Immigrants and Science Entrepreneurship

Transcription

1 Immigrants and Science Entrepreneurship Giulia La Mattina Boston University Department of Economics Donna Ginther University of Kansas Department of Economics Shulamit Kahn* Boston University School of Management and Megan MacGarvie Boston University School of Management and the NBER PRELIMINARY DRAFT DO NOT QUOTE This project is funded by National Science Foundation Grant SBE * Corresponding author. skahn@bu.edu 595 Commonwealth Avenue Boston MA

2 I. Introduction Entrepreneurial firms particularly those engaged in science or engineering -- have historically played a key role in developing and commercializing new technologies, and consequently in driving economic growth. Policies designed to encourage entrepreneurship will be more effective if we have a better understand what drives scientists and engineers to start new businesses. Furthermore, many high-profile entrepreneurial firms based in the United States were founded by individuals born outside the US, and it has been shown that US residents born outside the US are more likely to be self-employed or start new companies. Moreover, the foreign-born are becoming larger and larger proportions of the US science and engineering (S&E) workforce, and therefore are likely not only to start new firms, but to start new firms engaged in science or technology. This paper asks whether immigrants are particularly more likely to be self-employed in what we call science entrepreneurship, new businesses engaged in a scientific or technical area or occupation. To our knowledge, it is the first to use a national sample yet zero in on sciencerelated entrepreneurship. The paper also tests a wide variety of theories of who becomes an entrepreneur and why they become entrepreneurs, and asks both whether the data support these theories for both science and non-science entrepreneurship. It then asks whether the theories can help explain the immigrant entrepreneurship premium. To do this, we use the National Science Foundation s SESTAT database of over 100,000 people 1993 and SESTAT includes people in the US with a Bachelor s degree or higher in some way connected to science or engineering either due to their job or due to one of their degrees. We use a measure of entrepreneurship self-employed incorporated -- that we believe to be more appropriate to scientific entrepreneurship than a less specific self-employment measure. The paper proceeds as follows. After describing the data and giving background on the immigrant entrepreneurship premium, we first consider whether the increased likelihood of immigrants alone can explain the immigrant entrepreneurship premium. It cannot. Many authors have found that there seems to be a bimodal distribution of immigrants, at very low and very high ability levels, often denoted as hobos and stars. A growing literature is developing theories (typically based on non-convexities) to explain this bimodal distribution. 2

3 We address ability in a variety of ways. First, we consider whether within our highly educated sample, ability still has a bimodal impact on entrepreneurship and how much this contributes to the immigrant entrepreneurship premium. We measure ability alternatively in terms of education, experience and wages. To preview our findings, while educational level is important, having a PhD actually decreases science entrepreneurship but does not explain the immigrant entrepreneurship premium. There is evidence of a U-shaped relationship between wages and future entrepreneurship, consistent with a hobo and star effect. However, this non-linear relationship only exists for non-science entrepreneurship. There is no obvious relationship between current wages and future entrepreneurship in science-based ventures. We then test a theory of mismatch by looking at the effect of being over- or under-paid, holding constant wages and characteristics as measured by positive and negative wage residuals, in both non-science and science entrepreneurship. We find that it is not low ability levels per se, but rather being paid less than others with similar characteristics, that leads people to nonscience entrepreneurship. In fact, the fact that immigrants are much more likely to be observed at the low end of the residual distribution explains much of the immigrant premium in non-science entrepreneurship. We hypothesize that the fact that immigrants are mismatched into jobs that pay less than natives with similar characteristics because of language or cultural reasons, and therefore are more likely to choose the entrepreneurship route. We also find that especially high ability immigrants and natives are both more likely to be entrepreneurs, but the special advantage from being an extremely able person (given your characteristics) translates into entrepreneurship only for natives. In science entrepreneurship, there are no hobo s, no higher tendency of low-ability people to become science entrepreneurs. Also, very high ability natives, but not immigrants, are more likely to be science entrepreneurs. Instead, in science entrepreneurship, there remains a significant immigrant entrepreneurship premium that is spread across most wage deciles. We hypothesize that this immigrant premium in science entrepreneurship is driven by stronger preferences for entrepreneurship among immigrants. In a final section, we consider some specific preference variables (based on SESTAT survey questions about desired job characteristics). However, none of the specific immigrant-native differences in these job characteristic preferences explain the immigrant science-entrepreneurship premium. 3

