatment in
column (1) on call-back outcomes. Used abbreviations: LGBT Lesbian, Gay, Bisexual, and
Transgender; UK United Kingdom; US United States. This register is kept updated at the author’s
homepage []
S. Baert
16.7%) are insignicantly different from 0, and 5 (i.e. 4.6%) are signicantly
Most of the cases document discrimination against ethnic minorities. There are
two important exceptions with respect to this empirical pattern. First, in two recent
studies with experiments conducted in the United States, no ethnic discrimination in
hiring was found (Darolia etal. 2016; Decker etal. 2015). Second, in Malaysia the
(expected) unfavourable treatment of the ethnic majority was found (Lee and Khalid
2016).11 In addition, research in Belgium (Baert and Vujić 2016; Baert etal. 2015,
2017) revealed situations in which ethnic discrimination disappeared there, i.e.
when ethnic minorities mentioned volunteer work for mainstream organisations,
when they applied for occupations in which labour market tightness was high, and
when they had many years of work experience. For an in-depth review of a selection
of the studies in Panel A of Table 3.1, we refer to Bertrand and Duo (2016),
Neumark (in press), Rich (2014), and Zschirnt and Ruedin (2016).
With respect to evidence on gender discrimination, i.e. the experiments compar-
ing call-back for male and female candidates, the evidence is very mixed. This is
related to the particular occupations tested. Indeed, many authors mentioned that
gender discrimination was heterogeneous by occupational characteristics (Baert
etal. 2015; Petit 2007; Carlsson 2011). On the other hand, a signicant penalty for
being pregnant or being a mother was found in a study from Belgium and one from
the United States, respectively (Capéau etal. 2012; Correll etal. 2007). Disclosing
one’s transgender identity was found to be detrimental to labour market success in
the United States (Make the Road NewYork 2010).
With respect to discrimination based on religion, a majority of the studies
focussed on the signal of being a Muslim (directly mentioned or indicated by means
of a resume picture in which headscarves were worn), compared with being a
Christian (in countries where Christianity was the majority religion). Afliation
with Islam always yielded lower call-back rates (Adida etal. 2010; Banerjee etal.
2009; Pierné 2013; Weichselbaumer 2016). Somewhat surprisingly, no correspon-
dence experiments have been conducted yet with respect to other leading religions
(e.g., Hinduism, Buddhism, and Judaism) as well as to various folk religions.
Remarkably, all experiments on discrimination against the disabled have focussed
on different dimensions of disability. Thus, we are in favour of replication studies
for this dimension of discrimination. Nevertheless, each form of disability revealed
in the hiring process seems to result in adverse hiring outcomes. The same is true
with respect to age discrimination: across all studies listed in Table3.1, older age is
always punished.
10 These numbers do not sum up to 90, as some studies were included multiple times in Table3.1
(as mentioned in the rst paragraph of Sect. 3.3).
11 In general, comparing the results across the rows of Table3.1 is very tricky, as the experiments
differed substantially with respect to at least the following characteristics of their design: (i) region
of the experiment; (ii) experimental population (e.g., with respect to age and education level); and
(iii) sectors, occupations, and vacancies tested.
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
A minority sexual orientation, revealed by means of mentioning membership in
a rainbow organisation or the name of one’s (same-sex) marital partner in the
resume, has a non-positive effect on employment opportunities. Including an attrac-
tive facial picture (compared to a less attractive one) with one’s resume has a bene-
cial effect. Finally, Table3.1 lists little evidence for non-negative effects of military
service and higher wealth (Baert and Balcaen 2013; Kleykamp 2009), a negative
effect of trade union membership (Baert and Omey 2015), and zero effects for mari-
tal status (Arceo-Gomez and Campos-Vazquez 2014) and political afliation (Baert
etal. 2014).
3.3.2 Country ofAnalysis
Column (2) of Table 3.1 shows that the summarised literature on labour market
discrimination is unbalanced with respect to the country of analysis. Grouped at the
continental level, 59 of the 90 correspondence experiments were conducted in
Europe, compared to 20in North America, only 7in the largest continent of Asia,
2in South America, 2in Australia, and none in Africa.
At the country level, most experiments (19) were conducted in the United States.
