BLS WORKING PAPERS

U.S. DEPARTMENT OF LABOR 

Bureau of Labor Statistics 

OFFICE OF EMPLOYMENT AND UNEMPLOYMENT 

STATISTICS 

 

What Do Male Nonworkers Do? 

Jay Stewart, U.S. Bureau of Labor Statistics 

 Working Paper 371

 April 2004 

All views expressed in this paper are those of the author and do not necessarily reflect the views or policies of the 

U.S. Bureau of Labor Statistics. 
 April 2004 

What Do Male Nonworkers Do? 

Jay Stewart* 

Employment Research and 

 Program Development Staff 

Bureau of Labor Statistics 

2 Massachusetts Avenue, NE 

Room 4945 

Voice (202) 691-7376 

FAX (202) 691-7425 

E-Mail Stewart.Jay@BLS.GOV 

* Any views expressed in this paper are the author’s and do not necessarily reflect those of the 

BLS. I wish to thank Harley Frazis, Chinhui Juhn, Peter Meyer, Anne Polivka, James Spletzer, 

and Finis Welch for their comments and suggestions. All remaining errors are mine. 
What Do Male Nonworkers Do? 

Abstract 

Although male nonworkers have become a larger fraction of the population since the late 

1960s, labor economists know very little about them. Using data from several sources--the 

March CPS, the National Longitudinal Survey of Youth, and the 1992-94 University of 

Maryland Time Diary Study--this paper fills that void. The picture that emerges is that there is a 

small cadre of marginal workers who often do not work for periods of a year or more and tend to 

work relatively few weeks in the years that they do work. The vast majority of nonworking men 

(men who do not work at all during the year) receive unearned income from at least one source, 

and the amount of unearned income received varies significantly by reason for not working. 

Family members provide an important alternative source of support for nonworking men who 

have little or no unearned income of their own. For the most part, these nonworking men are not 

substituting nonmarket work for market work. Most of the time that is freed up by not working 

is spent in leisure activities and sleep. 
I. Introduction 

Over the past 30 or so years, the fraction of prime-age men who do not work has 

increased dramatically. In the late 1960s, virtually all 25-54 year-old men worked at least one 

week during the year. Only a small fraction--2.6 percent--went an entire calendar year without 

working. By the late 1990s, that fraction had increased to 6.7 percent. 

Papers by Juhn (1992) and Welch (1997) have found that the increase in the nonwork rate 

was mainly due to labor supply factors in the early 1970s and to labor demand factors in the 

1980s. Much of the decline in labor supply in the 1970s can be attributed to the increase in real 

Social Security Disability Insurance (DI) benefits during that time (see Parsons 1980),1 whereas 

much of the decline in the 1980s was due to a shift in demand away from less-skilled workers. 

For the late-1980s and the 1990s, Autor and Duggan (2003) present convincing evidence that the 

liberalization of the DI program in 1984 resulted in an increase in the nonwork rate, especially 

among less-skilled workers. This was due, in part, because the benefit formula is indexed to 

average wage changes, which have been larger than the wage changes experienced by people at 

the lower end of the earnings distribution. As a result, the replacement rate for the least-skilled 

workers increased over this period. As further evidence, Bound and Waidmann (2002) showed 

the growth in DI recipiency can account for all of the increase in the number of disabled 

individuals who do not work. Sheu (2003) argues that the availability of Medicaid benefits to 

individuals receiving DI have also contributed to the increase in the nonwork rate. 

Although we have a good idea of why these men do not work, we know relatively little 

about them. From earlier studies, we know that male nonworkers are less likely to be married 
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and are more likely to live alone or with relatives than are employed men (Welch 1990 and Juhn, 

Murphy, and Topel 1991); that they are more likely to receive government transfer payments 

than working men (Juhn 1992); that the amount of unearned income they receive increases with 

age (Welch 1999); and that income from other household members accounted for 52 percent of 

income in households with a nonworking man, compared with 31 percent for the overall sample 

(Juhn 1992). But we know nothing about the distribution of unearned income among male 

nonworkers, or the fraction of male nonworkers that are supported by their families. We also 

know nothing about what nonworkers do with their time or the total amount of time they will 

ultimately spend not working. These aspects of nonwork are of more than academic interest, 

because policy prescriptions likely will depend on the amount of support that can be expected 

from family members, whether or not nonworkers are merely substituting nonmarket work for 

market work, and whether or not it is the same men who do not work year after year. 

The goal of this study is to learn more about the growing population of prime-age male 

nonworkers by exploring the longitudinal aspects of nonwork, examining what nonworkers do 

with their time, and taking a more complete look at sources of support. To address the first two 

topics, I draw on data from the National Longitudinal Survey of Youth (NLSY) and the 1992-94 

University of Maryland (UMD) Time Diary Study. To address the last topic, I, like the earlier 

researchers, use the March CPS. I focus primarily on the 1990s, because this period is recent and 

has not been covered extensively by other studies, and because good data for examining the first 

two points are not available for earlier decades. 

The picture that emerges is that there is a small group of men that have a marginal 

attachment to the labor force and account for the lion’s share of the total amount of time spent 

 

1 Legislation was enacted in the late 1970s to stem the increases in real benefits. 
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not working. These men often do not work for periods of a year or more, and, in the years that 

they do work, they work fewer weeks than men who work every year. Most nonworking men 

receive unearned income from at least one source, and the sources of income are consistent with 

the reasons given for not working. For nonworking men who have little or no unearned income 

of their own, family members provide an important alternative source of support. Finally, it is 

clear from the time-diary data that these men are not substituting nonmarket work for market 

work to any great extent. Seventy percent of the time freed up by not working is spent in leisure 

activities and personal care. 

The paper is organized as follows. Section II updates trends in nonwork. Section III 

examines longitudinal aspects of nonwork. And Sections IV and V examine sources of support 

for male nonworkers and what they do with their time. Section V summarizes my findings. 

II. Trends in Nonwork 

Table 1 shows how nonwork rates have changed between the late-1960s and the late1990s. The data come from the March CPS data, with the sample being restricted to civilian, 

noninstitutional2 men age 25-54.3 I also dropped the Hispanic and State Children’s Health 

Insurance Program (SCHIP) oversamples to make the series consistent over time.4 I classified 

 

2 This sample restriction excludes the growing population of men who are incarcerated (see Chandra 2003). 

However, this exclusion is justified because we already have a good idea of what they do and who supports them. 

3 Age is as of March of the year in question. 

4 The CPS began oversampling Hispanic households in 1976. The Hispanic oversample is drawn from Hispanic 

households that were in the CPS in the previous November, December, or January and were still at the same address 

in March. Beginning in 2001, the March CPS sample was expanded to facilitate study of the SCHIP. The expanded 

sample has two components: (1) households that were in MIS 4 or 8 in February or that are scheduled to be in MIS 1 

or 5 in April, (2) households that were in the CPS in the previous November that were interviewed during the 

Febuary-April period. These oversample households are less representative than the rest of the CPS households, 

because they are less likely to have moved in the previous five months. Somewhat surprisingly, excluding these 

oversamples reduces estimated nonwork rates. The difference between the full sample and the nonoversample 

ranges from less than 0.2 of a percentage point in the late 1970s to approximately 0.6 of a percentage point in the 
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men as workers if they worked at least one week during the previous year or if they did not work 

during the year because they were in school.5 Men who worked but did not report earnings were 

dropped from the sample. All other men were classified as nonworkers. The time periods 

(1967-1969, 1977-79, 1987-1989, and 1997-1999) were chosen because they are ten years apart 

and represent similar points in the business cycle. 

There are two key points to take away from Table 1. First, there are significant 

differences in the magnitude and timing of increases in the nonwork rate by education level. 

