Exercise: Income inequality by education. (Worked Example)

library(tidyverse)
byCollege <- read_csv("https://info3370.github.io/sp23/assets/data/byCollege.csv")

A few observations about the data

• There are 4 variables and 360 observations.
• The year and income variables are numeric
• The education and quantity variables are characters

Below I create the ggplot. Students might have different labels, which is fine.

ggplot(data = byCollege,
       mapping = aes(x = year, y = income,
                     color = quantity)) +
  geom_point() +
  geom_smooth() +
  facet_wrap(~education) +
  scale_y_continuous(name = "Annual Wage and Salary Income\n(2022 Dollars)",
                     labels = scales::label_dollar()) +
  scale_x_continuous(name = "Year")

## `geom_smooth()` using method = 'loess' and formula = 'y ~ x'

Interpretation

Over the period from 1962 to 2022, the visualization shows one major change: the 90th percentile of real wage and salary incomes among college graduates rose substantially, from about $125,000 in 1962 to about $175,000 in 2022.

Meanwhile, the 50th and 10th percentiles among college graduate showed almost no change. Likewise, the 10th, 50th, and 90th percentiles are all fairly flat among those without a college degree. If anything, the median annual wage among those with less than a college degree declined slightly.

This evidence suggests a broader story: inequality in wage and salary incomes has risen primarily because of rising pay at the top of the earnings distribution among college-educated workers.