In this section, we present empirical evidence which shows that the policy efforts that we generally see implemented are an inefficient and ineffective way of promoting outcomes that we care about because of who typically becomes entrepreneurs and why people typically becomes entrepreneurs.
Most people would be better off not becoming entrepreneurs
First, we reiterate the three central tenets of good entrepreneurship public policy. Entrepreneurship policies would be clearly motivated if;
Lots of people are trapped in jobs at established businesses who would be better off self-employed;
We as a society are worse off because of this;
More policies like the ones we have would correct this social problem.
In this section, we will address the first two points, while the third point is discussed in Sect. 6. This section will show that neither one of the two first points is true, that is, there are not a lot of people trapped in jobs who would be better off self-employed, and our society will not be better off if more people leave employment for entrepreneurship.
The first evidence represents a stylized empirical fact that has been hard to disprove; that most people are economically better off staying employed rather than becoming an entrepreneur. Figure 1 shows an early and clear example which shows four earnings distributions, where three of them are different types of measures of earnings from self-employment, and the fourth is the earnings from wage work.Footnote 3 Wage earnings are the solid line. The data were taken from the USA in the mid-1980s and represent a stratified random sample of the population of income earners (Hamilton 2000).
Figure 1 clearly shows that all three measures of self-employment earnings have most of their density shifted to the left of the solid line wage earnings distribution. While there can be differences in background observable and unobservable characteristics between the two different groups, when Bart Hamilton controls for such differences, there still remains a significant negative difference in earnings for the average individual between self-employment and wage work. For example, Hamilton computes that the accumulated earnings for an average self-employed person for about 20 years would be 35 % less than if he would have stayed employed. This work has since been replicated several times across a number of different countries, and the typical “entrepreneurial discount” has been estimated between 5 and 15 % per year, which is a substantial annual penalty for becoming and staying an entrepreneur (for reviews, see Åstebro 2012; Åstebro and Chen 2014).
One might raise at least three counter-arguments to the usefulness of the above data for guiding public policy. The first is that it is not the earnings of the self-employed which we as a society care about, but the earnings (and employment) potential of the people which become “novel” entrepreneurs, as discussed in Sect. 3, however defined.Footnote 4 As we will show, it is clearly the case that if one examines a representative cross section of self-employed the typical “entrepreneur” is a sole proprietor with no other employees and who is working in a relatively mature and competitive industry such as the trades (e.g., construction), small-scale services, or who owns a restaurant or a retail business.
A second counter-argument is one of faulty measurements. It could be that since the data from Hamilton are cross sectional it does not represent well-calculated deliberate decisions to enter entrepreneurship, but rather a lot of “noise” and that earnings rise with time in entrepreneurship as those who has entered on mistaken grounds quickly exit.
A third argument is similar to the second, and it makes the claim that novel entrepreneurial earnings are much larger if one takes into account earnings which are not reported to the tax authorities and similarly not reported in surveys.
We will postpone a discussion on the types of businesses which people typically start until Sect. 4.3. However, we immediately note that if one implements a general entrepreneurship-friendly policy, then one obtains a response to this policy primarily from people starting the types of businesses which Hamilton’s study represents.
In order to indicate the earnings of novel entrepreneurs who base their new firms on intellectual property, Åstebro et al. (2013, 2015) examined the earnings of former academics in Sweden and the USA which decided to become full-time entrepreneurs. These represent the types of entrepreneurs one may care more about for the creation of wealth—they are likely to have created an invention at their university employer and are trying to commercialize this invention through an entrepreneurial act. In addition, they leave their former employer and become full-time entrepreneurs, so this is not a trivial decision. They typically forego a steady and well-paid job for the prospects of making something new under high uncertainty. Consulting or other part-time efforts are not included and so if entrepreneurial earnings appear, they are more likely to be large. Finally, academic institutions and universities have hosted inventors creating some of the most important inventions for society who in some cases have gone on to commercialize the inventions themselves, for example Herbert Boyer co-discovering genetic engineering and co-founding Genentech while his partner Stanley Cohen returned to the laboratory, and Craig Venter founding Celera Genomics to commercialize gene sequencing.
Figure 2 draws similar types of density functions as in Fig. 1. The figure shows the annual earnings from a representative sample of academics in the USA with Ph.D.s from Science, Technology, Engineering or Medicine (STEM) who either stay in academia all their life (the red line) or at some point in time leave their employer to become an entrepreneur (the blue line). Data are from the SESTAT database collected by the National Science Foundation through repeated surveys between 1993 and 2006, and the graph is found in Åstebro et al. (2015).
The story is not different from this specially created sample of top-potential earning entrepreneurs than for the self-employed in general. The academic entrepreneurs typically make a lot less money than those which remain employed. The estimated (individual fixed effects) earnings difference for a given person is around 15 % less when becoming an entrepreneur. The data are very similar when looking at Swedish academic entrepreneurs similarly defined. For the Swedish data, Åstebro et al. (2013) had the unique opportunity to also collect data on dividends and earnings from sales of their businesses. These additional earnings were inconsequential and did not change the general tendency of academics to earn more if they stayed employed.
