The gender wage gap in Krabbistan, 1995–2015

A synthetic three-census study inspired by Blau and Kahn (2017)

Overview

Krabbistan is a fictional small nation whose labor market is calibrated to be broadly reminiscent of the United States. This report creates census microdata for 1995, 2005, and 2015 and studies the gender wage gap using annual own earnings divided by annual hours.

The analysis follows the organizing logic of Blau and Kahn (2017):

1. raw mean log-wage gaps overall and by parenthood × marriage;
2. male-reference Oaxaca–Blinder decompositions of the mean gap, paralleling their Table 4;
3. decompositions of changes from 1995 into changing characteristics, changing male prices, and changing unexplained gaps, paralleling their Table 5; and
4. RIF-regression decompositions at the 10th, 50th, and 90th unconditional percentiles, as a rough analogue to their Table 6.

The data are entirely synthetic and the results are pedagogical, not estimates for any real country. The executable analysis is in R because this rendering environment does not contain a licensed Stata runtime. A complete Stata translation is supplied alongside the report.

set.seed(42042017)

library(dplyr)
library(tidyr)
library(ggplot2)
library(knitr)
library(scales)

dir.create("data", showWarnings = FALSE)
dir.create("results", showWarnings = FALSE)
dir.create("figures", showWarnings = FALSE)

years <- c(1995L, 2005L, 2015L)
n_per_wave <- 20000L
beta_groups <- list(
  Education = paste0("educ_", 2:5),
  Experience = c("experience", "experience_sq"),
  Industry = paste0("industry_", 2:8),
  Occupation = paste0("occupation_", 2:8)
)

theme_set(theme_minimal(base_size = 12.5))
gap_colors <- c(
  Education = "#56B4E9", Experience = "#E69F00",
  Industry = "#009E73", Occupation = "#CC79A7",
  Unexplained = "#777777"
)

Simulating the census

Each wave contains 20,000 residents ages 18–64. Education rises over time, women overtake men educationally, occupational and industrial segregation decline, women’s labor-force participation rises, and the direct wage penalty attached to being female shrinks. Parenthood and marriage retain additional female wage penalties, although both also diminish.

softmax_draw <- function(scores) {
  scores <- scores - apply(scores, 1, max)
  probabilities <- exp(scores)
  probabilities <- probabilities / rowSums(probabilities)
  cumulative <- t(apply(probabilities, 1, cumsum))
  u <- runif(nrow(scores))
  1L + rowSums(u > cumulative)
}

simulate_wave <- function(year, n = n_per_wave) {
  t <- (year - 1995) / 10
  sex <- rbinom(n, 1, 0.505) # 0 male, 1 female

  # Triangular working-age distribution with an early male age advantage.
  age <- round(runif(n, 18, 65) + (1 - sex) * (3.5 - 1.2 * t))
  age <- pmin(64L, pmax(18L, age))

  educ_latent <- rnorm(n) + 0.16 * t + sex * (-0.06 + 0.03 * t) +
    0.010 * (age - 40)
  education <- as.integer(cut(
    educ_latent,
    breaks = c(-Inf, -0.90, -0.20, 0.50, 1.18, Inf),
    labels = FALSE
  ))
  education_years <- c(10, 12, 14, 16, 18)[education]
  college <- education >= 4

  p_married <- plogis(
    -0.15 + 0.105 * (age - 25) - 0.0031 * (age - 41)^2 +
      0.17 * college - 0.10 * sex - 0.10 * t
  )
  married <- rbinom(n, 1, p_married)
  p_former <- plogis(-2.2 + 0.075 * (age - 30) + 0.12 * sex)
  formerly <- married == 0 & runif(n) < p_former
  marital_status <- ifelse(married == 1, 2L, ifelse(formerly, 3L, 1L))

  p_parent <- plogis(
    -1.15 + 0.115 * (age - 24) - 0.0030 * (age - 39)^2 +
      0.72 * married - 0.13 * (education - 3) - 0.05 * t
  )
  parent <- rbinom(n, 1, p_parent)

  edc <- education - 3
  occ_scores <- cbind(
    -0.55 + 0.48 * edc - sex * (0.72 - 0.20 * t),
    -0.75 + 0.70 * edc + sex * (0.12 + 0.03 * t),
    0.05 + 0.18 * edc + sex * (0.45 - 0.09 * t),
    -0.15 + 0.05 * edc + sex * 0.10,
    -0.05 - 0.18 * edc + sex * (0.53 - 0.07 * t),
    -0.35 - 0.08 * edc - sex * (1.15 - 0.16 * t),
    -0.25 - 0.16 * edc - sex * (0.76 - 0.11 * t),
    -0.45 - 0.24 * edc - sex * (0.28 - 0.04 * t)
  )
  occupation <- softmax_draw(occ_scores)

  professional <- occupation %in% 1:3
  manual <- occupation %in% 6:8
  ind_scores <- cbind(
    -0.15 + 0.35 * edc + 0.25 * professional - sex * (0.25 - 0.06 * t),
    -0.10 + 0.22 * edc + sex * (0.65 - 0.08 * t),
    0.05 - 0.12 * edc + 0.38 * manual - sex * (0.50 - 0.08 * t),
    -0.55 - 0.08 * edc + 0.70 * (occupation == 6) - sex * (1.20 - 0.16 * t),
    0.05 - 0.05 * edc + 0.18 * (occupation %in% 3:5),
    -0.35 + 0.42 * edc + 0.20 * professional - sex * (0.18 - 0.04 * t),
    -0.50 + 0.20 * edc + sex * 0.02,
    -0.20 - 0.10 * edc + 0.20 * (occupation == 5) + sex * 0.18
  )
  industry <- softmax_draw(ind_scores)

  potential_experience <- pmax(age - education_years - 6, 0)
  career_break <- sex * parent * (4.0 - 0.75 * t) +
    sex * married * (0.8 - 0.15 * t)
  effective_experience <- pmax(potential_experience - career_break, 0)

  p_lfp <- plogis(
    1.50 - 0.72 * sex + 0.22 * sex * t - 0.48 * sex * parent -
      0.14 * sex * married + 0.16 * (education - 3) -
      0.0035 * (age - 42)^2
  )
  lfp <- rbinom(n, 1, p_lfp)

  educ_premium <- c(0.00, 0.10, 0.22, 0.48, 0.72)[education]
  occ_premium <- c(0.45, 0.38, 0.15, 0.05, -0.15, 0.08, -0.05, -0.15)[occupation]
  ind_premium <- c(0.25, 0.05, 0.12, 0.15, -0.05, 0.30, 0.05, -0.10)[industry]
  base_penalty <- c(`1995` = 0.100, `2005` = 0.090, `2015` = 0.080)[as.character(year)]
  parent_penalty <- c(`1995` = 0.040, `2005` = 0.040, `2015` = 0.040)[as.character(year)]
  married_penalty <- c(`1995` = 0.020, `2005` = 0.020, `2015` = 0.015)[as.character(year)]

  log_hourly_wage <- 2.42 + educ_premium +
    0.045 * effective_experience - 0.00055 * effective_experience^2 +
    occ_premium + ind_premium -
    sex * (base_penalty + parent_penalty * parent + married_penalty * married) +
    rnorm(n, 0, 0.32)
  hourly_wage <- pmin(250, pmax(4, exp(log_hourly_wage)))

  log_hours <- log(1930) - 0.10 * sex - 0.13 * sex * parent -
    0.04 * sex * married + 0.035 * t * sex + rnorm(n, 0, 0.20)
  annual_hours <- ifelse(lfp == 1, round(pmin(3200, pmax(250, exp(log_hours)))), 0)
  own_earnings <- ifelse(lfp == 1, round(hourly_wage * annual_hours), 0)

  data.frame(
    year = as.integer(year), sex = as.integer(sex),
    own_earnings, age = as.integer(age),
    marital_status = as.integer(marital_status), married = as.integer(married),
    parent = as.integer(parent), education = as.integer(education),
    education_years = as.integer(education_years),
    annual_hours = as.integer(annual_hours), lfp = as.integer(lfp),
    industry = as.integer(industry), occupation = as.integer(occupation)
  )
}

census <- bind_rows(lapply(years, simulate_wave))

