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Implicit Age Cues in Resumes: Subtle Effects on Hiring Discrimination

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Anonymous resume screening, as assumed, does not dissuade age discriminatory effects. Building on job market signaling theory, this study investigated whether older applicants may benefit from concealing explicitly mentioned age signals on their resumes (date of birth) or whether more implicit/subtle age cues on resumes (older-sounding names/old-fashioned extracurricular activities) may lower older applicants’ hirability ratings. An experimental study among 610 HR professionals using a mixed factorial design showed hiring discrimination of older applicants based on implicit age cues in resumes. This effect was more pronounced for older raters. Concealing one’s date of birth led to overall lower ratings. Study findings add to the limited knowledge on the effects of implicit age cues on hiring discrimination in resume screening and the usefulness of anonymous resume screening in the context of age. Implications for research and practice are discussed.
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ORIGINAL RESEARCH
published: 10 August 2017
doi: 10.3389/fpsyg.2017.01321
Edited by:
Cort W. Rudolph,
Saint Louis University, United States
Reviewed by:
Justin Marcus,
Özye ˘
gin University, Turkey
Joseph M. Goodman,
Illinois State University, United States
*Correspondence:
Eva Derous
eva.derous@ugent.be
Specialty section:
This article was submitted to
Organizational Psychology,
a section of the journal
Frontiers in Psychology
Received: 29 March 2017
Accepted: 18 July 2017
Published: 10 August 2017
Citation:
Derous E and Decoster J (2017)
Implicit Age Cues in Resumes: Subtle
Effects on Hiring Discrimination.
Front. Psychol. 8:1321.
doi: 10.3389/fpsyg.2017.01321
Implicit Age Cues in Resumes: Subtle
Effects on Hiring Discrimination
Eva Derous1*and Jeroen Decoster2
1Department of Personnel Management, Work, and Organizational Psychology, Faculty of Psychology and Educational
Sciences, Ghent University, Ghent, Belgium, 2Thomas More University College, Antwerp, Belgium
Anonymous resume screening, as assumed, does not dissuade age discriminatory
effects. Building on job market signaling theory, this study investigated whether older
applicants may benefit from concealing explicitly mentioned age signals on their resumes
(date of birth) or whether more implicit/subtle age cues on resumes (older-sounding
names/old-fashioned extracurricular activities) may lower older applicants’ hirability
ratings. An experimental study among 610 HR professionals using a mixed factorial
design showed hiring discrimination of older applicants based on implicit age cues
in resumes. This effect was more pronounced for older raters. Concealing one’s date
of birth led to overall lower ratings. Study findings add to the limited knowledge on
the effects of implicit age cues on hiring discrimination in resume screening and the
usefulness of anonymous resume screening in the context of age. Implications for
research and practice are discussed.
Keywords: age, anonymous resume screening, hiring discrimination, job market signaling theory, recruitment
INTRODUCTION
In Western society people need to work long enough to maintain welfare levels (Administration
on Aging, 2015). Many people also prefer to stay active in the labor market until an older age
(Wöhrmann et al., 2016). However, and despite anti-discrimination legislation, chronologically
older compared to younger job applicants still have lower chances to hold and obtain jobs, even
when their competencies are alike (Neumark et al., 2016;Wanberg et al., 2016).
The present paper focuses on age discrimination in hiring, and more in particular on resume
screening, (Bal et al., 2011;Truxillo et al., 2015). Worldwide, resumes are one of the most
frequently used screening tools that encompass the first selection hurdle. Moreover, due to
the way impressions are formed, this hurdle seems vulnerable for hiring discrimination (Fiske
et al., 2002). Although chronological age has no validity for predicting future job performance
(Schmidt et al., 2016), correspondence audit studies1consistently show that explicitly presenting
one’s chronological age in a resume may decline older applicants’ job chances (Riach and Rich,
2006, 2010;Richardson et al., 2013;Neumark et al., 2016). Moreover, such ageism effects seem
substantial. Ahmed et al. (2012), for instance, found that younger (31 years) compared to equally
Abbreviation: AAP, anonymous application procedures.
1The correspondence audit technique is an experimental research technique that allows to compare labor market outcomes
of applicants who are equally qualified for a job (i.e., identical in all productive characteristics) but who differ in non-
job related or non-productive characteristics like -most typically- demographic characteristics (e.g., gender, ethnicity, and
age). In correspondence audit studies matched pairs of equivalent resumes are sent to the same employer and callback is
registered as the outcome variable in order to investigate whether differential treatment of applicants can be attributed to
hiring discrimination.
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Derous and Decoster Age Cues in Resumes
qualified older (46 years) applicants received over three times
more responses from employers looking to hire restaurant
workers and over four times more responses from employers
looking for sales assistants.
Anti-discrimination regulations have not prevented bias
in resume screening; therefore AAP are offered to combat
illegal discriminatory hiring practices (Åslund and Skans, 2012).
AAP like blind auditions (Goldin and Rouse, 2000), blind
interviewing (Buijsrogge et al., 2016), and anonymous resume
screening (Åslund and Skans, 2012), aim to blot non-job-related,
personal identifiers (like socio-demographic information) to
increase protected job applicants’ chances of advancing to the
next assessment stage, and hence, to increase their hiring
chances. Intriguingly, however, results of AAP are mixed.
Anonymous resume screening also dilutes hiring chances of job
applicants from protected social groups (Krause et al., 2012;
Behaghel et al., 2015), which suggests that resumes in which
demographic information is blotted still reveal information about
job applicants’ group membership, albeit in subtle ways. To
the best of our knowledge, the role of implicit markers of
applicants’ social group membership on their hiring chances
has not been investigated much in recruitment (i.e., resume
screening), especially not regarding applicants’ older age, and will
be considered here.
According to the signaling theory (like the job market
signaling theory; see Spence, 1973, 1974;Rynes, 1991) senders
(like applicants) exchange information with receivers (like
recruiters) through signals/cues (like resume information),
which correlate with unobservable characteristics of the sender
(Connelly et al., 2011;Bangerter et al., 2012). Hence building
on assumptions from both job market signaling theory and
impression formation theory, we first investigate whether older
job applicants benefit from concealing explicitly mentioned
age cues in their resumes (like date of birth) or whether
more implicit/subtle age cues in resumes – i.e., other than
one’s date of birth- may lower older job applicants’ hirability
ratings. Second, surprisingly and with a few exceptions (see
Fasbender and Wang, 2017), few studies investigated recruiter
characteristics on hiring discrimination, and ageism in particular.
Yet, recruiters might differ in their susceptibility to hiring bias
and recruiters’ chronological age has been suggested as one
potential boundary condition. As a second aim, we therefore
explore the potential moderating role of recruiters’ chronological
age on implicit/subtle age-related information in resumes on
the one hand and applicants’ hirability ratings on the other
hand. Some studies showed evidence for in-group favoritism with
chronologically older recruiters favoring older applicants (Krings
et al., 2011), whereas others have shown the opposite (Finkelstein
and Burke, 1998;Axt et al., 2014). Since hiring literature is
inconclusive and mainly considered explicit age cues, we further
explore whether older recruiters may (dis)favor resumes based
on implicit old-age cues (Axt et al., 2014;Marcus and Sabuncu,
2016).
In the next paragraphs, we first discuss why resume screening
is vulnerable to age discrimination, what is known about ageism
effects in this stage of the hiring procedure, and why anonymous
resume screening may fail to avert age discrimination. Next,
we turn our attention to job market signaling theory and
discuss ‘implicit’ age cues in resumes (i.e., applicants’ name and
affiliations). Finally, we explore the potential moderating role
of recruiters’ chronological age on implicit age cues and hiring
discrimination.
THEORETICAL BACKGROUND
Age Discrimination in Resume Screening
Resumes are one of the first and most important sources of
information when HR managers and recruiters initially screen
applicants for jobs, but they also appear very vulnerable to
bias (Derous et al., 2015). Job applicants are typically judged
on the basis of a one- or two-page resume. This resume
screening provides limited individuating information and is
vulnerable to categorization effects. That is, cognitive models of
impression formation (like the continuum model; see Fiske et al.,
1999) suggest that category-based information processing will
be particularly strong when limited individualized information
is available, such as on resumes (Abrams et al., 2016). Models
of impression formation further suggest that categorization
will occur automatically and once people have categorized
someone as belonging to a particular out-group, associated group
stereotypes may be activated, which can influence how people
judge others (Fiske et al., 1999).
