ECON 383 ASU How BTB Policies Affect the Employment of Groups Questions

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Midterm Exam
ECON 383
Due before class on Monday, Nov. 8 at 10:00AM
Instructions: Complete each of the questions. Your answers can contain a mixture of math,
words, and diagrams if you think it is appropriate. Submit your answers through Canvas (your
answers may be either typed or hand-written). There are four question, worth 25 points each (for
a total of 100 points). Each (sub)question can be sufficiently answered in one or two paragraphs.
1. Causal Identification (25 points) This question is based on the paper The Unintended
Consequences of “Ban the Box”: Statistical Discrimination and Employment Outcomes When
Criminal Histories Are Hidden by Jennifer Dolean and Ben Hansen. The paper is available
on Canvas.
(a) (5 points) Explain the main research question that this paper attempts to answer.
(b) (5 points) What are the main results that the authors find?
(c) (5 points) Explain what is meant by the term “statistical discrimination,” as it is used in
this paper.
(d) (10 points) How did the authors identify the causal effect in question? Do you think
there is any selection bias that might be present in the estimates?
2. Finance and Credit (25 points)
(a) (10 points) In an Arrow-Debreu model of the economy, rational agents are able to fully
insure themselves against idiosyncratic risk. Any unexpected negative event will have no
negative impact on a person’s economic well-being because the insurance (or neighbors
and community) have compensate them for their losses, with the understanding that
they will return the favor to other members of the community had they been affected
instead.
Suppose everyone in the world behaves like rational Arrow-Debreu agents. Explain how
having access to formal credit markets (banks, insurance companies) can improve economic outcomes even if everyone is fully rational.
(b) (15 points) In the real world, we often observe people who experience poverty engaging
in behavior that might be considered irrational. For instance, many people utilize shortterm loans with extremely high interest rates even though they can live debt-free by
forgoing “frivolous” consumption for a short period of time. Explain how poverty might
cause people to engage in this irrational behavior. Do we have any evidence that supports
this causal explanation?
1
3. The Becker Model of Discrimination (25 points) Consider a theoretical model of discrimination where the workers are prejudiced (rather than the employers, as discussed in class).
In particular, type A workers do not like to work with type B workers. Let wA and wB denote
the wage levels of type A and type B workers, respectively (assume perfectly competitive labor
markets). Type A workers have a “prejudice coefficient” of value P C. In other words, type
A workers are only willing to work with type B workers if they are paid a wage premium of
at least wA + P C. If their employer pays them less than this, type A workers will quit and
begin working for firms that do not employ type B workers (where they will earn wA ). What
will happen in the long run in this labor market? What type of workers will firms employ?
Will workers work together at the same firms? Are some firms more profitable than others?
Explain your reasoning. (You need not solve a formal model using math and diagrams if you
do not wish to–an intuitive response based on economic reasoning should be sufficient).
4. Neigborhoods and Mobility (25 points)
(a) (5 points) Describe the Moving to Opportunity program. How does the design of the
program allow us to make causal estimates about the effects of neighborhoods on outcomes?
(b) (10 points) According to the research of Chetty, Hendren, and Katz, what were the main
effects of the Moving to Opportunity program? Who benefited the most? Who benefited
the least?
(c) (10 points) In the United States, we observe strong residential segregation by race. Briefly
describe some of the historical reasons why we observe such segregation in our communities.
2
The unintended consequences of “ban the box”:
Statistical discrimination and employment outcomes when
criminal histories are hidden
Jennifer L. Doleac? and Benjamin Hansen†
October 2017‡
?
Frank Batten School of Leadership and Public Policy, University of Virginia, Charlottesville, VA 22904. Email:
jdoleac@virginia.edu.
†
Department of Economics, University of Oregon, Eugene, OR 97403. Email: bchansen@cas.uoregon.edu.
