Report | Jobs and Unemployment

Class of 2019: High school edition

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Press release

Summary

The members of the high school Class of 2019 who enter the labor market right after graduating have better job prospects than young people who graduated from high school into the aftermath of the recession, a result of the steady (if slow) progression of the economic recovery. However, compared with those who graduated into the strong 2000 labor market, the Class of 2019 still faces real economic challenges, as demonstrated by elevated levels of underemployment as well as low wages and worsened wage gaps for black workers.

Those high school graduates who wish to pursue further education also face significant challenges. Because of the sluggish growth in family incomes and the rising cost of a college degree, many young high school graduates are only able to access the benefits (economic and otherwise) of a college degree by taking on significant debt. And many take on such debt without actually experiencing significantly improved employment outcomes after college; this is particularly likely to be true for those who complete some college, but do not graduate, and for those who attend for-profit colleges.

The economy needs to continue on track toward full employment for economic growth to reach all corners of the labor market—including those workers without a college degree who make up the vast majority of the workforce—while ensuring equal access to the economic (and intrinsic) benefits of a college education.

Overview and key findings

In this study, we analyze data on recent young high school graduates (ages 18–21) to learn about the Class of 2019’s economic prospects as they start their careers. We begin the report by providing a snapshot of the educational attainment of all young adults in this age group (not just graduates) side by side with the educational attainment of all adults over age 21, to provide context and get a sense of these graduates’ likely future educational prospects. In the second section, we look specifically at those in this age group who have graduated from high school to learn what shares of these young adults are now enrolled in further schooling, employed, both, or neither. Third, we narrow our focus to only those graduates who are not enrolled in further schooling to find out how they are faring in the labor market—specifically, looking at their unemployment and underemployment rates. In the fourth section, we analyze the wages of those who are employed (and not enrolled in further schooling), making comparisons with wages in earlier periods as well as looking at important differences by gender and race/ethnicity. In the fifth and final section, we discuss the challenges facing those students who wish to pursue a college degree: stagnating family incomes, the rising price of college and resulting student loan debt, uncertain future wage prospects, and the complicating role of for-profit colleges.

This report focuses exclusively on those graduating from high school. Outcomes for recent college graduates are the subject of a separate report, Class of 2019: College Edition (Gould, Mokhiber, and Wolfe 2019).

Key findings

While 45.7 percent of all 18- to 21-year-olds have at least some college education, the vast majority (65.8 percent) of the population over age 21 do not have a four-year college degree.

  • About one in three young adults (ages 18–21) has a high school diploma only. One in five has less than a high school diploma.
  • Young women are more likely than young men to have completed high school already and to have enrolled in college right after graduating from high school.
  • Young black and Hispanic adults are less likely to have already completed high school than their white and Asian American/Pacific Islander peers.
  • Asian Americans/Pacific Islanders are significantly more likely to have begun on the college path at this age than any other racial/ethnic group.

While there’s been some growth in the last few years, a smaller share of young high school graduates are employed only (and not enrolled in further schooling) now than in 1989. The share that are enrolled only (and not employed) increased over most of the last three decades, although it has flattened in recent years.

  • After rising dramatically during the Great Recession, the share of high school graduates who are idled—neither employed nor enrolled in further schooling—has declined, but a larger share are idled now than they were when the economy was at full employment in 2000.
  • Young women graduates are more likely to be enrolled than their male peers.
  • Young black and Hispanic high school graduates are more likely to be idled than their white and Asian American/Pacific Islander peers. Asian Americans/Pacific Islanders are the group most likely to be enrolled, and they are far more likely to be enrolled without being employed.

Nearly one in 10 young high school graduates not enrolled in further schooling is unemployed. This share is on par with where it was when the economy was at full employment in 2000.

  • Young black high school graduates are roughly twice as likely to be unemployed as their white and AAPI peers.

The underemployment rate for high school graduates in this age group currently sits at 19.1 percent, a slight improvement over 2007, but still well above where it was in 2000.

  • Underemployment counts include those who are unemployed plus those part-time workers who want to work full time (involuntary part-time workers) plus those workers who want a job and have looked for work in the last year, but have given up actively seeking work in the last four weeks (and are therefore not officially counted as “unemployed”).
  • Over a quarter of young black high school graduates are underemployed, a much higher rate than among young white, Hispanic, and Asian American/Pacific Islander graduates.

From 1989 to 2019, average wages for young high school graduates grew only 11.2 percent in total. And if it hadn’t been for the expansionary economy of the late 1990s and 2000, wages would actually be 3.1 percent lower today than in 1989.

  • Average wages for young high school graduates recently surpassed their 2007 level, but remain just below their 2000 level, representing two lost decades of wage growth.
  • The gender wage gap for young high school graduates barely budged over the past 19 years, and the improvement that did occur was due to a small increase in women’s wages and a slight decline in men’s wages. The current gap is $1.29 per hour, or about $2,680 per year for a full-time worker.
  • Young Hispanic high school graduates saw faster wage growth than their white, black, and AAPI peers did between 2000 and 2019.
  • Between 2000 and 2019, white high school graduates’ wages grew by less than a percent over the entire period while black graduates experienced a 2.7 percent drop in pay, increasing the black–white pay gap to 11.1 percent. Black graduates have the lowest hourly pay at $10.92 per hour.

As incomes stagnate and the price of college increases, students must increasingly rely on loans to finance their education, further complicating the decision to enroll in college.

  • Black students take on a disproportionate amount of debt, in part because their families generally accumulate less wealth than white families.
  • Those who take on student debt but do not complete their degree are more likely to have trouble repaying their loans.
  • Students at for-profit colleges generally take on more debt than students at nonprofit private and public schools do, but they are less likely to finish their degrees and often do not get the same wage boost after attendance.

Notes about our data sample

Throughout this report, we examine the outcomes for young high school graduates, whom we define as adults between the ages of 18 and 21 with a high school diploma but without a bachelor’s degree.

We restrict our sample to ensure that its characteristics are as similar as possible to the characteristics of the high school graduating class of 2019. We limit it by age (to adults ages 18 to 21) to minimize variations in outcomes based on differing amounts of work experience, and we limit it to those who have a high school diploma but not a college degree since members of the graduating class of 2019 would not yet have had the opportunity to achieve a college degree.

When looking at labor market outcomes (unemployment rates, underemployment rates, and average wages), we further restrict our sample to only those young high school graduates who have not taken any college classes and are not enrolled in further schooling.

Most of the analysis in this report uses Current Population Survey (CPS) basic monthly microdata. For the wage analysis, we use CPS Outgoing Rotation Group (ORG) microdata; in the ORG survey, a quarter of the respondents to the CPS basic survey are asked additional questions about wages.

Because we are examining such a small subset of the population, we pool 12 or 36 months of data to increase the sample size and mitigate some of the volatility in the series. Unless otherwise specified, when looking at “overall” trends in the data, we pool 12 months of data to create a pooled moving average, which also has the added advantage of removing any seasonal effects. In these analyses, we highlight four key years: the most recent (2019), the peak before the Great Recession (2007), the last time the economy was closest to full employment (2000), and the earliest peak available in the data (1989).

We use 36-month pooled data to look at trends by gender and race/ethnicity, since breaking the population down by demographics restricts the sample further and therefore limits the conclusions we can draw from it. In general, that means that analyses for 2019 use the most recent 36-month period, specifically April 2016 through March 2019. Our comparison of longer-run trends by gender and race/ethnicity uses two fixed points in time: the most recent 36-month period and the pooled average of January 1998 through December 2000, when the economy was close to or at full employment.

The CPS asks respondents about both race and ethnicity, so respondents may be categorized as having Hispanic ethnicity and being of any race. To avoid including observations in multiple categories, we create five mutually exclusive categories for race/ethnicity: white (non-Hispanic), black (non-Hispanic), Hispanic (any race), Asian American/Pacific Islander (non-Hispanic; sometimes referred to as “AAPI” in this report), and “other.” Because of sample limitations, we do not report the results of our analysis for this “other” group nor are we able to analyze any other groups, such as Native American young high school graduates. Likewise, gender is restricted to the two predominant binary categories: women and men.

What are the likely future educational prospects for young high school graduates?

Figure A displays the shares of all 18- to 21-year-olds and all over-21–year-olds by highest level of educational attainment. Looking at these data allows us to compare the educational attainment of those just starting out with that of adults over age 21, and to draw conclusions about the likely eventual educational attainment of those just graduating from high school.

Figure A

The majority of adults have less than a four-year college degree: Shares of 18- to 21-year-olds and over-21-year-olds with a given level of education, 2019

Less than high school High school Some college Bachelor’s degree Advanced degree
18- to 21-year-olds 20.3% 34.0% 44.0% 1.5% 0.2%
Over-21-year-olds 10.2% 28.6% 27.0% 21.9% 12.3%
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The data below can be saved or copied directly into Excel.

Notes: “High school” refers to high school diploma or equivalent. The 2019 analysis here pools the most recent 36 months of data, March 2016–February 2019.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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One in five 18- to 21-year-olds has not graduated from high school; this share shrinks to 10.2 percent for the population over 21, suggesting that many of those young people who have not yet finished high school will eventually receive a high school diploma (or equivalent). Just over one-third of 18- to 21-year-olds have a high school diploma and no further education, while 44.0 percent have “some college.”1 Very few young adults between the ages of 18 and 21 have graduated from college.2

While many of the 18- to 21-year-olds with a high school diploma or some college will go on to obtain at least a bachelor’s degree, adults without a four-year college degree still make up the majority of the population over 21 years old (65.8 percent). When considering how to strengthen the economy, policymakers should remember that most workers will likely never attain a four-year college degree and that these workers need viable options in the labor market to reach a reasonable standard of living with decent wages, work supports, and benefits.

Figure B displays the shares of 18- to 21-year-olds at each level of educational attainment, overall and by gender and race/ethnicity. The overall shares clearly mask important differences among demographic groups. Young men in this age group are less likely to have completed high school than young women, and young women are more likely to go on to college right away than young men. (As discussed in The Class of 2019: College Edition, among 21- to 24-year-olds, women are also more likely than men to have completed a bachelor’s degree [Gould, Mokhiber, and Wolfe 2019].) Asian Americans/Pacific Islanders are significantly more likely to have begun on the college path by ages 18–21 than members of any other racial/ethnic group, while Hispanic and black young adults are less likely to have completed high school than their white and AAPI peers.

Figure B

Educational attainment among young adults varies by gender and race/ethnicity: Shares of 18- to 21-year-olds with given level of education, overall and by gender and race/ethnicity, 2019

Less than high school High school Some college Bachelor’s degree Advanced degree
All 20.3% 34.0% 44.0%  1.5% 0.2%
Women 18.3% 31.3% 48.3% 1.9% 0.2%
Men 22.2% 36.6% 39.8% 1.2% 0.2%
White 18.5% 32.9% 46.6% 1.8% 0.2%
Black 23.1% 37.8% 37.9% 1.1% 0.1%
Hispanic 23.9% 36.6% 38.6% 0.9% 0.1%
AAPI 14.4% 24.0% 58.0% 3.2% 0.4%
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The data below can be saved or copied directly into Excel.

Notes: AAPI stands for Asian American/Pacific Islander. “High school” refers to high school diploma or equivalent. The 2019 analysis here pools the most recent 36 months of data, March 2016–February 2019.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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What are recent high school graduates doing?

In this section, we look at the employment and enrollment outcomes of young adults with a high school diploma but without a college degree, whom we refer to as “young high school graduates.” We group these graduates into four mutually exclusive categories based on their outcomes: employed and not enrolled (“employed only”), employed and also enrolled in further schooling (“enrolled and employed”), enrolled in further schooling and not employed (“enrolled only”), and neither employed nor enrolled in further schooling (“idled”). Figure C, which shows the share of all young high school graduates that are experiencing each outcome, uses 12-month moving pools of data to ensure an adequate sample. Figure D shows outcomes by gender and race/ethnicity; these outcomes are based on a 36-month pool of data to ensure an adequate sample to allow us to make these comparisons.

