Self-Employment, Family Background, and Race
Author(s): Michael Hout and Harvey Rosen
Source: The Journal of Human Resources, Vol. 35, No. 4 (Autumn, 2000), pp. 670-692
Published by: University of Wisconsin Press
Stable URL: http://www.jstor.org/stable/146367
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Self-Employment, Family
Background, and Race
Michael Hout
Harvey Rosen
ABSTRACT
The offspring of self-employed fathers are more likely than others to become self-employed. Thus the historically low rates of self-employment
among African-Americans and Latinos may contribute to their low contemporary rates. National data show that African-Americans and Latinos
whose fathers were self-employed have lower rates of self-employment
than other men whose fathers were not self-employed. Other aspects of
family background explain only a small portion of the self-employment
gap between African-Americans and native-born white ancestry groups.
Male immigrants who have self-employed fathers overseas are no more
likely to be self-employed than other immigrants are.
I. Introduction
Self-employmentrates vary a great deal across Americanancestry groups.In 1990, for example,24 percentof Koreansand 4 percentof AfricanAmericans were self-employed; the economy-wide rate was 10.8 percent (Fairlie
and Meyer 1996). Understanding group differences in self-employment rates is important for several reasons. From the point of view of the economy as a whole, the
entrepreneurshipassociated with self-employment may be linked to the creation of
This work began when the authors were visiting scholars at the Russell Sage Foundation in New York.
We are grateful for the supportive environment that the foundation provided. We thank Lisa Kahraman
for outstanding assistance in preparing the manuscript, and Thomas Dunn, Andrew Greeley, and
Marta Tienda for useful comments. Subsequent work has been supported in part by Princeton's Center
for Economic Policy Studies and Berkeley's Survey Research Center. A version of the paper was presented at the winter meeting of the Methodology Section of the American Sociological Association,
Duke University, 13-15 March 1999 and distributed as NBER working paper no. 7344. The data are
in the public domain. See www. icpsr.umich.edu/contents for instructions on ordering or downloading
the data.
[SubmittedJanuary1998; acceptedJanuary2000]
THE JOURNAL OF HUMAN RESOURCES * XXXV * 4
Hout and Rosen
new jobs and the developmentof new technologies.1It has been suggestedthatthis
"job creation" aspect of self-employmentis particularlyinstrumentalin creating
opportunityin ethnicenclaves(see, for example,WilsonandPortes1980;Waldinger
1986; Loganet al. 1997). More generally,from the point of view of the individual
or his or her ancestrygroup, self-employmenthas traditionallybeen viewed as a
means of gettingaheadin life, socially and politicallyas well as economically.As
Glazerand Moynihan(1970, 30-31) arguein theirclassic work on immigrationin
the United States,
As againstthe worker,each businessmanhad the possibility, slim though it
was, of achievinginfluenceandperhapswealth.The smallbusinessmangenerally had access to thatspecial worldof creditwhich may give him for a while
greaterresourcesthan a job. He learnsaboutcreditand financeand develops
skills that are of value in a complex economy. He learnstoo aboutthe world
of local politics.
Some theories of self-employmentview differencesin attitudestowardrisk as
being the primaryfactor. Self-employmentis inherentlymore risky than wageearning(Hamilton1995), so the risk-averseavoid self-employment(Kihlstromand
Laffont1979).Othertheoriesfocus on differencesin managerialskill acrossindividuals-some peoplehave a comparativeadvantagein runningan enterprise,andthey
arethe ones who areinclinedto go into self-employmentin the firstplace or survive
longer once the businessis launched(Jovanovic1982). A thirdset of theoriesputs
financialconstraintsat the centerof the story(EvansandJovanovic1989). The idea
here is that runningand maintaininga business requireaccess to capital,but for
variousreasons,some individualshave access to capitalmarketsandothersdo not.2
The varioustheoriesare not mutuallyexclusive, nor is thereany reasonto think
that one cause predominates.In a way, all of these theoriesmerelypush back the
question-why do people differin theirattitudestowardrisk or theirabilitiesto run
a businessor in the constraintsthey face? This is whereethnicityandancestryenter
the story. People's ethnic ancestrymay expose them to a variety of culturaland
psychologicalfactorsthat affect risk-takingand managementskills. Ancestrymay
be correlatedwith the constraintsthey face as well. For example,even afterthe end
of slavery, African-Americansfaced severe institutionalconstraintson their selfemployment.Jim Crowlegislationrestrictedthe size and revenuesof black-owned
businessesin the states of the formerConfederacy,Maryland,Delaware,and Missouri from the 1890s to the 1930s (Woodward1955). Race riots in Wilmington,
Delaware(in 1898), andTulsa,Oklahoma(in 1921), wiped out thrivingdistrictsof
black-ownedbusinesses(Oliverand Shapiro1995). Even today,thereis some evidence thatblacksare subjectto discriminationin capitalmarkets(BrowneandTootell 1995).
This focus on ethnicityand race is particularlyrelevantin light of an important
fact:not only is the rateof self-employmentamongAfrican-Americans
low relative
to the rate amongwhites, but it has been that way for decades (Fairlieand Meyer
1. See Davis, Haltiwanger,and Schuh (1993) for a criticaldiscussionof the job creationissue.
2. See Holtz-Eakin,Joulfaian,andRosen(1994) forevidencethataccessto capitalincreasestheprobability
that an individualwill make a transitionfrom wage-earningto self-employment.
671
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The Journal of Human Resources
1997). The low self-employment rate among blacks has long been viewed as a puzzle
by both sociologists and economists: "Negro business did not develop, despite the
fact that business is in America the most effective form of social mobility for those
who meet prejudice" (Glazer and Moynihan 1970, 36).3 Glazer and Moynihan attribute this phenomenon to a legacy of slavery: "Negroes emerging from slavery had
no experience with money, and had no occasion to develop the skill in planning and
foresight that even the smallest businessman must have" (36).
The legacy-of-slavery argument would be of little relevance today were it not for
the strong intergenerational component in self-employment. Parents pass on selfemployment to their offspring (Blau and Duncan 1967; Hout 1984), but if members
of some group have historically been excluded from self-employment (or have chosen to exclude themselves) then the intergenerational chain from self-employed father to self-employed offspring never starts.4Several mechanisms can transmit the
propensity to be self-employed across generations. Self-employed parents may endow their children with human capital that is specific to running an enterprise (Lentz
and Laband 1990). They may provide role models and adopt child-rearing practices
that facilitate self-employment as well (Kerckhoff 1972). Previous work has in fact
documented that children of the self-employed are more likely to be self-employed
themselves (Blau and Duncan 1967; Hout 1984, 1988; Lentz and Laband 1990; Fairlie 1996; and Dunn and Holtz-Eakin 1996). One can imagine that other attributes
of family background could matter as well, but these have not received a great deal
of attention, presumably because of data limitations.5
In a model that focuses on the transmission of self-employment from parents
to offspring, the length of time required to "compensate" for a low initial selfemployment rate depends on what we will call the intergenerational pick-up rate
with respect to self-employment-in other words, the probability that the child of
a self-employed parent will become self-employed him- or herself.6Intergenerational
3. See Meyer(1990) for an econometricanalysisof the differencesin blackand white self-employment
rates,andFairlie(1996) for a studyof racialdifferencesin transitionratesbetweenwage-earningandselfemployment.
