Journal of Social Issues, Vol. 72, No. 1, 2016, pp. 105--121
doi: 10.1111/josi.12158
Old and Unemployable? How Age-Based Stereotypes
Affect Willingness to Hire Job Candidates
Dominic Abrams∗ , Hannah J. Swift, and Lisbeth Drury
University of Kent
Across the world, people are required, or want, to work until an increasingly old
age. But how might prospective employers view job applicants who have skills and
qualities that they associate with older adults? This article draws on social role
theory, age stereotypes and research on hiring biases, and reports three studies
using age-diverse North American participants. These studies reveal that: (1)
positive older age stereotype characteristics are viewed less favorably as criteria
for job hire, (2) even when the job role is low-status, a younger stereotype profile
tends to be preferred, and (3) an older stereotype profile is only considered hirable
when the role is explicitly cast as subordinate to that of a candidate with a younger
age profile. Implications for age-positive selection procedures and ways to reduce
the impact of implicit age biases are discussed.
Global population aging means that between 2000 and 2050 the percentage of
the world’s population aged over 60 years will double from 11% to 22% (WHO,
2014). In many industrialized nations, this may create an unavoidable obligation to
work longer (Feyrer, 2007). However, extending working life means older people
may face age stereotypes, resulting in discrimination and exclusion from the labor
market (McCann & Giles, 2002). Negative stereotypes that surround older people
and older workers (Ng & Feldman, 2012) can harm their performance (Lamont,
Swift, & Abrams, 2015) and influence employers’ hiring decisions (Gringart,
Helmes, & Speelman, 2005). However, research has yet to examine whether
people’s assumptions about a candidate’s age may affect hiring decisions even
∗ Correspondence concerning this article should be addressed to Dominic Abrams, Centre for the
Study of Group Processes, School of Psychology, University of Kent, Canterbury CT2 7NP, UK. Tel:
0044-1227-827475 [e-mail: D.Abrams@kent.ac.uk].
All authors are members of the EURAGE research team. The last author was supported by a grant
from the Economic and Social Research Council ES/J500148/1 and by AgeUK. We are grateful to
Giovanni A. Travaglino for support in setting up the experiments and to members of EURAGE and
GroupLab for their comments and suggestions as the research progressed.
The copyright line for this article was changed on July 28, 2016 after original online publication.
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C 2016 The Authors. Journal of Social Issues published by Wiley Periodicals, Inc. on behalf of Society for the Psychological Study of
Social Issues
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
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when there is no disclosure of actual age. In this article, drawing on theories of
age stereotypes, social roles and hiring bias, we report a series of studies that
investigate how age-stereotypical characteristics are used as criteria for job hire.
Many countries legislate against age discrimination in the workplace, preventing employers from limiting positions to particular ages (unless objectively
justified), and entitling applicants to omit their age from their resumés (Age Discrimination and Employment Act, 1967; Equality Act, 2010). However, information included in job applications and resumés (e.g., including dated qualifications)
often enable employers to discern an applicant’s age. This could consciously or
unconsciously lead to discrimination.
The present studies build on previous research on hiring biases but which
has typically varied applicants’ gender or their gender-stereotypic characteristics
to see how this affects judges’ preferences or hiring decisions (Eagly & Karau,
2002). For example, in a series of studies expanding on the “Think Manager—
Think Male” effect, Ryan, Haslam, Hersby, and Bongiorno (2011) established a
series of traits that are judged to characterize either men or women and discovered
that people associate managers of successful companies with masculine traits and
managers of unsuccessful companies with feminine traits.
To date, it appears that little research has explored how age-stereotypic characteristics rather than explicit age may affect judgments of hirability. Indeed, the one
study that has examined explicit age-based candidate preference for an age-neutral
job revealed that younger workers were rated slightly higher on their relevant job
qualifications (Cleveland & Landy, 1983). The present work therefore sought to
establish characteristics stereotypically associated with younger and older workers and then test whether profiles of candidates possessing these traits influence
perceivers’ willingness to hire them.
Based on theories of ageism, which demonstrate that people have implicit
preferences for young over old (Levy & Banaji, 2002), and on evidence that youth
is more often associated with competence and relatively higher status (Abrams,
Russell, Vauclair, & Swift, 2011; Cuddy, Norton, & Fiske, 2005; Fiske, Cuddy,
Glick, & Xu, 2002), we expect people to be more willing to hire a candidate with a
relatively younger stereotypic profile even though there is no explicit information
about that candidate’s age. However, given the multidimensional nature of old-age
workplace stereotypes (Dordoni & Argentero, 2015; Swift, Abrams, & Marques,
2012), we assume there may be circumstances that might moderate bias based on
tenure (Postuma & Campion, 2008) and status of the position (Abrams et al., 2011).
Moderators of Age Discrimination in the Workplace
Short- and Long-Term Goals
One reason why employers may avoid hiring older people for a new position is
that older people may provide fewer years of return on any training and investment
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(Finkelstein & Burke, 1998). Hirers may therefore have greater preference for
stereotypically younger candidates if the investment is viewed as long- rather than
short-term. In contrast, a review of moderators of workplace age discrimination
research revealed evidence for the opposing hypothesis—that older workers are
a better long-term investment because they are less likely to quit (Postuma &
Campion, 2008). Study 2 in this research tests these and a third (null) prediction
that, because age is not explicit, judges cannot make a rational calculation based
on age, and therefore implicit preferences for stereotypically younger candidates
would arise regardless of the time frame.
Role Fit
Social role theory holds that discrimination may occur when there is a mismatch between a person’s (gender) stereotypical characteristics and the requirements of the position for which they are applying (Eagly, 1987; Eagly & Karau,
2002). Translated to the age context, older workers are more likely to be discriminated against when there is a stereotypical mismatch between the worker’s
perceived age and characteristics of a particular position or profession (Postuma
& Campion, 2008). Based on societal perceptions that older adults hold lower
status than younger adults (Abrams et al., 2011), we expect that stereotypically
older candidates will be more likely to be hired if the job itself is lower-status.
We report pilot work and three empirical studies to test these predictions In
each study, people judge two candidates whose ages are not specified but who
differ in their age-stereotypic characteristics. Study 1 examines hiring preferences
for these candidates. Study 2 explores whether preferences are affected by the
time frame for potential benefit for the employer, and Study 3 examines the effect
of job status that is focal for the hiring decision.
Pilot Studies
Pilot work established two skill sets that would be viewed as stereotypically
“young” and “old” by U.S. participants by adapting and adding to the attributes
identified originally in U.K. research (Ray, Sharp, & Abrams, 2006; Swift et al.,
2013). Participants were recruited and paid to complete the questions via Amazon’s
Mechanical Turk. The questionnaire presented a set of 20 skills and abilities.
Participants were asked to choose whether each ability was “more typical of
people in their 20s” (scored 1), “more typical of people in their 60s” (scored 3),
or “equally typical of both” (scored 2).
The age stereotypicality of each ability was evaluated by 60 participants
(ranging from 18 to 72 years, M = 35.1, SD = 12.93, 57% Male). Attributes were
designated as stereotypical if they were distinctively typical of one group (half
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Abrams, Swift, and Drury
Table 1. Age-Stereotypic Ability Profiles and Hiring Preferences across Studies
Person A
Settling arguments
Understanding other’s views
Dealing with people politely
Solving crosswords
Being an effective complainer
Using a library
Carefulness
Pilot M (SD)
Age categorization
Valence
Study 1 hiring preference
for Person B (%)
Study 2 hiring preference
Long-term
Short-term
Study 3 hiring preference
Control
Supervised
Subordinate
2.42 (0.30)
5.39 (0.47)
Person B
Learning new skills
Being creative
Using new computer technology
(e.g., Smartphones)
Rapid decision making
Being open to new ideas/experiences
Communicativeness
Using social media (e.g., Facebook)
1.46 (0.31)
5.55 (0.50)
80
85
81
73
72
50
Note. Age categorization ranges from 1 = typical of a person in their 20s to 3 = typical of a person in
their 60s. Valence ratings can range from 1 = very negative to 7 = very positive.
or more of respondents assigned it to one age group and fewer than a quarter of
respondents assigned it to the other or both age groups). Abilities were defined as
neutral if at least half the respondents judged that it applied to both age categories,
and no more than 30% selected either age group.
A separate sample of 25 participants (ranging from 18 to 66 years, M =
32.9, SD = 13.1, 56% Male) rated each attribute on a 7-point scale (1 = very
negative, 7 = very positive). We then compiled two age-stereotypic profiles that
were matched in terms of mean valence and then added a neutral item to each
profile (carefulness and communicativeness).
The two profiles are shown in Table 1. The age categorization of the abilities
in the two profiles differed significantly, t (59) = 16.12, p < .001 and both differed
significantly from the scale-neutral point (2). The mean valence of the two profiles
did not differ significantly, t (24) = 1.61, p = .121. We therefore used these profiles,
which are equivalent in valence but differ in age stereotypicality, as the stimuli in
the studies that followed.
Old and Unemployable?
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Study 1
Study 1 tested preferences for these two positive profiles when participants
considered each as a candidate for a job. We explicitly stated that the candidates
had similar qualifications and neither had previous experience of the job. We
expected that the “younger” profile (Candidate B) would be more likely to be
selected as a potential job hire.
Method
Participants.
Participants were 40 MTURK workers (ranging from 21 to
62 years, M = 36.9, SD = 11.9, 54% Male). No constraints were placed on participants. The data stopping rule was 40 cases because, from lecture demonstrations
that had used a similar stimulus set, we anticipated a large effect size.
Procedure and measures.