The European countries of Belgium (13 experiments), France (8 experiments),
Greece (6 experiments), Sweden (9 experiments), and the UK (8 experiments) are
clearly overrepresented. On the other hand, these European countries are, together
with the United States, the only ones in which within-country comparisons can be
made of the discrimination measured for different grounds. In 6 of the 10 largest
countries by population (Indonesia, Brazil, Pakistan, Nigeria, Bangladesh, and
Russia), no correspondence experiments have been conducted yet.
3.4 Conclusion
This chapter provided the reader with a catalogue of all correspondence experi-
ments on hiring discrimination conducted after Bertrand and Mullainathan (2004)
that could be found through a systematic search. It shows that these experiments
have focussed on a few specic grounds for discrimination (race, gender, religion,
disability, age, sexual orientation, and physical appearance). An overwhelming
majority of these studies reported unfavourable treatment of the group hypothesised
to be discriminated against. On the other hand, other topical forms of potential hir-
ing discrimination (e.g., based on genetic information, citizenship status, or politi-
cal orientation) have hardly been assessed. Moreover, in 6 of the 10 largest countries
by population, no correspondence experiments have been conducted yet.
The register presented in Table3.1—enriched with hyperlinks to the electronic
versions of the included studies—is kept updated at the author’s homepage [http://].
S. Baert
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... Certain studies do indicate, however, that disability disclosure in the recruitment process is associated with negative behavioral responses. A small number of field experiments, which involved submitting fictional applications for real job listings, establish the fact that disability disclosure during the first stage of hiring leads to significantly lower rates of interview invitations (Ameri et al., 2018;Baert, 2018;Bellemare et al., 2018;Bjørnshagen, 2021;Bjørnshagen & Ugreninov, 2021;Hipes et al., 2016). A literature review by Brohan et al. (2012) regarding the disclosure of mental health conditions points to vignette and survey studies that indicate that disclosing a mental health condition leads to more of a disadvantage than disclosing a physical impairment. ...
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BACKGROUND: How to disclose an impairment during the hiring process is an important question for disabled people, yet the associated employer perspective remains overlooked in the literature. OBJECTIVE: The article investigates whether, when and how employers prefer jobseekers to disclose their impairment during the recruitment process. Stigma and impression management is used as a theoretical lens to interpret employer responses. METHODS: The article uses interview data from 38 Norwegian employers paired with behavioral data from a recruitment situation. Prior to the interviews, the employers were subjected to a field experiment wherein pairs of fictitious applications were submitted for real job listings. In these, one of the applicants disclosed either a mobility impairment or a mental health condition. RESULTS: The findings show that disability disclosure is a balancing act between appearing candid and demonstrating competence and that employers favor identity management strategies that present disability in a positive and unobtrusive manner and downplay the impairment. The employers favored disclosure but expected wheelchair users to disclose their impairment earlier than people with mental health conditions. Furthermore, employers with a relational view on disability were found to be more open to hiring disabled people. CONCLUSIONS: The article illustrates how disclosure expectations can represent a significant disability penalty, thus hampering employment advancement for disabled people.
... While there are original studies researching labor market discrimination based on various discrimination grounds, systematic reviews have been conducted mostly to aggregate data on ethnic discrimination (Zschirnt & Ruedin, 2016;Lippens et al., 2021). Some reviews have provided overviews of correspondence experiments focusing on the most common discrimination grounds (e.g., ethnicity, gender, religion, sexual orientation, age; Bertrand & Duflo, 2017), with the most comprehensive review being Baert et al.'s (2018) and their newly updated meta-analysis (Lippens et al., 2021). They included almost all studies on hiring discrimination, based on all available discrimination grounds. ...
