Excerpts from Pager, Western, Bonikowski (2008) In one case, for example, Zuri, an African American tester, reports his experience applying for a job as a warehouse worker: “The original woman who had herded us in told us that when we finished filling out the application we could leave because “there’s no interview today, guys!”…When I made it across the street to the bus stop …the woman who had collected our completed applications pointed in the direction of Simon, Josue and myself [the three test partners] motioning for us to return. All three of us went over…. She looked at me and told me she “needed to speak to these two” and that I could go back.” Zuri returned to the bus stop, while his white and Latino test partners were both asked to come back at 5pm that day to start work. Simon, the white tester, reports, “She said she told the other people that we needed to sign something—that that’s why she called us over—so as not to let them know she was hiring us. She seemed pretty concerned with not letting anyone else know.”

In one case, for example, the three testers inquired about a sales position at a retail clothing store. Joe, one of our African American testers, reports: “[The employer] said the position was just filled and that she would be calling people in for an interview if the person doesn’t work out.” Josue, his Latino test partner, was told something very similar: “She informed me that the position was already filled, but did not know if the hired employee would work out. She told me to leave my resume with her.” By contrast, when Simon, their white test partner, applied last, his experience is notably different: “…I asked what the hiring process was—if they’re taking applications now,interviewing, etc. She looked at my application. ‘You can start immediately?’ Yes. ‘Can you start tomorrow?’ Yes. ‘10 a.m.’ She was very friendly and introduced me to another woman (white, 28) at the cash register who will be training me.”

13

testers had extended interaction with the employer. If testers were acting in ways that fulfill their expectations of discrimination, we would expect outcomes for those tests conducted with interaction to show greater evidence of differential treatment than those without. If the results are consistent, or show weaker evidence of differential treatment, we can be more confident that experimenter effects are not driving the results. The results of this test indicate that personal contact served to weaken the effect of race on hiring decisions (see Table 1).11 When applicants has little chance to interact with the employer, for example, whites were 9.6 times more likely to receive a callback or job offer than their black partners (and 1.8 times more likely than their Latino partners). By contrast, when applicants had the opportunity to interact with employers, whites were just 1.9 times more likely to receive a callback or job offer than blacks, and equal to the outcomes of Latinos. Rather than enacting expectations of discrimination, then, interaction between testers and employers resulted in a substantial reduction in discrimination. The friendly, appealing qualities of the testers appear to mediate the effects of racial stereotypes, reducing the negative bias evident in more superficial reviews.

Table 1. Percentage of positive responses and race differences, by level of personal contact

White Latino Black Race Differences

Subsample (W) (L) (B) W / L W / B L / B Total 31.0 25.2 15.2 1.2 2.0 1.7 No personal contact 14.4 8.0 1.5 1.8 9.6 5.3 Personal contact 44.2 42.9 23.8 1.0 1.9 1.8 White felon Latino Black Race Differences Subsample (Wf) (L) (B) Wf / L Wf / B L / B Total 17.1 15.9 12.9 1.1 1.3 1.2 No personal contact 9.4 10.6 3.4 0.9 2.8 3.1 Personal contact 27.0 22.4 34.0 1.2 0.8 0.7 Note: Personal contact varies across testers within teams. Tests involving personal contact represent 56% by white testers, 49% by Latino testers, and 61% by black testers in the first team (N=171); 44% of white testers, 45% of Latino testers, and 31% of black testers in the second team (N=170).

11 Note: Because of the vastly different baseline positive response rates across groups, we calculate ratios as an indicator of disparate treatment rather than differences.

Source: Pager, Western, Bonikowski (2006)

35

Table 1. Job Channeling by Race original job title suggested job Blacks channeled down Server Busser (324) Counter person Dishwasher/porter (102) Server Busboy (189) Assistant manager Entry fast food position (258) Server Busboy/runner (269) Retail sales Maintenance (399) Counter person Delivery (176) Sales Stockboy (831) Sales Not specified(a) Hispanics channeled down Server Runner (199) Sales Stock (2) Steam cleaning Exterminator (79) Counter person Delivery (176) Sales Stock person (503) Whites channeled down Server Busboy (192) Hispanics channeled up Carwash attendant Manager (1058) Warehouse worker Computer/office (1001) Whites channeled up Line Cook Waistaff (254) Mover Office / Telesales (784) Dishwasher Waistaff (858) Driver Auto detailing (948) Kitchen job “Front of the house” job (5) Receptionist Company supervisor (347)

(a) employer told tester “sales might not be right for you…” Note: numbers in parentheses refer to employer ID codes.

997 VOL. 94 NO. 4 BERTRAND AND MULUINATHAN: RACE IN THE LABOR MARKET

Percent callback Percent callback for Percent difference for White names African-American names Ratio (D-value)

Sample: All sent resumes

Chicago

Boston

Females

Females in administrative jobs

Females in sales jobs

Males

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 Experimen~

We have already highlighted the strengths of this experiment relative to previous audit stud- ies. We now discuss its weaknesses. First, our outcome measure is crude, even relative to the previous audit studies. Ultimately, one cares about whether an applicant gets the job and about the wage offered conditional on getting the job. Our procedure, however, simply mea- sures callbacks for interviews. To the extent that the search process has even moderate frictions, one would expect that reduced interview rates would translate into reduced job offers. How- ever, we are not able to translate our results into gaps in hiring rates or gaps in earnings.

