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

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Abstract

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, https://doi.org/10.1007/978-3-319-71153-9_3
Chapter 3
Hiring Discrimination: AnOverview
of(Almost) All Correspondence Experiments
Since 2005
StijnBaert
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
e-mail: stijn.baert@ugent.be
64
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.
S. Baert
65
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: https://www.eeoc.gov/
3 Source: http://touchngo.com/lglcntr/akstats/Statutes/Title18/Chapter80/Section220.htm
4 Source: http://www.dfeh.ca.gov/
5 Source: http://touchngo.com/lglcntr/akstats/Statutes/Title18/Chapter80/Section220.htm
6 Source: www.ilga.gov/legislation/ilcs/ilcs5.asp?ActID=2266
7 Source: http://www.dfeh.ca.gov/
8 Source: http://www.lexisnexis.com/hottopics/michie/
9 Source: https://www.law.hawaii.edu/les/downloads/LAW%20589%20Appearance%20
Discrimination_0.doc
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
66
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.
S. Baert
67
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
(4)
Effect
A.Discrimination ground: race and national origin
A.1. African (versus native)
name
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)
name
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)
name
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
Czech
Republic
Bartoš etal. (2014)
A.9. Chinese, Greek, Indian,
or Pakistani (versus native)
name
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)
(continued)
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
68
Table 3.1 (continued)
(1) Treatment
(2) Country of
analysis (3) Study
(4)
Effect
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
(2012)
Attström (2007)
A.17. Mixed-race or
Indigenous (versus white) skin
Mexico Arceo-Gomez and Campos-Vazquez
(2014)
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)
name
Norway Midtbøen (2013) and Midtbøen (2016)
A.21. Turkish (versus native)
name
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
(2014)
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)
gender
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
identity
US Make the Road NewYork (2010)
C.Discrimination ground: religion
C.1. Muslim (versus majority
religion)
France Adida etal. (2010)
Pierné (2013)
India Banerjee etal. (2009) 0
(continued)
S. Baert
69
Table 3.1 (continued)
(1) Treatment
(2) Country of
analysis (3) Study
(4)
Effect
C.2. Pentecostal, Evangelical,
or Jehovah’s Witness (versus
majority religion)
Greece Drydakis (2010b)
C.3. Religious group
membership
US Wright etal. (2013)
C.4. Wearing headscarves Germany Weichselbaumer (2016)
D.Discrimination ground: disability
D.1. Blindness, deafness, or
autism
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
disability
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
66)
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
47)
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
younger)
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) +
(continued)
3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
70
Table 3.1 (continued)
(1) Treatment
(2) Country of
analysis (3) Study
(4)
Effect
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
upper-caste)
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
unmarried)
Mexico Arceo-Gomez and Campos-Vazquez
(2014)
0
K.Discrimination ground: sexual orientation
K.1. LGBT organisation
member
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
partner
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 [http://users.UGent.be/~sbaert]
S. Baert
71
16.7%) are insignicantly different from 0, and 5 (i.e. 4.6%) are signicantly
positive.10
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…
72
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://
users.UGent.be/~sbaert].
S. Baert
73
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3 Hiring Discrim ination: AnOverview of(Almost) All Correspondence Experiments…
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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.
Book
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
Chapter
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.
Article
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.