Participants were instructed: “In this study we
are asking you to imagine that you are an employer who is looking at applications
from two different people that are applying for the same job. As the employer,
your goal is to hire someone who will maximize the profits of your company.
Your task is quite difficult because there are a lot of candidates who have similar
qualifications and none have any previous experience in this kind of job. Each
candidate also completed a psychometric questionnaire about their interests, skills
and abilities, and this has given you a profile of ways in which each candidate is
distinctive from the other candidates. Using this information your task is to select
the person that you wish to employ to maximize the profits of your company. To
keep these names anonymous, we have labelled these candidates with letters A
and B rather than providing their actual names.”
Hiring decision.
Participants then viewed the two profiles simultaneously
before responding to the question: “Who would you hire?” They were asked to
select a button to show if they would hire Person A, Person B, or were unsure.
Age estimates.
On the next screen, participants were then asked to estimate
the age of each candidate using a slider scale (from 19 to 81).
Results
Hiring decision.
Eighty percent (32) of the participants chose to hire
Candidate B (the younger profile). Fifteen percent selected Candidate A and 5%
(2) were unsure, χ 2 = 14.40, p < .001 (see Table 1).
Point biserial correlation analyses showed that participants’ age and gender
were not significantly related to their candidate choice (ps > .70).
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Age estimates.
A repeated measure ANOVA showed that Candidate A
was judged to be older (M = 36.53, SD = 9.76) than Candidate B (M = 32.10,
SD = 9.65), F (1, 39) = 4.56, p = .039, ηp 2 = .105. Moreover, the participants who
chose Candidate B estimated Candidate B’s age to be lower than did participants
who did not choose Candidate B (point biserial r = -.39, p = .012). Finally, multiple regression analysis showed that when participants’ own age and gender and
their estimates of Candidate A’s age were included as covariates, the relationship
between estimates of Candidate B’s age and hiring choice remained significant
(β = -.36, t = 2.16, p = .038).
In summary, only a minority of participants chose to hire the stereotypically
older age profile (A). Participants’ assumed Candidate B was younger and the
more they did so, the more they preferred to hire Candidate B, consistent with the
idea that implicit age stereotypes affected hiring decisions.
Study 2
Given the goal of “maximizing profits,” a plausible explanation for hiring a
stereotypically younger candidate is based on “rational” cost-benefit calculations.
If participants had long-term profits in mind in Study 1, the “younger” candidate
could work for longer before reaching retirement and provide greater total profit
for the company. Alternatively, if participants had short-term profits in mind,
their preference for the younger profile may be because they discounted the
stereotypically older candidate’s potentially greater long-term value due to their
lower turnover intention (Posthuma & Campion, 2008). To test these possibilities,
Study 2 examined whether the selection chances of the stereotypically older profile
(Candidate A) would depend on whether the employer’s goal was short-, rather
than long-term profits. However, we noted from Study 1 that the age-stereotype link
generated quite a small explicit difference in age estimates for the two candidates.
This makes it less likely that it is the specific age of candidates that affects
decisions but rather perceptions of relative age and implicit ageism. In that case,
the preference for the “younger” profile may persist regardless of time perspective.
Method
Participants and design.
Eighty MTURK workers (ranging from 19 to
70 years, M = 35.3, SD = 11.7, 60% Male) were recruited as participants. Using
random assignment to condition (via Qualtrics software), we presented the profiles
for Candidates A and B and defined either short- or long-term objectives.
Procedure and measures.
In the short- and long-term conditions (respective differences shown in parentheses), participants were instructed as Study 1.
However, “maximize the profits of your company” was replaced with “be an ideal
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worker for the [short term/long term] benefit of your company over [the next
financial year/ a number of financial years].” Participants then completed the job
hire and age estimation measures as in Study 1.
Results
Hiring decision.
Eighty-three percent of participants selected Candidate B
(χ 2 = 37.33, p < .001). Moreover, time frame condition made no difference to the
selection of candidates, χ 2 = 0.34. Eighty-one percent and 85% chose Candidate
B in the short- and long-term conditions, respectively (see Table 1).
Age estimations.
Candidate B was judged to be significantly younger than
Candidate A, repeating the finding from Study 1, F (1, 79) = 20.58, ηp 2 = .207.
In summary, regardless of whether they were considering hiring for a
short-term or long-term position, participants strongly preferred a stereotypically
younger age profile.
Study 3
We extended our consideration of the stereotypical status differences between
older and younger people. Based on Eagly’s Role Theory (1987) and the stereotype
content model (Fiske et al., 2002), we considered that the warm/less competent
older stereotype would be more compatible with a low-status role. Therefore, in
Study 3, we compared whether specifying a position as low-status would increase
the probability that the stereotypically older candidate (A) would be hired.
Given that Studies 1 and 2 revealed a strong preference for hiring Candidate
B, and based on role theory, we wondered if low status per se would be sufficient
to make Candidate A attractive. Specifically, whereas stereotype-based models
of ageism have identified that being older (in general) is associated with lower
societal status (Abrams et al., 2011; Cuddy et al., 2005), a role-based interpretation
might assume that the low status might only affect a hiring decision if there is
certainty that the job position would be subordinate to someone who should have
higher status, thereby assuring role fit.
To test this idea, we compared the baseline condition of Study 1 (Control condition) against two alternative scenarios involving a low-status criterion for hiring.
We either specified that the task was to hire a person to occupy a supervisee role
(Supervisee condition), or we specified that participants should select which of the
two candidates should be supervised by (subordinate to) the other (Subordinate
condition). The Supervisee and Subordinate conditions both required participants
to select a person to be supervised, but the Subordinate condition involved explicit subordination of one candidate to the other, thus ensuring fulfillment of a
comparatively lower-status role. To explore how participants were thinking about
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the different roles, we also investigated perceptions of the candidates. If hiring
decisions are driven by implicit ageism and only one candidate can be hired, participants should still favor the “younger profile,” even as a supervisee. But this
“younger” preference should reduce if the selected candidate will be subordinate
to the other because of the less close role fit between being stereotypically younger
and a relatively lower status position.
Method
Participants and design.
One hundred and fifty MTURK participants
(ranging from 19 to 67 years, M = 35.6, SD = 12.4, 55% Female) were randomly assigned to condition (Control, Supervisee, Subordinate).
Procedure and measures.
The Control condition instructed participants
to hire a candidate to maximize profits, exactly as in Study 1. The Supervisee
condition and Subordinate condition instructions (distinguished by a slash in
parentheses) were as follows: “In this study we are asking people to imagine that
they are an employer who is looking at applications from two different people
who are applying for [a job/two jobs]. You will hire [one person/both people] so
you must decide which one should be hired to be [supervised /supervised by the
other]. As the employer, your goal is to choose which one should be [supervised/the
subordinate (supervised)]. The other one [will not be hired/will be the supervisor].
Your task is quite difficult because these have similar qualifications and neither
has any previous experience in this kind of job. But both people completed a
psychometric questionnaire about their interests, skills and abilities, and this has
given you a profile of ways in which each candidate is distinctive from the other.
Using this information your task is to select which person should hired to be
[supervised/subordinate (supervised)]. The other one will [not be hired/ be the
supervisor]. To keep these names anonymous, we have labeled candidates with
letters (Person A, and Person B) rather than providing actual names. Your task is
to decide whether Person A or Person B should be the one who should be [hired
to be supervised /subordinate (supervised)]. Click next to view the profile of each
candidate.”
Participants then completed the hiring decision and age estimation measures.
In order to understand reasons for hiring decisions, we asked participants to judge
how important each attribute was for the job, to evaluate the profiles of the two
candidates, and to infer demographic characteristics for the two profiles.
Job-related importance of attributes.
Participants were asked how important each of the following attributes was for the job (1 = not at all important,
7 = extremely important): settling arguments, understanding others’ views, dealing with people politely, solving crosswords, being an effective complainer, using
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a library, carefulness, learning new skills, being creative, using new computer
technology (e.g., smartphones), rapid decision making, openness to new ideas and
experiences, communicativeness, using social media (e.g., Facebook), and other
(free response). Presentation of all but the last item was randomized.
Trait inferences.
Participants were asked to rate (1 = very unlikely, 5 =
very likely) whether Person A and Person B were gentle, intelligent, warm, moral,
exciting, interesting, admirable, perform well at tasks, have a lot of potential,
are resourceful, reliable, loyal, open, efficient, motivated, experienced, needy,
financially smart, risk takers, and natural leaders. The presentation order of these
characteristics was randomized.
Demographic inferences.
Participants were asked to indicate whether
they thought Person A and/or Person B were male/female, White/Black/
Hispanic/Asian, heterosexual/gay or bisexual, religious/nonreligious, American.
Order of presentation was randomized.
Results
Hiring decision.
Overall, 64.8% selected Candidate B (χ 2 = 12.75, p <
.001). However, this proportion varied as a function of condition, χ 2 (2 df) = 7.38,
p = .029). Specifically, whereas 73.3% chose Candidate B in the control condition,
and 72% in the supervisee condition, this reduced to 50% in the subordinate
condition (see Table 1).
Age estimates.
Candidate B (M = 32.42, SD = 8.66) was judged as significantly younger than Candidate A (M = 37.92, SD = 9.66), repeating the findings
from Studies 1 and 2, F (1,138) = 23.26, p < .001, ηp 2 = .144. Moreover, estimates
of candidates’ ages did not vary by condition, suggesting that differences in hiring
decisions were not because the subordinate condition had altered the perceived
age difference between the candidates.
Inspection of correlations within conditions indicated that participants in
the Control condition who selected Person B were significantly more likely to
estimate Person B’s age as younger (r = -.40, p = .009). In contrast, participants
in the Subordinate condition who selected Candidate B were significantly more
likely to estimate Candidate B’s age as being older (r = .38, p = .007). In the
Supervisee condition, there was no significant correlation (r = .04, p = .790). This
suggested an interactive effect of condition and perceived age on hiring decisions.