# R-native and portable CSV files are generated directly. Stata-format files
# are written with the recommended foreign package included with R.
saveRDS(census, "data/krabbistan_census_1995_2015.rds")
write.csv(census, "data/krabbistan_census_1995_2015.csv", row.names = FALSE)
foreign::write.dta(census, "data/krabbistan_census_1995_2015.dta", version = 12)
for (yr in years) {
  wave <- census[census$year == yr, ]
  saveRDS(wave, sprintf("data/krabbistan_census_%s.rds", yr))
  foreign::write.dta(wave, sprintf("data/krabbistan_census_%s.dta", yr), version = 12)
}

coverage <- census |>
  group_by(year) |>
  summarise(
    observations = n(), female_share = mean(sex),
    labor_force_participation = mean(lfp),
    mean_age = mean(age), .groups = "drop"
  )
kable(coverage, digits = 3, caption = "Synthetic census coverage")

Synthetic census coverage

year	observations	female_share	labor_force_participation	mean_age
1995	20000	0.508	0.554	43.187
2005	20000	0.503	0.592	42.710
2015	20000	0.506	0.621	42.041

The public-use files retain all residents, including zero earnings and zero hours for nonparticipants. The wage analysis restricts attention to ages 25–64, positive earnings, labor-force participation, and at least 500 annual hours. Hourly wages are calculated—not simulated as a released variable—as own earnings divided by annual hours.

work <- census |>
  filter(age >= 25, age <= 64, lfp == 1, own_earnings > 0, annual_hours >= 500) |>
  mutate(
    hourly_wage = own_earnings / annual_hours,
    log_wage = log(hourly_wage),
    experience = pmax(age - education_years - 6, 0),
    experience_sq = experience^2
  )

for (j in 2:5) work[[paste0("educ_", j)]] <- as.numeric(work$education == j)
for (j in 2:8) {
  work[[paste0("industry_", j)]] <- as.numeric(work$industry == j)
  work[[paste0("occupation_", j)]] <- as.numeric(work$occupation == j)
}
saveRDS(work, "data/krabbistan_wage_analysis.rds")
foreign::write.dta(work, "data/krabbistan_wage_analysis.dta", version = 12)

Raw wage gaps

raw_sex <- work |>
  group_by(year, sex) |>
  summarise(mean_log_wage = mean(log_wage), N = n(), .groups = "drop") |>
  pivot_wider(names_from = sex, values_from = c(mean_log_wage, N), names_prefix = "sex_")

raw_overall <- raw_sex |>
  transmute(
    year,
    male_mean_log_wage = mean_log_wage_sex_0,
    female_mean_log_wage = mean_log_wage_sex_1,
    male_minus_female_log_gap = mean_log_wage_sex_0 - mean_log_wage_sex_1,
    female_male_wage_ratio = exp(mean_log_wage_sex_1 - mean_log_wage_sex_0),
    N = N_sex_0 + N_sex_1
  )

family_sex <- work |>
  group_by(year, parent, married, sex) |>
  summarise(mean_log_wage = mean(log_wage), N = n(), .groups = "drop") |>
  pivot_wider(names_from = sex, values_from = c(mean_log_wage, N), names_prefix = "sex_")

raw_family <- family_sex |>
  mutate(
    family_group = paste0(
      ifelse(parent == 1, "Parent", "Not parent"), " × ",
      ifelse(married == 1, "married", "not married")
    ),
    male_minus_female_log_gap = mean_log_wage_sex_0 - mean_log_wage_sex_1,
    female_male_wage_ratio = exp(mean_log_wage_sex_1 - mean_log_wage_sex_0),
    N = N_sex_0 + N_sex_1
  ) |>
  select(year, parent, married, family_group, male_minus_female_log_gap,
         female_male_wage_ratio, N)

write.csv(raw_overall, "results/raw_gap_overall.csv", row.names = FALSE)
write.csv(raw_family, "results/raw_gap_family.csv", row.names = FALSE)

kable(
  raw_overall |>
    mutate(
      male_minus_female_log_gap = round(male_minus_female_log_gap, 3),
      female_male_wage_ratio = percent(female_male_wage_ratio, accuracy = 0.1)
    ),
  caption = "Raw mean wage gaps"
)

Raw mean wage gaps

year	male_mean_log_wage	female_mean_log_wage	male_minus_female_log_gap	female_male_wage_ratio	N
1995	3.591023	3.288464	0.303	73.9%	10335
2005	3.618790	3.367863	0.251	77.8%	10883
2015	3.671828	3.476444	0.195	82.3%	11252

p_raw <- ggplot(raw_overall, aes(year, male_minus_female_log_gap)) +
  geom_line(linewidth = 1.1, color = "#0072B2") +
  geom_point(size = 3, color = "#0072B2") +
  scale_x_continuous(breaks = years) +
  labs(
    title = "Krabbistan's raw gender wage gap narrows",
    x = "Census year", y = "Male − female mean log wage gap"
  )
p_raw

ggsave("figures/raw_gap_over_time.png", p_raw, width = 7.5, height = 4.5, dpi = 180)

family_display <- raw_family |>
  transmute(
    year, `Family category` = family_group,
    `Log gap` = round(male_minus_female_log_gap, 3),
    `Female/male wage ratio` = percent(female_male_wage_ratio, accuracy = 0.1),
    N
  )
kable(family_display, caption = "Raw wage gaps by parenthood × marriage")

Raw wage gaps by parenthood × marriage

year	Family category	Log gap	Female/male wage ratio	N
1995	Not parent × not married	0.171	84.3%	1235
1995	Not parent × married	0.214	80.8%	1512
1995	Parent × not married	0.309	73.4%	1164
1995	Parent × married	0.328	72.0%	6424
2005	Not parent × not married	0.098	90.6%	1455
2005	Not parent × married	0.171	84.3%	1675
2005	Parent × not married	0.260	77.1%	1224
2005	Parent × married	0.281	75.5%	6529
2015	Not parent × not married	0.108	89.7%	1639
2015	Not parent × married	0.157	85.4%	1804
2015	Parent × not married	0.269	76.4%	1272
2015	Parent × married	0.189	82.8%	6537

p_family <- ggplot(
  raw_family,
  aes(year, male_minus_female_log_gap, color = family_group)
) +
  geom_line(linewidth = 1) +
  geom_point(size = 2.6) +
  scale_x_continuous(breaks = years) +
  scale_color_brewer(palette = "Dark2") +
  labs(
    title = "Raw gaps by parenthood and marriage",
    x = "Census year", y = "Male − female mean log wage gap", color = NULL
  ) +
  theme(legend.position = "bottom")
p_family

ggsave("figures/raw_gap_family_groups.png", p_family, width = 8.5, height = 5, dpi = 180)

Parents generally have larger gaps because family status affects women’s wage offers, labor-force attachment, and hours more strongly. Married parents have the largest gap in the first two waves; by 2015, compositional selection among working married mothers changes the ordering. The persistent parenthood gradient is more robust than the marriage gradient.

Mean Oaxaca–Blinder decomposition

For year \(t\), let \(\Delta\bar X_t=\bar X_{mt}-\bar X_{ft}\) and let \(\widehat\beta_{mt}\) be the male wage coefficients. The male-reference decomposition is

\[ G_t = \Delta\bar X_t'\widehat\beta_{mt} + U_t, \]

where \(G_t\) is the male–female mean log-wage gap. Group contributions sum the relevant dummy or polynomial terms. The experience proxy is potential experience, \(\max(\text{age}-\text{schooling}-6,0)\), and its square.