Addressing the second concern which claimed that we are mis-measuring the earnings potential of entrepreneurs in both above-reported studies by including a lot of short-term business, in the third graph we report on a study which compared earnings for the self-employed who had been in business for at least 10 years to the earnings of wage workers in Denmark. The figure is taken from Åstebro et al. (2014) and is reproduced below as Fig. 3. The figure also clearly shows that even if one excludes those who may have made a mistake by entering and quickly leave self-employment to go back to wage work, the earnings for the remaining self-employed are still predominantly less than the earnings for those in wage work.
This graph provides an additional interesting point which we will return to in the next subsection. Even though the expected utility of entrepreneurship appears less than the alternate wage work, the median income is clearly less, people persist in entrepreneurship, even after 10 years. Why would they persist? By the time they have been in business for 10 years, it surely must be obvious to them that they could make more money by working for someone else.
The final argument against using all these data for policy purposes is that income may be severely underreport by entrepreneurs but not by wage workers. Comparing reported earnings may then not be meaningful. Indeed, several papers have estimated that entrepreneurs underreport their income by between 10 and 40 % (see review in Åstebro and Chen 2014). However, even though people apparently can more easily hide income from the tax man by becoming entrepreneurs, the policy conclusions from these findings are not necessarily that it is a good idea to encourage people to become entrepreneurs. Indeed, this would make for very bizarre public policy. Take, for example, Greece, which has the highest rate of self-employment in the E.U. and also the largest difficulty of collecting taxes owed from these self-employed. It is not at all obvious that the remedy to the financial and economic problems in Greece is to encourage greater self-employment rates. Instead, one might argue that these results indicate that there are even greater opportunities to collect tax from entrepreneurs than what is typically accomplished (and in particular in Greece). Several papers also show that larger companies and entrepreneurs which start incorporated firms are likely to underreport their income substantially less than small sole proprietorships arguably due to the more detailed scrutiny of accounts in corporations (Engström and Holmlund 2009; Schuetze 2002), suggesting that tighter financial auditing of entrepreneurs may in fact be motivated.
Overall, the earnings data paint a picture of people behaving as if they were playing poker at the casino. Most lose money, but there is a small percentage of people that make a whole lot more money as entrepreneurs than they would as wage workers. A policy conclusion from these data is that subsidizing entrepreneurship would be like collecting taxes so we could give out free poker chips to encourage more people to play poker. This does not look like sound public policy.
People choose to become entrepreneurs predominantly because they like it
In the previous section, we showed evidence that most people are not better off becoming entrepreneurs. An immediate question following this evidence is: Why do people then become entrepreneurs? In this section, we will present compelling evidence, indicating that one of the most prominent explanations is that a lot of people like to become/be entrepreneurs. A preference for entrepreneurship immediately explains why people enter into entrepreneurship although they will be making less money—they simply trade off lower income for higher consumption utility.
We start by showing that there is a strong preference for becoming an entrepreneur. In fact, there are substantially greater fractions wanting to be entrepreneurs than the actual rates of self-employment across a wide variety of countries. The proportion of citizens who favor being an entrepreneur over wage worker vary from 80 to approximately 30 % (Blanchflower 2004, Table 7).Footnote 5 Poland, Portugal and the USA topped the league in 1997/1998, with roughly three quarters of citizens preferring to be entrepreneurs. These proportions seem extraordinarily large and cannot be motivated only by earnings opportunities. In the bottom of the league come Scandinavian countries. In these nations, roughly 30 % of citizens say they want to be an entrepreneur.
We continue by reporting that the preference for entrepreneurship is mostly driven by non-pecuniary reasons. Table 1 reproduces data reported by Hurst and Pugsley (2011) taken from the panel study of entrepreneurial dynamics, a survey conducted in 2006 representing a sample of “nascent” US entrepreneurs—those actively involved in the process of starting a business. Table shows percentages for the first reason given. There is direct evidence that people are mostly concerned about enjoying being an entrepreneur. The main reason for becoming an entrepreneur is various non-pecuniary motivations, while only 19.5 % reports making money as the main reason.
That non-pecuniary considerations dominate the decision to become entrepreneur is indirectly supported by several articles. For example, it is well documented that across a wide range of countries, self-employed are more satisfied with their work than wage workers (see Åstebro 2012 for references). Fixed-effect analysis shows that those who move to self-employment become happier. Further, using the unification of East and West Germany as a natural experiment, Benz and Frey (2008b) show that this result is not due to reverse causation (i.e., that more happy people enter entrepreneurship). Self-employed report they are more satisfied with their jobs because their work provides more autonomy, flexibility and skill utilization and (strangely) greater job security (Hundley 2001). Benz and Frey (2008a) discover that more interesting work and greater autonomy are mostly responsible for the difference in job satisfaction scores between entrepreneurs and employees.
Various types of data thus give a consistent opinion: People choose entrepreneurship primarily because they like it. There is absolutely nothing wrong with this. But supporting people who want to enjoy becoming entrepreneurs would be like taxing non-smokers so the government can buy cigarettes and give to people who enjoy smoking. This kind of policy does not make sense from a social welfare perspective. If people want to become entrepreneurs, they should do so without any subsidies collected from others.
