Getting Started with assemblykor

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Overview

assemblykor provides seven built-in datasets from the Korean National Assembly for teaching quantitative methods in political science:

legislators: 947 MP records (20th-22nd assemblies)
bills: 60,925 legislative bills
wealth: 2,928 legislator-year asset declarations
seminars: 5,962 legislator-year seminar activity records
speeches: 15,843 committee speech records (22nd, Science & ICT)
votes: 7,997 plenary vote tallies (20th-22nd)
roll_calls: 368,210 member-level roll call votes (22nd)

library(assemblykor)

1. Exploring legislator data

data(legislators)
str(legislators)
#> 'data.frame':    947 obs. of  15 variables:
#>  $ member_id    : chr  "XQ98168F" "60490713" "IH436704" "8I61593E" ...
#>  $ assembly     : int  20 20 20 20 20 20 20 20 20 20 ...
#>  $ name         : chr  "강길부" "강병원" "강석진" "강석호" ...
#>  $ name_hanja   : chr  "姜吉夫" "姜炳遠" "姜錫振" "姜碩鎬" ...
#>  $ name_eng     : chr  "KANG GHILBOO" "KANG BYUNGWON" "KANG SEOGJIN" "KANG SEOKHO" ...
#>  $ party        : chr  "무소속" "더불어민주당" "자유한국당" "자유한국당" ...
#>  $ party_elected: chr  "무소속" "더불어민주당" "새누리당" "새누리당" ...
#>  $ district     : chr  "울산 울주군" "서울 은평구을" "경남 산청군함양군거창군합천군" "경북 영양군영덕군봉화군울진군" ...
#>  $ district_type: chr  "constituency" "constituency" "constituency" "constituency" ...
#>  $ committees   : chr  "산업통상자원중소벤처기업위원회, 4차 산업혁명 특별위원회, 예산결산특별위원회, 교육문화체육관광위원회" "" "농림축산식품해양수산위원회, 국회운영위원회, 보건복지위원회, 예산결산특별위원회" "농림축산식품해양수산위원회, 외교통일위원회, 정보위원회, 정치개혁 특별위원회, 행정안전위원회, 안전행정위원회" ...
#>  $ gender       : chr  "M" "M" "M" "M" ...
#>  $ birth_date   : Date, format: "1942-06-05" "1971-07-09" ...
#>  $ seniority    : int  4 2 1 3 4 1 3 2 2 1 ...
#>  $ n_bills      : int  312 1078 694 583 1131 393 1478 932 1284 1054 ...
#>  $ n_bills_lead : int  21 94 53 43 89 74 75 55 78 64 ...

Gender composition by assembly

gender_tab <- table(legislators$assembly, legislators$gender)
gender_tab
#>     
#>        F   M
#>   20  53 267
#>   21  64 258
#>   22  64 241

prop.table(gender_tab, margin = 1)
#>     
#>              F         M
#>   20 0.1656250 0.8343750
#>   21 0.1987578 0.8012422
#>   22 0.2098361 0.7901639

Legislative productivity by seniority

boxplot(n_bills_lead ~ seniority, data = legislators,
        xlab = "Terms served", ylab = "Bills proposed (as lead)",
        main = "Seniority and Legislative Productivity",
        col = "lightblue")

Senior legislators produce more bills, but with high variance.

2. Bill outcomes

data(bills)

# Top 5 outcomes
outcome_counts <- sort(table(bills$result), decreasing = TRUE)
barplot(outcome_counts[1:5], las = 2, col = "steelblue",
        main = "Most Common Bill Outcomes")

Most bills expire at the end of the assembly term (임기만료폐기). Only a small fraction pass in their original form (원안가결).

