me Natimd Longituti Smeys ~S) pro~a suppofls my stidies &si~ed

to hcreme ~dasbtig of the labor mkti md methtis of &h mIlwtion, ~e

Dismssion Papers ties tico~orate mme of the r=-h cofig out of tie pro-.

My of the papm h this seria hve bem &hv=ed m M repfi co-oned =

M of a etiamal gmt pmpq mdm which gmti x. awmdd tiugb a

mmptitive procas. me sM= is titm&d ti ctidate tbe ti~s to ktme~d rad=s

titi md outside the Bweau of Labor S&tistics.

Pasom titaewd ti obx a copy of my Diwussion Paper, other ~S re~tis,

or gmerd Momtion about tie ~S pro~m md smeys ~odd wntit the Bmau of

hbor Stistics, Office & Econotic Reseach, W=tigton, DC 20212-0001, or dl

(202) 606-7405.

Mtti h this publimtion is h tbe pub~c doti md, titb appro@ak crediL

mY L ~rotid titiout phssion.

Optilms md mnclwiom .Tressd h this docuent a. those of the auti~s) md

do not nwestily qresmt m official ~sition “ofthe Bweau of bbor S@tistics or the

U.S. Dep~at of hhr.
Au@t 1995

We thank Donsdd Bruce and Ctis Furgiuele for outstanding research assistance, and Esther

Gay, Asm Wicks, and Jodi Woodson for their help in preparing the manuscript. We also thmrk

Jim Spletzer and participants at the NS Summer Workshop for helpfil comments.

Capital Market Constrainfi, Parental Wealth and tie Trasition

to Self-Emplo~ent among Men and Women

A Report to the Department of Labor

Purchase Order # B9J43507

Thomas Dunn

Douglas Holtz-Eakin

Center for Pohcy Research

Syracuse University
Dsmc T

The environment for business creation is central to economic poficy as entrepreneurs are

believed to be forces of irmowtion, employment and economic dynamism. We use data from the

Nationrd Lon@tudlnrd Surveys ~S) to investigate the relative impacts of parental weakh and

human capital on the probability that an individud will make the transition from a wage and

salary job to self-employment, and to examine differences between men and women in the

determinants of self-employment.

We find that the financial assets of young men exert a statistic~y si@ficmt, but

quantitatively modest effect on the probability of self-employment and the transition to selfemployment. In contrast, financial assets are not a significant detertinarrt of these activities for

young women, casting doubt on the importance of capital market constraints for female

entrepreneurs. For both males and females, parents exefi a large itiuence. The channel for this

effect mns not through financial means, but rather through intergeneratiod correlation in selfemployment. Moreover, parents are not “created equ~; the influence across generations is

stronger along gender fines.

Thomas Dunn Douglas Holtz-Eakin

Department of Economics Department ofEconomics

Center for Policy Reswch Center for Poficy Research

Syracuse University Syracuse University

Syracuse, ~ 13244-1090 Syracuse, N 13244-1090

(315) 443-5053 (315)443-3115

tdunn@maxwefl .syr.edu djheakin@maxwell. syr.edu
E~C~IW S~Y ,.,.: . .. ..,.,. _

New entrepreneurs””are valuable sources of innovation employment, and econotic

dynamism, thus making it important for pohcy makers and analysts to understand why people

become and remain entrepreneurs. Research into entrepreneurship has identified two important

empiricrd phenomena the greater propensity of the offspring of the self-employed to become

entrepreneurs, and the importance of access to capital in starting a new business. One possibih~

is that these two features of the data both reflect access to capital. Successful entrepreneurs may

be more able and wilhng to transfer financial wealth to their offspring, thereby relting capit~

market constraints. Ntematively, intergenerational links may operate because of the

transmission of managerial human capital. In short, understanding finks in seE-employment

across generations involves consideration of the intergenerational transmission of both human

and financial capital. Quanti&ng the importance of each feature of this process may permit one

to identifi those poficies that create an enviromnenf favorable to entrepreneurism. In doing so, it

is of interest is to examine whether the effects of human and financial capital differ by gender.

The rise in female self-employment has been a striking feature of the labor market in the 1980$

a rise thus far without a dofin~t explanation. Orie might conjecture that either capital markets

affect men and women differently, or that the transmission of human capital has gender-specific

elements, or both. We also explore these possibilities.

We examine data are drawn from the four original coho~s of the National Longitudind

Surveys of Labor Market Experience ~S). The. constmction of the NLS makes it possible to

both match parent-ctild pairs and to exploit the availability of panel data. We are able to

construct a rich panel data for both members ofa parent-child pair and for each member’s spouse
that encompasses demographic variables, income vtiables, education variables, asset measures,

and indicators of self-employment.

A examination of the propensity for selfiemployment in the ~S indicates several

important features of the data. SelfiempIoyrnent experiences are an important aspect of the

careers of young men. ~le the annual se~-employment rate reaches ody 15 percent, seKemployrnent touches one-fi~h of the young me% occupying nearly one-hrdf of their early

careers. For”young women, the self-employment rate is rougtiy one-third lower. ~le young

womens’ overall exposure to self-employment is lower than for young mem they enter seKemployment at roug~y the same age and spend nearly as much of their worbg time seKemployed. Even “agiance at the raw data suggest impotiant differences between “young men and

young women in the incidence and intensity of seIf-employment.

Turning to the intergenerationrd transmission of self-employment through financial or

human capital fifiages, one finds that the self-employment rate for sons and daughters is rm~ch

higher whenever either parent has a history of se~-employrnent. Thus, while parental

experiences do not seemingly affect either the intensity of self-emplo~ent or the age at which

self-employment begins, they are associated with a greater propensity to become self-employed

at dl, i.e., on entry into self-employment. In contrast, the effecti of financial capital differ

strongiy by gender. hong young men there is a positive correlation between asset holdlngs

and self-employment, especially at the upper end of the wealth distribution, while the data

suggests httle relationship between assets and self-employment rates among young”women.

& noted at the outset, parental wealth may provide indirect access to capital, thereby

easing the ent~ into self-employment, and the raw data display a moderate positive relationship

between self-employment and parental wealth.
k short, a simple, dlrea examination of the data rmeds a positive relatiomtip between

,.

self+mploymentpropensitiesand their p~ents’ self-employment and wdth. Moreover, there is

sugg=tive evidence that ow-wedth accumulation aids self-employment among young me~ but

not young women. It is usefil to e-e the degree to which these findings sufive in a

multivariate context.

The remits of cross-section based lo@t malyses reveal that for sons the effects of

parents’ assets are estimated precisely, but are quite small. For daughters, however, neither ow

assets nor parents’ assets are sigrrificarrt predictors of se~-employment. Thus, the basic

dflerence regarding the impotice of assets for mde and female self-employment sufives

irrtact. Whh regard to the role of human capital, parents’ self-employment enters si@ficmtly

tith a large positive coefficient for both sons and daughters. At the same time, the pwents’ asset

coefficients dlmitish and lose significance.

We conclude with an analysis of trrmsitions into self-employment. As tith the crosssectional analyses, parents’ financial wealth appears to be far less impofirort than their selfemployment experiences. Thus, these data suggest again a greater role for human capital than

finmcid assets in errhancing the probability of mating a transition to being an entrepreneur.

Interestingly, parents do not have equal effects on sons md daughters; the ifluence of fathers is

greater upon sore, while the itiuence of mothers is greater upon their daughters.
1. Introduction

Senator Robert Kasten has summarized the view that entrepreneurs are vrduable sources

of innovation, employment, and econofic dynamism “They create new jobs. They provide new

competition to existing businesses. They help to improve product qutihy, help to reduce prices,

add new goods and services never before thought of advance new technologies, &erica’s

competitive stance.”1 It is of interest to understand why people become and remain

entrepreneurs.

Because credit rationing can emerge even when all agents optimize (e.g. Stightz and

Weiss (198 l)), economists have explored the hypothesis that capitsd markets constrain

entrepreneurs. ~ important empirical phenomenon in this regard is the greater propensity of

the offspring of the self-employed to become entrepreneurs. Successful entrepreneurs maybe

more able and willing to transfer financial wealth to their offspring, thereby relting capital

market constraints. Mtematively, self-employment may be correlated across generations

because of the transmission of managerial human capital,

In short, understanding finks in self-employment across generations involves

consideration of the intergenerational transmission of both human and financial capital.

Moreover, identifying the quantitatively important features of this process may petit us to

identify those poficies that create an environment favorable to entrepreneurism.

1.1 Pretious Res earth

Empirical work on liquidity constraints and entrepreneurs has been guided by the

following logic. Initial capital is required for a new enterprise. Uindlviduals are price takers in

the credh marke~ then the ablbty to obtain capitrd and start the enterprise will be independent of

the entrepreneur’s personal finances. However, Meyer (1990) found that the probabltity of being

-1-
se~-employed increases with an indlvidud’s net worth, ceteris ~ibus. Similarly, BlancMower

and Oswtid’s (1990) study of British young men found that a receipt of a g5,000 ‘fieritance

doubled the probabili~ of setting up a business.

Meyer (1990) and others have argued that longitudinal data has an adv~tage because

explanatory v~ables maybe dated prior to the time of the decision, reducing the chances that

the explanatory variables are consequences, not causes, of being seK-employed. For example, in

a cross section, a positive sign on assets could suggest that wealth petits entrepreneurship, but

dso that entrepreneurs accumulate more wealth.

Using the National Lon@tudinal Survey of Young Men, Evsms and Leighton (1989)

found that the probablfi~ of becoming se~-employed increased tith assets. Evsms and

Jovanovic (1989) used data from the same survey”to “~dyze transitions to self-employment.

They found the coefficient on assets to be positive and statistically significant, pointing to the

presence of fiquidity constraints. Meyer (1990) also examined transitions from wage-earning to

sel~employment and found that horn a statistical point of view, assets were significant, but their

quantitative impact was quite minor. Finally, in a pair of studies Holtz-Eakln, Joulf&an and

Rosen (1994a, 1994b) found that receipt of an ikefitance increases the probability of becoming

self-employed, raises the capital invested in a new enterprise, enhances the survival probability

of etiant sole-proprietors, and increases the gross receipts of existing entrepreneurs. Each of

these effects suggests that the.windfrdl of capitrd relaxes capitrd market constraints.

For purposes of this research, an important related Eterature concerns intergenerational

transmission of labor market status. Recent studies (Mtonj and Dunn (1991), Solon (1992) and

Zlrmnerman (1 992)) have estimated ktergenerationd income comelations to be about 0.40,

indicating a smder degree of moblfity than previoudy documented (see Becker and Tomes

(1986)). However, few studies have looked at the channels through which such correlations

-2-
arise, Ntonji and Dunn estimate strong correlations in work hours, wages and etigs of.

fathers and sons and mothers and daughters. They dso show that the probabtity that a young

man belongs to a union is higher when his father is a unioncnernber.Lentzmd Labmd (1ggo)

show that the probability that a young man is self-employed is significmtly higher when MS

father is self-employed.

In short, the previous literature suggest a role for both financial and human capital in

explaining the intergenerational correlation in self-employment. Given this, we attempt to

determine in a more direct way the separate effects of family finrmcid resources and family

human capital on the Lkefihood of a young adult becoming seK-employed.

1.2 0r9anizatiom

The remainder is organized as follows. In Section 2, we develop a framework for our

analysis, while in Section 3 we describe briefly the data used in our analysis. Section 4 is

devoted to cross-sectional arrdyses of the probability of being self-employed, while Section 5

analyzes the transition from wage and salary jobs to self-employment. In Section 6 we

decompose the importance of intergenerational influences along gender fines. Section 7

summarizes our findings with particular emphasis on the relative importance of financial and

human capital.

2. Frame work for h alvsis

The model on which we base our empirical work focuses on the new entrepreneur’s

demnd for capital and its interaction with becoming an entrepreneur.2 We begin by ignoring

intergenerational or other family finks. For each individual, let ut~ky depend on income (~.) and

a vector (Zj) of personrd characteristics such as those available in our data education, race,

-3-
marital status, number of childre~ and number of sibhngs. If earnings ability in a wage and

salary job is w,, assets are AI and the net rate of return r, then income as a wage-earner is

Wi + rA,.

As a self-employed entrepreneur, the individud’s gross earnings are O,flki)&, where fo is

a production function using capitrd (k,), e, is the individua~s unobsemed abifity as an

entrepreneur and &is a random element. Abihty, Of,has a distribution across individuals md is

not revealed to the indlvidud untfi @er becoming an entrepreneur. It is also likely itiuenced

by the human capital acquired by the individual from parents and other source> we return to this

below. We assume that e has a mean of one and tite varimce, and that &is independent of ~.

Mer investing in the business, the individual has A,-k, available to earn capitrd income, and the

indlvidud’s net entrepreneurkd income is 0,ffi) e + r(Ar - k). By definition, ifk, > A,, then

k, -A, is the amount of capitrd financed by borrowing. We capture the possibihty that capitrd

market constraints fimit borrowing according to the individual’s net assets using the hquidhy

constraint: kl s lk(Ai). If the individual becomes an entrepreneur, his optimal amount of capital

maximizes expected entrepreneurial income subject to the fiquidity constraint. The solution to

this problem implies that kt’ = g (Ai, ef) , where e; is the individud’s expected ablfity as an

entrepreneur. The impact on ki’ of changing Al depends on whether the entrepreneurial venture

.

is hquidity constrained. Kso, ki. = l~(Ai), Ieadmg directly to ~ = z~(Ai) >0. Othe-,

&**

—=0.

M,

So far we have discussed behavior conditional on being an entrepreneur. The individud

will opt for entrepreneurship if expected utility is higher in that setting. That is, if

E{ U([B,f(k,”)& + r(Ai - k,”)]; zi)}>~{u([w, +rA,]; Z,)}. (1)

-4-
&(1 ) indicates, the decision to become an entrepreneur depends on relative abfity in each

mode, resources, and tastes. This suggests w empirical implementation of the fom

P~r = P(z{r,wIt, ‘ir, Pit), (

where p;, is the probablfity that isrdi~dual i is self-employed during yew f ~d pit is a stochastic

error tersn.3

Parentrd U&ages affect the decision-mting process.in two ways. First, it is possible that

the offspring of wealthy parents have better access to capital assets either directly or through the

itiuence of their parents’ wealth. If so, it suggests that equation (2) should include paren%’

assets as we~ as the individual’s assets. That is,

P,, = P(Z,(, W,,>A,,,A;, vi,),

where Ap denotes parents’ assets.

