Predicted Earnings Losses from Graduating during COVID-19
Bibliographic record
Abstract
Poor labour market conditions at the start of a worker's career can result in earnings losses for many years. The 2021 cohort of Canadian high school and post-secondary students have seen employment prospects diminish amid economic lockdowns to contain the spread of coronavirus disease 2019 (COVID-19). The goal of this article is to predict earnings losses for this cohort. We use Census of Population data to show that a 1 percent increase in unemployment at the time of graduation leads to a 1.5-4 percent average decrease in earnings. Then, using unemployment rate forecasts from various sources, we predict how this year's graduating class is expected to fare. Our approach assumes previous recessions are informative about the effects of the current recession. We estimate that a typical 2021 graduate loses 5-12 percent of the amount they would have earned over the first few years if the pandemic had not occurred.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 teacher head, 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".