4. While in principleeitherparentcan contributeto the offspring'spropensityto be self-employed,we
In thiscontext
focus on therole of fathersbecausewe haveonly limiteddataon mother'sself-employment.
it maybe usefulto noteDunnandHoltz-Eakin's(1996) resultthatthe presenceof a self-employedmother
has no statisticallydiscemableeffect on a son's probabilityof becomingself-employed(net of father's
self-employmentstatus),but it does matterfor daughters.
5. Fairlie(1996) considersthe educationof the father;Dunn and Holtz-Eakin(1996) includethe assets
of the parentsin their analysis.Glazerand Moynihan(1970) speculatethat the prevalenceof femalefamilies might lower rates of self-employment(as well as
headedhouseholdsamongAfrican-American
otherforms of occupationalachievement).
6. To be more precise, the intergenerational
pick-uprate is the probabilityof self-employmentamong
those adultswhose fatherswere self-employed.This retrospectivedefinitionhas severaladvantagesover
a prospectiveone thatdefinesthe pick-uprateas the probabilitythata self-employedpersonwill haveselfemployedoffspring.Mostimportantly,theretrospectivedefinitioncanbe combinedwiththe corresponding
conditionalprobabilityforthosewhosefatherwas not self-employedto arriveatthe cross-sectionalproportion self-employedat any time (Fairlieand Meyer 1996). The retrospectivedefinitionalso avoids the
difficultythatprospectivedefinitionshave adjustingfor the fact that some people have no childrenand
thatparentsdifferin the numberof childrenthattheyhave.Everyonehasjust one father.Somecomplexity
arisesbecausethatfathermay have morethanone job or may not live with all of his childrenall the time
whiletheyaregrowingup. Retrospectiveandprospectivedefinitionssharethe family-relatedcomplexities,
but the retrospectivedefinitionat least solves the initialmatchingproblem.
Hout and Rosen 673
pick-upratesare no morelikely to be the same acrossancestrygroupsthanoverall
self-employmentratesare. For example,to the extentthatthe natureof intrafamily
interactiondiffers across ancestrygroups(some fathersare "involved" with their
childrenandothersmay not be), thenthe father'sself-employmentstatuswill differentiallyaffect his offspring'sstatus.Alternatively,we posit thatthe likelihoodthat
a personsucceeds in self-employmentdependsin parton the humancapitalhe or
she receivesfroma self-employedfather,andthe amountof thathumancapitalmay
differfrom one ancestrygroupto another.For example,fathersin groupsthathave
long traditionsof self-employmentmight have more useful lore to pass on. As far
as we know, however,there are no estimatesof how much ancestrygroupsdiffer
in this crucialparameter.
Moregenerally,in the sociologicalandeconomicliteratures(notto mentionliteraturesin fieldslike developmentalpsychology)thereis evidencesuggestingthatfamily structureaffects educationaland occupationaloutcomes(see Butcherand Case
1994). In this paperwe use a datasourcethatis rich in informationaboutindividuals' family backgroundsto investigate how they affect the probabilityof selfandLatinosstand
employment.In particular,we documenthow African-Americans
out frompersonsof otherancestriesnot only in theirlow ratesof self-employment
but also in their low intergenerational
pick-uprates. We also presentdata on the
roles of immigration,family size, and family structurein self-employment.
SectionII describesin detailourdataset, the GeneralSocial Survey,andprovides
some suggestivetabulationsrelatingto the variationin pick-upratesacrossancestry
groups.The multivariateanalysisin Section III confirmsone of the key findingsof
thesetabulations:The intergenerational
pick-uprateis lowest for African-Americans
andLatinos,intensifyingthe low prevalencewithinthesetwo groupsof self-employmentamongfathers.We also find thatwhile familybackgrounddifferencesexplain
some of the racialgap in self-employmentrates,much of the gap is evidentwithin
family-typecategories.Section IV speculatesa bit furtheron the sourcesof racial
differencesin self-employmentrates,andSectionV concludeswith a briefsummary.
II. The Data
A. GeneralDescription
Our data come from the GeneralSocial Survey (GSS), which consists of face-toface interviewswith a (changing)representativesample of about 1,500 Englishspeakingadults(18 years old and older) living in the United States.The GSS was
conductedby the NationalOpinionResearchCenter(NORC)at the Universityof
Chicago with only a few changes in formatalmost every winter and springfrom
1972 to 1993 (1979, 1981, and 1992 were skippeddue to insufficientfunding);in
1994 the GSS shiftedto a surveyof approximately3,000 personsin even-numbered
yearsonly (see Davis andSmith 1996 for details).The GSS has an averageresponse
rate of 77 percent,higher than most social and political surveys achieve. We use
data from the 1973 through1996 surveys.Blacks were oversampledin 1982 and
1987. We employ samplingweights to adjustfor oversamplingin calculatingdescriptivestatisticsand simulationsbut not for the logistic regressionanalysis.
674
The Journalof HumanResources
An attractivefeatureof the GSS for ourpurposesis its relativelydetailedcharacterizationof familybackground,particularlyfather'semployment.The CurrentPopulationSurveyandthe Census,in contrast,tell us nothingaboutfamilybackground.
The Panel Studyof IncomeDynamicsprovidesfamily backgroundinformationon
the childrenof the membersof the originalsamplefrom the late 1960s, but these
individualsarenot a randomsampleof the workingpopulationin any year.7Similar
limitationsapplyto the variousNationalLongitudinalSurveys,which arecharacterized by age-censoring.In contrast,the GSS includesfather'soccupation,the family's
religion,ethnicancestry,and family structurewhen the individualwas growingup,
interalia. These variablescan be correlatedwith the individual'sself-employment
statusandhis orherfather'sself-employmentstatus.Includingthemin a multivariate
analysis of the propensityto be self-employedwill improveour ability to obtain
consistentestimatesof the determinantsof the intergenerational
pick-uprate.