‡
Thanks to Amanda Agan, Shawn Bushway, David Eil, Harry Holzer, Kirabo Jackson, Jonathan Meer, Steven
Raphael, Sonja Starr, and seminar participants at the following for helpful comments and conversations: 2016 IRP
Summer Research Workshop, University of Chicago Harris School, 2016 Southern Economic Association annual
meeting, West Point, University of Alabama at Birmingham, 2017 American Economic Association annual meeting,
American University, University of Waterloo, Duke University, the 2017 Society of Labor Economists annual meeting,
and the 2017 NBER Summer Institute Law and Economics meeting. Thanks also to Emily Fox and Anne Jordan for
excellent research assistance. This study was generously supported by the Russell Sage Foundation and the Bankard
Fund for Political Economy.
Abstract
Jurisdictions across the United States have adopted “ban the box” (BTB) policies preventing
employers from conducting criminal background checks until late in the job application process.
Their goal is to improve employment outcomes for those with criminal records, with a secondary goal of reducing racial disparities in employment. However, removing information about
job applicants’ criminal histories could lead employers who don’t want to hire ex-offenders to
guess who the ex-offenders are, and avoid interviewing them. In particular, employers might
avoid interviewing young, low-skilled, black and Hispanic men when criminal records are not
observable, guessing that these applicants are more likely to be ex-offenders. This would exacerbate racial disparities in employment. In this paper, we use variation in the details and timing
of state and local BTB policies to test BTB’s effects on employment for various demographic
groups. We find that BTB policies decrease the probability of being employed by 3.4 percentage
points (5.1%) for young, low-skilled black men, and by 2.3 percentage points (2.9%) for young,
low-skilled Hispanic men. These findings support the hypothesis that when an applicant’s criminal history is unavailable, employers statistically discriminate against demographic groups that
include more ex-offenders.
2
1
Introduction
Mass incarceration has been an important crime reduction policy for the past several decades, but
it has come under intense scrutiny due to high financial cost, diminishing public-safety returns,
and collateral damage to the families and communities of those who are incarcerated. There is
substantial interest in reallocating public resources to more cost-effective strategies, with greater
emphasis on rehabilitating offenders. Due in part to this change in focus, individuals are now being
released from state and federal prisons more quickly than they are being admitted. According to the
most recent data, over 637,000 people are released each year (Carson and Golinelli, 2014). However,
recent data also suggest that approximately two-thirds of those released will be re-arrested within
three years (Cooper et al., 2014). This cycle signals the country’s failure to help re-entering offenders
transition to civilian life, and limits our ability to reduce incarceration rates. Breaking this cycle is
a top policy priority.
Both theory and evidence suggest that connecting ex-offenders with jobs can keep them from
re-offending. The classic Becker (1968) model of criminal behavior suggests that better employment
options reduce crime. In practice, increasing the availability of jobs for re-entering offenders reduces
recidivism rates (Schnepel, 2015; Yang, 2017). But finding employment remains difficult for this
group. Part of the reason ex-offenders have difficulty finding employment is that, on average, they
have less education and job experience than non-offenders. However, as Pager (2003) and others
have shown, employers discriminate against ex-offenders even when other observable characteristics
are identical. This is likely due to statistical discrimination.1 Ex-offenders are more likely than nonoffenders to have engaged in violent, dishonest, or otherwise antisocial behavior, and – based on
current recidivism rates – are more likely to engage in similar behavior in the future.2 Ex-offenders
also have higher rates of untreated mental illness, addiction, and emotional trauma (Raphael, 2010;
Wolff and Shi, 2012; Justice Center, 2016). These are all valid concerns for employers seeking
reliable, productive employees. But this reasoning is little comfort to someone coming out of prison
1
Some employers’ discrimination could be taste-based – that is, they simply don’t like ex-offenders, and no additional information about individuals with records could change their feelings. This distinction does not alter the
predicted effects of “ban the box”, but does matter when considering alternative policies.
2
This not only affects an individual’s expected tenure on the job, but increases potential financial costs to the
employer. For instance, employers might worry about theft, or that future violent behavior could result in a negligenthiring lawsuit.
3
and hoping to find gainful legal employment. In addition, since black and Hispanic men are more
likely to have criminal records, making a clean record a condition for employment could exacerbate
racial disparities in employment.3
If even a few ex-offenders are more job-ready than some non-offenders, then employers’ statistical
discrimination against those with criminal records hurts the most job-ready ex-offenders. This has
motivated the “ban the box” (BTB) movement, which calls for employers to delay asking about
an applicant’s criminal record until late in the hiring process. Advocates of BTB believe that if
employers can’t tell who has a criminal record, job-ready ex-offenders will have a better chance at
getting an interview. During that interview, they may be able to signal their otherwise-unobservable
job-readiness to the employer. This could increase employment rates for ex-offenders, and thereby
decrease racial disparities in employment outcomes.
