Self-Employment, Entrepreneurship

40 Monthly Labor Review February 2005

Self-Employment, Entrepreneurshhip

A

 relatively small, but growing, body of

literature uses microdata from the 1979

National Longitudinal Survey of Youth

(NLSY79) to study self-employment and entrepreneurship among young adults. The topics

covered in these studies include, but are not

limited to, the determinants of entrepreneurship,

earnings growth among entrepreneurs, the returns to self-employment, the relationship

between criminal activities and self-employment,

and job satisfaction among the self-employed.

The NLSY79 is a nationally representative

sample of 12,686 men and women who were

between the ages of 14 and 22 when they were

first interviewed in 1979.1 Survey respondents

were interviewed annually from 1979 to 1994 and

biannually starting in 1996. Most previous

studies using this survey exclude the sample of

1,280 youths designed to represent the population enlisted in the four branches of the military

as of September 30, 1978, but retain the supplemental sample of 5,295 civilian black, Hispanic,

and economically disadvantaged nonblack, nonHispanic youth. The NLSY79 contains a wealth

of information on the demographic, economic,

family background, educational, and psychological characteristics of respondents. Detailed

measures of the group’s labor market and life

experiences from early adulthood to the midforties can also be created for survey respondents.

The NLSY79 is an excellent source of data for

conducting research on self-employment and

Robert W. Fairlie is

associate professor

and director of the

M.S. Program in

Applied Economics

and Finance,

Department of

Economics, University

of California, Santa

Cruz, California.

E-mail:

Researchers have used the rich data from the 1979 cohort

of the National Longitudinal Survey of Youth to investigate

the relationship between self-employment and various job

and earnings outcomes; future inquiry may afford valuable insights

 into other interesting consequences of self-employment

Self-employment, entrepreneurship,

and the NLSY79

Robert W. Fairlie entrepreneurship. The wealth of information

available in the survey allows one to build rich

empirical models of the entrepreneurial process.

Measures of previous wage and salary, selfemployment, and unemployment experience can

be created, and the NLSY79 contains several

uncommon variables, such as those associated

with detailed asset categories, family background information, data on criminal activities,

Armed Forces Qualification Test (AFQT) scores,

and psychological characteristics. Furthermore,

a plethora of measures of the dynamics of selfemployment may be extracted from the longitudinal data in the survey. For example, measures

of transitions to and from self-employment,

number of years of self-employment, and whether

an individual ever tries self-employment can

easily be created. Finally, the returns to selfemployment, measured as earnings, job satisfaction, net worth, or other outcomes, can be estimated. Changes over time in labor market status

can be used to identify the effects of self-employment, potentially removing biases created by

unobserved heterogeneity across individuals.

Given these advantages, it is somewhat surprising that more researchers have not used the

NLSY79 to study self-employment. In the sections that follow, this article presents estimates

of self-employment from the NLSY79, reviews

findings from previous studies that used the

survey, and discusses some of the merits of the

fairlie@ucsc.edu data sets making up the survey.
Monthly Labor Review February 2005 41

Self-employment in the NLSY79

In most previous studies using the NLSY79, self-employed

workers are defined as those individuals who identify themselves

as self-employed in their own business, professional practice, or

farm in response to the class-of-worker question relating to the

current or most recent job. Unpaid family workers are not

counted as self-employed. Individuals who report being

enrolled in school and workers who report working fewer

than 300 hours in the previous calendar year are often

excluded. The hours restriction rules out very small scale

business activities.

Self-employment rates increase rapidly as the NLSY79

cohort ages. (See chart 1.) The self-employment rate is defined

as the fraction of workers that is self-employed. At age 22,

only 5.1 percent of men and 2.6 percent of women are selfemployed. By age 42, however, 12.1 percent of men and 9.8

percent of women are self-employed.

The following tabulation shows that self-employment rates

also differ substantially by race and its ethnicity:

 NLSY79 data

 Men Women

Self- SelfRace or employment Sample employment Sample

ethnicity rate size rate size

 (percent) (percent)

Black ................... 5.3 14,448 3.2 13,469

Hispanic ............. 7.4 10,153 4.9 8,404

White .................. 10.1 31,803 6.9 29,006

As in previous studies, blacks and Hispanics are much

less likely to be self-employed than are whites.2 Only 5.3

percent of black men are self-employed, compared with 10.1

percent of white men. The Hispanic male rate of 7.4 percent is

also lower than the white rate, but higher than the black rate.