Figure C

What are young high school grads doing?: Shares of young high school graduates (ages 18–21) by employment and enrollment outcomes, 1990–2019

Employed only  Enrolled and employed  Enrolled only  Idled 
Dec-1989 44.5% 21.0% 21.0% 13.5%
Jan-1990 44.5% 21.0% 21.1% 13.4%
Feb-1990 44.3% 21.1% 21.1% 13.5%
Mar-1990 44.3% 21.1% 21.2% 13.4%
Apr-1990 44.3% 21.1% 21.3% 13.3%
May-1990 44.3% 21.1% 21.3% 13.3%
Jun-1990 44.3% 21.1% 21.4% 13.3%
Jul-1990 44.2% 21.0% 21.4% 13.4%
Aug-1990 43.8% 21.1% 21.6% 13.5%
Sep-1990 43.8% 21.1% 21.6% 13.5%
Oct-1990 43.7% 21.1% 21.6% 13.6%
Nov-1990 43.5% 21.2% 21.7% 13.6%
Dec-1990 43.3% 21.4% 21.7% 13.6%
Jan-1991 43.1% 21.5% 21.7% 13.8%
Feb-1991 42.9% 21.4% 21.7% 14.0%
Mar-1991 42.7% 21.5% 21.7% 14.1%
Apr-1991 42.6% 21.5% 21.7% 14.2%
May-1991 42.5% 21.4% 21.5% 14.6%
Jun-1991 42.2% 21.4% 21.6% 14.8%
Jul-1991 41.7% 21.5% 21.7% 15.1%
Aug-1991 41.5% 21.3% 21.8% 15.4%
Sep-1991 41.2% 21.3% 21.8% 15.6%
Oct-1991 40.9% 21.4% 21.9% 15.7%
Nov-1991 40.8% 21.5% 22.0% 15.7%
Dec-1991 40.6% 21.5% 22.1% 15.8%
Jan-1992 40.5% 21.5% 22.2% 15.8%
Feb-1992 40.4% 21.6% 22.3% 15.7%
Mar-1992 40.4% 21.5% 22.4% 15.7%
Apr-1992 40.4% 21.4% 22.5% 15.8%
May-1992 40.2% 21.6% 22.5% 15.7%
Jun-1992 40.1% 21.7% 22.5% 15.7%
Jul-1992 40.2% 21.7% 22.4% 15.7%
Aug-1992 40.4% 21.6% 22.3% 15.7%
Sep-1992 40.3% 21.9% 22.3% 15.5%
Oct-1992 40.3% 21.9% 22.3% 15.5%
Nov-1992 40.3% 21.9% 22.3% 15.5%
Dec-1992 40.4% 21.8% 22.3% 15.5%
Jan-1993 40.4% 21.8% 22.3% 15.5%
Feb-1993 40.4% 21.8% 22.3% 15.6%
Mar-1993 40.3% 21.9% 22.2% 15.6%
Apr-1993 40.3% 21.9% 22.1% 15.6%
May-1993 40.2% 22.0% 22.3% 15.5%
Jun-1993 40.3% 22.0% 22.4% 15.3%
Jul-1993 40.3% 22.0% 22.5% 15.3%
Aug-1993 40.2% 22.0% 22.5% 15.2%
Sep-1993 40.3% 21.8% 22.7% 15.1%
Oct-1993 40.4% 21.7% 22.8% 15.1%
Nov-1993 40.4% 21.7% 22.9% 15.0%
Dec-1993 40.5% 21.7% 23.0% 14.9%
Jan-1994 40.5% 21.7% 23.0% 14.9%
Feb-1994 40.5% 21.8% 23.0% 14.8%
Mar-1994 40.4% 21.9% 23.0% 14.8%
Apr-1994 40.3% 22.0% 23.0% 14.7%
May-1994 40.3% 22.1% 22.9% 14.6%
Jun-1994 40.4% 22.2% 22.8% 14.6%
Jul-1994 40.2% 22.5% 22.8% 14.5%
Aug-1994 40.0% 22.8% 23.0% 14.2%
Sep-1994 39.7% 23.2% 22.9% 14.2%
Oct-1994 39.7% 23.4% 22.8% 14.2%
Nov-1994 39.5% 23.6% 22.7% 14.2%
Dec-1994 39.5% 23.8% 22.5% 14.2%
Jan-1995 39.6% 24.0% 22.3% 14.1%
Feb-1995 39.7% 24.1% 22.2% 14.0%
Mar-1995 40.0% 24.0% 22.0% 14.0%
Apr-1995 40.1% 24.0% 21.9% 14.0%
May-1995 40.1% 24.1% 21.8% 14.0%
Jun-1995 40.0% 24.1% 21.9% 13.9%
Jul-1995 40.0% 24.2% 21.9% 13.8%
Aug-1995 40.0% 24.3% 21.9% 13.8%
Sep-1995 40.0% 24.3% 21.9% 13.8%
Oct-1995 40.0% 24.3% 22.0% 13.8%
Nov-1995 40.1% 24.1% 21.9% 13.9%
Dec-1995 40.0% 23.9% 22.0% 14.0%
Jan-1996 39.8% 23.8% 22.1% 14.2%
Feb-1996 39.6% 23.8% 22.3% 14.3%
Mar-1996 39.4% 23.9% 22.5% 14.3%
Apr-1996 39.2% 24.0% 22.7% 14.1%
May-1996 39.5% 23.9% 22.5% 14.1%
Jun-1996 39.4% 23.9% 22.6% 14.1%
Jul-1996 39.4% 23.8% 22.7% 14.1%
Aug-1996 39.0% 24.0% 23.1% 14.0%
Sep-1996 39.1% 23.9% 23.0% 13.9%
Oct-1996 39.1% 24.0% 23.0% 13.9%
Nov-1996 38.9% 24.2% 22.9% 13.9%
Dec-1996 38.8% 24.4% 22.9% 13.9%
Jan-1997 38.7% 24.7% 22.8% 13.8%
Feb-1997 38.7% 24.9% 22.7% 13.7%
Mar-1997 38.7% 25.0% 22.6% 13.7%
Apr-1997 38.8% 25.1% 22.4% 13.7%
May-1997 38.8% 25.1% 22.4% 13.7%
Jun-1997 38.7% 25.1% 22.5% 13.7%
Jul-1997 38.6% 25.2% 22.4% 13.7%
Aug-1997 38.6% 25.3% 22.5% 13.6%
Sep-1997 38.5% 25.3% 22.7% 13.5%
Oct-1997 38.5% 25.2% 22.9% 13.4%
Nov-1997 38.6% 25.3% 22.9% 13.2%
Dec-1997 38.6% 25.4% 23.0% 13.0%
Jan-1998 38.7% 25.4% 23.0% 12.9%
Feb-1998 38.7% 25.4% 23.1% 12.8%
Mar-1998 38.8% 25.3% 23.1% 12.8%
Apr-1998 38.7% 25.3% 23.3% 12.7%
May-1998 38.7% 25.3% 23.4% 12.6%
Jun-1998 38.7% 25.5% 23.4% 12.4%
Jul-1998 38.8% 25.5% 23.4% 12.3%
Aug-1998 38.9% 25.5% 23.3% 12.3%
Sep-1998 38.9% 25.6% 23.3% 12.2%
Oct-1998 38.9% 25.7% 23.2% 12.1%
Nov-1998 38.7% 25.9% 23.3% 12.1%
Dec-1998 38.7% 25.9% 23.3% 12.0%
Jan-1999 38.6% 25.9% 23.5% 12.1%
Feb-1999 38.5% 25.9% 23.5% 12.0%
Mar-1999 38.5% 26.0% 23.6% 12.0%
Apr-1999 38.5% 26.0% 23.6% 11.9%
May-1999 38.4% 26.1% 23.7% 11.8%
Jun-1999 38.4% 26.1% 23.7% 11.8%
Jul-1999 38.2% 26.2% 23.7% 11.9%
Aug-1999 38.2% 26.2% 23.7% 11.9%
Sep-1999 38.1% 26.3% 23.8% 11.9%
Oct-1999 38.2% 26.2% 23.7% 11.9%
Nov-1999 38.3% 26.1% 23.6% 12.0%
Dec-1999 38.4% 26.0% 23.5% 12.0%
Jan-2000 38.5% 26.1% 23.4% 12.0%
Feb-2000 38.6% 26.1% 23.3% 12.0%
Mar-2000 38.7% 26.1% 23.2% 12.0%
Apr-2000 38.8% 26.0% 23.1% 12.0%
May-2000 38.8% 26.0% 23.2% 12.0%
Jun-2000 39.0% 25.8% 23.1% 12.0%
Jul-2000 39.1% 25.8% 23.2% 11.9%
Aug-2000 39.1% 25.8% 23.3% 11.8%
Sep-2000 39.2% 25.8% 23.2% 11.8%
Oct-2000 39.3% 25.9% 23.0% 11.8%
Nov-2000 39.4% 26.0% 22.9% 11.7%
Dec-2000 39.3% 26.1% 22.8% 11.8%
Jan-2001 39.3% 26.1% 22.8% 11.8%
Feb-2001 39.2% 26.2% 22.8% 11.8%
Mar-2001 39.1% 26.3% 22.8% 11.8%
Apr-2001 39.0% 26.3% 22.9% 11.9%
May-2001 38.7% 26.3% 23.0% 12.0%
Jun-2001 38.3% 26.4% 23.2% 12.1%
Jul-2001 37.9% 26.6% 23.2% 12.2%
Aug-2001 37.2% 26.8% 23.7% 12.3%
Sep-2001 36.9% 26.7% 23.9% 12.5%
Oct-2001 36.7% 26.5% 24.2% 12.7%
Nov-2001 36.7% 26.2% 24.2% 12.9%
Dec-2001 36.5% 26.1% 24.4% 13.0%
Jan-2002 36.3% 26.0% 24.6% 13.1%
Feb-2002 36.1% 25.9% 24.7% 13.2%
Mar-2002 35.9% 25.9% 24.7% 13.4%
Apr-2002 35.7% 26.0% 24.8% 13.5%
May-2002 35.8% 25.9% 24.6% 13.6%
Jun-2002 35.8% 25.9% 24.7% 13.7%
Jul-2002 35.5% 25.8% 24.8% 13.9%
Aug-2002 35.5% 25.8% 24.8% 13.9%
Sep-2002 35.3% 25.9% 24.8% 14.0%
Oct-2002 35.2% 26.0% 24.9% 13.9%
Nov-2002 34.9% 26.1% 25.1% 13.9%
Dec-2002 34.8% 26.1% 25.2% 13.8%
Jan-2003 34.7% 26.1% 25.3% 13.8%
Feb-2003 34.6% 26.1% 25.5% 13.8%
Mar-2003 34.5% 26.0% 25.7% 13.8%
Apr-2003 34.5% 25.9% 25.8% 13.8%
May-2003 34.5% 25.8% 25.8% 13.9%
Jun-2003 34.4% 25.8% 25.7% 14.1%
Jul-2003 34.3% 25.7% 25.8% 14.2%
Aug-2003 34.2% 25.7% 25.8% 14.3%
Sep-2003 34.2% 25.7% 25.9% 14.3%
Oct-2003 34.1% 25.7% 25.9% 14.3%
Nov-2003 34.0% 25.6% 26.0% 14.4%
Dec-2003 34.0% 25.6% 26.0% 14.4%
Jan-2004 34.1% 25.4% 26.1% 14.4%
Feb-2004 34.1% 25.4% 26.2% 14.3%
Mar-2004 33.9% 25.5% 26.3% 14.3%
Apr-2004 33.7% 25.6% 26.4% 14.2%
May-2004 33.4% 25.9% 26.6% 14.1%
Jun-2004 33.3% 26.0% 26.7% 14.0%
Jul-2004 33.1% 26.2% 26.8% 13.9%
Aug-2004 33.2% 26.3% 26.7% 13.8%
Sep-2004 33.0% 26.3% 27.0% 13.7%
Oct-2004 32.9% 26.3% 27.0% 13.7%
Nov-2004 33.0% 26.4% 26.9% 13.7%
Dec-2004 33.0% 26.5% 26.9% 13.6%
Jan-2005 33.0% 26.4% 26.9% 13.6%
Feb-2005 33.0% 26.4% 26.9% 13.7%
Mar-2005 33.0% 26.4% 26.9% 13.7%
Apr-2005 33.0% 26.4% 26.9% 13.7%
May-2005 32.9% 26.4% 27.0% 13.7%
Jun-2005 33.0% 26.3% 26.9% 13.7%
Jul-2005 33.1% 26.4% 26.7% 13.8%
Aug-2005 33.2% 26.3% 26.6% 13.8%
