이중적 외부자, 노동시장과 사회보험 바깥의 노동자들은 누구인가?

GBTM을 통한 임금수준과 사회보험의 관계 유형화

연구
사회정책은 본래 소득불평등을 완화하는 기능을 수행하지만, 이중화된 노동시장 하에서 사회정책은 오히려 불평등을 더 강화하게 된다. 노동시장과 사회정책 모두에서 떠밀린 이들을 “이중적 외부자”라 부를만 하다.
Published

2025.05.29

Modified

2025.06.13

Abstract
작성중
주의

아직 미완성된 논문으로, 인용을 금합니다.

1 서론

사회정책은 자본주의 생산체제에서 필연적으로 발생하는 결과적 불평등을 조정하여, 체제의

1.1 개요

복지국가란:

  1. 자본주의 사회에서 필연적으로 발생하는 구조적 위험에 대해
  2. 집합주의적 보호를 제공함으로써,
  3. 자본주의의 존속가능성을 확대시키고,
  4. 사회효용 극대화에 근접시키는,
  5. 정책의 집합이다.

1의 구조적 위험은 실업, 퇴직, 산재 등 자본주의사회이기에 발생하는 위험을 말한다. 2의 집합주의적 보호란 개인-개인이나 지역사회(교회 등)-개인 사이의 도움이 아닌, 제도적 보호를 의미한다. 자선행위가 아무리 많아도 그건 복지국가가 아니다. 5는 한두개의 제도가 아닌 다수의 제도가 요구됨을 의미한다.

3, 4를 이해하기 위해서는 후생경제학적 배경지식이 필요하다. 간단히 말하면, 소득구조를 교정하여 사회 전체의 효용을 증가시킬 수 있음을 의미한다. 지나친 불평등은 자원분배의 비효율을 초래하여 결과적으로 사회 전체의 역동을 감소시킨다.

구조적 위험(1)에 대한 대응책으로 제시되는 제도(2)에는 대표적으로 사회보험이 있다. 퇴직, 질병, 노령, 실업, 산재는 국민연금, 건강보험, 노인장기요양보험, 고용보험, 산재보험과 정확히 대응된다. 사회보험은 복지국가의 근간을 이루는 핵심적 제도인 것이다. 미국을 포함한 대부분의 자본주의 국가는 사회보험제도를 운영하고 있다.

요약하자면, 사회정책이 잘 작동할 때, 자본주의 체제는 약점을 적절히 극복하고 최적화될 수 있다는 것.

Code
data_url <- "https://sdmx.oecd.org/public/rest/data/OECD.WISE.INE,DSD_WISE_IDD@DF_IDD,/JPN+ITA+FRA+DEU+AUT+NOR+CAN+FIN+DNK+SWE+USA+GBR+AUS+KOR.A.INC_DISP_GINI..._T.METH2012..?startPeriod=2010&endPeriod=2023&dimensionAtObservation=AllDimensions"
response <- GET(data_url)
json_text <- content(response, as = "text", encoding = "UTF-8")
json_data <- fromJSON(json_text)

obs_dim <- json_data$structure$dimensions$observation

time_values <- obs_dim$values[[10]]
time_labels <- time_values$id

ref_area_dim <- obs_dim$values[[1]]
ref_area_labels <- ref_area_dim$id

series_list <- obs_dim$values[[1]]$id
results <- data.frame()

obs <- json_data$data$observations
dims <- json_data$structure$dimensions$observation

# 각 차원의 인덱스를 실제 값으로 매핑
get_label <- function(dim_index, value_index) {
  dims[[dim_index + 1]]$values[[value_index + 1]]$id
}

# 결과 저장용
results <- data.frame()

# 관측값 순회하며 정리
for (key in names(obs)) {
  key_parts <- as.numeric(strsplit(key, ":")[[1]])
  time <- get_label(0, key_parts[1])
  country <- json_data$structure$dimensions$series[[1]]$values[[key_parts[2] + 1]]$id
  value <- obs[[key]][[1]]
  
  results <- rbind(results, data.frame(country = country, year = time, gini = value))
}

# 보기 좋게 정렬
results <- results %>% arrange(country, year)

print(results)

1.2 문제점

사회정책이 불평등을 강화시키고 있다는 몇몇 증거들

이중구조에 대한 얘기들

1.3 필요성

(작성중) 사회정책은 본래 소득불평등을 완화하는 기능을 수행하지만, 최근의 사회정책은 오히려 불평등을 더 강화하고 있다. 노동시장 이중구조 때문이다. 사회보험은 사회정책의 핵심제도 중 하나인데, 이 사회보험의 불평등 완화효과는 가입자 내에서만 작동하고, 미가입자에게는 작동하지 않는다. 반면 대개 사회보험 미가입 일자리는 저임금 일자리가 많다. 정작 재분배의 수혜자가 되어야 할 이들이 정책 대상에서 제외되어 있는 셈이다. 그럼에도 불구하고, 사회정책 분야 노동시장 연구는 대부분 “사회정책의 불평등 강화 효과”를 다루는 경우가 드물다. 그러나 연구자들은 이중노동시장의 외부자가 “불평등 강화효과”로 인해 사회정책에 대한 정치적 지지를 철회하고 사회정책의 지지 기반에서 이탈할 수 있음을 인식해야 한다. 사회정책의 잠재적 수혜자인 이들의 지지철회는 사회정책 논의의 정당성과 제도적 발전가능성에 위협이 될 수 있기 때문이다.

2 선행연구 검토

2.1 이론적 배경

2.2 경험적 배경

3 연구방법

3.1 자료

Code
wage.global <- tibble(klips = 5:26,
                      year = 2002:2023,
                      minwag = c(2100, 2275, 2510, 2840, 3100,
                                 3480, 3770, 4000, 4110, 4320,
                                 4580, 4860, 5210, 5580, 6030,
                                 6470, 7530, 8350, 8590, 8720,
                                 9160, 9620))