Among women, the black-white and Hispanic-white selfemployment rate ratios are similar to those for men. The main

difference is that women’s self-employment rates are lower

than men’s for all three racial and ethnic groups.

The determinants of self-employment. A few patterns are

beginning to emerge in the young and expanding literature

on self-employment. The empirical studies in this literature

generally find that being male, white, older, married, and an

immigrant and having a self-employed parent, more assets,

and more education increase self-employment. In contrast,

theoretical models of self-employment posit that attitudes

toward risk, entrepreneurial ability, and preferences for

autonomy are central to the individual’s decision to become

self-employed or engage in wage and salary work.3 Perhaps

not surprisingly, there is very little empirical evidence on the

importance of these unobservable characteristics in the selfemployment decision. One article that uses the NLSY79 offers

indirect evidence by examining the relationship between drug

dealing and legitimate self-employment:4 a review of ethnographic studies in the criminology literature indicates that

drug dealing may serve as a useful proxy for a low aversion

to risk, entrepreneurial ability, and a preference for autonomy.

The 1980 wave of the NLSY contains a special section on

participation in illegal activities, including questions on

selling marijuana and other “hard” drugs. The answers to

these questions, together with data from subsequent years

of the survey, are used to examine the relationship between

drug dealing as a youth and legitimate self-employment in

later years. Using various definitions of drug dealing and

specifications of the econometric model, the survey finds

drug dealers to be 11 percent to 21 percent more likely than

those who are not drug dealers to choose self-employment,

all else being equal. After ruling out a few alternative

explanations, this article interprets these results as providing

indirect evidence that aversion to risk, entrepreneurial ability,

and preferences for autonomy are important determinants of

self-employment.

In addition to offering detailed information on criminal

activities in the 1980 wave, the NLSY79 includes information

on whether respondents were interviewed in jail or prison in

each year. This information is useful because convictions

and incarcerations may have different effects on current and

future wage and salary and self-employment earnings. In

particular, ex-offenders who choose self-employment do not

face discrimination, either pure or statistical, by employers in

the labor market, but may face other forms of discrimination,

such as that by consumers or lending institutions. Using the

NLSY79, the aforementioned study by Robert Fairlie provides

evidence on the relationship between incarceration and selfemployment.5

 Estimates from probit regressions indicate that

having a previous incarceration increases the probability of

self-employment by 0.36 percentage point to 0.39 percentage

point, or 5.2 percent to 5.9 percent. Thus, self-employment

may provide an important alternative to wage and salary work

for at least some ex-convicts.

Another finding reported in Fairlie is that AFQT scores

have a small and insignificant effect in probit regressions for

the probability of self-employment.6

 Interestingly, previous research using the NLSY79 finds

that AFQT scores have a large positive effect on earnings.

The general argument is that the scores represent a measure

of basic skills that help predict job performance. Although

youths who have low levels of these basic skills may have

limited opportunities in the wage and salary sector, that

barrier does not translate into higher probabilities of selfemployment.
Self-Employment, Entrepreneurship

42 Monthly Labor Review February 2005

The longitudinal nature of the NLSY79 also allows one to

explore the effects of previous labor market experience on

current self-employment. Ellen Rissman analyzes one aspect

of the dynamic relationship between unemployment and selfemployment among men.7

 She finds that the probability of

being self-employed in the current year increases significantly if the person was unemployed in the previous year.

Stratifying her sample by race, she also finds a positive and

significant effect for whites, but not for nonwhites.

The dynamics of self-employment. Previous research on selfemployment generally takes a point-in-time focus;

longitudinal data in the NLSY79, however, allow for numerous

dynamic measures of the concept. For example, Rissman finds

that 3.4 percent of wage and salary workers in any given year

become self-employed the following year and, conversely,

36.9 percent of the self-employed during a given year make

the transition to wage and salary work the next year. 8 Also,

Marianne A. Ferber and Jane Waldfogel find that 24.8 percent

of men and 16.5 percent of women in their sample from the

NLSY79 report ever being self-employed.9 By contrast, they

find current self-employment rates of 8.8 percent and 5.5

percent for men and women, respectively. Finally, Donald

Williams finds that, by 1987, just 3.1 percent of NLSY79

respondents had 2 or more years of self-employment experience and only 1.2 percent of respondents had 3 or more