Another weakness is that the resumes do not directly report race but instead suggest race through personal names. This leads to various sources of concern. First, while the names are chosen to make race salient, some employers may simply not notice the names or not recog- nize their racial content. On a related note, because we are not assigning race but only race-specific names, our results are not repre- sentative of the average African-American (who may not have such a racially distinct

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, newspaper ads represent only one channel for job search. As is well known from previous work, social networks are another common means through which people find jobs and one that clearly cannot be studied here. This omis- sion could qualitatively affect our results if African-Americans use social networks more or if employers who rely more on networks differ- entiate less by race.29

111. Results

A. Is There a Racial Gap in Callback?

Table 1 tabulates average callback rates by racial soundingness of names. Included in brackets under each rate is the number of re- sumes sent in that cell. Row 1 presents our results for the full data set. Resumes with White

As Appendix Table A1 indicates, the African-American names we use are, however, quite common among African-Americans, making this less of a concern.

29 In fact, there is some evidence that African-Americans may rely less on social networks for their job search (Hany J. Holzer, 1987).

1012 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2004

TABLE AI-FIRST NAMESUSEDIN EXPERIMENT

White female Name L(WI/L(BI Percevtion White

Allison m

Anne m

Carrie m

Emily m

Jill Y2

Laurie m

Kristen m

Meredith m

Sarah m

Fraction of all births:

3.8 percent

White male Name L(W)k(B) Perception White

Brad m

Brendan m

Geoffrey m

Greg cc

Brett m

Jay a

Matthew a

Neil m

Todd m

Fraction of all births:

1.7 percent

African-American female Name L(BI/L(WI Perce~tion Black

Aisha Ebony Keisha Kenya Lakisha m

Latonya m

Latoya m

Tamika 284 Tanisha 32

Fraction of all births:

7.1 percent

African-American male Name L(B)/L(W) Perception Black

Darnell Hakim Jamal 257 Jermaine 90.5 Kareem m

Leroy 44.5 Rasheed m

Tremayne m

Tyrone 62.5 Fraction of all births:

3.1 percent

Notes: This table tabulates the different first names used in the experiment and their identifiability. The first column reports the likelihood that a baby born with that name (in Massachusetts between 1974 and 1979) is White (or African-American) relative to the likelihood that it is African-American (White). The second column reports the probability that the name was picked as White (or African-American) in an independent field survey of people. The last row for each group of names shows the proporrion of all births in that race group that these names account for.

REFERENCES

Aigner, Dennis J. and Cain. Glenn G. "Statistical Theories of Discrimination in Labor Mar- kets." Industrial and Labor Relations Re- view, January 1977, 30(1), pp. 175-87.

Altonji, Joseph G. and Blank, Rebecca M. "Race and Gender in the Labor Markey," in Orley Ashenfelter and David Card, eds., Handbook of labor economics, Vol. 30. Amsterdam: North-Holland, 1999, pp. 3143-259.

Arrow, Kenneth, J. "The Theory of Discrimina- tion," in Orley Ashenfelter and Albert Rees, eds., Discrimination in labor markets. Princeton, NJ: Princeton University Press, 1973, pp. 3-33.

. "What Has Economics to Say about Racial Discrimination?Voumal of Economic Perspectives, Spring 1998, 12(2), pp. 91-100.

Becker, Gary S. The economics of discrimina- tion, 2nd Ed. Chicago: University of Chicago Press, 1961.

Brown, Colin and Gay, Pat. Racial discrimina- tion 17 years after the act. London: Policy Studies Institute, 1985.

Cornell, Bradford and Welch, Ivo. "Culture, In- formation, and Screening Discrimination." Journal of Political Economy, June 1996, 104(3), pp. 542-7 1.

Council of Economic Advisers. Changing America: Indicators of social and economic well-being by race and Hispanic origin. September 1998, http://w3.access.gpo.gov/eop/ca/pdfs/ca.pdf.

Cross, Harry; Kenney, Genevieve; Mell, Jane and Zimmerman, Wendy. Employer hiring prac- tices: Differential treatment of Hispanic and Anglo job applicants. Washington, DC: Ur- ban Institute Press, 1990.

1003 VOL. 94 NO. 4 BERTRAND AND MULLAINATHAN: RACE IN THE LABOR MARKET

TABLE&-EFFECT ADDRESS OF CALLBACKOF APPLICANT'S ON LIKELIHOOD

Dependent Variable: Callback Dummy

Zip code characteristic:

Zip code characteristic

Zip code characteristic* African-American name

African-American name

Fraction college or Fraction Whites more Log(per capital income)

0.020 0.020 0.054 0.053 0.018 0.014 (0.012) (0.016) (0.022) (0.03 1) (0.007) (0.010) - -0.000 - -0.002 - 0.008

(0.024) (0.048) (0.015) - -0.031 - -0.03 1 - -0.112

(0.015) (0.01 3) (0.152)

Notes: Each column gives the results of a probit regression where the dependent variable is the callback dummy. Reported in the table is the estimated marginal change in probability. Also included in columns 1, 3, and 5 is a city dummy; also included in columns 2,4, and 6 is a city dummy and a city dummy interacted with a race dummy. Standard errors are corrected for clustering of the observations at the employment-ad level.

resumes with African-American-sounding names. Taken at face value, these results suggest that African-Americans may face relatively lower individual incentives to invest in higher skills.36

C. Applicants ' Address

An incidental feature of our experimental de- sign is the random assignment of addresses to the resumes. This allows us to examine whether and how an applicant's residential address, all else equal, affects the likelihood of a callback. In addition, and most importantly for our pur- pose, we can also ask whether African-Ameri- can applicants are helped relatively more by residing in more affluent neighborhoods.

We perform this analysis in Table 6. We start (columns 1,3, and 5 ) by discussing the effect of neighborhood of residence across all applicants. Each of these columns reports the results of a probit regression of the callback dummy on a specific zip code characteristic and a city dummy. Standard errors are corrected for clus- tering of the observations at the employment-ad level. We find a positive and significant effect of neighborhood quality on the likelihood of a callback. Applicants living in Whiter (column I), more educated (column 3) ,or higher-income (column 5) neighborhoods have a higher prob- ability of receiving a callback. For example, a 10-percentage-point increase in the fraction of college-educated in zip code of residence in-

36 This of course assumes that the changes in job and wage offers associated with higher skills are the same across races, or at least not systematically larger for African- Americans.

creases the likelihood of a callback by a 0.54 percentage point (column 3).

