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姓名 陳疆平(Chiang-Ping Chen)  查詢紙本館藏   畢業系所 經濟學系
論文名稱 國家研發效率與生產力之分析
(Three Essays on National R&D Efficiency and Productivity)
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摘要(中) 內生成長理論指出研發被廣泛認為是主要帶動持續經濟成長的驅使動力。許多國家因而致力於研發以促進經濟發展。然而,研發是否真能視為主要成長的來源,必須仰賴具效率且具生產力之研發過程。因此,衡量各國研發效率和生產力與了解其影響因素,對於制定相關研發政策與改善資源配置則為首要事務。基於此觀點之下,本論文包含了三種國家研發效率與生產力之跨國分析的議題。
議題一主要建立在多產出與多投入之隨機邊界法的架構之下,利用距離函數法來估計各國家整體研發效率,同時,探討國家創新系統與國家研發效率之關係。實證結果初步確認國家創新系統的確對於改善國家研發效率扮演著重要的角色,其中亦發現智慧財產權的保護、產業界之間技術合作、產學間知識移轉、研發設施的聚集與政府參與研發活動的程度將有明顯改善整體國家研發效率的效果。
議題二主要利用資料包絡分析法,建立各國不同產出項之研發效率指標,並加以進行跨國分析與評比,後續並探討國家創新系統對於各項產出研發效率之影響。實證結果發現,多數國家專利與技術授權金之研發效率的走勢較為相似,反觀學術期刊之研發效率的走勢,相較於前兩指標較為不同。此外,亦發現除了智慧財產權保護、知識資本與人力資本累積對於整體研發效率有影響之外,更重要地發現,產業界研發資金來源來自於國外與自行籌措,對於改善專利與技術授權金之研發效率,扮演重要的角色。然而,提升各國高等教育之研發密度,對於改善各國之學術期刊之研發效率則有明顯之效果。
由於研發效率指標屬於一種靜態指標,只能了解當期之下,各國研發投入轉換成研發產出之相對效率。因此,議題三利用方向距離函數的概念與Luenberger生產力指標,建立Luenberger研發生產力指標,進行國家整體研發生產力變動之跨國分析,並可將研發生產力變動拆解為研發效率之變動(追趕效果)與研發技術之變動(創新效果)兩部分之貢獻。實證結果主要發現,多數國家整體研發生產力成長主要來自創新效果之貢獻。同時,非OECD國家相對於OECD國家,不論在效率變動與技術變動皆有較佳的表現。其中亦發現,專利的研發生產力變動相對於學術期刊之研發生產力變動,可被視為國家研發生產力成長主要來源。此外,亦發現智慧財產權之保護與人力資本,主要是透過追趕效果影響國家整體研發生產力成長。產業界之間相互技術合作與政府參與研發活動的程度,則是透過創新效果影響國家整體研發生產力成長。
摘要(英) Endogenous growth theory indicates that research and development (R&D) is widely recognized as the primary driving force of sustainable economic growth. Most nations, therefore, have gradually devoted more efforts to R&D so as to foster economic development. Therefore, evaluating the R&D efficiency and productivity across nations and understanding its determinants are the prerequisites for designing R&D policies that intend to improve resources allocation. Based on this viewpoint, this dissertation includes three issues of R&D efficiency and productivity across nations.
In the first issue, we estimate national R&D efficiency score by using the distance function approach at the multiple inputs-outputs framework of stochastic frontier analysis (SFA). Simultaneously, this approach can also investigate the relationship between national R&D efficiency and national innovation system (NIS). Empirical results confirm that NIS indeed plays an essential role in improving the national R&D efficiency. Intellectual property rights protection, technological cooperation among business sectors, and knowledge transfer between business sectors and higher education institutions, the agglomeration of R&D facilities, and government sector involving in R&D activities significantly improve national R&D efficiency.
In the second issue, we compare R&D efficiency across nations based on various output-oriented R&D efficiency indexes that are developed by data envelopment analysis (DEA) approach. Moreover, we further examine the relationship between national innovation system and output-oriented R&D efficiency indexes. Empirical results show that nations have similar R&D efficiency in terms of patents and royalties, while their performance on journal publications is quite different. Intellectual property rights protection, knowledge stock, and human capital accumulation have significantly positive effects on overall output-oriented R&D efficiency. Importantly, private sector R&D funded by either foreign sources or funded and performed by private businesses plays an important role in improving the output-oriented R&D efficiency index for patents and for royalties and licensing fees. The R&D intensity performed by higher education institutions has a positive effect on the journal-oriented R&D efficiency index.