design_matrix <- function(frame, specification) {
  groups <- c("Education", "Experience")
  if (specification == "Full") groups <- c(groups, "Industry", "Occupation")
  variables <- unlist(beta_groups[groups], use.names = FALSE)
  X <- cbind(`_cons` = 1, as.matrix(frame[, variables, drop = FALSE]))
  list(X = X, variables = c("_cons", variables), groups = groups)
}

fit_ols <- function(y, X) as.numeric(qr.solve(X, y))

table4_rows <- list()
ob_store <- list()
row_id <- 1L

for (yr in years) {
  wave <- work[work$year == yr, ]
  men <- wave[wave$sex == 0, ]
  women <- wave[wave$sex == 1, ]
  gap <- mean(men$log_wage) - mean(women$log_wage)

  for (specification in c("Human capital", "Full")) {
    spec_key <- ifelse(specification == "Full", "Full", "HC")
    dm <- design_matrix(men, spec_key)
    df <- design_matrix(women, spec_key)
    beta <- fit_ols(men$log_wage, dm$X)
    names(beta) <- dm$variables
    dx <- colMeans(dm$X) - colMeans(df$X)
    contributions <- sapply(dm$groups, function(group) {
      variables <- beta_groups[[group]]
      sum(dx[variables] * beta[variables])
    })
    explained <- sum(contributions)
    unexplained <- gap - explained
    ob_store[[paste(yr, specification, sep = "::")]] <- list(
      beta = beta, dx = dx, gap = gap,
      explained = explained, unexplained = unexplained
    )
    values <- c(
      contributions,
      `Total explained` = explained,
      Unexplained = unexplained,
      `Total gap` = gap
    )
    table4_rows[[row_id]] <- data.frame(
      year = yr, specification, component = names(values),
      log_points = as.numeric(values),
      percent_of_gap = 100 * as.numeric(values) / gap
    )
    row_id <- row_id + 1L
  }
}

table4 <- bind_rows(table4_rows)
write.csv(table4, "results/table4_ob_decomposition.csv", row.names = FALSE)

Table 4 analogue

table4_display <- table4 |>
  mutate(entry = sprintf("%.3f (%.1f%%)", log_points, percent_of_gap)) |>
  select(specification, component, year, entry) |>
  pivot_wider(names_from = year, values_from = entry)
kable(
  table4_display,
  caption = "Male-reference decomposition: log points (percent of total gap)"
)

Male-reference decomposition: log points (percent of total gap)

specification	component	1995	2005	2015
Human capital	Education	0.018 (6.1%)	-0.004 (-1.8%)	-0.004 (-2.0%)
Human capital	Experience	0.028 (9.2%)	0.027 (10.6%)	0.016 (8.0%)
Human capital	Total explained	0.046 (15.3%)	0.022 (8.9%)	0.012 (5.9%)
Human capital	Unexplained	0.256 (84.7%)	0.229 (91.1%)	0.184 (94.1%)
Human capital	Total gap	0.303 (100.0%)	0.251 (100.0%)	0.195 (100.0%)
Full	Education	0.014 (4.5%)	-0.003 (-1.3%)	-0.003 (-1.5%)
Full	Experience	0.028 (9.2%)	0.027 (10.7%)	0.016 (8.1%)
Full	Industry	0.026 (8.7%)	0.020 (7.9%)	0.018 (9.0%)
Full	Occupation	0.025 (8.2%)	0.019 (7.7%)	0.014 (7.4%)
Full	Total explained	0.092 (30.5%)	0.063 (24.9%)	0.045 (23.1%)
Full	Unexplained	0.210 (69.5%)	0.188 (75.1%)	0.150 (76.9%)
Full	Total gap	0.303 (100.0%)	0.251 (100.0%)	0.195 (100.0%)

table4_plot_data <- table4 |>
  filter(
    specification == "Full",
    component %in% c("Education", "Experience", "Industry", "Occupation", "Unexplained")
  ) |>
  mutate(component = factor(
    component,
    levels = c("Education", "Experience", "Industry", "Occupation", "Unexplained")
  ))

p_table4 <- ggplot(table4_plot_data, aes(factor(year), log_points, fill = component)) +
  geom_col(width = 0.68) +
  scale_fill_manual(values = gap_colors) +
  labs(
    title = "Full Oaxaca–Blinder decomposition of the mean gap",
    x = "Census year", y = "Log points", fill = NULL
  ) +
  theme(legend.position = "bottom")
p_table4

ggsave("figures/table4_stacked_components.png", p_table4, width = 8.5, height = 5, dpi = 180)

Education contributes little because women’s schooling catches up and then surpasses men’s. Industry and especially occupation remain important sorting margins. The unexplained component includes the simulated direct gender penalty, unmeasured career interruptions, family penalties, and sampling noise; it must not be interpreted mechanically as discrimination alone.

Change decomposition relative to 1995

Blau and Kahn’s Table 5 emphasizes that the change in the gap depends on both changing gender differences in characteristics and changing prices. Two exact paths are reported. With 1995 coefficients as the base,

\[ G_t-G_{95} = (\Delta\bar X_t-\Delta\bar X_{95})'\widehat\beta_{m,95} +\Delta\bar X_t'(\widehat\beta_{mt}-\widehat\beta_{m,95}) +(U_t-U_{95}). \]

The alternative evaluates mean changes at current coefficients and coefficient changes at the 1995 characteristic gap.

table5_rows <- list()
row_id <- 1L

for (specification in c("Human capital", "Full")) {
  base <- ob_store[[paste(1995, specification, sep = "::")]]
  active_groups <- if (specification == "Full") names(beta_groups) else c("Education", "Experience")

  for (yr in c(2005L, 2015L)) {
    current <- ob_store[[paste(yr, specification, sep = "::")]]
    for (path in c("1995 coefficients / current gaps", "Current coefficients / 1995 gaps")) {
      group_rows <- lapply(active_groups, function(group) {
        variables <- beta_groups[[group]]
        dx0 <- base$dx[variables]
        dxt <- current$dx[variables]
        b0 <- base$beta[variables]
        bt <- current$beta[variables]
        if (startsWith(path, "1995")) {
          means <- sum((dxt - dx0) * b0)
          coefficients <- sum(dxt * (bt - b0))
        } else {
          means <- sum((dxt - dx0) * bt)
          coefficients <- sum(dx0 * (bt - b0))
        }
        data.frame(
          year = yr, specification, path, component = group,
          changing_means = means, changing_coefficients = coefficients
        )
      })
      group_rows <- bind_rows(group_rows)
      extras <- data.frame(
        year = yr, specification, path,
        component = c("All covariates", "Unexplained gap", "Total gap change"),
        changing_means = c(
          sum(group_rows$changing_means),
          current$unexplained - base$unexplained,
          current$gap - base$gap
        ),
        changing_coefficients = c(sum(group_rows$changing_coefficients), NA, NA)
      )
      table5_rows[[row_id]] <- bind_rows(group_rows, extras)
      row_id <- row_id + 1L
    }
  }
}

table5 <- bind_rows(table5_rows)
write.csv(table5, "results/table5_changes_from_1995.csv", row.names = FALSE)

Table 5 analogue

table5_display <- table5 |>
  filter(specification == "Full") |>
  mutate(
    `Changing means` = ifelse(is.na(changing_means), "", sprintf("%.3f", changing_means)),
    `Changing coefficients` = ifelse(
      is.na(changing_coefficients), "", sprintf("%.3f", changing_coefficients)
    )
  ) |>
  select(year, path, component, `Changing means`, `Changing coefficients`)
kable(table5_display, caption = "Contributions to the change in the gap relative to 1995: full specification")

Contributions to the change in the gap relative to 1995: full specification

year	path	component	Changing means	Changing coefficients
2005	1995 coefficients / current gaps	Education	-0.017	0.001
2005	1995 coefficients / current gaps	Experience	-0.002	0.001
2005	1995 coefficients / current gaps	Industry	-0.005	-0.002
2005	1995 coefficients / current gaps	Occupation	-0.007	0.001
2005	1995 coefficients / current gaps	All covariates	-0.031	0.001
2005	1995 coefficients / current gaps	Unexplained gap	-0.022
2005	1995 coefficients / current gaps	Total gap change	-0.052
2005	Current coefficients / 1995 gaps	Education	-0.017	-0.000
2005	Current coefficients / 1995 gaps	Experience	-0.002	0.001
2005	Current coefficients / 1995 gaps	Industry	-0.005	-0.002
2005	Current coefficients / 1995 gaps	Occupation	-0.007	0.001
2005	Current coefficients / 1995 gaps	All covariates	-0.030	0.000
2005	Current coefficients / 1995 gaps	Unexplained gap	-0.022
2005	Current coefficients / 1995 gaps	Total gap change	-0.052
2015	1995 coefficients / current gaps	Education	-0.016	0.000
2015	1995 coefficients / current gaps	Experience	-0.012	-0.000
2015	1995 coefficients / current gaps	Industry	-0.008	-0.000
2015	1995 coefficients / current gaps	Occupation	-0.011	0.001
2015	1995 coefficients / current gaps	All covariates	-0.047	-0.000
2015	1995 coefficients / current gaps	Unexplained gap	-0.060
2015	1995 coefficients / current gaps	Total gap change	-0.107
2015	Current coefficients / 1995 gaps	Education	-0.016	-0.001
2015	Current coefficients / 1995 gaps	Experience	-0.011	-0.001
2015	Current coefficients / 1995 gaps	Industry	-0.008	-0.000
2015	Current coefficients / 1995 gaps	Occupation	-0.011	0.000
2015	Current coefficients / 1995 gaps	All covariates	-0.046	-0.001
2015	Current coefficients / 1995 gaps	Unexplained gap	-0.060
2015	Current coefficients / 1995 gaps	Total gap change	-0.107