years of self-employment experience.10

Although numerous possibilities exist for measuring selfemployment dynamics, most previous research has focused

on annual transitions to and from self-employment. Estimates

for transition matrices that include wage and salary employment, self-employment, and nonemployment are reported in

table 1.11 One-year transition matrices are reported for the

1979–94 period, and 2-year transitions are reported for the

1994–2002 period. Estimates from 1979–94 indicate that 3.4

percent of young men who were wage and salary workers

became self-employed the following year. The entry rate for

the nonemployed is 2.2 percent. Estimates for 2-year transitions from 1994–2002 indicate a lower self-employment rate

from wage and salary work and a higher entry rate from

nonemployment. For men, the exit rates from self-employment

are 31.6 percent and 24.9 percent for the earlier and later

periods, respectively. Self-employment entry rates are generally lower, and exit rates higher, for young women. Overall,

the estimates indicate that substantial mobility exists between

sectors and into and out of employment among young workers.

The importance of assets has taken center stage in the

literature on the determinants of self-employment. Several

22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42

0

3

6

9

12

15

0

3

6

9

12

15

Chart 1. Self-employment rates by age, NLSY (1979–2002)

Age

Percent Percent

Women

Men
Monthly Labor Review February 2005 43

recent studies explore this issue by modeling the decision of

wage and salary workers or other nonbusiness owners to

switch into self-employment over a fixed period.12 The focus

on transitions to self-employment attempts to avoid the

endogeneity problem of including assets in a static model of

self-employment. The problem is that a positive relationship

found in a cross-sectional analysis may simply reflect the

possibility that business owners accumulate more wealth

instead of wealth increasing the likelihood of owning a

business. Although individuals may save in anticipation of

becoming self-employed, a measure of assets in year t – 1

should be more exogenous to the entrepreneurial decision

than a contemporaneous measure of assets.

Fairlie follows this approach, using net worth data from

the NLSY79.

13 Specifically, he estimates probit regressions

for the probability of entry into self-employment from wage

and salary work that include a measure of net worth. The

NLSY did not collect information on assets prior to 1985 and

in 1991. For other years, a measure of net worth can be created

from the detailed NLSY questions on assets.14 The coefficients

on net worth and its square are statistically significant and

indicate a concave relationship. Evaluated at the mean level

of net worth (which equals $36,900), the coefficients imply

that increasing net worth by $10,000 raises the probability of

a transition into self-employment by 0.00044. This percentage

represents only 1.5 percent of the sample’s entry rate into

self-employment. Thus, the estimates of the coefficients provide some evidence that young men face liquidity constraints, but these constraints do not appear to be overly

restrictive.

Creating detailed measures of previous work experience to

include in her regressions, Hiromi Taniguchi examines the

determinants of transitions from nonemployment to selfemployment and to wage and salary employment among

women in the NLSY79 .

15 Her results indicate that both

cumulative work experience and the number of jobs ever held

increase the rate of entry into self-employment and wage/

salary employment. She also finds that previous selfemployment increases the rate of entry into self-employment

and has a negative effect on entry into wage and salary

employment.

Self-employment earnings in the NLSY79

Although self-employment income can be identified in the

NLSY79, earnings among self-employed business owners are

typically measured as total annual earnings, which are calculated by summing the responses to questions on military

income, wage and salary income, and business or farm income

(after expenses) in the previous calendar year. The income

from all three sources is added because more than half of the

self-employed with positive earnings in the NLSY79 report

wage and salary income, but do not report business income.16

Labor market transition matrices, National Longitudinal Survey of Youth, 1979–2002

[In percent]

 Men

1979–94, year t:

Nonemployment ........................... 72.1 25.7 2.3 7.0 3,675

Wage and salary employment ........ 1.0 95.5 3.4 85.1 33,917

Self-employment .......................... .7 30.8 68.4 7.9 2,661

1994–2002, year t: .........................

