Criminal Record Checks, Drug Testing and the Employment of
Minority Men and Women in Professional and Blue-Collar Jobs
Joshua Guetzkow
Department of Sociology and Anthropology
Institute of Criminology
Hebrew University of Jerusalem
Mt. Scopus, Israel 91905
e-mail: joshua.guetzkow@mail.huji.ac.il
Alexandra Kalev
Department of Sociology and Anthropology
Tel Aviv University
e-mail: akalev@post.tau.ac.il
DRAFT. PLEASE DON’T CITE WITHOUT PERMISSION
COMMENTS ARE WELCOME
Keywords: Incarceration, reintegration, pre-employment screens, stereotypes,
discrimination, minorities.
Acknowledgments: We thank Ron Edwards and Bliss Cartwright for sharing the EEOC
data and their expertise with us. We also thank Frank Dobbin and Dan Schrage for
comments and assistance.
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ABSTRACT
Criminal record checks and drug testing have burgeoned among U.S. employers since the
1980s, alongside the wars on crime and drugs. Public and scholarly debates rage over
their impact on minority employment. Some argue that pre-employment checks will
reduce employers’ reliance on statistical discrimination in minority hiring. Others warn
that such screening expands institutional discrimination against minorities, who are more
likely to have criminal records and be suspect of drug use. Evidence feeding this debate is
still scarce. We use rich longitudinal data on a national sample of U.S. organizations to
examine the effect of employers’ adoption of criminal record checks and drug testing on
the share of minority employment in professional and blue-collar jobs. We find that
screening significantly improves minority employment in select jobs, and harm it in
others, and this varies by screen type, job type, the regulatory environment and
incarceration rates. We advance the debate over screens in three key ways. First, we show
that criminal record checks and drug testing improve minority employment share in jobs
where studies find that employers are more averse to hiring ex-convicts or drug users:
professional and blue-collar jobs, respectively. Conversely, we find negative effects in
jobs where employers are less averse to hiring ex-convicts or drug users: criminal record
checks reduce minority employment in blue-collar and drug testing in professional jobs.
Second, we examine the way the legal environment shapes the impact of screens.
Regulations protecting ex-convicts from blanket exclusion tempers negative effects of
criminal record checks in blue collar jobs, while the Drug-Free Workplace Act hampered
minority professional employment in testing firms. Finally, we show that rising
incarceration rates improve the effects of screens on minority employment in professional
jobs. We discuss the theory and policy implications of this research.
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INTRODUCTION
The wars on crime and drugs have had a profound impact on labor market outcomes and
ethno-racial inequality (REFS). As these wars raged, employers became ever more
reluctant to hire ex-convicts and drug users, (Bushway 2004; Comer 1994; Holzer,
Raphael, and Stoll 2006; Knudsen, Roman, and Johnson 2003; Todd 2004) and
increasingly adopted criminal records checks and drug tests to screen job applicants (REF
SHRM). The effects of these screens on minority employment have been highly debated.
Some warn that criminal record checks compound institutional discrimination against
blacks and Hispanics, because they have higher criminal conviction rates than whites.
Giving employers access to criminal records would disproportionately affect minorities,
lowering their employment chances even further by legal and legitimate means (Todd
2004; Decker et al. 2015; Pager 2003; Pager et al. 2009; Alexander 2012). And, although
minorities do not have higher drug use rates than whites, some worry that they might be
tested at higher rates (Becker et al 2014)—especially since the war on crime and drugs
have cemented in the public's mind an association between minority status, criminality,
and drug use. Others argue that, precisely due to this association, without background
checks and drug testing, employers are likely to engage in statistical discrimination
against minority job candidates (Beckett, Nyrop, and Pfingst 2006; Chiricos, Welch, and
Gertz 2004; Fishman 1998; Gee et al.2005; Mauer 2006; Russell-Brown 2008; Wisotsky
1987). In that case, criminal background checks and drug testing could actually increase
minority hiring by providing individuating information on job candidates (Decker et al.
2004; Finlay 2009; Holzer et al. 2006; Pager 2007; Uggen et al. 2014; Wozniak 2015).
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Given the high rates of incarceration and unemployment among minorities and
the growing movement to limit the use of pre-employment screens, it is essential to
inform the debate with empirical research. The limited evidence accumulated to date has
provided important insights, but findings are mixed (Finlay 2009; Holzer et al. 2006;
Pager 2007; Wozniak 2015). Researchers have focused primarily on men in low-skill
jobs and examined either criminal records checks or drug testing, although many
employers screen for both and may be more concerned about one trait (e.g. current drug
use) but not the other (e.g. certain past arrests). Researchers have tried to tease out the
effect of these screens using audit methods, cross-sectional employer surveys or variation
in state policies, yet we know very little about the aggregate impact of corporate
screening policies on the racial and ethnic composition of their workforce.
In this study, we use rich longitudinal data from a national sample of mid-sized
and large U.S. employers between 1971 and 2002 to answer a straightforward but crucial
question that has not been addressed: after employers adopt pre-employment criminal
background checks or drug testing, does the share of black and Hispanic men and women
in their workforce increase or decline? We examine this in both blue-collar and
professional jobs across different institutional contexts.
We advance the current debate in three key ways: First, we use existing evidence
on employer aversion to hiring ex-convicts or drug users to develop and test hypotheses
about screens’ effects on minority employment in both blue-collar and professional jobs.
Second, we examine how federal regulations on the employment of ex-convicts and drugusers shape screens’ effect. Third, we examine whether the effect of screens varies
depending on the rate of minority incarceration.
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By bridging research on mass imprisonment and research on organizations and
inequality, the present study expands our understanding of the organizational practices
and legal contexts that contribute to minorities’ exclusion from employment. With the
exception of a handful of studies, the effects of screens on minority employment have
received remarkably little attention, especially from scholars of organizations. Theorists
of racial discrimination emphasize the growing centrality of institutional discrimination
in shaping contemporary racial inequality (Feagin 2003; Alexander 2012). This study
provides an opportunity to gain insight into its working and help policy makers reach
scientifically informed decisions regarding pre-employment screens.
INSTITUTIONAL DISCRIMINATION, STATISTICAL DISCRIMINATION,
AND THE EFFECTS OF INDIVIDUATING INFORMATION
Criminal history records have been legally available to the public since the Supreme
Court’s 1976 decision in Paul v. Davis, in which the court ruled that the publication of
official acts, including arrests, convictions, and incarceration records, were not protected
by privacy rights (Finlay 2009:92). Drug testing techniques were first developed by the
military in the late 1960’s in response to drug-addicted soldiers returning from Vietnam
(Gee et al. 2005). But employer criminal record checks and drug testing became
widespread only in the 1980s with the moral panics swirling around the wars on crime
and drugs (Beckett et al. 2005; Gee et al. 2005; Knudsen et al. 2003; Alexander 2012;
Wozniak 2015). Employers began paying much closer attention to criminal records and
drug use as both salient markers of human capital (Decker et al. 2015; Holzer et al. 2006;
Pager 2007; Bushway 2004; Harris and Keller 2005) and a source of concern about
perceived or real legal liability (Holzer, Raphael, and Stoll 2004; Stoll and Bushway
4
2008; Todd 2004). Published surveys show that between 50% to 60% of employers use
criminal background checks (Stoll and Bushway 2008:381), and as many as 80% of all
employers use drug testing (American Management Association 1996; Gee et al. 2005).
We offer a theoretical framework for understanding the effects of employer
adoption of hiring screens that is based on the interplay between institutional and
statistical discrimination. Our basic argument is straightforward and largely mirrors the
public debate over these screens: if, prior to adopting screens, employers either did not
use race and ethnicity as a proxy for criminal background or drug use, or underestimated
the true rate of these traits among minorities, screens will reveal the higher underlying
rates and will reduce minorities’ employment chances (Pager 2007; Clifford and Shoag
2015). If employers did use race and ethnicity as a proxy for criminal background or drug
use, or overestimated the rate of these traits among minority groups, screens will reveal
the true, lower rates, and expand employment opportunity for those groups (Wozniak
2015; Holzer et al. 2006).
To use Autor and Scarborough’s (2008: 222) terms, our model suggests that
screens’ effects depend on the relative biases inherent in the formal and informal screens.
As long as the information provided by criminal background or drug-use screens about
minority applicants is not systematically more negative than employers’ informal
judgments, the adoption of screens should not disparately impact minority hiring. If the
information provided by screens is systematically worse than employers’ initially
assume, then screen adoption will reduce minority employment. If information provided
by screens is more positive than previously assumed, screen will increase minority
employment. It is important to note, however, that we do not directly observe whether
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employers’ make biased decisions absent screens. We can infer this based on what we
know about the rate of the screened trait in the population and the change in minority
employment following screen adoption (Autor and Scarborough 2008; Wozniak 2015).
We also draw on existing research on employers’ aversion to hiring ex-convicts or drug
users to bolster our inferences about employers likelihood of engaging in statistical
discrimination via racial proxies prior to adopting screens.
Below, we briefly discuss the concepts of institutional discrimination and
statistical discrimination and their implications for screens’ effects, and make predictions
about the contexts in which each might take place, according to what we know
employers’ aversion to drug use or criminal records across job types. We then discuss
and present hypotheses about how the institutional environment, regulations and
incarceration rates are expected to shape the impact of these pre-employment screens.
Institutional Discrimination
Black intellectuals since Frederick Douglass and W. E. B. Du Bois have long pointed to
the institutional nature of racial discrimination, and the 1960’s civil rights movement
brought a renewed emphasis among race scholars (Feagin 2014:144). While most
Americans believe that racial discrimination belongs to the past or to few “bad apples,”
contemporary race theorists explore the manifold, subtle, socially embedded and facially
neutral discriminatory norms and laws that perpetuate racial inequality (Kinder and
Sanders 1996; Bobo, Kluegel and Smith 1997; Balibar 2007; Bonilla-Silva 2006;
DiTomaso 2013; Feagin 2013). The wars on crime and drugs have been a source of
multiple mechanisms of institutional discrimination as they legally exclude ex-convicts,
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which are disproportionately minorities, from key areas of civic participation (Alexander
2012).
In a somewhat parallel development, organizational scholars began to examine
the ways organizational structures shape gender and racial inequalities even more
profoundly than individual determinants (Kanter 1977; Baron and Bielby 1984; Nkomo
1992). Organizational rules that are correlated with social inequality perpetuate that
inequality (Acker 1991; Nonet and Selznick 1978). Formal human capital criteria such as
soft skills, high credit scores, costumer-client match and clean criminal records or druguse checks have real or perceived racial correlates (Moss and Tilly 2001; Autor and
Scarborough 2008; Maroto 2012; Marantz et al. 2014; Becker 2015). Thus for example,
studies have shown that black financial consultants are disadvantaged when marketing
strategies match consultants’ and clients’ race (Bielby 2012); women are disadvantaged
when promotion policies require experience in jobs almost exclusively filled by men
(Williams 2002; DiPrete 1988); and women and black managers are disadvantaged when
re-structuring rules cut departments most distant from the core business (Kalev 2014).
These policies do not explicitly target specific racial, ethnic or gender groups, but they
capture, reproduce and expand inequalities along these lines (Wilson 2012). In this way,
institutional discrimination is carried out by formal and legitimate structures.
Criminal record checks similarly create an avenue for institutional discrimination,
wherein facially neutral policies, rationalized in business terms, may disparately impact
racial minorities. There are simply more blacks and Hispanics with arrests or convictions
than whites, and when these appear on a background check, employment chances are
significantly reduced. The lifetime likelihood of an African American, Hispanic and
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White male adult to have ever been incarcerated in state or federal prison was 16.6%,
7.7%, and 2.5% respectively in 2001 (Bonczar 2003). These rates are significantly lower
for the highly educated. Among black men born between 1975 and 1979, 68% of high
school dropouts are estimated to have ever been imprisoned by 2008, compared to only
6.6% with a college degree (Western and Pettit 2010). For white men, these numbers are
28% and 1%, respectively, and for Hispanic men, 19.6% versus 3.4%. Female
incarceration rates are significantly lower, although they have grown faster than men’s
since the 1980s (Beck, Karberg, and Harrison 2002, cited by Beckett and Sasson 2004:3;
Galgano 2009). By 2001, 1.7% of adult black women had ever been incarcerated,
compared with 0.7% for Hispanic women and 0.3% of white women (Bonczar 2003).
