R/data-law_resume.R

In openintro: Data Sets and Supplemental Functions from 'OpenIntro' Textbooks and Labs

#' Gender, Socioeconomic Class, and Interview Invites
#'
#' 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.
#'
#'
#' @name law_resume
#' @docType data
#' @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}.
#' @keywords datasets
#' @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")
"law_resume"