However, this policy does nothing to address the average job-readiness of ex-offenders. A criminal
record is still correlated with lack of job-readiness4 . For this reason, employers will still seek to avoid
hiring individuals with criminal records. When BTB removes information about a criminal record
from job applications, employers may respond by using the remaining observable information to try
to guess who the ex-offenders are, and avoid interviewing them. Even though ex-offenders could be
weeded out after the interview process, interviewing candidates is costly. Employers would rather
not spend time interviewing candidates that they are sure to reject when their criminal history is
revealed. Surveys by Holzer et al. (2006) show that employers are most concerned about hiring
those who were recently incarcerated. Since young, low-skilled, black and Hispanic men are the
most likely to fall in this category (Bonczar, 2003; Yang, 2017), employers may respond to BTB
by avoiding interviews with this group. Even black and Hispanic men without a record would lose
opportunities with employers who are worried these applicants have a record but are forbidden from
asking. As a result, racial disparities in employment could increase rather than decrease.
This paper estimates the effect of BTB policies on employment for young, low-skilled, black
and Hispanic men. To do this, we exploit variation in the adoption and timing of state and local
3
The best data available suggest that a black man born in 2001 has a 32% chance of serving time in prison at
some point during his lifetime, compared with 17% for Hispanic men and 6% for white men (Bonczar, 2003).
4
We use “job-readiness” to refer to a range of characteristics that make someone an appealing employee, including
reliability and productivity.
4
BTB policies to test BTB’s effects on employment outcomes, using individual-level data from the
2004-2014 Current Population Survey (CPS). We focus on the probability of employment for black
and Hispanic men who are relatively young (age 25-34)5 and low-skilled (no college degree), as
they are the ones most likely to be recently-incarcerated. This group contains the most intended
beneficiaries of BTB as well as the most people who could be unintentionally hurt by the policy.
If BTB does not exacerbate statistical discrimination by employers, and only helps ex-offenders,
then we would expect BTB to increase employment among groups that include a lot of ex-offenders.
If, however, BTB reduces employment for this group and no others, that is strong evidence that
employers are statistically discriminating, and the damage to innocent bystanders within this group
is greater than the aid given to ex-offenders.
We find net negative effects on employment for these groups: Young, low-skilled black men
are 3.4 percentage points (5.1%) less likely to be employed after BTB than before. This effect is
statistically significant (p < 0.05) and robust to a variety of alternative specifications and sample definitions. We also find that BTB reduces employment by 2.3 percentage points (2.9%) for young, low-skilled Hispanic men. This effect is only marginally significant (p < 0.10) but also fairly robust. Both effects are unexplained by pre-existing trends in employment, and – for black men – persist long after the policy change. The effects are larger for the least skilled in this group (those with no high school diploma or GED), for whom a recent incarceration is more likely. We expect BTB’s effects on employment to vary with the local labor market context. For instance, it would be difficult for an employer to discriminate against all young, low-skilled black men if the local low-skilled labor market consists primarily of black men, or if there are very few applicants for any open position. We find evidence that such differential effects exist. BTB reduces black male employment significantly everywhere but in the South (where a larger share of the population is black). Similarly, BTB reduces Hispanic male employment everywhere but in the West (where a larger share of the population is Hispanic). This suggests that employers are less likely to use race as a proxy for criminality in areas where the minority population of 5 We follow the literature and focus on individuals age 25 and over because most individuals have completed their education by that age. In our sample, only about 1% of low-skilled men ages 25-34 are enrolled in school. Since we are using education level as a proxy for skill level, using final education increases the precision of our estimates (relative to, for instance, considering all 19 year olds "low-skilled" because they don’t yet have a college degree). 