klips <- list()
for(k in sprintf("%02d", 5:26)) {
  klips[[as.integer(k) - 4]] <- paste0("../../../rawdata/klips", k, "p.dta") %>% 
    read_dta() %>% 
    mutate(# The demographicals
      empsta = !!sym(paste0("p", k, "0314")),
      male = ifelse(!!sym(paste0("p", k, "0101")) == 1,
                    1, 
                    0) %>% as.factor(),
      age = !!sym(paste0("p", k, "0107")),
      youth = case_when(
        age >= 15 & age < 35 ~ 1,
        age >= 35 & age < 65 ~ 0,
        TRUE ~ NA
      ) %>% as.factor(),
      wage = !!sym(paste0("p", k, "1642")) * 10000,
      worhou.week = case_when(
        !!sym(paste0("p", k, "1003")) == 1 ~ !!sym(paste0("p", k, "1006")),
        !!sym(paste0("p", k, "1003")) == 2 ~ !!sym(paste0("p", k, "1004")),
        TRUE ~ NA
      ),
      worday.week = case_when(
        !!sym(paste0("p", k, "1003")) == 1 ~ !!sym(paste0("p", k, "1007")),
        !!sym(paste0("p", k, "1003")) == 2 ~ !!sym(paste0("p", k, "1005")),
        TRUE ~ NA
      ),
      houwag = wage / (worhou.week * 4),
      minwag = wage.global$minwag[as.integer(k) - 4],
      higwag = case_when(
        is.na(minwag) == T ~ NA,
        houwag < minwag * 2.5 ~ 0,
        houwag >= minwag * 2.5 ~ 1,
        TRUE ~ NA
      ) %>% as.factor(),
    ) %>% 
    mutate(# Non-standard employments
      partim = ifelse(!!sym(paste0("p", k, "0315")) == 1, 
                      1, 
                      0) %>% as.factor(),
      fixter_1 = ifelse(!!sym(paste0("p", k, "0501")) == 1, 
                        1, 
                        0) %>% as.factor(),
      fixter_2 = case_when(
        !!sym(paste0("p", k, "0501")) == 2
        & !!sym(paste0("p", k, "0601")) == 2 
        & (!!sym(paste0("p", k, "0605")) >= 1 
           & !!sym(paste0("p", k, "0605")) <= 6) ~ 1,
        TRUE ~ 0
      ) %>% as.factor(),
      fixter_3 = ifelse(empsta == 2, 1, 0) %>% as.factor(),
      fixter = ifelse(fixter_1 == 1 
                      | fixter_2 == 1 
                      | fixter_3 == 1, 
                      1, 
                      0) %>% as.factor(),
      daiwor_1 = if_else(!!sym(paste0("p", k, "0508")) == 1, 
                         1, 
                         0, 
                         0) %>% as.factor(),
      daiwor_2 = ifelse(empsta == 3, 
                        1, 
                        0) %>% as.factor(),
      daiwor = ifelse(daiwor_1 == 1 | daiwor_2 == 1, 
                      1, 
                      0) %>% as.factor(),
      agewor_1 = ifelse(!!sym(paste0("p", k, "0611")) == 2, 
                        1, 
                        0) %>% as.factor(),
      agewor_2 = ifelse(!!sym(paste0("p", k, "0611")) == 3, 
                        1, 
                        0) %>% as.factor(),
      agewor = ifelse(agewor_1 == 1 | agewor_2 == 1, 
                      1, 
                      0) %>% as.factor(),
      domwor = ifelse(!!sym(paste0("p", k, "0613")) == 1, 
                      1, 
                      0) %>% as.factor(),
      speemp = ifelse(!!sym(paste0("p", k, "0612")) == 1, 
                      1, 
                      0) %>% as.factor(),
      staemp = ifelse(partim == "0" 
                      & fixter == "0"
                      & daiwor == "0"
                      & agewor == "0"
                      & domwor == "0"
                      & speemp == "0",
                      1,
                      0),
      jobsat = ifelse(!!sym(paste0("p", k, "4312")) < 3,
                      1,
                      0)
    ) %>% 
    mutate(# Social insurances
      natpen = ifelse(!!sym(paste0("p", k, "2101")) == 1, 
                      1, 
                      0) %>% as.factor(),
      spepen = ifelse(!!sym(paste0("p", k, "2102")) == 1, 
                      1, 
                      0) %>% as.factor(),
      totpen = ifelse(natpen == 1 | spepen == 1, 
                      1, 
                      0) %>% as.factor(),
      nathel = ifelse(!!sym(paste0("p", k, "2103")) == 1, 
                      1, 
                      0) %>% as.factor(),
      empins = ifelse(!!sym(paste0("p", k, "2104")) == 1 | spepen == 1, 
                      1, 
                      0) %>% as.factor(),
      indins = ifelse(!!sym(paste0("p", k, "2105")) == 1 | spepen == 1, 
                      1, 
                      0) %>% as.factor(),
      retben = ifelse(!!sym(paste0("p", k, "4101")) == 1, 
                      1, 
                      0) %>% as.factor(),
      socins = ifelse(totpen == "1"
                      & nathel == "1"
                      & empins == "1"
                      & indins == "1",
                      1, 
                      0)
    ) %>% 
    mutate(# Labor market class
      KSCO5 = !!sym(paste0("p", k, "0350")),
      occu_serv = case_when(
        KSCO5 >= 140 & KSCO5 < 160 ~ 1,
        KSCO5 >= 170 & KSCO5 < 190 ~ 1,
        KSCO5 >= 240 & KSCO5 < 260 ~ 1,
        KSCO5 >= 270 & KSCO5 < 290 ~ 1,
        KSCO5 >= 310 & KSCO5 < 540 ~ 1,
        TRUE ~ 0
      ), 
      occu3 = case_when(
        (KSCO5 >= 400 & KSCO5 < 600) | KSCO5 >= 900 ~ 1,
        (KSCO5 >= 300 & KSCO5 < 400) | (KSCO5 >= 700 & KSCO5<900) ~ 2,
        KSCO5 >= 0 & KSCO5 < 300 ~ 3
      ),
      occu5 = case_when(
        occu_serv == 1 & occu3 == 1 ~ 1,
        occu_serv == 0 & occu3 == 1 ~ 2,
        occu_serv == 1 & occu3 == 2 ~ 3,
        occu_serv == 0 & occu3 == 2 ~ 4,
        occu3 == 3 ~ 5,
        TRUE ~ NA
      ) %>% as.factor(),
      firmsize1 = !!sym(paste0("p", k, "0402")),
      firmsize2 = !!sym(paste0("p", k, "0403")),
      large_firm = case_when(
        (firmsize1 >= 1 & firmsize1 <= 299) | firmsize2 <= 7 ~ 0,
        firmsize1 >= 300 | (firmsize2 >= 8 & firmsize2 <= 10) ~ 1,
        is.na(firmsize1) == TRUE & is.na(firmsize2) == TRUE ~ NA,
        TRUE ~ NA
      ) %>% as.factor(),
      firm_group5 = case_when(
        (firmsize1 >= 1 & firmsize1 <= 4) | firmsize2 <= 1 ~ 1,
        (firmsize1 >= 5 & firmsize1 <= 29) 
        | (firmsize2 >= 2 & firmsize2 <= 3) ~ 2,
        (firmsize1 >= 30 & firmsize1 <= 99) 
        | (firmsize2 >= 4 & firmsize2 <= 6) ~ 3,
        (firmsize1 >= 100 & firmsize1 <= 299) 
        | (firmsize2 >= 7 & firmsize2 <= 7) ~ 4,
        firmsize1 >= 300 | (firmsize2 >= 8 & firmsize2 <= 10) ~ 5,
        TRUE ~ NA
      ) %>% as.factor(),
      firm_group10 = case_when(
        (firmsize1 >= 1 & firmsize1 <= 4) | firmsize2 == 1 ~ 1,
        (firmsize1 >= 5 & firmsize1 <= 9) | firmsize2 == 2 ~ 2,
        (firmsize1 >= 10 & firmsize1 <= 29) | firmsize2 == 3 ~ 3,
        (firmsize1 >= 30 & firmsize1 <= 49) | firmsize2 == 4 ~ 4,
        (firmsize1 >= 50 & firmsize1 <= 69) | firmsize2 == 5 ~ 5,
        (firmsize1 >= 70 & firmsize1 <= 99) | firmsize2 == 6 ~ 6,
        (firmsize1 >= 100 & firmsize1 <= 299) | firmsize2 == 7 ~ 7,
        (firmsize1 >= 300 & firmsize1 <= 499) | firmsize2 == 8 ~ 8,
        (firmsize1 >= 500 & firmsize1 <= 999) | firmsize2 == 9 ~ 9,
        firmsize1 >= 1000 | firmsize2 == 10 ~ 10,
        TRUE ~ NA
      ) %>% as.factor()
    ) %>% 
    filter(empsta <= 3, wage > 0) %>% 
    drop_na(youth)
    # drop_na(male, age, youth, wage, class5, firm_group5, large_firm, KSCO5)
  names(klips)[k %>% as.integer() - 4] <- paste0("p", k)
  rm(k)
}
Code
merge <- klips$p26 %>% 
  mutate(year = NA) %>% 
  select(pid, year, male:firm_group10,
         contains("sample")) %>% 
  slice()

