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R&D investment of start-up firms: does founders’ human capital matter?


This article examines whether founders’ human capital affects not only actual investment but also required investment in research and development (R&D), using the original data of Japanese start-up firms. The estimation results indicate that higher levels of founders’ human capital, especially their education levels, increase both actual and required investment in R&D and thus do not necessarily contribute to reducing the funding gap for R&D.

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  1. 1.

    Therefore, firms use external financing after exhausting internal financing, according to the pecking order theory or hierarchy of finance (e.g., Myers and Majluf 1984; Fazzari et al. 1988; Berger and Udell 1998).

  2. 2.

    For an overview of entrepreneurial finance, see, for example, Denis (2004).

  3. 3.

    Carpenter and Petersen (2002) also pointed out that physical investments designed to embody R&D results are likely to be firm-specific and therefore have little collateral value.

  4. 4.

    Avery et al. (1998) pointed out that personal commitments may be important for firms seeking certain types of loans as they serve as a signal of the quality of firms.

  5. 5.

    Parker and van Praag (2006) also indicated that a higher educational level is associated with lower financial constraints. In contrast, Cassar (2004) found that a major decision-maker’s characteristics do not have a significant influence upon start-up financing.

  6. 6.

    As Hall (2002) pointed out, the efforts of educated scientists and engineers create an intangible asset from which profits in future years will be generated as the firm’s knowledge. Moreover, Cressy (1996) emphasized that human capital is the “true” determinant of firm survival and that the correlation between financial capital and survival is spurious. According to his argument, human capital, rather than financial capital, may relate to R&D output and post-entry performance.

  7. 7.

    Guiso (1998) examined the determinants of credit rationing for Italian high-tech firms. Using the data of UK firms, Freel (2007) found that the most innovative firms are less successful in loan markets. However, these studies did not investigate the effects of human capital on R&D investment. For further discussions on the financing of technology-based small firms, see Revest and Sapio (2012).

  8. 8.

    Okamuro (2009) examined the effect of educational background on R&D intensity, without finding its significant effect.

  9. 9.

    However, if founders with higher levels of human capital tend to develop new technologies that have a high risk of failure, such as drug discovery, then higher levels of human capital may provide a signal to external suppliers of capital that the repayment probability is low; thus, in this case it is unclear whether founders’ human capital increases R&D funding. This point was suggested by a reviewer, and we thank the reviewer for this suggestion.

  10. 10.

    Among the 14,401 firms to which we sent the questionnaires, 819 firms could not be reached. For more detail on this survey, see Okamuro et al. (2011).

  11. 11.

    According to TSR, the coverage ratio of this database is estimated to be nearly 90 % of newly incorporated firms. Note that this database relies on the official register of corporations and thus does not include sole proprietors.

  12. 12.

    Since some firms start businesses with multiple founders, we asked the respondents about the number of co-founders. In fact, the sample includes firms with multiple founders. In the case of multiple founders, we asked for the characteristics of the presidents.

  13. 13.

    We can check a part of response bias by comparing the distribution regarding region and industry between the target firms and the respondents, while we cannot check response bias regarding firm size by using our data. We found few significant differences in the response rate between regions, suggesting little response bias. Meanwhile, software firms tended to be more highly represented than manufacturing firms among the respondents. Moreover, R&D-oriented firms may be overrepresented because we pay special attention to such firms in the survey.

  14. 14.

    The database compiled by TSR provides us with newly incorporated firms, but also includes those firms that were already established as sole proprietors before incorporation. In fact, we found from the questionnaire that one-third of the respondents were established before December 2006 and thus excluded them from the sample because they were not “real” start-ups.

  15. 15.

    One firm had no funding at start-up, and five firms reported a required amount of R&D investment of over 100 million yen. We regarded these firms as outliers.

  16. 16.

    In the questionnaire, we asked whether the firm was founded as an independent firm or a subsidiary/an affiliated firm when starting operations. In addition, we asked for the sources of initial funding in the questionnaire, and the firms for which the ratio of funds from parent and affiliated firms exceeded 20 % were also regarded as subsidiaries and affiliated firms.

  17. 17.

    As explained before, the sample comprises firms whose founders conducted R&D or that employed R&D personnel when starting their businesses or afterward. Therefore, the sample can include those without R&D investment; they informally conduct R&D without accounting for R&D expenditure or ceased to invest in R&D before November 2008 when the survey was conducted.

  18. 18.