Sep-2005 33.4% 26.5% 26.4% 13.8%
Oct-2005 33.3% 26.6% 26.3% 13.7%
Nov-2005 33.4% 26.5% 26.3% 13.7%
Dec-2005 33.5% 26.5% 26.3% 13.7%
Jan-2006 33.5% 26.4% 26.2% 13.8%
Feb-2006 33.7% 26.5% 26.1% 13.8%
Mar-2006 33.8% 26.4% 25.9% 13.8%
Apr-2006 33.8% 26.4% 25.9% 13.9%
May-2006 33.9% 26.3% 25.9% 13.9%
Jun-2006 33.9% 26.3% 25.9% 13.8%
Jul-2006 33.9% 26.3% 26.0% 13.8%
Aug-2006 33.9% 26.2% 26.1% 13.8%
Sep-2006 34.0% 26.0% 26.2% 13.8%
Oct-2006 34.1% 25.8% 26.2% 13.9%
Nov-2006 34.1% 25.7% 26.3% 13.9%
Dec-2006 34.1% 25.7% 26.3% 13.9%
Jan-2007 34.1% 25.8% 26.3% 13.7%
Feb-2007 34.0% 25.8% 26.4% 13.7%
Mar-2007 34.1% 25.8% 26.5% 13.6%
Apr-2007 34.1% 25.8% 26.6% 13.5%
May-2007 34.1% 25.7% 26.8% 13.5%
Jun-2007 34.0% 25.7% 26.8% 13.5%
Jul-2007 33.9% 25.7% 26.9% 13.5%
Aug-2007 33.5% 25.8% 27.4% 13.3%
Sep-2007 33.5% 25.8% 27.5% 13.2%
Oct-2007 33.3% 25.7% 27.7% 13.3%
Nov-2007 33.3% 25.6% 27.8% 13.2%
Dec-2007 33.1% 25.6% 28.0% 13.3%
Jan-2008 32.9% 25.6% 28.0% 13.4%
Feb-2008 32.7% 25.6% 28.2% 13.5%
Mar-2008 32.5% 25.6% 28.3% 13.7%
Apr-2008 32.5% 25.6% 28.2% 13.7%
May-2008 32.5% 25.7% 28.1% 13.8%
Jun-2008 32.3% 25.8% 28.2% 13.8%
Jul-2008 32.1% 25.8% 28.2% 13.9%
Aug-2008 32.0% 25.9% 28.0% 14.1%
Sep-2008 31.7% 26.0% 28.1% 14.2%
Oct-2008 31.7% 26.0% 28.1% 14.3%
Nov-2008 31.5% 26.0% 28.1% 14.5%
Dec-2008 31.3% 26.0% 28.2% 14.5%
Jan-2009 31.1% 25.9% 28.3% 14.7%
Feb-2009 31.0% 25.7% 28.4% 14.9%
Mar-2009 30.9% 25.5% 28.5% 15.1%
Apr-2009 30.6% 25.4% 28.7% 15.3%
May-2009 30.3% 25.4% 29.0% 15.4%
Jun-2009 29.9% 25.3% 29.1% 15.7%
Jul-2009 29.4% 25.2% 29.3% 16.1%
Aug-2009 29.0% 25.0% 29.6% 16.5%
Sep-2009 28.6% 24.8% 30.0% 16.6%
Oct-2009 28.2% 24.5% 30.5% 16.8%
Nov-2009 27.7% 24.5% 30.8% 17.0%
Dec-2009 27.4% 24.3% 31.1% 17.2%
Jan-2010 27.2% 24.1% 31.6% 17.2%
Feb-2010 26.8% 24.0% 31.9% 17.3%
Mar-2010 26.7% 23.9% 32.0% 17.3%
Apr-2010 26.5% 23.8% 32.2% 17.5%
May-2010 26.4% 23.7% 32.3% 17.6%
Jun-2010 26.3% 23.6% 32.5% 17.6%
Jul-2010 26.1% 23.5% 32.7% 17.7%
Aug-2010 25.9% 23.6% 32.8% 17.6%
Sep-2010 25.8% 23.7% 32.8% 17.6%
Oct-2010 25.9% 23.7% 32.8% 17.5%
Nov-2010 25.9% 23.7% 32.8% 17.5%
Dec-2010 25.8% 23.7% 32.9% 17.6%
Jan-2011 25.9% 23.7% 32.8% 17.6%
Feb-2011 26.1% 23.7% 32.7% 17.6%
Mar-2011 26.1% 23.7% 32.6% 17.5%
Apr-2011 26.2% 23.8% 32.6% 17.4%
May-2011 26.3% 23.8% 32.7% 17.2%
Jun-2011 26.3% 23.8% 32.8% 17.2%
Jul-2011 26.3% 23.8% 32.8% 17.1%
Aug-2011 26.3% 23.7% 32.9% 17.1%
Sep-2011 26.3% 23.7% 33.0% 17.0%
Oct-2011 26.3% 23.8% 32.9% 17.0%
Nov-2011 26.4% 23.7% 32.9% 16.9%
Dec-2011 26.5% 23.8% 32.9% 16.8%
Jan-2012 26.5% 23.9% 33.0% 16.7%
Feb-2012 26.5% 24.0% 33.0% 16.5%
Mar-2012 26.4% 24.0% 33.2% 16.3%
Apr-2012 26.4% 24.0% 33.3% 16.3%
May-2012 26.3% 24.2% 33.2% 16.3%
Jun-2012 26.4% 24.3% 33.1% 16.3%
Jul-2012 26.5% 24.2% 32.9% 16.3%
Aug-2012 26.3% 24.3% 33.2% 16.2%
Sep-2012 26.3% 24.3% 33.2% 16.2%
Oct-2012 26.4% 24.2% 33.2% 16.3%
Nov-2012 26.5% 24.2% 33.1% 16.3%
Dec-2012 26.5% 24.1% 33.1% 16.3%
Jan-2013 26.7% 24.0% 32.9% 16.4%
Feb-2013 26.7% 23.9% 33.0% 16.4%
Mar-2013 26.8% 23.7% 32.9% 16.5%
Apr-2013 27.0% 23.5% 33.0% 16.5%
May-2013 27.2% 23.2% 32.9% 16.7%
Jun-2013 27.4% 23.0% 32.7% 16.8%
Jul-2013 27.6% 22.9% 32.6% 16.9%
Aug-2013 28.0% 22.8% 32.2% 17.0%
Sep-2013 28.2% 22.7% 32.0% 17.1%
Oct-2013 28.3% 22.6% 32.0% 17.1%
Nov-2013 28.4% 22.5% 31.9% 17.2%
Dec-2013 28.7% 22.4% 31.7% 17.1%
Jan-2014 28.8% 22.3% 31.9% 17.1%
Feb-2014 28.9% 22.1% 31.9% 17.1%
Mar-2014 28.9% 22.1% 31.9% 17.1%
Apr-2014 28.9% 22.1% 31.8% 17.1%
May-2014 29.0% 22.2% 31.8% 17.0%
Jun-2014 29.1% 22.2% 31.9% 16.9%
Jul-2014 29.1% 22.2% 31.9% 16.8%
Aug-2014 29.3% 22.1% 31.9% 16.7%
Sep-2014 29.4% 22.1% 31.8% 16.7%
Oct-2014 29.6% 22.2% 31.7% 16.5%
Nov-2014 29.6% 22.2% 31.8% 16.4%
Dec-2014 29.5% 22.3% 31.9% 16.3%
Jan-2015 29.5% 22.4% 31.9% 16.2%
Feb-2015 29.5% 22.6% 31.8% 16.1%
Mar-2015 29.6% 22.7% 31.8% 16.0%
Apr-2015 29.7% 22.6% 31.9% 15.8%
May-2015 29.8% 22.6% 31.9% 15.7%
Jun-2015 29.9% 22.7% 31.8% 15.6%
Jul-2015 29.8% 22.7% 32.0% 15.5%
Aug-2015 29.8% 22.8% 31.9% 15.4%
Sep-2015 29.7% 22.9% 32.2% 15.2%
Oct-2015 29.7% 22.9% 32.3% 15.1%
Nov-2015 29.8% 22.9% 32.4% 15.0%
Dec-2015 29.8% 23.0% 32.2% 15.0%
Jan-2016 29.8% 23.1% 32.1% 15.0%
Feb-2016 30.0% 23.1% 32.0% 14.9%
Mar-2016 30.1% 23.3% 31.7% 14.9%
Apr-2016 30.1% 23.5% 31.5% 14.8%
May-2016 30.2% 23.6% 31.5% 14.7%
Jun-2016 30.2% 23.6% 31.4% 14.7%
Jul-2016 30.2% 23.7% 31.5% 14.6%
Aug-2016 30.4% 23.7% 31.3% 14.6%
Sep-2016 30.3% 23.6% 31.3% 14.7%
Oct-2016 30.2% 23.7% 31.4% 14.7%
Nov-2016 30.1% 23.9% 31.4% 14.6%
Dec-2016 30.1% 23.9% 31.6% 14.4%
Jan-2017 30.1% 23.9% 31.7% 14.3%
Feb-2017 30.1% 23.9% 31.7% 14.3%
Mar-2017 30.1% 23.9% 31.9% 14.1%
Apr-2017 30.2% 23.8% 32.0% 14.0%
May-2017 30.1% 23.8% 32.2% 13.9%
Jun-2017 30.1% 23.8% 32.3% 13.8%
Jul-2017 30.2% 23.8% 32.3% 13.7%
Aug-2017 30.2% 23.8% 32.3% 13.7%
Sep-2017 30.3% 23.9% 32.2% 13.6%
Oct-2017 30.4% 23.9% 32.1% 13.6%
Nov-2017 30.6% 23.7% 32.0% 13.7%
Dec-2017 30.8% 23.5% 32.0% 13.7%
Jan-2018 30.9% 23.5% 31.9% 13.7%
Feb-2018 31.0% 23.5% 32.0% 13.6%
Mar-2018 30.9% 23.5% 31.9% 13.6%
Apr-2018 30.9% 23.5% 32.0% 13.6%
May-2018 31.1% 23.5% 31.7% 13.7%
Jun-2018 31.1% 23.6% 31.7% 13.7%
Jul-2018 31.2% 23.6% 31.6% 13.6%
Aug-2018 31.0% 23.6% 31.9% 13.5%
Sep-2018 31.2% 23.4% 31.9% 13.5%
Oct-2018 31.1% 23.4% 32.0% 13.5%
Nov-2018 31.1% 23.4% 32.1% 13.5%
Dec-2018 31.1% 23.4% 32.0% 13.5%
Jan-2019 31.2% 23.3% 32.0% 13.5%
Feb-2019 31.3% 23.2% 31.8% 13.7%
Mar-2019 31.4% 23.2% 31.7% 13.7%
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Notes: “Idled” refers to those who are neither employed nor enrolled in further schooling. This series is based on 12-month moving pools of data. The most recent data point uses pooled data from March 2018 through February 2019. Shaded areas denote recessions.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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Figure C shows that the share of young high school graduates who are employed (the sum of those who are “employed only” and those who are “employed and enrolled”) has declined significantly since 1989, driven by the declining share of young graduates who are employed and not enrolled in further education (“employed only”). In 1989, 44.5 percent of young high school graduates were employed only. That share declined over the next 20 years, particularly during the Great Recession, bottoming out in 2010 when roughly a quarter of young graduates were employed only. Since 2010, the employed-only share has increased somewhat, although it remains below its pre-recession level.

