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If You Are So Smart, Why Aren’t You

an Entrepreneur? Returns to Cognitive

and Social Ability: Entrepreneurs

Versus Employees

JOOP HARTOG

Faculty of Economics and BusinessUniversity of Amsterdam

Roetersstraat 111018 WB Amsterdam, The Netherlands

Tinbergen Institute, [email protected]

MIRJAM VAN PRAAG

Max Planck InstituteTinbergen Institute, IZA

Amsterdam Center for EntrepreneurshipFaculty of Economics and Business

University of AmsterdamRoetersstraat 11

1018 WB AmsterdamThe Netherlands

[email protected]

JUSTIN VAN DER SLUIS

Amsterdam Center for EntrepreneurshipFaculty of Economics and Business

University of AmsterdamRoetersstraat 11

1018 WB Amsterdam, The [email protected]

How valuable are cognitive and social abilities for entrepreneurs’ relative toemployees’ earnings? We answer three questions: (1) To what extent does acomposite measure of ability affect an entrepreneur’s earnings relative to wagesearned by employees? (2) Do different cognitive abilities (e.g., math ability,

The authors thank Erik Plug, Hans van Ophem, and many seminar and conferenceparticipants for their helpful comments. Moreover, the authors gratefully acknowledgethe very valuable comments and suggestions by two referees and the coeditor. The usualdisclaimer applies. A previous version of this paper circulated under the title “Returns tointelligence: Entrepreneurs versus employees.”

C© 2010 Wiley Periodicals, Inc.Journal of Economics & Management Strategy, Volume 19, Number 4, Winter 2010, 947–989

948 Journal of Economics & Management Strategy

language, or verbal ability) and social ability affect earnings of entrepreneursand employees differently?, and (3) Does the balance in these measured abilitylevels affect an individual’s earnings? Our (difference-of-difference) estimatesof the returns to ability for spells in entrepreneurship versus wage employmentaccount for selectivity into entrepreneurial positions insofar as they aredetermined by fixed individual characteristics. Our robust results providethe following answers to the three questions: General ability has a strongerimpact on entrepreneurial incomes than on wages. Moreover, entrepreneursand employees benefit from different sets of specific abilities: verbal and clericalabilities have a stronger impact on wages, whereas mathematical, social, andtechnical ability are more valuable for entrepreneurs. The balance in the variouskinds of ability also generates a higher income, but only for entrepreneurs: Thisfinding supports Lazear’s Jack-of-all-Trades theory.

1. Introduction

Starting up and running a business, as any labor market activity, allowsan individual to exploit his or her personal characteristics in a particularway. Education, general ability, and specific types of ability may allhave their value in the complex operations involved in entrepreneurialactivities. Many (classic) economists, most notably Marshall, havestressed the relevance of general and specific abilities for succeedingas an entrepreneur.1

Recently, a literature has revived that measures the effects ofcognitive and noncognitive abilities of individuals on their labor mar-ket performance (and other outcome variables).2 Both cognitive andnoncognitive abilities have a marked effect on labor market perfor-mance. However, the evidence is mostly limited to employees. There isa separate but less developed literature for entrepreneurs, and seldomthe twain have met. Bringing them together is interesting for its ownsake and for highlighting any differences between distinct but relatedmarkets: A combined analysis of entrepreneurs and employees enablesa comparison of the relative value of abilities for the performance ofentrepreneurs and employees. The only performance measure available

1. A discussion of the measurement of the returns to education is beyond the scopeof this paper, we only focus on abilities. However, as we shall argue, measuring the effectof abilities on incomes requires acknowledging education as a channel for the effect ofabilities, and we will do so. Van der Sluis, Van Praag, and Van Witteloostuijn (2005) andVan der Sluis and Van Praag (2007) focus on education and show that the returns toeducation are consistently higher for entrepreneurs than for employees. Our results willturn out consistent with these findings.

2. See Section 2. Borghans et al. (2008) note that the contrast between cognitiveand noncognitive ability may have intuitive appeal but “creates the potential for muchconfusion because few aspects of human behavior are devoid of cognition.” Nevertheless,the “contrasting” terminology is widely used.

If You Are So Smart, Why Aren’t You an Entrepreneur? 949

for both groups and thereby allowing such a comparison, albeit withsome limitations (Hamilton, 2000), is the individual’s income.3 Thispaper, therefore, aims at comparing the effects of measured cognitiveand noncognitive abilities on incomes for entrepreneurs versus wageemployees.

In particular, we analyze three questions: (1) To what extent doesa composite measure of ability affect incomes for entrepreneurs andsalaried employees? (2) Do distinct measures of cognitive abilities (e.g.,verbal ability, mathematical ability, technical ability, and administrativeability) and noncognitive (social) ability affect incomes of entrepreneursand employees differently?, and (3), inspired by Lazear’s Jack-of-all-Trades theory (2005) of entrepreneurship, to what extent does thebalance in these measured ability levels affect entrepreneurs’ andemployees’ incomes differently?

To address these novel questions, we incorporate measures ofability into Mincerian income equations using panel data from theNational Longitudinal Survey of Youth 1979–2000. This data set isparticularly apt for the task at hand. It includes a number of distinctmeasures of specific abilities administered at early ages (15–23 yearsold), when respondents have probably not yet been affected by labormarket activities. Moreover, the panel character of the data set allowsus to estimate the differential returns to abilities in entrepreneurshipversus wage employment spells by means of a differences-of-differencesregression approach where we control for individual fixed effects. Thus,we can account for self-selection based on unobserved individual char-acteristics that are time-invariant (see Section 3.2). Finally, survivorshipbias is limited thanks to the (bi)annual (panel) data collections aboutlabor market activities and sources of income in the past period (seeSection 3.1).

Our results indicate markedly different returns to ability forentrepreneurs and employees. General ability has a higher payoff inentrepreneurship. The same individual has a 30% higher return togeneral ability when active as an entrepreneur than when working asan employee. As the measure of general ability is a weighted averageof measured specific abilities, it is interesting to assess which specificabilities render the returns to general ability in an entrepreneurialspell higher than in a spell in wage employment. As it turns out, itis, in particular, the science-oriented part of the set of abilities thatgenerates higher returns. Especially technical and, to a lesser extent,mathematical ability are more lucrative in entrepreneurship than in

3. Income is a commonly used performance measure for entrepreneurs, see themeta-analysis by Van der Sluis et al. (2008). The meta-analysis further shows a closecorrespondence between (levels and determinants of) income and other performancemeasures for entrepreneurs, such as profit and survival.


wage employment. In addition, social ability benefits entrepreneurialincomes more than wages. The other measured abilities, that is, verbalability and, in particular, clerical ability have better payoffs as anemployee. The third analysis in this paper assesses the validity ofLazear’s Jack-of-all-Trades theory. In support of this, we find that amore balanced portfolio of individual ability levels boosts earnings asan entrepreneur, while it leaves earnings as an employee unaffected.

The results, which survive various robustness checks, suggestthat, if occupational choices were based on expected earnings differ-entials, higher levels of general, math, technical and social abilities,and more balanced portfolio’s of abilities would all favor the choicefor entrepreneurship, whereas higher levels of clerical and verbalabilities would stimulate the choice for wage employment. However,we observe a discrepancy between the drivers of occupational choiceand the determinants of the premium income as an entrepreneurversus a wage employee. This observed discrepancy is in line withthe evidence collected in support of the “returns to entrepreneurshippuzzle.” Despite longer working hours and more variable and oftenlower incomes, people choose to become and remain entrepreneurs andturn out more satisfied as such (Benz and Frey, 2008; Blanchflower andOswald, 2008; Hamilton, 2000; Hyytinen et al., 2008; Parker, 2004; VanPraag and Versloot, 2007). Entrepreneurship choices are not primarilydriven by income maximization.

Our study fits neatly in the empirical literature of occupationalchoices between entrepreneurship and wage employment and thedrivers of performance in each of these “occupations” (e.g., Parker,2004 for an overview). Many studies have focused on the effects ofhuman capital (Van Der Sluis et al., 2008), most notably the levels ofeducation and experience or the balance in the portfolio’s of these (e.g.,Lazear, 2005). To the best of our knowledge a systematic study of thereturns to (the levels of and balance in) general and specific abilities inentrepreneurship vis-a-vis wage employment is novel.

Some limitations pertain to our study. We consider the self-employed and owner/managers of incorporated firms as the empiricalequivalent of the entrepreneur.4 We acknowledge the limitations ofthis definition, also including, for instance, independent shopkeepersand bookkeepers, in the set of entrepreneurs. Moreover, not all en-trepreneurial activities take place in (newly founded) firms, initiatedby their founders. Entrepreneurship in general refers to a type of

4. Thus, entrepreneurs in our sample are not necessarily founders, they may as wellhave bought an existing business. We presume that the majority will be founders, however.Parker and Van Praag (2010) calculate that 83% of the entrepreneurs in their representativeDutch sample have started up a firm whereas only 17% acquired their entrepreneurialpositions through takeover of a (family) firm.


behavior: (pro-)active, innovative, dynamic. In this sense, one canbe entrepreneurial both as an employee and as a business owner(Parker, 2004, 2010). Entrepreneurial behavior also implies choosingone’s own actions, of acting on one’s own account and responsibility.This dimension is usually captured in empirical studies by identifyingbusiness owners (self-employed and owner/managers of incorporatedfirms) as entrepreneurs. We define entrepreneurs accordingly.

The data set poses some other unavoidable challenges. For in-stance, ability measures administered at different ages and educationlevels within this age range are incomparable (Heckman et al., 2006).Age affects measured ability, whereas the causality of the relationshipbetween education and measured ability goes both ways: “schoolingcausing test scores and test scores causing schooling” (Hansen et al.,2004, p. 40). Therefore, we develop and employ two sets of abilitymeasures. Both control for the effect of age at measurement, whereasone does not control for the effect of education at measurement and theother does. The resulting estimates render upper and lower bounds ofthe returns to true ability, see Section 3.

Moreover, based on related studies, albeit for employees only,we acknowledge that separating the effect of ability on income fromeducation effects is hard (Roberts et al., 2000; Cawley et al., 2001;Tobias, 2003). Another limitation is that there is no way of ensuringthe comparability of the measurement of entrepreneurial and wageincomes (see Fairlie, 2005; Parker, 2004). In this respect, our study isno exception in the empirical literature, and it will be problematic onlyinsofar as the possibly distinct ways of measurement affect the returnsto ability. Moreover, little theory has been developed to guide empiricalanalysis of the value of abilities for entrepreneurs relative to employeesand we do not contribute to a theoretical perspective ourselves.5 Theonly exception is Lazear (2005) on the relative value of a balanced setof abilities for entrepreneurs vis-a-vis employees. Although we thusbelieve that our approach has some strengths and certainly novelty, itis not without limitations.