Hnrmcial capital is ordy one possible intergenerational Ii&age. Lentz and Laband (1983,

1990) and others have argued that the probablfity that an individual is self-employed increases if

his or her parents were also self-employed. This propensity may derive from similarities among

family members in attitudes or preferences for autonomy, or the transmission of human capital

Qeneral managerial expertise or job-specific howledge), or the transmission of non-human

capital (brand name Ioydty). Moreover, there maybe other attributes of famifies-hardwor~ng, disciplined, independent, etc.—that will be correlated both with family wealth and with

the probability of becoming self-employed. In these circumstances, a finding that parental

wdth “matters” may reflect not the role of access to capital, but rather the value of this human

capital.

-5-

(2)

(3)
In terms of the formal model, abifity (e) maybe ifluenced by parents’ experiences as

self-employed entrepreneurs. Expanding our spectication to accommodate this feature yields

(4)

h our empiric~ work below, access to matched family member data allows us to tiploit these

finks between children and their parents, permitting more refiable estimates of the impact of

human and financial capital as determinants of entrepreneurship.

A fird topic of interest is to permit the effect of both human and fmancird capital to

differ by gender. The rise in female self-emplo~ent has been a striking feature of the labor

market in the 1980s; a rise thus far without a dominant explanation (see, e.g., Detine (1994)).

One might conjecture that capital market constraints tiect men and women differently, and our

data permit us to examine directly this possibility. Moreover, it may be the case that the

transmission of human capital has gender-specific element% i.e., one possibility is that sons tend

to be more itiuenced by the self-employment experiences of their fathers while daughters tend

to follow their mothers. We explore this possibility below.

3. w

The data are drawn from the four original cohorts of the National Lorrgitudind Sumeys

of Labor Market Experience ~S). Specifically, we work tith the sample of young men who

were aged 14 to 24 in 1966 and were followed through 1981”(12 sumey years), the samples of

young women who were aged 14 to 24 in 1968 and mature women who were aged 30 to 44 in

1967 and continue to be followed, and the sample of older men who were aged 45 to 59.in 1966

and were last suweyed in 1990. We use data ody through 1981 (11 sumey years) in the case of

older men because in the later years of the sumey only very small number of respondents satisfi

-6-
the criteria destibed below. We use data through 1988 in the case of the young women (15

survey years) and through 1982 in the case of mature women(11 survey YS~S). M~Y Ofthe

households contributed more than one person to the young men and young women cohorts, and

in some cases the households contributed to both the youth surveys and the older men and

mature women surveys. Consequently, it is possible to match data for sibfing pairs and parentcMd pairs. Some of our analyses. below use the entire samples of young men and young womeu

while others are restricted to the subset of children who can be matched to a parent. The

Appendix provides summary statistics for the original cohorts, and the samples of matched

family members.4r5’$ .

Many of our analyses exploit the availabihty of panel data on the individuals in the

sample. However, data on a particular question maybe missing either because the individual Iefi

the sample prior to the year of a particular survey or because the response is invahd for some

reason. In the case of the young men and young women we restrict our attention to observations

from years in which the individual was out of school and did not return to school in a subsequent

year. We keep observations for the parents in the older cohorts until the parent reaches age 65 or

first reports being retired.’

3.1 Variables

The ~S respondents answer a wide variety of labor market, demographic and family

stmcture questions in each survey year. When we match young men and women to their parents

in the older cohorts, we get rich panel data for both members of the pair and for each member’s

spouse. We employ a standard set of demographic variables (age, race, mariti status, number of

children, number of sibfings, whether the individual fives in the South or in an SMSA) and

income variables, and create education, self-employment, and asset measures.

-7-
We use yearly enrollment and highest grade completed reports to construct a measure of

education attainment (a series of indicator variables for less than high school, high school

graduate, some college, coflege graduate, and post college) for the young men and women. We

ignore obsemations for each individual from years previous to his or her school-leaving year.

We count an irrdividud as sey-employed in a particulw survey year if his or her “class of

worker” report category for ody the current or most recent job is ‘own farm or business,”

incorporated or otherwise. g One might wish to exclude farmers from the analysis. We do not do

so because farmers account for ‘ody a small fraction of the self-employed young men and

women (O.7 percent and 4.3 percent, respectively). They do, however, represent a larger

fraction of the older cohorts 30.8 percent of the selfiemployed older men and 9.1 percent of the

self-employed mature women. In the case of spouses, we lack a direct report ofwbether the

spouse is self-employe~ so we rely on an affirmative response to whether the spouse had income

from a fa~ business or professiorrd practice.g

&ong with the yearly indcator, we constructed several other measures of seKemployment: whether the individual was ever self-employed over the course of his or her time

in the survey, the age when he or she f~st became se~-employed (for young men and young

women ody), the fraction of valid sumey years the indlvidurd is seK-employed, and this same

fraction conditional on ever being self-employed. In the older cohorts, we also created an

indicator variable to show if either the respondent or spouse was ever self-employed. 10

In each survey year, respondents in each cohort reported their income &om wages and

salary, income from farm, business or professional practice, their spouse’s income from these

sources, and total ftily income from rdl sources. In seversd survey years, detailed questions

were asked about the vahse of various household assets and fiablfities including the value of

savings accounts, stocks, bonds and mutual finds, residence and real estate market value and

-8-
fiabihty, and farm and business market value and tiabiti~. 11 In addition to the total net vahse of

assets, we construct another measure which excludes farm and business assets and fiablhties.

Using only the survey years in w~ch assets were reported would greatly reduce the number of

observations we have for each person. In order to more filly exploit the panel aspect of the dat%

we constructed an asset value for every survey year between each two consecutive asset reports.

We used the two asset reports to calculate an armu~lzed asset growth rate for each individurd

and then compound the earlier asset value by the growth rate for each intervening survey year.

We perform this smoothing routine for both non-business and total assets. M do~ar values have

been converted to 1982-84 dollars using the Consumer Price Index.

3.2 ~elf-Emolo~ent In the ~s

To gain a feel for the data, we present a preliminary look at the propensity for selfemployment as reverded by the ~S. Table 1 shows year-by-year (population-weighted) rates of

self-employment for the four groups covered by our data. young men ~boys~).. young women

~girls”), mature men ~men”), aid mature women ~women”).

As shown in the fust column of the table, the rate of se~-employment for boys rises

steadily as the cohort ages: from four percent in 1966 to 15 percent in 19gl. Nso shown at the

bottom of the table are our memures of the overau propensity toward self-emplo~ent. Using

these measures, 20 percent of the boys reported being self-ernptoyed at least once during the

1966~198”1”period, with the mean age of first self-employment being roug~y 27 y=r:. Using a

sfightly different” metric that captures iptey:]ty of self-employment, on average nine percent of

the working years of boys in the sample were devoted to self-employment. This computation

includes, however, those boys who were never self-employed. Restricting the wmple to those

-9-
(20 percent) who reported self-employment at some point during the sample, the fraction of time

devoted to seK-employment rises to 43 percent.

In short, seti-employment experienc~ are an important aspect of the careers of young

men. Self-employment touches one-fifth of the boys, occupying nearly one-hrdf of their early

careers.

How does self-employment differ across genders? The second column of Table 1

presents similar information for the girls in our sample. & with the boys, self-employment rises

steadily among the girls from one percent in 1967 to 10 percent in 1987. One clear pattern is

that, although their ages are similar, the self-employment rate is lower among girls than among

boys. The gap is especially apparent toward the end of the sample period. Comparing the boys’

rate in 1981 (when the boys are between 29 and 39 years old) to the girls’ rate in 1982 (when

they are between 28 and 38), shows the girls’ rate to be about two-thirds (0.09 versus O.15) of

the boys’.

Turning to the summary measures at the bottom of the table, a few interesting results

stand out. F]rst, girls’ overall exposure to self-employment is lower than boys: as of 1982, 14

percent of girls and 20 percent of boys experienced at least one episode of self-employment.

However, girls enter self-employment at the same age, on average, as boys and spend only a

fittle less of their workng time self-employed (36 percent for girls and 43 percent for boys).

Tting into account the three additiomd years of data, the girls’ “ever seIf-employe& rate (19

pe~cent through 1987) matches the boys in 1981, but their 1987 self-employment rate and time

spent self-employed are lower than the boys’ while the age first se~-employed is higher. T&en

together, the computations in Table 1 suggest differences between boys and girls in the incidence

and intensity of self-employment.

-1o-
The find two columns of Table 1 repotiour self-employment measures for men and

women, respectively. These samples seine as the source of parental information in our

investigation of the intergenerational finks in self-employment. For the moment, however, we

view them in isolation. Not surprisingly, self-employment rates are tigher among the men

(rising from 20 percent to 24 percent over the period) than among women (rising from four

percent to nine percent). [n contrast to the younger cohort$ exposure to self-employment is

quite different between men (28 percent) and women (15 percent), as are our measures of the

fraction of time spent in self-employment.’? Nevefiheless, when viewed as a whole Table I

suggesK substantird self-employment atiivity among the mature men and women in the sample.

A natural question arises as to whether the MS reports of self-employment me indicative

ofself-employment in the population as a whole. To shed some fight on this issue, we present in

Table 2 self-employment rates for each of our four age-gender goups calculated using both the

~S and the Current Population Survey (CPS).13 We restrict the comparisons to the three years

in which we have self-empl.o~ent rates for W four cohorts. A glance at Table 2 suggests that

the incidence of self-employment in the ~S is quite close to that of the population in general.*J

Our focus is upon aspects of the intergenerationsd transmission of self-employment

through financird or human capital linkages. It is usefil, therefore, to examine self-employment

within the samples of parents and children that can be successfully finked in the ~S. Table 3

presents our measures of self-employment in these groups; “sons” and “daughters” represent the

subset of ’’boys” and “girls,” respectively, that can be matched to parents while “fathers” and

“mothers” are similar subsets of.’’men” and “women.”

The natural comparison is between corresponding entries in Table 1 and Table 3. In

generrd, the corresponding entries are quite similar in value. To the extent that a difference

emerges, the age of first employment is shghtly younger among sons md daughters than among

-11-
boys and girls as a whole. Otherwise, the matched subsamples look very much Me the cohorts

from wtich they are drawn.

3.3 Inter- ~t

We turn now to an explicit arrdysis of the role of parental se~-employrnent experiences

h the self-employment propensities o~their children. Specifically, in Table 4 we compare the

self-employment rates between sons and daughters, respectively, based upon the selfemployrnent bistori~ of their parents. Consider, for example, the entries for 1971 shown in the

second row of the table. The entries show that seven percent of those sons whose fathers were

ever self-employed were themselves self-employed in 1971. k contrast, among sons whose

fathers were not ever self-employed during the sumey, the rate was ody four percent. Of course,

a father’s sel~employment is not the ody possible source of intergenerational finks in selfemployrnent; the next two cohrnms display sitiar comparisons using mothers’ self-employment

eWeriences as the key event. As shown, the self-employment rate among sons of mothers with

self-employment experience (six percent) is twice as high as for those sons whose mothers were

not self-employed (three percent).

The final four columns of the table display anrdogous computations for daughters. k

1971, a similar pattern emerges as daughters of either fathers or mothers ~th a ~story of seKemployment are more likely to be self-employed than daughters whose parents were not selfemployed.

Taken as a whole, what patterns does Table 4 reveal? First, in any given year the selfemployrnent rate for sons and daughters is much higher whenever either parent has a history of

self-employment than othetise. Second, using “any se~-employnrent” as our m~ure

summarizes the overrdl tendency quite well. On the whole, this self-employment measure is on

-12-
the order of twice as high whenever a parent has been self-employed.’5 The W rows of the

table indicate that parental experiences do not affect either the intensity of self-employment

(&action of yems spent self-employed condition upon self-employment) or the age at which

self-employment be@s (except in the case of boys and their fathers).lc Instead, the positive

intergenerationrd correlation appears to stem from a greater propensity to become self-employed

at dl, i.e., on the ent~ into self-employment.

3.4 s~ Inter ional Acc Wea

One possibihty is that the simple, bivariate correlation in self-employment across

generations displayed in Table 4 stems &om an omitted, common factor. And a likely candidate

for this role is werdth. As noted at the outset, a growing body of research documents the

importance of capital market constraints in determining entry into self-employment. One might

conjecture that those parents who survive as entrepreneurs may have greater access to financial

capital and are able to itiuence their children’s employment choices by example or by providing

start-up capital. We turn in this section to the pattern of self-employment and weakh

accumulation in the WS.

We begin by looklng at the direct relationships between asset holdings and selfemployment among boys (Table 5) and girls (Table 6), Consider first panel (a) of Table 5. The

entries indicate, for example, that in 1981 five percent of the boys in the first quartile of total

asset holdings were self-employed. Notice, however, that as one moves successively into higher

quartiles of the wealth distribution the self-employment rate rises to seven, nine, and ultimately

33 percent. That is, there is a positive correlation between asset holdkrgs and self-employment,

especially at the upper end of the wealth distribution. Moreover, this finding is not unique to

19S 1 as the same pattern prevails in each of the years in which asset values are reported.

-13-
One possibility is that the positive comelation in panel (a) is an artifict of business

success itse~, that i% it maybe the case that successful self-employed boys have accumulatti

business assets, rather than that financial assets have eased or enhanced the entry into selfemployment. As a crude means of controlhrrg for ttis possibihty, we present in panel (b) sitiar

computations in which wealth is measured as non-business asset holdings ordy. Remoting

business assets has the effect of moderating the positive correlation somewhat. In partictdw, the

relationship between seIf-employment and assets is no longer strictly monotonic. In general,

however, the results remain suggestive of m important role for wealth in the seF-employntent

process.

Is the same true for feties? Table 6 shows the per-qu~ile se~-employrnent rates for

girlsinthe sample. Wtile it remains true that there is a positive relationship between assets and

sek-employment in panel (a), it is less clear than the relationship for boys. Even more striking,

however, is the effect of removing business asse~. Pael (b) suggests little relationship between

non-business assets and self-employment rates among girls. Nthougb the simple comparisons in

Tables 5 and 6 are no substitute for a fill-fledged, multivariate analysis of the determinants of

seIf-employrnent, they are suggestive of important differences between boys and girls in the

factors that determine self-employment.

In addition to their own financial assets, individuals may have access to the wealth

accumulations of their parents: Hence, in analo=gyto the intergenerational finks in selfemplo~ent experiences presented in Table 4, we show in Tables 7 and 8 the links across

generations in assets and self-employment. Specificrdly, we show in Table 7 the propensity for

se~-employrnent among boys based upon the location of their - in the wealth distribution.

Under the hypothesis that children have access to their parents’ weahh, and that greater financial

assets enhances the trmsition to self-employment and survival of new businesses, one would

-14-
~ect to find a positive relationship between parents’ wealth and self+mployrnent among

ctildren.

