Economic uncertainty before and during the COVID-19 pandemic

Dave Altig, Scott Baker, Jose Maria Barrero, Nick Bloom, Philip Bunn, Scarlet Chen, Steven J. Davis, Julia Leather, Brent Meyer, Emil Mihaylov, Paul Mizen, Nick Parker, Thomas Renault, Pawel Smietanka and Greg Thwaites.

The unprecedented scale and nature of the COVID-19 crisis has generated an extraordinary surge in economic uncertainty. In a recent paper we review what has happened to different indicators of uncertainty in the US and UK before and during the COVID-19 pandemic. Three results emerge. All of the indicators that we consider show huge jumps in uncertainty in reaction to the pandemic and its economic fallout. Most indicators reach their highest values on record, although the extent of the increases differ. The time paths also differ: implied stock market volatility rose rapidly from late February, peaked in mid-March, and fell back by late March as stock prices partly recovered. In contrast, broader measures peaked later.

There is uncertainty about almost every aspect of the COVID-19 crisis: on the epidemiological side these include the infectiousness and lethality of the virus; the time needed to develop and deploy vaccines; whether a second wave of the pandemic will emerge; the duration and effectiveness of social distancing. On the economic side, these include the near-term economic impact of the pandemic and policy responses; the speed of economic recovery as the pandemic recedes; whether temporary government interventions will become permanent; the extent to which pandemic-induced shifts in consumer spending patterns, business travel, and working from home will persist; and the impact on business formation, and research and development.

In our paper, we examine several measures of economic uncertainty before and during the COVID-19 pandemic. Our focus is on forward-looking uncertainty measures that are available in near real-time. We adopt this focus for two main reasons. First, measures derived from statistical models fit to standard macroeconomic data and are essentially backward looking. As a result, they are not well suited to quickly capture the shifts associated with sudden, surprise developments. Second, when an enormous and unusual shock hits with such speed, it is especially vital for real-time forecasting and for policy formulation to work with measures that capture the uncertainties that economic agents actually perceive.

Different Uncertainty Measures

There are many ways to measure uncertainty. Different indicators can capture different aspects of uncertainty or the beliefs of different agents. We consider several types of forward-looking uncertainty measures.

Stock Market Volatility: These measures capture uncertainty among financial market participants about the future path of the stock market. They are derived from the price investors are willing to pay for options contracts to protect them against future stock price movements. Examples include the 1-month and 24-month VIX, which quantify the option-implied volatility of returns on the US S&P 500 index over their respective horizons. The 1-month VIX rose from about 15 in January 2020 to a peak daily value of 82.7 on 16 March before falling below 30 by early May (Figure 1). The 24-month VIX follows a similar profile to the 1-month but has a lower peak.

Figure 1: VIX, Implied Stock Returns Volatility

Newspaper-Based Uncertainty Measures: Newspaper-based measures of uncertainty are forward looking in that they reflect the real-time uncertainty perceived and expressed by journalists. They offer a ready ability to drill down into the sources of economic uncertainty and its movements over time. For example, over 90% of newspaper articles about economic policy uncertainty in March 2020 mention “COVID,” “Coronavirus,” “pandemic” or other term related to infectious diseases.

Examples include the Economic Policy Uncertainty Indices of Baker, Bloom and Davis (2016), which are available for many countries at www.policyuncertainty.com. The US daily version of this index reflects the frequency of newspaper articles with one or more terms about “economics,” “policy” and “uncertainty” in roughly 2,000 US newspapers. It is normalized to 100 from 1985 to 2010, so values above 100 reflect higher-than-average uncertainty. Figure 2 plots weekly averages of the daily EPU, which surges from around 100 in January 2020 to over 500 in March and April 2020, reaching its the highest values on record.

Figure 2: U.S. Economic Policy Uncertainty Index and Twitter Economic Uncertainty Index

Twitter-Based Economic Uncertainty: This is similar to the newspaper-based measure but instead uses Twitter rather than newspapers to measure the frequency with which particular terms are used. To construct a twitter-based economic uncertainty index (TEU), we scraped all tweets worldwide that contain both “economic” and “uncertainty” (including variants of each term) from 1 January 2010 to 1 July 2020. This yields over 175,000 tweets. We then calculated the weekly EU tweet frequency. Figure 2 shows that weekly TEU series behaves similarly to the weekly newspaper-based EPU index around the COVID-19 crisis.

Subjective Uncertainty Measures Computed from Business Expectation Surveys: These measures capture uncertainty around businesses expectations for their own sales. Both the US monthly panel Survey of Business Uncertainty (SBU) and the UK monthly Decision Maker Panel (DMP) contain regular questions that elicit five-point probability distributions (mass points and associated probabilities) over each firm’s own future sales growth rates at a one-year look-ahead horizon.  These data can be aggregated to produce uncertainty measures for the whole economy, particular industries, firm size categories and more.  

Figure 3 plots these survey-based time-series measures of sales growth uncertainty for the United States and the United Kingdom. These measures show pronounced increases in uncertainty in March 2020 and April 2020, before falling back slightly in May 2020. Since March 2020, all four months have been well above any previous peaks in their (short) histories.

