% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data-law_resume.R
\docType{data}
\name{law_resume}
\alias{law_resume}
\title{Gender, Socioeconomic Class, and Interview Invites}
\format{
A data frame with 316 observations on the following 3 variables. Each
row represents a resume sent a top law firm for this experiment.
\describe{\item{class}{The resume represented irrelevant details suggesting
either \code{"low"} or \code{"high"} socioeconomic class.} \item{gender}{The
resume implied the candidate was either \code{"male"} or \code{"female"}.}
\item{outcome}{If the candidate received an invitation for an
\code{"interview"} or \code{"not"}. } }
}
\source{
For a casual overview, see
\url{https://hbr.org/2016/12/research-how-subtle-class-cues-can-backfire-on-your-resume}.

For the academic paper, see Tilcsik A, Rivera LA. 2016. Class Advantage,
Commitment Penalty. The Gendered Effect of Social Class Signals in an Elite
Labor Market. American Sociological Review 81:6 p1097-1131.
\doi{10.1177/0003122416668154}.
}
\usage{
law_resume
}
\description{
Resumes were sent out to 316 top law firms in the United States, and there
were two randomized characteristics of each resume. First, the gender
associated with the resume was randomized by assigning a first name of
either James or Julia. Second, the socioeconomic class of the candidate was
randomly assigned and represented through five minor changes associated with
personal interests and other other minor details (e.g. an extracurricular
activity of sailing team vs track and field). The outcome variable was
whether the candidate was received an interview.
}
\examples{


tapply(law_resume$outcome == "interview", law_resume[, c("class", "gender")], mean)
m <- glm(I(outcome == "interview") ~ gender * class, data = law_resume, family = binomial)
summary(m)
predict(m, type = "response")
}
\keyword{datasets}
