Returns for Entrepreneurs vs. Employees: The Effect of ...

[Pages:34]DISCUSSION PAPER SERIES

IZA DP No. 4628

Returns for Entrepreneurs vs. Employees: The Effect of Education and Personal Control on the Relative Performance of Entrepreneurs vs. Wage Employees Mirjam van Praag Arjen van Witteloostuijn Justin van der Sluis December 2009

Forschungsinstitut zur Zukunft der Arbeit Institute for the Study of Labor

Returns for Entrepreneurs vs. Employees: The Effect of Education and Personal Control on the Relative Performance of Entrepreneurs vs. Wage Employees

Mirjam van Praag

University of Amsterdam, Tinbergen Institute, Max Planck Institute of Economics and IZA

Arjen van Witteloostuijn

University of Antwerp and Utrecht University

Justin van der Sluis

University of Amsterdam

Discussion Paper No. 4628 December 2009

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IZA Discussion Paper No. 4628 December 2009

ABSTRACT

Returns for Entrepreneurs vs. Employees: The Effect of Education and Personal Control on the Relative Performance of Entrepreneurs vs. Wage Employees*

How valuable is education for entrepreneurs' performance as compared to employees'? What might explain any differences? And does education affect peoples' occupational choices accordingly? We answer these questions based on a large panel of US labor force participants. We show that education affects peoples' decisions to become an entrepreneur negatively. We show furthermore that entrepreneurs have higher returns to education than employees (in terms of the comparable performance measure `income'). This is the case even when estimating individual fixed effects of the differential returns to education for spells in entrepreneurship versus wage employment, thereby accounting for selectivity into entrepreneurial positions based on fixed individual characteristics. We find these results irrespective of whether we control for general ability and/or whether we use instrumental variables to cope with the endogenous nature of education in income equations. Finally, we find (indirect) support for the argument that the higher returns to education for entrepreneurs is due to fewer (organizational) constraints faced by entrepreneurs when optimizing the profitable employment of their education. Entrepreneurs have more personal control over the profitable employment of their human capital than wage employees.

JEL Classification: J23, J24, J31, J44, M13

Keywords: entrepreneurship, self-employment, returns to education, performance, personal control, locus of control, human capital, wages, incomes

Corresponding author:

Mirjam van Praag Amsterdam Center for Entrepreneurship Faculty of Economics and Business University of Amsterdam Roetersstraat 11 1018 WB Amsterdam The Netherlands E-mail: C.M.vanPraag@uva.nl

* A very early version of this paper circulated entitled: "Why are the returns to education higher for entrepreneurs than for employees?" We are grateful to Robert Fairlie and Rob Alessie for their excellent comments. The usual disclaimer applies.

1. INTRODUCTION Entrepreneurship is a multidisciplinary field with a wide topical coverage. Shane and Venkataraman took stock of the entrepreneurship literature in a note in the Academy of Management Review of 2000. They defined the field "as the scholarly examination of how, by whom, and with what effects opportunities to create future goods and services are discovered, evaluated and exploited" (Shane and Venkataraman, 2000: 218). Indeed, by and large, the entrepreneurship literature revolves around these three key issues. First, the "how" question deals with entrepreneurship strategies such as firm entry and product innovation (e.g., Wu and Knott, 2006). Second, the "who" question relates to the examination of what makes an entrepreneur different from a non-entrepreneur, varying from family background to genetic heritage (e.g., Nicolaou, Shane, Cherkas, Hunkin and Spector, 2008). Third, the "what" question focuses on entrepreneurial performance, searching for the drivers of performance heterogeneity (e.g., Zott and Amit, 2007). The "what" question has received most scholarly attention, to date (Ireland, Reutzel and Webb, 2005).

The first question addressed in the current paper primarily relates to the "what" question, too. We study the effect of one particular source of human capital, i.e., education, on the relative performance of entrepreneurs vis-?-vis employees. How is formal education related to entrepreneurial performance? Any insight in the "what" question may have important implications. As demonstrated by a recent literature review (Van Praag and Versloot, 2007), there is hard evidence supporting the important contribution of entrepreneurs to the economic development of nations. However, Henrekson and Johansson (2009) show that the higher end of the distribution of entrepreneurs over performance levels is to a disproportionately large extent responsible for creating economic value. Successful entrepreneurs are responsible for economic growth, sustained levels of competition, the creation of jobs, and innovations. The established benefits imply positive external effects at the societal level, making insight in determinants of success, the "what" question, valuable: "Those business men who have pioneered new paths have often conferred on society benefits out of all proportion to their own gains, even though they have died millionaires" (Marshall, 1890: 598). Indeed, many developed countries and regions, including the US and the EU, have installed policies fostering successful entrepreneurship. One of these is providing people opportunities to develop their human capital by means of education. An underlying assumption of this approach is that investments in human capital increase people's performance as an entrepreneur. In other words, these policies are consistent with the belief that entrepreneurship competencies can be developed through education. But how valid is this assumption? Can entrepreneurship be learned in school?

