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Entrepreneurial human capital and the survival of new firms in high- and low-tech sectors

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Abstract

This paper explores the role of entrepreneurial human capital in the post-entry performance of firms in high- and low-tech sectors. Using a dataset from the Japanese manufacturing industry, we examine the determinants of new-firm survival, taking into account exit routes to differentiate ‘failure’ (bankruptcy) and ‘nonfailure’ (voluntary liquidation and merger) outcomes. Our results show that entrepreneurial human capital, measured as educational background, is important in reducing the probability of bankruptcy in high-tech sectors, although it does not help significantly in this regard in low-tech sectors. By contrast, we provide evidence that entrepreneurs with high levels of human capital are more likely to voluntarily close businesses both in high- and low-tech sectors. Furthermore, we find that firms managed by entrepreneurs with high levels of human capital are more likely to exit via merger than others, particularly in high-tech sectors. We provide evidence that entrepreneurs with scientific backgrounds are less likely to voluntarily exit than those with humanistic backgrounds, particularly in low-tech sectors.

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Notes

  1. Regarding empirical studies using Japanese data, Doi (1999) examined the determinants of firm exit at the industry level. Honjo (2000a, b) examined the determinants of business failure of new firms using a proportional hazards model. Harada (2007) also examined the determinants of small-firm exit in Japan by distinguishing exits forced by economic factors from other exits. See, for example, Storey and Greene (2010) for a cross-country survey of the evidence on new-firm survival and exit.

  2. Other empirical studies on the determinants of firm duration, including Buehler et al. (2006), Mata et al. (2007), and Esteve-Pérez et al. (2010), have considered exit routes.

  3. Malerba and Orsenigo (1997) also described how new entrepreneurs enter an industry with new ideas and innovations, launch new enterprises that challenge established firms, and continuously disrupt the current ways of production, organization, and distribution, thus wiping out the quasi rents associated with previous innovations.

  4. Honjo et al. (2014) found that entrepreneurial human capital, such as educational background, is positively related to the amount of funds for R&D at start-up, while R&D-oriented start-ups suffer from a funding gap between required and actual investment in R&D.

  5. As a public data source, the Establishment and Enterprise Census reports data, such as numbers of entries and exits, at the individual establishment level, for individual industries or regions. However, it is difficult to obtain data for individual firms from public data sources, and generally we could not use these sources to identify which establishments (or firms) have become active or extinct. Additionally, reliance on these sources is accompanied by the possibility that the relocation of an establishment to another region is recorded as an exit even if the establishment remains in the market. These sources thus create difficulties in identifying whether a firm actually exited the market.

  6. In this paper, although the OECD (2011) classified manufacturing industries into four groups, namely high-tech, medium-high-tech, medium-low-tech, and low-tech, we defined the first two and last two groups as high- and low-tech sectors, respectively. In addition, we divided the full sample into subsamples based on R&D intensity (industry R&D expenditures divided by sales). However, we do not report the results using this methodology because they were generally consistent with those obtained using the definition based on the OECD classification.

  7. We checked whether the results remained consistent if these firms were included in the sample by introducing dummies for firms whose entrepreneurs’ backgrounds are unknown. This test revealed similar results before and after dropping these firms from the sample.

  8. Additionally, we dropped 19 observations in one industry for which we could not match three-digit SIC classifications for data on capital intensity between the periods before and after the changes in SIC.

  9. While we dropped firms with 100 or more employees from the sample as outliers, the exclusion of these firms from the sample had little impact on the results. Moreover, the results generally held even when we tried alternative cutoff points.

  10. While some studies have paid attention to business exit, in this paper ‘exit’ means the disappearance of a firm.

  11. In this paper, a merging firm is regarded as surviving if it continues to operate as the same entity. On the other hand, a merging firm is regarded as exiting through merger if a new entity is created. A merged firm is also regarded as exiting through merger. With respect to acquisition cases, an acquiring firm and an acquired firm are regarded as surviving firms, because neither firm disappears, although ownership is transferred.

  12. However, this assumption regarding the exit year may contain bias. Therefore, we estimated the exit year for all exit routes, including firms with total deficits equal to or greater than 10 million yen, based on the year of the last reported statement of account, and also estimated the determinants of exit. The estimation results changed little, regardless of the method used to identify the exit year.

  13. The exit rate for our sample is much lower than that in some previous studies (e.g., Dunne et al. 1988; Audretsch 1995; Bartelsman et al. 2005). One reason is that the TSR Data Bank, on which our sample is based, comes from the company register, which does not include sole proprietorships. Therefore, the sample may exclude tiny firms, which would naturally exit the market faster than others.

  14. While some previous studies have used the continuous-time duration model to examine firm duration, others have used the discrete-time duration model (e.g., Fontana and Nesta 2009; Cefis and Marsili 2011; 2012). Because the timings of survival and exit are observable only to the year, we use the discrete-time duration model, following Fontana and Nesta (2009) and Cefis and Marsili (2011, 2012).

