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Efficiency Analysis of EU and Non-EU R&D Investor Firms on Matched Samples

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Abstract

This study is on the efficiency analysis of EU and non-EU R&D investor firms. The study mainly aims to understand if there is a difference between the efficiency level of EU and non-EU R&D investor firms and what the effecting factors of firm efficiency are. To construct an unbiased group of EU and non-EU firms, propensity score matching (PSM) is employed and thereby the analysis is made with the firms that have similar features. In the efficiency analysis stage, a slacks-based measure data envelopment analysis (SBM DEA) model is used for 2017–2019 period. After that, a panel Tobit regression model is used to examine the factors effecting the efficiency of the EU and non-EU firms. The results showed that EU firms have higher efficiency than non-EU firms only in 2018 and EU firms have very high improvement potential in market capitalization. By panel Tobit regression model, it was understood that capital expenditure intensity has negative effect on both the efficiency of EU and non-EU firms. Size of the firms has negative effect on only non-EU firms.

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Data Availability

The datasets used and/or analyzed during the current study are available from the author on reasonable request.

References

  • Agasisti, T., Shibanova, E., Platonova, D., & Lisyutkin, M. (2020). The Russian excellence initiative for higher education: A nonparametric evaluation of short-term results. International Transactions in Operational Research, 27, 1911–1929.

    Article  Google Scholar 

  • Agostino, M., Brancati, E., Giunta, A., Scalera, D., & Trivieri, F. (2019). Firms’ efficiency and global value chains: An empirical investigation on Italian industry. World Economy, 43, 1000–1033.

    Article  Google Scholar 

  • Agyemang, S. A., Ratinger, T., & Ahado, S. (2020). Has microcredit boosted poultry production in Ghana? Agricultural Finance Review, 80(2), 135–152. https://doi.org/10.1108/AFR-03-2019-0030

  • Aristovnik, A. (2012). The impact of ICT on educational performance and its efficiency in selected EU and OECD countries: A non-parametric analysis. SSRN 2187482.

  • Bae, Y., & Chang, H. (2012). Efficiency and effectiveness between open and closed innovation: Empirical evidence in South Korean manufacturers. Technology Analysis and Strategic Management, 24(10), 967–980.

    Article  Google Scholar 

  • Bogetoft, P., & Kromann, L. (2018). Evaluating treatment effects using data envelopment analysis on matched samples: An analysis of electronic information sharing and firm performance. European Journal of Operational Research, 270, 302–313.

    Article  Google Scholar 

  • Charnes, A., Cooper, W. W., & Rhodes, E. (1978). Measuring the efficiency of decision making units. European Journal of Operational Research, 2, 429–444.

    Article  Google Scholar 

  • Chen, K. H., Kou, M. T., & Fu, X. L. (2018). Evaluation of multi-period regional R&D efficiency: An application of dynamic DEA to China’s regional R&D systems. Omega, 74, 103–114.

    Article  Google Scholar 

  • Chiu, Y. H., Huang, C. W., & Chen, Y. C. (2012). The R&D value-chain efficiency measurement for high-tech industries in China. Asia Pacific Journal of Management, 29(4), 989–1006.

    Article  Google Scholar 

  • Choi, Y., Wen, H., Lee, H., & Yang, H. (2020). Measuring operational performance of major Chinese airports based on SBM-DEA. Sustainability, 12, 8234.

    Article  Google Scholar 

  • Chuang, L. M., Liu, C. C., & Chao, S. T. (2011). Data envelopment analysis in measuring R&D efficiency of semiconductor industry’s new product development in Taiwan. Actual Problems of Economics, 123, 418–429.

    Google Scholar 

  • Chun, D., Chung, Y., & Bang, S. (2015a). Impact of firm size and industry type on R&D efficiency throughout innovation and commercialisation stages: Evidence from Korean manufacturing firms. Technology Analysis & Strategic Management, 27(8), 895–909.

    Article  Google Scholar 

  • Chun, D., Chung, Y., Woo, C., Seo, H., & Ko, H. (2015b). Labor union effects on innovation and commercialization productivity: An integrated propensity score matching and two-stage data envelopment analysis. Sustainability, 7, 5120–5138.

    Article  Google Scholar 

  • Czarnitzki, D., & Hussinger, K. (2018). Input and output additionality of R&D subsidies. Applied Economics, 50(12), 1324–1341.

