{"id":71,"date":"2020-04-04T01:21:54","date_gmt":"2020-04-04T01:21:54","guid":{"rendered":"https:\/\/pressbooks.ccconline.org\/accbertelsen\/chapter\/the-economics-of-discrimination\/"},"modified":"2023-10-27T01:42:18","modified_gmt":"2023-10-27T01:42:18","slug":"the-economics-of-discrimination","status":"publish","type":"chapter","link":"https:\/\/pressbooks.ccconline.org\/accbertelsen\/chapter\/the-economics-of-discrimination\/","title":{"raw":"Chapter 13 - The Economics of Discrimination","rendered":"Chapter 13 – The Economics of Discrimination"},"content":{"raw":"
\r\n

What is discrimination?<\/h2>\r\n

Discrimination <\/strong>is the unjust or unequal treatment of an individual or group based on a specific characteristic, such as their race, age, or gender identity. In the United States, a number of laws forbid discrimination on the basis of age, disability, national origin, pregnancy, race\/color, religion, or sex.[footnote]U.S. Equal Employment Opportunity Commission, 2017.[\/footnote]<\/sup> Globally, the United Nations (UN) has passed conventions on eliminating all forms of racial discrimination and discrimination against women.[footnote]UN Women, 2009; United Nations Human Rights Office of the High Commissioner, 2017.[\/footnote]<\/sup><\/p>\r\n

Although the definition seems straightforward, identifying when an individual or entity is discriminating in practice is quite challenging. This difficulty is because disparities<\/strong> (differences in outcomes) may be the result of current or past discrimination. The fact that women earn 81 cents for every dollar men earn[footnote]U.S. Bureau of Labor Statistics, 2021.[\/footnote]<\/sup> may reflect employers\u2019 discrimination in setting wages, but may also reflect the fact that women choose different majors, or are more likely to take time out of the workforce to care for children. Of course, that women choose different majors may also<\/em> reflect discrimination in human capital accumulation.<\/p>\r\n

Likewise, the fact that Black men have a one in three lifetime likelihood of imprisonment, while white men have a one in seventeen chance[footnote]Bonczar, 2003.[\/footnote]<\/sup> may be due to a variety of factors, such as historical housing discrimination and poor local labor market opportunities, as well as discrimination in the criminal justice system. Identifying the source of disparities\u2014for instance in the case of imprisonment, whether disparities are due to unequal and discriminatory outcomes around education, employment, or poverty as factors in committing crimes, or in unequal chances of arrests, convictions, or sentences\u2014is critical to addressing and reducing these disparities.<\/p>\r\n\r\n

Causes of discrimination<\/h2>\r\n

Discriminatory \u201ctastes\u201d<\/h3>\r\n

Economists have two main theories concerning the causes of discrimination. The first theory is that individuals have \u201ctastes\u201d or preferences for discrimination.[footnote]Becker, 1971.[\/footnote]<\/sup> This taste-driven discrimination<\/strong> theory suggests that factors such as social and physical distance and relative socioeconomic status contribute to tastes for discrimination. Contact with a minority group and the size of a \u201cminority\u201d group matter as well (the minority in this case could actually be a majority that has historically been disempowered, e.g. women). Tastes for discrimination mean that individuals are effectively willing to forfeit income to avoid certain transactions or interactions. For instance, landlords may prefer to rent only to individuals of a certain race or religion, even though they could charge higher rents if they opened up to a broader market.<\/p>\r\n\r\n