B. Sample Restrictionsand Measurementof Key Variables
We begin our analysis with informationon men and women between the ages of
25 and64 for the calendaryears 1973 through1996 who hada nonfarmjob at which
they workedat least 15 hours the week before the interview.8The age and hours
restrictionsare imposedto limit attentionto personswho have a significantattachment to the labor force. We classify each individualas a wage earneror selfemployedfrom the answerto the direct question, "Are you self-employedor do
you workfor someoneelse?"9We leave farmersin the descriptivecalculationsbut
deletethemfromlogisticregressionanalysesbecausethe selectioninto self-employment may be significantlydifferentfor farmingthanfor otheroccupations.
A correspondingbatteryof questionsasks, "Whatkind of work did your father
normallydo while you weregrowingup?Washe self-employed
(orfather-substitute)
or did he workfor someoneelse?" Thereis clearlysome ambiguityassociatedwith
these questionsbecause many fatherswill have changedjobs and some will have
movedin andout of self-employment.Interviewerswere instructedto resolvethose
ambiguitiesby saying, "Tell me aboutwhat kind of workhe was doing when you
were 16 yearsold," if the personbeing interviewedmentionedthather or his father
had morethanone job or was self-employedat times but not always.Thusthe selfemployedfatherswe observe were self-employedwhen the individualwho is the
evenin theinitialPSIDsample,therewas notmuchinformationaboutfamilybackground.
7. Furthermore,
For example,questionsaboutfamily structureand father'soccupationare not very detailed.
8. Some variablesarenot availableeveryyearor for some subsamplesin some years.The 1972 GSS did
not ask abouthoursworked,so we cannotuse it. The immigrationquestionswere not askeduntil 1977,
so analysesthatincludeimmigrationvariablesuse datafrom 1977 to 1996.
9. This questionis partof a batterythat asks aboutemployment,occupation,and industry.For persons
with more thanone job (we do not know how manybut we assumethatthereare few), the interviewer
askedfor informationaboutthe mainjob, in otherwords,the one at which the respondentworkedmore
hours.The GSS questionconformsto the self-employmentquestionson the long formsof the 1970, 1980,
and 1990 Censusesandon the CurrentPopulationSurveysup to 1992.Recentchangesin CPSprocedures
probablyproduceslightlylower estimatesof the incidenceof self-employmentthanthe Censusand GSS
questions.
Hout and Rosen
subject of this study was facing important educational decisions and forming ideas
about possible careers.10For about 12 percent of the sample, father's self-employment status was missing because the father did not live with the subject of this study
when that person was 16 years old. Individuals who did not live with their fathers
are retained in the analysis and treated as not having a self-employed father. We
use detailed data on family structure to examine the difference between the impact
of absent fathers and fathers who were present but doing wage and salary work.
The GSS includes ancestry and race questions that allow us to replicate the classifications used in the CPS and Census. We begin with a question similar to the Census's ancestry question, in other words, "From what countries or part of the world
did your ancestors come?" The GSS codes up to three countries of origin (7 percent
of whites do not know enough about their ancestry to name a country). Persons are
asked to choose the one "you feel closest to" (slightly less than half of those who
name more than one can choose the one they feel closest to). We use the single
responses and the "closest" country to code 82 percent of persons; the remainder
are put in a residual category-' not elsewhere classified." The GSS data file identifies 42 countries of origin. For some countries of origin we had enough cases to
assign unique one-nation categories (for example, Ireland and Italy). We followed
two strategies to accommodate other, mostly smaller countries: (1) we combined
small countries with large ones to form cultural affinity groups (for example, we
combined Wales with England, we added Austria and Switzerland to Germany to
form a "German" category, and we combined France and French Canada); (2) we
combined nearby countries to form geographically proximate groups (for example, the Netherlands and Belgium); and (3) we treated the special cases of Englishspeaking Canada and "America" as a separate category (see Lieberson 1985; see
Lieberson and Waters 1993 on the subject of these "un-hyphenated whites").
Of special note are the "African-American," "Latino," and "East Asian" categories, as these are often considered "racial" distinctions. African-Americans are
all persons coded "black" (or equivalent) on the race item, regardless of the countries they named on the ancestry question. Latinos are persons who made any mention of Mexico, Puerto Rico, or the countries that NORC grouped together as "other
Hispanic" as their country of origin, even if it was not the country mentioned as
"closest." We did this to get a Latino category that comes as close as possible to
the Census Bureau's concept of "Hispanic origin." Likewise East Asians are persons
who made any mention of China, Japan, the Philippines, or "other Asia" on the
ancestry question, even if it was not the country mentioned as "closest." We ended
up with 17 ancestry categories, details on their composition are included in Table Al.
The GSS also includes questions about religion-both current religion (if any)
and the religion the person was raised in (if any). In combination with data on race
and ancestry, the religion data allow us to construct a more refined measure of ances10. Althoughit is reasonableto thinkthatretrospectivedatahavemoreerrorthancontemporary
measures,
Bielby, Hauser,and Featherman(1977) found that men underthe age of 65 made no greatererrorsin
reportingtheirfather'soccupationalcharacteristicsthan they made in reportingtheir own occupational
characteristics.
The detailsof responsepatternsdifferfor African-American
andothermen, but the errors
in retrospectivereportsare no greaterthanfor contemporary
reportsfor eithergroup.
675
676
The Journal of Human Resources
Ancestry
eroun
Latino
African American
East Asian
American Indian
Not classified
Irish
All groups
German
Central Southern European
"American"
English / Welsh
Italian
Scandinavian
French
Scottish
Polish / Russian
Dutch / Belgian
Jewish
Leeend
+ All fathers
* Self-employed
fathers
o Employee
fathers
Women
Men
0.O0
0-10
o*O
' 0
o
.
46I
0- *
*4 0
64
04
0g
0%
o0
0
04044
Om4-
0
4. am
4NO
.4.
0
0
4.-
0
_4*
13% 26% 39% 52%
0%
Self-employed
13%
26%
39%
Combined
rate
8%
6%
11%
11%
14%
13%
13%
14%
16%
13%
15%
14%
16%
15%
15%
9%
17%
25%
52%
Self-employed
Figure 1
Rate of Self-Employment by Father's Self-Employment, Gender, and Ancestry:
Employed Persons Who Work 15 or More Hours per Week, 25-64 Years Old,
United States, 1974-96
Note: The overall rate of self-employment is 13 percent. The vertical lines show multiples of that overall
rate. Horizontal gray lines show the 95 percent confidence interval for the series depicting all men and
all women. Source: General Social Survey, 1974-96
try than other commonly used data sources provide. In particular, the answer to the
question "In what religion were you raised?" allows us to distinguish a "Jewish"
ancestry. Persons of Jewish ancestry may have named any country of origin.1 This
eliminates the ambiguity that arises for ethnic ancestry groupings that preclude religious categories (for example, Lieberson and Waters 1988). We experimented with
a number of treatments of this information and found that the ability to identify
people who were raised in the Jewish religion was the single significant piece of
information on religion for the purposes of studying self-employment.12
C. Pick-up Rates by Ancestry and Gender
The probability that a man is self-employed as an adult varies according to whether
his father was self-employed and his ancestry group (see Figure 1). The probability
11. There are not enough observations to study differences among Jews from different countries.
12. Adding other religious distinctions to the final model we present below yielded no significant improvement of fit and did not appreciably change any of the coefficients we present. To the extent to which actual
religious affiliation reflects the orientation to saving and investment known as "the Protestant Ethic," we
find no evidence that it encourages entrepreneurship.