Figure 3: Firm-Level Subjective Sales Uncertainty

Forecaster Disagreement: Levels of disagreement on the outlook for real variables such as GDP growth are another proxy for uncertainty. Figure 4 compares US and UK disagreement among professional forecasters about one-year-ahead GDP growth rate forecasts. The US data are from the Survey of Professional Forecasters (SPF), while the UK data are from the Bank of England’s Survey of External Forecasters (SEF). To quantify disagreement, we calculate the standard-deviation of GDP growth rate forecasts across forecasters. As Figure 4 shows, the COVID-19 pandemic triggered historically high levels of disagreement in the growth rate forecasts.

Figure 4: Cross-sectional dispersion of GDP growth forecasts

Comparing the Uncertainty Measures

Armed with these uncertainty measures, we consider three questions: How much did uncertainty rise in the wake of the COVID pandemic? When did it peak? How much, if it all, has it fallen since the peak?

Table 1 summarizes our answers: First, every uncertainty measure we consider rose sharply in the wake of the COVID-19 pandemic. Most measures reached all-time peaks. The exceptions are the 24-month VIX, which peaked during the Global Financial Crisis, and the US GDP forecast disagreement measure, which peaked in the 1970s.

Table 1: Measures of Uncertainty for the COVID-19 Crisis

Measure	Average value in January 2020	Percentage jump: January 2020 to peak	Date of peak value during COVID	Source
VIX 1-Month implied volatility, US	13.8	497	March 16th	http://www.cboe.com/vix
VIX 24-Month implied volatility, US	16.2	108	March 18th	Dew-Becker and Giglio (2020)
Economic Policy Uncertainty Index, US	110.1	683	May 17th	http://www.policyuncertainty.com/
Twitter Economic Uncertainty, US	240.0	410	March 18th	Baker, Bloom, Davis and Renault (2020)
Subjective Sales Uncertainty, US	2.7	154	April 2020	https://www.frbatlanta.org/research/surveys/business-uncertainty
Subjective Sales Uncertainty, UK	4.3	91	April 2020	www.decisionmakerpanel.com
Forecaster disagreement, US	0.3	755	2020 Q2	https://www.philadelphiafed.org/research-and-data/real-time-center/survey-of-professional-forecasters/data-files/rgdp
Forecaster disagreement, UK	0.5	1960	2020 Q2	https://www.bankofengland.co.uk/report/2020/monetary-policy-report-financial-stability-report-may-2020

Second, there is huge variation in the magnitude of the increase. Subjective uncertainty over sales growth rates at a one-year forecast horizon roughly doubles, as does the 24-month VIX. In contrast, disagreement among professional forecasters about real GDP growth over the next year rises roughly 8-fold for the United States and 20-fold for the United Kingdom.

Third, the time profiles of uncertainty responses to the COVID-19 shock differ across the various measures. Figure 5 offers a close-up look at the recent behaviour of several uncertainty measures that we can track at sub-monthly intervals – including a Likert-based measure for the UK that shows the percentage of DMP respondents who rate overall uncertainty facing their business as high or very high. (Some of the series in Figure 5 are shown in a hidden axis). The stock market volatility measures peak in mid-March and then fall quickly to about half their peak levels by the end of June. In contrast, the real-side uncertainty measures peak later – or continue to remain extremely high through late June in the case of subjective uncertainty. This contrast highlights the Wall Street/Main Street distinction that is also apparent in first-moment outcomes.

Figure 5: High frequency measures of uncertainty

Conclusions

The unprecedented scale and nature of the COVID-19 crisis helps explain why it has generated such an extraordinary surge in economic uncertainty. Most of the indicators we consider have risen to historically high levels, although the extent of those increases differed and stock market measures peaked earlier.

This rise in uncertainty is likely to have important implications for the economic outlook. Previous research finds that elevated uncertainty generally makes firms and consumers cautious, holding back investment, hiring and expenditures on consumer durables. It remains to be seen which uncertainty measures will prove most useful in explaining economic developments during and after the COVID-19 pandemic. But several, and perhaps all, of these measures are likely to prove useful, because they capture different aspects of economic uncertainty and allow different aspects of theories to be tested in respect of how investment and consumption behave under uncertainty.

Dave Altig (Federal Reserve Bank of Atlanta), Scott Baker (Kellogg School of Management, Northwestern University), Jose Maria Barrero (ITAM Business School), Nick Bloom (Stanford University), Philip Bunn (Structural Economics Division, Bank of England), Scarlet Chen (Stanford University), Steven J. Davis (University of Chicago Booth School of Business), Julia Leather (University of Nottingham), Brent Meyer (Federal Reserve Bank of Atlanta), Emil Mihaylov (Federal Reserve Bank of Atlanta), Paul Mizen (University of Nottingham), Nick Parker (Federal Reserve Bank of Atlanta), Thomas Renault (University Paris 1 Panthéon-Sorbonne), Pawel Smietanka (Structural Economics Division, Bank of England), Greg Thwaites (LSE Centre for Macroeconomics)

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