for(k in sprintf("%02d", 05:26)) {
  temp <- klips[[paste0("p", k)]] %>% 
    mutate(year = k %>% as.numeric()) %>% 
    select(pid, year, male:firm_group10,
           contains("sample"))
  merge <- bind_rows(merge, temp)
  rm(temp, k)
}

merge09 <- merge %>% 
  mutate(year = year - 11) %>% 
  filter(sample09 == 1 | sample09 == 2)
obs09 <- merge09 %>% 
  group_by(pid) %>% 
  summarise(n = n())
baseline09 <- merge09 %>%
  filter(year == 1 | year == 15) %>%
  # filter((year == 1 & age >= 51) | (year == 15 & age >= 65)) %>% 
  group_by(pid) %>% 
  summarise(n = n()) %>% 
  filter(n == 2)
range09 <- table(obs09$n, useNA = "ifany") %>% names() %>% length()

df09 <- list()
for(k in 1:range09) {
  df09[[k]] <- merge09 %>% 
    filter(pid %in% baseline09$pid) %>% 
    left_join(obs09, by = "pid") %>% 
    filter(n >= k) %>% 
    select(-n)
  rm(k, range09)
}

3.2 변수

Code
set.seed(0509)
model09 <- list()
for(k in 1:7) {
  start_time <- Sys.time()
  if(k == 1) {
    cat("\n```\n")
    cat("fitting:\n")
    model09[[k]] <- lcmm(fixed = socins ~ year + higwag:year,
                         subject = "pid",
                         ng = k,
                         link = "thresholds",
                         data = df09[[5]],
                         nproc = 6)
  } else {
    model09[[k]] <- lcmm(fixed = socins ~ year + higwag:year,
                         mixture = ~ year + higwag:year,
                         subject = "pid",
                         ng = k,
                         link = "thresholds",
                         data = df09[[5]],
                         B = model09[[1]],
                         nproc = 6)
  }
  names(model09)[k] <- paste0("g", k)
  end_time <- Sys.time()
  cat("G =", k)
  cat(" takes", 
      round(as.numeric(end_time) - as.numeric(start_time, 2)),
      "second(s),\n")
  rm(k, start_time, end_time)
}

saveRDS(model09, "model09.rds")

3.3 분석방법

4 분석결과

Code
model09 <- readRDS("model09.rds")

sum.tab <- summarytable(
  model09$g2, model09$g3, model09$g4, model09$g5, model09$g6, model09$g7, 
  which = c("G", "AIC", "BIC", "SABIC", "entropy", "%class")
)

k <- 4
model.fin <- model09[[paste0("g", k)]]
model.fin$pprob <- model.fin$pprob %>% rename(!!paste0("class", k) := class)
df.fin <- eval(model.fin$call$data) %>% 
  left_join(model.fin$pprob, by = "pid")
rm(k)

nd0 <- tibble(year = seq(1, 15),
              higwag = factor(rep("0", 15), levels = c("0", "1")))
nd1 <- tibble(year = seq(1, 15),
              higwag = factor(rep("1", 15), levels = c("0", "1")))

observed <- df.fin %>% 
  mutate(socins = socins %>% as.numeric(),
         name = "Yobse") %>% 
  group_by(year, higwag, name, class4) %>% 
  summarise(value = mean(socins)) %>% 
  arrange(year) %>% 
  mutate(class4 = class4 %>% as.factor())
fitted0 <- predictY(model.fin, nd0)$pred %>%
  as_tibble() %>%
  mutate(year = seq(1, 15),
         higwag = "0") %>%
  gather(name, value, -c(year, higwag)) %>%
  separate(name, c("name", "class4"), sep = "_class") %>% 
  mutate(class4 = class4 %>% as.factor())
fitted1 <- predictY(model.fin, nd1)$pred %>%
  as_tibble() %>%
  mutate(year = seq(1, 15),
         higwag = "1") %>%
  gather(name, value, -c(year, higwag)) %>%
  separate(name, c("name", "class4"), sep = "_class") %>% 
  mutate(class4 = class4 %>% as.factor())
df.traj <- bind_rows(observed, fitted0, fitted1)