Statistical discrimination<\/h3>\r\n

The second theory is statistical discriminatio<\/strong>n<\/strong>, which assumes discrimination is essentially an information problem.[footnote]Phelps, 1972.[\/footnote]<\/sup> For instance, in the labor market, employers may have imperfect information about the productivity of individual workers. Consider the case of an employer hiring a new carpenter for a construction company. The employer has information from applicants\u2019 resumes on their education, training and past work experience. She can even administer a test to prospective employees to measure their skills, perhaps building a stair rail. The resume information and the test are, however, imperfect signals of the employee\u2019s productivity. This uncertainty and imperfect information cause the employer to take into account another factor: she believes that women are, on average, less productive carpenters than men. This potentially erroneous statistic, combined with the inability of the resume and skills test to fully signal productivity, will lead the employer to conclude that a man is likely to be more productive than an equally qualified woman. The job is then offered to the man instead of the woman.<\/p>\r\n

A \u201ctaste\u201d for discrimination and statistical discrimination are often framed as competing theories. However, as we will see in discussing the empirical studies on discrimination below, there is substantial evidence for each theory. One way of reconciling the theories may be to think of taste-driven discrimination as a potential source of assumptions about individuals\u2019 productivity in the face of incomplete information. Additionally, some individuals may change their assumptions in the face of additional evidence, a case which supports the existence of statistical discrimination. Others with more deeply ingrained prejudices would not reevaluate their assumptions, which lends credence to taste-driven discrimination. Different assumptions regarding individuals and groups may be more or less swayed by information.<\/p>\r\n\r\n

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Box <\/strong>6.<\/strong>1<\/strong>: <\/strong>Economists in Action: Lisa Cook Studies <\/strong>Competition and Discrimination<\/strong>[footnote]Cook et al., 2020; Cook, 2011; Cook, 2019; Board of Governors of the Federal Reserve, 2022.[\/footnote]<\/strong><\/sup><\/p>\r\n\r\n<\/header>\r\n

\r\n\r\nDr. Lisa D. Cook is a Professor of Economics and International Relations, currently serving on the Board of Governors for the Federal Reserve. She researches economic growth and development, along with financial institutions, innovation, and economic history. She was a Senior Economist for the Council of Economic Advisors, serving in the White House, and was elected to the board of the American Economic Association (AEA). She directed the AEA Summer Training Program, which increases diversity in the field of economics by preparing undergraduate students for graduate degrees in economics.\r\n

One important area of Dr. Cook\u2019s research is creating and analyzing data on discrimination, including gathering data on Jim Crow era firms that were friendly towards African Americans, as well as the creation of a national lynching database. In one of her papers Dr. Cook examines the determinants of firms\u2019 discrimination towards potential consumers during the Jim Crow era and prior to the Civil Rights Act. She shows that firm owners segregated and discriminated against African Americans based on white consumers\u2019 discrimination, a case of taste-based discrimination. Reductions in the number of white consumers reduced discrimination and activism among African Americans also helped.<\/p>\r\n\r\n<\/div>\r\n<\/div>\r\n

Labor market models of discrimination and its consequences<\/h2>\r\n

Regardless of whether discrimination is taste-driven or caused by statistical discrimination, it can essentially be modeled the same way. The approaches to reducing discrimination will be quite different, but the models for the impact of discrimination will be nearly identical. Consider discrimination for the case of the labor market. Recall that employers\u2019 demand for labor is based on productivity. We are now going to name that productivity the marginal revenue product<\/strong> of workers, how much revenue they create for their employers through their work. Operating under statistical discrimination, productivity is assumed to be lower for certain groups.[footnote]Hellerstein, Neumark, and Troske, 2002.[\/footnote]<\/sup> In the case of gender discrimination, employers may assume the marginal revenue product of women is lower because they disproportionately undertake caregiving responsibilities (a case of statistical discrimination). Alternatively, with taste-based discrimination, hiring a less-preferred group imposes a \u201ccost\u201d on employers, effectively modeled as a decrease in the marginal revenue product.<\/p>\r\n

