5. High-Growth Entrepreneurs, Public Policies, and Economic Growth

An earlier version of this chapter was published in Spanish in the Basque Journal of Economics EKONOMIAZ (Stam et al., 2007). Erik Stam and André van Stel are also affiliated with the Max Planck Institute of Economics in Jena, Germany.

There is another policy problem looming here if the focus is too much on achieving more high-growth firms. It should be kept in mind that high-growth firms are a means to an end: economic growth. The focus on targets like increasing the number of high-growth firms might become subject to “Goodhart’s Law” (Kay 2004, p. 80): any measure adopted as a target changes its meaning. This might happen if policy makers are judged by the number of additional high-growth firms they have “created.”

The difficulty to predict the growth of start-ups has led the English DTI to emphasize entrepreneurs with growth aspirations in her competitiveness policy (DTI 1998). The main rationale for this program is the potential welfare gains to the economy, which will result from enabling more new businesses with growth potential to achieve significant growth (see Smallbone et al. 2002). There is an implicit assumption of market failure in the sense of the support needs of high-growth start-ups not being adequately met by the private sector. The program is also legitimated because it is an addition to the existing start-up support program. High-growth potential of start-ups is defined as an aspiration of £1 million sales per annum. It is estimated that only about 1% of new business start-ups in the United Kingdom each year achieve annual sales of this amount. Achieving £150,000 sales within 12 months is provided as a stepping stone goal toward this threshold.

In 1971, the National Association of Securities Dealers set up the NASDAQ stock exchange, largely in response to pressure from the Securities and Exchange Commission to bring order into the unregulated over-the-counter market (Ingebretsen 2002).

These figures relate to the percentage of firms within the population of medium-sized firms (50–1,000 employees) that grow their business with at least 60% (in terms of employment) over a period of 3 years. (1998–2001; see Peeters and Verhoeven, 2005, p. 27).

According to Davidsson (1991, p. 424) persuasive attempts to stimulate growth motivation are likely to be most effective if directed at younger firms and younger owner-managers. Younger firms have a stronger objective need for expansion, and their values, attitudes, and “company cultures” are less likely to be firmly held. Younger individuals are also likely to be more sensitive to growth objectives than older entrepreneurs who have since long defined and lived up to a role as the manager of a stable firm.

When the growth expectations for the national economy are good, more entrepreneurs may expect to grow their business in the years to come. Hence, there may also be a (reversed) effect of economic growth on (high expectation) entrepreneurship. To limit the potential impact of reversed causality we include lagged GDP growth as an additional explanatory variable. We also measure TEA rates in a year (2002) preceding the period over which the dependent variable is measured (2002–2005). Still, the possibility of reversed effects cannot be ruled out completely.

The 36 countries in our sample are divided into three categories: rich, developing, and transitioning. The 24 rich countries are Australia, Belgium, Canada, Taiwan, Denmark, Finland, France, Germany, Hong Kong, Iceland, Ireland, Israel, Italy, Japan, Korea, the Netherlands, New Zealand, Norway, Singapore, Spain, Sweden, Switzerland, the United Kingdom, and the USA. Our seven developing nations are Argentina, Brazil, Chile, India, Mexico, South Africa, and Thailand. The five transition countries are China, Hungary, Poland, Russia, and Slovenia. With the exception of Slovenia, the developing and transitional countries are classified as (relatively) poor.

Van Stel et al. (2005) refer to a possible lack of (foreign) larger companies in these poorer countries as a possible explanation for the zero effect of entrepreneurial activity.

In particular, results for model 3 in Tables 5.2–5.4 might be sensitive to outliers. As a test of robustness we estimated model 3 leaving out one country at a time, i.e., we computed 36 auxiliary regressions, where each regression uses 35 observations (each time leaving one of the 36 countries out). For TEA, using the full sample, we found a significant positive impact for the highly developed countries (see Table 5.2). In the auxiliary regressions we always found a positive impact for the highly developed countries, which was significant at least at the 10% level, except for the regression excluding Korea. Here, we found a coefficient of.088 and a t value of 1.5. Similarly, for TEA medium growth, using the full sample, we found a significant positive impact for both the highly developed and the transition countries (see Table 5.3). In the auxiliary regressions we always found a significant positive impact for the highly developed countries except when Korea was excluded from the sample (coefficient 0.20; t value 1.4). For the transition countries we always found a significant positive impact except when China was excluded from the sample (coefficient 0.56; t value 1.4). Finally, for TEA high growth, using the full sample, we found a significant positive impact for the transition countries (see Table 5.4). In the auxiliary regressions we always found a significant positive impact, except when China (coefficient 0.76; t value 1.2) or India (coefficient 0.60; t value 1.4) were excluded. Note, however, that in all these cases, despite their insignificance, the estimated coefficients are close to the full sample estimates in Tables 5.2–5.4. Furthermore, the Jarque-Bera test on the normality of disturbances is passed for all models reported in Tables 5.2–5.4. Therefore, we feel that our results are quite robust to the potential influence of outliers.