Article

Measurement of inequality using household energy consumption data in rural China

  • Nature Energyvolume 2pages795803 (2017)
  • doi:10.1038/s41560-017-0003-1
  • Download Citation
Received:
Accepted:
Published:

Abstract

Measuring inequality can be challenging due to the limitations of using household income or expenditure data. Because actual energy consumption can be measured more easily and accurately and is relatively more stable, it may be a better measure of inequality. Here we use data on energy consumption for specific devices from a large nation-wide household survey (n = 3,404 rural households from 12 provinces) to assess inequality in rural China. We find that the overall inequality of energy consumption and expenditure varies greatly in terms of energy type, end-use demand, regions and climatic zones. Biomass, space heating and cooking, intraregional differences, and climatic zones characterized as cold or hot summer/cold winter contribute the most to total inequality for each indicator, respectively. The results suggest that the expansion of infrastructure does not accompany alleviation of energy inequality, and that energy affordability should be improved through income growth and targeted safety-net programmes instead of energy subsidies.

Additional access options:

Already a subscriber?  Log in  now or  Register  for online access.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    Xie, Y. & Zhou, X. Income inequality in today’s China. Proc. Natl Acad. Sci. USA 111, 6928–6933 (2014).

  2. 2.

    Kaiman, J. China gets richer but more unequal. The Guardian (28 July 2014).

  3. 3.

    Woellert, L. & Chen, S. China’s Income Inequality Surpasses US, Posing Risk for Xi. Bloomberg (28 March 2014).

  4. 4.

    Kanbur, R. & Zhang, X. Which regional inequality? The evolution of rural–urban and inland–coastal inequality in China from 1983 to 1995. J. Compar. Econ. 27, 686–701 (1999).

  5. 5.

    Cai, H., Chen, Y. & Zhou, L.-A. Income and consumption inequality in urban China: 1992–2003. Econ. Devel. Cult. Change 58, 385–413 (2010).

  6. 6.

    Benjamin, D., Brandt, L. & Giles, J. The evolution of income inequality in rural China. Econ. Devel. Cult. Change 53, 769–824 (2005).

  7. 7.

    Cutler, D. M. & Katz, L. F. Rising inequality? Changes in the distribution of income and consumption in the 1980’s. Amer. Econ. Rev 82, 546–551 (1992).

  8. 8.

    Hassett, K. A. & Mathur, A. A new measure of consumption inequality. AEI Econ. Stud. 2, (2012) http://www.aei.org/wp-content/uploads/2012/06/-a-new-measure-of-consumption-inequality_142931647663.pdf.

  9. 9.

    Reform and Innovation for Better Rural Health Services in China (The World Bank, Washington DC, 2015); http://www.worldbank.org/en/results/2015/04/02/reform-innovation-for-better-rural-health-services-in-china.

  10. 10.

    Li, J. J. & Su, C. How face influences consumption. Int. J. Market Res. 49, 237–256 (2007).

  11. 11.

    Corneo, G. & Jeanne, O. Conspicuous consumption, snobbism and conformism. J. Public Econ. 66, 55–71 (1997).

  12. 12.

    Krueger, D. & Perri, F. Does income inequality lead to consumption inequality? Evidence and theory. Rev. Econ. Stud. 73, 163–193 (2006).

  13. 13.

    Chen, X. & Nordhaus, W. D. Using luminosity data as a proxy for economic statistics. Proc. Natl Acad. Sci. USA 108, 8589–8594 (2011).

  14. 14.

    Arora, V. & Lieskovsky, J. Electricity Use as an Indicator of US Economic Activity EIA Working Paper Series (EIA, Washington DC, 2014).

  15. 15.

    Henderson, J. V., Storeygard, A. & Weil, D. N. Measuring economic growth from outer space. Amer. Econ. Rev. 102, 994–1028 (2012).

  16. 16.

    Joyeux, R. & Ripple, R. D. Household energy consumption versus income and relative standard of living: A panel approach. Energ. Policy 35, 50–60 (2007).

  17. 17.

    Aklin, M., Cheng, C.-Y., Urpelainen, J., Ganesan, K. & Jain, A. Factors affecting household satisfaction with electricity supply in rural India. Nat. Energy 1, 16170 (2016).

  18. 18.

    Jorgenson, A. K., Alekseyko, A. & Giedraitis, V. Energy consumption, human well-being and economic development in central and eastern European nations: A cautionary tale of sustainability. Energ. Policy 66, 419–427 (2014).

  19. 19.

    Anderson, D. in World Energy Assessment: Energy and the Challenge of Sustainability (eds. United Nations Development Programme) (UNDP, New York, 2000).

  20. 20.

    Kammen, D. M. & Kirubi, C. Poverty, energy, and resource use in developing countries. Ann. NY Acad. Sci 1136, 348–357 (2008).

  21. 21.

    China to realize nationwide electricity coverage. State Council (4 August 2015); http://english.gov.cn/state_council/ministries/2015/08/04/content_281475160764026.htm

  22. 22.

    Fleisher, B., Li, H. & Zhao, M. Q. Human capital, economic growth, and regional inequality in China. J. Dev. Econ 92, 215–231 (2010).

