A package to Tools to Retrieve Economic Policy Institute Data Library Extracts

Description

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.

Details

Set option 'epidata.show.citation' to 'FALSE' to suppress note and citation messages. Remember, though, that EPI requires attribution if you use their data.

Author(s)

Bob Rudis (bob@rud.is)

Retreive CPS ASEC Annual Wages and Work Hours

Description

Annual, weekly, and hourly wages and work hours show the average wages and work hours of wage and salary workers using data from the CPS ASEC (also known as the March CPS). Note that this data is not directly comparable to the CPS ORG data in median/average hourly wage.

Usage

get_annual_wages_and_work_hours()

Value

tbl_df

data frame

Note

CPS ASEC | Murphy and Welch (1989)

References

Economic Policy Institute Data Library

Examples

get_annual_wages_and_work_hours()

Annual wages by wage group

Description

Return the average annual salaries for select wage groups, with particular focus on the highest wage earners. Note that this data is not directly comparable to wage deciles/percentiles.

Usage

get_annual_wages_by_wage_group()

Details

Wages are in 2017 dollars. Population sample: All workers.

The average annual wages by wage group are taken from a 2010 article by Wojciech Kopczuk, Emmanuel Saez, and Jae Song. To extend this series, data for 2006 through 2017 are extrapolated from 2004 data using changes in wage shares computed from Social Security Administration wage statistics. We employ the midpoint of the bracket to compute total wage income in each bracket and sum all brackets. We then use interpolation to derive percentile cutoffs building from the bottom up to obtain the 0–90th percentile bracket and then estimate the remaining categories. This allows us to estimate the wage shares for upper wage groups. We use these wage shares computed for 2004 and later years to extend the Kopczuk, Saez, and Song series by adding the changes in share between 2004 and the relevant year to their series. To obtain absolute wage trends we use the SSA data on the total wage pool and employment and compute the real wage per worker (based on t heir share of wages and employment) in the different groups in 2017 dollars. For a detailed explanation, see the methodology for annual wages and hours.

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: SSA | Kopczuk, Saez, and Song (2010)

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_annual_wages_by_wage_group()

Retreive the percent by which hourly wages of black workers are less than hourly wages of white workers

Description

The black-white wage gap is the percent by which hourly wages of black workers are less than hourly wages of white workers. It is also often expressed as a wage ratio (black workers' share of white workers' wages) by subtracting the gap from 100 percent.

Usage

get_black_white_wage_gap(by = NULL)

Arguments

Details

• A median black-white wage gap of 26.2 percent means that a typical black worker is paid 26.2 percent less per hour than a typical white worker.

• An average black-white wage gap of 26.6 percent means that on average black workers are paid 26.6 percent less per hour than white workers.

• A regression-based black-white wage gap of 15.2 percent means that on average black workers are paid 15.2 percent less per hour than white workers, all else held equal (controlling for gender, race and ethnicity, education, experience, and geographic location).

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_black_white_wage_gap()

if (not_dos()) get_black_white_wage_gap("g")

Retreive the percent by which hourly wages of college graduates exceed those of otherwise equivalent high school graduates

Description

A regression-based college wage premium of 56.1 percent means that on average workers with a college degree are paid 56.1 percent more per hour than workers whose highest education credential is a high school diploma, all else held equal (controlling for gender, race and ethnicity, education, experience, and geographic location).

Usage

get_college_wage_premium(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_college_wage_premium()

if (not_dos()) get_college_wage_premium("g")

Compensation, wages, and benefits

Description

Return the nonwage payments, referred to as fringe benefits, and wages. Compensation includes employer payments for health insurance, pensions, and payroll taxes (primarily payments toward Social Security and unemployment insurance).

Usage

get_compensation_wages_and_benefits()

Details

Wages are in 2016 dollars. Wage and salary workers (NIPA) | Private-sector workers (ECEC)

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: NIPA | ECEC

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_compensation_wages_and_benefits()

Retreive the share of the civilian noninstitutional population that is employed

Description

Retreive the share of the civilian noninstitutional population that is employed

Usage

get_employment_to_population_ratio(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

data frame

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_employment_to_population_ratio()

if (not_dos()) get_employment_to_population_ratio("r")

if (not_dos()) get_employment_to_population_ratio("grae")

Retreive the percent by which hourly wages of female workers are less than hourly wages of male workers

Description

The gender wage gap is the percent by which hourly wages of female workers are less than hourly wages of male workers. It is also often expressed as a wage ratio (women's share of men's wages) by subtracting the gap from 100 percent.

