The Pitfalls of Work Requirements in Welfare-to-Work Policies: Experimental Evidence on Human Capital Accumulation in the Self-Sufficiency Project
Bibliographic record
Abstract
This paper investigates whether policies that encourage recipients to exit welfare for full-time employment influence participation in educational activity. The Self-Sufficiency Project ('SSP') was a demonstration project where long-term welfare recipients randomly assigned to the treatment group were offered a generous earnings supplement if they exited welfare for full-time employment. We find that treatment group members were less likely to upgrade their education along all dimensions: high-school completion, enrolling in a community college or trade school, and enrolling in university. Thus, 'work-first'; policies that encourage full-time employment may reduce educational activity and may have adverse consequences on the long-run earnings capacity of welfare recipients. We also find that there was a substantial amount of educational upgrading in this population. For instance, among high-school dropouts at the baseline, 19% completed their diploma by the end of the demonstration. Finally, we simulate the consequences of the earnings supplement in the absence of adverse effects on educational upgrading. Doing so alters the interpretation of the lessons from the SSP demonstration.
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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 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".