means_plot <- table5 |>
  filter(
    specification == "Full",
    path == "1995 coefficients / current gaps",
    component %in% names(beta_groups)
  ) |>
  transmute(year, component = paste("Means:", component), contribution = changing_means)

other_plot <- table5 |>
  filter(
    specification == "Full",
    path == "1995 coefficients / current gaps",
    component %in% c("All covariates", "Unexplained gap")
  ) |>
  transmute(
    year,
    component = ifelse(component == "All covariates", "All coefficient changes", "Unexplained-gap change"),
    contribution = ifelse(component == "All coefficient changes", changing_coefficients, changing_means)
  )

table5_plot <- bind_rows(means_plot, other_plot)
p_table5 <- ggplot(table5_plot, aes(factor(year), contribution, fill = component)) +
  geom_col(width = 0.68) +
  geom_hline(yintercept = 0, linewidth = 0.4) +
  labs(
    title = "Why the gap changed relative to 1995",
    subtitle = "1995-coefficient/current-characteristic-gap path",
    x = "Ending census year", y = "Contribution to change in log gap", fill = NULL
  ) +
  theme(legend.position = "bottom")
p_table5

ggsave("figures/table5_stacked_changes.png", p_table5, width = 8.8, height = 5.2, dpi = 180)

Negative bars narrow the gap. The two paths allocate interactions between changing characteristics and changing coefficients differently, but both sum to the same observed gap change once the unexplained component is included.

Distributional decomposition: Table 6 analogue

The original paper uses the Chernozhukov–Fernández-Val–Melly distribution-regression framework. Here, a deliberately rougher recentered-influence-function (RIF) approximation is used. For percentile \(p\), each sex-specific wage is transformed to

\[ \operatorname{RIF}(Y;q_p)=q_p+\frac{p-\mathbf{1}\{Y\le q_p\}}{f_Y(q_p)}, \]

where \(f_Y(q_p)\) is estimated with a Gaussian kernel. Separate male and female RIF regressions then yield an Oaxaca-style covariate effect and wage-coefficient effect at each unconditional percentile.

rif_values <- function(y, p) {
  q <- as.numeric(quantile(y, p, names = FALSE, type = 7))
  h <- max(1.06 * sd(y) * length(y)^(-1 / 5), 1e-4)
  density <- mean(dnorm((y - q) / h) / h)
  list(rif = q + (p - as.numeric(y <= q)) / density, quantile = q)
}

table6_rows <- list()
row_id <- 1L
for (yr in years) {
  wave <- work[work$year == yr, ]
  men <- wave[wave$sex == 0, ]
  women <- wave[wave$sex == 1, ]
  for (p in c(0.10, 0.50, 0.90)) {
    rm <- rif_values(men$log_wage, p)
    rf <- rif_values(women$log_wage, p)
    for (specification in c("Human capital", "Full")) {
      key <- ifelse(specification == "Full", "Full", "HC")
      dm <- design_matrix(men, key)
      df <- design_matrix(women, key)
      bm <- fit_ols(rm$rif, dm$X)
      bf <- fit_ols(rf$rif, df$X)
      covariate <- sum((colMeans(dm$X)[-1] - colMeans(df$X)[-1]) * bm[-1])
      coefficient <- sum(colMeans(df$X) * (bm - bf))
      table6_rows[[row_id]] <- data.frame(
        year = yr, percentile = as.integer(100 * p), specification,
        covariate_effect = covariate,
        coefficient_effect = coefficient,
        sum_effects = covariate + coefficient,
        raw_quantile_gap = rm$quantile - rf$quantile
      )
      row_id <- row_id + 1L
    }
  }
}

table6 <- bind_rows(table6_rows)
write.csv(table6, "results/table6_rif_quantile_decomposition.csv", row.names = FALSE)

table6_display <- table6 |>
  mutate(across(
    c(covariate_effect, coefficient_effect, sum_effects, raw_quantile_gap),
    ~ round(.x, 3)
  ))
kable(table6_display, caption = "RIF-regression decomposition by unconditional wage percentile")

RIF-regression decomposition by unconditional wage percentile

year	percentile	specification	covariate_effect	coefficient_effect	sum_effects	raw_quantile_gap
1995	10	Human capital	0.042	0.242	0.284	0.284
1995	10	Full	0.085	0.200	0.284	0.284
1995	50	Human capital	0.048	0.252	0.300	0.300
1995	50	Full	0.095	0.205	0.300	0.300
1995	90	Human capital	0.050	0.288	0.338	0.337
1995	90	Full	0.110	0.227	0.338	0.337
2005	10	Human capital	0.025	0.196	0.221	0.221
2005	10	Full	0.064	0.158	0.221	0.221
2005	50	Human capital	0.020	0.230	0.250	0.250
2005	50	Full	0.068	0.182	0.250	0.250
2005	90	Human capital	0.024	0.263	0.287	0.287
2005	90	Full	0.059	0.228	0.287	0.287
2015	10	Human capital	0.012	0.177	0.189	0.189
2015	10	Full	0.041	0.148	0.189	0.189
2015	50	Human capital	0.013	0.185	0.198	0.198
2015	50	Full	0.048	0.150	0.198	0.198
2015	90	Human capital	0.009	0.176	0.185	0.185
2015	90	Full	0.046	0.138	0.185	0.185

table6_plot <- table6 |>
  select(year, percentile, specification, covariate_effect, coefficient_effect) |>
  pivot_longer(
    c(covariate_effect, coefficient_effect),
    names_to = "effect", values_to = "log_points"
  ) |>
  mutate(
    effect = recode(
      effect,
      covariate_effect = "Covariates",
      coefficient_effect = "Wage coefficients"
    ),
    cell = paste0(year, "\np", percentile)
  )

p_table6 <- ggplot(table6_plot, aes(cell, log_points, fill = effect)) +
  geom_col(width = 0.72) +
  facet_wrap(~ specification, nrow = 1) +
  scale_fill_manual(values = c(Covariates = "#0072B2", `Wage coefficients` = "#D55E00")) +
  labs(
    title = "RIF approximation to Blau–Kahn Table 6",
    x = "Year and unconditional percentile", y = "Log-point contribution", fill = NULL
  ) +
  theme(axis.text.x = element_text(angle = 45, hjust = 1), legend.position = "bottom")
p_table6

ggsave("figures/table6_rif_stacked.png", p_table6, width = 11, height = 5.5, dpi = 180)

The percentile results separate compositional sorting from differences in the wage structure. Because RIF regression is a local linear approximation, sum_effects need not equal the raw sample quantile gap exactly. Standard errors are omitted; a substantive application should bootstrap the entire quantile, density, and regression procedure.

Where the design is feasible in IPUMS International

The IPUMS International sample catalogue and harmonized-variable documentation were audited on 11 August 2026 to identify real-data settings for the same exercise. A direct match must have (i) at least two census microdata samples whose endpoints are at least ten years apart, (ii) the demographic, earnings, education, employment, industry, and occupation variables below in each qualifying wave, and (iii) at least 100,000 person records in each wave. The record threshold is an analytical rule used here, not an IPUMS standard; it leaves ample scope for employed-sex and family-status cells after sample restrictions.

The hours requirement is deliberately flexible. A series passes if it supports at least one of three wage-sample constructions:

1. Computed hourly wage: earnings divided by exact usual or actual hours (HRSWORK1, HRSUSUAL1, HRSMAIN, or HRSACTUAL1), after aligning reference periods;
2. Reported hourly wage: a wage rate reported directly in hourly units, without dividing earnings by hours; or
3. Full-time earnings sample: wage earnings analyzed only among workers identifiable as full-time, using HRSFULL or a source variable with a documented full-time threshold.

