An Approach to Predicting Regional Labor Market Effects of Economic Shocks: The COVID-19 Pandemic in New England
Bibliographic record
Abstract
The emergence of the COVID-19 pandemic led state and local governments throughout New England and much of the nation to issue ordinances restricting activity that might otherwise contribute to the spread of the disease. Individuals also freely adjusted their behavior, hoping to reduce the chances of infecting themselves or others. As a result, many employers have experienced substantial reductions in sales revenue, which were expected to generate harmful effects on the labor market. Even though the reversal of mandated policies and voluntary behavior changes are well under way, the initial effects and ongoing public health concerns may extend the time needed for labor market outcomes to improve substantially. This study uses pre-pandemic employment data by occupation and a conceptual framework focused on labor costs to identify the subpopulation most vulnerable to the economic shock and predict layoffs and unemployment in the second quarter of 2020. The analysis allows for the possibility of wage cuts mitigating job losses. Further extensions incorporate indirect effects due to reduced product demand from directly affected workers, as well as offsetting effects of a federal policy response. Predicted second-quarter layoffs and unemployment due to the pandemic vary throughout New England, and such adverse labor market effects tend to be somewhat smaller in the region than in the country as a whole. Additionally, official estimates of unemployment from available second-quarter data fall within the range of predictions, after accounting for plausible measurement error. This approach, which builds on the work of other recent analysis, should be helpful in estimating the regional labor market impact of future economic shocks.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".