  23. 23.

    Jacobson, A., Milman, A. D. & Kammen, D. M. Letting the (energy) Gini out of the bottle: Lorenz curves of cumulative electricity consumption and Gini coefficients as metrics of energy distribution and equity. Energ. Policy 33, 1825–1832 (2005).

  24. 24.

    Han, J., Mol, A. P. & Lu, Y. Solar water heaters in China: a new day dawning. Energ. Policy 38, 383–391 (2010).

  25. 25.

    Dube, I. Impact of energy subsidies on energy consumption and supply in Zimbabwe. Do the urban poor really benefit? Energ. Policy 31, 1635–1645 (2003).

  26. 26.

    Kebede, B. Energy subsidies and costs in urban Ethiopia: The cases of kerosene and electricity. Renew. Energ 31, 2140–2151 (2006).

  27. 27.

    Pitt, M. M. Equity, externalities and energy subsidies The case of kerosine in Indonesia. J. Dev. Econ 17, 201–217 (1985).

  28. 28.

    Alam, M., Sathaye, J. & Barnes, D. Urban household energy use in India: efficiency and policy implications. Energ. Policy 26, 885–891 (1998).

  29. 29.

    Soile, I. & Mu, X. Who benefit most from fuel subsidies? Evidence from Nigeria. Energ. Policy 87, 314–324 (2015).

  30. 30.

    Coady, D., Parry, I. W. H., Sears, L. & Shang, B. How Large Are Global Energy Subsidies? International Monetary Fund Working Paper No. 15/105 42 (IMF, Washington DC, 2015).

  31. 31.

    Saboohi, Y. An evaluation of the impact of reducing energy subsidies on living expenses of households. Energ. Policy 29, 245–252 (2001).

  32. 32.

    Larson, D. F., Lampietti, J., Gouel, C. & Cafiero, C. Food security and storage in the Middle East and North Africa. World Bank Econ. Rev 28, 48–73 (2014).

  33. 33.

    Meyer, B. D., Mok, W. K. C. & Sullivan, J. X. Household surveys in crisis. J. Econ. Perspect. 29, 199–226 (2015).

  34. 34.

    Zheng, X. et al. Characteristics of residential energy consumption in China: Findings from a household survey. Energ. Policy 75, 126–135 (2014).

  35. 35.

    Bian, Y. & Li, L. The Chinese general social survey (2003–8). Chinese Sociol. Rev. 45, 70–97 (2012).

  36. 36.

    Tol, R. S., Downing, T. E., Kuik, O. J. & Smith, J. B. Distributional aspects of climate change impacts. Glob. Environ. Change 14, 259–272 (2004).

  37. 37.

    Groot, L. Carbon Lorenz curves. Resour. Energy Econ. 32, 45–64 (2010).

  38. 38.

    Jacmart, M. C., Arditi, M. & Arditi, I. The world distribution of commercial energy consumption. Energ. Policy 7, 199–207 (1979).

  39. 39.

    Druckman, A. & Jackson, T. Measuring resource inequalities: The concepts and methodology for an area-based Gini coefficient. Ecol. Econ. 65, 242–252 (2008).

  40. 40.

    Damgaard, C. & Weiner, J. Describing inequality in plant size or fecundity. Ecology 81, 1139–1142 (2000).

  41. 41.

    Shorrocks, A. F. Decomposition procedures for distributional analysis: a unified framework based on the Shapley value. J. Econ. Inequal. 11, 99–126 (2013).

  42. 42.

    Yang, D. T. Urban-biased policies and rising income inequality in China. Amer. Econ. Rev. 89, 306–310 (1999).

  43. 43.

    Wei, C., Wu, S. & Zheng, X. Figshare Digital Repository (2017); https://doi.org/10.6084/m9.figshare.5172496.v1

Download references

Acknowledgements

We thank J. Guo, Z.T. Huang, J.Q. Hu and J.S. Fu for their input in survey design and implementation and data collection. We are grateful to J. Tang and W.D. Wang for their technical guidance and Y.C. Liu, R.L. Yang and Y. Zhang for their support to CRECS. Funding for this work was provided by the National Science Foundation of China (Grant no. 71622014, 41771564, 71774165), Ministry of Education of China (Grant nos 14JJD790033, 16YJA790049) and Beijing Natural Science Foundation (Grant no. 9152011).

Author information

Affiliations

  1. Department of Energy Economics, School of Economics, Renmin University of China, No 59, Zhongguancun Street, Haidian district, Beijing, 100872, China

    • Shimei Wu
    • , Xinye Zheng
    •  & Chu Wei

Authors

  1. Search for Shimei Wu in:

  2. Search for Xinye Zheng in:

  3. Search for Chu Wei in:

Contributions

All authors contributed to survey design and data collection. S.M.W. conducted the data analysis. X.Y.Z. secured project funding. C.W. drafted the manuscript. S.M.W. and C.W. edited the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Chu Wei.

Electronic supplementary material

  1. Supplementary Information

    Supplementary Notes 1–2, Supplementary Figures 1–6, Supplementary Tables 1–2, and Supplementary References