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This project investigates gender-based wage disparities in the United States using data from the March 2022 Current Population Survey (CPS). The analysis examines earnings distributions, career patterns, and other contributing factors to the gender wage gap. By applying regression models and controlling for variables such as education, demographic characteristics, and household composition, the project aims to quantify and better understand the drivers of income differences between men and women.

This project uses microdata from the March 2022 Current Population Survey (CPS), obtained from IPUMS. The CPS is a monthly survey conducted by the U.S. Census Bureau and the Bureau of Labor Statistics, and the March supplement provides detailed demographic, labor force, and income information. The IPUMS extract used in this analysis contains 163,324 individuals and 397 variables.

cpsmar_e <- read_csv(here("data", "cpsmar_e.csv")) 

We use the March 2022 CPS extract, which contains data on 60,000 individuals and 397 variables.

cpsmar_a <- cpsmar_e %>%
  filter(
    age >= 23,
    age <= 62,
    earnings > 0
  ) %>%
  mutate(
    gender = if_else(female == 1, "Female", "Male"),
    wage = earnings/ (weeks * hours),
    lwage = log(wage),
    Black = case_when(race == 2~1, TRUE ~ 0),
    south = case_when(region == 3~1, TRUE ~ 0),
    married = case_when((marital == 1 | marital == 2 | marital == 3) ~ 1, TRUE ~ 0),
    age_centered = age - 23
  )

In this analysis sample, there are a total of 46,194 observations.

figure1 <- ggplot(cpsmar_a, aes(x = earnings, group = gender, fill = gender)) +
  geom_density(alpha = 0.4) +
  labs(
    title="Figure 1. Distribution of earnings by gender",
    x="Earnings",
    y="Density"
    )+
  theme_minimal()
earnings_fvm <- cpsmar_a %>%
  group_by(gender) %>%
  summarize(avg_earnings = round(mean(earnings, na.rm = TRUE),0))

avg_earnings_f <- earnings_fvm %>% 
  filter(gender == "Female") %>% 
  pull(avg_earnings) # `pull` extracts the "avg_earnings" value for "Female" from earnings_fvm, a single value since the data only record two genders.

avg_earnings_m <- earnings_fvm %>% 
  filter(gender == "Male") %>% 
  pull(avg_earnings) # `pull` extracts the "avg_earnings" value for "Male" from earnings_fvm, a single value since the data only record two genders. 

Women earn significantly less than men on average. The wage gap is large even before controlling for differences in age, education, or hours worked.

Table 1 shows that men earn substantially more than women, with an average hourly wage gap of $7.12. On top of that, men work 1.77 more hours per week on average, further widening the total earnings disparity.

cef_fvm_w <- cpsmar_a %>%
  group_by(gender, age_centered) %>%
  summarize(avg_lwage = mean(lwage, na.rm = TRUE))

figure2 <- ggplot(cef_fvm_w, aes(x = age_centered, y = avg_lwage, color = gender, linetype = gender, linewidth = gender)) +
  geom_point() +
  geom_line() +
  scale_linetype_manual(values = c("Female" = "longdash", "Male" = "solid")) + 
  scale_linewidth_manual(values = c("Female" = 0.7, "Male" = 0.5)) + 
  guides(linewidth = "none") +
  labs(
    title="Figure 2. Career log-wage profiles for women and men",
    x="Year",
    y="Average log wage"
    )+
  theme_minimal()

males <- cef_fvm_w %>%
  filter(gender == "Male") %>%
  rename(avg_lwage_male = avg_lwage) %>%
  select(-gender) 
females <- cef_fvm_w %>%
  filter(gender == "Female") %>%
  rename(avg_lwage_female = avg_lwage) %>%
  select(-gender)

diff_fvm <- inner_join(males, females, by = "age_centered") %>%
  filter(age_centered <= 30) %>%
  mutate(
    diff = avg_lwage_male - avg_lwage_female,
    age_group = cut(
      age_centered, 
      breaks = c(-1, 10, 20, 30), 
      labels = c("1-10", "11-20", "21-30"))
    ) %>%
  group_by(age_group) %>%
  summarize(mean_diff = mean(diff)*100) 
  
table2 <- kable(
  diff_fvm,
  digits = 2,
  col.names = c("Year Range", "Avg Pct Difference"),
  align = "cc",
  caption = "Table 2. Percent wage differences, first 30 years",
  ) %>%
  kable_styling(position = "center")