To answer this "what" question, we measure the effect of investing in human capital through formal education on the performance of entrepreneurs, and compare this to employees. In so doing, we believe we introduce a good proxy for the impact of opportunity cost. From the perspective of the management literature, key is that an entrepreneur's "performance advantage may be insufficient to compensate for the opportunity cost of the alternatives" (Shane and Venkataraman, 2000: 217). In line with this fundamental starting point, Shane and Venkataraman (2000) recommend to focus on the individual whilst taking into account opportunity costs. This is anything but easy, as profit opportunities and opportunity costs are difficult-to-observe constructs. The returns to education measure suits this purpose very well, we argue, by offering the opportunity to compare the entrepreneurs' returns to education with the employees'. In so doing, the entrepreneurs' returns are

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estimated in comparison with that which forms the key alternative career option: wage employment. Although the comparison is sometimes flawed with measurement issues, as we will discuss below, the income performance measure is the only one available for both entrepreneurs and employees, albeit in somewhat different fashions (Hamilton, 2000). It reflects an appropriate complementary measure of entrepreneurial performance, next to the standard objective and subjective performance measures currently in use in the management literature. It is the most widely used performance measure in the economics of entrepreneurship (see the survey of Van der Sluis, Van Praag and Vijverberg, 2008).

Basically, the measured effect of education on performance boils down to the measured return to education in terms of income. For the US, we show that formal education enhances the performance of entrepreneurs importantly and significantly. Even stronger, the returns to education for entrepreneurial spells are not only large and significant, they are also significantly higher than the returns to education in wage employment, at least in the US. Of course, we do not argue that income reflects the measure of entrepreneurial performance. Without any doubt, depending upon the research question at hand and the associated level of analysis, other performance measures such as new venture survival, small firm growth or subjective performance evaluation offer rich yardsticks in entrepreneurial research. Rather, we argue that our analysis provides an interesting complementary perspective by offering a way to deal with the tricky issue of opportunity costs. This is our first contribution.

The second, supplementary, issue we address relates to an important "who" question: "Are higher educated people more or less likely to enter into entrepreneurship?" One all too frequently hears those stories about very successful entrepreneurs who were early dropouts from the education system. For instance, at a list is (more or less proudly) presented consisting of the world's richest billionaire dropouts from school. Among them are Sir Richard Branson (Virgin), Michael Dell (Dell computers), Bill Gates (Microsoft) and Larry Ellison (Oracle), who all dropped out from their schools at various stages. It is often claimed that education is a waste of time in case you want to become an entrepreneur. Education would only be worthwhile for wage employees. Hence, these claims imply that entrepreneurship competencies cannot be (effectively) developed through formal education. However, perhaps, this popular rhetoric should be taken with a grain of salt, since the most recent list of the world's billionaires included 1,125 individuals with only 73 of them, i.e., six per cent, having dropped out at some stage of schooling. This popular narrative as to what may be coined the Bill Gates effect can be contrasted with the standard argument that formal education is associated with a higher likelihood of opting for entrepreneurship, the reason being that higher educated people are more likely to observe entrepreneurial opportunities. In this paper, we will test both alternative hypotheses. That is, is choice behavior more in line with Bill Gates' dropout example or with the belief that entrepreneurship performance (and thereby rewards) can be boosted by education? Is there a negative, positive or zero correlation between an individual's education level and the probability that they are observed as an entrepreneur? Our analyses show that the choice for entrepreneurship versus wage employment is negatively related to education. So, the choice behavior is inconsistent with (information about) higher returns to education in entrepreneurship than in wage employment. We put these seemingly contradictory findings in the perspective of the literature that has shown wide support for the fact that the choice for entrepreneurship is not primarily driven by income

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maximization. It is referred to as the `returns to entrepreneurship puzzle' (Benz and Frey, 2008; Blanchflower and Oswald, 2008; Hamilton, 2000; Hartog, Van Praag and Van der Sluis, 2009; Hyytinen, Ilmakunnas and Toivanen, 2008; Parker, 2004; Van Praag and Versloot, 2007). Since we study the effect of education on the proceeds from and choice for entrepreneurship, we can suggest a solution for this particular demonstration of the puzzle: the Bill Gates effect. This is our second contribution.

