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Understanding electricity billing preferences in rural and urban India: Evidence from a conjoint experiment

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  • Wong, Jason Chun Yu
  • Blankenship, Brian
  • Urpelainen, Johannes
  • Balani, Kanika
  • Ganesan, Karthik
  • Bharadwaj, Kapardhi

Abstract

To address issues of non-payment, high costs, and theft, paying a fixed fee for electricity is common among many developing countries. We use a conjoint experiment to study electricity billing preferences among urban and rural communities in Uttar Pradesh, India. We find that 59.5% of respondents (95% CI: 58.2%–60.9%) prefer consumption-based tariffs as opposed to fixed fee ones, favoring lower base charges among a number of factors. We additionally use Bayesian Additive Regression Trees to test for heterogeneous treatment effects. Respondents with more appliances, using more hours of electricity, and who live in rural areas with meters prefer consumption-based plans with lower base rates. Our results suggest that policy reforms should move beyond fixed rate schemes especially if respondents would accept higher unit tariffs with improved service.

Suggested Citation

  • Wong, Jason Chun Yu & Blankenship, Brian & Urpelainen, Johannes & Balani, Kanika & Ganesan, Karthik & Bharadwaj, Kapardhi, 2022. "Understanding electricity billing preferences in rural and urban India: Evidence from a conjoint experiment," Energy Economics, Elsevier, vol. 106(C).
    • Handle: RePEc:eee:eneeco:v:106:y:2022:i:c:s0140988321005831
    DOI: 10.1016/j.eneco.2021.105735
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    as
    1. Gilbert, Ben & Graff Zivin, Joshua, 2014. "Dynamic salience with intermittent billing: Evidence from smart electricity meters," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 176-190.
    2. Thaler, Richard H, 1990. "Saving, Fungibility, and Mental Accounts," Journal of Economic Perspectives, American Economic Association, vol. 4(1), pages 193-205, Winter.
    3. Shawn Cole & Xavier Gine & Jeremy Tobacman & Petia Topalova & Robert Townsend & James Vickery, 2013. "Barriers to Household Risk Management: Evidence from India," American Economic Journal: Applied Economics, American Economic Association, vol. 5(1), pages 104-135, January.
    4. Bhattacharyya, Ranajoy & Ganguly, Amrita, 2017. "Cross subsidy removal in electricity pricing in India," Energy Policy, Elsevier, vol. 100(C), pages 181-190.
    5. Hunt Allcott, 2011. "Consumers' Perceptions and Misperceptions of Energy Costs," American Economic Review, American Economic Association, vol. 101(3), pages 98-104, May.
    6. Mwaura, Francis M., 2012. "Adopting electricity prepayment billing system to reduce non-technical energy losses in Uganda: Lesson from Rwanda," Utilities Policy, Elsevier, vol. 23(C), pages 72-79.
    7. Wichman, Casey J., 2014. "Perceived price in residential water demand: Evidence from a natural experiment," Journal of Economic Behavior & Organization, Elsevier, vol. 107(PA), pages 308-323.
    8. O'Sullivan, Kimberley C. & Howden-Chapman, Philippa L. & Fougere, Geoff, 2011. "Making the connection: The relationship between fuel poverty, electricity disconnection, and prepayment metering," Energy Policy, Elsevier, vol. 39(2), pages 733-741, February.
    9. Susan Athey & Guido W. Imbens, 2019. "Machine Learning Methods That Economists Should Know About," Annual Review of Economics, Annual Reviews, vol. 11(1), pages 685-725, August.
    10. Michael D. Grubb, 2015. "Consumer Inattention and Bill-Shock Regulation," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 82(1), pages 219-257.
    11. Chattopadhyay, Pradip, 2004. "Cross-subsidy in electricity tariffs: evidence from India," Energy Policy, Elsevier, vol. 32(5), pages 673-684, March.
    12. Wichman, Casey J., 2017. "Information provision and consumer behavior: A natural experiment in billing frequency," Journal of Public Economics, Elsevier, vol. 152(C), pages 13-33.
    13. O'Sullivan, Kimberley C. & Howden-Chapman, Philippa L. & Fougere, Geoffrey M. & Hales, Simon & Stanley, James, 2013. "Empowered? Examining self-disconnection in a postal survey of electricity prepayment meter consumers in New Zealand," Energy Policy, Elsevier, vol. 52(C), pages 277-287.
