In Conversation

In progress: A tool to help policymakers safely re-open the economy

Yale economist Fabrizio Zilibotti discusses his work to develop a predictive model for guiding policymakers as they try to restart economies safely.
Fabrizio Zilibotti

Fabrizio Zilibotti

Governments across the globe have imposed strict lockdowns to slow the spread of COVID-19. These measures have reduced infection rates, but also triggered the most severe economic collapse since the Great Depression.

Yale economist Fabrizio Zilibotti is working with Hong Kong-based researchers Rongzhu Ke and Zheng Song to develop a predictive model for guiding policymakers as they try to restart economies safely. An expert in inequality in children’s education, Zilibotti is also studying the unequal effects of pandemic-related school closures on children.

The Tuntex Professor of International and Development Economics in the Faculty of Arts and Sciences, Zilibotti recently spoke to YaleNews about his work. Interview condensed and edited.

Can you describe the model you’re developing? How will it address the pandemic?

To effectively study COVID-19, you must begin with its diffusion. We started with a variant of an existing epidemiological model called the SIR model, which stands for “susceptible, infected, and recovered.” It is used to predict the spread of infectious diseases absent any policy intervention to limit their diffusion.

Our twist is that we introduce the notion of isolation directly into the model. We make explicit in the equations governing diffusion that some infected people will be detected and isolated and, as a result, will not infect others. This helps us to think about how to build up testing capacity and devise an effective exit strategy for the current economic lockdowns. We would like to develop an understanding of how many people need to be regularly tested in order to lift restrictions on economic activity.

What insight will this analysis provide policymakers?

We aim to help policymakers determine the amount of testing capacity required to safely open the economy. If you relax restrictions, there will be an increase in the number of infections. The point is that if you have sufficiently strong testing capacity, these outbreaks will be very limited. It’s very different from a situation in which you don’t do any testing or you do limited testing and essentially return to a herd immunity scenario. We would like to be able to tell policymakers that if they want an exit strategy, and they have “X” amount of testing capacity, here’s what they should expect in terms of new infections.

Some countries, such as Germany, are imposing new lockdowns when they experience a sudden spike in infections. I’m not saying this isn’t an effective approach, but lockdowns are very costly. It could be that you can accept a limited increase in infections, knowing it will subside within a week. If countries combine some social distancing measures and an emphasis on wearing masks with sufficient testing capacity, it could eliminate the need for costly lockdowns.

Do you have any sense of the amount of testing necessary to safely ease lockdowns in the United States?

It is too early for us to deliver a quantitative prediction, but we suspect that the amount of testing needed is significantly smaller than what is being reported in the press. We don’t think it’ll be necessary to test a large proportion of the U.S. population every second week. We’re trying to find the right number.

What sort of data are you analyzing?

We’re examining how the epidemic has evolved in countries that are already past the peak of infections. At the moment, we’re mostly studying data from Italy, South Korea and China.

There are pros and cons to all of the data. Of course, there are issues in China concerning the government’s opacity. We essentially have no data from there from before the lockdowns were imposed. I think we can probably trust the number of cases reported before the lockdowns in both South Korea and Italy. An important difference between those two countries is that Korea had a very high testing capacity from the very beginning, so it had a much greater ability to detect cases. Italy was quickly overwhelmed, and in the early stage of the epidemic lacked the testing capacity to follow up with people who had contacts with positive cases.

Italy is interesting because the health system there is governed by region, and different regions follow different procedures. For example, Veneto, a region in northern Italy, initially had a very high number of cases and a very robust approach to testing. This was partly because there are local diseases endemic there, and they were more prepared for that kind of intervention. At the same time, the testing capacity was very low relative to the need in Lombardy and in other northern regions.

You have studied how inequality shapes parenting decisions and children’s education. Are you thinking about the pandemic’s effects on schooling?

I am seeking to understand the consequences of school closures for children from different socioeconomic backgrounds. There have been some local studies showing a very diverse range of outcomes that are highly related to family background. Children always carry the inequality of their home conditions into school. That inequality is attenuated by the fact that all the students are interacting in a common setting with the same teacher.

Now, all of a sudden, students are confined to their respective situations at home. Some parents have the time to help their children with learning. They have the space to provide them a room to study. They have all the technology required for remote learning. In other households, there is one computer that must be shared among several children or no computer at all. The parents are working and struggle to find time for homeschooling. We want learn what this means for children’s academic progress.

Are you looking into any particular policy responses to alleviate the inequality inherent in remote instruction?

My colleagues and I previously have looked into the extent to which extra instruction during the summer can help close gaps in learning. The results were quite interesting. Every summer, children lose a portion of what they had learned over the school year. Children who are struggling in school can retain more than half the knowledge they would otherwise lose if they receive appropriate support over the summer. We think that this could be policy-relevant under the present circumstances.

In Italy, there’s been a big debate on how to handle the schooling. It seems like the main focus has been on how to structure examinations. This is important of course, but I would have focused the effort and resources on creating forms of support during the summer ahead of schools reopening. Given the emergency, it would be a good time to try to at least partially re-equalize the educational conditions for kids. This gap can carry over into next year, especially since we still don’t know the degree to which schools will function in the fall.

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Part of the In Focus Collection: Yale responds to COVID-19

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