Bidding against the odds? The impact evaluation of grants for young micro and small firms during the recession

Abstract

Impact evaluations of business development grants targeting young firms have been somewhat neglected in the literature. While most research studies focus on the impact of research and development grants, a larger percentage of young firms would benefit from grants that assist them in business development activities. In this paper, we examine the impact of small business development grants on young small firm survival, turnover growth, labor growth, and access to external finances. We study this topic in the context of a long recession in Croatia (2009 to 2014), which makes it possible to better observe the effect of the public instrument intervention. Results show positive effect on firm survival and on obtaining long-term bank loans and no significant effects on firm performance. The grant scheme was most successful for firms newest to the market.

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Notes

  1. 1.

    By this term we are referring to output growth (most often approximated by turnover growth and/or total revenue growth), labor growth or productivity growth (most often approximated by labor productivity and/or total factor productivity).

  2. 2.

    The policy basis for the programs can be found in the: Operational Plan for Development of Small and Medium Sized Enterprises (Government of the Republic of Croatia, 2008; 2009; 2010; 2011; indices: MINGORP – KLASA: 311–01/08–01/88; 311–01/09–01/05; 311–01/10–01/15; 311–01/10–01/393) and the Entrepreneurial Impulse – Entrepreneurship and Crafts Promotion Plan (Government of the Republic of Croatia, 2012; 2013; indices: MINPO – KLASA: 311–01/12–01/50; 311–01/13–01/04). These policies are in line with the Law for supporting the development of small businesses (NN 29/02; NN 63/07) and the Law of state support (NN 140/05).

  3. 3.

    Detailed description of these programs can be found in the Appendix A (Table 9).

  4. 4.

    All monetary values are expressed in Croatian kuna, HRK (1 EUR = 7.529 HRK, 2016 average).

  5. 5.

    Grant amount distribution in analyzed period is presented in Appendix A (Table 10).

  6. 6.

    Crafts report their income on the basis of the Income Tax Law (OG 177/2004), while limited liability firms are obliged to keep accounting books at a detailed level according to the Accounting Act, Croatian and International Financial Reporting Standards and International Accounting Standards.

  7. 7.

    Unless stated otherwise, all Figures and Tables in this paper are produced by authors themselves.

  8. 8.

    Table 4 is mentioned before Table 3 in text as it combines descriptive statistics of covariates both prior and after matching.

  9. 9.

    As robustness check, we also ran a several different Probit models: i) without synergetic effects of firm size and entrepreneurs’ age; ii) without entrepreneurship characteristics; iii) without liability measures; and iv) with aggregated liability measures. Using controls obtained this way does not make significant changes to our baseline results presented in Table 5 below. These results are available from authors upon request.

  10. 10.

    In addition to the three robustness checks, we also conducted matching procedure using single- and multiple-treated firms together. In addition to 222 single-treated firms, the new treated sample also included additional 58 firms that received a total of 149 grants in different years over the 2008–2013 time span. For those additional58 multiple-treated firms, we defined treatment as the first year firm received treatment. After matching and finding no statistically significant differences in all pre-treatment characteristics, (results are available upon request) we calculate the ATT (in Appendix A, Table 12). Results confirm the effect on survival in 2016 and growth in long-term bank loans. In addition, results show positive effect on sales growth (26.87% at t + 3) and employment growth (40.68% at t + 5), that are not found in the sample of single-treated firms only, pointing to the omitted variable bias stemming from the multiple treatment received.

  11. 11.

    Balancing property of placebo treated and control group show no statistically significant difference in means and are available upon request.

References

  1. Abadie, A., & Imbens, G. W. (2008). On the failure of the bootstrap for matching estimators. Econometrica, 76(6), 1537–1557. https://doi.org/10.3982/ECTA6474.

    Article  Google Scholar 

  2. Acevedo, G. L., & Tan, H. W. (Eds.). (2011). Impact evaluation of small and medium enterprise programs in Latin America and the Caribbean. Washington. D.C.: World Bank Publications.