In columns 2,4, and 6, we further interact the zip code characteristic with a dummy variable for whether the applicant is African-American or not. Each of the probit regressions in these columns also includes an African-American dummy, a city dummy, and an interaction of the city dummy with the African-American dummy. There is no evidence that African- Americans benefit any more than Whites from living in a Whiter, more educated zip code. The estimated interactions between fraction White and fraction college educated with the African- American dummy are economically very small and statistically insignificant. We do find an economically more meaningful effect of zip code median income level on the racial gap in callback; this effect, however, is statistically insignificant.

In summary, while neighborhood quality af- fects callbacks, African-Americans do not ben- efit more than Whites from living in better neighborhoods. If ghettos and bad neighbor-hoods are particularly stigmatizing for African- Americans, one might have expected African- Americans to be helped more by having a "better" address. Our results do not support this hypothesis.

D. Job and Employer Characteristics

Table 7 studies how various job requirements (as listed in the employment ads) and employer characteristics correlate with the racial gap in callback. Each row of Table 7 focuses on a specific job or employer characteristic, with

1004 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2004

TABLE7-EFFECT AND EMPLOYER ON RACIAL IN CALLBACKSOF JOB REQUIREMENT CHARACTERISTICS DIFFERENCES

Sample mean Marginal effect on callbacks Job requirement: (standard deviation) for African-American names

Any requirement? (Y = 1) 0.79 0.023 (0.41) (0.015)

Experience? (Y = 1) 0.44 0.01 1 (0.49) (0.013)

Computer skills? (Y = 1) 0.44 0.000 (0.50) (0.013)

Communication skills? (Y = 1) 0.12 -0.000 (0.33) (0.015)

Organization skills? (Y = I) 0.07 0.028 (0.26) (0.029)

Education? (Y = 1) 0.11 -0.031 (0.3 1) (0.017)

Total number of requirements 1.18 0.002 (0.93) (0.006)

Sample mean Marginal effect on callbacks Employer characteristic: (standard deviation) for African-American names

Equal opportunity employer? (Y = 1) 0.29 -0.013 (0.45) (0.0 12)

Federal contractor? (Y = 1) 0.11 -0.035 (N = 3,102) (0.32) (0.016) Log(emp1oyment) 5.74 -0.001 (N = 1,690) (1.74) (0.005) Ownership status: (N = 2,878) Privately held 0.74 0.01 1

(0.019) Publicly traded 0.15 -0.025

(0.015) Not-for-profit 0.11 0.025

(0.042) Fraction African-Americans in employer's zip code 0.08 0.1 17

(N = 1,918) (0.15) (0.062)

Notes: Sample is all sent resumes (N = 4,870) unless otherwise specified in column 1. Column 2 reports means and standard deviations (in parentheses) for the job requirement or employer characteristic. For ads listing an experience requirement, 50.1 percent listed "some," 24.0 percent listed "two years or less," and 25.9 percent listed "three years or more." For ads listing an education requirement. 8.8 percent listed a high school degree, 48.5 percent listed some college, and 42.7 percent listed at least a four-year college degree. Column 3 reports the marginal effect of the job requirement or employer characteristic listed in that row on differential treatment. Specifically, each cell in column 3 corresponds to a different probit regression of the callback dummy on an African-American name dummy, a dummy for the requirement or characteristic listed in that row and the interaction of the requirement or characteristic dummy with the African-American name dummy. Reported in each cell is the estimated change in probability for the interaction term. Standard errors are corrected for clustering of the observations at the employment-ad level.

summary statistics in column 2. Column 3 African-American dummy. The reported coef- shows the results of various probit regressions. ficient is that on the interaction term. Each entry in this column is the marginal effect We start with job requirements. About 80 of the specific characteristic listed in that row on percent of the ads state some form of require- the racial gap in callback. More specifically, ment. About 44 percent of the ads require some each entry is from a separate probit regression minimum experience, of which roughly 50 per-of a callback dummy on an African-American cent simply ask for "some experience," 24 per- dummy, the characteristic listed in that row and cent less than two years, and 26 percent at least the interaction of that characteristic with the three years of experience. About 44 percent of

1008 THE AMERICAN ECONOMIC REVIEW SEPTEMBER 2004

TABLE 8---CALLBACK RATE AND MOTHER'S BY FIRST NAME EDUCATION

White female African-American female

Name Percent callback Mother education Name Percent callback Mother education

Emily Aisha Anne Keisha Jill Tamika Allison Lakisha Laurie Tanisha Sarah Latoya Meredith Kenya Carrie Latonya Kristen Ebony

Average 91.7 Average Overall 83.9 Overall

Correlation -0.318 (p = 0.404) Correlation -0.383 (p = 0.309)

White male African-American male

Name Percent callback Mother education Name Percent callback Mother education

Todd 5.9 87.7 Rasheed 3.0 77.3 Neil 6.6 85.7 Tremayne 4.3 -Geoffrey 6.8 96.0 Kareem 4.7 67.4 Brett 6.8 93.9 Darnell 4.8 66.1 Brendan 7.7 96.7 Tyrone 5.3 64.0 Greg 7.8 88.3 Hakim 5.5 73.7 Matthew 9.0 93.1 Jamal 6.6 73.9 Jay 13.4 85.4 Leroy 9.4 53.3 Brad 15.9 90.5 Jermaine 9.6 57.5

Average 91.7 Average Overall 83.5 Overall

Correlation -0.0251 ( p = 0.949) Correlation -0.595 (p = 0.120)

Notes: This table reports, for each first name used in the experiment, callback rate and average mother education. Mother education for a given first name is defined as the percent of babies born with that name in Massachusetts between 1970 and 1986 whose mother had at least completed a high school degree (see text for details). Within each sextrace group, first names are ranked by increasing callback rate. "Average" reports, within each race-gender group, the average mother education for all the babies born with one of the names used in the experiment. "Overall" reports, within each race-gender group, average mother education for all babies born in Massachusetts between 1970 and 1986 in that race-gender group. "Correlation" reports the Spearman rank order correlation between callback rate and mother education within each race-gender group as well as the p-value for the test of independence.