Over the entire period, the largest proportional increase has not been among high school 

dropouts, but among high school graduates. The 1990s differed from earlier decades in that it 

was the most educated men that saw the largest proportional increases in the nonwork rate. The 

nonwork rates of college graduates and men with some college increased by 70 percent and 57 

percent, respectively. For college graduates, the increase in the 1990s accounts for more than 80 

percent of the increase over the entire 30-year period. Among high school dropouts and high 

school graduates, the relative increase in the nonwork rates were smaller in the 1990s than in 

previous decades (15 percent and 43 percent, respectively). Second, although most nonworkers 

are “Sick/Disabled,” a large fraction (35 percent in the late 1990s) are not. Further, except for 

the 1990s, there has been disproportionately greater growth in categories other than 

Sick/Disabled. In the late 1960s, the Sick/Disabled category accounted for 75 percent of 

nonworkers, but only 58 percent of the increase between the late 1960s and the late 1990s. In the 

late 1980s, this category was 62 percent of nonworkers, and accounted for 77 percent of the 

increase in the nonwork rate in the 1990s. 

 

mid-1990s. This implies that using the full sample overestimates the nonwork rate by up to 9 percent. As part of 

the SCHIP initiative, the CPS also increased the monthly sample in states with high sampling errors for uninsured 

children. I did not drop these observations. 
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These trends indicate that disability, while important, is not the entire story and that an 

increasing share of nonworkers are not disabled. Although the increase in the nonwork rate of 

non-college educated men during the 1990s is consistent with Autor and Duggan’s explanation,6

rising DI replacement rates cannot explain the large relative increase in the nonwork rate of 

college graduates during this time because their replacement rates did not increase. As further 

evidence, the Sick/Disabled category accounted for only 42 percent of the increase among 

college graduates, while the Family Care category accounted for 27 percent of the increase. 

III. Longitudinal Aspects of Nonwork 

Although March CPS data tell us a lot about trends in nonwork rates, they cannot tell us 

anything about the total amount of time that an individual will ultimately spend not working. In 

this section, I use NLSY data to examine this issue along with other longitudinal aspects of 

nonwork. For this analysis, I restricted the NLSY sample to civilian, noninstitutional men who 

were age 25-29 in 1987 and had valid data for all years in the 1987-1997 period.7 The resulting 

dataset has 20,581 observations on 1,871 men. Because sample attrition could have biased the 

results, I also ran the analyses on a more complete sample of 25,395 observations on 2,445 men 

who had valid data for at least 5 years of the 11 years in the 1987-1997 period. Although the 

nonwork rates were slightly higher in the expanded sample, the two datasets tell the same 

qualitative story.8

 

5 The classification of students as workers does not affect my results. 

6 In the 1990s, the Sick/Disabled category accounted for over 75 percent of the increase in the nonwork rate of noncollege educated men. 

7 As above, age is as of March of the year in question. 

8 I opted to use the balanced panel, because it simplifies the presentation of my results. As an added check, I 

compared the NLSY sample to a similarly defined sample from the CPS, and found that there were very similar to 

each other. See Table A1 and the Data Appendix for more details. 
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I defined nonworkers as above (zero weeks worked and not in school) using a weeksworked variable constructed from the detailed week-by-week employment information in the 

Work History file. Men who were nonworkers in one or more years of the 11 years of the 

sample are referred to as “marginal workers,” and, to simplify the presentation, are classified into 

three categories: frequent nonworkers (men who were nonworkers in four or more years), 

occasional nonworkers (nonworkers in two or three years), and one-time nonworkers (one year 

of nonwork). The nonwork rate for this cohort over the 1987-1997 period in the NLSY is 

somewhat lower than that in the comparable March CPS data--averaging 2.8 percent9 versus 5.1 

percent in the CPS. Some of this difference is due to attrition (the nonwork rate for the full 

NLSY sample averaged 3.5 percent), but differences in the way the data are collected likely 

account for most of the difference (see the Data Appendix for more details). 

Table 2 shows that a small percentage of men account for the vast majority of the time 

spent not working. Over the 1987-97 period, the 2.8 percent of men who were frequent 

nonworkers accounted for 68.3 percent of the years spent not working, and another 2.2 percent 

who were occasional nonworkers accounted for an additional 17.0 percent. Hence, a small 

fraction (5 percent) of men accounts for nearly all (85.3 percent) of the years spent not working. 

Put another way, in an average year 85.3 percent of full-year nonworkers are either frequent or 

occasional nonworkers and only 14.7 percent are one-time nonworkers. 

The amount of time spent not working varies predictably by demographic characteristics 

(see Table 3). High school dropouts spent an average of 1.1 years not working out of the 11 in 

the sample, compared with 0.3 of a year for high school graduates, 0.2 of a year for men with 

some college, and a negligible amount of time for college graduates. The fraction of men who 

 

9 The nonwork rate in the NLSY ranged from a low of 1.6 percent in 1989 to a high of 4.2 percent in 1997. 
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worked all 11 years increases monotonically with education from 71.7 percent for high school 

dropouts to 98.4 percent for college graduates. Similarly, the fraction of men who are frequent 

nonworkers is 11.3 percent among high school dropouts, about 2.6 percent among high school 

graduates and men with some college, and zero for college graduates. There are significant 

differences by race as well. Nonwhites spent an average of 0.7 of a year not working, while 

whites spent 0.2 of a year not working. Nonwhites are more likely to be marginal workers than 

whites (17.7 percent vs. 7.8 percent), and the difference between whites and nonwhites becomes 

more pronounced at higher levels of nonwork. 

Looking at the first and last columns in Table 3, it appears that much of the betweengroup differences in the total number of years spent not working are due to differences in the 

fraction who are marginal workers, rather than differences in the number of years spent not 

working conditional on being a marginal worker. The regression results in Table 4 help sort this 

out. Because the number of full years spent not working is essentially a count, I estimated the 

effects of covariates using a Poisson regression model. The results in the first column confirm 

that there is significant between-group variation in the average number of years spent not 

working. High school dropouts spend nearly three times as many full years not working as high 

school graduates, and college graduates spend significantly fewer years not working. Nonwhites 

and men who have spent some time in jail spend more than twice as many years not working as 

their respective reference groups. The remaining two columns support the hypothesis that most 

of the between-group differences are due to differences in the probability of being a marginal 

worker. The probit results10 in the second column show that the probability of being a marginal 

worker is monotonically decreasing in education and that nonwhites are about 6 percentage 

 

10 The coefficients are expressed as marginal effects. 
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points more likely than whites to be a marginal worker. These effects are large and statistically 

significant. In contrast, having spent time in jail does not seem to affect the probability of being 

a marginal worker. The Poisson regression in the third column was estimated over the subset of 

marginal workers. Among marginal workers, college graduates spend 63 percent fewer years not 

working and men who have spent time in jail spend about 73 percent more years not working. 

The other coefficients are not significant. Together, these results indicate that most of the 

between-group differences in the number of years not worked are due to differences in the 

probability of being a marginal worker and that unobserved differences account for much of the 

variation in the time spent not working among marginal workers. 

In the years that they do work, marginal workers work fewer weeks than men who 

worked every year (see Table 5). One-time nonworkers work a full year in only 75 percent of 

years that they work, compared with nearly 93 percent for men who worked every year. The 

fraction is even lower for occasional and frequent nonworkers (63 percent and 42 percent). 

Conversely, conditioning on the number of weeks worked, individuals who work fewer weeks in 

a year are more likely to be occasional and frequent nonworkers. Nearly 30 percent of men who 

worked 1-13 weeks are occasional or frequent nonworkers. In contrast, less than 2 percent of 

full-year workers (50+ weeks) are occasional or frequent nonworkers. 

