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Evaluation of credit guarantee policy using propensity score matching

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Abstract

In this article, we evaluate the effect of the credit guarantee policy by comparing a large sample of guaranteed firms and matched non-guaranteed firms from 2000 to 2003. The sample firms are compared with respect to growth rates of different performance indicators including: productivity, sales, employment, investment, R&D, wage level, and the survival of firms in the post crisis period. In order to avoid the selectivity problem, propensity score matching methodologies are adopted. Results suggest that credit guarantees influenced significantly firms’ ability to maintain their size, and increase their survival rate, but not to increase their R&D and investment and hence, their growth in productivity. Moreover, due to the adverse selection problem, firms with lower productivity were receiving guarantees.

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Notes

  1. Chaebol is a Korean corporate form consisting of a pyramid of subsidiary firms operated by a single family line.

  2. Hoshi (2006) introduces the concept of ‘zombie firms’ and reviews their effects on the entire economies.

  3. Korean Won, US Dollar (USD) 1$ = 1037 KRW, in November 2005.

  4. Operation multiple is defined as the outstanding guarantee balance divided by basic funds of the guarantee institution.

  5. The portion of credit guarantee based loan reached 35.9% of total SME loans from banks in 2003.

  6. There also exist 14 regional credit guarantee institutions founded by local governments, and their aggregated guarantee balance compared to the total guarantee balance provision from all guarantee institutions was 3.1% and 3.7% for the year 2001 and 2002, respectively. We could not identify firms supported from regional credit guarantee institutions. However more than half the firms supported by regional credit guarantee institutions were also beneficiaries of KOTEC and KCGF (Lee 2006a).

  7. The methodologies listed here are standardized in practical works and well explained in Caliendo (2006), Dehejia and Wahba (2002) and Heckman et al. (1997). Also Becker and Ichino (2002) developed programs and manuals to utilize various matching estimators in STATA software.

  8. However, to show the robustness of the estimation, we also conducted the same analyses with nearest neighbor matching with replacement and radius matching differing the radius of caliper for comparison. Advantages and drawbacks of various matching estimators, and topics of selecting appropriate estimators are extensively discussed in Caliendo (2006).

  9. Hereafter the KOTEC firms, KCGF firms and BOTH firms means the firms received credit guarantee from one of KOTEC, KCGF and both of institutions, respectively.

  10. The definition of input and output variables, and detailed calculation of TFP are available from the authors on request.

  11. However, Carpenter and Petersen (2002) reported that establishing a relationship between lenders and firms to know borrower characteristics, takes on average 9.4 years which is too long for SMEs requiring financing at a very young age.

  12. Matching with multiple treatments applied in Frölich et al. (2004) could be implemented in this kind of evaluation that there exist more than two institutions working under the same policy. However we are using seven performance variables including survival rate, and comparing all these by institutions will result in too many combinations, so here we concentrated on comparing supported firms with non-supported firms by institutions.

  13. See Lee (2006b) for reviews of various balancing tests used in practice.

  14. Results in Table 3 were estimated to investigate the performance difference with firms which survived during 2000–2003. However, we also estimated probit regression for firms which survived during 2000–2002 to investigate the difference in survival rate. Since two regressions provided essentially the same results, we only presented and discussed results in Table 3.

  15. The results from the other matching estimators are available from the authors on request. Kernel matching was based on Gaussian kernel function with the bandwidth: 0.06.

  16. We thank an anonymous referee for raising the issues of banks types of error and their possible sources and explanations.

  17. We thank an anonymous referee for pointing out the importance of the guarantee scheme parameters.

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Authors and Affiliations

  1. Technology Management, Economics and Policy Program, Seoul National University, San 56-1, Shilim-Dong, Kwanak-Gu, Seoul, 151-742, Republic of Korea

    Inha Oh & Jeong-Dong Lee

  2. Department of Economics, University of Kurdistan Hawler, Hawler, Federal Region of Kurdistan, Erbil, Iraq

    Almas Heshmati

  3. School of Business Administration, Dongguk University, 26, Pil-dong 3-ga, Jung-gu, Seoul, Korea

    Gyoung-Gyu Choi

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  1. Inha Oh
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  2. Jeong-Dong Lee
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  3. Almas Heshmati
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  4. Gyoung-Gyu Choi
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Correspondence to Inha Oh.

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Oh, I., Lee, JD., Heshmati, A. et al. Evaluation of credit guarantee policy using propensity score matching. Small Bus Econ 33, 335–351 (2009). https://doi.org/10.1007/s11187-008-9102-5

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