Figure 1 shows discrimination in the labor market for men and women. To simplify, we assume the same labor supply for men and women. However, the demand, which is equal to the marginal revenue product (MRP) is assumed to be lower for women than for men (discrimination). As a result, the equilibrium outcome is that fewer women are employed and women are earning lower wages than men. Although we focus on models of the labor market here, similar ideas apply to other markets. Housing is another example. A landlord might discriminate in supplying housing to individuals with a different religion than his own. This would shift the supply of housing to differentiate between religious groups, with a higher cost (reduced supply) for those of a different religion.<\/p>\r\n

In all cases, there is a substantial challenge when it comes to proving discrimination. It is difficult to measure the MRP of different workers. This then makes it difficult to distinguish between cases where individuals actually are differentially productive on average, such as workers with more training or experience, and when discrimination is occurring.<\/small><\/p>\r\n \r\n\r\n[caption id=\"attachment_308\" align=\"aligncenter\" width=\"1024\"]\"\" Figure 6.1. Labor market for men and women with discrimination[\/caption]\r\n

Evidence on Discrimination<\/h2>\r\n

There are a variety of forms of discrimination and different groups that are discriminated against globally. This section presents just some of the evidence on discrimination, primarily from the U.S., but from other global contexts as well. Economists rely on a host of different techniques to gather evidence on discrimination. One is multiple regression<\/strong>, also called multivariate regression, a statistical technique where economists try to account for differences in observable characteristics. When checking for wage discrimination, for example, these characteristics would include occupation and education. The remainder of the differences in outcomes would then be attributed to discrimination.<\/p>\r\n

Conducting experiments is another method to assess discrimination. Economists can randomize resumes with different characteristics to apply to jobs or randomize the economic equivalent of \u201cmystery shoppers\u201d with different characteristics to apply for housing. These experiments are typically referred to as audit studies<\/strong>. Experiments are the most effective for being certain about cause and effect, but can be challenging to implement and much more expensive than analyzing existing data with multiple regression. This section presents evidence from both multiple regression models and audit studies on discrimination in education, housing, the labor market, and the criminal justice system.<\/p>\r\n\r\n

In education<\/h3>\r\n

Discrimination in the education system leads to disparate human capital outcomes that also contribute to labor market disparities.[footnote]Carruthers and Wanamaker, 2017.[\/footnote]<\/sup> Teachers play a key role in education and their discriminatory attitudes can affect students in a variety of ways. For instance, one experiment demonstrated that teachers gave worse grades and lower secondary school recommendations when assignments (essays) had minority (Turkish) names.[footnote]Sprietsma, 2013.[\/footnote]<\/sup> Teachers also have lower expectations and negative attitudes that affect their behavior towards minority students, which may in turn affect those students\u2019 performance.[footnote]Van Ewijk, 2011.[\/footnote]<\/sup> Gender bias may be particularly important for Science, Technology, Engineering, and Math (STEM) fields. Science faculty presented with otherwise identical student resumes bearing either a female or male name rated women as less competent than men. Faculty were less likely to hire women, offered a lower salary, and were less likely to mentor women.[footnote]Moss-Racusin et al., October 9, 2012.[\/footnote]<\/sup><\/p>\r\n\r\n

In housing<\/h3>\r\n

Housing was one of the areas where discrimination in the United States was first measured effectively. Fair housing audits were developed by housing organizations to identify racial discrimination in housing opportunities after the passage of the Civil Rights Act.[footnote]Yinger, 1986.[\/footnote]<\/sup> For example, in assessing Black-white housing disparities, an audit will send two auditors to a housing agent, one white and one Black, for a random sample of advertised housing units. When individuals receive differential treatment, specifically in different offers of housing, and this treatment depends on their race, the audit indicates discrimination. Historically, as of 1981, Black housing seekers were told about 30% fewer available housing units than whites.[footnote]Ibid.[\/footnote]<\/sup><\/p>\r\n