4 II. Data SESTAT is collected by the National Science Foundation (NSF), and it is the most comprehensive database on the employment, educational, and demographic characteristics of U.S. scientists and engineers available. It contains observations on over 100,000 respondents that have science and engineering (or related) educations or work in science and engineering (S&E)occupations. It is a biennial panel data set that allows researchers to follow scientists and engineers over time. Individuals included in the sample reside in the United States during the survey reference period, are less than seventy-five years old, and have a bachelors degree or higher. These individuals have degrees in, or work in, the fields of computer and math sciences, life sciences, physical sciences, social sciences, engineering, health, or technology. The sample excludes medical doctors and associate degree holders. SESTAT has limited coverage of those receiving their highest degree outside of the United States and those that move into science and engineering jobs during the decade. SESTAT consists of three surveys, the National Survey of Recent College Graduates (NSRCG), the National Survey of College Graduates (NSCG) and the Survey of Doctorate Recipients (SDR). The NSRCG includes individuals with a science, engineering or health bachelor s or master s degree in the previous two to three academic years. The NSCG consists of individuals who responded to the decennial census and report having a bachelor s degree or higher. The first wave of the NSCG contains all degree holders. Subsequent waves of the NSCG integrated into SESTAT panels follow over time individuals with science, engineering, or health degrees. Subsamples of the NSRCG are included in subsequent NSCG panels. We exclude individuals from the SDR because the labor market for doctorates is very different from the labor market for all scientists and engineers. SESTAT collects information on employment, including labor force status, job and employer characteristics, work activities and training. Education questions include all degrees, years and institutions, and continuing education activities. SESTAT contains comprehensive 4

5 demographic information on gender, race/ethnicity, marital status, children, citizenship and immigration status, etc. 1 SESTAT creates a new panel of scientists each decade. The 1990s panel includes college graduates in the NSCG who were identified in the 1990 decennial census as well as individuals surveyed by the NSRCG and SDR in the 1990s. The 1990s panel includes four waves, 1993, 1995, 1997, and The 2000s panel includes individuals in the NSCG who reported being college graduates in the 2000 decennial census as well as recent college graduates surveyed in that decade. We use two waves of data from this panel, from 2003 and 2006 in our sample. There are some relevant differences in the 1990s and 2000s surveys and panel. An NSF review indicated that the self-employed were being under-reported because of the order of the choices given for employer type. This was rectified in the surveys beginning with the 2003 survey. Second, in the 2000s the target population was enlarged to include people with health or other science and engineering-related education and occupations. Our analysis does not concern time trends in entrepreneurship, so these differences should not bias our results. We do include survey year dummies in all analysis, and this will pick up any difference across surveys due to these composition factors as well as time-related factors. 2 In much of the analysis in this paper, we include only those whose highest degree was in the natural sciences or engineering. This excludes those whose highest degree was in the social sciences. We do include, however, people s whose highest degree was in management but whose previous degree was in the natural sciences. Throughout this study, we define as entrepreneurs people who are self-employed and working for an incorporated business. This variable is available for all the years of our sample. We prefer this variable to simple self-employment because those who incorporate are more likely to hire workers and supervise others (in the fill sample 65.3% of self-employed incorporated supervise others, while 50.8% of self-employed unincorporated do so). Those selfemployed who are incorporated are also more likely to be in a business related in some way to science than those who are self-employed non-incorporated (compare 23.6% v. 14.2% based on everyone.) Within the set of self-employed, incorporated entrepreneurs, we further refine our measure by dividing them into science entrepreneurs and non-science entrepreneurs. Science 1 More information about the SESTAT database is available at 2 For robustness,we have also done most of the analysis excluding health fields and results are qualitatively similar. 5

6 entrepreneurs include those self-employed (incorporated) whose occupation is given as a field within science, or whose occupation is management but their primary or secondary work activity relates to science. Science entrepreneurship expressly excludes people in professional services, most of whom are doctors or health professionals in private practices. We categorize these and all others not doing expressly science-related work as Non-science entrepreneurs.. III.Facts on immigrants and entrepreneurship (Section Incomplete) In our data set, 7.7% of workers are classified as entrepreneurs according to our definition (e.g., self-employed and incorporated). The rate of self-employment is higher among immigrants than among natives, but as Table 1 shows, when self-employment is divided into those incorporated and those not incorporated, this applies only to self-employed incorporated, where 9.5% of foreign-born were entrepreneurs compared to 7.5% of natives which translates into immigrants being 27% more likely than native to be entrepreneurs. In contrast, immigrants are somewhat less likely than natives to be self-employed and un-incorporated. When we limit ourselves to the subset of those with highest degrees in science, rates of entrepreneurship are somewhat higher while rates of unincorporated self-employment are lower (Table 1), again suggesting that the kinds of entrepreneurship that further technological advances are primarily in incorporated self-employment. There remains a 2 percentage point difference in entrepreneurship between immigrants and natives. We are most interested in those entrepreneurs whose new ventures are science-based i.e. science entrepreneurship. Here, the difference between natives and immigrants is far more striking (Table 1). Immigrants are 89% (2.8 v. 1.5 percentage points) more likely than nonimmigrants to be doing science entrepreneurship, while only 12% (6.7 v. 6.0 percentage points) more likely to be doing non-science entrepreneurship. Higher science entrepreneurship rates are obtained when we limit the sample to those with highest degrees in science, with a similar percentage point but smaller percentage immigrant-native gap (3.5 v. 2.2). In stark contrast, the rate of science entrepreneurship among those who are self-employed but not incorporated, even among those with a highest degree in science, is less than 0.8 for both immigrants and natives. 6