The remainder of this text is structured as follows. Section 2discusses the measurement of ability and its previously established—or sometimes just postulated—role for labor market outcomes of wageemployees and entrepreneurs. Section 3 discusses the data and how weemploy them for the analysis. In Section 4, we present the results of theempirical analysis. Section 5 concludes.

5. The most general model addressing the relevance of abilities for economic perfor-mance is the matching or assignment model, but it does not explicitly distinguish betweenentrepreneurs and employees (see the survey in Hartog, 2001).


2. Abilities

Since the beginning of the measurement of intelligence, psychologistshave always been divided on the nature of cognitive ability. Alfred Binet,who developed the first instrument to measure differences in childintelligence, assumed that intelligence was essentially unitary, that is,intelligence is one overriding quality that helps an individual deal withthe environment (Binet and Simon, 1911). Thorndike (1904) opposed theidea of one overriding factor and acknowledged the existence of specificabilities only. Besides these two extreme viewpoints, two early streamsacknowledge both. Spearman (1904) concludes that intelligence can bedivided into a general factor and specific factors of which one or morehave an additional influence on the ability to perform specific activities.Thurstone and Thurstone (1941) and Carrol (1993) represent the secondstream by positing that general ability is a (linear) combination ofvarious specific abilities.

We take an eclectic approach and, driven by the data, followThurstone and Thurstone (and Carrol) by assuming that general abilityis a weighted sum of the individual’s scores on specific abilities. Besidesspecific cognitive abilities, we analyze the (additional) effect of specificnoncognitive abilities, inspired by a recent stream of research (e.g.,Borghans et al., 2008; Heckman and Rubinstein, 2001; Heckman et al.,2006; Mueller and Plug, 2006).

2.1 General Ability

Economists and psychologists have shown ample evidence that general(cognitive) ability affects economic and social outcomes markedly (e.g.,Borghans et al., 2008; Gottfredson, 2002; Heckman et al., 2006; Hernsteinand Murray, 1994; Cawley et al., 2001).6

As noted in the introduction, there is no systematic empiricalevidence on the relative role of general ability for the labor marketoutcomes of entrepreneurs vis-a-vis employees.7 The claims by classiceconomists that general and specific abilities are required for successfulentrepreneurship have thus not been tested, as yet. Most notably,Marshall claimed, in 1890, that general ability and intelligence “arerequired to enable one to attain great success in any pursuit andespecially in business.” Marshall defined general ability as:

6. Cawley et al. analyze the same data set as we do, but only consider wage employees.7. Van Praag and Cramer (2001) find a significant positive effect of general intelligence

on the performance of entrepreneurs (measured by venture size). Their study allowsno comparison between entrepreneurs and employees. De Wit and Van Winden (1989)estimate an insignificant effect of general intelligence on income for both entrepreneursand employees.


To be able to bear in mind many things at a time, tohave everything ready when wanted, to act promptly andshow resource when anything goes wrong, to accommodateoneself quickly to changes, to be steady and trustworthy,to have always a reserve of force . . . Marshall (1890, 1930,pp. 206–207).

This definition is remarkably similar to the definition of intelligence(or cognitive ability) proposed by an official taskforce of the AmericanPsychological Association (APA): the “ability to understand complexideas, to adapt effectively to the environment, to learn from experience,to engage in various forms of reasoning, to overcome obstacles by takingthought” (Neisser et al., 1996, p. 77 [taken from Borghans et al., 2008]).We will test Marshall’s claim that general ability is required for success,especially in business.

Horn and Cattell (1966) distinguish, based on their earlier work,between fluid and crystallized general intelligence. Fluid intelligenceis abstract, adaptive intelligence used for solving new problems, andmeasured by tests that minimize the role of cultural knowledge. Crys-tallized intelligence, on the other hand, that has an applied character,is the ability to use skills and knowledge and is measured by teststhat maximize the role of cultural knowledge. Roberts et al. (2000)conclude that the APA-based construct of general intelligence thatwe will use, that is, the Armed Services Vocational Aptitude Battery(ASVAB), coincides with crystallized rather than fluid intelligence, thusreflecting acculturated learning. It is hard to differentiate this kind ofgeneral ability from the effects of an individual’s acquired education(Roberts et al., 2000; Cawley et al., 2001 and Tobias, 2003) because“Measured cognitive ability and schooling are so highly correlated thatone cannot separate their effects without imposing strong, arbitraryparametric structure in estimation which, when tested, is rejected by thedata.” (Cawley et al., 2001, p. 419). We shall thus pay careful attentionto this differentiation in our empirical approach.

2.2 Specific Abilities

As the assignment literature stresses, jobs are different sets of ac-tivities that provide opportunities for exploiting particular abilities.Entrepreneurship forms no exception. Among economists, Marshallwas the first who stressed the importance of various specialized abilitiesfor achieving successful entrepreneurship. The recent managementliterature has pursued the idea that certain (noncognitive) abilities (e.g.,social ability) are relatively important for entrepreneurs (Baron, 2000;


Baron and Markman, 2003; Hmieleski and Ensley, 2004). However, littleempirical evidence has supported such ideas so far.8

From the NLSY79, we extract five specific and distinct abilities, seeSection 3: (i) Verbal ability; the knowledge to understand and processwritten material; (ii) Mathematical ability; the knowledge to performmathematical calculations and logical thinking; (iii) Technical ability; theability to understand physical and mechanical principles; (iv) Clericalability or coding speed; the ability to process information quickly; and,finally (v) Social ability; the ability to form social contacts. The effects ofall these abilities have been studied in various contexts (but not in theirrelative value for entrepreneurs and employees).

To date, the results on verbal ability have been ambiguous. Verbalability is reported to have no value (Paglin and Rufolo, 1990; Dougherty,2000), a negative impact (Bishop, 1991a), and sometimes a positiveeffect on labor market outcomes (Hause, 1972). Nonlinearities may be atplay here (Dougherty, 2000; McIntosh and Vignoles, 2001). Mathematicalability has received most attention from researchers and returns aremostly found to be significantly positive (Taubman and Wales, 1974;Willis and Rosen, 1979; Paglin and Rufolo, 1990; Murnane et al., 1995;McIntosh and Vignoles, 2001). A minority of studies reports an insignifi-cant or even negative return to math ability (Hause, 1972; Bishop, 1991a).Evidence on a positive impact of technical ability on income comes fromBlackburn and Neumark (1993), but only for employees. Clerical abilityhas almost entirely been neglected. Exceptions are Bishop (1991a) andMurnane et al. (2001) who used the NLSY79 to find that clerical abilityenhances employees’ performance. The last type of ability we consider isnoncognitive, that is, social ability. As Heckman and Rubinstein (2001)claim: “No single factor has yet emerged to date in the literature onnoncognitive skills, and it is unlikely that one will ever be found, giventhe diversity of traits subsumed under the category of noncognitiveskills.” We pick social ability as it is receiving increasing attention inentrepreneurship research. Various studies have shown that the abilityto disentangle patterns of social relationships and deal with socialrelationships accordingly has a positive influence on entrepreneurs’performance (Hartog, 1980; Baron, 2000; Wong and Law, 2002; Baron andMarkman, 2003). Baron and Markman (2003) suggest that social ability ismore important for the performance of entrepreneurs than of employeesbecause entrepreneurs must interact with many different persons insideand outside the firm in environments that are often unstructured anduncertain. They find indeed that social perception, adaptability and

8. One reason for this is probably the lack of data. Most representative data sets donot include any measures of specific abilities and there are few data sets that includeboth entrepreneurs and employees as well as comparable measures of their labor marketperformance.


expressiveness are important determinants of entrepreneurial perfor-mance. Their analysis excludes employees.

2.3 Balanced Abilities

Recently, Lazear (2005) has developed a theory proposing that individ-uals with a balanced set of competencies across different fields, thatis, “Jacks-of-All-Trades” (JATs), are more apt for entrepreneurship thanthose who have unbalanced sets of competencies, that is, specialists.In this view, employees may be specialists, but entrepreneurs require abroad set of competencies and are as strong as the level of their weakestskill.9 Employees are the pawns in the division of labor, employersorganize this division.

Empirical evidence supporting this theory has been provided byLazear himself (2005), and Wagner (2003) and Silva (2007):10 JATs havea higher probability of becoming an entrepreneur. “JAT” measures arebased on individuals’ choices of schooling curriculum and the varietyof their job experience. Of course, curriculum and job variety may beendogenous to the decision to become an entrepreneur.

We modify Lazear’s test in three ways. First, we use an alternativemeasure of JAT. We use the balance in an individual’s scores across thefive measures of specific abilities, measured at a relatively young age.This measure is probably not influenced by the anticipated decision tobecome an entrepreneur or by the anticipated relative earnings as such,and thereby does not suffer from endogeneity and unclear causality.Second, we do not focus on the selection into entrepreneurship, but ratheron the performance of entrepreneurs relative to employees. The JATtheory states that JATs have a comparative advantage as entrepreneurs.A relevant way of testing this is measuring whether being a JAT hasa more positive effect on the performance of entrepreneurs than ofemployees. Third, we study the JAT–performance relationship acrossthe entire schooling distribution, unlike Lazear and Silva who bothstudy samples from the top part of the schooling distribution.

3. Data and Methodology

3.1 Data

Empirical answers to our central questions are obtained by incor-porating measures of ability into Mincerian income equations usingpanel data from the (representative part of) the National LongitudinalSurvey of Youth (NLSY) 1979–2000 consisting of 6,111 individuals

9. Team entrepreneurship is an exception.10. Astebro and Thompson (2007) test the theory and report no support.


aged between 14 and 22 years in 1979.11 They have been interviewedannually up to 1994, and since then on a biannual basis. The maximumnumber of observations per individual is 19. Within each observed year,our sample selection includes all persons who are entrepreneurs oremployees (defined below), while excluding students and people whoare unemployed or otherwise not working. Given this selection criterionand omitting missing person–year observations, the resulting sampleincludes, on average and per annum, 4,500 entrepreneurs/employees,leading to a total number of person–year observations of almost 50,000.The average number of year-observations per individual is 11. In whatfollows, we first define the key variables, that is, our measures ofability levels and dispersion, occupational status, and incomes. We thenpresent the descriptive statistics of both the key and control variablesand discuss the empirical methodology.

3.1.1 Abilities Measured

The NLSY contains information on cognitive test scores derived fromthe Armed Services Vocational Aptitude Battery (ASVAB) administeredin 1980 at age 15–23. The ASVAB is a test developed by the U.S.Department of Defense in the 1960s and used for recruiting purposes. Itwas added to the NLSY questionnaire with the purpose of generatinga benchmark intelligence measure representative of the total USApopulation. The measure correlates strongly with other intelligencetests that are frequently used, such as the Otis-Lennon Mental Abilitytest and the Lorge–Thorndike Intelligence Test (Frey and Detterman,2003). The ASVAB is highly rated among vocational psychologists andcounselors. Ryan Krane and Tirre (2004, p. 346) write: “The ASVABis distinguished by superior norms, a thorough investigation of testfairness and unsurpassed criterion related validity data.” Bishop (1991b,p. 5) cites an authority on educational and psychological testing to notethat data from the NLSY responses to the ASVAB are “ . . .free from majordefects such as high levels of guessing or carelessness, inappropriatelevels of difficulty, cultural test-question bias, and inconsistencies in testadministration procedures.” Ryan Krane and Tirre also note that “. . .factor analysis of an earlier form of ASVAB suggests that it measuresgeneral cognitive ability, a verbal-mathematical ability, clerical speedand technical knowledge” and that it is “heavily g-saturated.” Asdiscussed, Roberts et al. (2000) conclude that this g is mostly crystallizedability.