g1 <- ggplot() +
    theme_classic(base_size = 12) +
    theme(legend.position = "none") +
    labs(
      x = "Year",
      y = "Probability",
      title = "Low-wage workers",
      subtitle = "less than 2.5x National minimum wage"
    ) +
    geom_line(
      data = df.traj %>% filter(name == "Ypred", higwag == "0"),
      stat = "smooth",
      size = 1.25,
      aes(x = year, y = value, color = class4)
    ) +
    geom_line(
      data = df.traj %>% filter(name == "Yobse", higwag == "0"),
      alpha = 0.75,
      linetype = "dashed",
      aes(x = year, y = value, color = class4)
    ) +
    scale_x_continuous(
      limits = c(1, max(df.fin$year)),
      breaks = seq(1, max(df.fin$year), 2),
      labels = seq(min(df.fin$year) + 2008, max(df.fin$year) + 2008, 2)
    ) +
    scale_y_continuous(limits = c(0, 1)) +
    scale_color_manual(values = c("#FC4E07", "#E69F00", "#56B4E9", "#009E73"),
                       labels = c("Class 1", "Class 2", "Class 3", "Class 4"))

g2 <- ggplot() +
    theme_classic(base_size = 12) +
    theme(legend.position = "none") +
    labs(
      x = "Year",
      y = "Probability",
      title = "High-wage workers",
      subtitle = "2.5x National minimum wage or more"
    ) +
    geom_line(
      data = df.traj %>% filter(name == "Ypred", higwag == "1"),
      stat = "smooth",
      size = 1.25,
      aes(x = year, y = value, color = class4)
    ) +
    geom_line(
      data = df.traj %>% filter(name == "Yobse", higwag == "1"),
      alpha = 0.75,
      linetype = "dashed",
      aes(x = year, y = value, color = class4)
    ) +
    scale_x_continuous(
      limits = c(1, max(df.fin$year)),
      breaks = seq(1, max(df.fin$year), 2),
      labels = seq(min(df.fin$year) + 2008, max(df.fin$year) + 2008, 2)
    ) +
    scale_y_continuous(limits = c(0, 1)) +
    scale_color_manual(values = c("#FC4E07", "#E69F00", "#56B4E9", "#009E73"),
                       labels = c("Class 1", "Class 2", "Class 3", "Class 4"))

legend.color <- get_legend(
  ggplot() +
    theme_classic(base_size = 12) +
    theme(legend.title = element_blank(),
          legend.key.width = unit(1.5, "cm"),
          legend.direction = "horizontal",
          plot.margin = unit(c(0, 0, 0, 0), "cm")) +
    geom_line(
      data = df.traj, 
      size = 1.25,
      aes(x = year, y = value, color = class4)
    ) +
    scale_color_manual(
      values = c("#FC4E07", "#E69F00", "#56B4E9", "#009E73"),
      labels = c("Class 1", "Class 2", "Class 3", "Class 4")
    )
)

legend.linetype <- get_legend(
  ggplot() +
    theme_classic(base_size = 12) +
    theme(legend.title = element_blank(),
          legend.key.width = unit(1.5, "cm"),
          legend.direction = "horizontal",
          plot.margin = unit(c(0, 0, 0, 0), "cm")) +
    geom_line(
      data = df.traj, 
      size = 1.25,
      aes(x = year, y = value, linetype = name)
    ) +
    scale_linetype_manual(
      values = c("dashed", "solid"),
      labels = c("Observed value", "Fitted value")
    )
)
png("g1.png", width = 3000, height = 2500, res = 600)
g1
dev.off()

png("g2.png", width = 3000, height = 2500, res = 600)
g2
dev.off()

png("legend_color.png", width = 3000, height = 100, res = 600)
grid.newpage()
grid.draw(legend.color)
dev.off()

png("legend_linetype.png", width = 3000, height = 100, res = 600)
grid.newpage()
grid.draw(legend.linetype)
dev.off()

png("AIC.png", width = 3000, height = 1250, res = 600)
ggplot(data = sum.tab, 
       aes(x = G, y = AIC)) +
  theme_classic(base_size = 12) +
  theme(axis.title.x = element_blank(),) +
  labs(y = "AIC",
       x = "Year") +
  geom_line() +
  geom_point() +
  scale_y_continuous(
    limits = c(14500, 17000),
    labels = function(x) formatC(x, format = "f", big.mark = ",", digits = 0)
  )
dev.off()

png("BIC.png", width = 3000, height = 1250, res = 600)
ggplot(data = sum.tab, 
       aes(x = G, y = BIC)) +
  theme_classic(base_size = 12) +
  theme(axis.title.x = element_blank()) +
  labs(y = "BIC",
       x = "Year") +
  geom_line() +
  geom_point() +
  scale_y_continuous(
    limits = c(14500, 17000),
    labels = function(x) formatC(x, format = "f", big.mark = ",", digits = 0)
  )
dev.off()

png("SABIC.png", width = 3000, height = 1250, res = 600)
ggplot(data = sum.tab, 
       aes(x = G, y = SABIC)) +
  theme_classic(base_size = 12) +
  theme(axis.title.x = element_blank()) +
  labs(y = "SABIC",
       x = "Year") +
  geom_line() +
  geom_point() +
  scale_y_continuous(
    limits = c(14500, 17000),
    labels = function(x) formatC(x, format = "f", big.mark = ",", digits = 0)
  )
dev.off()

png("entropy.png", width = 3000, height = 1250, res = 600)
ggplot(data = sum.tab, 
       aes(x = G, y = entropy)) +
  theme_classic(base_size = 12) +
  theme(axis.title.x = element_blank()) +
  labs(y = "Entropy",
       x = "Year") +
  geom_line() +
  geom_point() +
  scale_y_continuous(
    limits = c(0.7500, 1.0000)
  )
dev.off()
Figure 1: Goodness of fits

Figure 2: 추정된 잠재집단추세유형별

Code
png("thumbnail.png", height = 700, width = 1000, res = 150)
model.fin.traj
dev.off()
Code
mytable_sub(class4
            ~ jobsat
            + empsta
            # + houwag
            + worday.week
            + worhou.week
            + higwag
            + male
            + youth
            + occu5
            + large_firm
            + socins
            + partim
            + fixter
            + daiwor
            + agewor
            + domwor
            + speemp,
            klips$p12 %>% 
              left_join(model.fin$pprob, by = "pid"),
            max.ylev = 5,
            show.total = TRUE,
            use.column.label = F)

