Human capital and energy in economic growth Evidence from Chinese provincial data

Transcription

1 Human capital and energy in economic growth Evidence from Chinese provincial data Zheng Fang, Singapore University of Social Sciences Yang Chen, Xi an Jiaotong-Liverpool University 40th IAEE International Conference June 2017 Singapore

2 Introduction 2016 Paris agreement Keep temperature rise within 2 degree above pre-industrial levels China s target achieving the peak before 2030 lowering the emission per unit of GDP by 60-65% from its 2005 level increasing the non-fossil fuel share in the primary energy consumption to 20% China s target is challenging due to its Unbalanced economic structure Diverse energy structure Shanxi: Coal (38%); Xinjiang: Oil (17%); Sichuan: Natural gas (24%) -> Policy suggestions on the basis of regional level analysis on disaggregated energy types

3 Literature review Existing energy-growth literature on China National time series Aggregate energy: Soytas and Sari, 2006; Zhang and Cheng, 2009; Wang et al., 2011; Shahbaz et al., 2013 Both aggregate and disaggregate energy: Yuan et al., 2008; Zhang and Yang, 2013; Bloch et al., 2015; Long et al., 2015; Fang and Wolski, 2016 Yalta and Cakar (2012): causality tests based on time series data has a high risk falsely rejecting the null hypothesis of no causality when the sample size is small Provincial panel Aggregate energy: Wang et al., 2011; Akkemik et al., 2012; Zhang and Xu, 2012 Disaggregate energy: Herrerias et al., 2013

4 Contributions This paper uses provincial panel data for to examine the causal link between individual energy sources and economic development in China. Contributions Incorporate human capital to the energy-augmented neoclassical production function (Pablo-Romero and Sanchez-Braza, 2015; Fang and Chang, 2016) Account for interdependence across provinces in the panel cointegrating and causality analysis (Westerlund, 2007; Bai et al., 2009; Emirmahmutoglu and Kose, 2011) Examine total energy use and consumption of raw coal, coke, crude oil, petroleum products (fuel oil, gasoline, diesel, kerosene), natural gas, and electricity in Chinese provinces

5 Energy Profile in China 18% 16% 14% 12% 10% 8% 6% 4% 2% Growth of output and total energy consumption ( ) 0% GDP Energy

6 The consumption trend of primary energy sources Million toe 2,700 2,400 2,100 1,800 1,500 1, % The composition of energy consumption Coal Crude oil Natural gas Coal Crude oil Natural gas Others (Hydro-, nuclear, and wind power)

7 Coal and coke Coal Top 5 in 1995 Shanxi Hebei Shandong Liaoning Jiangsu Top 5 in 2014 Shandong Shanxi Inner Monglia Hebei Jiangsu Last 5 in 1995 Fujian Qinghai Ningxia Hainan Chongqing Last 5 in 2014 Tianjin Shanghai Qinghai Beijing Hainan Coke Top 5 in 1995 Shanxi Hebei Liaoning Sichuan Shanghai Top 5 in 2014 Hebei Shandong Jiangsu Liaoning Henan Last 5 in 1995 Fujian Xinjiang Qinghai Ningxia Hainan Last 5 in 2014 Chongqing Qinghai Heilongjiang Beijing Hainan

8 Million tonnes Crude oil and petroleum products Oil production and consumption Production and consumption of petroleum products % 1000 barrels/day 1000 barrels/day % % % % % Production Consumption Surplus ratio Production: Petroleum Products: China Consumption: Petroleum Products: China Data Source: Wind Info

9 Data China Energy Statistical Yearbooks total energy consumption, coal, coke, crude oil, petroleum products (which consist of fuel oil, gasoline, diesel, kerosene), natural gas, and electricity China Statistical Yearbooks GDP, fixed asset investment, population China Human Capital Index Report 2016 (China Center for Human Capital and Labor Market Research) Human capital Provincial panel: 30 provinces, 20 years ( ) All variables in per capita logarithm form

10 Variables Mean Std. Dev. Min. Max. No. of obs. lngdp lninv lnhc lnenergy lncoal lncoke lncrude Oil lnpetroleum Products lngas lnelectricity