In many Western European societies, it is common to indicate
date of birth on resumes, which explicitly signals applicants’
chronological age. Such an explicit signal might provide
recruiters with information about one’s life/work experiences
but at the same time might come with a cost and lower older
applicants’ hiring chances. When explicit age markers are present
in resumes, recruiters seem to prefer younger applicants over
older ones as evidenced by many recently conducted (field)
experiments (Lahey, 2008;Albert et al., 2011;Krings et al., 2011;
Ahmed et al., 2012;Richardson et al., 2013). For instance, Lahey
(2008) showed in a field experiment (i.e., correspondence audit
study) that chronologically older women received fewer positive
reactions to their applications than comparable younger women.
In another correspondence audit study, Albert et al. (2011)
sent out resumes of equally qualified 24-, 28-, and 38-years old
applicants in response to existing job ads. Resumes from 38-
years old applicants received a significant lower callback than
those from the former two age groups. Similarly, Ahmed et al.
(2012) found 46-years old applicants to get lower callback than
applicants aged 31, whereas Richardson et al. (2013) showed
resumes of applicants aged 54 to less likely be hired than those
of equally qualified applicants aged 42 or 48.
These correspondence audit studies show age discrimination
to be one of the reasons why older workers have a higher
chance of dropping out of the labor force (Neumark et al.,
2016). A survey of the AARP Public Policy Institute also revealed
that 51% of older unemployed workers (aged 47–70) reported
to be discriminated against because of their older age (Koenig
et al., 2015). In support of this, Wanberg et al. (2016) showed
negative relationships between job seekers’ chronological age,
reemployment status, and reemployment speed. Moreover, these
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negative relations became stronger over age 50 (see also Perry
et al., 2016). Comparable findings have been reported in several
other Western countries outside the United States, showing less
positive callbacks for older job seekers when compared to their
equally qualified younger counterparts (e.g., Krings et al., 2011;
Åslund and Skans, 2012), indicating age discrimination in resume
screening to be a widespread, substantial, and pressing issue.
Anonymous Resume Screening
To avert age discrimination in the first phase of the screening
procedure, policy makers as well as researchers recommended
AAP (see Edin and Lagerström, 2006;Furtmueller et al., 2010;
Åslund and Skans, 2012). Furtmueller et al. (2010, p. 10),
for instance, concluded that “since employers are prohibited
to select employees based on gender, birth date, nationality
and marital status, resume forms should not ask for this
personal information.” Anonymous resume screening omits
explicit demographic cues from resumes that are non-job-related,
like date of birth, ethnic-sounding name, or gender (Edin and
Lagerström, 2006;Åslund and Skans, 2012;Krause et al., 2012;
Hiemstra et al., 2013).
Intriguingly, however, studies have shown that anonymous
resume screening may not be as effective as typically assumed
as ethnic minority or otherwise stigmatized applicants (e.g.,
like female or older applicants) are still more rejected when
they apply anonymous compared to equally qualified ethnic
majority or their non-stigmatized counterparts (Behaghel et al.,
2015;Maurer, 2016). For instance, in a field experiment in
Germany (Krause et al., 2012), female applicants were less
likely to receive a job interview invitation for a post doc
position in economics compared to equally qualified male
applicants when they applied with an AAP. In France,
organizations were less likely to invite minority applicants
when they received anonymous resumes (Behaghel et al., 2015).
The French government, therefore, abandoned the idea of
making anonymous resume screening mandatory in public
employment service offices. Recently, the Behavioral Economics
Team of the Australian Government (BETA) also showed that
de-identifying applications for senior positions decreased the
number of female and ethnic minority applicants shortlisted
for senior positions in the Australian Public Service (Hiscox
et al., 2017). Corroborating these findings, a recent scenario
study in which both American and European participants had
to screen resumes of chronologically older/younger applicants
(Kaufmann et al., 2016) revealed that hiring intentions of the
chronologically older applicants (i.e., who applied with resumes
that included age cues) did not differ significantly from those
of the ‘anonymous’ candidates (i.e., who applied with resumes
without age cues).
In a Dutch study, Hiemstra et al. (2013) showed that in the
absence of demographic information (as in anonymous resume
screening), real recruiters still gave lower job suitability ratings
to resumes of ethnic minority applicants compared to those of
their majority counterparts. Whereas human capital factors could
explain these findings to some extent, Hiemstra et al. (2013)
could not exclude hiring discrimination, either. Specifically,
other resume information (like applicants’ affiliations) than
explicit signals of one’s demographic background (like applicants’
chronological age or ethnic background) might operate as
‘implicit’ or subtle markers of applicants’ protected group status
and affect hirability ratings (Dovidio and Gaertner, 2000;Cole
et al., 2007;Derous et al., 2012).
Indeed, over the past decades, workplace discrimination
has become more subtle (Dovidio and Gaertner, 2000;Rosette
et al., 2016). In line with this, one could expect recruiters to
also turn their attention to more subtle cues in resumes to
gain information. Specifically, recruiters may infer applicants’
protected group membership from subtle cues (like one’s
affiliations, Dovidio and Gaertner, 2000;Hiemstra et al., 2013),
which contrasts with the often discussed view that anonymity
prevents recruiters from favoring majority over minority
applicants when credentials are equal, at least in the initial stage
of the hiring process. The above-mentioned findings raise the
question: Is there more into a resume than one’s date of birth
(i.e., or any other explicit age cue, like years of work experience)
that might disclose one’s chronological age and might instigate
age discrimination in hiring, albeit in more subtle ways?
Implicit Cues in Resumes
Job Market Signaling Theory
Recruitment researchers rely on job market signaling theory
(Spence, 1973, 1974) to explain how actors determine what
information is reliable for making job market choices. In its
more general and original sense, signaling theory refers to how
individuals (i.e., job applicants and recruiters/organizations)
with -partly- conflicting interests will communicate and
interpret signals/cues of unknown characteristics (i.e., of the
organization/job seeker) to obtain the biggest gains, like getting
hired or getting the best employees on board. Hence, signaling
systems are characterized by information asymmetry between
senders and receivers but are at the same time shaped by mutual
interests between signalers and receivers (Connelly et al., 2011).
Typically, signaling theory in recruitment research (i.e., job
market signaling theory; see Rynes, 1991) is used to explain the
cues job applicants use to make inferences about the prospective
employer/organization (Carter and Highhouse, 2014). Put
differently, the recruitment literature considers how observable
recruitment characteristics (like recruiter behavior) serve as
signals or cues of the unknown (to job seekers) organizational
quality or organizational characteristics. A warm recruiter, for
instance, may signal to applicants an organization that looks
after its employees (Slaughter et al., 2004). However, recruiters
also look for ‘cues’2about applicants’ overall and unknown
qualities in their resumes (Popken, 1993;Cable and Gilovich,
1998;Cole et al., 2003, 2009;Aguinis et al., 2005;Burns
et al., 2014). According to the normative-predictive model of
2In layman’s terms ‘cues’ and ‘signals’ are used intertwined (as synonyms). In
signaling theory, the notions ‘signal’ and ‘cue’ may differ conceptually from each
other in that ‘signals’ refer to traits or characteristics of the sender that might
change the behavior of the receiver to the benefit of the sender whereas ‘cues’ refer
to traits or characteristics of the sender that might benefit the receiver (Connelly
et al., 2011). Given the overall lower labor market outcomes of chronologically
older compared to younger persons (Wanberg et al., 2016), in the present paper,
implicit resume information referring to an applicant’s older age might be captured
more by the notion ‘cue’ than ‘signal.’