  • Espostio, A., Alfiero, S., Elba, F., & Resce, G. (2016). Italian saving banks efficiency, is unity strength? Bank groups versus stand-alone. 34th International Conference Mathematical Methods in Economics MME, 2016, 7–12.

  • European Commission. (2020). R& D Monitoring. https://iri.jrc.ec.europa.eu/rd_monitoring. Accessed 29 April 2021.

  • Fang, S., Xue, X., Yin, G., Fang, H., Li, J., & Zhang, Y. (2020). Evaluation and improvement of technological innovation efficiency of new energy vehicle enterprises in China based on DEA-Tobit model. Sustainability, 12, 7509.

  • Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society, 120, 253–290.

    Article  Google Scholar 

  • Ferrier, G. D., & Valdmanis, V. G. (2004). Do mergers improve hospital productivity? Journal of the Operational Research Society, 55(10), 1071–1080.

    Article  Google Scholar 

  • Halaskova, M., Gavurova, B., & Kocisova, K. (2020). Research and development efficiency in public and private sectors: An empirical analysis of EU countries by using DEA methodology. Sustainability, 12, 7050.

    Article  Google Scholar 

  • Han, U., Asmild, M., & Kunc, M. (2016). Regional R&D efficiency in Korea from static and dynamic perspectives. Regional Studies, 50(7), 1170–1184.

    Article  Google Scholar 

  • Han, C. J., Thomas, S., Yang, M., & Cui, Y. M. (2019). The ups and downs of open innovation efficiency: The case of Procter & Gamble. European Journal of Innovation Management, 22(5), 747–764.

    Article  Google Scholar 

  • Hashimoto, A., & Haneda, S. (2008). Measuring the change in R&D efficiency of the Japanese pharmaceutical industry. Research Policy, 37(10), 1829–1836.

    Article  Google Scholar 

  • Hong, J., Hong, S., Wang, L., Xu, Y., & Zhao, D. (2015). Government grants, private R&D funding and innovation efficiency in transition economy. Technology Analysis and Strategic Management, 27(9), 1068–1096.

    Article  Google Scholar 

  • Hu, J. L., Yang, C. H., & Chen, C. P. (2014). R&D efficiency and the national innovation system: An international comparison using the distance function approach. Bulletin of Economic Research, 66(1), 55–71.

    Article  Google Scholar 

  • Jang, H., Lee, S., & Suh, E. (2016). A comparative analysis of the change in R&D efficiency: A case of R&D leaders in the technology industry. Technology Analysis & Strategic Management, 28(8), 886–900.

    Article  Google Scholar 

  • Jing, R., Xu, T., Lai, X., Mahmoudi, E., & Fang, H. (2020). Technical efficiency of public and private hospitals in Beijing, China: A comparative study. International Journal of Environmental Research and Public Health, 17(1), 82.

    Article  Google Scholar 

  • Karadayi, M. A., & Ekinci, Y. (2019). Evaluating R&D performance of EU countries using categorical DEA. Technology Analysis & Strategic Management, 31(2), 227–238.

    Article  Google Scholar 

  • Khoshnevis, P., & Teirlinck, P. (2018). Performance evaluation of R&D active firms. Socio-Economic Planning Sciences, 61, 16–28.

    Article  Google Scholar 

  • Lee, K., & Yoon, B. (2015). The idiosyncrasy of research and development efficiency across types of small-and medium sized enterprises: Evidence from Korea. R&D Management, 45(3), 250–266.

    Article  Google Scholar 

  • Mete, M. H., & Belgin, O. (2022). Impact of knowledge management performance on the efficiency of R&D active firms: Evidence from Turkey. Journal of the Knowledge Economy, 13, 830–848.

    Article  Google Scholar 

  • OECD. (2015). Frascati manual 2015: Guidelines for collecting and reporting data on research and experimental development. OECD Publishing, Paris.

  • Park, H. S., Kim, T. Y., & Kim, D. (2019). Efficiency analysis of zinc refining companies. Sustainability, 11, 6528.

    Article  Google Scholar 

  • Rosenbaum, P. R., & Rubin, D. B. (1983). The central role of the propensity score in observational studies for causal effects. Biometrika, 70, 41–55.

    Article  Google Scholar 

  • Shimura, H., Masuda, S., & Kimura, H. (2014). Research and development productivity map: Visualization of industry status. Journal of Clinical Pharmacy and Therapeutics, 39(2), 175–180.

