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Electricity supply reliability and households decision to connect to the grid

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Abstract

The 7th Sustainable Development Goal aims to "ensure access to affordable, reliable, sustainable and modern energy for all". Because the cost to increase electrical capacity in Africa alone has been estimated at $800bn, this article investigates the extent to which electricity reliability could contribute to a reduction in the marginal cost of grid extension by attracting more customers. Using lightning as an instrument for outages severity, the article evaluates the assumption that less uncertainty about electricity availability would lead to a larger number of connected households. The article finds that a one percentage point increase in electricity reliability would yield a 0.67 percentage point increase in connections. Therefore, delivering fully reliable electrical power would allow an electricity company to achieve its targeted growth of customer base 15 months earlier than planned. The effect of reliability is highest for middle-rich households, which are the most reluctant to subscribe in the presence of total, severe or partial outages. A one-percentage-point upgrade in reliability increase the likelihood that these households will be connected by 1.28 percentage points. This article also finds that households are more sensitive to outages in areas where outages are less frequent. In addiction, the impact of reliability on households decision to connect could be at least 5% greater than the effect of poverty; if the frequency of outages is too high, the wealth or poverty effect might vanish and households would respond only to the excessively low reliability. These results confirm the uncertainty assumption, that is, regular and severe outages yield an uninsurable context that deters households from subscribing to the electric service

Suggested Citation

  • Arnaud Millien, 2017. "Electricity supply reliability and households decision to connect to the grid," Documents de travail du Centre d'Economie de la Sorbonne 17031, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
  • Handle: RePEc:mse:cesdoc:17031
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    File URL: ftp://mse.univ-paris1.fr/pub/mse/CES2017/17031.pdf
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    Cited by:

    1. Abiodun, Kehinde & Gilbert, Ben, 2022. "Does universal electrification shield firms from productivity loss?," World Development Perspectives, Elsevier, vol. 28(C).
    2. Le Viet Phu, 2020. "Electricity price and residential electricity demand in Vietnam," Environmental Economics and Policy Studies, Springer;Society for Environmental Economics and Policy Studies - SEEPS, vol. 22(4), pages 509-535, October.
    3. Kennedy, Ryan & Mahajan, Aseem & Urpelainen, Johannes, 2019. "Quality of service predicts willingness to pay for household electricity connections in rural India," Energy Policy, Elsevier, vol. 129(C), pages 319-326.
    4. Bajo-Buenestado, Raúl, 2021. "The effect of blackouts on household electrification status: Evidence from Kenya," Energy Economics, Elsevier, vol. 94(C).
    5. Enrico Nano, 2022. "Electrifying Nigeria: the Impact of Rural Access to Electricity on Kids' Schooling," IHEID Working Papers 03-2022, Economics Section, The Graduate Institute of International Studies.

    More about this item

    Keywords

    electrification; reliability; outages; Kenya; instrumental variable;
    All these keywords.

    JEL classification:

    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q01 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General - - - Sustainable Development
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure
    • O55 - Economic Development, Innovation, Technological Change, and Growth - - Economywide Country Studies - - - Africa
    • C26 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Instrumental Variables (IV) Estimation
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection

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