ResumeNames: Are Emily and Greg More Employable Than Lakisha and Jamal?

Description

Cross-section data about resume, call-back and employer information for 4,870 fictitious resumes.

Usage

data("ResumeNames")

Arguments

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?
email: 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.

Source

Online complements to Stock and Watson (2007). http://wps.aw.com/aw_stock_ie_2/

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

Run this code

data("ResumeNames")
summary(ResumeNames)
prop.table(xtabs(~ ethnicity + call, data = ResumeNames), 1)

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