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Article

December 2020

Male prime-age nonworkers: evidence from the NLSY97

The labor force participation rate of prime-age men (ages 25 to 54) has been mostly falling since the late 1960s, with steeper declines during recessionary periods. This article uses longitudinal data to examine whether men’s prior trajectories of schooling, work, family, neighborhood, health, incarceration, and living situations are associated with nonwork status. It also investigates whether nonwork status is a transitory state and whether nonworkers are supported by family members. The data in this article are from the National Longitudinal Survey of Youth 1997 (NLSY97), which provides detailed histories of respondents’ lives across multiple domains. When the 2015–16 NLSY97 interview was conducted, about 8.5 percent of men, who, at the time, ranged in age from 30 to 36 years, had not worked in the prior year. More than two-thirds (70.0 percent) of these men had never married, nearly a third (30.6 percent) lived in a household with a parent, and 16.3 percent were incarcerated at the time of the interview. The vast majority of these men also did not work much in earlier years. Nonworkers not only are more disadvantaged in many aspects of their current lives—such as education, health, incarceration, and finances—but they also were disadvantaged earlier in their lives in terms of family and neighborhood background.

The labor force participation rate of men in their prime working age (25 to 54 years) has been mostly falling since the late 1960s, with steeper declines occurring during recessionary periods. In 1969, the labor force participation rate of prime-age men was 96.1 percent, whereas in 2015, the rate was 88.3 percent.1 Prime-age men who were out of the labor force in a given month increasingly reported that they had not worked at all in the previous year. According to a report by the Council of Economic Advisors, data from the Current Population Survey (CPS) show that, in 2015, 83 percent of prime-age men who were not in the labor force during the reference week had not worked at all in the previous year, compared with 73 percent in 1988.2 When men do not work in their prime years, it has has implications for future job and earnings potential, as well as for the well-being of the nonworker and his family.

Several recent studies document and try to explain the decline in labor force participation of prime-age men over time. In a 2017 study, for example, Alan B. Krueger finds that health conditions, disability, and the rise of opioid prescriptions may be important contributing factors.3 In another 2017 study, John Coglianese suggests that much of the decline in prime-age men’s labor force participation is due to the increase of “in-and-outs”—that is, men who temporarily leave the labor force between jobs.4 He credits the rise in this phenomenon to the increase in young men living with parents and to a wealth effect from married or cohabiting men’s partner’s growth in earnings. Mark Aguiar et al. posit that more recent declines in the labor supply of young men are due to the advancement of video game technology.5 In a series of studies, David H. Autor et al. argue that the pain of more recent trade shocks is often locally concentrated, causing a decline in manufacturing employment in those local areas, which particularly affects those with lower levels of education.6 Katharine G. Abraham and Melissa S. Kearney provide an extensive review of the literature on the decline in employment over time and evaluate which factors they believe are most important for the decline from 1996 to 2016.7 They posit that factors associated with labor demand, primarily related to trade and automation, are the most responsible for the decline over this period. Labor supply factors related to disability caseloads and compensation (Social Security Disability Insurance and the U.S. Department of Veterans Affairs disability compensation program), the real value of the minimum wage, and the rise in incarceration and the growth in the number of people with prison records also had an impact. Ariel J. Binder and John Bound point out that declining labor force participation rates are more pronounced among prime-age men who are less educated.8 They argue that feedback between labor demand, marriage markets, and the increase in men living with parents or other relatives plays a role in declining labor force participation rates of prime-aged men with less than a college education. Jay Stewart provides descriptive statistics of male nonworkers and their sources of financial support.9 He uses the National Longitudinal Survey of Youth 1979 (NLSY79) to look at work behavior from 1987 to 1997 and finds that a small fraction of men account for the majority of person-years spent not working. Using data from the CPS, Stewart finds that a substantial proportion of nonworkers live with family members and receive financial support from those members.

This article takes a deeper look at the characteristics of male prime-age nonworkers and the paths that led them to that status.10 Specifically, it uses longitudinal data to examine the extent to which these men’s prior trajectories of schooling, work, family, neighborhood, health, incarceration, and living situations are associated with their nonwork status. It also investigates whether nonwork status is a transitory state and whether nonworkers are supported by parents, spouses, partners, or others. Data in this article are from the National Longitudinal Survey of Youth 1997 (NLSY97), which contains detailed histories about respondents’ lives across multiple domains. Compared with much of the previous literature on these issues, this article focuses more closely on the characteristics and histories of nonworkers themselves, by using data that provide a more nuanced picture involving support systems, incarceration, substance use, early family and neighborhood characteristics, health, disability, and youth expectations regarding future employment.

Data

The data used in this article are from the NLSY97, which is a cohort of people who were born in the years 1980 to 1984 and were living in the United States when they were first interviewed in 1997. In the 2015–16 interview (Round 17), the latest round of the survey from which data were available when I began working on this article, respondents were ages 30 to 36. The Round 17 interviews were conducted from October 2015 to August 2016. This data set is well suited for the study of nonworkers because it contains a complete work history of the respondents since their teens. It also contains cognitive test scores,11 incarceration history, levels of schooling, residence census tract information,12 income sources, and information about health and living situations, among other topics.

I limit my sample to men who participated in the 2015–16 interview and delete a small number for whom key labor force status information was missing, which brought the sample size to 3,499. I define nonworkers as the 365 men in the sample who did not work in the 52 weeks immediately preceding their interview, which resulted in 8.5 percent of the (weighted) sample.13 I classify the other 3,134 men in the sample—those who worked at least some weeks prior to the 2015–16 interview—as workers (or 91.5 percent of the weighted sample).

Worker and nonworker characteristics

Table 1 provides descriptive statistics about the work behavior of the men in the NLSY97 in the years leading up to the 2015–16 interview; the right-most column shows p-values for whether the means differ for workers and nonworkers.14 The general picture that emerges from this table is that the vast majority of men who did not work in the year prior to the 2015–16 interview also did not work in earlier years. For example, 79.3 percent did not work in the second year before the interview, 64.7 percent did not work in the third year before the interview, and 61.2 percent did not work in the fourth year before the interview. More than half (56.4 percent) did not work in the 4 years before the 2015–16 interview. In contrast, those who worked at least some weeks in the year prior to the 2015–16 interview (most worked at least 75 percent of weeks) also mostly worked at least 75 percent of weeks in each of the prior years—89.0 percent in the second year before the interview, 85.8 percent in the third year before the interview, and 84.5 percent in the fourth year before the interview. The bottom portion of table 1 shows the number of years of low levels of work (less than 25 percent of weeks) or no work in the 4 years prior to the 2015–16 interview. The table shows that 60.2 percent of nonworkers minimally worked in all 4 years prior to the 2015–16 interview, and another 13.3 percent minimally worked in 3 of the 4 years prior to the interview. By contrast, relatively few of the men who worked in the year prior to the 2015–16 interview fall into these minimal-work categories.

