Lecture 12: Discrimination

Transcription

1 Lecture 12: Discrimination Natalia Zinovyeva March 30, 2017 Lecture Slides 1 / 45

2 Outline Discrimination Definition Theories Taste-based discrimination Statistical discrimination Empirical evidence Audit and Correspondence studies Correspondence studies Blind evaluations Taste vs Statistical Discrimination 2 / 45

3 Change of the U.S. Gender Wage Gap, [Goldin 2006] Based on median earnings of full-time, year-round workers 15 years old and over. 3 / 45

4 Gaps between ethnic groups 4 / 45

5 Outline Discrimination Definition Theories Taste-based discrimination Statistical discrimination Empirical evidence Audit and Correspondence studies Correspondence studies Blind evaluations Taste vs Statistical Discrimination 5 / 45

6 Definition of Discrimination Discrimination is a term that is widely used in public although it is surprisingly difficult to define. Standard economic definition (Bertrand and Duflo 2016): members of a minority group (women, Blacks, Muslims, immigrants, etc.) are treated differentially (less favorably) than members of a majority group with otherwise identical characteristics in similar circumstances. Note that some people might use an alternative definition: on average members of a minority group (women, Blacks, Muslims, immigrants, etc.) are treated differentially (less favorably) than members of a majority group 6 / 45

7 Ambiguity of the definition Let the wage Y be equal to Y = X β + αz + ɛ where X is a vector of exogenous productivity characteristics and Z is an indicator variable for membership in a minority group. Assuming that X β fully captures the set of productive characteristics and their returns, then discrimination occurs when α < 0. Difficulties already with this definition: Productivity may directly depend on Z. E.g., entertainment (cinema, sports), i.e. customer discrimination. Is it legitimate? Is production technology (β) truly exogenous? (Are requirements for job truly legitimate?) The X s could be endogenous due to expectations of discrimination (investment in schooling, etc.) 7 / 45

8 Outline Discrimination Definition Theories Taste-based discrimination Statistical discrimination Empirical evidence Audit and Correspondence studies Correspondence studies Blind evaluations Taste vs Statistical Discrimination 8 / 45

9 Taste-Based Discrimination Gary Becker s 1957 book The Economics of Discrimination has been the starting point of an enormous economic literature on discrimination. In his model, employers have a taste for discrimination, meaning that there is a disamenity value to employing minority workers (the theory equally applies to all minorities who may be the target of discrimination). 9 / 45

10 Employers Utility Function Let a denote majority group membership and b denote minority group membership. Employers maximize the utility function that is the sum of profits plus the monetary value of utility from employing members of particular groups: U = pf (N a + N b ) w a N a w b N b dn b In this function, F is the production function p is the price of goods N j is the number of workers of group j = {a, b} wj is the wage paid to workers of group j d 0 is the taste parameter of the employer (measuring the distaste against workers of group b) 10 / 45

11 Prejudiced Employers Employers who are prejudiced (d > 0) will act as if the wage of group b is w b + d. Hence, they will only hire individuals of group b if w a w b d 11 / 45

12 A Simple Example: Setup The key argument of Becker s model can be illustrated for the following simple case: The number of firms N f is equal to the number of workers N f = N a + N b Each firm wants to hire one worker Each firm is willing to pay wa = w to workers of group a A share α of firms is not prejudiced and thus willing to pay w b = w to workers of group b The remaining share (1 α) of firms has a prejudice parameter of d > 0 and is only willing to hire workers of group b at the wage w b = w a d < w 12 / 45

13 A Simple Example: Equilibrium Wages Workers of group b will seek employers who are not prejudiced. Equilibrium wages thus critically depend on the relative share of minorities among workers, and of unprejudiced employers among firms: If αnf N b, then there are enough unprejudiced employers to hire all workers of group b. In equilibrium, w a = w b = w. If αn f < N b, then some workers of group Y will end up at prejudiced employers. The equilibrium wages which are determined by the marginal firm are w a = w and w b = w d. An important insight is that the existence of prejudiced employers does not cause discrimination in equilibrium as long as the marginal employer is unprejudiced. 13 / 45

14 The Becker Model Puzzle In an equilibrium with taste-based wage discrimination, non-discriminating employers make positive profits. These positive profits should attract market entry (or expansion) of non-discriminating firms, and once the share of non-discriminating firms becomes sufficiently large, discrimination will be eliminated. Kenneth Arrow thus memorably remarked that Becker s employer discrimination model "predicts the absence of the phenomenon it was designed to explain." 14 / 45

