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Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel

Author

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  • Liddle, Brantley
  • Smyth, Russell
  • Zhang, Xibin

Abstract

We estimate time-varying income and price elasticities for energy demand for a 26-country, middle-income balanced panel that spans 1996–2014. To do so, we employ a recently developed local linear dummy estimation method to estimate the trend and coefficient functions. We find that the price elasticity for energy demand is either insignificant or positive and small. While the income elasticity for energy demand behaves in a non-linear fashion over-time, it is always less than unity and is generally within 0.6–0.8. A GDP elasticity of less than one suggests that these middle-income countries are on the right-hand-side of an inverted-U energy intensity-GDP path that is consistent with the dematerialization process. Also, this finding suggests that energy intensity — but not energy consumption — in these countries will fall with economic growth. Hence, intensity-based targets may be met in a business-as-usual setting, but aggregate or per capita-based carbon emissions targets would likely require policy interventions.

Suggested Citation

  • Liddle, Brantley & Smyth, Russell & Zhang, Xibin, 2020. "Time-varying income and price elasticities for energy demand: Evidence from a middle-income panel," Energy Economics, Elsevier, vol. 86(C).
  • Handle: RePEc:eee:eneeco:v:86:y:2020:i:c:s0140988320300207
    DOI: 10.1016/j.eneco.2020.104681
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    References listed on IDEAS