More recent studies have taken advantage of the power of the internet; an experiment in the U.S. rental apartment market varied first names, using those commonly associated with whites and African Americans. In some cases, it also included information about credit history and smoking. African-American sounding names had a 9.3 percentage point lower positive response rate than applicants with white-sounding names, indicating discrimination. The additional information on credit history and smoking did differentially affect the gap in response rates, indicating that information and statistical discrimination contributed to disparities.[footnote]Ewens, Tomlin, and Wang, 2014.[\/footnote]<\/sup> In India, a study using India\u2019s largest real estate website showed that, while an upper-caste Hindu had a 35% chance of a response to a housing application, this was only 22% for a Muslim applicant.[footnote]Datta and Pathania, 2016.[\/footnote]<\/sup> An experiment in Sweden varied distinctive ethnic and gender names in applying for rental housing. Arabic\/Muslim names received fewer responses than\u00a0the Swedish male names, and Swedish female names had an easier time accessing housing than Swedish male names.[footnote]Ahmed and Hammarstedt, 2008.[\/footnote]<\/sup> In addition to long-term rentals, these disparities extend to short-term rentals, such as Airbnb vacation rentals. Applications from guests with African-American names were 16% less likely to be accepted relative to otherwise identical guests with distinctively white names.[footnote]Edelman, Luca, and Dan, 2017.[\/footnote] <\/sup>Discrimination also occurs against Airbnb ethnic minority hosts.[footnote]Laou\u00e9nan and Rathelot, 2022.[\/footnote]<\/sup><\/p>\r\n\r\n

In the labor market<\/h3>\r\n

Discrimination in the labor market manifests in substantial hiring disparities by race, ethnicity, gender, and disability status. A study sending fake resumes to help-wanted ads in Boston and Chicago found that white names received 50% more callbacks for interviews than African-American names.[footnote]Bertrand and Mullainathan, 2004.[\/footnote]<\/sup> A similar study in New York City found that Black applicants were half as likely to receive a callback or job offer than white applicants.[footnote]Pager, Western, and Bonikowsi, 2009.[\/footnote]<\/sup> In interviews for waitstaff jobs in Philadelphia, job applications from women had a 40 percentage point lower chance of receiving a job offer from high-price (and high earning) restaurants than men, in part embodying customer discrimination.[footnote]Neumark, Bank, and Van Nort, 1996.[\/footnote]<\/sup> An experiment that randomized disclosure of disability status found disability halved the chances of a callback.[footnote]Bellemare et al., 2018.[\/footnote]<\/sup><\/p>\r\n

In Toronto, a study demonstrated that individuals with foreign experience or with Indian, Pakistani, Chinese, and Greek names were less likely to be hired than those with English names.[footnote]Oreopoulos, 2011.[\/footnote]<\/sup> In Germany, which has a substantial number of Muslim migrants, especially from Turkey, it is common for applicants to send photos with resumes. A study of female applicants that randomized German names, Turkish names, and whether the migrant was wearing a headscarf found significant discrimination against Turkish names and more so against those wearing a headscarf. This discrimination is so pronounced that a female applicant who wears a headscarf and who has a Turkish name would have to send 4.5 times as many applications to receive the same number of callbacks as a woman with a German name and no headscarf.[footnote]Weichselbaumer, 2019.[\/footnote]<\/sup><\/p>\r\n

