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International Tfp Dynamics And Human Capital Stocks: A Panel Data Analysis, 1960–2003

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  • Adriana Di Liberto
  • Francesco Pigliaru
  • Piergiorgio Chelucci

Abstract

This paper adopts a fixed-effect panel methodology that enables us to take into account both TFP and neoclassical convergence. We use a sample of 76 countries, 1960-2003 and estimate TFP values obtained by using different estimators such as LSDV, Kiviet-corrected LSDV, and GMM à la Arellano and Bond. In our estimates, cross-country TFP dynamics shows that most countries in the sample do not catch up with the USA. We also find conditional convergence in TFP levels and that human capital acts as a robust enhancing factor of technology adoption, as suggested by Nelson and Phelps in 1966. In contrast with previous evidence, in our results even very low level of human capital stocks allow a country to enter a "conditional TFP convergence club" – a result again consistent with the original version of the Nelson-Phelps hypothesis. Further, our results imply a plausible link between stages of development and returns to different levels of education. Finally, the positive influence of human capital on technology catch up is robust to the inclusion of controls for a country's institutional quality.
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  • Adriana Di Liberto & Francesco Pigliaru & Piergiorgio Chelucci, 2011. "International Tfp Dynamics And Human Capital Stocks: A Panel Data Analysis, 1960–2003," Review of Income and Wealth, International Association for Research in Income and Wealth, vol. 57(1), pages 156-182, March.
    • Handle: RePEc:bla:revinw:v:57:y:2011:i:1:p:156-182
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    3. Simplice Asongu & Christelle Meniago & Raufhon Salahodjaev, 2022. "The role of value added across economic sectors in modulating the effects of FDI on TFP and economic growth dynamics," International Journal of Emerging Markets, Emerald Group Publishing Limited, vol. 18(11), pages 5087-5108, February.
    4. Ruba A. Aljarallah & Andrew Angus, 2020. "Dilemma of Natural Resource Abundance: A Case Study of Kuwait," SAGE Open, , vol. 10(1), pages 21582440198, January.
    5. ADACHI Yusuke & OGAWA Hikaru & TSUBUKU Masafumi, 2019. "Productivity Dynamics during Major Crises in Japan: A Quantile Approach," Discussion papers 19015, Research Institute of Economy, Trade and Industry (RIETI).
    6. Bosco, Bruno, 2016. "Old and new factors affecting corruption in Europe: Evidence from panel data," Economic Analysis and Policy, Elsevier, vol. 51(C), pages 66-85.
    7. Antonelli Cristiano & Fassio Claudio, 2012. "Academic knowledge and economic growth: are scientific fields all alike," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 201203, University of Turin.
    8. Indunil De Silva & Sudarno Sumarto, 2015. "Dynamics Of Growth, Poverty And Human Capital: Evidence From Indonesian Sub-National Data," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 40(2), pages 1-33, June.
    9. Edinaldo Tebaldi, 2016. "The Dynamics of Total Factor Productivity and Institutions," Journal of Economic Development, Chung-Ang Unviersity, Department of Economics, vol. 41(4), pages 1-25, December.

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    More about this item

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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