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Do Firms Supported by Credit Guarantee Schemes Report Better Financial Results 2 Years After the End of Intervention?

Ondřej Dvouletý ORCID logo, Jan Čadil and Karel Mirošník

Abstract

The study contributes to underdeveloped knowledge on effects of SME policies in Central and Eastern Europe. We evaluate two Czech credit guarantee schemes funded from EU funds during years 2007–2013. We conduct micro-econometric firm-level impact evaluation based on propensity score matching approach. We estimate average treatment effect on the treated (ATET) for six financial outcome variables (total assets, tangible fixed assets, personnel costs, sales, price-cost-margin and return on assets) measuring firm´s competitiveness. Two years after the programme, no statistically conclusive results were obtained for the most of the outcome variables. We found only a positive change in tangible fixed assets for the programme participants. However, we cannot say, that the supported firms would be better off, compared to those non-supported in a short-term. Our analysis shows that without reliable data gathered by public sector authorities, no rigorous evaluations can be made and thus no evidence driven policies can be formed.

JEL Classification: L53; L26; L38

Appendix

Figure 4: Kernel matching technique diagnostics graphs (distributions of the standardised % bias on the top and propensity scores on the bottom, for both groups before and after the application of the matching procedures).Source: Own calculations

Figure 4:

Kernel matching technique diagnostics graphs (distributions of the standardised % bias on the top and propensity scores on the bottom, for both groups before and after the application of the matching procedures).

Source: Own calculations

Figure 5: Radius with caliper (0.001) matching technique diagnostics graphs (distributions of the standardised % bias on the top and propensity scores on the bottom, for both groups before and after the application of the matching procedures).Source: Own calculations

Figure 5:

Radius with caliper (0.001) matching technique diagnostics graphs (distributions of the standardised % bias on the top and propensity scores on the bottom, for both groups before and after the application of the matching procedures).

Source: Own calculations

Figure 6: Nearest neighbour (1) matching technique diagnostics graphs (Distributions of the standardised % bias on the top and propensity scores on the bottom, for both groups before and after the application of the matching procedures).Source: Own calculations

Figure 6:

Nearest neighbour (1) matching technique diagnostics graphs (Distributions of the standardised % bias on the top and propensity scores on the bottom, for both groups before and after the application of the matching procedures).

Source: Own calculations

Acknowledgements

This work was supported by the University of Economics, Prague by the Internal Grant Agency of FBA UEP IP300040 and within the EU collaborative research project CUPESSE (Cultural Pathways to Economic Self-Sufficiency and Entrepreneurship; Grant Agreement No. 613257). An earlier version of this paper was presented at the 10th Joint IOS/APB/EACES Summer Academy on Central and Eastern Europe: Firm Behaviour in Central Europe, held in Tutzing in June, 2018. Authors thank to Martin Lukeš, Oto Potluka and the anonymous reviewers for their contributions to paper development.

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Published Online: 2018-10-26

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