Are Emily and Greg More Employable Than Lakisha and Jamal?
ResumeNames.RdCross-section data about resume, call-back and employer information for 4,870 fictitious resumes.
Usage
data("ResumeNames")Format
A data frame containing 4,870 observations on 27 variables.
- name
factor indicating applicant's first name.
- gender
factor indicating gender.
- ethnicity
factor indicating ethnicity (i.e., Caucasian-sounding vs. African-American sounding first name).
- quality
factor indicating quality of resume.
- call
factor. Was the applicant called back?
- city
factor indicating city: Boston or Chicago.
- jobs
number of jobs listed on resume.
- experience
number of years of work experience on the resume.
- honors
factor. Did the resume mention some honors?
- volunteer
factor. Did the resume mention some volunteering experience?
- military
factor. Does the applicant have military experience?
- holes
factor. Does the resume have some employment holes?
- school
factor. Does the resume mention some work experience while at school?
factor. Was the e-mail address on the applicant's resume?
- computer
factor. Does the resume mention some computer skills?
- special
factor. Does the resume mention some special skills?
- college
factor. Does the applicant have a college degree or more?
- minimum
factor indicating minimum experience requirement of the employer.
- equal
factor. Is the employer EOE (equal opportunity employment)?
- wanted
factor indicating type of position wanted by employer.
- requirements
factor. Does the ad mention some requirement for the job?
- reqexp
factor. Does the ad mention some experience requirement?
- reqcomm
factor. Does the ad mention some communication skills requirement?
- reqeduc
factor. Does the ad mention some educational requirement?
- reqcomp
factor. Does the ad mention some computer skills requirement?
- reqorg
factor. Does the ad mention some organizational skills requirement?
- industry
factor indicating type of employer industry.
Details
Cross-section data about resume, call-back and employer information for 4,870 fictitious resumes sent in response to employment advertisements in Chicago and Boston in 2001, in a randomized controlled experiment conducted by Bertrand and Mullainathan (2004). The resumes contained information concerning the ethnicity of the applicant. Because ethnicity is not typically included on a resume, resumes were differentiated on the basis of so-called “Caucasian sounding names” (such as Emily Walsh or Gregory Baker) and “African American sounding names” (such as Lakisha Washington or Jamal Jones). A large collection of fictitious resumes were created and the pre-supposed ethnicity (based on the sound of the name) was randomly assigned to each resume. These resumes were sent to prospective employers to see which resumes generated a phone call from the prospective employer.
References
Bertrand, M. and Mullainathan, S. (2004). Are Emily and Greg More Employable Than Lakisha and Jamal? A Field Experiment on Labor Market Discrimination. American Economic Review, 94, 991–1013.
Stock, J.H. and Watson, M.W. (2007). Introduction to Econometrics, 2nd ed. Boston: Addison Wesley.
Examples
data("ResumeNames")
summary(ResumeNames)
#> name gender ethnicity quality call city
#> Tamika : 256 male :1124 cauc:2435 low :2424 no :4478 boston :2166
#> Anne : 242 female:3746 afam:2435 high:2446 yes: 392 chicago:2704
#> Allison: 232
#> Latonya: 230
#> Emily : 227
#> Latoya : 226
#> (Other):3457
#> jobs experience honors volunteer military holes
#> Min. :1.000 Min. : 1.000 no :4613 no :2866 no :4397 no :2688
#> 1st Qu.:3.000 1st Qu.: 5.000 yes: 257 yes:2004 yes: 473 yes:2182
#> Median :4.000 Median : 6.000
#> Mean :3.661 Mean : 7.843
#> 3rd Qu.:4.000 3rd Qu.: 9.000
#> Max. :7.000 Max. :44.000
#>
#> school email computer special college minimum
#> no :2145 no :2536 no : 874 no :3269 no :1366 none :2746
#> yes:2725 yes:2334 yes:3996 yes:1601 yes:3504 some :1064
#> 2 : 356
#> 3 : 331
#> 5 : 163
#> 1 : 142
#> (Other): 68
#> equal wanted requirements reqexp reqcomm reqeduc
#> no :3452 manager : 741 no :1036 no :2750 no :4262 no :4350
#> yes:1418 supervisor : 376 yes:3834 yes:2120 yes: 608 yes: 520
#> secretary :1621
#> office support: 578
#> retail sales : 818
#> other : 736
#>
#> reqcomp reqorg industry
#> no :2741 no :4516 manufacturing : 404
#> yes:2129 yes: 354 transport/communication : 148
#> finance/insurance/real estate : 414
#> trade :1042
#> business/personal services :1304
#> health/education/social services: 754
#> unknown : 804
prop.table(xtabs(~ ethnicity + call, data = ResumeNames), 1)
#> call
#> ethnicity no yes
#> cauc 0.90349076 0.09650924
#> afam 0.93552361 0.06447639