Should We Teach Old Dogs New Tricks? The Impact of Community College Retraining on Older Displaced Workers. WP 2003-25.
Bibliographic record
Abstract
This paper estimates the returns to retraining for older displaced workers--those 35 or older--by estimating the impact that community college schooling has on their subsequent earnings. Our analysis relies on longitudinal administrative data covering workers who were displaced from jobs in Washington State during the first half of the 1990s and who subsequently remained attached to the state’s work force. Our database contains displaced workers' quarterly earnings records covering 14 years matched to the records of 25 of the state's community colleges. We find that older displaced workers participate in community college schooling at significantly lower rates than younger displaced workers. However, among those who participate in retraining, the per-period impact for older and younger displaced workers is similar. We estimate that one academic year of such schooling increases the long-term earnings by about 8 percent for older males and by about 10 percent for older females. These per-period impacts are in line with those reported in the schooling literature. These percentages do not necessarily imply that retraining older workers is a sound social investment. We find that the social internal rates of return from investments in older displaced workers' retraining are less than for younger displaced workers and likely less than those reported for schooling of children. However, our internal rate of return estimates are very sensitive to how we measure the opportunity cost of retraining. If we assume that these opportunity costs are zero, the internal rate of return from retraining older displaced workers is about 11 percent. By contrast, if we rely on our estimates of the opportunity cost of retraining, the internal rate of return may be less than 2 percent for older men and as low as 4 percent for older women.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".