Chart notes | Wages

Tables

Table 4.1. Average wages and work hours, 1967–2010.Productivity data, which measure output per hour of the total economy, including the private and public sectors, are from an unpublished series available from the Bureau of Labor Statistics Labor Productivity and Costs program on request. The wage-level data are based on the authors’ tabulations of Current Population Survey Annual Social and Economic Supplement (CPS-ASEC, also known as the March CPS) microdata files using a series on annual, weekly, and hourly wages for wage and salary workers. See Appendix B for the sample definition and other information. The weekly and hourly wage data are “hour weighted,” obtained by dividing annual wages by weeks worked and annual hours worked. The 1967 and 1973 values are derived from unpublished tabulations provided by Kevin Murphy from an update of Murphy and Welch (1989); they include self-employment as well as wage and salary workers. The values displayed in this table were bridged from CPS 1979 values using the growth rates in the Murphy and Welch series. Hours of work were derived from differences between annual, weekly, and hourly wage trends.

Table 4.2. Average hourly pay and pay inequality, 1948–2011.The data in the top panel are computed from the Bureau of Economic Analysis National Income and Product Accounts (NIPA) tables. “Wages and salaries” are calculated by dividing wage and salary accruals (NIPA Table 6.3) by hours worked by full-time and part-time employees (NIPA Table 6.9). “Total compensation” is the sum of wages and salaries and benefits (it includes payroll taxes and health, pension, and other nonwage benefits). Payroll taxes are calculated as total compensation (NIPA Table 6.2) minus the sum of volunteer benefits (sum of health and nonhealth benefits; see NIPA Table 6.11) and wages and salaries. “Benefits” is the difference between total compensation and wages and salaries. These data were deflated using the NIPA personal consumption expenditure (PCE, chain-weighted) index, with health insurance adjusted by the PCE medical care (chained) index. These data include both public- and private-sector workers.

The data in the Employer Costs for Employee Compensation (ECEC) panel come from the BLS National Compensation Survey’s employment cost trends and benefits data and provide cost levels for March for private-sector workers, available starting in 1987. We categorize wages and salaries differently than BLS, putting all wage-related items (including paid leave and supplemental pay) into the hourly wage/salary column. This makes the definition of wages and salaries comparable to workers’ W-2 earnings and to the definition of wages in the CPS Outgoing Rotation Group (ORG) data that are tabulated for other tables in this chapter. Benefits, in our definition, include only payroll taxes, pensions, insurance, and “other” benefits. The sum of wages and salaries and benefits makes up total compensation. It is important to use the ECEC (the current-weighted series) rather than the other series from the same National Compensation Survey (NCS) data, the ECI (the fixed-weighted series), because composition shifts (in the distribution of employment across occupations and industries) can have large effects over time. Employer costs for insurance are deflated by the medical-care component of the CPI-U-RS (Consumer Price Index Research Series Using Current Methods). All other pay is deflated by the CPI-U-RS for “all items.” Inflation is measured for the first quarter of each year. Wage and compensation inequality measures are drawn from Pierce (2010). Pierce computes these from the NCS microdata, the data used to calculate the ECI and ECEC data.

Table 4.3. Hourly and weekly earnings of private production and nonsupervisory workers, 1947–2011. Underlying data are from the Bureau of Labor Statistics Current Employment Statistics program data from the Employment, Hours, and Earnings–National database, deflated using CPI-U-RS.

Table 4.4. Hourly wages of all workers, by wage percentile, 1973–2011. Table is based on analysis of CPS wage data described in Appendix B.

Table 4.5. Hourly wages of men, by wage percentile, 1973–2011. Table is based on analysis of CPS wage data described in Appendix B.

Table 4.6. Hourly wages of women, by wage percentile, 1973–2011. Table is based on analysis of CPS wage data described in Appendix B.

Table 4.7. Change in wage groups’ shares of total wages, 1979–2010. Data are taken from Kopczuk, Saez, and Song (2010), Table A-3. Data for 2006 through 2010 are extrapolated from 2004 data using changes in wage shares computed from Social Security Administration wage statistics (data for 2010 at http://www.ssa.gov/cgi-bin/netcomp.cgi). The final results of the paper by Kopczuk, Saez, and Song printed in a journal used a more restrictive definition of wages so we employ the original definition, as recommended in private correspondence with Kopczuk. SSA provides data on share of total wages and employment in annual wage brackets such as for those earning between $95,000.00 and $99,999.99. We employ the midpoint of the bracket to compute total wage income in each bracket and sum all brackets. Our estimate of total wage income using this method replicates the total wage income presented by SSA with a difference of less than 0.1 percent. We use interpolation to derive 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 their share of wages and employment) in the different groups in 2011 dollars.

