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Human capital, energy and economic growth in China: evidence from multivariate nonlinear Granger causality tests

Abstract

Using a novel nonlinear multivariate Granger causality test and an augmented production function, which incorporates both physical and human capital, this study investigates the causal link between economic development and aggregate and disaggregate energy consumption in China during the period of 1965–2014. This is the first time the multivariate nonlinear Granger causality test is applied to measure the dependencies between growth, energy use and human capital in the context of China. Our results confirm the neutrality hypothesis for aggregate energy use as well as for the consumption of coal, natural gas and hydroelectricity. We also find unidirectional causality running from economic growth to oil consumption. Weak evidence in favor of the substitution effect between human capital and energy/coal use is observed in the linear approach. The findings suggest that energy conservation policies are feasible in China and policies advocating an improvement in human capital, associated with energy-specific skills, may be helpful in pollution reduction.

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Notes

  1. 1.

    As pointed out by Fang and Chang (2016), one of the reasons that human capital is seldom taken into consideration in this strand of literature may be the lack of reliable data on the human capital variable. The recently built human capital index by Feenstra et al. (2015), however, makes more studies on this topic possible and thus we expect that the role of human capital in the link of energy and growth will draw more attention to the topic.

  2. 2.

    For instance, one could project the multivariate setting on a bivariate plane before performing the test. Nevertheless, such methods can suffer from substantial information losses and, as a consequence, they can lead to biased inference.

  3. 3.

    There are a few other nonlinear Granger causality tests proposed in the literature. For example, Péguin-Feissolle et al. (2013) proposed a method to test the Granger noncausality based on a Taylor expansion, and Troster (2018) develops a semi-parametric test of Granger-causality in quantiles. In this paper, we choose to focus on the multivariate extensions of the popular nonlinear Granger causality test proposed by Diks and Panchenko (2006) and the distributional Granger causality test across quantiles proposed by Jeong et al. (2012), which, we believe, together offer good power against a wide range of alternatives hypotheses of no Granger causality.

  4. 4.

    Other variables that have been considered in the multivariate framework include price (Zhang and Xu 2012), international trade (Shahbaz et al. 2013), technology (Bhattacharya et al. 2015) and carbon dioxide emission (for example, Zhang and Cheng 2009; Wang et al. 2011a, b; Fei et al. 2011; Bloch et al. 2012, 2015; Lin and Moubarak 2014).

  5. 5.

    The results of the joint test can be found in the online appendix Table A3.

  6. 6.

    The differenced data are not fully stationary. Therefore, to guarantee the consistency of the nonlinear framework, we take into account detrended first differences as suggested by stationarity test results.

  7. 7.

    The p values for the 5th percentile and 95th percentile are almost the same for some cases because the indicator functions in the test statistics are the same for more extreme quantiles due to the small sample size.

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Correspondence to Zheng Fang.

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We are grateful for Professor Robert Kunst and the two anonymous reviewers for their constructive and helpful comments, which help us improve the paper greatly.

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Fang, Z., Wolski, M. Human capital, energy and economic growth in China: evidence from multivariate nonlinear Granger causality tests. Empir Econ 60, 607–632 (2021). https://doi.org/10.1007/s00181-019-01781-7

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Keywords

  • Multivariate nonlinear causality test
  • Human capital
  • Energy–growth nexus
  • China

JEL Classification

  • Q43
  • Q48
  • C50