A larger share of young high school graduates enroll in additional education now than did in 1989. Now, over half (54.9 percent) of young high school graduates are enrolled in additional schooling, up from two in five (42.0 percent) in 1989. This trend has been driven by the increasing share who are enrolled in further education without being employed (“enrolled only”). In 1989, equal shares (21.0 percent) of young high school graduates were enrolled only and enrolled while employed. The share that are enrolled while employed has held relatively steady since then, while the share who are enrolled only has increased to nearly one in three.

While some of the decline in the employment shares during the Great Recession is reflected in the increasing enrolled-only share, there was also a pickup in the share that were idled (neither employed nor enrolled). In 2007, on the eve of the recession, 13.3 percent of young high school graduates found themselves idled. During the recession, that share peaked at 17.7 percent, and it has since declined to just above its pre-recession level, now at 13.7 percent. Still, a larger share of high school graduates are idled now than were when the economy was at full employment in 2000, when the idled rate dropped to a low of 11.8 percent.

Figure D illustrates, by gender and race/ethnicity, what young high school graduates are doing now, using the same four mutually exclusive categories: employed only (dark orange), employed and enrolled (light orange), enrolled only (yellow), and idled (gray). The first set of bars shows the differences in outcome shares by gender and the next set shows differences by race and ethnicity.

Figure D

Most young high school graduates are enrolled in further schooling: Employment and enrollment outcomes of young high school graduates (ages 18–21), by gender and race/ethnicity, 2019

Employed only Enrolled and employed Enrolled only Idled
Women 27.2% 26.5% 32.8% 13.5%
Men 34.5% 20.5% 30.9% 14.1%
White 31.7% 25.7% 30.8% 11.9%
Black 30.7% 19.1% 31.4% 18.9%
Hispanic 33.0% 22.5% 28.8% 15.7%
AAPI 16.0% 21.5% 52.3% 10.2%
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Notes: AAPI stands for Asian American/Pacific Islander. “Idled” refers to those who are neither employed nor enrolled in further schooling. The 2019 analysis here pools the most recent 36 months of data, March 2016–February 2019.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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To highlight total employment, we examine the dark orange (“employed only”) and light orange (“enrolled and employed”) bars combined. The overall employment rate for men and women is quite similar. However, men are more likely to be employed only, while women are more likely to be enrolled and employed simultaneously. Similar shares of men and women are enrolled only, but, because of their higher enrolled-and-employed rate, women have a higher overall enrollment rate.

Three-quarters of young Asian American/Pacific Islander (AAPI) graduates are enrolled in further education, a much larger share than any other racial group. This difference is driven by the fact that these graduates are far more likely to be enrolled only. In fact, 52.3 percent of young AAPI graduates are enrolled only, a slightly higher share than the overall enrollment shares for both Hispanic and black graduates (the sum of enrolled-only shares and enrolled-and-employed shares for those groups). Only 16.0 percent of AAPI graduates are employed only, making them about half as likely as the other groups to be employed but not enrolled.

Asian Americans and Pacific Islanders are the group least likely to be idled (10.2 percent), followed by young white graduates (11.9 percent). Young black high school graduates have the highest likelihood of being sidelined, with nearly one in five (18.9 percent) being neither enrolled in further education nor employed. Hispanic graduates are also more likely to be idled (15.7 percent) than their white or AAPI peers.

What are the employment prospects for recent high school graduates not enrolled in further schooling?

In this section, we examine unemployment and underemployment rates for young high school graduates. To do this, we narrow the sample of young high school graduates to those who are not currently enrolled in further schooling. This allows us to better assess the employment prospects of otherwise similar groups. Figure E presents unemployment and underemployment rates for young high school graduates, showing that both of these rates shot up during the Great Recession and its immediate aftermath.

Figure E

The unemployment and underemployment rates for young high school grads are still higher than in 2000: Unemployment and underemployment for young high school graduates (ages 18–21) not enrolled in further schooling, 1990–2019

Unemployment rate Underemployment rate
Dec-1989 10.2% 18.9%
Jan-1990 10.1% 18.8%
Feb-1990 10.1% 19.0%
Mar-1990 10.0% 19.0%
Apr-1990 10.0% 18.9%
May-1990 10.0% 18.8%
Jun-1990 9.7% 18.5%
Jul-1990 9.6% 18.6%
Aug-1990 9.6% 18.5%
Sep-1990 9.7% 18.5%
Oct-1990 9.8% 18.8%
Nov-1990 9.9% 18.9%
Dec-1990 10.0% 19.2%
Jan-1991 10.3% 19.6%
Feb-1991 10.8% 20.2%
Mar-1991 11.0% 20.6%
Apr-1991 11.1% 20.9%
May-1991 11.4% 21.2%
Jun-1991 11.8% 21.6%
Jul-1991 12.1% 22.0%
Aug-1991 12.3% 22.3%
Sep-1991 12.5% 22.6%
Oct-1991 12.6% 23.0%
Nov-1991 12.7% 23.4%
Dec-1991 12.9% 23.6%
Jan-1992 12.9% 23.8%
Feb-1992 12.9% 23.7%
Mar-1992 12.9% 23.8%
Apr-1992 13.0% 24.0%
May-1992 13.2% 24.4%
Jun-1992 13.4% 24.7%
Jul-1992 13.4% 24.8%
Aug-1992 13.4% 25.0%
Sep-1992 13.4% 25.0%
Oct-1992 13.3% 24.9%
Nov-1992 13.4% 24.9%
Dec-1992 13.4% 24.9%
Jan-1993 13.3% 25.0%
Feb-1993 13.2% 25.0%
Mar-1993 13.2% 25.1%
Apr-1993 13.2% 25.1%
May-1993 13.1% 25.0%
Jun-1993 12.8% 24.7%
Jul-1993 12.8% 24.8%
Aug-1993 12.7% 24.7%
Sep-1993 12.6% 24.6%
Oct-1993 12.6% 24.8%
Nov-1993 12.5% 24.6%
Dec-1993 12.4% 24.6%
Jan-1994 12.3% 24.4%
Feb-1994 12.3% 24.3%
Mar-1994 12.3% 24.2%
Apr-1994 12.1% 23.9%
May-1994 12.0% 23.7%
Jun-1994 11.8% 23.6%
Jul-1994 11.7% 23.0%
Aug-1994 11.6% 22.9%
Sep-1994 11.6% 22.7%
Oct-1994 11.5% 22.5%
Nov-1994 11.4% 22.4%
Dec-1994 11.2% 22.2%
Jan-1995 11.1% 22.0%
Feb-1995 10.8% 21.7%
Mar-1995 10.7% 21.6%
Apr-1995 10.7% 21.5%
May-1995 10.8% 21.3%
Jun-1995 11.0% 21.4%
Jul-1995 11.0% 21.5%
Aug-1995 10.9% 21.3%
Sep-1995 11.1% 21.3%
Oct-1995 11.2% 21.2%
Nov-1995 11.2% 21.1%
Dec-1995 11.3% 21.2%
Jan-1996 11.5% 21.2%
Feb-1996 11.6% 21.2%
Mar-1996 11.6% 21.0%
Apr-1996 11.5% 20.9%
May-1996 11.6% 21.0%
Jun-1996 11.5% 20.9%
Jul-1996 11.5% 20.8%
Aug-1996 11.5% 20.8%
Sep-1996 11.5% 20.8%
Oct-1996 11.5% 20.9%
Nov-1996 11.6% 20.8%
Dec-1996 11.6% 20.8%
Jan-1997 11.7% 21.0%
Feb-1997 11.7% 21.2%
Mar-1997 11.6% 21.1%
Apr-1997 11.6% 21.3%
May-1997 11.5% 21.0%
Jun-1997 11.5% 20.9%
Jul-1997 11.4% 20.6%
Aug-1997 11.5% 20.7%
Sep-1997 11.4% 20.5%
Oct-1997 11.4% 20.4%
Nov-1997 11.3% 20.3%
Dec-1997 11.1% 20.0%
Jan-1998 10.9% 19.7%
Feb-1998 10.9% 19.4%
Mar-1998 10.8% 19.2%
Apr-1998 10.6% 19.1%
May-1998 10.6% 19.3%
Jun-1998 10.6% 19.2%
Jul-1998 10.4% 18.9%
Aug-1998 10.3% 18.7%
Sep-1998 10.3% 18.6%
Oct-1998 10.1% 18.3%
Nov-1998 9.9% 18.2%
Dec-1998 9.8% 18.0%
Jan-1999 9.7% 17.9%
Feb-1999 9.7% 17.8%
Mar-1999 9.6% 17.8%
Apr-1999 9.6% 17.7%
May-1999 9.5% 17.4%
Jun-1999 9.3% 17.2%
Jul-1999 9.3% 17.2%
Aug-1999 9.2% 17.1%
Sep-1999 9.2% 17.1%
Oct-1999 9.3% 17.0%
Nov-1999 9.5% 17.1%
Dec-1999 9.6% 17.1%
Jan-2000 9.6% 17.0%
Feb-2000 9.5% 16.8%
Mar-2000 9.6% 16.8%
Apr-2000 9.6% 16.7%
May-2000 9.6% 16.8%
Jun-2000 9.7% 16.7%
Jul-2000 9.6% 16.6%
Aug-2000 9.7% 16.7%
Sep-2000 9.6% 16.5%
Oct-2000 9.5% 16.5%
Nov-2000 9.5% 16.5%
Dec-2000 9.5% 16.5%
Jan-2001 9.5% 16.6%
Feb-2001 9.5% 16.6%
Mar-2001 9.5% 16.5%
Apr-2001 9.5% 16.6%
May-2001 9.5% 16.5%
Jun-2001 9.5% 16.6%
Jul-2001 9.6% 16.7%
Aug-2001 9.6% 16.7%
Sep-2001 9.9% 17.2%
Oct-2001 10.1% 17.5%
Nov-2001 10.3% 18.0%
Dec-2001 10.6% 18.5%
Jan-2002 10.7% 18.7%
Feb-2002 11.1% 19.1%
Mar-2002 11.4% 19.5%
Apr-2002 11.6% 19.8%
May-2002 11.9% 20.2%
Jun-2002 12.1% 20.4%
Jul-2002 12.3% 20.9%
Aug-2002 12.5% 21.1%
Sep-2002 12.4% 21.3%
Oct-2002 12.4% 21.1%
Nov-2002 12.4% 21.1%
Dec-2002 12.2% 20.9%
Jan-2003 12.4% 21.2%
Feb-2003 12.4% 21.3%
Mar-2003 12.3% 21.3%
Apr-2003 12.2% 21.4%
May-2003 12.4% 21.5%
Jun-2003 12.5% 21.5%
Jul-2003 12.5% 21.7%
Aug-2003 12.6% 21.8%
Sep-2003 12.7% 21.9%
Oct-2003 12.8% 22.2%
Nov-2003 13.0% 22.4%
Dec-2003 13.0% 22.4%
Jan-2004 12.9% 22.3%
Feb-2004 12.7% 22.2%
Mar-2004 12.8% 22.3%
Apr-2004 12.8% 22.2%
May-2004 12.6% 22.2%
Jun-2004 12.5% 22.2%
Jul-2004 12.5% 22.0%
Aug-2004 12.3% 21.9%
Sep-2004 12.3% 21.8%
Oct-2004 12.3% 21.8%
Nov-2004 12.1% 21.7%
Dec-2004 12.1% 21.7%
Jan-2005 12.1% 21.6%
Feb-2005 12.2% 21.5%
Mar-2005 12.1% 21.5%
Apr-2005 12.1% 21.5%
May-2005 12.2% 21.4%
Jun-2005 12.3% 21.4%
Jul-2005 12.2% 21.4%
Aug-2005 12.3% 21.5%
Sep-2005 12.2% 21.4%
Oct-2005 12.0% 21.1%
Nov-2005 11.8% 20.7%
Dec-2005 11.7% 20.4%
Jan-2006 11.7% 20.4%
Feb-2006 11.7% 20.3%
Mar-2006 11.5% 20.1%
Apr-2006 11.6% 20.1%
May-2006 11.5% 20.0%
Jun-2006 11.4% 19.9%
Jul-2006 11.4% 19.8%
Aug-2006 11.4% 19.8%
Sep-2006 11.4% 19.7%
Oct-2006 11.5% 19.9%
Nov-2006 11.6% 20.1%
Dec-2006 11.7% 20.3%
Jan-2007 11.6% 20.3%
Feb-2007 11.5% 20.2%
Mar-2007 11.4% 20.2%
Apr-2007 11.2% 20.1%
May-2007 11.2% 20.1%
Jun-2007 11.2% 20.0%
Jul-2007 11.2% 20.2%
Aug-2007 11.2% 20.1%
Sep-2007 11.1% 20.1%
Oct-2007 11.1% 19.9%
Nov-2007 11.0% 19.9%
Dec-2007 11.2% 20.1%
Jan-2008 11.3% 20.2%
Feb-2008 11.4% 20.4%
Mar-2008 11.8% 20.8%
Apr-2008 11.8% 21.1%
May-2008 12.0% 21.5%
Jun-2008 12.1% 21.8%
Jul-2008 12.4% 22.3%
Aug-2008 12.6% 22.5%
Sep-2008 12.9% 22.9%
Oct-2008 13.2% 23.5%
Nov-2008 13.5% 24.1%
Dec-2008 13.7% 24.7%
Jan-2009 14.2% 25.4%
Feb-2009 14.8% 26.3%
Mar-2009 15.1% 27.1%
Apr-2009 15.6% 27.9%
May-2009 16.0% 28.7%
Jun-2009 16.5% 29.5%
Jul-2009 16.9% 30.3%
Aug-2009 17.5% 31.3%
Sep-2009 17.8% 32.0%
Oct-2009 18.4% 32.8%
Nov-2009 18.9% 33.5%
Dec-2009 19.3% 33.9%
Jan-2010 19.5% 34.2%
Feb-2010 19.6% 34.6%
Mar-2010 19.7% 34.7%
Apr-2010 19.9% 34.8%
May-2010 19.8% 34.7%
Jun-2010 19.8% 34.7%
Jul-2010 19.6% 34.5%
Aug-2010 19.5% 34.4%
Sep-2010 19.5% 34.5%
Oct-2010 19.3% 34.4%
Nov-2010 19.2% 34.5%
Dec-2010 19.0% 34.4%
Jan-2011 18.9% 34.4%
Feb-2011 18.8% 34.2%
Mar-2011 18.9% 34.3%
Apr-2011 18.8% 34.4%
May-2011 18.9% 34.5%
Jun-2011 18.8% 34.6%
Jul-2011 19.0% 35.0%
Aug-2011 19.0% 35.1%
Sep-2011 18.9% 35.0%
Oct-2011 18.8% 35.0%
Nov-2011 18.8% 34.8%
Dec-2011 18.8% 34.7%
Jan-2012 18.7% 34.6%
Feb-2012 18.6% 34.6%
Mar-2012 18.5% 34.3%
Apr-2012 18.5% 34.1%
May-2012 18.5% 34.0%
Jun-2012 18.5% 33.8%
Jul-2012 18.2% 33.3%
Aug-2012 18.1% 33.2%
Sep-2012 18.0% 33.1%
Oct-2012 18.0% 33.0%
Nov-2012 17.9% 32.9%
Dec-2012 17.8% 32.8%
Jan-2013 17.7% 32.8%
Feb-2013 17.5% 32.6%
Mar-2013 17.3% 32.4%
Apr-2013 17.1% 32.2%
May-2013 16.9% 32.1%
Jun-2013 17.0% 32.2%
Jul-2013 16.8% 32.1%
Aug-2013 16.9% 31.9%
Sep-2013 16.9% 31.8%
Oct-2013 16.9% 31.7%
Nov-2013 16.6% 31.4%
Dec-2013 16.2% 31.1%
Jan-2014 16.0% 30.8%
Feb-2014 15.9% 30.7%
Mar-2014 16.0% 30.7%
Apr-2014 15.8% 30.4%
May-2014 15.6% 30.1%
Jun-2014 15.2% 29.7%
Jul-2014 15.2% 29.6%
Aug-2014 14.7% 29.2%
Sep-2014 14.5% 28.8%
Oct-2014 14.2% 28.4%
Nov-2014 14.2% 28.4%
Dec-2014 14.2% 28.1%
Jan-2015 14.2% 27.9%
Feb-2015 14.0% 27.6%
Mar-2015 13.8% 27.3%
Apr-2015 13.6% 27.1%
May-2015 13.5% 27.0%
Jun-2015 13.4% 26.7%
Jul-2015 13.1% 26.2%
Aug-2015 13.0% 26.0%
Sep-2015 12.7% 25.7%
Oct-2015 12.6% 25.5%
Nov-2015 12.4% 25.1%
Dec-2015 12.5% 25.1%
Jan-2016 12.4% 24.9%
Feb-2016 12.3% 24.6%
Mar-2016 12.3% 24.3%
Apr-2016 12.3% 24.1%
May-2016 12.2% 23.8%
Jun-2016 12.2% 23.8%
Jul-2016 12.2% 23.8%
Aug-2016 12.0% 23.4%
Sep-2016 12.1% 23.3%
Oct-2016 12.0% 23.1%
Nov-2016 12.0% 23.1%
Dec-2016 11.7% 22.8%
Jan-2017 11.6% 22.5%
Feb-2017 11.5% 22.4%
Mar-2017 11.2% 22.0%
Apr-2017 10.9% 21.8%
May-2017 10.8% 21.6%
Jun-2017 10.5% 21.0%
Jul-2017 10.2% 20.5%
Aug-2017 10.2% 20.5%
Sep-2017 10.0% 20.4%
Oct-2017 9.8% 20.0%
Nov-2017 9.7% 19.7%
Dec-2017 9.7% 19.7%
Jan-2018 9.7% 19.7%
Feb-2018 9.6% 19.4%
Mar-2018 9.5% 19.4%
Apr-2018 9.6% 19.3%
May-2018 9.6% 19.4%
Jun-2018 9.7% 19.4%
Jul-2018 9.7% 19.4%
Aug-2018 9.6% 19.2%
Sep-2018 9.5% 19.0%
Oct-2018 9.7% 19.3%
Nov-2018 9.4% 19.2%
Dec-2018 9.2% 18.9%
Jan-2019 9.2% 19.0%
Feb-2019 9.2% 19.0%
Mar-2019 9.3% 19.1%
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Notes: This series is based on 12-month moving pools of data. The most recent data point uses pooled data from March 2018 through February 2019. Shaded areas denote recessions.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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The unemployment rate reflects the share of people in the labor market who are jobless and have reported that they are actively seeking work. Nearly one in 10 (9.3 percent) young high school graduates is unemployed—an improvement over 2007 and in line with 2000, when the overall labor market was near or at full employment. And the current unemployment rate likely understates the slack in the labor market given that, in recent months, seven out of 10 newly employed workers were not actively searching for work in the prior month3—these workers would not have been counted in the official unemployment rate, even though they were clearly interested in working. Still, it is encouraging that the recovery is now reaching some of the more vulnerable populations, including young people with only a high school degree.

