KDI Journal of Economic Policy

Korea Development Institute (한국개발연구원)
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Government R&D Support for SMEs: Policy Effects and Improvement Measures

  • LEE, SUNGHO (Innovation & Growth Strategy Research Group, The Korea Chamber of Commerce & Industry) ;
  • JO, JINGYEONG (Korea Development Institute)
  • Received : 2018.08.21
  • Published : 2018.11.30
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Abstract

Government R&D grants for SMEs have risen to three trillion Korean won a year, placing Korea second among OECD nations. Indeed, analysis results have revealed that government support has not only expanded corporate R&D investment and the registration of intellectual property rights but has also increased investment in tangible and human assets and marketing. However, value added, sales and operating profit have lacked improvement owing to an ineffective recipient selection system that relies solely on qualitative assessments by technology experts. Nevertheless, if a predictive model is properly applied to the system, the causal effect on value added could increase by more than two fold. Accordingly, it is important to focus on economic performance rather than technical achievements to develop such a model.

Keywords

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FIGURE 1. Q-Q PLOT OF SALES AND IP RIGHTS REGISTRATIONS BY THE MATCHING METHOD

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FIGURE 2. DECISION-TREE MODEL THAT PREDICTS THE VALUE ADDED INCREMENT (∆𝑡+2)

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FIGURE 3. COMPARISON OF TREATMENT EFFECTS BY DECILE:VALUE ADDED INCREMENT IN MATCHED SMES

TABLE 1- INTERNATIONAL COMPARISON OF TOTAL CORPORATE R&D INVESTMENT AND GOVERNMENT-FUNDED R&D COSTS BY FIRM SIZE

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TABLE 2-BASIC SME STATISTICS COMPARISON

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TABLE 3-COMPARISON OF MEAN DIFFERENCE AND REDUCTION RATE BY THE MATCHING METHOD

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TABLE 4 - OLS ANALYSIS OF VALUE ADDED INCREMENT (∆t+2) IN THE MATCHED DATASET

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TABLE 5 - SUMMARY ANALYSIS OF THE TREATMENT EFFECT ON THE INCREMENT OF TEN PERFORMANCE INDICATORS AMONG SMES IN THE MATCHED DATASET

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TABLE 6-OLS ANALYSIS COMPARISON OF TREATMENT EFFECTS BY FUND SIZE: MATCHED SMES

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TABLE 7- COMPARISON OF THE CAUSAL EFFECT ON VALUE ADDED INCREMENT (∆𝑡+2) FOR EACH DECIMAL SUBGROUP OF THE MATCHED SMES

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References

  1. Abadie, A. and G. Imbens. 2006. "Large Sample Properties of Matching Estimators for Average Treatment Effects," Econometrica, 74: 235-267. https://doi.org/10.1111/j.1468-0262.2006.00655.x
  2. Athey, S., G. Imbens, Y. Kong, and V. Ramachandra. 2016. "An Introduction to Recursive Partitioning for Heterogeneous Causal Effects Estimation Using the causalTree Package."
  3. Edison, T. R. 2010. "Estimation of the Department of Defense Small Business Innovation Research Program Treatment Effect," RAND Dissertation.
  4. Ho, D. E., K. Imai, G. King, and E. A. Stuart. 2007. "Matching as Nonparametric Preprocessing for Reducing Model Dependence in Parametric Causal Inference," Political Analysis, 15: 199-236. https://doi.org/10.1093/pan/mpl013
  5. Howell, S. T. 2017. "Financing Innovation: Evidence from R&D Grants," American Economic Review, 107(4): 1136-1164. https://doi.org/10.1257/aer.20150808
  6. Iacus, S. M., G. King, and G. Porro. 2009. "CEM: Software for Coarsened Exact Matching," Journal of Statistical Software, 30(9): 1-27.
  7. Kim, Sangsin. 2016. "Understanding and Using the CEM Method: Focusing on the Effect of Public R&D Subsidies." The Korean Journal of Economic Studies, 64(3): 125-151 (in Korean).
  8. King, G. and R. Nielsen. 2016. "Why Propensity Scores Should Not Be Used for Matching," Working Paper, Harvard Institute for Quantitative Social Science (http://j.mp/2ovYGsW).
  9. Korea Federation of SMEs. 2017. 2016 Survey on Technology of SMEs, Small and Medium Business Administration (in Korean).
  10. Korea Intellectual Property Office. 2014. Intellectual Property Statistics FOCUS, Vol. 4 (in Korean).
  11. Korea Intellectual Property Office. 2017. Intellectual Property Statistics FOCUS, Vol. 9 (in Korean).
  12. Ministry of Science and ICT.Korea Institute of Science & Technology Evaluation and Planning. 2017. 2016 National R&D Project Report and Analysis (in Korean).
  13. OECD. Main Science and Technology Indicators, http://www.oecd.org/sti/msti.htm (accessed October 31, 2017).
  14. Oh, Seunghwan and Sunwoo Kim. 2017. "R&D Support for SMEs: Current Status and Performance Analysis," STEPI Insight, Vol. 211, Science and Technology Policy Institute (in Korean).
  15. Park, Chansu, Chaeyoon Im, and Dongwoo Lee. 2016. Evaluating SME's Innovation Capability and Analyzing SME Innovation Policy VII, Research Paper 2016-10, Science and Technology Policy Institute (in Korean).
  16. Prust, A. C. and R. H. H. Prasad. 2015. "Application of Uplift Modeling and Propensity Score Matching to Develop an Industrialized Marketing Progression Service," DMA Analytics Journal.
  17. Sekhon, J. S. 2011. "Multivariate and Propensity Score Matching Software with Automated Balance Optimization: The Matching Package for R," Journal of Statistical Software.
  18. The Hankyoreh. "Government R&D Budget Wasted on Management Expenses, Instead of Researchers," (October 7, 2016) (in Korean).
  19. Wager, S. and S. Athey. 2017. "Estimation and Inference of Heterogeneous Treatment Effects using Random Forests," Stanford University.