                       Descriptive Statistics by 'class4'                       
————————————————————————————————————————————————————————————————————————————————— 
                  1            2            3           4         Total       p  
               (N=246)      (N=1369)     (N=231)     (N=227)     (N=2073)  
————————————————————————————————————————————————————————————————————————————————— 
 jobsat                                                                     0.000
   - 0       130 (52.8%)  651 (47.6%)  158 (68.4%) 181 (79.7%) 1120 (54.0%)      
   - 1       116 (47.2%)  718 (52.4%)  73 (31.6%)  46 (20.3%)  953 (46.0%)       
 empsta                                                                     0.000
   - 1       219 (89.0%)  1259 (92.0%) 128 (55.4%) 107 (47.1%) 1713 (82.6%)      
   - 2        19 ( 7.7%)   94 ( 6.9%)  64 (27.7%)  52 (22.9%)  229 (11.0%)       
   - 3        8 ( 3.3%)    16 ( 1.2%)  39 (16.9%)  68 (30.0%)  131 ( 6.3%)       
 worday.week  5.4 ±  0.7   5.3 ±  0.6   5.5 ±  1.1  5.4 ±  1.1  5.3 ±  0.9  0.160
 worhou.week 45.0 ± 10.3  44.1 ±  8.0  46.6 ± 16.5 47.8 ± 15.8 45.0 ± 11.7  0.000
 higwag                                                                     0.000
   - 0       108 (43.9%)  515 (37.6%)  175 (75.8%) 172 (75.8%) 970 (46.8%)       
   - 1       138 (56.1%)  854 (62.4%)  56 (24.2%)  55 (24.2%)  1103 (53.2%)      
 male                                                                       0.000
   - 0        95 (38.6%)  446 (32.6%)  120 (51.9%) 108 (47.6%) 769 (37.1%)       
   - 1       151 (61.4%)  923 (67.4%)  111 (48.1%) 119 (52.4%) 1304 (62.9%)      
 youth                                                                      0.000
   - 0       171 (69.5%)  720 (52.6%)  153 (66.2%) 183 (80.6%) 1227 (59.2%)      
   - 1        75 (30.5%)  649 (47.4%)  78 (33.8%)  44 (19.4%)  846 (40.8%)       
 occu5                                                                      0.000
   - 1        41 (16.7%)  124 ( 9.1%)  58 (25.2%)  60 (26.5%)  283 (13.7%)       
   - 2        13 ( 5.3%)   63 ( 4.6%)  31 (13.5%)  41 (18.1%)  148 ( 7.2%)       
   - 3        59 (24.0%)  384 (28.1%)  35 (15.2%)  14 ( 6.2%)  492 (23.8%)       
   - 4        69 (28.0%)  368 (26.9%)  55 (23.9%)  79 (35.0%)  571 (27.6%)       
   - 5        64 (26.0%)  428 (31.3%)  51 (22.2%)  32 (14.2%)  575 (27.8%)       
 large_firm                                                                 0.000
   - 0       142 (71.4%)  733 (64.2%)  179 (89.1%) 177 (94.7%) 1231 (71.2%)      
   - 1        57 (28.6%)  409 (35.8%)  22 (10.9%)  10 ( 5.3%)  498 (28.8%)       
 socins                                                                     0.000
   - 0        27 (11.0%)  114 ( 8.3%)  181 (78.4%) 196 (86.3%) 518 (25.0%)       
   - 1       219 (89.0%)  1255 (91.7%) 50 (21.6%)  31 (13.7%)  1555 (75.0%)      
 partim                                                                     0.000
   - 0       234 (95.1%)  1338 (97.7%) 192 (83.1%) 195 (85.9%) 1959 (94.5%)      
   - 1        12 ( 4.9%)   31 ( 2.3%)  39 (16.9%)  32 (14.1%)  114 ( 5.5%)       
 fixter                                                                     0.000
   - 0       215 (87.4%)  1209 (88.3%) 144 (62.3%) 146 (64.3%) 1714 (82.7%)      
   - 1        31 (12.6%)  160 (11.7%)  87 (37.7%)  81 (35.7%)  359 (17.3%)       
 daiwor                                                                     0.000
   - 0       236 (95.9%)  1343 (98.1%) 188 (81.4%) 150 (66.1%) 1917 (92.5%)      
   - 1        10 ( 4.1%)   26 ( 1.9%)  43 (18.6%)  77 (33.9%)  156 ( 7.5%)       
 agewor                                                                     0.777
   - 0       236 (95.9%)  1320 (96.4%) 221 (95.7%) 221 (97.4%) 1998 (96.4%)      
   - 1        10 ( 4.1%)   49 ( 3.6%)  10 ( 4.3%)   6 ( 2.6%)   75 ( 3.6%)       
 domwor                                                                     0.000
   - 0       246 (100.0%) 1367 (99.9%) 223 (96.5%) 224 (98.7%) 2060 (99.4%)      
   - 1         0 ( 0.0%)   2 ( 0.1%)    8 ( 3.5%)   3 ( 1.3%)   13 ( 0.6%)       
 speemp                                                                     0.000
   - 0       244 (99.2%)  1359 (99.3%) 218 (94.4%) 215 (94.7%) 2036 (98.2%)      
   - 1        2 ( 0.8%)    10 ( 0.7%)  13 ( 5.6%)  12 ( 5.3%)   37 ( 1.8%)       
————————————————————————————————————————————————————————————————————————————————— 
Code
mytable_sub(class4
            ~ jobsat
            + empsta
            + houwag
            + worday.week
            + higwag
            + male
            + youth
            + occu5
            + large_firm
            + socins
            + partim
            + fixter
            + daiwor
            + agewor
            + domwor
            + speemp,
            klips$p26 %>% 
              left_join(model.fin$pprob, by = "pid"),
            max.ylev = 5,
            show.total = TRUE,
            use.column.label = F)