Looking at the underemployment rate broadens our understanding of the labor market for young high school graduates. This rate includes the officially unemployed (see above), but also includes “involuntary” part-timers (those who are working part time but want full-time work) and “marginally attached” workers (those who want a job and have looked for work in the last year but who have given up actively seeking work in the last four weeks and therefore are not captured in the official unemployment rate). Nearly one in five (19.1 percent) young high school graduates is underemployed, a slightly smaller share than in 2007 (20.1 percent) but a larger share than in 2000 (16.5 percent). This suggests that more young high school graduates are having difficulty finding full-time jobs or have been discouraged from searching, compared with young high school graduates in 2000.

Figure F compares unemployment rates by gender and race/ethnicity in 2019 with rates in 2000, the last time the economy was at or close to full employment. For most groups of young high school graduates, their unemployment rate is at or below its 2000 level.

Figure F

Among young high school grads, almost all gender and racial/ethnic groups face higher unemployment rates today than in 2000: Unemployment rates of young high school graduates (ages 18–21) not enrolled in further schooling, by gender and race/ethnicity, 2000 and 2019

2000 2019
Women 9.0% 9.0%
Men 10.2% 10.8%
White 7.7% 8.2%
Black 17.2% 15.9%
Hispanic 9.8% 9.6%
AAPI 9.8% 7.4%
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Notes: AAPI stands for Asian American/Pacific Islander. Data for 2000 and 2019 use pooled data from January 1998–December 2000 and March 2016–February 2019, respectively.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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Young white graduates are the only race/ethnicity group with an unemployment rate that is higher than in 2000. Young black graduates saw a slight dip in their unemployment rate, although it is still far higher than the rate for any other group and is about twice as high as the white unemployment rate (15.9 percent versus 8.2 percent).

One would think there would be little disparity in the unemployment rates of young high school graduates, who have the same basic level of education and are in the same labor market position (i.e., high school diploma only, ages 18–21, not enrolled in school, and either employed or actively seeking work). It is notable that having an equivalent amount of education and little variation in work experience (given their young age) still does not result in parity in unemployment rates across races and ethnicities. This suggests other factors may be at play, such as discrimination or unequal access to the informal networks that often lead to job opportunities.

Figure G shows underemployment rates for young high school graduates. While unemployment rates (the share of young graduates who are actively seeking work) for most groups have recovered to their 2000 levels, underemployment rates generally remain elevated above their 2000 levels. This means that a larger share of young graduates from most groups are now either discouraged from the job search or are working part time when they would rather work full time.

Figure G

Among young high school grads, all gender and racial/ethnic groups face significantly higher underemployment rates today than in 2000: Underemployment rates of young high school graduates (ages 18–21) not enrolled in further schooling, by gender and race/ethnicity, 2000 and 2019

2000 2019
Women 17.1% 19.1%
Men 17.3% 20.9%
White 14.8% 18.1%
Black 27.1% 27.8%
Hispanic 16.7% 19.3%
AAPI 16.9% 16.8%
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Notes: AAPI stands for Asian American/Pacific Islander. Data for 2000 and 2019 use pooled data from January 1998–December 2000 and March 2016–February 2019, respectively.

Source: EPI analysis of Current Population Survey basic monthly microdata from the U.S. Census Bureau (EPI 2019)

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The underemployment rates for both young women and men with a high school diploma are still significantly higher today than in 2000. Just under one in five young white and Hispanic high school graduates is underemployed—a significantly larger share than in 2000. Black underemployment is 27.8 percent, just above its 2000 level (27.1 percent) and much higher than the 2019 levels for their white, Hispanic, and AAPI peers.

What are the wages of young high school graduates not enrolled in further schooling?

Over much of the last three decades, wage growth for young high school graduates has been essentially flat. Figure H presents average hourly wages for young high school graduates (ages 18–21, not enrolled in further schooling) from 1989 to 2019 (in 2018 dollars). Over that entire period, average wages cumulatively grew only 11.2 percent. If it hadn’t been for the wage growth spurred by the extended period of very low unemployment in the late 1990s and 2000, wages would be 3.1 percent lower today than in 1989. Wages at the last business cycle peak in 2007 were below where they were in 2000. And then the Great Recession hit, and young high school graduates experienced the loss in wages felt throughout the economy. Wages for young high school graduates have been slowly recovering lost ground since 2013 and recently reached the level they were at in 2007, immediately before the Great Recession hit; however, they are just below where they were in 2000.