Hout and Rosen
that a womanis self-employedas an adultalso variesby father'sself-employment
and ancestry,but not as much.For each combinationof ancestrygroupand gender,
a solid blackcircle shows the intergenerational
pick-uprate,in otherwords,the rate
of self-employmentamongindividualswhose fatherswere self-employed;an open
circle shows the rateof self-employmentamongindividualswhose fatherswere not
self-employed(or absent).Theplus sign (+) betweenthe solidblackandopencircles
shows the gender-specificself-employmentrate for all persons,regardlessof their
father'sself-employmentstatus.The overall rate in our sample is 13 percent;the
verticallines in the figuremarkoff the multiplesof this average.'3We show the 95
percentconfidenceintervalsaroundthe rate for all personsin each ancestrygroup
with gray bars that branchout to the left and right from the plus sign. For each
ancestrygroup,the self-employmentrate for men and women combinedis printed
in a columnalong the right-handside of the figure.Ancestrygroupsare sortedaccordingto the intergenerational
pick-uprate among men; the groupsat the top of
the charthave the lowest male intergenerational
pick-uprates while the groupsat
the bottomof the charthave the highestrates.
Withfew exceptions,ancestrygroupswith higherintergenerational
pick-uprates
among men have higher self-employmentrates overall. The correlationbetween a
group'smale intergenerational
pick-uprate and its overall self-employmentrate is
0.71; the slope relatingthe male intergenerational
pick-uprate to the overall selfrate
for
an
is
almost
employment
ancestrygroup
unity(b = 0.97). The ratesof selfemploymentamongmen whose fatherswere not self-employedvarymuchless from
groupto groupthan the male intergenerational
pick-uprates do. Indeed,for most
ancestrygroupsself-employmentamongmen whose fatherswere not self-employed
departsonly slightly from the averagerate for all ancestrygroups.Exceptionsto
this generalizationare the very low ratesof self-employmentamongAfricans,East
Asians, and Latinos,and the above-averagerateof self-employmentamongJewish
and Southernor CentralEuropeanmen whose fatherswere not self-employed.
Women are substantiallyless likely than men to be self-employed,as previous
studies have repeatedlyshown. We find that 16 percentof men and 9 percentof
women are self-employed;this seven-pointdifferenceis statisticallysignificantat
all conventionallevels. Women's self-employmentratesvary relativelylittle from
group to group. However, two importantregularitiesthat we noted for men also
occuramongwomen. The daughtersof self-employedfathersare more likely to be
self-employedthanareotherwomenfromthe sameancestrygroup,andthe women's
intergenerational
pick-uprateis as closely correlatedwith overallself-employment
as men's ratesare(r = 0.71, the samefigureas for men). On the otherhand,because
women's intergenerational
pick-up rates vary over a relativelynarrowrange, the
slope relatingthe intergenerational
pick-uprateto the overallself-employmentrate
is only 0.63 for women.Tryingto explainthe narrowrangeof variationin women's
intergenerational
pick-upratesis not likely to be fruitful,so we choose to focus on
men in the rest of this analysis.
13. The 95 percentconfidenceintervalaroundthis averagein a sampleof 15,820 is just ?0.8 percentage
points.
677
678
The Journalof HumanResources
III. Multivariate Analyses
A. Definition of Variables
Figure 1 focuses on father'sself-employmentstatus,ancestry,and genderas variables relatedto an individual'sself-employmentstatus.But previousresearchsuggests that otheraspectsof family backgroundmay be involved.We now turnto a
statisticalanalysisthatallows us to considereducation,immigrationexperience,and
age along the lines suggestedby, for example,Fairlieand Meyer (1996). We also
considerthe individual'sfamilystructurewhilehe was growingup,his contemporary
family structure(marriageand children),and his abilityto speakEnglish (Section
IIID).
The GSS providesa numberof useful variables.Respondentsare asked, "Were
you living with your own motherand fatheraroundthe time you were 16?" If the
responseis "No," she/he is asked,"Withwhomwereyou living aroundthattime?"
Eight specific family configurationsand a residual"other" categoryare recorded.
They are listed in Table 1, which shows the categorieswe use for all variablesand
the proportionaldistributionof cases acrossthose categories.Aboutthree-fourthsof
respondentsgrewup living with theirown motherandfather;most of the otheronefourthwere with "motheronly" or "motherand stepfather."For the father-absent
categories,we do not know father's self-employmentstatus,so it is importantto
controlfor family type in orderto interpretthe resultsfor father'sself-employment
correctly.
Beginning in 1977 the GSS asked respondentswhetherthey were born in the
United States or some othercountry.It also asks whetherone or both parentsare
were bornin the United States.The survey
U.S. born and how many grandparents
also asks where the respondentwas living when he or she was 16 years old. From
this informationwe classify respondentsto the 1977-96 GSSs as immigrants(forsecond
eign born, living abroadat age 16, and at least one foreign-bornparent),14
generation(U.S.-bornwith at least one foreign-bornparentor foreignbornbutliving
in the United States at age 16), thirdgeneration(two U.S.-bornparentsbut one or
and fourthgeneration(two U.S.-bornparentsand
moreforeign-borngrandparents),
no foreign-borngrandparents).
Completingthe rosterof family backgroundvariablesare the numberof siblings
and father'soccupation.The numberof siblings includes all brothersand sisters,
Father'soccupationis codedaccordingto U.S. Censtepsiblings,andhalf siblings.15
sus Bureaustandards(1970 codes were used through1988 and 1980 codes since).
We recoded the detailed occupationinto seven categories:professional,business
related(in other words, managerialand nonretailsales occupations),clerical and
retail,skilledblue collar(foremenandcraftsworkers),semiskilledbluecollar(operatives), unskilledblue collar (laborers,includingfarmlaborers),and farmers.Oneeighthof respondentswere living in a family with no male presentwhen they were
14. The few cases of foreign-bornrespondentswhose parentswere both born in the United States are
coded as thirdor fourthgeneration,dependingon theirgrandparents'
nativity.