5 interest is larger – perhaps because discriminating against that entire set of job applicants is simply infeasible. In addition, we find evidence that statistical discrimination based on race is less prevalent in tighter labor markets: BTB’s negative effects on black and Hispanic men are larger when national unemployment is higher. In other words, employers are more able to exclude broad categories of job applicants in order to avoid ex-offenders when applicants far outnumber available positions. Our hypothesis is that employers are less likely to interview young, low-skilled black and Hispanic men because these groups include a lot of ex-offenders with recent convictions and incarcerations. This hypothesis suggests that employers will instead interview and hire individuals from demographic groups unlikely to include recent offenders. We find some evidence suggesting that this does indeed happen. Older, low-skilled black men are significantly more likely to be employed after BTB.6 (This supports our hypothesis that the racial discrimination at work is statistical, not taste-based.) Effects on white men are also positive and significant when BTB targets private firms. However, total employment might go down when employers are not able to see which applicants have criminal records. BTB increases the expected cost of interviewing job applicants, because there’s a higher chance that any interview could end in a failed criminal background check. In addition, while employers might be willing to substitute college graduates or others who are clearly job-ready, those individuals might not be willing to accept a low-skilled job at the wage the employer is willing to pay.7 Consistent with this, we find no effect on employment for men with college degrees. Controlling for local unemployment rates has little effect on our estimates, suggesting that BTB simply shifts employment, rather than reducing it – at least in the short run. We are not the only researchers interested in the effects of BTB on employment. Two other papers, written concurrently with this one, study the effect of this policy. However, ours is the only one to focus on real-world employment outcomes for young, low-skilled men – the group with the most to gain or lose from BTB. Agan and Starr (2016) exploited the recent adoption of BTB in New Jersey and New York to conduct a field experiment testing the effect of the policy on the likelihood of getting an interview. 6 Highly-educated black women are also more likely to be employed after BTB, but this effect could represent intrahousehold substitution, rather than substitution by employers. That is, women might be more likely to work when their partners are unable to find jobs. 7 This is related to the well-known "lemons problem" in economics, where asymmetric information between a buyer and seller causes a market to unravel and no transactions to be made (Akerlof, 1970). 6 They submitted thousands of fake job applications from young, low-skilled men, randomizing the race and criminal history of the applicant. They found that before BTB white applicants were called back slightly more often than black applicants were. That gap increased six-fold after BTB went into effect. White ex-offenders benefited the most from the policy change: after BTB, employers seem to assume that all white applicants are non-offenders. After BTB, black applicants were called back at a rate between the ex-offender and non-offender callback rates from before BTB – that is, those with records were helped, but those without records were hurt. Since the researchers create the applications themselves, they could keep other factors like education constant. The differences in interview rates before and after the policy change are therefore solely due to the changing factors – race and criminal history. The limitation of this approach is that fake applicants can’t do real interviews that lead to real jobs. It’s possible that the few ex-offenders granted interviews would be more likely to get the job after BTB implementation than before. However, if employers are reluctant to hire ex-offenders, those applicants might be rejected once their criminal history is revealed late in the process (between the interview and the job offer). These later steps are critical in determining the true social welfare consequences of BTB. Our paper complements this one by showing that these changes in callback rates do result in changes in hiring, with a net negative effect on employment for young, low-skilled black men. We also confirm that young, low-skilled white men are more likely to get hired when BTB laws target private firms. Shoag and Veuger (2016) use a difference-in-difference strategy to consider the effects of BTB on residents of high-crime neighborhoods (a proxy for those with criminal records), using those living in low-crime neighborhoods (a proxy for those without criminal records) as a control group. They focus on a subset of BTB cities (though they are not listed in the paper). Neighborhoods are deemed high- or low-crime based on violent crime rates in 2000, and employment is measured for 2002-2013. Using aggregated data, they find that more people are employed in high-crime neighborhoods after BTB, relative to employment in low-crime neighborhoods, and interpret this as evidence that BTB has a beneficial effect on ex-offenders. However, no effort is made to control for changes in the compositions of these neighborhoods over time, and the authors are unable to control for residents’ demographic characteristics. It seems likely that the residents of both types of neighborhoods changed over the course of two economic downturns, the housing bubble, and 7 the housing crash, and places that were high-crime in 2000 might not be by 2013. It is therefore unclear from this analysis who (if anyone) is benefiting from BTB. A supplementary analysis uses annual data from the American Community Survey (ACS) to consider effects of BTB on the full working-age population, divided by race and gender. They find a net increase in employment for black men. As in their previous analysis, they make no effort to break results down by education level or age, and so cannot test the effect of the policy on the groups most likely to be affected. In addition, the way