Route 3 estimates an earnings gap among full-time workers, not a literal hourly-wage gap. It is therefore a useful fallback but not estimand-equivalent to routes 1 or 2.

ipums_crosswalk <- tibble::tribble(
  ~Krabbistan_measure, ~IPUMS_variable, ~Interpretation,
  "Sex", "SEX", "Reported sex",
  "Own earnings", "INCWAGE or INCEARN", "Wage-and-salary income, or broader earned income when the former is unavailable",
  "Age", "AGE", "Age in years",
  "Marital status", "MARST", "Harmonized marital status",
  "Parenthood", "NCHILD/MOMLOC/POPLOC; CHBORN/CHSURV/LASTBYR", "Co-resident parent for both sexes; fertility-history proxy for women",
  "Education", "EDATTAIN", "Internationally harmonized attainment",
  "Wage denominator/sample", "HRSWORK1, HRSUSUAL1, HRSMAIN, HRSACTUAL1, or HRSFULL", "Exact hours, a direct hourly rate, or a documented full-time indicator",
  "Labor-force status", "EMPSTAT", "Employment status",
  "Industry", "INDGEN", "Coarse general industry",
  "Occupation", "OCCISCO", "ISCO-harmonized occupation"
)

kable(
  ipums_crosswalk,
  col.names = c("Krabbistan measure", "IPUMS field", "Interpretation"),
  caption = "Crosswalk used in the feasibility screen"
)

Crosswalk used in the feasibility screen

Krabbistan measure	IPUMS field	Interpretation
Sex	SEX	Reported sex
Own earnings	INCWAGE or INCEARN	Wage-and-salary income, or broader earned income when the former is unavailable
Age	AGE	Age in years
Marital status	MARST	Harmonized marital status
Parenthood	NCHILD/MOMLOC/POPLOC; CHBORN/CHSURV/LASTBYR	Co-resident parent for both sexes; fertility-history proxy for women
Education	EDATTAIN	Internationally harmonized attainment
Wage denominator/sample	HRSWORK1, HRSUSUAL1, HRSMAIN, HRSACTUAL1, or HRSFULL	Exact hours, a direct hourly rate, or a documented full-time indicator
Labor-force status	EMPSTAT	Employment status
Industry	INDGEN	Coarse general industry
Occupation	OCCISCO	ISCO-harmonized occupation

Reconstructing parenthood

NCHILD is not essential. The preferred replacement is a co-resident-parent indicator constructed by reversing the IPUMS parent pointers: a person is a parent if another household member’s MOMLOC or POPLOC equals that person’s PERNUM. This has the same conceptual limitation as NCHILD—children outside the household are missed—but it works symmetrically for women and men. Where pointers are unavailable, household relationship and spouse links can often identify children of the head or spouse, though they miss more complex families.

Fertility histories provide a second route. CHBORN > 0, CHSURV > 0, or a valid LASTBYR identifies women who have ever had a birth. These variables capture nonresident children but usually apply only to women, sometimes only within a restricted age range. They therefore cannot by themselves define the same parent category for men and women. The recommended design uses co-resident parenthood as the common main measure and women’s fertility history as a sensitivity check.

Removing NCHILD as a hard screen and admitting INCEARN (total own labor income) materially expands the usable set. Brazil, Jamaica, Mexico, the United States, and Venezuela support repeated-census analyses with a common parent construct; Canada is conditionally usable with a source-level relationship reconstruction or an asymmetric fertility-history sensitivity analysis. Puerto Rico is a technical match but a U.S. territory. Brazil, Mexico, and Venezuela use broader labor earnings rather than wage-and-salary income alone, so their estimand includes self-employment income.

ipums_feasibility <- tibble::tribble(
  ~jurisdiction, ~assessment, ~samples, ~minimum_records, ~parent_route, ~wage_status,
  "Brazil", "Feasible; broader earnings", "1991, 2000, 2010 censuses", "17,045,712", "Co-resident links; CHBORN sensitivity", "INCEARN / exact hours; includes business and farm income",
  "Canada", "Conditional", "1981, 1991, 2001 censuses", "486,875", "CHBORN for women; source relationship reconstruction needed for a sex-symmetric measure", "INCWAGE / exact hours or full-time restriction",
  "Jamaica", "Feasible", "1982, 1991, 2001 censuses", "205,179", "Co-resident links; CHBORN sensitivity", "INCWAGE / exact hours",
  "Mexico", "Feasible; broader earnings", "1990, 2000, 2010 censuses", "8,118,242", "Co-resident links; CHBORN sensitivity", "INCEARN / exact hours; includes self-employment income",
  "United States", "Feasible", "1980, 1990, 2000 censuses", "11,343,120", "Co-resident links; CHBORN available through 1990", "INCWAGE / exact hours",
  "Venezuela", "Feasible; broader earnings", "1981 and 2001 censuses", "1,441,266", "Co-resident links; CHBORN available in 2001", "INCEARN / exact hours; 20-year endpoint comparison",
  "Puerto Rico", "Technical match; territory", "1990 and 2000 censuses", "177,655", "Co-resident links; CHBORN in 1990", "INCWAGE / exact hours"
)

write.csv(
  ipums_feasibility,
  "results/ipums_international_feasibility.csv",
  row.names = FALSE
)

kable(
  ipums_feasibility,
  col.names = c(
    "Country or territory", "Assessment", "Potential waves",
    "Minimum IPUMS records", "Parent construction", "Wage construction/deviation"
  ),
  caption = "Repeated-census settings that can support the analysis after flexible parent construction"
)

Repeated-census settings that can support the analysis after flexible parent construction

Country or territory	Assessment	Potential waves	Minimum IPUMS records	Parent construction	Wage construction/deviation
Brazil	Feasible; broader earnings	1991, 2000, 2010 censuses	17,045,712	Co-resident links; CHBORN sensitivity	INCEARN / exact hours; includes business and farm income
Canada	Conditional	1981, 1991, 2001 censuses	486,875	CHBORN for women; source relationship reconstruction needed for a sex-symmetric measure	INCWAGE / exact hours or full-time restriction
Jamaica	Feasible	1982, 1991, 2001 censuses	205,179	Co-resident links; CHBORN sensitivity	INCWAGE / exact hours
Mexico	Feasible; broader earnings	1990, 2000, 2010 censuses	8,118,242	Co-resident links; CHBORN sensitivity	INCEARN / exact hours; includes self-employment income
United States	Feasible	1980, 1990, 2000 censuses	11,343,120	Co-resident links; CHBORN available through 1990	INCWAGE / exact hours
Venezuela	Feasible; broader earnings	1981 and 2001 censuses	1,441,266	Co-resident links; CHBORN available in 2001	INCEARN / exact hours; 20-year endpoint comparison
Puerto Rico	Technical match; territory	1990 and 2000 censuses	177,655	Co-resident links; CHBORN in 1990	INCWAGE / exact hours

Twenty-country longlist and the binding constraint

The broader audit produces a longlist of 21 sovereign countries, plus Puerto Rico. All have large IPUMS samples and some substantial portion of the desired demographic/labor design. It is not honest, however, to label all twenty-one feasible wage-gap settings: once parenthood is reconstructed, the binding limitation is repeated individual earnings, not children. The table makes those deviations explicit instead of silently changing the estimand to household income or occupation-imputed wages.