These results have several implications. Given that a small fraction of men account for 

the vast majority of the time spent not working, the composition of nonworking men does not 

change much from year to year. Thus, we can learn a lot about nonworkers using cross-section 

datasets such as the CPS. From a policy perspective, this result means that programs designed to 

reduce the nonwork rate need only target a relatively small fraction of the population. The fact 

that men who work part year are more likely to be marginal workers implies that potential 
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experience understates actual experience for these workers. To illustrate the effect of this 

understatement, I estimated wage equations on full-year and part-year workers and found that the 

return to experience is about 30-40 percent lower for part-year workers. If full-year and partyear workers are pooled together, the estimated parameters slightly underestimate the returns to 

experience for full-year workers, and significantly overestimate the returns for part-year workers. 

IV. Who Supports Them? 

This section builds on earlier studies by providing a more detailed description of the 

sources of support for nonworking men. For this analysis, I pooled data from the 1994-2000 

March CPS files (covering the 1993-1999 period) using the sample restrictions and definitions 

described in Section II. Unearned income is broken into six categories: asset income,11 disability 

income,12 income from Social Security,13 income from retirement sources, unemployment 

compensation, and other unearned income.14 

Table 6 shows the percent of nonworkers that have any unearned income and the percent 

with each type of income by reason for not working. About 70 percent of nonworkers have at 

least one source of unearned income, although there is significant variation by reason for not 

working. Nonworkers who were Sick/Disabled or Retired, whom together comprise 75 percent 

of nonworkers, were by far the most likely to have unearned income (82 and 84 percent, 

respectively). The remaining three categories (25 percent of nonworkers) were significantly less 

 

11 This includes interest, dividends and rental income. For married couples, I assume that the CPS correctly 

allocates income to each spouse. 

12 This includes worker’s compensation and disability benefits from pension plans and private insurance policies. 

13 This includes Social Security and SSI. The main reason for combining these two sources is that respondents seem 

to have confused them. There are a large number of men who claim to be receiving Social Security, but who are too 

young to receive retirement benefits. 
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likely to have unearned income. Among all nonworkers, the most common sources are Social 

Security (42 percent), asset income (20 percent), and disability (12 percent), although there are 

large differences by reason for not working. Among the Sick/Disabled, the most common 

sources of income were Social Security (62 percent), disability benefits, and income from assets 

(17 percent each). Slightly over half of all Retired nonworkers received asset income, 42 percent 

received retirement income, and 26 percent received Social Security. The relatively small 

number receiving Social Security likely reflects the fact that these men are too young for old-age 

benefits. Hence, they must have received DI or SSI payments, but reported being Retired rather 

than Sick/Disabled. For those who were Unable to Find Work, unemployment benefits, asset 

income, and other income were the most common sources, but relatively few received income 

from any of these sources (between 13 and 15 percent for each source). In the Family Care 

category, asset income (28 percent) and other income (14 percent) are the most common sources. 

Table 7 shows the amount of unearned income received (conditional on receiving any 

unearned income) by reason for not working, and the relative contribution from each source of 

income. Unearned income amounts were adjusted to account for income and FICA taxes (to 

make them comparable to earned income)15 and deflated to 1998 dollars using the CPI. Of 

nonworkers who have any unearned income, the average amount is $12,990. Most of this total 

comes from Social Security (42 percent), disability benefits (19 percent), retirement income (11 

percent), and asset income (10 percent). 

As might be expected, both average income (conditional on having any income) and the 

share from each source vary considerably by reason for not working. Retired nonworkers have 

 

14 Other unearned income includes veteran’s benefits, public assistance, child care, alimony, and money received 

from non-household friends and relatives. 
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by far the highest average income at $23,261. About half of this amount comes from pensions 

and other retirement sources, another 19 percent comes from assets, and 14 percent comes from 

Social Security. Sick/Disabled nonworkers averaged $12,644 in unearned income, with nearly 

four-fifths of this coming from Social Security (53 percent) and disability benefits (25 percent). 

Nonworking men who were Unable to Find Work and doing Family Care received significantly 

less unearned income, only $7,725 and $6,838, respectively. Among nonworking men who were 

Unable to Find Work, a little over half came from unemployment benefits (38 percent) and other 

income (34 percent). While for those doing Family Care most of their income came from assets 

(31 percent) and other income (38 percent). 

Looking at average incomes potentially can be misleading, because income distributions 

tend to be skewed.16 As it turns out, median incomes (shown in the second row of Table 7) tell a 

similar story. The main differences show up in the Family Care and Other Reason categories. 

Overall, median income is about 35 percent less than mean income, but for these two categories 

the relative differences are about twice as large (62 and 75 percent). Thus it appears that a large 

fraction of nonworkers in these two categories receive very little income. 

The box-and-whisker plots in Figure 1 further illustrate the differences in income by 

reason for not working.17 The universe for Figure 1 is all nonworkers, not just those with 

positive incomes, so that we can more clearly see the effect of income nonreceipt. Retired 

 

15 When I adjusted for income taxes, I assumed that income from programs not subject to income taxes was 

marginal so that the marginal tax rates on the Unicon March CPS files applied. 

16 Interestingly, the distribution of income among nonworkers is considerably more skewed than the distribution of 

either earnings or total income among workers. The skewness coefficient is about 2.8 for workers and about 4.7 for 

nonworkers. Perhaps not surprisingly, the skewness coefficient for retired nonworkers is about 2.6--approximately 

the same as for workers. 

17 The box in the box-and-whisker plots extends from the 25th to the 75th percentile, while the line inside the box is 

at the median. The endpoints of the whiskers are equal to inf (Y | Y < (Y75 + 1.5· (Y75 - Y25)) and 

sup (Y | Y > (Y75 - 1.5· (Y75 - Y25)). 
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nonworkers stand out from the rest, because they have significantly higher median income and 

their unearned income exhibits greater dispersion than any other category. Focusing on the other 

four reasons, we see that income nonreceipt plays an important role. For example, the collapsed 

boxes for the Family Care and Other Reasons indicate that 75 percent of the men in these 

categories have little or no income. This and the higher fraction of Sick/Disabled nonworkers 

with unearned income account for the fact that there is no overlap between the interquartile range 

of Sick/Disabled category and the other three categories (Family Care, Unable to Find Work, and 

Other Reasons), indicating that 75 percent non-Sick/Disabled nonworkers (except Retired) 

receive less income than the 25th percentile of Sick/Disabled nonworkers. If the universe for 

Figure 1 had been restricted to nonworkers with unearned income, these four groups would look 

much more similar to each other.18 

Putting Tables 6 and 7 and Figure 1 together, we can see that the income received by 

male nonworkers reflects the high proportion that are Sick/Disabled and that there is significant 

variation in the incidence and amount of income received by reason for not working. More 

importantly, a large fraction of nonworking men have little or no income, which suggests that 

they have other sources of support. 

Support from Other Family Members 

The first step in this analysis is to examine the living arrangements of nonworkers and 

compare them to those of workers. For the purposes of this discussion, I consider living 

arrangements defined by the presence of family members, where “family” includes all immediate 

 

18 These plots are available from the author on request. 
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and extended family members living in the household.19 The top panel of Table 8 shows that 42 

percent of nonworkers live with a spouse and 29 percent live alone. The rest live either with 

their parents (21 percent) or other relatives (7 percent). In contrast, 68 percent of working men 

live with a spouse, 23 percent live alone, and only 6 percent live with their parents. So, 

compared with working men, nonworking men are much less likely to be living with a spouse 

and are more likely to be living alone, with their parents, or with other relatives. 

