IDEAS home Printed from https://ideas.repec.org/a/taf/applec/v49y2017i54p5570-5578.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/00036846.2017.1313952
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/00036846.2017.1313952?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Havranek, Tomas & Kokes, Ondrej, 2015. "Income elasticity of gasoline demand: A meta-analysis," Energy Economics, Elsevier, vol. 47(C), pages 77-86.
    2. John Eakins, 2013. "The Determinants of Household Car Ownership: Empirical Evidence from the Irish Household Budget Survey," Surrey Energy Economics Centre (SEEC), School of Economics Discussion Papers (SEEDS) 144, Surrey Energy Economics Centre (SEEC), School of Economics, University of Surrey.
    3. Espey, James A. & Espey, Molly, 2004. "Turning on the Lights: A Meta-Analysis of Residential Electricity Demand Elasticities," Journal of Agricultural and Applied Economics, Cambridge University Press, vol. 36(1), pages 65-81, April.
    4. Roger Koenker & Kevin F. Hallock, 2001. "Quantile Regression," Journal of Economic Perspectives, American Economic Association, vol. 15(4), pages 143-156, Fall.
    5. Bernstein, Ronald & Madlener, Reinhard, 2011. "Responsiveness of Residential Electricity Demand in OECD Countries: A Panel Cointegation and Causality Analysis," FCN Working Papers 8/2011, E.ON Energy Research Center, Future Energy Consumer Needs and Behavior (FCN).
    6. Meier, Helena & Rehdanz, Katrin, 2010. "Determinants of residential space heating expenditures in Great Britain," Energy Economics, Elsevier, vol. 32(5), pages 949-959, September.
    7. Amemiya, Takeshi, 1982. "Two Stage Least Absolute Deviations Estimators," Econometrica, Econometric Society, vol. 50(3), pages 689-711, May.
    8. Harold, Jason & Lyons, Seán & Cullinan, John, 2015. "The determinants of residential gas demand in Ireland," Energy Economics, Elsevier, vol. 51(C), pages 475-483.
    9. Koenker, Roger W & Bassett, Gilbert, Jr, 1978. "Regression Quantiles," Econometrica, Econometric Society, vol. 46(1), pages 33-50, January.
    10. Nesbakken, Runa, 1999. "Price sensitivity of residential energy consumption in Norway," Energy Economics, Elsevier, vol. 21(6), pages 493-515, December.
    11. Chitnis, Mona & Sorrell, Steve & Druckman, Angela & Firth, Steven K. & Jackson, Tim, 2014. "Who rebounds most? Estimating direct and indirect rebound effects for different UK socioeconomic groups," Ecological Economics, Elsevier, vol. 106(C), pages 12-32.
    12. Sorrell, Steve & Dimitropoulos, John & Sommerville, Matt, 2009. "Empirical estimates of the direct rebound effect: A review," Energy Policy, Elsevier, vol. 37(4), pages 1356-1371, April.
    13. Murray, Cameron K., 2013. "What if consumers decided to all ‘go green’? Environmental rebound effects from consumption decisions," Energy Policy, Elsevier, vol. 54(C), pages 240-256.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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, June.
    4. 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).
    5. 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).
    6. 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.
    7. Aslam, Misbah & Ahmad, Eatzaz, 2023. "Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan," Energy, Elsevier, vol. 282(C).
    8. 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).
    9. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Biying Yu & Junyi Zhang & Akimasa Fujiwara, 2016. "Who rebounds in the private transport sector? A comparative analysis between Beijing and Tokyo," Environment and Planning B, , vol. 43(3), pages 561-579, May.
    2. Chitnis, Mona & Sorrell, Steve, 2015. "Living up to expectations: Estimating direct and indirect rebound effects for UK households," Energy Economics, Elsevier, vol. 52(S1), pages 100-116.
    3. Hendrik Schmitz & Reinhard Madlener, 2020. "Heterogeneity in price responsiveness for residential space heating in Germany," Empirical Economics, Springer, vol. 59(5), pages 2255-2281, November.
    4. Tilov, Ivan & Farsi, Mehdi & Volland, Benjamin, 2020. "From frugal Jane to wasteful John: A quantile regression analysis of Swiss households’ electricity demand," Energy Policy, Elsevier, vol. 138(C).