1 9 8 6 . ~ ~ name and, in that gender-race cell, whose moth- For each first name in our experiment, we compute the fraction of babies with that ers have at least completed a high school

degree.-In Table 8, we display the average callback

46 This longer time span (compared to that used to assess name frequencies) was imposed on us for confidentiality rate for each firstname with this proxy for reasons. When fewer than 10 births with education data social background. Within each race-gender available are recorded in a particular education-name cell, group, the names are ranked by increasing call- the exact number of births in that cell is not reported and we back rate, Interestingly, there is significantimpute five births. Our results are not sensitive to this imhutation. One African-American female name (Latonya) and two male names (Rasheed and Hakim) were imputed in this way. One African-American male name (Tremayne) tatively similar when we use a larger data set of California had too few births with available education data and was births for the years 1989 to 2000 (kindly provided to us by therefore dropped from this analysis. Our results are quali- Steven Levitt).

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 NoTrait: Ethnicity; Attractiveness Low attractiveness hurts white females

Eriksson and Rooth (2014) Sweden 8,466 - Employed to long-term unemployed: 1.25 NoTrait: Unemployment duration

Blommaert, Coenders, and van Tubergen (2014) Netherlands 636 - Dutch-to-foreign: 1.62 (unconditional NoTrait: 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 statisticalTrait: Race discrimination, consistent with

taste-based discrimination

Ghayad (2013) US 3360 600 Employed-to-unemployed: 1.47 NoTrait: Unemployment duration

Bartoš, Bauer, Chytilová, and Matějka (2013) Czech Rep. 274 (Czech R.) - Czech-to-Vietnamese: 1.34 Consistent with attentionTrait: Ethnicity (Roma, Asian, Turkish) and Germany 745 (Ger.) Lower requests for CVs if candidate discrimination

is Turkish

Wright, Wallace, Bailey, and Hyde (2013) US 6,400 1,600 White-to-Muslim: 1.58 Consistent with theoreticalTrait: Religion / ethnicity models of secularization

and cultural distate theory

Kroft, Lange, and Notowidigdo (2013) US (largest 12054 3,040 1 log point change in unemployment NoTrait: Unemployment duration 100 MSAs) duration: 4.7 percentage

points lower call-back probability

Baert, Cockx, Gheyle, and Vandamme (2013) Belgium 752 376 Flemish-to-Turkish: 1.03 to 2.05, NoTrait: Nationality (Turkish-sounding name) depending on the occupation

Bailey, Wallace, and Wright (2013) US 4,608 1,536 No effect NoTrait: Sexual orientation

Ahmed, Andersson, and Hammarstedt (2013) Sweden 3,990 - Heterosexual-to-homosexual (male): 1.14 NoTrait: Sexual orientation Heterosexual-to-homosexual (female): 1.22

Acquisti and Fong (2013) US 4183 - Christian-to-Muslim: 1.16 NoTraits:Sexual orientation and religion

Patacchini, Ragusa, and Zenou (2012) Italy 2,320 - Heterosexual-to-Homosexual: 1.38 NoTraits: Sexual orientation and attractiveness

Kaas and Manger (2012) Germany 1,056 528 German-to-Turkish: 1.29 Consistent with statisticalTrait: Immigrant (race/ethnicity) (if no reference letter is included) discrimination

Maurer-Fazio (2012) China 21,592 10,796 Han-to-Mongolian: 1.36 NoTrait:Ethnicity Han-to-Tibetan: 2.21

14

Source: Bertrand and Duflo (2016)

...Continued from previous page

Paper Country CVs / apps Vacancies Effect (Call-back ratio) Theory

Jacquemet and Yannelis (2012) US 330 990 English-to-foreign names: 1.41 Consistent with patternsTrait: Race / Nationality English-to-Black names: 1.46 of ethnic homophily

Ahmed, Andersson, and Hammarstedt (2012) Sweden 466 - 31 year old-to-46 year old: 3.23 NoTrait: Age

Oreopoulos (2011) Canada 12910 3225 English name-to-Immigrant: ranged NoTrait: 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 NoTrait: Gender

Booth, Leigh, and Varganova (2011) Australia Above 4000 - White-to-Italian: 1.12 NoTrait: Ethnicity White-to-Chinese: 1.68

Booth and Leigh (2010) Australia 3,365 - Female-to-male: 1.28 NoTrait: Gender (female-dominated professions)

Riach and Rich (2010) UK 1,000+ - 2.64 favoring younger candidates NoTrait: Age

Rooth (2009) Sweden 1,970 985 Non-obese/attractive-to-obese/unattractive: NoTrait: Attractiveness/Obesity ranged from 1.21 to 1.25 (but higher for some

occupations)

McGinnity, Nelson, Lunn, and Quinn (2009) Ireland 480 240 1.8, 2.07, 2.44 in favor of Irish and against NoTrait: Nationality / race Asians, Germans and Africans respectively

Banerjee, Bertrand, Datta, and Mullainathan (2009) India 3,160 371 Upper Caste-to-Other Backward Castes: 1.08Traits: Caste and religion (software jobs, insignificant), 1.6 (call-center jobs) No

Lahey (2008) US App. 4,000 - Young-to-older: 1.42 NoTrait: Age

Petit (2007) France 942 157 Ranged from 1.13 to 2.43 against 25-year-old, NoTraits: Age, gender, number of children childless women

