Predicted Earnings Losses from\nGraduating during COVID-19
Bibliographic record
Abstract
Les travailleurs dont les conditions sur le marché du travail laissent à désirer en début de carrière sont exposés à des pertes de revenus pendant plusieurs années. Les étudiants de la cohorte 2021 de diplômés d'établissements canadiens d'enseignement secondaire et postsecondaire ont vu leurs perspectives d'emploi s'étioler par suite des interruptions de l'activité économique visant à freiner la propagation de la maladie provoquée par le coronavirus 2019 (COVID-19). Notre objectif, dans le présent article, est de prédire les pertes de revenu que risquent les membres de cette cohorte. Nous utilisons les données du recensement de la population pour démontrer qu'une hausse de 1 pour cent du taux de chômage au moment de la diplomation entraîne une diminution moyenne de 1,5 à 4 pour cent des revenus. À l'aide de prévisions du taux de chômage provenant de diverses sources, nous prédisons ensuite de quelle façon les diplômés de 2021 devraient s'en sortir. Nous supposons, dans l'application de cette méthode, que les récessions antérieures nous éclairent sur les conséquences de la récession actuelle. Nous estimons que le diplômé moyen de 2021 sera privé de 5 à 12 pour cent du revenu qu'il aurait gagné au cours de ses premières années de carrière si la pandémie n'avait pas sévi. 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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".