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

TL;DR

This paper presents a method using high-frequency electricity market data to quickly estimate COVID-19's short-term economic impact, providing timely insights beyond traditional delayed statistics.

Contribution

It introduces a novel approach leveraging electricity market data for real-time economic impact assessment of COVID-19, demonstrated on Italian power market data.

Findings

Containment measures significantly reduced economic activity.

GDP was approximately 11% lower in May 2020 due to COVID-19.

Method enables daily monitoring of economic impact.

Abstract

The COVID-19 pandemic has caused more than 8 million confirmed cases and 500,000 death to date. In response to this emergency, many countries have introduced a series of social-distancing measures including lockdowns and businesses' temporary shutdowns, in an attempt to curb the spread of the infection. Accordingly, the pandemic has been generating unprecedent disruption on practically every aspect of society. This paper demonstrates that high-frequency electricity market data can be used to estimate the causal, short-run impact of COVID-19 on the economy. In the current uncertain economic conditions, timeliness is essential. Unlike official statistics, which are published with a delay of a few months, with our approach one can monitor virtually every day the impact of the containment policies, the extent of the recession and measure whether the monetary and fiscal stimuli introduced to address the crisis are being effective. We illustrate our methodology on daily data for the Italian day-ahead power market. Not surprisingly, we find that the containment measures caused a significant reduction in economic activities and that the GDP at the end of in May 2020 is still about 11% lower that what it would have been without the outbreak.