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
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).
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).
Detailed description of these programs can be found in the Appendix A (Table 9).
All monetary values are expressed in Croatian kuna, HRK (1 EUR = 7.529 HRK, 2016 average).
Grant amount distribution in analyzed period is presented in Appendix A (Table 10).
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.
Unless stated otherwise, all Figures and Tables in this paper are produced by authors themselves.
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.
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.
Balancing property of placebo treated and control group show no statistically significant difference in means and are available upon request.
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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
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 56, 83–103 (2021). https://doi.org/10.1007/s11187-019-00200-6
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DOI: https://doi.org/10.1007/s11187-019-00200-6