7 Previous authors have noted the greater likelihood of immigrants to be self-employed than natives, particularly in the U.S. 6 Many of these authors have suggested possible explanations for this fact and tested specific hypotheses about such explanations. Much of this work are based on careful empirical work with a large number of controls. Table 2 [note: incomplete] surveys some of this literature. Seminal work by George Borjas (Borjas 1986; Borjas and Bronars, 1989) found self-employment rates exceeding 15% for some nationalities in the US and found that self-employment increased the longer the immigrant was in the US and the later the cohort. Borjas argued that much of the increased tendency of immigrant selfemployment was due to the enclave effect, where immigrants start businesses that serve fellow-immigrants since immigrants know the language and preferences of those from their country living in a local enclave, it expands their opportunities for serving this market giving them a comparative advantage) Clark and Drinkwater (1998, 2000) believe discrimination against ethnic minorities are one reason for enclave effects in England but find that those with less English fluency and more recent immigrants are actually less likely to be self-employed, even those living in enclaves. Fairlie (2008) also found that immigrants are more likely to start a business than natives and that immigrants represent 16.7% of new business owners. He also finds that they are the least educated business owners. Yuengert (1995) did not find enclave effects. He did find that immigrants who became self-employed tended to come from countries with more self-employment and argued that this is likely have led to greater preferences for self-employment or lower start-up costs. However, Akee et. al (2007) found that this is because immigrants themselves were more likely to have been self-employed in their home country. Studying Spain, Irastorza and Pena (2007) found that immigrants in Spain are more likely to become self-employed. Concentrating on more scientific self-employment/entrepreneurship, Axe and Hart (2011) survey the high tech industry and find that 16% of the companies in their sample reported at least one founder who was foreign-born. Wadwha et al () collected information on engineering and technology companies founded between 1995 and 2005 and interviewed 144 of them. Among other things, they found that 25% had foreign born CEO s or CTO s and that 53% of whom completed their highest degree in U.S. universities, and that the majority of these had come to 6 General literature on the causes of entrepreneurship is discussed in the individual sections below. 7

8 the US to study. Finally, Anderson and Platzer (2006) found that in the period , immigrants started 40 percent of U.S. public venture-backed companies operating in high technology. IV. Is foreign entrepreneurship due to field only? The establishment of an entrepreneurial venture in many cases reflects the commercialization of a new idea. One of the most fruitful sources for profitable new ideas is recent scientific knowledge (whether discovered by the potential entrepreneur or not). The foreign-born are an increasing proportion of U.S. graduates in science at all levels of higher education but particularly at the highest doctoral level (see Bound, Turner and Walsh, forthcoming). It is possible that the immigrant-native differential in entrepreneurship is due mainly to field of study. Hunt (2011) highlighted field as an important source of immigrant-native differentials in innovation and knowledge creation. Using one year of SESTAT data, she found that the advantage of immigrants over natives in wages is entirely due to the immigrants field of study, while 90% of the immigrant advantage in patenting and about 50% of the advantage in publishing can be attributed to field. Hunt also addressed entrepreneurship but using a different measure of entrepreneurship than ours one that averaged less than 1% of those working and therefore had more noise than our measure. (Her measure was whether the person was selfemployed in a business they had started within the last five years with at least 10 employees.) She found a marginally significant difference between natives and immigrants that did not decrease with the inclusion of field and highest degree dummies. Table 3 examines whether the immigrant-native differential in entrepreneurship is due mainly to detailed field (of highest degree). Note that even in Table 1, we saw a much smaller immigrant-native entrepreneurship differential among those whose highest degree is in science than in the survey as a whole (10.0% immigrants compared to 7.9% of natives, or 26% higher.) The first two columns of Table 3 give the distribution of natives compared to immigrants across the detailed field of highest degree. We find that immigrants are twice as likely as natives to be in computer and information sciences, statistics, chemistry and many engineering subfields, about half as likely to be in social sciences (excluding economics), and 63% as likely to be in a field besides science and engineering. 8

9 The next columns give the coefficients of probit analyses, converted to marginal effects. Field alone explains 2.1 percent of the variation in entrepreneurship (Table 3 regression 2). Somewhat surprisingly, when limiting only to those with highest degrees in science, field alone explains a larger percent of the variation in entrepreneurship (2.8 percent). Are the substantial differences in fields between natives and immigrants responsible for the immigrant-native entrepreneurship gap, measured in regression #1 as 2.03 percentage points (26% of the average entrepreneurship)? Adding detailed field only changes the immigrant-native gap a bit, to 1.81 percentage points (23% of average). Moving to the smaller sample of those with highest degrees in science (with detailed field controls) there is a somewhat larger immigrant-native gap (2.25 percentage points, representing 27% of this smaller sample). Of course, field of study can be correlated with many different demographic features or with the year or region, all of which affect the propensity to become an entrepreneur. In Table 3 s last 3 regressions, we control for these characteristics including gender, race, and age (allowing for nonlinear effects of age using a cubic) as well as dummies for year, region, and year-region interactions. (We leave controls for human capital levels until the following section.) Adding these controls more than triples the R-squared. With these controls and detailed field dummies, the immigrant-native gap for those with highest degree in science falls to 1.2 percentage points. The immigrant-native gap within science-based entrepreneurship is smaller only 0.3 percentage points, but statistically significant at the 5% level. It is more than twice as high for non-science entrepreneurship, at 0.7 percentage points. One striking regularity in these regressions with covariates and field is that the immigrant-native gap is 12.5 percent of the average entrepreneurship rates for both science and non-science entrepreneurship among those with highest degrees in science 7. This hints that there is a quite constant immigrant-native gap across both types of entrepreneurship. V. Is foreign entrepreneurship due to ability? A. Theory A good deal of the theoretical literature that addresses why some people are more likely to become entrepreneurs than others has linked entrepreneurship to ability and human capital. 7 In science entrepreneurship, this is 0.30/2.39 and in non-science it is 0.74/