    Comparing KD across industries (table not shown), we find that the mean values of KD are larger in R&D-intensive industries, such as chemical (20.2 million yen), electrical machinery (18.9 million yen), and electronic parts and devices industries (13.7 million yen). The medians of KD are 3.6 million yen in the chemical, 5.0 million yen in the electrical machinery, and 4.0 million yen in the electronic parts and devices industries. Moreover, the ratio of the mean value of KD to the mean value of KS in these three industries is 2.9, 3.7, and 2.0, respectively.

  19. 19.

    In the questionnaire, the other alternatives were “junior high school/high school,” “technical college/junior college,” and “others.” That is, the reference category for formal education included all education levels below university degrees.

  20. 20.

    In the questionnaire, we asked for the founder’s work experience related to the current business and used this variable in the estimation, but without obtaining any significant results. Therefore, we omitted this variable from the model.

  21. 21.

    In the traditional investment model, cash flow has often been used to capture the availability of internal funding that hypothetically mitigates financial constraints (e.g., Fazzari et al. 1988). However, cash flow is not appropriate as a proxy for the availability of internal funding at start-up because most start-up firms have not yet established cash flow.

  22. 22.

    Several studies have addressed equity financing of R&D because the cost of equity financing may differ from that of debt financing for start-up firms. For instance, Carpenter and Petersen (2002) argued that new equity has many advantages over debt for financing high-tech investment. More recently, Müller and Zimmermann (2009) emphasized the importance of equity finance for the R&D activity of small- and medium-sized enterprises. Although we do not distinguish equity financing from debt financing to reduce respondent burden in answering the questionnaire, we may be able to regard initial funding from external suppliers of capital as debt financing because, in practice, most firms do not obtain equity financing from banks at start-up.

  23. 23.

    There is the possibility that internal funding is affected by founders’ human capital. In other words, we should regard internal funding as endogenous when founders with higher levels of human capital tend to raise more R&D funding by themselves. However, according to the correlation matrix of the independent variables (table not shown), the correlation coefficients between the variables for internal funding and founder-specific characteristics are found to be low. Therefore, we assume that for our sample firms, internal funding is independent of founders’ human capital to avoid complexity in estimation. Further investigation into how internal funding is determined may be warranted. Moreover, the variable for public subsidy was included in the model, in addition to this variable. However, we did not find any significant effect of public subsidy on actual and required R&D investment.

  24. 24.

    The sample comprises many firms with less than 1 year of operations when we conducted this survey. Such firms may usually use initial funding to cover operational costs, including R&D expenditure.

  25. 25.

    We found similar results, using industry dummies based on the two-digit Standard Industrial Classification instead of this variable.

  26. 26.

    Honjo (2001), for example, found a positive correlation between IPO intention and R&D intensity using the data on Japanese electric manufacturing firms.

  27. 27.

    In practice, we attempted to use several variables that were obtained in the questionnaire as instruments. Among them, as will be shown later, IPO intention has a strongly significant effect on required R&D investment (KD), and the F statistic for the first equation of KD in 3SLS is 11.83 (R 2 is 0.255) when we add this instrument to the model of KD, which seems to avoid the weak instruments problem.

  28. 28.

    To control for the differences in firm size among the sample firms, we included the variable for firm size, measured by the number of employers and employees at start-up, in the empirical models. However, we did not find any significant effect of firm size on actual and required R&D investment. Therefore, the estimation results with this variable are not shown in this article. Moreover, to test the robustness of the results, we estimated the models using the subsample of financially restricted firms that have positive values for the funding gap and obtained similar results to those in Tables 4 and 5.

  29. 29.

    Since the data on IPO intention were not obtained for two firms in the sample, the number of observations was reduced from 359 (Table 4) to 357 (Table 5).

  30. 30.

    When using a 2SLS method, we obtained similar results to those shown in columns (iii) and (iv) of Table 5.


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This study is supported by a Grant-in-Aid for Scientific Research (A) (no. 20243018) from the Japan Society for the Promotion of Science. We are grateful to Luca Grilli, Itxaso del Palacio, and the participants in a seminar at Politecnico di Milano, the 9th Interdisciplinary European Conference on Entrepreneurship Research (IECER), and the VICO Final Conference for their helpful comments and suggestions. We thank the editor and two anonymous reviewers for their valuable comments and suggestions. We also thank Kosei Fukuda for valuable advice on estimation methods and Kenta Ikeuchi for excellent research assistance. Needless to say, any remaining errors are our own.

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Correspondence to Yuji Honjo.

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Honjo, Y., Kato, M. & Okamuro, H. R&D investment of start-up firms: does founders’ human capital matter?. Small Bus Econ 42, 207–220 (2014).

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  • Founder
  • Funding gap
  • Human capital
  • R&D
  • Start-up

JEL Classifications

  • G30
  • M13
  • O32
  • L26