    Google Scholar 

  3. Afcha, S., & García-Quevedo, J. (2016). The impact of R&D subsidies on R&D employment composition. Industrial and Corporate Change, 25(6), 955–975. https://doi.org/10.1093/icc/dtw008.

    Article  Google Scholar 

  4. Alperovych, Y., Hübner, G., & Lobet, F. (2015). How does governmental versus private venture capital backing affect a Firm's efficiency? Evidence from Belgium. Journal of Business Venturing, 30(4), 508–525. https://doi.org/10.1016/j.jbusvent.2014.11.001.

    Article  Google Scholar 

  5. Aristei, D., Sterlacchini, A., & Venturini, F. (2017). Effectiveness of R&D subsidies during the crisis: Firm-level evidence across EU countries. Economics of Innovation and New Technology, 26(6), 554–573. https://doi.org/10.1080/10438599.2016.1249543.

    Article  Google Scholar 

  6. Backman, M., Mellander, C., & Gabe, T. (2016). Effects of human capital on the growth and survival of Swedish businesses. Journal of Regional Analysis and Policy, 46(1), 22–38.

    Google Scholar 

  7. Bertoni, F., & Tykvová, T. (2015). Does governmental venture capital spur invention and innovation? Evidence from young European biotech companies. Research Policy, 44(4), 925–935. https://doi.org/10.1016/j.respol.2015.02.002.

    Article  Google Scholar 

  8. Bertoni, F., Martí, J., & Reverte, C. (2019). The impact of government-supported participative loans on the growth of entrepreneurial ventures. Research Policy, 48(1), 371–384. https://doi.org/10.1016/j.respol.2018.09.006.

    Article  Google Scholar 

  9. Binks, M. R., Ennew, C. T., & Reed, G. V. (1992). Information asymmetries and the provision of finance to small firms. International Small Business Journal, 11(1), 35–46. https://doi.org/10.1177/026624269201100103.

    Article  Google Scholar 

  10. Burger, A., & Rojec, M. (2018). Impotence of crisis-motivated subsidization of firms: The case of Slovenia. Eastern European Economics, 1–27. https://doi.org/10.1080/00128775.2017.1416294.

  11. Butler, I., Galassi, G., & Ruffo, H. (2016). Public funding for startups in Argentina: An impact evaluation. Small Business Economics, 46(2), 295–309. https://doi.org/10.1007/s11187-015-9684-7.

    Article  Google Scholar 

  12. Bruhn, M., Karlan, D. S., & Schoar, A. (2012). The Impact of Consulting Services on Small and Medium Enterprises: Evidence from a Randomized Trial in Mexico. Yale Economics Department Working Paper No. 100. http://dx.doi.org/10.2139/ssrn.2010710

  13. Caliendo, M., & Kopeinig, S. (2008). Some practical guidance for the implementation of propensity score matching. Journal of Economic Surveys, 22(1), 31–72. https://doi.org/10.1111/j.1467-6419.2007.00527.x.

    Article  Google Scholar 

  14. Calvino, F., Criscuolo, C., & Menon, C. (2015). Cross-country evidence on start-up dynamics. In OECD science, technology and industry working papers, no. 2015/06. Paris: OECD Publishing.

    Google Scholar 

  15. Chen, W. R. (2008). Determinants of firms’ backward- and forward-looking R&D search behavior. Organization Science, 19(4), 609–622. https://doi.org/10.1287/orsc.1070.0320.

    Article  Google Scholar 

  16. Coad, A. (2018). Firm age: A survey. Journal of Evolutionary Economics, 28(1), 13–43. https://doi.org/10.1007/s00191-016-0486-0.

    Article  Google Scholar 

  17. Colombo, M. G., & Grilli, L. (2005). Founders’ human capital and the growth of new technology-based firms: A competence-based view. Research Policy, 34(6), 795–816. https://doi.org/10.1016/j.respol.2005.03.010.