The bottom panel of Table 8 examines the extent to which the nonworker’s income rather 

than employment status accounts for differences in living arrangements. The income quantile 

ranges in Table 8 were chosen to facilitate comparisons between workers and nonworkers with 

similar incomes. Nonworking men in the 30-75 percentile range have roughly the same income 

as workers in the first decile, and nonworking men in the top quartile have roughly the same 

income as working men in the 10-90 percentile range. Nonworking men in the first three deciles 

have no income. 

The lower panel of Table 8 shows that working and nonworking men with roughly the 

same income have similar distributions of living arrangements. The main difference between the 

distributions is that nonworkers in the 30-75 and 75+ percentile ranges are about 8-10 percentage 

points less likely to be living with a spouse compared to workers with similar incomes. 

Nonworkers in the 30-75 percentile range are 5 percentage points more likely to live with their 

parents, whereas those in the 75+ range are 6 percentage points more likely to live alone. But 

these differences are relatively small compared to the differences between nonworkers with 

different levels of income. Comparing nonworkers in the 30-75 percentile range to those in the 

75+ range, the higher income nonworkers are 20 percentage points more likely to be living with 

 

19 Alternatively, I could have used the household or the nonworker’s immediate family. The results are not sensitive 
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a spouse and are 14 percentage points less likely to be living with their parents. Nonworkers in 

the first three deciles (those with no income) are the most likely to be living with their parents or 

other relatives (32 percent and 11 percent) and are the least likely to be living with a spouse (35 

percent). 

The next step is to investigate the extent to which nonworkers are supported by other 

family members living in the household. Table 9 shows family per capita income and the 

contributions to family per capita income from nonworkers, their spouses, parents, and other 

relatives conditional on having any family income. Over all living arrangements, average per 

capita income is $11,048.20 Per capita income is highest when the nonworker is living alone or 

with a spouse and is lowest for nonworkers who live with other relatives, although there is not 

much variation across living arrangements. There is, however, quite a bit of variation in the 

fraction contributed by the nonworker. Of the nonworkers who live with family members, those 

who live with their wives contribute the most (38 percent) to per capita family income, while 

those living with their parents contribute the least (17 percent). Households where the 

nonworker lives with other relatives fall between these two figures, with the nonworker 

contributing 24 percent to family per capita income. 

Table 10 provides insight to some of this variation by showing how the contributions to 

total family income are broken down into the fraction of nonworkers’ families receiving income 

from a given source and the amount conditional on receipt. Nonworkers who live with their 

parents or other relatives are the least likely to have unearned income. Only 54 percent of 

nonworkers who live with their parents have any income of their own, and the average amount is 

considerably smaller --$8,515 versus $12,990 overall. A similar percentage of nonworkers who 

 

to the definition used. 
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live with other relatives have income, though the average amount is somewhat higher ($10,901). 

In contrast, 75 percent of nonworkers who live with a spouse received income, with the average 

amount being $15,614. A similar fraction of nonworkers who live alone received unearned 

income, but the average amount was only $11,902. 

There is significant variation in the income contributed by family members. Spouses 

provide much of the income in married couples. In Table 9, we can see that their incomes, 

which are mostly from earnings, comprise 53 percent of family per capita income. Table 10 

indicates that the 60 percent of wives who have earned income earned an average of $23,398 per 

year. A similar fraction have unearned income, but the average amount is considerably smaller, 

only $5,677. In contrast, when nonworkers live with their parents (no spouse present), the 

parents income, most of which is unearned, accounts for 71 percent of per capita income (see 

Table 9). Only 38 percent of parents of nonworkers in this arrangement have earned income, 

although the amount is relatively large, $26,916. These parents are twice as likely to have 

unearned income (82 percent) with the average amount being $14,427 (see Table 10). Overall, 

other relatives contribute 11 percent to per capita family income. Interestingly, of the 

nonworkers who live with a spouse or with their parents, 41 and 46 percent have other relatives 

present who also contribute income. 

The data in Tables 9 and 10 indicate that a significant portion of family income comes 

from other family members and that the extent of that support varies considerably by living 

arrangement. But averages do not tell us much about what fraction of nonworking men receive 

substantial or total support from household family members. Ideally, I would like to compare 

the resources consumed by the nonworker to the income received by the nonworker, but 

 

20 As a point of reference, the average per capita income in families with a working man is $24,938. 
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consumption data are not available in the CPS. However, if I assume that resources are 

distributed approximately equally among family members, then I can shed light on this issue by 

looking at the ratio of the nonworker’s income to the average income of other adults in the 

family (I will refer to this as the relative income ratio). I use 75 percent and 125 percent as my 

main cutoffs. A relative income ratio between 75 and 125 percent indicates that the nonworker’s 

income is about the same as other adults in the family, and I assume that he neither receives 

substantial support from nor provides substantial support to other family members. Below 75 

percent, I assume that the nonworker receives substantial support from other family members, 

and above 125 percent the nonworker provides substantial support to other family members. 

Table 11 shows the distribution of the relative income ratio by living arrangement, reason for not 

working, income percentile range, and selected demographic characteristics. The universe for 

this table includes nonworking men in who live in families with positive family income. 

From Table 11, we can see that there is considerable variation in the fraction of 

nonworkers who receive substantial support from family members by living arrangement, and 

reason for not working. Fifty-three percent of nonworking men who live with a spouse receive 

substantial support from other family members, compared with 77 percent of nonworkers who 

live with their parents and 70 percent of those who live with other relatives. Nonworkers in the 

latter two living arrangements also are much more likely to be completely supported by other 

family members. Nonworkers who said they were Sick/Disabled or Retired are the least likely to 

be receiving substantial support from family members (53 percent and 41 percent, respectively). 

Over 80 percent of men who report the other three reasons receive substantial support from 

family members, with between half and two-thirds of them receiving total support. 
-17-

Looking at the level of support by income quantile range makes it clear that support from 

family members is a substitute for nonworkers’ own income, and that the differences described 

above are related to differences in income across living arrangements and reason for not 

working. Not surprisingly, nearly 60 percent of nonworking men in the 30-75 percentile income 

range receive substantial support from family members, while only 24 percent provide 

substantial support. In contrast, only 21 percent of nonworkers in the top quartile receive 

substantial support, while 60 percent provide substantial support. 

There is surprisingly little variation by race and especially by education, although black 

nonworkers are significantly more likely to have no income compared with white nonworkers. 

The fraction receiving substantial support decreases with age, but this is not surprising given 

Welch’s (1999) finding that nonworkers’ incomes increase with age. 

Putting all of these results together, it is clear that family members provide an important 

alternative source of support for nonworking men who have little or no unearned income of their 

own. This can be a double-edged sword, because the availability of potential support from 

family members, while providing an important alternative safety net, may, like the availability of 

DI benefits, reduce incentives to work. 

Taking a quick look at the NLSY data suggests that living arrangements are jointly 

determined with employment status, which casts doubt on the safety-net aspect of family 

support. If the safety net aspect is important, then one would expect that transitions from work 

to nonwork to be accompanied changes in living arrangements. But of the 212 nonwork spells 

that began in 1988 or later, only 13.7 percent (on a weighted basis) were accompanied by a 
-18-

change in living arrangements.21 Of those, only one third (4.4 percent of all transitions) moved 

in with their parents at the same time they made the transition from work to nonwork. Moreover, 

the distributions of living arrangements before and after the transition are nearly identical. 

Hence, it appears that living arrangements and employment status are jointly determined, and 

that certain living arrangements facilitate nonwork. 

