Predicting Covid-19's impact on GDP: how can high-frequency data help us?

The purpose of Bank Overground is to share our internal analysis. Each bite-sized post summarises a piece of analysis that supported a policy or operational decision.
Published on 19 October 2020
Governments’ restrictions related to Covid-19, and households’ voluntary efforts to lessen health risks, are affecting economic activity. High-frequency measures of formal restrictions and household mobility can help assess Covid-19’s impact on GDP.

Across a wide range of countries, we have daily indices measuring the stringency of restrictions on activity imposed by governments to contain the spread of Covid-19. These indices are produced by Oxford University’s Coronavirus Government Response Tracker. Broadly speaking, GDP fell by more in countries with more restrictions (Chart A).

For example, China imposed severe restrictions early in the year and GDP fell by around 11% in Q1. Reflecting the later spread of the virus, other countries did not impose restrictions until towards the end of Q1 and so GDP fell more modestly.

We also have daily measures of the mobility of populations, sourced from Apple Mobility Trends Reports. This complements measures of formal government restrictions because households voluntarily restrict their activities in response to the health risks. Falls in GDP were greater in countries where households were less mobile (Chart B).

This very timely information about formal rules and mobility across countries can be combined in a model to generate predictions for GDP growth. The model controls for a range of country-specific characteristics, which can affect the impact on GDP of policy or mobility trends.

As the virus spread, most countries imposed the tightest restrictions early in Q2 and mobility fell sharply (red observations in Charts A and B). Our model gave an early indication in June of the much sharper falls in GDP in Q2, confirmed by official estimates first published in August. It also correctly predicted that GDP would fall by much more in the United Kingdom than in Germany for example, where restrictions remained in place for a shorter period of time, allowing mobility to recover during Q2.

With the easing of restrictions on activity and increases in mobility since around May in many countries, the model predicts a sharp rebound in GDP in Q3, consistent with the August Monetary Policy Report. This type of model can complement other high-frequency indicators on spending and more traditional measures of economic activity, particularly given the reimposition of some restrictions on activity through September and October in response to rising virus cases in many countries.

Chart A: GDP fell by more in countries with more restrictive policy responses to Covid-19

X axis plots Containment policy index. Y axis shows quarter on quarter percentage change in GDP growth.

Footnotes

Note: Containment policy index is derived from sub-indices produced by Oxford University’s Coronavirus Government Response Tracker.

Sources: Blavatnik School of Government, Oxford University and Bank calculations.

Chart B: Falls in GDP were greater in countries where households were less mobile

X axis plots Apple mobility index. Y axis shows quarter on quarter percentage change in GDP growth.

Footnotes

Note: We use the Apple mobility index for all countries apart from China, for which we use an internal metric (excluded from this chart).

Sources: Apple and Bank calculations.

This post was prepared with the help of Oliver Davies, Sevim Kösem and Simon Whitaker.

This analysis was presented to the Monetary Policy Committee in June 2020.

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