To test this possibility, we dummy coded conditions and created interaction terms
between the Control condition and estimates of Candidate B’s age, and between
the Subordinate condition and Candidate B’s estimated age. We then conducted
a regression analysis to test the effects of participants’ age and gender, Control
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condition and Subordinate condition, age estimate of Candidate B, and the two
interaction terms on whether participants selected Candidate B.
The analysis confirmed that there were no significant effects of participants’
age or gender (βs = -.09, -.15) or their estimates of Candidate A’s age (β = -.05).
Both the Control condition and the Subordinate condition differed from the means
of the alternative conditions (βs = .65, -1.04, ps = .01, < .001, respectively).
More interesting were the Control x estimated age of Candidate B interaction,
β = -.65, t = -2.62, p = .01, and the Subordinate x age of Candidate B interaction,
β = .86, t = 3.21, p = .002. The addition of these interaction terms increased the
R2 from .08 to .18, and F for the final equation was F (7,132) = 4.24, p < .001.
To summarize this finding, when participants simply had the goal of selecting the
best candidate, the younger they estimated Candidate B’s age, the more likely they
were to select Candidate B. When participants had the goal of selecting which
candidate should be subordinate, the older they perceived Candidate B to be the
more likely they were to select Candidate B.
We repeated these analyses but with the estimated age of Candidate A as the
independent variable, whether Candidate A was chosen as the dependent variable,
and estimated age of Candidate B as a covariate. This revealed no effects except a
significant Subordinate condition versus other conditions effect (β = .23, t = 2.44,
p = .028), all other ps > .10. This simply reflects that finding that Candidate A was
more likely to be selected in the Subordinate condition than in other conditions.
Job-related importance of attributes.
The job characteristics were averaged into two scores, one for the importance of the characteristics presented in the
profile of Candidate A (the older profile) and one for Candidate B (the younger
profile.). We conducted a repeated measure ANCOVA (Condition x Profile), with
Condition as a between participants factor and Profile (older, younger) as a within
participants factor. Participant age and gender were covariates. This revealed no
significant effects of the covariates, but a significant effect of Condition, F (2,135)
= 4.50, p = .013, ηp 2 = .062, a significant effect of Profile, F (1,135) = 8.18,
p = .005, ηp 2 = .057, and a significant Condition x Profile interaction, F (2,135)
= 5.94, p = .003, ηp 2 = .081.
Attributes were regarded as less important when no role was specified (M =
4.88, SD = 0.72), than when the role was either supervised (M = 5.18, SD = 0.61)
or subordinate (M = 5.26, SD = 0.53). The older profile attributes were regarded
as less important (M = 4.58, SD = 0.87), than the younger profile attributes (M =
5.64, SD = 0.76). Simple effects tests showed that whereas the importance of the
young profile attributes did not differ between conditions, F (2,135) = 0.79, p =
.457, ηp 2 = .012, the importance of the older profile attributes did differ, F (2,135)
= 8.87, p < .001, ηp 2 = .116. Pairwise comparisons showed that the attributes
were accorded less importance in the Control condition (M = 4.18, SD = 1.05)
than in either the Supervisee (M = 4.64, SD = 0.81) or Subordinate (M = 4.92,
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SD = 0.58) conditions (ps = .017, < .001, respectively) and that the importance
was greater in the Subordinate than in the Supervisee condition (p = .062).
Trait inferences.
The items were averaged into mean positivity ratings for
each candidate (alphas > .7) and these were subjected to analysis by ANCOVA.
This revealed a significant main effect of Condition, F (1,134) = 10.59, p < .001,
ηp 2 = .137, and a marginal interaction, F (2,134) = 2.58, p = .08, ηp 2 = .037).
However, the simple effect of Condition was significant only for Candidate A,
F (1,134) = 10.89, p < .001, ηp 2 = .140. Candidate A was rated less positively
in the Control condition (M = 3.25, SD = 0.67) than in either the Supervisee
condition (M = 3.52, SD = 0.51, p = .02) or the Subordinate condition (M = 3.78,
SD = 0.42, p < .001), and less favorably in the Supervisee condition than the
Subordinate condition (p = .017). In contrast, the simple effect of Condition was
nonsignificant for Candidate B, F (1,134) = 1.58, p = .209, ηp 2 = .023, as this
candidate was rated equally positively in all conditions (Ms = 3.71, 3.82, 3.89,
SDs = 0.53, 0.50, 0.45, respectively, all pairwise ps > .07). Moreover, whereas
ratings of A and B differed significantly in both the Control, F (1,134) = 16.02,
p < .001, ηp 2 = .107, and the Supervisee condition, F (1,134) = 8.87, p = .003,
ηp 2 = .062, they did not differ significantly in the Subordinate condition, F (1,134)
= 1.33, p = .250, ηp 2 = .01.
Demographic inferences.
These data were coded first according to
whether or not the candidate was judged to have a majority group characteristic
(White, male, American, religious, heterosexual). Repeated measure MANCOVA
revealed no significant differences due to Condition, Candidate, or participant gender or age. These scores were factor analyzed for each candidate. Because they all
loaded significantly on the first principle component, an average “majority” score
was created for each candidate. This score could range from 0 (no majority characteristics) to 1 (entirely majority characteristics). Overall, participants judged
that at least half of the candidates’ characteristics were majority memberships
(M = 0.58, SD = 0.28). A repeated measure ANCOVA on this score confirmed
the MANCOVA findings and revealed no significant differences due to Condition,
candidate, or participant gender or age. These analyses confirm that the profiles
differed only in terms of their stereotypical age and were not associated with other
major demographic characteristic.
Mediation analyses.
Because hiring choices differed between the Subordinate and other conditions, we sought to explain why preferences shifted in the
Subordinate condition. To simplify analyses, we constructed a difference score for
the relative importance of the profile characteristics for the job (Candidate B minus
Candidate A), and a difference score for the relative positivity ratings of Candidate
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Relative Importance of
‘Older’ Attributes for Job
-.59** (.18)
2.50*** (.69)
Total Effect: -1.07** (.38)
Subordinate (=1) vs other (0)
Decision to Hire ‘Older’ Profile
Direct Effect -.83 ns (.48)
-.25* (.13)
Relative Favorability
toward ‘Older’ Traits
.84** (.32)
Fig. 1. Effect of condition on hiring decisions, mediated by attribute importance and favorability
ratings.
Note. Indirect effects via relative importance, B = -.501, SE = .25, 95% CI [-1.11, -0.14] and relative
favorability, B = -.633, SE = .37, 95% CI [-1.46, -0.07], do not differ from one another, B = .132,
SE = .43, 95% CI [-1.12, 0.56].
B minus the positivity ratings of Candidate A. ANCOVAs showed that these two
scores differed significantly between the Subordinate and other conditions.
The relative importance and relative favorability measures were significantly
correlated with one another (r = .49, p < .001), and each was significantly correlated with hiring choice (point biserial r = .48, .49, respectively). Given that both
could potentially mediate between conditions and hiring decisions, we conducted
a parallel mediation analysis using Hayes’ (2013) Process SPSS Macro (model 4
with 5,000 bootstraps), including participant age and gender as covariates.
The covariates were nonsignificant and the total effect of Subordinate condition was significant, B = -1.07, SE = .38, 95% CI [-1.81, -0.33]. There were
significant indirect effects of both job importance B = -.50, SE = .25, 95% CI
[-1.11, -0.14], and profile ratings, B = -.63, SE = .70, 95% CI [-1.46, -0.07], and
the direct effect of Subordinate condition was not significant, B = -.83, SE = .48,
95% CI [-1.77, 0.11] (see Figure 1).
In summary, the subordinate condition increased participants’ relative favorability toward Candidate A’s characteristics, and also their judgments of whether
those characteristics were relatively important for the job. These two effects accounted for increased selection of Candidate A.
Discussion
This research is the first, to our knowledge, to have systematically tested
whether exhibiting age-stereotypic characteristics per se may affect a candidate’s chances of being hired. We established profiles of age-stereotyped job
Old and Unemployable?
117
characteristics that are more stereotypical of a person in their 20s or in their 60s,
respectively. We established that the characteristics, judged without reference to
the job context, are judged equally positively. In the studies that followed, we
ascribed the profiles to Candidate A (older profile) and Candidate B (younger
profile), respectively. Across three studies, these profiles led participants to assume Candidate B was younger than Candidate A. In Study 1, 80% of participants
selected the younger profile (B) to maximize their company’s profits. Study 2 established that this preference could not be attributed to judges’ adoption of a longor short-term time frame. Therefore, decisions were unlikely to be rationally based
on candidates’ likely cumulative contribution or turnover intention. Candidate B
was strongly preferred, regardless of whether the goal was to maximize short- or
long-term profits.
Study 3 tested whether role fit accounted for whether the older age profile
would be “hirable.” Even when the role involved being supervised, selection of
Candidate A only increased when the role was explicitly subordinate to that of the
younger profile (B).
Study 3 also examined the perceptions and inferences that participants made
about the attributes of the two candidates and what would be necessary for the job.
Overall, participants rated Candidate B’s characteristics as more important for the
job and rated them more positively. Note that the latter finding appears to contradict the idea that the two profiles shared a similar valence. However, whereas
the pilot research showed that the characteristics themselves had similar valence,
these evaluations were clearly altered when participants considered them as being
relevant to a job rather than in a context-free manner. Importantly, we found that
the differences in the job relevance and ratings of the two sets of attributes were
significantly lower when participants were considering them for the subordinate
role. Moreover, this reduced differentiation also explained, statistically, why participants were willing to select Candidate A, the older profile, when considering a
subordinate role.
