epidata

Tools to Retrieve Economic Policy Institute Data Library Extracts

Description

The Economic Policy Institute (https://www.epi.org/) 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.

What’s Inside The Tin

The following functions are implemented:

• get_annual_wages_and_work_hours: Retreive CPS ASEC Annual Wages and Work Hours
• get_annual_wages_by_wage_group: Annual wages by wage group
• get_black_white_wage_gap: Retreive the percent by which hourly wages of black workers are less than hourly wages of white workers
• get_college_wage_premium: Retreive the percent by which hourly wages of college graduates exceed those of otherwise equivalent high school graduates
• get_compensation_wages_and_benefits: Compensation, wages, and benefits
• get_employment_to_population_ratio: Retreive the share of the civilian noninstitutional population that is employed
• get_gender_wage_gap: Retreive the percent by which hourly wages of female workers are less than hourly wages of male workers
• get_health_insurance_coverage: Retreive Health Insurance Coverage
• get_hispanic_white_wage_gap: Retreive the percent by which hourly wages of Hispanic workers are less than hourly wages of white workers
• get_labor_force_participation_rate: Retreive the share of the civilian noninstitutional population that is in the labor force
• get_long_term_unemployment: Retreive the share of the labor force that has been unemployed for six months or longer
• get_median_and_mean_wages: Retreive the hourly wage in the middle of the wage distribution
• get_minimum_wage: Minimum wage
• get_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 school
• get_pension_coverage: Retreive Pension Coverage
• get_poverty_level_wages: Poverty-level wages
• get_productivity_and_hourly_compensation: Retreive Productivity and hourly compensation
• get_underemployment: Retreive the share of the labor force that is “underemployed”
• get_unemployment_by_state: Retreive the share of the labor force without a job (by state)
• get_unemployment: Retreive the share of the labor force without a job
• get_union_coverage: Retreive Union Coverage
• get_wage_decomposition: Retreive Wage Decomposition
• get_wages_by_education: Retreive the average hourly wages of workers disaggregated by the highest level of education attained
• get_wages_by_percentile: Retreive wages at ten distinct points in the wage distribution
• not_dos: Not DoS’ing EPI/Cloudflare

Installation

install.packages("epidata") # NOTE: CRAN version is 0.3.0
# or
install.packages("epidata", repos = c("https://cinc.rud.is", "https://cloud.r-project.org/"))
# or
remotes::install_git("https://git.rud.is/hrbrmstr/epidata.git")
# or
remotes::install_git("https://git.sr.ht/~hrbrmstr/epidata")
# or
remotes::install_gitlab("hrbrmstr/epidata")
# or
remotes::install_github("hrbrmstr/epidata")

NOTE: To use the ‘remotes’ install options you will need to have the {remotes} package installed.

Usage

library(epidata)

# current version
packageVersion("epidata")
## [1] '0.4.0'

NOTE: You can use options(epidata.show.citation = FALSE) to disable the messages that show citation requirements and notes about the datasets. Please remember that EPI requires attribution and that the notes convey very important information about the datasets.

get_black_white_wage_gap()
## # A tibble: 47 x 8
##     date white_median white_average black_median black_average gap_median gap_average gap_regression_based
##    <dbl>        <dbl>         <dbl>        <dbl>         <dbl>      <dbl>       <dbl>                <dbl>
##  1  1973         17.9          20.7         14.0          16.3      0.223       0.215              NA     
##  2  1974         17.5          20.3         14.0          16.0      0.198       0.209              NA     
##  3  1975         17.4          20.4         14.1          16.1      0.191       0.208              NA     
##  4  1976         17.5          20.5         14.2          16.8      0.19        0.182              NA     
##  5  1977         17.5          20.4         14.2          16.5      0.188       0.19               NA     
##  6  1978         17.7          20.5         14.2          16.7      0.201       0.186              NA     
##  7  1979         17.4          20.7         14.6          17.1      0.164       0.173               0.086 
##  8  1980         17.4          20.3         14.4          16.7      0.173       0.174               0.086 
##  9  1981         17.0          20.2         14.0          16.6      0.175       0.174               0.0820
## 10  1982         17.2          20.4         13.9          16.5      0.194       0.191               0.099 
## # … with 37 more rows