 Table 1. Recent employment history of men ages 30 to 36 in the National Longitudinal Survey of Youth 1997, by work status in year prior to the 2015–16 interview
CharacteristicWorked in prior year (in percent)Did not work in prior year (in percent)p-value

Percent of weeks employed

Year before interview

0 percent

0.0100.00.000

Greater than 0 percent to less than 25 percent

2.30.00.000

25 percent to less than 75 percent

7.60.00.000

75 percent or more

90.10.00.000

Second year before interview

0 percent

3.179.30.000

Greater than 0 percent to less than 25 percent

2.27.00.002

25 percent to less than 75 percent

5.79.50.031

75 percent or more

89.04.20.000

Third year before interview

0 percent

4.564.70.000

Greater than 0 percent to less than 25 percent

2.37.50.001

25 percent to less than 75 percent

7.410.30.135

75 percent or more

85.817.60.000

Fourth year before interview

0 percent

6.461.20.000

Greater than 0 percent to less than 25 percent

2.23.80.165

25 percent to less than 75 percent

6.97.60.652

75 percent or more

84.527.40.000

No work in second and third years

2.064.30.000

No work in second, third, and fourth years

1.356.40.000

In 4 years prior to the 2015–16 interview, number of years with little work (less than 25 percent of weeks) or no work

0 years

86.20.00.000

1 year

7.99.60.342

2 years

3.517.00.000

3 years

1.713.30.000

4 years

0.760.20.000

Sample size

3,134365

Note: Descriptive statistics are weighted with 2015 National Longitudinal Survey of Youth 1997 (NLSY97) survey weights. Dash indicates not applicable.

Source: U.S. Bureau of Labor Statistics, NLSY97.

Table 2 displays various characteristics of men in the NLSY97 by their work status in the year prior to the date of their 2015–16 interview. Workers and nonworkers differ in many ways. Among the men who did not work in the prior year, for example, nearly a third (32.7 percent) reported that they had experienced at least some weeks of unemployment during that year, meaning that they had actively searched for work and were unable to find it or were on layoff; this compares with 11.3 percent of the men who did at least some work in the prior year. Of the men who did not work in the prior year, 40.9 percent stated that health had limited their ability to work, compared with 4.3 percent of those who had worked at least part of the prior year. Those who did not work in the prior year were more likely than those who worked to have been interviewed in prison (16.3 percent versus 0.4 percent).

 Table 2. Current characteristics of men in the National Longitudinal Survey of Youth 1997, by work status in year prior to the 2015–16 interview
CharacteristicWorked in prior year (in percent)Did not work in prior year (in percent)p-value

Any weeks unemployed in prior year

11.332.70.000

Collected unemployment insurance in prior year

3.83.00.474

Work limited for health reasons

4.340.90.000

Proxy interview due to disability

0.12.20.001

Interviewed in prison

0.416.30.000

Incarcerated in prior year

1.320.60.000

Ever incarcerated

12.336.20.000

Enrolled in school at interview date

5.77.30.331

Veteran

11.28.60.174

Race or ethnicity

Non-Black, non-Hispanic

71.652.40.000

Black, non-Hispanic

13.633.50.000

Hispanic

13.413.10.858

Other

1.41.00.614

Education level

Less than high school

6.718.60.000

General Education Development (GED)

10.520.80.000

High school diploma

23.129.40.027

Some college

24.822.20.321

Bachelor’s degree or higher

35.09.00.000

Armed Forces Qualification Test (AFQT) percentile score

Less than 25 percent

23.853.40.000

25 percent to less than 50 percent

24.120.50.222

50 percent to less than 75 percent

23.518.80.103

75 percent or higher

28.67.30.000

AFQT score missing

17.523.30.029

Marital status

Never married

43.670.00.000

Married

47.818.10.009

Separated

1.02.70.090

Divorced or widowed

7.69.20.396

Cohabiting (sample not married)

35.715.90.000

Live in household with parent

13.630.60.000

Child under age 18 in household

54.023.20.000

Child under age 6 in household

37.712.90.000

Respondent ages 30 to 32

40.238.30.538

Respondent ages 33 to 36

59.861.70.538

Time use in a typical week

Watch television 21 or more hours per week

9.623.70.000

Use computer 10 or more hours per week

58.630.10.000

Have health insurance

78.253.30.000

Self-rated health

Excellent or very good

61.943.50.000

Good

28.432.40.177

Fair

9.118.50.000

Poor

0.65.60.000

Census region and division of residence

Northeast

17.315.60.472

New England

4.93.30.219

Middle Atlantic

12.412.30.958

Midwest

24.719.70.049

East North Central

16.013.30.208

West North Central

8.86.40.165

South

35.344.30.003

South Atlantic

17.021.10.100

East South Central

6.38.80.143

West South Central

12.014.50.231

West

22.720.30.363

Mountain

8.66.10.125

Pacific

14.114.20.967

Residence in a core-based statistical area

No

4.28.10.021

Yes, but not central city

55.949.20.034

Yes, central city

39.742.20.426

Yes, central city status unknown

0.20.50.563

Local area unemployment rate of residence

Less than 4 percent

15.28.70.000

4 percent to less than 6 percent

67.972.60.092

6 percent or higher

16.918.70.435

Note: Descriptive statistics are weighted with 2015 National Longitudinal Survey of Youth 1997 (NLSY97) survey weights. The Census Bureau defines a core-based statistical area (CBSA) as a statistical geographic entity consisting of the county or counties associated with at least one core (urbanized area or urban cluster) with a population of at least 10,000, plus adjacent counties that have a high degree of social and economic integration with the core as measured through commuting ties with the counties containing the core. For more information, see the CBSA page on the U.S. Census Bureau website at https://www.census.gov/topics/housing/housing-patterns/about/core-based-statistical-areas.html.

Source: U.S. Bureau of Labor Statistics, NLSY97.

Black men were more likely to have not worked in the prior year than to have worked (33.5 percent versus 13.6 percent), whereas men who were not Black and not Hispanic were more likely to have worked (71.6 percent versus 52.4 percent). Hispanic men were about equally likely to have worked as to have not worked (13.4 percent versus 13.1 percent). Men who did not work in the prior year were more likely than those who worked to have less than a high school diploma (18.6 percent versus 6.7 percent), to have a General Educational Development (GED) credential (20.8 percent versus 10.5 percent), and to have an Armed Forces Qualification Test (AFQT) percentile score of less than 25 (53.4 percent versus 23.8 percent). Men who did not work in the prior year were much more likely than those who worked to have never married (70.0 percent versus 43.6 percent). Nonworking men were less likely than working men to be cohabiting (15.9 percent versus 35.7 percent), and they were much more likely to be living in a household with a parent (30.6 percent versus 13.6 percent). Regarding time use in a typical week, men who did not work in the prior year were more likely than those who worked to watch at least 21 hours of television per week (23.7 percent versus 9.6 percent), and nonworking men were less likely than working men to spend 10 or more hours on the computer (30.1 percent versus 58.6 percent).