Nonemployment ........................... 65.6 29.1% 5.3 6.9 1,284

Wage and salary employment ........ 2.8 94.0 3.1 83.6 11,249

Self-employment .......................... 2.0 22.8 75.1 9.5 1,112

 Women

1979–94, year t:

Nonemployment ........................... 75.4 21.4 3.3 19.1 9,584

Wage and salary employment ........ 3.7 94.1 2.2 76.1 31,452

Self-employment .......................... 4.6 29.0 66.5 4.8 1,641

1994–2002, year t:

Nonemployment ........................... 68.7 28.1 3.2 18.8% 2,844

Wage and salary employment ........ 6.1 91.5 2.4 75.0% 10,585

Self-employment .......................... 10.2 28.6 61.2 6.2% 727

 1 Those unemployed and not in the labor force.

2 Measured in year t + 1 for 1979–94 and year t + 2 for 1994–2002.

NOTE: The sample consists of youths aged 22 to 45 years who are not

enrolled in school. All estimates are calculated with the use of annual

sample weights provided by the National Longitudinal Survey of Youth.

Table 1.

Wage and salary

 employment,2

t + 1/t + 2

Selfemployment,2

t + 1/t + 2

Nonemployment,1 2

t + 1/t + 2

Gender, category, and year t Share of total, year t N
Self-Employment, Entrepreneurship

44 Monthly Labor Review February 2005

This is partly due to incorporated business owners reporting

their income as wage and salary income, as roughly 50 percent

of unincorporated business owners with positive total

earnings report zero business income. As suggested by

Zagorsky, it may also be due to the ordering of questions on

the questionnaire.17 Respondents were asked, (1) “How much

money did you get from the military?”; (2) “Excluding military

pay, how much money did you get from wages, salary,

commissions, or tips?”; and (3) “Excluding anything you

already mentioned, did you receive any business income?”

Thus, some of the self-employed may have reported their

income in the second question and did not correct their mistake. Another possibility is that the self-employed report only

their labor income from the business under wage and salary

income.

The following tabulation shows the mean, median, and

standard deviation of total annual earnings for self-employed

and wage and salary workers:

Men Women

Self- Wage and Self- Wage and

employed salary employed salary

Mean ..................... $52,300 $38,258 $28,217 $27,131

Median .................. 38,000 33,021 20,029 23,407

Standard deviation . 48,159 25,769 30,040 17,794

Sample size........... 3,725 43,852 1,570 35,367

Only full-time workers, defined here as those working at

least 1,400 hours in the previous calendar year, are included,

in order to control for differences in hours worked. Earnings

observations in all years are inflated to 2002 dollars. The

responses for each of the three sources of income (the selfemployed, wage and salary workers, and the military) are top

coded at $75,000 from 1979 to 1984, $100,000 from 1985 to

1994, and the top 2 percent for more recent years. Instead of

these top codes, however, the 1994 top code, in 2002 dollars,

which equals $121,390, is used for all years in what follows.18

As is customary, all top-coded values are set to $150,000.

For men, the self-employed earn substantially more, on

average, than do wage and salary workers. Men’s average

earnings are $14,042 higher among the self-employed, and

median earnings are $4,979 higher. For women, average earnings among the self-employed are slightly higher than average earnings among wage and salary workers, but median

earnings are lower. For both men and women, the standard

deviation of self-employment income is substantially greater

than that of wage and salary income.

Returns to capital. One issue that arises in comparing selfemployment earnings with wage and salary earnings from

survey data is the treatment of returns to capital. In the NLSY,

the question regarding self-employment income asks, “How

much did you receive after expenses?” from your farm or

business in the past calendar year. Although some uncertainty is involved in answering this question, respondents

are likely to interpret the question to include both the returns

to labor and the returns to capital. As noted earlier, however,

most of the self-employed report their earnings as wage and

salary income and not business income. In the case of the

respondent who does report self-employment income as

business income, it would be preferable to remove the returns

to capital before making comparisons with the earnings of

wage and salary workers.

Unlike most other data sets, the NLSY79 contains detailedenough information on assets to enable researchers to

conduct a careful analysis of the issue of how returns to

capital are treated. The NLSY79 contains data on the market

value of the individual’s farm, business, and other real estate

and the total amount of debt owed on those assets. This

information can be used to calculate the opportunity cost of

capital and remove it from business income. With Standard &

Poor’s 500 as the alternative investment, adjusted self-employment earnings are 5.2 percent lower than unadjusted selfemployment earnings for white men and 4.0 percent lower for

white women.19 Simple adjustments for the opportunity cost

of capital also have a small effect on self-employment earnings for blacks and Latinos. Overall, estimates from the

NLSY79 suggest that unadjusted self-employment earnings

from survey data may provide reasonably accurate measures

of the returns to labor.

