epidata : Tools to Retrieve Economic Policy Institute Data Library Extracts
The Economic Policy Institute provides researchers, media, and the public with easily accessible, up-to-date, and comprehensive historical data on the American labor force. It is compiled from Economic Policy Institute analysis of government data sources. Use it to research wages, inequality, and other economic indicators over time and among demographic groups. Data is usually updated monthly.
The following functions are implemented:
get_black_white_wage_gap: Retreive the percent by which hourly wages of black workers are less than hourly wages of white workersget_college_wage_premium: Retreive the percent by which hourly wages of college graduates exceed those of otherwise equivalent high school graduatesget_employment_to_population_ratio: Retreive the share of the civilian noninstitutional population that is employedget_gender_wage_gap: Retreive the percent by which hourly wages of female workers are less than hourly wages of male workersget_hispanic_white_wage_gap: Retreive the percent by which hourly wages of Hispanic workers are less than hourly wages of white workersget_labor_force_participation_rate: Retreive the share of the civilian noninstitutional population that is in the labor forceget_long_term_unemployment: Retreive the share of the labor force that has been unemployed for six months or longerget_median_and_mean_wages: Retreive the hourly wage in the middle of the wage distributionget_non_high_school_wage_penalty: Retreive the percent by which hourly wages of workers without a high school diploma (or equivalent) are less than wages of otherwise equivalent workers who have graduated from high schoolget_underemployment: Retreive the share of the labor force that is "underemployed"get_unemployment: Retreive the share of the labor force without a jobget_unemployment_by_state: Retreive the share of the labor force without a job (by state)get_wages_by_education: Retreive the average hourly wages of workers disaggregated by the highest level of education attainedget_wages_by_percentile: Retreive wages at ten distinct points in the wage distributionget_wage_ratios: Retreive the level of inequality within the hourly wage distribution.devtools::install_github("hrbrmstr/epidata")library(epidata)
# current verison
packageVersion("epidata")## [1] '0.1.0'get_black_white_wage_gap()## # A tibble: 43 × 8
## date white_median white_average black_median black_average gap_median gap_average gap_regression_based
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1973 17.19 19.68 13.50 15.43 0.215 0.216 0.124
## 2 1974 16.73 19.22 13.34 15.19 0.203 0.210 0.105
## 3 1975 16.54 19.23 13.43 15.14 0.188 0.213 0.107
## 4 1976 16.73 19.39 13.45 15.74 0.196 0.188 0.089
## 5 1977 16.71 19.33 13.41 15.51 0.198 0.197 0.095
## 6 1978 16.72 19.43 13.38 15.72 0.200 0.191 0.092
## 7 1979 16.88 19.64 13.85 16.09 0.180 0.181 0.090
## 8 1980 16.58 19.23 13.50 15.73 0.185 0.182 0.092
## 9 1981 16.22 19.10 13.33 15.64 0.178 0.181 0.087
## 10 1982 16.47 19.26 13.17 15.45 0.200 0.198 0.103
## # ... with 33 more rowsget_underemployment()## # A tibble: 416 × 2
## date all
## <date> <dbl>
## 1 1989-12-01 0.093
## 2 1990-01-01 0.093
## 3 1990-02-01 0.093
## 4 1990-03-01 0.094
## 5 1990-04-01 0.094
## 6 1990-05-01 0.094
## 7 1990-06-01 0.094
## 8 1990-07-01 0.094
## 9 1990-08-01 0.095
## 10 1990-09-01 0.095
## # ... with 