ipums_longlist <- tibble::tribble(
  ~country, ~candidate_censuses, ~minimum_records, ~parent_option, ~status_or_blocker,
  "Brazil", "1991/2000/2010", "17,045,712", "Resident child links; fertility history", "Feasible with broader earned income",
  "Canada", "1981/1991/2001", "486,875", "Fertility history; source relationship reconstruction", "Conditional: common male/female parent proxy needs source work",
  "Jamaica", "1982/1991/2001", "205,179", "Resident child links; fertility history", "Feasible",
  "Mexico", "1990/2000/2010", "8,118,242", "Resident child links; fertility history", "Feasible with broader earned income",
  "United States", "1980/1990/2000", "11,343,120", "Resident child links; fertility history", "Feasible",
  "Venezuela", "1981/2001", "1,441,266", "Resident child links; fertility history", "Feasible with broader earned income",
  "Indonesia", "1980/1990 censuses", "912,544", "Resident child links; fertility history", "Censuses lack earnings; 1976/1995 surveys are usable alternatives",
  "Panama", "1990/2000/2010", "232,737", "Resident child links; fertility history", "Earnings present; qualifying waves lack hours/full-time route",
  "Israel", "1983/1995/2008", "403,474", "Resident links through 1995; fertility history", "Earnings present; no repeated hours/full-time route",
  "Trinidad and Tobago", "1980/1990/2000/2011", "105,464", "Resident child links; fertility history", "Individual earnings only in 2000",
  "Uruguay", "1975/1985/1996/2011", "279,994", "Resident child links; fertility history", "Individual wage income only in the 2006 household survey",
  "Switzerland", "1970/1980/1990/2000", "312,538", "Resident child links; fertility history in 2000", "No individual earnings measure",
  "Ecuador", "1990/2001/2010", "966,234", "Resident child links; fertility history", "No individual earnings measure",
  "Spain", "2001/2011", "2,039,274", "Resident child links; earlier fertility history", "No individual earnings measure",
  "France", "1990/1999/2006/2011", "2,360,854", "Resident child links", "No individual earnings measure",
  "Greece", "1991/2001/2011", "969,407", "Resident child links; fertility history", "No individual earnings measure",
  "Italy", "2001/2011", "2,968,065", "Resident child links", "No census individual earnings measure",
  "Mauritius", "1990/2000/2011", "106,710", "Resident child links; fertility history", "No individual earnings measure",
  "Nicaragua", "1995/2005", "435,728", "Resident child links; fertility history", "No individual earnings measure",
  "El Salvador", "1992/2007", "510,760", "Resident child links; fertility history", "No individual earnings measure",
  "South Africa", "1996/2001/2011", "3,621,164", "Resident child links; fertility history", "Excellent labor covariates, but IPUMS provides household—not own—income in these waves",
  "Puerto Rico", "1990/2000", "177,655", "Resident child links; fertility history", "Feasible, but a U.S. territory"
)

write.csv(ipums_longlist, "results/ipums_twenty_country_longlist.csv", row.names = FALSE)

kable(
  ipums_longlist,
  col.names = c(
    "Country or territory", "Candidate census years", "Minimum records",
    "Parent option", "Assessment or binding blocker"
  ),
  caption = "Expanded IPUMS International longlist: 21 sovereign countries plus Puerto Rico"
)

Expanded IPUMS International longlist: 21 sovereign countries plus Puerto Rico

Country or territory	Candidate census years	Minimum records	Parent option	Assessment or binding blocker
Brazil	1991/2000/2010	17,045,712	Resident child links; fertility history	Feasible with broader earned income
Canada	1981/1991/2001	486,875	Fertility history; source relationship reconstruction	Conditional: common male/female parent proxy needs source work
Jamaica	1982/1991/2001	205,179	Resident child links; fertility history	Feasible
Mexico	1990/2000/2010	8,118,242	Resident child links; fertility history	Feasible with broader earned income
United States	1980/1990/2000	11,343,120	Resident child links; fertility history	Feasible
Venezuela	1981/2001	1,441,266	Resident child links; fertility history	Feasible with broader earned income
Indonesia	1980/1990 censuses	912,544	Resident child links; fertility history	Censuses lack earnings; 1976/1995 surveys are usable alternatives
Panama	1990/2000/2010	232,737	Resident child links; fertility history	Earnings present; qualifying waves lack hours/full-time route
Israel	1983/1995/2008	403,474	Resident links through 1995; fertility history	Earnings present; no repeated hours/full-time route
Trinidad and Tobago	1980/1990/2000/2011	105,464	Resident child links; fertility history	Individual earnings only in 2000
Uruguay	1975/1985/1996/2011	279,994	Resident child links; fertility history	Individual wage income only in the 2006 household survey
Switzerland	1970/1980/1990/2000	312,538	Resident child links; fertility history in 2000	No individual earnings measure
Ecuador	1990/2001/2010	966,234	Resident child links; fertility history	No individual earnings measure
Spain	2001/2011	2,039,274	Resident child links; earlier fertility history	No individual earnings measure
France	1990/1999/2006/2011	2,360,854	Resident child links	No individual earnings measure
Greece	1991/2001/2011	969,407	Resident child links; fertility history	No individual earnings measure
Italy	2001/2011	2,968,065	Resident child links	No census individual earnings measure
Mauritius	1990/2000/2011	106,710	Resident child links; fertility history	No individual earnings measure
Nicaragua	1995/2005	435,728	Resident child links; fertility history	No individual earnings measure
El Salvador	1992/2007	510,760	Resident child links; fertility history	No individual earnings measure
South Africa	1996/2001/2011	3,621,164	Resident child links; fertility history	Excellent labor covariates, but IPUMS provides household—not own—income in these waves
Puerto Rico	1990/2000	177,655	Resident child links; fertility history	Feasible, but a U.S. territory

This expanded screen also clarifies why India is not in the census longlist: its income-bearing IPUMS files are employment surveys, not census microdata. Mexico has both survey and census material, but the census sequence itself is usable through INCEARN.

South Africa is indeed a strong candidate for the employment and child-penalty parts of the project. IPUMS supplies the 1996, 2001, and 2011 censuses, millions of observations, parent links and fertility histories, schooling, labor-force status, and industry/occupation (through 2007 in the harmonized series). The available income fields for these census samples are household income or household-head income, not the worker’s own earnings. Household income is mechanically contaminated by partners’ earnings and cannot serve as the dependent variable in an individual Oaxaca–Blinder gender-pay decomposition. South Africa should therefore remain on the longlist, with another microdata source required for the wage portion.

Child Penalty Atlas source audit

Kleven, Landais, and Leite-Mariante’s Child Penalty Atlas covers 134 countries. Its core outcome is employment, so inclusion in the Atlas does not imply that its cited file contains own earnings, hours, education, occupation, and industry. The following audit starts from Appendix Table A.1 and asks whether the cited source could support the richer gender-pay analysis here. “Accessible” includes free registration or a research-use application; it does not mean unrestricted anonymous download.

