Pandemic Macro: Stimulus vs. Insurance

The pandemic recession from March to April 2020 and its aftermath–up to and including the current surge of inflation and the risk of another recession as the Fed raises interest rates to choke off inflation–seems to require a different set of lessons than basic textbook models. Th bottom line is to shift away from conventional thinking about aggregate demand and aggregate supply during recessions, and in particular to shift away from conventional thinking about fiscal and monetary stimulus, and instead to think more broadly about social insurance.

The standard stories about recessions start with aggregate demand or aggregate supply. Consider first if there is a drop in aggregate demand–an event often linked to a drop in investment, a drop in household purchases of durable goods, and to stress in the banking/financial system. However, the underlying capacity of the economy to supply goods as measured by workers, capital, and technology hasn’t changed. One option is this situation is to wait for all the effect of this drop in aggregate demand to filter through individual product and labor markets, where prices are likely to adjust slow and sticky ways, and hope the economy doesn’t get stuck on the way to a new equilibrium. The other option is to use fiscal or monetary policy to inject more demand into the economy. The standard belief, backed up by considerable evidence, is that a degree of activism will return the economy to good health faster than a wait-and-see approach.

But the pandemic wasn’t a demand-side shock, at least not when if first hit in spring 2020. Instead, it changed how work and industries functioned, and shut some industries down altogether. The productive capacity of the economy was disrupted, with some sectors suffering far more disruption than others. The banking/financial sector was largely OK; the public health center faced crisis-level challenges; and real-world supply chains were a mess. Employment first plummeted, but then has bounced back strongly. Instead of an unemployment job problem, an inflation problem has now emerged.

How should policy-makers think about this sort of short, sharp, disruptive shock, as opposed to a conventional recession? Christina D. and David H. Romer lay out an alternative perspective in “A Social Insurance Perspective on Pandemic Fiscal Policy: Implications for Unemployment Insurance and Hazard Pay” (Journal of Economic Perspectives, Spring 2022, freely available online). As usual, I acknowledge when discussing JEP articles that I work as Managing Editor of the journal, and thus may be predisposed to find the articles of interest.

Romer and Romer set up their discussion this way:

During a pandemic, workers in certain sectors face prolonged unemployment because their industries can’t operate safely, while workers in other sectors remain relatively unscathed. Had workers foreseen this possibility, they would have liked to purchase insurance against the risk that their sector would be closed. The social insurance framework can show which types of government fiscal actions best approximate what a well-functioning insurance market would provide.

A social insurance perspective is more appropriate for designing and evaluating pandemic fiscal policy than simple aggregate-demand-based models. Conventional Keynesian models of fiscal policy suggest that the way to deal with a recession is to increase aggregate demand quickly, and by enough to return output to its normal or potential level. And in this framework, it is not necessary for fiscal policy to closely target the workers or industries most affected by the recession. Raising aggregate demand anywhere will raise incomes and spending throughout the economy, and so help will eventually flow to those most affected. These models and policy prescriptions don’t hold in a pandemic recession. Because the virus thrives on human interaction (and hence on some types of
economic activity), fiscal policy should not be aimed at quickly raising aggregate demand and attempting to return the economy to full employment. Doing so would make the pandemic worse and increase illness and deaths. Similarly, in a pandemic, some types of economic activity—such as in-restaurant dining and cruise travel—simply can’t take place safely. As a result, broad stimulus measures like one-time payments or tax cuts can do little to put workers in those industries back to work.

Their discussion focuses on two main aspects: What’s the useful way to think about social insurance for those made unemployed by a pandemic-style recession? What’s the useful way to think about supporting workers who become especially essential during a recession–and who society really wants to keep on the job?

For workers who become unemployed in a pandemic, the key incentive issue is that the government may not have much ability to determine, for the vast and diverse US economy, who cannot work because of the pandemic recession and who is choosing not to work. Thus, the appropriate policy design is to design the unemployment support so that those who continue working are better off than those who are not working–thus providing an incentive to work. Notice that the policy goal here is focused just on assisting a group of workers: it is not a broad-based fiscal or monetary stimulus for the economy as a whole.

They argue that the appropriate policy focus here is that lower-income unemployed workers should have a larger share of their income replaced in a pandemic than higher-income workers, because the higher-income workers are more likely to have other personal resources and wealth on which they can draw. However, the replacement rate of income needs to be less than 100%, so that there is an incentive to keep working. Based on a variety of evidence, they suggest that replacement of about 85% of previous income might make sense for lower-income workers; less for those with higher incomes. But the boost to unemployment insurance policies adopted in 2020 were so generous that they often replaced more than 100% of lost income. Romer and Romer write:

Taken together, these studies indicate that unemployed workers may have limited ability to self-insure, and that this ability may be substantially smaller among lower-income workers. This suggests that the replacement rate for unemployment benefits may need to be fairly substantial, though clearly less than 100 percent, to result in a loss of consumption in the 10 to 15 percent range. It also suggests that replacement rates should decline as prior income rises. However, the existing evidence is not enough to pin down optimal replacement rates precisely.