Our set of specific abilities consists of four of the ten measuresof cognitive abilities included in the ASVAB: (i) language or verbalability measured by “paragraph comprehension”; (ii) mathematical

11. This is the original sample of 12,686 individuals minus the supplementary militaryand minority samples.


ability measured by “mathematical knowledge”; (iii) technical abilitymeasured by “mechanical comprehension”; and (iv) clerical abilitymeasured by “coding speed.”12 Aiming at including a varied set ofabilities into the analysis, these four measures have been selected out ofthe ten available measures such that a reasonable number of measurescan be used with minimal correlation levels between each of them. Theresulting upper limit on correlation is 0.60 (The correlation matrix isavailable upon request from the authors).

The noncognitive specific ability measure we use, social ability,is formed by a measure of sociability at age six, measured by recall inthe NLSY in 1980. Respondents were asked, “Thinking of yourself whenyou were 6 years old, would you describe yourself as: (1) extremely shy;(2) somewhat shy; (3) somewhat outgoing; or (4) extremely outgoing?”13

This leaves us with five specific ability measures in total.As was discussed in the introduction, ability measures taken at

different ages and education levels within this age range are incompa-rable (Heckman et al., 2006). Age affects measured ability, whereas thecausality of the relationship between education and measured abilityis: “schooling causing test scores and test scores causing schooling”(Hansen et al., 2004, p. 40). Therefore, we develop two sets of the abilitymeasures. The first set is calculated as the residual when regressingeach measure on a set of age dummies in 1980 at the time the testwas taken. We refer to this as “ability | age.” The second set is formedby the residuals when regressing each measure on a set of age andeducation dummies in the year 1980 (cf. Blackburn and Neumark, 1993),“ability | age + schooling.” Both sets of (corrected) ability measures are nottruly exogenous (innate endowments) because they are clearly affectedby childhood rearing. But they are unlikely to be affected by futureentrepreneurship status or prospects. The correlation levels betweenthese five used measures are shown in Table I, both without (Panel A)and with (Panel B) correction for education.

A composite score of general ability is constructed from the(corrected) ASVAB sections including social ability by means of factoranalysis (as in Cawley et al., 2001). Again, two scores are calculated,one based on ability | age and the other on ability | age + schooling.Table I shows the correlation between these composite measures ofgeneral ability and the specific ability measures. The correlation with

12. Other measured abilities in the ASVAB are: (v) general science, (vi) arithmetic rea-soning, (vii) word knowledge, (viii) numerical operations, (ix) auto and shop information,and (x) electronic information.

13. The distribution of answers over these categories is 19, 43, 26, and 12%, respec-tively. Borghans et al. (2005) use the same sociability measure from the NLSY whenstudying determinants of task assignment and the effect of tasks involving personalinteraction on wages.


Table I.

Correlations Between the Used Measures for

General Ability, Specific Abilities, Spread in

Abilities and Education

Correlation Coefficients 1. 2. 3. 4. 5. 6. 7. 8.

Panel A: Ability measures without controls for education level at the time of the test(1980)

1. General ability 1.0002. Verbal ability 0.856 1.0003. Math ability 0.861 0.664 1.0004. Technical ability 0.748 0.554 0.607 1.0005. Clerical ability 0.683 0.559 0.523 0.336 1.0006. Social ability 0.154 0.086 0.064 0.052 0.057 1.0007. Spread in abilities 0.022 0.021 0.004 0.021 0.028 0.002 1.0008. Education 0.582 0.487 0.616 0.349 0.382 0.085 0.000 1.000

Panel B: Ability measures with controls for education level at the time of the test (1980)

1. General ability 1.0002. Verbal ability 0.839 1.0003. Math ability 0.857 0.643 1.0004. Technical ability 0.702 0.494 0.557 1.0005. Clerical ability 0.633 0.506 0.487 0.235 1.0006. Social ability 0.151 0.081 0.056 0.053 0.047 1.0007. Spread in abilities 0.016 −0.001 0.016 0.011 0.035 −0.041 1.0008. Education 0.420 0.331 0.470 0.267 0.212 0.084 0.005 1.000

Notes: The correlation levels have been calculated while using 1 year of observations.

social ability is low and explains why the results from our analysesremain the same when excluding social ability from the compositemeasure.14 The composite score approximates the standard ArmedForces Qualifications Test (AFQT) score that is included in the NLSY.The results are the same when using that score.15

To test Lazear’s JAT theory, a measure of the balance in the specificmeasured ability levels is required. The coefficient of variation of theindividual scores on the five corrected measures of specific abilities willserve as an inverse measure of balance.16 Unlike Lazear, we do not use

14. These results are not shown and can be obtained from the authors.15. “The AFQT is a general measure of trainability and a primary criterion of eligibility

for service in the armed forces. It has been used extensively as a measure of cognitive skillsin the literature” (Heckman et al., 2006, p. 415). The results upon replacing the ASVABbased measure of general ability with the AFQT score can be obtained from the authors.

16. The balance in abilities is possibly also (negatively) affected by an individual’sage and education at the time of the test. However, based on insignificant correlation


the variance as a measure of spread because it is a function of the meansof the specific ability measures. Table I shows indeed that the correlationbetween the measure of spread, based on the coefficient of variation,and all ability scores is low.

Finally, to facilitate interpretation of the regression coefficients,we have standardized all ability variables (including the coefficientof variation): we subtracted the mean and divided by the standarddeviation. Panel A of Table II shows the descriptive statistics of thetwo sets of standardized ability measures, separately for spells inentrepreneurship and paid employment. The different average scoresof the two groups and the issue of selectivity will be discussed inSection 3.2 along with the rationale of using these two “corrected” setsof ability measures.

3.1.2 Occupational Status Measured

As noted in the Introduction, we define entrepreneurs conventionally aslabor market participants whose main occupation is in self-employmentor who are owner–director of an incorporated business. As usual,farmers are excluded from the sample of entrepreneurs. Furthermore,in line with common practice (Fairlie, 2005), we exclude “hobby”entrepreneurs from the sample by using a lower boundary of 300 hoursper year worked as an entrepreneur.17 An employee is defined as aperson whose main occupation is a salaried job.

Occupational positions are administered at each interview. Allentrepreneurship spells of at least six months have been recordedin case they were the main labor market activity of the respondent.Table II shows that we observe 3,000 entrepreneur spells in a totalof 50,000 spells. On average, at any moment, 6% of the sample is anentrepreneur.18 Nine hundred and forty-two individuals are observedin both entrepreneurship and in paid employment spells. Among their15,749 spells, a quarter has been in entrepreneurship. These numberswill be relevant for the diff-of-diff analysis, that will effectively useonly these “mixed”: observations to measure the differential returns tohigh/low ability levels in entrepreneurship versus wage employment(see Section 3.2). Moreover, our sample of entrepreneurs does not suffermuch from survivorship bias that plagues single cross-section samples:Returns to ability will not pertain to surviving entrepreneurs only.

coefficients between the measure of spread and respondents’ ages and education levelsat the time of the test, we conclude no correction is required.

17. We tested whether the results in Section 4 are sensitive to increasing this number.Similar results are obtained when increasing the threshold number of hours to 500, 800and 1,200, respectively.

18. The percentage is below the national average as we study a relatively young cohort(see Fairlie, 2005).


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labi

lity

0.22

410.

9914

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146

0.99

880.

2025

0.97

89−0

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20.

9999

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leri

cala

bilit

y−0

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80.

9429

0.00

231.

0036

−0.0

519

0.94

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0034

1.00

366.

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alab

ility

0.11

740.

9991

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077

0.99

960.

1192

1.00

06−0

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80.

9995

7.Sp

read

inab

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es−0

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90.

8007

0.00

031.

0116

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61.

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150.

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rly

inco

me)

2.16

511.

0135

2.10

920.

6458

2.26

282.

0882

Con

tinu

ed


Tab

le

II.

Co

ntin

ued

Abi

lity

Mea

sure

sW

itho

utE

duc

atio

nC

ontr

ols

Abi

lity

Mea

sure

sw

ith

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ucat

ion

Con

trol

s

Ent

r=

1(n

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052)

Ent

r=

0(n

=46

,713

)E

ntr=

1(n

=3,

026)

Ent

r=

0(n

=46

,272

)

Mea

nSt

dD

evM

ean

Std

Dev

Mea

nSt

dD

evM

ean

Std

Dev

Pane

lC:C

ontr

olva

riab

les

1.E

duc

atio

n(i

nye

ars)

13.1

261

2.43

5513

.071

42.

3369

2.E

duc

atio

nfa

ther

(in

year

s)12

.324

93.

2070

11.8

252

3.34

213.

Ed

ucat

ion

mot

her

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62.

2579

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020

2.43

584.

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e(d

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3.1.3 Incomes Measured

We use incomes as an indicator of labor market performance. It is theonly indicator that is (to some extent) comparable for entrepreneurs andemployees; measures like company growth or supervisor ratings cannotbe used. Gross hourly income is used as the performance measure forboth groups and is constructed as the average annual total earnings(from wage and business income, see Fairlie (2005)), divided by thenumber of hours worked in that year. The second panel of Table IIshows the income statistics of entrepreneurs and employees separately.Entrepreneurs have higher incomes (in line with Fairlie, 2005), but themagnitude depends strongly on the statistic.19 The mean is 45% higher,the median is 19% higher but the mean of the logs is less than 6% higher.This signals markedly different distributions; indeed the variance issubstantially higher for entrepreneurs (almost four times for hourlyearnings and 2.5 times for the log of hourly earnings), though both havesimilar extents of (positive) skewness.

Parker (2004) documents factors limiting the comparability ofentrepreneurs’ incomes to wages. For instance, the self-employed havemore opportunity to underreport (tax) income (Levitt and Dubner, 2006,p. 237); there may be failure to deal properly with negative incomesand “top-coding,” with employee fringe benefits unavailable for en-trepreneurs. Moreover, entrepreneurs’ incomes may include returns tocapital besides returns to labor. For all these reasons, income levels ofentrepreneurs may only be compared with wages with great caution.