atlas_source_text <- "country|source|years
Afghanistan|DHS|2015
Albania|DHS|2008-2017
Algeria|MICS|2012-2019
Angola|Census|2014
Argentina|IPUMS|1970-2001
Armenia|IPUMS|2001-2011
Australia|Panel Data|2001-2019
Austria|Panel Data|1981-2017
Bangladesh|IPUMS|1991-2011
Belarus|IPUMS|1999-2009
Belgium|LIS|1985-2017
Benin|IPUMS|1979-2013
Bolivia|IPUMS|1976-2001
Botswana|IPUMS|1991-2011
Brazil|IPUMS|1991-2010
Bulgaria|SILC|2007-2020
Burkina Faso|IPUMS|1996-2006
Burundi|DHS|2010-2016
Cambodia|IPUMS|1998-2008
Cameroon|IPUMS|1976-2005
Canada|IPUMS|2011
Chad|DHS|1996-2014
Chile|LIS|1990-2017
China|Mini Census|2005
Colombia|IPUMS|1973-2005
Congo, Dem. Rep.|DHS|2007-2013
Congo, Rep.|DHS|2005-2011
Costa Rica|IPUMS|1973-2011
Cote d'Ivoire|LIS|2002-2015
Croatia|SILC|2010-2020
Cuba|IPUMS|2002-2012
Cyprus|SILC|2009-2020
Czech Republic|LIS|1992-2016
Denmark|Panel Data|1980-2017
Dominican Rep.|IPUMS|1981-2010
Ecuador|IPUMS|1982-2010
Egypt|IPUMS|1996-2006
El Salvador|IPUMS|1992-2007
Estonia|SILC|2009-2020
Ethiopia|DHS|2000-2016
Fiji|IPUMS|1976-2014
Finland|SILC|2009-2020
France|LFS|1990-2020
Gabon|DHS|2000-2012
Gambia|LFS|2010-2015
Georgia|LFS|2020-2021
Germany|LIS|1989-2005
Ghana|IPUMS|2000-2010
Greece|IPUMS|1981-2001
Guatemala|IPUMS|1964-2002
Guinea|IPUMS|1983-2014
Guyana|LFS|2017-2021
Haiti|IPUMS|1971-2003
Honduras|IPUMS|1974-2001
Hungary|IPUMS|1990-2011
Iceland|SILC|2004-2018
India|DHS|2005-2015
Indonesia|IPUMS|1971-2010
Iran|IPUMS|2006
Iraq|IPUMS|1997
Ireland|LIS|1994-2018
Israel|LIS|1986-2013
Italy|LIS|1986-2020
Jamaica|IPUMS|1982-2001
Japan|Panel Data|2004-2020
Jordan|IPUMS|2004
Kenya|IPUMS|1989-2009
Kyrgyz Republic|DHS|1997-2012
Laos|IPUMS|2005
Latvia|SILC|2009-2020
Lesotho|IPUMS|1996-2006
Liberia|IPUMS|2008
Lithuania|LIS|2009-2018
Luxembourg|LIS|1985-2013
Madagascar|DHS|1992-2008
Malawi|IPUMS|1987-2008
Malaysia|IPUMS|1991-2000
Maldives|DHS|2009-2016
Mali|IPUMS|1987-2009
Mauritius|IPUMS|1990-2011
Mexico|IPUMS|1970-2015
Moldova|DHS|2005
Mongolia|IPUMS|2000
Morocco|IPUMS|1982-2004
Mozambique|IPUMS|1997-2007
Myanmar|IPUMS|2014
Namibia|DHS|1992-2013
Nepal|IPUMS|2001-2011
Netherlands|LIS|1990-2018
Nicaragua|IPUMS|1995-2005
Niger|DHS|1992-2012
Nigeria|DHS|1990-2018
Norway|Panel Data|1993-2017
Pakistan|LFS|2010-2021
Panama|IPUMS|1960-2010
Papua New Guinea|IPUMS|1980-2000
Paraguay|IPUMS|1962-2002
Peru|IPUMS|1993-2007
Philippines|IPUMS|1990
Poland|LIS|1992-2020
Portugal|IPUMS|1981-2011
Puerto Rico|IPUMS|1990-2010
Romania|IPUMS|1992-2011
Russia|IPUMS|2002-2010
Rwanda|IPUMS|2002-2012
Senegal|IPUMS|1988-2002
Serbia|SILC|2013-2020
Sierra Leone|IPUMS|2004
Slovakia|LIS|1992-2018
Slovenia|LIS|1997-2012
South Africa|IPUMS|1996-2011
South Korea|LIS|2006-2016
South Sudan|IPUMS|2008
Spain|IPUMS|1991-2001
Sudan|IPUMS|2008
Suriname|IPUMS|2012
Sweden|Panel Data|1997-2017
Switzerland|Panel Data|1981-2020
Taiwan|LIS|1981-2016
Tanzania|IPUMS|1988-2012
Thailand|IPUMS|1990-2000
Timor-Leste|DHS|2009-2016
Togo|IPUMS|2010
Trinidad & Tobago|IPUMS|1970-2011
Tunisia|Census|2004
Turkey|IPUMS|1985-2000
Uganda|IPUMS|1991-2014
United Kingdom|APS|2012-2020
United States|CPS/ACS|1968-2020
Uruguay|IPUMS|1963-2011
Venezuela|IPUMS|1971-2001
Vietnam|IPUMS|1989-2009
Zambia|IPUMS|1990-2010
Zimbabwe|IPUMS|2012"

atlas_audit <- read.delim(
  textConnection(atlas_source_text), sep = "|", quote = "",
  stringsAsFactors = FALSE, check.names = FALSE
)
stopifnot(nrow(atlas_audit) == 134)

ipums_pay_yes <- c("Brazil", "Canada", "Jamaica", "Mexico", "Panama", "Puerto Rico", "Venezuela")
ipums_household_only <- "South Africa"

atlas_audit <- atlas_audit |>
  mutate(
    public_access = case_when(
      source == "IPUMS" ~ "Yes: free registration and approved extract",
      source == "DHS" ~ "Yes: free registration and project request",
      source == "MICS" ~ "Yes: free registration/download",
      source == "SILC" ~ "Restricted: Eurostat scientific-use application",
      source == "LIS" ~ "Restricted: LIS membership/remote execution",
      source == "Panel Data" ~ "Restricted; country-specific application",
      source == "LFS" ~ "Varies by national statistical office; verify access",
      source == "APS" ~ "Yes: UK Data Service registration/licence",
      source == "CPS/ACS" ~ "Yes: public-use microdata",
      source == "Mini Census" ~ "Restricted; public microdata not confirmed",
      source == "Census" ~ "Not confirmed from the paper"
    ),
    pay_gap_data = case_when(
      country %in% ipums_pay_yes ~ "Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates",
      country %in% ipums_household_only ~ "No: household income only; own earnings absent in cited IPUMS censuses",
      source == "IPUMS" ~ "Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination",
      source %in% c("DHS", "MICS") ~ "No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient",
      source == "SILC" ~ "Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted",
      source == "LIS" ~ "Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave",
      source == "Panel Data" ~ "Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted",
      source == "LFS" ~ "Unconfirmed: hours, occupation, industry, and education exist, but earnings and public access vary",
      source == "APS" ~ "Yes: earnings, paid hours, education, occupation, industry, and family variables",
      source == "CPS/ACS" ~ "Yes: earnings, hours, education, occupation, industry, and household parent links",
      source == "Mini Census" ~ "Unconfirmed: access and individual earnings documentation not established",
      source == "Census" ~ "Unconfirmed: Atlas documents employment, not a public own-earnings/hourly-wage file"
    )
  )

write.csv(atlas_audit, "results/child_penalty_atlas_data_audit.csv", row.names = FALSE)

atlas_summary <- atlas_audit |>
  mutate(category = case_when(
    grepl("^Yes", pay_gap_data) ~ "Yes",
    grepl("^(Partial|Unconfirmed|Not confirmed)", pay_gap_data) ~ "Partial or unconfirmed",
    TRUE ~ "No"
  )) |>
  count(category, name = "countries")

kable(
  atlas_summary,
  col.names = c("Assessment", "Number of countries"),
  caption = "Summary of the 134-country Atlas audit"
)

Summary of the 134-country Atlas audit

Assessment	Number of countries
No	19
Partial or unconfirmed	91
Yes	24

kable(
  atlas_audit,
  col.names = c("Country", "Atlas data source", "Atlas years", "Public/research access", "Gender-pay-gap readiness"),
  caption = "Country-level review of the data sources in Kleven et al. (2025), Appendix Table A.1"
)

Country-level review of the data sources in Kleven et al. (2025), Appendix Table A.1

