Tracking GDP in real-time using electricity market data: insights from the first wave of COVID-19 across Europe

TL;DR

This paper introduces a real-time GDP tracking method using electricity market data, demonstrating its accuracy during COVID-19's first wave in Europe and highlighting policy implications.

Contribution

It presents a novel methodology for real-time GDP estimation from high-frequency electricity data, validated during COVID-19, offering more timely insights than traditional indicators.

Findings

GDP estimates closely match official statistics (correlation 0.98)

Delaying interventions increased economic and health impacts

International policy coordination reduces spillover effects

Abstract

This paper develops a methodology for tracking in real time the impact of shocks (such as natural disasters, financial crises or pandemics) on gross domestic product (GDP) by analyzing high-frequency electricity market data. As an illustration, we estimate the GDP loss caused by COVID-19 in twelve European countries during the first wave of the pandemic. Our results are almost indistinguishable from the official statistics of the recession during the first two quarters of 2020 (correlation coefficient of 0.98) and are validated by several robustness tests. However, they are also more chronologically disaggregated and up-to-date than standard macroeconomic indicators and, therefore, can provide crucial and timely information for policy evaluation. Our results show that delaying intervention and pursuing 'herd immunity' have not been successful strategies so far, since they increased both economic disruption and mortality. We also find that coordinating policies internationally is fundamental for minimizing spillover effects from NPIs across countries.