Overwhelmingly entrepreneurs do not create any value beyond private benefits
Even if we have been able to convincingly show that most entrepreneurs would be better off staying employed, and most people enter entrepreneurship because they like to rather than to make money, it might be that entrepreneurs create a lot of social welfare (for others) even if they do not make much money for themselves. Take, for example, the two cases of Herbert Boyer and Craig Venter we discussed before. Even if they happened to get rich, there might be a plurality of entrepreneurs who do not get rich but where society got much better from their efforts. In this section, we will show that this is an unlikely conclusion.
To illustrate that welfare gains from entrepreneurship are likely very small, we return to data compiled by Hurst and Pugsley (2011) from the panel study of entrepreneurial dynamics.Footnote 6 We use answers regarding innovation and R&D activities to indicate the potential for welfare gains. Our point here is that if there are welfare gains from entrepreneurs, these would be most likely to appear if entrepreneurs innovate, as innovative activities are the most difficult to appropriate and which gains might more easily spill over to others. Authors have previously shown that small firms are proportionally more likely to innovate than large firms (the seminal work being Acs and Audretsch 1990), so this seems on the face of it a plausible argument.
However, it turns out that most entrepreneurs are unlikely to innovate or conduct R&D. Table 2 reveals that only a small fraction of entrepreneurs have produced a patent (4.9 %) or developed a proprietary technology (6.5 %), as part of their start-up activities. And rather surprising, only a quarter believes that R&D is a major priority for them. Instead, a rather large fraction (35.7 %) state when they enter that many existing firms already offer the same product or service to expected customer base. Many new firms are thus of a me-too character, simply imitating what is already in the market. The fractions which focus on innovation increases after 4 years of operations, indicating that successful entry indeed is associated with innovating. Nevertheless, R&D still does not weight heavily in the minds of the entrepreneurs and four out of ten firms still focus on providing me-too products.
A different approach of looking at the potential for spillovers is to examine the type of industries which the typical start-up enters into. If start-ups are more likely in high-tech industries, then maybe that would be an argument for supporting them with policies. We have already mentioned some of the most likely industries where entrepreneurs go into and so a more detailed analysis will bring no surprises. Hurst and Pugsley (2011) rank all 294 four-digit level industries in the USA by the fraction of firms within the industry which have <20 employees, a proxy for the intensity of entrepreneurship by industry. Their analysis shows that most small businesses are either restaurants, skilled professionals (physicians, dentists, lawyers, accountants, architects, consultants), skilled crafts persons (general contractors, plumbers, electricians, masons, painters, roofers), professional service providers (clergy, insurance agents, real estate agents), general service providers (auto repair, building services such as landscaping, barbers and beauticians) or small retailers (grocery stores, gas stations, clothing stores).
Maybe entrepreneurs are not very good at generating economic welfare, but they might be the source of most new employment? Indeed, studies have recently shown that it is primarily the new firms which generate most aggregate employment growth (Haltiwanger et al. 2013). However, Hurst and Pugsley (2011) provide some convincing evidence from the USA that, while aggregate job creation is higher among new firms, most new firms (with employees) create very little amount of new jobs. The point is that the distribution of job growth among new firms is highly skew and any policy aimed at stimulating the average entrepreneur would thus be ineffective. Maybe the most interesting evidence they report is the following. Posed with the question in the PSED “Which of the following two statements best describe your preference for the future size of this new business: I want this new business to be as large as possible, or I want a size I can manage myself or with a few key employees?” only one-quarter of entrepreneurs answers that they want the business to be as large as possible.
We turn to some Swedish data to indicate the lack of job creation by entrepreneurs in general. The benefits of the Swedish data are that it can track employment in all new firms. Åstebro and Tåg (2015) show that in Sweden, 84 % of all entrepreneurs are sole proprietors, and among them, it requires 10 entrepreneurs to create one job for another person within the first 2 years of operations.Footnote 7 Those entrepreneurs who start a limited liability firm have better employment growth in the first 2 years, creating 1.73 additional jobs. Unfortunately, only 16 % of all new firms are started as limited liability businesses, and employment in these firms retract to 0.36 additional employees per entrepreneur after 6 years of operations. The latter statistic reflects that the failure rates are high among these companies due to the inherent risk of entrepreneurship. See Table 3.
Unfortunately, we must therefore disappoint the policy maker also when it comes to job creation. Most new firms create no additional jobs beyond those for the entrepreneurs themselves. If these entrepreneurs in addition arrive from paid employment, then there is no new net job creation, only a reshuffling of work. Of course, such reshuffling is part of the animal spirit of entrepreneurship and not a bad thing. But one should not look to the average entrepreneur as the giant job creator.
This section has shown that most entrepreneurs enter into highly contested markets, with products and services that are typically already offered, and where there is already a large supply present. Few new firms enter to innovate, and very few entrepreneurs hire anyone except themselves and have no interest or ability to expand after creating a job for themselves. In conclusion, supporting people to become entrepreneurs would mostly support one-man me-too shops in low-growth, low-margin industries where there is no or little innovation undertaken.