In the United States, women\u2019s pay is, as of 2020, 82% of men\u2019s pay.[footnote]U.S. Bureau of Labor Statistics, 2021.[\/footnote]<\/sup> Notably, women and men are approaching convergence in their pay at the start of their careers. Figure 6.2[footnote]Ibid.[\/footnote]<\/sup> shows median weekly earnings by sex, as well as the ratio of women\u2019s wages to men\u2019s. Early on in the life course, women\u2019s wages are 95% (ages 16-24) that of men\u2019s. However, pay diverges over the lifespan, with a major expansion in the gender gap. At ages 25-34, women\u2019s pay is 89% of men\u2019s pay. By ages 35-44 women\u2019s pay is only 81% of men\u2019s, dropping to 78% at ages 45-64. Two key drivers for the gap expansion are differences in career interruptions and differences in weekly hours\u2014both largely associated with motherhood.[footnote]Bertrand, Goldin, and Katz, 2010; Bailey et al., 2019.[\/footnote]<\/sup> In contrast, when men become parents, they tend to receive a premium, an increase in pay, rather than a penalty.[footnote]Lundberg and Rose, 2002.[\/footnote]<\/sup> As of 2010, differences in human capital contributed little to the gender wage gap. However, differences in occupations were still important, as women tended to be in traditionally female occupations that are generally lower paying, such as nursing and teaching.[footnote]Blau and Kahn, 2016.[\/footnote]<\/sup><\/p>\r\n\r\n\r\n[caption id=\"attachment_496\" align=\"aligncenter\" width=\"1024\"]\"\" Figure 6.2. Median weekly earnings for full time workers by gender and women\u2019s wages as a percentage of men\u2019s, U.S., 2020[\/caption]\r\n

Gender pay gaps can be compounded by racial disparities. Figure 6.3[footnote]U.S. Bureau of Labor Statistics, 2021.[\/footnote]<\/sup> shows median weekly earnings among workers as a percentage of white men\u2019s earnings. People of color tend to earn less than whites, with disparities further exacerbated by the gender pay gap. For instance, Black men earn 75% of what white men earn, while Hispanic men earn 72% of what white men earn. Asian men earn more than white men, at 130% and Asian women earn 103% of what white men earn. In contrast, other groups of women earn less on average. Black women earn 69% of white men and Hispanic women 64%. Relative to white women, who earn 82% of what white men earn, Asian women are better off but still at a disadvantage when compared to the relatively higher earnings of Asian men.<\/p>\r\n\r\n\r\n[caption id=\"attachment_495\" align=\"aligncenter\" width=\"1024\"]\"Figure Figure 6.3. Median weekly earnings as a percentage of white men\u2019s earnings, by race\/ethnicity and sex, 2020[\/caption]\r\n

In studying pay gaps by race, what are referred to as pre-market factors, such as human capital, explain an important share of pay gaps. However, an important share of gaps are also discrimination in the labor market\u2014estimated to be at least one-third of the Black-white wage gap.[footnote]Fryer Jr., Pager, and Spenkuch, 2013.[\/footnote]<\/sup> Discrimination feeding into pay gaps can occur in complex ways. For example, when Black job-seekers attempt to negotiate for a higher salary, they are penalized in terms of their salary outcomes.[footnote]Hernandez et al., 2019.[\/footnote]<\/sup> Likewise, women tend to be perceived more negatively than men when they try to negotiate, in part due to gender stereotypes around being \u201cnice.\u201d[footnote]Bowles, Babcock, and Lai, 2007.[\/footnote]<\/sup><\/p>\r\n\r\n

In the criminal justice system<\/h3>\r\n

Discrimination is a challenge throughout the criminal justice system and contributes to the large disparities in incarceration by race and gender that were discussed in the crime chapter. Racial disparities in drug arrests are not<\/em> due to differential drug or nondrug offending, nor residing in areas with a police focus on drug offenses; there is strong evidence of discrimination and disparities in police practices driving disparities.[footnote]Mitchell and Caudy, 2015; Welty et al., 2016.[\/footnote]<\/sup> Likewise, studies using the differential ability to tell driver race in the daytime versus the nighttime have demonstrated racial bias in traffic stops in some localities, but not others.[footnote]Ritter and Bael, 2009; Antonovics and Knight, 2009.[\/footnote]<\/sup> Once arrested, individuals may be discriminated against in terms of the process from pre-trial processing (for instance, setting bail) through setting their sentences.<\/a>[footnote]Schlesinger, 2007; Starr and Rehavi, 2013; Bielen, Marneffe, and Mocan, 2018.[\/footnote]<\/sup> Offenders who are Black, male, less educated, and lower income receive longer sentences.[footnote]Mustard, 2001; Cook et al., 2020.[\/footnote]<\/sup><\/p>\r\n\r\n