We aimed to identify effect sizes of age discrimination in recruitment based on evidence from correspondence studies and scenario experiments conducted between 2010 and 2019. To differentiate our results, we separated outcomes (i.e., call-back rates and hiring/invitation to interview likelihood) by age groups (40-49, 50-59, 60-65, 66+) and assessed age discrimination by comparing older applicants to a control group (29-35 year-olds). We conducted searches in PsycINFO, Web of Science, ERIC, BASE, and Google Scholar, along with backward reference searching. Study bias was assessed with a tool developed for this review, and publication bias by calculating R-index, p-curve, and funnel plots. We calculated odds ratios for callback rates, pooled the results using a random-effects meta-analysis and calculated 95% confidence intervals. We included 13 studies from 11 articles in our review, and conducted meta-analyses on the eight studies that we were able to extract data from. The majority of studies were correspondence studies (k=10) and came largely from European countries (k=9), with the rest being from the U.S. (k=3) and Australia (k=1). Seven studies had a between-participants design, and the remaining six studies had a within-participants design. We conducted six random-effects meta-analyses, one for each age category and type of study design and found an average effect of age discrimination against all age groups in both study designs, with varying effect sizes (ranging from OR = 0.38, CI [0.25, 0.59] to OR = 0.89, CI [0.81, 0.97]). There was moderate to high risk of bias on certain factors, e.g., age randomisation, problems with application heterogeneity. Generally, there’s an effect of age discrimination and tends to increase with age. This has important implications regarding the future of the world’s workforce, given the increase in the older workforce and later retirement.
... Both employees and employers face the negative consequences of this discrimination. On the one hand, minority employees repeatedly experience unfavourable treatment when applying for a job and are often remunerated worse than their majority counterparts (Altonji and Pierret 2001;Baert 2018; Barr and Oduro 2002;Charles and Guryan 2008;Epstein, Gafni, and Siniver 2016;Lippens, Vermeiren, and Baert 2021). As a consequence, they are less likely to be satisfied with their job or committed to the organisation they work for, and are more prone to experiencing mental and physical health issues (Paradies et al. 2015;Pascoe and Smart Richman 2009;Triana, Jayasinghe, and Pieper 2015). ...
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To mitigate ethnic labour market discrimination, it is essential to understand its underlying mechanisms because different mechanisms call for different counteracting measures. To this end, we reviewed the recent literature that confronts the theories of taste-based and statistical discrimination against the empirical reality. Whereas the empirical evidence for both mechanisms is generally mixed, (field) experimental research, which predominantly focuses on hiring outcomes, appears to yield proportionately more evidence in favour of taste-based discrimination vis-à-vis statistical discrimination. This finding suggests that the taste-based mechanism may better explain ethnic discrimination in hiring. However, we also observe that the measurement operationalisations of the mechanisms vary substantially between studies and that alternative theoretical interpretations of some of the evidence are plausible. Taken together, additional research efforts, using clear measurement standards and appropriate synthesis methods, are required to solidify the review’s main finding.
... In wider society, specific groups of racially marked "second and third generation descendants of immigrants" (van de Weerd 2019: 245) are constructed as "categorically different" (van de Weerd 2019: 248) from those who can be assumed to be understood as autochthonous; in that context, Muslims are iconized as the archetypal allochtoon. As a consequence, Belgians with non-Belgian sounding names, irrespective of their linguistic competencies, experience racial profiling (Van Praet 2020), discrimination on the labor market (Baert 2018), and racism on the housing market (Van der Bracht, Coenen, and Van de Putte 2015; P.-J. Verhaeghe and Dumont 2019). ...
This thesis explores how the requirement of Dutch-French bilingualism on the labor market intersects with the racial stratification of work in Brussels, Belgium. In particular, it focuses on the context of security work for public transport, and presents the results of 18 months of ethnographic fieldwork, consisting of participant observation conducted among security workers in training as well as at work in metro stations in Brussels, and interviews and informal conversations with company executives, trainers, and trainees. The security workers that this study followed were recruited in 2017, after the government freed funds for increasing the safety of the Brussels public transport system in the wake of the terrorist attacks of March 2016. Due to the legal requirement of Dutch-French bilingualism for all jobs that involve contact with the public in Brussels, the security workers that this study focuses on had to be bilingual too. Since job seekers on the Brussels labor market generally have good knowledge of French, but limited or no competence in Dutch, the six-month training these men followed comprised fifteen weeks of full time Dutch language classes. Moreover, the training primarily attracted unemployed adult men of color, which is in line with the general strong representation of racialized men in the public transport company and in security work. At the same time, the public transport company is strongly racially stratified, with a majority of racially marked men working as technicians, drivers, and security workers, and a virtually all white management. The aim of this thesis was to investigate the potential interconnection between language and racial stratification in the Brussels public transport sector. By investigating the tensions between on the one hand the required knowledge of Dutch and the high investment it requires from job seekers, employers and the government, and on the other hand the virtual absence of Dutch at work, this study seeks to demonstrate two things. First, it shows how in Brussels Dutch-French bilingualism and the indexical meaning of the Dutch language are instrumentalized to rationalize the racial stratification of labor force. Second, this thesis demonstrates how racially marked security workers use Dutch-French bilingualism in their attempt to navigate the tension between on the one hand being members of a racialized and criminalized minority, and on the other hand becoming legitimate bilingual guardians of the social order.