  15. In this paper, t corresponds to calendar years, which implies that the baseline function is determined by macroeconomic conditions.

  16. Although some firms may be established by multiple entrepreneurs, because of data unavailability, we assume the president to be the entrepreneur.

  17. According to Kato and Odagiri (2012), the difficulty of entry is the best proxy for measuring the quality of universities in Japan. To identify top-ranked universities, we used the score book published by Benesse (formerly Fukutake Shoten), one of the major firms selling services to university entrance examinees. It is well recognized in Japan that these 12 universities have been top ranked for a long time. While we checked whether the results are sensitive to the identification of top-ranked universities by trying other cutoff points between top-ranked and the other universities, the results are generally consistent with those using the dummy for the 12 top-ranked universities.

  18. We classified the schools where entrepreneurs were educated into these three groups, based on information from their official websites and other sources. For high schools, agricultural, fisheries, and technical high schools were classified as having scientific courses only, while commercial high schools were regarded as having humanistic courses only. For junior colleges, technical and commercial junior colleges were included in the former and latter groups, respectively. Both for high schools and junior colleges, the schools with general courses were classified as having both scientific and humanistic courses. To classify universities into the groups, we used a data source, Zenkoku Daigaku Ichiran (List of Universities in the Nation), published annually by Bunkyo Kyokai, which listed all the educational and research organizations in Japanese universities and colleges.

  19. We use the dummies instead of a covariate for continuous ages, because there is the possibility that the effects of age are not linear.

  20. Fairlie and Robb (2009) suggested that female-owned businesses have lower survival rates because of less start-up capital. They also concluded that female business owners have different preferences in terms of goals for their businesses.

  21. Instead of paid-in capital, we used the number of employees as a measure of firm size. However, the results are generally consistent with those using paid-in capital. Data on paid-in capital and the number of employees are not measured for the year of entry, because the TSR Data Bank provides information at the latest available year.

  22. Additionally, we examined the effects of entry rates by industry. As is well known, entry rate is positively correlated with exit rate (e.g., Dunne et al. 1988; Geroski 1995; Caves 1998; Disney et al. 2003). Furthermore, entry rate is considered to be positively correlated with industry growth, because the latter induces the former. To avoid reverse causality and multicollinearity, we excluded the covariate for entry rates, despite it having positive effects on each exit route.

  23. Additionally, we estimated our model using a multinomial logit model. The results are generally consistent with those obtained using the cloglog models.

  24. For more details, see the Stata Manual.

  25. Furthermore, we estimate the cloglog model by restricting the observation period to a fixed amount of time for each firm (e.g., 5 or 7 years), in order to take into account the possibility that the role of entrepreneurial human capital changes after firm foundation. However, we do not report the results, because they are generally consistent with those of Tables 4 and 5.

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Acknowledgments

We extend our thanks for comments from Alex Coad, Kim Huynh, Francine Lafontaine, Jose Mata, Jose Maria Millan, Masayuki Morikawa, Sadao Nagaoka, Hiroyuki Odagiri, Hiroyuki Okamuro, and the participants in seminars at Hitotsubashi University, Erasmus University Rotterdam, the University of Groningen, and the University of Frankfurt, and in the EARIE Annual Conference (Istanbul), the JEA Autumn Meeting (Hyogo), the CAED Conference (London), the RENT Annual Conference (Maastricht), the Competition Policy Research Center Conference (Tokyo), and the Japan Productivity Center Workshop (Tokyo). We also thank the editors (Uwe Cantner and Roberto Fontana) and two anonymous referees for their useful comments. Financial supports from Kwansei Gakuin University Special Grant for Individual Research (A) for the first author and Grant-in-Aid for Scientific Research (B) (No. 26285060) for the first and second authors are gratefully acknowledged. Needless to say, any remaining errors are our own.

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Authors and Affiliations

  1. School of Economics, Kwansei Gakuin University, 1-155 Uegahara Ichiban-cho, Nishinomiya, Hyogo, 662-8501, Japan

    Masatoshi Kato

  2. Faculty of Commerce, Chuo University, 742-1 Higashinakano, Hachioji, Tokyo, 192-0393, Japan

    Yuji Honjo

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  1. Masatoshi Kato
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Correspondence to Masatoshi Kato.

Appendix

Appendix

See Tables 6–8.

Table 6 Number of firms in the sample according to high- and low-tech sectors
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Table 7 Estimation results using the random-effects cloglog model: the full sample
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Table 8 Estimation results using the random-effects cloglog model: high- and low-tech subsamples
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Kato, M., Honjo, Y. Entrepreneurial human capital and the survival of new firms in high- and low-tech sectors. J Evol Econ 25, 925–957 (2015). https://doi.org/10.1007/s00191-015-0427-3

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