Finally, since R&D efficiency index belongs to a static measurement, it only enables us to understand the relative efficiency of transforming R&D inputs to outputs in the current period. Based on the concept of directional distance function and Luenberger productivity index, the third issue develops a Luenberger R&D productivity change (LRC) index and then decomposes it into R&D efficiency change (catch-up effect) and R&D technical change (innovation effect). Empirical results show that the R&D productivity growth is mainly attributed to the innovation effect; meanwhile, non-OECD nations have better performance on both efficiency change and technical change than their OECD counterparts. Also, patent-oriented R&D productivity growth can serve as the main source of national R&D productivity growth than the journal article-oriented one. Furthermore, human capital and intellectual property rights (IPR) protection improve national R&D productivity growth by the catch-up effect. Technological cooperation among business sectors and government sector involving in R&D activities promote national R&D productivity by enhancing the innovation effect.
關鍵字(中) ★ 國家研發效率
★ 國家研發生產力
關鍵字(英) ★ National R&
★ D Efficiency
★ National R&
★ D Productivity
論文目次 Chapter 1 Introduction 1
Chapter 2 R&D Efficiency and National Innovation System: An International Comparison Using the Distance Function Approach 5
2.1 Introduction 5
2.2 Literature Review 7
2.3 Empirical Methodology and Data 9
2.3.1 The distance function approach for R&D efficiency 10
2.3.2 Data description of inputs and outputs 13
2.3.3 Variable description of efficiency model 15
2.4 Estimation of Distance Function and R&D Efficiency 18
2.4.1 Estimation results of distance function 18
2.5 The Relationship between National R&D Efficiency and NIS 26
2.6 Conclusions 28
Chapter 3 An International Comparison for R&D Efficiency of Multiple Innovative Outputs: The Role of the National Innovation System 31
3.1 Introduction 31
3.2 Methodology and Output-oriented R&D Efficiency Index 34
3.2.1 Methodology of DEA and data 34
3.2.2 Construction of the output-oriented R&D efficiency index 37
3.3 Estimation of Output-oriented R&D Efficiency Indexes 38
3.4 The Role of the NIS on R&D Efficiency 38
3.4.1 Empirical specification of the output-oriented R&D efficiency index 38
3.4.2 Empirical results and discussions 38
3.5 Conclusions 38
Chapter 4 An International Comparison of R&D Productivity Change: Technical Progress or Efficiency Change ? 38
4.1 Introduction 38
4.2 Empirical Methodology 38
4.3 Empirical Results 38
4.3.1 Estimation of Luenberger R&D productivity change 38
4.3.2. Components of Luenberger R&D productivity change 38
4.4 The Influence of NIS on R&D Productivity Change 38
4.5 Conclusions 38
Chapter 5 Concluding Remarks and Policy Implications 38
5.1 Concluding Remarks 38
5.2 Policy Implications 38
References 38
參考文獻 Acemoglu, D., Aghion P. and Zilibotti, F. (2006). ‘Distance to frontier, selection, and economic growth,’ Journal of European Economic Association, 4, pp. 37-74.
Ahn, S.C., Lee, Y.H. and Schmidt, P. (2001). ‘GMM estimation of linear panel data models with time-varying individual effects,’ Journal of Econometrics, 101, pp. 219-255.
Banker, R.D., Charnes, A. and Cooper, W.W. (1984). ‘Some models for estimating technical and scale inefficiencies in data envelopment analysis,’ Management Science, 30, pp. 1078-1092.
Battese, G.E. and Coelli, T.J. (1995). ‘A model for technical inefficiency effects in stochastic frontier production function for panel data,’ Empirical Economics, 20, pp. 325-332.
Beath, J., Poyago-Theotoky, J. and Ulph, D. (1998). ‘Organization design and Information-sharing in a research joint venture with spillovers,’ Bulletin of Economic Research, 50, pp. 47-59.
Boussemart, J.P., Briec, W., Kerstens, K. and Poutineau, J.C. (2003). ‘Luenberger and Malmquist productivity indices: Theoretical comparisons and empirical illustration,’ Bulletin of Economic Research, 55, pp. 391-405.
Brown, M.G. and Svensen, R.A. (1998). ‘Measuring R&D productivity,’ Research and Technology Management, 41, pp. 30-35.
Cave, D.W., Christensen, L.R. and Diewet, W.E. (1982). ‘The economic theory of index numbers and measurement of input, output and productivity,’ Econometrica, 50, pp. 1393-1414.
Chambers, R.G., Chung, Y.H. and Färe, R. (1996). ‘Benefit and distance functions,’ Journal of Economic Theory, 70, pp, 407-419.
Charnes, A., Cooper, W.W. and Rhodes, E. (1978). ‘Measuring efficiency of decision making units,’ European Journal of Operational Research, 2, pp. 429-444.
Chen, Y. and Puttitanun, T. (2005). ‘Intellectual property rights and innovation in developing countries,’ Journal of Development Economics, 78, pp. 474-493.