Taken together, these findings are in line with a social role account (Eagly,
1987) and strongly indicate that job applicants may well be vulnerable to implicit
age stereotyping and ageist assumptions that older workers belong in low-status
roles. Ironically, even when an applicant highlights positively valued traits and
skills, if mentioning these skills invokes old-age stereotypes, they could well create
implicit beliefs that the candidate is “older” than others, and this could place
them at a disadvantage relative to applicants who only highlight their “young”
stereotypical attributes.
Limitations, Future Directions, and Implications for Policy and Practice
These studies are novel and we acknowledge several limitations. International
generalizability of the findings has yet to be established because, whilst drawing
118
Abrams, Swift, and Drury
initially from U.K. evidence, the studies all involve only North American participants. Nor is it known whether the requirement to “maximize profits” affected the
level of bias. Many organizations define profit as their primary objective but the
salience of other goals (e.g., providing excellent services) might tilt biases in other
directions (cf. Finkelstein & Burke, 1998). The decisions of actual managers and
recruitment staff may differ if they are motivated to avoid stereotype-based bias
(Singer & Sewell, 1989). More generally, making judges feel more accountable
for their decisions (Gordon, Rozelle, & Baxter, 1988), reducing their cognitive
busyness (Perry, Kulik, & Bourhis, 1996), or reducing intergenerational resource
scarcity (North & Fiske, 2016) may moderate their reliance on stereotypes for
hiring decisions.
Based on role theory (Eagly, 1987), we argued that older = lower status.
Even if this is not always true for high levels of certain occupations (e.g., judges,
surgeons, politicians, CEOs), these older high-status roles may still involve significant “younger” stereotypic attributes. Thus, even for these roles there may be
an advantage in highlighting a higher proportion of such attributes at the selection
stages. These are all questions for future research.
Implicit age bias in hiring has policy relevance for individuals, organizations,
and society. For individuals, exclusion from the labor market can increase the
likelihood of depression and mental health problems for older adults (Aquino,
Russell, Cutrona, & Altmaier, 1996; Gallo et al., 2006) whereas there are significant psychological benefits for older people that remain in the workforce (Schooler,
Mulatu, & Oates, 1999). If job candidates who present or reveal older-stereotypic
abilities and skills activate recruiters’ implicitly ageist hiring preferences, this suggests that both applicants and recruiters should be made aware of these potential
biases in order to avoid or challenge them directly. Strategically, candidates could
tailor their resumés to display only competence and other stereotypically “young”
traits that organizations have preferences for, such as learning new skills, creativity, and competence using technology to mitigate the application of old stereotypes. Although this may actually increase resentment toward older workers in
conditions where resources are scarce and if older workers are perceived to violate prescriptive norms (North & Fiske, 2016). Ideally, however, employers would
learn to recognize the actual advantages and strengths of both older and younger
stereotypical qualities rather than to assume one set is inevitably better.
Even for objectively or stereotypically younger people these findings are
troubling. Younger people eventually become older, so the perpetual application
of ageist hiring assumptions means that people may approach aging with growing
anxiety and dread of age discrimination. This not only poses a stereotype threat that
could well harm their actual capacity to perform well at work (Lamont et al., 2015),
but has potential to decrease job satisfaction and job commitment (Macdonald &
Levy, 2016).
Old and Unemployable?
119
The implication of these findings is that implicit age bias could lead organizations to fail to select the best candidates because of unacknowledged assumptions
about candidates’ age. Thus, organizations may well underperform because age inferences are drawn that downplay the strengths of candidates who show relatively
more “older” characteristics.
At a societal level, the need to retain people in the workforce longer and to
sustain incomes and resources into later old age all mean that biases against “older”
abilities and skills will lead to reduced opportunities, greater impoverishment,
and ultimately more dependency among the oldest members of society. This
research shows that even positive age stereotypes may be a substantial driver of
age discrimination in employment.
References
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Aquino, J. A., Russell, D. W., Cutrona, C. E., & Altmaier, E. M. (1996). Employment status, social
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Cleveland, J. N., & Landy, F. J. (1983). The effects of person and job stereotypes on two personnel
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Cuddy, A. J. C., Norton, M. I., & Fiske, S. T. (2005). This old stereotype: The pervasiveness and
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Dordoni, P., & Argentero, P. (2015). When age stereotypes are employment barriers: A conceptual analysis and a literature review on older workers stereotypes. Ageing International. doi:
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Eagly, A. H. (1987). Sex differences in social behavior: A social role interpretation. Hillsdale, NJ:
Lawrence Erlbaum.
Eagly, A. H., & Karau, S. J. (2002). Role congruity theory of prejudice towards female leaders,
Psychological Review, 109, 573–598. doi: 10.1037/0033-295X.109.3.573.
Equality Act. (2010). Available online at http://www.legislation.gov.uk/ukpga/2010/15. Accessed at
October 1, 2015.
Feyrer, J. (2007). Demographics and productivity. The Review of Economics and Statistics, 89, 100–
109. doi: org/10.1162/rest.89.1.100
Finkelstein, L. M., & Burke, M. J. (1998). Age stereotyping at work: The role of rater and contextual
factors on evaluations of job applicants. Journal of General Psychology, 125, 317–345. doi:
10.1080/00221309809595341.
Fiske, S. T., Cuddy, A. J. C., Glick, P., & Xu, J. (2002). A model of (often mixed) stereotype content:
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of Personality and Social Psychology, 82, 878–902. doi: 10.1037/0022-3514.82.6.878.
Gallo, W. T., Bradley, E. H., Dubin, J. A., Jones, R. N., Falba, T. A., Teng, H. M., & Kasl, S. V.
(2006). The persistence of depressive symptoms in older workers who experience involuntary
job loss: Results from the health and retirement survey. Journals of Gerontology Series B, 61,
S221–S228. doi: 10.1093/geronb/61.4.S221.
Gordon, R. A., Rozelle, R. M., & Baxter, J. C. (1988). The effect of applicant age, job level, and accountability on the evaluation of job applicants. Organizational Behavior and Human Decision
Processes, 41, 20–33. doi: 10.1016/0749-5978(88)90044-1.
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Gringart, E., Helmes, E., & Speelman, C. P. (2005). Exploring attitudes toward older workers among
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org/10.1300/J031v17n03_05
Hayes, A. F. (2013). Introduction to mediation, moderation and conditional process analysis: A
regression-based approach. NY: Guilford Press.
Lamont, R.A., Swift, H. J., & Abrams, D. (2015). A review and meta-analysis of age-based stereotype
threat: Negative stereotypes, not facts, do the damage. Psychology and Aging, 30, 180–193.
doi: 10.1037/a0038586.
Levy, B., & Banaji, M. R. (2002). Implicit ageism. In T. D. Nelson (Ed.), Ageism, stereotyping and
prejudice against older persons (pp. 163–199). Cambridge, MA: MIT Press.
Macdonald, J. L., & Levy, S. R. (2016). Ageism in the workplace: The role of psychosocial factors
in predicting job satisfaction, commitment, and engagement. Journal of Social Issues, 72(1),
169–190.
McCann, R., & Giles, H. (2002). Ageism in the workplace: A communication perspective. In T.
D. Nelson (Ed.), Ageism, stereotyping and prejudice against older persons (pp. 163–199).
Cambridge, MA: MIT Press.
Ng, T. W. H., & Feldman, D. C. (2012). Evaluating six common stereotypes about older workers with
meta-analytical data. Personnel Psychology, 65, 821–858. doi: 10.1111/peps.12003.
North, M. S., & Fiske, S. T. (2016). Resource scarcity and prescriptive attitudes generate subtle,
intergenerational older-worker exclusion. Journal of Social Issues, 72(1), 122–145.
Perry, E. L., Kulik, C. T., & Bourhis, A. C. (1996). Moderating effects of personal and contextual
factors in age discrimination. Journal of Applied Psychology, 81, 628. doi: 10.1037/00219010.81.6.628.
Posthuma, R. A., & Campion, M. A. (2008). Age stereotypes in the workplace: Common stereotypes, moderators, and future research directions. Journal of Management, 35, 158–188. doi:
10.1177/0149206308318617.
Ray, S., Sharp, E., & Abrams, D. (2006). Age discrimination 2006: A benchmark for public attitudes.
London: Age Concern England, Policy Unit. Retrieved from http://www.ageuk.org.uk/
documents/en-gb/for-professionals/equality-and-human-rights/ageism%20%20-%20a%20
benchmark%20of%20public%20ageist%20attitudes_pro.pdf
Ryan, M. K., Haslam, S. A., Hersby, M. D., & Bongiorno, R. (2011). Think crisis–think female: The
glass cliff and contextual variation in the think manager–think male stereotype. Journal of
Applied Psychology, 96, 470. doi: 10.1037/a0022133.
Schooler, C., Mulatu, M. S., & Oates, G. (1999). The continuing effects of substantively complex
work on the intellectual functioning of older workers. Psychology and Aging, 14, 483–506.
doi: 10.1037/0882-7974.14.3.483.
Singer, M. S., & Sewell, C. (1989). Applicant age and selection interview decisions: Effect of information exposure on age discrimination in personnel selection. Personnel Psychology, 42,
135–154. doi: 10.1111/j.1744-6570.1989.tb01554.x.
Swift, H. J., Abrams, D., & Marques, S. (2013). Threat or boost: Social comparison affects older
people’s performance differently depending on task domain. Journals of Gerontology, Series
B, 68, 23–30. doi:10.1093/geronb/gbs044.
