Treatment Effect Analysis of Technology-Based Credit Guarantee

  • Received : 2013.09.17
  • Accepted : 2013.11.10
  • Published : 2013.12.31

Abstract

This paper analyzes the effect of technology-based credit guarantees on small and medium-sized enterprises with firm-level panel data and various estimation methods. To estimate the impact of technology-based credit guarantees granted by the Korea Technology Credit Guarantee Fund (KTCG), we investigate changes in profitability, financial stability, and production efficiency among firms that have been the recipients of such technologybased credit guarantees. We find that technology-based credit guarantees tend to increase productivity of firms and ease firms' financing constraints in the short run. Moreover, receiving such guarantees significantly influences firms' performance by improving their long-term financial stability and efficiency. We conclude that the KTCG satisfies its policy objective in the long run as well as in the short run, contributing to the growth of the national economy.

Keywords

References

  1. Angrist, J. D., Imbens, G. W., Rubin, D. B. (1996). Identification of Causal Effects Using Instrumental Variable, Journal of the American Statistical Association, 91(434), 444-472. https://doi.org/10.1080/01621459.1996.10476902
  2. Ashenfelter, O. (1978). Estimating the Effect of Training Programs on Earnings, Review of Economics and Statistics, 60(1), 47-57. https://doi.org/10.2307/1924332
  3. Ashenfelter, O., Card, D. (1985). Using the Longitudinal Structure of Earnings to Estimate the Effect of Training Programs, Review of Economics and Statistics, 67(4), 648-660. https://doi.org/10.2307/1924810
  4. Augurzky, B., Tauchmann, H. (2011). Less Social Health Insurance, More Private Supplementary Insurance? Empirical Evidence from Germany, Journal of Policy Modeling, 33, 470-480 https://doi.org/10.1016/j.jpolmod.2010.12.002
  5. Cowling, M., Mitchell, P. (2003). Is the Small Firms Loan Guarantee Scheme Hazardous for Banks or Helpful to Small Business?, Small Business Economics, 21, 63-71. https://doi.org/10.1023/A:1024408932156
  6. Craig, B. R., Jackson, W. E., Thomson, J. B. (2005). SBA-loan Guarantees and Local Economic Growth, Federal Reserve Bank of Cleveland Working Paper, 05-03.
  7. Dehejia, R., Wahba, S. (1999). Causal Effects in Nonexperimental Studies: Reevaluating the Evaluation of Training Programs, Journal of the American Statistical Association, 94(448), 1053-1062. https://doi.org/10.1080/01621459.1999.10473858
  8. Eichenbaum, M., Hansen, P., Singleton, K. (1988). A Time Series Analysis of Representative Agent Models of Consumption and Leisure Choice under Uncertainty, Quarterly Journal of Economics, 103(1), 51-78 https://doi.org/10.2307/1882642
  9. 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
  10. Feldstein, M. (2003). Why Is Productivity Growing Faster?, Journal of Policy Modeling, 25, 445-451 https://doi.org/10.1016/S0161-8938(03)00039-5
  11. Gale, W. G. (1991). Economic Effects of Federal Credit Programs, American Economic Review, 81(1), 133-152.
  12. Gianfranco, E. A., Oliviero, A. C. (2008). The Effects of Grant Policy on Technology Investment in Italy, Journal of Policy Modeling, 30, 381-399. https://doi.org/10.1016/j.jpolmod.2007.10.004
  13. Hahn, J. Y., Todd, P., Klaauw, W. (2001). Identification and Estimation of Treatment Effects with a Regression-Discontinuity Design, Econometrica, 69(1), 201-209. https://doi.org/10.1111/1468-0262.00183
  14. Heckman, J. J. (1976). The Common Structure of Statistical Models of Truncation, Sample Selection, and Limited Dependent Variables and a Simple Estimator for Such Models, Annals of Economic and Social Measurement, 5(4), 475-492.
  15. Heckman, J. J. (1979). Sample Selection Bias as a Specification Error, Econometrica, 47(1), 153-162. https://doi.org/10.2307/1912352
  16. Heckman, J. J., 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(457), 313-348. https://doi.org/10.1111/1468-0297.00451
  17. Iichiro, U., Koji, S., Yamashiro, G. M. (2006). Effectiveness of Credit Guarantees in the Japanese Loan Market, RIETI Discussion Paper Series, 06-E-004.
  18. Imbens, G. W. (2004). Nonparametric Estimation of Average Treatment Effects under Exogeneity: A Review, The Review of Economics and Statistics, 86, 4-29. https://doi.org/10.1162/003465304323023651
  19. Imbens, G., Angrist, J. (1994). Identification and Estimation of Local Average Treatment Effects, Econometrica, 62(2), 467-475. https://doi.org/10.2307/2951620
  20. Kang, J. W., Heshmati, A. (2007). Effect of Credit Guarantee Policy on Survival and Performance of SMEs in Republic of Korea, Small Business Economics, 31(4), 445-462.
  21. Kim, T. (2008). An Empirical Study on the Characteristics and Bankruptcy Factors of the Guaranteed Small and Medium Businesses, Journal of the Korean Data Analysis Society, 10(2), 909-922. (in Korean).
  22. Lalonde, R. J. (1986). Evaluating the Econometric Evaluations of Training Programs with Experimental Data, American Economic Review, 76(4), 604-620.
  23. Lee, J., Jeong, J. Y. (2006). A Research on Firm Value Determinants: from the Point of View of Growth Options and Debt Policies, Journal of the Korean Data Analysis Society, 8(1), 273-290. (in Korean).
  24. Lee, K., Noh, M., Hong, J. (2009). A Reliability Analysis of Technology Rating with Rasch Model: Korea Technology Credit Guarantee Fund Case, Journal of the Korean Data Analysis Society, 11(3), 1537-1548. (in Korean).
  25. Mankiw, G. N. (1986). The Allocation of Credit and Financial Collapse, Quarterly Journal of Economics, 101(3), 455-470. https://doi.org/10.2307/1885692
  26. Newey, K. W. (1985). Generalized Methods of Moments Specification Testing, Journal of Econometrics, 29(3), 229-256. https://doi.org/10.1016/0304-4076(85)90154-X
  27. Riding, A. L., Haines, G. Jr. (2001). Loan Guarantees: Costs of Default and Benefits to Small Firms, Journal of Business Venturing, 16, 595-612. https://doi.org/10.1016/S0883-9026(00)00050-1
  28. Rosenbaum, P., Rubin, D. (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
  29. Rosenbaum, P., Rubin, D. (1985). Constructing a Control Group Using Multivariate Matched Sampling Methods that Incorporate the Propensity Score, American Statistician, 39, 33-38.
  30. Seo, J., Noh, M., Nam, J. (2011). The Selection of Post-Evaluation Target Companies for Technology Guarantee Fund using Sampling Method, Journal of the Korean Data Analysis Society, 13(1), 245-255. (in Korean).
  31. Stiglitz, J. E., Weiss, A. (1981). Credit Rationing in Markets with Imperfect Information, American Economic Review, 71(3), 393-410.
  32. Tongeren van, F. W. (1998), Microsimulation of Corporate Response to Investment Subsidies, Journal of Policy Modeling, 20, 55-75. https://doi.org/10.1016/S0161-8938(97)00009-4