Earnings Losses of Job Losers During the 2001 Economic Downturn,” Federal Reserve Bank of Philadelphia Business Review (Fourth Quarter
Bibliographic record
Abstract
ob losses may involve not only lost earnings during unemployment but also declines in earnings at subsequent jobs. After a timeconsuming job search, workers may need to restart their careers from scratch, accepting a lower wage. Workers may also need time to acquire new skills, and total earnings lost during such a period of re-adjustment can be considerable. But experiences may vary widely. In this article, using a novel data set, Shigeru Fujita and Vilas Rao provide evidence on earnings losses after unemployment. Although the usefulness of the evidence is limited by the short sample period, the data set allows us to ask some important questions, the answers to which may help inform us about important macroeconomic issues such as the cost of business-cycle fluctuations and the benefits of policies intended to avoid such fluctuations. acquiring new skills, establishing a new personal network of business associates, and so on, all of which may take a significant amount of time to accomplish. This re-adjustment period can be quite long, and thus total earnings lost can be considerable. This painful story would be relevant for at least some workers. But experiences may vary widely across individuals. In contrast to the example above, it is possible to imagine a situation in which workers make the same amount of money (or more) after a short unemployment spell or one where workers make less at the new job initially, but the losses are recovered quickly as a result of subsequent earnings growth. In these cases, earnings losses associated with the job loss are minor relative to one’s lifetime earnings, and unemployment may not be as costly and painful as the previous example suggests. 2 During economic downturns, more workers become unemployed and finding a new job becomes harder. Consequently, unemployment rises. Higher unemployment also means that there is a more intensive reallocation of workers from one job to another during downturns. 1 Shigeru Fujita is a senior economist in the Research
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".