                                    Descriptive Statistics by 'class4'                                    
——————————————————————————————————————————————————————————————————————————————————————————————————————————— 
                     1                 2                 3                4               Total         p  
                  (N=246)          (N=1369)           (N=231)          (N=227)          (N=2073)     
——————————————————————————————————————————————————————————————————————————————————————————————————————————— 
 jobsat                                                                                               0.000
   - 0          122 (49.6%)       451 (32.9%)       118 (51.1%)      166 (73.1%)       857 (41.3%)         
   - 1          124 (50.4%)       918 (67.1%)       113 (48.9%)       61 (26.9%)      1216 (58.7%)         
 empsta                                                                                               0.000
   - 1          172 (69.9%)      1321 (96.5%)       177 (76.6%)       79 (34.8%)      1749 (84.4%)         
   - 2          43 (17.5%)        42 ( 3.1%)        43 (18.6%)        60 (26.4%)       188 ( 9.1%)         
   - 3          31 (12.6%)         6 ( 0.4%)        11 ( 4.8%)        88 (38.8%)       136 ( 6.6%)         
 houwag      21947.3 ± 11539.4 26663.1 ± 13277.6 18609.2 ± 15339.6 16116.8 ± 9441.1 20620.2 ± 11899.6 0.000
 worday.week     5.0 ±  0.8        5.0 ±  0.3        5.2 ±  0.6       4.7 ±  1.1        5.0 ±  0.6    0.000
 higwag                                                                                               0.000
   - 0          162 (65.9%)       688 (50.3%)       199 (86.1%)      196 (86.3%)      1245 (60.1%)         
   - 1          84 (34.1%)        681 (49.7%)       32 (13.9%)        31 (13.7%)       828 (39.9%)         
 male                                                                                                 0.000
   - 0          95 (38.6%)        446 (32.6%)       120 (51.9%)      108 (47.6%)       769 (37.1%)         
   - 1          151 (61.4%)       923 (67.4%)       111 (48.1%)      119 (52.4%)      1304 (62.9%)         
 youth                                                                                                0.001
   - 0         246 (100.0%)      1363 (99.6%)       225 (97.4%)      226 (99.6%)      2060 (99.4%)         
   - 1           0 ( 0.0%)         6 ( 0.4%)         6 ( 2.6%)        1 ( 0.4%)        13 ( 0.6%)          
 occu5                                                                                                0.000
   - 1          40 (16.3%)        108 ( 7.9%)       57 (24.9%)        47 (20.9%)       252 (12.2%)         
   - 2          33 (13.4%)        76 ( 5.6%)        30 (13.1%)        56 (24.9%)       195 ( 9.4%)         
   - 3          46 (18.7%)        381 (27.9%)       34 (14.8%)        13 ( 5.8%)       474 (22.9%)         
   - 4          61 (24.8%)        353 (25.8%)       59 (25.8%)        76 (33.8%)       549 (26.5%)         
   - 5          66 (26.8%)        450 (32.9%)       49 (21.4%)        33 (14.7%)       598 (28.9%)         
 large_firm                                                                                           0.000
   - 0          156 (88.1%)       734 (68.2%)       174 (92.1%)      163 (94.8%)      1227 (76.0%)         
   - 1          21 (11.9%)        342 (31.8%)       15 ( 7.9%)        9 ( 5.2%)        387 (24.0%)         
 socins                                                                                               0.000
   - 0          138 (56.1%)       26 ( 1.9%)        31 (13.4%)       199 (87.7%)       394 (19.0%)         
   - 1          108 (43.9%)      1343 (98.1%)       200 (86.6%)       28 (12.3%)      1679 (81.0%)         
 partim                                                                                               0.000
   - 0          200 (81.3%)      1346 (98.3%)       210 (90.9%)      171 (75.3%)      1927 (93.0%)         
   - 1          46 (18.7%)        23 ( 1.7%)        21 ( 9.1%)        56 (24.7%)       146 ( 7.0%)         
 fixter                                                                                               0.000
   - 0          155 (63.0%)      1260 (92.0%)       162 (70.1%)       94 (41.4%)      1671 (80.6%)         
   - 1          91 (37.0%)        109 ( 8.0%)       69 (29.9%)       133 (58.6%)       402 (19.4%)         
 daiwor                                                                                               0.000
   - 0          210 (85.4%)      1359 (99.3%)       217 (93.9%)      131 (57.7%)      1917 (92.5%)         
   - 1          36 (14.6%)        10 ( 0.7%)        14 ( 6.1%)        96 (42.3%)       156 ( 7.5%)         
 agewor                                                                                               0.000
   - 0          234 (95.1%)      1340 (97.9%)       220 (95.2%)      208 (91.6%)      2002 (96.6%)         
   - 1          12 ( 4.9%)        29 ( 2.1%)        11 ( 4.8%)        19 ( 8.4%)       71 ( 3.4%)          
 domwor                                                                                               0.051
   - 0          243 (98.8%)      1366 (99.8%)      231 (100.0%)      225 (99.1%)      2065 (99.6%)         
   - 1           3 ( 1.2%)         3 ( 0.2%)         0 ( 0.0%)        2 ( 0.9%)         8 ( 0.4%)          
 speemp                                                                                               0.000
   - 0          242 (98.4%)      1367 (99.9%)       226 (97.8%)      214 (94.3%)      2049 (98.8%)         
   - 1           4 ( 1.6%)         2 ( 0.1%)         5 ( 2.2%)        13 ( 5.7%)       24 ( 1.2%)          
——————————————————————————————————————————————————————————————————————————————————————————————————————————— 
Code
df.fin %>% 
  filter(class4 == 4) %>% 
  select(pid, year, socins) %>% 
  arrange(pid) %>% 
  spread(year, socins) %>% 
  print(n = 200)
# A tibble: 227 × 16
         pid   `1`   `2`   `3`   `4`   `5`   `6`   `7`   `8`   `9`  `10`  `11`
       <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
  1     2001     0     0     0     0     0    NA     0     1     0     0    NA
  2     3302     0     0     0     1     0     0     0     0     1     0     1
  3     4101     0     0     0     0     0     0     0     0     0     0     0
  4     8701     1    NA    NA    NA     0     0     0     0     0     0     0
  5     9303     0     0     0     0     0     0     0     0     0     0     0
  6    10803     0     0    NA     0     0     0     0     0     0     1     0
  7    15703     0     0     1     0     0     0     0     0     0     0     0
  8    19401     0     0     0     0     0     0     0     0     0     0     0
  9    21603     0     1     0     0    NA    NA     0     0     0     0    NA
 10    24402     0    NA    NA    NA    NA    NA    NA    NA    NA     0     0
 11    26102     0     0     0     0    NA    NA     0    NA     0    NA    NA
 12    27201     1     1     1    NA    NA     0     0     0     0     0     0
 13    27202     0     0     0    NA    NA    NA    NA    NA    NA    NA    NA
 14    27704     1    NA     0     0    NA    NA    NA     0    NA     0     1
 15    29202     0     0     0    NA    NA     0     0     0     0     0     0
 16    29704     0     0    NA    NA     0     1     1     1     1     0     0