The third issue that we focus on deals with a key question associated with may be coined the "why" question, given the finding of the employee-entrepreneur returns to education differential in favor of entrepreneurs: "Why are the returns to education higher for entrepreneurs than for wage employees?" Using insights from personal control theory (e.g., Benz and Frey, 2008; Douhan and Van Praag, 2009; Hyytinen et al., 2008), we argue that the higher returns to education for entrepreneurs are due to fewer (organizational) constraints faced by entrepreneurs when optimizing the profitable employment of their education. As an entrepreneur, an individual can operate as a residual claimant. S/he can freely decide to engage in those activities where s/he believes that her / his talents are most likely to generate high returns. In contrast, as an employee, an individual is bounded by organizational processes and structures, with the higher ranked making the key decisions. So, there are limits to what an individual can decide to do inside the "iron cage" of an organization owned by others. We will test this personal control hypothesis, as well as a number of alternative explanations of the higher entrepreneurial returns to education. This is our third contribution.

A fourth motivation for this paper relates to the way in which we deal with three important econometric issues. Shane (2006) concludes that entrepreneurship (performance) studies have to deal with three econometric issues: unobserved heterogeneity, sample selection and endogeneity (see his "Introduction to the Focused Issue on Entrepreneurship" of Management Science). Only then, we can hope to generate unbiased estimates. Our data are from the 1979 National Longitudinal Survey of Youth (NLSY1979), which runs from 1979 to 2000. The nationally representative part of this survey panel includes 6,111 individual respondents aged between 14 and 22 in 1979. These respondents have been interviewed annually up to 1994, and on a bi-annual basis since then. Apart from entrepreneurship-employment status, money income and formal education, this survey includes rich information about a variety of other issues. Exploiting the panel nature and richness of this dataset, we take great care in dealing with the three econometric pitfalls of entrepreneurial (performance) research. In so doing, we respond to "the desire among entrepreneurship scholars to form longitudinal or panel samples and then to use appropriate methods for testing purposes" (Ireland et al., 2005: 562; see ?zcan and Reichstein, 2009, for a recent example). We benefit from the returns to education literature in labor economics, albeit almost exclusively applied to employees, by adopting the estimation strategies developed in that literature that adequately deal with critical econometric pitfalls that are endemic in earlier entrepreneurship performance work. Thus, we form econometrically sound answers to relevant questions about the role that education plays for entrepreneurship choices and performance, and why this is so. This is our fourth contribution.

The structure of this paper is as follows. First, we briefly review the labor economics literature on returns to education, specifically focusing on work on entrepreneurship in relation with econometric issues (Section 2). Subsequently, we develop a set of four hypotheses that help to structure our series of empirical analyses (Section 3). After that, we introduce our data and method (Section 4). Next, we present the empirical

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evidence (Section 5). Finally, we conclude with an appraisal, reflecting on this study's limitations and the implied opportunities for future research (Section 6).

2. THE RETURNS TO EDUCATION LITERATURE The study of returns to education has a long tradition in labor economics. By and large, this tradition is based on standard human capital theory. Human capital refers to the stock of skills and knowledge relevant to performing labor to produce economic value. It is the skills and knowledge gained through education and experience that was first defined as such by Adam Smith (1776). Thus, schooling is viewed as an investment in human capital (Mincer, 1958; Becker, 1964), implying that the returns to schooling may be measured in terms of the extra income due to additional schooling. More precisely, the internal rate of return is the discount rate that equates the present values of the lifetime earnings flows in the case of x years of education versus the case of x-1 years of education (Hartog and Oosterbeek, 2007). Mincer (1974) has introduced a simplified estimation strategy of rates of return to schooling, which boils down to estimating the rate of return as the coefficient of schooling years in a cross-section regression for individual earnings (Hartog and Oosterbeek, 2007). The rate of return thus measures how much extra units of income are generated, ceteris paribus, by an extra unit of education. The more recent labor economics literature has produced estimates of the rate of return to education that, apart from controlling for a variety of alternative explanations, address the three critical econometric pitfalls identified by Shane (2006): unobserved heterogeneity, sample selection and endogeneity (e.g., Card, 1999; Oreopoulos, 2006). However, these estimates pertain almost exclusively to the returns to education in wage employment. There is a separate but less developed educational returns literature for entrepreneurs. Bringing them together is interesting since a combined analysis of entrepreneurs and employees enables a comparison of the relative value of education for the performance of entrepreneurs versus employees, so indirectly capturing the critical notion in the entrepreneurship literature of opportunity cost.