Policies to reduce discrimination<\/h2>\r\n

Competition<\/h3>\r\n

The idea of taste-based discrimination has, historically, been linked with the idea that competition may play a key role in reducing discrimination. Consider a case where all workers are equally productive, but some employers have discriminatory tastes. It would follow that the non-discriminating employers would be able to make a greater profit by hiring individuals who tend to be discriminated against but are equally productive. This idea would suggest that the solution to discrimination in any market is simply competition. However, empirical evidence suggests that, while competitive markets deter discrimination, firms that have market power exist and do discriminate.[footnote]Hellerstein, Neumark, and Troske, 2002.[\/footnote]<\/sup> Simply \u201cwaiting out\u201d discrimination will not be effective. Other interventions are required.<\/p>\r\n\r\n

Changing the available information<\/h3>\r\n

An important set of interventions to reduce discrimination focus on changing the available information about individuals. Interventions can remove markers of protected categories, such as gender and race, from the set of available information to reduce discrimination. For example, when symphony orchestras adopted blind auditions\u2014where the candidate plays music behind a screen and is not visible to the hiring committee\u2014this approach led to gender equity in hiring, increasing the proportion of women in symphony orchestras.[footnote]Goldin and Rouse, 2000.[\/footnote]<\/sup> However, policies to remove all potentially revealing information are challenging to design, and employers may be resistant to their implementation. For instance, orchestras have to lay down carpet, to muffle the sounds of heeled shoes that are associated with women, or ask women to take off their shoes.<\/p>\r\n

Removing names from the available information may reduce discrimination in a variety of areas. This approach can be particularly effective for reducing discrimination in models like Uber and Lyft[footnote]Ge et al., 2016.[\/footnote]<\/sup> or Airbnb[footnote]Edelman, Luca, and Dan, 2017.[\/footnote]<\/sup> where such information could be readily removed without interrupting transactions. Other approaches to removing potential markers of protected categories include anonymizing resumes and using skills-based tests (like the orchestra auditions) for other jobs as well. A number of European countries have experimented with anonymizing applications.[footnote]Krause, Rinne, and Zimmermann, 2012; Behaghel, Cr\u00e9pon, and Le Barbanchon, 2015.[\/footnote]<\/sup> Doing so can reduce disparities and equalize the probability of receiving an interview. However, the process still allows for discrimination in hiring after the interview and precludes affirmative actions for otherwise equivalent applicants. In France, anonymous resumes ultimately led to a lower probability of interviewing and hiring minority candidates.[footnote]Behaghel, Cr\u00e9pon, and Le Barbanchon, 2015.[\/footnote]<\/sup><\/p>\r\n

Depending on the nature of discrimination, there may be cases where removing information could be potentially harmful and adding<\/em> information may be more helpful. One of the studies that identified discrimination in Airbnb determined that discrimination against African-American names disappeared when there was a positive public review.[footnote]Cui, Li, and Zhang, 2016.[\/footnote]<\/sup> Essentially, positive information about individuals helped reduce discrimination. Having to report gender-disaggregated information about pay has been shown to reduce the gender pay gap.[footnote]Bennedsen et al., 2018.[\/footnote]<\/sup> However, having individuals disclose their past salaries when applying to new jobs can perpetuate discrimination, as new employers will use those as a basis for salary offers. This problem has led some states to ban asking applicants about their salary history.[footnote]Abbott Watkins, 2018.[\/footnote]<\/sup><\/p>\r\n\r\n

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<\/a>Box <\/strong>6.<\/strong>2<\/strong>: <\/strong>The Lilly Ledbetter Fair Pay Act<\/strong>[footnote]Sorock, 2010.[\/footnote]<\/sup><\/p>\r\n\r\n<\/header>\r\n