As the first panel of the table indicates, there is a moderate positive relationship between

self-emplo~ent and parental wedt& with the largest effect concentrated in the dflerence h

self-employment rates for the sons whose parents are in the third versus the fourth quartile of the

asset distribution.

One potential concern about the computations in panel (a) is that parents’ assets maybe

dominated by their ownership of a business. Kso, the self-employment among sons may

possibly reflect assuming a family business, and not the role of parents’ assets in relaxing

hquidhy constraints. 17 As before, we control cmdely for this possibility by removing ~1

business assets from our wealth measures. The results of this procedure are shown in panel (b)

of the table. Whh the exception of survey year 1971, the positive relationship between parents’

assets and son’s self-emplo~ent survives virtually unchanged. Indeed a perusal of Table 8

indicates that this result is not unique to sons as the patterns in panels (a) and (b) of Table 8 for

girls are remarkably similar to those in Table 7.

3.5 F.m?“irical Strateg

A relatively simple examination of our data reveals that for both boys and girls there is a

positive relationship between their self-employment propensities and their parents’ selfemployment and wealth. This suggests that the intergenerational transmission of human and

financial capital may play a significant role in the processes that generate self-employment

among men and women. Moreover, there is suggestive evidence that owrr:weakh accumulation

aids self-employment among boys, but not girls. Of course, it is usefil to examine the degree to

which these findings survive a multivariate anrdysis of self-employment. In conducting our

-15-

,..
mdysis, we proceed along two dimensions. In the first, we contrast the resdts of cross-sectiod

@yses of the probability of self-employment with explicit analyses of the transition from wage

and dary employment to self-employment. Mong the second dimensio~ we v- our

specification of the human capital and fiancid capital variables. We begin with specifications

that focus on boys’ and girls’ own financial attributes, and then successively auqent our

equations with variables capturing the potentird contributions of their parents.

4. ~ ~n rOss- i

The discussion in Section 3 has established suggestive patterns of intergenerational

correlation in the propensity for self-employment that may stem from the transmission of human

capital, financial capital, or both. In this section we begin our multivariate analysis of selfemployment decision by examining the cross-sectional determinants of self-employment.

To begin, we present in Table 9 the estimates of a series of cross-sectiorrd Iogit equations

predicting the probability of self-employment among boys for each year in which assets were

reported. For each variable we report the logit coeticient, the p-value for the coeticient and the

marginal effect of the variable evaluated at the variable’s mean. Table 10 contains estimates of

analogous equations for our samples of girls in the ~S.

We leave it to the reader to examine each of the many coefficient estimates in the tables.

Instead, we focus on a strong difference in the performance of the non-business asset variable in

the two tables. For boys, the asset variable is always statistic~ly significant at conventional

levels and-with the exception of the anomalous estimate for 198 l—has a positive sign.” In

contrast, among the girls, the own-asset variable is never statistically significant and varies in

sign from yew to year. Thus, our cross-sectiorrd estimates suggest a very different role for

persod wealth depending upon the gender of the individual.

-16-
To gain a better feel for the underlying patterns in the data and to enhance the precision

of our estimates, we ‘augment the basic specification with year-specific indicator variables, pool

the data for all survey years, and re-estimate the equations. In order to use the data from dl the

survey years (rather than ody from the years in which assets were reported) we construct asset

values for the interverdng survey years using the smoothing routine described in Section 3.1. We

present these results in Table 11.

Begin with the results for boys in the fist column of the table. The coefficients on the

age and age squared vtiables capture the typical fife-cycle pattern of seti-employment

experiences. The estimated coefficients indicate an inverted-U shaped pattern, with the

probability of self-employment rising untfl age 39 and fdhng thereafter.

The next four variables are indicators of levels of educational attainment, which must be

interpreted relative to completing high school (the omitted category). For the boys, the

probabihty of being self-employed is diminished by the failure to complete high school (the

coefficient on LTHS is negative and significantly different from-zero) and enhanced by at least

some college education. Interestingly, however, neither completion of college nor post-college

education has a significant effect on the probability of self-employment.

Turning to the other variables, our estimates replicate the common finding that the

propensity for self-employment is lower among blacks. We find no effec~ however, of the

number of sibfings on the probabihty of self-employment. The estimates indicate that selfemployment is lower within standard metropolitan statistical areas (SMSAS), either in the central

city itself or within the metropolitan area. Enrdly, we find no effect of region, marital status, or

the number of dependents on the self-employment process.

The find two variables capture alternative aspects of the economic situation within the

household. The estimates indicate that the probability of self-employment dectines as the level

-17-
of spousal earrdngs rises. Lastly, the final coefficient indicates that afier controlling for the other

factors, increased levels of assets are correlated with a higher probability of self+mployrnent.

The estimates for girls in the second column of the table present some intriguing

differences from the results for boys. Rrst, while the sign pattern on the age coefficients is

sitiar, the estimates indicate that the probablfity of self-employment is -tied at age 54, a

figure that is far out of the sample and much later than for the boys. (Recall that the mean age of

self-employment in Table 1 was higher for girls, although the difference was not.so dramatic.)

Second, the coefficients on the fo~ schooling variables indicate essentially no effect; the ody

caveat being the marginally si@ficant, positive effect of post-graduate education.

Nefi, for girls the measures of family background and m~tal status are significant.

Specifically, the probablfity of self-employment declines as the number of sibfings increases, and

is positively related to being married and the number of dependents.

Lastly, the coefficient of the asset variable, while positive, is statistically insignificmt by

conventionrd standards. This result indicates that the findings of our simple investigation of

assets and self-employment in Section 3 survive in a more elaborate statistical setting.

As noted earfier, one potentisd concern is that the estimated coefficient on the asset

variable may be contaminated by simultaneity bias in a cross-sectio~ assets are figher for the

self-employed because they are successful and sorvive in the self-employed status. The find two

columns of Table 11 attempt to address this concern by modi&Ing our specification, replacing

the. contemporaneous measure of assets with the “lagge& value of asset$ i.e., the value of assets

horn the previous survey year.

Comparing corresponding columns of Table 11 indicates that this specification change

has fittle effect on the basic results. Whh regard to the asset variable coefficients themselves,

comparing the third and first columns indicates that for boys the coefficient remains positive and

-18-
19 For ~rIS, however, the coefficient statistically significant. Indeed, the estimated value rises.

decfines in magnitude and remains statisticrdly insignificant. ,Thus fm, ,at least, this basic

difference regarding the importance of assets for male and female self-employment sufives

intact.

We turn now to the ifiuences of parents’ self-employment and assets on children’s selfemployment that were seen in the tabulations in Section 3.3. We begin with our specification

using the cMd’s ow lagged non-business assets to reduce the potential endogeneity of the asset

measure. From the parents’ data we draw our memures of non-business and total assets for each

survey year, indicators for whether either parent was self-employed in each survey year, and

whether either parent was ever seK-employed during the course of the responding parent’s time

in the survey (PMSE). We augment our basic Iogh specification to enmirre the effects of

parents’ assets and self-employment on the cfild’s probability of self-employment in the pooled

cross-section.zo

The first column of Table 12 reproduces the specification in column 3 of Table 11 for the

sample of sons. While the same control variables are included in the regression, we focus on the

son’s own asset and parents’ variables. The sample probablfity of self-employment is slightly

lower in the matched sample of young men (0.078 versus 0,089 for all young men), and the

effect of own assets is much smaller and is imprecisely estimated: a $10,000 increase in own

assets raises the probablfity of seK-employment in the sons’ sample by 0.0007, whereas the

effect in the sample of young men is 0.0034.

In column 2 we add parents’ non-business assets, while in column 3 we include instead

parents’ total assets. The effects of parents’ assew are estimated precisely, but are quite small.

Increasing their non-business assets (total assets) by $10,000 raises the sons’ self-employment

probabihty by ordy 0.0004 (0.001 1). For daughters (see Table 13), neither own assets nor

-19-

—
parents’ assets are significant predictors of self-employment. Ow assets enters with a relatively

large, negative, and insi@cant coefficient. (Recrdl that in the sample of dl young women the

coefficient on own assets was dso essentially zero.) Parents’ assets, net or total, enter with very

smd, imprecisely estimated, and negative coefficients:

We turn nefi to the role of human capital. Columns 4 through 6 of each table add the

parents’ ever self-employed indicator either in isolation or in conjunction with the parenti

w~th v~ables,21 In the last tkee columns of both Table 12 and Table 13, P~SE enters

significantly with a large positive coefficient. For example, in column 4 of Table 12, the

marginal effect of P-SE is 0.051, an enormous change relative to the probtilhty of seKemployment (0,078) in the sample. Parents’ assets have very httle effect on the P~SE

coefficient or its significance in any of our specifications in Table 12. In practice, the parents’

asset coefficients diminish and non-business assets lose significance when P~SE is added

(compare columns 2 to 5 and 3 to 6). A similar pattern appears for daughters (Table 13):

P-SE has a large positive significant coefficient (the mar~nal effect in column 4 is 0.018

compared tith the sample probability of 0.02S), while parents’ assets remain inconsequential.

5. Dete rrninants of Transitions to Self-Emplovw

In this section, we turn from analyzing the probabihty of self-employment per se to an

examination of transitions into self-employment. As noted at the outset, focusing on transitions

eases concerns regarding the simultaneity of asset accumulation and entrepreneurship in a crosssection by includlng variables that are dated at a time prior to the entry into self-emplo~ent,

In the case of boys, the sample probablfity of entering se~-crnplo~ent is 3.4 percent.=

To begin, consider the estimates presented in Table 14. The first column presents estimates of

the probability of mting a transition from wage and dary employment into self-employment

-20-
for boys, while the corresponding estimates for @rls are in the second column. The estimates for

age and age squared imply that the probablfity of making a transition into self-employment pe~s

at age 26 and decfines thereafter. x In contrast, the probability is mtirnized seven years later, at

age 33, for girls.z~

AS in the cross-sections, the family status variables have very different effects for boys

and girls. Among girls, the probablfity of a transition increases if married and with the number

of dependents, wtie there are no such effects for the boys.

The final coefficients in each column show the effects of individuds’ assets on the

probtillity of self-employment. For boys, the estimated coefficient is 0.0053 and statistically

si~ficant, a finding consistent with the literature in~lcating th? impO@nce of c?Pitd market

25 The e5timated coefficient for girls, however,’s constraints on the entry into entrepreneurship.

both negative and insignificant.z’

As with our cross-sectional analysis, we devote the nefi two tables to the itiuence of the

parents’ variables on the child’s transition into self-employment. As stated earfier, the use of

transitions has the advantage of minimizing the endogeneity of own assets. The first column of

Table 1j is analogous to the young men’s colum in Table 14. Nthough the estimated

coefficient on own assets is larger for sons than for all boys (0.0076 versus 0.0053), the marginrd

effect of assets on the probability of a transition is shghtly smaller (0.000 14 versus 0.0002 I).

We then add parents’ non-business assets to the specification in column 2; the estimated

coefficient is positive, but small and imprecisely estimated. Using instead total assets

(column 3) gives Sfightly stronger and significant estimate, but the effect is still quite small. A

$10,000 increase in parents’ totrd assets raises the probabihty of a son’s transition into selfemployrnent by 0.0002.

-21-
In the remaining columns of the table we measure parents’ assets in 1966, rather than in

the current year, in an attempt to minimize the possibility that parents’ assets maybe

endogenous. This is the begitirrg of the survey period and the children are quite young, thus

lessening the possibility that parental asset accumulation is driven by the business plans of the

offspring. For symmetry, we sdso use an indicator for whether either parent was self-employed

in 1966, rather than the “ever self-employer indicator. The effect of parents’ assets remains

small (see columns 4 and 5). In the last three columns the specifications control for parents’

seK-emplo~ent. This variable has a strong positive effect, as PNSE did in the previous

estimates, and including itreduces the effect of “parents=%sets. Notice, however, that the own

asset effect Is largely unchanged. 27 Hol&ng the son, s characteristics at the mean v~ues and

switching the parents’ self-employment indicator from zero to one raises the probability ofa

transition from 0.010 to 0.034, an increase of 0.024 which is large compared to the sample

transition probability of 0.032.”

For daughters (Table 16), a familiar pattern prevails: none of the own assets or parents’

assets variables help to explain the transition into self-employment. However, parents’ selfemployment has a large and positive effect. Changing the value oftbis variable from zero to one

raises the transition probability by 0.001. This is the largest effect of any variable in the

specification.

In summary, parents’ assets exert a positive, but very small itiuence on sons’ seKemployment, and none on daughters’. On the other hand, parents’ seIf-employment expedience

has very large positive and sigrdficant effects, more than tripfing the probability of entering seKemployment for sons. It is the strongest and clearest predictor of seti-employment of young men

and women.

-22-
6. Effects of Parerrt’s G~~n s Self ., -Emolomw

The results presented thus far suggest an important role for theintergeneratiorrd

transmission of entrepreneurisd human capital. However, to this point we have examined the

effects of parents’ self-employment experiences using an indicator for whether at least one

parent was ever seK-employed. Ttis approach precludes identifig any separate effects that

fathers’ self-employment and mothers’ self-employment may exert on chldren.

my might we expect these effects to be dlffeient? One possibility is that children

observe and take as their example the labor supply of the parent with the stronger labor force

attachment. In these cohorts, the older mens’ labor force participation is on average “muchhigher

than the womens’, so we would expect the father’s effect to be stronger for both sons and

daughters. Another possibility is that, given the relative ifiequency of female self-employment,

it maybe the case that a mothers’ self-employment has a disproportionate effect on the children.

If so, we would expect that it would show up as a stronger predictor of children’s selfemployment propensities than would a fathers’ self-employment. A third alternative is that the

parent of the same gender as the child is the more important role model. In this case we would

expect the mother’s self-employment to have a larger impact than the father’s on daughters and

the reverse for sons.

In Tables 17 and 18 we examine these conjectures by allowing separate effects of each

parents’ self-employment on the probabihty of self-employment and the transition into selfemployment for boys and for girls. In the tables, DADMSE ~OMNSE) indicates

whether the father (mother) was ever self-employed during his (her) time in the survey. This

variable is zero if the parent never worked or held only wage or salary jobs over the course of the

survey. Column 1 in Table 17 reproduces column 5 in Table 12 which showed the very strong

effect of parents’ self-employment on the probab]hty that a son is self-employed. The next three

-23-
columns demonstrate that for sons, the father’s se~-employnrent is a much stronger predictor

than the mother’s. men both are entered in the specification (column 2), the margiml effect of

father’s self-employment is 0.049, while the mother’s is 0.032. In columns 3 and 4 we enter the

respective parental indicators separately. Even given the opportunity to capture” the fill effect,

the mothers’ ifluence is dominated by the correspondkg estimate for the father. Viewed

another way, the results in columns (2) and (3) indicate that part of the large positive effect of

father’s seti-employment is attributable to the ifiuence of mother’s self-employment.

