Do Youth Employment Programs Work? Evidence from the New Deal
Bibliographic record
Abstract
We study the Civilian Conservation Corps (CCC) -the first and largest youth training program in the U.S. in operation between 1933 and 1942 -to provide the first comprehensive assessment of the short-and long-term effects of means-tested youth employment programs. We use digitized enrollee records from the CCC program in Colorado and New Mexico and matched these records to the 1940 Census, WWII enlistment records, Social Security Administration records, and death certificates. We find that enrollees who spent more time in CCC training grew taller, lived longer lives and had higher lifetime earnings as a result of their participation in the program. We also find modest increases in the educational attainment of the participants and increases in short term geographic mobility. In contrast, we find no evidence that their labor force participation or wages increased in the short run. To assess the internal and external validity of the results, we compare our estimates to those derived from a randomized evaluation of Job Corps, the modern version of the CCC, conducted in the 1990s. The RCT's results show that our empirical strategy delivers estimates that are in line with the experimental estimates. Overall, we find significant long-term benefits in both longevity and earnings, suggesting short and medium-term evaluations underestimate the returns of training programs, as do those that fail to consider effects on longevity.
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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 | 0.010 | 0.034 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".