Working Paper: CEPR ID: DP16663
Authors: Nicola Fuchs-Schündeln; Dirk Krueger; Andre Kurmann; Etienne Lale; Alexander Ludwig; Irina Popova
Abstract: Using a structural life-cycle model and data on school visits from Safegraph and schoolclosures from Burbio, we quantify the heterogeneous impact of school closures during theCorona crisis on children affected at different ages and coming from households with differentparental characteristics. Our data suggests that secondary schools were closed forin-person learning for longer periods than elementary schools (implying that younger childrenexperienced less school closures than older children), and that private schools experiencedshorter closures than public schools, and schools in poorer U.S. counties experiencedshorter school closures. We then extend the structural life cycle model of private andpublic schooling investments studied in Fuchs-Schündeln, Krueger, Ludwig, and Popova(2021) to include the choice of parents whether to send their children to private schools,empirically discipline it with data on parental investments from the PSID, and then feedinto the model the school closure measures from our empirical analysis to quantify thelong-run consequences of the Covid-19 school closures on the cohorts of children currentlyin school. Future earnings- and welfare losses are largest for children that started publicsecondary schools at the onset of the Covid-19 crisis. Comparing children from the top tochildren from the bottom quartile of the income distribution, welfare losses are ca. 0.8percentage points larger for the poorer children if school closures were unrelated to income.Accounting for the longer school closures in richer counties reduces this gap by about 1/3.A policy intervention that extends schools by 3 months (6 weeks in the next two summers)generates significant welfare gains for the children and raises future tax revenues approximatelysufficient to pay for the cost of this schooling expansion.
Keywords: COVID-19; school closures; inequality; intergenerational persistence
JEL Codes: D15; D31; E24; I24
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| School closures (I21) | Future earnings and welfare (D69) |
| School closures (I21) | Human capital accumulation (J24) |
| School closures (I21) | Future earnings potential (J17) |
| Longer closures in richer counties (R59) | Welfare losses for children in bottom quartile (I38) |
| Policy intervention (extending school openings) (I21) | Welfare gains for children (I38) |
| Policy intervention (extending school openings) (I21) | Future tax revenues (H29) |