ANALYSIS OF EFFICIENCY OF R&D ACTIVITIES AMONG COUNTRIES WITH DEVELOPED AND DEVELOPING ECONOMIES INCLUDING REPUBLIC OF BELARUS WITH STOCHASTIC FRONTIER APPROACH
https://doi.org/10.21122/2227-1031-2016-15-6-528-535
Abstract
This study evaluates efficiency of R&D activities based on the stochastic frontier analysis across 69 counties with developed and developing economies. Gross domestic expenditures on R&D in purchasing power parity, researchers per million inhabitants, technicians per million inhabitants are treated as inputs while patents granted to residents and scientific and technical journal articles are considered as outputs. According to the analysis results Costa Rica, Israel and Singapore are the most efficient in terms of transformation of available resources into the R&D results. What concerns Belarus it is necessary that additional investments in R&D go together with increasing efficiency of available resources’ usage.
About the Authors
I. V. Zhukovski
Belarusian State University
Belarus
Belarus
Post-graduate student
Address for correspondence: Zhukovski Igor V.– Belarusian State University Karl Marx str., 31, 220050, Minsk, Republic of Belarus Tel.: +375 17 327-25-21 6786544@gmail.com
A. V. Gedranovich
Belarusian State University
Belarus
Belarus
Associate Professor, PhD in Economics
References
1. Edquist C., McKelvey M. (2000) Systems of Innovation Approaches – their Emergence and Characteristics. Systems of Innovation: Growth. Competitiveness and Employment. Cheltenham, Edward Elgar, 3–37.
2. Kalotay K., Pollan T., Fredriksson T. (2005) Globalization of R&D and developing countries: proceedings of the expert meeting. Geneva and New York, United Nations Publication. UNSTAD. 242.
3. Pearce R. D. (1999) Decentralised R&D and strategic Compe-Titiveness: Globalised Approaches to Generation and use of Technology in Multinational Enterprises (MNEs). Research Policy, 28 (2-3), 157–178. doi: 10.1016/s0048-7333(98)00115-2
4. Lee M., Son B., Om K. (2007) Evaluation of national R&D projects in Korea. Research Policy, 25 (5), 805–818. doi: 10.1016/0048-7333(96)00879-7
5. Roessner J. D. (1989) Evaluating government innovation programs: Lessons from the US experience. Research Policy, 18 (6), 343–359. doi: 10.1016/0048-7333(89)90022-x
6. Wang E. C., Huang W. (2007) Relative efficiency of R&D activities: A cross-country study accounting for environmental factors in the DEA approach. Research Policy, 36 (2), 260–273. doi: 10.1016/j.respol.2006.11.004
7. Kim J., Cho H., Park Y., Kim Y., Jeon J. (2013) A Balanced Scorecard for Identifying Factors of Strategic Fit of National R&D Program on the Creative Economy Policy. World Academy of Science. Engineering and Technology. International Journal of Mechanical. Aerospace. Industrial. Mechatronic and Manufacturing Engineering, 7 (10), 980–984.
8. Meliciani V. (2000) The relationship between R&D. investment and patents: a panel data analysis. Applied Economics, 32 (11), 1429–1437. doi: 10.1080/00036840050151502
9. Hartmann G. C. (2003) Linking R&D spending to revenue growth. Research-Technology Management, 46 (1), 39–46.
10. 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.doi: 10.1016/j.jpolmod.2006.12.005
11. Lee H., Park Y., Choi H. (2008) Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach. European Journal of Operational Research, 196 (3), 847–855. doi: 10.1016/j.ejor.2008.06.016
12. Pakes A., Griliches Z. (1980) Patents and R&D at the firm level: A first report. Economics letters, 5 (4), 377–381. doi: 10.1016/0165-1765(80)90136-6
13. Griliches Z. (1990) Patent Statistics as Economic Indicators: A Survey. Journal of Economic Literature, 28 (4), 1661–1707. doi:10.3386/w3301
14. Griliches Z. (1986) Productivity. R&D. and Basic Research at the Firm Level in the 1970s. American Economic Review, 76 (1), 141–154. doi: 10.3386/w1547
15. Mansfield E. (1988) Industrial R&D in Japan and the United States: A comparative study. The American Economic Review, 78 (2), 223–228.
