R/data.R
#' @title US House elections dataset from Lee (2008)
#'
#' @description Data on elections to the U.S. House of Representatives
#'
#' @details Lee (2008) uses the data on elections to the U.S. House of
#' Representatives (1946-1998) to analyze the incumbency advantage. For a
#' party's candidate who barely won the election (and became incumbent) or
#' barely lost the previous election, their districts usually share many
#' common characteristics. The electoral success for the two groups of common
#' characteristics. The electoral success for the two groups of candidates in
#' the next election can be used to identify the causal incumbency advantage.
#' We subtract the number 50, equivalent to fifty percent, from the vote share
#' data so that the cutoff is 0 for treatment assignment. Each row of the data
#' frame represents a party's candidate.
#'
#' @format A data frame with 6,558 rows and 2 variables. \describe{
#' \item{\code{voteshare}}{the treatment outcome variable \code{y}, vote
#' share in the next election} \item{\code{margin}}{the covariate \code{x},
#' democratic margin of victory at the previous election} } Each row
#'
#' @source Mostly Harmless Econometrics data archive at
#' \url{https://economics.mit.edu/faculty/angrist/data1/mhe}
#'
#' @references{
#'
#' \cite{Lee, D. S. (2008) "Randomized experiments from non-random selection in
#' U.S. House elections," Journal of Econometrics, 142 (2), 675-697.}
#'
#' }
"lee"
#' @title The Head Start dataset from Ludwig and Miller (2007)
#'
#' @description The Head Start policy dataset on the mortality rates for
#' children
#'
#' @details Ludwig and Miller (2007) studies the impact of Head Start funding on
#' children's schooling and health. This subset of their data focuses on the
#' change in mortality rates for children due to the Head Start funding cutoff
#' set by the Office of Economic Opportunities (OEF). Changes in morality
#' rates near the funding cutoff can be used to identify the causal effect of
#' such policy. We subtract the average poverty rate (59.198) from the
#' poverty rate data so that the cutoff is 0 for treatment assignment. Each
#' row in the data frame represents a county.
#'
#' @format A data frame with 3,103 rows and 2 variables: \describe{
#' \item{\code{mortality}}{the treatment outcome variable \code{y}, morality
#' rates per 10,000 children between 5 and 9 years old between 1973-1983.}
#' \item{\code{poverty}}{the covariate \code{x}, a county's poverty rate in
#' 1960 relative to the 300th poorest county (59.198)} }
#'
#' @references{
#'
#' \cite{Ludwig, J. and D. L. MIller (2007) "Does Head Start improve children's
#' life chances? Evidence from a regression discontinuity design," Quarterly
#' Journal of Economics, 122 (1), 159-208.}
#'
#' }
"headstart"
