Expirations of Pandemic Jobless Programs Caused an Unprecedented Drop in Access to UI
Bibliographic record
Abstract
At the start of the pandemic, Congress temporarily expanded Unemployment Insurance (UI) programs through the federal CARES Act In this report, we mainly focus on Pandemic Emergency Unemployment Compensation (PEUC), which expanded the weeks of UI eligibility This and other emergency provisions were set to expire on September 4, 2021, which is commonly referred to as a “benefts clif ” However, policymakers in some states chose to end these programs before that date This policy report evaluates the impact the expansion and expiration of these programs had on the US labor market using data from the U S Department of Labor, the Current Population Survey, and California’s Employment Development Department Using national data, we fnd the benefts clif caused a dramatic drop in the proportion of unemployed workers that are covered by UI, which was three times as large as a similar clif at the end of the Great Recession To measure coverage, we focus on the UI recipiency rate, which is the share of jobless workers who received UI benefts In comparison, gains to employment were modest at most Our primary measure of employment is the percentage of the adult population that is employed in each month Data on the demographics of extension program benefciaries is only available in California, and it suggests that workers from disadvantaged backgrounds were disproportionately afected by the expirations In California, UI claimants who were relying on the PEUC extension program when it expired were more likely to identify as Black or women Older and less educated workers were also more impacted by these programs expiring As a whole, our results paint a positive picture of the efects of the federal UI expansions on the U S labor market Our fndings of a dramatic decline in UI recipiency without a meaningful rise in employment imply the pandemic UI expansions increased coverage and bolstered incomes of unemployed workers without a substantial efect on employment As in many other studies, these results rely on comparisons of changes in outcomes between states over time The assumptions underlying our research design are discussed in further detail in the report.This work has been supported, in part, by the University of California Multicampus Research Programs and Initiatives grants MRP-19-600774 and M21PR3278
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".