Men who did not work in the year prior to the 2015–16 interview were more likely than those who worked to reside in the South Census region (44.3 percent versus 35.3 percent), and nonworking men were less likely than working men to reside in the Midwest (19.7 percent versus 24.7 percent). The data do not show any statistically significant differences by Census division, including in the East North Central division, which includes the states of Michigan and Ohio, both of which saw large declines in manufacturing jobs over the past 15 to 20 years. Nonworkers and workers were similarly likely to reside in a core-based statistical area (CBSA) within a central city (42.2 percent versus 39.7 percent), and nonworkers were almost twice as likely as workers to reside in an area that is not designated as a CBSA (8.1 percent versus 4.2 percent). Nonworkers were less likely to reside in a local area with an unemployment rate of less than 4.0 percent (8.7 percent versus 15.2 percent), but nonworkers and workers were similarly likely to reside in a local area with an unemployment rate of 6.0 percent or more (18.7 percent versus 16.9 percent).

The nonworkers in the sample can be broken down into different ordered subgroups. Of the 365 nonworkers in the sample, 81 had current or recent incarceration (in the past year), 127 of those remaining reported that health issues limited their ability to work or that they had a proxy interview because they were disabled; another 16 were enrolled in school at the interview date, 28 had a child of their own who was under age 6 living in their household, and 15 had a child who was under age 18 living in their household (these are potentially stay-at-home fathers). Subtracting those with recent incarceration, health limitations, school enrollment, and young children as potential reasons for nonwork leaves only a little over a quarter (98 men) of the sample of nonworkers remaining. Of these, 56 percent reported being unemployed at some point during the year prior to the 2015–16 interview (averaging 32 weeks of unemployment, conditional on any unemployment), and about 28 percent had worked at least some weeks in the second year prior to the 2015–16 interview.

Table 3 includes descriptive statistics related to earnings, finances, and program participation in order to show how nonworkers are financially supported. At the 2015–16 interview date, 38.5 percent of men who did not work in the prior year assessed their financial situation as “comfortable,” compared with 67.3 percent of men who worked at some point in the prior year. A combined 43.6 percent of nonworkers assessed their financial situation as either “tough to make ends meet” or that they were “in over [their] head,” compared with 11.0 percent of those who had worked in the prior year. Very small percentages of workers and nonworkers indicated that they had financial issues such as late rent or mortgage payments or a cash advance on credit cards in the past 12 months, although about a tenth in both groups responded to feeling pressure to pay bills by stores, creditors, or bill collectors (these figures are not shown in the table). Table 3 also displays the incidence of program participation since the date of the last interview (respondents are interviewed every 2 years) for the respondent and his spouse or partner. Of note is that higher percentages of men who did not work in the prior year than those who did work reported that they had received some form of food assistance (27.0 percent versus 8.8 percent) and Supplemental Security Income (17.2 percent versus 1.1 percent).

Table 3. Earnings, finances, and program participation of men in the National Longitudinal Survey of Youth 1997, by work status in the year prior to the 2015–16 interview
CharacteristicWorked in prior year (in percent)Did not work in prior year (in percent)p-value

Self-assessed financial situation at interview date

Comfortable

67.338.50.000

Occasional difficulties

21.718.00.135

Tough

9.230.50.000

In over their head

1.813.10.000

Program participation since date of last interview

Respondent and/or spouse or partner

Lived in public housing

0.63.70.000

Rental voucher

0.52.20.037

Transportation assistance

0.54.30.000

Help paying energy bills

1.43.40.078

Food assistance from the Women, Infants, and Children (WIC) or Supplemental Nutrition Assistance or Food Stamps programs

8.827.00.000

Cash assistance from Supplemental Security Income

1.117.20.000

Cash assistance from Aid to Families with Dependent Children or Temporary Assistance for Needy Families

0.62.40.031

Other noncash assistance

0.32.40.007

Calendar year 2014

Collected unemployment insurance in 2014

3.74.60.546

Collected worker’s compensation in 2014

0.91.30.540

Wage and salary income in 2014

Received income from job

93.718.90.000

Income less than $10,000

5.341.70.000

Income $10,000 to less than $20,000

10.221.50.053

Income $20,000 to less than $40,000

28.224.20.532

Income $40,000 to less than $70,000

32.19.90.000

Income $70,000 or more

24.22.70.000

Received income in 2014 from business, farm, or practice

4.41.40.000

Had spouse or partner in 2014

65.033.70.000

Wage and salary income in 2014 of spouse or partner

Spouse or partner received income from a job or jobs

73.059.80.021

Income of less than $10,000

8.012.00.379

Income of $10,000 to less than $20,000

16.516.10.947

Income of $20,000 to less than $40,000

36.137.10.902

Income of $40,000 to less than $70,000

26.713.40.014

Income of more than $70,000

12.621.40.185

Hours worked per week in 2014 by spouse or partner 

1 to 20 hours

8.75.20.332

21 to 39 hours

21.121.00.989

40 or more hours

70.373.80.588

Income from other sources

4.125.60.000

Other income of less than $10,000

49.748.10.840

Other income of $10,000 to less than $20,000

27.736.50.251

Other income of $20,000 to less than $40,000

14.55.00.013

Other income of $40,000 to less than $70,000

3.47.90.263

Other income of more than $70,000

4.72.40.372

Income of other relatives in household in 2014

16.128.20.000

Relatives’ income of less than $10,000

5.410.00.201

Relatives’ income of $10,000 to less than $20,000

7.220.00.009

Relatives’ income of $20,000 to less than $40,000

20.030.60.084

Relatives’ income of $40,000 to less than $70,000

19.715.10.369

Relatives’ income of $70,000 or more

47.624.30.000

Total family income in 2014

Family income of less than $10,000

4.843.20.000

Family income of $10,000 to less than $20,000

5.014.40.000

Family income of $20,000 to less than $40,000

15.011.20.074

Family income of $40,000 to less than $70,000

24.815.10.000

Family income of $70,000 or more

50.416.10.000

Family income missing

9.216.40.001

Note: Descriptive statistics are weighted with 2015 National Longitudinal Survey of Youth 1997 (NLSY97) survey weights.

Source: U.S. Bureau of Labor Statistics, NLSY97.

Much of the income section of the NLSY97 in the 2015–16 interview asks about income sources in the 2014 calendar year. Among the men who worked in the prior year, 93.7 percent reported that they had received income from a job in calendar year 2014, compared with only 18.9 percent of men who did not work in the year prior to the 2015–16 interview. Of those who received income from a job in 2014, only 5.3 percent of men who worked in the year prior to the 2015–16 interview reported an annual income of less than $10,000, compared with 41.7 percent of men who did not work in the prior year. Nearly two-thirds (65.0 percent) of men who worked in the prior year had a spouse or partner in 2014, compared with about one-third (33.7 percent) of men who did not work in the prior year. Of those with a spouse or partner in 2014, 73.0 percent of men who worked in the prior calendar year and 59.8 percent of men who did not work in the prior year had a spouse or partner who received income from a job. Of men with a spouse or partner who received income from a job in 2014 and worked in the year prior to the 2015–16 interview, 39.3 percent had a spouse or partner who earned at least $40,000 from their job, compared with 34.8 percent of nonworkers. For both groups, more than 70 percent of the spouses or partners worked 40 or more hours per week. (See table 3.)

