IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/103403.html
   My bibliography  Save this paper

A simulation-based estimation model of household electricity demand and appliance ownership

Author

Listed:
  • Poblete-Cazenave, Miguel
  • Pachauri, Shonali

Abstract

Understanding how electricity demand is likely to rise once households gain access to it is important to policy makers and planners alike. Current approaches to estimate the latent demand of unelectrified populations usually assume constant elasticity of demand. Here we use a simulation-based structural estimation approach, employing micro-data from household surveys for four developing nations, to estimate responsiveness of electricity demand and appliance ownership to income considering changes both on the intensive and extensive margin. We find significant heterogeneity in household response to income changes, which suggest that assuming a non-varying elasticity can result in biased estimates of demand. Our results confirm that neglecting heterogeneity in individual behavior and responses can result in biased demand estimates.

Suggested Citation

  • 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.
    • Handle: RePEc:pra:mprapa:103403
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/103403/1/MPRA_paper_103403.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Catherine Wolfram & Orie Shelef & Paul Gertler, 2012. "How Will Energy Demand Develop in the Developing World?," Journal of Economic Perspectives, American Economic Association, vol. 26(1), pages 119-138, Winter.
    2. Bente Halvorsen & Bodil M. Larsen, 2013. "How serious is the aggregation problem? An empirical illustration," Applied Economics, Taylor & Francis Journals, vol. 45(26), pages 3786-3794, September.
    3. François Lescaroux, 2012. "Household electricity demand, worldwide: climbing the ‘ladder of needs’," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 36(3), pages 247-271, September.
    4. Arthur, Maria de Fátima S.R. & Bond, Craig A. & Willson, Bryan, 2012. "Estimation of elasticities for domestic energy demand in Mozambique," Energy Economics, Elsevier, vol. 34(2), pages 398-409.
    5. Sowmya Dhanaraj & Vidya Mahambare & Poonam Munjal, 2018. "From Income to Household Welfare: Lessons from Refrigerator Ownership in India," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 16(2), pages 573-588, June.
    6. Pachauri, Shonali & Jiang, Leiwen, 2008. "The household energy transition in India and China," Energy Policy, Elsevier, vol. 36(11), pages 4022-4035, November.
    7. Paul J. Gertler & Orie Shelef & Catherine D. Wolfram & Alan Fuchs, 2016. "The Demand for Energy-Using Assets among the World's Rising Middle Classes," American Economic Review, American Economic Association, vol. 106(6), pages 1366-1401, June.
    8. Zhou, Shaojie & Teng, Fei, 2013. "Estimation of urban residential electricity demand in China using household survey data," Energy Policy, Elsevier, vol. 61(C), pages 394-402.
    9. Samad,Hussain A. & Zhang,Fan, 2018. "Electrification and Household Welfare : Evidence from Pakistan," Policy Research Working Paper Series 8582, The World Bank.
    10. Dubin, Jeffrey A & McFadden, Daniel L, 1984. "An Econometric Analysis of Residential Electric Appliance Holdings and Consumption," Econometrica, Econometric Society, vol. 52(2), pages 345-362, March.
    11. Maximilian Auffhammer & Catherine D. Wolfram, 2014. "Powering Up China: Income Distributions and Residential Electricity Consumption," American Economic Review, American Economic Association, vol. 104(5), pages 575-580, May.
    12. Filippini, Massimo & Pachauri, Shonali, 2004. "Elasticities of electricity demand in urban Indian households," Energy Policy, Elsevier, vol. 32(3), pages 429-436, February.
    13. Twerefou, Daniel Kwabena & Abeney, Jacob Opantu, 2020. "Efficiency of household electricity consumption in Ghana," Energy Policy, Elsevier, vol. 144(C).
    14. Richard Blundell & Joel Horowitz & Matthias Parey, 2017. "Nonparametric Estimation of a Nonseparable Demand Function under the Slutsky Inequality Restriction," The Review of Economics and Statistics, MIT Press, vol. 99(2), pages 291-304, May.
    15. Yanan Liu & Yixuan Gao & Yu Hao & Hua Liao, 2016. "The Relationship between Residential Electricity Consumption and Income: A Piecewise Linear Model with Panel Data," Energies, MDPI, vol. 9(10), pages 1-11, October.
    16. Jay Taneja, 2018. "If You Build It, Will They Consume? Key Challenges for Universal, Reliable, and Low-Cost Electricity Delivery in Kenya," Working Papers 491, Center for Global Development.
    17. van Ruijven, Bas J. & Schers, Jules & van Vuuren, Detlef P., 2012. "Model-based scenarios for rural electrification in developing countries," Energy, Elsevier, vol. 38(1), pages 386-397.
    18. 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.
    19. Dagnachew, Anteneh G. & Lucas, Paul L. & Hof, Andries F. & van Vuuren, Detlef P., 2018. "Trade-offs and synergies between universal electricity access and climate change mitigation in Sub-Saharan Africa," Energy Policy, Elsevier, vol. 114(C), pages 355-366.
    Full references (including those not matched with items on IDEAS)