World Health Organisation (WHO). (2014). Ageing and the life course: Facts about ageing. Retrieved
from http://www.who.int/ageing/about/facts/en. Accessed at October 1, 2015.
DOMINIC ABRAMS is Professor of Social Psychology and Director of the Centre
for the Study of Group Processes at the University of Kent. His research focuses
on the psychological dynamics of social exclusion and inclusion within and between groups. He is codirector and founder of the European Research Group on
Attitudes to Age, which designed the European Social Survey Round 4 module on experiences and expressions of ageism, (http://www.eurage.com). He is
coeditor with Michael A. Hogg of the journal Group Processes and Intergroup
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Relations, and recently (with Melanie Killen) of a JSI issue on social exclusion and
children. He is a past president of SPSSI, and Fellow of the British Academy and
the Academy of Social Sciences.
HANNAH J. SWIFT is Eastern ARC Research Fellow at the Centre for the Study
of Group Processes at the University of Kent, from where she also completed her
PhD (2012) as an ESRC-CASE grant holder in conjunction with AgeUK. Her
research focuses on attitudes to aging, ageism, stereotype threat, well-being and
factors that contribute to loneliness in later life as well as active, healthy aging. As
well as contributing to U.K. government reports and European Social Survey and
Gerontological Society of America policy briefings, her work has been published
in journals including Psychology and Aging, Journal of Gerontology, BMJ-Open,
Addiction, and Journal of Applied Social Psychology.
LISBETH DRURY is currently an ESRC-AgeUK CASE award holder completing
her PhD in Social Psychology at the University of Kent. Her interests include
intergenerational contact, age stereotypes, and the intersection of ageism and
sexism. Her research includes reports for the U.K. Government’s Foresight project
on enablers of positive attitudes to age. She is Managing Editor of Group Processes
and Intergroup Relations.
The Mark of a Criminal Record1
Devah Pager
Northwestern University
With over 2 million individuals currently incarcerated, and over
half a million prisoners released each year, the large and growing
number of men being processed through the criminal justice system
raises important questions about the consequences of this massive
institutional intervention. This article focuses on the consequences
of incarceration for the employment outcomes of black and white
job seekers. The present study adopts an experimental audit
approach—in which matched pairs of individuals applied for real
entry-level jobs—to formally test the degree to which a criminal record affects subsequent employment opportunities. The findings of
this study reveal an important, and much underrecognized, mechanism of stratification. A criminal record presents a major barrier
to employment, with important implications for racial disparities.
While stratification researchers typically focus on schools, labor markets,
and the family as primary institutions affecting inequality, a new institution has emerged as central to the sorting and stratifying of young and
disadvantaged men: the criminal justice system. With over 2 million individuals currently incarcerated, and over half a million prisoners released
each year, the large and growing numbers of men being processed through
the criminal justice system raises important questions about the consequences of this massive institutional intervention.
This article focuses on the consequences of incarceration for the em1
Support for this research includes grants from the National Science Foundation (SES0101236), the National Institute of Justice (2002-IJ-CX-0002), the Joyce Foundation,
and the Soros Foundation. Views expressed in this document are my own and do not
necessarily represent those of the granting agencies. I am grateful for comments and
suggestions from Marc Bendick, Jr., Robert M. Hauser, Erik Olin Wright, Lincoln
Quillian, David B. Grusky, Eric Grodsky, Chet Pager, Irving Piliavin, Jeremy Freese,
and Bruce Western. This research would not have been possible without the support
and hospitality of the staff at the Benedict Center and at the Department of Sociology
at the University of Wisconsin—Milwaukee. Direct correspondence to Devah Pager,
Department of Sociology, Northwestern University, 1810 Chicago Avenue, Evanston,
Illinois 60208. E-mail: pager@northwestern.edu
䉷2003 by The University of Chicago. All rights reserved.
0002-9602/2003/10805-0001$10.00
AJS Volume 108 Number 5 (March 2003): 937–75
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ployment outcomes of black and white men. While previous survey research has demonstrated a strong association between incarceration and
employment, there remains little understanding of the mechanisms by
which these outcomes are produced. In the present study, I adopt an
experimental audit approach to formally test the degree to which a criminal record affects subsequent employment opportunities. By using
matched pairs of individuals to apply for real entry-level jobs, it becomes
possible to directly measure the extent to which a criminal record—in the
absence of other disqualifying characteristics—serves as a barrier to employment among equally qualified applicants. Further, by varying the
race of the tester pairs, we can assess the ways in which the effects of
race and criminal record interact to produce new forms of labor market
inequalities.
TRENDS IN INCARCERATION
Over the past three decades, the number of prison inmates in the United
States has increased by more than 600%, leaving it the country with the
highest incarceration rate in the world (Bureau of Justice Statistics 2002a;
Barclay, Tavares, and Siddique 2001). During this time, incarceration has
changed from a punishment reserved primarily for the most heinous offenders to one extended to a much greater range of crimes and a much
larger segment of the population. Recent trends in crime policy have led
to the imposition of harsher sentences for a wider range of offenses, thus
casting an ever-widening net of penal intervention.2
While the recent “tough on crime” policies may be effective in getting
criminals off the streets, little provision has been made for when they get
back out. Of the nearly 2 million individuals currently incarcerated,
roughly 95% will be released, with more than half a million being released
each year (Slevin 2000). According to one estimate, there are currently
over 12 million ex-felons in the United States, representing roughly 8%
of the working-age population (Uggen, Thompson, and Manza 2000). Of
those recently released, nearly two-thirds will be charged with new crimes
and over 40% will return to prison within three years (Bureau of Justice
Statistics 2000). Certainly some of these outcomes are the result of desolate
opportunities or deeply ingrained dispositions, grown out of broken fam2
For example, the recent adoption of mandatory sentencing laws, most often used for
drug offenses, removes discretion from the sentencing judge to consider the range of
factors pertaining to the individual and the offense that would normally be taken into
account. As a result, the chances of receiving a state prison term after being arrested
for a drug offense rose by 547% between 1980 and 1992 (Bureau of Justice Statistics
1995).
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ilies, poor neighborhoods, and little social control (Sampson and Laub
1993; Wilson 1997). But net of these contributing factors, there is evidence
that experience with the criminal justice system in itself has adverse consequences for subsequent opportunities. In particular, incarceration is associated with limited future employment opportunities and earnings potential (Freeman 1987; Western 2002), which themselves are among the
strongest predictors of recidivism (Shover 1996; Sampson and Laub 1993;
Uggen 2000).
The expansion of the prison population has been particularly consequential for blacks. The incarceration rate for young black men in the
year 2000 was nearly 10%, compared to just over 1% for white men in
the same age group (Bureau of Justice Statistics 2001). Young black men
today have a 28% likelihood of incarceration during their lifetime (Bureau
of Justice Statistics 1997), a figure that rises above 50% among young
black high school dropouts (Pettit and Western 2001). These vast numbers
of inmates translate into a large and increasing population of black exoffenders returning to communities and searching for work. The barriers
these men face in reaching economic self-sufficiency are compounded by
the stigma of minority status and criminal record. The consequences of
such trends for widening racial disparities are potentially profound (see
Western and Pettit 1999; Freeman and Holzer 1986).
PRIOR RESEARCH
While little research to date has focused on the consequences of criminal
sanctions, a small and growing body of evidence suggests that contact
with the criminal justice system can lead to a substantial reduction in
economic opportunities. Using longitudinal survey data, researchers have
studied the employment probabilities and income of individuals after
release from prison and have found a strong and consistent negative effect
of incarceration (Western and Beckett 1999; Freeman 1987; Nagin and
Waldfogel 1993).
This existing research has been instrumental in demonstrating the possible aggregate effects of incarceration on labor market outcomes. Unfortunately, however, there are several fundamental limitations of survey
data that leave the conclusions of this research vulnerable to harsh criticism. First, it is difficult, using survey data, to rule out the possibility
that unmeasured differences between those who are and are not convicted
of crimes may drive the observed results. Figure 1 presents one possible
model of the relationship between incarceration and employment outcomes, with a direct causal link between the two. In this model, an individual acquires a criminal record, which then severely limits his later
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Fig. 1.—Model of direct causation
employment opportunities. But what evidence can we offer in support of
this causal relationship? We know that the population of inmates is not
a random sample of the overall population. What if, then, the poor outcomes of ex-offenders are merely the result of preexisting traits that make
these men bad employees in the first place? Figure 2 presents a model of
spurious association in which there is no direct link between incarceration
and employment outcomes. Instead, there are direct links between various
preexisting individual characteristics (e.g., drug and alcohol abuse, behavioral problems, poor interpersonal skills), which increase the likelihood
of both incarceration and poor employment outcomes.3 In this model, the
association between incarceration and employment is entirely spurious—the result of individual predispositions toward deviance.
Consistent with figure 2, Kling (1999), Grogger (1995), and Needels
(1996) have each argued that the effect of incarceration on employment
is negligible, at an estimated 0%–4%. Using administrative data from
unemployment insurance (UI) files matched with records from various
state departments of corrections, these authors contend that the observed
association is instead largely determined by unmeasured individual characteristics.4 The findings of these authors stand in stark contrast to the
majority of literature asserting a strong link between incarceration and
employment (Western and Beckett 1999; Bushway 1998; Sampson and
Laub 1993; Freeman 1987; Grogger 1992). While it remains an open
question as to whether and to what extent incarceration causes employ3
The variables listed here are just a few of the many potential sources of spuriousness
that are virtually untestable using survey data.