Bills per month

bills$month <- format(bills$propose_date, "%Y-%m")
monthly <- aggregate(bill_id ~ month, data = bills, FUN = length)
names(monthly) <- c("month", "count")
monthly <- monthly[order(monthly$month), ]

plot(seq_len(nrow(monthly)), monthly$count, type = "l",
     xlab = "Month (index)", ylab = "Bills proposed",
     main = "Monthly Bill Proposals (20th-22nd Assembly)")

3. Wealth panel

The wealth dataset is a legislator-year panel ideal for practicing fixed-effects regression.

data(wealth)

# Distribution of net worth
hist(wealth$net_worth / 1e6, breaks = 50, col = "coral",
     main = "Legislator Net Worth Distribution",
     xlab = "Net Worth (billion KRW)")

Real estate concentration

wealth$re_share <- ifelse(wealth$total_assets > 0,
                          wealth$real_estate / wealth$total_assets, NA)

boxplot(re_share ~ year, data = wealth,
        xlab = "Year", ylab = "Real estate / total assets",
        main = "Real Estate as Share of Legislator Wealth",
        col = "lightyellow")

Korean legislators hold a large share of their wealth in real estate, reflecting broader patterns in Korean household wealth.

4. Policy seminars and cross-party cooperation

data(seminars)

# Governing vs opposition party
gov_means <- tapply(seminars$cross_party_ratio,
                    seminars$is_governing, mean, na.rm = TRUE)
barplot(gov_means, names.arg = c("Opposition", "Governing"),
        ylab = "Cross-party ratio", col = c("dodgerblue", "tomato"),
        main = "Cross-Party Seminar Collaboration")

Governing-party legislators tend to have lower cross-party collaboration in policy seminars, a pattern consistent with the “closing ranks” hypothesis.

5. Joining datasets

All datasets share the member_id and/or assembly columns:

library(dplyr)

# Merge legislators with wealth
leg_wealth <- legislators %>%
  inner_join(wealth, by = "member_id", relationship = "many-to-many")

# Productivity vs wealth
leg_wealth %>%
  group_by(district_type) %>%
  summarise(
    n = n(),
    median_net_worth = median(net_worth / 1e6, na.rm = TRUE),
    median_bills = median(n_bills_lead, na.rm = TRUE)
  )
#> # A tibble: 2 × 4
#>   district_type     n median_net_worth median_bills
#>   <chr>         <int>            <dbl>        <dbl>
#> 1 constituency   4489             1.48           62
#> 2 proportional    466             1.23           65

6. Plenary votes

data(votes)

# Yes-vote share distribution
votes$yes_rate <- votes$yes / votes$voted
hist(votes$yes_rate, breaks = 40, col = "lightgreen",
     main = "Distribution of Yes-Vote Share",
     xlab = "Proportion yes")

Most bills pass with near-unanimous support. The left tail reveals contested legislation where party discipline breaks down.

7. Roll call analysis

data(roll_calls)
library(dplyr)

# Party discipline: how often do members vote with their party majority?
party_votes <- roll_calls %>%
  group_by(bill_id, party) %>%
  mutate(party_majority = names(which.max(table(vote)))) %>%
  ungroup() %>%
  mutate(with_party = vote == party_majority)

party_votes %>%
  group_by(party) %>%
  summarise(
    n_members = n_distinct(member_id),
    discipline = mean(with_party, na.rm = TRUE)
  ) %>%
  filter(n_members >= 5) %>%
  arrange(desc(discipline))
#> # A tibble: 4 × 3
#>   party        n_members discipline
#>   <chr>            <int>      <dbl>
#> 1 조국혁신당          13      0.888
#> 2 더불어민주당       169      0.856
#> 3 국민의힘           107      0.762
#> 4 무소속               6      0.753

8. Speech patterns

data(speeches)

# Who speaks most in committee?
leg_speeches <- speeches[speeches$role == "legislator", ]
speaker_counts <- sort(table(leg_speeches$speaker_name), decreasing = TRUE)
barplot(speaker_counts[1:10], las = 2, col = "plum",
        main = "Top 10 Most Active Speakers (Sci & ICT Committee)")

Next steps

For text analysis, download the bill propose-reason texts:

texts <- get_bill_texts()

For network analysis, download the full co-sponsorship records:

proposers <- get_proposers()

See vignette("codebook") for the full data dictionary, or ?get_bill_texts and ?get_proposers for download function details.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.