David E. Altig, Scott Baker, Jose Maria Barrero, Nicholas Bloom, Philip Bunn, Scarlet Chen, Steven Davis, Julia Leather, Brent Meyer, Emil Mihaylov, Paul Mizen, Nicholas Parker, Thomas Renault, Pawel Smietanka, Gregory Thwaites 24 July 2020
There is uncertainty surrounding almost every aspect of the COVID-19 crisis: on the epidemiological side, uncertainties include the infectiousness and lethality of the virus (Fauci et al. 2020, Li et al. 2020), the time needed to develop and deploy vaccines (Koirala et al. 2020), and the duration and effectiveness of social distancing (Anderson et al. 2020, Atkeson 2020, Eichenbaum et al. 2020). On the economic side, uncertainties include the near-term economic impact of the pandemic and policy responses (Baquee et al. 2020), the speed of recovery as the pandemic recedes (Congressional Budget Office 2020), and the extent to which pandemic-induced shifts in consumer spending patterns, business travel, and working from home will persist (Barrero et al. 2020).
In Altig et al. (2020a), we consider forward-looking measures of economic uncertainty before and during the COVID-19 pandemic. We focus on forward-looking measures available in near real time for two main reasons. First, measures derived from statistical models fit to standard macroeconomic data 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 great speed, it is vital for real-time forecasting and for policy formulation to use measures that capture the uncertainties economic agents actually perceive.
Stock market volatility
These measures are derived from prices 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 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. Examples include the economic policy uncertainty indices of Baker et al. (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 normalised 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 highest values on record.
Figure 2 US economic policy uncertainty index and twitter economic uncertainty index
Newspaper-based indices 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 (Baker et al. 2020).
Twitter-based economic uncertainty
Similarly, we can use Twitter rather than newspapers to measure the frequency with which particular terms appear. Baker et al. (2020) construct a twitter-based economic uncertainty index (TEU) by scraping tweets worldwide that contain both “economic” and “uncertainty” (including variants of each term) from 1 January 2010 to 1 July 2020. They then calculate the scaled weekly frequency of tweets that mention economic uncertainty. Figure 2 shows that their 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 that business executives have about the sales outlooks of their own firms. 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 rate uncertainty for the US and the UK. 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
Levels of disagreement about the outlook for real variables such as GDP growth are another proxy for uncertainty. Figure 4 displays disagreement among professional forecasters about one-year-ahead GDP growth rate forecasts for the US and the UK. 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 at each point in time. 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-19 pandemic? When did it peak? How much, if it all, has it fallen since the peak?
Table 1 Measures of uncertainty for the COVID-19 crisis
Notes: The VIX is the implied volatility (over the next month and over the next 24 months) on the S&P500 index from the Chicago Board of Options Exchange, expressed in annualized units. Values downloaded from here. The daily Economic Policy Uncertainty index values are from here and constructed as described in Baker, Bloom and Davis (2016). Subjective sales growth uncertainty is computed as the activity-weighted average of firm-level subjective uncertainty values, which are computed as the standard deviation of each firm’s subjective forecast distribution over its own future sales growth rate from the current quarter to four quarters hence. See Altig et al., 2020b). US data are form the Survey of Business Uncertainty conducted by the Federal Reserve Bank of Atlanta, Stanford University, and the University of Chicago Booth School of Business. UK data are from the Decision Maker Panel Survey conducted by the Bank of England, Nottingham University and Stanford University. Forecast disagreement is measured as the standard deviation across forecasters of one-year-ahead annual real GDP growth rate forecasts. US data are from the Survey of Professional Forecasters conducted by the Philadelphia Fed. UK data are from the Survey of External Forecasters conducted by the Bank of England.
Table 1 summarises 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 Crisis, and the US GDP forecast disagreement measure, which peaked in the 1970s.
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 US and 20-fold for the UK.
