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Electricity Use as an Indicator of U.S. Economic Activity

Author

Listed:
  • Arora, Vipin
  • Lieskovsky, Jozef

Abstract

We argue for the resurrection of an old idea: electricity use as an indicator of U.S. economic activity. Our analysis relies on associations–the 40-year correlation between growth rates in real GDP and electricity use can be as high as 89% –and intuition. Electricity use and economic conditions should move together. The vast majority of goods and services are still produced using electricity; services may require less electricity, but they still require some. Electricity use also has other strengths –it is broad-based and the data are available weekly, possibly hourly by 2015.

Suggested Citation

  • Arora, Vipin & Lieskovsky, Jozef, 2016. "Electricity Use as an Indicator of U.S. Economic Activity," EconStor Research Reports 126147, ZBW - Leibniz Information Centre for Economics.
    • Handle: RePEc:zbw:esrepo:126147
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    References listed on IDEAS

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    3. Kajal Lahiri & Wenxiong Yao, 2012. "Should transportation output be included as part of the coincident indicators system?," OECD Journal: Journal of Business Cycle Measurement and Analysis, OECD Publishing, Centre for International Research on Economic Tendency Surveys, vol. 2012(1), pages 1-24.
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    Cited by:

    1. Changkeun LEE & Olivia Hye KIM & Hwanoong LEE, 2026. "How will automation reshape worker well-being? Evidence from a highly automated economy," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 13(1), pages 1-9, December.
    2. Chen, Haitao & Zhang, Bin & Liu, Hua & Cao, Jiguo, 2024. "The inequality in household electricity consumption due to temperature change: Data driven analysis with a function-on-function linear model," Energy, Elsevier, vol. 288(C).
    3. Jinning Wang & Fangxing Li & Hantao Cui & Qingxin Shi & Trey Mingee, 2022. "Electricity consumption variation versus economic structure during COVID-19 on metropolitan statistical areas in the US," Nature Communications, Nature, vol. 13(1), pages 1-11, December.
    4. Jaewon Chung & Beakcheol Jang, 2022. "Accurate prediction of electricity consumption using a hybrid CNN-LSTM model based on multivariable data," PLOS ONE, Public Library of Science, vol. 17(11), pages 1-16, November.

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    Keywords

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    JEL classification:

    • E32 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Business Fluctuations; Cycles
    • E37 - Macroeconomics and Monetary Economics - - Prices, Business Fluctuations, and Cycles - - - Forecasting and Simulation: Models and Applications
    • Q43 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy and the Macroeconomy

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