10 Thus, those people who are particularly good in what they do (the stars in a given field) may enter entrepreneurship in order to capture their entire marginal product (e.g. Elfenbein 2009) or because having a variety of abilities often referred to as being a jack-of-all-trades is particularly useful when starting one s own business (e.g. Lazear 2004). Empirically, however, entrepreneurship seems common at both ends of the ability spectrum. Thus entrepreneurship rates have been shown to have a U-shaped relationship to education levels, higher for those with low and high education levels but lower for those with more average education levels. The same U-shaped relationship has been shown for the relationship between entrepreneurship and wages in previous paid employment (or in potential alternative employment). To explain the high rates of entrepreneurship at the bottom of the ability scale, some of the literature has appealed to completely different reasons than those that dominate at the top. Thus, they are considered people who enter self-employment because they cannot find a job or believe they are under-employed the grass is greener syndrome. The term hobo is sometimes applied to these low end entrepreneurs borrowed from the job mobility literature or the word misfit. Poor job fits can be found at any level of ability, however, so here we will call them simply low ability. Several recent papers have developed equilibrium models that endogenously lead to the observed bimodal relationship between entrepreneurship v. ability. These models are all based on some convexity in the relationship between productivity as entrepreneurs and wage in paid employment. Thus Poschke (2008) estimates a bimodal incidence of entrepreneurship assuming either a convex relationship between productivity in entrepreneurship and ability and/or assuming that the entrepreneurial firm s productivity is a convex function of the person s productivity in entrepreneurship. Ohyama (2007) finds a bimodal incidence of entrepreneurship assuming convexity in the relationship between a person s cost of gaining entrepreneurship skills as a function of their ability, such that ability is more valued in entrepreneurship than in paid employment. Finally, in Astebro et al (2011), complementarity of skills leads to a convexity of entrepreneurship productivity v. ability, but this alone is insufficient to produce the bimodal incidence of entrepreneurship. The complete Astebro et al model is discussed in the following section. The convexities in Poschke (2008) and Ohyama (2007) do lead to higher productivity 10

11 rates in self-employment relative to paid employment for those with very low or very high ability. In the next subsections, we investigate whether both immigrants and natives demonstrate a U-shaped relationship between entrepreneurship and ability, measuring ability first in terms of education and experience levels and then in terms of wage. B. Education Some of the literature linking self-employment to education does not differentiate between levels of post-ba degrees (e.g. Astebro et al 2011) or even groups all with at least a college degree together (Borjas and Bronars, Hamilton 2000). Since in our dataset all people have a minimum of a college degree, entrepreneurship patterns at lower levels of education are not relevant to our sample. Note that it may be that hobo arguments are most appropriate for individuals with lower levels of education than those in our sample. However, Poschcke (2008) (using the NLSY, with no controls) differentiates between levels of post-ba education, and finds a U-shape, or more exactly a J-shape for the entrepreneurship rates at the educational levels of college and above. He finds that self-employment rates among college graduates are greater than those among people with masters degrees, approximately equal to those with a professional degree, and less than those among doctoral recipients. Using SESTAT with only those with highest degrees in science and thus eliminating professional degrees, Elfenbein et al. (2009) find (without including controls) something between a U-shape and a J-shape, with selfemployment rates much lower for masters degrees than for BA s which have self-employment rates slightly lower than those with a Ph.D. The numbers in Ohyama (2007) based on SESTAT (for years in the 1990s only) finds a similar shape as Elfenbein et al (2009). We find similar shapes for self-employment, but not for our measure of entrepreneurship self-employed incorporated. The unconditional descriptive statistics are shown in Figure 1. Let us first consider what happens as education rises from BA to MA to Ph.D. Figure 1A includes the total SESTAT sample and looks at all self-employment for the total population, the closest comparison to previous work. As some others have found, there is a U-shaped relationship although with PhD less like than BA to be self-employed. However, limiting ourselves to selfemployed incorporated entrepreneurship (Figure 1B), there are sharply decreasing rates of entrepreneurship as we move from BA to Masters to Ph.D. level (although the difference 11

