Hugo Reis
Pedro Carneiro
Raul Santaeulalia-Llopis
Diego Restuccia
Chaoran Chen
Brad J. Hershbein
Claudia Macaluso
Chen Yeh
Xuan Tam
Xin Tang
Marina M. Tavares
Adrian Peralta-Alva
Carlos Carillo-Tudela
Felix Koenig
Joze Sambt
Ronald Lee
James Sefton
David McCarthy
Bledi Taska
Carter Braxton
Alp Simsek
Plamen T. Nenov
Gabriel Chodorow-Reich
Virgiliu Midrigan
Corina Boar
Sauro Mocetti
Guglielmo Barone
Steven J. Davis
Nicholas Bloom
José María Barrero
Thomas Sampson
Adrien Matray
Natalie Bau
Darryl Koehler
Laurence J. Kotlikoff
Alan J. Auerbach
Irina Popova
Alexander Ludwig
Dirk Krueger
Nicola Fuchs-Schündeln
Taylor Jaworski
Walker Hanlon
Ludo Visschers
Carlos Carillo-Tudela
Henrik Kleven
Kristian Jakobsen
Katrine Marie Jakobsen
Alessandro Guarnieri
Tanguy van Ypersele
Fabien Petit
Cecilia García-Peñalosa
Yonatan Berman
Nina Weber
Julian Limberg
David Hope
Pedro Tremacoldi-Rossi
Tatiana Mocanu
Marco Ranaldi
Silvia Vannutelli
Raymond Fisman
John Voorheis
Reed Walker
Janet Currie
Roel Dom
Marcos Vera-Hernández
Emla Fitzsimons
José V. Rodríguez Mora
Tomasa Rodrigo
Álvaro Ortiz
Stephen Hansen
Vasco Carvalho
Gergely Buda
Gabriel Zucman
Anders Jensen
Matthew Fisher-Post
José-Alberto Guerra
Myra Mohnen
Christopher Timmins
Ignacio Sarmiento-Barbieri
Peter Christensen
Linda Wu
Gaurav Khatri
Julián Costas-Fernández
Eleonora Patacchini
Jorgen Harris
Marco Battaglini
Ricardo Fernholz
Alberto Bisin
Jess Benhabib
Cian Ruane
Pete Klenow
Mark Bils
Peter Hull
Will Dobbie
David Arnold
Eric Zwick
Owen Zidar
Matt Smith
Ansgar Walther
Tarun Ramadorai

The long-term distributional and welfare effects of Covid-19 school closures

What is this research about and why did you do it?

This paper provides estimates on the likely effects of the Covid-induced school closures in the US on the long-term earnings of the affected children. Almost all countries worldwide closed schools in response to the Covid outbreak, with relatively little discussion about these closures. While closing businesses leads to immediate losses for the affected owners and employees, closing schools leads to losses in human capital, which translate into lower earnings only in the future. We wanted to provide a number for these losses in order to inform policy makers about the costs of this policy.

How did you answer this question?

Clearly, we cannot empirically investigate the future earnings losses of the affected children yet. Therefore, we chose to construct a life cycle model in which children accumulate human capital while young by receiving three forms of input, namely schooling and time and monetary investments of their parents. The human capital of the children at age 16affects their choice of higher education, and final educational attainment together with human capital affects wages earned in the labour market. We calibrate the model to US data prior to the Covid outbreak, and then analyse the model’s predictions for the effect of the school closures on children’s future earnings.

What did you find?

We find that on average, children with school closures of half a year, and without virtual schooling, should expect lifetime earnings losses of -2.1%. These losses arise despite the fact that parents increase their own time and monetary investments into the education of the children significantly during the school closures. Discounting these expected earnings losses to today, and summing them up over all children, they amount to -3.0% of US GDP. Therefore, these hidden economic costs of the pandemic are high. There is significant heterogeneity in the effects: younger children and children from disadvantaged backgrounds suffer larger losses.

Heterogeneity or simply main result

Notes: Share s ∈ {no, hs, co}, the education share in each respective education category where s = no denotes less than high school, s = hs denotes high school and s = co denotes college. Average HK, the average acquired human capital at age sixteen; PDV gross earn, the present discounted value of gross earnings assuming labour market entry at age twenty-two and retirement at age sixty-six; PDV net earn, present discounted value of net earnings; CEV, consumption equivalent variation. Columns for biological ages 4–14 show the respective percentage point changes of education shares, the percentage changes of acquired human capital and average earnings, and the CEV expressed as a percentage change, for children at various ages at the time of the school closures. Column ‘Average’ gives the respective average response. The CEV is the consumption equivalent variation of welfare measure.

What implications does this have for the study (research and teaching) of wealth concentration or economic inequality?

Intergenerational mobility is crucial for equality of opportunity. Our study shows that the Covid-induced school closures had larger effects on children from poor households than from rich ones. Therefore, we should expect intergenerational mobility to decline and income inequality in the next generation to increase due to the closures.

What are the next steps in your agenda?

We are using the model to address the effects of other developments, e.g. increased assortative mating, on intergenerational mobility. Moreover, we empirically investigate the effects of the school closures on children and their parents, now that data slowly become available.

Citation and related resources

Fuchs-Schündeln, N., Krueger, D., Ludwig, A., and Popova, I. (2022). "The long-term distributional and welfare effects of Covid-19 School closures". The Economic Journal, 132(645), pp. 1647-1683

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