4
Studies using administrative data have the advantage of analyzing large samples of
ex-offenders over extended periods of time, before and after incarceration. However,
this line of research also suffers from several important limitations: First, employment
and wage data from UI administrative records are available only for those jobs covered
by and in compliance with unemployment insurance laws, thus excluding many temporary, contingent, or “grey-market” jobs, which may be more likely held by ex-offenders. Second, administrative data are typically limited to one state or jurisdiction;
individuals who move to other states during the period of observation are thus mistakenly coded as unemployed or as zero-earners. And finally, missing social security
numbers or difficulties in matching records often results in fairly substantial reduction
in sample representativeness. See Kornfeld and Bloom (1999) for an in-depth discussion
of these issues.
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Fig. 2.—Model of spurious effects
ment difficulties, survey research is poorly equipped to offer a definitive
answer. The Achilles heel of the survey methodology is its inability to
escape from the glaring problems of selection that plague research in this
field (see Winship and Morgan 1999; Rubin 1990; Heckman et al. 1998).5
A second, related limitation of survey research is its inability to formally
identify mechanisms. From aggregate effects, we can infer plausible causal
processes, but these are only indirectly supported by the data. Because
numerous mechanisms could lead to the same set of outcomes, we are
left unable to assess the substantive contribution of any given causal
process. Survey researchers have offered numerous hypotheses regarding
the mechanisms that may produce the observed relationship between incarceration and employment. These include the labeling effects of criminal
stigma (Schwartz and Skolnick 1962), the disruption of social and familial
ties (Sampson and Laub 1993), the influence on social networks (Hagan
1993), the loss of human capital (Becker 1975), institutional trauma (Parenti 1999), legal barriers to employment (Dale 1976), and, of course, the
possibility that incarceration effects may be entirely spurious (Kling 1999;
Grogger 1995; Needels 1996). Without direct measures of these variables,
it is difficult, using survey data, to discern which, if any, of these causal
explanations may be at work.
The uncertainty surrounding these mechanisms motivates the current
project. Before addressing some of the larger consequences of incarcer5
Researchers have employed creative techniques for addressing these issues, such as
looking at pre- and postincarceration outcomes for the same individuals (e.g., Grogger
1992; Freeman 1991), comparing ex-offenders to future offenders (e.g., Waldfogel 1994;
Grogger 1995), estimating fixed- and random-effects models (Western 2002), and using
instrumental variables approaches to correct for unmeasured heterogeneity (e.g., Freeman 1994). There remains little consensus, however, over the degree to which these
techniques effectively account for the problems of selection endemic to this type of
research.
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ation, it is essential to first establish conclusively the mechanism—or at
least one of the mechanisms—driving these results. In the present study,
I focus on the effect of a criminal record on employment opportunities.
This emphasis directs our attention to the stigma associated with criminal
justice intervention and to the ways in which employers respond to this
stigma in considering applicants. While certainly there are additional ways
in which incarceration may affect subsequent employment, this focus
allows us to separate the institutional effect from the individual (or from
the interaction of the two) and to directly assess one of the most widely
discussed—but rarely measured—mechanisms of carceral channeling
(Wacquant 2000). While incarceration may in fact additionally transform
individuals (and/or their social ties) in ways that make them less suited
to work, my interest here is in what might be termed the “credentialing”
aspect of the criminal justice system. Those sent to prison are institutionally branded as a particular class of individuals—as are college graduates or welfare recipients—with implications for their perceived place
in the stratification order. The “negative credential” associated with a
criminal record represents a unique mechanism of stratification, in that
it is the state that certifies particular individuals in ways that qualify them
for discrimination or social exclusion.6 It is this official status of the negative credential that differentiates it from other sources of social stigma,
offering greater legitimacy to its use as the basis for differentiation. (See
Pager [2002] for a more extensive discussion of negative credentials and
their implications for stratification).
In order to investigate this question, I have chosen an experimental
approach to the problem, a methodology best suited to isolating causal
mechanisms. There have, in the past, been a limited number of studies
that have adopted an experimental approach to the study of criminal
stigma. These studies have relied on a “correspondence test” approach,
whereby applications are submitted by mail with no in-person contact.
The most notable in this line of research is a classic study by Schwartz
and Skolnick (1962) in which the researchers prepared four sets of resumes
to be sent to prospective employers, varying the criminal record of applicants. In each condition, employers were less likely to consider appli-
6
Numerous opportunities become formally off-limits to individuals following a felony
conviction, including (depending on the state of residence) access to public housing,
voting rights, and employment in certain occupational sectors (e.g., health care occupations, public sector positions, child and elder care work). In addition, the widespread availability of criminal background information allows for the information to
be further used as the basis for allocating opportunities not formally off-limits to exoffenders, as studied here.
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cants who had any prior contact with the criminal justice system.7 Several
later studies have verified these findings, varying the types of crimes
committed by the hypothetical applicant (Finn and Fontaine 1985; Cohen
and Nisbett 1997) or the national context (Boshier and Johnson 1974;
Buikhuisen and Dijksterhuis 1971). Each of these studies reports the similar finding that, all else equal, contact with the criminal justice system
leads to worse employment opportunities.
Unfortunately, the research design of Schwartz and Skolnick and others
using this approach has several limitations. First, Schwartz and Skolnick’s
study, while clearly demonstrating the substantial effect of criminal
stigma, is limited to one job type only (an unskilled hotel job). It remains
uncertain how these effects generalize to the overall population of entrylevel jobs. Ex-offenders face a diverse set of job openings, some of which
may be more or less restricted to applicants with criminal records.
Second, correspondence tests are poorly equipped to address the issue
of race. While it is possible to designate national origin using ethnic names
(see, e.g., Riach and Rich 1991), it is much more difficult to clearly distinguish black and white applicants on paper.8 Given the high rates of
incarceration among blacks and the pervasive media images of black
criminals, there is good reason to suspect that employers may respond
differently to applicants with criminal records depending on their race
(see discussion below). Prior research using correspondence tests to study
the effect of criminal records, however, has not attempted to include race
as a variable.
Finally, the type of application procedure used in correspondence
tests—sending resumes by mail—is typically reserved for studies of administrative, clerical, and higher-level occupations. The types of job openings ex-offenders are most likely to apply for, by contrast, typically request
in-person applications, and a mailed resume would therefore appear out
of place.
The present study extends the work of Schwartz and Skolnick to include
a more comprehensive assessment of the hiring process of ex-offenders
across a full range of entry-level employment. By using an experimental
audit design, this study effectively isolates the effect of a criminal record,
while observing employer behavior in real-life employment settings. Fur7
The four conditions included (1) an applicant who had been convicted and sentenced
for assault, (2) an applicant who had been tried for assault but acquitted, (3) an
applicant who had been tried for assault, acquitted, and had a letter from the judge
certifying the applicant’s acquittal and emphasizing the presumption of innocence,
and (4) an applicant who had no criminal record. In all three criminal conditions—even
with a letter from the judge—applicants were less likely to be considered by employers
relative to the noncriminal control.
8
For an excellent exception, see Bertrand and Mullainathan (2002).
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ther, by using in-person application procedures, it becomes possible to
simulate the process most often followed for entry-level positions, as well
as to provide a more direct test of the effects of race on hiring outcomes.
RESEARCH QUESTIONS
There are three primary questions I seek to address with the present study.
First, in discussing the main effect of a criminal record, we need to ask
whether and to what extent employers use information about criminal
histories to make hiring decisions. Implicit in the criticism of survey research in this area is the assumption that the signal of a criminal record
is not a determining factor. Rather, employers use information about the
interactional styles of applicants, or other observed characteristics—which
may be correlated with criminal records—and this explains the differential
outcomes we observe. In this view, a criminal record does not represent
a meaningful signal to employers on its own. This study formally tests
the degree to which employers use information about criminal histories
in the absence of corroborating evidence. It is essential that we conclusively document this effect before making larger claims about the aggregate consequences of incarceration.
Second, this study investigates the extent to which race continues to
serve as a major barrier to employment. While race has undoubtedly
played a central role in shaping the employment opportunities of AfricanAmericans over the past century, recent arguments have questioned the
continuing significance of race, arguing instead that other factors—such
as spatial location, soft skills, social capital, or cognitive ability—can
explain most or all of the contemporary racial differentials we observe
(Wilson 1987; Moss and Tilly 1996; Loury 1977; Neal and Johnson 1996).
This study provides a comparison of the experiences of equally qualified
black and white applicants, allowing us to assess the extent to which
direct racial discrimination persists in employment interactions.
The third objective of this study is to assess whether the effect of a
criminal record differs for black and white applicants. Most research
investigating the differential impact of incarceration on blacks has focused
on the differential rates of incarceration and how those rates translate
into widening racial disparities. In addition to disparities in the rate of
incarceration, however, it is also important to consider possible racial
differences in the effects of incarceration. Almost none of the existing
literature to date has explored this issue, and the theoretical arguments
remain divided as to what we might expect.
On one hand, there is reason to believe that the signal of a criminal
record should be less consequential for blacks. Research on racial stere944
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otypes tells us that Americans hold strong and persistent negative stereotypes about blacks, with one of the most readily invoked contemporary
stereotypes relating to perceptions of violent and criminal dispositions
(Smith 1991; Sniderman and Piazza 1993; Devine and Elliott 1995). If it
is the case that employers view all blacks as potential criminals, they are
likely to differentiate less among those with official criminal records and
those without. Actual confirmation of criminal involvement then will provide only redundant information, while evidence against it will be discounted. In this case, the outcomes for all blacks should be worse, with
less differentiation between those with criminal records and those without.
On the other hand, the effect of a criminal record may be worse for
blacks if employers, already wary of black applicants, are more hesitant
when it comes to taking risks on blacks with proven criminal tendencies.