Persistent hiring discrimination as demonstrated by correspondence experiments incites immigrant job candidates and their descendants to modify their résumé to hide their immigrant status, that is, résumé whitening. To date, we have little to no empirical evidence on how common this is in practice. We test the extent of résumé whitening with a representative survey of immigrants in Switzerland (N = 7659). Around 9% of the immigrants used some résumé whitening. Immigrants appear to use résumé whitening strategically when experiencing or anticipating discrimination. Future correspondence experiments should take this into account to maximize external validity.
We present a natural field experiment to examine if priming can influence behavior in a market for credence goods. 40 testers took 600 taxi journeys in Vienna, Austria, and using a between–subject design we vary the script they spoke, each designed to prime either honesty, dishonesty, or a competitor. We find that the honesty prime increases taxi fares by 5.5% relative to a baseline, the result of overcharging rather than overtreatment. Priming dishonesty and a competitor have no impact on fares. We find that the effects of priming on behavior are likely to be small compared to information asymmetries.
Purpose The purpose of this study is to investigate whether ethnic discrimination is present in the Russian labor market and whether it has a significant economic effect on the potential salaries of applicants. Design/methodology/approach Data were collected using a correspondence audit for four experimental male applicants with identical professional and personal characteristics while differing only in applicant name as a signal of applicants' ethnic background. Implied ethnicities include Russians, Armenians, Jews and North Caucasians. Résumés were sent out to 800 real unique vacancies on behalf of the experimental applicants with a geographic focus on the capital Moscow. Findings The results of the analysis suggest that there is a significant difference in treatment in both response rate and potential average salaries on ethnic grounds. Disadvantaged groups were found to be systematically pushed into jobs paying 15% less monthly wage. Originality/value The study investigates the existence of ethnic discrimination in the Russian labor market and furthermore economically quantifies the effects of discrimination.
A large experimental literature is devoted to studying discrimination. An important question for policymakers and firms is what drives the discrimination uncovered by those experiments. However, motivations are hard to determine when decision makers pay selective attention to information because their learning is private. We overcome this challenge by deriving conditions on average outcomes that reveal decision makers are prejudiced no matter what they learn about individuals in each demographic group before making their decisions. This provides a test of prejudice that is general, simple, and robust and that can potentially be used to identify prejudice in a wide range of important settings, such as hiring, consumer lending, and housing access. We demonstrate our test of prejudice using two influential labor market experiments. This paper was accepted by Yan Chen, decision science.
Does cultural dissimilarity explain discrimination against immigrant-origin minorities in the labour market? I conducted a factorial field experiment (N = 1350) to explore how explicit group cues trigger differential treatment and whether individuating information that counters cultural-based stereotypical representations mitigate discrimination. Employers were randomly assigned a job application with a putative female ethnic majority or immigrant-origin minority alias and CV photographs portraying the minority candidate with or without a headscarf—perhaps the quintessential marker of Muslim identity. Moreover, half the job applications conveyed information intended to reduce cultural distance by indicating a liberal lifestyle and civic participation. The results demonstrate that immigrant-origin women are significantly less likely to receive an invitation to a job interview, especially if they also wear a headscarf. Contrary to expectations, the differential treatment is not moderated by the individuating information in the applications. This indicates that the differential treatment is persistent and also targets immigrant-origin minorities who have acquired soft skills and signals cultural proximity.