Columns 5 through 8 consider the tiamition into self-employment. In column 5 we

reproduce column 7 of Table 15 to provide a basetine. k columns 6 through 8, the effects of

fathers’ and mothers’ self-employment are large and of roughly the same magnitude, but the

mother’s is not significant (even when the father’s is omitted from the specification (column 8)).

The effect of the father is large and statistically significant whether or not the mother’s

experience is included.

Table 18 repeats this analysis for girls. Interestin@y, the table shows that the parents’

effects are reversed”for girls. Column 2 shows that when both fathers’ and mothers’ .seIfemployment are included, the mother’s effect is about twice as large as the .fath.er’s (which. is not

significant), raising. the probability of the daughter’s self-employment by 0.020, large compared

with the probabihty in the sample of 0.028. men entered done, the father’s effect is a bit more

than half as large as the mother’s (columns 3 and 4). Turning to the equations for the transition

to self-employment in columns 5 through 8, the differences between the mother’s and father’s

effects are not as pronounced as in the probablfity equations. fiso, the gender effect$”~e. much

more similar than for the boys.

In summary, these tables show that father’s self-employment experience exerts a stronger

effect than mother’s on a son’s self-employment, while for girls, the mother’s effect isjust

-24-
shghtly stronger than the father’s. These results are largely consistent with those of Mtonji and

DUM (1 994) who find strong similarities in the work hours of parents and children that run rdong

gender fines and that this may be traced to intergenerational correlations in work preferences

rather than to labor supply responses to similarities ti wages.

7.

Recent research has foased on the importance of fiquidity constraints and human capital

accumulation in the determination of self-employment. Clearly, the intergeneratioml

transmission of parental financird assets and job-market experiences is a potentially cruciti

aspect of the process the generates and sustains entrepreneurs. Our investigation of data from

the National Longitudind Suweys suggests that the roles of financial and human capital differ

by gender and source.

Specifically, the results reported above indicate that the financial assets of young men

exert a statistically significant, but quantitatively modest effect on the probabihty of selfemployment and the transition to self-employment. We find no such effect for young women.

Using ttis as our metric, we find a relatively small impad of capitrd market constraints in the

NLs.

Similarly, our education variables do not indicate a monotonicrdly increasing importance

for forrnrd schootirrg. We find that a high-school education appears to be the key hurdle among

young men, while there is an additiond effect of greater schoofing among young women.

Hence, using this as our measure of human capital does not support a scenario in which human

capital is “the” key to successful self-employment.

In contrast, parents exert a large itiuence. The channel for this effect runs not through

financial means, but rather through intergeneratiomd correlation in self-employment. Thus, once

-25-
agti these data suggest a greater role for husn capital than financial resets b enhantig the

probabtity of m~ng a transition to or stiting as an entrepreneur. kterestingly, parents are

not “created equal.” The itiuence of fathers is greater upon sons, wtile the itiuence of

mothers is geater upon theti daughters.

-26-
Notes

1. Hearings before fhe Subcommittee on Entrepreneurship and Special ProbIems Facing

SmaIIBusiness of the Committee on SmaIIBusiness, United Stafes Senate, S. Hrg. 99-

677, U.S. Government Printing Office, Washington, D. C., 1986.

2. See Evans and Jovanovic (1989) or Holtz-Eakin, JouEkian, and Rosen (1994a).

3. The interest rate, r, does not vary across individuals and is suppressed.

4. It also should be noted that although the ~S provides yearly sarnphng weights for each

member of each coho~ we do not employ a weighting scheme in most of our analyses.

Because we work with a large number of family relationships and rely on unbrdanced

panel designs, it is difficult to formulate an appropriate weighting procedure.

Furthermore, it is not clear how to apply the sarnpting weights for individuals to the

family matches that we create. The original ~S samples were designed to be

representative of the civifian non-institutionalized population at the time of the first

survey, but because of the age requirements in the originfl cohorts the family member

matches are not nationally representative. Black and low-income households were

oversarnpled to insure their representation in the samples.

5. A standard concern in analyses of sibkng or intergenerational data is that the very fact

that it was possible to colleti data on several family members makes the data

unrepresentative. In the case of the ~S, both the parent and the child must satisfi the

age restrictions of the sample design in the base year of the survey. Since a substantial

number of children leave the household by age 24, one might expe~ that the matched

sample would over-represent individuals who are still Iiting with their parents when they

are in their early twenties. This problem is mitigated to some extent by the fact that the

young men matched to a parent average about 1.4 years younger than the entire young

men’s sample: 16.7 versus 18.1 years in 1966. Young women matched to a parent are

also younger on average than the entire young women’s cohort: 17.5 versus 18.7 years in

1968.

6. Because sample members are asked questions about the labor market activities of their

spouses, it is possible to examine relationships between individuals who are related by

marriage. For example, one could exatine the relationship between the self-employment

histories of fathers- and sons-in-law using the reports on spouses provided by members of

the young women’s cohort who can be matched to their fathers in the older men’s cohort.

Such an analysis is beyond the scope of this study.

7. It is possible that ehminating observations for individuals based on retirement may lead

to an undercount of the prevalence of self-employment. Fuchs ( 1982) shows that the

self-employed retire later than the non-self-employed and that many people become selfemployed tier retiring from a wage and salary job. Our age and retirement restrictions

affect this second group of switchers.

-27-
8.

9.

10.

11.

12.

13.

14.

15.

16.

17,

Unfortunately, survey participants were not asked about activity in dud or second jobs in

each survey year. Hence, we are unable to gauge the importance of self-employment as a

secondary job.

One might be concerned over the accuracy of spousd reports. In the younger cohorts,

the se~-employment rates we derive for the husbands of the young women closely match

those for the married subset of the young men’s cohort. The same holds for the seKemployment rates for the wives of the young men and the married young women. The

spouse’s seK-employrnent rates in the older cohorts appear less refiable due to

inconsistencies over time in the way the farm and business income questions were asked.

For example, in the early survey years the farm and business questions did not attribute

income separately to the respondent and spouse. Consequently, in any year we tend to

have a smaller reported fraction of seti-employed husbands than of married men in the

older men’s cohort and a slightly smaller reported fraction of self-employed wives than

of married women in the mature women’s cohort. However, these year-by-year reporting

differences eventually average out; our “ever self-employed rates are nearly identical for

each of the two comparison groups.

In the discussion below we cdl this variable “parent ever self-employe& even though the

mother’s husband might not be her child’s father. The husband of a mature women

would not appear in the older men’s cohort if he dld not meet the age firnits of that

sample (aged 45 to 59 in 196Q, but he still could be the father of the child to whom the

mother is matched.

There are five years of asset reports for young men and older me~ seven asset reports for

young wome~ and five for mature women.

Recrdl, however, that these comparisons till be ifluenced by the fact that, at any point

in time, the women are, on average, 15 years younger than the men.

We use the March CPS for each year shown.

We focus in the ~S on boys and girls who are out of school and on men md women

who are not oIder than 65 and not retired. Otii CPS rates are calculated on the basis of

age rdone, Presumably, applying the schoo~ig and retirement criteria to the CPS would

make the rates even more sitiar.

For each pair of columns, t-tests reject at conventional significance levels the hypothesis

that the “any self-employment” rates are equal.

In Leritz and Labarrd’s (1 990) sampleof self-employed me% second-generation

proprietors on average entered self-employment at a younger age (34.7) than firstgenerstion proprietors (36.6).

The data do not allow us to distinguish between self-employment which reflects starting

a new business and self-employment resulting from entering an etisting family business:

Lentz andLaband(1990) find that second-generation proprietors had higher earnings

than first-generation proprietors, but they find no etidence that those second-generation

-28-
18.

19.

20.

21.

22.

23.

24.

25.

26.

27.

proprietors who Merited or bought their family business had higher e-gs t.harrthose

who dld not. In their sample, 14.2 percent of proprietors (26.5 percent of secondgeneration proprietors) had inherited or bought a ftily business.

The coefficient intimate in 1981 seems to be an artifact of a handfil of out~ers. When

we omit these observations the coefficient @-value) on own assets in 1981 is 0.0026

(0.0001).

Returning to the year-by-year cross-sectional results in Table 9 reveals the underpitings

of this result. The coefficient on assets in the last year, 1981, is negative. The use of

lagged assets removes this ifluence.

We focus on the pooled results for two reasons: to”economize on space and because,

especially in the early years of the surveys, the number of cross-sectional observations is

small. The yearly cross-sectional results appear in Appendix Tables 2A and 2B.

It m~es very httle difference whether we use the parents’ “ever self-employer indicator

or the parents’ yearly indicator. We use the former because it increases the sample size.

This is very closetoFairlie’s(1994) estimate of the transition probability for men in the

PS~ @.3.percent).

Evans and Leighton (1989) ~so use the ~S but find the age profile of entry to selfemployment is essentially flat up through age 40.

The correspondence between these estimates and the mean age first self-employed in

Table 1 is quite close.

Fairfie (1994) uses a net of business asset measure similar to ours and finds that a

$10,000 increase at the mean raises the probability of a transition by 0.005, our estimate

is 0.0021. Using asse~ variation generated by the receipt of an inheritance, Holtz-Etiln,

Joulfaian, and Rosen (1994b) find a substantial effect: a $100,000 inheritance raises the

annual transition probtillity by 0.00825. In contrast, Meyer (1990) finds tkt an

additional $100,000 of net worth raises the transition probability by ordy 0.00017.

Some experirnentition indicates that the size and significance if the coefficient on the

asset variable is sensitive to a few outfiers. For example, deleting obsemations in both

the top and bottom one percent of the asset distribution raises the estimate in column (1)

of Table 14 from 0.0053 to 0.0078, an increase of nearly 50 percent. It remains

statistically si~ficant.

As noted earher, deleting observations in the etiremes of the asset distribution alters the

estimated coefficients. For example, deleting the top and bottom one percent of the

parents’ asset distribution has essentially no effect on the own-asset coefficient (although

its significance level rises to 0.0055). The coefficient on parents’ asset, however, rises to

0.0032 (compared to -0.0007 in column (7)) with ap-value of O.085. At the same time,

the coefficient in PNSE falls to 0.7998 @-value of 0,.0005). The role of asset outfiers

remains an interesting topic for fiture research.

-29-
28. Ftirhe (1 994) fin& sitiar strong effe~-father’s seK-emplopent rtises the transition

probabihty at the mean by 53 percent.

-30-
Appendix

Summary of~S Data Sets

There are 5225 young men, 5159 young wome~ 5020 mature me% md 5083 mature

women in the MS panel. Young men were first sumeyed in 1966 when they were between the

ages of 14 and 24, and were surveyed 12 tbes between 196”6and 1981. Young women were

fist surveyed in 1968 (when they were also between the ages of 14 and 24) and were surveyed

15 times between 1968 and 1988. Older men were surveyed first in 1966 when they were

between the ages of 45 md 59. Older women were between 30 and 44 when they were fist

sumeyed in 1967. Both of the older cohorts were surveyed 11 times between 1966 and 1981.

Data beyond 1981 is available for both older cohorts, but is not used here.

Survey data for the younger cohorts was kept k each year only if the respondent was out

of school in that particular year and never returned thereafter. Responses from the older men

were kept ordy if the man was less than age 65 and not retired. al of the responses from older

women were kept as they never reached the age of 65 during the portion of the survey used here.

~ n.

Mer imposing these screens we are Iefi with 4737 young men. The number of

observations per person depends on the amount of time spent in school as well as question

refids, invahd responses, and survey attrition. Of our initial sample of 5225 young me~ 2495

of them can be matched to a ptient: 1097 to fathers in the older men’s cohort and 167 I to

mothers in the older women’s cohort (273 young men can be matched to both a father and a

mother). Of these, 2200 have at least one vflld obsemation during the survey. Appendk Table

1A compares the characteristics of the samples of young men and sons before and after the

schoofing screen is apphed. About 9 percent of the young men are lost from the sample because

-31-
they did not finish school before they exited the survey. In sho~ the young men look ve~

sifilar before and ~er the schoohng screen is apphed, except that assets are larger in the

screened sample. Similarly, the mean characteristics of the sample of sons are untiected by the

schoofing screen. There are differences, however, between the young men and the sons the sons

are younger on average and have substantially lower assets in dl yws.

You rur Women’s Data Set

After imposing our screens we are left with 4SS0 young women (a 12 percent reduction

in the sample) who have at least one vfid survey repoti. Of our initial sample of S1S9 young

wome~ 2569 can be matched to a parent 988 to fathers, 1848 to mothers, and 267 to both. Of

these, 2214 young women have at least one v~ld observation after the schoofing screen is

imposed.

Appendix Table IB compares the samples of young women to the sample of daughters.

The same patterns show up as in the young men’s sample: the daughters are younger on average

and have lower assets, and the scboofing screen has little effect on the mean characteristics of the

samples.

-32-
References

Akonji, Joseph G. and Thomas A. Dunn, “Relationships among the Family Incomes and Labor

Market Outcomes of Relatives,” in Research in Labor Economics, Volume 12, 1991,

edhed by R. Ehrenberg, JM Press Inc., pp. 269-310.

Akonji, Joseph G. and Thomas A. Dunn, “An Intergenerational Model of Wages, Hours and

Earnings; NBER Working Paper No. 4950, December 1994.

Becker, Gary S, and Nigel Tomes, ‘Wuman Capital and the Rise and Fdl of Farnihes,” Journal

of Labor Economics, 4, 3 (Part 2), July 1986, pp. S 1- S39.

BlancMower, David and Andrew Oswald, “What Makes an Entrepreneur?,” London School of

Economics, mimeo, 1990.

DeVine, Theres% “Changes in Wage-and-Salary Returns to Still and the Recent Rise in Female

Self-Employment~ America Economic Reviw, May 1994, pp. 108-113.

Evans, David S. and Boyan Jovanovic, “h Estimated Model of Entrepreneurial Choice Under

Liquidity Constraints: Journal of Political Economy, 1989, pp. ‘808-827.

Evans, D“avid S. and Linda Leighton, “Some Empirical Aspects of Entrepreneurship,” Amtican

Economic Revim, 1989, pp. 519-535.

Fairhe, Robeti W., “The Absence of the African-American Owned Business: An Analysis of the

Dynamics of SeE-Employnrent,” Center for Urban Affairs and Poticy Research Working

Paper No. 94-9, Northwestern University, February 1994.