However, the extent to which all such biases affect the marginalreturns to regressors, such as measured ability is probably limited. In anearlier study, Van der Sluis et al. (2005) explicitly evaluate the presenceand effect of several of the potential problems of the income measurefor entrepreneurs mentioned by Parker (2004) and Fairly (2005). Theyconclude that, based on the NLSY79, the returns to education forentrepreneurs (relative to employees) are robust under correctionsfor possible differential underreporting of income, entrepreneurs er-roneously including capital income, possible risk premiums includedin entrepreneurs’ incomes and differences in the incidence of part-time work. Moreover, Van der Sluis and Van Praag (2007), analyzethe relative returns to education upon the inclusion and exclusion ofnegative incomes.

Based on this, we are rather confident that regression coeffi-cients on ability measures in income equations are comparable for

19. As will become clear when discussing the regression results in Section 4, theincome premium for entrepreneurs turns negative when controlling for individual char-acteristics such as education and abilities, cohort effects, age effects and macroeconomiccircumstances. This is a common finding for the US (Parker, 2004).


entrepreneurs and employees. Any comparison of the income levels ofentrepreneurs versus employees will be interpreted with great caution.

3.1.4 Control Variables

Panel C of Table II shows the statistics of the control variables weuse. Schooling is measured as the number of completed years ofeducation (with a topcode of 20 years of schooling). Parental educationlevels are measured in the same fashion. Dummies are included forgender, marital status, geographic location in the United States, healthconditions, and race. Moreover, each income equation includes a setof transformed year, birth year, and age dummies according to themethod proposed by Deaton (2000) such that estimates are obtainedwhile controlling for cohort effects, age effects, and macroeconomiccircumstances.

Panel D of Table II shows the distributions of entrepreneur andemployee spells over industries. As is well documented there aremarked differences between the groups in terms of their distributionacross industries; entrepreneurs are overrepresented in constructionand various kinds of services, whereas employees are more active in,for instance, manufacturing (Parker, 2004). The basic specifications willexclude industry dummies as they are endogenous (see also Fairlie andRobb, 2008) and likely to bias the effects of ability on income.20 We shallonly add industry dummies to the set of controls included in our incomeregressions as a robust check.

3.2 Empirical Methodology

To answer the three questions posed, the estimation of income equationsis required where we measure the returns to general ability, specificabilities and ability spread for entrepreneurs relative to employees. Inwhat follows, we first discuss how we deal with the issue of separatingthe effects of ability and education. Second, we elaborate on ourapproach to dealing with selectivity. We then present the econometricspecifications that we will employ.

3.2.1 Separating the Effects of Ability and Education

Separating the effects of education and ability on incomes is a difficultempirical matter (Roberts et al., 2000; Cawley et al., 2001; Hansen et al.,2004; Heckman et al., 2006). In particular, because ASVAB is said tomeasure mostly acculturated learning (crystallized intelligence). The

20. For example, certain (knowledge intensive) industries can be accessed only withsuperior levels of ability and these abilities will pay off accordingly, but this may notbecome evident in the within-industry distribution of incomes (where most people willbe high ability/high income).


first decision is whether to include education levels (S) in incomeequations that aim at measuring the effect of ability measures. OmittingS would lead to positive omitted variable bias because schooling iscorrelated positively to both ability and income, albeit expectedly lessso for the second than for the first set of corrected measures, see TableI. Its exclusion would hence lead to an overestimate of the true effect ofability on incomes.21

Including S as a regressor in the equation, though, leads in this caseto the problem of proxy control, that Angrist and Pischke describe as “theinclusion of variables that might partially control for omitted factorsbut are themselves affected by the variable of interest” (2008, p. 66).They show intuitively that the “use of a proxy control that is increasedby the variable of interest generates a coefficient below the desiredeffect” (p. 67). Angrist and Pischke value the use of proxy controlsas follows: “While proxy control does not generate the regressioncoefficient of interest, it may be an improvement on no control at all”(p. 68). Angrist and Pischke conclude for the case we have, that is,income equations with controls for education and ability and whereability affects education: “You can safely say that the causal effect ofinterest lies between these two,” that is, between the estimated effectwith and without including education controls in the equation. Thus,the coefficient obtained without controlling for education should resultin an upper boundary of the true effect whereas the one obtained whilecontrolling for education yields a lower boundary of the true effect.22

On top of that the data are plagued by an additional issue, al-ready discussed: Ability measures are incomparable across individualsbecause they have been administered at varying ages and educationlevels. We proceeded by creating two sets of ability measures from theoriginal test scores: “ability | age” and “ability | age + schooling.” The firstwill attribute part of the education effect also to measured ability. Theomitted variable bias is positive in this case and the resulting ability

21. A large empirical literature investigates the consequences of omitting abilityvariables from schooling equations (see Angrist and Pischke, 2008, for some key earlyreferences, p. 61), but not vice versa.

22. Perhaps it is interesting to note here that an instrumental variables approach(where S is replaced by S, which is then independent from ability) would result in anestimate of the relevant coefficient that is closer to the true causal value (and would renderbetter estimates of the true effect of education on income). However, the quality of suchan estimate hinges on the available identifying instruments for education. Using familybackground measures as such, a choice that has received some critique, we find coefficientsof the ability measures whose values are indeed between the lower and upper boundariesimplied by the estimates we have without using IV. The background measures used arethe presence of a library card when young; of a newspapers/magazines, the numberof siblings; the presence of a stepparent in the household, and education levels of theparents. The results are available upon request.


measures will pick up some of the effect of education in the incomeequations and thus provide an overestimate. However, the second setof ability measures will attribute the effect that ability has on educationto education fully and will result in an underestimate of the true effectof ability on incomes.

In combination, we employ four ways of dealing with the complexinteraction between ability, education, and incomes: including versusexcluding education controls in the income equation and using abilitymeasures that do versus do not control for education. The combinationof not controlling for education in the income equation and not control-ling for education in the ability equation will provide an overestimate,whereas the combination of controlling for education in the incomeequation and controlling for education in the ability equation willprovide us with an underestimate of the true effect that must, hence, bein between these two limits.

3.2.2 Selectivity: Entrepreneurship or Wage

Employment?

The available panel data include repeated measurements of each in-dividual’s labor market position (entrepreneur versus employee) andtheir incomes (and other time varying covariates). However, abilitieshave been measured only once in 1980. Thus, estimation by meansof a fixed effects approach is not possible and we perforce apply arandom effects approach (estimated by means of GLS) and assume zerocorrelation between the individuals and regressors.

However, the choice between entrepreneurship and salaried em-ployment might be endogenous in an income equation. Occupationalchoices might be guided by better returns to ability and (related)unobserved characteristics in either one of the choices, leading to biasedestimates of the returns to ability in a random effects framework. Toaddress the issue of selectivity, we also use a diff-of-diff regressionapproach (which effectively comes down to introducing individualfixed effects into the equation, see Angrist and Pischke, 2008, p. 227). Wethus estimate the differential effect of each ability measure on income inthe case that the respondent is an entrepreneur versus a wage employee.Obviously, the ability measures as stand alone variables—and othervariables that do not vary over time—are omitted from this specification.This specification contains only the interaction of ability (and schooling)with entrepreneurial status (that varies over time and is included alsoon a stand-alone basis) and we estimate the difference in returns to abilityin entrepreneurial and employee spells. Identification is based on the942 individuals who are observed to change status at least once. Thediff-of-diff approach controls for unobserved individual characteristics


that do not change over time and thus eliminates the bias originatingin permanent disposition, inclination and aptness for entrepreneurialactivity. It will not eliminate bias from unobserved characteristics thatvary over time or from unobservable circumstances that stimulate anindividual to seize an opportunity at a particular point in time and thatare grasped more easily by some individuals (with specific abilities?)than others.23

3.2.3 Econometric Specifications

The income equations we estimate can all be derived from addingrestrictions to the following equation that cannot be estimated in thisgeneral specification:

ln yit = α + (β + γ Eit)Ai + (δ + φEit)SAi + (ϕ + ηEit)ADi

+ (κ + λEit)Si + νXi + μEit + π Zit + ci + θi + εi t.

The dependent variable is the log of hourly income (y), as definedabove, A is general ability and SA consists of the five specific abilities.The measure of general ability and the measures of specific abilities arenever included simultaneously into one equation, because A is a linearcombination of the specific abilities. Moreover, ability measures are justentered linearly.24

AD is the measure of ability dispersion, that is, the inverse measureof JATness. Its coefficient is constrained to zero in the equations thatare focusing on the effects of ability levels. As discussed, the abilitymeasures come in two flavors: those obtained including controls forschooling at the time and those obtained without. S is schoolingand is, as discussed, included in half of the income equations. Xrepresents a vector of other time-constant individual characteristics(gender, ethnicity, parental education levels, birthyear dummies). Eis the employment status dummy (1 for entrepreneurship), whereasZ includes time-varying controls (age and year dummies, health status,marital status, urbanization, region). Finally, ci is an individual fixedeffect, θ i a random effect (the two are never used simultaneously, seebelow) and εit is a random draw for individual i at time t.

To answer the first question, that is, to what extent does a compositemeasure of ability affect an entrepreneur’s earnings relative to wages?, weestimate three sets of equations (results are reported in Table III, Panels

23. We do not try to estimate a selection-model where the choice for entrepreneurshipis modelled separately in a regression equation (2SLS) because of the lack of a valididentifying instrument.

24. We experimented with nonlinearities and interactions, see footnote 26, but thesewere hardly significant, whereas the interpretation of results became cumbersome.Heckman et al. (2006) estimate linear earnings functions for given education levels andimpose linear separability of cognitive and noncognitive ability across occupations.


A to C). They have the following restrictions in common: δ, φ , ϕ, η ≡ 0,because we only look at the effects of general ability and exclude specificabilities and ability dispersion from the equations. Our first set of(random effects) estimates (Panel A) serves as a benchmark and does notdistinguish between entrepreneurs and employees, that is, we imposean additional restriction on the parameter values of all interaction effects

Table III.

The Effect of General Ability on Income

I. II. III. IV.

Panel A: Base line model (GLS-Random effects)

General ability 0.1935 0.1572 0.1314 0.1095(0.0068)∗∗∗ (0.0069)∗∗∗ (0.0074)∗∗∗ (0.0069)∗∗∗

Entrepreneur dummy −0.0894 −0.0904 −0.0879 −0.0884(0.00093)∗∗∗ (0.00094)∗∗∗ (0.00093)∗∗∗ (0.0094)∗∗∗

Education 0.0439 0.0523(0.0023)∗∗∗ (0.0022)∗∗∗

Ability regr includeeducation controls

No Yes No Yes

Income regr includeseducation controls

No No Yes Yes

R2 overall 0.4362 0.4212 0.4489 0.4458No. of observations 49,764 49,297 49,764 49,297No. of individuals 4,472 4,423 4,472 4,423

Panel B: Distinguishing entrepreneurs (GLS-Random effects)

General Ability 0.1911 0.1546 0.1300 0.1076(0.0068)∗∗∗ (0.0070)∗∗∗ (0.0074)∗∗∗ (0.0069)∗∗∗

General Ability ∗ 0.0359 0.0416 0.0236 0.0330Entrepreneur (0.0100)∗∗∗ (0.0101)∗∗∗ (0.0120)∗∗ (0.0106)∗∗∗


Education 0.0433 0.0517(0.0024)∗∗∗ (0.0022)∗∗∗

Education ∗ 0.0076 0.0089Entrepreneur (0.0046)∗ (0.0040)∗∗


No Yes No Yes


No No Yes Yes


Continued


Table III.