Coelli, T.J. and Perelman, S. (2000). ‘Technical efficiency of European railways: a distance function approach,’ Applied Economics, 32, pp. 1967-1976.
Coelli, T.J., Prasada Rao, D.S., O’Donnell, C.J. and Battese, G.E. (2005). An Introduction to Efficiency and Productivity Analysis, Second Edition, Kluwer Academic Publishers, Boston.
Coelli, T.J., Rao, D.S.P., and Battese, G.E. (1998). An Introduction to Efficiency and Productivity Analysis, Kluwer Academic Publishers, Boston.
Cullmann, A., Schmidt-Ehmcke, J. and Zloczysti, P. (2009). ‘Innovation, R&D efficiency and the impact of the regulatory environment – A two stage semi-parametric DEA approach,’ DIW Discussion Paper, No. 883.
Edquist, C. (1997). Systems of Innovation: Technologies, Institutions, and Organizations, Pinter, London and Washington.
Etzkowitz, H. and Leydesdorff, L. (1997). Universities and the Global Knowledge Economy: A Triple Helix of University–Industry–Government Relations, London: Cassell Academic.
Etzkowitz, H. and Leydesdorff, L. (2000). ‘The dynamics of innovation: from national systems and mode 2 to a triple helix of university-industry-government relations,’ Research Policy, 29, pp. 109–123.
Färe, R., Grosskopf, S., Norris, M. and Zhang, Z. (1994). ‘Productivity growth, technical change, and efficiency changes in industrialized countries.’ American Economic Review, 84, pp.66-83.
Färe, R. and Primont, D. (1995). Multi-output Production and Duality: Theory and Applications, Boston: Kluwer Academic.
Farrell, M.J. (1957). ‘The measurement of productive efficiency,’ Journal of the Royal Statistical Society: Series A, 120, pp. 253-281.
Freeman, C. (1987). Technology Policy and Economic Performance: Lessons from Japan, Pinter, London.
Fu, X. and Yang, Q.G. (2009). ‘Exploring the cross-country gap in patenting: a stochastic frontier approach,’ Research Policy, 38, pp. 1203-1213.
Furman, J.L., Porter, M.E. and Stern, S. (2002). ‘The determinants of national innovative capacity,’ Research Policy, 31, pp. 899-933.
Geisler, E. (1995). ‘An integrated cost-performance model of research and development evaluation,’ International Journal of Management Science, 23, pp.281-294.
Ginarte, J.C. and Park, W.G. (1997). ‘Determinants of patent rights: a cross-national study,’ Research Policy, 26, pp. 283-301.
Greene, W. (2005). ‘Reconsidering heterogeneity in the panel data estimators of the stochastic frontier model,’ Journal of Econometrics, 126, pp.269-303.
Griffith, R., Huergo, E., Mariesse, J. and Peters, B. (2006). ‘Innovation and productivity across four European countries,’ Oxford Review of Economic Policy, 22, pp. 483-498.
Griliches, Z. (1990). ‘Patent statistics as economic indicators: a survey,’ Journal of Economic Literature, 28, pp. 1661–1707.
Grosskopf, S., Hayes, K., Taylor, L. and Weber, W. (1996). ‘Budget constrained frontier measures of fiscal equality and efficiency in schooling,’ Review of Economics and Statistics, 79, pp. 116-124.
Grossman, G. and Lai, E.L.C. (2005). ‘International protection of intellectual property,’ American Economic Review, 94, pp. 1635-1653.
Guan, J. and Chen, K. (2010). ‘Modeling macro-R&D production frontier performance: An application to Chinese province-level R&D,’ Scientometrics, 82, pp. 165-173.
Guellec, D. and van Pottelsberghe de la Potterie, B. (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, pp. 353–378.
Hall, B.H. and Mairesse, J. (1995). ‘Exploring the relationship between R&D and productivity in French manufacturing firms,’ Journal of Econometrics, 65, pp. 263-293.
Hashimoto, A. and Haneda, S. (2008). ‘Measuring the change in R&D efficiency of the Japanese pharmaceutical industry,’ Research Policy, 37, pp. 1829-1836.
Hu, M.C. and Mathews, J.A. (2008). ‘China’s national innovative capacity,’ Research Policy, 37, pp. 1465-1479.
Kumbhaker, S.C. and Lovell, C.A.K. (2000). Stochastic Frontier Analysis, New York: Cambridge University Press.
Kumbhaker, S.C., Ghosh, C.S. and McGuckin, J.T. (1991). ‘A generalized production frontier approach for estimating determinants of inefficiency in U.S. dairy farms,’ Journal of Business and Economic Statistics, 9, pp. 279-286.
Lee, H.Y. and Park, Y.T. (2005). ‘An international comparison of R&D efficiency: DEA approach,’ Asian Journal of Technology Innovation, 13, pp. 207-222.