Bursell (2007) Sweden 3,552 1,776 Swedish-to-foreign names: 1.82 Inconsistent with statisticalTrait: Ethnicity discrimination

Bertrand and Mullainathan (2004) US 4,870 1300+ White-to-African-American: 1.5 NoTrait: Race (1.22 for females in sales jobs)

Jolson (1974) US 300 - White-to-black: 4.2 for selling positions NoTrait: Race and religion

15

Source: Bertrand and Duflo (2016)

Table 2: Rental market papers

Study Country Inquiries Effect Theory

Carlsson and Eriksson (2014) Sweden 5,827 Swedish-to-Arabic (females): 1.37 NoTrait: Minority status (Arabic name) Swedish-to-Arabic (males): 1.62

Ewens, Tomlin, and Wang (2014) US 14,237 White-to-Black: 1.19 Consistent with statisticalTrait: Race discrimination, inconsistent with

taste-based discrimination

Bartoš, Bauer, Chytilová, and Matějka (2013) Czech Republic 1,800 Czech-to-minority: 1.27 (site available), Consistent with attentionTrait: Minority status (Roma or Asian name) and Germany 1.9 (pooled Asian and Roma names) discrimination

Hanson and Hawley (2011) US 9,456 White-to-African American: 1.12 Consistent with statisticalTrait: Race (varied by neighborhood and unit type) discrimination

Baldini and Federici (2011) Italy 3,676 Italian-to-East European: 1.24 NoTrait: Immigrant status; Language ability Italian-to-Arab: 1.48

Ahmed, Andersson, and Hammarstedt (2010) Sweden 1,032 Swedish-to-Arab/Muslim: 1.44 (no NoTrait: Minority status (Arabic name) information), 1.24 (detailed

information about the applicant)

Bosch, Carnero, and Farré (2010) Spain 1,809 Spanish-to-Moroccan: 1.44 (no NoTrait: Immigrant status information), 1.19 (with positive

information)

Ahmed and Hammarstedt (2009) Sweden 408 Straight-to-gay: 1.27 NoTrait: Sexual orientation

Ahmed and Hammarstedt (2008) Sweden 1,500 Swedish-to-Arab male: 2.17 NoTrait: Immigrant (race/ethnicity/religion)

Carpusor and Loges (2006) US (Los Angeles County) 1,115 White-to-Arab: 1.35 NoTrait: Race / Ethnicity (Arab, African-American) White-to-Black: 1.59, conditional

on hearing back, 1.98 unconditional

21

Source: Bertrand and Duflo (2016)

1. Procedure

The goal of this study is to isolate the effect of race on market outcomes rigorously via acarefully constructed field experiment. We have developed a procedure that avoidsseveral confounding factors present in other studies and which is replicable in a varietyof settings. We posted online classified advertisements on locally focused websitesthroughout the US over the course of one year, with variation along three keydimensions: race or social group of the seller (as indicated by a photograph), askingprice and the ‘quality’ of the advertisement text. This results in a 39392 design (threetypes of sellers, three prices and two qualities). The photographs used are shown inFigure 1. Online Appendix Table A2 tabulates these advertisement characteristicswhich – along with the markets in which the advertisements are posted and our postingand negotiation procedures – are discussed in greater detail below.

1.1. Overview: Online Classified Advertisements

We posted advertisements for new Apple iPod Nano 8 GB Silver portable digital musicplayers6 on local classified advertising websites in approximately 300 geographical

(a) (b) (c)

(d) (e) (f)

(g) (h) (i)

Fig. 1. Advertisement PhotographsNote. These photographs have been slightly scaled down from the size included in ouradvertisements.

6 Apple released an updated iPod Nano model in the midst of our experiment. Our advertisements offerthe current model – the ‘fourth generation’ (model MB598LL/A) before 9 September 2009 and the ‘fifthgeneration’ (model MC027LL/A) after that date. The two models appear almost identical in their packaging.

© 2013 Royal Economic Society.

2013] TH E V I S I B L E H AN D F473Doleac and Stein (2013)

Table 2; including additional controls merely allows us to increase the estimates’precision.

Online Appendix E reports these results for specifications without controls,excluding prematurely removed advertisements, using linear models, replacing themarket controls with market fixed effects and with alternate weighting.

2.1. Premature Advertisement Removal

The sites on which we posted provide tools for users to mark advertisements asinappropriate or unwelcome. If enough users protest a particular advertisement in thisway, it is removed from the website: 4.1% of our advertisements were removed in thismanner. In addition to legitimate use of this feature, other sellers may disingenuouslymark competing advertisements as inappropriate to reduce competition.14

Table 2

Key Outcome Averages by Advertisement Type

White Black Tattoo Total

Prematurely removed 0.028 0.056 0.041 0.041

Number of responsesNumber of non-scams 2.46 2.06 2.07 2.21Number of offers 1.70 1.36 1.44 1.50

Received ≥1 offer 0.624 0.559 0.586 0.590

Indicators of trust in responses (given ≥1 non-scam response)Includes name 0.391 0.301 0.315 0.339Uses polite language 0.415 0.370 0.354 0.383Includes personal story 0.038 0.046 0.048 0.044

Offer amountMean offer 53.51 46.84 48.93 49.86Best offer 58.51 50.36 52.93 54.05

Offer amount (given ≥1 offer)Mean offer 85.76 83.78 83.45 84.46Best offer 93.79 90.07 90.25 91.56

Reaction to delivery proposal (given delivery proposed)Scam/payment concern 0.075 0.107 0.084 0.088No response 0.376 0.424 0.398 0.398Other 0.191 0.139 0.199 0.176Prefer to wait 0.303 0.297 0.260 0.289Willing to ship 0.056 0.033 0.059 0.049

iPod shipped 0.037 0.017 0.031 0.028

Notes. Mean values are reported. Observations are weighted by state population/number of advertisementsposted in each state.