    Article  Google Scholar 

  • Somaya, D., Williamson, I. O., & Zhang, X. (2008). Combining patent law expertise with R&D for patenting performance. Working Paper Melbourne: Intellectual Property Institute of Australia.

  • Škrinjarić, T. (2021). Evaluating R&D efficiency of selected European countries: A dynamic analysis for period 2007–2017. In Ferreira J.J.M., Teixeira S.J. & Rammal H.G. (eds), Technological Innovation and International Competitiveness for Business Growth, Palgrave Studies in Democracy, Innovation, and Entrepreneurship for Growth. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-51995-7_10.

  • Teirlinck, P., & Khoshnevis, P. (2020). Within-cluster determinants of output efficiency of R&D in the space industry. Omega, 94, 102039.

    Article  Google Scholar 

  • Thomas, V. J., Sharma, S., & Jain, S. K. (2011). Using patents and publications to assess R&D efficiency in the states of the USA. World Patent Information, 33(1), 4–10.

    Article  Google Scholar 

  • Thompson, M. A., Huerta, T. R., & Ford, E. W. (2012). Mandatory Insurance coverage and hospital productivity in Massachusetts: Bending the curve? Health Care Management Review, 37(4), 294–300.

    Google Scholar 

  • Tone, K. (2001). A slacks-based measure of efficiency in data envelopment analysis. European Journal of Operational Research, 130(3), 498–509.

    Article  Google Scholar 

  • Wang, E. C. (2007). R&D efficiency and economic performance: A cross-country analysis using the stochastic frontier approach. Journal of Policy Modeling, 29(2), 345–360.

    Article  Google Scholar 

  • Wang, Q., Hang, Y., Sun, L., & Zhao, Z. (2016). Two-stage innovation efficiency of new energy enterprises in China: A non-radial DEA approach. Technological Forecasting and Social Change, 112, 254–261.

    Article  Google Scholar 

  • Wang, C. Y., Wang, Y., Li, N. N., & Ma, T. F. (2019). Spatial differentiation of China’s industrial enterprise R&D efficiency. Erdkunde, 73(3), 199–210.

    Article  Google Scholar 

  • Wu, T. H., Ting, P. J. L., Lin, M. C., & Chang, C. C. (2020). Corporate ownership and firm performance: A mediating role of innovation efficiency. Economics of Innovation and New Technology. https://doi.org/10.1080/10438599.2020.1799140

    Article  Google Scholar 

  • Wu, H. Y., Chen, I. S., Chen, J. K., & Chien, C. F. (2019). The R&D efficiency of the Taiwanese semiconductor industry. Measurement, 137, 203–213.

    Article  Google Scholar 

  • Yeh, L. T., & Chang, D. S. (2020). Using categorical DEA to assess the effect of subsidy policies and technological learning on R&D efficiency of IT industry. Technological and Economic Development of Economy, 26(2), 311–330.

    Article  Google Scholar 

  • Zhang, B., Bi, J., Fan, Z., Yuan, Z., & Ge, J. (2008). Eco-efficiency analysis of industrial system in China: A data envelopment analysis approach. Ecological Economics, 68(1–2), 306–316.

    Article  Google Scholar 

  • Zhen, L., & Yingqi, L. (2018). A measurement of China’s new energy vehicle industry using the improved general combined-oriented CCR model. Journal of Discrete Mathematical Sciences and Cryptography, 21(4), 895–906.

    Article  Google Scholar 

  • Zhukovski, I. V., & Gedranovich, A. B. (2016). Analysis of efficiency of research & development activities among countries with developed and developing economies including Republic of Belarus while using method of stochastic frontier approach. Science & Technique, 15(6), 528–535.

    Article  Google Scholar 

  • Zuo, K. R., & Guan, J. C. (2017). Measuring the R&D efficiency of regions by a parallel DEA game model. Scientometrics, 112(1), 175–194.

    Article  Google Scholar 

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Authors and Affiliations

  1. Republic of Türkiye Ministry of Industry and Technology, Ankara, Türkiye

    Onder Belgin

  2. School of Business, Department of International Trade and Logistics, Atılım University, Ankara, Türkiye

    Onder Belgin

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Belgin, O. Efficiency Analysis of EU and Non-EU R&D Investor Firms on Matched Samples. J Knowl Econ (2023). https://doi.org/10.1007/s13132-023-01605-1

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