 17    30901     0     0     0     0    NA     0     0     0     0     0     0
 18    31002     0     1    NA    NA     0     0     0     0     0     0     0
 19    31902     0     0     0     0     0     0     0     0     0     0     0
 20    32402     0     0     0     0     0     0     0     0     0     0     0
 21    32803     0     0     0     0     0     0    NA    NA    NA    NA    NA
 22    33105     0     1     1     0     0     0     0     0     0     0     0
 23    33201     0     0     0     0    NA    NA    NA    NA    NA    NA     0
 24    33701     0     0     0     0     0     0     0     0     0     0     0
 25    35201     0     0     0     0     0     0     0     0     0     0     0
 26    39302     0     0     0    NA    NA    NA    NA    NA    NA    NA    NA
 27    41801     0     0     0     0     0     0     0     0     0     0     0
 28    50102     0     0     0     0    NA    NA     0     0    NA     1     1
 29    51502     0     0     0     0     0     0    NA    NA    NA    NA    NA
 30    55002     0     0     0     0     0     0     0     0     0     0     0
 31    55103     0     0     0     0     0     0    NA    NA     0     1     0
 32    58402     0     0     0     0     0     0     0     0     1     1     1
 33    58404     1     0     0     0     0    NA    NA    NA    NA    NA    NA
 34    64103     0     0     0     0     0     0     0     0     0     0     0
 35    64104     0     0     0     0     0     0     0     0     0     0     0
 36    69301     0     0     0     0     0     0     0     0     0     0     0
 37    73902     0     0     0     0     0     0     1     0     1     1     1
 38    74702     0     0     0     0     0     1     1     1     1     0     0
 39    82903     1     1     1     0     0    NA     0     0     0     0     0
 40    88602     0     0     0     0     0     0     0     0     0     0     0
 41    90501     1     0     0     0     0     0     0     0     0     1     0
 42    91402     0     0     0     0     0     0     0     0     0     0     0
 43    91404     0    NA     0    NA    NA     0     0     0     0     0     0
 44    92302     0     0     0     0     0     0     0     0     0     0     0
 45    97801     0     0     0     0     0     0     0     0     0     0     0
 46   103603     0     0     1    NA     1    NA     0    NA     0     0     0
 47   106104     1     1     0    NA     0     0     0     0     0     0     0
 48   108301     0     0     0     0     0     0     0     0     0     0     0
 49   108801     0     0     0     0     0     0     0     0     0     0     0
 50   109201     0     0     0     0     0     0     0     0     0     0     0
 51   110803     0    NA     1    NA    NA    NA    NA    NA     0     0     0
 52   110902     1     0     0     0     0     0     0     0     0     0     0
 53   121902     0     0    NA     0     0    NA     1    NA    NA     0    NA
 54   125702     0    NA     0     1     0     0     0     0     0     0     1
 55   127701     1     0     0     0     0     0    NA    NA     0     1    NA
 56   130703     0     0     0     0     0     0     0     0     0     0     0
 57   145102     0     0     0     0    NA    NA    NA    NA    NA    NA     0
 58   147802     0     0     0     0     0     0     0     0     0     0     0
 59   149203     0     0     0     0     0     1     1     1     0     0     0
 60   156404     0     0     0     0     0     0     0     0     0     0     1
 61   156407     0     0     0     0     0     0     0     0     0     0     0
 62   157005     0     0     0     0     0     0     0     0     0     0     0
 63   158002     0     0     0     0     0     0     0     0     0     0     0
 64   158101     0     0     0     0     0     0     0     0     0     0     0
 65   160506     0     0     1     1     0     0     0     0     0     0    NA
 66   166002     0    NA    NA    NA    NA    NA    NA    NA    NA    NA     0
 67   168701     0    NA     0     1     0     0     0     0     0     0     1
 68   168801     0     0     0     0     0     0     0     0     0     0     0
 69   168802     0    NA    NA    NA     0     0     0    NA    NA    NA    NA
 70   169101     0     0     0     0     0     0     0     0     0     0     0
 71   170301     0     0     0     0     0     0     0     0     0     0     0
 72   175801     0     0     0     0     0     0     0     0     0     0     0
 73   180701     0     0     0    NA     0     0     0     0     0     0     0
 74   181002     0     0     0     0    NA     0     0     0     0    NA    NA
 75   182101     0     0    NA    NA     1     0    NA     0     0     0     0
 76   182102     0     0    NA     0     1     1     0    NA     0     0     0
 77   182603     0     0     0    NA    NA    NA     0     0     0     0     0
 78   190601     0     0     0     0     1     0     1    NA     0     0     0
 79   193301     0     0     0     0     0     0     0     0     0     0     0
 80   207601     0     0     0     0     0     0     0     0     0     1     0
 81   212802     0     0     1     1     0     0     0     0     0     0     0
 82   217901     0     0     0     0     0     0     0     0     0     0     0
 83   218106     0    NA     1     1     0     0     0     0    NA    NA     0
 84   222102     0     0     0     0     0     0    NA     0     0     0     0
 85   230805     0     0     0     1     0    NA    NA    NA    NA    NA    NA
 86   230904     0    NA    NA    NA     1     1     1     0     0    NA    NA
 87   234403     0     0     0     0     0     0     0     0     0     0     0
 88   236503     0     0     0    NA     0     0     1     1     0     0     0
 89   239003     0    NA    NA     0    NA     0     0     0     0     0     0
 90   239103     1     0     1     0     0     0     0     0     0     0     0
 91   241503     0     0     0     0     0     0     0     0     0     0     0
 92   243901     1     0     0     0     0     0     0     0     0     0     0
 93   244605     1     1    NA    NA     0    NA     0     0     0     0     0
 94   245002     0    NA     0     0     0     0     0     0     0     0     0
 95   245003     0     0     0     0     0     0     0     0     0     0     0
 96   245403     1     0     1     0     0     0    NA    NA    NA    NA    NA
 97   245803     1     1     1     1     0     0     0     0     0     0     0
 98   248901     0     0     0     0     0     0     0     0     0     0     0
 99   249901     0     1     0     0    NA    NA    NA    NA    NA    NA    NA
100   253102     0     0     0     0    NA     0     0     0     0     0     0
101   253904     0     0     0    NA     0     0    NA     0     0     0     0
102   256002     0     0     0     0     0     0     0     0     0     1     0
103   256805     0     0    NA    NA    NA    NA    NA     0     0     0     0
104   258301     0    NA     0     0     0     0     0     0     0     0     0
105   259601     0     0     0     0     0     0     0     0     0     0     0
106   259802     0    NA     0     0     0     0     0     0     0     0     0
107   264401     0     0     0     0     0     0     0     0     0    NA     0
108   264603     1     0    NA     0     0     0     0     0     0     0     0
109   264701     0     0     0     1     1     0     0     1    NA     0     0
110   272102     0    NA     0     0     0    NA    NA     0     1     0     0