12 between Masters and PhDs is small and insignificant.) Further, as we narrow the sample to those with a highest degree in science (Figure 1C), there are again decreasing rates of entrepreneurship as education levels increase (although now the BA and MA rates are much closer to each other than MA and PhD). The regressions in Table 4 look at how entrepreneurship varies with education (BA excluded category), controlling for age (cubic), experience (cubic) as well as all variables used in Table 3 (field, demographics, age, year and region). Overall, the patterns indicate that entrepreneurship falls with education level. Again, we first estimate on the entire sample using the broader measure used in the other literature, all self-employment. With controls, we no longer see a U-shape. Instead, the rate of self-employment is highest for BA s and falls by about 2 percentage points (26%) for the higher degrees of Masters and PhD s. We then narrow consideration to self-employed incorporated (entrepreneurship) and see a similar pattern, with Masters and PhD s both 1.7 percentage points (22%) less likely to be entrepreneurs than BA s. Further, as we narrow the sample to those with a highest degree in science, entrepreneurship declines with education also, now declining both from BA to MA and from MA to Ph.D. Dividing into science and non-science entrepreneurship indicates only a small difference. PhD s continue to have the lowest entrepreneurship rates for both types of entrepreneurship, with a BA- PhD gap more than twice the size in non-science entrepreneurship. However, BA s and MA s are essentially equally likely to start a science-related company, while MA s are 1.4 percentage points (23.5%) less likely than BA s to start a non-science company. In our discussion, we have separated MD s from other doctorates, because MD s may go into private practice, a kind of self-employment different from what we intend by science entrepreneurship. Compared to all other kinds of education, Figure 1 indicates that MD s have the highest levels of self-employment and incorporated self-employment for the sample as a whole and for people with highest degrees in science. However, practically none are science entrepreneurs by our definition (because our definition excludes professional services.) 8 The same is true controlling for other characteristics. In Table 4, MD s are by far the most likely to be self-employed or entrepreneurs, between 15 and 28 percentage points more likely than BA s (twice to three and ½ times more likely than the average.) However, the next columns indicate that those with MDs are very likely to be what we here deem non-science entrepreneurs which 8 Note that SESTAT categorizes PhD as higher than MD, so the PhD rate includes MD-PhD s. 12

13 includes private medical practices but less likely than those with any other degree (including PhD s) to be science entrepreneurs. 9 We also separated MBA s from other masters (and professional degrees) 10 since people may pursue MBA s particularly to learn how to start and/or run their own business. Yet the descriptive statistics show that for all measures of entrepreneurship, rates among MBA s are remarkably similar to those among BA s, sometimes a bit more and sometimes a bit less. However, the regression results indicate that ceteris paribus, MBA s are about 4.5 to 6 percentage points more likely than even BA s to be self-employed and./or self-employed incorporated. Limiting to those whose highest degree is in science but including MBAs with BA s in science MBA s are once again not significantly different from BA s (or MA s). 11 Is the higher rate of entrepreneurship among immigrants even controlling for field (as well as demographics, region and year) explained by different levels of education? Looking at Figure 2, in both our full sample and limited to those with highest degrees in science, more immigrants are Masters and MD s and far more are PhDs relative to natives, not surprising since a substantial proportion of these immigrants came to the US to pursue these higher degrees. And education has a large impact on non-science entrepreneurship. In results not shown, adding education alone (and not experience as well) raises the Pseudo-R-square from.121 (Table 3) to.148 in non-science entrepreneurship ( highest degree in science) and therefore also substantially increases when all types of entrepreneurship are combined. We therefore expect to see the immigrant coefficient be affected by the addition of education dummies. However, because immigrants are more likely to have higher degrees and those with higher degrees are less likely to be entrepreneurs, we expect the immigrant coefficient to fall when education is added. In unreported regressions adding only education levels and not experience, the coefficients on immigrant all fall somewhat (for all measures of entrepreneurship). 12 The decrease is not statistically significant for any entrepreneurship measure, but it is consistent with our intuition. The difference is larger for non-science than for 9 The difference between MD s and PhD s is not statistically significant. 10 Note that we only include other professional degrees for the entire sample, where most in this category are lawyers. The dozen or so people holding other professional degrees among those whose highest degree is in science are dropped from the analysis. 11 Results dividing into science and non-science entrepreneurship also show insignificant differences with BA s. 12 This and all results not included in the tables are available from the authors on request. 13