Criminal record checks then can officially disadvantage minority job candidates by
adding the negative mark of a criminal record (Pager 2007).
In contrast, drug-use rates are not correlated with race and ethnicity. Blacks and
Hispanics are as likely to use drugs as whites, with women and the more educated
reporting lower drug use rates (Wozniak 2015). Based on this, drug testing should not
disproportionately impact minorities’ employment chances. There is evidence that
employers of minority workers are more likely to test for drug use compared to
employers of white workers (Becker et al. 2014). Minorities may therefore be more
exposed to the downsides of drug testing overall.
But whether the adoption of criminal background or drug-use screens reduces
minority hiring depends not only on whether minorities are more likely to fail these tests
than whites, but also on employers’ hiring patterns prior to having access to information
provided by screens.
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Statistical Discrimination
In the absence of information about job candidates’ criminal history or drug-use,
employers anxious to avoid hiring candidates with these traits might rely on potentially
inaccurate information or beliefs about the likelihood an applicant has these traits, based
on proxies related to the group to which the individual belongs. Economists refer to this
decision making as statistical discrimination and assume that employers’ estimates will
eventually align with the true rate (Aigner and Cain 1977). However, empirical evidence
indicates that statistical discrimination can also be based on sustained, inaccurate
estimations (Autor and Scarborough 2008; Finlay 2009; Holzer et al. 2006).
It is likely that employers use candidates’ race and ethnicity as markers for
criminality and drug use. There is rich evidence that the American public commonly
associates both traits with racial and ethnic minorities (Becker et al. 2014; Beckett et al.
2006; Burston, Jones, and Roberson-Saunders 1995; Chiricos et al. 2004; Fishman 1998;
Gee et al. 2005; Holzer et al. 2006; Pager and Shepherd 2008). Results from audit
studies suggest that hiring managers associate minorities with criminality and hire them
at lower rates (Pager 2003; Pager et al. 2009; Decker et al. 2015). For example, Uggen et
al. (2014) find that black men who disclose misdemeanor records on job applications
have higher odds of getting a callback than black male candidates who offer no criminal
background information; without this information, employers presumably assume the
worst. A weaker racial effect in hiring was observed for black and white women
(Galgano 2009). As for drug use, Wozniak (2011) documents a prevalent belief among
hiring managers that blacks are likely to fail drug tests. We take these studies to support
our claim that in the absence of individuating information, employers that are averse to
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hiring ex-convicts or drug users will engage in statistical discrimination associating race
and ethnicity with the undesired trait and hire fewer blacks and Hispanics (Bushway
2004; Holzer et al. 2006; Wozniak 2015). This will significantly shape the impact of
screen adoption on minority employment. If employers did not use race/ethnicity as a
proxy for past convictions, then the adoption of criminal background screens will harm
minorities’ employment chances (Pager 2003). Drug testing should have null effect on
minorities. If employers did use proxies and overestimated the rates of criminal records
and drug use among minorities, screens will provide more positive, individualized
information that can improve the employment chances of minorities (Bushway 2004;
Holzer et al. 2006; Wozniak 2015).
It is important to emphasize that although our theoretical model for predicting
screens outcome is based on the match between the exclusionary biases in informal and
formal screening, we do not observe whether during informal screening employers indeed
engage in statistical discrimination on the basis of race and ethnicity. Instead, we theorize
that such hiring patterns are more common among employers that express stronger
concern about employing ex-convicts or drug use and we split our analysis accordingly
(Holzer et al. 2006:463).
The Labor Process, Employer Aversion and the Effect of Screens
Existing research, albeit limited, shows that the value employers place on avoiding hiring
drug users differs from that placed on lack of a criminal record, and it varies by the type
of labor process they manage. Key is whether the job is professional or blue collar. In a
nutshell, studies find that employers of low-skill workers are more concerned about
workers’ drug use and less about their criminal history, and employers of professional
10
workers are worry more about employing ex-convicts and less about drug users (Holzer
et al. 2004; Wozniak 2015; Hartwell et al. 1996; Knudsen et al. 2003).
Aversion to Hiring Ex-Convicts –In a survey of 600 employers in Los Angeles County in
2001, Holzer et al. (2004:41) find that employers in the manufacturing, transportation and
construction sectors, and those with a larger fraction of unskilled workers express more
willingness and less aversion to hiring ex-offenders. In contrast, employers in the service
sector and those who employ only professional workers disproportionately express high
aversion to hiring ex-offenders. A similar pattern is reported in Holzer et al. (2006: Table
A1) based on data from the Multi-City Survey of Urban Inequality.
Holzer et al. (2006:469) show that the effects of criminal record checks are
associated with employers’ willingness or aversion: willing employers that use criminal
background checks hire fewer blacks than do averse employers that use these checks. The
reasoning is that after the adoption of criminal records checks, blue-collar employers are
likely to exclude candidates convicted of certain crimes, if only to avoid the potential
liability of knowingly hiring someone with a problematic criminal history (Stoll and
Bushway 2008). This will have a disproportionate impact on minority men, who have
significantly higher conviction rates than white men. The effect should be smaller for
minority women, because they have lower rates of criminal records, especially for serious
offenses (Holzer et al. 2006; Galgano 2009). We therefore expect that:
H1a: The adoption of criminal records checks will reduce the share of black and
Hispanic workers, especially men, in blue-collar jobs.
Employers of blue collar workers may also be less averse to employing illegal
immigrants, largely due to labor market considerations (Moss and Tilly 2001). The
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introduction of criminal record screens might have chilling effects on undocumented
Hispanics, thereby reducing their employment shares (Borjas, Freeman, and Katz 1992;
Borjas and Katz 2007).
In contrast, because employers of professionals are more averse to hiring exconvicts, in the absence of formal checks, they are more likely to engage in statistical
discrimination and exclude minorities (Holzer et al. 2006). Because conviction rates are
so low among highly educated blacks and Hispanics, when criminal background checks
are adopted they will likely reveal lower-than-expected rates of conviction, and minority
hiring will increase. Therefore:
H1b: The adoption of criminal record checks will increase the share of black and
Hispanics workers, especially men, in professional jobs.
Aversion to Hiring Drug Users.-- In contrast to their greater willingness to hire exconvicts, several studies conclude that employers of blue-collar workers are concerned
about drug use and more likely to have drug testing than employers of white collar,
college-educated workers (Hartwell et al. 1996; Knudsen et al. 2003; Haar and Pell 2007;
Becker et al. 2014). Using the 1997 National Employee Survey, Knudsen et al. (2003)
find that firms that are rule-oriented and have a high degree of mechanization are more
likely to adopt drug testing while firms in service and FIRE industries are significantly
less likely to adopt drug testing. This is consistent with evidence that employers view
drug use as a safety problem (Spell and Baum 2001:116; Gee et al. 2005:756).
We therefore assume that, absent individuating information, employers of blue
collar workers are more likely to engage in statistical discrimination that associates
minority status with drug use. Given that drug-use rates are similar across racial and
12
ethnic groups, the introduction of drug testing in these contexts will reveal lower-thanbelieved rates of drug use and thereby lead to an increase in the hiring of minorities.
Wozniak (2015:560) finds significant employment increases for low-skill black men and,
to a lesser extent, black women, after states adopt pro-testing legislation. Therefore:
H2a: The adoption of drug testing will increase the share of black and Hispanic
men and women in blue-collar jobs.
Employers are less concerned about drug use among professional workers (Hartwell et al
1996; Knudsen et al. 2003). In a national employer survey from 1993, Hartwell et al
(1996) find an inverse relationship between the share of workers with college degrees and
the likelihood that employers have drug testing. Once testing is adopted, minorities
should not show higher rates of drug use than whites, and so the introduction of
individualized information will not change minority hiring patterns. Indeed Wozniak
(2015) finds no effects of pro-drug testing laws on black professional workers. Therefore:
H2b: The adoption of drug testing will not change the share of black and
Hispanic workers, both men and women, in professional jobs.
Figure 1 includes a summary of hypotheses 1 and 2 regarding the effects of screens on
minority employment in professional and low-skill jobs.
INSERT FIGURE 1 ABOUT HERE
THE LEGAL ENVIRONMENT AND SCREENS’ EFFECTS
There are numerous, overlapping and frequently ambiguous state and federal laws and
regulations regarding the employment of ex-convicts or drug users and the use of screens,
leaving many employers, as well as researchers, confused regarding how to comply
(Holzer et al. 2004; Stoll and Bushway 2008). We focus on relatively straightforward
13
federal regulations: the EEOC guidelines to avoid blanket exclusion of ex-convicts and
the Drug Free Workplace Act of 1988. Both can shape screens outcomes by affecting
employers’ willingness or aversion to hiring ex-convicts and drug users.
Title VII and Criminal Record Checks.— A blanket exclusion from employment of exconvicts is unlawful under Title VII of the Civil Rights Act, due to the disparate impact
on blacks and Hispanics. The Equal Employment Opportunity Commission (EEOC) has
been enforcing these regulations since at least 1969.1 All private employers with more
than 15 workers are subject to this regulation, but federal contractors are under
heightened antidiscrimination oversight and thus should be more concerned than noncontractors about avoiding disparate impact (Dobbin et al. 2015). Survey data indeed
show that federal contractors are less averse to hiring ex-offenders (Holzer and Neumark
2000). Therefore, we expect contractors to hire more ex-convicts once checks are in
place. Thus, other things being equal:
H3a: Being a federal contractor will improve the effect of criminal record screens
on minority employment.
The Drug-Free Workplace Act and Drug Testing.— The Drug-Free Workplace Act of
1988 requires certain categories of federal contractors and grantees not to employ
workers that use illicit drugs in jobs related to the contract.2 This Act should make federal
contractors more averse to hiring drug users compared to non-contractors and so more
likely to engage in statistical discrimination and exclude minorities absent individuating
information. Once contractors start testing prospective employees for drugs, they will
find that minorities do not use drugs at higher rates than whites. Thus, we can expect that:
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H3b: Being a federal contractor will improve the effect of drug testing on
minority employment after 1988.
Disparate Screening.— We present a caveat for this hypothesis. This caveat links
statistical discrimination to the formal screening phase as well. We claim that it is
possible that employers do not equally test all candidates, but rather, use racial and ethnic
proxies in their decisions whom to test. If this is the case screens will reduce minority
employment simply because minorities are tested at a higher rate.
There is rich evidence of firms’ unequal use of formal skill tests, personnel
policies and disciplinary actions across racial lines (Dobbin 2009; Dobbin, Schrage, and
Kalev 2015; Roscigno 2007; Wilson and McBrier 2005; Zwerling and Silver 1992).
Although minorities report more drug testing at work than whites (Becker et al. 2014)
there is no direct evidence of testing disparities within the same workplace.
Our data are at the organizational level and do not include information on which
job candidates were screened. But if there is an uneven use of drug testing, screen
adoption may be followed by unexpected negative effects on minority employment. This
will show most clearly in the case of professional workers, because we assume there is
less statistical discrimination against minorities prior to testing, and because drug-use
rates are similar for race/ethnic groups.
H2c: The adoption of drug testing will decrease the share of black and Hispanic
workers, men and women, in professional jobs.