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resume screening (Vieira Campos Proença and Valente Dias
de Oliveira, 2009;Guion, 2011), resume screening should be
based on objective and job-related information like applicants’
work experiences and educational background as mentioned in
resumes (Cole et al., 2007). Objective and job-related information
(like qualifications) might function as ‘explicit cues’ about
applicants’ competencies (Bangerter et al., 2012). Yet, Popken
(1993) indicated that recruiters also reply upon inference and
indirect speech acts when they read resumes. Cole et al. (2003,
2009) indeed showed recruiters to infer applicants’ personality
(like agreeableness) from work experiences as mentioned on
resumes (see also Burns et al., 2014). Recently, Kaufmann
et al. (2016) even showed trait-related inferences from resume
pictures to lower applicants’ hiring chances (i.e., with pictures of
applicants with old-appearing faces triggering impressions of low
health and fitness). Hence, recruiters also tend to infer subjective
attributes and even personality characteristics from resume
content in an indirect way (from educational credentials, work
experiences, and so on), albeit often not in very accurate ways
(see Apers and Derous, 2017;Cole et al., 2007). Put differently,
some objective and job-related information in resumes might also
function as ‘implicit cues’ about other applicant characteristics
(like attributes and traits) than this information is originally
intended to be used for.
Interestingly, most studies consider qualification-based
inferences (e.g., cues to applicants’ personality) but do not
consider social group status inferences (i.e., cues to applicants
protected group status, like age), which may also affect recruiters’
impressions of applicants’ overall job qualification (i.e., given
age-based inferences) and even impact recruiters’ hiring
judgments. For instance, applicant skills as mentioned in
resumes might signal applicants’ overall qualification in a very
explicit way but at the same time might communicate something
else being useful to recruiters, namely applicants’ chronological
age (Abrams et al., 2016). Whereas job market signaling theory
mainly focuses on actions taken by senders to communicate
positive, imperceptible qualities of the signaler to the receiver
(like acquired skills), other information could also be conveyed
(i.e., co-vary with positive qualities) that might turn-out to
be less beneficial or even harmful to senders (like their older
age). Implicit cues to one’s social group status, therefore, can
be considered as an unintended consequence of actions taken
by senders to communicate positive qualities through resume
information (Connelly et al., 2011). As shown by Abrams et al.
(2016), applicants’ skills’ set (i.e., observable signal of one’s
qualifications) might co-vary with receivers’ perceptions of
applicants’ chronological age (i.e., indirectly cause chronological
age is not explicitly mentioned in the applicants’ skills or any
other section). Hence, when job applicants provide explicit
information in their resumes about their skills (like ‘being a rapid
decision-maker’; ‘understanding others’ views well’), they might
indirectly signal their age too (when ‘being a rapid decision-
maker’ is associated with chronologically younger age and
‘understanding others’ is associated with chronologically older
age) which might affect recruiters’ stereotypical impressions of
the applicant’s potential productivity. In doing so, recruiters may
subtly factor-in job-irrelevant information, like one’s social group
status, that they infer from implicit age cues in applicant resumes
(Hiemstra et al., 2013;Abrams et al., 2016).
Implicit cues (e.g., regarding one’s chronological age) differ
further from explicit cues in that they may be much more ‘hard-
to-fake’ by applicants and ‘hard-to-resist’ by recruiters (Bangerter
et al., 2012;Abrams et al., 2016). First, cues are considered
‘hard-to-fake’ (or ‘honest’) if signaling happens beyond one’s
conscious control regarding one’s unobserved qualities (Connelly
et al., 2011;Bangerter et al., 2012). Information about one’s social
group status (e.g., age) is implicitly conveyed through ‘honest’
cues, meaning that these cues convey truly useful information to
the receiver about the sender’s social group status in an indirect,
non-manipulable way (i.e., beyond the sender’s awareness; see
Bangerter et al., 2012). Second, implicit cues are ‘hard-to-resist’
by recruiters. Recruiters might look for such implicit, hard-
to-fake cues in resumes because applicants are less likely to
consciously cheat on these implicit age cues as applicants might
not be aware of the age-related associations they indirectly send
to recruiters through this information (e.g., skills as ‘proxy’
of applicants’ chronological age; Abrams et al., 2016). Given
that applications are characterized by information asymmetry
between applicants and recruiters, such information might be
of specific interest to recruiters. Moreover, when implicit cues
refer to one’s social group status, they may increase category
salience and lessen recruiters’ ability to inhibit categorization
(Fiske et al., 1999), which may make them even much ‘harder-to-
resist.’ Hence, implicit age cues may fuel recruiters’ categorization
processes and ageist hiring preferences in rather subtle ways, as
discussed next.
Implicit Age Cues
The present study integrates predictions from theories that
explain hiring discrimination (i.e., cognitive models of
impression formation) with job market signaling theory by
considering recruiters’ use of age-related cues in resumes.
First, according to impression formation theories (Fiske et al.,
2002), resumes may trigger social categorization processes
and instigate hiring discrimination because of the limited
amount of individuating information (i.e., a one or two-page
resume). Second, given the limited amount of individuating
information on resumes and given that resume-screening is
characterized by information asymmetry (Bangerter et al., 2012),
recruiters may particularly look for both explicit and implicit
cues about applicant characteristics to base their hiring decisions
upon. Certain of these cues may signal applicants’ age and
may trigger age-related associations (like applicants’ physical
and psychological ‘fitness’) to recruiters. Whereas effects of
explicit age cues on recruiters’ hiring decisions have already
been demonstrated (e.g., using correspondence audit tests; see
Neumark et al., 2016), effects of implicit age cues remain largely
understudied and are considered here. Specifically, given that
implicit cues are ‘hard-to-fake’ and ‘hard-to-resist,’ one could
expect strong categorization effects from implicit age cues,
thereby affecting job suitability ratings of applicants with old vs.
young-age cues in a different way.
Two implicit age cues of interest to this study are applicants’
first names and extracurricular activities. First, as the popularity
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of first names changes over time (Christopher, 1998;Sigurd
et al., 2005), names might indicate in a subtle, indirect way
a person’s chronological age and applicants might not be
aware of such a subtle and honest signal (i.e., age-related
association) in their resumes. Furthermore, because first names
are tied with one’s social and personal identity, they may
activate social categorization processes and ageism (Young et al.,
1993;Bennington and Wein, 2002;King et al., 2006;Derous
et al., 2009). Therefore, first names might be both hard-to-fake
(applicant perspective) and hard-to-resist (recruiter perspective).
For instance, Young et al. (1993) and Christopher (1998) both
showed that recruiters infer age from applicants’ first names.
Moreover, age associations seemed hard to resist as equally
qualified applicants with young-sounding names were perceived
more positively and received higher job suitability ratings than
those with old-fashioned names, showing there is more into a
resume than one’s date of birth (or any other explicit age cue)
that might disclose one’s chronological age and instigate age
discrimination (Perdue et al., 1990;Rudman et al., 1999).
Second, recruiters also evaluate applicants based on their
extracurricular activities/affiliations with socio-cultural groups
(Dovidio and Gaertner, 2000;Cole et al., 2007;Derous et al.,
2009). Cole et al. (2007) showed that if asked directly, recruiters
considered extracurricular activities as the least important
resume characteristic in judging applicants’ employability. Actual
employability ratings, however, showed exactly the opposite,
hence indicating that extracurricular activities were factored
in (hard-to-resist) when recruiters evaluated applicants. This
is buttressed by studies showing ethnic affiliations to lower
ethnic minorities’ job suitability ratings (Dovidio and Gaertner,
2000;Derous et al., 2009): Affiliations with certain socio-cultural
groups seem to affect recruiters’ information processing and
decision-making in subtle ways. Berger (2009) also suggests that
extracurricular activities in resumes can be beneficial to older
applicants when such activities counter stereotypical inferences
about the (older) applicant. Hence, as with names, extracurricular
activities might serve as hard-to-fake cues about applicants’
chronological age, particularly when applicants are unaware of
the age-related associations and such cues seem hard-to-resist
from a recruiter’s perspective (Cole et al., 2007;Abrams et al.,
2016).
Building further on both impression formation (Fiske et al.,
2002), and job market signaling theory (Bangerter et al., 2012)
to explain age discrimination in resume-screening, we expected
effects of implicit age cues in such a way that:
Hypothesis 1. Applicant resumes with old-sounding names
(Hypothesis 1a) and old-fashioned activities (Hypothesis
1b) will receive lower job suitability ratings than those from
equally qualified applicants with young implicit age cues.