    Article  Google Scholar 

  18. Colombo, M. G., Giannangeli, S., & Grilli, L. (2012). Public subsidies and the employment growth of high-tech start-ups: Assessing the impact of selective and automatic support schemes. Industrial and Corporate Change, 22(5), 1273–1314. https://doi.org/10.1093/icc/dts037.

    Article  Google Scholar 

  19. Colombo, M. G., Piva, E., Quas, A., & Rossi-Lamastra, C. (2016). How high-tech entrepreneurial ventures cope with the global crisis: Changes in product innovation and internationalization strategies. Industry and Innovation, 23(7). https://doi.org/10.1080/13662716.2016.1196438.

  20. Costa, S., Pappalardo, C., & Vicarelli, C. (2017). Internationalization choices and Italian firm performance during the crisis. Small Business Economics, 48(3), 753–769. https://doi.org/10.1007/s11187-016-9799-5.

    Article  Google Scholar 

  21. Cowling, M. (2006). Early stage survival and growth. In S. C. Parker (Ed.), The life cycle of entrepreneurial ventures (pp. 479–506). NY: Springer.

    Google Scholar 

  22. Crépon, B., & Duguet, E. (2003). Bank loans, start-up subsidies and the survival of the new firms: An econometric analysis at the entrepreneur level. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.421921.

  23. Cumming, D. J., Grilli, L., & Murtinu, S. (2017). Governmental and independent venture capital investments in Europe: A firm-level performance analysis. Journal of Corporate Finance, 42, 439–459. https://doi.org/10.1016/j.jcorpfin.2014.10.016.

    Article  Google Scholar 

  24. Czarnitzki, D. (2006). Research and development in small and medium-sized enterprises: The role of financial constraints and public funding. Scottish Journal of Political Economy, 53(3), 335–357. https://doi.org/10.1111/j.1467-9485.2006.00383.x.

    Article  Google Scholar 

  25. Czarnitzki, D., & Delanote, J. (2015). R&D policies for young SMEs: Input and output effects. Small Business Economics, 45(3), 465–485. https://doi.org/10.1007/s11187-015-9661-1.

    Article  Google Scholar 

  26. Decramer, S., & Vanormelingen, S. (2016). The effectiveness of investment subsidies: Evidence from a regression discontinuity design. Small Business Economics, 47(4), 1007–1032. https://doi.org/10.1007/s11187-016-9749-2.

    Article  Google Scholar 

  27. Désiage, L. J., Duhautois, R., & Redor, D. (2010). Do public subsidies have an impact on new firm survival? An empirical study with French data. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1640560.

  28. Dimos, C., & Pugh, G. (2016). The effectiveness of R&D subsidies: A meta-regression analysis of the evaluation literature. Research Policy, 45(4), 797–815. https://doi.org/10.1016/j.respol.2016.01.002.

    Article  Google Scholar 

  29. DiPrete, T. A., & Gangl, M. (2004). Assessing Bias in the estimation of causal effects: Rosenbaum bounds on matching estimators and instrumental variables estimation with imperfect instruments. Sociological Methodology, 34(1), 271–310. https://doi.org/10.1111/j.0081-1750.2004.00154.x.

    Article  Google Scholar 

  30. Eddleston, K. A., Ladge, J. J., Mitteness, C., & Balachandra, L. (2016). Do you see what I see? Signaling effects of gender and firm characteristics on financing entrepreneurial ventures. Entrepreneurship Theory and Practice, 40(3), 489–514. https://doi.org/10.1111/etap.12117.

    Article  Google Scholar 

  31. Evans, D. S., & Jovanovic, B. (1989). An estimated model of entrepreneurial choice under liquidity constraints. Journal of Political Economy, 97(4), 808–827. https://doi.org/10.1086/261629.