THE DYNAMIC EFFECT OF THE WAR ON CRIME AND DRUGS
Our inquiry about the effects of drug testing and criminal record checks taps into the
question of the mechanisms by which the wars on crime and drugs hurt minorities’
15
employment disproportionately. Is it through the institutionalization of hiring screens, or
through the institutionalization of employer association of minority status with
convictions or drug use (Pager 2007; Alexander 2012; Wozniak 2015)? This question is
important because it has implications for how to lessen the harms of the wars on crime
and drug. We look at how state incarceration rates shape screens’ impact.
According to the institutional discrimination hypothesis, rising incarceration rates
will lead to more minorities failing criminal records checks (though not drug testing) and
therefore increase minority exclusion from hiring after adoption of such checks.
According to the statistical discrimination hypothesis, rising incarceration rates can
intensify employer’s reluctance to hire ex-convicts and drug-users, and therefore their use
of statistical discrimination in informal screening. The adoption of formal criminal
background checks and especially drug testing would then reveal lower rates of
convictions and drug use relative to what was assumed prior to adoption and will increase
minority employment. We therefore offer no formal hypothesis, but note that the results
provide an indication of underlying mechanisms.
DATA
We use fixed effects analysis of rich longitudinal data on the workforce composition and
hiring policies of 805 establishments to estimate changes in the proportion of black and
Hispanic women and men in professional and blue collar jobs following the adoption of
criminal record checks and drug testing programs between 1971 and 2002. The dataset
used for this study combines an employer survey with federal data on these employers’
workforce composition, local labor markets and incarceration statistics.
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Workforce Composition Data.— The workforce composition data come from annual
reports (EEO-1 reports) submitted to the EEOC by all private sector employers with more
than 100 employees and government contractors with more than 50 employees and
$50,000 worth of contracts.3 These data were obtained for research purposes from the
EEOC under an Intergovernmental Personnel Act (IPA) agreement. The reports detail the
sex, racial and ethnic composition of their workforce in nine broad occupations. We
analyze the composition of professional jobs, which is a single occupational category on
the EEO-1 form, and of blue-collar jobs, which is a composite of four categories: Craft,
Operatives Laborers and Service workers.4 EEO-1 data were obtained for 1971-2002,
excluding the years 1973, 1974 and 1976 that are not available and were interpolated
using a linear function. The results are not sensitive to the inclusion of these years.
Employer survey.— Data on employer practices come from a retrospective survey of 833
organizations that was conducted in 2002 by the Princeton Survey Research Center. The
sampling frame was based on the EEO-1 reports submitted in the year 1999. The sample
was stratified by industry (including industries in the manufacturing, retail and service
sectors), age in the data (half of the establishments had been in the data for at least 8
years, half for at least 20 years) and size (with 35% of the establishments having less than
500 workers). Experienced Human resources or line managers were surveyed by phone
about their pre-employment screening policies and the years in which they were first
adopted and about other organizational policies and characteristics. Respondents’ average
tenure was 11 years. The response rate was 67 percent.
The survey data were merged with the EEO-1 data and organized in a crosssection time-series structure, with organization-year as the unit of analysis. After
17
excluding cases for unusable data, the number of organizations participating in the
analysis of blue-collar jobs is 778 and the number of organization-years is 17,134. For
professional jobs these numbers are 773 and 16,247 respectively. The median
organization appears in the dataset for 25 years. The minimum number of years is four
(by design) and the maximum is 32.
MEASURES OF KEY VARIABLES
Dependent Variables: The Composition of Professional and Blue-Collar Jobs
Our dependent variables are the proportion of black and Hispanic men and women in
professional and blue-collar occupations in each establishment. Figures 2a and 2b show
the changes in the share of each group in blue-collar and professional jobs between 1971
and 2002 in our sample. Women made more gains than men in professional jobs and
blacks have made the least gains in blue collar jobs. Our sample excludes younger and
smaller organizations as well as public sector and non-profit organizations, and thus it
somewhat under-represents the demographic diversity of the national labor market.
FIGURES 2A, 2B ABOUT HERE
Because of the high levels of racial segregation at work (Stainback and TomaskovicDevey 2012), wherein minorities may hold very few professional jobs in the typical firm
but many jobs in some firms—and vice versa in the case of blue-collar jobs—
composition distributions are skewed. We therefore transform the proportions of each
group in the focal job (blue-collar or professional) to log odds (Fox 1997:78).5
Key Independent Variables: Criminal Record Checks and Drug Testing
The main independent variables of interests are two binary variables based on survey
responses, indicating whether the establishment has a program of drug testing or criminal
18
record checks of job candidates. The variables are coded 1 in each organization-year
where the policy existed and 0 otherwise (before adoption, after revocation, if applicable,
or in all years in organizations that never adopted). In this national sample of mid-size
and large workplaces, a total of 65 percent adopted criminal record checks and 75 percent
adopted drug testing by 2002. Figure 3 shows the proportion of workplaces with criminal
record and drug-use pre-employment screens from 1971 to 2002. The percentages are
based on the number of surveyed workplaces that existed in each year. The sample
represents older and more bureaucratic and stable work establishments, which are more
likely to adopt these screens (Holzer et al. 2004). Patterns might be less pronounced in a
sample of younger firms. The trend in Figure 3 suggests an effect of the 1988 Drug Free
Workplace Act on the adoption of drug testing, while the percent of criminal record
checks grows relatively uniformly.
FIGURE 3 ABOUT HERE
The distribution of screens across employers where the core job is professional versus
blue-collar is consistent with findings about employers’ aversion to hiring of ex-convicts
or drug users to different jobs. By 2002, criminal record checks were adopted by 78
percent of employers with mostly professional jobs, compared to 58 percent in
workplaces with blue-collar core jobs. Drug tests were adopted by 66 percent of
professional workers’ employers compared to 85 percent of employers of mainly bluecollar workers.
Variables on Federal Regulations and Incarceration Rates
Data on federal contractor status, for examining the effect of federal regulations on hiring
of ex-convicts and drug users, come from employers’ EEO-1 reports. About 48 percent of
19
the organization-year spells in our data have federal contractor status. As expected, given
the legal requirement for a drug free workplace, the adoption rate of drug testing among
federal contractors is higher than among non-contractors (85 versus 66 percent
respectively by 2002). We see smaller differences in the adoption of criminal record
checks (62 versus 68 percent respectively by 2002). To measure the period where the
Drug Free Workplace Act was in place, we use a binary variable that equals 1 in all years
from 1988 onward and 0 before that.
We include measures of state incarceration rates using data from the Bureau of
Justice Statistics’ Correctional Populations in the United States series. The incarceration
rate is measured as the total number of prisoners under a state’s jurisdiction serving
sentences of a year or more per 100,000 people. Incarceration rates rose from 8 in 1971 to
42 in 2002. The mean incarceration rate across state was 26, the first quartile was 22 and
the 3rd quartile 31. We divided the rates by a 100 in the analysis.
MEASURES OF OTHER FACTORS SHAPING WORKFORCE COMPOSITION
We incorporate in our analyses a host of time-varying measures of organizational and
labor market features that can affect the organization’s demographic makeup. Some of
these may also be correlated with employers’ decision to adopt criminal record checks or
drug testing, thus omitting them may lead to spurious results (Wozniak 2015; Finlay
2009; Stoll and Bushway 2008). Table 1 includes information on variable means,
standard deviations, ranges, form, and data sources for all variables included in the
analyses in both subsamples used. Variation stemming from stable characteristics, such
as industry or geographical area is accounted for by the establishment fixed-effects.
TABLE 1 ABOUT HERE
20
Labor Market Characteristics
Labor market composition will affect the supply of workers from different demographic
groups, and might affect employers’ decisions to adopt certain screens (Finlay 2009;
Wozniak 2015). We include the proportions of black, Hispanic and white men and
women in the establishment’s two-digit industry workforce and state workforce, obtained
from the Current Population Survey. The category for Hispanic first appears in 1977, thus
we used the 1970 census and interpolated the years in between. The results we report are
not sensitive to the inclusion of these interpolated measures. We include measures for
state unemployment rate and two-digit industry employment size, obtained from the
Bureau of Labor Statistics. When unemployment rises, minority employment tends to
shrink first (Farber 1997). Growing industries should experience a tighter labor market,
thus expanding opportunities for minorities (Reskin and Roos 1990). We include a
measure of the share of federal contractors among industry employers, as their demand
for minority workers is higher (McTague, Stainback, and Tomaskovic-Devey 2009).
Organizational Structures
We include in our analyses organizational measures found to affect workforce
demographics, some of which may also affect the adoption of screens, and so their
inclusion eases concerns about endogeneity.
Personnel Structures.— We measure the adoption of employer diversity programs
(including the adoption of a diversity officer, a diversity committee, diversity mentoring
and networking programs, diversity evaluations and diversity training) and affirmative
action plans, all of which affect the demographic makeup of the workforce (Edelman and
Petterson 1999; Hirsh and Kmec 2009; Kalev, Dobbin, and Kelly 2006). Work-family
21
programs may reduce caregivers’ career obstacles and improve women’s employment
(Glass and Riley 1998). Unionization may affect workforce diversity as well as the
adoption of pre-employment screening (Holzer et al. 2006; Kelly 2003). Employers’
formal hiring and promotion procedures shape workforce composition (Reskin and
McBrier 2000; Dobbin et al. 2015). We use a count of the adoption of a human resources
department, written hiring and promotion guidelines, job descriptions, promotion ladders,
performance evaluations, a pay grade system and internal posting of jobs. We include a
measure for the adoption of a skill test in hiring and a count of
Legal Awareness.-- Employers’ awareness of antidiscrimination laws and experience
with charges may reduce discrimination and affect workforce composition (Edelman and
Petterson 1999; Holzer and Neumark 2000 Kalev and Dobbin 2006; Skaggs 2008;
Skaggs 2009). We include measures for a legal department and an experience of
antidiscrimination lawsuit or EEOC charge or a Department of Labor compliance review.
Demand for Workers.— Organizational expansion can increase demand for minorities
(Baron, Mittman, and Newman 1991; Haveman, Broschak, and Cohen 2009) and also
lead to screen adoption. We measure establishment size and the size of the focal job
category (professional or blue-collar).
Organizational Workforce Composition.— The demographic composition of an
organization’s leadership and workforce affects hiring patterns (Cohen, Broschak, and
Haveman 1998; Moss and Tilly 2001). We measure of the percent of women and blacks
among the top ten executive positions using data obtained from the survey for the years
1982, 1992 and 2002, and linearly interpolated for the years in between. For nonmanagerial composition we measure the share of the focal group in non-managerial, non-
22
focal jobs. Finally, we include a flag variable for whether there is no representation of the
focal group in the focal job. The results are not sensitive to the inclusion of this measure.
ANALYTIC STRATEGY
We use OLS regressions with fixed effects for establishment and year to estimate the
change in each demographic group’s employment share in the focal jobs in the years after
screens were put in place. The mean number of years that criminal record checks and
drug testing were in effect in our data is 10 and 12 respectively. We lag the outcome
variables by a year to allow time for screens adopted during the previous year to affect
job demographics.
In addition to the important control variables used in the analysis, the fixed effects
for each workplace account for variance that comes from unobserved stable features of
these workplaces, such as organizational culture. The reported R2 in these models do not
include variation captured by establishment fixed effects. The year fixed effects account
for unmeasured environmental changes that affect all organizations, such as change in the
content of stereotypes. The establishment and year fixed effects also offer an efficient
means of dealing with the non-constant variance of the errors (heteroskedasticity) that
stems from the cross-sectional and over-time aspect of the pooled data (Sayrs 1989). In
addition we use the Huber-White robust standard errors to account for the clustering of
errors due to the panel data. We discuss a series of robustness checks at the end of finings
section. Results for these are presented in the appendix.