14 In addition, the websites implement filters (based on unknown algorithms that appear to changefrequently) to identify unwelcome advertisements. On several occasions, all of the advertisements we postedon a given morning or evening were immediately removed from the site. This universal, simultaneouspremature removal suggested that our advertisements were caught in the websites’ filters. Similarly, someadvertisements did not show up in search results despite appearing to have posted successfully; this is also dueto the websites’ screening for unwelcome advertisements. All of these advertisements are entirely excludedfrom our analyses. Stratifying the advertisements we posted simultaneously by advertisement quality andmarket size greatly reduced this automatic removal.

© 2013 Royal Economic Society.

2013] TH E V I S I B L E H AN D F479Doleac and Stein (2013)

Table 10 Means and Partial Regression Coefficients of Explanatory Variables1) from Separate NLSY Log Wage Regressions

for Men and Women Ages 35-43 in 2000Means Female Male

Female MaleM2 M4 M2 M4

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-statRace

Hispanic (0,1) 0.182 0.193 0.063 2.57 0.060 2.61 -0.025 -1.02 -0.018 -0.75Black (0,1) 0.316 0.282 0.053 2.42 0.066 3.14 -0.022 -0.92 0.005 0.20

Education and skill level<10 yrs. 0.031 0.052 -0.089 -1.76 -0.078 -1.64 -0.028 -0.65 -0.025 -0.6010-12 yrs (no diploma or GED) * 0.103 0.124 --- --- --- --- --- --- --- ---HS grad (diploma) 0.300 0.326 -0.003 -0.10 -0.008 -0.27 -0.018 -0.65 -0.013 -0.50HS grad (GED) 0.045 0.056 -0.015 -0.34 -0.046 -1.12 0.027 0.63 0.015 0.38Some college 0.308 0.232 0.090 2.99 0.060 2.09 0.166 5.31 0.123 4.08BA or equiv. degree 0.153 0.155 0.276 7.61 0.216 6.19 0.373 10.23 0.260 7.08MA or equiv. degree 0.053 0.041 0.391 8.49 0.348 7.76 0.562 10.84 0.446 8.62Ph.D or prof. Degree 0.007 0.015 0.758 7.47 0.654 6.71 0.806 10.60 0.639 8.53

AFQT percentile score (x.10) 3.981 4.238 0.042 9.92 0.032 7.84 0.042 9.92 0.029 7.04

L.F. withdrawal due to family responsibilities (0,1) 0.549 0.130 -0.081 -4.16 -0.082 -4.46 -0.080 -3.14 -0.066 -2.74Lifetime Work Experience

Weeks worked in civilian job since age 18 ÷ 52 15.565 17.169 0.030 13.85 0.023 11.13 0.038 12.54 0.034 11.39Weeks worked in military since 1978 ÷ 52 0.062 0.573 0.046 3.53 0.040 3.22 0.025 5.15 0.020 4.46Weeks PT ÷ total weeks workd since age 22 0.137 0.050 -0.203 -4.24 -0.084 -1.81 -0.779 -7.90 -0.540 -5.70

Employment typeGov't employer (0,1) 0.215 0.144 -0.030 -1.50 -0.027 -1.13Non-profit employer (0,1) 0.100 0.049 -0.056 -2.13 -0.121 -3.20

OCC. Characteristics of Person's 3-digit OCC.SVP required in occup. (months) (DOT) 26.961 28.773 0.001 2.44 0.003 5.43Hazards (0,1) (DOT) 0.013 0.084 0.327 4.66 0.131 3.97Fumes (0,1) (DOT) 0.004 0.043 -0.293 -2.27 -0.075 -1.72Noise (0,1) (DOT) 0.080 0.307 0.005 0.18 0.019 0.83Strength (0,1) (DOT) 0.092 0.215 0.011 0.37 -0.049 -1.99Weather extreme (0,1) (DOT) 0.033 0.188 0.120 2.56 0.000 -0.01Prop. using computers (CPS) 0.557 0.415 0.157 2.19 0.045 0.49Prop. using computer for analysis (CPS) 0.143 0.139 0.497 4.62 0.258 2.22Prop. using computer for word proc. (CPS) 0.345 0.236 -0.255 -3.19 -0.007 -0.06Relative rate of transition to unemployment 0.772 1.092 -0.022 -1.11 -0.023 -1.91Relative rate of transition to OLF 1.046 0.789 -0.144 -7.30 -0.073 -3.57% female in OCC. X 0.1. (CPS ORG) 6.348 2.695 0.005 1.08 -0.019 -3.55

Adj. R-Square 0.392 0.464 0.403 0.467Dependent mean (Log Hourly Wage) 2.529 2.764Sample size 2704 26941) Model also controls for age, central city, MSA, region, and occupation missing.* Reference group.Source: National Longitudinal Survey of Youth (NLSY79) merged with measures of occupational characteristics (3-digit level) from the September 2001 CPS, the March CPS, the CPS ORG, and the Dictionary of Occupational Titles (1991).