get_underemployment()
## # A tibble: 367 x 2
##    date         all
##    <date>     <dbl>
##  1 1989-12-01 0.094
##  2 1990-01-01 0.093
##  3 1990-02-01 0.094
##  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 0.096
## # … with 357 more rows

get_median_and_mean_wages("gr")
## # A tibble: 47 x 25
##     date median average men_median men_average women_median women_average white_median white_average black_median
##    <dbl>  <dbl>   <dbl>      <dbl>       <dbl>        <dbl>         <dbl>        <dbl>         <dbl>        <dbl>
##  1  1973   17.3    20.1       21.0        23.6         13.2          15.1         17.9          20.7         14.0
##  2  1974   16.9    19.7       20.7        23.1         13            14.9         17.5          20.3         14.0
##  3  1975   16.9    19.8       21.0        23.1         13.2          15.1         17.4          20.4         14.1
##  4  1976   16.9    20.0       20.7        23.4         13.3          15.4         17.5          20.5         14.2
##  5  1977   16.9    19.9       20.9        23.4         13.2          15.2         17.5          20.4         14.2
##  6  1978   17.1    19.9       21.2        23.5         13.2          15.3         17.7          20.5         14.2
##  7  1979   16.8    20.1       21.1        23.7         13.4          15.5         17.4          20.7         14.6
##  8  1980   16.7    19.7       20.9        23.2         13.3          15.3         17.4          20.3         14.4
##  9  1981   16.5    19.6       20.4        23.0         13.4          15.3         17.0          20.2         14.0
## 10  1982   16.4    19.8       20.4        23.2         13.2          15.6         17.2          20.4         13.9
## # … with 37 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>

Extended Example

library(tidyverse)
library(epidata)
library(ggrepel)
library(hrbrthemes)

unemployment <- get_unemployment()
wages <- get_median_and_mean_wages()

glimpse(wages)
## Rows: 47
## Columns: 3
## $ date    <dbl> 1973, 1974, 1975, 1976, 1977, 1978, 1979, 1980, 1981, 1982, 1983, 1984, 1985, 1986, 1987, 1988, 1989,…
## $ median  <dbl> 17.27, 16.93, 16.94, 16.90, 16.92, 17.07, 16.79, 16.68, 16.50, 16.43, 16.47, 16.55, 16.81, 16.92, 17.…
## $ average <dbl> 20.09, 19.72, 19.77, 19.99, 19.88, 19.92, 20.10, 19.70, 19.59, 19.76, 19.80, 19.87, 20.08, 20.57, 20.…

glimpse(unemployment)
## Rows: 510
## Columns: 2
## $ date <date> 1978-01-01, 1978-02-01, 1978-03-01, 1978-04-01, 1978-05-01, 1978-06-01, 1978-07-01, 1978-08-01, 1978-09…
## $ all  <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 0.061, 0.061, 0.060, 0.060, 0.059, 0.059, 0.059, 0.058, 0.05…

unemployment %>% 
  group_by(date = as.integer(lubridate::year(date))) %>%
  summarise(rate = mean(all)) %>%
  left_join(select(wages, date, median), by = "date") %>%
  filter(!is.na(median)) %>%
  arrange(date) -> xdf

cols <- ggthemes::tableau_color_pal()(3)

update_geom_font_defaults(font_rc)

ggplot(xdf, 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(xdf)-2)), 1),
    size = c(5, rep(3, (nrow(xdf)-2)), 5),
    color = c(cols[2], rep("#2b2b2b", (nrow(xdf)-2)), cols[3]),
    family = font_rc
  ) +
  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"
  ) +
  theme_ipsum_rc(grid="XY")

epidata Metrics

clock Package Metrics for epidata

Code of Conduct

Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.