Table 4.8. Change in annual wages, by wage group, 1979–2010. See note to Table 4.7.

Table 4.9. Specific fringe benefits, 1987–2011. Table is based on ECEC data described in note to Table 4.2.

Table 4.10. Employer-provided health insurance coverage, by demographic and wage group, 1979–2010. Table is based on tabulations of CPS-ASEC data samples of private wage-and-salary earners ages 18–64 who worked at least 20 hours per week and 26 weeks per year. This sample is chosen to focus on those with regular employment. Coverage is defined as being included in an employer-provided plan for which the employer paid for at least some of the coverage. As with other CPS microdata analyses presented in the book, race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race).

Table 4.11. Employer-provided pension coverage, by demographic and wage group, 1979–2010. Table is based on CPS-ASEC data on pension coverage, using the sample described in the note to Table 4.10. As with other CPS microdata analyses presented in the book, race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race).

Table 4.12. Share of workers with paid leave, by wage group, 2011.Computed from BLS Employee Benefits Survey, Holiday, Vacation, Sick, and Other Leave Benefits, March 2011, data tables 34, 36, and 38; http://www.bls.gov/ncs/ebs/benefits/2011/benefits_leave.htm.

Table 4.13. Dimensions of wage inequality, by gender, 1973–2011. All of the data are based on analyses of the CPS-ORG data described in Appendix B and used in various tables. The measures of “total wage inequality” are natural logs of wage ratios (multiplied by 100) computed from Tables 4.5 and 4.6. The exception is 1979 data for women, which are 1978–1980 averages; we use these to smooth the volatility of the series, especially at the 10th percentile. The “between-group inequalities” are computed from regressions of the log of hourly wages on education categorical variables (advanced, college only, some college, less than high school with high school omitted), experience as a quartic, marital status, race, and region (4). The college/high school and high school/less-than-high-school premiums are simply the coefficient on “college” and “less than high school” (expressed as the advantage of “high school” over “less than high school” wages). The experience differentials are the differences in the value of age (calculated from the coefficients of the quartic specification) evaluated at 25, 35, and 50 years old. “Within-group wage inequality” is measured as the root mean square error from the same log wage regressions used to compute age and education differentials.

Table 4.14. Hourly wages by education, 1973–2011. Table is based on tabulations of CPS wage data described in Appendix B. See Appendix B for details on how a consistent measure of education was developed to bridge the change in coding in 1992.

Table 4.15. Hourly wages of men, by education, 1973–2011. See note to Table 4.14.

Table 4.16. Hourly wages of women, by education, 1973–2011. See note to Table 4.14.

Table 4.17. Educational attainment of the employed, by gender and nativity, 2011. Table is based on analysis of CPS wage earners. The data are described in Appendix B. The categories are as follows: “less than high school” is grade 1–12 or no diploma; “high school/GED” is high school graduate diploma or equivalent; “some college” is some college but no degree; “associate degree” is occupational or academic associate degree; “college degree” is a bachelor’s degree; and “advanced degree” is a master’s, professional, or doctoral degree.

Table 4.18. Hourly wages of entry-level and experienced workers, by gender and education, 1973–2011. Table is based on analysis of CPS wage data described in Appendix B. Entry-level wages are measured for a seven-year window starting a year after normal graduation, which translates to ages 19–25 for high school graduates and ages 23–29 for college graduates.

Table 4.19. Hourly wages by wage percentile, gender, and education, 1973–2011. Table is based on analysis of CPS wage data described in Appendix B.

Table 4.20. Contribution of within-group and between-group inequality to total wage inequality, 1973–2011. Data are from the CPS-ORG sample described in Appendix B. “Overall wage inequality” is measured as the standard deviation of log wages. “Within-group wage inequality” is the mean square error from log wage regressions (the same ones used for Table 4.13). “Between-group wage inequality” is the difference between the overall and within-group wage inequalities and reflects changes in all of the included variables: education, age, marital status, race, ethnicity, and region.

