Trento
29 Settembre 2021

Real-time estimation of the short-run impact of COVID-19 on economic activity using electricity market data

Tracking GDP in real-time using electricity market data: insights from the first wave of COVID-19 across Europe”. The study published on European Economic Review 

Electricity market data can play a vital role in measuring the impact of sudden economic shocks, like COVID-19 and related lockdown policies, according to a new study. 
Researchers from University of Trento have pioneered a generalized methodology for tracking in real-time the impact of shocks on economic activity by using publicly available electricity consumption data. Their methodology builds upon and significantly extends the approach they introduced in a paper published last year (Fezzi and Fanghella, 2020), that studied the effect of the COVID-19 pandemic on the Italian GDP

This new approach is widely applicable for the cross-country comparisons of the economic disruption caused by the COVID-19 pandemic, but can also be applied to monitor the health of the economy under more general shocks, such as financial crises and natural disasters.
“Official GDP statistics are typically released with a delay of three months and in aggregate form. Therefore, they are not timely enough to provide information in time of crisis” said lead author Prof. Carlo Fezzi, of the Department of Economics of the University of Trento “Our approach takes advantage of the fact that electricity contributes to virtually every human activity, and that information on electricity consumption is reported in real-time in most nations and regions in the world. Our estimates for the first wave of COVID-19 across Europe are almost indistinguishable from official OECD statistics but, of course, do not suffer from the three-months delay problem and are available at the weekly level rather than quarterly”. 
The researchers apply their methodology to twelve European countries using data from the European day-ahead power market where electricity is typically traded on an hourly basis. They select countries to ensure heterogeneity in terms of severity of virus outbreaks and strength and timing of policies implementation, and compare their GDP estimates with mortality data. Doing so enables the authors to discuss the trade-offs (or lack thereof) between financial and public health costs.
First, they find that countries that experienced the most severe initial outbreaks (e.g. Italy, Spain) also grappled with some of the hardest economic recessions. Second, countries with low initial exposure that introduced early and relatively less stringent containment policies (e.g., Denmark, Norway) experienced low financial and mortality impacts. Finally, nations that failed to coordinate with others, and at least initially tried to pursue a “herd immunity” strategy (e.g., Great Britain and Sweden), underperformed under both perspectives. 
Dr. Valeria Fanghella, Researcher at the Grenoble Ecole de Management, commented: “Taken together, our findings suggest that not implementing any lockdown does not protect from economic recession, since supply and demand shifts affect countries regardless of their policies. The introduction of early and relatively less stringent containment policies coordinated among neighboring countries appears to have been the most effective strategy to minimize both financial and mortality impacts during the first wave of the COVID-19 pandemic in Europe.”

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This research is part of the OCEN-COVID19 project, funded by the University of Trento under the “Covid 19” research fund. It is currently being extended by a team of researchers at the University of Trento, which continues to monitor the impact of the pandemic on economic activities in real-time. For more information see the website of the Observatory on COVID-19, Economics, and Energy Transition 

“Tracking GDP in real-time using electricity market data: insights from the first wave of COVID-19 across Europe”, European Economic Review DOI: https://doi.org/10.1016/j.euroecorev.2021.103907

Info: https://oceet.unitn.it/