Fuchs, Victor R., “Self Employment and Labor Force Participation of Older Men; Journal of

Hzlman Resources, 17,3, 1982: pp. 339-357.

Holtz-Eakin, D., D. Joulfaian, and H. S. Rosen, “Sticting it Out: Entrepreneurial Survival and

Liquidity Constraints,” Journal of Political Economy, February 1994% pp. 53-75.

Holtz-Eakin, D., D. Joulfaian, and H. S. Rosen, “Entrepreneurird Decisions and Liquidity

Constraints,” Rand Joumai of Economics, 23, 2, Summer 1994b, pp. 53-75.

Lentz, Bernard S. and David N. Laband, “Entrepreneurial Success and Occupational Inheritance

among Proprietors,” Canodian Jozimat of Economics, =11, August 1990, pp. 563-579.

Lentz, Bernard S. and David N. Laband, “LIke Father, Like Son: Toward and Economic Theory

of Occupatioti Following,” Southern Economic Journal, 50(2), 1993, pp. 474-493.

Meyer, Bruce, “Why Are There So Few Black Entrepreneurs National Bureau of Economic

Researc~ Working Paper No. 3537, 1990.

-33-
Solon, GV R, “Intergenerational Income MobfiT in the United States,” Amen’- Economic

Reviti”, 82,3, June 1992, pp. 393-408.

Stigfitz, Joseph and Adrew Weiss, “Credit Rationing in Markets with Impetiti ~ormatio~”

Amerim Economic Reviw, Jurte 1981, pp. 393410.

Zlmmerm~ Datid J., aRegession Toward Mediocri~ in Economic Statire,” Ametica

Economic Revim, 82,3, June 1992, pp. 409-429.

-34-
APPE~~ TAB~ 1A SUM~RY STA~ST3CS FOR SA~US OF BOYS ~ SONS

Boys Sons

AU Out of School AU Out of Schml

N=5225 N+737 N=2495 N=2200

Ige h 1966 18.1 18.2 16.7 16.9

(3.16) (3.18) (2.43) (2.47)

*@est made Completd 12.9 12.8 13.0 13.0

(2.86) (2.89) (2.77) (2.81)

31ack 0.28 0.27 0.31 0.31

(0.45) (0.44) (0.46) (0.46)

{mber of Sibkgs 3.41 3.43 3.48 3.51

(2.59) (2.60) (2.s9) (2.60)

:ver SeK-Employd 0.17 0.19” 0.15 0.17

(0.38) (0.39) (0.36) (0.37)

ige Fist SeK-hployed 31.1 31.1 29.8 29.8

(4.85) (4.86) (4.27) (4.26)

N=891 N=879 N=371 N=363

<On-Bwtiess kets

1966 I577 1641 847 88s

(8288) (8s8s) (4S06) (472j)

N=4783 N=434o N=2314 N=2036

1970 j401 5484 2527 2514

(2644S) (27070) (17S52) ( 18087)

N=3721 N=3482 N=1743 N= 1603

1971 6803 6909 4233 4374

(31471) (32072) (3 S617) (36835)

N=3703 N=3500 N=1733 N=161S

1976 20793 20915 14261 14265

(j4600) (5 S277) (44s31) (4S598)

N=3438 N=3270 N=1623 N=lj29

1981 26747 26326 16698 IS8S0

(1 S7826) (161S64) (1 S2838) (lj7125)

N=2687 N=2S48 N=1313 N=1236

fwba of Smey Rqom S.73 6.33 4.92 5.58

(3.8S) (35s) (3.50) (3.19)
APPEND~ TABLE lB S~RY STAT3ST3CS FOR SMLES OF

GW AND DAUG~RS

GUIS Daughtem

AU Out of School AU Out of Schml

N=5159 N4550 N=2569 N=2214

Ageti 1966” ““” 1s.7 1s.s 17.s 17.5”

(3.02) (3.02) (2.s0) (2.s2)

Mghest &ade Gmpleted 12.7 12.6 13.0 12.9

(2.67) (2.66) (2.63) (2.64)

Black 0.2s 0.2s 0,32 0.32 ~

(0.4s) (0.4s) (047) (0.47) j

I

Nmbm of Sibtigs

3,s6.

3.s9 3.6s 3.73

(2.6I) (2.63) (2.66) (2.69j

Eva SeK-Employed 0.15 0.16” 0.13 0.15

(0.3s) (0.37) (0.34) (0.36)

Ase Fist SeE-Employed 32.9 32.S 31.s 31.s

(6.29) (6.2S) (s.60) (j.59)

N=7S 1 N=744 N=342 N=33S

NOn-Buti-s AsseB

I 967 2039 21s2 1022 10s2

(171SS) (IS124) (s713) (9245)

N=jlOl N=4S04 N=2549 N=2202

1970 3271 3361 19s9 21j4

(17sss) (17932) (16604) (16473)

N4390 N=3963 N=2216 N=19S0

1971 4S31 4631 2636 2s92

(3s90s) (36956) (1 S69S) (13693)

N423j N=3S49 N=2124 N=l 906

1972

6376. -.

6432 34s0 3260

(39102) (40222) (21944) (21917)

N4027 N=367S N=201 S N=1S2S

1977 16j~2 16122 1214S 1I 299

(67S20) (69S52) (60443) (62209)

N=38S0 N=3S6S N=192S N=1793

19s2 2401S 23649 ‘2044 1 19711

(64610) (6SS94) (s10ss) (s0906)”

N=3S24 N=3226 N=17SS N=IS90

19s7 36773 36393 32420 31 S30

(94424) (9643S) (s1s17)

N=3494

@224S)

N=317S N=1735 N=1S63

Nwbm of S@q RWO- 784 S.S9 7,70 S.3S

(5.12) (4.S2) (4,s1) (4.lj)

Notes Enrnes ae mwtightd s=ple mems. Stidad &viatiom shorn h pwenti=es. N ffldcat= tie

nub= of obsaations wh- appropriate.
APPE~= TABM 2A CROSS-SE~ONAL LOG~ ES~~S OF

PROBAB~~Y OF SELF-E~LOW~ SONS I

1970 1971 1976 1981

COntit -53.79 -3j.96 6.489 36.16

(0.0078) (0.0700) (0.6952) (0,0890)

AGE 4.409 2.240 -0.6735

(age)

-2.390

(00141) (0.1491) (0.57 I2) (0.0660)

AGE2 0.0956 -0.0400 0.0131 0.0369

(age sqwd) (0.01S8) (0.2096) (0.S380) (0,06 15)

L~S Q.3344 -0.4349 -0.0344 -0.7606

(=1 tiless &m M@ Schml) (0.6262) (0.5182) (0.9390) (0.2225)

Soco -0.249s -0.438S -0.3482 0.s013

(= I Ksome college) (0.7179) (0.4871) (0.4198) (O.1764)

COLG 0.3589 -1.742 -0.0134 0.1679

(=1 Kmllege ~tiuate) (0.6523) (0. 1289j (0.9788) (0.6856)

POST -23.806 -0.4656 -0.4841 0.9962

(=1 fpo<-gaduate edcation) (0,9998) (0.5874) (0.4399) (0.0139)

BLACK -0.9680 -0.5814 -1.472

(= I if Black)

-1.907

(O.1807j (0.4268) (00107) (0,001 lj

~Bs -0,1169 0.0207 -0.0175 0.0366

(nmbw of tibltigs) (0.2070) (0.8263) (0.80jO) (0.5784)

SMSAN 0.1027 0.3501 -0.8806 -0.8885

(=1 f SMSA, non-cenud city) (0.8793j (0.5388) (0.0 170) (0.0047)

SMSAC 0.6657 0.330s -0.9847 -1.278

(= IfSMSA, cenrd city) (o.29s3j (0,j795) (0.0222) (0.001 I)

Som -0.4458 -0.2036 0.1s68 -0.0629

(=1 ifi Soum) (o.4410j (0.6993) (0.6586) (0.8373)

WT -0.2312 1.0s7 0,4056 0.0187

(=1 Kmtied) (0.6761) (oo777j (0.3207) (0.96S6)

~P 0.s104 -0.5036 0.1217 0.1212

(nmber of depmda@) (0.0105) (0.1199) (0.4355) (0.3s79)

SEM -0,1380 -0.092s -0.1639 0.0333

(spowe etigs) (o. 126) (0.1 173) (0.0023) (0, 1304)

ASSET 0.0494 0.0022 -0.0005 -0.0011

(&se@) (0.02S4) (0.6808) (0.8997) (0.07S2)

NPASSET 0.0005 0.0007 0.000s 0.0004

(paent’s non-bwtiess mse~j (0.3999) (0.6010j (0.8452) (0.6273)

PANYSE 1.474 1.168 1.092 1.0S8.

(pint’s %E-mployd) (0.0033) (oo179j (0.000s) (0.0001)

N (obsmations) 569 640 673 642

Smple probability 0.040 0.037s 0.080 0. I 20

Noks: Enties me abatd lo@t cwfflcimfi. Fi~es h pwentieses m p-valuti for &@flcmce of etiates.
APPE~H TABLK 2B: CROSS=E~IONAL LOGTf ESMTES OF

PROBAB~ OF SELF-EWLO~E~ DAUG~RS

1970 1971 1977 1982

:Otimt -33.22 18,.37 -18.48 -41.92

(0.3692) (0.4485) (0.4489) (0,2753)

AGE 2,568 -2.082 0.9394 2.302

[age) (0.4646) (0.3671) (0.6002) (0.3408)

AGE2 -0.0584 0.0477 -0..0161 -0.0354

(age squxed) (0.4834) (0.3748) (0.6233) (0.3504)

L~S 0.6345 1.211 0.6410 -0.2032

(=1 ifIess tim M@ Sch@l) (0.5216)- (0.2884) (0.3860) (0.8127)

Soco 0.1016 26.07 0.1805 0.5506

(=1 I wme college) “’” (0.9344) (0.9999) (0.7527) (0.2688)

COLG 0.6650 0.1013 0.5344 0.5994

(=1 if college gad:ate) (0.6363) (0.9426) (0.4215) (0.3272)

POST -20.41 -27.05 -24.38 -0.7458

(= I fpos-gaduate ducation) (09999) (1 .0000) (0.9998) (0.5044)

BLACK -0.9459 -25.21 -2.537 -1.650

(= I if Black) (0.4646) (0.9999) (0.0433) (0.0247)

NOMSBS 0.0122 -0.32S5 0,0300 0.014?

(nmbm of siblti~) (0.9401) (0.16j7) (0.79.45) (0.8799)

SMSAN 0.0475 -1.802 -0.2412 0.0617

(=1 if SMS& non-card ciy) (0.9587) (0.0993) (0.64 18) (0.9047)

SMSAC -0.9293. -1.087 -0.7585 0.6148

(=ltiSMSA, cential city) (0.348S) (0.2606) (0.3035) (0.2426)

Souw 0.3022 -0.4009 -0.7816 -0.1005

(= 1 Kti south) (0.7013) (0.6784) (0.1530) (0.8306)

MST 3.118 0.7585 2.201 1.119

(=1 ifmtid) (0.0136) (0. S814) (00570) (0.0570)

NUMOP 0.1561 4.j185 0.3601 0.2840

(nubm of d~mdm~) (0.6240) (0.6405) (0.0444) (0.0918)

SEARN -0.0965 0.0519 0.0005 -0.0443

(spowe etigs) (o. 1379) (0.4211) (0.9822) (0.0312)

ASSET -0.0108 -0.0 I 57 0.0023 -0.0070

(Axek) (0,4962) (0.5047) (0.6850) (0.1371)

NPASSET -0.0034 0.0027 -0.0013 -0.0002

(pint’s non-btiess mx&) (0.5767) (0.3172) (0.7241) (0.8462)

PANYSE I 0.8004 I 2.277 I 0.9639 I 1.720

@wmt’s seE-mpIOyed) (0.3201) (0.0208) (0,0364) (0.0002)

N (obsaatims) 404 445 S09 582

S~ple probability 0.022 0.018 0.053 0.051

Not=: Enti= m esttiatd 10@t-ffIcienk. FIw= in pwmtiew =e p-valu= for ti~flcace of =timates.
TAB~ 1: SEM-E~LO~~ WTES ~ ~ ~

Year BOYS Clrls Men Women Y-r BOYS Guk Men Women

1966 0.04 na 0.20 0.04 1976

[1513]

0.12

[4970]

0.05

[4860]

0.23 0.08

[2870] [2427] [1762] [2288]

1967 0.04 0.01 0.21 na

[1832]

1977 na

[1689] [4709]

0.07 na na

[1818]

1968 0.05 0.02 0.20 0.05 1978

[2060]

0.14 na 0.22

[1515] [4108] [2727]

0.08

[3076] [1296] [2520]

1969 0.06 0.01 0.20 0.07

[2274]

1979 na

[1 169]

0.08 na na

[4016] [2943] [2257]

1970 0.07 0.04 na 0.07 1980

[2317] [1307]

0.15 m

[2499]

0.23 0.08

[3208] [866] [2432]

1971 0.07 0.03 0.21 0.07 1981

[2564]

0.15

[1458]

0.09

[3469]

0.24 0.09

[2456] [3288] [2350] [612] [2211]

1972 m 0.04 na na 1982 na 0.09 na na

[lj67] [2260]

1973 0.09 na 0.21 0.06

[2901]

1984 na

[2789] [2925]

0.10 na na

[2609]

1974 M 0.04. na na 1986 na 0.10 na na

[2298] [2747]

1975 0.11 na 0.21 0,07

[2914]

1987 na

[2077] [2851]

0.10 na na

[2782]

BOYS Girls Men Women

c;$8;lated til~8$

hy seti+mplo~mt 0.20 0.14 0.19 0.28

[4958]

0.15

[4184] [4609] [5020] [4957]

Age fmt seE-employed 27.4 27.4 29.9 na na

[891] [525] [751]

Fmction of y= s~~t

seE-emplOyd

Overall 0.09 0.05 0.06 0.20

[4958]

0.06

[4184] [4609] [5003] [4957]

COn&tiOnd on my ;:; 0.36 $;; 0.71 0.41

~ SK-m pp10 mt [] 525 [1281] []694

Notes ‘Ml fi~es have b- wti@ted to populatim m~s.

b%~-mpl~mt rates me -dltional upon wmtig at all.

Wmba ti bracke= ~es~ le tia for =ch mm utation.

‘ ‘.bys” we agd 14 m 24 mY 966. “GuIs” ze ag~ 14 to 24 k 1968. “Mm., we agd45 to 59 h 1966.

