The Fiscal and Welfare Effects of Policy Responses to the Covid-19 School Closures
Bibliographic record
Abstract
Based on data on school visits from Safegraph and on school closures from Burbio, we document that during the Covid-19 crisis secondary schools were closed for in-person learning for longer periods than elementary schools, private schools experienced shorter closures than public schools, and schools in poorer US counties experienced shorter school closures. To quantify the long-run consequences of these school closures, we extend the structural life cycle model of private and public schooling investments by Fuchs-Schündeln et al. (Econ J 132:1647–1683, 2022) to include private school choice and feed into the model the school closure measures from our empirical analysis. Future earnings and welfare losses are largest for children that started public secondary schools at the onset of the Covid-19 crisis. Comparing children from the top to children from the bottom quartile of the income distribution, welfare losses are 0.5 percentage 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 1/4. A policy intervention that extends schools by 6 weeks generates significant welfare gains for children and raises future tax revenues sufficient to pay for the cost of this schooling expansion.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".