Country	Atlas data source	Atlas years	Public/research access	Gender-pay-gap readiness
Afghanistan	DHS	2015	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Albania	DHS	2008-2017	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Algeria	MICS	2012-2019	Yes: free registration/download	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Angola	Census	2014	Not confirmed from the paper	Unconfirmed: Atlas documents employment, not a public own-earnings/hourly-wage file
Argentina	IPUMS	1970-2001	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Armenia	IPUMS	2001-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Australia	Panel Data	2001-2019	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Austria	Panel Data	1981-2017	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Bangladesh	IPUMS	1991-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Belarus	IPUMS	1999-2009	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Belgium	LIS	1985-2017	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Benin	IPUMS	1979-2013	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Bolivia	IPUMS	1976-2001	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Botswana	IPUMS	1991-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Brazil	IPUMS	1991-2010	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Bulgaria	SILC	2007-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
Burkina Faso	IPUMS	1996-2006	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Burundi	DHS	2010-2016	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Cambodia	IPUMS	1998-2008	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Cameroon	IPUMS	1976-2005	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Canada	IPUMS	2011	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Chad	DHS	1996-2014	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Chile	LIS	1990-2017	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
China	Mini Census	2005	Restricted; public microdata not confirmed	Unconfirmed: access and individual earnings documentation not established
Colombia	IPUMS	1973-2005	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Congo, Dem. Rep.	DHS	2007-2013	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Congo, Rep.	DHS	2005-2011	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Costa Rica	IPUMS	1973-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Cote d’Ivoire	LIS	2002-2015	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Croatia	SILC	2010-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
Cuba	IPUMS	2002-2012	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Cyprus	SILC	2009-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
Czech Republic	LIS	1992-2016	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Denmark	Panel Data	1980-2017	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Dominican Rep.	IPUMS	1981-2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Ecuador	IPUMS	1982-2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Egypt	IPUMS	1996-2006	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
El Salvador	IPUMS	1992-2007	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Estonia	SILC	2009-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
Ethiopia	DHS	2000-2016	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Fiji	IPUMS	1976-2014	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Finland	SILC	2009-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
France	LFS	1990-2020	Varies by national statistical office; verify access	Unconfirmed: hours, occupation, industry, and education exist, but earnings and public access vary
Gabon	DHS	2000-2012	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Gambia	LFS	2010-2015	Varies by national statistical office; verify access	Unconfirmed: hours, occupation, industry, and education exist, but earnings and public access vary
Georgia	LFS	2020-2021	Varies by national statistical office; verify access	Unconfirmed: hours, occupation, industry, and education exist, but earnings and public access vary
Germany	LIS	1989-2005	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Ghana	IPUMS	2000-2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Greece	IPUMS	1981-2001	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Guatemala	IPUMS	1964-2002	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Guinea	IPUMS	1983-2014	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Guyana	LFS	2017-2021	Varies by national statistical office; verify access	Unconfirmed: hours, occupation, industry, and education exist, but earnings and public access vary
Haiti	IPUMS	1971-2003	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Honduras	IPUMS	1974-2001	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Hungary	IPUMS	1990-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Iceland	SILC	2004-2018	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
India	DHS	2005-2015	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Indonesia	IPUMS	1971-2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Iran	IPUMS	2006	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Iraq	IPUMS	1997	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Ireland	LIS	1994-2018	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Israel	LIS	1986-2013	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Italy	LIS	1986-2020	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Jamaica	IPUMS	1982-2001	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Japan	Panel Data	2004-2020	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Jordan	IPUMS	2004	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Kenya	IPUMS	1989-2009	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Kyrgyz Republic	DHS	1997-2012	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Laos	IPUMS	2005	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Latvia	SILC	2009-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
Lesotho	IPUMS	1996-2006	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Liberia	IPUMS	2008	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Lithuania	LIS	2009-2018	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Luxembourg	LIS	1985-2013	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Madagascar	DHS	1992-2008	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Malawi	IPUMS	1987-2008	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Malaysia	IPUMS	1991-2000	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Maldives	DHS	2009-2016	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Mali	IPUMS	1987-2009	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Mauritius	IPUMS	1990-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Mexico	IPUMS	1970-2015	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Moldova	DHS	2005	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Mongolia	IPUMS	2000	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Morocco	IPUMS	1982-2004	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Mozambique	IPUMS	1997-2007	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Myanmar	IPUMS	2014	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Namibia	DHS	1992-2013	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Nepal	IPUMS	2001-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Netherlands	LIS	1990-2018	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Nicaragua	IPUMS	1995-2005	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Niger	DHS	1992-2012	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Nigeria	DHS	1990-2018	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Norway	Panel Data	1993-2017	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Pakistan	LFS	2010-2021	Varies by national statistical office; verify access	Unconfirmed: hours, occupation, industry, and education exist, but earnings and public access vary
Panama	IPUMS	1960-2010	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Papua New Guinea	IPUMS	1980-2000	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Paraguay	IPUMS	1962-2002	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Peru	IPUMS	1993-2007	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Philippines	IPUMS	1990	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Poland	LIS	1992-2020	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Portugal	IPUMS	1981-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Puerto Rico	IPUMS	1990-2010	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Romania	IPUMS	1992-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Russia	IPUMS	2002-2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Rwanda	IPUMS	2002-2012	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Senegal	IPUMS	1988-2002	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Serbia	SILC	2013-2020	Restricted: Eurostat scientific-use application	Yes: earnings, hours/full-time, education, occupation, industry, and household structure; access restricted
Sierra Leone	IPUMS	2004	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Slovakia	LIS	1992-2018	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Slovenia	LIS	1997-2012	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
South Africa	IPUMS	1996-2011	Yes: free registration and approved extract	No: household income only; own earnings absent in cited IPUMS censuses
South Korea	LIS	2006-2016	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
South Sudan	IPUMS	2008	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Spain	IPUMS	1991-2001	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Sudan	IPUMS	2008	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Suriname	IPUMS	2012	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Sweden	Panel Data	1997-2017	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Switzerland	Panel Data	1981-2020	Restricted; country-specific application	Yes for annual-earnings gaps; exact hours/occupation/industry vary and administrative access is restricted
Taiwan	LIS	1981-2016	Restricted: LIS membership/remote execution	Partial/likely: labor earnings and demographics exist; hours and industry/occupation vary by country-wave
Tanzania	IPUMS	1988-2012	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Thailand	IPUMS	1990-2000	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Timor-Leste	DHS	2009-2016	Yes: free registration and project request	No: lacks monetary own earnings and hours; industry/occupation detail is also insufficient
Togo	IPUMS	2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Trinidad & Tobago	IPUMS	1970-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Tunisia	Census	2004	Not confirmed from the paper	Unconfirmed: Atlas documents employment, not a public own-earnings/hourly-wage file
Turkey	IPUMS	1985-2000	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Uganda	IPUMS	1991-2014	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
United Kingdom	APS	2012-2020	Yes: UK Data Service registration/licence	Yes: earnings, paid hours, education, occupation, industry, and family variables
United States	CPS/ACS	1968-2020	Yes: public-use microdata	Yes: earnings, hours, education, occupation, industry, and household parent links
Uruguay	IPUMS	1963-2011	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Venezuela	IPUMS	1971-2001	Yes: free registration and approved extract	Yes/partial: own earnings plus hours/full-time route; verify wave-specific covariates
Vietnam	IPUMS	1989-2009	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Zambia	IPUMS	1990-2010	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination
Zimbabwe	IPUMS	2012	Yes: free registration and approved extract	Not confirmed: Atlas employment file lacks a verified own-earnings + hours/full-time combination

The Atlas table is a source audit, not a guarantee of variable access. In particular, “Panel Data” often means confidential administrative registers; LIS provides harmonized output through a remote system rather than distributing national microdata; EU-SILC scientific-use files require approval; and national labor-force-survey earnings modules vary. The machine-readable audit is available in results/child_penalty_atlas_data_audit.csv.

Access documentation: IPUMS International, DHS Program, UNICEF MICS, Luxembourg Income Study, Eurostat microdata access, UK Annual Population Survey, and U.S. CPS.

Five comparability cautions apply even to feasible settings. First, co-resident parenthood misses nonresident and older children, while fertility-history parenthood is generally women-only. Second, INCWAGE and INCEARN are different concepts; the latter includes business and farm earnings. Third, income can be reported weekly, monthly, or annually and is subject to sample-specific universes and top-codes; numerator and hours must be placed on a common period within each sample. Fourth, HRSFULL definitions vary across countries and waves. Fifth, broad industry and occupation harmonization can still contain source-classification breaks. A real analysis should read every sample-specific comparability tab and apply IPUMS person weights.

Sources: sample catalogue and country sample-detail pages; variable documentation for INCWAGE, INCEARN, HRSWORK1, HRSUSUAL1, HRSMAIN, HRSACTUAL1, HRSFULL, NCHILD, MOMLOC, POPLOC, CHBORN, CHSURV, LASTBYR, INDGEN, and OCCISCO.

Conclusions

• The synthetic mean log-wage gap narrows over the three censuses.
• Parenthood remains associated with larger gaps, while selection into employment makes the marriage gradient vary across waves.
• Women’s educational gains remove education as an explanation and can make its contribution negative.
• Occupational and industrial sorting remain meaningful explained components even as segregation declines.
• Both changing characteristics and a declining unexplained component contribute to convergence from 1995.
• Distributional decompositions reveal that a single mean gap can conceal different composition and coefficient effects across the wage distribution.

Reproduction and files

The report source is krabbistan-gender-gap.qmd. Running

quarto render krabbistan-gender-gap.qmd

regenerates the census files, all CSV result tables, figures, and this self-contained HTML. The optional code/krabbistan_gender_gap.do contains a standalone Stata translation of the simulation and mean/distributional decomposition workflow.

Downloads:

• Combined census, Stata format
• Combined census, CSV format
• 1995 Stata file, 2005 Stata file, and 2015 Stata file
• R/Quarto source
• Optional Stata translation
• Machine-readable result tables

Reference

Blau, Francine D., and Lawrence M. Kahn. 2017. “The Gender Wage Gap: Extent, Trends, and Explanations.” Journal of Economic Literature 55 (3): 789–865. https://doi.org/10.1257/jel.20160995

Kleven, Henrik, Camille Landais, and Gabriel Leite-Mariante. 2025. “The Child Penalty Atlas.” Review of Economic Studies 92 (5): 3174–3207. https://doi.org/10.1093/restud/rdae104. Country sources are transcribed from Appendix Table A.1.