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Understanding segmentation in rural electricity markets: Evidence from India

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  • Robert Thomas, Daniel
  • Agrawal, Shalu
  • Harish, S.P.
  • Mahajan, Aseem
  • Urpelainen, Johannes

Abstract

How can demand for electricity be estimated without fine-grained usage data? Employing an original and large dataset, we develop a novel method for determining drivers of demand without electricity meter data. We first segment Indian consumers by their willingness to pay for electricity service, their level of usage, and their satisfaction with lighting, and then use cluster membership as a dependent variable in order to determine which household-level factors predict electricity usage. Our approach employs machine-learning and more traditional regression techniques to determine the optimal number of segments, generate the segments, and determine the predictors of segment membership. The dataset consists of more than 10,000 households in more than 200 villages in the states of Bihar, Odisha, Rajasthan, and Uttar Pradesh. We find that the rural Indian electricity market can be segmented into three clusters based on households' willingness to pay, satisfaction with lighting, and appliance wattage. The clusters consist of potential customers, low-demand customers, and high-use customers. We then determine the predictors of membership in these clusters. We show that different types of consumers can be identified along easily observable measures. Moreover, we show that there are clear groups of consumers that vary along their satisfaction, willingness to pay, and existing appliance usage.

Suggested Citation

  • Robert Thomas, Daniel & Agrawal, Shalu & Harish, S.P. & Mahajan, Aseem & Urpelainen, Johannes, 2020. "Understanding segmentation in rural electricity markets: Evidence from India," Energy Economics, Elsevier, vol. 87(C).
  • Handle: RePEc:eee:eneeco:v:87:y:2020:i:c:s0140988320300360
    DOI: 10.1016/j.eneco.2020.104697
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    References listed on IDEAS

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    Cited by:

    1. Syed Hasan & Odmaa Narantungalag, & Martin Berka, 2022. "The intended and unintended consequences of large electricity subsidies: evidence from Mongolia," Discussion Papers 2202, School of Economics and Finance, Massey University, New Zealand.
    2. Majid Hashemi, 2021. "The Effect of Reliability Improvements on Household Electricity Consumption and Coping Behavior: A Multi-dimensional Approach," Working Paper 1469, Economics Department, Queen's University.

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    More about this item

    Keywords

    Electricity; Energy; Demand; India; Clustering; Segmentation; Rural development;
    All these keywords.

    JEL classification:

    • Q40 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - General
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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