Among the men who did not work in the year prior to the 2015–16 interview, 28.2 percent had other relatives in the household in 2014, compared with 16.1 percent of those who worked in the prior year. For the men with relatives in their household, the relatives’ combined income was at least $40,000 for 39.4 percent of nonworkers, compared with 67.3 percent for workers. The NLSY97 does not ask a separate question about income amount from Social Security Disability Insurance; instead, that income is grouped with other income sources: “During 2014 did [you/you or your spouse/you or your partner] receive income from any other sources, such as Social Security payments, pension or retirement income including survivor's benefits, alimony, veterans or GI benefits, payments from life insurance policies or any other regular or periodic source of income?” Among nonworkers, 25.6 percent reported that they had received other income in 2014, compared with only 4.1 percent of workers. A substantial majority of recipients in both groups—84.6 percent of nonworkers and 77.4 percent of workers—received less than $20,000 in other income in 2014. (See table 3.)

The last measure shown in table 3 is total family income for 2014, which includes the respondents’ own earnings, their spouses’ or partners’ earnings, their relatives’ earnings, rental income, income from dividends, and other income. Nonworkers had substantially lower family income in 2014 than workers: 43.2 percent of nonworkers had a family income of less than $10,000, compared with only 4.8 percent of workers. A much smaller percentage of nonworkers than workers had family income of $40,000 or more (31.2 percent versus 75.2 percent). Support for a substantial portion of nonworkers appears to have come from a spouse or partner, other relatives in the household, as well as income from “other” sources such as Supplemental Security Income.

Background characteristics of workers and nonworkers in early life and at age 25

Tables 1–3 show that nonworkers are more disadvantaged in terms of many aspects of their current lives, such as education, health, incarceration, and finances. Table 4 displays early background characteristics and shows that workers and nonworkers differed early in their lives, in terms of family and neighborhood resources, delinquency, experiences from ages 12 to 18, and expectations about their futures. On the whole, nonworkers appear to come from less advantaged backgrounds than workers. Nonworkers were more likely to have a mother with less than a high school diploma, compared with their working peers (31.0 percent versus 16.9 percent). Nonworkers were also less likely to live with both of their biological parents at the time of the 1997 (Round 1) interview, and they were more likely to have a mother who was age 18 or younger when they were born. Compared with workers, nonworkers were much more likely to report that they had been shot at or had seen someone shot at with a gun when they were between the ages of 12 and 18 (26.9 percent versus 12.5 percent). Nonworkers were also much more likely to have been arrested at some point when they were age 18 or younger (41.2 percent versus 26.9 percent), and they were more likely to have used marijuana by age 19 (63.1 percent versus 54.3 percent). Nonworkers were less likely to have graduated from high school by age 20, compared with their working peers (50.4 percent versus 78.1 percent).

Table 4. Early background characteristics of men in the National Longitudinal Survey of Youth 1997, by work status in the year prior to the 2015–16 interview
CharacteristicWorked in prior year (in percent)Did not work in prior year (in percent)p-value

Biological mother’s education level

Less than high school

16.931.00.000

High school diploma

37.233.20.200

Some college

24.220.50.160

Bachelor’s degree or higher

21.715.30.015

Mother’s education level missing

7.010.00.096

Family structure at Round 1 interview (1997)

Two biological or adoptive parents

55.941.20.000

Two parents, one biological

13.422.90.001

Biological or adoptive mother only

22.526.50.112

Biological or adoptive father only

3.92.80.280

Other

3.36.60.014

Mother age 18 or younger at birth

5.714.30.000

Mother’s age at birth missing

6.210.40.017

Youth experiences, ages 12 to 18

Victim of repeated bullying

11.613.00.531

Home broken into

10.39.90.801

Shot at or saw someone shot at with a gun

12.526.90.000

Youth received high school diploma by age 20

78.150.40.000

Youth arrested while age 18 or younger

26.941.20.000

Youth used marijuana by age 19

54.363.10.004

Youth used hard drugs by age 19

17.920.90.256

Youth expectations about school and work for 5 years from 2000 interview date

Percent chance in school

Less than 75 percent

70.276.00.037

75 percent or more

29.824.00.037

If in school, percent chance of working 20 or more hours per week

Less than 75 percent

31.139.10.013

75 percent or more

68.960.90.013

If not in school, percent chance of working 20 or more hours per week

Less than 75 percent

5.215.60.000

75 percent or more

94.884.40.000

Missing 2000 interview

6.67.40.603

Youth neighborhood characteristics

Poverty rate

Less than 10 percent

55.036.10.000

10 percent to less than 20 percent

28.831.80.306

20 percent to less than 40 percent

13.524.60.000

40 percent or more

2.77.50.000

Percent Black

Less than 10 percent

73.552.60.000

10 percent to less than 75 percent

22.034.20.000

75 percent or more

4.513.20.000

Percent Hispanic

Less than 10 percent

72.466.60.043

10 percent to less than 50 percent

21.526.10.095

50 percent or more

6.07.30.335

Percent ages 25 or older with bachelor’s degree or higher

Less than 10 percent

20.535.90.000

10 percent to less than 25 percent

45.944.10.576

25 percent or more

33.620.00.000

Percent of men employed

Less than 50 percent

5.813.40.000

50 percent to less than 75 percent

71.571.70.952

75 percent or more

22.714.90.001

Income rank measure for men who grew up in low-income families

Less than 30 percent

4.710.80.000

30 percent to less than 40 percent

36.751.60.000

40 percent to less than 45 percent

30.323.30.013

45 percent or more

28.214.30.000

Note: Descriptive statistics are weighted with 2015 National Longitudinal Survey of Youth 1997 (NLSY97) survey weights. For more information on intergenerational mobility and the income rank measure for men who grew up in low-income families, see Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, and Sonya Porter, “The Opportunity Atlas: mapping the childhood roots of social mobility” (U.S. Census Bureau and Opportunity Insights, 2020), https://opportunityinsights.org/wp-content/uploads/2018/10/atlas_summary.pdf.

Source: U.S. Bureau of Labor Statistics, NLSY97.

The bottom portion of table 4 displays youth neighborhood characteristics obtained by linking the youth’s 2000 census tract code obtained from the 1997 interview to information from the Census 2000 Summary Files or to a tract-level neighborhood quality measure available from the Census Bureau.15 Numerous studies have found that a child’s neighborhood affects his or her subsequent outcome as an adult.16 Nonworkers tend to have grown up in less advantaged neighborhoods than those of their working peers. For example, 24.6 percent of nonworkers grew up in a neighborhood with a poverty rate of between 20 and 40 percent, compared with 13.5 percent of nonworkers. In addition, 7.5 percent of nonworkers grew up in a neighborhood with a poverty rate of 40 percent or more (often referred to as concentrated poverty), compared with 2.7 percent of workers. Compared with workers, nonworkers grew up in neighborhoods with a higher percentage of minorities and a lower percentage of people with a bachelor’s degree or more. Male employment was also lower in nonworkers’ childhood neighborhoods, compared with workers’ childhood neighborhoods. For example, 13.4 percent of nonworkers grew up in a neighborhood with very low male employment (less than 50 percent), compared with 5.8 percent of workers.