Lee, H.Y., Park, Y.T. and Choi, H. (2009). ‘Comparative evaluation of performance of national R&D programs with heterogeneous objectives: A DEA approach,’ European Journal of Operational Research, 196, pp. 847-855.
Lichtenberg, F.R. (1993). ‘R&D investment and international productivity differences,’ in Siebert H. (Ed.), Economics Growth in the World Economy, Mohr, Tubingen, pp. 47-68.
Lovell, C.A.K., Richardson, S., Travers, P. and Wood, L.L. (1994). ‘Resources and functions: a new view of inequality in Australia,’ in W. Eichhorn (ed.), Models and Measurement of Welfare and Inequality, Berlin: Springer-Verlag, pp. 787-807.
Lovell, C.A.K. and Schmidt, S.S. (1993). Production Frontiers and Productive Efficiency, the Measurement of Productive Efficiency: Techniques and Applications, Oxford University Press, Oxford.
Lu, W.C. and Liu, T.K. (2010). ‘Malmquist indices of R&D productivity growth in Taiwanese IC-design industry,’ Global Journal of Business Research, 4, pp.105-114.
Lundvall, B. (1992). National Systems of Innovation, Pinter, London and New York.
Mairesse, J. and Hall, B.H. (1996). ‘Estimating the productivity of research and development in French and US manufacturing firms: an exploration of simultaneity issues with GMM methods,’ in Wagner, K. and B. van Ark (eds.), International Productivity Differences and Their Explanations, Elsevier Science, pp. 285-315.
Managi, S. (2003). ‘Luenberger and Malmquist productivity indices in Japan, 1955-1995,’ Applied Economics Letters, 10, pp. 581-584.
Mastromarco, C. (2008). ‘Foreign capital and efficiency in developing countries,’ Bulletin of Economic Research, 60, pp. 351-374.
Nelson, R.R. (1993). National Innovation Systems: A Comparative Analysis, Oxford University Press, Oxford.
Nunnenkamp, P. and Spatz, J. (2003). ‘Foreign direct investment and economic growth in developing countries: how relevant are host-country and industry characteristics?’ Kiel Working Papers, No. 1176.
OECD (2001). OECD Science, Technology, and Industry Scoreboard, Paris: OECD.
Pakes, A. and Griliches, Z. (1984). ‘Patents and R&D at the firm Level: a first look in: Griliches, Z. (Ed.),’ R&D Patents and Productivity, University of Chicago Press, Chicago.
Parisi, M.L., Schiantarelli, F. and Sembenelli, A. (2006). ‘Productivity, innovation and R&D: Micro evidence for Italy,’ European Economics Review, 50, pp. 2037-2061.
Pitt, M.M. and Lee, L.F. (1981). ‘The measurement and sources of technical inefficiency in the Indonesian weaving industry,’ Journal of Development Economics, 9, pp. 43-64.
Sharma, S. and Thomas, V.J. (2008). ‘Inter-country R&D efficiency analysis: An application of data envelopment analysis,’ Scientometrics, 76, pp. 483-501.
Shephard, R.W. (1970). Theory of Cost and Production Functions, Princeton, NJ: Princeton University Press.
Thomas, V.J., Sudhir, K.J. and Sharma, S. (2009). ‘Analyzing R&D efficiency in Asia and the OECD: An application of the Malmquist productivity index,’ IEEE Proceedings of the 2009 Atlanta Conference on Science and Innovation Policy.
Tödtling, F., Lehner, P. and Kaufmann, A. (2009). ‘Do different types of innovation rely on specific kinds of knowledge interactions?’ Technovation, 29, pp. 59-71.
Wang, E.C. (2007). ‘R&D efficiency and economic performance: a cross-country analysis using the stochastic frontier approach,’ Journal of Policy Modeling, 29, pp. 345-360.
Wang, E.C. and Huang, W.C. (2007). ‘Relative efficiency of R&D activities: a cross-country study accounting for environmental factors in the DEA approach,’ Research Policy, 36, pp. 260-273.
Wang, H.J. and Schmidt, P. (2002). ‘One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels,’ Journal of Productivity Analysis, 18, pp. 129-144.
Werner, B.M. and Souder, W.E. (1997). ‘Measuring R&D performance: state of the art,’ Research Technology Management, 40, pp. 34-41.
Wu, W.Y., Tsai, H.J., Cheng, K.Y. and Lai, M. (2006). ‘Assessment of intellectual capital management in Taiwanese IC design companies: Using DEA and the Malmquist productivity index,’ R&D Management, 36, pp. 531-545.
指導教授 楊志海、胡均立
(Chih-Hai Yang、Jin-Li Hu)
審核日期 2011-7-22
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