FINDINGS
Tables 2, 3, 4a, 4b and 5 include results from our analysis of the effect of screen adoption
on workforce composition. Exponentiating the coefficients, [exp(β) -1]*100, gives the
23
average percent change in the odds that professional or blue-collar workers are from a
focal demographic group in the years following the adoption of either screen, net of all
other variables and fixed effects. When the coefficient’s absolute value is smaller than
0.1, the percent change can be calculated simply as β*100. The error of such
approximation is about 0.005. Coefficients for control variables are presented in the
Appendix Tables.
The analysis confirms our expectation that the effect of screens varies across
types of labor process and institutional environments. In a nutshell, coefficients in Table
2 show that, on average throughout the period, the adoption of drug testing reduces the
share of Hispanics in professional jobs and increases black men and Hispanics in bluecollar jobs. The adoption of criminal record checks is followed by increases in the share
of black men and women in professional jobs and reductions in Hispanic men and women
in blue-collar jobs. Tables 3a shows that black men, too, experience a negative impact of
criminal record checks in blue-collar jobs, but only among non-contractors. Table 4b
shows that contrary to expectations, the Drug-Free Workplace Act significantly amplified
the negative effects of drug testing on minorities in professional jobs. Table 4a shows that
in blue-collar jobs, the Act improved the effects of drug testing on black women. Finally
Table 5 show that the effects of both screens on blacks in professional jobs improve as
incarceration rates go up.
24
Criminal Background Checks and the Share of Minorities in Blue-Collar and
Professional Jobs
Estimates for the average effect of screens in the period after adoption on the composition
of blue-collar jobs are presented in Table 2, under Model A, and for professional jobs
under Model B.
The adoption of criminal record checks is followed by declines of about 10
percent in the odds of Hispanics employed in blue-collar jobs. In Table 3 we see that
black men also experience a similar decline but only among employers that are not
government contractors. The negative results for Hispanics could be partially a result of
criminal record checks deterring undocumented Hispanics from applying to these jobs.
This interpretation is especially likely for Hispanic women, who do not have high
incarceration rates. That the negative effects on Hispanics are not sensitive to contractor
status further supports this interpretation.
To examine the magnitude of these effects relative to the overall change of
minority employment shares in blue-collar jobs during the period under study, we
calculated the percent change between the mean proportion of blue-collar workers from a
certain group and the predicted proportion following the adoption of criminal record
checks. These magnitudes are calculated using the coefficients in Tables 2 and 3a and are
associated only with the adoption of the focal screen (Petersen 1985). The average
employer in our sample employed 5.4 percent Hispanic men in blue collar jobs. But the
introduction of criminal record checks in the average organization is responsible for
almost 10 percent decline in their mean share, to a mean of 4.9 percent. As figure 2a
shows, during the entire period under study, between 1971 and 2002, the percent of
25
Hispanic men increased from 3 percent in 1971 to 7.4 percent in 2002. For Hispanic
women, the increase during the period is from being 1.6 to 4.2 percent of the blue-collar
workforce, while criminal record screens reduced their share from 2.9 to 2.6 percent in
the average organization, a decline of almost 12 percent.
The average non-contractor employer had 7.4 percent black men in their bluecollar jobs during the period under study. The adoption of criminal records checks
reduces the percent of black men in blue-collar jobs by eleven percent, to 6.6 percent.
During the entire period under study, the share of black men in blue-collar jobs has
hardly changed. Criminal record checks seem to have contributed to this stagnation.
Model B in Table 2 shows that in professional jobs, in contrast, criminal
background screens are followed increases in the odds of black men and women and no
significant change in the share of Hispanics, although the coefficients are positive and
standard errors are relatively small. Based on our theory of the relative bias of the
informal and formal screening, these results suggest that prior to screen adoption
employers were overestimating blacks’ conviction rates and excluding them from hiring.
Calculating the percent change in the percent of each group we see that in the average
adopting organization in our sample, the mean proportion of black women in professional
jobs increased due to these checks by 11.5 percent - from 1.9 to 2.2 percent. For black
men, the increase is 7 percent, from a mean of 1.3 percent of professional workers to 1.4.
As Figure 2b shows throughout the entire period black women increased in professional
jobs from 1 percent to 3.1 percent and black men from 0.5 to 1.7 percent. Contractor
interaction had no effect in the models for professional jobs. We therefore do not present
these models.
26
Drug Testing and the Share of Minorities in Blue-Collar and Professional Jobs
The adoption of drug testing shows more or less the opposite effects from criminal
records checks, as predicted. In blue-collar jobs, drug testing is followed by increases of
about 8 percent in the share of black men and Hispanic men and women. Given that black
men’s share in blue collar jobs did not increase throughout the period this increase is
especially significant. This is consistent with Wozniak’s findings (2015) and with the
theory that employers more averse to hiring drug users, as previous studies find blue
collar employers to be, are more likely to statistically discriminate prior to adoption of
formal testing, and thus testing will increase minority employment.
In professional jobs, Hispanics’ employment share declines after employers adopt
drug testing. Tests should reveal similar rates of drug use for minorities as for whites. We
hypothesized that negative effects can occur if employers of professionals test Hispanics
at higher rates, but this deserves further research as we do not observe individual
screening. We discuss this in the conclusion section.
Tables 4a and 4b include coefficients from an analysis of the effects of the 1988
Drug-Free Workplace Act. Because the Act only applies to government contractors, we
constructed a three-way interaction between drug testing, being a government contractor,
and the period from 1988 onward in order to examine the effect of the Act on drug testing
outcomes. Regardless of the Act, federal contractors are also under heightened
regulatory Department of Labor oversight to avoid employment discrimination of women
and minorities. We estimate the baseline effects of being a federal contractor on minority
employment using the variable for whether an employer is a federal contractor and the
27
interaction of federal contractor with the post-1988 period. The variable for post-1988
accounts for the baseline period effect.
Results for professional jobs are included in table 4a. The coefficients for “drug
testing” show that before 1988, adopting drug testing reduces the share of Hispanic men
and women in professional jobs among non-contractors. And contractor status makes no
significant difference in this period (drug testing*contractor). After 1988, the negative
effects of drug testing on Hispanic men and women disappear among non-contractors
(drug testing*p88). But among government contractors – those employers covered by the
Act – drug testing shows significant negative effects on black men and Hispanic men and
women. That is, drug testing is more likely to reduce minorities’ share in professional
jobs among contractors after 1988. This result is surprising, given that minority drug use
rates are not higher than whites. We discuss this unexpected finding in the conclusion.
The results in table 4a also tell us that the negative effects of drug testing on
Hispanics we saw in table 2 characterize all adopting employers in the early period, but
only contractors after 1988, where black men also saw negative effects of drug testing.
During this later period, minority employment in professional jobs shrank overall
(“Period 1988)” though less so among federal contractors (“Contractor*P88”), perhaps
due to antidiscrimination pressures. Yet, these relatively better odds for minorities among
contractors are significantly offset when drug testing is adopted.
The Drug-Free Workplace Act had a smaller but positive effect on minority
employment in blue-collar jobs, improving the odds of black women in testing firms, as
seen in Table 4b. Prior to 1988, the employment share of both black women and men
declined following drug testing adoption among contractors (drug testing*contractor).
28
After the passage of the Act in 1988, black women no longer experienced a negative
effect of drug testing among covered employers, while black men continue to experience
such negative effect. Another way to look at the results is that, after 1988, the
employment share of black women and Hispanic men and women in blue-collar jobs
increased, and for black women these increases were significantly larger among covered
employers that had drug testing. That is, drug testing help increase black women’s
employment among covered employers, but no other groups.
Do Rising Incarceration Rates Increase Statistical or Institutional Discrimination?
Table 4 includes results from an analysis examining whether screens’ effects are
shaped by incarceration rates. The interaction of incarceration rates with criminal record
checks or drug testing were only significant in the models for professional workers.
Hence we present only those. Rising incarceration rates, which are disproportionately of
minorities, may deepen institutional discrimination because more minorities are likely to
fail checks. Incarcerations rates could also strengthen employer aversion to hiring exconvicts or drug users. We made no formal hypothesis. In the baseline model (see
Appendix tables A2a and A2b) growing state incarceration rates show no effect in bluecollar jobs and improve odds for blacks in professional jobs. In Table 4 we see that this
positive effect works through the interaction with criminal record checks, which is
significant and positive for black men and women in professional jobs. That is, as state
incarceration rates grow, criminal records checks are followed by increases in the share
black men and women in professional jobs. When incarceration rates were at their period
mean 26, criminal record checks were followed by about a 5 percent (0.155+0.792*0.26=0.051) increase in the odds that professional workers are black
29
women. For black men the increase is much smaller, about 0.7 percent (0.172+0.688*0.26=0.007). In 2002 the mean incarceration rate was 42. At this level of
incarceration, criminal record checks increase black women’s odds in professional jobs
by about 20 percent and of black men by 12 percent. Drug testing effects on black men’s
employment share also vary by incarceration rates. When incarceration rates are at the 25
percentile, drug testing reduces the odds of black men by about 5 percent. At the period
mean of incarceration rates, drug testing has no effect. But when incarceration rates are at
the 75 percentile (36), drug testing improved black men’s odds by about 4 percent and
when they were at their 2002 mean, this was a 6 percent increase. Note that the analysis
includes fixed effects for year, and that these findings remain after adding to the model a
separate time interaction for each state, as discussed below. These findings are consistent
with a theory that as state incarceration rates grow, the exclusion of minorities absent
screens seems to be higher.
Robustness Analyses
We have taken several steps to increase confidence that the results are not affected by
unobserved heterogeneity. The establishment and year fixed effects implicitly control for
unobserved stable heterogeneity unique to each workplace analyzed, such as an
especially discriminatory workplace culture, and unobserved time-varying changes that
may affect all firms, such as changes in national cultural or legal environments. The host
of control variables we include accounts for variance related to organizational and labor
market features that may cause changes in the employment of minorities. In addition, we
examined several other specifications, the results of which are presented in the Appendix
30
Table 6-9. Each table includes results for the main variables from the 3 robustness checks
discussed here, and for each model presented in the finding section.
First, to account for unobserved state-level legal or cultural changes that might
affect both screen adoption and workforce composition, we added to all analyses reported
in this paper a series of interaction variables for each establishment’s state and a
continuous count variable for time (where 1971=1). Because legal changes may affect
specific industries (such as the health or insurance industry), we conducted a similar
analysis with interaction variables for each establishment’s industry and a count variable
for time. As Appendix Tables 6-9 show the results overall are robust to these
specifications (although some coefficients shrink a bit and so significant levels move to
be at the 10 percent). All observed patterns of interest are robust to adding the interaction
between state and time. The coefficient for the interaction drug testing*incarceration rates
for black men significantly declines in size and becomes non-significant. Yet, calculating
the effects of drug testing at different incarceration rates shows a very similar pattern to
the main model, with the negative effect of the adoption of drug testing declining and
disappearing when incarceration rates are above the median of 25. Adding an interaction
between industry and time to the models reduces the coefficient for the effect of drug
testing on Hispanic employment share in blue-collar jobs in the baseline model but not in
the model with interaction with incarceration rates. Yet, it is possible that some of the
observed pattern is a product of industry dynamics.
Second, out of concern that adopters are different than non-adopters in ways that
affect our results but are not captured by our control variables and fixed effects, we
repeated the analyses presented in this paper while limiting the sample to adopters only,
31
once for criminal record checks and once for drug testing adopters. Appendix Tables 6-9
includes results from the adopters-only analyses for each model specification. Here too,
the key findings remain robust. While most coefficients of the baseline model
significantly shrink in size, all other model specifications are highly robust. Taken
together, the fixed effects and the rich set of control variables, along with the results of
additional robustness checks, increase our confidence in the results reported in this paper,
especially as it concerns the heterogeneity of processes that shape screen outcomes.
