# Chart notes | Overview

## Tables

**Table 1.1. Key labor market indicators and living-standards benchmarks, 2000–2011. **Underlying data are from the Current Population Survey (CPS) public data series; the CPS Annual Social and Economic Supplement microdata and *Historical Income Tables* Table H-5, “Race and Hispanic Origin of Householder–Households by Median and Mean Income: 1967 to 2010”; CPS Outgoing Rotation Group microdata (see Appendix B for details on CPS-ORG microdata); the Bureau of Labor Statistics Current Employment Statistics; and unpublished Total Economy Productivity data from the Bureau of Labor Statistics Labor Productivity and Costs program.

**Table 1.2. Key labor market indicators and living-standards benchmarks, 1979–2011. **See note for Table 1.1.

**Table 1.3. Middle-fifth household income, minus selected key sources, 1979–2007. **Underlying data for income, transfers, and pensions are from the Congressional Budget Office Web resource, *Average Federal Taxes by Income Group*, “Sources of Income for All Households, by Household Income Category, 1979 to 2007” [Excel spreadsheet] and unpublished data related to the resource. Underlying data for health care deflation are from the Bureau of Labor Statistics *Consumer Price Indexes *database. Underlying data for hours worked are from Current Population Survey Annual Social and Economic Supplement microdata; see Appendix A for details on microdata. Income data are deflated using a health care deflator, and then the contributions of additional transfers, hours worked, and pensions since 1979 are taken out in sequence. Note that the unpublished CBO data are unrounded, and produce slightly different income dollar values and thus an income growth rate for the middle fifth (19.1 percent) that differs by .1 percentage point from the income growth rate from the rounded, publicly available CBO data underlying Figure 1I. Note that the “hours worked” increases in some periods because total earnings in the CBO data dropped *more *than hourly earnings in the CPS data (which is where the hourly earnings are measured from) over this period. This implies that hours dropped more than hourly earnings over this period in the CBO data. In other words, if you remove the effect of hours (i.e., leave only the effect of hourly earnings), total earnings will rise.

**Table 1.4. Employer-provided health insurance and pension coverage, by race and ethnicity, 1979–2010. **Underlying data are from Current Population Survey Annual Social and Economic Supplement microdata; see Appendix A for details.

**Table 1.5. Employer-provided health insurance and pension coverage, by gender, 1979–2010. **Underlying data are from Current Population Survey Annual Social and Economic Supplement microdata; see Appendix A for details.

## Figures

**Figure 1A. Payroll employment and the number of jobs needed to keep up with the growth in the potential labor force, Jan. 2000–Dec. 2011. **Underlying data are from the Bureau of Labor Statistics Current Employment Statistics public data series and a 2012 Congressional Budget Office report, *The Budget and Economic Outlook*, Table 2-3, “Key Assumptions in the CBO’s Projection of Potential GDP.” Since the CBO estimates of the size of the potential labor force are annual, the annual values are assigned to June of each year and extrapolated for the monthly figure.

**Figure 1B. Home prices and their impact on residential investment and housing wealth, 1995–2011. **Underlying data are from Shiller (2005 and 2012), Bureau of Economic Analysis National Income and Product Accounts,Table 1.1.5, “Gross Domestic Product,” and Federal Reserve Board (2012), Flow of Funds Accounts of the United States. Home prices are indexed such that 1997=100, and residential investment and the wealth effect on consumption are relative to 1997 average as a share of GDP.

**Figure 1C. Employment-to-population ratio, age 25–54, Jan. 1995–Dec. 2011. **Underlying data are from the Current Population Survey public data series.

**Figure 1D. Unemployment rate and real median wage decline, 1991–2011. **Underlying data for the unemployment rate are from the Current Population Survey public data series. The unemployment rate is lagged by one year in the figure. Underlying data for median wages are from CPS Outgoing Rotation Group microdata; see Appendix A for details.

**Figure 1E. Change in real family income of the middle fifth, actual and predicted, 2000–2018. **Underlying data are from the Current Population Survey public data series on unemployment and from CPS Annual Social and Economic Supplement *Historical Income Tables*, Table F-2, “Share of Aggregate Income Received by Each Fifth and Top 5 Percent of All Families, All Races: 1947– 2010”; Table F-3, “Mean Income Received by Each Fifth and Top 5 Percent of Families, All Races: 1966 to 2010”; and Table F-5, “Race and Hispanic Origin of Householder—Families by Median and Mean Income.” Real family income is indexed such that 2000=100. The projections are based on a regression analysis, based roughly on Katz and Krueger (1999), that uses the annual change in inflation-adjusted income of families in the middle fifth of the money income distribution as the dependent variable and the level of unemployment as the independent variable. The projections then use the regression parameters to forecast annual changes in middle-fifth family income based on unemployment forecasts through 2018 that are made by the Congressional Budget Office and Moody’s Economy.com, a division of Moody’s Analytics.