406 more rowsget_median_and_mean_wages("gr")## # A tibble: 43 × 25
## date median average men_median men_average women_median women_average white_median white_average black_median
## <int> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 1973 16.53 19.05 19.89 22.32 12.47 14.30 17.19 19.68 13.50
## 2 1974 16.17 18.67 19.63 21.89 12.39 14.04 16.73 19.22 13.34
## 3 1975 16.05 18.64 19.76 21.81 12.43 14.14 16.54 19.23 13.43
## 4 1976 16.15 18.87 19.41 22.05 12.56 14.53 16.73 19.39 13.45
## 5 1977 16.07 18.77 19.84 22.05 12.51 14.36 16.71 19.33 13.41
## 6 1978 16.36 18.83 20.04 22.18 12.56 14.44 16.72 19.43 13.38
## 7 1979 16.15 19.06 20.30 22.46 12.66 14.63 16.88 19.64 13.85
## 8 1980 16.07 18.66 19.98 22.00 12.60 14.46 16.58 19.23 13.50
## 9 1981 15.66 18.52 19.52 21.82 12.53 14.44 16.22 19.10 13.33
## 10 1982 15.75 18.65 19.30 21.96 12.61 14.68 16.47 19.26 13.17
## # ... with 33 more rows, and 15 more variables: black_average <dbl>, hispanic_median <dbl>, hispanic_average <dbl>,
## # white_men_median <dbl>, white_men_average <dbl>, black_men_median <dbl>, black_men_average <dbl>,
## # hispanic_men_median <dbl>, hispanic_men_average <dbl>, white_women_median <dbl>, white_women_average <dbl>,
## # black_women_median <dbl>, black_women_average <dbl>, hispanic_women_median <dbl>, hispanic_women_average <dbl>library(tidyverse)
library(epidata)
library(ggrepel)
unemployment <- get_unemployment()
wages <- get_median_and_mean_wages()
glimpse(wages)## Observations: 43
## Variables: 3
## $ date <int> 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 198...
## $ median <dbl> 16.53, 16.17, 16.05, 16.15, 16.07, 16.36, 16.15, 16.07, 15.66, 15.75, 15.71, 15.71, 15.80, 16.27, 1...
## $ average <dbl> 19.05, 18.67, 18.64, 18.87, 18.77, 18.83, 19.06, 18.66, 18.52, 18.65, 18.67, 18.75, 18.96, 19.41, 1...glimpse(unemployment)## Observations: 456
## Variables: 2
## $ date <date> 1978-12-01, 1979-01-01, 1979-02-01, 1979-03-01, 1979-04-01, 1979-05-01, 1979-06-01, 1979-07-01, 1979-...
## $ all <dbl> 0.061, 0.061, 0.060, 0.060, 0.059, 0.059, 0.059, 0.058, 0.058, 0.058, 0.059, 0.059, 0.059, 0.059, 0.05...group_by(unemployment, date=as.integer(lubridate::year(date))) %>%
summarise(rate=mean(all)) %>%
left_join(select(wages, date, median), by="date") %>%
filter(!is.na(median)) %>%
arrange(date) -> df
cols <- ggthemes::tableau_color_pal()(3)
ggplot(df, aes(rate, median)) +
geom_path(color=cols[1], arrow=arrow(type="closed", length=unit(10, "points"))) +
geom_point() +
geom_label_repel(aes(label=date),
alpha=c(1, rep((4/5), (nrow(df)-2)), 1),
size=c(5, rep(3, (nrow(df)-2)), 5),
color=c(cols[2],
rep("#2b2b2b", (nrow(df)-2)),
cols[3]),
family="Hind Medium") +
scale_x_continuous(name="Unemployment Rate", expand=c(0,0.001), label=scales::percent) +
scale_y_continuous(name="Median Wage", expand=c(0,0.25), label=scales::dollar) +
labs(title="U.S. Unemployment Rate vs Median Wage Since 1978",
subtitle="Wage data is in 2015 USD",
caption="Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata") +
hrbrmisc::theme_hrbrmstr(grid="XY")
library(epidata)
library(testthat)
date()## [1] "Thu Jan 5 13:37:48 2017"test_dir("tests/")## testthat results ========================================================================================================
## OK: 15 SKIPPED: 0 FAILED: 0
##
## DONE ===================================================================================================================