The literature on racial stereotypes also tells us that stereotypes are most
likely to be activated and reinforced when a target matches on more than
one dimension of the stereotype (Quillian and Pager 2002; Darley and
Gross 1983; Fiske and Neuberg 1990). While employers may have learned
to keep their racial attributions in check through years of heightened
sensitivity around employment discrimination, when combined with
knowledge of a criminal history, negative attributions are likely to
intensify.
A third possibility, of course, is that a criminal record affects black and
white applicants equally. The results of this audit study will help to adjudicate between these competing predictions.
THE AUDIT METHODOLOGY
The method of audit studies was pioneered in the 1970s with a series of
housing audits conducted by the Department of Housing and Urban Development (Wienk et al. 1979; Hakken 1979). Nearly 20 years later, this
initial model was modified and applied to the employment context by
researchers at the Urban Institute (Cross et al. 1990; Turner, Fix, and
Struyk 1991). The basic design of an employment audit involves sending
matched pairs of individuals (called testers) to apply for real job openings
in order to see whether employers respond differently to applicants on
the basis of selected characteristics.
The appeal of the audit methodology lies in its ability to combine
experimental methods with real-life contexts. This combination allows for
greater generalizability than a lab experiment and a better grasp of the
causal mechanisms than what we can normally obtain from observational
data. The audit methodology is particularly valuable for those with an
interest in discrimination. Typically, researchers are forced to infer dis945
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crimination indirectly, often attributing the residual from a statistical
model—which is essentially all that is not directly explained—to discrimination. This convention is rather unsatisfying to researchers who seek
empirical documentation for important social processes. The audit methodology therefore provides a valuable tool for this research.9
Audit studies have primarily been used to study those characteristics
protected under Title VII of the Civil Rights Act, such as race, gender,
and age (Ayres and Siegelman 1995; Cross et al. 1990; Turner et al. 1991;
Bendick, Brown, and Wall 1999; Bendick 1999; Bendick, Jackson, and
Reinoso 1994; Neumark 1996). The employment of ex-offenders, of course,
has not traditionally been thought of as a civil rights issue, but with the
rapid expansion of the criminal justice system over the past three decades,
there has been heightened concern over the growing population of men
with criminal records. Recognizing the increasing importance of this issue,
several states (including Wisconsin) have passed legislation expanding the
fair employment regulations to protect individuals with criminal records
from discrimination by employers. Employers are cautioned that crimes
may only be considered if they closely relate to the specific duties required
of the job, however “shocking” the crime may have been. If anything,
then, this study represents a strong test of the effect of a criminal record.
We might expect the effect to be larger in states where no such legal
protection is in place.10
STUDY DESIGN
The basic design of this study involves the use of four male auditors (also
called testers), two blacks and two whites. The testers were paired by
race; that is, unlike in the original Urban Institute audit studies, the two
black testers formed one team, and the two white testers formed the second
9
While the findings from audit studies have produced some of the most convincing
evidence of discrimination available from social science research, there are specific
criticisms of this approach that warrant consideration. Heckman and Siegelman (1993)
identify five major threats to the validity of results from audit studies: (1) problems
in effective matching, (2) the use of “overqualified” testers, (3) limited sampling frame
for the selection of firms and jobs to be audited, (4) experimenter effects, and (5) the
ethics of audit research. For a useful discussion of these concerns, see the series of
essays published in Fix and Struyk (1993). See also app. A below.
10
Indeed, in a survey of employer attitudes, Holzer, Raphael, and Stoll (2002) found
that Milwaukee employers were significantly more likely to consider hiring ex-offenders
than were employers in Boston, Atlanta, Los Angeles, or Detroit, suggesting that
Wisconsin may represent a best case scenario for the employment outcomes of exoffenders relative to other major metropolitan areas (see also Holzer and Stoll 2001).
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team (see fig. 3).11 The testers were 23-year-old college students from
Milwaukee who were matched on the basis of physical appearance and
general style of self-presentation. Objective characteristics that were not
already identical between pairs—such as educational attainment and
work experience—were made similar for the purpose of the applications.
Within each team, one auditor was randomly assigned a “criminal record”
for the first week; the pair then rotated which member presented himself
as the ex-offender for each successive week of employment searches, such
that each tester served in the criminal record condition for an equal number of cases. By varying which member of the pair presented himself as
having a criminal record, unobserved differences within the pairs of applicants were effectively controlled. No significant differences were found
for the outcomes of individual testers or by month of testing.
Job openings for entry-level positions (defined as jobs requiring no
previous experience and no education greater than high school) were
identified from the Sunday classified advertisement section of the Milwaukee Journal Sentinel.12 In addition, a supplemental sample was drawn
from Jobnet, a state-sponsored web site for employment listings, which
was developed in connection with the W-2 Welfare-to-Work initiatives.13
The audit pairs were randomly assigned 15 job openings each week.
The white pair and the black pair were assigned separate sets of jobs,
with the same-race testers applying to the same jobs. One member of the
pair applied first, with the second applying one day later (randomly varying whether the ex-offender was first or second). A total of 350 employers
were audited during the course of this study: 150 by the white pair and
200 by the black pair. Additional tests were performed by the black pair
because black testers received fewer callbacks on average, and there were
thus fewer data points with which to draw comparisons. A larger sample
11
The primary goal of this study was to measure the effect of a criminal record, and
thus it was important for this characteristic to be measured as a within-pair effect.
While it would have been ideal for all four testers to have visited the same employers,
this likely would have aroused suspicion. The testers were thus divided into separate
teams by race and assigned to two randomly selected sets of employers.
12
Occupations with legal restrictions on ex-offenders were excluded from the sample.
These include jobs in the health care industry, work with children and the elderly,
jobs requiring the handling of firearms (i.e., security guards), and jobs in the public
sector. An estimate of the collateral consequences of incarceration would also need to
take account of the wide range of employment formally off-limits to individuals with
prior felony convictions.
13
Employment services like Jobnet have become a much more common method of
finding employment in recent years, particularly for difficult-to-employ populations
such as welfare recipients and ex-offenders. Likewise, a recent survey by Holzer and
Stoll (2001) found that nearly half of Milwaukee employers (46%) use Jobnet to advertise vacancies in their companies.
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Fig. 3.—Audit design: “C” refers to criminal record; “N” refers to no criminal record
size enables me to calculate more precise estimates of the effects under
investigation.
Immediately following the completion of each job application, testers
filled out a six-page response form that coded relevant information from
the test. Important variables included type of occupation, metropolitan
status, wage, size of establishment, and race and sex of employer.14 Additionally, testers wrote narratives describing the overall interaction and
any comments made by employers (or included on applications) specifically related to race or criminal records.
One key feature of this audit study is that it focuses only on the first
stage of the employment process. Testers visited employers, filled out
applications, and proceeded as far as they could during the course of one
visit. If testers were asked to interview on the spot, they did so, but they
did not return to the employer for a second visit. The primary dependent
variable, then, is the proportion of applications that elicited callbacks
from employers. Individual voicemail boxes were set up for each tester
to record employer responses. If a tester was offered the job on the spot,
this was also coded as a positive response.15 The reason I chose to focus
only on this initial stage of the employment process is because this is the
stage likely to be most affected by the barrier of a criminal record. In an
audit study of age discrimination, for example, Bendick et al. (1999) found
that 76% of the measured differential treatment occurred at this initial
stage of the employment process. Given that a criminal record, like age,
14
See Pager (2002) for a discussion of the variation across each of these dimensions.
In cases where testers were offered jobs on the spot, they were instructed to tell the
employer that they were still waiting to hear back from another job they had interviewed for earlier. The tester then called the employer back at the end of the same
day to let him or her know that the other job had come through and he was therefore
no longer available.
15
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is a highly salient characteristic, it is likely that as much, if not more, of
the treatment effect will be detected at this stage.
TESTER PROFILES
In developing the tester profiles, emphasis was placed on adopting characteristics that were both numerically representative and substantively
important. In the present study, the criminal record consisted of a felony
drug conviction (possession with intent to distribute, cocaine) and 18
months of (served) prison time. A drug crime (as opposed to a violent or
property crime) was chosen because of its prevalence, its policy salience,
and its connection to racial disparities in incarceration.16 It is important
to acknowledge that the effects reported here may differ depending on
the type of offense.17
In assigning the educational and work history of testers, I sought a
compromise between representing the modal group of offenders, while
also providing some room for variation in the outcome of the audits. Most
audit studies of employment have created tester profiles that include some
college experience, so that testers will be highly competitive applicants
for entry-level jobs and so that the contrast between treatment and control
group is made clear (see app. B in Cross et al. 1989). In the present study,
however, postsecondary schooling experience would detract from the representativeness of the results. More than 70% of federal and nearly 90%
of state prisoners have no more than a high school degree (or equivalent).
16
Over the past two decades, drug crimes were the fastest growing class of offenses.
In 1980, roughly one out of every 16 state inmates was incarcerated for a drug crime;
by 1999, this figure had jumped to one out of every five (Bureau of Justice Statistics
2000). In federal prisons, nearly three out of every five inmates are incarcerated for a
drug crime (Bureau of Justice Statistics 2001). A significant portion of this increase
can be attributed to changing policies concerning drug enforcement. By 2000, every
state in the country had adopted some form of truth in sentencing laws, which impose
mandatory sentencing minimums for a range of offenses. These laws have been applied
most frequently to drug crimes, leading to more than a fivefold rise in the number of
drug arrests that result in incarceration and a doubling of the average length of sentences for drug convictions (Mauer 1999; Blumstein and Beck 1999). While the steep
rise in drug enforcement has been felt across the population, this “war on drugs” has
had a disproportionate impact on African-Americans. Between 1990 and 1997, the
number of black inmates serving time for drug offenses increased by 60%, compared
to a 46% increase in the number of whites (Bureau of Justice Statistics 1995). In 1999,
26% of all black state inmates were incarcerated for drug offenses, relative to less than
half that proportion of whites (Bureau of Justice Statistics 2001).
