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Is power generation really the gold measure of the Chinese economy? A conceptual and empirical assessment

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  • Zhang, Jin
  • Li, Pujiang
  • Zhao, Guochang

Abstract

Amid widespread concern about the quality of the Chinese GDP data, power generation has become probably the single most cited indicator about the Chinese economy other than official statistics. For the first time, we provide an assessment on the efficacy of power generation as such an indicator, using both conceptual and empirical analyses. We show that (i) conceptually, it is somewhat misleading to use power generation as coincident indicator to infer the growth rate of GDP, due to the change of power intensity; (ii) empirically, power generation beats railway cargo and bank loan, the two other variables in the Li Keqiang index, as the best predictor in forecasting the Chinese GDP over the full sample; however, this superiority holds mainly during period of sharp change, i.e., the financial crisis period, but not when the economy is relatively stable, especially the current New Normal period. These two findings place important caveats on the common trust we have in power generation as an influential variable for measuring the Chinese economy.

Suggested Citation

  • Zhang, Jin & Li, Pujiang & Zhao, Guochang, 2018. "Is power generation really the gold measure of the Chinese economy? A conceptual and empirical assessment," Energy Policy, Elsevier, vol. 121(C), pages 211-216.
    • Handle: RePEc:eee:enepol:v:121:y:2018:i:c:p:211-216
    DOI: 10.1016/j.enpol.2018.06.030
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    References listed on IDEAS

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    1. Mehrotra, Aaron & Pääkkönen, Jenni, 2011. "Comparing China's GDP statistics with coincident indicators," Journal of Comparative Economics, Elsevier, vol. 39(3), pages 406-411, September.
    2. Chai, Jian & Guo, Ju-E & Wang, Shou-Yang & Lai, Kin Keung, 2009. "Why does energy intensity fluctuate in China?," Energy Policy, Elsevier, vol. 37(12), pages 5717-5731, December.
    3. Rawski, Tom, 1993. "How fast has Chinese industry grown?," Policy Research Working Paper Series 1194, The World Bank.
    4. Liao, Hua & Fan, Ying & Wei, Yi-Ming, 2007. "What induced China's energy intensity to fluctuate: 1997-2006?," Energy Policy, Elsevier, vol. 35(9), pages 4640-4649, September.
    5. Zhang, ZhongXiang, 2003. "Why did the energy intensity fall in China's industrial sector in the 1990s? The relative importance of structural change and intensity change," Energy Economics, Elsevier, vol. 25(6), pages 625-638, November.
    6. Mr. Shaun K. Roache, 2012. "China's Impacton World Commodity Markets," IMF Working Papers 2012/115, International Monetary Fund.
    7. James H. Stock & Mark W.Watson, 2003. "Forecasting Output and Inflation: The Role of Asset Prices," Journal of Economic Literature, American Economic Association, vol. 41(3), pages 788-829, September.
    8. Beirne, John & Beulen, Christian & Liu, Guy & Mirzaei, Ali, 2013. "Global oil prices and the impact of China," China Economic Review, Elsevier, vol. 27(C), pages 37-51.
    9. Eric Ghysels & Arthur Sinko & Rossen Valkanov, 2007. "MIDAS Regressions: Further Results and New Directions," Econometric Reviews, Taylor & Francis Journals, vol. 26(1), pages 53-90.
    10. Holz, Carsten A., 2014. "The quality of China's GDP statistics," China Economic Review, Elsevier, vol. 30(C), pages 309-338.
    11. Janet Koech & Jian Wang, 2012. "China's slowdown may be worse than official data suggest," Economic Letter, Federal Reserve Bank of Dallas, vol. 7(8), August.
    12. Michelle T. Armesto & Kristie M. Engemann & Michael T. Owyang, 2010. "Forecasting with mixed frequencies," Review, Federal Reserve Bank of St. Louis, vol. 92(Nov), pages 521-536.
    13. John G. Fernald & Israel Malkin & Mark M. Spiegel, 2013. "On the reliability of Chinese output figures," FRBSF Economic Letter, Federal Reserve Bank of San Francisco, issue mar25.
    14. J. Vernon Henderson & Adam Storeygard & David N. Weil, 2012. "Measuring Economic Growth from Outer Space," American Economic Review, American Economic Association, vol. 102(2), pages 994-1028, April.
    15. Ghysels, Eric & Santa-Clara, Pedro & Valkanov, Rossen, 2004. "The MIDAS Touch: Mixed Data Sampling Regression Models," University of California at Los Angeles, Anderson Graduate School of Management qt9mf223rs, Anderson Graduate School of Management, UCLA.
    16. Keola, Souknilanh & Andersson, Magnus & Hall, Ola, 2015. "Monitoring Economic Development from Space: Using Nighttime Light and Land Cover Data to Measure Economic Growth," World Development, Elsevier, vol. 66(C), pages 322-334.
    17. repec:zbw:bofitp:2011_001 is not listed on IDEAS
    Full references (including those not matched with items on IDEAS)

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    More about this item

    Keywords

    Power generation; GDP forecasting; China; Li Keqiang index; MIDAS;
    All these keywords.

    JEL classification:

    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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