CCWomm” m aced 30 to 44 k 1967.
I

TABLE > SELF-E~LO~NT WTES: NATIONAL LONG~D~AL

S~VEY VEMUS C~NT POPULATION SWVEY I

BOYS Girls Men Women

Y=r m CPS NLs CPS NLs es NLs as

1971 0.07 0.04 0.03 0.02 0.21 0.17 0.07 0.05

1976 0,12 0.07 0.05 0.02 0.23 0.18 0.08 0.05

1981 0.15. 0.10 0.09 0.07 0.24 0.22 0.09 0.08

Notw. ‘ NLS seF-plo~ent rak computed wtig wtigh@ dati md reqtie tiat boys md @ls be out

of %hool, md that ma md womm xe yomger thm 66 ad w not retid

bCPS seK-~plO~mt mta co~uted wtig the Mach CPS for ach yea. CPS weighs wed h

the cmptiations. Age wges ae comlstat titb com~ondmg ~S smple.
Year sons

1966 0.02

[412]

1967 0.03

[595]

1968 0.03

P421

1969 0.04

[888]

I970 0.05””

[897]

1971 0.04

[1040]

1972 na

1973 0,08

[1264]

1974 na

1975 0.09

[1281]

TABLE 3 SELF-E~LOYED UTES ~ n ~:

MAT=D PA~~-C_ SA~~S

Daughters Fathers Mothem Yam sons Daughtem Fathem Mothem

M 0.22 0.05 1976 0.10 0.0s 0.23 0.08

[1290] [2080] [1968] [1223] [j94J [941]

0.00. 0.23 na 1977 na 0.07 na na

[548] [1240] [898]

0.01 0.21 0.06 1978 0.13 na 0.23 0.08.

[620] [1109] [1230] [1406] [4s4] [1019]

0.01 0.21 0.08 1979 Da 0.07 n. n.

[s09] [1103] [1322] [1114]

0,03 na 0.07 1980 0.14 na 0.23 0.08

[628] [108S] [1487] [3 16] [991]

0.03 0.22 0.08 1981 0.1s 0.08 0.24 0.09

[741] [984] [1087] [1ss4] [11s4] [222] [874]

0.03 na na 1982 na 0.08 na na

[779] [1102]

na 0.22 0.07 1984 na 0.10 na na

[8S4] [1272] [1274]

0.02 na na 19.86 na 0.10 na na

[1142] [136S]

na 0.22 0.08 1987 na 0.09 na na

[674] [1192] [2569]

I

sons Daughters Fathers Mothem

calcdatd tiou~

1982. .1987

hy seE-aplO~ent 0.18 0.13 0,18 0.30 0.16

[2363] [204S] [2269] [1294] [212s]

Age fist se~-aployd 26.8 26.3 29.0 na na

[371] [227] [342]

Fraction of yews Vat ““

seK-aployed

Overall 0.08 0.04 0.06 0.22 0.07

[2363] [204S] [2269] [1293] [212s]

COn&tiOnd m my 0.43 014 0.32 0.72 0.43

se~-mplopmt [371] [227] [342] [36S] [302]

Notes .MI fi~es have bem weighted to population mems.

5SeK-ap10~mt rates me conditional upon wortig at all.

Wubem k bracke~ ze smple += for each computation.

d“Sons” me agd 14 tO 24 k 1966. “Daughta” m aged 14@ 24 k 1968. “Fa$a” =e aged 45 to S9 in 1966.

“Mothas” ue aged 30 to 44 h 1967.
Year

1966

1971

1976

1981

Age fust w~-employd

Fraction of yem ~nt

xMaplOyd

Ovemll

Conditional on my

wlf-empl~ent

TABLE 4: R4TERGENERATIONAL LIN~ ~ SELF-E~LO~ENT I

Sons’ Sc[f-EmnlOvmcnt Rate

Father Mother

Self- Not Sclf- self- Not SelfEmployed Employed Employed Employed

0.05 0.00 0.00 0.02

(0.21) --- (0.00) (0.15)

0.07 0.04 0.06 0.03

(0,26) (0. 19) (0.24) (0.16)

O.ls 0.05 0.12 0.0s

(0.38) (Q.23) (().32) (0.28)

0.28 0,09 0.21 0.11

(0.45) (0.28) (0.41) (0.32)

0.32 0.12 0.19 0.13

(0,47) (0.32) (0.40) (0.34)

[292] [746] [221] [1306]

26.0 27.5 26.0 26.4

(4.68) (4.94) (4.57) (4.25)

[94] [87] [43] [171]

w

Dauzbters’ Self-EmnlOvment Rate I

Fat

sclfEmployed

0,00

. ..

0.05

(0.22)

0.07

(0.25)

0,12

(0.32)

0.24

(0.43)

[271]

29,6

(5.46)

[66]

0.08

(0.18)

0.31

(0,24)

*

!r MO

Nat Self- SelfEmployed Employed

0,00 0.00

... . ..

*

0.01 0.03

(0.12) (0.18)

0.03 0.08

(O:k8) (0,27)

0.07 0.09

(0,25) (0.29)

%

0.12 0.23

(0.33) (0,42)

[621] [226]

30.2 28.1

(5.53) (4,78)

[77] [51]

0.04 0.08

(0.14) (0.18)

3

er

Not Self.

Employed

0.00

. ..

0.02

(015)

4

0.03

(0.17)

a

3

0.13

(0.33)

[1343]

28.6

(5.26)

[170]

(0:13)

0,32

(0.22)

Notes `Self-emploWentratesarcmndtionolupon\vorkhgatallnndurem\vei@lted

Nlmbers h Dwentheses we slaadwd deviationsnnmbersinbncketsaresamDlesizes.

C“Sons’’we;gd14t024h196b. “Dau@t~sia reagti14t024inl%S. ‘~ahem’’ areagd45to 59inl96b. “Motbem’’areagd30t044in1967.
TABLE 5: ASSET HOLD3NGS AND SELF-E~LO~E~: BOYS

(a) Tatal Asset Holdings @) Non-Business Asset Holdings

QuartiIe 1 Quatiile 2 Quartile 3 Quartile 4 Quatiile 1 Q.atiUe 2 Quadile 3 QuatiUe 4

1966 0,01 0.01 0.02 0.16 0.04 0.o1 0.05 0.10

(0.12) (0.08) (0.13) (0.37) (0,20) (0,12) (0.22) (0.30)

[136] [143] [123] [138] [138] [147] [122] [133]

I 970 0.03 0,01 0.04 0.13 0.05 0.03 0,05 0.08

(0,16) (0.11) (o. 19) (0.34) (0.22) (0.16) (0.22) (0.28)

[328] [315] [311] [341] [327] [316] [313] [339]

1971 0.02 0.02 0.04 0.15 0.03 0.03 0,07 0.10

(0.12) (0.12) (021) (0,36) (0.18) (0.18) (0.26) (0,30)

[385] [379] [38Q] [430] [394] [372] [383] [425]

i976 0,04 0.06 0.08 0.22 0.06 0.08 0.11 0.16

(0,21) [0.24) (0,27) (0.42) (0.24) (0.28) (0.31) (0.37)

[521] [527] [562] [597] [526] [531] [560] [588]

1981 0.05 0.07 0.09 0.33 0.08 0.12 0.09 0,22

(0,21) (0.26) (029) (0.47) ;:; (0.32) (0.29) (0.41)

[522] [528] [536] [536] [504] [503] [515]

Notes ‘Smple restictd ~ bays out of schwl.

bEnties *OW esdmatd self-emplowent rate, standard deviation of se~-emplo~ent rate (m p~entheses) md nmber of obsmatiom (m brackets).

“AWE me~wd net of tinbilities.
TABLE 6: ASSET HOLD3NGS AND SELF-E~LO~ENT: GIRM

(a) Total Asset Holdings (b) Non-Business Asset Holdings

Quatiile 1 Quatiile 2 Quatiilc 3 Quatiile 4 Quartile 1 Quatiile 2 Quatiile 3 Quatiilc 4

1967 0.03 0.00 0.02 0.02 0.02 0,00 0.02 0,02

(0.16) (0.07) (0. 1,3) (o. 1s)

[267]

(0.15)

[213]

(0.00)

[226]

(0.14) (0.14)

[269] [290] [301] [193] [295]

1970 0.07 0.03 0.02 0,05 0.07 0.03 0.04 0.04

(0,25) (0.17) (0, 14) (0.21)

[173]

(0,26)

[169]

(o.”f7)

[194]

(0.18)

[191]

(o,.19)

[1s0] [163] [199] [196]

1971 0,05 0.03 0.03 0.07 0.06 0.05 0,03 0.05

(0.21) (o. 17) (0. 17) (0.25) (0.23)

[198] [204]

(0.21)

[227]

(0.18) (0.23)

[224] [19s] [216] [237] [223]

1972 0.04 0.04 0.04 0.06 0.04 0.05 0.04 0,05

(0.20) (0.1?) (0.19) (0.24)

[245]

(0.20) (0.22)

[239] [292]

(0.21) (0.22)

[264] [248] [245] [296]” [272]

!977 0.04 0,02 0.07 0.15 0.08 0.04 0.05 0.14

(0.19) (o. 14) (0.25) (0,36) (0.26)

[281]

(0.20)

[342] [3 12]

(0.21) (0.35)

[290] [226] [474] [384] [330]

1982 0,04 0.06 0.07 0.12 0.08 0.06 0.09 0.10

(0.19) (0.24) (0.26) (0.33) (0,28) (0.24)

[274] [313] [288]

(0.29) (0.30)

[276] [2 19] [454] [372] [32S1

1987 0.05 0.05 0.08 0.16 0.13 0.08 0.07 0.13

(0.22) (0.22) (0.27) (0.37) (0.33)

[477]

(0.26)

[534] ~508]

(0.25) (0.34)

[493] [350] [883] [541]; [611]

Notes: ‘Snmple restictd 10girls out of schml.

~ti~ show csdmatd =lf.emplo~ent rote, slmdtrd deviation of wlf-employment rate fill pwenties~) md number of obxwations fin bracke&).

c~sets mcaswti netofliabilities.
TABLE 1 PARENTS’ ASSETS ANR SELF-EMPLOYME~ SONS

(a) Parents’ Totnl Assets (b) Parents’ Non-Business Assets

Quatiil. 1 Quatiilc 2 Quartile 3 Quatiile 4 Quatilc 1 Quartile 2 Quatitie 3 Quatiile 4

1966 0.00 0.00 0.00 0.29 0.00 0.20 0.00

(0.00)

0.20

(0.00) (0.00) (0.49) (0.00) (0.45)

[7]” [4] [lo]

(0.00) (0.4s)

[7] [7] [s] [11] [s]

1969 0.00 0.02 0.06 0.14 0.01 0,05 0.02 0.13

(0.w) (0.14) (0.24) (0.3s)

[70]

(0.12) (0.22)

[54] [68]

(0.13) (0.34)

[s9] [69] [S8] [58] [52]

1971 0.00 0.02 0.05 0.09 0.03 0.0s 0.06 0.0s

(0.00) (0,1s) (0.22) (0.29)

[76]

(0.16) (0.21)

[94] [101],

(0.24)

[117]

(0.23)

[7s] [86] [103] [112]

1976 o.a3 0.07 0.08 0.14 0.06 0.09 0.07 0.11

(0.18) (0.26) (0.28) (0.3s) (0.23)

[124] [126]

(0.29)

[169]

(0.26) (0,31)

[188] [126] [127] [1s3] [190]

1981 0.0s 0.13 0.1s 0.2s 0.12 0.14 0.1s 0.21

(0.21) ;;:; (0.3s) (0.43)

[129]

(0,32) (0,3s)

[192]

(0.3s) (0.41)

[208] [130] [lk5] [179] [207]

Notes ‘Smple resticld to mns out of scb~l.

bEnties show estimatd wlf-emplo~ent rote, stmdard deviation of self-enlpl~ent rate on pwe}ltileses) mld lmmber of observations fin brackets).

‘As%k memwd net of liabilities.
TABLE 8 PARENTS’ ASSETS AND SELF-EMPLOYMENfi DAUGHTERS

(a) Parents’ Total Assets . ,

Quatile 1 Quadile 2

*

1981 0,.02 0.04

(0.14) (0,,20)

[88] [81]

Notes ‘SmDleresticti bdauglltelxoutofschml

Quatillc 3

0.00

...

[47]

0.02

(0.13)

‘[61]”

0.03

(O.18)

[94]

0.04

(o. 19)

[161]

0.03

(0.17)

[1 17]

Quartile 4

0.00

.. .