V. What Do Nonworkers Do? 

To examine this question, I use data from the 1992-94 University of Maryland (UMD) 

Time Diary Study. In this study, households were sampled using random digit dialing (RDD), 

and one household member was randomly selected and interviewed. Respondents were asked to 

sequentially report everything they did during the previous day. For each episode, the survey 

collected a verbatim description of what was done, the start and stop times, where the respondent 

was, and who the respondent was with. Each verbatim response was coded into one of 90 fairly 

detailed activity codes. In addition to the time-diary information, the UMD data contain basic 

demographic information (age, race, sex, education) and employment status. 

As with the March CPS data, I restricted the sample to civilian, noninstitutional men age 

25-54.22 I also excluded observations where the respondent reported four or fewer episodes. I 

further restricted the analysis to nonworkers and full-time workers, because part-time workers 

looked very different both from full-time workers and nonworkers and there were not enough of 

them to do a separate analysis.23 Nonworkers were further broken down by reason for not 

 

21 I dropped the 28 transitions for which living arrangement was unknown either before or after the transition from 

work to nonwork. 

22 Age is at the time of the interview. 

23 For this analysis, I excluded nonworking students. 
-19-

working: disabled, unemployed, and other reasons.24 Individuals were classified as workers if 

they reported that they were employed during the previous week. About 6 percent of individuals 

who were not employed full time during the previous week reported working during the diary 

day. Although these observations appear to be legitimate transitions out of nonemployment,25 I 

dropped them from the sample because I could not determine their full-time/part-time status. 

To keep the presentation manageable, I aggregated the 90 2-digit activities into 6 broad 

activity groups. The Work category includes time spent in job search and commuting. 

Household Work includes housework and purchasing goods and services. Child Care refers only 

to care that was done as a primary activity, and does not include looking after children while 

doing something else. It also excludes playing with children if the activity falls into another 

category.26 Leisure and Recreation includes active sports, exercise, outdoor activities, arts and 

crafts, games, computer use, watching TV, reading, and conversation. And Personal Care 

includes time spent eating, sleeping, bathing, and dressing. The six columns of Table 12 show 

the average number of hours per day spent in each activity for each of the broadly defined 

activities defined above. The rows do not sum to 24 hours, because I omitted activities related to 

organizations, which are not household work, leisure, or personal care. Table 12 shows that 

nonworking men spend 3.6 hours per day (25.2 hours per week) doing unpaid work (Household 

Work plus Child Care), 8.4 hours per day (58.6 hours per week) in leisure and recreational 

 

24 Keep in mind that these categories are not exactly the same as the corresponding categories in the March CPS 

data. 

25 I looked into this by comparing the implied monthly transition rate of about 24 percent in the UMD sample to the 

average monthly transition rates of men for the same period from the CPS gross flows data. Average monthly 

transition rates of men for the period from the gross flows were 26.3 percent for unemployment-to-employment 

transitions and 5.2 percent for NILF-to-employment. Hence, the transition rate implied by the UMD data is about 

right for unemployment-to-employment transitions, and too large for NILF-to-employment transitions. The results 

are virtually identical when these observations are included in the sample and classified as employed. 

26 For example, bike riding with young children is likely to be recorded as active sports rather than child care. 
-20-

activities, and 11.1 hours per day (77.6 hours per week) doing personal care activities. To put 

these numbers into perspective, it is useful to compare nonworking men to nonworking women 

and to working men. 

Nonworking women spend 6.0 hours per day (41.7 hours per week) doing unpaid work, 

or 2.4 hours more per day (16.5 hours more per week) than nonworking men. Comparing 

nonworking men and women by reason for not working, we see that the greatest difference in 

unpaid work is for the unemployed. Unemployed women spend 5.4 hours (37.5 hours per week) 

doing unpaid work compared with 3.2 hours (22.4 hours per week) for unemployed men. The 

differences for the other two reasons are smaller, which suggests that much of the overall 

difference is due to differences in the distributions of reasons for not working--three-quarters of 

nonworking men reported being disabled or unemployed, whereas only one-quarter of 

nonworking women gave those reasons. 

To illustrate the effect of difference in the distributions of reasons for not working, I 

generated counterfactual estimates of the time that nonworking men spend doing unpaid work by 

calculating weighted averages across the three reasons using the fraction of nonworking women 

giving those reasons as weights. Using these counterfactual estimates, the difference in unpaid 

work fell from 2.4 hours (16.5 hours per week) to 1.7 hours (12.0 hours per week), with all of the 

reduction being due to household work. The difference in the time spent in child care increased 

slightly. However, it is likely that this counterfactual estimate does not fully account for the 

differences in reasons for not working, because the composition of the Other Reasons category 

differs significantly between men and women. The vast majority (84 percent) of women in the 

Other Reasons category report keeping house, whereas only 18 percent of men in the Other 

Reasons category gave that reason. Tabulations of CPS data further support this hypothesis. 
-21-

Nonworking women are nearly twice as likely as nonworking men to be living with children 

under 18 (64 percent versus 34 percent) and are nearly 10 times as likely to be taking care of 

family (70.0 percent versus 7.4 percent). It is also worth noting that the estimated differences in 

the time spent in child care likely understates the true difference in child care between 

nonworking men and nonworking women, because they do not include child care as a secondary 

activity, which accounts for most child care. 

To compare nonworking men to working men, it is useful to examine what nonworkers 

do with the time that is freed up by not working. Compared with men who work full time, 

nonworking men spend about 6.6 fewer hours per day (46.1 hours per week) less in work-related 

activities than working men (0.2 of an hour versus 6.8 hours). About 25 percent of this 

difference (1.7 hours) is spent doing household work and child care, and another 6 percent (0.4 

hours) is spent getting additional education or training. The remaining 69 percent is spent in 

leisure and recreation activities (3.6 hours) and personal care (1.0 hour, about 3⁄4 of which is 

sleep). The differences between workers and nonworkers shrink considerably when the 

comparison is restricted to nonwork days.27 Workers and nonworkers spend similar amounts of 

time in unpaid work (3.6 versus 3.7 hours per day for workers) and leisure activities (8.4 versus 

8.0 hours per day). If one looks at less-aggregated 1-digit activities, the differences are 

statistically significant for only three of the ten activities (Work, Education, and Passive Leisure) 

compared with eight of ten activities when workdays are included for working men. Thus, the 

average day of a nonworking man looks very much like the average day-off of a man who works 

full time. In contrast, nonworking women substitute nonmarket work for market work to a much 

 

27 I did not omit days when the respondent reported looking for work. 
-22-

greater degree. Of the 6 hours per day freed up by not working, they spend 50 percent doing 

unpaid work, 35 percent doing leisure and recreation activities, and 10 percent in personal care. 

To help quantify the differences described above, the bottom panel of Table 12 shows 

values of the activity profile index for selected comparisons. This weighted absolute deviation 

index (TWAD) is given by the following formula: 

 

( )

∑

∑ =

= 



















































+

+

+

− =

k

i 1

k

i 1

i i

i i

i i

i i

WAD

a b

a b

a b

a b T . 

where ai is the time spent in activity i by group a, bi is the time spent in activity i by group b, and 

k is the number of 1-digit activities. This index ranges between 0 and 1, with 0 indicating 

identical activity profiles and 1 indicating no activities in common, and is best described as a 

weighted average of the absolute percentage difference in time spent in all activities.28 

The values of the index in the first column compare full-time workers to nonworkers for 

both men and women. Comparing the average day of a full-time worker to the average day of 

nonworker, the activity profile index values are 0.27 for men and 0.24 for women, indicating 

significant differences between the two groups for both men and women. In contrast, comparing 

an average day of nonworkers to an average nonwork day of full-time workers the index value is 

 

28 I used this index, because it has an intuitive interpretation and is not sensitive to the level of aggregation. See 

Stewart 2000 for a discussion of other activity profile indexes used in the time-use literature. This index is 

equivalent to the Duncan segregation index when ∑ ∑ = =

=

k

i 1

i

k

i 1

ai b 
-23-

about 0.06 for both men and women, indicating a strong similarity.29 Comparing nonworking 

men and women reveals that nonworkers of both sexes are similar to one another, but that the 

activities of nonworking men are more similar to those of working men on their day off than to 

those of nonworking women. 

