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

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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.

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Notes

  1. See Lane and Pollner (2008), Iles et al. (2010) and Zhang et al. (2010).

  2. Examples include Gui et al. (1997), Gui (2009), Wang et al. (1999), Shao et al. (2001), Chen (2002), Sun (2006) and Lin and Yu (2011).

  3. \(A_{ij}\) here stands for the Gaussian fuzzy set with \(u_{ij}\) representing the cluster center and \(\sigma _{ij}\) representing the cluster width.

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Acknowledgments

The authors would like to express their sincere appreciation for the support provided for this research by the National Natural Science Foundation of China (Grant numbers 71301030,71303061), the Humanities and Social Science Foundation at the Ministry of Education of China (Grant number 11YJCZH057) and the College Humanities and Social Science Project of Guangdong Province (Grant number 12ZS0080).

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Authors and Affiliations

  1. School of Management, Guangdong University of Technology, Guangzhou, 510520, People’s Republic of China

    Yong He & Nuo Liao

  2. School of Business, Eastern Kentucky University, Richmond, KY, 40475, USA

    Siwei Gao

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  1. Yong He
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  2. Siwei Gao
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  3. Nuo Liao
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Correspondence to Nuo Liao.

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He, Y., Gao, S. & Liao, N. An Intelligent Computing Approach to Evaluating the Contribution Rate of Talent on Economic Growth. Comput Econ 48, 399–423 (2016). https://doi.org/10.1007/s10614-015-9536-1

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