14 science entrepreneurship, because the PhD entrepreneurship penalty is much larger. 13 In results not shown, we also tested whether education affects immigrants and natives equally. In general, we found that immigrants and natives have the same patterns in their relationship between entrepreneurship and the level of education, with two small exceptions. In science entrepreneurship, immigrants with masters actually were more likely to be entrepreneurs than bachelors (Ph.D. was still the lowest). Second, further dividing immigrants by where they got their degree, immigrants were less likely to be non-science entrepreneurs as education increased (like natives), but the difference between PhD s and MA s/ba s was far greater for immigrants with highest degrees from US. Thus, PhD immigrants with US degrees were more than 80% less likely to be non-science entrepreneurs than masters or BA s and about 65% less likely than native PhDs. Finally, it is possible that for immigrants, not only education level affects ability but also where that education was obtained, inside the US or outside the US. Indeed, if we add both education levels and whether that education was obtained in the US into a wage equation, we find that the impact of education level on wage is the same for natives and immigrants whose highest degree was earned in the US. However, wages are substantially lower for immigrants whose highest degree was not earned in the US. Of course, it is possible that the location of an immigrants education does not affects true ability, but only ability as perceived (or rather, misperceived) by the employer. We found that those immigrants with highest degrees in the US were most likely to be entrepreneurs (coefficient.159) but those immigrant whose highest degree was earned abroad were still more likely to be entrepreneurs than natives (coefficient.068). Assuming that earning the highest degree in the US creates higher ability rather than just correcting misperceptions, we might infer that those with only BA s not earned in the US would be lowest quality in our sample and as a result more likely to be entrepreneurs (given our general education-entrepreneurship patterns). However, when we tested this using education-immigrant interactions in the entrepreneurship equation, we did not find that to be the case. (Results available upon request.) Instead, we found that, while like the natives, immigrants were less 13 A larger percentage of immigrants have MDs as well, however, so controlling for MD should increase the immigrant coefficient in science entrepreneurship (where MDs are a disadvantage) but decrease it in non-science. The MD impact, however is small. 14

15 likely to be nonscience entrepreneurs as education increased, the difference between PhD s and MA s/ba s was far greater for immigrants with highest degrees from US. Thus, PhD immigrants with US degrees were more than 80% less likely to be non-science entrepreneurs than masters or BA s and about 65% less likely than native PhDs. C. Experience Experience may be another ability measure that has a relationship with entrepreneurship. Experience (controlling for age) is likely to be related to entrepreneurship for reasons not related to the ability experience confers. No doubt for this reason, other studies of the distribution of ability and entrepreneurship have not used experience as a measure of ability, although several have included it as controls. Here, we ask whether the U-shaped pattern expected or the decreasing pattern actually seen for entrepreneurship v. education extends to experience, using a measure of experience not based entirely on age: age minus age at highest degree. We control for age as well, allowing both experience and age to have a cubic form. Note that age and experience will still be highly collinear, and highest degree will also be (negatively) correlated with experience conditional on age. Thus we are getting separate identification purely from the functional form. We therefore make no claims that our variable actually can be interpreted as experience. Using the coefficients on the experience terms in Table 4, we can calculate the entrepreneurship-experience relationship. For entrepreneurship (or self-employment) in the aggregate and non-science entrepreneurship, entrepreneurship is an upward sloping and generally convex function of experience (with some concavity at low levels of experience). However, science entrepreneurship decreases with experience (beyond the first year) at a slow but increasing rate (by.1 percentage point a year at 5 years but.5 percentage points a year at 15 years). Thus to the extent that our experience variable actually isolates experience, we see that more experience means more non-science entrepreneurship but somewhat less scienceentrepreneurship. Is the higher level of entrepreneurship of immigrants due to their different levels of experience? Based on results not shown, we first note that adding experience adds relatively little explanatory power to the entrepreneurship regressions of Table 4, with the R square increasing only at most.007 when we add experience to the other controls in Table 3 (or similarly when we 15

16 add experience conditional on education and all other variables). Experience does not change the explanatory power of science entrepreneurship at all. Immigrants on average also have about one more year of experience which should have increased their non-science entrepreneurship rates but slightly decreased the science entrepreneurship rate. When we added only experience and not education to the Table 3 regressions, we found that adding experience slightly increases the coefficient on immigrant for non-science entrepreneurship (from to ) and also very slightly increases it for science entrepreneurship (from to ). However, the immigrant coefficient was more influenced by education than by experience. D. Wages in paid employment In competitive labor markets without frictions or other market imperfections, wages reflect employees marginal value to its firm. Several articles have found a U-shaped (or J- shaped) relationship between self-employment and previous wages in paid employment. This includes Poschke (2008) (assuming a quadratic form and controlling for experience) and Elfenbein (2009) (who added dummies for top and bottom deciles.) In this section, we ask whether this is true in our data and with our definition of entrepreneurship, and whether it affects natives and immigrants and science and non-science entrepreneurship similarly. To study the impact of paid-employment wage on the likelihood of moving into entrepreneurship, this section (and the next) are based on a sub-sample of our data set consisting of those who were observed in at least one survey wave in full-time paid employment and were observed in the following survey as well. 14 Figure 3 shows the unconditional distribution of entrepreneurship for those with the highest degree in science as a function of the person s previous real wage in a paid-employment job in the previous period. Because it excludes anyone who is never observed in paid employment, had previously been observed in self-employment, or who was not observed for at least two consecutive surveys, it is about half the size of the samples in the previous tables, with 90,063 instead of 195,854 observations. As Table 1 14 As a result, it does not include people observed only in 1999 or 2006 because we don t observe these people in a subsequent survey. We drop people who transitioned from paid employment to unemployment or who left the labor force. In order to differentiate those with a tendency to become entrepreneurs from others, we drop people who were observed in self-employment before they were observed in paid-employment or who moved into selfemployment two or more surveys later. 16