The fact that there is not much substitution of nonmarket work for market work suggests 

that the relatively high reservation wages of male nonworkers are due to something other than 

opportunities for nonmarket production, and provides some additional corroboration of the 

findings of the Autor and Duggan (2003) and Bound and Waidmann (2002) studies. 

VII. Summary 

The increase in the nonwork rate of prime-age males, which has been documented 

through the 1980s by Juhn (1992) and Welch (1997), has continued unabated through the 1990s. 

Much of the increase in the 1990s was in the Sick/Disabled category, which is consistent with 

the Autor and Duggan (2003) and Bound and Waidmann (2002) studies. But this is not the 

entire story, because the non-Sick/Disabled categories have increased as well. 

Examination of NLSY data indicates that there is a small cadre of men who spend large 

amounts of time not working. In an average year, about 85 percent of male nonworkers are men 

who will end up spending nearly one in five years not working, and 68 percent will end up 

spending one in three years not working. Most of the between-group variation in the time spent 

not working is due to differences in the probability of being a marginal worker, rather than 

 

29 Findings from Stewart 2000 can help put these numbers in to perspective. For the population as a whole, the 

activity profile index takes on values of between 0.02 and 0.04 when comparing weekdays to each other, and 

between 0.13 and 0.20 when comparing weekend days to weekdays. For groups that are identical except for random 

noise, the activity profile index is approximately 0.03. See Stewart (2000) for a description of this procedure. 
-24-

differences in the time spend not working conditional on being a marginal worker. In the years 

that they do work, these marginal workers tend to work fewer weeks. 

Most nonworking men have income from at least one source, although fully 30 percent 

receive no income. As might be expected, there is significant variation in the fraction receiving 

income by reason for not working. Nonworkers who are Sick/Disabled or Retired are the most 

likely to have unearned income, and, conditional on receiving any income, the amounts received 

by men in these categories are larger as well. There is a large fraction of nonworking men that 

have little or no income, which suggests that they have other sources of support such as other 

family members living in the household. 

Compared with workers, nonworkers are less likely to be living with a spouse and are 

more likely to be living with their parents or with other relatives. However, after controlling for 

income, many of these differences disappear. Hence, most of the differences in the distribution 

of living arrangements between workers and nonworkers are related to differences in income, 

rather than differences in employment status per se. 

These differences in living arrangements by income level suggest that family members--

especially parents--are an important source of support for nonworkers who have little or no 

income. Overall, three-fifths of nonworkers who live with family members receive substantial or 

total support from those family members. Nonworkers who live with their parents or who report 

not working because of Family Care, they are Unable to Find Work, or Other Reasons have the 

lowest incomes and are by far the most likely to receive substantial or total support from family 

members living in the household. 

Although nonworking men receive substantial support from family members, they do not 

substitute nonmarket work for market work to any great extent. Nonworking men do more 
-25-

unpaid household work than men who work full time, but household work and education account 

for only 31 percent of the time that is freed up by not working. The remaining 69 percent is 

spent in leisure and personal care activities. Compared with nonworking women, nonworking 

men spend significantly less time doing household work, although much of the difference is due 

to differences between men and women in the distribution of reasons for not working. It is also 

worth noting that these estimates likely understate the differences between nonworking men and 

women, because nonworking women are much more likely to be living in a household with 

children and these time-diary data do not capture child care that is done as a secondary activity. 

The small sample size of the UMD data limits the analyses that can be performed. With 

more time-diary data, it would be possible to make finer distinctions. For example, it would be 

possible to determine how the amount of unpaid work varies by living arrangement (are stay-athome Dads different from stay-at-home Moms?) and the length of the nonwork spell (do longterm nonworkers differ from short-term nonworkers?). Once data from the American Time-Use 

Survey become available, it will be possible to answer these questions and more. 
-26-

Data Appendix 

Panel A of Table A1 shows summary statistics for the NSLY sample in 1997 and a 

comparably defined sample from the March CPS. The two samples look very similar, except for 

the distribution of educational attainment and the distribution of weeks worked. Much of this 

difference is due to the fact that the CPS uses a degree based measure, while the NLSY uses a 

years-of-schooling measure.30 The more important difference is in the nonwork rate, which is 

nearly 50 percent higher in the CPS (6.1 percent vs. 4.2 percent in the NLSY). Attrition can 

account for some of the difference--the nonwork rate for the full NLSY sample is 5.0 percent, 

which is still well below the CPS rate. Moreover, the difference in 1997 understates the 

difference between the two datasets over the entire 1987-97 period. The average over all years 

was 5.1 percent in the CPS compared with 3.5 percent in the full NLSY sample and 2.8 percent 

in the balanced panel. 

Differences in survey design seem to be the most likely explanation for the lower 

nonwork rate in the NLSY. The NLSY collects information directly from the individual, 

whereas information for about half of the CPS sample comes from proxy respondents, who may 

be less likely to remember or know about short spells of employment. Also, the NLSY collects 

the work history information by showing respondents a calendar and asking what they were 

doing each week, whereas the CPS asks a single question about number of weeks worked during 

the previous year. Again, short spells of employment are less likely to remembered. Apart from 

the fraction with zero weeks worked, the main difference in weeks worked shows up in the 

fraction of full year workers (50+ weeks per year): 85.4 percent in NLSY vs. 80.0 in the CPS. If 

 

30 See Frazis and Stewart (1999) for a discussion of the two approaches to collecting educational attainment in the 

context of the 1992 change in the CPS question. 
-27-

differences in survey design are responsible, then they affect reporting at all points in the weeksworked distribution. 

Panel B of Table 1 compares the UMD data to the March 1993 and 1994 CPS data. The 

CPS data are from the monthly questions, so they refer to March of those years. In general, the 

UMD data are very similar to the CPS data. The age distributions are nearly identical, although 

the UMD sample is better educated. This is probably an artifact of the RDD sampling, because 

high school dropouts are less likely to live in households with a telephone. There are somewhat 

more nonwhites in the UMD sample than in the CPS. Probably the biggest difference is the 

distribution of employment status. A much higher fraction of the CPS sample is not employed. 

Again, this may be an artifact of the RDD sampling. Apart from the fact that there are only 152 

respondents who are not employed, this should not affect my results, because I am comparing 

time use across employment status. 
-28-

References 

Autor, David and Mark Duggan (2003) “The Rise in the Disability Rolls and the Decline in 

Unemployment,” Quarterly Journal of Economics 118(1), February 2003, pp. 157-205. 

Bound, John and Waidmann (2002) “Accounting for Recent Declines in Employment Rates 

among the Working-aged Men and Women with Disabilities,” Journal of Human 

Resources 37(2), Spring 2002, pp. 231-250. 

Chandra, Amitabh (2003) “Is the Convergence in the Racial Wage Gap Illusory?” NBER 

Working Paper 9476, January 2003. 

Frazis, Harley and Jay Stewart (1999) “Tracking the Returns to Education in the 1990s: 

Bridging the Gap Between the New and Old Current Population Survey Education 

Items,” Journal of Human Resources 34(3), Summer 1999, pp. 629-641. 

Juhn, Chinhui. (1992) “ The Decline Of Male Labor Market Participation: The Role of 

Declining Market Opportunities,” Quarterly Journal of Economics 107(1), February 

1992, pp. 79-121. 