Earnings regressions. As mentioned earlier, the NLSY79

contains detailed information on individual characteristics

such as age, race, education, AFQT scores, and various measures of previous work experience. Earnings regressions that

include these observable controls can be used to estimate

the between self-employment earnings and wage and salary

earnings. Unobserved differences, however, such as entrepreneurial ability and aversion to risk, may also exist between

self-employed business owners and wage and salary workers. To address this issue, fixed-effects earnings regressions

can be estimated with the longitudinal data in the NLSY79.

The individual-level fixed effects control for all observable

and unobservable characteristics that do not change over

time. Because, over time, individuals make transitions between self-employment, on the one hand, and wage and

salary work, on the other, comparisons of self-employment

earnings with wage and salary earnings for the same individual in different years contribute to identifying the associated

coefficients.

Estimating fixed-effects earnings regressions for young

men from disadvantaged families yields some evidence that

self-employed business owners earn more than do wage and

salary workers.20 Estimates for young women, however,
Monthly Labor Review February 2005 45

provide some evidence of lower earnings among self-employed

business owners than among wage and salary workers. The

results from these earnings comparisons are somewhat

sensitive to the use of different measures of income and

different econometric models.

In a related study, Justin van der Sluis, Mirjam van Praag

and Arjen van Witteloostuijn (2004) estimate the returns to

education for entrepreneurs and for wage and salary

workers.21 Using instrumental-variable regressions, they find

that the returns to education are 14 percent for the selfemployed, much higher than the 10-percent estimated return

for wage and salary workers. The detailed data available in

the NLSY79 allow these researchers to control for ability and

to use family background characteristics, including the

mother’s and father’s education, the presence of library cards

in the household at age 14, and magazines present in the

household at age 14, as instrumental variables for education.

Earnings profiles. The longitudinal nature of the NLSY79

enables one to compare earnings profiles for self-employed

workers and wage and salary workers. Charts 2 and 3 display

earnings-age profiles for full-time self-employed and wage

and salary workers. For men (chart 2), average self-employment earnings are always higher and appear to grow at a rate

similar to that of wage and salary earnings. For women,

average self-employment earnings start out lower than wage

and salary earnings, but then grow at a faster rate.

To investigate the question of whether the self-employed

experience faster earnings growth than do wage and salary

workers, the NLSY79 allows fixed-effects regressions that

include interactions between self-employment, on the one

hand, and experience, potential experience, or tenure, on the

other, to be estimated. Estimating fixed-effects regressions

for hourly earnings for a sample of white, non-Hispanic men,

Daiji Kawaguchi finds flatter earnings-experience/tenure

profiles for self-employed workers than for wage and salary

workers.22 At 10 years of experience and job tenure, selfemployed business owners earn 18 percent less than wage

and salary workers. An earlier work by Fairlie compares men’s

and women’s earnings profiles for whites, blacks, and

Hispanics.23 For white men, the point estimates from these

earnings regressions indicate that the self-employed initially

experience slower earnings growth than do wage and salary

workers. After several years, the trend reverses, and selfemployed persons experience faster earnings growth and

higher earnings. For Hispanic men, the relative self-employment earnings coefficients suggest that the self-employed

start at much lower earnings levels than do wage and salary

workers, but experience faster growth rates. For white women,

relative self-employment earnings start out positive and

then become negative. Relative self-employment earnings

coefficients are not statistically significant for black men,

black women, or Hispanic women, possibly due to small

sample sizes.

Self-employment and other outcomes. The detailed information available in the NLSY79 also allows for the analysis of

the relationship between self-employment and other outcomes, such as future wage and salary income, job satisfaction, and net worth. One possibility is to examine the

relationship between early-career self-employment experience and future labor market outcomes. The NLSY79 is an

excellent instrument for this type of analysis because it

follows individuals from ages 14 to 22 in 1979 to 37 to 45 in 2002.