Figure H

Wages of young high school graduates today are still below their 2000 levels: Real hourly wages (2018$) of young high school graduates (ages 18–21) not enrolled in further schooling, 1990–2019

Wages
Dec-1989 $11.03
Jan-1990 $11.06
Feb-1990 $11.07
Mar-1990 $11.05
Apr-1990 $11.12
May-1990 $11.10
Jun-1990 $11.15
Jul-1990 $11.17
Aug-1990 $11.18
Sep-1990 $11.16
Oct-1990 $11.11
Nov-1990 $11.10
Dec-1990 $11.11
Jan-1991 $11.08
Feb-1991 $11.07
Mar-1991 $11.06
Apr-1991 $11.03
May-1991 $11.04
Jun-1991 $11.04
Jul-1991 $11.02
Aug-1991 $10.99
Sep-1991 $10.97
Oct-1991 $10.97
Nov-1991 $10.93
Dec-1991 $10.89
Jan-1992 $10.91
Feb-1992 $10.91
Mar-1992 $10.89
Apr-1992 $10.89
May-1992 $10.84
Jun-1992 $10.79
Jul-1992 $10.77
Aug-1992 $10.78
Sep-1992 $10.75
Oct-1992 $10.73
Nov-1992 $10.76
Dec-1992 $10.74
Jan-1993 $10.68
Feb-1993 $10.67
Mar-1993 $10.64
Apr-1993 $10.57
May-1993 $10.61
Jun-1993 $10.61
Jul-1993 $10.56
Aug-1993 $10.50
Sep-1993 $10.49
Oct-1993 $10.47
Nov-1993 $10.46
Dec-1993 $10.45
Jan-1994 $10.43
Feb-1994 $10.42
Mar-1994 $10.49
Apr-1994 $10.54
May-1994 $10.51
Jun-1994 $10.51
Jul-1994 $10.53
Aug-1994 $10.55
Sep-1994 $10.67
Oct-1994 $10.80
Nov-1994 $10.82
Dec-1994 $10.88
Jan-1995 $10.94
Feb-1995 $10.95
Mar-1995 $10.92
Apr-1995 $10.87
May-1995 $10.89
Jun-1995 $10.87
Jul-1995 $10.87
Aug-1995 $10.90
Sep-1995 $10.82
Oct-1995 $10.73
Nov-1995 $10.76
Dec-1995 $10.73
Jan-1996 $10.70
Feb-1996 $10.75
Mar-1996 $10.77
Apr-1996 $10.77
May-1996 $10.71
Jun-1996 $10.73
Jul-1996 $10.71
Aug-1996 $10.70
Sep-1996 $10.71
Oct-1996 $10.71
Nov-1996 $10.73
Dec-1996 $10.77
Jan-1997 $10.81
Feb-1997 $10.77
Mar-1997 $10.74
Apr-1997 $10.76
May-1997 $10.82
Jun-1997 $10.95
Jul-1997 $11.00
Aug-1997 $10.98
Sep-1997 $10.95
Oct-1997 $10.98
Nov-1997 $10.99
Dec-1997 $10.98
Jan-1998 $10.98
Feb-1998 $11.03
Mar-1998 $11.04
Apr-1998 $11.10
May-1998 $11.17
Jun-1998 $11.17
Jul-1998 $11.27
Aug-1998 $11.40
Sep-1998 $11.44
Oct-1998 $11.50
Nov-1998 $11.53
Dec-1998 $11.62
Jan-1999 $11.68
Feb-1999 $11.69
Mar-1999 $11.74
Apr-1999 $11.82
May-1999 $11.80
Jun-1999 $11.75
Jul-1999 $11.87
Aug-1999 $11.88
Sep-1999 $11.94
Oct-1999 $11.96
Nov-1999 $12.03
Dec-1999 $11.98
Jan-2000 $12.02
Feb-2000 $12.05
Mar-2000 $12.11
Apr-2000 $12.10
May-2000 $12.17
Jun-2000 $12.26
Jul-2000 $12.19
Aug-2000 $12.19
Sep-2000 $12.20
Oct-2000 $12.22
Nov-2000 $12.26
Dec-2000 $12.30
Jan-2001 $12.34
Feb-2001 $12.31
Mar-2001 $12.26
Apr-2001 $12.27
May-2001 $12.23
Jun-2001 $12.26
Jul-2001 $12.27
Aug-2001 $12.30
Sep-2001 $12.33
Oct-2001 $12.36
Nov-2001 $12.32
Dec-2001 $12.31
Jan-2002 $12.25
Feb-2002 $12.29
Mar-2002 $12.34
Apr-2002 $12.32
May-2002 $12.36
Jun-2002 $12.30
Jul-2002 $12.20
Aug-2002 $12.20
Sep-2002 $12.11
Oct-2002 $12.09
Nov-2002 $12.07
Dec-2002 $12.07
Jan-2003 $12.06
Feb-2003 $12.01
Mar-2003 $12.03
Apr-2003 $12.01
May-2003 $11.97
Jun-2003 $11.98
Jul-2003 $12.04
Aug-2003 $12.13
Sep-2003 $12.24
Oct-2003 $12.19
Nov-2003 $12.23
Dec-2003 $12.25
Jan-2004 $12.27
Feb-2004 $12.33
Mar-2004 $12.31
Apr-2004 $12.37
May-2004 $12.42
Jun-2004 $12.44
Jul-2004 $12.38
Aug-2004 $12.23
Sep-2004 $12.25
Oct-2004 $12.17
Nov-2004 $12.09
Dec-2004 $12.13
Jan-2005 $12.03
Feb-2005 $11.96
Mar-2005 $11.86
Apr-2005 $11.74
May-2005 $11.72
Jun-2005 $11.73
Jul-2005 $11.79
Aug-2005 $11.79
Sep-2005 $11.71
Oct-2005 $11.80
Nov-2005 $11.82
Dec-2005 $11.72
Jan-2006 $11.84
Feb-2006 $11.84
Mar-2006 $11.91
Apr-2006 $12.02
May-2006 $12.00
Jun-2006 $11.97
Jul-2006 $11.97
Aug-2006 $11.95
Sep-2006 $12.01
Oct-2006 $11.97
Nov-2006 $11.92
Dec-2006 $11.93
Jan-2007 $11.90
Feb-2007 $11.95
Mar-2007 $12.01
Apr-2007 $12.00
May-2007 $12.01
Jun-2007 $11.99
Jul-2007 $11.94
Aug-2007 $12.02
Sep-2007 $11.94
Oct-2007 $11.98
Nov-2007 $12.00
Dec-2007 $12.09
Jan-2008 $12.09
Feb-2008 $12.08
Mar-2008 $11.98
Apr-2008 $11.96
May-2008 $11.93
Jun-2008 $11.89
Jul-2008 $11.87
Aug-2008 $11.81
Sep-2008 $11.77
Oct-2008 $11.78
Nov-2008 $11.86
Dec-2008 $11.81
Jan-2009 $11.79
Feb-2009 $11.72
Mar-2009 $11.75
Apr-2009 $11.68
May-2009 $11.69
Jun-2009 $11.66
Jul-2009 $11.67
Aug-2009 $11.70
Sep-2009 $11.68
Oct-2009 $11.58
Nov-2009 $11.49
Dec-2009 $11.46
Jan-2010 $11.42
Feb-2010 $11.45
Mar-2010 $11.34
Apr-2010 $11.30
May-2010 $11.28
Jun-2010 $11.34
Jul-2010 $11.45
Aug-2010 $11.33
Sep-2010 $11.39
Oct-2010 $11.37
Nov-2010 $11.34
Dec-2010 $11.28
Jan-2011 $11.33
Feb-2011 $11.29
Mar-2011 $11.31
Apr-2011 $11.23
May-2011 $11.20
Jun-2011 $11.18
Jul-2011 $10.96
Aug-2011 $10.97
Sep-2011 $10.95
Oct-2011 $10.95
Nov-2011 $10.89
Dec-2011 $10.89
Jan-2012 $10.81
Feb-2012 $10.76
Mar-2012 $10.76
Apr-2012 $10.84
May-2012 $10.79
Jun-2012 $10.78
Jul-2012 $10.79
Aug-2012 $10.87
Sep-2012 $10.76
Oct-2012 $10.76
Nov-2012 $10.75
Dec-2012 $10.73
Jan-2013 $10.71
Feb-2013 $10.74
Mar-2013 $10.73
Apr-2013 $10.78
May-2013 $10.79
Jun-2013 $10.71
Jul-2013 $10.75
Aug-2013 $10.70
Sep-2013 $10.78
Oct-2013 $10.78
Nov-2013 $10.85
Dec-2013 $10.86
Jan-2014 $10.85
Feb-2014 $10.87
Mar-2014 $10.88
Apr-2014 $10.82
May-2014 $10.85
Jun-2014 $10.93
Jul-2014 $10.88
Aug-2014 $10.92
Sep-2014 $10.94
Oct-2014 $10.97
Nov-2014 $10.98
Dec-2014 $11.04
Jan-2015 $11.10
Feb-2015 $11.19
Mar-2015 $11.28
Apr-2015 $11.31
May-2015 $11.32
Jun-2015 $11.38
Jul-2015 $11.49
Aug-2015 $11.49
Sep-2015 $11.54
Oct-2015 $11.61
Nov-2015 $11.63
Dec-2015 $11.65
Jan-2016 $11.66
Feb-2016 $11.62
Mar-2016 $11.59
Apr-2016 $11.62
May-2016 $11.69
Jun-2016 $11.65
Jul-2016 $11.72
Aug-2016 $11.74
Sep-2016 $11.80
Oct-2016 $11.77
Nov-2016 $11.85
Dec-2016 $11.85
Jan-2017 $11.84
Feb-2017 $11.84
Mar-2017 $11.88
Apr-2017 $11.95
May-2017 $11.90
Jun-2017 $11.94
Jul-2017 $11.95
Aug-2017 $11.97
Sep-2017 $11.90
Oct-2017 $11.96
Nov-2017 $11.93
Dec-2017 $11.91
Jan-2018 $12.05
Feb-2018 $12.12
Mar-2018 $12.09
Apr-2018 $12.07
May-2018 $12.08
Jun-2018 $12.05
Jul-2018 $12.02
Aug-2018 $12.04
Sep-2018 $12.10
Oct-2018 $12.08
Nov-2018 $12.14
Dec-2018 $12.19
Jan-2019 $12.22
Feb-2019 $12.23
Mar-2019 $12.26
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Notes: The wage series is based on 12-month moving pools of data. The most recent data point uses pooled data from March 2018 through February 2019. Dollar amounts are adjusted for inflation to 2018 dollars. Shaded areas denote recessions.

Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau (EPI 2019)

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Fortunately, low-wage workers in general, of which high school graduates make up a disproportionate share, have been showing larger wage gains over the last five years thanks to an economy approaching full employment as well as a series of state-level minimum wage increases (Gould 2019b). In today’s tightening labor market, we should expect to see continued and stronger wage growth, which should help make up for losses experienced by young high school graduates in the aftermath of the Great Recession. However, a high-pressure labor market will have to be sustained for quite some time to offset the longer-run wage stagnation young high school graduates have experienced.

Although it may be tempting to point to young graduates’ age or lack of previous work experience as the reason their wages have failed to grow since 2000, we observe similar wage trends for the population as a whole (Gould 2019a). Like graduates ages 18–21, high school graduates in the labor force at large (all workers ages 16 and older) saw a brief period of strong wage growth in the 1990s, but have had stagnant wages since—with their wages rising only 3.4 percent from 2000 to 2019 (Gould 2019a). This is indicative of an economywide slowdown in wage growth, driven both by a lack of demand for workers and by the erosion of workers’ power to bargain with their employers for higher wages (Bivens et al. 2014).

In 2019, young workers with a high school diploma have an average hourly wage of $12.26, which translates to annual earnings of around $25,500 for a full-time, full-year worker. This overall average masks important differences in wages by gender and race. Figure I looks at average wages for young men and women with a high school diploma as well as for young white, black, Hispanic, and Asian American/Pacific Islander (AAPI) high school graduates, in 2000 and 2019, using a three-year pool of data for more reliable comparisons among groups and across time. Figure J compares wage gaps between women and men as well as between white workers and black, Hispanic, and AAPI workers, in turn.

Figure I

Among young high school grads, men and black workers have lower wages today than in 2000: Real hourly wages (2018$) of young high school graduates (ages 18–21) not enrolled in further schooling, by gender and race/ethnicity, 2000 and 2019

2000 2019
Women $11.00 $11.32
Men $12.74 $12.61
White $12.19 $12.29
Black $11.23 $10.92
Hispanic $11.49 $12.33
AAPI $12.50 $12.08
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Notes: AAPI stands for Asian American/Pacific Islander. Average wages for 2000 and 2019 use pooled data from January 1998–December 2000 and March 2016–February 2019, respectively, adjusted for inflation to 2018 dollars.

Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau (EPI 2019)

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Figure J

Among young high school grads, the gender wage gap has narrowed and the black–white wage gap has widened since 2000: Average gender and racial/ethnic wage gaps for employed young high school graduates (ages 18–21) not enrolled in further schooling, 2000 and 2019

Wage gap 2000 2019 
Gender wage gap <br>(women–men) -13.7% -10.2%
Black–white wage gap -7.9% -11.1%
Hispanic–white wage gap -5.7% 0.3%
AAPI–white wage gap 2.6% -1.7%
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* The AAPI–white wage gap in 2000 and the Hispanic–white and AAPI–white gaps in 2019 are not statistically different from zero.

Notes: AAPI stands for Asian American/Pacific Islander. Wage gaps are calculated from average wages for 2000 and 2018 using pooled data from January 1998–December 2000 and March 2016–February 2019, respectively, adjusted for inflation to 2018 dollars.