[361

0.02

(0.14)

‘[j I]”

0.06

(0.23) ‘[w]’

0.07

[0.25)

[162]

O.OY

(0.29)

[139]

Quatiilc 1

O.po

(0.00)

[58]

0.00

(0,00)

‘[30]”

0.02

(0.14)

‘[4Y]’

0,02

(0, I 5)

[138j

0.03

(0. 18)

[w]

(b) ., Parents’ Non-Business Assets

Quatiilc 2

0.00

(0.00)

[45]

0.00

(0.00)

[45]

0.03

(0.17)

[67]

0.03

(0.17)

[138]

0.04

(0.20)

[89]

Quatiile 3

0.00

(0.00)

[47]

0.02

(0.14)

[50]

0.00

(0.00)

[711

0.02

(014)

[156]

0.03

(0.18)

[106]

3

Qw.tiile 4

0.00

(0.00)

[34]

4

().02

(o. 14)

[48]

0.07

(0.25)

[88]

0.08

(0.27)

[154]

0.07

(0.26)

[133]

bEn~= show estimatd &f-mPloymenlrate,sbmdmddeviationofself-employmeIlt rateOnparentheses)and number of obxmatimw 011brackets). I

CA*ISmemwd net ofhnbiliti~s.
TAB~ 9: CROSS-SECTIONAL LOG3T ES~~S OF PROBABETY

OF SELF-E~LO~E~ BOYS

1966 1970 1971 1976 1981

Commt -15.99 -20.32 -16.18 -3.190 I ,23g

(0.3 174) (00075) (0.027S) (0.6189) (0.8778)

[-0.12091] [-0.72086] [-0.59232] [-0.2433S] [0. 10771]

AGE 1.0897 1.398 1.0137 0.06270

(age)

-0.214S

(0.4782) (0.0309) (0.0925) (0.8878)

[0.00824]

(0.6j30)”

[0.049j8] [0.0371 1] [0.00478] [-0.0186s]

AGE2 4.0209 -0.0263 -0.0180S 0.0000s222 0.003928

(age squ=d) (0. S678) (0.0j32) (0.1394) (0.994s) (0.5753)

[-0.00016] [-0.00093] [-0.00066] [0.00000] [0.00034]

L~S -0.7383 4.1922 -0.2239 -0.193s -0.1980

(=1 til~S tim bi@ school) (0.0727) (0.4939) (0.4244) (0.3804) (0.3954)

[-0.00S58] [-0.00682] [-0.00820] [-0.01477] [-0.01721]

Soco -0.9010 0.s047 0.39S6 0.2098 0.1492

(=1 Kmme cOUege) (0.1257) (00s39) (0.1134) (0.2463) (0.3933)

[-0.00688] [0.01791] [0.01448] [0,01600] [0.01297]

COLG -25.92 -0.09j7 -0.03212 0.0250s 0.1883

(=1 FcolIege ~aduate) (0.9997) (0.8149) (0.92ss) (0.9064) (0.3328)

[-0. 19j96] [-0.00340] [-0.00118] [0.00191] [0.0 1637]

POST -0.41s3 -1.047 -0.2431 -0.001666 0.4j84

(=1 fpost-~aduate education) (0.7353) (0.1021) (0.5520) (0.9940)

[-0.00314]

(0.0139)

[-0.0371 j] [-0.00890] [-0.00013] [0.0398S]

BLACK -1.94j -1.123 -0.9898 -1.208 -1.6jj

(=1 KBlack) (0.0120) (0.0017) (0.0047) (0.000 1) (0.0001)

[-0.0 1470] [-0.03984] [-0.03624] [-0.09220] [-0.14366]

msms 0.0463 0.00581 -0.008860 -0.01836 -0.0027s9

(nmbm of tibligs) (0.s01 9) (0.8943) (0.8357) (0.j.S64) (0.3s91)

[0.00035] [0.00021] [-0.00032] [-0.00140] [-0.00240]

SMSN -1.056 -0.6S38 -0.6383 -0.832S -0.6198

(=1 if SMSA, non-ctial civ) (0.0087) (0.0079) (0.0042) (0.0001) (0.0001)

[-0.00798] [-0.02320] [-0.02341] [-0.06351] [-0.05388]

SMSAC -1.178 4.6613 -0.8719 -0.9992 -07354

(=1, ti SMSA, cmmd .i~) (0.0073) (0.0141) (0.0008) (0.000 I) (0.000 I)

[-0.00891] [-0.02346] [-0.03 192] [-0.07623] [-0.06393]

sow -0.3S68 0.0s437 0.006770 0.1107 -0,09833

(=1 fh muti) (0.3448) (0.8148) (0.97s1) (0.4s49) (0.4784)

[-0.00270] [000193] [0.0002s] [0.00845] [-0.00863]
TABLE 9 (CO~~ED)

I

1966 1970 1971 1976 19s1

WT 0.5826 4.3904 0.04100 4,246% 0.0S785

(=1 timtid) (0.1853) (0.1630) (0.8761) (0.1874) (0.7556)

[0.00440] [-0.01385] [0.00150] [-0.01883] [0.00503]

-P -0.3036 0.1008 0.03842 0.02620 0.09237

(nmbm of @mdmk) (0. 1872) (0.32S2) (0.6862) (0.6773) (0.0821)

[-0..00230] [0.00358] [0.00141] [0.00200] [0.00803]

Sw -0.0974 -0.04211 -0.04413 -0..02777 -0.009173

(SPOUX efi~) (o. 1375) (o. 1s03) (0.0688j (0.0378)

[4.00074]

(0.3926j

[-0,00149] [-0.00162] [-0.00212] [-0.00080]

ASSET 0.0288 0.006015 0.007811 0.004358:

(asses)

-0.0006345

(0.0002) (0.0173) (0.0007) (0.0001)

[0.00022]

(0.0323)

[0.00021] [0..00029] [0.00033] [-0.00006]

N (obsematiom) 1391 2111 2346 2627 2572

Smple Probability 0.032 0.050 0.053 0. I03 0.121

Notes Enties we Gitiald lo~tboe5cimk. R.wes k pwtitheses we p-vdua fm-si~~lcmce of ~~ates.

Mmgtial effeck of ea& vtiable (evdmkd at the smple mem) we show b brackek.
TAB~ 10: CROSS-SE~IONAL LOG~ EST~TES OF PROBABL~

OF SELP-E~LO~E~: GW

1970 1971 1972 1977 1982 1987

Comtat -35.jl -11.31 -11.98 -16.74 7.546 -0.01033

(0.0209) (0.2j98) (0.2216) (0.0827) (0.4j67) (0.9993)

[-O.j2918] [-0.21741] [-O.2937j] [-0.62625] [0.41414] [-0.00072]

AGE .“ 2.986 0.7214 0.6360 0.978j -0.6976 -0.1798

(age) (0.0356) (0.4240) (0.4508) (0. 1546) (0.2581) (0.764 1)

[0.04450] [0.01387] [001559] [0.03661] [-0.03829] [-0.01248]

AGE2 -0.07021 -0.01j33 -0.01273 -0.01703 0,01113 0.002900

(age sqwed) (0.0314) (0.4452) (0,4777) (0.161j) (0.2321) (0,7122)

[-000105] [-0.00029] [-0.0003 I] [-0.00064] [0.00061] [0.00020]

L~ 0.2065 0.4644 -0.1223 -0.1268 -0.4067 0.08722

(=1 flew &m tigh school) (0.6586) (0,2480~ (0,7772) (0.6979) (0.1780) (0.6988)

[0.00308] [0.00893] [-0.00300] [-000474] [-0.02232] [0.0060j]

Soco -0.1686 -0.9903 -0.1386 0.0609 0.4317 0.0j861

(=1 Fsome college) (0.7724) (0.1131) (0.74 I 4) (0.8156) (0.0410) (0.7j22)

[-0.00251] [-001904] [-0.00340] [0.00228] [0.02369] [000407]

COLG 0.5j90 -0.6308 -0.73j8 -0.3745 0.5186 0.1686

(=1 ~college @duate) (0.4085) (o.4075j (o.3315j (0.3518) (0.0488) (0.4343)

[0.00833] [-0.01213] [-0.01804] [-00 140”1] [0,02846] [0.01170]

POST 1.408 -0.15j8 05371 -0.02912 0.j384 0.06943

(=1 if post-qaduate education) (0.0946) (0.8838j (0.3620) (0.9378) (0.0432) (0.7j38)

[002099] [-0.00300] [0.01317] [-0.00109] [0.02955] [0.00482]

BLACK -1.884 -1.419 -1.520 -1.647 -1.484 -1.269

(=1 if Black) (0.0103) (0.034 1) (0.0056) (0.0004) (0.000 i) (0.0001)

[-0.02808] [-0.02728] [-0.03726] [-0,06164] [-O.08142] [-008808]

msms -0.03324 -0.1569 0.02177 -0.063j7 0.007729 -0,012jl

(nmbw of siblti~) (0.6924) (0.0469) (0.7369) (0.2051) (0.8413) (0.703j)

[-0.000jO] [-0.00302] [0.000j3] [-0.00238] [0.00042] [-O.0008j]

SMS~ -0.3612 -0.3058 0.07900 -0.4608 a.008178 0.03660

(=1 ti SMSA, non-cenkal ci~) (0.3877) (04113) (0.823j) (0.0503) (0.6822)

[-0.00538]

(0.8227j

[-O.00j88] [0.00193] [-0.0 1724] [-0.00449] [0.00254]

SMSAC -0.6100 -0.6927 0.01458 -0.5708. 0.1075 -0.002264

(=1, ti SMSA. cmkal .i&) (O.1839) (0.1 163) (0.9702) (00473) (0.6264) (0.9906)

[-0.00909] [-0.01332] [0.00036] [-0.02136] [0.00590] [-0.00016]

Soum 0,08216 -0.5133 0.2768 -0.1635 -0.08727

(=1 f“h WUti)

0.03154

(0.S262) (0.1630) (0.36S0) (0,4577) (0..6225) (0.s2s3)

[000122] [-0.009s7] [0.00679] [-0.00612] [-0.00479] [000219]
TABLE 10 (CO~ED) I

1970 1971 1972 1977 1982 1987

WT 1.698 0,5653 0,8462 0.9596 0.8166 0.7217

(=1 fmtied) (0.0047) (0.2926) (0.0779) (0.0063) (0.0013) (0,0004)

[0.0253 1] [0.01087] [0.02075] [0.03590] [0.04482] [0.05008]

~P 0.4191 0.3173 0.2494 0.1780 0.1912 na

(nuber of d~enda~) (o.m51 ) (0.0293) (0.0347) (0.0314)

[0.00625] [0,00610]

(0.0009)

[0.0061 1] [0.00666] [0.01049]

SE~ -0.02433 0.01433 0.0101 -0.008035 -0.01219

(wO~, etigij

-0.008j81

(o.2968j (0.s010) (0.4s07) (0.40s0) (0.0888) (0.0811)

[-0.00036] [0.00028] [0.0002s] [-0.00030] [-0.00067] [-0.00060]

ASSET -0.01127 -0.006468 -0.001481 0.001881 0.00096S3” 0.001176

(Xxs) (0.0761) (0.4490) (0.7224) (0.1749) (0.3399) (0.0647)

[4,00017] [-0.00012] [.0.00004] [0.00007] [0.0000s] [0.00008]

N (obsmations) 1196 1329 1407 1790 2234 27S9

Smple Robabiliy 0.043 0.033 0.037 0.061 0.076 0.089

Notes Enties we esti”td Iogit cmfficimts. fi~es ti pwmtheses ~e p-valu~ for si@cmce of tiat~.

Mzgtial effeck of each vtiable (evaluated at the smple mem) we show h bmckeb.
1

1

(

TABL32 11: POOLED LOGYf ES~TE OF PROBAB~ OF SELF-E~LO~E~

Contemporanmus Assets kgged Assets

BOYS ark BOYS Grk

COmmt -7.1850 -6.5723 -6.6125 -6.3823

(0.0001) (0.0001) (0.0001) (0.0001)

[-O.36q [-0.235] [-0.371] [-0.310]

&GE 0.2962 0.1089 0.2722 0.1278

[age) (0.0001) (0.0864) (0.0004) (0.0808)

[0015] [0.004] [0.015] [0.006]

&GE2 -0.0038 -0.0010 -0.0038 4.0012 ~

(age squzed) (0.0005) (0.3099) (0.0029) (0,2659)

[0.000] [0.000] [0.000] [0.om]

L= -0.2598 -0.0913 -0.2255 -0.1348

(=1 til~S thm tish school) (0.0037) (0.3498) (om22) (0.2292)

[-0.013] [0.003] [.0.013] [-0.007]

Soco 0.2000 0.0678 0.1612 0.0208

(= I if some college) (0.0063) (0.3993) (0.0522) (0.8175)

[0.010] [0.002] [0.009] [0.001]

COLG -0.03 I2 0. I494 -0.1325 0.0993

(=1 fcOllege ~aduate) (0.7241) (0.1327) (0.1817)

[-0.002]

(0,3674)

[0.005] [-0.007] [0.005]

POST 0.0780 0.2119 0.0126 0.1960

(=1 ifpost-~aduate education) (0.3844) (0.0412) (0.8988) (0.0882)

[0.004] [0.008] [aooI I [0010]

BLACK -1.3907 -1 ,49j7 -1.3332 -1.5744

(=1 EBlack) (0.0001) (0.0001) (0.0001) (0.0001)

[-0.071] [-O.0j4] [-0.075] [-0.077]

WBS 0.0062 -0.0345 0.0214 -0.0369

(nub= of sibb~) (0.6206) (0,0158) (0.1290) (0.0224)

[0,000] [0.001] [0.ool] [-0.002]

Smm -0.7127 -0.1562 -0.6915 -0.0978

(=1 if SMSA, nm-cakal ci~) (0.0001) (0.0272) (0.0001) (0,2150)

[-0.036] [-0.006] [-0.039] [-0.005]

SMSAC -0.8259 4.0876 -0.7804 -0.0724

(=1 KSMSA mti ci~) (0.0001) (0.2762) (0.0001) (0.4269)

[0.042] [-0.003] [-0.044] [-0.004]

SOUTH -0.0480 -0.0415 -0.0537 -0.0092

(=1 ifk muth) (0.4316) (0.5189) (0.4451) (0.8988)

[-0.002] [-0.001] [-0.003] [0.000]
I

TABLE 11 (CO~NED) I

Contempormeous Assets Lagged Assets

I I I ,

BOYS Girls BOYS Girls

I

MST 0.0579 0.8929-. -0.0058 0.85j6

(=1 timtied) (0.6450) (0.000 1) (0.9524) (0.001)

[0.002] [0.032] [0.000] [0,042]

~P o.oj59 0.2534 0.0347 0.2667

(nuber of d~mdab) (0. I j.j4) (0.0001) (0.2286) (0.0001)

[0.002] [0.009] [0.002] [0.013]

SE~ -0.0216 -0.0091 -0.0167 -0.0093

(spoue etigs) (0.0001) (0.0002) (0.0066) (0.0009)

[-o.omI]”- [0.000] [a.ool] [0.wo]

ASSET 0.00193 0.00029 0.00608 0.00003

(as-) (0,0001) (0.1253) (0.0001) (0.915j)

[0.00010] [0.0001] [0.00034] [000000]

N (obsaations) 19080 21970 14193 16904

Smple Probability 0.083 0.058 0.089 0.060

NOkS Enti~ ~e Iogit coefficiat =tiates. p-values foF sf~,cmce leveIs m sho~ ti pmtiwes. Enties h

brackek m mw@al effecs for each variable evalwted at tie smple mems. “Contmporaeow As*t” WS

=seb h wey ye=, “Lagged Asse@” m asse@ horn prtiou mey y-. See teti for details.
TABLE 12: PARENTS’ FINANCIAL CAPITAL, PARENTS’ HUMAN CAPITAL

AND TIIE PROBABILITY OF SELF-EMPLOYMENT SONS

(1) (2) (3) (4) (s) (6)

ASSETS 0.0015 0.0011 0.0006 0,0016 0.0014 0.0010

(Iaggd asets) (0.3390) (0.5042) (0.7510) (0.3040) (b.3827) 0.5655

[0,00007] [0.00004] [o.mm3j [0,00006] [0,00005] [0,00005]

NPASSET 0.0010 0.0005

(pwenk’ non-b~hess axets) (0.0229) (0.2700)

[0,00004] [0.00002]

TPASSET 0.0020 0.0014

@umk’ total asxk) (0.0001) (0.0001)

[0,00011] [0,00007]

PANYSE 1.256 1.235 1.036

@wents wlf-employd) (0.0001) (Oml) (0.0001)

[0,05072] [0.04518] [0.,04831]