The last measure of neighborhood quality at the census-tract level shown in table 4 is based on research by Raj Chetty and Nathaniel Hendren for a sample of children born from 1980 to 1986 (which is similar to the NLSY97 sample birth dates).17 For the Census Bureau’s Opportunity Atlas, the authors provided (among other variables) a measure of mean household income rank for children (male children here) whose parents were at the 25th percentile of the national income distribution (derived from tax data). Incomes for (grown-up) children were measured as mean earnings in 2014–15 when they were between the ages of 31 and 37. Household income is defined as the sum of the respondent’s own and his spouse’s income. The data show, by neighborhood, the extent of intergenerational income mobility attained by male children from low-income households. Compared with workers, nonworkers were more likely to grow up in neighborhoods with lower intergenerational mobility for men in low-income families. Nonworkers were more than twice as likely to grow up in a neighborhood with a mobility income rank of less than 30 percent (10.8 percent versus 4.7 percent) and about half as likely to grow up in a neighborhood with a higher mobility income rank of 45 percent or more (14.3 percent versus 28.2 percent). (Note that Chetty and Hendren do not directly look at the relationship between child neighborhood and subsequent work status as I have done in this article.)

Table 5 shows characteristics for men at an intermediate stage in the NLSY97 sample, when they were age 25 (in 2005–09), by whether they worked in the year prior to the 2015–16 interview. Men who did not work in the year before the 2015–16 interview were much more likely than those who worked to have not worked in the year they turned 25: 30.8 percent of nonworkers did not work in the year they turned 25, compared with 4.9 percent of workers. Among those who did not work in the year before the 2015–16 interview, 41.7 percent of nonworkers worked at least 75 percent of weeks in the year they turned 25, compared with 79.8 percent of those who worked. Nonworkers were more likely than workers to have been incarcerated at some point in the year they turned 25 (10.5 percent versus 2.3 percent). Nonworkers were much less likely than workers to have been married at age 25 (9.7 percent versus 24.0 percent), and nonworkers were much more likely than workers to have lived in a household with a parent when they were 25 (40.2 percent versus 27.9 percent). Nonworkers were more than twice as likely as workers to rate their health as fair or poor (14.1 percent versus 6.7 percent). At each life stage shown in tables 4 and 5, nonworkers were more likely than workers to have been less advantaged with respect to the neighborhoods they grew up in, family background, educational attainment, health status, early employment experience, and incarceration.

Table 5. Age-25 characteristics of men in the National Longitudinal Survey of Youth 1997, by work status in the year prior to the 2015–16 interview
CharacteristicWorked in prior year (in percent)Did not work in prior year (in percent)p-value

Percent of weeks employed in the year respondents turned age 25

0 percent

4.930.80.000

Greater than 0 percent to less than 25 percent

2.87.70.002

25 percent to less than 75 percent

12.519.80.004

75 percent or more

79.841.70.000

Incarcerated in year they turned age 25

2.310.50.000

Married

24.09.70.000

Cohabitating

17.716.20.534

Living with parent

27.940.20.000

Self-rated health

Excellent or very good

68.949.20.000

Good

24.436.70.000

Fair

6.411.30.112

Poor

0.32.80.003

Used marijuana since date of last interview

23.524.50.734

Used hard drugs since date of last interview

5.310.10.026

Note: Descriptive statistics are weighted with 2015 National Longitudinal Survey of Youth 1997 (NLSY97) survey weights.

Source: U.S. Bureau of Labor Statistics, NLSY97.

Probability of being a nonworker

This section examines whether early youth and teen characteristics and age-25 characteristics can predict nonworker status. More specifically, I use a linear probability model to estimate the effects of earlier youth and age-25 characteristics on the probability of not working in the year prior to the 2015–16 interview. Table 6 displays estimates from linear probability models of the relationship between youth characteristics and the subsequent likelihood of being a nonworker.18 Three specifications are shown in Table 6, and each builds on the previous one by adding additional controls. Specification 1 includes basic demographic and family background controls, specification 2 adds youth and teen characteristics as controls, and specification 3 adds controls for age-25 characteristics.

The results shown in specification 1 suggest that family background characteristics are statistically significant predictors of the likelihood of being a nonworker in the year prior to the 2015–16 interview. Having a mother who gave birth as a teen increases the likelihood of being a nonworker, for example, and having a mother who attained higher levels of education decreases the likelihood of being a nonworker in the year before the 2015–16 interview. Being Black increases the likelihood of being a nonworker by 8.7 percentage points. Growing up in a neighborhood with concentrated poverty (40 percent or more) increases the likelihood of being a nonworker by 9.8 percentage points, while growing up in a neighborhood that has a poverty rate ranging from 20 percent to less than 40 percent increases the likelihood of being a nonworker by 3.5 percentage points.

Table 6. Probability of men not working in the year prior to the 2015–16 interview, ordinary least squares linear probability model
CharacteristicSpecification 1Specification 2Specification 3
CoefficientRobust standard errorsCoefficientRobust standard errorsCoefficientRobust standard errors

Black

0.087*0.0160.064*0.0160.034*0.015

Hispanic

–0.0100.014–0.0190.014–0.0210.014

Family background characteristics

Family with two biological parents in 1997

–0.025*0.0100.0000.0110.0020.010

Mother age 18 or younger at birth

0.052*0.0250.0400.0250.0360.023

Mother’s education: high school

–0.037*0.017–0.0130.017–0.0040.016

Mother’s education: some college

–0.041*0.018–0.0070.0180.0000.018

Mother’s education: bachelor’s degree or higher

–0.049*0.0180.0000.0190.0010.018

Neighborhood (census tract) poverty rate

10 percent to less than 20 percent

0.0180.0120.010.0120.0070.011

20 percent to less than 40 percent

0.035*0.0170.0220.0170.0140.016

40 percent or more

0.098*0.0320.077*0.0320.069*0.030

Youth, early and teen characteristics

Armed Forces Qualification Test percentile score

Less than 25 percent

0.069*0.0160.067*0.015

25 percent to less than 50 percent

0.0180.0130.027*0.012

50 percent to less than 75 percent

0.029*0.0120.034*0.012

Youth ages 12 to 18 shot at or seen someone shot at with a gun

0.040*0.0170.0300.017

Youth received high school diploma by age 20

–0.081*0.015–0.055*0.015

Youth arrested while age 18 or younger

0.0150.013–0.0030.013

Youth used marijuana by age 19

0.0160.0110.0120.011

Youth used hard drugs by age 19

0.0010.015–0.0060.015

Youth expectations 5 years from 2000 interview

If not in school, percent chance of working 20 or more hours per week is less than 75 percent

0.059*0.0270.0300.026

Age-25 characteristics

Percent of weeks employed in the year respondents turned age 25

0 percent

0.285*0.031

Greater than 0 percent to less than 25 percent

0.106*0.035

25 percent to less than 75 percent

0.053*0.017

Incarcerated in year respondent turned age 25

0.0240.040

Married at age 25

–0.027*0.011

Cohabitating at age 25

–0.0130.014

Living with parent at age 25

0.0220.012

Self-rated health at age 25

Good

0.0150.012

Fair

0.0220.022

Poor

0.286*0.108

* Coefficients are statistically significant at the .05 level.