Table 2 highlights a notable wage gap between men and women under age 30, pointing to persistent disparities in early-career earnings.

formula <- y ~ x + I(x^2)
figure3 <- figure2 +  
  geom_smooth(
    method = "lm", 
    formula = formula, 
    aes(group = gender), 
    se = FALSE
    ) +
  stat_poly_eq(
    aes(label =  after_stat(eq.label)),
    formula = y ~ x + I(x^2),
    parse = TRUE
    ) +
  labs(
    title="Figure 3. Career log-wage profiles with quadratic fits",
    x="Year",
    y="Average log wage"
    )+
  theme_minimal()+
  theme(legend.position = "bottom")

Women are less likely to be married and less likely to have children under age 6. Men are more likely to have college degrees.

singles <- cpsmar_a %>%
  filter(
    married==0,
    child_u6==0
    )
models <- list(
  "Baseline"      = lm(lwage ~ gender +
                       age_centered + I(age_centered^2),
                       data = cpsmar_a),
  "Add Education" = lm(lwage ~ gender +
                       age_centered + I(age_centered^2) + CollDeg + SomeColl + HSGrad,
                       data = cpsmar_a),
  "Add Person"    = lm(lwage ~ gender +
                       age_centered + I(age_centered^2) + CollDeg + SomeColl + HSGrad +
                       Black + hisp + south + married,
                       data = cpsmar_a),
  "Add Household" = lm(lwage ~ gender +
                       age_centered + I(age_centered^2) + CollDeg + SomeColl + HSGrad +
                       Black + hisp + south + married +
                       earnings + child_u6,
                       data = cpsmar_a),
  "Only Singles"  = lm(lwage ~ gender +
                       age_centered + I(age_centered^2) + CollDeg + SomeColl + HSGrad +
                       Black + hisp + south + married, 
                       data = singles)
)

cm <- c(
  'female'            = 'Female',
  'age_centered'      = 'Age',
  'I(age_centered^2)' = 'Age$^2$',
  '(Intercept)'       = 'Constant'
)
gm <-  tibble::tribble(
  ~raw, ~clean, ~fmt,
  "nobs", "$N$", 0,
  "r.squared", "$R^2$", 2
)
rows <- tribble(~term, ~Baseline, ~Add_Education, ~Add_Person, ~Add_Household, ~Only_Singles,
  'Education controls',   ' ',         'X',           'X',          'X',           'X',
  'Demographic controls', ' ',         ' ',           'X',          'X',           'X',
  'Household controls',   ' ',         ' ',           ' ',          'X',           'X'
)
attr(rows, 'position') <- c(9, 10, 11) # Positions where you want these rows to appear

table5 <- modelsummary(
  models,
  add_rows = rows,
  coef_map = cm,
  gof_map = gm,
  vcov = c("robust","robust","robust","robust","robust"),
  title = "Table 5. OLS estimates of the gender wage gap",
  notes = "Robust standard errors in parentheses.",
  escape = FALSE
  )

The analysis shows that the gender wage gap persists even after controlling for various factors. In the baseline model, women earn significantly less than men, as indicated by the negative and statistically significant coefficient for the Female variable. Adding controls for education reduces the gap slightly, suggesting that differences in educational attainment explain part of the disparity. Demographic controls, such as race and region, further narrow the wage gap.

This project analyzed the gender wage gap using data from the March 2022 CPS, focusing on differences in earnings distributions, career progression, and the influence of education and demographics. The analysis revealed that: On average, women earn significantly less than men. Educational attainment and demographic characteristics account for some of the gap, but not all. Even after controlling for a range of factors, the wage gap persists, suggesting other unmeasured influences.

This is a list of the main variables used in this project with their definitions.