A previously mentioned work by Fairlie examines the

earnings patterns of less educated individuals who are selfemployed early in their careers and makes comparisons with

young, less educated wage and salary workers.24 Selfemployment status is determined between ages 22 and 26,

and earnings are measured starting at age 27. Estimates from

fixed-effects regressions indicate that the self-employed

experience faster earnings growth, on average, than do wage

and salary workers after a few initial years of slower growth.

In a similar vein, Williams examines the relationship between

self-employment at ages 16 to 20 and outcomes at ages 25

and 27.25 He finds that self-employment as a youth is

associated with a substantially higher probability of being

self-employed in early adulthood (age 27), but also is

associated with lower earnings at that age.

Another interesting question that can be answered with

the NLSY79 is whether self-employment experience is

rewarded in the wage and salary sector. Do self-employment

spells limit opportunities for acquiring valuable labor market

experience, especially firm- and sector-specific human capital,

or do they provide workers with skills that are rewarded in

the wage and salary sector? Again using data from the

NLSY79, as well as data from the National Longitudinal Survey

(NLS) of Young Women, Williams examines the effects of selfemployment experience on future wage and salary earnings

of men and women.26 His estimates indicate a negative return

for women and little or no return for men.

Most of the focus in the self-employment literature is on

earnings, but other outcomes also are of interest. In particular,

lower hourly earnings among the self-employed with high

levels of tenure may be explained by nonpecuniary factors of

the job, such as being one’s own boss. Kawaguchi uses the

NLSY79 to investigate whether self-employment is associated

with higher levels of job satisfaction.27 He finds that 65

percent of the self-employed report liking their job “very

much,” whereas only 45 percent of wage and salary workers

report that level of job satisfaction. Estimates from regression

models which control for individual heterogeneity confirm

that the self-employed have higher levels of job satisfaction

than wage and salary workers have.
Self-Employment, Entrepreneurship

46 Monthly Labor Review February 2005

22-23 24-25 26-27 28-29 30-31 32-33 34-35 36-37 38-39 40-41 42-43

20,000

30,000

40,000

50,000

60,000

70,000

20,000

30,000

40,000

50,000

60,000

$ $70,000

Earnings Earnings

Age

Self-employed

Wage/salary

Chart 2. Average annual earnings by age for men, NLSY (1979–2002)

22-23 24-25 26-27 28-29 30-31 32-33 34-35 36-37 38-39 40-41 42-43

15,000

20,000

25,000

30,000

35,000

40,000

45,000

15,000

20,000

25,000

30,000

35,000

40,000

$ $45,000

Age

Earnings Earnings

Self-employed

Wage/salary

Chart 3. Average annual earnings by age for women, NLSY (1979–2002)
Monthly Labor Review February 2005 47

RESEARCH USING THE NLSY79 has undoubtedly improved

our understanding of the determinants of entrepreneurship,

the dynamic process of self-employment, and self-employent

earnings patterns. Although the relationship between selfemployment and a few outcomes, such as future wage and

salary earnings and job satisfaction, has been explored with

the exceptionally rich data available in the NLSY79, more

research may provide valuable insights into the consequences of self-employment. For example, the detailed data

available in the survey allow one to explore the causal relationship between self-employment and several outcomes

of interest, such as net worth, business net worth, health

insurance and other fringe benefits, and public assistance

programs.

Notes

1

 See Center for Human Resource Research, NLSY79 Users’ Guide

(Columbus, OH, The Ohio State University, 1999), for a detailed

description of the NLSY79.

2 Estimates from the NLSY79 are comparable to those from 1990

census microdata using a similar age group. See Robert W. Fairlie,

“Does Business Ownership Provide a Source of Upward Mobility for

Blacks and Hispanics?” in Douglas Holtz-Eakin, ed., Entrepreneurship

and Public Policy (Cambridge, MA, MIT Press, 2004), pp. 634–59. The

census shows slightly lower rates, but the relative differences between

the races are similar.

3 See, for example, Richard Kihlstrom and Jean-Jacques Laffont,

“A General Equilibrium Entrepreneurial Theory of Firm Formation

Based on Risk Aversion,” Journal of Political Economy, vol. 87, no.

4, 1979, pp. 719–48; Robert E. Lucas,“On the Size Distribution of

Firms,” Bell Journal of Economics, vol. 9, no. 2, 1978, pp. 508–23;

Boyan Jovanovic, “Selection and the Evolution of Industry,”

Econometrica, vol. 50, no. 3, 1982, pp. 649–70; and David Evans

and Boyan Jovanovic, “An Estimated Model of Entrepreneurial Choice

under Liquidity Constraints,” Journal of Political Economy, vol. 97,

no. 4, 1989, pp. 808–27.