Continued

I. II. III. IV.

Panel C: Distinguishing entrepreneurial spells (Diff-of-diff)

General ability ∗ 0.0432 0.0458 0.0282 0.0347entrepreneur (0.0104)∗∗∗ (0.0105)∗∗∗ (0.0125)∗∗ (0.0110)∗∗∗


Education ∗ entrepreneur 0.0096 0.0116(0.0047)∗∗ (0.0042)∗∗∗


No Yes No Yes


No No Yes Yes


Notes: Significance levels: ∗10%; ∗∗5%; ∗∗∗1%. All regressions include the control variables discussed in Section 3, seealso Table II, Panels A–C. Standard errors are adjusted for intragroup correlations where necessary.

to zero, that is, γ , λ ≡ 0. Whenever we estimate random effects weassume ci = 0, the fixed effect, as is the case in the first set of equationsand also in the second set that distinguishes between entrepreneursand employees in a random effects framework (Panel B). The third setof estimates is based on the diff-of-diff approach and perforce assumesthat σ, β, κ, ν = 0, whereas θi is omitted from the equation. γ is thecoefficient of interest when answering the first question.

To answer the second question, Do different cognitive abilities(e.g., math ability, technical ability) and social ability affect earningsof entrepreneurs and employees differently?, we assume throughoutβ, γ , ϕ, η = 0, because general ability as well as ability dispersion are re-moved from this set of equations. Again, we first estimate a benchmarkwithout distinction between entrepreneurs and employees (φ , λ ≡ 0)in a random effects framework (ci = 0), then include the distinctionbetween entrepreneurs and employees in a random effects framework(ci = 0) and finally employ a diff-in-diff framework (α, δ, κ, ν = 0,whereas θi is omitted from the equation). The results of these estimationswill be reported in Table IV, Panels A, B, and C, respectively. φ is thevector of coefficients of interest when answering the second question.

Both the answer to question 1 in Table III and question 2 in Table IVconsist of four columns: two including/excluding controls for educationand each one based once on the first and once on the second set of


Table IV.

The Effect of Specific Abilities on Income


No Yes No Yes


No No Yes Yes

No. of observations 49,764 49,297 49,764 49,297No. of individuals 4,472 4,423 4,472 4,423

Panel A: Base line model (GLS-Random effects)

Verbal ability 0.0310 0.0135 0.0175 0.0094(0.0083)∗∗∗ (0.0080)∗ (0.0081)∗∗ (0.0076)

Math ability 0.1170 0.1098 0.0688 0.0611(0.0079)∗∗∗ (0.0078)∗∗∗ (0.0081)∗∗∗ (0.0077)∗∗∗

Technical ability 0.0081 0.0129 0.0126 0.0146(0.0080) (0.0079) (0.0078) (0.0075)∗

Clerical ability 0.0525 0.0391 0.0451 0.0402(0.0070)∗∗∗ (0.0070)∗∗∗ (0.0068)∗∗∗ (0.0066)∗∗∗

Social ability 0.0181 0.0179 0.0168 0.0161(0.0053)∗∗∗ (0.0054)∗∗∗ (0.0051)∗∗∗ (0.0052)∗∗∗

Entrepreneur dummy −0.0881 −0.0893 −0.0874 0.0881(0.0094)∗∗∗ (0.0094)∗∗∗ (0.0093)∗∗∗ (0.0094)∗∗∗

Education 0.0435 0.0514(0.0024)∗∗∗ (0.0023)∗∗∗

R2 overall 0.4364 0.4231 0.4486 0.4459


Verbal ability 0.0335 0.0154 0.0203 0.0114(0.0083)∗∗∗ (0.0081)∗ (0.0081)∗∗ (0.0077)

Math ability 0.1150 0.1081 0.0683 0.0602(0.0079)∗∗∗ (0.0078)∗∗∗ (0.0081)∗∗∗ (0.0078)∗∗∗

Technical ability −0.0001 0.0057 0.0043 0.0072(0.0080) (0.0080) (0.0078) (0.0076)

Clerical ability 0.0586 0.0442 0.0514 0.0452(0.0070)∗∗∗ (0.0070)∗∗∗ (0.0068)∗∗∗ (0.0067)∗∗∗

Social ability 0.0163 0.0162 0.0150 0.0144(0.0053)∗∗∗ (0.0055)∗∗∗ (0.0052)∗∗∗ (0.0052)∗∗∗


Verbal ability ∗Entrepreneur

−0.0341 −0.0260 −0.0375 −0.0250

(0.0137)∗∗ (0.0131)∗∗ (0.0137)∗∗∗ (0.0131)∗Math ability ∗ Entrepreneur 0.0389 0.0332 0.0100 0.0203

(0.0138)∗∗∗ (0.0136)∗∗∗ (0.0155) (0.0145)Technical ability ∗ 0.1147 0.1056 0.1205 0.1072

Entrepreneur (0.0119)∗∗∗ (0.0116)∗∗∗ (0.0119)∗∗∗ (0.0116)∗∗∗

Continued


Table IV.

Continued


Clerical ability ∗ −0.1051 −0.0850 −0.1081 −0.0840Entrepreneur (0.0125)∗∗∗ (0.0119)∗∗∗ (0.0124)∗∗∗ (0.0119)∗∗∗

Social ability ∗ 0.0318 0.0278 0.0311 0.0270Entrepreneur (0.0092)∗∗∗ (0.0093)∗∗∗ (0.0092)∗∗∗ (0.0092)∗∗∗

Education 0.0425 0.0509(0.0024)∗∗∗ (0.0023)∗∗∗

Education ∗ 0.0177 0.0083Entrepreneur (0.0049)∗∗∗ (0.0042)∗∗

R-sq overall 0.4379 0.4244 0.4501 0.4471

Panel C: Distinguishing entrepreneurial spells (Diff-of-Diff)

Verbal ability ∗ −0.0414 −0.0333 −0.0468 −0.0342Entrepreneur (0.0143)∗∗∗ (0.0137)∗∗ (0.0144)∗∗∗ (0.0137)∗∗

Math ability ∗ 0.0474 0.0388 0.0188 0.0257Entrepreneur (0.0143)∗∗∗ (0.0142)∗∗∗ (0.0162) (0.0151)∗

Technical ability ∗ 0.1232 0.1137 0.1290 0.1156Entrepreneur (0.0124)∗∗∗ (0.0121)∗∗∗ (0.0125)∗∗∗ (0.0121)∗∗∗

Clerical ability ∗ −0.1036 −0.0841 −0.1067 −0.0832Entrepreneur (0.0130)∗∗∗ (0.0124)∗∗∗ (0.0130)∗∗∗ (0.0124)∗∗∗

Social ability ∗ 0.0287 0.0254 0.0279 0.0243Entrepreneur (0.0096)∗∗∗ (0.0096)∗∗∗ (0.0096)∗∗∗ (0.0096)∗∗

Entrepreneur −0.1095 −0.1065 −0.1121 −0.1077(0.0101)∗∗∗ (0.0101)∗∗∗ (0.0101)∗∗∗ (0.0101)∗∗∗

Education ∗ 0.0194 0.0106Entrepreneur (0.0051)∗∗∗ (0.0044)∗∗

R2 overall 0.3059 0.3058 0.3550 0.3552

Notes: Significance levels: ∗10%; ∗∗5%; ∗∗∗1%. All regressions include the control variables discussed in Section 3, seealso Table II, Panels A–C. Standard errors are adjusted for intragroup correlations where necessary.

corrected ability measures. To answer question 3 (in Table V), Does thebalance in these measured ability levels affect an individual’s earnings? weproceed in the same way but estimate four columns controlling forA but not for SA (δ, φ = 0) and four columns the other way around(β, γ = 0), where each column includes the main variable of interesthere, η pertaining to AD, ability dispersion. Panels A to C are otherwisecomparable to the other tables.

Besides these income equations, we also estimate selection equa-tions (Table VI) that are meant to give some insight, beyond what issuggested by the descriptive statistics in Table II, in the observablefactors affecting occupational choice and the kind and extent of biasin the relevant income effects, resulting from selectivity. We estimate


Tab

le

V.

Th

eE

ffect

of

In

divid

ual

Var

iatio

nin

Ab

ilities

on

In

co

me

Abi

lity

regr

incl

ude

educ

atio

nco

ntro

lsN

oYe

sN

oYe

sN

oYe

sN

oYe

sIn

com

ere

grin

clud

esed

ucat

ion

cont

rols

No

No

Yes

Yes

No

No

Yes

Yes

Inco

me

regr

cont

rols

for

gene

rala

bilit

yYe

sYe

sYe

sYe

sN

oN

oN

oN

oIn

com

ere

grco

ntro

lsfo

rsp

ecifi

cab

iliti

esN

oN

oN

oN

oYe

sYe

sYe

sYe

sN

o.of

obse

rvat

ions

49,7

6449

,297

49,7

6449

,297

49,7

6449

,297

49,7

6449

,297

No.

ofin

div

idua

ls4,

472

4,42

34,

472

4,42

34,

472

4,42

34,

472

4,42

3

Pane

lA:B

ase

line

mod

el(G

LS-

Ran

dom

effe

cts)

Var

iati

onin

abili

ties

mea

sure

0.00

14−0

.000

90.

0015

0.00

110.

0021

0.00

010.

0019

0.00

14(0

.004

8)(0

.005

6)(0

.004

6)(0

.005

3)(0

.004

8)(0

.005

6)(0

.004

6)(0

.005

3)E

ntre

pren

eur

dum

my

−0.0

894

−0.0

904

−0.0

879

−0.0

884

−0.0

881

−0.0

893

−0.0

874

−0.0

881

(0.0

093)

∗∗∗

(0.0

094)

∗∗∗

(0.0

093)

∗∗∗

(0.0

094)

∗∗∗

(0.0

094)

∗∗∗

(0.0

094)

∗∗∗

(0.0

093)

∗∗∗

(0.0

094)

∗∗∗

R2

over

all

0.43

620.

4212

0.44

890.

4459

0.43

640.

4231

0.44

860.

4459 Con

tinu

ed


Tab

le

V.

Co

ntin

ued

Pane

lB:D

isti

ngui

shin

gen

trep

rene

urs

(GL

S-R

and

omef

fect

s)

Var

iati

onin

abili

ties

mea

sure

0.00

250.

0006

0.00

26.0

0247

0.00

300.