15. One man claims to have 68 siblings.This responsehas been verifiedby NORC as the answerthat
the respondentintended,but we deletethe case anywaybecausewe suspectit mighthave undueleverage
over the logistic regressionresults.
Hout and Rosen
Table 1
Percentage Distributions of Categorical Variables and Means and Standard
Deviations of Continuous Variables for Full Sample and Subsample Used in
Logistic Regression Analyses: Persons 25-64 Years Old, Who Worked 15 or
More Hours at Their Main Job
Men with All
CovariatesPresent
1977-96
VariableName
Self-employed
Fatherself-employed
Male
Ethnicity
English/Welsha
Irish
Scottish(or Scots-Irish)
French(or FrenchCanadian)
Italian
Southernor CentralEuropean
Dutch/Belgian
Scandinavian
German(or German-speaking
country)
Polish, Russian,or formerSoviet Union
Jewishb
East Asian
Latino
Native AmericanIndian
AfricanAmericanc
"American"
Not elsewhereclassifiedd
Familystructureat age 16
Father& mothera
Father& stepmother
Mother& stepfather
Fatheronly
Motheronly
Male relative
Femalerelative
Male & female relatives
Otherarrangement
Age
(Standarddeviation)
Siblings
(Standarddeviation)
Father'soccupation
ProfessionalP
Businessrelated
Clerical& retail
Skilledblue collar
Semiskilledblue collar
Unskilledblue collar
Farm
Fatherabsent
Full Sample
1974-96
All
African
American
12.4%
24.2%
53.0%
15.4%
24.1%
100.0%
5.7%
15.5%
100.0%
11.3%
9.8%
2.8%
2.9%
4.7%
3.6%
1.5%
4.0%
16.0%
3.1%
2.4%
1.0%
3.5%
2.7%
11.0%
1.2%
18.4%
11.9%
9.2%
3.0%
2.9%
4.6%
3.4%
1.6%
3.9%
16.8%
3.1%
2.6%
1.0%
3.7%
2.8%
9.1%
1.2%
19.3%
100.0%
-
78.0%
1.7%
3.9%
1.9%
11.0%
0.3%
1.0%
1.9%
0.2%
40.49
10.32
3.60
2.94
58.8%
1.3%
5.1%
2.9%
22.4%
0.4%
4.4%
4.2%
0.5%
39.87
10.46
5.15
3.68
9.9%
17.9%
4.5%
24.5%
15.4%
6.9%
9.1%
11.8%
3.4%
6.6%
1.7%
16.2%
18.1%
15.1%
11.6%
27.2%
679
680
The Journalof HumanResources
Table 1 (continued)
Men with All
CovariatesPresent
1977-96
VariableName
Highestdegree
Less thanhigh school diplomaa
High school diploma
Juniorcollege degree
Four-yearcollege degree
Advanceddegree
Region
Mid-Atlantic(NJ, NY, PA)a
New England(CT, MA, ME, NH, RI, VT)
EasternMidwest(IL, IN, MI, OH, WI)
WesternMidwest(IA, KS, MN, MO, NB, ND, SD)
SouthAtlantic(DC, DE, FL, GA, MD, NC, SC, VA, WV)
EasternSouthCentral(AL, KY, MS, TN)
WesternSouthCentral(AR, LA, OK, TX)
Mountain(AZ, CO, ID, MT, NM, NV, UT, WY)
Pacific(AK, CA, HA, OR, WA)
City size
12 largestSMSAs, centralcity
SMSAs 13-100, centralcity
12 largestSMSAs, suburb
SMSAs 13-100, suburb
Urban,not in 100 largestSMSAs
Rural,not in 100 largestSMSAsa
Generationssince immigration
Immigrant
At least one parentwas immigrant
was immigrant
At least one grandparent
were bornin the UnitedStatesa
Grandparents
Observations
Full Sample
1974-96
15,800
All
African
American
13.8%
50.3%
6.0%
18.9%
11.0%
22.0%
56.3%
7.6%
10.3%
3.8%
15.0%
5.4%
18.8%
7.8%
17.4%
6.6%
9.2%
5.8%
14.1%
14.7%
1.2%
16.3%
4.8%
30.7%
11.9%
9.2%
1.3%
9.8%
7.3%
13.1%
12.8%
17.3%
38.1%
11.3%
20.0%
23.8%
10.1%
13.2%
25.9%
7.0%
6.7%
9.6%
26.6%
57.1%
6,379
7.6%
2.1%
3.8%
86.5%
578
Source:GeneralSocial Survey(full sample, 1974-96; subsample,1977-96).
a Reference
categoryin the logistic regressionanalysis.
b The "Jewish"
categoryis coded fromthe questionaboutreligiousorigins.Personsclassifiedas Jewish
could have given any country-or no country-in responseto the ancestryquestion.
c The "AfricanAmerican"
categoryis coded fromthe questionaboutracialidentity.Personsclassified
as AfricanAmericancouldhave given any country-or no country-in responseto the ancestryquestion.
d Responsesincludedto the "notelsewhereclassified"categoryareof threetypes:the namesof countries
thatdid not fit into any of the categorieslisted here,for example,"India"(all small countriescombined
addupto 8 percentof thiscategory);multipleresponsesthatcouldnotbe resolvedby thefollowupquestion,
"Whichof these countriesdo you feel closer to?" (55 percent);andnonresponsebecausethe respondent
did not know much abouthis or her ancestry(37 percent).
Hout and Rosen 681
16 years old; father'soccupationis missing for them but we include them in the
analysis, coding them as "fatherabsent." We also make use of several standard
demographicvariablesthatmightbe relatedto both self-employmentand ancestry:
age (and age squared),education(five categories),region (the nine U.S. Census
Bureauregions),and type of place of residence(six categories).
Table 1 shows the percentagedistributionof each categoricalvariableand the
mean and standarddeviationfor age and the numberof siblings. The first column
shows the distributionsof self-employment,father'sself-employment,gender,and
ancestryfor the full sample(n = 15,820).The secondcolumnshows the descriptive
statisticsfor all variableswe use in our multivariateanalysisof men's self-employment(n = 6,379). The thirdcolumnshows descriptivestatisticsfor the 578 AfricanAmericanmen includedin the multivariateanalysis;we use these distributionsto
help interpretthe results.
B. StatisticalStrategyand Results
We have two goals for our multivariateanalysis.One is to determinewhetherthe
relationshipbetweenthe intergenerational
pick-uprateandoverallself-employment
for most ancestrygroupssuggestedby Figure 1 standsup to formalanalysis.The
second is to assess whetherthe observeddifferencesamongancestrygroupsmight
actuallyreflectthe more directinfluencesof otherfactors,correlatedwith ancestry,
on self-employmentprobabilities.