16. Goto A., Suzuki K. (1989) R & D Capital, Rate of Return on R & D Investment and Spillover of R & D in Japanese Manufacturing Industries. The Review of Economics and Statistics, 71 (4), 555–564. doi: 10.2307/1928096
17. Lee H. Y., Park Y. T. (2005) An international comparison of R&D efficiency: DEA approach. Asian Journal of Technology Innovation, 13 (2), 207–222. doi:10.1080/19761597.2005.9668614
18. Kocher M. G., Luptacik M., Sutter M. (2006) Measuring productivity of research in economics: A cross-country study using DEA. Socio-Economic Planning Sciences, 40 (4), 314–332. doi: 10.1016/j.seps.2005.04.001
19. Zhukovski I. V., Gedranovich A. B. (2016) Inter-country efficiency evaluation in innovation activity on the basis of method for data envelopment analysis among countries with developed and developing economy including the Republic of Belarus. Nauka i Tekhnika [Science & Technique], 15 (2), 154–163. doi: 10.21122/2227-1031-2016-15-2-154-163 (in Russian).
20. Sharma S., Thomas V. (2008) Inter-country R&D efficiency analysis: An ap-plication of data envelopment analysis. Scientometrics, 76 (3), 483–501. doi: 10.1007/s11192-007-1896-4
21. Rousseau S., Rousseau R. (1997) Data envelopment analysis as a tool for constructing scientometric indicators. Scientometrics, 40 (1), 45–56. doi: 10.1007/bf02459261
22. Aigner D., Lovell C. K., Schmidt P. (1977) Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6 (1), 21–37. doi: 10.1016/0304-4076(77)90052-5
23. Meeusen W., Broeck J.V.D. (1977) Efficiency estimation from Cobb-Douglas production functions with composed error. International economic review, 18 (2), 435–444. doi: 10.2307/2525757
24. Kevin Cullinanea, Teng-Fei Wangb, Dong-Wook Songc, Ping Jid (2006) The technical efficiency of container ports: comparing data envelopment analysis and stochastic frontier analysis. Transportation Research Part A: Policy and Practice, 40 (4), 354–374. doi: 10.1016/j.tra.2005.07.003
25. Bogetoft P., Otto L. (2010) Benchmarking with DEA, SFA, and R. New York, Springer, 157. 365. doi: 10.1007/978-1-4419-7961-2.
26. Jiancheng G., Junxia W. (2004) Evaluation and interpretation of knowledge production efficiency. Scientometrics, 59 (1), 131–155. Doi: https://doi.org/10.1023/b:scie.0000013303.25298.ae
27. Pavitt K. (1985) Patent statistics as indicators of innovative activities: possibilities and problems. Scientometrics, (7), (1–2), 77–99. doi: 10.1007/bf02020142
28. Fleischer M. (1999) Innovation, patenting and performance. Economie Appliquée, 52. (2), 95–120.
29. Adams J. D., Griliches Z. (2000) Research productivity in a system of universities. The economics and econometrics of innovation. US, Springer, 105–140. doi: 10.1007/978-1-4757-3194-1_5
30. Hall B. H., Griliches Z., Hausman J. A. (1986) Patents and R&D: Is There a Lag? International Economic Review, 27 (2), 265–284. doi: https://doi.org/10.2307/2526504
31. Griliches Z. (1979) Issues in assessing the contribution of research and development to productivity growth. The Bell Journal of Economics, 10 (1), 92–116. doi: 10.2307/3003321
32. Guellec D., Van Pottelsberghe de la Potterie D. (2004) From R&D to productivity growth: Do the institutional settings and the source of funds of R&D matter? Oxford Bulletin of Economics and Statistics, 66 (30), 353–378. doi: 10.1111/j.1468-0084.2004.00083.x
Review
For citations:
Zhukovski I.V., Gedranovich A.V. ANALYSIS OF EFFICIENCY OF R&D ACTIVITIES AMONG COUNTRIES WITH DEVELOPED AND DEVELOPING ECONOMIES INCLUDING REPUBLIC OF BELARUS WITH STOCHASTIC FRONTIER APPROACH. Science & Technique. 2016;15(6):528-535. https://doi.org/10.21122/2227-1031-2016-15-6-528-535
Views:
1104
Email this article 