Juhn, Chinhui, Kevin M. Murphy, and Robert H. Topel (1991) “Why has the Natural Rate of 

Unemployment Increased over Time?” Brookings Papers on Economic Activity, 1991:2, 

pp. 75-142. 

Parsons, Donald O. (1980) “The Decline in Male Labor Force Participation,” Journal of 

Political Economy 88(1), Feb. 1980, pp. 117-134. 

Sheu, Sheng-jang (2003) “Why are more men choosing not to work?” Unpublished paper, 

University of Western Ontario. 

Stewart, Jay (2000) “Alternative Indexes for Comparing Activity Profiles,” presented at the 

International Association for Time Use Research, June 2000. 

Welch, Finis (1990) “The Employment of Black Men,” Journal of Labor Economics 8(1, pt. 

2), January 1990, pp. S26-S74. 

Welch, Finis (1997) “Wages and Participation,” Journal of Labor Economics 15(1, pt. 2), 

January 1997, pp. S77-S103. 

Welch, Finis (1999) “In Defense of Inequality,” American Economic Review, Papers and 

Proceedings 89(2), May 1999, pp. 1-17.
Table 1: Trends in the percent of 25-54 year-old men who did not work during the year, 

 by education level and reason for not working 

1967-69 1977-79 1987-89 1997-99

Education

High school dropout 4.4 10.7 15.1 17.4

High school graduate 1.6 3.8 5.6 8.0

Some college 1.4 3.1 3.4 5.3

College graduate 1.2 1.3 1.5 2.5

1967-69 1977-79 1987-89 1997-99

Reason for Not Working

Sick/Disabled 1.9 3.1 3.4 4.4

Family Care 0.0 0.1 0.2 0.6

Retired 0.2 0.4 0.4 0.7

Unable to Find Work 0.1 0.5 0.9 0.6

Other 0.3 0.5 0.5 0.5

All Reasons 2.6 4.7 5.5 6.7

Note: Author's tabulations of March CPS data.

Percent of all 25-54 year-old men who did not work during the 

year (3-year averages)

Percent of 25-54 year-old men in each education category who 

did not work during the year (3-year averages)
Table 2: Percent of years spent not working accounted for by occasional and frequent 

 nonworkers 

Number of years spent not working

4+ (frequent nonworkers) 2.8 2.8 68.3 68.3

2-3 (occasional nonworkers) 2.2 5.0 17.0 85.4

1 (one-time nonworkers) 4.5 9.5 14.6 100.0

0 90.5 100.0 0.0 100.0

Note: Author's tabulations of NLSY data (balanced panel of men who were 25-29 in 1987).

 

Percent

Cumulative 

percent

Percent of years not 

working accounted for by 

men who did not work ... 

years

 

Percent

Cumulative 

percent

Percent of men who did 

not work ... years
Table 3: Distribution of years spent not working, by education level and race

0 1 2 - 3 4+ Mean

Education

High School Dropout 71.7 10.7 6.3 11.3 1.1

High School Graduate 88.3 5.6 3.4 2.7 0.3

Some College 94.1 2.8 0.5 2.6 0.2

College Graduate 98.4 1.5 0.1 0.0 0.0

Race

White 92.2 4.0 1.8 1.9 0.2

Nonwhite 82.3 6.5 4.0 7.2 0.7

All men 90.5 4.5 2.2 2.8 0.3

Note: Author's tabulations of NLSY data (balanced panel of men who were 25-29 in 1987).

Percent of men in each category who spent ... years not 

working (out of 11)
Table 4: Determinants of the time spent not working 

High School Dropouts 2.744 ** 0.111 ** 1.230

(0.661) (0.029) (0.220)

Some College 0.623 -0.045 ** 1.188

(0.202) (0.011) (0.263)

College Graduates 0.058 ** -0.091 ** 0.373 **

(0.024) (0.011) (0.051)

Nonwhite 2.275 ** 0.064 ** 1.287

(0.448) (0.019) (0.182)

Ever in military 1.307 0.031 0.881

 (prior to 1987) (0.395) (0.023) (0.205)

Ever in jail/prison 2.605 ** 0.048 1.735 **

 (prior to 1987) (0.945) (0.060) (0.324)

Observations 1,871 1,871 224

Notes: Estimated using NLSY data (balanced panel of men who were 25-29 in 1987).

The coefficients from the Poisson regressions (first and third columns) are expressed

as incidence rate ratios, and the coefficients from the probit (second column) are expressed 

as discrete marginal effects.

Number of years 

not worked

Probability of being 

a marginal worker

Number of years 

not worked 

(marginal workers)

Dependent Variable
Table 5: Distribution of weeks worked during the year by years spent not working 

 (conditional on working any weeks during the year) 

0 1 2 - 3 4+ Total

Weeks worked during year

 1-13 57.5 13.7 14.0 14.9 100

0.9 4.5 10.7 21.2 1.4

 14-26 71.9 14.9 6.8 6.4 100

1.8 8.5 8.9 15.8 2.4

 27-39 80.7 9.5 5.9 3.9 100

4.6 12.1 17.4 21.5 5.3

 40-49 87.9 7.1 3.6 1.4 100

8.3 15.0 17.7 12.9 8.8

 50+ 95.6 3.0 1.0 0.3 100

84.4 60.0 45.3 28.7 82.2

Total 93.1 4.2 1.8 1.0 100

100 100 100 100 100

Note: Author's tabulations of NLSY data (balanced panel of men who were 25-29 in 1987). The 

universe for this table is all observations in which the individual worked at least one week.

Number of years spent not working
Table 6: Percent of 25-54 year-old male nonworkers with income from various sources -- 

 1993-1999 average 

All 

nonworkers Sick/disabled Family care Retired

Unable to find 

work

Other 

reasons

Any unearned income 69.6 82.0 45.3 83.6 38.2 33.1

Asset income 20.3 16.6 27.8 53.9 13.4 20.8

Disability income 11.7 17.0 1.1 6.9 0.9 1.9

Social Security income 42.1 61.8 5.5 25.7 3.7 1.5

Retirement income 5.2 2.5 0.8 42.2 1.2 0.7

Unemployment compensation 3.6 1.9 1.2 0.5 14.0 4.6

Other income 15.0 16.0 14.6 11.3 15.1 11.5

Notes: Author's tabulations of March CPS data. The universe for this table is all male nonworkers.
Table 7: Income of 25-54 year-old male nonworkers by source (conditional on having any income) -- 

1993-1999 average 

All 

nonworkers Sick/disabled Family care Retired

Unable to find 

work

Other 

reasons

Unearned income

Mean $12,990 $12,644 $6,838 $23,261 $7,725 $12,058

Median $8,491 $8,651 $2,577 $18,632 $5,206 $3,006

Percent of (mean) income 

from...