0012

0.00

280.

0024

(0.0

048)

(0.0

056)

(0.0

046)

(0.0

053)

(0.0

048)

(0.0

056)

(0.0

046)

(0.0

053)

Ent

repr

eneu

rd

umm

y−0

.093

9−0

.095

0−0

.092

2−0

.093

7−0

.118

0−0

.117

4−0

.120

7−0

.117

8(0

.009

4)∗∗

∗(0

.009

5)∗∗

∗(0

.009

4)∗∗

∗(0

.009

4)∗∗

∗(0

.009

7)∗∗

∗(0

.009

8)∗∗

∗(0

.009

7)∗∗

∗(0

.009

8)∗∗

∗V

aria

tion

inab

iliti

es∗e

ntre

pren

eur

−0.0

296

−0.0

252

−0.0

287

−0.0

239

−0.0

227

−0.0

286

−0.0

213

−0.0

274

(0.0

086)

∗∗∗

(0.0

088)

∗∗∗

(0.0

086)

∗∗∗

(0.0

088)

∗∗∗

(0.0

086)

∗∗∗

(0.0

088)

∗∗∗

(0.0

086)

∗∗(0

.008

8)∗∗

∗R

2ov

eral

l.4

361

.421

2.4

488

.445

8.4

380

.424

5.4

502

.447

3

Pane

lC:D

isti

ngui

shin

gen

trep

rene

uria

lspe

lls(D

iff-

of-D

iff)

Var

iati

onin

abili

ties

∗ent

repr

eneu

r−0

.029

7−0

.022

1−0

.029

1−0

.020

8−0

.022

6−0

.025

9−0

.021

3−0

.024

6(0

.008

6)∗∗

∗(0

.008

9)∗∗

(0.0

086)

∗∗∗

(0.0

089)

∗∗(0

.008

6)∗∗

∗(0

.009

0)∗∗

∗(0

.008

6)∗∗

(0.0

090)

∗∗∗

Ent

repr

eneu

rd

umm

y−0

.083

5−0

.083

3−0

.082

7−0

.083

4−0

.109

4−0

.107

5−0

.112

0−0

.108

6(0

.009

7)∗∗

∗(0

.009

8)∗∗

∗(0

.009

8)∗∗

∗(0

.009

8)∗∗

∗(0

.010

1)∗∗

∗(0

.010

1)∗∗

∗(0

.010

1)∗∗

∗(0

.010

2)∗∗

∗R

2ov

eral

l.3

028

.302

8.3

513

.351

7.3

060

.306

0.3

550

.355

3

Not

es:S

igni

fican

cele

vels

:∗10

%;∗

∗ 5%

;∗∗∗

1%.A

llre

gres

sion

sin

clud

eth

eco

ntro

lvar

iabl

esd

iscu

ssed

inSe

ctio

n3,

see

also

Tabl

eII

,Pan

els

A–C

.St

and

ard

erro

rsar

ead

just

edfo

rin

trag

roup

corr

elat

ions

whe

rene

cess

ary.


Tab

le

VI.

Th

eE

ffect

of

Ab

ilities

on

En

tr

epr

en

eu

rsh

ip

Selectio

n

Abi

lity

regr

incl

.ed

ucat

ion

cont

rols

No

Yes

No

Yes

No

Yes

No

Yes

Inco

me

regr

incl

.ed

ucat

ion

cont

rols

No

No

Yes

Yes

No

No

Yes

Yes

Pane

lA:R

and

omef

fect

spr

obit

mod

el(G

EE

popu

lati

onav

erag

edm

odel

)#

Gen

eral

abili

ty−0

.062

2−0

.049

1−0

.027

8−0

.025

1(0

.023

1)∗∗

∗(0

.023

1)∗∗

(0.0

264)

(0.0

244)

Ver

bala

bilit

y−0

.008

40.

0036

−0.0

058

0.00

43(0

.028

2)(0

.026

9)(0

.028

4)(0

.026

9)M

ath

abili

ty−0

.164

−0.1

592

−0.1

517

−0.1

426

(0.0

267)

∗∗∗

(0.0

259)

∗∗∗

(0.0

285)

∗∗∗

(0.0

271)

∗∗∗

Tech

nica

labi

lity

0.13

130.

1156

0.13

010.

1151

(0.0

272)

∗∗∗

(0.0

264)

∗∗∗

(0.0

273)

∗∗∗

(0.0

264)

∗∗∗

Cle

rica

labi

lity

−0.0

037

0.00

57−0

.002

80.

0046

(0.0

240)

(0.0

234)

(0.0

241)

(0.0

234)

Soci

alab

ility

0.05

180.

0527

0.05

360.

0539

(0.0

181)

∗∗∗

(0.0

182)

∗∗∗

(0.0

182)

∗∗∗

(0.0

183)

∗∗∗

Var

iati

onin

abili

ties

0.00

34−0

.012

50.

0031

−0.0

132

0.00

33−0

.012

10.

0033

−0.0

125

(0.0

159)

(0.0

178)

(0.0

161)

(0.0

179)

(0.0

156)

(0.0

175)

(0.0

157)

(0.0

176)

Wal

dC

hi-s

quar

e62

7.9

623.

063

9.6

638.

767

8.7

671.

568

1.0

678.

7N

o.of

obse

rvat

ions

50,2

1549

,627

50,0

9449

,627

50,2

1549

,627

50,0

9449

,627

No.

ofin

div

idua

ls4,

508

4,45

24,

501

4,45

24,

508

4,45

24,

501

4,45

2 Con

tinu

ed


Tab

le

VI.

Co

ntin

ued

Pane

lB:P

robi

t,d

epen

den

tvar

iabl

e=

ever

been

entr

epre

neur

insa

mpl

epe

riod

##,d

eriv

ativ

eef

fect

ses

tim

ated

Gen

eral

abili

ty−0

.014

4−0

.011

70.

0079

0.00

09(0

.008

4)∗

(0.0

079)

(0.0

095)

(0.0

083)

Ver

bala

bilit

y−0

.005

50.

0018

−0.0

020

0.00

11(0

.011

2)(0

.010

6)(0

.011

2)(0

.010

5)M

ath

abili

ty−0

.056

2−0

.051

8−0

.038

8−0

.037

6(0

.011

0)∗∗

∗(0

.010

5)∗∗

∗(0

.011

8)∗∗

∗(0

.011

0)∗∗

∗Te

chni

cala

bilit

y0.

0463

0.03

520.

0427

0.03

52(0

.011

0)∗∗

∗(0

.010

4)∗∗

∗(0

.011

0)∗∗

∗(0

.010

4)∗∗

∗C

leri

cala

bilit

y0.

0038

0.01

050.

0049

0.00

91(0

.009

4)(0

.009

1)(0

.009

5)(0

.009

1)So

cial

abili

ty0.

0198

0.01

980.

0209

0.02

10(0

.007

5)∗∗

∗(0

.007

5)∗∗

∗(0

.007

5)∗∗

∗(0

.007

5)∗∗

∗V

aria

tion

inab

iliti

es0.

0024

−0.0

129

0.00

18−0

.013

80.

0014

−0.0

123

0.00

13−0

.013

0(0

.006

4)(0

.009

0)(0

.006

3)(0

.009

4)(0

.006

5)(0

.009

0)(0

.006

4)(0

.009

3)W

ald

Chi

-squ

are

32.0

34.9

52.8

55.3

71.9

68.4

81.0

81.8

No.

ofob

serv

atio

ns3,

333

3,29

43,

326

3,29

43,

333

3,29

43,

326

3,29

4 Con

tinu

ed


Tab

le

VI.

Co

ntin

ued

Pane

lC:R

Epr

obit

mod

el(G

EE

popu

lati

onav

erag

edm

odel

),d

epen

den

t=tr

ansi

tion

toen

trep

rene

ursh

ip##

#

Gen

eral

abili

ty−0

.052

7−0

.028

0−0

.030

9−0

.009

2(0

.019

0)∗∗

∗(0

.019

7)(0

.021

6)(0

.020

7)V

erba

labi

lity

−0.0

017

0.01

750.

0016

0.01

78(0

.023

9)(0

.023

3)(0

.024

1)(0

.023

2)M

ath

abili

ty−0

.113

8−0

.107

9−0

.106

7−0

.094

0(0

.023

3)∗∗

∗(0

.022

5)∗∗

∗(0

.025

3)∗∗

∗(0

.023

7)∗∗

∗Te

chni

cala

bilit

y0.

0706

0.05

940.

0678

0.05

90(0

.023

4)∗∗

∗(0

.022

3)∗∗

(0.0

235)

∗∗∗

(0.0

223)

∗∗∗

Cle

rica

labi

lity

−0.0

110

0.00

82−0

.010

40.

0076

(0.0

207)

(0.0

198)

(0.0

208)

(0.0

161)

Soci

alab

ility

0.04

170.

0419

0.04

310.

0424

(0.0

160)

∗∗∗

(0.0

161)

∗∗∗

(0.0

160)

∗∗∗

(0.0

161)

∗∗∗

Var

iati

onin

abili

ties

−0.0

122

−0.0

247

−0.0

126

−0.0

260

−0.0

119

−0.0

228

−0.0

120

−0.0

236

(0.0

125)

(0.0

158)

(0.0

125)

(0.0

159)

(0.0

123)

(0.0

155)

(0.0

123)

(0.0

156)

Wal

dC

hi-s

quar

e15

2.5

148.

415

7.8

156.

918

3.7

177.

418

4.9

181.

0N

o.of

obse

rvat

ions

48,6

7048

,087

48,5

4948

,087

48,6

7048

,087

48,5

4948

,087

Not

es:S

igni

fican

cele

vels

:∗10

%;∗

∗ 5%

;∗∗∗

1%.A

llre

gres

sion

sin

clud

eth

eco

ntro

lvar

iabl

esd

iscu

ssed

inSe

ctio

n3,

Tabl

eII

,Pan

els

A–C

.The

esti

mat

esin

Pane

lBha

vebe

enob

tain

edw

hen

omit

ting

regr

esso

rsth

atva

ryov

erti

me.

