The Effects of Wealth, and Unemployment Benefits on Search Behavior and Labor Market Transitions
Bibliographic record
Abstract
During the past decade, many researchers have examined the theoretical predictions of labor search models with endogenous job search intensity. For a risk adverse individual, search intensity depends on variables such as individual wealth and the level of unemployment benefits. Since wealth and unemployment benefits affect search intensity, they also affect the duration of unemployment spells. Although there are a small number of papers that empirically estimate the relationship between search intensity and unemployment benefits, none focus on the effects of savings on search intensity. This omission is primarily due to the lack of suitable datasets. To determine the effects of wealth and unemployment benefits on search intensity and unemployment duration, we estimate a simultaneous equation model of search intensity, reservation wages, labor market transitions and wealth using a sample from the 1984 Survey of Income and Program Participation. We examine whether wealth and unemployment insurance have different effects on the intensive search margin (the number of contacts) and the extensive search margin (the number of search methods). Our results yield insights into the effectiveness of different methods of search, the effect of the unemployment insurance benefits, and the magnitude of the discouraged worker effect in the U.S
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".