N (obxwntions) 2702 2702 2702 2702 2702 2702

Smple Probability 0.078 0.078 0.078 0.078 0.078 0.078

Note EachequatiOn alwkcludes yem-specific intercept, age, ~gesql!wed, !~wl~erofsiblmgs, nmbcrOfdepndents, spOusc'semings, andin&cahr

vwtibles foreducati0n81 attaiment, raw, mgiOnj SMSAresidence, mdmnritals@t~. Entries meestiatd logitmefflcients. Figurcsin

pwenti=~e~values forsi~ificmw of estimates, Mmginaletiwtsofeacbvtiable(evaluald atsmplemean) aresl1o~inbmckets.
TABLE 13: PARENTS’ F~ANCML CAPITAL, PARENTS’ HUMAN CAPITAL

AND THE PROBABILITY OF SELF-EMPLOYMENT: DAUGHTERS

NASSET -0.0004

(pwmts’ non-bwinms a~ts) (07436)

[-000001]

TPASSET

(pwents’ total aswk)

PANYSE

@wenk xlf-employd)

N (obmations) 2463 2463

Smple Pmbabifity 0.028 0.028

(3) (4) (5) (6)

-0.0036 -0.0055 -0.0055 4.0054

(0.3708) (0,1391) (0,1511) (0,1491)

[-0,0M06] [-0,00010] [-0,00006] [-0.00006]

-0.0006

(0.5845)

[-0.00001)

-0.moo -0.0005

(0,8436) (0,5142)

[4,0WOO] [-0.00001]

1.042 1.049 I.092

(Oowl) (0.0001) (0.0001)

[0.01824] [0,01 149] [0.01218]

2463 2463 2463 2463

0.028 0,028 0.028 0,02s

NOIe EachequatiOn alsOincludes yew-spwific intercepts, age, agesquwed, nmberofsiblings, nulliberOfdcpendents, spOuw'semings, mdin&cat0r

vtinbles forduoatiOnal attainmmt, race, regiOn, SMSAresidence, mdmaribls!a[us, Entiies weestima!d lo~itcoefflcients, Fiwesin

pwenfiewsarepvalum fOrsi~Lcanceof~stiInaks. Mnrginaleflccts Ofeachvaliable (cvaluatdat mplem~an)we show b~rackek.
TABLE 14: LOG~ ES~TES OF PROBAB~ OF TRANSITION TO SELF-E~LO~NT I

I BOYS I Girls I

CONSTANT -5.7027 -9.0879

(0.0003) (0.0001)

[-0.230] [-0. 124]

AGE 0.154s 0.3129

(age) (0.197s) (0.0138)

[0.006] [0.004]

AGE2 -0.0029

(age s~~ed)

-0.0047

(O.1827) (0.0233)

[-0.000] [-0.000]

SMSAN -0.1727 0.1241

(=1 if SMSA, non-cmmal”ci~) (0.1267) (0.3362)

[-0.007] [0002]

SMSAC -0,46j7 -0.1718

(=1 if SMSA, cmkal .i&) (0.0002) (0.234S)

[-0.019] [-0.002]

SO~H -0.3603 -0.3887

(=1 ifksoufi) (00007) (0.0007)

[-0.015] [-0.00s]

MARST -0.2477 0.9811

(= 1 ifmtid) (0.0759) (0.000 I)

[-0010] [0013]

~P 0.0439 0.lj28

(n~bm of depmdenk) (0.313s) (0.000 1)

[0.002] [0.002]

SEARN 0.0189 -0.0081

(Spo- -*) (0.0329) (0. 1122)

[0.008] [-0.000]

ASS~ I

0.00s30

I

-0.000I9

(asses) (0.0001) (0.73s7)

ro.000211 r-o.000ool

N (obsmatiom) I I 4060 I 1S404 I

Smple ProbAiIiV I 0.034 I 0.024 I

TABLE 16: PARENTS’ FR4ANCMLCAPITA~ PARENTS’ HUMAN CAPITAL

AND TW TRANSITION TO SELF-EMPLOYME~ DAUGHTERS

(1) (2) (3) (4) (5) (6) (7) (8)

iSSETS -0.0004 .0.0005 -0.0004 .0.0009 4.0008 -0.0013 -0,0016 4.0015

laggd awts) (0,8843) (0,8748) (0,8842) (0,7298) (0,7G14) (O-5645) (0.4602)

[-O,WOO]

(0,5012)

[-0;00000] [-0.00000] [-O.00000] [-0,00000] [-0,00000] [-o,oom] [-0;00000]

4PASSET 0.0006 0.00230 0.0013 I

pwents’ non-bwtiess awk) (0.6G59) (0.0383) (0,2896)

[0,00000] [O;ooooo] [0.00000]

:PASSET 0.000001 0.00169 0.00 I22

pwents’ total aswts) (0,9782) (0,0137)

[0,00000]

(0.1605)

[0,00000] [0,00000]

‘~YSE 1.411 1.505 1.451

pmentsxf-employd) (0.mol) (o.om4) (9.0006)

rOiOO029] [0.00027] [0.00037]

J (obwwstions) 2473 2473 2473 2112 2112 2469 2108 2108

:mple Probability 0.015 0.015 0.015 0.014 0.014 0.010 0.014 0.014

{ote Each equation alm includes yem-specific interwpts, age, age squared, nuber of dependents, md hdcator variables forregion,SMSAresidenm,md mmital

stare, Entriesweesttiatd Iogitcwfflcients.Figwes in pmentiews are p-valua for si~ficanw of etimates. Mmgiml effwk tieach vtiable (evaluatd at

smple mem) we AOM i bmckets

h mlm (4) flu0ugb(8), pments’ nwts md xk-emplo~ent experience we measwd in 1%6 (tic sw ofthesmple ptid).
TABLE 17: EFFE~S OF PARENT’S GENDER ON SELF-EMPLOYME~: SONS

on Prnhabili(y on Trmsition

(1) (2) (3) (4) (5) (6) (7) (8)

4SSETS 0.0014 0.0020 0.oo1o 0.002 I

Iaggd Mwts)

0.0W4 0.0105 0,0099 0.o1oo

(0.3827) (0,2175) (0.5461) (0.1578) (0.0097) (0.0044) (0,0079)

[o.moo5]

(0,0059)

[0,00010] [0,00005] [0,00009] [0.@016] [0.00017] [O,OM13] [0.0017]

4PASSET 0.0005 0.0005 0.0005 0.0007 4.W7 -0.0005 -0.0007 0.0007

pwenh’ non-bwiaes asxs) (0.2700) (0.2960) (0.2841) (0.1s43)

[0,0W2]

(0,6788) (0,7262) (0.6692) (9.5766)

[0,00003] [0,00002] [0,00003] [4,00001] [-o.mool] [-0,00001] [0.0000[]

‘ANYSE 1,235 1.225

pwenb*W-emplOyd) (0.ml) (0.ml)

[0,04518] [0,02023]

)Af3ANYSE 0.9}31 1.262 1.258 1.139

(O,ouol) (0.ouol) (0.ml) (0.mol)

[0.04965] [0.05590] [0.01990] [0.01476]

dOWYSE 0.5959 0.7705 1.230 1.092

(0.0048) (0.0001) (O.1152) (0.1593)

[0,03241J [0,03238] [0,01945] [0;01812]

( (obwrvatiom) 2702 2029 2481 2250 25U8 2088 2146 2450

;nmpleProbablfity 0.078 0,087 0.083 0,080 0.030 0.033 0.033 0.030

?ote Eachequationalwkcludesyew-spwificintercepts,age,agesq~rd, nmberofde\>enden@,spouses'emingsandtidicators forre@on,SMSAresidenw,md

Atal status,'rhespecifica(ionsincolims (l)tljrough(4)alwinclWcindicatorsforraw,nmherofsiblings, andducationalattaim~mt.Entieswe

estimatd logitwficicn~. FigwesiIi pwmfllews arep-values forsi~ificanw ofes[ imates. Marginal effec!s ofeachvminble (evaluate atsmplemem)we

show in bracke~s.

h mhnrms (5) ti0ugh(8), pwenE’ awts and wlf+mployment experimm arc rneaswd in 1966 (the mafi of the sample pmid).

/

(

I
TABLE 18: EFFE~S OF PARENT’S GENDER ON SELF-EMPLOYMENT: DAUG~ERS

I on Probabiliw I onTlamition

(1) (2) (3) (4) (5) (G) (7) (s)

ASSETS -0.0055 -0.0048 -0.0050 -0.s037 -0.0016 -0.0013 -0.0013 4.0010

(laggd aw~) (0.1511) (0,4142) (0,2177) (0.5180) (0,4602) (0,5535)

[-0,0W06] [-0.00010]

(0.5701)

[-o,ooao9]

(0.69S1)

[-pomoo] [4.000@] [-0,00000] [,O,OWOO] [4.om]

NPASSET -0.0006 4.0003 -0.0006 -0.0001 0.0013 0.0014 0.0013 0.0024

(paents’ nmr-b~incss assets) (0,5845) (0,8198) (0,5862) (0,9223) (0,2896) (0.2585)

[-0,00MI]

(0.2994)

[-0,00001] [-0.00001]

(0,0322)

[-0,00000] [0.wom] [0,00000] [0.000oo] [0,00000]

PANYSE 1.049 I.505

@menk =K-emp!oyd) (0.0001) (0.0004)

[0.01149] [0,00027]

DADANYSE 0.4049 0.6570 1.041 1.0s3

(0.1863) (0.0128)

[0.00826]

(0,0193) (0.0141)

[0,01129] [0,00035] [0,00034]

MOMANYSE 0.9647 1.066 1.329 1.646

(0.0046) (0,0012) (0.1 1s0)

[0,01969]

(0,0467)

[0.01924] [0,0W44] [0.00037]

N (obwmations) 2463 1950 2261 2152 2108 1733 1733 210s

Smple Probability 0.028 0.028 0.030 0.026 0.014 0.0 I7 0.017 0.014

Note Eachequationalsoincludesyeu-specificinterwpls,a8e,agesquarti, nunberofdependellts,spouws'emhgs andindcatorsforregion,SMSAresidenw,and

mtital status.Thespwificationsincolms (l)hrou@ (4)almincludcindicatorsfol-race,numberofsiblings,andeducalionalattainment.

Entiesme&fmtd logitwficienls. Fi@esinpwenlhexs urep-vultlesforsi~ificonceofestimates.Mwginuleffeclsofeachvmiable(evaluatdatsample

mem) me how h brwkeh.

h mlm (5) ti0ugb(8), pments’ as%~ ad wlf-.mplo~enl expeticnw al-emeasurti ia 1966 (the sw of the sample perid).
National Longitudinal Sumeys @LS)

Discussion Paper Seriw

- ___._.m

9241 MichaelR Pergmit

9242

9243

92-04

92-05

92-06

9247

9248

92.09

92-1o

92-11

92-12

92-13

Nomm M. Bradbm

Mtin K Frdel

Re@aId P. B*er

Micbel R Pergtit

Saul Schwanz

Wbefi Hutchens

George Jtibson

A. Colin Cammon

R, Mmk fhim

ThomasMatidy

Hew S. F=ber

Fr~ L. Mott

Pada B&r

Au&eyLight

Mmuefita Ureta

Lisa M. Lynch

Emgelos M. Faltis

H, Ek%eth Peters

he Hill

Jme E. ONeill

Adeen Leibotiti

JacobMex Me=

Linda Wtite

Lee A. Llllard

Joseph G. AJto@i

Thorns A. Dum

A Comparisfin of Computer-Assisted Perso~

Intefiws (CAPD tith Paper-red Penal htertiews @API) in the Natioti Longimditi SWey

of Youth

Dytic Models of the Joint Determination of

Labor Supply md F=ily Swtie

The effects of Unemplopent Compensation

on the UnemplO~ent of YOuti

Evaluating Competing Thmties of Worker

Mobility

Evaluation of the 19S9 CMld-cue Supplemmt

in the National Longitudind Smey of Youth

Gender Differences in the Qtit Behgtior of

Young Workers

The Impad of Private Sector Ttining on Wce

ad Gender Wage DMerentis ad the Cz=r

Patterns of Yowg Workcra

Wsponses of Fade bbor Supply ad Fetility

to the Demographic” Cycle

A Smdy of htercohofl Chmge in Women’s

Work Patterns and Earnings

Women’s Emplopent Dting Pre~nw

ad Folloting Bifi

Work EWerience, Job Tenu~, Job S~mtion,

and Wage Groti

Ftily Backgomd and Labor M&et Outcomes
92-14

92-15

93-16

94-17

94-18

94-19

95-20

95-21

95-22

95-D

95-24

95-25

95-26

95-27

95-28

95-29

George J, Bo~as

StephenG. Bmus

StephenJ. Trejo

James J. H=~n

Stephen V. Cameron

Petti Z, Schwhet

R &k Griti

Thnmas MCurdy

Mm L, G@an

Thomas L. Stetieiel

Au&ey Light

Ctistopher J. Rti

tik Lowe@ein

Jamw Sple@er

Jacob &ex Memm

StephenG.Bron=s

tiol Mwre

Donald O. P~<ons

Donald O. Pamons

Amr Batiel

Nachw Sichem

Au&ey Light

Lawa Brmda

R ~k Griti

Michael R. Pergamit

Thomas Dmm

Drm@as Holti-Eakin

SeM-Selection md Inteti ~~ation in the

Utited Statw

The Detemrimmta mrd Co~uences of Wblic

S@or and Priwk SectorTraiting

Pticipatimr in Low-Wagebbnr ~keta by

Yomg Mm

Retirementin a Family Contem A Stitid Model

for Husbmds ad WIv~

Trmsitions from SchooIto Work: A Smq of

Reseach Utig the Natirmd bngitidimd SWqs

Mgh School Employment Commption or

Investment

Ifiomd Traiting A Rtiew of Etistig Data md

Some New Evidence

Chmacteritig Leme for Matemiw Modehg the

NLSY Data

Employment Continui@ bong New Mothers

Incentive Pay, Ifiormatiow ad Etings Etidace

from the National LongimdinaJ SDV of Yonth

The Evolving Stm@es of Fede Work Activities

Etidence from the Natio@ LongimW SWCYS nf

Wtire Women S.mey, 1967-1989

Povew Dyhcs Among Matue Womeu Etidence

from the Natimml bngimdind SWCYS, 1967-1989

Tcctiologid Chm& mrd the Skill Acquisition of

Yowg Workers

Wgh Schwl Employment

The Effect of Intetiw Length on Atitimr in the

National Longitudid SUWW of Youth

Capiti Mwket Constminta, P=enti Wdth md b

Trmsition to *M-Employment among Men md

Women