Asset income 9.9 5.1 30.7 18.5 6.9 52.2

Disability income 19.4 24.6 7.5 6.4 2.0 3.8

Social Security income 42.0 53.4 11.6 14.3 10.5 3.1

Retirement income 11.4 2.7 3.2 52.4 7.3 4.6

Unemployment compensation 2.8 1.1 3.0 0.1 37.9 8.8

Other income 14.5 13.0 44.0 8.3 35.3 27.4

100.0 100.0 100.0 100.0 100.0 100.0

Notes: Author's tabulations of March CPS data. The universe for this tables is male nonworkers with unearned income
Figure 1: Box plots of full-time workers’ earnings and earnings-equivalent income 

of nonworkers -- 1993-1999 average 

0 50,000 100000

Other Reasons

Unable to Find Work

Retired

Family Care

Sick/Disabled

All Nonworkers

 

by reason for not working -- 1993-1999 

Figure 1: Distribution unearned income

Notes: Author’s tabulations of March CPS data. The box in the box-and-whisker 

plots extends from the 25th to the 75th percentile, while the line inside the box is 

at the median. The endpoints of the whiskers are equal to inf (Y | Y < (Y75 + 1.5· 

(Y75 - Y25)) and sup (Y | Y > (Y75 - 1.5· (Y75 - Y25)). 
Table 8: Distribution of living arrangements by employment status and 

income quantile range -- 1993-1999 average 

Alone Spouse

Parents 

(No spouse)

Other 

Relatives Total

All Nonworkers 29.1 42.4 21.0 7.5 100

All Workers 23.0 67.7 6.1 3.2 100

By Income Quantile Range

Nonworkers

 0-30 22.2 35.1 32.0 10.7 100

 30-75 33.7 38.4 21.1 6.8 100

 75+ 29.4 58.5 7.3 4.8 100

Workers

 0-10 30.0 46.2 16.8 7.1 100

 10-90 23.1 68.5 5.4 3.0 100

 90+ 15.1 82.7 1.1 1.2 100

Notes: Author's tabulations of March CPS data. 
Table 9: Family per capita income by source and type of living arrangement (conditional on receipt 

of any family income) -- 1993-1999 average 

Parents Other

Total Alone Spouse (No spouse) Relatives

Total $11,048 $11,199 $11,331 $10,700 $9,983

Percent of income from...

Self 47.7 100.0 37.7 16.8 23.7

Spouse

Earned Income 19.7 42.9

Unearned Income 4.8 10.4

Parents

Earned Income 6.8 0.4 30.3

Unearned Income 9.2 0.8 40.6

Other relatives 11.8 7.9 12.4 76.3

100.0 100.0 100.0 100.0 100.0

Notes: Author's tabulations of March CPS data. The universe for this table is male nonworkers who have unearned 

 income or are living in families that have income (earned or unearned).
Table 10: Incidence and amount (conditional on receipt) of income by source and 

 type of living arrangement -- 1993-1999 average 

Parents Other

Total Alone Spouse (No spouse) Relatives 

Total 90.8 76.8 95.8 97.7 96.9

$28,669 $11,902 $34,915 $33,104 $32,803

Self 69.6 76.8 74.9 53.5 56.5

$12,990 $11,902 $15,614 $8,515 $10,901

Spouse

Earned Income 25.5 60.0

$23,335 $23,335

Unearned Income 25.4 60.0

$5,677 $5,677

Parents

Earned Income 8.3 1.0 37.6

$26,631 $21,449 $26,916

Unearned Income 18.6 3.1 82.3

$14,344 $13,243 $14,427

Other relatives 33.7 40.6 45.2 93.7

(Earned and unearned) $14,005 $9,205 $12,864 $27,344

Notes: Author's tabulations of March CPS data. The universe for this table is all male nonworkers.
Table 11: Distribution of nonworker income relative to income of other adults in family, by selected 

 characteristics -- 1993-1999 average 

Nonworker is 

sole source 

0 <75 75-125 >125 of family Total

Living Arrangement

Spouse 21.9 30.9 13.5 23.4 10.4 100

Parents (No spouse) 45.2 31.8 11.1 10.1 1.8 100

Other Relatives 41.7 28.0 11.7 14.3 4.3 100

Reason for Not Working

Sick/Disabled 18.9 34.0 15.7 23.1 8.4 100

Family Care 52.5 36.1 5.5 3.9 2.2 100

Retired 13.7 27.0 17.9 31.2 10.3 100

Unable to Find Work 65.0 20.2 3.7 5.6 5.5 100

Other Reasons 66.4 22.7 4.4 4.7 1.8 100

Income Quantile Range

0-30 100.0 0.0 0.0 0.0 0.0 100

30-75 0.0 58.7 17.8 15.5 8.0 100

75+ 0.0 21.4 18.9 45.5 14.2 100

Age

25-34 48.8 28.2 7.9 10.2 4.9 100

35-44 30.3 33.0 13.2 17.1 6.4 100

45-54 20.2 30.6 15.1 24.9 9.3 100

Race

White 27.0 32.2 13.4 19.8 7.6 100

Black 42.5 27.3 11.1 14.5 4.6 100

Other 36.5 27.7 8.2 16.6 11.0 100

Education

High School Dropouts 31.8 27.4 12.1 20.6 8.2 100

High School Graduates 34.5 30.2 13.4 15.4 6.5 100

Some College 24.7 34.1 10.9 22.7 7.7 100

College Graduates 25.5 38.8 14.2 16.0 5.4 100

All Nonworking Men Living 

With Family Members 31.0 30.9 12.6 18.5 7.2 100

Notes: Author's tabulations of March CPS data. The universe for this table is male nonworkers living in families with 

positive family income. 

Nonworker's income is ... percent of 

average income of other adults in family
Table 12: Time spent in various activities by working and nonworking men and women (hours per day) 

WorkRelated 

Activities

Education 

and Training

Household 

Work

Active 

Childcare

Leisure and 

Recreation

Personal 

Care N

Men

FT workers (average day) 6.75 0.12 1.72 0.23 4.80 10.10 1,682

FT workers (nonwork day) 0.03 0.15 3.43 0.27 8.03 11.57 538

All Nonworkers 0.17 0.48 3.33 0.27 8.37 11.08 151

Disabled 0.05 0.57 3.17 0.25 9.12 10.70 55

Unemployed 0.33 0.63 2.83 0.37 8.50 11.00 56

Other 0.08 0.13 4.38 0.17 6.95 11.82 40

Women

FT workers (average day) 5.90 0.13 2.68 0.40 4.28 10.40 1,484

FT workers (nonwork day) 0.02 0.17 4.47 0.53 6.70 11.77 547

All Nonworkers 0.07 0.18 4.73 1.22 6.35 10.97 478

Disabled 0.00 0.42 3.62 0.77 7.58 11.42 58

Unemployed 0.42 0.02 4.60 0.75 7.18 10.45 83

Other 0.00 0.18 4.93 1.38 5.98 11.02 336

Average day FT Workers 0.06

Men 0.27 All nonworkers 0.10

Women 0.24 Disabled 0.08

Nonwork day Unemployed 0.13

Men 0.06 Other reasons 0.10

Women 0.07

Note: Author's tabulation of data from the UMD Time Diary Study.

FT workers and nonworkers Men and Women

Activity Profile Index Comparison of ...
Table A1: Comparisons to March CPS 

A. Comparison of NLSY to March CPS* 

All Balanced CPS

Education

High school dropout 10.6 9.8 11.8

High school graduate 43.4 43.2 36.5

Some college 21.0 21.2 24.5

College graduate 25.0 25.9 27.3

Age

35 20.1 20.5 21.0

36 20.3 20.8 20.2

37 20.6 20.2 20.2

38 19.1 18.8 19.4

39 20.0 19.7 19.3

Race

White 80.0 82.8 83.2

Nonwhite 20.0 17.3 16.8

Weeks worked in 1997

0 5.0 4.2 6.1

1-13 1.5 1.3 1.5

14-26 2.3 1.8 2.5

27-39 2.4 2.2 3.8

40-49 5.5 5.1 6.0

50+ 83.4 85.4 80.0

B. Comparison of UMD data to March CPS**

UMD CPS

Education

High school dropout 7.71 11.72

High school graduate 31.61 34.63

Some college 23.71 25.34

College graduate 36.97 28.31

Age

25 - 34 37.68 37.11

35 - 44 35.09 36.88

45 - 54 27.23 26.01

Race

White 81.80 84.85

Nonwhite 18.20 15.15

Employment Status

Employed Full-time 92.47 84.20

Not Employed 7.53 15.80

* Data are for 1997

** Data are for Sept. 1992-Oct 1994 (EPA) and March 1993 & March 1994 (CPS)

NLSY