Results for Control Variables
Results for control variables are generally consistent with expectations. The effects of
growth in group’s share in the establishment industry or state workforce show that such
dynamics do not necessarily mean entrance to the older and more stable establishments
that are in our sample. Industry growth improves employment chances for all minority
groups in professional jobs, and reduces the odds of Hispanic men and women in bluecollar jobs. Growth in the share of federal contractors in industry reduces the share of
minorities in professional jobs in our sample and increases their share in low-skill jobs,
though only the coefficient for Hispanic men is significant.
Organizational structures affect workforce demography as well. Most research to
date established these effects for managerial jobs but not for non-managerial jobs. We
find that employer adoption of diversity programs show strong positive effects for all
groups in professional jobs, consistent with findings about managerial jobs (Kalev et al.
2006) and no effect in blue-collar jobs. Work-family programs increase the share of black
women in professional jobs but have surprising negative effects on black women and
Hispanic men in low-skill jobs, perhaps because they make these jobs more attractive to
32
white women (Kelly, Kalev and Dobbin 2007). Unions do not benefit minority
employment, and show a significant negative effect on Hispanic men in blue-collar jobs.
Formalized human resources practices show positive estimates only for Hispanics in
blue-collar jobs, and significant only for Hispanic women. Pre-employment skill tests
show no effects. Having a legal department shows significant positive effects and black
men and women in blue-collar and professional jobs and of Hispanic men in professional
jobs. Experiences with an antidiscrimination lawsuit, charge or review show mostly
negative estimates, though only the one for black women in professional jobs is statistical
significant. Growth in the size of the organization and the size of the focal jobs reduces
the share of groups in the focal job. Managerial and workforce diversity generally, but
not uniformly, increases diversity in both job types.
DISCUSSION AND CONCLUSIONS
Pre-employment criminal record checks and drug testing grew in popularity alongside the
wars on crime and drugs and have been hotly debated since. Opponents see screens as an
expansion of the penal system, with all its inequities, into the workplace. They argue that
not only do screens breach civil rights, cement stigma against ex-convicts and drug users,
and stymie their rehabilitation, they also hurt the labor market outcomes of minorities,
who are more likely to have criminal records and be suspected of drug use (Garland
1996; Pager 2007; Alexander 2012). The movement to “ban the box” seeks to limit
screen-based exclusion by moving record checking to later stages of the hiring process,
when employers have more knowledge of the candidate. Supporters of screens argue that,
without them, employers are likely to engage in statistical discrimination that associates
criminality and drug use with black and Hispanic origin. Pre-employment screens allow
33
minorities to show clean records or minor misdemeanors and to prove they are not drugusers, thereby reducing their exclusion (Holzer et al. 2006; Wozniak 2015).
Understanding how screens impact minority employment is of crucial importance in this
period of social activism on the topic. If this is a time of change, it ought to be informed
by empirical evidence. The stakes in this debate are high, since improving minority
employment in general, and ex-convicts’ employment in particular, are central to
reducing racial and ethnic inequality in the U.S. (REF). Yet, empirical research on the
effects of screens on minority employment has been relatively scarce—this, despite the
tidal wave of research on the determinants of workforce demographics (Stainback et al.
2010). This study sought to contribute to current knowledge about screens’ effects by
examining the aggregate impact of screen adoption on workplace diversity using
longitudinal data on a national sample of workplace establishments. We examined how
the impact of screens differs for blue-collar versus professional workers, and how it is
shaped by different regulatory environments and in the context of higher incarceration
rates.
Our theoretical argument is twofold. First, in line with prior research (Wozniak
2015; Holzer et al 2006; Autor and Scarbrough 2008), we posit that the effect of screens
depends, in part, on the relationship between the bias embedded in the screens (a feature
of institutional discrimination) and the bias embedded in informal selection processes
prior to screen adoption (an example of statistical discrimination). If the former
dominates, then screens will have negative effects; if the latter plays a bigger role, then
screens are likely to have positive effects.
34
Second, we argue that employers who are more averse to hiring people with the
past convictions or drug users are more likely to engage in statistical discrimination prior
to adopting the related screen, in which case screens will have a positive effect. Where
they are less averse, screens will have a negative effect. And, since employers generally
worry more about drug use in blue-collar jobs and about criminality in professional jobs
(and vice versa), we hypothesized that the effects of criminal background checks and
drug testing will vary by job type—although we do not directly observe employer biases
prior to screen adoption.
Consistent with our theory, we find that criminal record checks reduce the share
of minority employment in blue-collar jobs but increase their share in professional jobs;
conversely, drug testing increases the share of black men and Hispanic in blue-collar jobs
and hurts their share in professional jobs.
This latter finding is somewhat unexpected by our main model, because
minorities are no more likely to use drugs than whites. Drug testing thus should not lead
to higher rates of minority exclusion. It is possible however that employers
disproportionately screen minorities for drug use when hiring professional workers
(SAMSHA 1997; Western 2008. See also Gee et al. 2005). We made this prediction
based on studies and courts decisions that have shown that formal policies are not always
universally applied. For example, employers are more likely to administer skill tests to
minorities (Dobbin 2009; Dobbin et al. 2015) and use disciplinary procedures for blacks
more than whites (Roscigno 2007). Yet, another possibility is that employers of minority
professionals are simply more averse to drug use than expected.
35
Our second finding is that federal regulations have a significant impact on
employers’ hiring patterns. Inclusive regulation (the EEOC prohibition of blanket
exclusion of ex-convicts) makes screens more inclusive, too. And exclusionary regulation
(the Drug-Free Workplace Act) makes screen more exclusionary. In firms that are under
heightened oversight of antidiscrimination regulation, criminal record checks do not
reduce black men odds in blue collar jobs, as they do in non-contractor firms. Throughout
the period under study, the employment of black men in non-contractor firms in our
sample declined slightly (from a mean of 8.4 percent of blue collar workforce in firms
that existed in 1971 to 8.1 in 2002), but it grew in contractor firms, (from 8.3 in 1971 to
9.7 in 2012). In our models, the estimate of the effect of government contract by itself
does not show an effect on minority employment in blue-collar jobs. The positive
interaction with criminal records checks suggests that this is has been one mechanism by
which contractors’ employment of black men increased.
The Drug Free Workplace Act of 1988 made drug testing more exclusionary for
black men and Hispanic men and women in professional jobs. This was surprising, since
we expected the Act to increase contractor aversion and hence the use of statistical
discrimination prior to adopting drug tests. Further research is needed to better
understand these dynamics. While we can only speculate, it may be that firms that were
more likely to employ minorities were also more likely to adopt drug tests after the Act
passed, which limited the growth in minority employment. Indeed, other things being
equal, the findings in Table 3a show that contractor status increases minority employment
in the period after 1988, when the Act was in place, but only among non-testing firms.
36
Out third main finding shows that screen effects in professional jobs are improved
when state incarceration rates are higher. If all criminal record checks did was to transfer
disadvantage in one system (criminal justice) to another (labor market), then higher
incarceration rates should have amplified the negative effects of criminal record checks,
as more minorities would fail them. And since drug testing should reveal similar rates
minorities and whites, we would expect rising incarceration rates to not affect minority
employment exclusion due to tests. The positive results suggest otherwise. One
possibility is that rising incarceration rates intensify employer reluctance to hire exconvicts and drug-users, and therefore their use of statistical discrimination in informal
screening. The adoption of formal screening then reveals lower rates of convictions and
drug use relative to what was assumed and so increases minority employment. This
finding thus lends further support to our theory that the effect of screens depends on the
relative bias between informal and formal screening.
The results varied by gender and race in a manner that requires further research as
well. Most patterns in the findings vary by race, not by gender, although underlying
distribution fo the screened trait vary by gender, not by race. Yet, black women are less
likely to be adversely affected than all other groups. We also need to further understand
whether criminal records checks have a chilling effects on Hispanics in Blue Collar Jobs.
The study allows for some more general conclusions as well. First, the fact that
these screens have any effect at all is an indication of the institutionalization of
conviction background and drug-use as components of human capital (Wozniak 2015).
This is consistent with other research on the growing importance of signals beyond those
37
of skill (Maroto 2012; Sharone 2014), a phenomenon that requires new understandings of
mechanisms of social inequality.
Scholarly, public and policy discourses often tie pre-employment checks together
and use knowledge about the outcomes of one to learn about the potential impact of the
other. This approach ignores the difference between labor processes, which affects which
qualities employers worry about. No study we know of has empirically examined both
types of screens simultaneously. Yet, our study shows that we cannot generalize from one
screen effects to another (Holzer et al. 2006; Wozniak 2015). Further, because employers
often adopt both screening mechanisms, failure to control for each may lead to incorrect
conclusions. For example, failure to account for the positive effect of drug testing on
blue-collar minority employment may explain why some researchers have not found a
negative effect of criminal record checks on minorities (Finlay 2009). Or, if the positive
effect of criminal background checks in professional jobs is not accounted for, studies
may fail to find negative effects of drug testing on professional minorities’ employment
(Wozniak 2015).
If employers’ use of inaccurate beliefs in hiring absent screens shapes the effect
of screens as much as it seems from our findings, then much of the debate around preemployment screens is misplaced (Bushway 2004). For example, banning the box might
improve the employment chances of ex-convicts in blue collar jobs, but it could reduce
minority employment in professional jobs if it prompts employers to rely more on
statistical discrimination in early stages of the hiring process.
Overall, there are two sets of employers’ beliefs that seem to shape, at least
partially, the effects of screen: beliefs associating ex-convicts and drug users with worse
38
work outcomes, which underly employer aversion to hiring such workers; and beliefs
associating blacks and Hispanics with criminality and drug use. We need to better
understand the working of these biases in hiring and how to de-institutionalize them.
Sociological research has shown that both federal and internal oversight
mechanisms can significantly reduce the disparate impact that seemingly universal
personnel procedures have on minority employment (Dobbin et al. 2015; Edelman and
Pettersen 1999; Kalev et al. 2006; Kalev 2014). In 2012, the EEOC issued renewed
enforcement guidelines that detail how to evaluate criminal records. These may already
be helping to improve the employment odds of ex-convicts. Policymakers need to
strengthen enforcement of these guidelines. Our finding on the positive effect of
regulatory antidiscrimination oversight holds promise for the effective use of such
regulatory initiatives. The rich academic research and discourse on workforce diversity
has mostly disregarded issues surrounding criminal record checks and drug tests (Castilla
and Benard 2010; Correll, Benard, and Paik 2007; Kalev 2014; Kalev et al. 2006). Yet,
diversity managers that want to increase diversity need to check whether some screens
work to screen out their diverse candidates.
Researchers have found that employers often inflate legal requirements regarding
the adoption of screens or the exclusion of ex-convicts or drug users (Stoll and Bushway
2008). This is a common outcome when legal requirements are complex and ambiguous
(Dobbin 2009; Edelman 2008; Guetzkow and Schoon 2015). Federal, and especially state
legislators, need to offer employers more clarity regarding the employment of ex-convicts
and drug users, as well as more protection against negligent hiring liability.
39
Our research is the first to use rich longitudinal data on a national sample of
organizations to examine both criminal record checks and drug testing side-by-side.
While more research is needed to explore specific mechanisms, our findings show that
the effects of screens and their absence are important for understanding patterns of
inequality, especially for black men, who have seen the lowest employment gains during
the period under study. Our results are clearly not encouraging for ex-convicts and illicit
drug users, since an increase in minority employment does not mean an increase in exoffenders’ and drug users’ employment. In most cases, it means more efficient exclusion
of candidates with these characteristics. On the other hand, the drive to “ban the box” and
other efforts to reduce reliance on pre-employment checks may result in employers
falling back on racial and ethnic statistical discrimination based on incorrect beliefs,
which would hurt minority employment overall.
Taken together, this study shows that the problems that the use of screens poses to
minority employment cannot be solved by banning the box or even eliminating the
screens entirely. Instead, solutions must be aimed at reducing employers’ use of bias in
hiring, both about minorities and about ex-convicts and drug users. We have offered
some lessons from research on organizations and inequality about effective remedies that
can be promoted by policy makers and organizational actors.
