Key lessons of pandemic are resilience and adaptability
The size and scale of the economic shock arising from the Covid-19 pandemic is unprecedented in the context of the modern economy. Record contractions in economic activity and employment, rapidly expanding central bank balance sheets and sovereign debt and financial market volatility have conspired to create a heightened sense of uncertainty about the future.
It is important to consider two key properties of the Covid-19 virus: it is contagious and has profound health consequences.
Due to these specific properties, there are two types of economic reactions. The first is from people who choose to engage less in the types of activities that may expose them to the virus and begin, for instance, to practice social distancing.
However, the population is not homogenous, and people will vary in their assessment of the risks associated with various activities.
As a result, some people will continue to engage in activities that expose them to the virus and risk spreading it to others.
This is what economists refer to as a “negative externality”, in which the behaviour of a person fails to consider the negative impact on those around them.
Consequently, various restrictions on activities are imposed by governments to minimise the negative externality, leading to the second economic reaction that arises from the pandemic.
The question then arises is which feature exerts the greatest blow to economic activity: individual behaviour or government-mandated restrictions.
Fortunately, a data-rich environment has allowed economists to work this out.
One such paper by Goolsbee and Syverson (2020) used mobile phone data on customer visits to more than 2.25 million businesses across the US.
Given the varying levels of restrictions imposed across counties and states, the authors were able to estimate the extent to which risk aversion and policy drove a reduction in customer visits. Findings indicated that while overall customer visits fell by 60 per cent, legal restrictions only accounted for about 7 per cent of that.
While the existence of the pandemic influences behaviour at the level of the individual, a sustained economic recovery is unlikely to occur until the pandemic is brought under control.
In short, it is the pandemic that caused the recession, not the response.
As with any downturn, the 2020 recession has also disproportionately affected some segments of the economy while leaving others relatively intact.
However, one element of how this recession differs from others is the disproportionate effect on women. Since the 1970s, recessions have typically affected men’s employment disproportionately, with the term “mancession” first used to describe the phenomena in 2009.
In this recession – dubbed a “shecession” by some economists – women’s unemployment in the US was 2.9 percentage points more than men’s unemployment at the peak of the pandemic.
Such disproportionate impact arises as the sectors most affected are “contact intensive”, such as restaurants, where women have a high share of employment.
So why does this matter? Aside from the distribution issues that arise, such as the
gender pay gap, the “shecession” has a significant blow at the household level, which then propagates across the broader economy.
Since the outbreak began, many economists have sought to estimate its economic damage and provide forecasts on the performance of a range of economic and financial variables over the next year or two.
A debate has also arisen as to what shape the recovery will take, with various letters (U, V, W and L, for instance) and shapes, such as ticks and swooshes, used to depict the expected recovery.
Yet, forecasting output growth to within one decimal place, or the shape of the economic recovery with any degree of certainty, is next to impossible, given the underlying uncertainties.
A key assumption feeding into any economic forecasting model is how long the pandemic will last.
The longer it lasts, the more likely a degree of voluntary social distancing will persist
and the more likely government restrictions on activity will remain.
Economic recovery is, therefore, dependent on the path of future Covid-19 cases and herein lies a key forecasting problem: pandemics are highly non-linear events. That is, an output – such as the total number of people infected – responds in a disproportionate manner to a change in inputs such as the reproducibility rate, or the “R0” figure.
One also needs to factor in assumptions about the progress of a vaccine, when it will be available and its efficacy.
This single assumption about how long the pandemic will last is difficult to know with any degree of certainty, yet the path of economic growth is highly reliant upon this assumption.
Once an assumption is made about how long the pandemic will last, one then needs to consider how economic agents will react to an event that has no precedent in the modern globalised economy. For example,
will consumers become more cautious and increase their savings long after the pandemic is over?
Another unknown to consider is whether working from home has become more normalised and whether this leads to adjustment costs in some sectors of the economy or whether the pandemic leads to more onshoring, resulting in a reduction in global trade.
Given the extreme uncertainty arising from the pandemic, resilience and adaptability have never been more important. The economy may recover earlier than expected or much later, but the fact is we just do not know.
It is, therefore, important to be able to respond to and deal with a wide range of possible scenarios over the next year or two rather than being overly reliant on a forecast worldview generated under a specific set of assumptions.
In this recession, women’s unemployment in the US was more than that for men at the peak of the pandemic