Note: Each specification builds on the previous one by adding additional controls. Specification 1 includes basic demographic and family background controls, specification 2 adds controls for youth and teen characteristics, and specification 3 adds controls for age-25 characteristics. The model also includes indicators for year of birth, other race, and variables with missing observations. Dash indicates not applicable. Sample size: 3,499.

Source: U.S. Bureau of Labor Statistics, National Longitudinal Survey of Youth 1997.

Because the neighborhood measures shown in table 4 are highly correlated, I only include the poverty measure in the regressions shown in table 6. However, alternative neighborhood measures from table 4 (percentage Black, percentage with a bachelor’s degree or more, percentage of men employed, and degree of intergenerational mobility) also have statistically significant effects on the likelihood of being a nonworker in the expected direction when they are each put into the regression in place of the neighborhood poverty measure.

Specification 2 in table 6 adds youth teenage characteristics; note that several of the background variables from specification 1 are no longer statistically significant (family structure and mother’s education) or decrease in magnitude. Having an AFQT percentile score of less than 25 increases the likelihood that respondents did not work in the year prior to the 2015–16 interview by 6.9 percentage points, and obtaining a high school diploma by age 20 decreases the likelihood by 8.1 percentage points. Early arrests and drug use are not statistically significant predictors, but having had lower expectations about the likelihood of working 5 years after the 2000 interview increases the likelihood of being a nonworker in the year before the 2015–16 interview. Specification 3 adds age-25 characteristics. With that addition, the size of several of the family background characteristics coefficients diminishes, while many of the early youth and teen characteristics, such as obtaining a high school diploma by age 20 and the AFQT percentile score categories, remain statistically significant. The strongest predictor of future nonwork is the percentage of weeks worked in the year the respondents turned 25, with not working or working less than 25 percent of weeks having very large effects: 28.5 and 10.6 percentage points, respectively. Incarceration and drug use at age 25 are not statistically significant, but rating their own health at age 25 as poor has a large statistically significant effect (28.6 percentage points).

Comparison with the NLSY79 cohort

The rate of nonwork among prime-age men has risen over time, and this section examines the extent to which nonworking men’s characteristics have changed. It uses data from the National Longitudinal Survey of Youth 1979 (NLSY79) to compare characteristics of nonworkers from the older cohort with those from the NLSY97 cohort when they were the same age. The NLSY79 is a nationally representative sample of 12,686 men and women born from 1957 to 1964 and living in the United States at the time of the initial survey, in 1979. Respondents were interviewed annually from 1979 to 1994 and biennially since then. At the time of the 2015–16 interview, NLSY97 respondents were ages 30 to 36. To make the cohorts as comparable as possible, I only use NLSY79 cohort data for those born from 1960 to 1964. When the 1996 interview was conducted, these NLSY79 respondents were ages 31 to 36. Figure 1 provides information about the percentage of men not working in the years leading up to the 1996 (NLSY79) and 2015–16 (NLSY97) interviews. The figure shows that 4.9 percent of the NLSY79 sample did not work in the year prior to the 1996 interview, compared with 8.5 percent of the NLSY97 sample who did not work in the year prior to the 2015–16 interview.19 In the second year before the interview, 5.3 percent of men in the NLSY79 and 9.6 percent of men in the NLSY97, respectively, did not work, with similar percentages for the third year before the interview. These percentages reflect the increase in nonwork for men in the later NLSY97 cohort.

Although nonwork among prime-age men appears to be less of an issue in the NLSY79 cohort, it is interesting that nonworkers from both cohorts tend to have similar characteristics in terms of health limitations, education, AFQT scores, and living situation, among other characteristics. Table 7 displays descriptive statistics for those who worked in the year prior to the 1996 (NLSY79) and 2015–16 (NLSY97) interviews and those who did not. In the NLSY79 cohort, 50.6 percent of men who did not work in the year prior to the 1996 interview stated that health issues limited their ability to work, compared with 4.6 percent of workers. Similarly, 40.9 percent of nonworkers in the NLSY97 cohort report that health issues limited their ability to work, compared with 4.3 percent of workers. Men in both surveys who did not work in the year prior to their interview were more likely than those who worked at some point in the prior year to have been interviewed in prison (24.0 percent of nonworkers in the NLSY79 and 16.3 percent in the NLSY97). Black men in both surveys were overrepresented among those who did not work in the prior year and underrepresented among those who worked in the prior year: 40.7 percent in the NLSY79 did not work, compared with 12.7 who worked; and 33.5 percent in the NLSY97 did not work, compared with 13.6 who worked. Conversely, Non-Black, non-Hispanic men in both surveys were overrepresented among those who worked in the prior year and underrepresented among those who did not work in the prior year.

 Table 7. Descriptive statistics of men, ages 30 to 36, in the National Longitudinal Survey of Youth 1979 (NLSY79) and National Longitudinal Survey of Youth 1997 (NLSY97) samples, by work status in the year prior to the 1996 (NLSY79) and 2015–16 (NLSY97) interviews
CharacteristicNLSY79 (in percent)NLSY97 (in percent)
Worked in prior yearDid not work in prior yearWorked in prior yearDid not work in prior year

Work limited for health reasons

4.650.64.340.9

Interviewed in prison

0.724.00.416.3

Enrolled in school at interview date

3.26.65.77.3

Race or ethnicity

Non-Black, non-Hispanic

80.948.571.652.4

Black, non-Hispanic

12.740.713.633.5

Hispanic

6.410.713.413.1

Other

1.41.0

Education level

Less than high school

10.526.76.718.6

General Education Development (GED)

8.227.210.520.8

High school diploma

35.624.123.129.4

Some college

19.915.724.822.2

Bachelor’s degree or higher

25.86.435.09.0

Armed Forces Qualification Test (AFQT) percentile score

Less than 25 percent

26.063.723.853.4

25 percent to less than 50 percent

22.216.424.120.5

50 percent to less than 75 percent

25.415.623.518.8

75 percent or higher

26.44.328.67.3

AFQT score missing

4.17.117.523.3

Marital status

Never married

24.551.643.670.0

Married

60.522.647.818.1

Separated

2.86.41.02.7

Divorced or widowed

12.219.47.69.2

Cohabiting (sample not married)

21.412.535.715.9

Live in household with parent

9.830.213.630.6

Child under age 6 in household

36.820.037.712.9

Sample size

2,6472073,134365

Note: Descriptive statistics are weighted with 1996 (NLSY79) and 2015 (NLSY97) survey weights. Dash indicates not applicable.

Source: U.S. Bureau of Labor Statistics, NLSY79 and NLSY97.

Nonworkers in both surveys were much more likely than workers to have never married (51.6 percent versus 24.5 percent in the NLSY79, and 70.0 percent versus 43.6 percent in the NLSY97). Nonworkers were also much more likely than workers to live in a household with a parent (30.2 percent versus 9.8 percent in the NLSY79, and 30.6 percent versus 13.6 percent in the NLSY97). Nonworkers are much less likely than workers to have a bachelor’s degree in both surveys and much more likely than workers to have less than a high school education or GED. In both cohorts, workers are fairly evenly distributed across the four quarters of AFQT percentile scores. In comparison, nonworkers are much less likely to have AFQT scores in the 75th percentile or higher (4.3 percent versus 26.4 percent in the NLSY79, and 7.3 percent versus 28.6 percent in the NLSY97). Nonworkers were also much more likely to have AFQT scores that were below the 25th percentile (63.7 percent versus 26.0 percent in the NLSY79, and 53.4 percent versus 23.8 percent in the NLSY97).