The traditional view of bias considers membership in a
particular social group (e.g., with a protected status) as having
the same effect on employment outcomes for all members of
that group; typically studies do not consider multiple cues in
conjunction and within-category differences (i.e., differences
between members from the same social category; see Kaiser
and Pratt-Hyatt, 2009;Marcus and Fritzsche, 2014). That is,
compared to a single cue on a resume (e.g., name only) one would
expect multiple cues (like old-sounding name and old-fashioned
extracurricular activities) to increase category salience and lessen
the ability of even a motivated decision maker to inhibit the
activation of a social category (Kulik et al., 2007;Derous et al.,
2017). Similarly, research has suggested that minority group
members may be rejected in proportion to their ‘outgroupness’
(Crisp and Hewstone, 1999). Several studies offer evidence that
multiple cues to minority membership may lead to greater
hiring discrimination (Uhlmann et al., 2002;Segrest Purkiss
et al., 2006;Derous et al., 2009, 2015). Segrest Purkiss et al.
(2006), for instance, found that two ethnic cues (name; accent)
led to more negative interviewer reactions than one cue only.
Derous et al. (2009) also showed that the strength of applicants’
ethnic in-group identification (or social category salience) as
appearing on resumes affected their job suitability ratings with
highly Arab-identified minority applicants receiving lower job
suitability ratings compared to equally qualified but less highly
Arab-identified applicants. Similarly, because category salience
will affect attention to that category (Kulik et al., 2007) and
because protected group members (like older workers) may be
rejected in proportion to their degree of identification with the
protected social group of interest (Kaiser and Pratt-Hyatt, 2009),
we expected for implicit age cues that:
Hypothesis 2. Applicant resumes with more implicit cues
referring to older age will receive lower job suitability
ratings than those with less implicit cues referring to
older age.
Recruiter Age
Recruiters may differ in their susceptibility to bias. Given the
limited number of studies that considered effects of recruiter
characteristics on ageism in hiring (Kulik et al., 2000) and in
resume-screening in particular (Fasbender and Wang, 2017), we
focus on recruiters’ chronological age as a potential boundary
condition of implicit age effects. Two competing perspectives
have been put forward regarding implicit social evaluation.
On the one hand, implicit evaluations may favor members
from the in-group (i.e., in-group favoritism). In a simulated
hiring study, Krings et al. (2011) found recruiters’ own age to
attenuate age bias to some extent: The probability of selecting
the older candidate instead of the younger candidate increased
with increasing age of the evaluator. Specifically, age bias was
no longer observed or even turned into older worker favoritism
when recruiters were equal in age or older than the older
job candidate himself. These findings support the idea of in-
group favoritism. Recently, Axt et al. (2014) showed that for
both race and gender, the hierarchy of implicit evaluations
places in-group members at the top, therefore also evidencing
in-group favoritism. Indeed, to preserve one’s social identity,
one might favor in-group members (Tajfel and Turner, 1979).
Interestingly, however, Axt et al. (2014; see also Nosek et al., 2002)
showed a peculiar feature of implicit age effects: The hierarchy
of implicit evaluations did not reflect in-group favoritism for
age. Instead, older participants also placed older adults at the
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bottom of the social hierarchy (Axt et al., 2014). Thus, older
adults were preferred considerably less than younger adults, also
by older-aged participants, which runs counter to the idea of
in-group favoritism as found in the context of implicit race and
gender evaluations. Corroborating with this, Christopher (1998)
also reported older-age effects (based on first names) among both
younger and older participants. Why implicit in-group favoritism
does not occur for age cannot be inferred from these data.
Yet, Marcus and Sabuncu (2016) recently suggest evolutionary
explanations for ageism. Individuals may tend to systematically
avoid and even discriminate against older individuals that are
reminiscent of potential sickness or decline, in order to protect
one’s social group status and one’s individual ego from the
threat of sickness, decline, and eventually death. Since thoughts
about decline and death tend to particularly operate at the
implicit level (Levy and Banaji, 2002), they may exacerbate
the negative effects of implicit old-age cues on both older and
younger recruiters’ hiring decisions. Hence, evolutionary theories
of ageism consider prejudice and discrimination against older
applicants as a ‘defense mechanism’ that equally affects older and
younger evaluators. In line with these assumptions, one could
also expect the opposite, namely a lack of in-group favoritism
from the part of the older-aged recruiter in case he/she evaluates
resumes with implicitly mentioned old-age cues. Yet, given the
limited empirical evidence in the context of resume-screening,
we formulated the following research question on the potential
effect of recruiters’ chronological age:
Research question. Will applicant resumes with more
implicit age cues referring to older age receive higher job
suitability ratings from chronologically older recruiters than
younger recruiters (in-group favoritism) or will applicant
resumes with more implicit old-age cues receive equally low
job suitability ratings from older recruiters as from younger
recruiters (lack of in-group favoritism)?
MATERIALS AND METHODS
Ethics Statement
The study was carried out in accordance with the guidelines
of the ‘General Ethical Protocol for Scientific Research at
the Faculty of Psychology and Educational Sciences’ of
the Ethical Commission of the Faculty of Psychology and
Educational Sciences at Ghent University, which is the relevant
university institutional review board that considers ethical
aspects. In accordance with the Declaration of Helsinki,
participants provided informed written consent prior to their
participation. Participants were debriefed after all the data were
collected.
Participants
Participants of the main study were HR professionals in
organizations who regularly recruited applicants and who were
identified through researchers’ professional contacts, databases,
and networks. In total, 1424 HR professionals were emailed the
study link to participate, of which 45.86% (N=653) agreed to
participate. Of this group, 93.42% (N=610) were eligible because
they recruited applicants on a daily base (i.e., spending about 38%
of their daily activities on recruiting). Hence, the final sample
comprised 610 participants who recruited applicants frequently
(also called ‘recruiters’ or ‘raters’) with a mean age of 41.15 years
(SD =11.25 years), 50.70% males, of which 87% had a university
degree (bachelor or higher), and about 1/3rd held a lower/junior
(32.8%), middle/senior (36.4%), or higher (24.9%) position.
Procedure and Design
A field-based randomized experimental study (i.e.,
resume-screening experiment over the internet) was conducted.
Participants received an email with an url and personal code
that asked for participation in a study on the development of a
tool aimed to train/assess recruiters’ competencies (see Derous
et al., 2015, for a similar approach). To mask the study purpose
to a further extent and to reduce potential item priming, we also
included several filler items (Podsakoff et al., 2003).
After having completed the informed consent form,
participants read a job description for a ‘project manager’
(i.e., age-neutral as pilot tested; Perry, 1994). Subsequently,
participants read and evaluated four resumes (i.e., equally
qualified, see ‘Development of study materials and pilot studies’).
Specifically, we conducted a 2 (Date of birth: absent vs. present)
by [2 (Name: young vs. old-sounding) by 2 (Extracurricular
activities: modern vs. old-fashioned)] mixed factorial design.
Date of birth (i.e., explicit cue about applicants’ chronological
age) was the between-subjects factor, with chronologically
younger applicants born in 1987 and older applicants in 1959.
The younger applicants were 26 years’ old (born in 1987) whereas
the chronologically older applicants were 54 years’ old (born
in 1959) at the moment of the data collection. These birth
years were selected because previous studies showed more age
discrimination for people over 30, and especially when being
over 50 (Albert et al., 2011;Ahmed et al., 2012;Richardson
et al., 2013). Name and Extracurricular activities (i.e., implicit
age cues) were the within-subjects factors. We paired the explicit
age cue with the corresponding (young vs. old-sounding) name
on the job applicants’ resumes to avoid unrealistic combinations
(Christopher, 1998). Job applicants’ sex was kept constant (male
applicants only3), and applicants’ resumes were counterbalanced
to avoid order effects. The dependent variable was job suitability
(or hirability rating; see ‘Measures’). After the resume-sifting
task, participants completed a filler task, consisting of several
distractor items (e.g., asking how they screen applicants). As part
of this filler task, we also asked participants to indicate applicants’
age based on names and affiliations (i.e., manipulation checks).
In the end, participants were asked to fill-out an ‘opinion survey’
that included measures of old-age stereotypes4, social desirability,
3Only male applicants were considered because men participate more in the labor
market where this study was conducted than women and because of the need to
remove gender as a potentially confounding factor in the design (Sidanius and
Veniegas, 2000;OECD, 2015).