    as
    1. Neto, David, 2012. "Testing and estimating time-varying elasticities of Swiss gasoline demand," Energy Economics, Elsevier, vol. 34(6), pages 1755-1762.
    2. Labandeira, Xavier & Labeaga, José M. & López-Otero, Xiral, 2017. "A meta-analysis on the price elasticity of energy demand," Energy Policy, Elsevier, vol. 102(C), pages 549-568.
    3. repec:aen:journl:2009v30-03-a05 is not listed on IDEAS
    4. repec:aen:journl:2008v29-01-a06 is not listed on IDEAS
    5. repec:aen:journl:2006v27-04-a07 is not listed on IDEAS
    6. Karimu, Amin & Brännlund, Runar, 2013. "Functional form and aggregate energy demand elasticities: A nonparametric panel approach for 17 OECD countries," Energy Economics, Elsevier, vol. 36(C), pages 19-27.
    7. Inglesi-Lotz, R., 2011. "The evolution of price elasticity of electricity demand in South Africa: A Kalman filter application," Energy Policy, Elsevier, vol. 39(6), pages 3690-3696, June.
    8. Eberhardt, Markus & Presbitero, Andrea F., 2015. "Public debt and growth: Heterogeneity and non-linearity," Journal of International Economics, Elsevier, vol. 97(1), pages 45-58.
    9. Roger Fouquet, 2014. "Editor's Choice Long-Run Demand for Energy Services: Income and Price Elasticities over Two Hundred Years," Review of Environmental Economics and Policy, Association of Environmental and Resource Economists, vol. 8(2), pages 186-207.
    10. Davidson, James & Monticini, Andrea & Peel, David, 2007. "Implementing the wild bootstrap using a two-point distribution," Economics Letters, Elsevier, vol. 96(3), pages 309-315, September.
    11. Chang, Yoosoon & Choi, Yongok & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y., 2016. "Disentangling temporal patterns in elasticities: A functional coefficient panel analysis of electricity demand," Energy Economics, Elsevier, vol. 60(C), pages 232-243.
    12. Pesaran, M. Hashem & Smith, Ron, 1995. "Estimating long-run relationships from dynamic heterogeneous panels," Journal of Econometrics, Elsevier, vol. 68(1), pages 79-113, July.
    13. repec:aen:journl:ej41-3-liddle is not listed on IDEAS
    14. Silvapulle, Param & Smyth, Russell & Zhang, Xibin & Fenech, Jean-Pierre, 2017. "Nonparametric panel data model for crude oil and stock market prices in net oil importing countries," Energy Economics, Elsevier, vol. 67(C), pages 255-267.
    15. M. Hashem Pesaran, 2021. "General diagnostic tests for cross-sectional dependence in panels," Empirical Economics, Springer, vol. 60(1), pages 13-50, January.
    16. Degui Li & Jia Chen & Jiti Gao, 2011. "Non‐parametric time‐varying coefficient panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 14(3), pages 387-408, October.
    17. Nguyen-Van, Phu, 2010. "Energy consumption and income: A semiparametric panel data analysis," Energy Economics, Elsevier, vol. 32(3), pages 557-563, May.
    18. Valderrama, Diego, 2007. "Statistical nonlinearities in the business cycle: A challenge for the canonical RBC model," Journal of Economic Dynamics and Control, Elsevier, vol. 31(9), pages 2957-2983, September.
    19. Richmond, Amy K. & Kaufmann, Robert K., 2006. "Is there a turning point in the relationship between income and energy use and/or carbon emissions?," Ecological Economics, Elsevier, vol. 56(2), pages 176-189, February.
    20. Chang, Yoosoon & Kim, Chang Sik & Miller, J. Isaac & Park, Joon Y. & Park, Sungkeun, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand with an Application to Korea," Energy Economics, Elsevier, vol. 46(C), pages 334-347.
    21. Brookes, L G, 1972. "More on the Output Elasticity of Energy Consumption," Journal of Industrial Economics, Wiley Blackwell, vol. 21(1), pages 83-92, November.
    22. Jeyhun I. Mikayilov & Fakhri J. Hasanov & Carlo A. Bollino & Ceyhun Mahmudlu, 2017. "Modeling of Electricity Demand for Azerbaijan: Time-Varying Coefficient Cointegration Approach," Energies, MDPI, vol. 10(11), pages 1-12, November.
    23. Hailemariam, Abebe & Smyth, Russell & Zhang, Xibin, 2019. "Oil prices and economic policy uncertainty: Evidence from a nonparametric panel data model," Energy Economics, Elsevier, vol. 83(C), pages 40-51.
    24. Wang, Nan & Mogi, Gento, 2017. "Industrial and residential electricity demand dynamics in Japan: How did price and income elasticities evolve from 1989 to 2014?," Energy Policy, Elsevier, vol. 106(C), pages 233-243.
    25. repec:aen:journl:2001v22-02-a04 is not listed on IDEAS
    26. repec:aen:journl:1998v19-04-a04 is not listed on IDEAS
    27. Yoosoon Chang & Chang Sik Kim & J. Isaac Miller & Joon Y. Park & Sungkeun Park, 2014. "Time-varying Long-run Income and Output Elasticities of Electricity Demand," Working Papers 1409, Department of Economics, University of Missouri.
    28. Luzzati, T. & Orsini, M., 2009. "Investigating the energy-environmental Kuznets curve," Energy, Elsevier, vol. 34(3), pages 291-300.
    29. repec:aen:journl:1999v20-02-a02 is not listed on IDEAS
    30. Fouquet, Roger, 2014. "Long run demand for energy services: income and price elasticities over two hundred years," LSE Research Online Documents on Economics 59070, London School of Economics and Political Science, LSE Library.
    31. M. Hashem Pesaran, 2007. "A simple panel unit root test in the presence of cross-section dependence," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(2), pages 265-312.
    32. Adom, Philip Kofi & Bekoe, William, 2013. "Modelling electricity demand in Ghana revisited: The role of policy regime changes," Energy Policy, Elsevier, vol. 61(C), pages 42-50.
    33. Awaworyi Churchill, Sefa & Inekwe, John & Smyth, Russell & Zhang, Xibin, 2019. "R&D intensity and carbon emissions in the G7: 1870–2014," Energy Economics, Elsevier, vol. 80(C), pages 30-37.
    34. Neftci, Salih N, 1984. "Are Economic Time Series Asymmetric over the Business Cycle?," Journal of Political Economy, University of Chicago Press, vol. 92(2), pages 307-328, April.
    35. Arthur A. van Benthem, 2015. "Energy Leapfrogging," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(1), pages 93-132.
    36. Park, Sung Y. & Zhao, Guochang, 2010. "An estimation of U.S. gasoline demand: A smooth time-varying cointegration approach," Energy Economics, Elsevier, vol. 32(1), pages 110-120, January.
    37. Chudik, Alexander & Pesaran, M. Hashem, 2015. "Common correlated effects estimation of heterogeneous dynamic panel data models with weakly exogenous regressors," Journal of Econometrics, Elsevier, vol. 188(2), pages 393-420.
    38. Ashley, Richard A & Patterson, Douglas M, 1989. "Linear versus Nonlinear Macroeconomies: A Statistical Test," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 30(3), pages 685-704, August.
    39. Arisoy, Ibrahim & Ozturk, Ilhan, 2014. "Estimating industrial and residential electricity demand in Turkey: A time varying parameter approach," Energy, Elsevier, vol. 66(C), pages 959-964.
    40. Yonghui Zhang & Liangjun Su & Peter C. B. Phillips, 2012. "Testing for common trends in semi‐parametric panel data models with fixed effects," Econometrics Journal, Royal Economic Society, vol. 15(1), pages 56-100, February.
    41. repec:aen:journl:ej37-2-stern is not listed on IDEAS
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    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy
    • Q5 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Environmental Economics

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