Cross-section data about resume, call-back and employer information for 4,870 fictitious resumes.
data("ResumeNames")A data frame containing 4,870 observations on 27 variables.
factor indicating applicant's first name.
factor indicating gender.
factor indicating ethnicity (i.e., Caucasian-sounding vs. African-American sounding first name).
factor indicating quality of resume.
factor. Was the applicant called back?
factor indicating city: Boston or Chicago.
number of jobs listed on resume.
number of years of work experience on the resume.
factor. Did the resume mention some honors?
factor. Did the resume mention some volunteering experience?
factor. Does the applicant have military experience?
factor. Does the resume have some employment holes?
factor. Does the resume mention some work experience while at school?
factor. Was the e-mail address on the applicant's resume?
factor. Does the resume mention some computer skills?
factor. Does the resume mention some special skills?
factor. Does the applicant have a college degree or more?
factor indicating minimum experience requirement of the employer.
factor. Is the employer EOE (equal opportunity employment)?
factor indicating type of position wanted by employer.
factor. Does the ad mention some requirement for the job?
factor. Does the ad mention some experience requirement?
factor. Does the ad mention some communication skills requirement?
factor. Does the ad mention some educational requirement?
factor. Does the ad mention some computer skills requirement?
factor. Does the ad mention some organizational skills requirement?
factor indicating type of employer industry.
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.
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.
# NOT RUN {
data("ResumeNames")
summary(ResumeNames)
prop.table(xtabs(~ ethnicity + call, data = ResumeNames), 1)
# }
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