Source: EPI analysis of Current Population Survey Outgoing Rotation Group microdata from the U.S. Census Bureau (EPI 2019)

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Young women with a high school diploma have average hourly wages of $11.32 in 2019, just above their 2000 wage of $11.00, an increase of 3.0 percent. Over this same time, men’s wages fell slightly from $12.74 to $12.61, a dip of 1.0 percent. These different trends have meant that the gender wage gap for young high school graduates has fallen over the last 19 years from 13.7 percent to 10.2 percent (as shown in Figure J). The current gap of $1.29 per hour translates into about $2,680 per year for a full-time worker, still a substantial difference in pay.

The second set of bars in Figure I shows average wages for young white, black, Hispanic, and AAPI high school graduates in 2000 and 2019. Hispanic graduates experienced the fastest wage growth (7.3 percent), and their wages are now in line with the wages of their white peers. Young black and AAPI graduates actually saw slight declines over this period. Young black graduates have the lowest hourly pay at $10.92 per hour.

The second set of bars in Figure J compares black, Hispanic, and AAPI wages with white wages in both 2000 and 2019. Gains in Hispanic pay alongside mild losses in white pay have essentially closed the Hispanic–white wage gap. White graduates saw a slight wage increase over this period, while black graduates saw a decline in pay, increasing the black–white pay gap to 11.1 percent by 2019.

Financial challenges facing those who want to pursue higher education

As they prepare to graduate from high school, young people are faced with one of their first major life decisions: whether to enter the workforce or enroll in some form of higher education. There is immense societal pressure on high school graduates to go to college; for many, the idea that a college degree is needed to achieve a middle-class lifestyle is a foregone conclusion. Statements like “from almost any individual’s perspective, college is a no-brainer. It’s the most reliable ticket to the middle class and beyond,” from The New York Times, reinforce this idea (Leonhardt 2014).

To be clear, the average economic benefits an individual gains from attending college are large. But the decision of whether to attend college is more complicated than just examining average differences in wages or employment between those with and without a college degree. This is particularly true for those who are starting out with limited financial resources. Even if it is true that they will likely earn more money after graduation, increasing costs of college mean additional obstacles to enrolling—and staying—in college. And a corresponding rise in the financing of education through student loans means that students who choose to go to college often take on financial risks and burdens that could have long-term consequences for their financial security and well-being.

Amid the immense pressure to go to college combined with the financial barriers to entry, many for-profit institutions have sprung up in recent decades, using aggressive marketing strategies while facilitating financial aid processing in order to enroll large numbers of students (Cottom 2017). These for-profits schools are often more costly than traditional public or private nonprofit schools and yet confer lower economic returns to their graduates (Looney and Yannelis 2015). Students who borrow money to attend for-profit colleges take on more student loan debt, on average, than traditional students (NCES 2017a); they are more likely to leave college without finishing their degree; and they are more likely to default or be delinquent on student loans (Looney and Yannelis 2015).

Family incomes have stagnated while college costs have risen dramatically

Though the Great Recession officially ended in June 2009, the recovery following it has been slow, and median family incomes by 2017 were just slightly above their 2007 levels.4 It is likely that many of the families of the students in the Class of 2018 faced real financial challenges—e.g., because of job loss or depressed wages after the Great Recession—and have only recently seen their incomes begin to recover.

The cost of higher education has risen faster than typical family incomes, making it harder for families to pay for college. Many students face financial challenges in addition to paying tuition, such as food and housing insecurity or the need to contribute to their family’s household income (Goldrick-Rab 2016). From the 1978–1979 enrollment year to the 2017–2018 enrollment year, the inflation-adjusted cost of a four-year education—including tuition, fees, and room and board—increased 173.6 percent for private school and 159.4 percent for public school. Median family income increased only 24.5 percent over this 39-year period, leaving families and students increasingly unable to pay for most colleges and universities in full (College Board 2018; U.S. Census Bureau 2018).

During the downturn, colleges had to rely more on tuition to make up for endowment losses (mostly at private universities) and funding cuts (at public universities), further shifting the costs of college onto students and their families. Between the 2007–2008 school year and the 2015–2016 school year, state appropriations for higher education per full-time enrolled student fell by 18 percent; in response, public colleges and universities steeply increased tuition (Mitchell, Leachman, and Masterson 2016). Other sources show a similar trend. Figure K shows that from 1992 to 2018, full-time enrollment increased while educational appropriations per full-time-equivalent student decreased in real terms (SHEEO 2019). Figure K also shows that over time students have taken on an increasing share of the cost burden for public education, with tuition’s share of total educational revenue rising from less than one-third (28.8 percent) in 1992 to nearly one-half (46.4 percent) in 2018. In 27 states, the tuition share was greater than 50 percent in 2018 (SHEEO 2019).

In the 2017–2018 school year, the total cost of attendance for an on-campus student—including in-state tuition, books, room and board, transportation, and other expenses—at a four-year in-state public school averaged $25,290. For a four-year private nonprofit school, it was $50,900 (College Board 2017). When total grant aid and tax benefits are taken into account, public four-year in-state costs still averaged about $19,460, while costs for a four-year private nonprofit school averaged about $30,690.5

Figure K

Public spending on education has not kept pace with rising enrollment: Public FTE enrollment (millions), educational appropriations per FTE (2018$), and net tuition per FTE (2018$), FY 1992–2018

Year Net tuition per FTE Educational appropriations per FTE FTE enrollment
1992 $3,439 $8,492           8.11
1993 $3,657 $8,200           8.19
1994 $3,785 $8,307           8.12
1995 $3,884 $8,578           8.07
1996 $4,018 $8,671           8.08
1997 $4,075 $8,997           8.09
1998 $4,101 $9,290           8.19
1999 $4,105 $9,533           8.33
2000 $3,916 $9,493           8.58
2001 $4,060 $9,765           8.65
2002 $4,074 $9,405           9.01
2003 $4,168 $8,712           9.48
2004 $4,385 $8,141           9.68
2005 $4,551 $8,077           9.87
2006 $4,854 $8,481           9.81
2007 $4,934 $8,695           9.94
2008 $4,898 $8,848         10.21
2009 $4,969 $8,265         10.68
2010 $5,218 $7,690         11.33
2011 $5,400 $7,360         11.61
2012 $5,876 $6,689         11.53
2013 $6,159 $6,820         11.30
2014 $6,335 $7,155         11.18
2015 $6,524 $7,454         11.06
2016 $6,696 $7,650         10.98
2017 $6,786 $7,841         10.95
2018 $6,788 $7,853         10.93
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Notes: “FTE enrollment” refers to the number of full-time-equivalent students enrolled in public (state) colleges and universities. “Educational appropriations per FTE” refers to the dollar amount contributed by the states, on average, for each student’s education. “Net tuition per FTE” refers to the average net dollar amount spent by each student on tuition after financial grants have been taken into account. Constant 2018 dollars adjusted by SHEEO Higher Education Cost Adjustment (HECA).

Source: State Higher Education Executive Officers Association, State Higher Education Finance: FY 2018, Figure 1

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Students are forced to take on increasing levels of debt if they want to attend college

As tuition costs have risen at rates vastly exceeding household income growth, it is not surprising that many students have to take on debt to pay for college. Using the Survey of Consumer Finances, Richard Fry (2014) shows that in 2010, 37 percent of the nation’s households headed by an adult younger than age 40 owed money on student debt, a share that has more than doubled since 1989. For households with student loan debt, the average amount in 2010 was $26,682 while the median was $13,410 (reported in 2011 dollars). The average amount is higher than the median because of very high amounts of debt owed by some: 10 percent of households owe $61,894 or more (Fry 2012).

The real average student debt amount has nearly tripled since 1989, and household incomes have failed to keep up. In 1989, student loan debt was equivalent to 1.2 percent of all household income on average; this ratio had steadily increased to 6.1 percent by 2010 (Fry 2012).The growth in student loan debt in younger households—in which the head of household is younger than 35—was even more dramatic, rising from 3.6 percent in 1989 to 21.9 percent in 2010. Using the Federal Reserve Board of New York’s Consumer Credit Panel, Brown et al. (2015) find that between 2004 and 2014, the number of student loan borrowers increased by 92 percent, and average debt per borrower increased by 39 percent, in real terms.

Debt can be damaging to graduates’ future incomes and lifelong earnings. After graduation, those with higher student debt are more likely to accept jobs that offer higher initial wages but have slower wage growth over time (Minicozzi 2005; Rothstein and Rouse 2011). High debt can also steer graduates into worse-fitting careers than their debtless peers; for example, some workers might prefer to be in the public or nonprofit sector but feel compelled to take corporate jobs because they need the high salary to pay off their debts. Moreover, many students who take on debt do not actually complete their degree, putting them at an economic disadvantage in the workforce; while the prospect of paying off student loans is daunting for college graduates entering the workforce, those who took on debt but never completed a degree face a greater likelihood of defaulting or becoming delinquent on loans (Nguyen 2012).

Those who have borrowed to finance their education are increasingly struggling to repay their debts. In the first quarter of 2019, 10.9 percent of student loan debt was seriously delinquent (Federal Reserve Bank of New York 2019)6—this is a higher rate than for any other type of consumer debt. In the first quarter of 2003, the first period for which this data is available, that rate was 6.1 percent. Unlike for other forms of consumer debt, serious delinquency rates for student loan debt spiked during the recovery from the Great Recession and have remained high ever since.7

Black students, in particular, rely disproportionately on loans to finance their education, largely because black families tend to hold much less wealth than white families, even at the same income levels (Goldrick-Rab, Kelchen, and Houle 2014). Furthermore, low-income students of color are disproportionately more likely to leave college before completing a degree (Huelsman 2015); as discussed above, students who attend college but don’t graduate face even steeper challenges to paying off their debt (Nguyen 2012).

For-profit college students had even higher levels of debt than students at nonprofit private or public schools. In the 2011–2012 school year, the cumulative amount borrowed by full-time undergraduate students at for-profit institutions was $24,950, compared with $22,810 for private nonprofit institutions and $17,320 for public institutions, in 2015–2016 dollars (NCES 2017a). Outcomes for for-profit students tend to be worse as well. A Brookings Institution study compared those who took on student loan debt to attend a for-profit college (“for-profit borrowers”) with traditional four-year college borrowers from 2002 to 2011. For-profit borrowers had worse labor market outcomes than traditional four-year college borrowers. They tended to have higher unemployment rates, and median earnings of for-profit borrowers in 2011 were $20,900 (in 2014 dollars), compared with $29,100 for borrowers from nonselective four-year institutions and $42,300 for those graduating from selective four-year institutions. For-profit borrowers also tended to come from lower-income families and were much less likely to finish their degree. As a result of all of these factors, they were much more likely to default or be delinquent on their loans (Looney and Yannelis 2015).

Furthermore, students attending for-profit institutions were 1.5 percentage points less likely to be employed after attending the institution compared with their peers at public institutions, and those who were employed had 11 percent lower earnings (Cellini and Turner 2019). In addition, these students were found to have no meaningful difference in annual earnings after their attendance at a for-profit school when compared with their peers who did not attend college at all. These findings, considered together with the amount of debt the typical student at a for-profit institution takes on, make it clear that for-profit colleges are a questionable investment.

Slow wage growth for recent college graduates makes it harder to pay back debt

The rising cost of college and stagnating public investment in higher education, combined with sluggish wage growth for college graduates, signals that entering college is becoming a potentially more risky investment.

The college premium, or the relative edge workers receive in earnings from obtaining a college degree, experienced rapid growth in the 1980s and 1990s, but the growth has been relatively slow since 2000 and is mostly attributable to sluggish wage growth for high school graduates rather than to strong wage growth for college graduates (Gould 2019a). As shown in The Class of 2019: College Edition, young college graduates (ages 21–24) have an average hourly wage of $20.74, which translates to an annual salary of roughly $43,100 (in 2018 dollars) for a full-time, full-year worker (Gould, Mokhiber, and Wolfe 2019). This is only slightly higher than what a typical young college graduate would have made in 2000 ($41,300). In contrast, from the 1999–2000 enrollment year to the 2018–2019 enrollment year, the average cost of college (including room and board) rose 75.0 percent for a public university and 49.4 percent for a private school (College Board 2018).

To be clear, the college premium—despite recent slow growth—is still significant: The regression-adjusted log-wage difference between the wages of college-educated and high school–educated workers is estimated at 48.4 percent in 2018 (Gould 2019a). But although wages of college graduates continue to be much stronger than those of high school graduates, wages of college graduates are clearly failing to keep pace with the rising cost of college and rising student loan debt, meaning that college is becoming an increasingly risky investment.

