한국전자거래학회지 (The Journal of Society for e-Business Studies)

  • 제25권4호
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  • Pages.77-109
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  • 2020
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  • 2288-3908(pISSN)
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  • 2765-3846(eISSN)
한국전자거래학회 (Society for e-Business Studies)
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Gradient Boosting 모형을 이용한 중소기업 R&D 지원금 결정요인 분석

Who Gets Government SME R&D Subsidy? Application of Gradient Boosting Model

  • 투고 : 2020.09.15
  • 심사 : 2020.10.29
  • 발행 : 2020.11.30
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⟨ 이전 논문 다음 논문 ⟩

초록

본 논문에서는 그래디언트 부스팅 모형을 활용하여 정부의 중소기업 연구개발 지원 결정에 영향을 미치는 요인들을 파악하였다. 기존 연구가 사후적으로 정부의 연구개발 지원이 수혜 기업에 미친 영향을 분석하는 것에 중점을 두었다면, 본 논문은 정부의 연구개발 지원 결정 방식을 파악하고, 그 방식이 기업에게 제공하는 유인을 분석하고자 하였다. 이를 위하여 본 논문은 지원금 결정에 영향을 미치는 다양한 잠재적 요인들을 선택하고, 기계학습 접근법을 활용하여 추정오차 축소효과가 큰 요인들을 선별하였다. 구체적으로 본 논문은 한국과학기술평가원이 구축한 국가연구개발조사분석 자료와 한국신용평가자료를 연결한 자료에 그래디언트 부스팅(Gradient Boosting) 모형을 적용하여 지원금 추정모형을 구축하였다. 본 논문에서 구축한 그래디언트 부스팅 모형은 선형회귀분석 응용모형에 비해 평균제곱근오차를 7.20% 축소할 수 있었다. 각 변수의 순열 중요도(permutation importance)를 분석한 결과 연구성과지표 및 연구개발비가 추정오차 축소에 기여가 큰 것으로 파악되었다. 그리고 각 변수의 부분의존도(Partial Dependence Plot: PDP) 및 SHAP 값(SHAP value: SHapley Additive exPlanation value)을 분석한 결과 연구성과지표가 좋고 연구개발비 지출이 큰 기업이 많은 연구개발 지원금을 받는 반면, 영업이익이 크고 자기자본회전율이 높은 기업은 적은 지원금을 받는 경향이 발견되었다. 본 연구의 결과는 현재 중소기업 연구개발 지원금 배분 방식이 연구성과지표 제고 및 연구개발투자 증가 유인은 제공하나, 기업 경영성과 제고 유인은 취약함을 시사한다.

In this paper, we build a gradient Boosting model to predict government SME R&D subsidy, select features of high importance, and measure the impact of each features to the predicted subsidy using PDP and SHAP value. Unlike previous empirical researches, we focus on the effect of the R&D subsidy distribution pattern to the incentive of the firms participating subsidy competition. We used the firm data constructed by KISTEP linking government R&D subsidy record with financial statements provided by NICE, and applied a Gradient Boosting model to predict R&D subsidy. We found that firms with higher R&D performance and larger R&D investment tend to have higher R&D subsidies, but firms with higher operation profit or total asset turnover rate tend to have lower R&D subsidies. Our results suggest that current government R&D subsidy distribution pattern provides incentive to improve R&D project performance, but not business performance.