11 Econometric methodologies Y it = f K it, HC it, E it Research questions: 1. Whether the variables GDP, physical and human capital and energy consumption are cointegrated and what is the magnitude of output elasticity with respect to each factor input if there is a cointegrating relationship? Panel unit root test by Pesaran (2007) Cointegration test by Westerlund (2007) Cup-FM estimator by Bai et al. (2009) 2. Whether there is a Granger causal link between energy consumption and economic growth; if so, what is the direction of the Granger causality? Bootstrap panel Granger causality test (Emirmahmutoglu and Kose, 2011) is applied

12 Cross-sectional dependence test Test Energy Coal Coke Crude oil Petroleum product Natural gas Electricity Frees Q statistic Friedman s χ 2 statistic Pesaran s statistic

13 Panel unit root tests, CIPS Pesaran (2007) Level First difference Intercept Intercept and trend Intercept Intercept and trend lngdp *** *** lninv *** *** lnhc *** *** lnenergy *** ** *** *** lncoal *** *** lncoke *** *** *** *** lncrude Oil *** *** lnpetroleum Products *** *** lngas *** *** lnelectricity *** *** ***

14 Panel cointegration test (Westerlund, 2007) Test Energy Coal Crude oil Petroleum product Natural gas Electricity Without constant Gt [0.010] [0.080] [0.040] [0.010] [0.130] [0.040] Ga [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Pt [0.070] [0.170] [0.040] [0.060] [0.030] [0.140] Pa [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] With constant Gt [0.070] [0.120] [0.000] [0.030] [0.010] [0.210] Ga [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Pt [0.080] [0.090] [0.000] [0.010] [0.000] [0.410] Pa [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]

15 Cup-FM estimation Energy Coal Crude oil Petroleum product Natural gas Electricity lninv 0.099*** 0.110*** 0.206*** 0.108*** 0.102*** 0.065*** (8.969) (10.129) (3.836) (9.521) (9.109) (6.409) lnhc 0.203*** 0.263*** 0.625*** 0.309*** 0.272*** *** (16.047) (25.648) (8.064) (23.187) (28.149) (-5.286) lne 0.051*** 0.027*** 0.047*** 0.037*** 0.006*** 0.114*** (3.697) (2.575) (3.069) (3.687) (2.709) (8.091)

16 Panel Granger non-causality test Country Energy Granger not-cause GDP? GDP Granger not-cause energy? Fisher test statistic λ Fisher test statistic λ Energy ** *** Coal * *** Coke ** *** Crude oil Petroleum product *** Natural gas * ** Electricity **

17 Country Finding Energy Granger not-cause GDP? GDP Granger not-cause energy? E?GDP W i p i W i p i Beijing Tianjin Hebei Shanxi Inner Mongolia Liaoning Jilin Heilongjiang Shanghai Jiangsu Zhejiang Anhui Fujian Jiangxi Shandong Henan Hubei Hunan Guangdong Guangxi Hainan Chongqing Sichuan Guizhou Yunnan Shaanxi Gansu Qinghai Ningxia Xinjiang

18 Coal?GDP Coke?GDP Crude Oil?GDP Petroleum product?gdp Natural gas?gdp Electricity?GDP Beijing ** * Tianjin * * *** Hebei ** *** Shanxi *** *** Inner Mongolia * *** *** * ** Liaoning ** ** * Jilin ** ** * * ** Heilongjiang * ** Shanghai * * ** ** Jiangsu ** ** *** *** *** Zhejiang * * ** *** Anhui ** * Fujian *** ** * Jiangxi *** * * ** Shandong *** *** *** Henan *** *** * *** Hubei * * * Hunan ** Guangdong Guangxi * * *** ** Hainan ** Chongqing * * ** Sichuan ** ** * *** *** Guizhou * *** *** ** Yunnan ** *** Shaanxi ** Gansu Qinghai * *

19 Conclusions We find strong evidence of cross-sectional dependence, and confirm the presence of cointegration relationship between GDP, investment, human capital and various energy variables even after taking into consideration interdependence across provinces. We find that human capital is a crucial factor input which exerts about 2-3 times effects that investment has on GDP; energy, both in aggregate or disaggregate forms, plays a significant role in economic development in China as well. Therefore, both human capital and energy are indispensable in stimulating economic growth. Granger causal relationships running from GDP to petroleum products and electricity, and bi-directional relationship between GDP and coal, coke, natural gas and total energy consumption in the whole China. For individual provinces, causal relationships vary significantly.

20 Thank You!