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REFERENCES

  • Acemoglu, D., Aghion, P. and Zilibotti, F. (2006). ‘Distance to frontier, selection, and economic growth’, Journal of European Economic Association, 4, pp. 3774.
  • Battese, G. E. and Coelli, T. J. (1992). ‘Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India’, Journal of Productivity Analysis, 3, pp. 15369.
  • 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. 32532.
  • 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. 4759.
    Direct Link:
  • Coelli, T. J. and Perelman, S. (2000). ‘Technical efficiency of European railways: a distance function approach’, Applied Economics, 32, pp. 196776.
  • Coelli, T. J., Prasada Rao, D. S., O'Donnell, C. J. and Battese, G. E. (2005). An Introduction to Efficiency and Productivity Analysis, 2nd edition, Boston, MA: Kluwer Academic Publishers.
  • 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. 10923.
  • Färe, R. and Primont, D. (1995). Multi-Output Production and Duality: Theory and Applications, Boston, MA: Kluwer Academic.
  • Farrell, M. J. (1957). ‘The measurement of productive efficiency’, Journal of the Royal Statistical Society: Series A, 120, pp. 25381.
  • Fu, X. and Yang, Q. G. (2009). ‘Exploring the cross-country gap in patenting: a stochastic frontier approach’, Research Policy, 38, pp. 120313.
  • Furman, J. L., Porter, M. E. and Stern, S. (2002). ‘The determinants of national innovative capacity’, Research Policy, 31, pp. 899933.
  • Ginarte, J. C. and Park, W. G. (1997). ‘Determinants of patent rights: a cross-national study’, Research Policy, 26, pp. 283301.
  • Greene, W. (2005). ‘Reconsidering heterogeneity in the panel data estimators of the stochastic frontier model’, Journal of Econometrics, 126, pp. 269303.
  • 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. 11624.
  • 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. 35378.
  • Hall, B. H. and Mairesse, J. (1995). ‘Exploring the relationship between R&D and productivity in French manufacturing firms’, Journal of Econometrics, 65, pp. 26393.
  • Kumbhakar, S. C. and Lovell, C. A. K. (2000). Stochastic Frontier Analysis, New York: Cambridge University Press.
  • Kumbhakar, 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. 27986.
  • Lee, H. Y. and Park, Y. T. (2005). ‘An international comparison of R&D efficiency: DEA approach’, Asian Journal of Technology Innovation, 13, pp. 20722.
  • Lovell, C. A. K., Richardson, S., Travers, P. and Wood, L. L. (1994). ‘Resources and functions: a new view of inequality in Australia’, in Eichhorn, W. (ed.), Models and Measurement of Welfare and Inequality, Berlin: Springer-Verlag, pp. 787807.
  • 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 Van Ark, B. (eds), International Productivity Differences, Measurement and Explanations, Amsterdam: Elsevier Science, pp. 285315.
  • Mastromarco, C. (2008). ‘Foreign capital and efficiency in developing countries’, Bulletin of Economic Research, 60, pp. 35174.
  • Nelson, R. R. (1993). National Innovation Systems: A Comparative Analysis, Oxford: Oxford University Press.
  • 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 Paper 1176, Kiel Institute for the World Economy.
  • 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, Chicago, IL: University of Chicago Press.
  • 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. 4364.
  • Sharma, S. and Thomas, V. J. (2008). ‘Inter-country R&D efficiency analysis: an application of data envelopment analysis’, Scientometrics, 76, pp. 483501.
  • Shephard, R. W. (1970). Theory of Cost and Production Functions, Princeton, NJ: Princeton University Press.
  • Tödtling, F., Lehner, P. and Kaufmann, A. (2009). ‘Do different types of innovation rely on specific kinds of knowledge interactions?Technovation, 29, pp. 5971.
  • 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. 34560.
  • 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. 26073.
  • 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. 12944.
  • Werner, B. M. and Souder, W. E. (1997). ‘Measuring R&D performance: state of the art’, Research Technology Management, 40, pp. 3441.