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Why Economic Forecasting Is So Difficult in the Pandemic

The Case for Reopening Economies by Sector

The coronavirus pandemic has introduced extreme uncertainty into nearly every aspect of society. Will health care systems hold up? Will scientists develop a vaccine? Are essential workers safe? When can regular employees go back to the office? The answers to these questions — when there are answers — seem to change daily. And with each change the stock market (and our hopes) rises or falls.

Since the Covid-19 pandemic began, we’ve seen pervasive uncertainty manifest in a sudden and massive divergence in macroeconomic projections. For example, in early February, the spread among economic growth forecasts for Q2 in the U.S. was 3.5 percentage points according to FocusEconomics data. By April 29, the most optimistic forecast among the 28 institutions in our weekly coronavirus survey saw the U.S. economy contracting 8.2%. The most pessimistic projected a huge 65.0% contraction — a spread of 56.8 percentage points — with an average of -31.4%. While most institutions expected a rebound in Q3, some saw further declines. And in Q4, although all economists projected growth of some form, forecasts ranged from a minimum of +1.1% and a maximum of +70.0%. The spreads observed in recent weeks are by far the widest recorded since we started covering the U.S. a decade ago.

Looking at countries with a longer time horizon, the current forecast spread among analysts is far larger than at any point during the past 20 years, and significantly above that seen during the height of the financial crisis — the last period of extreme, prolonged global uncertainty. For example, during the 2008 financial crisis, both Brazil and Mexico saw the spread for annual GDP forecasts widen to close to six percentage points, before returning to under three for most of the 2010s. The percentage point spread is now well over seven percentage points.

Why So Much Divergence?

The short answer to why there is so much divergence is because no one knows for sure what is going to happen. Digging deeper, three key factors are causing forecasters particular difficulties.

First, the economic impact and speed of policy changes have never been higher. In normal times, most governments can be relied on to at least attempt to encourage economic growth and preserve employment. Today, however, they are deliberately provoking recessions to save lives, and containment measures are crushing domestic activity. Simply miscalculating the end date of a nationwide lockdown by a couple of weeks throws annual GDP forecasts completely off-kilter. Moreover, bills which generally endure months of parliamentary ping-pong are being rushed through legislatures in days as governments and central banks race to respond to the rapid advance of the virus. Many governments have adopted emergency powers allowing them to rule by decree. What’s more, the fiscal and monetary stimulus being announced to palliate the downturn dwarfs that seen during the financial crisis. For economic forecasters, keeping up with the constant flurry of measures and correctly incorporating them into models poses challenges.

Second, the pandemic is undermining the reliability of economic data — the bedrock of any good macroeconomic model. In particular, survey-based data of businesses and households is suffering as lockdown measures reduce response rates, amplifying sampling error. The U.S. Bureau of Labor Statistics data for March, for instance, saw establishments’ and households’ response rates fall by nine and 10 percentage points respectively relative to their recent average.

Paul Donovan, chief economist at UBS Global Wealth Management, explained the problem this way: “If you are filling in survey forms in a lockdown, you are likely to be an unusual person, and possibly not representative. Sentiment affects answers to surveys. Data, like consumer price inflation, includes restaurant prices, but restaurants are closed. What happens when you survey something that is not there? Online spending is likely to have increased in lockdowns. Online spending may stay higher after the lockdowns end. It may not be properly captured in official data.”

The third reason the models are diverging so much is because economic forecasters are having to delve into the unfamiliar world of epidemiology to better understand the likely evolution of the coronavirus outbreak in each country. However, this is a challenge even for health experts. Predicting the scope and effectiveness of future public health interventions, or how health care systems will respond under pressure, is tough — particularly for countries at early stages of their epidemics. There is no firm timeline for the arrival of game-changing treatments or vaccines, or clarity over the likelihood or severity of a second wave of cases.

Handling Uncertainty

The divergence in economic forecasts should narrow somewhat going forward. Greater clarity will emerge on the effectiveness of recent stimulus, and any further fiscal or monetary measures will likely be more modest in scope. The gradual lifting of lockdowns will facilitate the collection of economic data. Our knowledge of the virus and its spread will improve. But a return to pre-virus levels of economic uncertainty will have to wait until a lasting solution to the pandemic — likely in the form of a vaccine — is found.

While the coronavirus pandemic poses a unique challenge to macroeconomic forecasters, the profession has faced similarly profound shocks in the past and emerged fortified. The global financial crisis of 2008 caught most economists napping. But the insights gained in areas such as the economics of financial contagion and the impact of unconventional monetary measures have improved the quality of today’s forecasting models. In the same way, Covid-19 will shine new light on areas such as behavioral economics — how consumer spending is affected by fear of contagion for instance — and the economic effects of radical fiscal policy. The knowledge gained could make the uncertainty of future economic crises, whenever and however they come, that bit less extreme.

Source: Harvard Business Review

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