Discussion and comparison with prior literature

The  8.5 percent of men in the later NLSY97 cohort who did not work in the year prior to the 2015–16 interview also did not work much in earlier years, with more than half working zero weeks in each of the 4 years prior to the 2015–16 interview. That is, unlike those of the Coglianese study, my findings show a persistence of nonwork for these men, rather than a situation in which they move in and out of the labor force.20 An examination of the 52 weeks following the 2015–16 interview indicates that the nonworking status of prime-age men remains mostly unchanged, with almost 82 percent not working in the following year, compared with about 3 percent of workers (numbers not shown in tables).21 Nonwork status among men is less prevalent in the earlier (NLSY79) cohort (4.9 percent) than in the later (NLSY97) cohort (8.5 percent), when I use the same definition and ages for both cohorts. This finding is not surprising, given the increase over the last several decades in the percentage of prime-age men who are not working, as documented in several recent studies.22

The NLSY97 data suggest two likely reasons for the prevalence of nonwork among prime-age men. The first is related to health issues, as found in the Krueger study, and the second relates to current or recent incarceration.23 As shown in table 2, nonworkers are much more likely to report that they have health issues that limit their ability to work (40.9 percent) and 20.6 percent were incarcerated in the year prior to the 2015–16 interview.24 Nonworkers are less advantaged on other fronts as well. As other researchers have found, nonworking men tend to have lower levels of education than their working peers.25 Nonworkers also have lower levels of cognitive skills, as measured by AFQT score, with 53.4 percent in the lowest 25th percentile, compared with 23.8 percent of workers. (See table 2.) Race is also a notable factor, as Black men were nearly 20 percentage points more likely to have not worked in the year prior to the 2015–16 interview than to have worked (33.5 percent versus 13.6 percent). Nonworkers were only slightly more likely to reside in local areas with high levels of unemployment (6.0 percent or higher). (See table 2.)

Data from the NLSY97 show that family members play a substantial role in financially supporting nonworking men, a finding reported by other researchers using alternative data sets.26 Nonworking men are much more likely than working men to live in a household with a parent, but they are less likely to be married or cohabiting. Among those who are married or cohabiting, nonworkers and/or their spouse or partner are more likely to receive transfers such as Supplemental Security Income and food assistance. At the time of the 2015–16 interview, nonworkers were less likely than workers to report that their financial situation was comfortable (38.5 percent versus 67.3 percent), and they were more likely to report that their financial situation was tough or that they were in over their head (43.6 percent versus 11.0 percent). (See tables 2 and 3.)

As far as I know, no earlier studies have specifically looked into the early backgrounds of nonworking men (family and neighborhood resources, delinquency, teen experiences and expectations) as I have done in this article. However, research from the Moving to Opportunity experiment has examined how moving to a less disadvantaged neighborhood affects youths’ subsequent outcomes.27 Previous research has also found that early characteristics and resources substantially affect subsequent educational and employment outcomes.28 Similarly, Adam Looney and Nicholas Turner found that early family and neighborhood environments are strong predictors of future incarceration.29 The NLSY97 data also show that nonworkers tend to be less advantaged than workers, in terms of their early background characteristics. For example, nonworkers are less likely to grow up in a two-parent family, more likely to have a mother with a lower level of education, and more likely to have a mother who had a teen birth. Nonworkers also are more likely to have been arrested at age 18 or younger, less likely to have received a high school diploma by age 20, and to have lower expectations regarding the likelihood of their working 20 or more hours per week in the future. Nonworkers are much more likely than workers to grow up in an impoverished neighborhood. Moreover, nonworkers’ neighborhoods are more likely to be disadvantaged in other ways, including with respect to intergenerational income mobility. Linear probability models suggest that certain early characteristics affect the likelihood of becoming a nonworker. Factors such as early cognitive test scores, whether the respondent attained a high school diploma by age 20, and growing up in a disadvantaged neighborhood, for example, are important predictors of the likelihood of not working in the year prior to the 2015–16 interview. The models suggest that other characteristics, such as early drug use and whether they had been arrested at age 18 or younger, are not.

Conclusion

NLSY97 data show that 8.5 percent of men in the NLSY97 did not work in the year prior to the 2015–16 interview and a majority had also not worked much in earlier years. Two main (supply-side) reasons for nonwork are underscored by the NLSY97 data. First, 40.9 percent of nonworkers respond that health issues limit their ability to work. Research conducted by Priyanka Anand and Purvi Sevak suggests that inaccessible workplaces or lack of transportation provide barriers to employment for many disabled people and that workplace accommodations to address these barriers could increase the likelihood of employment for those with limitations.30 The second reason for nonwork relates to incarceration, as 20.6 percent of nonworkers report that they had been incarcerated in the year prior to the 2015–16 interview and 36.2 percent say they have been incarcerated at some point in their lives. Having a criminal record can be a barrier to employment.31 The NLSY97 data also show that nonworkers tend to be more disadvantaged in the early part of their lives, particularly with respect to family and neighborhood environment.

Suggested citation:

Donna S. Rothstein, "Male prime-age nonworkers: evidence from the NLSY97," Monthly Labor Review, U.S. Bureau of Labor Statistics, December 2020, https://doi.org/10.21916/mlr.2020.25.

Notes


1 Data are annual averages for 1969 and 2015. See U.S. Bureau of Labor Statistics (BLS), Current Population Survey (CPS), labor force participation rate for men, ages 25 to 54, data series LNS11300061; these data can be accessed with the BLS “Series Report” data retrieval tool, at https://data.bls.gov/cgi-bin/srgate.

2 Council of Economic Advisors (CEA), The Long-Term Decline in Prime-Age Male Labor Force Participation (Executive Office of the President of the United States, June 2016), p. 7, https://obamawhitehouse.archives.gov/sites/default/files/page/files/20160620_cea_primeage_male_lfp.pdf. The CEA authors used CPS microdata to make these calculations. This particular measure—current labor force status by previous year’s work experience—is not available in the CPS databases or in BLS publications.

3 Alan B. Krueger, “Where have all the workers gone? An inquiry into the decline of the U.S. labor force participation rate,” Brookings Papers on Economic Activity (Washington, DC: The Brookings Institution, September 2017), pp. 1–59, https://www.brookings.edu/bpea-articles/where-have-all-the-workers-gone-an-inquiry-into-the-decline-of-the-u-s-labor-force-participation-rate/.

4 John Coglianese, “The rise of in-and-outs: declining labor force participation of prime age men,” Harvard University working paper, 2017; the latest version of the paper (2018) can be accessed on the author’s website at https://www.johncoglianese.com/publication/in-and-outs/.

5 Mark Aguiar, Mark Bils, Kerwin Kofi Charles, and Erik Hurst, “Leisure luxuries and the labor supply of young men,” Working Paper 23552 (Cambridge, MA: National Bureau of Economic Research, June 2017), https://www.nber.org/papers/w23552.pdf.

