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Human capital, mechanisms of technological diffusion and the role of technological shocks in the speed of diffusion: Evidence from a panel of Mediterranean countries

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Author Info
Marta Simões () (GEMF and Faculdade de Economia, Universidade de Coimbra)
Adelaide Duarte () (GEMF and Faculdade de Economia, Universidade de Coimbra)

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Abstract

Our main goal is to ascertain the importance of human capital as a facilitator of technological diffusion in a sample of seven Mediterranean countries (Algeria, Cyprus, Israel, Egypt, Syria, Tunisia, and Turkey) for the period 1960-2000. First, we estimate the technological progress growth rate and the technological gap between each country in our sample and the technological leader (the USA), following the methodology of Benhabib and Spiegel (2002). We then address the issue of the importance of technology diffusion for the TFP growth rate through the Nelson and Phelps (1966) hypothesis - the potential speed of technology diffusion is inversely related to the degree of technological backwardness of the follower country and its ability to absorb new technologies will depend positively on its human capital level. The non-linear specification of the TFP growth rate proposed by Benhabib and Spiegel (2002) is estimated to control for the type of technological diffusion: logistic or exponential. The empirical analysis is applied to two samples: a smaller one consisting of the above-mentioned countries, and a larger one that includes some European countries. First, we studied the unit root characteristic of the TFP growth rate series using unit root panel tests. The results obtained allowed the use of traditional econometric methods for both equations. For the first equation estimations were performed using the NLLS estimation procedure, as it is a non-linear equation. The second equation, was estimated using OLS with robust errors, the fixed effects model and the random effects model, as it is a linear equation. The empirical importance of human capital in fostering technological diffusion is also addressed through the FDI channel, by which technology is transferred from the leader to the followers. The host economy needs a sufficient level of human capital in order to apply the technology of the leader, i.e., the stock of human capital of the follower country limits its absorptive capability. We also analyse the role of human capital as a facilitator of the diffusion of a particular type of technology, ICT, where there is a role for different educational levels. In both cases we take Lee (2000) as the basic framework for our estimations. Finally, the last part of the paper discusses the importance of technological shocks to the process of technological diffusion. The speed of technological diffusion, and consequently the evolution of cross-country differences in GDP growth rates and levels, depend, to a large extent, on exogenous shocks. We propose to model technological shocks for each of the seven countries in our sample in a simple VAR model with four variables: their TFP growth rate, the logarithm of GDP per capita, the logarithm of investment per capita, and the logarithm of the stock of human capital.

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Paper provided by GEMF - Faculdade de Economia, Universidade de Coimbra in its series GEMF Working Papers with number 2004-03.

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Length: 42 pages
Date of creation: 2004
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Publication status: Published in Notas Económicas, 0(20), December 2004
Handle: RePEc:gmf:wpaper:2004-03

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Related research
Keywords: Economic growth Education Human capital Panel data VAR models

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Find related papers by JEL classification:
C33 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Models with Panel Data
O5 - Economic Development, Technological Change, and Growth - - Economywide Country Studies

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  1. Robert J. Barro & Jong-Wha Lee, 2000. "International Data on Educational Attainment Updates and Implications," NBER Working Papers 7911, National Bureau of Economic Research, Inc. [Downloadable!] (restricted)
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  2. Arellano, Manuel & Bond, Stephen, 1991. "Some Tests of Specification for Panel Data: Monte Carlo Evidence and an Application to Employment Equations," Review of Economic Studies, Blackwell Publishing, vol. 58(2), pages 277-97, April. [Downloadable!] (restricted)
  3. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January. [Downloadable!] (restricted)
  4. Benhabib, Jess & Spiegel, Mark M., 1994. "The role of human capital in economic development evidence from aggregate cross-country data," Journal of Monetary Economics, Elsevier, vol. 34(2), pages 143-173, October. [Downloadable!] (restricted)
  5. Arellano, Manuel & Bover, Olympia, 1995. "Another look at the instrumental variable estimation of error-components models," Journal of Econometrics, Elsevier, vol. 68(1), pages 29-51, July. [Downloadable!] (restricted)
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  6. Blundell, Richard & Bond, Stephen, 1998. "Initial conditions and moment restrictions in dynamic panel data models," Journal of Econometrics, Elsevier, vol. 87(1), pages 115-143, August. [Downloadable!] (restricted)
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  7. Mankiw, N Gregory & Romer, David & Weil, David N, 1992. "A Contribution to the Empirics of Economic Growth," The Quarterly Journal of Economics, MIT Press, vol. 107(2), pages 407-37, May. [Downloadable!] (restricted)
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  8. Borensztein, E. & De Gregorio, J. & Lee, J-W., 1998. "How does foreign direct investment affect economic growth?1," Journal of International Economics, Elsevier, vol. 45(1), pages 115-135, June. [Downloadable!] (restricted)
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  9. Andrew Levin & Chien-Fu Lin, 1993. "Unit Root Tests in Panel Data: New Results," University of California at San Diego, Economics Working Paper Series 93-56, Department of Economics, UC San Diego. [Downloadable!]
  10. Lucas, Robert Jr., 1988. "On the mechanics of economic development," Journal of Monetary Economics, Elsevier, vol. 22(1), pages 3-42, July. [Downloadable!] (restricted)
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