4We administered the ‘Beliefs About Older Workers Scale’ (Hassell and Perrewe,
1995). Because confirmatory factor analysis with maximum likelihood estimation
(Lisrel v. 9.2) showed a bad fit for a one-factor model (i.e., meaning that the
scale was not unidimensional as suggested by the authors), we did not consider
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and participants’ demographics. We ended the study with an
open-ended probe to ask for any suspicion regarding the study
purpose. After data were collected, participants were debriefed.
Development of Study Materials and
Pilot Studies
Prior to the main study, study materials (i.e., advertisement
and resumes) were developed and a series of pilot studies
(Ntotal =183) were conducted to ensure relevance and
equivalence of study materials. (see Supplementary Material for
a more detailed description).
First, 25 jobs were selected to evaluate whether these jobs were
equally accessible for older and younger workers. Given that there
is evidence for the effect of job-related age stereotypes on hiring
outcomes (Perry, 1994;Abrams et al., 2016), we aimed to select
an age-neutral job. Results of Pilot Study 1 (n=47, Mage =27.39,
SDage =5.1, 68% males) showed that the job of ‘project manager’
was perceived as equally accessible for younger and older workers
and therefore selected for this study.
Second, implicit age cues were developed and pilot tested.
Applicant names were selected based on the annual statistical
reports of the local government and onomastics that indicate
the popularity of first names in the geographical area where
this study was conducted. We also selected 24 extracurricular
activities. Based on the results of Pilot Study 2 (n=60,
Mage =27.86, SDage =7.51; 73% males), we selected ‘Fons’
and ‘Frans’ as names of older people and ‘Jens’ and ‘Niels’ as
names of younger people. We further selected old-fashioned
extracurricular activities (i.e., being a member of a bridge club;
being a pigeon/finches fancier; being a walking club member)
and extracurricular activities that are seen as modern/typically
performed by younger people (i.e., being a member of boy scouts;
being a snowboarder; being a life board crew member/rescuer).
Third, we listed all other information needed for the
resume template (i.e., educational degree/level, work experiences,
language, IT proficiency, home address) based on actual resumes
posted on job search websites in the area of interest (note that
identifying information was deleted). We had to make sure this
information to be relevant, age neutral, and equivalent across
resumes (as in the case of work experiences) and had to keep
this resume information constant (as in the case of language, IT
proficiency, home address/neighborhood). Based on Pilot Study 3
(n=76, Mage =25.91, SDage =9.8, 65.4% males), we therefore
selected a ‘Master of Science degree in economics’ (relevant
as pilot tested) and four work experiences (equivalent as pilot
tested). Finally, we held language (English, French, German, and
Dutch) and IT proficiency (SAP, MS office, team foundation
server) constant as well as applicants’ neighborhood (middle
class).
Based on the results of the pilot studies, we then integrated
all information to create the final resumes. In sum, the resume
template included information about the applicant’s name (old-
sounding vs. young-sounding first names as pilot tested), sex
(male), date of birth (born in 1987 vs. 1959), home address
recruiters’ beliefs about older workers in any further analyses (see also Discussion
section).
(middle-class area), relevant educational background and level
(Master of Science degree in economics as pilot tested), relevant
work experiences (without any indication of the number of
years of professional/work experience and considered equivalent
across resumes as pilot tested), language and IT proficiency
(held constant), and extracurricular activities (modern vs. old-
fashioned activities as pilot tested).
Measures
After participants observed the job ad and the four resumes,
they responded to questions using a 5-point Likert-type response
scale (unless otherwise mentioned). Job suitability was measured
with a 4-item measure adapted from Derous et al. (2009). An
example item is “Given all information you read about this
applicant, how suitable do you believe this applicant is for
this function?” (1 =not suitable at all to 5 =very suitable).
Cronbach’s alpha for the four resumes ranged from 0.91 to 0.93
(see Table 1). Second, to check manipulations, participants rated
applicants’ perceived age based on the job applicant’s name and
extracurricular activities using the following, self-constructed
item: “With what age do you associate [extracurricular activities]
or [name of applicant as appearing on the resume]” (1 =very
young to 5 =very old). Third, to control for social desirable
responding, eight items were adapted from the Impression
Management Scale (BIDR, Paulhus, 1991). An example item
is: “When I hear people talking privately, I avoid listening”
(1 =strongly disagree to 5 =strongly agree). Cronbachs alpha was
0.70. Finally, participants indicated demographics including their
chronological age (open answer), their sex (0 =female; 1 =male),
educational level (1 =college; 2 =university), job level (1 =lower,
2=middle, 3 =higher level) as well as recruiting experience.
Recruiting experience was measured with one self-constructed
item, namely “How much time do you spend on recruiting
activities on a daily base?” (1 =1–30%to4=70–100%).
RESULTS
Preliminary Analyses
Before testing the hypotheses, preliminary analyses were
conducted to check model assumptions, randomization,
manipulations, intercorrelations and potential covariate
variables. First, we tested assumptions of normality and
homogeneity of variances, which were met for each of the
applicant resumes. Inspection of the PP-plots, skewness and
kurtosis showed that distributions were approximately normal,
so that there is support for the assumption of normality. (Since
the repeated measure variables only had two levels, assumption
of sphericity was not tested.). Levene’s test further showed that
homogeneity of variances was met for all repeated measures.
Second, female and male applicants were equally distributed
across conditions, χ2(1,610) =0.22, p=0.64, and experimental
conditions did not differ from each other in participant age,
F(1,608) =0.15, p=0.70, participants’ educational level,
χ2(1,610) =0.74, p=0.39, job level, χ2(2,574) =0.32, p=0.85,
and recruiting experience, χ2(3,610) =4.19, p=0.24, supporting
the assumption of randomization.
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TABLE 1 | Descriptives, reliabilities and correlations among study variables.
M SD 12345678910
(1) Job suitability ‘Jens’a3.79 0.67 (0.92)
(2) Job suitability ‘Frans’b3.54 0.71 0.361∗∗ (0.91)
(3) Job suitability ‘Niels’c3.21 0.76 0.461∗∗ 0.307∗∗ (0.93)
(4) Job suitability ‘Fons’d3.32 0.70 0.310∗∗ 0.328∗∗ 0.395∗∗ (0.91)
(5) Social desirability 3.73 0.55 0.069 0.006 0.011 0.010 (0.70)
(6) Chronological agee41.15 11.3 0.066 0.0990.0890.111∗∗ 0.114∗∗
(7) Recruiting experiencef1.37 0.83 0.031 0.078 0.0940.0880.015 0.359∗∗
(8) Genderg0.51 0.50 0.033 0.0840.147∗∗ 0.0860.0850.333∗∗ 0.275∗∗
(9) Educational levelh1.87 0.33 0.058 0.043 0.021 0.050 0.031 0.063 0.063 0.097
(10) Job leveli1.92 0.77 0.008 0.070 0.025 0.042 0.0950.270∗∗ 0.174∗∗ 0.288∗∗ 0.015 -
Note. Cronbach’s alpha are on the diagonal. a‘Jens’: Resume with young-sounding name and modern extracurricular activities; b‘Frans’: resume with old-sounding name
and modern extracurricular activities; c‘Niels’: resume with young-sounding name and old-fashioned extracurricular activities; d‘Fons’: resume with old-sounding name
and old-fashioned extracurricular activities. eChronological age of the participants. fOnly participants who actually recruited applicants were included in the final sample
(N =610); recruiting experiences were coded as: 1 =less than or about 30% recruiting activities per day, 2 =30–50% recruiting activities per day; 3 =50–70% recruiting
activities per day; 4 =70% or more recruiting activities per day. gGender: 0 =female; 1 =male. hEducational level: 1 =college, 2 =university. iJob level: 1 =lower,
2=middle, 3 =higher. p<0.05, ∗∗ p<0.01.
Third, manipulation checks were successful: Young-sounding
names were perceived as significantly younger (M=2.07;
SD =0.47) than old-sounding names (M=4.00; SD =0.47),
t(609) =59.82, p=0.00. Old-fashioned activities were perceived
as significantly older (M=4.09; SD =0.52) than modern
activities (M=1.96; SD =0.46), t(609) = −64.64, p=0.00.