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 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
The extraordinary scale and unusual nature of the COVID-19 crisis helps explain why it has generated such a tremendous surge in economic uncertainty. Much previous research finds that elevated uncertainty generally makes firms and consumers cautious, holding back investment, hiring and expenditures on consumer durables.1
It remains to be seen which uncertainty measures will prove most useful in explaining economic developments during and after the COVID-19 pandemic. Several, perhaps all, of the measures we consider may prove useful, because they capture different aspects of uncertainty and facilitate different approaches to assessing the relationship of uncertainty to consumption, investment, employment, and other outcomes.
Authors’ note: The views expressed in this column are those of the authors, and not necessarily those of the Bank of England or its committees or the Federal Reserve Bank of Atlanta.
Altig, D, B Scott, J M Barrero, N Bloom, P Bunn, S Chen, S J Davis, B Meyer, E Mihaylov, P Mizen, N Parker, T Renault, P Smietanka and G Thwaites (2020a), “Economic Uncertainty Before and During the COVID-19 Pandemic”, Bank of England Staff Working Paper No. 876, June.
Altig, D, J M Barrero, N Bloom, S J Davis, B Meyer and N Parker (2020b), “Surveying Business Uncertainty”, NBER Working Paper 25956. Revised, March, and Journal of Econometrics, forthcoming
Anderson, R M, H Heersterbeek, D Klinkenberg and T D Hollingsworth (2020), “How will country-based mitigation measures influence the course of the covid-19 epidemic?” The Lancet, 395, no. 10228 (March).
Atkeson, A (2020), “What Will Be the Economic Impact of COVID-19 in the US? Rough Estimates of Disease Scenarios”, NBER Working Paper 26867, March.
Baker, S, N Bloom and S J Davis (2016), “Measuring Economic Policy Uncertainty”, Quarterly Journal of Economics, November.
Baker, S B, N Bloom, S J Davis and T Renault (2020), “Economic Uncertainty Measures Derived from Twitter”, Working paper, June.
Baker, S, N Bloom, S J Davis, K Kost, M Sammon and T Viratyosin (2020), “The Unprecedented Stock Market Reaction to COVID-19”, Covid Economics: Vetted and Real-Time Papers, 1, 3 April.
Baqaee, D, E Farhi, M J Mina and J H Stock (2020), “Policies for a Second Wave”, BPEA Conference Draft, Summer.
Barrero, J, N Bloom and S J Davis (2020), “COVID-19 and Labour Reallocation: Evidence from the US”, VoxEU.org, 14 July.
Bloom, N (2014), “Fluctuations in Uncertainty”, Journal of Economic Perspectives, Spring.
Congressional Budget Office (2020), “An Update to the Economic Outlook: 2020 to 2030”, July.
Dew-Becker, I and S Giglio (2020), “Cross-sectional uncertainty and the business cycle: Evidence from 40 years of options data”, Working paper.
Eichenbaum, M S, S Rebelo and M Trabandt (2020), “The Macroeconomics of Epidemics”, NBER Working Paper 26882, March.
Fauci, A S, H C Lane and R R Redfield (2020), “Covid-19 – Navigating the Uncharted”, New England Journal of Medicine, 26 March.
Koirala, A, Y J Joo, A Khatami, C Chiu and P N Britton (2020), “Vaccines for COVID-19: The Current State of Play”, Pediatric Respiratory Review, in press, 18 June.
Li, R, S Pei, B Chen, Y Song, T Zhang, W Yang and J Shaman (2020), “Substantial Undocumented Infection Facilitates the Rapid Dissemination of Novel Coronavirus (SARS-CoV2)”, Science, 16 March.
Linton, N M et al. (2020), “Incubation period and other epidemiological characteristics of 2019 novel Coronavirus infections with right truncation: A statistical analysis of publicly available case data”, Journal of Clinical Medicine 9, no. 2 (17 February).
1 See Bloom (2014) for references to the relevant literature.