Usage

get_gender_wage_gap(by = NULL)

Arguments

Details

• A median gender wage gap of 17.3 percent means that a typical woman is paid 17.3 percent less per hour than a typical man.

• An average gender wage gap of 19.7 percent means that on average women are paid 19.7 percent less per hour than men.

• A regression-based gender wage gap of 21.7 percent means that on average women are paid 21.7 percent less per hour than men, all else held equal (controlling for gender, race and ethnicity, education, experience, and geographic location).

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_gender_wage_gap()

if (not_dos())
get_gender_wage_gap("r")

Retreive Health Insurance Coverage

Description

Employer-sponsored health insurance (ESI) coverage shows the share of workers who received health insurance from their own job for which their employer paid for at least some of their health insurance coverage.

Usage

get_health_insurance_coverage(by = NULL)

Arguments

Details

Population sample: Private-sector workers age 18–64 & at least 20 hours/week and 26 weeks/year

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: CPS ASEC

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_health_insurance_coverage()

if (not_dos()) get_health_insurance_coverage("r")

if (not_dos()) get_health_insurance_coverage("gr")

Retreive the percent by which hourly wages of Hispanic workers are less than hourly wages of white workers

Description

The Hispanic-white wage gap is the percent by which hourly wages of Hispanic workers are less than hourly wages of white workers. It is also often expressed as a wage ratio (Hispanic workers' share of white workers' wages) by subtracting the gap from 100 percent.

Usage

get_hispanic_white_wage_gap(by = NULL)

Arguments

Details

• A median Hispanic-white wage gap of 29.6 percent means that a typical Hispanic worker is paid 29.6 percent less per hour than a typical white worker.

• An average Hispanic-white wage gap of 30.1 percent means that on average Hispanic workers are paid 30.1 percent less per hour than white workers.

• A regression-based Hispanic-white wage gap of 11.1 percent means that on average Hispanic workers are paid 11.1 percent less per hour than white workers, all else held equal (controlling for gender, race and ethnicity, education, experience, and geographic location).

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_hispanic_white_wage_gap()

if (not_dos()) get_hispanic_white_wage_gap("g")

Retreive the share of the civilian noninstitutional population that is in the labor force

Description

(i.e., working or looking for work)

Usage

get_labor_force_participation_rate(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

get_labor_force_participation_rate()

get_labor_force_participation_rate("r")

get_labor_force_participation_rate("grae")

Retreive the share of the labor force that has been unemployed for six months or longer

Description

Retreive the share of the labor force that has been unemployed for six months or longer

Usage

get_long_term_unemployment(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

get_long_term_unemployment()

get_long_term_unemployment("r")

get_long_term_unemployment("grae")

Retreive the hourly wage in the middle of the wage distribution

Description

The median wage is the hourly wage in the middle of the wage distribution; 50 percent of wage earners earn less and 50 percent earn more. The average wage is the arithmetic mean of hourly wages; or, the sum of all workers' hourly wages divided by the number of workers.

Usage

get_median_and_mean_wages(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

get_median_and_mean_wages()

get_median_and_mean_wages("r")

get_median_and_mean_wages("gr")

Minimum wage

Description

Return tthe hourly minimum wage set by federal law. The real minimum wage is the federal hourly minimum wage adjusted for inflation.

Usage

get_minimum_wage()

Details

Wages are in 2016 dollars, excluding the nominal federal minimum wage. Share of average wages based on the average wages of production and nonsupervisory workers. For state minimum wages, see EPI’s minimum wage tracker.

Population sample: Production and nonsupervisory workers (average wages)

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: U.S. Department of Labor Wage and Hour Division | CES

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_minimum_wage()

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

Description

A regression-based non-high school wage penalty of 21.8 percent means that on average workers without a high school diploma are paid 21.8 percent less per hour than workers with a high school diploma, all else held equal (controlling for gender, race and ethnicity, education, experience, and geographic location).

Usage

get_non_high_school_wage_penalty(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

## Not run: 
if (not_dos()) get_non_high_school_wage_penalty()

if (not_dos()) get_non_high_school_wage_penalty("g")

## End(Not run)

Retreive Pension Coverage

Description

Employer-provided pension coverage shows the share of workers included in an employer-provided plan for which the employer paid for at least some of their pension coverage.

Usage

get_pension_coverage(by = NULL)

Arguments

Details

Population sample: Private-sector workers age 18–64 & at least 20 hours/week and 26 weeks/year

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: CPS ASEC

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_health_insurance_coverage()

if (not_dos()) get_health_insurance_coverage("r")

if (not_dos()) get_health_insurance_coverage("gr")

Poverty-level wages

Description

Return the share of workers earning equal to or less than the poverty-level wage, or the hourly wage that a full-time, year-round worker must earn to sustain a family of four with two children at the official poverty threshold.