Source: O'Neil and O'Neil (2005)

Table 11

Gender Wage Gap: Decomposition Results (NLSY, 2000)

Using male coefficients Using female coefficients

M1 M2 M3 M4 M1 M2 M3 M4

Log Wage Gap (Male-Female) Attributable to:Age, race, region, central city, MSA 0.0044 0.0112 0.0089 0.0089 0.0040 0.0089 0.0064 0.0064AFQT 0.0132 0.0107 0.0073 0.0074 0.0143 0.0107 0.0081 0.0081Education level -0.0138 -0.0128 -0.0094 -0.0096 -0.0147 -0.0068 -0.0054 -0.0052L.F. withdrawal due to family responsibilities 0.0335 0.0272 0.0277 0.0340 0.0344 0.0343Lifetime work experience 0.1425 0.1135 0.1116 0.0901 0.0649 0.0655Nonprofit, government 0.0088 0.0081 0.0048 0.0050

Occupational characteristics: Investment related

SVP (Specific Vocational Preparation) 0.0062 0.0053 0.0020 0.0021Computer usage 0.0122 -0.0040 -0.0054 -0.0024

Compensating differencesDisamenities (physical) 0.0167 0.0040 0.0252 0.0267Unemployment risk; labor force turnover 0.0116 0.0028 0.0226 0.0259

TYP: % female in occupation 0.0721 -0.0137

Unadjusted log wage gap 0.2351 0.2351 0.2351 0.2351 0.2351 0.2351 0.2351 0.2351Total explained by model 0.0037 0.1851 0.2030 0.2342 0.0036 0.1370 0.1578 0.1526Unexplained log wage gap 0.2314 0.0500 0.0321 0.0009 0.2315 0.0981 0.0773 0.0825

Unadjusted hourly wage ratio (Female/Male) : 79.0 79.0 79.0 79.0 79.0 79.0 79.0 79.0Adjusted hourly wage ratio (Female/Male) : 79.3 95.1 96.8 99.9 79.3 90.7 92.6 92.1

Note: Decomposition results shown are derived from results of separate regressions for men and women. See Table 10 for variable means and coefficients using Model 2 and 4. Wage ratios are based on the exponentiated log hourly wage.Source: National Longitudinal Survey of Youth (NLSY79) merged with measures of occupational characteristics (3-digit level) from the September 2001 CPS, the March CPS, the CPS ORG, and the Dictionary of Occupational Titles (1991).

Source: O'Neil and O'Neil (2005)

Table 4

Means and Partial Regression Coefficients of Explanatory Variables1) from Separate Log Wage Regressions for Black, White, and Hispanic MEN Ages 35-43 in 2000 (NLSY)

Mean White Black Hispanic

White Black Hisp.M1 M2 M1 M2 M1 M2

Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat Coef. t-stat

Education and skill level<10 yrs. 0.043 0.041 0.093 -0.051 -0.68 -0.036 -0.49 0.069 0.80 0.024 0.30 -0.064 -0.81 -0.082 -1.0810-12 yrs (no diploma or GED) * 0.083 0.149 0.198 --- --- --- --- --- --- --- --- --- --- --- ---HS grad (diploma) 0.328 0.358 0.274 0.064 1.33 0.009 0.19 0.072 1.51 0.005 0.12 -0.007 -0.12 -0.063 -1.10HS grad (GED) 0.041 0.079 0.062 -0.018 -0.24 0.031 0.43 0.042 0.62 0.078 1.22 -0.080 -0.87 -0.077 -0.89Some college 0.216 0.239 0.264 0.236 4.42 0.215 4.13 0.205 3.76 0.151 2.89 0.085 1.32 0.068 1.11BA or equiv. degree 0.207 0.109 0.079 0.419 7.31 0.427 7.66 0.335 4.88 0.294 4.51 0.355 3.77 0.369 4.13MA or equiv. degree 0.059 0.021 0.019 0.524 7.14 0.561 7.84 0.634 5.29 0.624 5.48 0.465 2.94 0.484 3.23Ph.D or prof. Degree 0.023 0.004 0.012 0.645 6.50 0.780 8.00 1.302 5.07 1.359 5.58 0.593 2.95 0.774 4.02

AFQT percentile score (x.10) 5.538 2.411 3.360 0.046 7.63 0.039 6.49 0.058 6.68 0.048 5.80 0.059 6.04 0.046 4.91Lifetime work experience (Year equivalents)

Weeks worked in civilian job since age 18 ÷ 52 17.828 15.865 17.279 0.047 9.17 0.040 9.20 0.049 7.55Weeks worked in military since 1978 ÷ 52 0.483 0.835 0.436 0.033 4.31 0.028 4.00 0.036 2.89

Adj. R-Square 0.296 0.337 0.287 0.359 0.262 0.335Dependent mean (Log Hourly Wage) 2.898 2.559 2.700Sample size 1416 759 519

1) Model also controls for age, central city, MSA and region. The analysis is restricted to wage and salary workers employed within the past month. * Reference group.Source: National Longitudinal Survey of Youth (NLSY79).

Source: O'Neil and O'Neil (2005)

Table 5White-Black and White-Hispanic Wage Gaps: Decompositon Results for MEN (NLSY)

White-Black Differential White-Hispanic Differential

Using black male coef.

Using white male coef.

Using hispanic male coef.

Using white male coef.

M1 M2 M1 M2 M1 M2 M1 M2

Log Wage Gap Attributable to:Age, region, central city, MSA 0.0622 0.0589 0.0354 0.0334 0.0282 0.0292 -0.0004 -0.0079AFQT 0.1800 0.1504 0.1435 0.1204 0.1276 0.1001 0.1000 0.0839Education 0.0731 0.0714 0.0663 0.0713 0.0709 0.0741 0.0768 0.0771Lifetime work experience 0.0691 0.0810 0.0286 0.0275

Unadjusted log wage gap 0.3387 0.3387 0.3387 0.3387 0.1982 0.1982 0.1982 0.1982Total explained by model 0.3153 0.3499 0.2451 0.3061 0.2267 0.2321 0.1764 0.1805Unexplained log wage gap 0.0234 -0.0112 0.0936 0.0326 -0.0285 -0.0339 0.0218 0.0177

Unadjusted minority/white hourly wage ratio: 71.3 71.3 71.3 71.3 82.0 82.0 82.0 82.0

Adjusted minority/white hourly wage ratio: 97.7 101.1 91.1 96.8 102.9 103.4 97.8 98.2

Note: Decomposition results shown are derived from results of separate regressions for men ages 35-43 by race and by model using NLSY79 data from the 2000 survey. See Table 4 for variable means and coefficients. Hourly wages are the exponentiated hourly log wages.