\r\n\r\nLilly Ledbetter was an employee of Goodyear from 1979 until 1998. Initially Ledbetter was paid the same as the men in the same position. By 1997, Ledbetter was paid $3,727 per month. Male managers were paid between $4,286 and $5,236 per month. In part because Goodyear kept pay information confidential (as is common practice), Ledbetter did not find out about the pay disparity until long after the disparity had occurred. When she sued, under Title VII of the Civil Rights Act, a 5-4 decision in the case before the Supreme Court determined that she had not filed within the statute of limitations\u2014the legal time frame for filing after discrimination occurs. The case treated the discrimination as the decision about her salary by her supervisor, some time ago, not the ongoing disparate paychecks, because the paychecks themselves did not have discriminatory intent, which is required under Title VII. The problem that faced Lilly Ledbetter, that she learned about discrimination long after it occurred and the statute of limitations expired, led to the Lilly Ledbetter Fair Pay Act. The Act, passed in 2009, broadened the definition of discriminatory practice to include, for instance, each disparate paycheck. The case and subsequent act illustrate some of the challenges in identifying and remedying discriminatory practices.\r\n\r\n<\/div>\r\n<\/div>\r\n

The \u201cBan the box\u201d campaign is an example of an information removal effort that appears to have achieved the opposite of its goal. We learned in the chapter on crime that ex-offender rehabilitation depends in part on employment opportunities and holding a legitimate job. Yet employers tend to discriminate against those with a criminal record.[footnote]Pager, 2003.[\/footnote]<\/sup> Employers commonly ask about past criminal convictions on initial job applications. \u201cBan the box\u201d campaigns forbid asking at the initial job application stage but allow for the question in interviews and with conditional job offers. The goal was that ex-offenders would have better job opportunities. An additional goal was to reduce racial disparities and discrimination in employment, given racial disparities in the criminal justice system.[footnote]Henry and Jacobs, 2007.[\/footnote]<\/sup> Although well intentioned, \u201cban the box\u201d laws appear to be counterproductive in reducing discrimination. Employers, without information on criminal history, operate under statistical discrimination and are less likely to interview young, low-skilled Black and Hispanic men.[footnote]Doleac and Hansen, 2016; Agan and Starr, 2018.[\/footnote]<\/sup><\/p>\r\n\r\n

Affirmative action<\/h3>\r\n

Affirmative action<\/strong> is a \u201cset of procedures designed to eliminate unlawful discrimination between applicants, remedy the results of such prior discrimination, and prevent such discrimination in the future. Applicants may be seeking admission to an educational program or looking for professional employment.\u201d[footnote]Cornell University Law School, 2017.[\/footnote]<\/sup> Affirmative action in the United States came about as a 1961 executive order by President John F. Kennedy, with a requirement mandating affirmative action among government contractors. Affirmative action subsequently expanded to other areas, such as education.<\/p>\r\n

Those in favor of affirmative action argue that it equalizes opportunities, benefits qualified women and minorities, and that it is beneficial to society as a whole. Proponents also suggest that affirmative action improves equity and either improves efficiency or has at most minor reductions in efficiency through the reallocation of jobs. Opponents suggest that there are efficiency losses and that the policy itself is inherently racist.[footnote]Holzer and Neumark, 2006; Ibanez and Riener, 2018.[\/footnote]<\/sup><\/p>\r\n

Historically, affirmative action has helped promote the employment of minorities and women.[footnote]Leonard, 1990.[\/footnote]<\/sup> The magnitude of the effects is generally fairly small, although they can cause substantial relative shifts for minority groups.[footnote]Holzer and Neumark, 2006.[\/footnote]<\/sup> While minorities who benefit on the labor market may have poorer credentials, they have equal performance, suggesting that efficiency concerns have relatively little merit. Further, white males face costs, but they are relatively small. Affirmative action has also increased the probability that under-represented minority groups graduate from selective institutions. However, affirmative action or some approaches to affirmative action have been banned in making university admissions decisions.[footnote]Hinrichs, 2010.[\/footnote]<\/sup><\/p>\r\n\r\n