Further, not everyone who takes on college debt has the same level of access to the college premium. For one thing, the college wage premium of 48.4 percent is an average. A large share of workers with a college degree do not experience such a large wage boost—particularly those on the margin. In fact, the bottom 60 percent of those with a college degree still have lower wages than they did in 2000 (Gould 2019c)—meaning that wages for a majority of college grads have not only not kept pace with the rising cost of education, they’ve gone in the opposite direction.

On top of this, the only way to access the full college wage premium is by completing a four-year college degree. Of the 69.3 percent of young adults who have at least some college education, over half (54.4 percent) haven’t completed a bachelor’s degree by age 31 (BLS 2018); often, these young adults are leaving college with substantial debt but without the relative benefits in employment and wages that the college premium offers.

Finally, wage levels vary significantly by gender and race/ethnicity. Among young college graduates (ages 21–24), women are paid 12.9 percent less than men. Young black college graduates are paid, on average, 12.2 percent less than their white counterparts, while young Hispanic graduates are paid 5.8 percent less (Gould, Mokhiber, and Wolfe 2019). For those who think that having a larger share of the population attain college degrees carries large, positive spillovers for society at large (and we certainly think this), these additional wage disadvantages for some groups (particularly young women and black graduates), together with the rising cost of education, should be very worrisome indeed.

Financial challenges are often exacerbated by for-profit schools’ tactics

Even though students who attend for-profit colleges tend to have worse outcomes than those who attend traditional four-year institutions, over the past two decades for-profit students have grown to represent a significant share of all those enrolled. From 2000 to 2016, the number of students enrolled at for-profit institutions grew by 127 percent, compared with 25 and 27 percent enrollment growth at public and private nonprofit institutions, respectively. In total, about 915,000 of the 13.1 million students enrolled in college in 2016 attended a for-profit institution (NCES 2018). For many for-profit institutions, the amount spent per student on actual instruction is much lower than the amount spent at a traditional four-year institution, while the amount spent on marketing, recruitment, and lobbying is disproportionately higher. In the 2015–2016 school year, for-profit institutions spent $3,948 on instruction per student, compared with $10,221 at public nonprofits and $17,567 at private nonprofits (NCES 2017b).

Targeted advertisements and aggressive sales strategies are used to recruit students. Admissions officers often enroll—and facilitate financial aid processing for—low-income workers, people working multiple jobs, or other people in precarious positions who don’t have the time or necessary information to gain a full understanding of the type of education they are signing up for or the debt they are taking on. Even after students are enrolled, 65 percent never know that they are enrolled at a for-profit organization (Cottom 2017). In an environment where people recognize that they need to get some sort of qualification in order to achieve financial stability, but are unsure how to get it, the for-profit colleges’ aggressive strategies are quite effective in getting people to sign up for their programs. Unfortunately, these programs are too often not worth the cost.

Conclusion

While recent high school graduates may have many reasons for choosing to enter the labor force after high school rather than attending college, college should at least be a viable option; a person’s economic resources should not be the determining factor in whether they get to go to (and complete) college. But, as things stand, the prospect of staggering debt may discourage students from less wealthy families from enrolling in further education or prevent them from completing a degree.

In addition to the intrinsic value of education, both to individuals and society, college graduates tend to have better employment outcomes and higher wages than workers without a degree. These benefits, economic and otherwise, should be made available to all those who wish to pursue them though increased state and federal funding for higher education, stemming of tuition hikes, debt relief for past students, debt-free options for future students, additional support for the students who are most in need both financially and academically, and appropriate monitoring of loan terms as well as regulations to protect consumers from the predatory practices of for-profit colleges.

The policies that will give young people a fighting chance as they enter the labor market in the aftermath of the Great Recession are the same policies that will help workers overall. The most direct way to quickly bring down the unemployment rate and spur wage growth of young workers—and all workers—is to institute measures that would boost aggregate demand and encourage full employment, bolster labor standards, and strengthen workers’ collective bargaining rights. Most immediately, this means ensuring high aggregate demand, particularly through strategic public investments targeting the communities that need them most in areas such as infrastructure, energy efficiency, and early child care and education. Policies that generate demand for U.S. goods and services in turn generate demand for the workers who provide them—bringing down unemployment, giving workers more leverage, and raising workers’ wages. Policies that reduce work hours, including paid family and medical leave and overtime protections, will also ensure that job growth spurred by high aggregate demand is more widely shared (Bivens 2018).

Additional policies that will improve young high school graduates’—and all workers’—job quality include raising the minimum wage; protecting workers from wage theft; providing undocumented workers with a path to citizenship (which will give these workers, as well as authorized workers in similar fields, more leverage to command higher pay); and ending discriminatory practices that contribute to race and gender inequities (Bivens et al. 2014). Further, we should pursue stronger safety nets, such as more generous unemployment insurance and more affordable health care, which would allow for basic economic security that is not directly dependent on employment (Bivens 2018)—ensuring that new graduates don’t fall through the cracks as they navigate the challenges of the labor market.

Endnotes

1. The category “some college” includes anyone who has taken a college course but does not hold a four-year degree. People in this category may have begun a college program but left college without completing a four-year degree; they may be currently enrolled; or they may have an associate degree.

2. For a discussion of labor market outcomes for young college graduates, see The Class of 2019: College Edition (Gould, Mokhiber, and Wolfe 2019).

3. EPI analysis of monthly jobs and unemployment data from the Bureau of Labor Statistics, December 2018–May 2019. See, e.g., Gould 2018.

4. EPI analysis of U.S. Census Bureau 2018, which accounts for the redesign in the CPS ASEC income questions in 2013.

5. Total costs of attendance are from College Board 2017, Figure 1. Grant aid and tax benefits are from College Board 2017, Figures 9 and 10; net costs are Figure 1 costs minus these offsets.

6. An account is considered seriously delinquent if it is delinquent by 90 days or more.

7. The delinquency rates for student debt are likely understated, since they are calculated as a share of all borrowers, including students who are currently enrolled or have recently graduated and therefore are exempt from making payments (and therefore cannot be delinquent) (Brown et al. 2012).

References

Bivens, Josh. 2018. Recommendations for Creating Jobs and Economic Security in the U.S.: Making Sense of Debates About Full Employment, Public Investment, and Public Job Creation. Economic Policy Institute, March 2018.

Bivens, Josh, Elise Gould, Lawrence Mishel, and Heidi Shierholz. 2014. Raising America’s Pay: Why It’s Our Central Economic Policy Challenge. Economic Policy Institute Briefing Paper no. 378, June 2014.

Brown, Meta, Andrew Haughwout, Donghoon Lee, Maricar Maubutas, and Wilbert van der Klaauw. 2012. “Grading Student Loans.” Liberty Street Economics (Federal Reserve Bank of New York blog), March 5, 2012.

Brown, Meta, Andrew Haughwout, Donghoon Lee, Joelle Scally, and Wilbert van der Klaauw. 2015. “The Student Loan Landscape.” Liberty Street Economics (Federal Reserve Bank of New York blog), February 18, 2015.

Bureau of Labor Statistics (BLS). 2018. “Table 1. Highest Grade Completed by Young Adults at Ages 25 and 31 in 2005–15 by Sex, Race, and Hispanic or Latino Ethnicity.” In Employment Experience of Youths: Results from a Longitudinal Survey. Last modified April 17, 2018.

Cellini, Stephanie R., and Nicholas Turner. 2019. “Gainfully Employed? Assessing the Employment and Earnings of For-Profit College Students Using Administrative Data.” Journal of Human Resources 54, no. 2: 342–370. https://doi.org/10.3368/jhr.54.2.1016.8302R1.

College Board. 2017. Trends in College Pricing 2017, downloadable from the report archive at https://trends.collegeboard.org/college-pricing. Accessed May 2019.

College Board. 2018. “Table 2: Average Tuition and Fees and Room and Board (Enrollment-Weighted) in Current Dollars and in 2017 Dollars, 1971–72 to 2018–19.” Downloadable Excel file accompanying the report Trends in College Pricing 2018. Accessed May 2019.

Cottom, Tressie McMillan. 2017. Lower Ed: The Troubling Rise of For-Profit Colleges in the New Economy. New York: The New Press.

Economic Policy Institute (EPI). 2019. Current Population Survey Extracts, version 0.6.7.

Federal Reserve Bank of New York. 2019. Quarterly Report on Household Debt and Credit. Center for Microeconomic Data, May 2019.

Fry, Richard. 2012. A Record One-in-Five Households Now Owe Student Loan Debt. Pew Research Center: Social & Demographic Trends project, September 2012.

Fry, Richard. 2014. Young Adults, Student Debt and Economic Well-Being. Pew Research Center: Social & Demographic Trends project, May 2014.

Goldrick-Rab, Sara. 2016. Paying the Price. Chicago: Univ. of Chicago Press.

Goldrick-Rab, Sara, Robert Kelchen, and Jason Houle. 2014. “The Color of Student Debt: Implications of Federal Loan Program Reforms for Black Students and Historically Black Colleges and Universities.” Wisconsin Hope Lab Working Paper, September 2014.

Gould, Elise. 2018. “What to Watch on Jobs Day: Will We See Signs of Stronger Wage Growth?Working Economics Blog (Economic Policy Institute), December 6, 2018.

Gould, Elise. 2019a. State of Working America: Wages 2018. Economic Policy Institute, February 2019.

Gould, Elise. 2019b. “Wage Growth for Low-Wage Workers Has Been Strongest in States with Minimum Wage Increases.” Working Economics Blog (Economic Policy Institute), March 5, 2019.

Gould, Elise. 2019c. “Higher Returns on Education Can’t Explain Growing Wage Inequality.” Working Economics Blog (Economic Policy Institute), March 15, 2019.

Gould, Elise, Zane Mokhiber, and Julia Wolfe. 2019. Class of 2019: College Edition. Economic Policy Institute, May 2019.

Huelsman, Mark. 2015. The Debt Divide: The Racial and Class Bias Behind the “New Normal” of Student Borrowing. Demos.

Leonhardt, David. 2014. “Is College Worth It? Clearly, New Data Say.” New York Times, May 27, 2014.

Looney, Adam, and Constantine Yannelis. 2015. “A Crisis in Student Loans? How Changes in the Characteristics of Borrowers and in the Institutions They Attended Contributed to Rising Loan Defaults.” Brookings Papers on Economic Activity, Fall 2015.

Minicozzi, Alexandra. 2005. “The Short Term Effect of Educational Debt on Job Decisions.” Economics of Education Review 24, no. 4: 417–430. https://doi.org/10.1016/j.econedurev.2004.05.008.

Mitchell, Michael, Michael Leachman, and Kathleen Masterson. 2016. Funding Down, Tuition Up: State Cuts to Higher Education Threaten Quality and Affordability at Public Colleges. Center on Budget and Policy Priorities, August 2016.

National Center for Education Statistics (NCES). 2017a. “Table 331.50. Amount Borrowed, Aid Status, and Sources of Aid for Full-Time and Part-Time Undergraduates, by Control and Level of Institution: 2007–08 and 2011–12.” Digest of Education Statistics. U.S. Department of Education.

National Center for Education Statistics (NCES). 2017b. “Fast Facts: How Much Do Colleges and Universities Spend on Students?” (web page). U.S. Department of Education.

National Center for Education Statistics (NCES). 2018. The Condition of Education: Undergraduate Enrollment. Last updated May 2018.

Nguyen, M. 2012. Degreeless in Debt: What Happens to Borrowers Who Drop Out. Education Sector, February 2012. Downloadable at https://www.newamerica.org/documents/668/degreeless-in-debt.

Rothstein, Jesse, and Cecelia Elena Rouse. 2011. “Constrained After College: Student Loans and Early-Career Occupational Choices.” Journal of Public Economics 95, no. 1–2: 149–163.

State Higher Education Executive Officers Association (SHEEO). 2019. State Higher Education Finance: FY 2018.

U.S. Census Bureau. 2018. “Table F-5. Race and Hispanic Origin of Householder—Families by Median and Mean Income: 1947 to 2017” [Excel file]. Data from the Current Population Survey Annual Social and Economic Supplement (CPS-ASEC), downloadable from Historical Income Tables: Families. Last revised August 28, 2018.

 


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