Table 4.21. Hourly wage growth by gender and race/ethnicity, 1989–2011. Table is based on analysis of CPS wage data described in Appendix B. As with other CPS microdata analyses presented in the book, race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race).

Table 4.22. Gender wage gap, 1973–2011. Wages and ratios are based on 50th-percentile wages from Tables 4.5 and 4.6 (CPS-ORG data).

Table 4.23. Factors contributing to the productivity/compensation gap, 1973–2011. Table is based on analysis of Mishel and Gee (2012), Table 1. Mishel and Gee present a decomposition of the gap between productivity and median hourly compensation. This has been reconfigured to eliminate the gap between median hourly wages and compensation so the decomposition is between productivity and median hourly compensation.

Table 4.24. Impact of rising and falling unemployment on wage levels and gaps, 1979–2011. Table is based on analyses of yearly wage decile data from Tables 4.5 and 4.6 (see Appendix B), and of unemployment data using model from Katz and Krueger (1999). The unemployment rate is from the Current Population Survey. The simulated effect of change of unemployment presented in the table was calculated by regressing the log-change of nominal wages on the lagged log-change of the CPI-U-RS (but, following Katz and Krueger [1999], the coefficient is constrained to equal 1), the unemployment rate, lagged productivity growth, and dummies for various periods (1989–1995, 1996–2000, 2001–2007). Using these models, wages were predicted for the periods in the table given a simulated unemployment rate series in which unemployment remains fixed at its starting-year level. So in the 1979 to 1985 period, unemployment was fixed at its 1979 level and not allowed to rise (as actually happened) throughout the period. The “estimated cumulative impact of unemployment” shows the difference between actual wages and the wages when unemployment was held fixed in the starting year.

Table 4.25. Annual pay in expanding and contracting industries, 1979–2007. These data reflect the average (annual) wages, benefits, and compensation of the net new employment in each period based on changes in industry composition. The employment data are payroll counts from the BLS Current Employment Statistics, and the pay data are from 2008 Bureau of Economic Analysis NIPA tables (calculated per payroll employee). The pay of the net new employment is a weighted average of the pay by industry in which the weights are the changes in each industry’s employment share over the period.

Table 4.26. Employer health care costs as a share of wages, 1948–2010. Table is based on analysis of National Income and Product Accounts data. Wage data are from NIPA Table 6.3, and group health insurance data are from NIPA Tables 6.11A-C, and 6.11D.

Table 4.27. Employer health care costs as a share of wages, by wage fifth, 1996–2008. Table is based on analysis of Burtless and Milusheva (2012) based on Medical Expenditure Panel Survey. The authors provide data by decile which we aggregated to fifths. The premiums include both those enrolled and not enrolled in employer plans. The premiums were estimated by Burtless and Milusheva using various imputation methods.

Table 4.28. Impact of trade balance in manufacturing on employment and wages, by education, 1979–2005. Table is based on analysis of Bivens (2008).

Table 4.29. Impact of trade with low-wage countries on college/noncollege wage gap, 1973–2011. Table is an update of Bivens’s (2008) reanalysis of Krugman (1995) using 2011 data.

Table 4.30. Characteristics of offshorable and non-offshorable jobs. Table reflects authors’ analysis of the Bernstein, Lin, and Mishel (2007) analysis of data of Blinder (2007), matching Blinder’s occupational codes to the BLS Occupational Employment Statistics (OES) survey (http://www.bls.gov/oes/) and Blinder and Krueger (2009) Table 4.

Table 4.31. Mexican and other immigrants’ share of U.S. workforce, by gender, 1940–2011. Data are from Figure 1 in Borjas and Katz (2005) and authors’ computations of Current Population Survey basic monthly microdata for 2000 and 2011.

Table 4.32. Educational attainment of immigrants, by gender, 1940–2011.Data are from Table 2 in Borjas and Katz (2005) and authors’ computations of Current Population Survey basic monthly microdata for 2000 and 2011.

Table 4.33. Union wage premium by demographic group, 2011. “Percent union” is tabulated from CPS-ORG data (see Appendix B) and includes all those covered by unions. “Union premium” values are the coefficients on union in a model of log hourly wages with controls for education, experience as a quartic, marital status, region, industry (12) and occupation (9), race/ethnicity, and gender where appropriate. For this analysis we only use observations that do not have imputed wages because the imputation process does not take union status into account and therefore biases the union premium toward zero. See Mishel and Walters (2003). As with other CPS microdata analyses presented in the book, race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race).

