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Original data from the experiment run by Bertrand and Mullainathan (2004).

Usage

labor_market_discrimination

Format

A tibble with 4870 observations of 63 variables.

education

Highest education, with levels of 0 = not reported; 1 = high school diploma; 2 = high school graduate; 3 = some college; 4 = college or more.

n_jobs

Number of jobs listed on resume.

years_exp

Number of years of work experience on the resume.

honors

Indicator variable for which 1 = resume mentions some honors.

volunteer

Indicator variable for which 1 = resume mentions some volunteering experience.

military

Indicator variable for which 1 = resume mentions some military experience.

emp_holes

Indicator variable for which 1 = resume mentions some employment holes.

occup_specific

1990 Census Occupation Code. See sources for a key.

occup_broad

Occupation broad with levels 1 = executives and managerial occupations, 2 = administrative supervisors, 3 = sales representatives, 4 = sales workers, 5 = secretaries and legal assistants, 6 = clerical occupations

work_in_school

Indicator variable for which 1 = resume mentions some work experience while at school

email

Indicator variable for which 1 = email address on applicant's resume.

computer_skills

Indicator variable for which 1 = resume mentions some computer skills.

special_skills

Indicator variable for which 1 = resume mentions some special skills.

first_name

Applicant's first name.

sex

Sex, with levels of 'f' = female; 'm' = male.

race

Race, with levels of 'b' = black; 'w' = white.

h

Indicator variable for which 1 = high quality resume.

l

Indicator variable for which 1 = low quality resume.

call

Indicator variable for which 1 = applicant was called back.

city

City, with levels of 'c' = chicago; 'b' = boston.

kind

Kind, with levels of 'a' = administrative; 's' = sales.

ad_id

Employment ad identifier.

frac_black

Fraction of blacks in applicant's zip.

frac_white

Fraction of whites in applicant's zip.

l_med_hh_inc

Log median household income in applicant's zip.

frac_dropout

Fraction of high-school dropouts in applicant's zip.

frac_colp

Fraction of college degree or more in applicant's zip

l_inc

Log per capita income in applicant's zip.

col

Indicator variable for which 1 = applicant has college degree or more.

expminreq

Minimum experience required, if any (in years when numeric).

school_req

Specific education requirement, if any. 'hsg' = high school graduate, 'somcol' = some college, 'colp' = four year degree or higher

eoe

Indicator variable for which 1 = ad mentions employer is 'Equal Opportunity Employer'.

parent_sales

Sales of parent company (in millions of US $).

parent_emp

Number of parent company employees.

branch_sales

Sales of branch (in millions of US $).

branch_emp

Number of branch employees.

fed

Indicator variable for which 1 = employer is a federal contractor.

frac_black_emp_zip

Fraction of blacks in employers's zipcode.

frac_white_emp_zip

Fraction of whites in employer's zipcode.

l_med_hh_inc_emp_zip

Log median household income in employer's zipcode.

frac_dropout_emp_zip

Fraction of high-school dropouts in employer's zipcode.

frac_colp_emp_zip

Fraction of college degree or more in employer's zipcode.

l_inc_emp_zip

Log per capita income in employer's zipcode.

manager

Indicator variable for which 1 = executives or managers wanted.

supervisor

Indicator variable for which 1 = administrative supervisors wanted.

secretary

Indicator variable for which 1 = secretaries or legal assistants wanted.

off_support

Indicator variable for which 1 = clerical workers wanted.

sales_rep

Indicator variable for which 1 = sales representative wanted.

retail_sales

Indicator variable for which 1 = retail sales worker wanted.

req

Indicator variable for which 1 = ad mentions any requirement for job.

exp_req

Indicator variable for which 1 = ad mentions some experience requirement.

com_req

Indicator variable for which 1 = ad mentions some communication skills requirement.

educ_req

Indicator variable for which 1 = ad mentions some educational requirement.

comp_req

Indicator variable for which 1 = ad mentions some computer skill requirement.

org_req

Indicator variable for which 1 = ad mentions some organizational skills requirement.

manuf

Indicator variable for which 1 = employer industry is manufacturing.

trans_com

Indicator variable for which 1 = employer industry is transport or communication.

bank_real

Indicator variable for which 1 = employer industry is finance, insurance or real estate.

trade

Indicator variable for which 1 = employer industry is wholesale or retail trade.

bus_service

Indicator variable for which 1 = employer industry is business or personal services.

oth_service

Indicator variable for which 1 = employer industry is health, education or social services.

miss_ind

Indicator variable for which 1 = employer industry is other or unknown.

ownership

Ownership status of employer, with levels of 'non-profit'; 'private'; 'public'

Source

Bertrand, Marianne, and Mullainathan, Sendhil. Replication data for: Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. Nashville, TN: American Economic Association [publisher], 2004. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2019-12-06. doi: 10.3886/E116023V1 .

NBER Working Papers

1990 Census Occupation Codes

Note: The description of the variables follows closely the labels provided in the original dataset, with small edits for clarity.

Details

From the summary: "We study race in the labor market by sending fictitious resumes to help-wanted ads in Boston and Chicago newspapers. To manipulate perceived race, resumes are randomly assigned African-American- or White-sounding names. White names receive 50 percent more callbacks for interviews. Callbacks are also more responsive to resume quality for White names than for African-American ones. The racial gap is uniform across occupation, industry, and employer size. We also find little evidence that employers are inferring social class from the names. Differential treatment by race still appears to be prominent in the U. S. labor market."

Examples

library(dplyr)

# Percent callback for typical White names and typical African-American names (table 1, p. 997)

labor_market_discrimination %>%
  group_by(race) %>%
  summarise(call_back = mean(call))
#> # A tibble: 2 × 2
#>   race  call_back
#>   <chr>     <dbl>
#> 1 b        0.0645
#> 2 w        0.0965