17
Survey results indicate that employers are substantially more averse to applicants
convicted of violent crimes or property crimes relative to those convicted of drug
crimes (Holzer et al. 2002; Pager 2002).
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The education level of testers in this study, therefore, was chosen to represent the modal category of offenders (high school diploma).18
There is little systematic evidence concerning the work histories of
inmates prior to incarceration. Overall, 77.4% of federal and 67.4% of
state inmates were employed prior to incarceration (Bureau of Justice
Statistics 1994). There is, however, a substantial degree of heterogeneity
in the quality and consistency of work experience during this time (Pager
2001). In the present study, testers were assigned favorable work histories
in that they report steady work experience in entry-level jobs and nearly
continual employment (until incarceration). In the job prior to incarceration (and, for the control group, prior to the last short-term job), testers
report having worked their way from an entry-level position to a supervisory role.19
DESIGN ISSUES
There are a number of complexities involved in the design and implementation of an audit study.20 Apart from the standard complications of
carrying out a field experiment, there were several specific dilemmas posed
in the development of the current study that required substantial deliberation. First, in standard audit studies of race or gender, it is possible to
construct work histories for test partners in such a way that the amount
of work experience reported by each tester is identical. By contrast, the
present study compares the outcome of one applicant who has spent 18
months in prison. It was therefore necessary to manipulate the work
histories of both applicants so that this labor market absence did not bias
the results.21 The solution opted for here was for the ex-offender to report
six months of work experience gained while in prison (preceded by 12
18
In 1991, 49% of federal and 46.5% of state inmates had a high school degree (or
equivalent; Bureau of Justice Statistics 1994).
19
Testers reported working either as an assistant manager at a national restaurant
chain or as a supervisor at a national home retail store. While it is unlikely that the
modal occupational attainment for high school graduates (with or without criminal
records) would be a supervisory position, this feature was added to the tester profiles
in order to make them more competitive applicants. The solid job histories of these
applicants should affect the results in a conservative direction, offering cues about the
tester’s reliability and competence, which may offset some of the negative associations
with a criminal background.
20
See app. A for a discussion of additional methodological concerns.
21
Though time out of the labor market is in fact one component of the total impact
of incarceration, this study sought to isolate the effect of criminal stigma from other
potential consequences of incarceration. Again, an estimate of the total effect of incarceration would also need to take account of employment difficulties resulting from
a prolonged labor market absence.
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months out of the labor force, representing the remainder of the total
prison time). The nonoffender, on the other hand, reported graduating
from high school one year later (thereby accounting for 12 months) and,
concurrent to his partner’s six months of prison work time, worked for
a temporary agency doing a similar kind of low-skill work. Thus, the
actual amount of work experience was equivalent for both testers. The
effect of having the noncriminal graduate from high school one year later
should impose a conservative bias, as graduating from high school late
may indicate less motivation or ability.
A second major difference between audit studies of race or gender and
the present study is that criminal status is not something that can be
immediately discerned by the employer. The information had to be explicitly conveyed, therefore, in order for the interaction to become a “test.”
In most cases, the tester was given the opportunity to communicate the
necessary information on the application form provided, in answer to the
question “Have you ever been convicted of a crime?”22 However, in the
26% of cases where the application form did not include a question about
criminal history, it was necessary to provide an alternate means of conveying this information. In the present study, testers provided two indirect
sources of information about their prior criminal involvement. First, as
mentioned above, the tester in the criminal record condition reported work
experience obtained while in the correctional facility. Second, the tester
listed his parole officer as a reference (calls to whom were recorded by
voicemail). These two pieces of evidence provided explicit clues to employers that the applicant had spent time in prison; and both of these
strategies are used by real ex-offenders who seek to account for empty
time by reporting work experience in prison or who wish to have their
parole officer vouch for their successful rehabilitation.23 Pilot tests with
employers in a neighboring city suggested that this strategy was an effective means of conveying the criminal record condition without arousing
suspicion.
STUDY CONTEXT AND DESCRIPTIVES
The fieldwork for this project took place in Milwaukee between June and
December of 2001. During this time, the economic condition of the met22
To the extent that real ex-offenders lie about their criminal record on application
forms, this approach may lead to an overestimate of the effect of a criminal record.
See app. A for a discussion of this issue.
23
This approach was developed in discussion with several Milwaukee employment
counselors and parole officers and is based on a composite profile of resumes belonging
to real ex-offenders.
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ropolitan area remained moderately strong, with unemployment rates
ranging from a high of 5.2% in June to a low of 4% in September.24 It is
important to note that the results of this study are specific to the economic
conditions of this period. It has been well-documented in previous research
that the level of employment discrimination corresponds closely with the
tightness of the labor market (Freeman and Rodgers 1999). Certainly the
economic climate was a salient factor in the minds of these employers.
During a pilot interview, for example, an employer reported that a year
ago she would have had three applications for an entry-level opening;
today she gets 150.25 Another employer for a janitorial service mentioned
that previously their company had been so short of staff that they had
to interview virtually everyone who applied. The current conditions, by
contrast, allowed them to be far more selective. Since the completion of
this study, the unemployment rate has continued to rise. It is likely, therefore, that the effects reported here may understate the impact of race and
a criminal record in the context of an economic recession.
As mentioned earlier, the job openings for this study were selected from
the Sunday classified section of the Milwaukee Journal Sentinel and from
Jobnet, a state-sponsored Internet job service. All job openings within a
25-mile radius of downtown Milwaukee were included, with 61% of the
resulting sample located in the suburbs or surrounding counties, relative
to only 39% in the city of Milwaukee. Because a limited boundary was
covered by this project, the distribution of jobs does not accurately represent the extent to which job growth has been concentrated in wider
suburban areas. According to a recent study of job growth in Milwaukee,
nearly 90% of entry-level job openings were located in the outlying counties and the Milwaukee county suburbs, with only 4% of full-time openings located in the central city (Pawasarat and Quinn 2000).
The average distance from downtown in the present sample was 12
miles, with a substantial number of job openings located far from reach
by public transportation. Again, testers in this study represented a best
case scenario: all testers had their own reliable transportation, allowing
them access to a wide range of employment opportunities. For the average
entry-level job seeker, by contrast, the suburbanization of low wage work
can in itself represent a major barrier to employment (Wilson 1997).
24
Monthly unemployment rates followed a U-shaped pattern, with higher levels of
unemployment in the first and last months of the study. Specifically: June (5.4%), July
(5.2%), August (4.8%), September (4.4%), October (4.7%), November (4.9%), December
(4.5%). National unemployment rates were nearly a point lower in June (4.6%), but
rose above Milwaukee’s unemployment rate to a high of 5.8% in December (Bureau
of Labor Statistics 2002).
25
The unemployment rate in Milwaukee had been as low as 2.7% in September of
1999 (Bureau of Labor Statistics 2002).
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Similar to other metropolitan labor markets, the service industry has
been the fastest growing sector in Milwaukee, followed by retail and
wholesale trade, and manufacturing (Pawasarat and Quinn 2000). Likewise, the sample of jobs in this study reflects similar concentrations,
though quite a range of job titles were included overall (table 1).
The most common job types were for restaurant workers (18%), laborers
or warehouse workers (17%), and production workers or operators (12%).
Though white collar positions were less common among the entry-level
listings, a fair number of customer service (11%), sales (11%), clerical
(5%), and even a handful of managerial positions (2%) were included.26
Figure 4 presents some information on the ways employers obtain background information on applicants.27 In this sample, roughly 75% of employers asked explicit questions on their application forms about the applicant’s criminal history. Generally this was a standard question, “Have
you ever been convicted of a crime? If yes, please explain.”28 Even though
in most cases employers are not allowed to use criminal background
information to make hiring decisions, a vast majority of employers nevertheless request the information.
A much smaller proportion of employers actually perform an official
background check. In my sample, 27% of employers indicated that they
would perform a background check on all applicants.29 This figure likely
represents a lower-bound estimate, given that employers are not required
to disclose their intentions to do background checks. According to a national survey by Holzer (1996), 30%–40% of employers perform official
background checks on applicants for noncollege jobs. The point remains,
26
As noted above, this sample excludes health care workers—which represented the
largest category of entry-level job openings—and other occupations with legal restrictions on ex-felons (see app. A).
27
These are nonexclusive categories and are thus not meant to sum to 100.
28
An overwhelming proportion of employers used generic questions about criminal
backgrounds (with the only major source of variation stemming from an emphasis on
all prior convictions vs. felonies only). A handful of large national companies, however,
used questions that reflected a more nuanced understanding of the law. One company,
e.g., instructed applicants not to answer the question if they were a resident of certain
specified states; another asked only about prior convictions for theft and burglary,
ignoring all other possible offenses.
29
The issue of official background checks raises some concern as to the validity of the
experimental condition, given that the information provided by testers can be
(dis)confirmed on the basis of other sources of information available to employers. In
cases where employers in this study did perform background checks on testers, the
check would come back clean (none of the testers in this study actually had criminal
records). It is my expectation that because employers would not expect someone to lie
about having a criminal record, and because employers know that criminal history
databases are fraught with errors, they would be inclined to believe the worst case
scenario—in this case, the self-report.
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TABLE 1
Occupational Distribution
Job Title
%
Waitstaff . . . . . . . . . . . . . . . .
Laborer/warehouse . . . . .
Production/oper...
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