Table 4.34. Union premiums for health, retirement, and paid leave benefits. Table is based on Table 4 in Mishel and Walters (2003), which draws on Buchmueller, DiNardo, and Valletta (2001).

Table 4.35. Union impact on paid leave, pension, and health benefits. Table is based on Table 3 in Mishel and Walters (2003), which draws on Pierce (1999), Tables 4, 5, and 6.

Table 4.36. Effect of union decline on male wage differentials, 1978–2011. This analysis replicates, updates, and expands on Freeman (1991), Table 2, using the CPS-ORG sample used in other analyses (see Appendix B). The year 1978, rather than 1979, is the earliest year analyzed because we have no union membership data in our 1979 sample. “Percent union” is the share covered by collective bargaining. The “union wage premium” for a group is based on the coefficient on collective bargaining coverage in a regression of hourly wages on a simple human capital model (the same one used for estimating education differentials, as described in note to Table 4.13), with major industry (12) and occupation (9) controls in a sample for that group. The change in union premium across years, therefore, holds industry and occupation composition constant. Freeman’s analysis assumed the union premium was unchanged over time. We allow the union premium to differ across years so changes in the “union effect” on wages (the union wage premium times union coverage) are driven by changes in the unionization rate and the union wage premium. The analysis divides the percentage-point change in the union effect on wage differentials by the actual percentage-point change in wage differentials (regression-adjusted with simple human capital controls plus controls for other education or occupation groups) to determine the deunionization contribution to the change in the wage gaps among men, which, as a negative percent, indicates contribution to the growth of the wage gaps.

Table 4.37. Union wage premium for subgroups. The analysis builds on Mishel and Walters (2003), Table 2.3A and Gundersen (2003), Table 5.1 and Appendix C. Premium estimates by fifth are from Schmitt (2008); Card, Lemieux, and Riddle (2002); and Gittleman and Pierce (2007). Union coverage by fifth is from Schmitt (2008).

Table 4.38. Impact of deunionization on wage inequality, 1973–2007. Table is based on analysis of Western and Rosenfeld (2011), Table 2.

Table 4.39. Value of the minimum wage, 1960–2011. Data, deflated using CPI-U-RS, are from the U.S. Department of Labor Wage and Hour Division (2009); http://www.dol.gov/whd/minwage/chart.htm.

Table 4.40. Characteristics of workers affected by proposed minimum-wage increase to $9.80 in 2014. Table is based on Cooper (2012) analysis of CPS Outgoing Rotation Group microdata. As with other CPS microdata analyses presented in the book, race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race).

Table 4.41. Minimum-wage impact on 50/10 wage gap, 1979–2009. Analysis is of Autor, Manning, and Smith (2010), Table 5.

Table 4.42. Role of executives and financial sector in income growth of top 1.0% and top 0.1%, 1979–2005. Table is based on authors’ analysis of Bakija, Cole, and Heim (2012) Tables 4, 5, 6a, and 7a, using tables that include capital-gains income. The Bakija, Cole, and Heim paper tabulates IRS tax returns and exploits the information on the primary and secondary taxpayer occupation data provided there.

Table 4.43. CEO compensation and CEO-to-worker compensation ratio, 1965–2011. Complete details on the data used to compute CEO compensation trends and the CEO-to-worker compensation ratio can be found in Mishel and Sabadish (2012), Methodology for Measuring CEO Compensation and the Ratio of CEO-to-Worker Compensation at http://www.epi.org/publication/wp293-ceo-to-worker-pay-methodology. We use executive compensation data from the ExecuComp database of Compustat, a division of Standard & Poor’s. The ExecuComp database contains data on many forms of compensation for the top five executives at publicly traded U.S. companies in the S&P 1500 Index for 1992–2010. We employ two definitions of annual CEO compensation based on different ways of measuring option awards. “Realized direct compensation,” referred to as “Options realized” in the table, is the sum of salary, bonus, restricted stock grants, options exercised, and long-term incentive payouts. It follows the definition of compensation used in previous editions of The State of Working America, which in turn adapted this definition from the Wall Street Journal (WSJ) annual report on CEO compensation (compensation reported by the WSJ has been compiled by various companies over the years, including Pearl Meyer, the Mercer Group, and the Hay Group and is the longest CEO pay series available to us). “Total direct compensation” (also a definition used in the WSJ series and labeled “Options granted” in the table) is the sum of salary, bonus, restricted stock grants, options granted (Compustat Black Scholes value), and long-term incentive payouts.

