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An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth

Author

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  • Yong He

    (Guangdong University of Technology)

  • Siwei Gao

    (Eastern Kentucky University)

  • Nuo Liao

    (Guangdong University of Technology)

Abstract

We set out in this study to develop an intelligent computing method for the evaluation of the ‘economic contribution rate of talent’ (ECRT). We begin by constructing an indicator system for comprehensive evaluation of the talent environment and then go on to classify our (country or region) target system using our proposed GA-DE-FCM methodology. We subsequently identify total ‘human capital’ as comprising of ‘talent capital’ and ‘general labor’, which, along with ‘fixed assets’, are used as the input variables of the economic system, whilst the corresponding gross domestic product is used as the output variable. The mapping between the inputs and output is modeled in this study by a ‘fuzzy artificial neural network’ from which several fuzzy rules can be extracted. Having extracted these fuzzy rules, we subsequently go on to investigate the effect of each input factor (fixed assets, talent capital and general labor) on the level of economic growth within each category (obtained in Step 1), and then carry out an examination of the ECRT within each category, as well as that within the whole target system. The traditional methods of evaluating ECRT are not regarded as satisfactory, given that the ECRT problem is non-linear and involves lags; however, we argue that based upon intelligent computing, the model proposed here can effectively deal with these issues. The results, based upon a 2001–2010 sample of 31 provinces in mainland China, indicate that during this period, China could be classified into three categories according to the talent environment. The first category (high level talent environment) comprises of just two regions, with an average ECRT of 44.61 per cent, whilst the second category (median level talent environment) comprises of five regions, with an average ECRT of 37.57 per cent, and the third category (low level talent environment) comprises of 24 regions, with an average ECRT of 14.8 per cent. The average ECRT for China as a whole is 25.67 per cent.

Suggested Citation

  • Yong He & Siwei Gao & Nuo Liao, 2016. "An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth," Computational Economics, Springer;Society for Computational Economics, vol. 48(3), pages 399-423, October.
    • Handle: RePEc:kap:compec:v:48:y:2016:i:3:d:10.1007_s10614-015-9536-1
    DOI: 10.1007/s10614-015-9536-1
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    References listed on IDEAS

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    1. Mulligan, C. B. & Sala-i-Martin, X., 1997. "A labor income-based measure of the value of human capital: An application to the states of the United States," Japan and the World Economy, Elsevier, vol. 9(2), pages 159-191, May.
    2. Zhang, Weiying & Cooper, W.W. & Deng, Honghui & Parker, Barnett R. & Ruefli, Timothy W., 2010. "Entrepreneurial talent and economic development in China," Socio-Economic Planning Sciences, Elsevier, vol. 44(4), pages 178-192, December.
    3. Iles, Paul & Chuai, Xin & Preece, David, 2010. "Talent Management and HRM in Multinational companies in Beijing: Definitions, differences and drivers," Journal of World Business, Elsevier, vol. 45(2), pages 179-189, April.
    4. Jeong, Byeongju, 2002. "Measurement of human capital input across countries: a method based on the laborer's income," Journal of Development Economics, Elsevier, vol. 67(2), pages 333-349, April.
    5. Shaozhuang Ma & Virginia Trigo, 2008. "Winning the War for Managerial Talent in China: An Empirical Study," Chinese Economy, Taylor & Francis Journals, vol. 41(3), pages 34-57, May.
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    Cited by:

    1. Maolin Cheng, 2019. "A Grey CES Production Function Model and Its Application in Calculating the Contribution Rate of Economic Growth Factors," Complexity, Hindawi, vol. 2019, pages 1-8, April.
    2. He, Yong & Liao, Nuo & Zhou, Ya, 2018. "Analysis on provincial industrial energy efficiency and its influencing factors in China based on DEA-RS-FANN," Energy, Elsevier, vol. 142(C), pages 79-89.
    3. Liao, Nuo & He, Yong, 2018. "Exploring the effects of influencing factors on energy efficiency in industrial sector using cluster analysis and panel regression model," Energy, Elsevier, vol. 158(C), pages 782-795.
    4. Christos Alexakis & Michael Dowling & Konstantinos Eleftheriou & Michael Polemis, 2021. "Textual Machine Learning: An Application to Computational Economics Research," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 369-385, January.
    5. Maolin Cheng & Bin Liu, 2019. "Analysis on the Influence of China’s Energy Consumption on Economic Growth," Sustainability, MDPI, vol. 11(14), pages 1-25, July.

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