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 18-21 June 2017 Singapore
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
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
Contributions This paper uses provincial panel data for 1995-2014 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
1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 Energy Profile in China 18% 16% 14% 12% 10% 8% 6% 4% 2% Growth of output and total energy consumption (1980-2015) 0% GDP Energy
1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002 2004 2006 2008 2010 2012 2014 The consumption trend of primary energy sources Million toe 2,700 2,400 2,100 1,800 1,500 1,200 900 600 300 - % 100 90 80 70 60 50 40 30 20 10 0 The composition of energy consumption Coal Crude oil Natural gas Coal Crude oil Natural gas Others (Hydro-, nuclear, and wind power)
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
Million tonnes Crude oil and petroleum products Oil production and consumption Production and consumption of petroleum products 600.0-70% 1000 barrels/day 1000 barrels/day 10800 10800 500.0-50% 9900 9900 400.0-30% 9000 9000 300.0-10% 8100 8100 200.0 10% 7200 7200 100.0-30% 6300 6300 5400 5400 Production Consumption Surplus ratio 4500 4500 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Production: Petroleum Products: China Consumption: Petroleum Products: China Data Source: Wind Info
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 (1995-2014) All variables in per capita logarithm form
Variables Mean Std. Dev. Min. Max. No. of obs. lngdp 2.422 0.773 0.595 4.264 600 lninv 1.646 1.029-0.774 3.846 600 lnhc 5.910 0.669 4.466 7.863 600 lnenergy 0.649 0.617-0.872 2.073 600 lncoal 0.576 0.713-1.683 2.688 600 lncoke 4.654 1.362-3.781 7.056 600 lncrude Oil 4.661 2.514-5.656 7.375 560 lnpetroleum Products -1.943 0.789-3.767-0.108 600 lngas 3.177 1.983-3.196 6.606 600 lnelectricity 0.613 0.724-0.816 2.551 600
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
Cross-sectional dependence test Test Energy Coal Coke Crude oil Petroleum product Natural gas Electricity Frees Q statistic 6.910 6.792 6.705 6.256 6.623 6.234 6.608 Friedman s χ 2 statistic 222.021 221.244 192.312 188.231 190.867 180.766 200.438 Pesaran s statistic 35.255 34.162 28.958 28.231 29.144 27.392 29.713
Panel unit root tests, CIPS Pesaran (2007) Level First difference Intercept Intercept and trend Intercept Intercept and trend lngdp -2.029-2.457-2.634*** -2.921*** lninv -1.572-1.999-3.161*** -3.662*** lnhc -1.289-2.221-3.193*** -3.358*** lnenergy -2.475*** -2.879** -4.309*** -4.723*** lncoal -2.067-2.421-4.079*** -4.560*** lncoke -2.441*** -3.231*** -4.209*** -4.303*** lncrude Oil -1.864-2.182-3.956*** -4.195*** lnpetroleum Products -1.934-2.591-4.210*** -4.715*** lngas -1.988-2.299-4.136*** -4.338*** lnelectricity -2.397*** -2.195-4.217*** -4.567***
Panel cointegration test (Westerlund, 2007) Test Energy Coal Crude oil Petroleum product Natural gas Electricity Without constant Gt -2.040-1.767-1.632-1.905-1.510-1.908 [0.010] [0.080] [0.040] [0.010] [0.130] [0.040] Ga -14.115-10.971-10.007-14.777-10.694-13.614 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Pt -7.951-6.387-7.620-8.272-7.671-6.503 [0.070] [0.170] [0.040] [0.060] [0.030] [0.140] Pa -8.616-8.249-9.887-11.260-9.530-9.013 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] With constant Gt -2.185-1.905-2.443-2.081-1.972-1.893 [0.070] [0.120] [0.000] [0.030] [0.010] [0.210] Ga -17.698-16.780-15.312-14.432-13.552-12.255 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000] Pt -8.318-6.358-11.170-7.510-7.474-7.186 [0.080] [0.090] [0.000] [0.010] [0.000] [0.410] Pa -10.011-9.844-12.839-11.591-9.335-8.702 [0.000] [0.000] [0.000] [0.000] [0.000] [0.000]
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*** -0.155*** (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)
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 108.594 ** 157.867 *** Coal 102.442 * 122.583 *** Coke 127.194 ** 146.504 *** Crude oil 70.551 62.533 Petroleum product 69.429 170.487 *** Natural gas 103.695 * 104.088 ** Electricity 79.009 110.910 **
Country Finding Energy Granger not-cause GDP? GDP Granger not-cause energy? E?GDP W i p i W i p i Beijing 1.652 0.199 0.2 0.655 Tianjin 0.013 0.910 1.801 0.180 Hebei 5.348 0.021 0.047 0.828 Shanxi 6.527 0.011 0.026 0.872 Inner Mongolia 0.557 0.455 27.102 0.000 Liaoning 0.809 0.368 1.871 0.171 Jilin 0.080 0.778 3.316 0.069 Heilongjiang 0.000 0.997 3.004 0.083 Shanghai 1.897 0.168 0.064 0.800 Jiangsu 0.355 0.551 20.831 0.000 Zhejiang 14.987 0.000 3.701 0.054 Anhui 0.545 0.460 4.577 0.032 Fujian 5.736 0.017 0.330 0.566 Jiangxi 1.085 0.298 0.611 0.435 Shandong 1.979 0.159 10.977 0.001 Henan 5.347 0.021 0.261 0.609 Hubei 1.464 0.226 0.021 0.885 Hunan 0.231 0.631 0.638 0.424 Guangdong 2.545 0.111 1.375 0.241 Guangxi 1.818 0.178 0.791 0.374 Hainan 1.085 0.298 5.447 0.020 Chongqing 0.067 0.796 5.853 0.016 Sichuan 4.124 0.042 0.905 0.341 Guizhou 0.036 0.849 4.050 0.044 Yunnan 6.569 0.010 0.303 0.582 Shaanxi 0.239 0.625 6.800 0.009 Gansu 1.013 0.314 0.033 0.856 Qinghai 0.655 0.418 3.782 0.052 Ningxia 0.194 0.660 2.447 0.118 Xinjiang 0.459 0.498 0.079 0.779
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 * *
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.
Thank You! fangzheng@suss.edu.sg