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

Sung Won Kang, HeeChan Kang


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. 


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Bergstra, J. S., Bardenet, R., Bengio, Y., and Kégl, B., Algorithms for hyper-parameter optimization, NIPS’11: Proceedings of the 24th International Conference on Neural Information Processing Systems, pp. 2546-2554, 2011.

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.

Chang, W. H., “Is Korea’s Public Funding for SMEs Achieving Its Intended Goals?,” KDI Focus, No. 63, 2016. 2. 3.

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.

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.

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.

Friedman, J. H., “Greedy function approximation: a gradient boosting machine,” Annals of statistics, Vol. 29, No. 5, pp. 1189-1232, 2001.

Gerath, J., Witten, D., Hastie, T., and Tibshirani, R., An Introduction to Statistical Learning, New York: Springer, 2013.

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.

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.

Ivezić, Ž., 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.

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.

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.

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.

Kang et al., “Big Data Analysis: Application to Environmental Research and Service II,” Korea Environment Institute, 2018.

Kang et al., “Big Data Analysis: Application to Environmental Research and Service,” Korea Environment Institute, 2017.

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.

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.

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.

Kuhn, M. and Johnson, K., Applied predictive modeling(Vol. 26), New York: Springer, 2013.

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.

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.

Li, T., Jing, B., Ying, N., and Yu, X., “Adaptive Scaling,” arXiv preprint arXiv: 1709. 00566, 2017.

Lundberg, S. M. and Lee, S. I., “A unified approach to interpreting model predictions,” In Advances in neural information processing systems (pp. 4765-4774), 2017.

Lundberg, S. M., Erion, G. G., and Lee, S. I., “Consistent individualized feature attribution for tree ensembles,” arXiv preprint arXiv:1802.03888, 2018.

Molnar, Christoph. Interpretable Machine Learning, Lulu.com, 2020.

National Assembly Budget Office, Analysis on Government R&D Program : Overview, Seoul, 2019.

OECD, The SME Financing Gap (Vol. I): Theory and Evidence, OECD Publishing, Paris, 2006.

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.

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.

Zhao, Q. and Hastie, T., “Causal interpretations of black-box models,” Journal of Business & Economic Statistics, DOI: 10.10870/07350015, 2019.

Zúñiga-Vincente, J. A., Alonso-Borrego, C., Forcadell, F. J., and Galán, 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.


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