# The

dep

end

entv

aria

ble

inPa

nelA

is1

for

each

ind

ivid

ual/

year

obse

rvat

ion

inen

trep

rene

ursh

ipan

dze

roin

wag

eem

ploy

men

t.##

The

dep

end

entv

aria

ble

inPa

nelB

is1

for

ind

ivid

uals

wit

hat

leas

tone

spel

lin

entr

epre

neur

ship

and

zero

for

ind

ivid

uals

who

have

neve

rbe

enen

trep

rene

urs

(and

atle

asto

neye

arw

age

empl

oyee

).##

# The

dep

end

entv

aria

ble

inPa

nelC

is1

for

atr

ansi

tion

toen

trep

rene

ursh

ip,t

hat

is,f

orpe

rson

–yea

rob

serv

atio

nsw

hoar

een

trep

rene

urs

inye

art

but

not

int-

1an

dze

rofo

rpe

rson

–yea

rco

mbi

nati

ons

who

are

empl

oyee

sin

year

t.In

div

idua

l–ye

arob

serv

atio

nsof

entr

epre

neur

sin

year

tand

t-1

have

been

excl

uded

.Inc

lud

ing

them

leav

esth

ere

sult

sun

chan

ged

.


three common indicators of occupational choice. First we estimate arandom effects model where the dependent variable takes on the value1 for each individual/year observation for which we observe a spellin entrepreneurship and zero for observations in wage employment.The results from this random effects model are shown in Panel A. Thesecond dependent variable (Panel B) takes on the value 1 for individualsfor whom we observe at least one spell in entrepreneurship and zero forothers (who have at least once been observed as a wage employee). Timevarying independent variables are excluded here. The third approachemploys a dependent variable (Panel C), which takes on the value 1for an observed transition to entrepreneurship, that is, for person–yearobservations who are entrepreneur in year t but not in t − 1. It is zerofor all other person–year combinations who are employees in year t.

4. Results

We now present the results from estimating the income equations inTables III–V followed by the occupational choice results (Table VI). Theresults from controls are omitted from the tables.

4.1 Returns to General Ability

Table III presents the estimation results for the income equationconfined to the effect of (a standardized measure of) general ability.In the benchmark case in Panel A, where the interaction betweenoccupational status and ability is suppressed, β is between 11 and20%. Thus, increasing the measured level of general ability by onestandard deviation, leads to a 11–20% increase in income. The lowerestimate is obtained while including education into both the incomeand ability equations, whereas the upper bound is obtained whileexcluding education from both equations, κ = 0. Moreover, irrespectiveof whether ability and/or income are corrected for education levels,entrepreneurs earn, ceteris paribus and on average 9% lower incomesthan comparable employees (see also Hamilton, 2000). Panel B showsthat the return to general ability is 30% higher as an entrepreneur thanas an employee (10.7 + 3.3, rather than 10.7) while education paysa 17% premium, consistent with earlier evidence of higher returns toeducation in entrepreneurship than wage employment (See footnote 1).A comparison of the four columns in Panel B reveals that the measuredpremium for entrepreneurs is barely sensitive to the corrections foreducation. The premium for entrepreneurship itself is estimated to be, asin Panel A, a negative 9% and independent of the specification. The third


FIGURE 1. SURPLUS INCOME IN ENTREPRENEURIAL SPELLSGIVEN GENERAL ABILITY LEVELS

Notes: The graph is based on the estimates of Panel C (fixed effects) of Table III. Whencalculating the lines based on Columns 3 and 4, where education is included as acontrol variable, the individual’s education level varies with the level of general abilitycorresponding with the correlation between general ability and education. To be precise,whenever general ability is increased by × standard deviations, education is increased by× times corr(general ability, education) times sd(education).

panel shows that our results are not biased by unobserved permanentindividual factors: the diff-of-diff estimates tell the same tale. Thuswe find clear support for Marshall’s claim that general ability, whileobviously relevant for economic success, is particularly relevant forentrepreneurs.

Figure 1 shows suggestive evidence about the implications of thedifferent returns to ability for the absolute income levels individualsearn as entrepreneurs versus employees based on the four sets of esti-mates presented in Panel C.25 The figure shows the percentage (points)premium earned as an entrepreneur versus a wage employee (on the

25. The discussion in Section 3 about the limited comparability of income levelsfor entrepreneurs and employees motivates why we refer to “suggestive evidence.” Inaddition, the comparison in the graph is valid if two underlying assumptions hold.First, that the relationships between earnings and measured ability levels and dispersionare linear and second, that the income measures for employees and entrepreneursare comparable in absolute levels. The first assumption seems to hold in our data.When we include abilities in quadratic form into the equations, their coefficients arealmost all insignificant (results available from the authors) and the conclusions are notupset. However, there is little proof of the validity of the second assumption (althoughresearchers commonly assume this comparability). Therefore, we interpret the resultsbased on comparisons of income levels with great caution.


vertical axis), dependent on where a person is located in the distributionof general ability measured in standard deviations (horizontal axis’,where “0” is equivalent to the average level of general ability and isassociated with a negative income premium for entrepreneurs of 8%,which is equal to the coefficient of the entrepreneur dummy in PanelC). The slopes of the lines indicate the differential income effects forentrepreneurs and employees of increasing general ability, includingindirect effects brought about by the fact that higher ability levels areassociated with higher education levels that lead, in turn, to higherincomes.

The differences between the lines for the different specificationsare very small and they all bring out the same message: The higherreturns to general ability for entrepreneurs only lead to higher incomelevels as an entrepreneur than as an employee for the top of the abilitydistribution. The minimum level of general ability at which estimatedearnings are higher for entrepreneurs than for employees is between1.74 and 1.92 standard deviations above the mean, corresponding to theupper 7.8% and 7.3% of the distribution of general ability.26

4.2 Returns to Specific Abilities

Table IV shows the results for the returns to specific abilities. Thestructure of the table is similar to Table III. For the pooled data, ignoringpossibly differential returns, mathematical ability has the highest payoff(significant coefficients of between 0.12 and 0.06, depending on thespecification), a common finding in the literature. The return to clericalability is between 4% and 5% per standard deviation, whereas thereturn to verbal ability is between 1% and 3% (but insignificant wheneducational controls are included, in accordance with the mixed resultsfound in our survey, Section 2). Technical ability is barely significant,and social ability boosts earnings significantly by 1.5 to 2% per standarddeviation. As before, estimated coefficients are sensitive to controllingfor education, especially for the abilities that are associated moststrongly with scholastic achievement, that is, verbal and mathematicalability.

Considering differential returns by occupational status (PanelB), we see a substantial positive premium of technical ability inentrepreneurial status (10–12%) and a substantial negative premium

26. The accuracy of these estimates can be indicated by 95% confidence intervals(results are available from the authors upon request. For instance, for one of the lines(Line II) the lower and upper bounds of the estimated ability level in terms of standarddeviations above the mean for which entrepreneurial spells are associated with higherincomes than employee spells are 0.8 and 3.3, respectively.


of clerical ability (8–11%). Social ability has 3 percentage points higherreturns per standard deviation when entrepreneur, corroborating theanticipation formulated in Section 2. Verbal ability has a similar pre-mium return when employee. The premium return on mathematicalability for entrepreneurs is of the same magnitude as the premiumreturn on social ability, although the effect becomes smaller and lesssignificant when education controls are included. Except for mathemat-ical ability, the differences are barely affected by controls for educationand are robust when applying a diff-of-diff estimation (Panel C).

Figure 2 shows, similar to Figure 1, (suggestive evidence about)the earnings advantage in entrepreneurial status for the three abilitiesthat affect entrepreneurial incomes relative to employee incomes pos-itively.27 The lines within the figures (for different specifications) onlydiffer visibly for technical ability, but they all bring the same message asbefore: To realize an earnings gain from entrepreneurial status seems torequire top levels of abilities. The only exception is technical ability: thethreshold is between 1.04 and 1.43 standard deviations, correspondingto the upper 18.6–29.6% of the technical ability distribution. FigureA2 shows that the 95% confidence interval for this estimate (lineII) lies between 0.5 and 1.5 standard deviations. For mathematicaland social ability, only individuals positioned in the very top of thedistribution are financially better off as entrepreneurs. Earnings arehigher as an entrepreneur than as an employee for the upper 3.9% of thedistribution of math ability and for the upper 3.4% of the social abilitydistribution.

4.3 Returns to Being a Jack-of-All-Trades

Table V shows the results from testing the Jack-of-all-Trades theory ofentrepreneurship (Lazear, 2005). As Panel A indicates, an individual’sspread in measured ability levels is irrelevant for earnings if we do notdistinguish between employee and entrepreneur status, irrespective ofthe inclusion of controls for education or ability levels into the equation.

Lazear’s theory predicts that entrepreneurs benefit from beingJack-of-all-Trades, whereas employees would benefit from being spe-cialists. We find significant support for the first prediction. Althoughearnings as an employee are unaffected by ability dispersion, the inversemeasure of JAT, the impact for entrepreneurs is significant and negative,thereby supporting the positive effect of being a Jack-of-all-Trades.

27. Variation in the levels of the other measured abilities, that is, clerical and languageability, can never lead to higher incomes in entrepreneurship because both the coefficientfor entrepreneurship and the coefficients for the effects of these abilities on the incomesfor entrepreneurs relative to employees are negative.


FIGURE 2. SURPLUS INCOME IN ENTREPRENEURIAL SPELLSGIVEN SPECIFIC ABILITY LEVELS

Notes: The graphs are based on the estimates of Panel C (fixed effects) of Table IV; linescorrespond to columns. Along any line, levels of other specific abilities and educationcovary according to correlations. The effect of a change in a specific ability is not calculatedat fixed values for the other abilities, but they vary according to the correlation with thedepicted ability. Whenever the depicted ability is increased by × standard deviations,the other specific ability measures and education are increased by × times corr(depictedability, other ability). The same holds for education.


FIGURE 3. SURPLUS INCOME IN ENTREPRENEURIAL SPELLSGIVEN ABILITY DISPERSION

Notes: The graph is based on the estimates in Columns 1–4 of Panel C (fixed effects), TableV; the lines correspond to the first four columns. Along a line, the individual’s generalability level (all lines) and education level (lines III and IV only) covary according tocorrelations (see text).

An increase by a standard deviation of variability reduces earningsin entrepreneurial status by 3%. This estimate is also independentof controls for education and abilities. Once again, the diff-of-diffresults (Panel C) are virtually identical to the random-effects estimates(Panel B).

Figure 3 shows that, once again, the earnings advantage as anentrepreneur only holds for the top of the distribution: Earnings arehigher as an entrepreneur for the upper 4.0 to 4.5% of the distributionof ability dispersion.

We conclude that labor market participants benefit more from theirgeneral ability as entrepreneurs than employees. Regarding specificabilities, entrepreneurship is associated with higher returns to technical,social and mathematical ability, whereas wage employment offers apremium return on clerical and verbal abilities. In support of the JATtheory, being a JAT is profitable when an entrepreneur, but not asemployee.

4.4 Selection: Entrepreneurship

Versus Wage Employment

Table VI shows the determinants of occupational choice in a probitframework, including the control variables discussed. The first of the


three panels shows the estimates from a random effects probit model,as explained before, the second the estimates from a probit modelexplaining whether an individual has ever been an entrepreneur and thethird specification explains transitions to entrepreneurship. All panelstell the same tale again: The effect of general ability is not so clearcut, but, if anything, affects the choice for entrepreneurship negatively.Regarding specific abilities, verbal and clerical ability have no effect onoccupational choice, technical and social ability have a positive effecton the choice for entrepreneurship and mathematical ability a negativeeffect. These results for occupational choice are only partially in linewith the relative returns we estimated, shown in Table IV.