키워드

참고문헌

  1. Bergstra, J. S., Bardenet, R., Bengio, Y., and Kegl, B., Algorithms for hyper-parameter optimization, NIPS'11: Proceedings of the 24th International Conference on Neural Information Processing Systems, pp. 2546-2554, 2011.
  2. Bloom, N., Reenen, J. V., and Williams, H., "A Toolkit of Policies to Promote Innovation," Journal of Economic Perspectives, Vol. 33, No 3, pp. 163-84, 2019.
  3. Chang, W. H., "Is Korea's Public Funding for SMEs Achieving Its Intended Goals?," KDI Focus, No. 63, 2016. 2. 3.
  4. Choi, J. M., "A Study of the Effects of Government R&D Support on Product Innovation in Small and Medium-sized Enterprises(SMEs): Focusing on the Moderating Effect of Firm Characteristics," Korean Journal of Public Administration, Vol. 56, No. 2, pp. 213-248, 2018. https://doi.org/10.24145/kjpa.56.2.9
  5. Cin, B., Kim, Y., and Vonortas, N. S., "The Impact of Government R&D Subsidy on Firm Performance: Evidence from Korean SMEs," Small Business Economics, Vol. 48, No. 2, pp. 345-360, 2017. https://doi.org/10.1007/s11187-016-9786-x
  6. Fisher, A., Rudin, C., and Dominici, F., "All Models are Wrong, but Many are Useful: Learning a Variable's Importance by Studying an Entire Class of Prediction Models Simultaneously," Journal of Machine Learning Research, Vol. 20, No. 177, pp. 1-81, 2019.
  7. Friedman, J. H., "Greedy function approximation: a gradient boosting machine," Annals of statistics, Vol. 29, No. 5, pp. 1189-1232, 2001. https://doi.org/10.1214/aos/1013203451
  8. Gerath, J., Witten, D., Hastie, T., and Tibshirani, R., An Introduction to Statistical Learning, New York: Springer, 2013.
  9. Hall, B. H. and Lerner, J., Chapter 14-The financing of R&D and innovation, In Handbook of the Economics of Innovation, Vol. 1, pp. 609-639, 2010.
  10. Hong, J. P. and Kim, J. H., "Impacts of Financial Policies for SMEs on Firms Performance: Role of Supplier Network between Large Firms and SMEs," Journal of Korean Economic Analysis, Vol. 21, No. 3, pp. 185-240, 2015.
  11. Ivezic, Z., Connolly, A. J., VanderPlas, J. T., and Gray, A., Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data. Princeton University Press, 2019.
  12. Ji, M. W., "Did Legal Criteria for Receiving Governmental Support Cause a Negative Effect in Employment Growth of SMEs?:Evidence from the Korean Manufacturing Industry," The Journal of Korean Public Policy, Vol. 17, No. 3, pp. 3-31, 2015.
  13. Jun, B. W. and Choi, E., "Review on Tax Expenditures for Small-and-Mid Sized Firms," Asia Pacific Journal of Small Business, Vol. 37, No. 3, pp. 1-24, 2015.
  14. Kang et al., "An empirical Study on the Impact of Government R&D Investment on SMEs in Korea," Korea Institute of S&T Evaluation and Planning, Report no. 2016-027, 2016.
  15. Kang et al., "Big Data Analysis: Application to Environmental Research and Service II," Korea Environment Institute, 2018.
  16. Kang et al., "Big Data Analysis: Application to Environmental Research and Service," Korea Environment Institute, 2017.
  17. Kim, K. H. and Yang, J. Y., "Government R&D Support and Apply Strategy for SMEs," Regional Industry Review, Vol. 41, No. 3, pp. 299-324, 2018. https://doi.org/10.33932/rir.41.3.14
  18. Kim, K. W., Kim, J., Shin, J. K., and Hong, S. B., How to Improve the efficiency of Government R&D Investment, Korea Development Institute, 2011.
  19. Ko, H. S., Chung, Y. H., Seo, H. K., and Song, L. K., "A Study on the Effectiveness of the SMEs Consulting Support Project:Focused on Hidden Champion Business Supporting in Daejeon," Asia Pacific Journal of Small Business, Vol. 38, No. 1, pp. 169-188, 2016.
  20. Kuhn, M. and Johnson, K., Applied predictive modeling(Vol. 26), New York: Springer, 2013.
  21. Lee, D. H. and Kim, K. H., "Deep Learning Based Prediction Method of Long-term Photovoltaic Power Generation Using Meteorological and Seasonal Information," The Journal of Society for e-Business Studies, Vol. 24, No. 1, pp. 1-16, 2019.
  22. Lerner, J., Boulevard of broken dreams:why public efforts to boost entrepreneurship and venture capital have failed and what to do about it. Princeton University Press, 2009.
  23. Li, T., Jing, B., Ying, N., and Yu, X., "Adaptive Scaling," arXiv preprint arXiv: 1709. 00566, 2017.
  24. Lundberg, S. M. and Lee, S. I., "A unified approach to interpreting model predictions," In Advances in neural informatio processing systems (pp. 4765-4774), 2017.
  25. Lundberg, S. M., Erion, G. G., and Lee, S. I., "Consistent individualized feature attribution for tree ensembles," arXiv preprint arXiv:1802.03888, 2018.
  26. Molnar, Christoph. Interpretable Machine Learning, Lulu.com, 2020.
  27. National Assembly Budget Office, Analysis on Government R&D Program : Overview, Seoul, 2019.
  28. OECD, The SME Financing Gap (Vol. I):Theory and Evidence, OECD Publishing, Paris, 2006.
  29. Pyo, H. H. and Choi, H. H., "The Effects of Export Promotion on Korean Manufacturing SMEs' Performance," Kukje Kyungje Yongu, Vol. 24, No. 3, pp. 29-56, 2018. https://doi.org/10.17298/kky.2018.24.3.002
  30. Strobl, C., Boulesteix, A., Zeileis, A., and Hothorn, T., "Bias in random forest variable importance measures: Illustrations, sources and a solution," BMC Bioinformatics, Vol. 25, No. 8, pp. 1-21, 2007.
  31. Zhao, Q. and Hastie, T., "Causal interpretations of black-box models," Journal of Business & Economic Statistics, DOI:10.10870/07350015, 2019.
  32. Zuniga-Vincente, J. A., Alonso-Borrego, C., Forcadell, F. J., and Galan, J. I., "Assessing the effect of public subsidies on firm R&D investment: a survey," Journal of Economic Surveys, Vol. 28, No. 1, pp. 36-67, 2014 https://doi.org/10.1111/j.1467-6419.2012.00738.x