17 indicates, this sample has much smaller rates of entrepreneurship (3.46% instead of 7.94%) than all of those with higher degrees in science because it excludes entrepreneurs not previously observed in paid employment. Table 7 gives the pseudo-r-squared and immigrant coefficients for key regressions in the results from the other tables. It gives two sets of numbers for the key Table 3 and Table 4 results: one for the entrepreneurship rate in the entire Highest-degree-inscience sample, and one for the likelihood of becoming an entrepreneur next period available only for those with at least two years of data who start in paid employment. Table 7 shows that we can explain more of the smaller (2 year) sample s probability of entering science-based entrepreneurship by the explanatory variables but slightly less of the probability of entering nonscience entrepreneurship. It also shows that the immigrant-native difference in science entrepreneurship rates is much larger for the start-in-paid-employment survey, suggesting that immigrants who become entrepreneurs are more likely than natives to have left paid jobs to become entrepreneurs. 15 Table 5 estimates the probability of entering entrepreneurship by the next survey year as a function of wage deciles in paid employment. Note that these equations do not include the standard human capital variables -- education and experience as controls. Instead, they assume that wages are a sufficient statistic for the value of the worker to his or her employer. The equations do control for demographic characteristics including immigrant status and for field, all aspects that are also often included in wage equations. However, we include these variables because they are all expected to have strong direct effects on the propensity to be an entrepreneur. The first two columns of Table 5 and Figure 4A are not limited to those with highest degrees in science and first model all self-employment and then entrepreneurship (incorporated self-employment). The shape of the wage deciles is very similar for both, with a J-shape that bottoms out at 1.9 percentage points and 1.2 percentage points respectively below the first deciles rate and tops out at 3.4 percentage points and 2.9 percentage points respectively at the top decile. Thus the choice of entrepreneurship measure does not affect the shape of the entrepreneurship-wage relationship. 15 We can imagine various reasons why this could be. For instance, they may need to have some paid employment experience to learn about business in the US or for visa reasons. Alternatively, their choice to enter entrepreneurship could be due to negative experiences in the paid-workforce. 17

18 The next three columns of Table 5 and Figure 4B are on the population of most interest, those with highest degrees in science. We see a J-shaped relationship between entrepreneurship (self-employed incorporated) and wage decile, with individuals in the 10 th decile exhibiting the highest rate of entrepreneurship, the first decile the second highest, and particularly low rates for the third through 7 th deciles. Note that this is not that dissimilar to Elfenbein s result, although ours is much more J-shaped (much lower in the lower decile than in the highest) than theirs. 16 This conceals different patterns for science and non-science entrepreneurship (shown in Figure 4B). In non-science entrepreneurship, we see a pattern similar to entrepreneurship as a whole, but with the top and bottom deciles more balanced. However, in science entrepreneurship we see a different and somewhat striking pattern. The first decile has the lowest level of entrepreneurship, the tenth has the highest level of entrepreneurship, and the rest vary a lot. 17 Thus, there is a star effect in science entrepreneurship and both a low-ability and a star effect in non-science entrepreneurship. Is the higher rate of entrepreneurship among immigrants due to their different levels of ability as captured by wages? In Table 7 we compare the coefficient on the immigrant dummy in these three regressions to the comparable ones in Table 3 (i.e. without education and experience variables) and see that the immigrant dummy remains unchanged in the science entrepreneurship equation. In contrast, the coefficient on immigrant in the non-science entrepreneurship regression has fallen and become insignificant. Thus, the entrepreneurship-wage nonlinear relationship is not causing the immigrant-native entrepreneurship gap in science but definitely contributes to the gap in non-science engineering. We have also re-estimated these entrepreneurship-wage regressions with separate sets of wage deciles for natives and immigrants to assess whether the immigrant-entrepreneurs are drawn from different parts of the wage distribution than native-entrepreneurs. These are given in the final three columns of Table 5 and shown in Figures 4C and 4D. For non-science entrepreneurship, natives and immigrants have a similar U-shaped pattern. However, the pattern is somewhat different for natives and immigrants in science entrepreneurship. For natives, there is no clear pattern for the bottom 9 deciles, and the top decile 16 This is somewhat different from the result seen in Elfenbein, which was much more U-shaped with the lowest and highest deciles more similar. 17 with the second, sixth, seventh, and ninth having high levels, and the third having an entrepreneurship rate most similar to the first 18

