Researchers and students at the University of Exeter Business School developed a way to measure the immediate impact of COVID-19 lockdowns on an economy’s Gross Domestic Product, or GDP. To calculate this data, the scientists analyzed hourly electricity consumption rates during Italy’s spring lockdown to measure the impact of those activities on their economy during that time. Traditional metrics and official statistics take at least three months to calculate and will not remove other factors affecting consumption and production.

Data was analyzed from the Italian power market from January 2015 through June 2020. The results gave the researchers an idea of what the electrical consumption would have been if not for COVID-19 restrictions. The hypothetical model was then cross-referenced with actual consumption rates during and after the lockdown. The end result was then rescaled to focus on the implications for GDP.

In the first quarter, Italy’s GDP fell 5.1 percent, and during the most intense three-week lockdown period (March and April, when many factories were also shut down), the researchers calculated a reduction in the GDP by about 30 percent.

As Italy gradually reopened socially and economically, the GDP was still about 8.5 percent lower than it would have been without the outbreak at the end of June.

Several caveats were noted, including possible shortcomings of solely examining a short amount of time versus a more extended period. The lengthier timeframe would include added influences from other national procedures, as well as potential spillover effects from pandemic policies. Moreover, as adaptation strategies such as working from home are implemented, the researchers noted, “The effect of future lockdown restrictions may become less severe. This consideration has to be balanced against the potential for long-term and repeated lockdowns exerting cumulative and progressively more adverse economic effects, including through the impacts on human wellbeing.”

Read the complete research study conducted by students at the University of Exeter Business School for a detailed examination of the mathematical formula implemented to calculate the data.
 

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