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This report will document the gender wage gap, showcasing a plausible relationship between wages and gender, but will also explore other contributors that impact these factors. The data this report utilizes includes information on individuals living within America and factors in their life which will be used to document plausible connections for the gender wage gap.

The data is the 2024 Current Population Survey (CPS), a monthly survey to gain necessary insights on households, and the Annual Social and Economic (ASEC), demographic information and labor force data. supplemented from the US Census Bureau from 54000 households.

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

The data set includes 50388 observations and 20 The variables chosen for this report specifically for personal characteristics are an individual’s race, region, as well as their age. The household factors refers to their marital status as well as any children under 6 being present within the house. The data restricts the region and city to specific principle ones.

cpsmar_a <- cpsmar_e %>%
  filter(
    age >= 23, 
    age <= 62,
    earnings > 0
  ) %>%
  mutate(
    gender = ifelse(female == 1, "Female", "Male"),
    wage = earnings/(hours*weeks), 
    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
  )

figure1 <- ggplot(data = 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. 

When taking a look at the current graph, it is clear to see there is a larger peak for female wages and its location is lower than the male equivalent’s. Another major observation to be made is to understand there is a larger variation in men’s earnings.

The table shows a clear distinction between men and women average wages, exemplifying a significant increase of 7.26 percentage points in men wages. The men standard deviation for wages is 9.21 percentage points higher than the women, showing more variation in their earnings. When looking at the hours logged for each gender, there is less of a difference between the two, with men higher a mean that is 1.64 percentage points higher and standard deviation that is 1.46 percentage points higher.

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

figure2 <- ggplot(data = cef_fvm_w, aes(x = age, 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") %>%
  filter(age <= 30) %>%
  mutate(
    diff = avg_lwage_male - avg_lwage_female,
    age_group = cut(
      age, 
      breaks = c(-1, 10, 20, 30), 
      labels = c("1-10", "11-20", "21-30"))
    ) %>%
  group_by(age_group) %>%
  summarise(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")

When looking at Figure 2, it is clear to see that the women earnings plateau much lower than the men. Table 2 quantifies the percentage difference of 7.7 for 21-30 year old age group to give a better idea of the gap. The graph confirms this gap by showing how men also start off with a higher starting salary, while women salaries decrease substantially more in the last 10 years.

formula <- lwage ~ age + I(age^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,
    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")

Table 3 presents educational attainment of the men and women within the study and shows more women have completed a college degree over men by 10 percentage points. Despite this, it is clear to see women still are getting paid less despite the higher proportion of women attaining a higher degree of education. While Table 3 focuses on educational characteristics, table 4 hones into demographic related factors such as whether an individual is age, race, region, and marital status. In terms of demographics, there is not as much variation between the two gender groups. It can be seen that women have a greater mean in the marital status. This can signify that men are more likely to be married than women.

singles <- cpsmar_a %>%
  filter(
    married== 0,
    child_u6== 0
    )


models <- list(
  "Baseline"      = lm(lwage ~ female +
                       age_centered + I(age_centered^2),
                       data = cpsmar_a),
  "Add Education" = lm(lwage ~ female +
                       age_centered + I(age_centered^2) + HSGrad + SomeColl + CollDeg,
                       data = cpsmar_a),
  "Add Person"    = lm(lwage ~ female +
                       age_centered + I(age_centered^2) + HSGrad + SomeColl + CollDeg +
                       hisp + race + region + married,
                       data = cpsmar_a),
  "Add Household" = lm(lwage ~ female +
                       age_centered + I(age_centered^2) + HSGrad + SomeColl + CollDeg +
                       hisp + race + region + married +
                       child_u6,
                       data = cpsmar_a),
  "Only Singles"  = lm(lwage ~ female +
                       age_centered + I(age_centered^2) + HSGrad + SomeColl + CollDeg +
                       hisp + race + region + marital, 
                       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("HC1","HC2","HC3","HC4","HC5"),
  title = "Table 5. OLS estimates of the gender wage gap",
  notes = "robust standard errors in parentheses.",
  escape = FALSE
  )

Within the baseline model, the estimated gender wage gap is seen to be around -16.5 log points showcasing that women are earn less than men holding age constant.Once we add education as a factor, we can see the coefficient becomes more negative, indicating that the gap in wages becomes larger, showcasing the female gender’s conditional disadvantage. When including demographic controls it can be seen that these characteristics can connect and possibly account for the wage gap. However, once including household factors, there is not a significant difference when controlling for these factors. This contrasts with singles as the gap significantly decreases possibly due to the less variation in household structure. This large difference can be attributed with difference in demand for marital and child responsibilities for women.

These results describe and provide background for factors that can be possibly correlated with the gender wage gap.

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