19 is modestly higher than rest. However, for immigrants, entrepreneurship levels are high starting in the fifth decile (and in fact peak at that decile). Moreover, all of the immigrant deciles are higher than the native deciles. One possible explanation for the difference in the shape of the wage-entrepreneurship relationship between immigrants and natives could be that the wage is a less useful measure of ability for immigrants, because their skills may be undervalued by employers. For this reason, some of the immigrants in the middle of the wage distribution who become entrepreneurs may have ability levels closer to those of the natives at the high end of the wage distribution. The next section will investigate this possibility. VI. Is foreign entrepreneurship due to being undervalued in paid employment? A. Wage residuals as a measure of mismatch The discussion in the previous section linked entrepreneurship to ability as captured by human capital or by the wage in paid-employment. However, some of the arguments in the literature on the entrepreneurship-wage relationship are based on the fact that wages may not even accurately capture ability in paid employment. As we argue below, these arguments seem to us to apply particularly well to immigrants. For instance, a key part of the Astebro et al. (2011) model is that there are frictions leading to workers not being assigned efficiently to either tasks or firms in paid employment. The mismatch leads them to have different wages in paid employment than their ability alone would imply. Some underpaid workers will be able to make more in self-employment. In equilibrium in their model, because of convexities these include workers at the top of the ability distribution and those near but not at the bottom of the ability distribution. (The extreme bottom of the ability distribution does not work at all.) The empirical evidence that Astebro et al. bring to test their model is based on the relationship between entrepreneurship and either education or the paid-employment wage (as well as self-reported job mismatch and past job switches and unemployment.) However, assuming frictions or mismatch are not identical for everyone, those moving to entrepreneurship are not only within given wage ranges, but they are also underpaid relative to their ability. 19

20 How can we measure whether a person is underpaid relative to their ability? The residuals from a wage equation run on human capital variables are likely to at least partially measure underpayment or overpayment as a result of mismatch. Thus, the Astebro model would predict that (a) entrepreneurship would be greater at the top and near the bottom of the wage distribution and (b) large negative residuals in a wage equation would also predict entrepreneurship. This could be tested in a model that included both wage variables and residual variables on the right hand side. In contrast, in a model where wages accurately reflect ability in paid employment, the residuals in a wage equation measure ability characteristics unobserved to us but observed (and paid for) by the employer. Therefore, negative residuals from a wage equation would not indicate mismatch and for that reason should not predict entrepreneurship due to being undervalued in paid-employment. Undervaluing could also be due to discrimination or an inability to evaluate the person s ability, an argument particularly applicable to immigrants or minorities. This undervaluing would mean negative wage residuals for the group discriminated against, leading to entrepreneurship. This argument could apply at all levels of wages. 18 Finally, undervaluing might occur for organizational reasons such as pay structures that emphasize equity as noted by Elfenbein (2009) among others. Although Elfenbein (2009) did not make this link, this undervaluing would also suggest that those with negative wage residuals would be more likely to be entrepreneurs. Moreover, an equity-based reason is more likely to occur among those with high ability. Table 6 tests these predictions by including two new variables. One is the absolute value of negative wage residuals; the second is the value of positive wage residuals. First, consider the first three columns. Negative wage residuals do have strong positive effects on non-science entrepreneurship (beyond its contribution to wages.) However, while in Table 5 we saw a U- shaped relationship between non-science entrepreneurship and wage deciles, in Table 6 there is only an increase at the top decile. Thus, it is not low wages per se that encourage non-science entrepreneurship, but rather being paid less than you are worth (as predicted by a wage equation). 18 Immigrants may also have residuals that are lower than natives with the same ability level due to discrimination. However, it is impossible to identify this in the data. 20

21 However, Table 6 also shows that positive residuals increase non-science entrepreneurship as well, above and beyond their contribution to wage (note that the wage decile is included as a control). This is inconsistent with the Astebro model which would predict a U- shaped relationship between entrepreneurship and wages which is now absent. In science-based entrepreneurship, only positive and not negative residuals are associated with increases in entrepreneurship. Also, as in Table 5 (with no residual variables), there is no discernible relationship between entrepreneurship and wages, even controlling for residuals. One difference from Table 5 is that now individuals in the top wage decile are the least likely to be entrepreneurs rather than the most likely. Why would positive wage residuals (controlling for wage levels) increase entrepreneurship? One of the few articles linking wage residuals to self-employment, Carnahan et al. (2010), links positive residuals to entrepreneurship. Carnahan et al. assumes that people are paid their true value in paid employment but that those paid much higher than others with the same credentials have unusually high ability ( extreme performance ). The positive link they make from this to entrepreneurship is not based on an equilibrium model and requires that this unusual ability would not be rewarded by other firms and that mobility is exogenously determined. 19 However, it is also possible to combine the simple idea that positive wage residuals indicate unusual abilities with a star model like Lazear s (2004) 20 which predicts that those with unusually high ability will become entrepreneurs. The positive impact of positive residuals that we observed on both kinds of entrepreneurship is suggestive of this type of explanation. There are many reasons why we would expect mismatch theories to apply more to immigrants than to natives. If negative residuals signal mismatch or frictions leading to employers under-rewarding abilities, this mismatch is likely to be more common among immigrants. Immigrants abilities might be underestimated because native employers do not understand foreigners due to language or cultural differences, or do not give credence to the immigrants education since they are unfamiliar with educational systems in foreign countries. Or, negative residuals could be due to employers undervaluing immigrants due to bias. 19 Carnahan 2010 find that lawyers with high positive wage residuals are less likely to leave a firm, but if they leave they are more likely than others to start their own firm. Those with high negative residuals are more likely to leave the firm but, if they leave, they are less likely to go to self-employment. 20 Ability across skills is highly correlated and firms pay for one s best skill whereas a person can use all skills simultaneously as an entrepreneur. 21