Ourmodelingstrategyis informedby a tendencyrevealedin Figure 1: for men
whose fatherswere wage earners,self-employmentratesgenerallydo not differvery
much acrossancestrygroups,but ancestry-groupdifferencesare importantfor men
whose fatherswere self-employed.We begin with a simple model that expresses
this notionin its strictestform.We specifythe probabilitythata mani fromancestry
groupk is self-employedin year t(pikt) as a functionof ancestry-group
membership
(Xik)if his fatherwas self-employedwhile he was growingup (St = 1) but not if
his fatherwas a wage earneror absent(Si = 0). Because the data span 24 years,
we also includetime effects (Ti,= 1 if observationi is fromyear t and0 otherwise):
eYikt
(1)
Pikt
1+
eyikt'
where
(2)
Si) + PkXikSi +
Yikt= P + P1(l
tTit,
whereXil (EnglandandWales) is set as the referenceancestrygroup,Ti77(1977) is
set as the referenceyear, and the Ps and ys are parameters.We choose the logistic
functionfor convenience,but the curvilinearpatternthatcharacterizesthis distribution is evident in the observeddata. As alreadysuggested,Equations(1) and (2)
impose the constraintthat the expectedprobabilityof self-employmentfor a man
whose fatherwas not self-employedis the same for all ancestrygroups.16
The maximumlikelihood estimatesof the 3 parametersare shown in the first
columnof Table2; theirstandarderrorsarein Column2. The Ps varyconsiderably;
16. That expected probability is given by: exp(p3 + PI)/(1 + exp(po + P,)).
Table 2
Logit Analysis of Self-Employment Pick-Up Rates: Men, 25-64 Years Old Who Work 15 or More Ho
United States
Model 1 1974-96
(n = 8,174)
VariableName
j
Ancestry X pick-up rate
Latino
African American
East Asian
Native AmericanIndian
Not elsewhere classified
German
Scandinavian
Irish
"American"
English/Welsh
French
Southern/CentralEuropean
Italian
Dutch/Belgian
Scottish
Polish/Russian
Jewish
Model 2 1974-96
(n = 8,174)
Model 2 1977
(n = 6,383
P
Standard
Error
P
Standard
Error
P3
Sta
E
- 1.233*
-1.185*
-0.850
-0.403
-0.318
-0.297
-0.196
-0.122
-0.076
0.000
0.045
0.078
0.209
0.290
0.394
0.462
0.824*
0.447
0.361
0.556
0.411
0.195
0.196
0.303
0.243
0.532
0.000
0.400
0.321
0.296
0.409
0.316
0.449
0.246
- 1.234*
-1.186*
-0.850
-0.404
-0.318
-0.297
-0.198
-0.124
-0.074
0.000
0.044
0.075
0.210
0.289
0.393
0.464
0.826*
0.447
0.361
0.556
0.411
0.195
0.196
0.303
0.243
0.533
0.000
0.400
0.321
0.296
0.409
0.316
0.449
0.246
-1.013*
-0.973*
-0.603
-0.221
-0.255
-0.302
-0.305
-0.016
-0.717
0.000
0.318
-0.133
0.489
0.241
0.515
0.693
1.061*
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
0
Fathernot self-employed
Group-specificeffects
African American
Latino
Polish/Russian
Southern/CentralEuropean
Jewish
Age
Age squared/1000
Family structureat age 16
Father& stepmother
Mother & stepfather
Fatheronly
Mother only
Male relative
Famale relative
Male & female relatives
Other
Siblings
Father's occupation
Business related
Clerical & retail
Skilled blue collar
Semiskilled blue collar
Unskilled blue collar
Farm
Highest degree
High school diploma
Juniorcollege degree
Four-yearcollege degree
Advanced degree
-0.848*
0.155
-0.787*
0.156
-0.752
0.
-0.968*
-0.299
-0.598*
0.420*
0.931*
0.182
0.216
0.247
0.181
0.233
-1.169*
-0.288
-0.607*
0.660*
0.951*
0.
0.
0.
0.
0.
Table 2 (continued)
Model 1 1974-96
(n = 8,174)
VariableName
Region
New England
Midwest (Eastern)
Midwest (Western)
South Atlantic
South Central(Eastern)
South Central(Western)
Mountain
Pacific
City size
12 largest SMSAs, central city
SMSAs 13-100, centralcity
12 largest SMSAs, suburb
SMSAs 13-100, suburb
Urban,not-in 100 largest SMSAs
Generation
Son of immigrant
Grandsonof immigrant
Grandsonof native
employee
Immigrant*Father
Constant
-2 initial log likelihood
-2 log likelihood
(residualdegrees of freedom)
Psuedo R2
P
-1.321
6,851
6,659
(8,137)
0.028
Standard
Error
0.214
Model 2 1974-96
(n = 8,174)
3
-1.312
6,851
6,593
(8,132)
0.038
Standard
Error
0.215
Note: Eachequationis estimatedwith time effects. The initiallog likelihoodincludesthe time effects.
* Significantat the 0.05 level (two-tailedtest).
Model 2 1977
(n = 6,383
P
-1.454
5,488
5,256
(6,346)
0.042
Sta
E
0
Hout and Rosen 685
a likelihoodratiotest stronglyrejectsthe null hypothesisof no ancestrydifferences
in pick-up rates (L2 = 193 with 16 degrees of freedom).
Ourinspectionof Figure1 suggeststhatAfrican-Americans,
Latinos,andEastern
whose
fathers
were
not
have
lower
self-employment
self-employment
Europeans
thanothergroups,while Jewishmen andmen fromSouthernor CentralEuropehave
higher self-employmentratesthanothers.We add dichotomousvariablesfor these
five groups to the model, thus relaxing the preliminarymodel's assumptionthat
individualswith wage-earning(or absent)fathershave the same self-employment
probabilitiesregardlessof ancestrygroup.Doing so improvesthe fit significantly
(L2 = 66 with 5 degreesof freedom).The estimatesof the relevantparametersunder
this augmentedmodel-Model 2-and theirstandarderrorsare shown in the third
and fourthcolumnsof Table 2.
As notedearlier,ancestrygroupsdifferin a numberof ways thatmaybe correlated
with self-employment.Age, father'soccupation,featuresof the subject'sfamily of
origin (for example,the numberof siblings and the presenceor absenceof one or
bothbiologicalparents),education,region,city size, andimmigrationexperienceare
correlatedwith both ancestryand self-employment.Therefore,while the parameter
estimatesin the augmentedmodel are suggestive,they may be biased. Model 3 in
Table 2 controlsfor all of these potentiallyconfoundingcovariates.Missing data
on the covariatesreducethe numberof cases availablefor analysisby almost2,000
(immigrationstatuswas not askeduntil 1977), so to reassureourselvesthatthe deleted observationsdo not containuniqueinformation,we reestimateModel 2 using
the smallersample.As indicatedin the fifth column of Table 2, the only change
thatwould lead to a differencein the expectedself-employmentrate of as much as
five percentagepoints is the changeof 0.6 for "Americans"-the second smallest
group.17
We entermost of the covariatesas simpleadditiveeffects with one exception:an
interactionbetween the immigrationvariableand father'sself-employmentstatus.