the ACS asked about employment changed in 2008, and this change seems to have increased employment estimates (Kromer and Howard, 2010). It’s unclear how the authors account for this, and so their finding that black employment increased after BTB could be an artifact of this change in the survey. Given concerns about the integrity of ACS employment data during this time period, the CPS is better suited to measuring the impact of BTB. We use the CPS to consider impacts on the groups most likely to be affected BTB, in the full set of BTB communities instead of a non-random subset. We also use individual-level data with a full set of demographic controls, to account for the composition of local labor markets. Ban the box policies seek to limit employers’ access to criminal histories. This access itself is relatively new. Before the internet and inexpensive computer storage became available in the 1990s, it was not easy to check job applicants’ criminal histories. This is the world that BTB advocates would like to recreate. Of course this world differs from our own in many other respects, but nevertheless it is helpful to consider how employment outcomes changed as criminal records became more widely available during the 1990s and early 2000s. A number of studies address this, and their findings foreshadow our own: when information on criminal records is available, firms are more likely to hire low-skilled black men (Bushway, 2004; Holzer et al., 2006; Finlay, 2009; Stoll, 2009). In fact, many of those studies explicitly predicted that limiting information on criminal records, via BTB or similar policies, would negatively affect low-skilled black men as a group.8 8 A few striking quotes from that literature: [S]ome advocates seek to suppress the information to which employers have access regarding criminal records. But it is possible that the provision of more information to these firms will increase their general willingness to hire young black men, as we show here and since we have previously found evidence that employers who do not have such information often engage in statistical discrimination against this demographic group. (Holzer et al., 2004) Employers have imperfect information about the criminal records of applicants, so rational employers may use observable correlates of criminality as proxies for criminality and statistically discriminate against groups with high rates of criminal activity or incarceration. (Finlay, 2009) 8 There is plenty of evidence that statistical discrimination increases when information about employees is less precise. Autor and Scarborough (2008) measure the effects of personality testing by employers on hiring outcomes. Conditioning hiring on good performance on personality tests (such as popular Myers-Briggs tests) was generally viewed as disadvantaging minority job candidates because minorities tend to score lower on these tests. However, the authors note that this will only happen if employers’ assumptions about applicants in the absence of information about test scores are more positive than the information that test scores provide. If, in contrast, minorities score better on these tests than employers would have thought, adding accurate information about a job applicant’s abilities will help minority applicants. They find that in a national firm that was rolling out personality testing, the use of these tests had no effect on the racial composition of employees, though they did allow the firm to choose employees who were more productive. Wozniak (2015) found that when employers required drug tests for employees, black employment rates increased by 7-30%, with the largest effects on low-skilled black men. As in the personality test context, the popular assumption was that if black men are more likely to use drugs, employers’ use of drug tests when making hiring decisions would disproportionately hurt this group. It turned out that a drug test requirement allowed non-using black men to prove their status when employers would otherwise have used race as a proxy for drug use. In another related paper, Bartik and Nelson (2016) hypothesize that banning employers from checking job applicants’ credit histories will negatively affect employment outcomes for groups that have lower credit scores on average (particularly black individuals). The reasoning is as above: in the absence of information about credit histories, employers will use race as a proxy for credit scores. They find that, consistent with statistical discrimination, credit check bans reduce job-finding rates by 7-16% for black job-seekers. As with BTB policies, one goal of banning credit checks was to reduce racial disparities in employment, so this policy was counterproductive. Our study therefore contributes to a growing literature showing that well-intentioned policies [Ban the box] may in fact have limited positive impacts on the employment of ex-offenders....More worrisome is the likelihood that these bans will have large negative impacts on the employment of those whom we should also be concerned about in the labor market, namely minority – especially black – men without criminal records, whose employment prospects are already poor for a variety of other reasons. (Stoll, 2009) 9 that remove information about racially-imbalanced characteristics from job applications can do more harm than good for minority job-seekers.9 Advocates for these policies seem to think that in the absence of information, employers will assume the best