Fourth, inspection of the correlation table (see Table 1)
indicated that correlations were not overly strong and in line with
what could be expected (e.g., no relation between job suitability
ratings of any of the applicant profiles on the one hand and
social desirable responding, educational level, and job level on
the other hand). Finally, as literature suggests that recruiters’
gender and recruiting experience might affect resume evaluations
(e.g., Cole et al., 2003, 2007;Waung et al., 2015), we also
checked whether participants’ gender and recruiting experience
needed to be controlled for in the main analyses (Bernerth and
Aguinis, 2016). Gender and resume screening experience related
significantly to the job suitability ratings of some of the applicant
profiles/resumes. However, because further analyses showed that
homogeneity of regressions was not supported, participants’
gender and recruiting experience did not appear to be good
covariates and therefore were not included in the final analyses5
(Weinfurt, 1995;Tabachnick and Fidell, 2007).
Testing of Hypotheses and Research
Question
Table 1 presents descriptives, reliabilities, and correlations
among study variables. Hypotheses were tested with a series of
mixed analyses of covariances and simple effects analyses, given
the experimental set-up and nature of this study (see Derous et al.,
2015, for a similar approach). Hypothesis 1 investigated whether
applicant resumes with old-sounding names (Hypothesis 1a) and
old-fashioned activities (Hypothesis 1b) would receive lower job
5Additional analyses in which participants’ gender and recruiting experience were
included did not result in different effects than when gender and recruiting
experience were not included (see Results section).
TABLE 2 | Results of mixed analyses of variance for job suitability.
Source df F p η2
Between subjects
Date of birth (A) 1 5.32 0.02 0.02
Error (A) 608 (0.26)
Within subjects
Name (B) 1 9.32 0.00 0.02
Date of birth (A) ×Name (B) 1 0.56 0.45 0.00
Error (B) 608 (0.36)
Activities (C) 1 278.91 0.00 0.31
Date of birth (A) ×Activities (C) 1 0.69 0.41 0.00
Error (C) 608 (0.34)
Name (B) ×Activities (C) 1 73.72 0.00 0.11
Date of birth (A) ×Name (B) ×Activities (C) 1 0.53 0.47 0.00
Error (B ×C) 608 (0.27)
Note. The between-subjects factor refers to the explicit age cue (date of birth:
absent vs. present) whereas the within-subjects factors refer to the implicit age
cues (name/extracurricular activities: old vs. young) on resumes. The pattern of
results remained the same when participants’ recruiting experience and gender
were controlled for.
suitability ratings than those from equally qualified applicants
with young implicit age cues (i.e., young-sounding names;
modern activities) in their resumes. Job suitability was lower for
resumes with old-sounding names than young-sounding names,
F(1,608) =9.32, p<0.01, η2=0.02, and old-fashioned activities
than modern activities, F(1,608) =278.91, p<0.001, η2=0.31,
thereby supporting effects of implicit age cues (Hypotheses 1a
and 1b were supported). Interestingly, results further showed that
job suitability was lowest when the explicit age cue (i.e., date
of birth) was omitted from resumes, F(1,608) =5.32, p=0.02,
η2=0.02 (see Table 2).
Hypothesis 2 further investigated whether resumes with more
implicit cues referring to older age would receive lower job
suitability ratings than those with less implicit cues referring
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FIGURE 1 | Interaction of implicit age cues (names; extracurricular activities)
in resumes on job suitability ratings.
to older age. A significant two-way interaction was found
for implicit age cues (Name ×Extracurricular activities),
F(1,608) =73.72, p<0.001, η2=0.11, and this effect did
not depend on explicit age cues, F(1,608) =0.53, p=0.47,
η2=0.00. Simple effects analyses (i.e., to break down the
interaction term) further showed significant effects of each
of the repeated measures variable (i.e., name or activities) at
levels of the other repeated measures variable (i.e., activities
or name, respectively) with p=0.00 (in all cases). Inspection
of simple effects and Figure 1 showed that the resume with
both a young-sounding name and modern activities received the
highest ratings, followed by the resume with an old-sounding
name/modern activities, and the resume with an old-sounding
name/old-fashioned activities. The resume with an old-sounding
name and old-fashioned activities was rated significantly higher
than the one with a young-sounding name and old-fashioned
activities (Table 2 and Figure 1). Hypothesis 2, therefore, was
partially supported.
Finally, we formulated a research question to explore potential
effects of recruiters’ chronological age. Specifically, we questioned
whether resumes with more implicit cues referring to the
applicant’s older age would receive higher job suitability ratings
when recruiters were chronologically older, or whether applicant
resumes with more implicit cues referring to the applicant’s
older age would receive equally low job suitability ratings
from both older and younger recruiters? Results showed that
participants’ chronological age moderated effects of implicit age
cues (i.e., name and activities) on applicants’ job suitability
ratings, F(1,607) =6.07, p=0.014, η2=0.01. The test of
within-subjects effects further showed that chronologically older
participants gave lower scores to resumes of applicants with old-
sounding names, whereas no differences were found for resumes
of applicants with young-sounding names, F(1,607) =6.73,
p=0.01, η2=0.01. In a similar vein, chronologically older
participants gave lower scores to resumes of applicants with
old-fashioned activities, whereas no differences were found for
resumes of applicants with modern activities, F(1,607) =6.31,
p=0.01, η2=0.01 (see Figure 2).
FIGURE 2 | Moderating effects of recruiters’ chronological age and implicit
age cues (names; extracurricular activities) on job suitability ratings.
DISCUSSION
Intrigued by the ongoing debate about the usefulness of
anonymous resume screening in many Western societies (e.g.,
Behaghel et al., 2015;Maurer, 2016), coupled with limited studies
that considered AAP effectiveness and subtle mechanisms in
hiring discrimination, this study investigated whether omitting
explicit age cues (like date of birth) might be beneficial to
older applicants or whether more implicit/subtle age cues in
resumes may still affect older job applicants’ hirability ratings.
Further, because research in personnel recruitment and selection
has often failed to consider differences in raters’ tendencies to
differentiate among applicants (e.g., Fasbender and Wang, 2017),
we investigated raters’ chronological age as a potential moderator.
Overall Findings
Antidiscrimination legislation and diversity policies typically
focus on the impact of explicit age markers on resumes.
Unfortunately, the implicit age cues on hiring decisions may
pass somewhat unnoticed. Building on the principles of job
market signaling theory, we found support for the assumption
that recruiters make inferences about applicants’ age based on
implicit (i.e., ‘hard-to-fake’ and ‘hard-to-resist’) cues in resumes
(i.e., applicant first names; extracurricular activities), even in the
absence of explicit age cues. Specifically, resumes with both a
young-sounding name and modern activities received the highest
job suitability ratings, followed by resumes with an old-sounding
name/modern activities, and resumes with an old-sounding
name/old-fashioned activities. This provides support for so-
called ‘within-category’ effects (i.e., Kaiser and Pratt-Hyatt, 2009):
Resumes with more implicit cues referring to older age received
lower job suitability ratings than less strongly ‘old-age’ identified
resumes or those from presumably younger applicants.
Remarkably and somewhat unexpectedly, resumes with a
young-sounding name and old-fashioned activities received
the lowest job suitability ratings. Because this resume
might not have matched the ‘prototypical’ image of young
applicants, recruiters might have rated this applicant
lowest in overall suitability due to attributional discounting
(Kelley, 1973). Indeed, as young applicants are expected
to engage rather in modern activities than old-fashioned
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activities, young applicants (as perceived on the basis of
their young-sounding name) who do not do so might
have disconfirmed and discounted the dominant young age
stereotypes.
Furthermore, omitting explicit age cues led to overall lower job
suitability ratings. In many Western European societies (as where
this study was conducted) it is good practice to indicate date of
birth on one’s resume. Not doing so might deviate from the social
norm and result in overall lower ratings. Alternatively, implicit
age cues might have subtle though stronger effects in the absence
of explicit age cues, given that they are hard-to-fake by applicants
and hard-to-resist by raters. Moreover, some resume information
(like unexplained interruptions in one’s work history) might
not be understood properly when recruiters do not know the
applicant’s socio-demographic background (like age). Although
this was not the case in the present study design (and no
significant interaction of the implicit age cues with the explicit
age cue was found, either), it has been suggested that anonymous
resume screening might not be ideal when there are structural
differences between majority-minority applicants (like disparate
length of unemployment or educational attainment) as explicit
age cues (like age) might also explain, contextualize, and alleviate
any subgroup differences (Behaghel et al., 2015). The latter could
be investigated to a further extent.