    14. C. Chris Ofonyelu & Eguabor, R. E., 2014. "Metered and Unmetered Billing: How Asymmetric are the Phcn Bills?," Journal of Social Economics Research, Conscientia Beam, vol. 1(5), pages 97-107.
    15. Koichiro Ito, 2014. "Do Consumers Respond to Marginal or Average Price? Evidence from Nonlinear Electricity Pricing," American Economic Review, American Economic Association, vol. 104(2), pages 537-563, February.
    16. Jacome, Veronica & Ray, Isha, 2018. "The prepaid electric meter: Rights, relationships and reification in Unguja, Tanzania," World Development, Elsevier, vol. 105(C), pages 262-272.
    17. Idalina Baptista, 2015. "‘We Live on Estimates': Everyday Practices of Prepaid Electricity and the Urban Condition in Maputo, Mozambique," International Journal of Urban and Regional Research, Wiley Blackwell, vol. 39(5), pages 1004-1019, September.
    18. Aklin, Michaël & Bayer, Patrick & Harish, S.P. & Urpelainen, Johannes, 2015. "Quantifying slum electrification in India and explaining local variation," Energy, Elsevier, vol. 80(C), pages 203-212.
    19. Michaël Aklin & Chao-yo Cheng & Johannes Urpelainen & Karthik Ganesan & Abhishek Jain, 2016. "Factors affecting household satisfaction with electricity supply in rural India," Nature Energy, Nature, vol. 1(11), pages 1-6, November.
    20. Shaun McRae, 2015. "Infrastructure Quality and the Subsidy Trap," American Economic Review, American Economic Association, vol. 105(1), pages 35-66, January.
    21. Sendhil Mullainathan & Jann Spiess, 2017. "Machine Learning: An Applied Econometric Approach," Journal of Economic Perspectives, American Economic Association, vol. 31(2), pages 87-106, Spring.
    22. Brent, Daniel A. & Ward, Michael B., 2019. "Price perceptions in water demand," Journal of Environmental Economics and Management, Elsevier, vol. 98(C).
    23. Carroll, James & Lyons, Seán & Denny, Eleanor, 2014. "Reducing household electricity demand through smart metering: The role of improved information about energy saving," Energy Economics, Elsevier, vol. 45(C), pages 234-243.
    24. Doll, Christopher N.H. & Pachauri, Shonali, 2010. "Estimating rural populations without access to electricity in developing countries through night-time light satellite imagery," Energy Policy, Elsevier, vol. 38(10), pages 5661-5670, October.
    25. Blankenship, Brian & Wong, Jason Chun Yu & Urpelainen, Johannes, 2019. "Explaining willingness to pay for pricing reforms that improve electricity service in India," Energy Policy, Elsevier, vol. 128(C), pages 459-469.
    26. Oseni, Musiliu O., 2015. "Assessing the consumers’ willingness to adopt a prepayment metering system in Nigeria," Energy Policy, Elsevier, vol. 86(C), pages 154-165.
    27. Athey, Susan & Imbens, Guido W., 2019. "Machine Learning Methods Economists Should Know About," Research Papers 3776, Stanford University, Graduate School of Business.
    28. Balachandra, P., 2011. "Dynamics of rural energy access in India: An assessment," Energy, Elsevier, vol. 36(9), pages 5556-5567.
    29. C Chris Ofonyelu & Eguabor R. E, 2014. "Metered and Unmetered Billing: How Asymmetric are the Phcn Bills?," Journal of Social Economics Research, Conscientia Beam, vol. 1(5), pages 97-107.
    30. Pepermans, Guido, 2014. "Valuing smart meters," Energy Economics, Elsevier, vol. 45(C), pages 280-294.
    31. Telles Esteves, Gheisa Roberta & Cyrino Oliveira, Fernando Luiz & Antunes, Carlos Henggeler & Souza, Reinaldo Castro, 2016. "An overview of electricity prepayment experiences and the Brazilian new regulatory framework," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 704-722.
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    More about this item

    Keywords

    Billing; Consumer preferences; Rural–urban differences; Universal access;
    All these keywords.

    JEL classification:

    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • O21 - Economic Development, Innovation, Technological Change, and Growth - - Development Planning and Policy - - - Planning Models; Planning Policy
    • C83 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Survey Methods; Sampling Methods
    • Q48 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Government Policy
    • D10 - Microeconomics - - Household Behavior - - - General
    • H31 - Public Economics - - Fiscal Policies and Behavior of Economic Agents - - - Household

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