    Article  Google Scholar 

  32. Forbes, D. P., Borchert, P. S., Zellmer-Bruhn, M. E., & Sapienza, H. J. (2006). Entrepreneurial team formation: An exploration of new member addition. Entrepreneurship Theory and Practice, 30(2), 225–248. https://doi.org/10.1111/j.1540-6520.2006.00119.x.

    Article  Google Scholar 

  33. Galindo-Rueda, F., & Verger, F. (2016). OECD taxonomy of economic activities based on R&D intensity. Paris: OECD Publishing.

    Google Scholar 

  34. García-Teruel, P. J., & Martínez-Solano, P. (2008). On the determinants of SME cash holdings: Evidence from Spain. Journal of Business Finance & Accounting, 35(1–2), 127–149. https://doi.org/10.1111/j.1468-5957.2007.02022.x.

    Article  Google Scholar 

  35. Gilchrist, S., & Sim, J. W. (2007). Investment during the Korean financial crisis: A structural econometric analysis. NBER Working Paper No. w13315. https://doi.org/10.3386/w13315

  36. González, X., & Pazó, C. (2008). Do public subsidies stimulate private R&D spending? Research Policy, 37(3), 371–389. https://doi.org/10.1016/j.respol.2007.10.009.

    Article  Google Scholar 

  37. Grilli, L., & Murtinu, S. (2014). Government, venture capital and the growth of European high-tech entrepreneurial firms. Research Policy, 43(9), 1523–1543

  38. Guerini, M., & Quas, A. (2016). Governmental venture capital in Europe: Screening and certification. Journal of Business Venturing, 31(2), 175–195. https://doi.org/10.1016/j.jbusvent.2015.10.001.

    Article  Google Scholar 

  39. Hall, B., & Maffioli, A. (2008). Evaluating the impact of technology development funds in emerging economies: Evidence from Latin America. European Journal of Development Research, Taylor and Francis Journals, 20(2), 172–198. https://doi.org/10.3386/w13835.

    Article  Google Scholar 

  40. Heckman, J. J., Ichimura, H., & Todd, P. (1998). Matching as an econometric evaluation estimator. The Review of Economic Studies, 65(2), 261–294. https://doi.org/10.1111/1467-937x.00044.

    Article  Google Scholar 

  41. Hsu, F.-M., Horng, D.-J., & Hsueh, C.-C. (2009). The effect of Government-sponsored R&D Programmes in additionality in recipient firms in Taiwan. Technovation, 29(3), 204–217. https://doi.org/10.1016/j.technovation.2008.05.001.

    Article  Google Scholar 

  42. Huber, P., Oberhofer, H., & Pfaffermayr, M. (2017). Who creates jobs? Econometric modeling and evidence for Austrian firm level data. European Economic Review, 91, 57–71. https://doi.org/10.1016/j.euroecorev.2016.09.008.

    Article  Google Scholar 

  43. Hud, M., & Hussinger, K. (2015). The impact of R&D subsidies during the crisis. Research Policy, 44(10), 1844–1855. https://doi.org/10.1016/j.respol.2015.06.003.

    Article  Google Scholar 

  44. Huergo, E., & Trenado, M. (2010). The application for and the awarding of low-interest credits to finance R&D projects. Review of Industrial Organization, 37(3), 237–259. https://doi.org/10.1007/s11151-010-9263-7.

    Article  Google Scholar 

  45. Koski, H., & Pajarinen, M. (2013). The role of business subsidies in job creation of start-ups, gazelles and incumbents. Small Business Economics, 41(1), 195–214. https://doi.org/10.1007/s11187-012-9420-5.

    Article  Google Scholar 

  46. Lerner, J. (1999). The government as venture capitalist: The long-run impact of the SBIR program. The Journal of Private Equity, 72(3), 55–78. https://doi.org/10.1086/209616.

    Article  Google Scholar 

  47. Lerner, J. (2009). Boulevard of broken dreams: why public efforts to boost entrepreneurship and venture capital have failed--and what to do about it. Princeton University Press

  48. Martí, J., & Quas, A. (2018). A beacon in the night: Government certification of SMEs towards banks. Small Business Economics, 50(2), 397–413. https://doi.org/10.1007/s11187-016-9828-4.