Finally, compared to applicant characteristics, recruiter
characteristics have been investigated to a considerable lesser
extent in hiring discrimination literature (Finkelstein and Burke,
1998); we explored HR professionals’ chronological age to
address this gap. First, individuals of different ages may differ
in their attitudes toward older adults (Gordon and Arvey,
2004). Indeed, and although effects were small, older participants
compared to younger participants gave somewhat lower job
suitability scores to older applicants than to younger applicants.
Interestingly, this finding does not support in-group favoritism
which predicts participants to prefer rather than to disfavor
in-group members. According to Levy and Banaji (2002), the
psychologically permeable nature of boundaries between age
groups might allow one to dissociate from his/her own age
group. This finding further seems to fit evolutionary theories
on (implicit) ageism, that explain bias against older persons
as a function of mortality salience, and – at the individual
level- as one’s fear of aging and a way to avoid psychologically
and physically weak and parasitized individuals to protect one’s
own ego and sense of self-esteem (Martens et al., 2005;Marcus
and Sabuncu, 2016). Alternatively, findings also remind of the
‘black sheep effect’ (Marques et al., 1988;Brewer, 2007): Older
recruiters may reject in-group members as a self-enhancement
strategy (i.e., positive distinctiveness) or as a manner of securing
one’s position in an organization (i.e., optimal distinctiveness).
Finkelstein and Burke (1998) found similar results on in-group
bias. However, they used the availability heuristic to explain why
older raters disfavored older applicants when applicants’ age was
highly salient and when the raters identified with their age groups.
Specifically, older HR professionals might be more aware of
potential risks when hiring peers (e.g., negative attributes, more
expensive, early retirement intentions; Kite et al., 2005;Rau and
Adams, 2014). Younger generations of workers, on the other
hand, may grow-up in a climate where discrimination issues
receive much more attention than previously was the case, which
may make younger HR professionals somewhat more cautious
about age-related bias.
One might also consider other recruiter characteristics
than chronological age, like recruiters’ old-age stereotypes.
Old-age stereotypes typically include views that older people
are less productive, economically beneficial, competent, creative,
flexible, and harder to train (Finkelstein and Burke, 1998;
Kulik et al., 2000;Fiske et al., 2002). However, old-age
stereotypes are not unequivocal negative in nature (Fiske
et al., 2002). Finkelstein et al. (2013), for instance, revealed
a balanced view of older workers’ stereotypes, with many
positive older-worker stereotypes (e.g., well-mannered, strong
work ethic, and reliable). Hence, old-age stereotypes may
affect hiring decisions in a complex way (Krings et al., 2011).
Yet, few studies considered recruiters’ old-age stereotypes as
potential moderators of hiring decisions (Krings et al., 2011;
Lu et al., 2011;Fasbender and Wang, 2017). Research might
therefore include validated measures of old-age stereotypes (like
the Work-related Age-based Stereotypes scale; Marcus et al.,
2016). Also more implicit measures of old-age stereotypes
might be particularly interesting when one aims to investigate
effects of implicit older-age cues on resumes (like the Implicit
Association Test; see Levy and Banaji, 2002;Axt et al.,
2014).
In sum, this paper explored one recruiter characteristic that
might contribute to implicit old-age bias in the context of resume
screening, namely recruiters’ own chronological age. No evidence
was found for in-group favoritism, which is in line with previous
findings on implicit age attitudes (Axt et al., 2014). Older-age
bias seems a pressing and complex issue. Yet, as illustrated, and
with the exception of a few studies (e.g., Marcus and Sabuncu,
2016;Fasbender and Wang, 2017), its psychological roots are still
less well understood, particularly as regards implicit age bias, and
therefore in need for further investigation.
Limitations and Further Research
Opportunities
As with any study, limitations need to be mentioned. First,
we used a hypothetical job with job applicants in the form
of ‘paper’ people (Landy, 2008). Yet, in an early phase of
the application procedure, real applicants are ‘paper’ people
as the only information we have is the information applicants
provide on their resume. Moreover, in preliminary screening
situations, individuating information about applicants is still
limited, therefore, the use of paper people/resumes is ecologically
valid in this setting (Copus, 2005).
Second, in our study we employed a mixed-factorial design
with implicit age cues as within-subjects factors, which is more
realistic than using them in a between-subjects manner (i.e.,
as this is what recruiters do, namely comparing applicants’
resumes to each other), but which has also been criticized
for its potential to inflate hiring discrimination scores (e.g.,
Bal et al., 2011). A meta-analysis on age bias in evaluations
(i.e., hiring, promotion, and performance appraisal) by Gordon
and Arvey (2004), however, showed higher mean ds for
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between-subjects designs than for within-subjects designs.
Nevertheless, given that age may become more salient if
operationalized in a within-subjects manner, follow-up studies
may use between-subjects designs to further investigate implicit
age effects.
Third, scenario-based studies can also be criticized on the
ground of their lower external validity, but serve other purposes
than field studies (like correspondence audit tests) as they
may be conducted in a more controlled way and may test
contingencies surrounding discriminatory decisions in hiring.
Future studies, however, could investigate more and other
moderators related to the applicant (like gender; Sidanius
and Veniegas, 2000), the job (like type of job, differences
in responsibility, and decision-making power; Abrams et al.,
2016), and the recruiter (like prejudices and other individual
difference variables of relevance; see Self et al., 2015;Fasbender
and Wang, 2017). For instance, Brtek and Motowidlo (2002)
and Self et al. (2015) showed that recruiters’ accountability
might affect discriminatory decision-making, with some types
of accountability (like identity-blind accountability) leading to
less bias and more objective decision-making than other types
of accountability (like identity-conscious accountability). In our
scenario-based study, accountability was not primed in any way
and participants were randomized over conditions. Therefore, we
believe accountability may not have played a large role in affecting
findings. Further research on implicit age effects, however, might
either control for types of rater accountability or might look
at potential effects of recruiters’ accountability on implicit age
effects (e.g., by priming accountability). Further, despite our
initial attempt to consider both recruiters’ chronological age and
older worker stereotypes as potential moderators of implicit age
effects, we refrained from further investigating and reporting
effects of raters’ older-age stereotypes because of the suboptimal
psychometric properties of the ‘Beliefs About Older Workers
scale (Hassell and Perrewe, 1995) in our sample. Researchers
interested in investigating moderating effects of recruiters’ older
worker stereotypes may use more recently developed and
validated scales such as, for instance, the ‘Beliefs about Older
Workers’ Ability and Desire for Learning and Development’ scale
(Maurer et al., 2008) or the ‘Work-related Age-based Stereotypes’
scale (Marcus et al., 2016).
Fourth, we distinguish explicit from implicit age cues.
Whether cues are rather ‘implicit’ or ‘explicit’ may depend on
the nature of the (protected) group status under consideration.
For instance, applicants’ names may signal in a rather explicit,
direct way one’s ethnic group membership (e.g., Mohammed
vs. Mark), but in a rather implicit, indirect manner one’s age.
Furthermore, according to signaling theory, implicit age cues are
more ‘hard-to-fake’ (or ‘honest’). However, one could argue that
both names and extracurricular activities are ‘fakeable’ to some
extent. This would be the case when applicants are aware of
the particular cues and associations they communicate through
affiliations/names (Bangerter et al., 2012).
Another potential study limitation to our field-based,
randomized experimental study may be non-response bias.
About half of the HR professionals we emailed the study link
also participated in this study, which is considerable. Yet, as
the likelihood of non-response bias is inversely related to the
response rate, this also means that about 50% of respondents
did not participate in the study for some or another reason.
Non-response bias may be a threat to the external validity of
an experimental study if non-respondents’ profiles and answers
differ substantially from the profiles and answers of those who
did respond to the study (Stone-Romero, 2002). We were not
able to log personal information of non-respondents, nor were we
able to register reasons of non-response. Given that participants
were randomly assigned to the experimental conditions, one
might expect non-response also to be random. Yet, whether
non-response is random should be investigated empirically.
