Bibliographic record
Abstract
Vulnerable workers, workers who have recently experienced a shock that could adversely affect their labor market prospects, experience large, long-lasting earnings losses -- on average. This dissertation investigates the mechanisms behind the losses of three groups of vulnerable workers and the role of public policy in mitigating these losses. In the first essay, I identify which displaced workers, workers who lose their job as a result of a firm or plant closing, are the most vulnerable. I find that a worker's duration of joblessness depends much more on conditions within that worker's occupation than conditions within that worker's industry. This suggests a worker's vulnerability is a function of their skills and less related to the goods and services they were previously producing. In the second essay, my collaborators and I estimate the causal impacts of benefits in California's Paid Family Leave program on a second group of vulnerable workers: new mothers. We find no evidence that a higher weekly benefit amount increases leave duration or leads to adverse future labor market outcomes for mothers with earnings near the maximum benefit threshold. In the third essay, my collaborators and I find strong evidence that Disability Insurance and Paid Family Leave program take-up is substantially higher in firms with high earnings premiums. Our results suggest that changes in firm behavior have the potential to impact social insurance use and thus reduce an important dimension of inequality in America.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".