We define a CEO as an executive labeled a CEO by the variable CEOANN. Note that the executive flagged as the CEO may not necessarily be the highest-paid executive at the company. The CEOs included in our series are CEOs at the top 350 firms based on sales each year for 1992–2010.

Because no data for the compensation of an average worker in a firm exist, we create a proxy: the hourly compensation of a “typical” worker in a firm’s key industry. The wage measure is the production/nonsupervisory worker hourly earnings in that industry, the same series used in Table 4.3 for the entire private sector. We obtain compensation by multiplying the compensation wage ratio computed from NIPA Tables 6.3C and 6.3D. The hourly wages of production and nonsupervisory employees in 2011 were $19.47, 21 percent higher than the median hourly wage, so our proxy severely overstates the compensation of a typical worker and understates the CEO-to-worker pay ratio.

We use the growth in CEO compensation in the WSJ series to extend the CEO compensation series and the CEO-to-worker compensation ratio series backward. The WSJ series conducted by Pearl Meyer covered the years 1965, 1968, 1973, 1978, 1989, and 1992. We convert the compensation series to constant dollars using the CPI-U-RS and calculate the ratio of CEO compensation in each year as a fraction of the 1992 CEO compensation level. We then apply these ratios to the CEO compensation for 1992 calculated from the ExecuComp data. This moves the series backward in time so that the growth of CEO pay is the same as in the Pearl Meyer/WSJ series but is benchmarked to the levels in the ExecuComp series.

We make a similar set of computations to obtain a historical series for the CEO-to-worker compensation ratio. We start with the Pearl Meyer/WSJ series in constant dollars and divide it by an estimate of private-sector annual compensation of production/nonsupervisory workers in the same year. The compensation series is the real hourly compensation series presented in Figure 4B multiplied by 2,080 hours.

Table 4.44. Trends in education wage gaps, key wage group wage gaps, and relative supply of education, 1979–2011. The gross wage gap data are computed from underlying yearly data with selected years presented in Tables 4.4 and 4.8. The education wage gaps are computed from the same regressions for which results on college/high school and high school/less-than-high-school wage premiums are reported in Table 4.13, regressions of the log of hourly wages on education categorical variables (advanced degree, college only, some college, less than high school with high school omitted), experience as a quartic, marital status, race, and region (4). The college or more/noncollege differential is drawn from a similar regression except there is only one education dummy variable for those with a college degree or advanced degree. This estimate was also used in the analysis of trade’s impact on the college wage gap presented in Table 4.29.

Table 4.45. Inflation-adjusted hourly wage trends of college graduates, by occupation, 2000–2011. Table is based on tabulations of CPS-ORG data with a sample of those with a college degree (but no advanced degree). See Appendix B for information on the wage data.

Table 4.46. Effect of changing occupational composition on wages and on education and training requirements, 2010–2020. Table is based on analysis of Thiess (2012), Tables 5 and 6, and BLS Employment Projections Program (2012), Table 9.

Figures

Figure 4A. Cumulative change in total economy productivity and real hourly compensation of selected groups of workers, 1995–2011. Productivity data, which measure output per hour of the total economy, including private and public sectors, are from an unpublished series available from the Bureau of Labor Statistics Labor Productivity and Costs program on request. Wage measures are the annual data used to construct tables in this chapter: median hourly wages (at the 50th percentile) from Table 4.4 and hourly wages by education from Table 4.14. These are converted to hourly compensation by scaling by the real compensation/wage ratio from the Bureau of Economic Analysis National Income and Product Accounts (NIPA) data used in Table 4.2.

Figure 4B. Real hourly earnings and compensation of private production and nonsupervisory workers, 1947–2011. Wage data are from series used in Table 4.3. Wages are converted to hourly compensation by scaling by the real compensation/wage ratio from the NIPA data used in Table 4.2.

Figure 4C. Cumulative change in real hourly wages of men, by wage percentile, 1979–2011. See note to Table 4.5.