Thisallows for the possibilitythatthe impactof the father'sself-employmentexperience may differ when the experiencetook place abroad.
The covariatesimprovethe fit of the model, as shown by the likelihood ratio
statistic(L2= 169 with 38 degrees of freedom).However,the coefficientsof the
fuller model suggest that the ancestryeffects estimatedfrom the simplerModel 2
are not, for the most part,affectedby the exclusion of several significantfactors.
Eyeballingthe estimatedPk's from the two models reveals no differenceslarge
enoughto be substantivelyinteresting.Somewhatmore systematically,we note that
the rangeof the estimated0k's from Model 3 is 90 percentas greatas the rangeof
the estimatedPk'sfrom Model 2. Thus, while the exclusion of covariatesdoes not
affect the rankorderof ancestrygroupsmuch, it does lead to an overstatementof
the variationamonggroups.
An alternativemodelingstrategywouldbegin with a full set of dichotomousvariablesfor ancestryandthen augmentthe equationwith interactioneffects as needed.
Adding the 11 main effects that would be partof such a specificationto Model 2
resultsin a likelihoodratiotest statisticof just 5.75 with 11 degreesof freedom;the
correspondingcalculationfor Model 3 resultsin a likelihoodratio test statisticof
17. This is the P-hatwith the largeststandarderror.
686
The Journal of Human Resources
5.32 with 11 degrees of freedom. Thus parsimony favors our model over this alternative one.18
C. Interpretation
Father's self-employment is a major factor affecting who becomes self-employed.
Previous research has shown a strong intergenerational continuity in self-employment (for example, Blau and Duncan 1967). To this we have added evidence that the
gap between the offspring of self-employment fathers and employee fathers varies
considerably among major ancestry groups. Indeed we have shown that, with some
exceptions (notably Jewish and African-Americans), ethnic variation in the pick-up
rate accounts for ethnic differences in overall self-employment because the selfemployment of men whose fathers were employees or absent varies little across
ancestry groups.
Self-employment is lowest among African-Americans, Latinos, and Asians. These
three ancestry groups have very low levels of self-employment among the sons of
employees and absent fathers-far below the average for whites-and exceptionally
low pick-up rates. On the other hand, intergenerational persistence is high enough
among men of Scottish, Polish, Russian, and Jewish ancestries that we might think
in terms of family traditions.
Our results reveal the triple disadvantage of Latino and African-American men
with respect to the pursuit of self-employment: (1) The men from these two groups
are less likely to have fathers who were self-employed, putting them at an initial
disadvantage. (2) Among men whose fathers were not self-employed, men from these
groups are the least likely to become self-employed (on their own as it were). (3)
Among men whose fathers were self-employed, blacks and Latinos are significantly
less likely to follow their father into self-employment. This third factor is particularly
telling. The self-employment rate among African-Americans and Latinos whose fathers were self-employed is below that of the average man from a European ancestry
whose father was not self-employed.
Immigrants are another group of people who are of special interest. So far, our
focus has been on the pick-up rates for native sons (and immigrants who came to
the United States before their sixteenth birthday). The results for Model 3 (Table 2)
indicate that a father's self-employment in a foreign country has no effect on the
likelihood of his son being self-employed. The coefficient for the interaction between
immigrating after age 16 and father's self-employment is almost exactly as large as
the main effect of father's self-employment but opposite in sign, thereby canceling out the main effect. Immigration weakens the link between fathers' and sons'
self-employment probabilities, so it is hard to ascribe the difference in ethnic selfemployment rates that we observe to "tradition." With respect to the main effects
of immigration, we find that immigrants and the sons of immigrants are more likely
than third- and fourth-generation Americans to be self-employed.
18. Of course,approachingthe model-buildingprocessin this way wouldnot resultin a modelthatlooks
exactlylike Models2 and 3. Enteringthe maineffects of ancestryandthenselectivelyaddinginteraction
effects may lead some researchersto choose a modelthathas moremaineffects andfewer effects for the
interactionbetweenfather'sself-employmentandancestrythanwe havein Models2 and3. Theadvantage
of our specificationis thatit allows us to focus on differencesin pick-upratesacrossancestrygroups.
Hout and Rosen
We have focused on the interactionof ancestry,father'sself-employment,and a
person'sown self-employment,butModel 3 also containsresultsconcerningseveral
covariatesthat are also of interest.Father'soccupationalcategorysignificantlyaffects the likelihoodof self-employmentfor his son. Men from clerical, retail, and
manualoriginshave significantlylower self-employmentratesthando men whose
fathersheld professional,business, and farmoccupations.Family compositionhas
little net effect on self-employment.Men who grewup in householdsthatcontained
neither parent are less likely to be self-employed than other men, but origins
in mother-headedhouseholdsof varioustypes do not impede the pursuitof selfhouseholds.Family
employmentwhen comparedwith two-parentandfather-headed
size, on the otherhand,is significant;men from large families are 14 percentless
likely thanonly childrento be self-employed.
D. AlternativeSpecifications
Immigrationis the only variablein Model 3 thatinteractswith father'sself-employment.Otherdemographicfactorsmightalso conditionthe pick-uprate.Forexample,
it mightbe reasonableto expectthatone of the advantagesof havinga self-employed
fatherincludesthe possibilityof enteringself-employmentat a youngerage. While
Model 3 implies thatthe sons of self-employedfathersare more likely than other
men to be self-employedat each age, the advantagemay well be bigger at younger
ages than later in the life cycle. However, the data show no evidence of such a
"quick start"effect-interaction termsfor age and age squaredwith father'sselfemploymentresultin a likelihoodratiotest of 2.31 with 2 degreesof freedom,which
is only significantat the 0.32 level. Thus, we cannotrejectthe hypothesisthat the
life-cycle patternof self-employmentis the same for the sons of self-employedand
otherfathers.
