The Changing Time use of U.S. Welfare Recipients between 1992 and 2005
Bibliographic record
Abstract
Abstract This paper looks at the changes in the time allocation of welfare recipients in the United States following the 1996 welfare reform and other changes in their economic environment. Time use is a major determinant of well-being, and for policymakers to understand the broad influences that their policies can have on a population they ought to consider changes in all activities, not simply paid work. While an increase in market work of the welfare population has been well documented, little is known on the evolution of the balance of their time. Using the Current Population Survey to model the propensity to receive welfare, together with a multiple imputation procedure, I replicate previous difference-in-differences estimates that found an increase in child care and a decline in nonmarket work. However when additional data sources are used, I find that time spent providing child care does not increase. This is especially relevant as welfare recipients are overwhelmingly poor single mothers and the welfare reform increased time at work with ambiguous effects on time spent with children. I also find that time at work follows business cycles, with dramatic increases in work time throughout the strong economy of the late 1990s, accompanied by less time in leisure activities.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".