ResumeNames {AER} | R Documentation

## 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")

### 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.

### 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.

### Source

Online complements to Stock and Watson (2007).

### 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.

### See Also

`StockWatson2007`

### Examples

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

[Package AER version 1.2-6 Index]