Marriageis a majordecision in any man's life. It is likely to have far-reaching
effects on otherchoices, includingthe decisionto seek employmentor to startone's
own business.Fully awareof this,we nonethelessleft maritalstatusout of the canonical specificationbecausethe decisionto becomeself-employedmaybe madejointly
with the decision aboutmarriage;we cannottreatmaritalstatusas exogenouswith
any confidence.Still it seemed worthwhileto see what would happenif we added
maritalstatusto Model3. We foundthatsinglemenhavethelowest self-employment
rates, all else being equal. Men who are now marriedor have been in the past,
regardlessof their currentmaritalstatus,are significantlymore likely than nevermarriedmen to pursueself-employment(the likelihoodratiotest is 4.49 with 4 degrees of freedomfor all maritalstatuscontrasts;4.39 with 1 degreeof freedomfor
the contrastbetweennever marriedand all othermaritalstatuses).
In the same spirit,we checkedto see whetherthe presenceof childrenaffects the
propensityto choose self-employment.Fathers are not more likely to be selfemployed than other men. The numberof childrenenteredin several functional
forms, and a dichotomousdistinctionbetween fathers and other men yielded no
significantresults.
Finally, we addressthe role of languageskills in self-employment.Some have
arguedthat immigrantsfall back on self-employmentbecausetheirlack of facility
withEnglishreducestheirvalueas employees.FairlieandMeyer(1996) investigated
687
688
The Journalof HumanResources
this issue by includingin theirmodel a dichotomousvariableindicatingwhetheror
not the individualreportedthathe had a problemspeakingEnglish;they foundthat
men with a languageproblemare less likely than othermen to be self-employed.
A potentialcriticismof this surprisingresultis theway thatthe CPScollectslanguage
data-it asksrespondentsto reportfor themselves.The GSS providesa moreobjective measure-a ten-wordvocabularytest.19With this bettertest, we unfortunately
neitherreplicateFairlieandMeyer's resultnorreverseit. Addedto ourmodel(which
alreadyincludeseducationalcredentials),score on the vocabularytest has no effect
on self-employment.
IV. More on the Impact of Race
We have shown that African-Americans
come from circumstances
thatdiminishall black men's self-employmentprospects.Researcherssince Glazer
andMoynihanin the early 1960s have speculatedas to whetherthose disadvantages
accountfor the deficitin blacks' self-employment.We find thatthey do so only to
a modestextent.The prevalenceof family disruptionand fathers'low ratesof selfmen. Howemploymenthold down self-employmentfor today'sAfrican-American
ever, adjustingfor the effects of these additionalfactors accountsfor at most 15
men andwhitesof a varietyof ancespercentof the gap betweenAfrican-American
tries. Ourmost importantobservationconcernsthe self-employmentof the sons of
they are less likely to be self-employedthanthe
self-employedAfrican-Americans:
man
of
American
Europeanancestrywhose fatherwas not self-employed.
average
Psychologicalfactors,particularlyattitudestowardrisk,areoften cited to explain
individualdifferencesin the pursuitof self-employment.Althougha carefulexaminationof such theoriesis beyond the scope of this paper,they do not seem to be
a promising way to explain differences in self-employmentbetween AfricanAmericansand othergroups.Ethnographicevidence points to the widespreaduse
of risky entrepreneurial
strategiesby African-Americanhigh-schoolstudentswho
circumstances
aremotivatedto escapethepovertyof theirparents'andgrandparents'
1999).
(Sanchez-Jankowski
Lack of access to capitalalso affects individualsbut fails to accountfor group
differences.It is well documentedthat an increasein an individual'swealthraises
the probabilitythathe or she will become self-employed,ceterisparibus(for example, Evans and Jovanovic 1989 and Holtz-Eakin,Joulfaian,and Rosen 1994). As
have less wealth,on average,thanwhites do, this accountsfor
African-Americans
some of the gapin theirself-employmentrates.Nevertheless,the racialgapbetween
wealthlevels is not largeenoughto explainthe differencesin self-employmentrates
(Meyer1990).In short,neitherriskpreferencesnoropportunitysets seem to provide
a completeexplanationfor racialdifferencesin self-employment.
19. The test was partof the GSS in 1978, 1982, 1984, and 1987-96. Beginningin 1988 only two-thirds
of respondentswere given the test.
Hout and Rosen
V. Conclusion
The primary family factor affecting an individual's self-employment
is the self-employment status of his or her father. Ancestry and immigration also
affect self-employment. Furthermore, they can alter the link between one generation's self-employment and next generation's. These results confirm with direct observations the conjectures of several recent studies of self-employment and extend
the range of variables that have been considered. However, even after taking into
account a rich set of variables relating to family background, we cannot fully "explain" differences in self-employment rates among ancestry groups. In particular
the unexplained difference between African-Americans and most other Americans
is large. The residual differences among ancestry groups are a puzzle that continues
to challenge both sociology and economics.
689
690
The Journal of Human Resources
Appendix
Table Al
Countries or Parts of the World Cited by Persons in Each Ancestry Group
Irish
Ireland
Scottish
Scotland
English/Welsh
England and Wales
Dutch/Belgiam
The Netherlands
Belgium
Scandinavian
Denmark
Norway
Sweden
Finland
German
Germany
Austria
Switzerland
Japan
Philippines
"Other East
Asian"
French
France
Quebec/French Canada
Latinob
Mexico
Puerto Rico
"Other Spanish-speaking"
"American'
"America'
Canada (except Quebec)
Native American
American Indian
Specific tribe
Jewishc
Any country
African-Americand
Any country
Not Elsewhere Classified
Arabic nation
Africa
India
"Other"
Italian
Italy
Southern/Central European
Greece
Spain
Portugal
Czechoslovakia
Hungary
Bulgaria
Romania
Yugoslavia (and constituents)
"Other European"
Polish/Russian
Poland
Russia
Soviet Union
Estonia
Lithuania
East Asiana
China
Note: Countryof originis the only countrylisted for roughlyhalf of respondents.The remainderlisted
more thanone but chose one as the countrythey felt "closest to," or we chose the firstcountrylisted,
except wherenoted.
a
Respondentswho chose at leastone of thesecountriesfirst,second,or thirdwerecodedas "EastAsian."
b
Respondentswho chose at least one of these countriesfirst,second, or thirdwere coded as "Latino"
regardlessof the othercountrieslisted. Of the 557 Latinocases, 358 listed Mexico firstor "closest" and
98 listed PuertoRico firstor "closest." The other 101 madeotherchoices (includingSpain).
c Personswho saidthattheywereraisedJewishor wereJewishat age 16 werecoded "Jewish"regardless
of the countryof ancestralorigin.Of the 255 Jewish cases, 115 listed Russiafirstor "closest" and 41
listed Polandas firstor "closest."
d Personscoded "black" on the race item were coded "African-American"regardlessof countryof
ancestralorigin. Of the 1,312 African-American
cases, 943 listed Africa first or "closest," 150 listed
"America"firstor "closest," 78 listedWest Indiesas firstor "closest," and72 listed "AmericanIndian
or Native American"firstor "closest."
Hout and Rosen
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