    Article  Google Scholar 

  49. McKenzie, D. (2017). Identifying and spurring high-growth entrepreneurship: Experimental evidence from a business plan competition. American Economic Review, 107(8), 2278–2307. https://doi.org/10.1257/aer.20151404.

    Article  Google Scholar 

  50. McKenzie, D., Assaf, N., & Cusolito, A. P. (2017). The additionality impact of a matching grant programme for small firms: Experimental evidence from Yemen. Journal of Development Effectiveness, 9(1), 1–14. https://doi.org/10.1080/19439342.2016.1231703.

    Article  Google Scholar 

  51. Meuleman, M., & De Maeseneire, W. (2012). Do R&D subsidies affect SMEs’ access to external financing? Research Policy, 41(3), 580–591. https://doi.org/10.1016/j.respol.2012.01.001.

    Article  Google Scholar 

  52. Michalek, J., Ciaian, P., & D’Artis, K. (2016). Investment crowding out: Firm-level evidence from northern Germany. Regional Studies, 50(9), 1579–1594. https://doi.org/10.1080/00343404.2015.1044957.

    Article  Google Scholar 

  53. Musso, P., & Schiavo, S. (2008). The impact of financial constraints on firm survival and growth. Journal of Evolutionary Economics, 18(2), 135–149. https://doi.org/10.1007/s00191-007-0087-z.

    Article  Google Scholar 

  54. Neicu, D., Teirlinck, P., & Kelchtermans, S. (2016). Dipping in the policy mix: do R&D subsidies foster behavioral additionality effects of R&D tax credits?. Economics of Innovation and New Technology, 25(3), 218-239. https://doi.org/10.1080/10438599.2015.1076192

  55. OECD, ETF, EU, EBRD, & SEECEL. (2016). SME Policy Index: Western Balkans and Turkey 2016. Assessing the Implementation of the Small Business Act for Europe. Paris. https://doi.org/10.1787/9789264254473-en

  56. Pellegrini, G., & Muccigrosso, T. (2017). Do subsidized new firms survive longer? Evidence from a counterfactual approach. Regional Studies, 51(10), 1483–1493. https://doi.org/10.1080/00343404.2016.1190814.

    Article  Google Scholar 

  57. Pfeiffer, F., & Reize, F. (2000). Business start-ups by the unemployed—An econometric analysis based on firm data. Labour Economics, 7(5), 629–663. https://doi.org/10.1016/s0927-5371(00)00016-6.

    Article  Google Scholar 

  58. Rosenbaum, P. R. (2002). Observational studies. In Observational studies (pp. 1–17). New York, NY: Springer.

    Google Scholar 

  59. Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70(1), 41–55. https://doi.org/10.1093/biomet/70.1.41.

    Article  Google Scholar 

  60. Rubin, D. B. (1977). Assignment to treatment group on the basis of a covariate. Journal of Educational Statistics, 2(1), 1–26. https://doi.org/10.2307/1164933.

    Article  Google Scholar 

  61. Segarra-Blasco, A., & Teruel, M. (2016). Application and success of R&D subsidies: What is the role of firm age? Industry and Innovation, 23(8), 713–733. https://doi.org/10.1080/13662716.2016.1201649.

    Article  Google Scholar 

  62. Shane, S. (2009). Why encouraging more people to become entrepreneurs is bad public policy. Small Business Economics, 33(2), 141–149. https://doi.org/10.1007/s11187-009-9215-5.

    Article  Google Scholar 

  63. Stucki, T. (2013). Success of start-up firms: The role of financial constraints. Industrial and Corporate Change, 23(1), 25–64. https://doi.org/10.1093/icc/dtt008.

