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Financial performance analysis of guaranteed firms using propensity scores

성향점수를 활용한 보증기업의 재무성과 분석

  • Received : 2016.02.05
  • Accepted : 2016.02.12
  • Published : 2016.02.29

Abstract

In this paper, we examine the financial performance of credit guarantee programs. We compared financial performance of guaranteed firms of KODIT and non-guaranteed firms. The of covariate adjusted propensity score method is used because a selection bias problem could occur if t-test or regression analysis were used. The results show that a credit guarantee program enhances the financial performance of beneficiary firms.

본 연구에서는 신용보증기금으로부터 보증받은 기업의 미시적 성과를 분석하기 위해 비보증기업과 비교 분석하였다. t-test나 회귀모형과 같은 단순모형으로 비교하게 되면 선택편의에 의해 실제 보증성과를 나타낼 수 없다. 이러한 문제점을 해결하기 위해서, 선택편의를 보정한 회귀모형을 제안하였고 실제 자료에 적용하였다. 분석결과 비보증기업에 비하여 보증기업의 미시적 성과를 확인할 수 있었다.

Keywords

References

  1. Alam, M., Noh, M., and Lee, Y. (2013). Likelihood estimate of treatment effects under selection bias, Statistics and Its Interface, 6, 349-359. https://doi.org/10.4310/SII.2013.v6.n3.a5
  2. Garen, J. (1984). A selectivity bias approach with a continuous choice variable, Econometrica, 52, 1199-1218. https://doi.org/10.2307/1910996
  3. Glynn, A. N. and Quinn, K. M. (2009). An introduction to the augmented inverse propensity weighted estimator, Political Analysis, 18, 36-56.
  4. Heckman, J. J. and Smith, J. (1999). The pre-program earnings dip and the determinants participation in a social program: implication for simple program evaluation strategies, Economic Journal, 109, 313-348. https://doi.org/10.1111/1468-0297.00451
  5. Heckman, J. J., Tobias, J. L., and Vytlacil, E. (2003). Simple estimator for treatment parameters in a latent-variable framework, Review of Economical Statistics, 85, 748-755. https://doi.org/10.1162/003465303322369867
  6. Hirano, K. and Imbens, G. W. (2004). The propensity score with continuous treatments in Gelman, A. and Meng, X. (eds.), Applied Bayesian Modeling and Causal Inference from Incomplete-Data Perspectives, Wiley.
  7. Iichiro, U., Koji, S., and Yamashiro, G. M. (2006). Effectiveness of credit guarantees in the Japanese loan market, RIETI Discussion Paper Series, 06-E-004.
  8. Kim, S. and Kim, J. R. (2013). A study on the performance measurement of credit guarantee, Korean Industrial Economic Association, 26, 1381-1399.
  9. Rosenbaum, P. R. and Rubin, D. (1983). The central role of the propensity score in observational studies for causal effects, Biometrika, 70, 41-55. https://doi.org/10.1093/biomet/70.1.41
  10. Rubin, D. (1978). Bayesian inference for causal effect: the role of randomization, Annals of Statistics, 6, 34-58. https://doi.org/10.1214/aos/1176344064