6 David H. Autor, David Dorn, and Gordon H. Hanson, “The China Syndrome: local labor market effects of import competition in the United States,” American Economic Review, vol. 103, no. 6, October 2013, pp. 2,121–2,168, https://www.aeaweb.org/articles?id=10.1257/aer.103.6.2121; David H. Autor, David Dorn, and Gordon H. Hanson, “The China shock: learning from labor market adjustment to large changes in trade,” Annual Review of Economics, vol. 8, October 2016, pp. 205–240, https://doi.org/10.1146/annurev-economics-080315-015041.

7 Katharine G. Abraham and Melissa S. Kearney, “Explaining the decline in the U.S. employment-to-population ratio: a review of the evidence,” Working Paper 24333 (Cambridge, MA: National Bureau of Economic Research, February 2018), https://www.nber.org/papers/w24333.pdf. An updated version of the article was recently published in the Journal of Economic Literature, vol. 58, no. 3, September 2020, https://doi.org/10.1257/jel.20191480.

8 Ariel J. Binder and John Bound, “The declining labor market prospects of less-educated men,” Journal of Economic Perspectives, vol. 33, no. 2, spring 2019, pp. 163–190, https://www.aeaweb.org/articles?id=10.1257/jep.33.2.163.

9 Jay Stewart, “Male nonworkers: Who are they and who supports them?” Demography, vol. 43, no. 3, August 2006, pp. 537–552, https://link.springer.com/article/10.1353/dem.2006.0028.

10 For an earlier analysis of this topic by the present author, see Donna Rothstein, “Men who do not work during their prime years: What do the National Longitudinal Surveys of Youth data reveal?” Beyond the Numbers, vol. 8, no. 11, August 2019, https://www.bls.gov/opub/btn/volume-8/male-nonworkers-nlsy.htm?view_full.

11 The Armed Forces Qualification Test (AFQT) is the cognitive test used in the National Longitudinal Survey of Youth 1997 (NLSY97) cohort examined for this article; The AFQT covers four sections of the Armed Services Vocational Aptitude Battery (ASVAB) and measures math and verbal aptitude. This test was given to NLSY97 respondents in 1997–98.

12 Restricted-use data available upon application approval for use at the BLS or Federal Statistical Research Data Centers.

13 Descriptive statistics in the tables that follow use Round 17 (2015–16 interview) survey weights. The NLSY97 sample weights reflect that Black and Hispanic individuals are oversampled in the survey, as well as other factors. As a result, the weighted percentages of nonworkers and workers differ from unweighted sample percentages.

14 A p-value of .05 or less denotes a statistically significant difference in mean values.

15 Data are available on the U.S. Census Bureau website; see https://www.census.gov/programs-surveys/ces/data/public-use-data/opportunity-atlas-data-tables.html and https://www2.census.gov/ces/opportunity/Codebook-for-Table-2.pdf. See also Raj Chetty, John Friedman, Nathaniel Hendren, Maggie R. Jones, And Sonya Porter, “The Opportunity Atlas: mapping the childhood roots of social mobility” (U.S. Census Bureau and Opportunity Insights, 2020), https://opportunityinsights.org/wp-content/uploads/2018/10/atlas_summary.pdf. 

16 See, for example, Raj Chetty and Nathaniel Hendren, “The impacts of neighborhoods on intergenerational mobility I: childhood exposure effects,” Quarterly Journal of Economics vol. 133, no. 3, August 2018, pp. 1107–1162, https://doi.org/10.1093/qje/qjy007; and Raj Chetty, Nathaniel Hendren, and Lawrence F. Katz, “The effects of exposure to better neighborhoods on children: new evidence from the Moving to Opportunity experiment,” American Economic Review, vol. 106, no. 4, April 2016, pp. 855–902, https://www.aeaweb.org/articles?id=10.1257/aer.20150572.

17 Chetty and Hendren, “The impacts of neighborhoods on intergenerational mobility I.”

18 Note that these results are descriptive and do not imply a causal relationship between the covariates and the likelihood of being a future nonworker.

19 Descriptive statistics in figure 1 (and table 7) use 1996 survey weights (National Longitudinal Survey of Youth 1979) and 2015–16 interview survey weights (NLSY97).

20 Coglianese, “The rise of in-and-outs.”

21 About 87 percent of the 3,499 men in my NLSY97 subsample were interviewed in 2017–18; the data from this interview recently became available.

22 See, for example, Abraham and Kearney, “Explaining the decline in the U.S. employment-to-population ratio.”

23 See Krueger, “Where have all the workers gone?” See also, Cherrie Bucknor and Alan Barber, “The price we pay: economic costs of barriers to employment for former prisoners and people convicted of felonies” (Washington, DC: Center for Economic and Policy Research, June 2016), https://cepr.net/images/stories/reports/employment-prisoners-felonies-2016-06.pdf.

24 Michael Mueller-Smith finds incarceration decreases men’s likelihood of employment during and after their release. See, Mueller-Smith, “The criminal and labor market impacts of incarceration,” unpublished working paper, https://sites.lsa.umich.edu/mgms/wp-content/uploads/sites/283/2015/09/incar.pdf.

25 See, for example, Binder and Bound, “The declining labor market prospects of less-educated men.”

26 See Stewart, “Male nonworkers”; Coglianese, “The rise of in-and-outs”; and Binder and Bound, “The declining labor market prospects of less-educated men.”

27 See, for example, Chetty, Hendren, and Katz, “The effects of exposure to better neighborhoods on children.”

28 See, for example, Joseph G. Altonji, Prashant Bharadwaj, and Fabian Lange, “Changes in the characteristics of American youth: implications for adult outcomes,” Journal of Labor Economics, vol. 30, no. 4, July 2020, pp. 783–828, https://www.journals.uchicago.edu/doi/abs/10.1086/666536; and James J. Heckman, Jora Stixrud and Sergio Urzua, “The effects of cognitive and noncognitive abilities on labor market outcomes and social behavior,” Journal of Labor Economics, vol. 24, no. 3, July 2006, pp. 411–482, https://www.journals.uchicago.edu/doi/abs/10.1086/504455.

29 Adam Looney and Nicholas Turner, “Work and opportunity before and after incarceration,” Brookings Report (Washington, DC: The Brookings Institution, March 2018), pp. 1–23, https://www.brookings.edu/wp-content/uploads/2018/03/es_20180314_looneyincarceration_final.pdf.

30 Priyanka Anand and Purvi Sevak, “The role of workplace accommodations in the employment of people with disabilities,” IZA Journal of Labor Policy, vol. 6, no. 12, October 2017, pp. 1–20, https://izajolp.springeropen.com/articles/10.1186/s40173-017-0090-4.

31 See, for example, Bucknor and Barber, “The Price We Pay”; and Mueller-Smith, “The criminal and labor market impacts of incarceration.”

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About the Author

Donna S. Rothstein
rothstein.donna@bls.gov

Donna S. Rothstein is a research economist in the Office of Employment and Unemployment Statistics, U.S. Bureau of Labor Statistics.

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