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Western countries have experienced a large influx of Muslim immigrants, and concomitantly the Muslim headscarf has become the subject of major controversy. Drawing on theories of stigma, social identity, and multiple discrimination/intersectionality, this study examines the effect of wearing this headscarf in the German labor market. The author applies the method of correspondence testing that allows measuring discrimination in a controlled field setting. Findings show that when applying for a job in Germany, women with a Turkish migration background are less likely to be invited for an interview, and the level of discrimination increases substantially if the applicant wears a headscarf. The results suggest that immigrant women who wear a headscarf suffer discrimination based on multiple stigmas related to ethnicity and religion.
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We design and implement a large-scale resume correspondence study to address limitations of existing field experiments testing for age discrimination that may bias their results. One limitation that may bias results is giving older and younger applicants similar experience to make them “otherwise comparable.” A second limitation is that greater unobserved differences in human capital investment of older applicants may bias the results against finding age discrimination. On the basis of over 40,000 job applications, we find robust evidence of age discrimination in hiring against older women, especially those near retirement age, but considerably less evidence of age discrimination against men.
Understanding whether labor market discrimination explains inferior labor market outcomes for many groups has drawn the attention of labor economists for decades—at least since the publication of Gary Becker’s The Economics of Discrimination in 1957. The decades of research on discrimination in labor markets began with a regression-based “decomposition” approach, asking whether raw wage or earnings differences between groups—which might constitute prima facie evidence of discrimination—were in fact attributable to other productivity-related factors. Subsequent research—responding in large part to limitations of the regression-based approach—moved on to other approaches, such as using firm-level data to estimate both marginal productivity and wage differentials. In recent years, however, there has been substantial growth in experimental research on labor market discrimination—although the earliest experiments were done decades ago. Some experimental research on labor market discrimination takes place in the lab. But far more of it is done in the field, which makes this particular area of experimental research unique relative to the explosion of experimental economic research more generally. This paper surveys the full range of experimental literature on labor market discrimination, places it in the context of the broader research literature on labor market discrimination, discusses the experimental literature from many different perspectives (empirical, theoretical, and policy), and reviews both what this literature has taught us thus far, and what remains to be done.
This book offers practical instruction on the use of audit studies in the social sciences. It features essays from sociologists, economists, and other experts who have employed this powerful and flexible tool. Readers will learn how to implement an audit study to examine a variety of questions in their own research. The essays first discuss situations where audit studies are the most effective. These tools allow researchers to make strong causal claims and explore questions that are often difficult to answer with observational data. Audit studies also stand as the single best way to conduct research on discrimination. The authors highlight what these studies have uncovered about labor market processes in the past decade. The next section gives some guidance on how to design an audit study. The essays cover the difficult task of getting a study through an institutional review board, the technical setup of matching procedures, and statistical power and analysis techniques. The last part focuses on more advanced aspects. Coverage includes understanding context, what variables may signal, and the use of technology. The book concludes with a discussion of challenges and limitations with an eye towards the future of audit studies. This book brings together a number of interesting and useful perspectives on these field experiments. Many different kinds of readers will find it valuable, ranging from those interested in getting an overview of the evidence, to researchers looking for guidance on the nuts and bolts of conducting these complex experiments.” David Neumark, Chancellor’s Professor of Economics at the University of California – Irvine This volume provides the first deep examination of the audit method, with details on the practical, political, analytical, and theoretical considerations of this research. Social scientists interested in consuming or contributing to this literature will find this volume immensely useful.” Devah Pager, Professor of Sociology and Public Policy at Harvard University
An audit study is a specific type of field experiment primarily used to test for discriminatory behavior when survey and interview questions induce social desirability bas. In this chapter, I first review the language and definitions related to audit studies and encourage adoption of a common language. I then discuss why researchers use the audit method as well as when researchers can and should use this method. Next, I give an overview of the history of audit studies, focusing on major developments and changes in the overall body of work. Finally, I discuss the limitations of correspondence audits and provide some thoughts on future directions.
We use an audit study approach to investigate how unemployment duration, age, and holding a low-level interim job while applying for a better job affect the likelihood that experienced college-educated females applying for an administrative support job receive a callback from potential employers. First, the results show no relationship between callback rates and unemployment duration. Second, workers age fifty and older are significantly less likely to receive a callback. Third, taking an interim job significantly reduces the likelihood of receiving a callback. Finally, employers who have higher callback rates respond less to observable differences across workers in determining whom to call back.