Even though we are unable to say what exactly replacement rates from unemployment
insurance during a pandemic should be, it is clear that actual replacement rates have differed sharply from the prescriptions of a social insurance perspective. Ganong, Noel, and Vavra (2020) show that the flat $600 per week of additional [unemployment] benefits raised replacement rates to well over 100 percent for most workers. There appear to have been two forces behind the policies involving greater than 100 percent replacement. One is the pursuit of other objectives, especially redistribution toward lower-wage workers and aggregate demand stimulus. The other is idiosyncratic factors: Ganong, Noel, and Vavra (2020) report that an overestimate of the average wage of workers who would lose their jobs led policymakers to underestimate the impact of the $600 weekly adjustment on replacement rates, and that the very limited capacities of state unemployment insurance systems led policymakers to adopt the fixed supplement rather than more complicated additions to benefits.

Yes, these kinds of unemployment payments will raise aggregate demand in a recession, and that is a useful side-effect. But in a pandemic recession, it isn’t the main issue.

Another aspect of the Romer/Romer discussion is focused on essential workers. Some obvious examples during a pandemic might be those who work in health care and nursing homes. However, by late in 2020 there was some evidence that “healthcare workers no longer had significantly elevated COVID-19 risk. Instead, the occupations with the highest odds ratios of infection were food service workers (food counter attendants, bartenders, and waiters), transit workers, and cleaners …” Of course, it may also be that those in health care were more explicitly focused on holding down infection rates and might have been taking greater precautions.

Is it possible to design some form of “hazard pay” according to which occupations faced the greatest health risks from the pandemic? For example, the government could reach out to firms in certain industries and encourage them to apply, on behalf of their employees, for those frontline workers who might qualify for government-provided hazard pay. Romer and Romer discuss several different ways of estimating the number of workers who might qualify and the amount they might receive (based on risk of exposure). A broad-based program, for example, might have included about 10% of all workers. One could also imagine a system with perhaps two tiers: say, those who work directly with pandemic patients in the top tier betting perhaps an additional $4/hour, and those who are exposed because of broader public contact getting an additional $2/hour.

As it turned out, the state of Pennsylvania enacted a hazard pay premium of $3/hour. A federal hazard pay premium was proposed, but not enacted into law. It might be useful to design such a program now, specifying in broad terms how eligibility and hazard pay would be determined, so that it would be ready to go in a future pandemic.

Again, a hazard pay premium would raise aggregate demand in a recession, and that is a useful side-effect. But in a pandemic recession, it isn’t the main issue.

In the next day or two, I’ll return to this question of the appropriate response to a short, sharp, disruption like the spring 2020 COVID pandemic. This question of how to think about appropriate policy for a pandemic recession is generating its own wave of macroeconomic research. Again, some common themes of this literature are that thinking in conventional terms of fiscal and monetary stimulus, or aggregate demand and aggregate supply, may miss the point when when faced with a pandemic recession. For those interested in getting up to speed on the research literature, a starting point is the first three papers in the May 2022 issue of the American Economic Review (subscription required). These papers are also cited as background in the JEP Romer and Romer essay. To give a flavor of this research, I’ll just quote from the introduction by the editors:

“Supply and Demand in Disaggregated Keynesian Economies with an Application to the COVID-19 Crisis,” by David Baqaee and Emmanuel Farhi beautifully illustrates how a complex shock like COVID-19, combined with sectoral nominal rigidities, can lead simultaneously to the coexistence of tight and slack labor markets in different sectors. The paper highlights the importance of input-output networks and complementarities in production. …

“Macroeconomic Implications of COVID-19: Can Negative Supply Shocks Cause Demand Shortages?” by Veronica Guerrieri, Guido Lorenzoni, Ludwig Straub, and Ivan Werning explores similar territory. It shows how negative supply shocks can trigger even larger declines in aggregate demand. Both papers illustrate how traditional stimulus policies—such as monetary policy and standard fiscal stimulus—can have muted effects. Instead, transfer policies that offer insurance against sectoral shutdowns can deliver large gains.

“Effective Demand Failures and the Limits of Monetary Stabilization Policy,” by Michael Woodford, builds on the previous two papers by considering, formally, a model of the “circular flow of payments.” Disruptions in this circular flow can lead to a dramatic collapse of effective demand that cannot be remedied with conventional monetary policy, even away from the zero lower bound. As in the Guerrieri et al. paper, Woodford finds an important role for public transfers.

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