15 Why Taste-Based Discrimination May Persist Many economists dismiss the notion that minorities are hurt by taste-based discrimination. After all, theory tells us that such discrimination is hard to sustain in a competitive labor market. These predictions, however, critically depend on the assumption of a frictionless labor market. Taste-based employer discrimination can persist if workers face search costs and have imperfect information about employers tastes or labor markets are monopsonistic Moreover, taste-based discrimination may persist if firms customers have a distaste against a minority group. (i.e. with customer discrimination) 15 / 45

16 Statistical Discrimination The theory of taste-based discrimination starts from the presumption that discrimination stems from people s tastes. The theory of statistical discrimination sets a fundamental counterpoint: Discrimination against a group can result simply from imperfect information about workers and does not require that anyone has a distaste against the discriminated group. 16 / 45

17 Signal Extraction Statistical discrimination is the solution to a signal extraction problem. It s plausible that employers cannot perfectly assess worker productivity (particularly at the time of hire) and instead make educated guesses. Due to a lack of precise information, firms will use easily observable characteristics such as race or gender to infer the expected productivity of applicants if these characteristics are correlated with productivity. 17 / 45

18 Basic idea Consider a labor market with two groups of applicants (female/male, White/Black, native/migrant), j = {a, b}. Applicant have different levels of the value of the marginal product to the employer, VMP i. Employers cannot directly observe the productivity of job applicants. Therefore, they give workers a standardized test (or check their grades from school, or employ them for a trial period). Denote the result of the test for individual i by T i. The test is a noisy (but unbiased) predictor of VMP i. Employers observe some statistical information (hence, statistical discrimination): VMP a, VMP b. 18 / 45

19 Interpreting the Signal The test gives the employer an informative but noisy signal about applicants productivity. If group belonging is correlated with average productivity, it provides an additional, imperfect signal for applicants productivity. Employers will now estimate the expected marginal productivity of the candidate, given the signal provided by the test: E(VMP i T i, T j ) = αt i + (1 α)vmp j When the test provide a precise signal, α 1. When the test is uninformative, employers rely on their priors, α = / 45

20 The Impact of Statistical Discrimination on Wages Dollars Dollars White White Black Black T Test Score * T - Test Score (a) Whites have higher average score (b) Test is better predictor for white workers The worker s wage depends not only on his own test score, but also on the mean test score of workers in his racial group. (a) If black workers, on average, score lower than white workers, a white worker who gets a score of T earns more than a black worker with the same score. (b) If the test is a better predictor of productivity for white workers, high-scoring whites earn more than high-scoring blacks, and low-scoring whites earn less than low-scoring blacks.

21 Statistical vs. Taste-Based Discrimination The models of statistical and taste-based discrimination claim fundamentally different causes for discrimination: In taste-based models, discrimination arises from a distaste against a group of people. This is the concept that people usually have in mind when they talk about racism or sexism. In statistical models, discrimination arises from an information problem. It occurs even when no one has a distaste against the discriminated group. The observed result of discrimination is however the same: The average wage of group b members is generally below that of equally productive type a members. It is therefore very difficult to determine whether an observed discriminatory wage gap is due to taste-based or due to statistical discrimination. 21 / 45

22 Efficiency of Statistical Discrimination An important difference between taste-based and statistical discrimination is that the latter is efficient in a economic sense because it is the optimal solution to an information extraction problem. Therefore, many economists would say that employers should statistically discriminate because it is profit-maximizing, not motivated by animus, and arguably even fair since it treats people with the same expected productivity equally. 22 / 45

23 Legality of Statistical Discrimination Despite economic efficiency, statistical discrimination is often unlawful. In the U.S., it is illegal in to make hiring, pay, or promotion decisions using a performance prediction that is based on race, sex, age or disability. Other Western countries have similar laws in particular with regard to gender and racial discrimination. Statistical discrimination is difficult to detect, however, and so it is plausible that it occurs frequently despite the law. 23 / 45

24 Fairness of Statistical Discrimination So, given that statistical discrimination is efficient, why is it outlawed? In many situations, statistical discrimination violates common notions of fairness: It s not acceptable to most people that someone receives a lower wage merely because of having a certain race or gender. It can be very hard to draw a line between acceptable and unacceptable statistical discrimination. 24 / 45