4

 Robert W. Fairlie, “Drug Dealing and Legitimate SelfEmployment,“ Journal of Labor Economics, vol. 20, no. 3, 2002, pp.

Capital, Human Capital, and the Transition to Self-Employment:

Evidence from Intergenerational Links,” Journal of Labor Economics,

vol. 18, no. 2, 2000, pp. 82–305; Robert W. Fairlie,“The Absence of

the African-American Owned Business: An Analysis of the Dynamics

of Self-Employment,” Journal of Labor Economics, vol. 17, no. 1,

1999, pp. 80-108; and Erik Hurst and Annamaria Lusardi, “Liquidity

Constraints, Household Wealth, and Entrepreneurship,“ Journal of

Political Economy, vol. 112, no. 2, 2004, pp. 319–47.

13 Fairlie, “Drug Dealing.”

14 The variable having to do with assets is not available in the publicuse data, but can be obtained from Jay L. Zagorsky at the Center for

Human Resource Research. See Jay L. Zagorsky, “Young Baby Boomers’

Wealth,” working paper (Columbus, OH, Center for Human Resource

Research, 1998), for more details on the construction of this variable.

15 Hiromi Taniguchi, “Determinants of Women’s Entry into SelfEmployment, “ Social Science Quarterly, vol. 83, no. 3, 2002, pp.

875–93.

16 Fairlie, “Does Business Ownership Provide a Source of Upward

Mobility for Blacks and Hispanics?”

17 Telephone conversation, August 1999.

18 Especially problematic is the fact that 36 individuals have topcoded wage and salary income of more than $4 million each in 1996.

19 Fairlie, “Earnings Growth.”

20 Robert W. Fairlie, “Entrepreneurship and Earnings among Young

Adults from Disadvantaged Families,” Small Business Economics,

forthcoming.

21 Justin Van der Sluis, Mirjam van Praag, and Arjen van

Witteloostuijn, “Comparing the Returns to Education for

Entrepreneurs and Employees,” working paper (Amsterdam,

University of Amsterdam, 2004).

22 Daiji Kawaguchi, “Positive, Non-Earnings Aspects of SelfEmployment: Evidence from Job Satisfaction Scores,” working paper

(Tsukuba City, University of Tsukuba, Institute of Policy and Planning

Sciences, 2004).

23 Fairlie, “Does Business Ownership Provide a Source of Upward

Mobility for Blacks and Hispanics?”

24 Fairlie, “Earnings Growth.”

25 Williams, “Youth Self-Employment.”

26 Donald R. Williams, “Consequences of Self-Employment for

Women and Men in the United States,” Labour Economics, vol. 7,

no. 5, 2000, pp. 665–87.

27 Kawaguchi, “Aspects of Self-Employment.”

538–67.

5

Ibid.

6

Ibid.

7

 Ellen R. Rissman, “Self-Employment as an Alternative to

Unemployment,” working paper no. 2003–34 (Chicago, Federal

Reserve Bank of Chicago, 2003).

8

Ibid.

9

 Marianne A. Ferber and Jane Waldfogel, “The long-term

consequences of nontraditional employment,” Monthly Labor Review,

May 1998, pp. 3–12.

10 Donald R. Williams, “Youth Self-Employment: Its Nature and

Consequences,” Small Business Economics, vol. 23, no. 4, 2004, pp.

323–36.

11 All estimates are calculated with annual sample weights provided

by the NLSY. “Nonemplyment” denotes those not in the labor force.

12 See, for example, Evans and Jovanovic, “Estimated Model of

Entrepreneurial Choice”; David Evans and Linda Leighton, “Some

Empirical Aspects of Entrepreneurship,” American Economic Review,

vol. 79, no. 3, 1989, pp. 519–35; Douglas Holtz-Eakin, David

Joulfaian, and Harvey Rosen, “Entrepreneurial Decisions and Liquidity

Constraints,” Rand Journal of Economics, vol. 23, no. 2,1994 pp.

334-47; Thomas A. Dunn and Douglas J. Holtz-Eakin, “Financial