Figure 4D. Cumulative change in real hourly wages of women, by wage percentile, 1979–2011. See note to Table 4.6.

Figure 4E. Share of workers earning poverty-level wages, by gender, 1973–2011. Figure is based on analysis of Current Population Survey (CPS) wage data described in Appendix B. The poverty-level wage is calculated using an estimate of the four-person weighted average poverty threshold in 2011 of $23,010 (based on the 2010 threshold updated for inflation). This is divided by 2,080 hours to obtain a poverty-level wage of $11.06 in 2011. The poverty-level wage is roughly equal to two-thirds of the median hourly wage. This figure is deflated by CPI-U-RS (Consumer Price Index Research Series Using Current Methods) to obtain the poverty-level wage levels for other years. The threshold is available at the U.S. Census Bureau website.

Figure 4F. Share of workers earning poverty-level wages, by race and ethnicity, 1973–2011. As with other CPS microdata analyses presented in the book, race/ethnicity categories are mutually exclusive (i.e., white non-Hispanic, black non-Hispanic, and Hispanic any race).

Figure 4G. Share of total annual wages received by top earners, 1947–2010. See note to Table 4.7.

Figure 4H. Cumulative change in real annual wages, by wage group, 1979–2010. See note to Table 4.7.

Figure 4I. Share of private-sector workers with employer-provided health insurance, by race and ethnicity, 1979–2010. See note to Table 4.10.

Figure 4J. Share of pension participants in defined-contribution and defined-benefit plans, 1980–2004. Figure is based on Center for Retirement Research (2006), which used data from the Current Population Survey and the Department of Labor’s Annual Return/Report Form 5500 Series.

Figure 4K. Wage gaps among men, 1973–2011. Figure is based on ratios of yearly hourly wage by decile data presented in Table 4.5.

Figure 4L. Wage gaps among women, 1973–2011. Figure is based on ratios of yearly hourly wage by decile data presented in Table 4.6.

Figure 4M. Wage gap between the 95th and 50th percentiles, by gender, 1973–2011. Figure is based on ratios of yearly hourly wage by percentile data presented in Tables 4.5 and 4.6.

Figure 4N. College wage premium, by gender, 1973–2011. Differentials are estimated with controls for experience (as a quartic), region (4), marital status, race/ethnicity, and education, which are specified as dummy variables for less than high school, some college, college, and advanced degree. Log of hourly wage is the dependent variable. Estimates were made on the CPS-ORG data as described in Appendix B, and presented in Table 4.13.

Figure 4O. Share of the employed lacking a high school degree, by race/ethnicity and nativity status, 2011. Figure is based on tabulations of the full monthly CPS. See Appendix B for details on data.

Figure 4P. Real entry-level wages of high school graduates, by gender, 1973–2011. See note to Table 4.18.

Figure 4Q. Real entry-level wages of college graduates, by gender, 1973–2011. See note to Table 4.18.

Figure 4R. Share of recent high school graduates with employer health/pension coverage, 1979–2010. Data are computed from annual data series developed for Tables 4.10 and 4.11.The definition of recent high school graduates is the same as used in Table 4.18 for entry-level workers who are high school graduates; ages 19–25.

Figure 4S. Share of recent college graduates with employer health/pension coverage, 1979–2010. Data are computed from annual data series developed for Tables 4.10 and 4.11. The definition of recent college graduates is the same as used in Table 4.18 for entry-level workers who are college graduates; ages 23–29.

Figure 4T. Gender wage gap, by age cohort. See Moore and Shierholz (2007).

Figure 4U. Cumulative change in total economy productivity and real hourly compensation of production/nonsupervisory workers, 1948–2011. Productivity is based on unpublished Total Economy Productivity data from the Bureau of Labor Statistics Labor Productivity and Costs program. Hourly compensation for production/nonsupervisory workers is based on the wage data series used in Table 4.3. Wages are converted to hourly compensation by scaling by the real compensation/wage ratio from the NIPA data used in Table 4.2.