25 Outline Discrimination Definition Theories Taste-based discrimination Statistical discrimination Empirical evidence Audit and Correspondence studies Correspondence studies Blind evaluations Taste vs Statistical Discrimination 25 / 45

26 Audit and Correspondence studies To assess the the scope of potential discrimination, one could in principle estimate Y = X β + αz + ɛ However, we don t really know whether the workers with comparable observable characteristics indeed have comparable unobserved characteristics (Cov(Z, ɛ) 0). Audit and correspondence studies try to minimize the scope for heterogeneity in unobserved characteristics in job applications. 26 / 45

27 Audit studies Audit studies are a well known tool for measuring discrimination. In the typical audit study, matched testers (who are, in effect, actors) of different genders or races with substantively identical resumes are sent sequentially to employers advertising job vacancies. These studies evaluate whether the minority members of these pairs fare systematically worse as measured by callbacks and job offers. 27 / 45

28 Audit studies A major drawback is that audit studies are not double-blind, i.e., minority testers who presumably are committed to the cause of bringing discrimination to the public eye may unconsciously take actions that reduce their odds of receiving a job offer. Two additional disadvantages are small sample sizes and maybe a non-random selection of employers. Given these limitations, it is easy to dismiss audit studies out of hand. This is somewhat harder to do, however, if one considers the striking evidence that some of these studies provide. 28 / 45

29 Pager, Western, Bonikowski (ASR 2009) Pager, Western, and Bonikowski (ASR 2009) pair whites, blacks, and Latinos to apply for entry-level jobs in New York City. All testers were well-spoken, clean-shaven young men ages 22 to 26. Most were college graduates. Testers were given training to standardize/equalize their modes of communication. Testers presented themselves as high school graduates with steady work experience in 340 entry-level jobs in Employers were randomly sampled from job listings in newspapers that refer to jobs requiring no previous experience and no education greater than high school. Some of the white applicants were instructed to reveal that they had recently been released from prison after serving 18 months for a drug felony (possession with intent to distribute cocaine). 29 / 45

30 Results Pager, Western, Bonikowski (ASR 2009) Pager et al. Page 25 NIH-PA Author Manuscript NIH-PA Author Manus Figure 1. Positive Response Rates and Paired Comparisons by Race and Ethnicity Notes:Positive responses refer to callbacks or job offers. Hollow circles in Figure 1b indicate point estimates of the ratio. Solid circles indicate ratios obtained by sequentially dropping testers from the analysis. We estimated 95 percent confidence intervals from a hierarchical logistic regression with employer and tester random effects. Number of employers = 171.

31 Results Pager, Western, Bonikowski (ASR 2009) Pager et al. Page 26 NIH-PA Author Manuscript NIH-PA Author Man Figure 2. Positive Response Rates and Paired Comparisons by Race, Ethnicity, and Criminal Background Notes: Positive responses refer to callbacks or job offers. Hollow circles in Figure 2b indicate point estimates of the ratio. Solid circles indicate ratios obtained by sequentially dropping testers from the analysis. We estimated 95 percent confidence intervals from a

32 Discussion Pager, Western, Bonikowski (ASR 2009) PWB conduct a careful audit study that pays a lot of attention to methodological issues (e.g., random sampling of employers). While one might still have some reservations about the research strategy of audit studies, it s hard to dismiss the evidence presented here. This paper suggests that employers of low-skilled workers in New York City are indeed racially discriminating, particularly against blacks. 32 / 45

33 Correspondence studies Bertrand and Mullainathan (AER 2004) Bertrand and Mullainathan s well-known paper (AER 2004) "Are Emily and Greg More Employable than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination" is close in spirit to audit studies but eliminates effects by which experimenters could influence the results. Instead of sending actors to job interviews, they mail near-identical resumes to advertised job positions. The goal is to compare callback rates for blacks and whites. In the U.S., it is deemed inappropriate to mail a photo with the resume. How can employers then determine the race of applicants? 33 / 45

