A Novel Near Real-time System to Estimate Impact of Sudden Economic Shocks (Such as COVID-19) on the Economy

As the novel coronavirus progressed from Wuhan to various parts of the globe, governments were faced with a tradeoff: to go into lockdown to save lives but hurt the economy, or stay open to save the economy but lose lives. At the time, those in favour of the lockdown had empirical data to show the lethality of the virus and the need to go into lockdown. Whereas those against lockdowns didn’t have data to show the severe reduction in economic activity that would follow the lockdown. Hence, numerous governments enforced lockdowns and realized the need to re-open much after the damage had already been done. This delayed realization was purely due to the lack of a real-time system to quantitatively gauge the economic impact of various policies. In this paper, we develop a near real-time framework to estimate the economic impact of such sudden shocks at any geographical level (city, state or country). We use novel data sources, specifically satellite recorded Night Light and Electricity Consumption data. Such alternate sources are advantageous as they can answer economic questions related to isolated locations and the informal economy (a sizable constituent of developing economies). We demonstrate the applicability of our framework across a variety of cities across countries (both developing and developed) and present our results specifically for the COVID-19 pandemic. We quantitatively estimate the economic decline in various cities, how government policies affected this decline, and the impact on intercity transport. Using Night Light, we estimated a GDP decline of 12.3% for New York and Miami (Apr - June quarter), and the U.S. government reported 7.2% at the country level. This difference is expected since these would have had a higher decline than the overall country. Moreover, our framework could make these predictions one month before the official numbers were released, reflecting the framework’s accuracy and real-time nature. This framework could revolutionize the way governments, policymakers and stakeholders receive economic data, leading to more rational and proactive decision making.