Figure 4V. Cumulative change in hourly productivity, real average hourly compensation, and median compensation, 1973–2011. Productivity and average hourly compensation are based on unpublished Total Economy Productivity data from the Bureau of Labor Statistics Labor Productivity and Costs program. Average hourly compensation includes those who are self-employed as well as wage and salary workers. See Mishel and Gee (2012) for more details. Median wages for all, men, and women are based on the data presented in Tables 4.4, 4.5, and 4.6, respectively. Wages are converted to hourly compensation by scaling by the real compensation/wage ratio from the NIPA data used in Table 4.2.

Figure 4W. Increase in worker wages from a 1 percentage-point fall in unemployment, by wage group. Estimates are based on a model employed by Katz and Krueger (1999). Annual changes in log wages are regressed on unemployment, lagged log-changes in the CPI-U-RS (but, following Katz and Krueger the coefficient on this is constrained to equal 1), lagged productivity growth, and dummies for 1989–1995, 1996–2000, and 2001–2007 (excluded period is 1979–1988). The sample covers the years 1979–2007.

Figure 4X. Employer health care costs as a share of annual wages, by wage fifth, 1996–2008. Figure is based on analysis of Burtless and Milusheva (2012), based on Medical Expenditure Panel Survey. See note to Table 4.27.

Figure 4Y. Imports, exports, and trade balance in goods as a share of U.S. GDP, 1947–2011. Figure is based on authors’ analysis of Bureau of Economic Analysis National Income and Product Accounts data.

Figure 4Z. Manufacturing imports as a share of U.S. GDP, 1973–2011. Figure is based on analysis of U.S. International Trade Commission Tariff and Trade data (series on manufacturing trade) and Bureau of Economic Analysis National Income and Product Accounts data on gross domestic product.

Figure 4AA. Relative productivity of U.S. trading partners, 1973–2011.Figure is based on analysis of United States International Trade Commission Tariff and Trade data and the Penn World Table (Heston, Summers, and Aten 2011). For each trading partner, their share of total imports was multiplied by their levels of GDP per worker relative to the United States (using data from the Penn World Tables). The resulting products were then summed to get the average productivity level of import trading partners. The same exercise was done for exports.

Figure 4AB. Wage premium of offshorable jobs, by gender and education. Figure is based on analysis of Bernstein, Lin, and Mishel (2007).

Figure 4AC. Union coverage rate in the United States, 1973–2011. Data are from Hirsch and Macpherson (2003), http://unionstats.gsu.edu/Hirsch-Macpherson_ILRR_CPS-Union-
Database.pdf; updated at unionstats.com. The data on union coverage begin in 1977 and are extended back to 1973, based on percentage-point changes in union membership shares in Hirsh and Macpherson (2003).

Figure 4AD. Real value of the minimum wage, 1960–2011. Underlying data are from U.S. Department of Labor Wage and Hour Division (2009), deflated using CPI-U-RS; see note to Table 4.39.

Figure 4AE. Minimum wage as a share of average hourly earnings, 1964–2011. The data are the minimum wage divided by the average hourly earnings of production and nonsupervisory workers. Minimum-wage levels are from Table 4.39, and average hourly earnings are from the series used in Table 4.3.

Figure 4AF. Real value of the federal minimum wage and share of workforce covered by higher state minimums, 1979–2011. Cooper (2012) update of Shierholz (2009).

Figure 4AG. Share of worker hours paid at or below the minimum wage, by gender, 1979–2009. Figure is based on analysis of Autor, Manning, and Smith (2010), Figure 1. Estimates are of the share of hours worked for reported wages equal to or less than the applicable state or federal minimum wage.

Figure 4AH. CEO-to-worker compensation ratio (options granted and options realized), 1965–2011. Figure is based on data developed for Table 4.43.

Figure 4AI. Growth in relative demand for college graduates, 1940–2005. Figure is based on authors’ analysis of Goldin and Katz (2008), Table 1.

Figure 4AJ. Cumulative change in real hourly wages of college graduates, by decile, 2000–2011. Figure is based on authors’ analysis of CPS-ORG data using a sample of college graduates (but no advanced degree). See Appendix B for data details.

Figure 4AK. Underemployment of college graduates, by age, 2000–2010. Figure is based on authors’ analysis of Fogg and Harrington (2011), Table 1. “Underemployment” occurs when a college graduate works in an occupation that does not require a college education.

Figure 4AL. Education needed in 2020 workforce and education levels of the 2011 workforce. Figure is based on authors’ analysis of Thiess (2012) for Table 4.46 and education attainment data from Table 4.17.

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