34 Results Bertrand and Mullainathan (AER 2004) VOL. 94 NO. 4 BERTRAND AND MULLAINATHAN: RACE IN THE LABOR MARKET 997 TABLE 1-MEAN CALLBACK RATES BY RACIAL SOUNDINGNESS OF NAMES Percent callback Percent callback for Percent difference for White names African-American names Ratio (p-value) Sample: All sent resumes [2,435] [2,435] (0.0000) Chicago [1,352] [1,352] (0.0057) Boston [1,083] [1,083] (0.0023) Females [1,860] [1,886] (0.0003) Females in administrative jobs [1,358] [1,359] (0.0003) Females in sales jobs [502] [527] (0.3523) Males [575] [549] (0.0513) Notes: The table reports, for the entire sample and different subsamples of sent resumes, the callback rates for applicants with a White-sounding name (column 1) an an African-American-sounding name (column 2), as well as the ratio (column 3) and difference (column 4) of these callback rates. In brackets in each cell is the number of resumes sent in that cell. Column 4 also reports the p-value for a test of proportion testing the null hypothesis that the callback rates are equal across racial groups. employers rarely, if ever, contact applicants via postal mail to set up interviews. E. Weaknesses of the Experiment name).28 We return to this issue in Section IV, subsection B. Finally, and this is an issue pervasive in both our study and the pair-matching audit studies,

35 Correspondence studies Correspondence studies have proliferated during the last decade, analyzing the labor market impact of race, gender, sexual orientation, age, unemployment / 45

36 Labor Market Correspondence Studies (i) Source: Bertrand and Duflo (2016) Table 1: Labor market correspondence studies Paper Country CVs / apps Vacancies Effect (Call-back ratio) Theory Galarza and Yamada (2014) Peru 4,820 1,205 White-to-indigenous ratio: 1.8 No Trait: Ethnicity; Attractiveness Low attractiveness hurts white females Eriksson and Rooth (2014) Sweden 8,466 - Employed to long-term unemployed: 1.25 No Trait: Unemployment duration Blommaert, Coenders, and van Tubergen (2014) Netherlands Dutch-to-foreign: 1.62 (unconditional No Trait: Arabic name ratio). No difference, if views held fixed Nunley, Pugh, Romero, and Seals (2014) US 9,396 - White-to-black: 1.18 (unconditional) Inconsistent with statistical Trait: Race discrimination, consistent with taste-based discrimination Ghayad (2013) US Employed-to-unemployed: 1.47 No Trait: Unemployment duration Bartoš, Bauer, Chytilová, and Matějka (2013) Czech Rep. 274 (Czech R.) - Czech-to-Vietnamese: 1.34 Consistent with attention Trait: Ethnicity (Roma, Asian, Turkish) and Germany 745 (Ger.) Lower requests for CVs if candidate discrimination is Turkish 14 Wright, Wallace, Bailey, and Hyde (2013) US 6,400 1,600 White-to-Muslim: 1.58 Consistent with theoretical Trait: Religion / ethnicity models of secularization and cultural distate theory Kroft, Lange, and Notowidigdo (2013) US (largest ,040 1 log point change in unemployment No Trait: Unemployment duration 100 MSAs) duration: 4.7 percentage points lower call-back probability Baert, Cockx, Gheyle, and Vandamme (2013) Belgium Flemish-to-Turkish: 1.03 to 2.05, No Trait: Nationality (Turkish-sounding name) depending on the occupation Bailey, Wallace, and Wright (2013) US 4,608 1,536 No effect No Trait: Sexual orientation Ahmed, Andersson, and Hammarstedt (2013) Sweden 3,990 - Heterosexual-to-homosexual (male): 1.14 No Trait: Sexual orientation Heterosexual-to-homosexual (female): 1.22 Acquisti and Fong (2013) US Christian-to-Muslim: 1.16 No Traits:Sexual orientation and religion Patacchini, Ragusa, and Zenou (2012) Italy 2,320 - Heterosexual-to-Homosexual: 1.38 No Traits: Sexual orientation and attractiveness Kaas and Manger (2012) Germany 1, German-to-Turkish: 1.29 Consistent with statistical Trait: Immigrant (race/ethnicity) (if no reference letter is included) discrimination Maurer-Fazio (2012) China 21,592 10,796 Han-to-Mongolian: 1.36 No Trait:Ethnicity Han-to-Tibetan: 2.21

