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The income elasticity of household energy demand: a quantile regression analysis

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  • J. Harold
  • J. Cullinan
  • S. Lyons

Abstract

This article examines variation in the income elasticity of household energy demand across the energy expenditure distribution using expenditure data from the five most recent Household Budget Surveys (HBSs) in Ireland: the 1987, 1994/1995, 1999/2000, 2004/2005 and 2009/2010 HBS. The analysis uses a two-stage instrumental variable quantile regression approach and is based on each HBS cross section, as well as the overall pooled observations. The estimated elasticities are compared across low- and high-energy-consumption scenarios and to a benchmark elasticity estimated using two-stage least squares. The results provide evidence that there is significant variation in the income elasticities across the energy expenditure distribution and that care must be taken when using the constant mean elasticity for policy purposes. More specifically, any examination of the future impact of a change in income support policy measures on energy consumption should recognize the substantial context-dependent variation in the income elasticity.

Suggested Citation

  • J. Harold & J. Cullinan & S. Lyons, 2017. "The income elasticity of household energy demand: a quantile regression analysis," Applied Economics, Taylor & Francis Journals, vol. 49(54), pages 5570-5578, November.
    • Handle: RePEc:taf:applec:v:49:y:2017:i:54:p:5570-5578
    DOI: 10.1080/00036846.2017.1313952
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    Cited by:

    1. Michael Chesser & Jim Hanly & Damien Cassells & Nikolaos Apergis, 2019. "Household Energy Consumption: A Study of Micro Renewable Energy Systems in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 50(2), pages 265-280.
    2. Ghoddusi, Hamed & Rodivilov, Alexander & Roy, Mandira, 2021. "Income elasticity of demand versus consumption: Implications for energy policy analysis," Energy Economics, Elsevier, vol. 95(C).
    3. Kshitij Sharma & Yogesh K. Dwivedi & Bhimaraya Metri, 2024. "Incorporating causality in energy consumption forecasting using deep neural networks," Annals of Operations Research, Springer, vol. 339(1), pages 537-572, August.
    4. Walsh, Brendan & Wren, Maev-Ann & Smith, Samantha & Lyons, Seán & Eighan, James & Morgenroth, Edgar, 2019. "An analysis of the effects on Irish hospital care of the supply of care inside and outside the hospital," Research Series, Economic and Social Research Institute (ESRI), number RS91.
    5. Gorus, Muhammed Sehid & Karagol, Erdal Tanas, 2022. "Reactions of energy intensity, energy efficiency, and activity indexes to income and energy price changes: The panel data evidence from OECD countries," Energy, Elsevier, vol. 254(PA).
    6. Wang, Yuanping & Hou, Lingchun & Hu, Lang & Cai, Weiguang & Wang, Lin & Dai, Cuilian & Chen, Juntao, 2023. "How family structure type affects household energy consumption: A heterogeneous study based on Chinese household evidence," Energy, Elsevier, vol. 284(C).
    7. Poblete-Cazenave, Miguel & Pachauri, Shonali, 2020. "A simulation-based estimation model of household electricity demand and appliance ownership," MPRA Paper 103403, University Library of Munich, Germany.
    8. Aslam, Misbah & Ahmad, Eatzaz, 2023. "Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan," Energy, Elsevier, vol. 282(C).
    9. Kostakis, Ioannis & Lolos, Sarantis & Sardianou, Eleni, 2021. "Residential natural gas demand: Assessing the evidence from Greece using pseudo-panels, 2012–2019," Energy Economics, Elsevier, vol. 99(C).
    10. Poblete-Cazenave, Miguel & Pachauri, Shonali, 2021. "A model of energy poverty and access: Estimating household electricity demand and appliance ownership," Energy Economics, Elsevier, vol. 98(C).

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