40
ENDNOTES
1
Retrieved from http://www.eeoc.gov/laws/guidance/qa_arrest_conviction.cfm. Last
accessed on June 20, 2015.
2
Retrieved from http://www.dol.gov/elaws/drugfree.htm. Last accessed on June 20,
2015.
3
Excluded employers, such as state and local governments, schools and colleges provide
different reports (EEOC n.d.)
4
A sample form, including detail of each job category is available online
http://www.eeoc.gov/employers/eeo1survey/2007instructions.cfm. Last access on August
6, 2015.
5
Logit (i)=Log (Pi/1-Pi), where Pi is the proportion of group i among managers. The logit
is undefined when P=0 or P=1. We thus substituted 0 with 1/2Nj, and 1 with 1-1/2Nj,
where Nj is the number of workers in the focal job type in establishment j (Hanushek and
Jackson 1977; Reskin and McBrier 2000). The results of the analysis are robust to
different strategies for substituting zeros. We chose the one that kept the distribution unimodal and closest to normal. Finally, the results are robust to the choice of log proportion
versus log odds. The log odds transformation keeps the distribution closer to normal.
41
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Table 1: Variable Names, Means, Standard Deviations, Ranges, Form, and Data Sources For Variables Included in the
Analyses of The Log Odds of Blacks and Hispanics in Blue Collar and Professional Jobs
Focal Job in Analysis:
Blue Collar Jobs
Professional Jobs
Mean
S.D.
MIN
MAX
Mean
S.D.
MIN
MAX
Form
Data Source
0.058
0.078
0.030
0.057
0.118
0.121
0.076
0.123
0
0
0
0
0.988
1.000
0.711
1.000
0.023
0.015
0.008
0.012
0.067
0.048
0.029
0.043
0
0
0
0
1
1
1
1
Continuous
Continuous
Continuous
Continuous
EEO-1
EEO-1
EEO-1
EEO-1
0.279
0.423
0.449
0.494
0
0
1
1
0.286
0.427
0.452
0.495
0
0
1
1
Binary
Binary
Survey
Survey
0.481
0.262
0.500
0.150
0
0.020
1
0.880
0.508
0.268
0.500
0.150
0
0.020
1
0.880
Binary
Continuous
EEO-1
BJS
0.44
0.33
0.04
0.04
0.05
0.06
.156434
.1497224
.0252402
.0191503
0.02
.0381777
.1 454153
.1 25224
.0 44732
.0 86372
0.00
.0 59583
0.74
0.62
0.11
0.11
0.14
0.23
0.439
0.328
0.043
0.040
0.045
0.064
0.156
0.148
0.025
0.019
0.023
0.037
0.145
0.103
0.004
0.009
0.000
0.006
0.742
0.624
0.114
0.106
0.141
0.231
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
CPS
CPS
CPS
CPS
CPS
CPS
0.39
0.36
0.05
0.04
0.04
0.05
.0603076
.063516
.0349179
.0299425
.0463711
.0645547
.1160593
.0 929853
-.0 37157
.0 4132
.0 6739
.0 7047
0.57
0.50
0.20
0.19
0.25
0.29
0.385
0.354
0.048
0.042
0.039
0.054
0.060
0.064
0.035
0.030
0.047
0.065
0.116
0.093
-0.004
0.000
0.001
0.001
0.574
0.496
0.201
0.186
0.249
0.286
Continuous
Continuous
Continuous
Continuous
Continuous
Continuous
CPS
CPS
CPS
CPS
CPS
CPS
Industry Employment (In 1000)
Unemployment Rate
Federal Contractors in Industry
Organizational Structures
3.800
6.059
0.493
2.845
2.048
0.229
0.996
2
0.061
11.458
18
0.821
3.783
6.023
0.497
2.915
2.033
0.225
0.996
2
0.061
11.458
18
0.821
Continuous
Continuous
Continuous
CPS
BLS
EEO-1
Diversity Programs 2
Affirmative Action Plans
0.395
0.467
0.947
0.268
5.011
0.439
0.265
0.482
0.879
0.499
0.990
0.443
2.521
0.496
0.441
0.500
0
0
0
0
0
0
0
0
5
1
4
1
9
1
1
1
0.440
0.489
0.998
0.255
5.245
0.461
0.312
0.512
0.922
0.500
1.008
0.436
2.425
0.498
0.464
0.500
0
0
0
0
0
0
0
0
5
1
4
1
9
1
1
1
Count
Binary
Count
Binary
Count
Binary
Binary
Survey
Survey
Survey
Survey
Survey
Survey
Survey
Count
Survey
Continuous
Continuous
EEO-1
EEO-1
Percent in Focal Job that are
Black Women
Black Men
Hispanic Women
Hispanic Men
Screens:
Criminal Record Checks
Drug Testing
Institutional Environment
Federal Contractors
State Incarceration Rates (per 1000)
External Labor Markets:
Demographics of Industry Labor Force:
White Men
White women
Black Women
Black Men
Hispanic Women
Hispanic Men
Demographics of State Labor Force
White Men
White women
Black Women
Black Men
Hispanic Women
Hispanic Men
Work Family Policies 3
Union
Human Resources Policies 1
Skill Tests
Legal Department
Antidiscrimination
Lawsuits/Charges/Reviews
Demand for Workers
Size (number of workers)
Share of Focal Job in Establishment
Workforce Demography
% among Managers that are:
780
1007
0.474 .3069683
18
. 156
14195
0.998
791
0.159
953
0.156
13
0
14195
0.948
16.935 24.144
0
100
17.105 23.892
0
100
Continuous
Survey
% Blacks among Top Managers 4
3.378
9.757
0
100
3.164
9.201
0
100
Continuous
Survey
% Women among Top Managers 4
% Non-Focal Jobs that are:
White Men
0.454
0.231
0
1
0.426
0.244
0
1
Continuous
EEO-1
White women
0.426
0.227
0
1
0.367
0.236
0
1
Continuous
EEO-1
Black Women
0.037
0.073
0
1
0.062
0.102
0
1
Continuous
EEO-1
Black Men
0.022
0.040
0
1
0.049
0.079
0
1
Continuous
EEO-1
Hispanic Women
0.016
0.038
0
1
0.028
0.062
0
1
Continuous
EEO-1
Hispanic Men
0.017
0.044
0
1
0.039
0.091
0
1
Continuous
EEO-1
None from Focal Group in Focal Job:
Black Women
0.396
0.489
0
1
0.587
0.492
0
1
Continuous
EEO-1
Black Men
0.246
0.431
0
1
0.592
0.491
0
1
Continuous
EEO-1
Hispanic Women
0.565
0.496
0
1
0.716
0.451
0
1
Continuous
EEO-1
Hispanic Men
0.414
0.493
0
1
0.652
0.476
0
1
Continuous
EEO-1
N
17,134
16,247
Note: All independent variables, excluding the proportion of managerial jobs, are measures one year before the outcome variables.
1
Includes adoption of a formal HR department, written hiring, promotion and discharge guidelines, written job description, written promotion ladder,
written performance evaluations, pay grade system and internal posting of jobs.
2
Includes diversity staff, diversity committee, diversity mentoring and networking, diversity evaluations and diversity training
3
Includes paid maternity leave, paid paternity leave, policy allowing flexible work hours and top management support for work-family balance.
4
Percents were obtained in 10 years intervals (2002, 1992 and 1982). Values
49for the years in between were interpolated using a linear function.
Table 2: Estimates and Standard Errors of the Log Odds of White, Black and
Hispanic Workers in Low Skill and Professional Jobs After the Adoption of
Corporate Drug Testing and Criminal Background Checks - Key Variables
Black Women Black Men
Hispanic
Women
Hispanic Men
Model A: Blue Collar Jobs
Criminal Record
Checks
Drug Testing
R-Square (not including
fixed effects)
-0.019
-0.064
-0.100*
-0.127**
(0.040)
(0.042)
(0.043)
(0.046)
0.043
0.076*
0.088*
0.094*
(0.038)
(0.038)
(0.038)
(0.041)
0.184
0.111
0.311
0.267
0.117**
0.066*
0.054
0.050
(0.036)
(0.033)
(0.029)
(0.032)
-0.024
0.002
-0.058*
-0.059*
(0.032)
(0.029)
(0.028)
(0.027)
0.481
0.494
0.614
0.555
Model B: Professional Jobs
Criminal Record
Checks
Drug Testing
R-Square (not including
fixed effects)
Note: Blue Collar model: N Organizations/Org-years=778/17,134; LR Chi2 (12)=61.97; p<0.000; Professional jobs
model: N Organizations/Org-years=773/16,247; LR Chi212=82.98; p<0.000. All control variables listed in the text
are included in the models and reported in Appendix Tables 2a and 2b respectively. Year and establishment fixed
effects are included but not reported. * P<.05; ** P<.001; *** P<.0001.
50
Table 3: Estimates and Standard Errors of the Log Odds of Black and Hispanic Workers in Blue
Collar Jobs After the Adoption of Corporate Criminal Background Checks and Drug Testing by
Contractor Status
Black Women
Criminal Record Checks
* Federal Contractor
Federal Contractor
Drug Testing
* Federal Contractor
R-Square not including fixed effects
Log Likelihood Ratio Test
Black Men
Hispanic
Women
Hispanic Men
-0.082
-0.130*
-0.151**
-0.168**
(0.055)
(0.053)
(0.056)
(0.062)
0.130
0.135*
0.105
0.083
(0.071)
(0.067)
(0.066)
(0.074)
0.060
0.027
-0.022
0.028
(0.052)
(0.053)
(0.057)
(0.058)
0.066
0.126*
0.120*
0.120*
(0.049)
(0.051)
(0.053)
(0.060)
-0.052
-0.105
-0.068
-0.056
(0.059)
(0.063)
(0.071)
(0.079)
0.184
0.112
0.311
0.267
Chi-sq (48) = 412.77
p<0.001
Note: N Organizations/Org-years=778/17,134; All control variables listed in the text are included in the models
and reported in Appendix Table 3a. Year and establishment fixed effects are included but not reported. * P<.05;
** P<.001; *** P<.0001. Two tailed tests.
51
Table 4a: Estimates and Standard Errors of the Log Odds of Black and Hispanic Workers in Blue
Collar Jobs After the Adoption of Corporate Drug Testing and Criminal Background Checks by
Contractor Status and Period - Interaction Model
Black Women
Drug Testing
* P88
* Federal Contractor
* Federal Contractor* P88
Federal Contractor*P88
Federal Contractor
P88 (Period 1988 and Up)
Criminal Record Checks
* Federal Contractor
R-Square not including fixed effects
Black Men
Hispanic
Women
Hispanic Men
0.266**
0.135
0.238**
0.117
(0.087)
(0.081)
(0.091)
(0.095)
-0.245**
-0.006
-0.170
-0.028
(0.088)
(0.090)
(0.097)
(0.098)
-0.325**
-0.196*
-0.152
-0.100
(0.103)
(0.096)
(0.102)
(0.108)
0.340**
0.105
0.126
0.130
(0.115)
(0.112)
(0.116)
(0.122)
-0.044
0.030
-0.062
-0.125
(0.063)
(0.066)
(0.065)
(0.071)
0.082
0.018
0.003
0.066
(0.056)
(0.057)
(0.060)
(0.062)
0.268*
0.193
0.544***
0.781***
(0.135)
(0.138)
(0.141)
(0.146)
-0.064
-0.109*
-0.146*
-0.169**
(0.053)
(0.052)
(0.058)
(0.063)
0.116
0.112
0.110
0.095
(0.069)
(0.068)
(0.070)
(0.076)
0.316
0.314
0.405
0.362
Log Likelihood Ratio Test
p<0.001
Note: N Organizations/Org-years=778/17,134. All control variables listed in the text are included in the models and
reported in Appendix Table 4a. Year and establishment fixed effects are included but not reported.