Therefore, further research should take this potential limitation
into account, for instance, by finding ways to examine reasons
for non-response and by controlling for non-respondents’
demographic attributes.
Finally, this study was conducted in a Western European
country. Whereas there seems little evidence of a connection
between cultural practices and recruitment and selection
practices (see Ryan et al., 2017 for a recent review), cross-cultural
differences could exist in the interpretation of age-related cues
and attitudes toward older workers (e.g., North and Fiske,
2015), which was not considered here and may warrant further
investigation.
Theoretical and Practical Implications
Our study aimed to extend work on age bias in resume screening
in several ways. First, although the Nobel Prize winning paper
of Spence (1973) included hiring as an example (Bangerter
et al., 2012), relatively little research has applied Spence’s
job market signaling theory to (age-based) discrimination in
recruitment and more particularly to the act of resume screening.
Studies that applied job market signaling theory in recruitment
typically considered how applicants interpret unobservable
characteristics from signals sent by employers/recruiters through
recruitment devices (like job advertisements; see Carter and
Highhouse, 2014). Recruitment, however, is a two-way process
in which applicants also send signals or cues upon which
recruiters/employers may make hiring decisions, which we
considered here. Second, research suggests that bias has become
more subtle (Rosette et al., 2016), but few studies have measured
this in the context of early screening and resume screening
in particular, which we did. Hence, with our study, we extend
applications of job market signaling theory in recruitment by
considering how employers/recruiters may interpret age-related
information from subtle cues in job applicants’ resumes. Third,
studies that did consider recruiters’ inferences and indirect
speech acts mainly focused on applicants’ qualifications including
personality inferences (Cole et al., 2007;Aguinis et al., 2005;
Burns et al., 2014;Apers and Derous, 2017) but not on social
group status, which we investigated and -to the best of our
knowledge- has not been considered much. Finally, given the
widespread use of resume screening there is a growing interest
in studying age effects in this screening stage. Although job
applicants’ age shows no validity for predicting future job
performance (Schmidt et al., 2016), age discrimination seems
substantial as evidenced by a large number of recently conducted
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correspondence audit studies. Correspondence audits offer a
great amount of control and are a very powerful tool to detect
labor market discrimination but fail to examine unobserved
factors, such as recruiter characteristics. Our study adds to the
literature on age discrimination in hiring that just began to
investigate contingencies in recruiters (see for another example:
Fasbender and Wang, 2017).
Findings might also be relevant to practitioners. When more
individuating information about candidates becomes available
in later stages of the screening procedure (such as in the job
interview or assessment centers), category-based biases might
have less of a chance to color decisions; however, this suggests
the critical importance of ensuring a lack of discrimination
at the earliest stage of resume screening. Anonymous resume
sifting may be one tool to level the playing field, but is much
debated by HR professionals and society at large (Maurer,
2016;Hiscox et al., 2017). Indeed, anonymous resume screening
might be much more complex than it appears at first glance
and our results bear that out. First, omitting explicit cues
to one’s chronological age (like one’s date of birth) led to
overall lower job suitability ratings, and this might have
to do with codes of conduct (i.e., what recruiters deem
appropriate to be mentioned in resumes). However, it has also
been suggested that explicit age cues in resumes might help
recruiters understand to a better extent some other, potentially
stigmatizing resume information (Behaghel et al., 2015) and -
hence- make recruiters even more attentive/sensitive to ageism,
which could paradoxically counter age-related bias. Finally,
blind screening is at odds with targeted recruitment initiatives
if one aims to hire for more diversity (Newman and Lyon,
2009).
Whereas names are considered as identifying information
and –hence- blotted in anonymous resume screening,
extracurricular activities are typically not blotted. Yet, HR
professionals still made age inferences based on implicit age cues
like extracurricular activities. This raises the question whether
one needs to entirely eliminate resumes or whether structured
sifting processes with competency and experience checklists
should be considered instead of blotting personal information?
For instance, some consider resumes to be ‘dead’ and have
moved to requiring anonymous work samples from applicants
(Feintzeig, 2016), which seems promising given the fact that work
samples mirror relevant future work behavior (Joseph, 2016).
Despite this promising approach, applicants’ socio-demographic
group characteristics (such as age) will always become apparent
at later stages of the hiring procedure. Therefore, any effect of
(partially) blind recruiting might be nullified if such procedures
do not safeguard against hiring bias in later assessment stages,
too.
Given these findings, as well as older raters’ slight tendency
to in-group bias, organizations may deploy a mix of strategies
to minimize ageism in hiring. Aside from screening and training
recruiters for diversity (e.g., in how to deal with/interpret explicit
and implicit stigmatizing cues in resume information), team-
based hiring consisting of a mixed age group of raters (e.g., both
older and younger recruiters) might counter age bias in screening
too. Furthermore, both initiatives may signal to job seekers that
the organization is committed to diversifying the workforce, at
least as regards age (Barber, 1998), thereby also affecting the
overall organizational image positively.
Conclusion
For a better understanding and averting hiring discrimination,
one needs to move beyond prevalence studies and investigate
determinants of hiring discrimination. Results of an experimental
study among HR professionals showed hiring discrimination
of older applicants based on implicit age cues in resumes
and may help understand mixed effects of anonymous resume
screening initiatives. Such findings may help organizational
decision makers to understand the complexity of fair hiring
and the effectiveness of interventions in order to limit age
discrimination upon organizational entry.
AUTHOR CONTRIBUTIONS
ED designed, coordinated, helped collecting the data, and wrote
the study/paper. JD supervised research assistants and collected
data.
SUPPLEMENTARY MATERIAL
The Supplementary Material for this article can be found
online at: http://journal.frontiersin.org/article/10.3389/fpsyg.
2017.01321/full#supplementary-material
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Conflict of Interest Statement: The authors declare that the research was
conducted in the absence of any commercial or financial relationships that could
be construed as a potential conflict of interest.
Copyright © 2017 Derous and Decoster. This is an open-access article distributed
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original author(s) or licensor are credited and that the original publication in
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Supplementary resource (1)

... Negative effects in resume screening studies are not inevitable, and can range (based on Cohen's (1992) effect size benchmarks) from small (η 2 = .02; Derous & Decoster, 2017) to large (η 2 = .48; Diekman & Hirnisey, 2007). ...
... Thus, more research is required to improve understanding of the psychological processes, conditions or study methodologies contributing to these disparities. For instance, evidence of bias following implicit age cues in resumes is consistent but not following explicit cues (e.g., Abrams et al., 2016;Derous & Decoster, 2017;Kaufmann et al., 2016Kaufmann et al., , 2017Richardson et al., 2013), and this may be linked to the impression formation route taken (Fiske & Neuberg, 1990). Furthermore, moderators alter effects sizes, such as cognitive load and age attitudes (Perry et al., 1996;Zaniboni et al., 2019). ...
... More often, samples include both students and employees (Kaufmann et al., 2016;Richardson et al., 2013). Some studies sample only HR professionals (Derous & Decoster, 2017;Kaufmann et al., 2017), while other papers feature studies using student samples followed by replication with HR professionals (Krings et al., 2011). Notably, the latter series of studies report similarly large effects of age bias across student (η 2 = .22) ...
... Although this tool is promoted by several Belgian policymakers, its effectiveness is still much debated: Some studies found promising (debiasing) effects (Kang, DeCelles, Tilcsik, & Jun, 2016), whereas others showed no or even negative effects (Hiscox et al., 2017). Even if explicit markers of applicants' social group membership (like ethnic-sounding names) are blotted, recruiters may still infer applicants' social group membership via implicit, subtle markers (like affiliations; Derous & Decoster, 2017). Moreover, our study results also show that hiring discrimination (e.g., double jeopardy) might depend on the particular job context that might facilitate or inhibit biasing effects (Kulik et al., 2007). ...
... In CV's zitten naast expliciete verwijzingen ook impliciete verwijzingen naar iemands etniciteit of leeftijd, bijvoorbeeld via het soort lidmaatschap en affiliaties dat men heeft (zoals 'lid van de Koninklijke Nederlandse Roeiclub' vs. 'Duivenbond'). Zo kunnen recruiters op basis van extra-curriculaire activiteiten in CV's zowel iemands leeftijd (Derous & Decoster, 2017) als etniciteit afleiden. ...
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