Usage

get_poverty_level_wages(by = NULL)

Arguments

Details

Population sample: Wage and salary workers age 18–64. Data source: CPS ORG | Census Bureau (poverty threshold)

Value

tbl_df with data filtered by the selected criteria.

data frame

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_poverty_level_wages()

if (not_dos()) get_poverty_level_wages("r")

if (not_dos()) get_poverty_level_wages("gr")

Retreive Productivity and hourly compensation

Description

Productivity is how much workers produce per hour, or the growth of output of goods and services minus depreciation per hour worked. Compensation is made up of both nonwage payments and wages.

Usage

get_productivity_and_hourly_compensation(by = NULL)

Arguments

Details

Wages are in 2015 dollars. Median compensation is calculated using hourly wage medians from the CPS ORG and compensation from NIPA.

Population sample: All workers & Production and nonsupervisory workers

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: NIPA (compensation) | BLS Productivity Data

References

Economic Policy Institute Data Library

Examples

if (not_dos()) get_productivity_and_hourly_compensation()

if (not_dos()) get_productivity_and_hourly_compensation("g")

Retreive the share of the labor force that is "underemployed"

Description

Underemployment is the share of the labor force that either 1) is unemployed, 2) is working part time but wants and is available to work full time (an "involuntary" part timer), or 3) wants and is available to work and has looked for work in the last year but has given up actively seeking work in the last four weeks ("marginally attached" worker).

Usage

get_underemployment(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

get_underemployment()

get_underemployment("r")

get_underemployment("grae")

Retreive the share of the labor force without a job

Description

Retreive the share of the labor force without a job

Usage

get_unemployment(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

Note

See get_unemployment_by_state() for information on retrieving unemployment by state+race.

References

Economic Policy Institute Data Library

Examples

get_unemployment()

get_unemployment("r")

get_unemployment("grae")

Retreive the share of the labor force without a job (by state)

Description

Retreive the share of the labor force without a job (by state)

Usage

get_unemployment_by_state(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

Note

See get_unemployment() for other unemployment extracts..

References

Economic Policy Institute Data Library

Examples

get_unemployment_by_state()

get_unemployment_by_state("r")

Retreive Union Coverage

Description

The union coverage rate shows the percentage of the workforce covered by a collective bargaining agreement.

Usage

get_union_coverage()

Value

tbl_df

data frame

Note

Data source: CPS ORG | Hirsch and Macpherson (2003)

References

Economic Policy Institute Data Library

Examples

if (interactive()) get_union_coverage()

Retreive Wage Decomposition

Description

Wage inequality data shows the overall wage inequality and the within-group and between-group wage inequality over time. These measures allow an examination of how much of the change in overall wage inequality in particular periods was due to changes in within-group and between-group wage inequality.

Usage

get_wage_decomposition(by = NULL)

Arguments

Details

Population sample: Wage and salary workers age 18–64

Value

tbl_df with data filtered by the selected criteria.

data frame

Note

Data source: CPS ORG

References

Economic Policy Institute Data Library

Examples

get_wages_by_percentile()

get_wages_by_percentile("g")

Retreive the average hourly wages of workers disaggregated by the highest level of education attained

Description

Wages by education are the average hourly wages of workers disaggregated by the highest level of education attained. Employment shares provide the distribution of educational attainment for workers of each gender, racial, and ethnic group as a share of total employed for each group.

Usage

get_wages_by_education(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

References

Economic Policy Institute Data Library

Examples

get_wages_by_education()

get_wages_by_education("r")

get_wages_by_education("gr")

Retreive wages at ten distinct points in the wage distribution

Description

Wage percentiles are wages at ten distinct points in the wage distribution: deciles and the 95th percentile. The 95–50 and 50–10 wage ratios show how much greater wages are at the top than the middle, and at the middle than the bottom, respectively.

Usage

get_wages_by_percentile(by = NULL)

Arguments

Value

tbl_df with data filtered by the selected criteria.

data frame

References

Economic Policy Institute Data Library

Examples

get_wages_by_percentile()

get_wages_by_percentile("r")

get_wages_by_percentile("gr")

Not DoS'ing EPI/Cloudflare

Description

The EPI site/API is protected by Cloudflare. CF thinks CRAN is DoS'ing it when it runs examples and tests. This function is being used to prevent tests and examples from failing on CRAN.

Usage

not_dos()

Value

logical