about all job applicants. This is often not the case. In the above examples, providing information about characteristics that are less favorable, on average, among black job-seekers – criminal records, drug tests, and credit histories – actually helped black men and black women find jobs. These outcomes are what we would expect from standard statistical discrimination models. More information helps the best job candidates avoid discrimination. The availability of criminal records is just one facet of an ongoing debate about data availability. Improvements in data storage and internet access have made a vast array of information about our pasts readily available to those in our present, including to potential employers, love interests, advertisers, and fraudsters. This often seems unfair to those who – like many ex-offenders – are trying to put their pasts behind them. The policy debate about whether and how to limit this data availability is complicated both by free speech concerns and logistical issues – once information is distributed publicly, what are the chances of being able to make it private again? Even so, a great deal of effort has gone into defining who should have access to particular data, often with the goal of improving the economic outcomes of disadvantaged groups.10 As this and related studies have shown, well-intentioned policies of this sort often have unintended consequences, and providing more information is often a better strategy. This paper proceeds as follows: Section 2 provides background on BTB policies. Section 3 describes our data. Section 4 presents our empirical strategy. Section 5 describes our results. Section 6 presents robustness checks. Section 7 discusses and concludes. 9 An additional study focuses on a different population but its findings are consistent with the same statistical discrimination theory as those described above: Thomas (2016) finds that when the Family and Medical Leave Act limited employers’ information about female employees’ future work plans, it decreased employers’ investment in female employees as a group. After the FMLA, women were promoted at lower rates than before the law. 10 See for example, the "right to be forgotten" movement in Europe, which included a ruling that – at a person’s request – search engines must "remove results for queries that include the person’s name" (Google, 2016). See also the White House’s recent recommendations on consumer data privacy, available at https://www.whitehouse.gov/ sites/default/files/privacy-final.pdf. 10 2 Background on BTB policies When allowed, employers commonly include a box on job applications that applicants must check if they have been convicted of a crime, along with a question about the nature and date(s) of any convictions. Anecdotally, many employers simply discard the application of anyone who checks this box. BTB policies prevent employers from asking about criminal records until late in the hiring process, when they are preparing to make a job offer. The first BTB law was implemented in Hawaii in 1998, and – as of December 2015 – similar policies exist in 34 states and the District of Columbia. President Obama "banned the box" on employment applications for federal government jobs in late 2015. BTB policies fall into three broad categories: (1) those that target public employers (that is, government jobs only), (2) those that target private employers with government contracts, and (3) those that target all private employers. We’ll refer to these as "public BTB", "contract BTB", and "private BTB" policies, respectively. Every jurisdiction in our sample with a contract BTB policy also has a public BTB policy. Similarly, every jurisdiction in our sample with a private BTB policy also has a contract BTB policy. Thirteen percent of jurisdictions adopt a contract and/or private BTB policy during this time period. Our analysis focuses primarily on the effects of having any BTB policy, but we consider differential effects by policy type in Section 5.5. Public BTB laws can affect both public and private sector employment. These policies were typically implemented due to public campaigns aimed at convincing employers to give ex-offenders a second chance. Public BTB policies were intended in part to model the best practice in hiring, and there is anecdotal evidence that this model – in combination with public pressure – pushed private firms to adopt BTB even before they were legally required to. Several national private firms such as Wal-Mart, Target, and Koch Industries, voluntarily "banned the box" on their employment applications during this period, in response to the BTB social movement.11 Public BTB laws might also affect private sector employment because workers are mobile between the two sectors, and likely sort themselves based on where they feel most welcome. Ex-offenders who 11 We do not consider the effects of those voluntary bans here, but do note that a principal-agent problem could lead to the same effects as for government bans. A CEO might be inclined to hire ex-offenders, but the managers who are actually making the hiring decisions might still want to avoid supervising individuals with criminal records. 11 would have been employed in the private sector might not get those jobs if they target job openings in the public sector due to a public BTB law; if applicants change their application strategies and where they spend their time interviewing for jobs, these such policies could quickly have meaningful