37 Labor Market Correspondence Studies (ii) Source: Bertrand and Duflo (2016)...Continued from previous page Paper Country CVs / apps Vacancies Effect (Call-back ratio) Theory Jacquemet and Yannelis (2012) US English-to-foreign names: 1.41 Consistent with patterns Trait: Race / Nationality English-to-Black names: 1.46 of ethnic homophily Ahmed, Andersson, and Hammarstedt (2012) Sweden year old-to-46 year old: 3.23 No Trait: Age Oreopoulos (2011) Canada English name-to-immigrant: ranged No Trait: Nationality (and race) from 1.39 to 2.71 (against Indian Pakistani and Chinese applicants) Carlsson (2011) Sweden 3,228 1,614 Female-to-Male: 1.07 No Trait: Gender Booth, Leigh, and Varganova (2011) Australia Above White-to-Italian: 1.12 No Trait: Ethnicity White-to-Chinese: 1.68 Booth and Leigh (2010) Australia 3,365 - Female-to-male: 1.28 No Trait: Gender (female-dominated professions) 15 Riach and Rich (2010) UK 1, favoring younger candidates No Trait: Age Rooth (2009) Sweden 1, Non-obese/attractive-to-obese/unattractive: No Trait: Attractiveness/Obesity ranged from 1.21 to 1.25 (but higher for some occupations) McGinnity, Nelson, Lunn, and Quinn (2009) Ireland , 2.07, 2.44 in favor of Irish and against No Trait: Nationality / race Asians, Germans and Africans respectively Banerjee, Bertrand, Datta, and Mullainathan (2009) India 3, Upper Caste-to-Other Backward Castes: 1.08 Traits: Caste and religion (software jobs, insignificant), 1.6 (call-center jobs) No Lahey (2008) US App. 4,000 - Young-to-older: 1.42 No Trait: Age Petit (2007) France Ranged from 1.13 to 2.43 against 25-year-old, No Traits: Age, gender, number of children childless women Bursell (2007) Sweden 3,552 1,776 Swedish-to-foreign names: 1.82 Inconsistent with statistical Trait: Ethnicity discrimination Bertrand and Mullainathan (2004) US 4, White-to-African-American: 1.5 No Trait: Race (1.22 for females in sales jobs) Jolson (1974) US White-to-black: 4.2 for selling positions No Trait: Race and religion

38 Evidence from Finland Ministry of Employment and the Economy (2012) Finnish-named vs Russian-named applicants (educated in Finland and fluent in Finnish, but with slight accent!) Positions: Waiter/ waitress, cook, construction, drivers, office clerk Phone call + one-page CV and cover letter 38 / 45

39 Evidence from Finland Ministry of Employment and the Economy (2012) Finnish-named vs Russian-named applicants (educated in Finland and fluent in Finnish, but with slight accent!) Positions: Waiter/ waitress, cook, construction, drivers, office clerk Phone call + one-page CV and cover letter 26% of the applicants with a Finnish name but only 13% with a Russian name got invited to an interview. 38 / 45

40 Potential concerns with correspondence studies (i) An open question is whether higher callback rates eventually translate to a higher hiring probability. This seems probable but one could also imagine scenarios where it s not the case. Very coarse outcome variable (callback vs ranking) Only entry level jobs Ethical concerns 39 / 45

41 Potential concerns with correspondence studies (ii) One might also ask whether names transmit, apart from race, other signals about applicants background that influence callback probabilities. A similar concern applies for studies that identify other characteristics in the CV such as religion or sexual orientation. How could you signal religion or sexual orientation in a more natural way? Acquisti and Fong (2013): social media Up to 1/3 of employers use social media No significant impact of religion and sexual orientation (unlike previous studies) And how would you signal race in ebay? (Doleac and Stein 2013) 40 / 45

42 Blind evaluations Quasi-experiments where race/gender is alternatively revealed or concealed. There are few studies in this genre. Symphonic orchestras - gender (Goldin and Rouse 2000) Anonymized CVs in France - gender and ethnic minorities (Behaghel, Crepon, and Le Barbanchon 2015) 41 / 45

43 Taste vs Statistical Discrimination Why are applicants with a minority applicants less likely to get a callback? The above evidence is consistent both with statistical and taste discrimination (more on this below) How can we disentangle between the two theories? Provide additional information! Example 1: Kaas and Manger (2012) Correspondence study: German vs Turkish applicants Germans are 29% more likely to receive a callback The gap disappears when the application includes a reference letter Example 2: List and Gneezy (2013) Disabled vs Non-disabled participants go to an auto repair shop and simply ask for a price quote to fix their car disabled are given quotes 30 percent higher Extra sentence: getting three price quotes today. 42 / 45

44 List and Gneezy / 45

45 Policy implications Depending on the source of the problem, policy implications may be very different! For instance: Affirmative action Education Insurance in the rental market Providing information about the applicant. What about the criminal record? ("Ban the Box" debate in the U.S.) 44 / 45

46 Thank you! 45 / 45