**Figure 1F. 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 Chapter 4: 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 1G. Share of total household income growth attributable to various income groups, 1979–2007. **Underlying data are from the Congressional Budget Office *Average Federal Taxes by Income Group*, “Sources of Income for All Households, by Household Income Category, 1979 to 2007” [Excel spreadsheet]. Each group’s contribution to overall income growth is calculated by multiplying the change in its average income from 1979 to 2007 by its share of the distribution (where, for example, the share of the distribution for the top 1 percent is .01), and dividing the result by the change in overall average income growth over the same time period. For pretax income calculations of the 90th–<95th percentile and 95th–99th percentile, see Figure 2M notes.

**Figure 1H. Share of average income growth accounted for by the top 5 percent and top 1 percent, by dataset and income concept, 1979–2007. **Underlying data are from Piketty and Saez (2012, Table A-6); Congressional Budget Office, *Average Federal Taxes by Income Group*, “Sources of Income for All Households, by Household Income Category, 1979 to 2007” [Excel spreadsheet]; and Burkhauser, Larrimore, and Simon (2011), Table 4, “Quintile Income Growth by Business Cycle Using Each Income Series.” Each income concept’s contribution to overall income growth is calculated by multiplying the change in its average income from 1979 to 2007 by its share of the distribution (where, for example, the share of the distribution for the top 1 percent is .01,) and dividing the result by the change in overall average income growth over the same time period.

**Figure 1I. Change in real annual household income, by income group, 1979–2007. **Underlying data are from the Congressional Budget Office, *Average Federal Taxes by Income Group*, “Sources of Income for All Households, by Household Income Category, 1979 to 2007” [Excel spreadsheet]. Cumulative growth is calculated by dividing the average pretax income in the base year (1979) into average pretax income in each subsequent year (1980–2007). The data provide average pretax income for the bottom, second, middle, fourth, and top fifths, and for the top 10, 5, and 1 percent. For the 80th–<90th percentile, average pretax income is calculated by subtracting the aggregate income of the top 10 percent from aggregate income of the top fifth and dividing by the total number of households in the 80th–<90th percentile. Aggregate income is calculated by multiplying the number of households in each income group by average pretax income. The number of households is calculated by subtracting the number of households in the top 10 percent from the number of households in the top fifth. This same procedure is done between the top 10 percent and top 5 percent to calculate average pretax income for the 90th–<95th percentile and between the top 5 percent and top 1 percent to calculate the average pretax income for the 95th–<99th percentile. Data are inflated to 2011 dollars using the CPI-U-RS and then indexed to 1979=0. Note that this publicly available CBO dataset is rounded, and produces slightly different income dollar values and thus an income growth rate for the middle fifth (19.2 percent) that differs by .1 percentage point from the income growth rate from the unpublished, unrounded CBO data underlying Table 1.3.

**Figure 1J. Average family income growth, by income group, 1947–2007.** CPS Annual Social and Economic Supplement *Historical Income Tables*, Table F-2, “Share of Aggregate Income Received by Each Fifth and Top 5 Percent of All Families, 1947–2010”; Table F-3, “Mean Income Received by Each Fifth and Top 5 Percent of Families, All Races: 1966–2010”; and Table F-5, “Race and Hispanic Origin of Householder—Families by Median and Mean Income.” Data are inflated to 2011 dollars using the CPI-U-RS.

**Figure 1K. Income of middle-fifth households, actual and projected assuming growth equal to growth rate of overall average income, 1979–2007. **Underlying data are from the Congressional Budget Office *Average Federal Taxes by Income Group*, “Sources of Income for All Households, by Household Income Category, 1979 to 2007” [Excel spreadsheet]. Data for the middle fifth are shown as is and when applying the cumulative growth rate of the average income for all households.

**Figure 1L. Cumulative change in real annual wages, by wage group, 1979–2010. **Data 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 are 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 used 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 used the SSA data on the total wage pool and employment and computed the real wage per worker (based on their share of wages and employment) in the different groups in 2011 dollars.

**Figure 1M. Intergenerational correlations between the earnings of fathers and sons in OECD countries.** The figure is adapted from Corak (2011), Figure 1, “Comparable Estimates of the Intergenerational Elasticity between Father and Son Earnings for the United States and Twenty Four Other Countries.” “Earnings” refers to wages.

**Figure 1N. Elasticities between parental income and sons’ earnings, 1950–2000.** Data are from Aaronson and Mazumder (2007), Table 1,“Estimates of the IGE Using Census IPUMS Data.” Data reflect annual family income for the parents and annual earnings for the sons.

**Figure 1O. Unemployment rate, by race and ethnicity, 1979–2011.** Underlying data are basic monthly Current Population Survey 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).

**Figure 1P Median wealth by race, 1983–2010. **Underlying data are from the 2010 Survey of Consumer Finances (SCF) data prepared in 2012 by Edward Wolff for the Economic Policy Institute. The definition of wealth used in this analysis of the SCF is the same definition of wealth used in the analysis of the SCF conducted by Bricker et al. (2012), except that the Bricker et al. analysis includes vehicle wealth, while this analysis does not.