In the entrepreneurship literature, a stream of studies has focused on determinants of performance of entrepreneurs. In many of these studies, education has been included as a determinant. Van der Sluis et al. (2008) draw four conclusions relevant in our context, based on a meta-analysis of more than hundred studies of the relationship between education and entrepreneurship (entry and performance). First, the relationship between education and selection into entrepreneurship is mostly insignificant ? i.e., in 75 per cent of the cases. Second, the relationship between schooling and entrepreneurship performance is unambiguously positive, and significant in 67 per cent of the studies, irrespective of the performance measure used (such as survival, profit, income or firm growth).2 Third, the meta-analysis identifies approximately twenty studies that have actually measured the relationship between education and earnings for both entrepreneurs and employees in a comparable fashion. The conditional correlations between education and income turn out to be similar for entrepreneurs and employees, though somewhat higher for entrepreneurs in the US.

The fourth conclusion from the meta-analysis is that only few studies try to cope (in credible manners) with endogenous selection of individuals into entrepreneurship vis-?-vis wage employment. Moreover, earlier

2 The meta-analysis further reveals that the most widely used performance measure for entrepreneurs is their income, in line with the measure used in this study.

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studies did not yet employ estimation strategies that account for the endogeneity of schooling in performance equations, nor did they deal with unobserved individual characteristics that may drive the result, possibly leading to inconsistent estimates. This may perhaps be explained by the fact that many of the earlier studies measure the relationship between education and entrepreneurship outcomes as a by-product, while focusing on different issues. This is in sharp contrast with the common practices developed in labor economics, when studying the returns to education for employees (see Ashenfelter, Harmon and Oosterbeek, 1999). The first strategy used to cope with unobserved ability is trying to make the unobservable observable. Various proxies of intelligence and other test scores have been included in income equations. The effects of adding such controls on the estimated returns to education have been ambiguous (see Table 3 in Ashenfelter et al., 1999, for an overview).3 Inclusion of ability proxies in the income function does not completely shield the estimated returns against ability bias due to an imperfect correlation between such proxies and ability. Similarly, the endogeneity issue is not solved since ability is not necessarily perfectly correlated with the optimization behavior of individuals. Additional approaches are thus used to estimate the returns to education for employees, such as the employment of samples of monozygotic twins (e.g., Ashenfelter and Krueger, 1994; Bonjour et al., 2003), where identification comes from those twins who differ in their schooling and income, assuming that all unobserved factors are approximately equal. The usual finding is that treating education as an exogenous variable leads to downward biased estimates of the returns to education (e.g., Ashenfelter et al., 1999).

The most widely used identification strategy is the instrumental variables (IV) approach. Instruments are identified that explain a substantial proportion of the variance of the endogenous variable, education in this case, but are unrelated to the dependent variable ? i.e., income. Key is that the instrumented endogenous variable is not related to the error term anymore. This method strongly hinges on the quality and validity of the identifying instruments used. Like using twins, the IV-strategy leads to higher estimates of the returns to education of employees than when treating education as an exogenous variable. This is not only the case when parental background variables are used as identifying instruments (Blackburn and Neumark, 1993), but also when changes in compulsory schooling laws are introduced (e.g., Angrist and Krueger, 1991; Oreopoulos, 2006). Harmon, Oosterbeek and Walker (2003: Figure 6 on page 139) show that, based on a meta-analysis of models with endogenous schooling, IV-estimates of returns to schooling are higher than OLS-estimates, and that IVestimates based on exogenous variation in schooling attainment are even higher than when instruments are based on family background variables.

Since the meta-analysis by Van der Sluis et al. (2008), and prior to this study, three studies have used IVmethodologies to measure the returns to education for entrepreneurs: Van der Sluis and Van Praag (2004, 2007) and Parker and Van Praag (2006). In the current study, we re-evaluate the returns to education for entrepreneurs (relative to employees), without some of the drawbacks that characterized the earlier attempts. Like Van der Sluis and Van Praag (2004, 2007), and unlike Parker and Van Praag (2006), we measure the returns to education for entrepreneurs as well as employees. Unlike Van der Sluis and Van Praag (2004), we measure the returns to education for both groups within one framework (income equation) such that the (significance of the) difference

3 Theory predicts that omitting ability in the wage equation causes OLS-estimates to be upward biased (Griliches, 1977; Harmon and Walker, 1995; Ashenfelter et al., 1999).

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