* P<.05; **
P<.001; *** P<.0001. Two tailed tests.
52
Table 4b: Estimates and Standard Errors of the Log Odds of Black and Hispanic Workers in Professional
Jobs After the Adoption of Corporate Drug Testing and Criminal Background Checks by Contractor Status
and Period - Interaction Model
Black Women
Drug Testing
* P88
* Federal Contractor
* Federal Contractor* P88
Federal Contractor*P88
Federal Contractor
P88 (Period 1988 and Up)
Criminal Record Checks
* Federal Contractor
R-Square not including fixed effects
Black Men
Hispanic
Women
Hispanic Men
-0.010
-0.045
-0.134*
-0.158*
(0.074)
(0.085)
(0.066)
(0.064)
0.048
0.106
0.156*
0.152*
(0.083)
(0.086)
(0.074)
(0.072)
-0.036
0.079
0.074
0.116
(0.091)
(0.100)
(0.077)
(0.081)
-0.084
-0.207*
-0.212*
-0.195*
(0.105)
(0.104)
(0.094)
(0.091)
0.197***
0.203***
0.222***
0.175***
(0.059)
(0.056)
(0.058)
(0.053)
-0.494***
-0.503***
-0.410***
-0.353***
(0.127)
(0.111)
(0.101)
(0.103)
-0.038
-0.075
-0.067
-0.089
(0.060)
(0.052)
(0.048)
(0.054)
0.133**
0.057
0.051
0.036
(0.046)
(0.040)
(0.040)
(0.042)
-0.037
0.012
0.012
0.026
(0.062)
(0.059)
(0.058)
(0.068)
0.316
0.314
0.405
0.362
Log Likelihood Ratio Test
p<0.001
Note: N Organizations/Org-years=773/16,247. All control variables listed in the text are included in the models and
reported in Appendix Table 4b. Year and establishment fixed effects are included but not reported.
* P<.05; **
P<.001; *** P<.0001. Two tailed tests.
53
Table 5: Estimates and Standard Errors of the Log Odds of Black and Hispanic Workers in
Professional Jobs After the Adoption of Corporate Criminal Background Checks and Drug
Testing by State Incarceration Rates
Black Women
Criminal Record Checks
Black Men
Hispanic
Women
Hispanic Men
-0.155*
-0.172*
-0.066
-0.049
(0.072)
(0.072)
(0.071)
(0.071)
0.792***
0.688***
0.355
0.285
(0.211)
(0.194)
(0.195)
(0.189)
0.066
-0.111
-0.050
-0.028
(0.279)
(0.249)
(0.236)
(0.230)
R-Square not including fixed effects
0.316
0.315
0.403
0.360
Drug Testing
-0.079
-0.107
-0.105
-0.106
(0.068)
(0.066)
(0.057)
(0.054)
0.218
0.405*
0.195
0.198
(0.230)
(0.206)
(0.200)
(0.177)
0.414
-0.002
0.049
0.009
(0.314)
(0.257)
(0.253)
(0.247)
0.314
0.313
0.402
0.360
* Incarceration rates
Incarceration rates
* Incarceration rates
Incarceration rates
R-Square not including fixed effects
Note: N Organizations/Org-years=773/16,247; Interactions were tested in separate models. Each model
includes a measure for the non interacted screen. Incarceration rates are divided by 100. All control variables
listed in the text are included in the models and reported in Appendix Table 5. Year and establishment fixed
effects are included but not reported. * P<.05; ** P<.001; *** P<.0001. Two tailed tests.
54
Appendix Table 2a: Estimates and Standard Errors of the Log Odds of Black and Hispanic Workers in Blue
Collar Jobs Following the Adoption of Corporate Drug Testing and Criminal Background Checks
Black Women Black Men
Federal Contractors
State Incarceration Rates (/100)
Percent Group in Industry Labor
White Men
White women
Black Women
Black Men
Hispanic Women
Hispanic Men
Percent Group in State Labor
White Men
White women
Black Women
Black Men
Hispanic Women
Hispanic Men
Industry Employment Rate (log)
Unemployment Rate
Federal Contractors in Industry
Hispanic
Women
Hispanic Men
0.067
0.014
-0.027
0.023
(0.048)
(0.050)
(0.052)
(0.053)
-0.000
-0.000
-0.000
-0.001
(0.000)
(0.000)
(0.000)
(0.000)
0.197
-0.427
-0.452
-0.479
(0.230)
(0.232)
(0.273)
(0.290)
0.198
-0.252
-0.806***
-0.757***
(0.160)
(0.175)
(0.195)
(0.211)
0.034
-0.068
-0.009
0.067
(0.041)
(0.042)
(0.045)
(0.053)
-0.136**
-0.055
-0.152***
-0.134**
(0.049)
(0.045)
(0.046)
(0.049)
0.009
-0.007
0.022
0.039
(0.036)
(0.030)
(0.032)
(0.032)
0.077
-0.127*
-0.184***
-0.207***
(0.049)
(0.053)
(0.051)
(0.055)
1.379
1.734*
0.955
0.975
(0.881)
(0.871)
(0.790)
(0.898)
0.963
0.914
-2.464*
-3.182**
(0.874)
(0.939)
(0.977)
(1.012)
3.891*
0.557
0.699
0.411
(1.740)
(1.597)
(1.544)
(1.775)
-0.619
1.784
-2.320
-1.226
(1.498)
(1.473)
(1.789)
(2.001)
-1.735
-0.281
-4.264
-3.578
(1.670)
(1.831)
(2.257)
(1.988)
0.386
0.849
2.190
3.127*
(1.310)
(1.258)
(1.537)
(1.339)
0.212
0.013
-0.286*
-0.581***
(0.117)
(0.107)
(0.139)
(0.129)
0.016*
-0.015
-0.003
-0.006
(0.008)
(0.009)
(0.009)
(0.009)
0.169
0.165
0.278**
0.216
(0.088)
(0.094)
(0.094)
(0.118)
55
Appendix Table 2a (Continued):
Organizational Structures
Diversity Programs
Affirmative Action Plans
Work Family Policies
Union
Human Resources Policies
Skill Tests
Legal Department
Antidiscrimination
Lawsuits/Charges/Reviews
Demand for Workers
Size (log employees)
Share of Low Skill Jobs in
Establishment
Workforce Demography
% Blacks among Top Managers
Black Women
Hispanic Women
Hispanic Men
0.002
0.033
-0.016
-0.011
(0.023)
(0.024)
(0.024)
(0.028)
0.038
0.034
-0.055
-0.061
(0.046)
(0.046)
(0.050)
(0.056)
-0.077**
-0.032
-0.031
-0.075**
(0.024)
(0.024)
(0.023)
(0.027)
-0.136
0.008
-0.196
-0.226*
(0.105)
(0.089)
(0.118)
(0.106)
0.014
-0.001
0.031*
0.021
(0.011)
(0.010)
(0.012)
(0.013)
-0.030
-0.063
-0.017
-0.039
(0.042)
(0.044)
(0.043)
(0.047)
0.190**
0.196**
-0.011
-0.013
(0.071)
(0.069)
(0.077)
(0.076)
-0.054
0.027
-0.043
-0.020
(0.034)
(0.034)
(0.036)
(0.041)
-0.405***
-0.211***
-0.364***
-0.257***
(0.047)
(0.044)
(0.051)
(0.050)
-0.318***
-0.182***
-0.490***
-0.369***
(0.045)
(0.043)
(0.038)
(0.037)
0.004
-0.005
0.008
0.018*
(0.006)
(0.006)
(0.008)
(0.009)
% Women among Top Managers -0.003
(0.002)
% Focal Group among Non Focal 0.196***
(0.025)
Jobs
None from Focal Group in Low -0.613***
(0.046)
Skill Jobs
Constant
-2.758*
-0.001
0.002
0.001
(0.002)
(0.003)
(0.003)
0.168***
0.229***
0.184***
(0.021)
(0.026)
(0.025)
-0.461***
-0.771***
-0.642***
(0.046)
(0.049)
(0.042)
-3.741***
-0.217
2.033
(0.984)
(1.112)
(1.086)
0.184
0.111
Chi-sq 12 = 61.97
0.311
0.267
(1.127)
R-Square (not including fixed effects)
Log Likelihood Ratio Test
Black Men
p<0.001
Note: N Organizations/Org-years=778/17,134; All independent variables are lagged by one year. The analysis includes 30
binary variables for the years 1972-2001 1971 is the omitted year and 2002 is included in the analysis only for measuring
the outcome variable.
*** p<0.001; ** p<0.01; * p<0.05 a two tailed test
56
Appendix Table 2b: Estimates and Standard Errors of the Log Odds of Black and Hispanic Workers in
Professional Jobs Following the Adoption of Corporate Drug Testing and Criminal Background Checks
Black Women Black Men
Federal Contractors
State Incarceration Rates (/100)
Percent Group in Industry Labor
White Men
White women
Black Women
Black Men
Hispanic Women
Hispanic Men
Percent Group in State Labor
White Men
White women
Black Women
Black Men
Hispanic Women
Hispanic Men
Industry Employment Rate
Unemployment Rate
Federal Contractors in Industry
Hispanic
Women
Hispanic Men
0.007
-0.013
0.003
-0.020
(0.047)
(0.038)
(0.036)
(0.043)
0.773**
0.534*
0.496*
0.416*
(0.241)
(0.211)
(0.215)
(0.190)
-0.173
0.228
0.085
0.425*
(0.204)
(0.189)
(0.174)
(0.181)
0.294*
0.217
0.206
0.096
(0.127)
(0.122)
(0.112)
(0.119)
-0.075*
-0.062
-0.050
-0.063*
(0.035)
(0.032)
(0.030)
(0.029)
0.027
0.020
0.048
0.059
(0.041)
(0.035)
(0.034)
(0.033)
-0.013
0.032
-0.003
0.009
(0.024)
(0.028)
(0.017)
(0.024)
0.183**
0.057
0.036
0.025
(0.058)
(0.042)
(0.041)
(0.043)
-0.733
0.087
-1.318*
-1.190*
(0.749)
(0.660)
(0.600)
(0.605)
-1.379
-1.729**
-1.189*
-0.606
(0.841)
(0.658)
(0.581)
(0.609)
4.344**
1.788
-2.737**
-0.773
(1.398)
(1.222)
(0.975)
(1.026)
-4.314**
-1.416
0.325
-1.054
(1.547)
(1.480)
(0.961)
(1.205)
-0.986
2.670
6.396***
4.937***
(1.779)
(1.473)
(1.728)
(1.398)
0.533
-1.300
-0.419
0.280
(1.176)
(1.034)
(1.311)
(1.108)
0.789***
0.455***
0.641***
0.425***
(0.105)
(0.105)
(0.095)
(0.106)
-0.005
-0.005
-0.005
-0.008
(0.007)
(0.006)
(0.006)
(0.006)
-0.285***
-0.106
-0.217**
-0.215**
(0.079)
(0.076)
(0.074)
(0.069)
57
Appendix Table 2b Continued:
Organizational Structures
Diversity Programs
Affirmative Action Plans
Work Family Policies
Union
Human Resources Policies
Skill Tests
Legal Department
Antidiscrimination
Lawsuits/Charges/Reviews
Demand for Workers
Size (log employees)
Share of Professional Jobs in
Establishment
Workforce Demography
% Blacks among Top Managers
Black Women
Hispanic Men
0.113***
0.108***
0.093***
(0.023)
(0.021)
(0.022)
(0.019)
-0.052
0.014
0.009
0.020
(0.038)
(0.033)
(0.032)
(0.031)
-0.026
-0.099
0.025
0.031
(0.075)
(0.059)
(0.072)
(0.054)
0.061**
0.024
0.031
0.032
(0.019)
(0.017)
(0.016)
(0.018)
-0.013
0.001
-0.004
-0.009
(0.011)
(0.009)
(0.009)
(0.009)
-0.048
-0.055
-0.052
-0.031
(0.047)
(0.039)
(0.037)
(0.038)
0.189**
0.166**
0.071
0.116*
(0.058)
(0.061)
(0.052)
(0.057)
-0.068*
-0.029
-0.011
-0.023
(0.033)
(0.028)
(0.026)
(0.025)
-0.741***
-0.690***
-0.814***
-0.745***
(0.037)
(0.037)
(0.027)
(0.031)
-0.765***
-0.767***
-0.861***
-0.810***
(0.029)
(0.029)
(0.022)
(0.024)
0.012*
0.012**
-0.001
0.006
(0.005)
(0.004)
(0.003)
(0.003)
0.004*
0.004*
-0.000
(0.002)
(0.002)
(0.002)
(0.002)
R-Square (not including fixed effects)
Log Likelihood Ratio Test
Hispanic Women
0.126***
% Women among Top Managers 0.004
% Focal Group among Non
Professional Jobs
None from Focal Group in
Professional Jobs
Constant
Black Men
0.073***
0.057***
0.056***
0.034**
(0.015)
(0.014)
(0.013)
(0.013)
-0.638***
-0.610***
-0.638***
-0.602***
(0.029)
(0.025)
(0.028)
(0.022)
-5.263***
-3.158**
-4.121***
-2.817**
(1.056)
(0.983)
(0.845)
(0.901)
0.481
0.494
Chi-sq (12) = 82.98
0.614
0.555
p<0.001
Note: Organizations/Org-years=773/16,247. All independent variables are lagged by one year. The analysis includes 30
binary variables for the years 1972-2001 1971 is the omitted year and 2002 is included in the analysis only for
measuring the outcome variable.