    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. 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).
    2. Falchetta, Giacomo & Mistry, Malcolm N., 2021. "The role of residential air circulation and cooling demand for electrification planning: Implications of climate change in sub-Saharan Africa," Energy Economics, Elsevier, vol. 99(C).
    3. Debnath, Ramit & Bardhan, Ronita & Sunikka-Blank, Minna, 2019. "How does slum rehabilitation influence appliance ownership? A structural model of non-income drivers," Energy Policy, Elsevier, vol. 132(C), pages 418-428.
    4. Huse, Cristian & Lucinda, Claudio & Cardoso, Andre Ribeiro, 2020. "Consumer response to energy label policies: Evidence from the Brazilian energy label program," Energy Policy, Elsevier, vol. 138(C).
    5. Richmond, Jennifer & Urpelainen, Johannes, 2019. "Electrification and appliance ownership over time: Evidence from rural India," Energy Policy, Elsevier, vol. 133(C).
    6. Davis, Lucas & Martinez, Sebastian & Taboada, Bibiana, 2018. "How Effective is Energy-efficient Housing?: Evidence From a Field Experiment in Mexico," IDB Publications (Working Papers) 8767, Inter-American Development Bank.
    7. Pablo-Romero, María del P. & Pozo-Barajas, Rafael & Yñiguez, Rocío, 2017. "Global changes in residential energy consumption," Energy Policy, Elsevier, vol. 101(C), pages 342-352.
    8. Shigeru Matsumoto, 2015. "Electric Appliance Ownership and Usage: Application of Conditional Demand Analysis to Japanese Household Data," Working Papers e098, Tokyo Center for Economic Research.
    9. Pereira Uhr, Daniel de Abreu & Squarize Chagas, André Luis & Ziero Uhr, Júlia Gallego, 2019. "Estimation of elasticities for electricity demand in Brazilian households and policy implications," Energy Policy, Elsevier, vol. 129(C), pages 69-79.
    10. Aslam, Misbah & Ahmad, Eatzaz, 2023. "Untangling electricity demand elasticities: Insights from heterogeneous household groups in Pakistan," Energy, Elsevier, vol. 282(C).
    11. Huse, Cristian & Lucinda, Claudio & Ribeiro, Andre, 2021. "Assessing the effects of a large temporary energy savings program: Evidence from a developing country," Journal of Environmental Economics and Management, Elsevier, vol. 110(C).
    12. Li, Chuan-Zhong & Wei, Chu & Yu, Yang, 2020. "Income threshold, household appliance ownership and residential energy consumption in urban China," China Economic Review, Elsevier, vol. 60(C).
    13. Çetinkaya, Murat & Başaran, Alparslan A. & Bağdadioğlu, Necmiddin, 2015. "Electricity reform, tariff and household elasticity in Turkey," Utilities Policy, Elsevier, vol. 37(C), pages 79-85.
    14. Cao, Jing & Ho, Mun Sing & Li, Yating & Newell, Richard G. & Pizer, William A., 2019. "Chinese residential electricity consumption: Estimation and forecast using micro-data," Resource and Energy Economics, Elsevier, vol. 56(C), pages 6-27.
    15. Liddle, Brantley & Huntington, Hillard, 2021. "How prices, income, and weather shape household electricity demand in high-income and middle-income countries," Energy Economics, Elsevier, vol. 95(C).
    16. Daniel de Abreu Pereira Uhr & Júlia Gallego Ziero Uhr, André Luis Squarize Chagas, 2017. "Estimation of price and income elasticities for the Brazilian household electricity demand," Working Papers, Department of Economics 2017_12, University of São Paulo (FEA-USP).
    17. Li, Yating & Fei, Yinxin & Zhang, Xiao-Bing & Qin, Ping, 2019. "Household appliance ownership and income inequality: Evidence from micro data in China," China Economic Review, Elsevier, vol. 56(C), pages 1-1.
    18. Amoako, Samuel & Andoh, Francis Kwaw & Asmah, Emmanuel Ekow, 2023. "Household structure and electricity consumption in Ghana," Energy Policy, Elsevier, vol. 182(C).
    19. Matsumoto, Shigeru, 2016. "How do household characteristics affect appliance usage? Application of conditional demand analysis to Japanese household data," Energy Policy, Elsevier, vol. 94(C), pages 214-223.
    20. Agrawal, Shalu & Harish, S.P. & Mahajan, Aseem & Thomas, Daniel & Urpelainen, Johannes, 2020. "Influence of improved supply on household electricity consumption - Evidence from rural India," Energy, Elsevier, vol. 211(C).

    More about this item

    Keywords

    Energy Access; Household Energy Demand; Appliances Uptake; Simulation-based Econometrics; Scenario Analysis;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • D12 - Microeconomics - - Household Behavior - - - Consumer Economics: Empirical Analysis
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy

    NEP fields

    This paper has been announced in the following NEP Reports:

    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:pra:mprapa:103403. 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: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    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.