111   277902     0     0     0     0    NA    NA    NA     0     0     0     0
112   278102     0     0     0     0     0     0     0    NA     0     1    NA
113   286602     0     0     0     0     0     0     0     0     0     1     0
114   291905     0     0     1    NA     0     1     0     0     0     0     0
115   298601     0     0     0     0     0     0     0     0     0    NA    NA
116   300101     0    NA    NA     0    NA    NA    NA    NA    NA    NA     0
117   301501     0     0     0     1     0     0     0     0     0     0     0
118   302402     0     0     0     0     0     0     0     0     0     0     0
119   304302     0     0     0     0     0     0     0    NA     0     0     0
120   304306     0    NA     0     0     0     0     0     0     0     0     0
121   305201     0     0     0     0     0     0     0     0     0     0     0
122   305504     0    NA    NA     0    NA    NA    NA    NA    NA    NA    NA
123   305703     1     1     0     1     0     0     0     0     0    NA     0
124   306904     0     0     0     0     0     0     0     1     0     0     0
125   312701     0     0     0     1     0     0     0     0     0     0     0
126   313403     0     0     0     0     0    NA    NA     0     0     0     0
127   322804     0     0     0     0     0     0     0     0     0     0     1
128   329202     0     0     0     0     0     0     0     0     0     0     0
129   336203     0     0     0     0     0     0     1     0     0     0     0
130   339103     0     0     0     0     0     0     0     0     1     1     0
131   342203     0     1    NA    NA    NA    NA    NA    NA    NA    NA    NA
132   342504     0     0    NA    NA     1    NA    NA    NA    NA    NA     0
133   347102     0     0     0     0    NA    NA    NA     0     1     0     0
134   348303     0     0    NA    NA     0     0     0     0     0     0     0
135   349901     1     0     0     0     0     0     0     1     0     0     0
136   350404     0     0    NA     0     0     0     0    NA     0     0     0
137   353903     0    NA     0    NA    NA    NA    NA    NA    NA     0     0
138   358803     1    NA     0     0     1     0     0     1     1     0     0
139   363802     0     1     1     0     0     0     0     0     1     0     0
140   369001     0     0     0     0     0     0     1    NA    NA    NA     0
141   378202     0     0     0     0     0     0     0     0     1     0     0
142   382501     0     0     0     0     0     0     0    NA     0     0    NA
143   386603     0     0     0    NA    NA     0     0     0     0     0     0
144   387003     0     0     0     0     0     0     0     0     0     0     0
145   391204     0     0     0     0     0     0     0    NA     0    NA    NA
146   391801     0     0     0     0     0     0     0     0    NA    NA    NA
147   394102     0     0     0     0     0    NA     0     0     0     0     0
148   394701     1     0     0     0     0     0     0     0     0     0     0
149   396401     0     1     0     1     1     1     0     0     0     0    NA
150   400002     0     0     0     0     0     0     0     0     0     0     0
151   403001     0     0     0     0    NA     0     0     0    NA    NA     0
152   407003     0    NA     0     0     0     0     0    NA    NA    NA    NA
153   409003     0     0     0     0     0     0    NA    NA    NA     1     1
154   416801     0     0     0     0     0     0     0     0     0     0     0
155   420103     0    NA    NA     0     0    NA     0     0     0     0     1
156   422202     0     0     0     0     0     0     0     0     0     0     0
157   422302     0     0     0     0    NA    NA     0     0     0     1     0
158   425401     0     0     0    NA    NA     0     0     0     1     1     0
159   426201     1     0     0     0     0     0     0     0     0     0     0
160   430902     0     0     0     0     0     0     0     0     0     0     0
161   437901     0     0     0     0     0     0     0     0     0     0     0
162   443004     1     0     0     0     0     0     0     0     0     0     0
163   444202     0     0     0     0     0     0     0     0     0     0     0
164   445403     0    NA     0     0    NA    NA    NA    NA    NA    NA    NA
165   446101     0     0     0     0     0     0     0     0     0     0     0
166   450002     0     0    NA    NA    NA    NA    NA    NA    NA    NA    NA
167   450404     0    NA    NA    NA    NA    NA    NA    NA    NA    NA    NA
168   459001     1    NA    NA    NA     0     0    NA    NA     0     0     0
169   459002     1     1     0     0     0     0     0     0     1     1     1
170   462204     0     0     0     0     0     0     0    NA     0     0     0
171   472001     0     0     0     0     0     0     0     0     0     0     0
172   477902     0     0     1    NA    NA    NA    NA    NA     0    NA    NA
173   483201     0     0     0     0     0     0     0     0     0     0     0
174   483405     1     0     0     0     0     0    NA     0     0     0     1
175   484603     1     1     1    NA     0     0     0     0     0     0     0
176   490101     0     0     0     1     0     0     0     0     0     0     0
177   490102     1     1    NA    NA    NA    NA     0     0     0     0     0
178   490303     0     0     0     0     0     0     0     0     0     0    NA
179   527402     1     1     0    NA    NA    NA    NA     0     0     0     0
180   556101     0     0     0     0     0     0     0     0     0     0     0
181   557802     0     0     0     0     0     0     1     0     0     1     1
182   627602     0    NA     0     0     0     0     0     1     0    NA    NA
183   694902     0     0     1     0     0     0     0     0     0     0     0
184   716202     1    NA    NA    NA    NA    NA    NA    NA    NA    NA     0
185   724802     1     1     1     1    NA     0     0     0     0     0     0
186   745002     0     0    NA    NA    NA    NA     1     1    NA     0     0
187 10005101     0     0     0    NA    NA    NA    NA    NA    NA     0     0
188 10005302     0     0     0     0     0     0     0     0     0    NA     1
189 10013201     0     0     0     0     0     0     1     1     1     0     0
190 10015901     0     0     0     0     1     0     0     0     0     0     0
191 10017301     0     0     0     0     0     0     0     0     0     0     0
192 10018702     0     0     0     0     0     0     0     0     0     0     0
193 10024201     0     1    NA     0     0     0     0     0    NA     0     0
194 10029402     0     0     0     0     0     0     0     0    NA     0     0
195 10030101     0     0     0     0     0     0     0     0     0     0     0
196 10031701     0     1     0     0     0     0     0     0     0     0     0
197 10033101     0     0     0     0     0    NA    NA     0     0     0     0
198 10040402     1    NA     0     0     0     0     0     0     0     0     0
199 10042702     0    NA    NA    NA    NA    NA     0     0     0     0     0
200 10043601     0     0     0     0    NA    NA     0     0     0     0     0
# ℹ 27 more rows
# ℹ 4 more variables: `12` <dbl>, `13` <dbl>, `14` <dbl>, `15` <dbl>
ggplot(data = df.fin %>% filter(year == 15), aes(x = age)) +
  facet_grid(~ class4) +
  geom_histogram()
`stat_bin()` using `bins = 30`. Pick better value with `binwidth`.