Reducing bias in individuals<\/h3>\r\n

Individuals\u2019 biases, for example their gender biases, are key drivers of discrimination.[footnote]E.g. Moss-Racusin et al., October 9, 2012.[\/footnote]<\/sup> Legal changes can potentially change individuals\u2019 attitudes and behavior. For example, the passage of same-sex marriage reforms in U.S. states reduced individuals\u2019 discrimination against sexual minorities. This reduced discrimination in turn contributed to improvements in labor market outcomes for same-sex couples.[footnote]Sansone, 2019.[\/footnote]<\/sup><\/p>\r\n

Individuals may not be aware of their biases, in which case they are referred to as implicit biases<\/strong>. Training can also reduce implicit biases, particularly those that may be caused by lack of exposure or familiarity with other races.[footnote]Lebrecht et al., 2009.[\/footnote]<\/sup> Treating implicit bias like a habit that can be combated through awareness, concern about its effects, and the use of strategies to reduce bias is helpful, particularly for people who are concerned about discrimination in the first place.[footnote]Devine et al., 2012.[\/footnote]<\/sup> This suggests that training to reduce bias in individuals requires some commitment on their part to change their thinking and behaviors, and therefore is likely to work better for some individuals and biases than others.<\/p>\r\n

Professionalizing human resources functions may also help reduce bias in the hiring process. Research in Canada demonstrated that employers discriminated against those with Asian-sounding names. Asian applicants had a 20% disadvantage for large employers but double the disadvantage, 40%, for small employers. Larger organizations may devote more resources to recruitment, have professional human resource strategies, and also have more experience with diverse staff.[footnote]Banerjee, Reitz, and Oreopoulos, 2017.[\/footnote]<\/sup> This professionalism may reduce (although not necessarily eliminate) discrimination.<\/p>\r\n

It may even be possible to reduce the role of biased human decision making in areas such as sentencing. Risk assessments are a potential, but controversial, approach to reducing bias in sentencing, parole, and rehabilitation.[footnote]Desmarais and Singh, 2013.[\/footnote]<\/sup> Risk assessment instruments model the probability of reoffending based on a number of factors, including criminal history. In part because of different criminal histories, the policy can have disparate impact across racial groups. For example, Black offenders receive higher risk assessments, on average, than white offenders.[footnote]Skeem and Lowenkamp, 2016.[\/footnote]<\/sup> Especially with disparities in the criminal justice system, such instruments may perpetuate disparities. However, improvements in computing, such as machine learning algorithms, have the potential to reduce jail populations and crime rates, including reducing the percentage of minorities in jail.[footnote]Kleinberg et al., 2017.[\/footnote]<\/sup> Yet machine learning and artificial intelligence can also pick up and replicate existing biases.[footnote]Caliskan, Bryson, and Narayanan, 2017.[\/footnote]<\/sup><\/p>\r\n\r\n

Conclusions<\/h2>\r\n

Discrimination occurs in education, employment, housing, and the criminal justice system, as well as many other dimensions of individuals\u2019 lives. Economists tend to understand discrimination through one of two models\u2014based on prejudicial \u201ctastes\u201d for discrimination or based on incomplete information leading to statistical discrimination. Both theories of discrimination show how discrimination contributes to disparate outcomes, such as different wages and employment rates for men and women. Although discrimination is pervasive, the good news is that progress is being made, and (some) disparities have decreased over time as a result of effective policies. Designing effective policies is, however, extremely challenging, as the efforts to \u201cban the box\u201d illustrate. The challenges of designing effective policies underline an important role for economists and statisticians in the fight against discrimination: carefully evaluating the impact of different policy and program attempts to reduce discrimination.
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List of terms<\/strong><\/p>\r\n\r\n<\/header>\r\n

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