    Article  Google Scholar 

  64. Vitezic, V., Srhoj, S., & Peric, M. (2018). Investigating industry dynamics in a recessionary transition economy. South East European Journal of Economics and Business, 13(1), 43–67. https://doi.org/10.2478/jeb-2018-0003.

    Article  Google Scholar 

  65. Wren, C., & Storey, D. J. (2002). Evaluating the effect of soft business support upon small firm performance. Oxford Economic Papers, 54(2), 334–365. https://doi.org/10.1093/oep/54.2.334.

    Article  Google Scholar 

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Acknowledgments

We thank the associate editor László Szerb and the two anonymous referees for their comments and suggestions. Draft version of the paper was presented during Training Workshop ‘Evaluations of Innovation Policies’ held from 21 to 22 November 2017 in Zagreb, within the project “Strengthening scientific and research capacity of the Institute of Economics, Zagreb as a cornerstone for Croatian socioeconomic growth through the implementation of Smart Specialization Strategy” (H2020-TWINN-2015-692191-SmartEIZ). This research is also supported by TVOJ GRANT@EIZ, financed by the Institute of Economics, Zagreb. The manuscript has been awarded the Hans Raupach 2018 Award by the Leibniz Institute for East and Southeast European Studies Economics Department, best paper award at the final conference of the EU Horizon 2020 Twinning project H2020-TWINN-2015-692191-SmartEIZ, and was also presented at the DRUID2018 conference in Copenhagen in June 2018. The views expressed in this paper are solely of the authors and do not represent the views of the either of two projects.

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Appendices

Appendix A

Table 9 Basic information on the grant schemes
Table 10 Distribution of government grants
Table 11 Result comparison between our main specification and other matching algorithms
Table 12 Estimation of ATT (single- and multiple-treated)

Appendix B: The derivation of Rosenbaum bounds

Let us assume that the probability of the treatment D for observation i is a function of observed vector of covariates xi and unobserved variable ui. More precisely, P(Di = 1| xi, ui) = F(βxi + γui), where F is the logistic function and γ is the effect of the unobserved variable on the probability of treatment. When γ = 0, this means that the study is free of hidden bias and the selection into treatment is determined solely by xi. When γ ≠ 0, two observations, say i and j, which have the same covariates xi = xj, can have different probabilities of treatment if ui ≠ uj. Since F is logistic, the odds of treatment for the two observations are \( \frac{P_i}{1-{P}_i} \) and \( \frac{P_j}{1-{P}_j} \), and the odds ratio is given by \( \frac{P_j\left(1-{P}_i\right)}{P_i\left(1-{P}_j\right)}=\frac{e^{\beta {x}_i+\gamma {u}_i}}{e^{\beta {x}_j+\gamma {u}_j}}={e}^{\gamma \left({u}_i-{u}_j\right)} \). If the unobserved variable does not exert any influence (i. e. if γ = 0), or if ui = uj, then \( {e}^{\gamma \left({u}_i-{u}_j\right)}=1 \). Rosenbaum (2002) showed that the following bounds can be put on the odds ratio\( :\kern0.5em \frac{1}{\Gamma}=\frac{1}{e^{\gamma }}\le \frac{P_j\left(1-{P}_i\right)}{P_i\left(1-{P}_j\right)}\le {e}^{\gamma }=\Gamma \). Both observations have the same probability to be in treatment only if Γ = eγ = 1. If for example Γ = eγ = 2,that means that the probability that observation i receives treatment can be up to twice as big as the probability for observation j, regardless of the fact that probability should be the same for both units according to the observables, which is the result of hidden bias. This is how Rosenbaum bound Γ measures the extent of the hidden bias.

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Srhoj, S., Škrinjarić, B. & Radas, S. Bidding against the odds? The impact evaluation of grants for young micro and small firms during the recession. Small Bus Econ (2019). https://doi.org/10.1007/s11187-019-00200-6

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Keywords

  • Grants
  • Recession
  • Young firms
  • Survival
  • Firm performance
  • Bank loans

JEL classification

  • H25
  • L26