    5. Hache, Emmanuel & Leboullenger, Déborah & Mignon, Valérie, 2017. "Beyond average energy consumption in the French residential housing market: A household classification approach," Energy Policy, Elsevier, vol. 107(C), pages 82-95.
    6. Muller, Christophe, 2018. "Heterogeneity and nonconstant effect in two-stage quantile regression," Econometrics and Statistics, Elsevier, vol. 8(C), pages 3-12.
    7. Volland, Benjamin, 2017. "The role of risk and trust attitudes in explaining residential energy demand: Evidence from the United Kingdom," Ecological Economics, Elsevier, vol. 132(C), pages 14-30.
    8. Christophe Muller, 2019. "Linear Quantile Regression and Endogeneity Correction," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 9(5), pages 123-128, August.
    9. Jamal Bouoiyour & Amal Miftah & Refk Selmi, 2019. "The economic contribution of immigration on Europe: Fresh evidence from a “hybrid” quantile regression model," Working Papers hal-02346700, HAL.
    10. Coyne, Bryan & Lyons, Sean & McCoy, Daire, 2016. "The Effects of Home Energy Efficiency Upgrades on Social Housing Tenants: Evidence from Ireland," Papers WP544, Economic and Social Research Institute (ESRI).
    11. Cristina Bernini & Silvia Emili & Federica Galli, 2021. "Does urbanization matter in the expenditure‐happiness nexus?," Papers in Regional Science, Wiley Blackwell, vol. 100(6), pages 1403-1428, December.
    12. Tae-Hwan Kim & Christophe Muller, 2020. "Inconsistency transmission and variance reduction in two-stage quantile regression," Post-Print hal-02084505, HAL.
    13. Gilles Dufrenot & Valerie Mignon & Charalambos Tsangarides, 2010. "The trade-growth nexus in the developing countries: a quantile regression approach," Review of World Economics (Weltwirtschaftliches Archiv), Springer;Institut für Weltwirtschaft (Kiel Institute for the World Economy), vol. 146(4), pages 731-761, December.
    14. Gabriela Bezerra De Medeiros & Marcelo Savino Portugal & Edilean Kleber Da Silva Bejarano Aragon, 2016. "Endogeneity And Nonlinearities In Central Bank Of Brazil’S Reaction Functions: An Inverse Quantile Regression Approach," Anais do XLIII Encontro Nacional de Economia [Proceedings of the 43rd Brazilian Economics Meeting] 061, ANPEC - Associação Nacional dos Centros de Pós-Graduação em Economia [Brazilian Association of Graduate Programs in Economics].
    15. Gabriela Bezerra Medeiros & Marcelo Savino Portugal & Edilean Kleber da Silva Bejarano Aragón, 2017. "Endogeneity and nonlinearities in Central Bank of Brazil’s reaction functions: an inverse quantile regression approach," Empirical Economics, Springer, vol. 53(4), pages 1503-1527, December.
    16. Belaïd, Fateh & Youssef, Adel Ben & Lazaric, Nathalie, 2020. "Scrutinizing the direct rebound effect for French households using quantile regression and data from an original survey," Ecological Economics, Elsevier, vol. 176(C).
    17. Medina, Eva & Vicéns, José, 2011. "Factores determinantes de la demanda eléctrica de los hogares en España: una aproximación mediante regresión cuantílica/Determinants of Household Electricity Demand in Spain: An Approach through Quant," Estudios de Economia Aplicada, Estudios de Economia Aplicada, vol. 29, pages 515-538, Agosto.
    18. Huang, Wen-Hsiu, 2015. "The determinants of household electricity consumption in Taiwan: Evidence from quantile regression," Energy, Elsevier, vol. 87(C), pages 120-133.
    19. Billger, Sherrilyn M., 2007. "Principals as Agents? Investigating Accountability in the Compensation and Performance of School Principals," IZA Discussion Papers 2662, Institute of Labor Economics (IZA).
    20. Kenichi Mizobuchi & Kenji Takeuchi, 2015. "Replacement or Additional Purchase: The Impact of Energy-Efficient Appliances on Household Electricity Saving," Discussion Papers 1520, Graduate School of Economics, Kobe University.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:taf:applec:v:49:y:2017:i:54:p:5570-5578. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/RAEC20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.