*** p<0.001; ** p<0.01; * p<0.05 a two tailed test
58
ADDITIONAL APPENDIX TABLES FOR CONTROL VARIABLES TO BE ADDED.
59
Robustness Checks
Appendix Table 6
Robustness Checks for Main Models Presented in Table 2
Professionals
State*Time added
Criminal Record
Checks
Drug Testing
Industry*Time added Criminal Record
Checks
Drug Testing
Adopters Only
Criminal Record
Checks
Drug Testing
Black
Hispanic
Women Black Men Women
0.111**
0.069*
0.050
Hispanic
Men
0.041
(0.035)
(0.033)
(0.028)
(0.032)
-0.028
0.010
-0.054*
-0.052*
(0.031)
(0.029)
(0.027)
(0.026)
0.080*
0.062†
0.037
0.045
(0.035)
(0.032)
(0.028)
(0.030)
0.004
0.009
-0.049†
-0.054*
(0.032)
(0.029)
(0.027)
(0.027)
0.034
0.032
-0.018
-0.004
(0.036)
(0.034)
(0.029)
(0.032)
-0.006
0.015
-0.034
-0.037
(0.028)
(0.030)
(0.024)
(0.028)
Blue-Collar
Criminal Record
Checks
Black
Women
-0.035
Drug Testing
Criminal Record
Checks
Drug Testing
Criminal Record
Checks
Drug Testing
Hispanic
Black Men Women
-0.057
-0.115**
Hispanic
Men
-0.131**
(0.040)
(0.041)
(0.043)
(0.044)
0.031
0.071†
0.083*
0.079*
(0.036)
(0.036)
(0.037)
(0.039)
-0.018
-0.056
-0.083*
-0.085†
(0.039)
(0.041)
(0.042)
(0.044)
0.034
0.066†
0.068†
0.053
(0.036)
(0.037)
(0.038)
(0.040)
-0.056
-0.050
0.008
-0.030
(0.042)
(0.042)
(0.038)
(0.040)
0.035
0.044
0.081*
0.025
(0.036)
(0.036)
(0.037)
(0.038)
Appendix Table 7
Robustness Checks for Contractor Interaction Model Presented in Table 3
Professionals
State*Time added
Criminal Record
Checks
Hispanic
Men
0.026
(0.045)
(0.039)
(0.039)
(0.041)
0.001
0.027
0.047
0.038
(0.061)
(0.057)
(0.057)
(0.064)
0.041
0.027
-0.011
0.010
(0.044)
(0.039)
(0.039)
(0.043)
* Government
Contractor
0.074
0.060
0.088
0.074
(0.062)
(0.060)
(0.059)
(0.070)
Criminal Record
Checks
0.048
0.005
-0.050
-0.043
(0.044)
(0.038)
(0.040)
(0.040)
* Government
Contractor
-0.027
0.045
0.056
0.083
(0.063)
(0.062)
(0.060)
(0.067)
* Government
Contractor
Industry*Time added Criminal Record
Checks
Adopters Only
Black
Hispanic
Women Black Men Women
0.108*
0.051
0.024
60
Appendix Table 8
Robustness Checks for Contractor and Period Interaction Models Presented in Table 4a and 4b
Professionals
State*Time added
Drug Testing
* Period '88 +
Hispanic
Men
-0.163*
(0.077)
(0.082)
(0.070)
(0.064)
0.065
0.100
0.166*
0.159*
(0.084)
(0.083)
(0.078)
(0.072)
* Government
Contractor
-0.003
0.077
0.087
0.119
(0.091)
(0.097)
(0.081)
(0.078)
* Government
Contractor*P88
-0.104
-0.196†
-0.241*
-0.183*
(0.106)
(0.104)
(0.095)
(0.091)
Government
Contractor*P88
0.175**
0.179**
0.211***
0.148**
Period '88 +
Industry*Time added Drug Testing
* Period '88 +
* Government
Contractor
* Government
Contractor*P88
Government
Contractor*P88
Period '88 +
Adopters Only
Black
Hispanic
Women Black Men Women
-0.038
-0.033
-0.135†
Drug Testing
* Period '88 +
(0.058)
(0.055)
(0.056)
(0.053)
-0.068
-0.101
-0.177**
-0.219***
(0.062)
(0.062)
(0.061)
(0.057)
-0.007
-0.039
-0.132*
-0.156*
Drug Testing
* Period '88 +
Black
Hispanic
Women Black Men Women
0.241**
0.108
0.206*
Hispanic
Men
0.084
(0.089)
(0.078)
(0.086)
(0.089)
-0.241**
0.005
-0.147
-0.007
(0.090)
(0.089)
(0.092)
(0.091)
* Government
Contractor
-0.286**
-0.147
-0.111
-0.049
(0.104)
(0.093)
(0.099)
(0.101)
* Government
Contractor*P88
0.299**
0.073
0.090
0.073
(0.115)
(0.110)
(0.112)
(0.116)
Government
Contractor*P88
-0.028
0.052
-0.041
-0.081
Period '88 +
Drug Testing
(0.060)
(0.065)
(0.065)
(0.069)
0.045
-0.007
0.027
0.071
(0.064)
(0.073)
(0.073)
(0.071)
0.270**
0.128
0.231**
0.102
(0.075)
(0.084)
(0.063)
(0.064)
(0.083)
(0.080)
(0.089)
(0.094)
0.066
0.105
0.157*
0.155*
-0.256**
-0.003
-0.179
-0.041
(0.083)
(0.085)
(0.072)
(0.073)
0.003
0.100
0.100
0.109
(0.090)
(0.098)
(0.074)
(0.080)
-0.111
-0.222*
-0.231*
-0.181*
(0.104)
(0.103)
(0.091)
(0.091)
0.222***
0.184**
0.243***
0.160**
(0.061)
(0.056)
(0.059)
(0.054)
-0.022
-0.070
-0.091
-0.167**
(0.064)
(0.060)
(0.062)
(0.057)
0.007
-0.028
-0.130*
-0.141*
(0.076)
(0.085)
(0.065)
(0.062)
0.044
0.108
0.223**
0.181*
(0.086)
(0.088)
(0.071)
(0.074)
* Government
Contractor
-0.039
0.090
0.083
0.103
(0.091)
(0.099)
(0.076)
(0.077)
* Government
Contractor*P88
-0.032
-0.205†
-0.257**
-0.175†
(0.105)
(0.107)
(0.089)
(0.091)
Government
Contractor*P88
0.133*
0.183**
0.255***
0.143*
(0.065)
(0.066)
(0.057)
(0.059)
Period '88 +
Blue-Collar
-0.512*** -0.542*** -0.536*** -0.441***
(0.145)
(0.127)
(0.110)
(0.116)
* Period '88 +
(0.083)
(0.089)
(0.093)
(0.096)
* Government
Contractor
* Government
Contractor*P88
-0.321**
-0.201*
-0.167
-0.122
(0.112)
(0.111)
(0.114)
(0.120)
Government
Contractor*P88
-0.072
0.030
-0.097
-0.181*
Period '88 +
Drug Testing
* Period '88 +
* Government
Contractor
(0.099)
(0.095)
(0.101)
(0.106)
0.323**
0.108
0.135
0.140
(0.062)
(0.067)
(0.068)
(0.072)
0.034
-0.042
-0.039
-0.036
(0.064)
(0.078)
(0.075)
(0.074)
0.243**
0.117
0.228*
0.079
(0.088)
(0.083)
(0.093)
(0.098)
-0.219*
-0.001
-0.153
-0.062
(0.085)
(0.091)
(0.097)
(0.098)
-0.292**
-0.155
-0.148
-0.055
(0.103)
(0.097)
(0.103)
(0.110)
* Government
Contractor*P88
0.257*
0.035
0.076
0.051
(0.111)
(0.113)
(0.117)
(0.119)
Government
Contractor*P88
0.037
0.095
-0.015
-0.037
Period '88 +
61
(0.068)
(0.069)
(0.073)
(0.075)
0.260
0.221
0.607***
0.970***
(0.153)
(0.153)
(0.163)
(0.164)
Appendix Table 9
Robustness Checks for Contractor and Period Interaction Models Presented in Table 5
Professionals
State*Time added
Criminal Record
Checks
* Incarceration
rates
Drug Testing
* Incarceration
rates
Industry*Time added Criminal Record
Checks
* Incarceration
rates
Drug Testing
Adopters Only
* Incarceration
rates
Criminal Record
Checks
* Incarceration
rates
Drug Testing
* Incarceration
rates
Black
Hispanic
Women Black Men Women
-0.094
-0.124
-0.086
Hispanic
Men
-0.085
(0.068)
(0.069)
(0.066)
(0.065)
0.608**
0.565**
0.418*
0.396*
(0.193)
(0.185)
(0.182)
(0.171)
-0.043
-0.074
-0.117*
-0.127*
(0.065)
(0.064)
(0.059)
(0.054)
0.064
0.308
0.246
0.294
(0.220)
(0.203)
(0.202)
(0.180)
-0.174*
-0.178*
-0.072
-0.060
(0.071)
(0.069)
(0.071)
(0.070)
0.741***
0.690***
0.321
0.319
(0.206)
(0.192)
(0.197)
(0.191)
-0.089
-0.114
-0.094
-0.113*
(0.067)
(0.067)
(0.058)
(0.055)
0.350
0.450*
0.197
0.244
(0.227)
(0.210)
(0.203)
(0.182)
-0.179*
-0.167*
-0.038
-0.004
(0.078)
(0.079)
(0.073)
(0.074)
0.691**
0.632**
0.061
0.003
(0.240)
(0.229)
(0.208)
(0.205)
-0.143
-0.111
-0.113
-0.097
(0.073)
(0.071)
(0.060)
(0.056)
0.579*
0.535*
0.378
0.299
(0.257)
(0.233)
(0.218)
(0.199)
62
Figure 1: Predicted Screens Effects
Job Type
Professional
Blue Collar
Screen Type
Criminal
Background
Drug Use
Aversion to
Screened Trait
Predicted Screen
Effect
Screen Type
Aversion to
Screened Trait
Predicted Screen
Effect
High
Positive
Drug Use
Low
Null or Negative
High
Positive
Criminal
Background
Low
Negative
63
64
65
66