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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. 105 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. 106 Abrams, Swift, and Drury 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 Old and Unemployable? 107 (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 108 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? 109 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). 110 Abrams, Swift, and Drury 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 Old and Unemployable? 111 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 112 Abrams, Swift, and Drury 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 Old and Unemployable? 113 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 114 Abrams, Swift, and Drury 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, Old and Unemployable? 115 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 116 Abrams, Swift, and Drury 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 Abrams, D., Russell, P. S., Vauclair, C.-M., & Swift, H. (2011). Ageism in Europe, findings from the European Social Survey. London: Age UK. Retrieved from http://www.ageuk.org.uk/documents/ en-gb/id10704%20ageism%20across%20europe%20report%20interactive.pdf?dtrk=true Age Discrimination and Employment Act. (1967). Pub. L., No. 90–202, 81 Stat 602. 1967. (coded as amended at 29 U.S.C.A) §§ 621–634. West, 2000. Aquino, J. A., Russell, D. W., Cutrona, C. E., & Altmaier, E. M. (1996). Employment status, social support, and life satisfaction among the elderly. Journal of Counseling Psychology, 43, 480. doi: org/10.1037/0022-0167.43.4.480. Cleveland, J. N., & Landy, F. J. (1983). The effects of person and job stereotypes on two personnel decisions. Journal of Applied Psychology, 68, 609–619. doi: org/10.1037/0021-9010.68.4.609. Cuddy, A. J. C., Norton, M. I., & Fiske, S. T. (2005). This old stereotype: The pervasiveness and persistence of the elderly stereotype. Journal of Social Issues, 61, 267–285. doi: 10.1111/j/15404560.2005.00405.x. 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: 10.2466/pr0.1960.7.2.203. 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: Competence and warmth respectively follow from perceived status and competition. Journal 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. 120 Abrams, Swift, and Drury Gringart, E., Helmes, E., & Speelman, C. P. (2005). Exploring attitudes toward older workers among Australian employers: An empirical study. Journal of Aging & Social Policy, 17, 85–103. doi: 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 Old and Unemployable? 121 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): 937–75 937 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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). 938 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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 939 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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. 940 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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. 941 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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. 942 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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). 943 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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). 946 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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. 947 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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. 15 948 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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). 949 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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. 950 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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. 951 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology 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). 952 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). Criminal Record 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. 953 This content downloaded from 132.174.251.106 on March 05, 2018 13:32:31 PM All use subject to University of Chicago Press Terms and Conditions (http://www.journals.uchicago.edu/t-and-c). American Journal of Sociology TABLE 1 Occupational Distribution Job Title % Waitstaff . . . . . . . . . . . . . . . . Laborer/warehouse . . . . . Production/oper...
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Annotated bibliography
Abrams, Dominic, Hannah J. Swift and Lisbeth Drury. "Old a nd Unemployable? H ow
Age-Based S tereotypes Affect Willingness to H ire Job Candidates." Journal of
Social Issues (2016): 105-121.
This article puts forward that employers are usually more unwilling to offer job
opportunities to older individuals as compared to the young. Old age is steriotyped with
incompetence, slow execution of tasks, poor cordination and lack of modern technoogy
application, which limits their employability. The atudies also found that employers are willing
to give old individuals low role jobs but not senior and top jobs. Steretyping and profiling makes
them lose out on accessing jobs, which has led to a big chunk of the aging population to lack
jobs. This scenario is exhibited from mid age and continues thoughout ones aging process.
Accordignt to the findings, the role theory is applicabel in this case where the old are directly
linked with low jobs.
Chasteen, Alison L. "The role of age and age related Attitudes in Perceptions of Elderly
Individuals." Basic and Applied...


Anonymous
Really useful study material!

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