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Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe

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  • Fezzi, Carlo
  • Fanghella, Valeria

Abstract

This paper develops a methodology for tracking in real-time the impact of shocks (such as natural disasters, financial crises or pandemics) on gross domestic product (GDP) by analyzing high-frequency electricity market data. As an illustration, we estimate the GDP loss caused by COVID-19 in twelve European countries during the first wave of the pandemic. Our results are almost indistinguishable from the official statistics during the first two quarters of 2020 (the correlation coefficient is 0.98) and are validated by several robustness tests. We provide estimates that are more chronologically disaggregated and up-to-date than standard macroeconomic indicators and, therefore, can provide timely information for policy evaluation in time of crisis. Our results show that pursuing “herd immunity” did not shelter from the harmful economic impacts of the first wave of the pandemic. They also suggest that coordinating policies internationally is fundamental for minimizing spillover effects from non-pharmaceutical interventions across countries.

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  • Fezzi, Carlo & Fanghella, Valeria, 2021. "Tracking GDP in real-time using electricity market data: Insights from the first wave of COVID-19 across Europe," European Economic Review, Elsevier, vol. 139(C).
    • Handle: RePEc:eee:eecrev:v:139:y:2021:i:c:s0014292121002178
    DOI: 10.1016/j.euroecorev.2021.103907
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    Cited by:

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    4. Xiaoyan Mu & Xiaohu Zhang & Anthony Gar-On Yeh & Yang Yu & Jiejing Wang, 2023. "Structural Changes in Human Mobility Under the Zero-COVID Strategy in China," Environment and Planning B, , vol. 50(9), pages 2527-2542, November.
    5. Hirth, Lion & Khanna, Tarun M. & Ruhnau, Oliver, 2024. "How aggregate electricity demand responds to hourly wholesale price fluctuations," Energy Economics, Elsevier, vol. 135(C).
    6. Costa, Vinicius B.F. & Pereira, Lígia C. & Andrade, Jorge V.B. & Bonatto, Benedito D., 2022. "Future assessment of the impact of the COVID-19 pandemic on the electricity market based on a stochastic socioeconomic model," Applied Energy, Elsevier, vol. 313(C).
    7. Sam Jones & Ivan Manhique, 2022. "Digital labour platforms as shock absorbers: Evidence from COVID-19," WIDER Working Paper Series wp-2022-108, World Institute for Development Economic Research (UNU-WIDER).
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    9. Caixia Wang & Huijie Li, 2022. "Public Compliance Matters in Evidence-Based Public Health Policy: Evidence from Evaluating Social Distancing in the First Wave of COVID-19," IJERPH, MDPI, vol. 19(7), pages 1-13, March.

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    More about this item

    Keywords

    COVID-19; Economic impact; Mortality; Electricity demand; Real-time indicators;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • E01 - Macroeconomics and Monetary Economics - - General - - - Measurement and Data on National Income and Product Accounts and Wealth; Environmental Accounts
    • L94 - Industrial Organization - - Industry Studies: Transportation and Utilities - - - Electric Utilities

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