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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