Source: National Longitudinal Survey of Youth (NLSY79).

Source: O'Neil and O'Neil (2005)

 Reference Group: Male Coef.

A: Raw log wage gap : Qτ[ln(wm )]-Qτ[ln(wf)] 0.170 ( 0.023) 0.249 ( 0.019) 0.258 ( 0.026)

Estimated log wage gap: Qτ[ln(wm )]-Qτ[ln(wf)] 0.192 ( 0.015) 0.239 ( 0.016) 0.276 ( 0.026) Total explained by characteristics 0.257 ( 0.028) 0.198 ( 0.027) 0.143 ( 0.019) Total wage structure -0.065 ( 0.027) 0.041 ( 0.024) 0.133 ( 0.025)

Mean RIF gap: E[RIFτ(ln(wm))]-E[RIFτ(ln(wf))] 0.180 ( 0.023) 0.241 ( 0.019) 0.260 ( 0.026)Composition effects attributable to Age, race, region, etc. 0.015 ( 0.005) 0.013 ( 0.004) 0.002 ( 0.004) Education -0.011 ( 0.005) -0.017 ( 0.006) -0.005 ( 0.01) AFQT 0.005 ( 0.02) 0.013 ( 0.004) 0.013 ( 0.005) L.T. withdrawal due to family 0.022 ( 0.021) 0.042 ( 0.014) 0.039 ( 0.017) Life-time work experience 0.234 ( 0.026) 0.136 ( 0.014) 0.039 ( 0.023) Industrial Sectors 0.008 ( 0.012) 0.020 ( 0.008) 0.047 ( 0.011) Total explained by characteristics 0.274 ( 0.035) 0.208 ( 0.025) 0.136 ( 0.028)

Wage structure effects attributable to Age, race, region, etc. -0.342 ( 0.426) 0.168 ( 0.357) 0.860 ( 0.524) Education 0.023 ( 0.028) -0.030 ( 0.031) 0.023 ( 0.045) AFQT -0.007 ( 0.03) 0.003 ( 0.042) 0.008 ( 0.062) L.T. withdrawal due to family -0.075 ( 0.032) -0.005 ( 0.025) 0.018 ( 0.032) Life-time work experience 0.084 ( 0.148) -0.085 ( 0.082) -0.078 ( 0.119) Industrial Sectors 0.015 ( 0.06) -0.172 ( 0.046) -0.054 ( 0.052) Constant 0.208 ( 0.349) 0.154 ( 0.323) -0.653 ( 0.493)Total wage structure -0.094 ( 0.044) 0.033 ( 0.028) 0.124 ( 0.036)

Table 4. Gender Wage Gap: Quantile Decomposition Results (NLSY, 2000)

10th percentile 50th percentile 90th percentile

Note: The data is an extract from the NLSY79 used in O'Neill and O'Neill (2006). Industrial sectors have been added to their analysis to illustrate issues linked to categorical variables. The other explanatory variables are age, dummies for black, hispanic, region, msa, central city. Bootstrapped standard errors are in parentheses. Means are reported in Table 2.

B: Decomposition Method: Machado-Mata-Melly

C: Decomposition Method: RIF regressions without reweighing

OECD EMPLOYMENT OUTLOOK – ISBN 92-64-19778-8 – ©2002

– Women at work: who are they and how are they faring?104

(cf. Annex Table 2.B.1 for estimation results). It must be noted that decomposition out-comes are only partially robust to the choice of the benchmark country (Blau and Kahn,1996) and a different choice might lead to somewhat different results from those presentedbelow. Similar problems arise as regard to the choice of the reference group for categor-ical variables (Oaxaca and Ransom, 1999).

Before proceeding further with the examination of the decomposition results, thereader deserves some guidance to their interpretation. On the basis of the available evi-dence, it is not possible to determine whether the residual term can be ascribed only togender differences in unobserved characteristics and/or in their remuneration or rather tolabour market discrimination.25 However, comparing the full decomposition with onefocussing on the first and second terms of equation [2] only, thus leaving the residualunexplained, provides estimates of upper and lower bounds to the effect of gender gaps inproductive characteristics and the effect of the wage structure. This comparison is high-lighted in Chart 2.7, which presents three different measures of the gender wage gap:i) the unadjusted wage gap, defined as the percentage difference between male and femaleaverage gross hourly wages; ii) the wage gap adjusted for cross-country differences inremuneration rates for observed characteristics, computed by subtracting the first term onthe right-hand side of equation [2] from the unadjusted wage gap; and iii) the wage gapadjusted for cross-country differences in the whole wage structure, computed by subtract-ing both the first and third terms of the right-hand side of equation [2]. This way, the

Chart 2.7. The gender wage gap adjusted for the effect of the wage structurea

Percentage difference between male and female average gross hourly wages, persons aged 20 to 64 yearsb

a) The gender wage gap adjusted for cross-country differences in the remuneration rates of observed characteristics is obtained as follows:, where i indexes countries, k denotes the benchmark country, ¯ and ∆ refer to country averages

and differences between men and women, respectively, W stands for gross hourly wages, X for the vectors of observed characteristics, andβ for the vector of estimated coefficients from the male wage regressions (cf. Annex Table 2.B.1). The gender wage gap adjusted for cross-country differences in the whole wage structure is obtained as follows: ,where ε stands for the residuals from the male wage regressions (defined as the difference between actual and predicted values) and η forthe theoretical residuals that would be obtained in country i if it had the same residual wage structure as country k.

b) Countries are ranked by decreasing hourly wage gap adjusted for the whole wage structure.Sources and definitions: See Annexes 2.A and 2.B respectively.

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)(loglog kiiiadjobs

i XWW ββ∆∆∆ −−=

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i XXWW η∆β∆η∆ε∆ββ∆∆∆ +=−−−−= )()(loglog