The coefficient of “variation in abilities,” the inverse JAT mea-sure, is insignificant in all regressions explaining occupational choice,whereas the return to being a JAT was positive for entrepreneurs (andzero for employees). One might argue that the effect of JATness onentrepreneurial performance is a more relevant test of Lazear’s theorythan its effect on occupational choices that Lazear (2005) actually uses,assuming rational expectations: Entrepreneurship offers a relativelyhigh return to being a JAT and would thus attract JATs. We do not findthat a balanced set of abilities stimulates to opt for entrepreneurship,but we do find that the payoff to a balanced skill portfolio is higherin entrepreneurship than in salaried employment. The relationshipbetween investments in JATness and occupational choice has beenstudied empirically and was supported by Lazear himself (2005) andWagner (2003), whereas mixed support was reported by Silva (2007) andno support by Astebro and Thompson (2007). The effect of the innaterather than acquired skill portfolio on entrepreneurial success has notyet been studied.

If the choice for entrepreneurship would be dominated by earningsmaximization, we would expect an alignment between the resultsin Table VI and Tables III–V. The abilities that increase the relativereturns from entrepreneurship in terms of income as compared towages (general ability, technical, social ability and being a JAT) shouldstimulate the inclination to become an entrepreneur, whereas higherlevels of clerical and verbal abilities would stimulate the choice forwage employment. Moreover, one should observe relatively many Jack-of-all-Trades choosing to become entrepreneurs. These patterns of oc-cupational choice are only partly borne out by the data. In fact, the onlyabilities that affect relative income from entrepreneurship and the choicefor entrepreneurship in the same way are technical and social ability,which have a positive effect on both. Thus we observe a discrepancybetween the drivers of occupational choice and the determinants ofthe premium income as an entrepreneur versus a wage employee.


This observed discrepancy is consistent with the evidence collected inempirical studies of the choice for and performance in entrepreneurshipversus wage–employment: The “returns to entrepreneurship puzzle.”Despite longer working hours and more variable and often lowerincomes on average for entrepreneurs, people not only choose to becomeand remain entrepreneurs but they also turn out more satisfied as such(Benz and Frey, 2008; Blanchflower and Oswald, 2008; Hamilton, 2000;Hyytinen et al., 2008; Parker, 2004; Van Praag and Versloot, 2007).We show that the puzzle remains when looking at a specific driverof the differential financial returns in entrepreneurship versus wageemployment, that is, abilities, instead of studying the differences inincome levels themselves (that may be flawed with measurement issues,see Hyytinen et al., 2008). Recent empirical studies trying to solve thispuzzle find that the choice for entrepreneurship is mainly governed bymore autonomy and control over (the accruals from) one’s own work asan entrepreneur compared to positions in wage employment (Benz andFrey, 2008; Hyytinen et al., 2008). In line with this, we conclude that theoccupational choice between entrepreneurship and wage employmentis not primarily driven by the maximization of expected income.

4.5 Robustness of the Results

The results lead to answers to the three core questions of the paperand they turned out quite robust to changes in the empirical definitionsof the ability measures and the definition of the entrepreneur in termsof the minimum number of hours devoted to this activity. Moreover,the results are largely unaffected by the various choices made on howwe deal with the relation between education and ability as well as withselectivity. Moreover, the determinants of occupational choice turn outto be invariant to changes in the definition of the dependent variable(compare Panels A, B, C of Table VI). We performed three additionalchecks.

First, the ASVAB tests have been executed in English. Forindividuals for whom English is not their first language, lan-guage problems may have affected their results, leading to a dif-ferent relationship between measured ability and labor market out-comes. To analyze this issue, we replaced the entrepreneurship sta-tus dummy in the income equations with a “foreign language”dummy that is one for the 15% individuals in the sample who answeredyes to “Was a foreign language spoken at home during the respondent’schildhood”? and zero otherwise. Thus, we measure the difference inthe income effects of ability between native and nonnative speakers of


English.28 Note that we cannot run “diff-of-diff” analyses here becausethe foreign language variable does not vary over time. The results, thatare available from the authors upon request, show that the effect ofgeneral ability, specific abilities and ability variation are no different forpeople raised with a foreign language, with one exception: Individualswho spoke a foreign language at home during their childhood benefitmore from verbal ability than others in terms of their incomes. They earna premium of 4.5% per standard deviation on this ability. When raisedin a foreign language, a higher level of verbal ability probably makesmore of a difference. As the results are otherwise similar to what wefound before, we conclude that nonnative speakers have not influencedthe results.

As a second robustness check, we have included controls for indus-tries (see Panel D of Table II) into the income equations (available uponrequest). One might suspect that the (differences in) returns to (specific)abilities are related to differences in earnings across industries. Thisturned out not to be the case. Although there are marked average incomedifferences across industries (some 50% difference between the highestand the lowest paying industry), signs, magnitudes and significancelevels of the parameters of interest were unaffected by including indus-try dummies. The only parameter value whose magnitude changed wasthe entrepreneur dummy: the penalty to being an entrepreneur reducedfrom 8–10% to 4–7%. Entrepreneurs are apparently overrepresented inindustries with lower average incomes. However, this does not impacttheir returns to ability.

Third, we should not overlook that we have investigated theeffect of abilities on the levels of income as entrepreneurs comparedto employees. However, if the higher level of income as an entrepreneurdue to higher levels of abilities is associated with higher income risk,the premium benefit of being an entrepreneur versus an employee fora risk averse individual with high ability levels could be lower thanthe premium return to ability in terms of income levels. We explore thisissue briefly—we do certainly not aim at a complete risk-analysis. Wefirst briefly look at the income variation measured by the cross-sectionalincome variance within the group of entrepreneurs vis-a-vis the groupof wage earners, which is substantially higher in the first group (seeTable II), a common finding in other data sets too, even when controllingfor observables (Elfenbein et al., 2010; Astebro et al., 2007; Ohyama,2008). We note that the larger intragroup spread for entrepreneurscannot be caused by a larger variance in ability levels: Table II shows

28. Strictly speaking, the NLSY-variable that we use as a basis for the “foreignlanguage” dummy will (wrongly) assign individuals who are raised bilingually (speakingEnglish and a foreign language at home) to the group of nonnatives.


that the variance in abilities (general ability, specific abilities, and abilitydispersion) is not larger in the group of entrepreneurs than amongemployees.

We next calculate the standard deviation of the incomes over theyears per individual and split the sample into a group of people whohave ever been entrepreneur and others. We compare the variationof individual incomes over time between these two groups. Underconditions, one could take the income variance over time as a measureof income risk. In line with the cross-sectional results, the variance ismuch higher for individuals who have ever been entrepreneur than forthe others (0.6018 vs. 0.4964 and the difference is significant, [t-value =17.2]). However, the estimates of the determinants of this type of incomevariation—available from the authors upon request—show that noneof the abilities affect income risk in general, and there are no significantdifferences between the two groups in the relationship between incomerisk and abilities.

We conclude that the results on the (distinct) effects of abilitieson incomes for entrepreneurs and employees are not affected by (i)nonnatives for whom the relationship between test scores and incomemight be different (ii) industry differences. Moreover, some explorativeevidence suggests that the effects of ability on income levels are notassociated with similar effects of ability on income risk.

5. Conclusion

On average, 10% of the labor force in any developed countryare entrepreneurs, that is, business owners (either self-employed orowner–managers of incorporated businesses), see Parker, 2004. More-over, successful entrepreneurship has a profound effect on economicgrowth, labor demand, and innovation (see the survey by Van Praagand Versloot, 2007). Therefore, knowledge about the determinants ofentrepreneurs’ performance is relevant.

We find markedly different returns to ability for entrepreneursand employees. The same individual has a 30% higher return to generalability when active as an entrepreneur than when working as anemployee. Nevertheless, the results suggest that the expected earningslevels in entrepreneurship relative to wage employment are higher onlyfor the upper echelon of the general ability distribution. This is due tothe fact that, for the average individual, the expected earnings levels inspells of entrepreneurship are lower than in wage employment.

We also find differential returns to measures of specific abil-ities for entrepreneurs versus wage employees. In particular, the


science-oriented part of the set of abilities generates higher returnsin spells of entrepreneurship. One standard deviation increase intechnical ability pays 12% more when active as an entrepreneur,whereas mathematical ability has a much smaller positive premiumfor entrepreneurs. In addition, social ability benefits entrepreneurialincomes more than wages. As in the case of general ability, expectedearnings levels are higher in entrepreneurship than wage employmentonly for observations in the very right-hand tail of the distributionof these specific abilities on which entrepreneurs earn a premiumrelative to wage employees. This is due to the lower expected incomein entrepreneurship for the average individual. The other measuredabilities, that is, verbal ability and, in particular, clerical ability havebetter payoffs as an employee. Increasing one’s measured level ofclerical ability by one standard deviation increases the income premiumin wage employment relative to entrepreneurship by 8 to 10%.

In support of Lazear’s Jack-of-all-Trades theory, we find that amore balanced portfolio of individual ability levels boosts earnings asan entrepreneur. However, it leaves earnings as an employee unaffected.

The results are generally quite robust. For instance, all our con-clusions are insensitive to including or excluding education in theregression equation or in the correction of ability scores. In this mannerwe are confident that our main conclusion is probably not affectedby important empirical issues regarding the interrelationship betweenabilities and education. Furthermore, all our conclusions are obtainedusing various econometric specifications: The results obtained whenestimating a random effects model are very similar to these obtainedfrom a diff-of-diff specification where effects are identified based uponchanges in incomes due to changes in occupational positions for variousability levels. This indicates that self-selection into occupations basedon unobserved fixed individual characteristics is not driving the results.However, we cannot conclude that selectivity plays no part at all. Wecannot exclude that some individuals (with particular ability sets) arebetter at identifying lucrative opportunities when they pass by andthus time their decisions to become entrepreneurs better. In that casethe measured effect of abilities on income may not be completely causal.

Comparing these results to the (ability related) drivers of oc-cupational choice, we conclude that there is a discrepancy betweenthe drivers of occupational choice and the relative income from en-trepreneurship versus wage employment. This conclusion is in line withthe “returns to entrepreneurship puzzle” (Hyytinen et al., 2008). Despitelower average and more risky incomes, people not only choose tobecome and remain entrepreneurs but they also turn out more satisfiedas such (Benz and Frey, 2008; Blanchflower and Oswald, 2008; Hamilton,2000; Hyytinen et al., 2008; Parker, 2004; Van Praag and Versloot, 2007).


We show that the puzzle remains when looking at a specific driver offinancial returns, that is, abilities, instead of income levels themselves.Recent empirical studies trying to solve this puzzle find that the choicefor entrepreneurship is mainly governed by more autonomy and controlover (the accruals from) one’s own work as an entrepreneur comparedto positions in wage employment (Benz and Frey, 2008; Hyytinen et al.,2008) and not so much by income maximization.

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