% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/data.R
\docType{data}
\name{seminars}
\alias{seminars}
\title{Policy Seminar Activity by Legislator-Year (2000-2025)}
\format{
A data frame with 5,962 rows and 18 variables:
\describe{
\item{name}{Legislator name in Korean}
\item{member_id}{Legislator identifier (MONA_CD, links to
\code{legislators$member_id}). Available for ~95\\% of rows;
\code{NA} for unmatched or ambiguous (homonym) cases.}
\item{year}{Calendar year}
\item{assembly}{Assembly number (17-22)}
\item{party}{Party affiliation}
\item{camp}{Political camp: "liberal", "conservative",
"progressive", or "other" (values are in Korean)}
\item{seniority}{Number of terms served}
\item{n_seminars}{Number of policy seminars hosted that year}
\item{n_cross_party}{Number of seminars co-hosted with other-party legislators}
\item{cross_party_ratio}{Share of seminars that were cross-party (0-1)}
\item{avg_coalition_size}{Average number of co-hosts per seminar}
\item{is_governing}{Logical: belongs to the governing (presidential) party}
\item{is_female}{Logical: female legislator}
\item{is_proportional}{Logical: proportional-representation member}
\item{is_seoul}{Logical: represents a Seoul district}
\item{province}{Province/metro area of electoral district}
\item{total_terms}{Total assembly terms served across career}
\item{n_bills_led}{Number of bills proposed as lead proposer that year}
}
}
\source{
National Assembly Seminar Database, collected via API.
}
\usage{
seminars
}
\description{
Annual panel of policy seminar hosting activity for legislators in the
16th through 22nd Korean National Assembly. Policy seminars (jeongchaek semina)
are informal legislative events where MPs invite experts, stakeholders,
and colleagues from other parties to discuss policy issues.
}
\details{
Policy seminars are a distinctive feature of the Korean National Assembly.
Unlike floor speeches or committee hearings, seminars are voluntary and
allow legislators to signal policy expertise and build cross-party ties.
The \code{cross_party_ratio} variable captures how often a legislator
cooperates across party lines in this informal arena.

The \code{is_governing} variable enables difference-in-differences designs:
when a party transitions from opposition to governing (or vice versa),
does its members' cross-party collaboration change?
}
\examples{
data(seminars)

# Cross-party collaboration by governing status
tapply(seminars$cross_party_ratio, seminars$is_governing, mean, na.rm = TRUE)

# Seminar activity over time
agg <- aggregate(n_seminars ~ year, data = seminars, FUN = sum)
plot(agg, type = "b", main = "Total Policy Seminars by Year")

# Gender gap in seminar hosting
tapply(seminars$n_seminars, seminars$is_female, median, na.rm = TRUE)
}
\keyword{datasets}
