THREE ESSAYS ON THE EMPLOYMENT AND ECONOMIC WELL-BEING OF VULNERABLE POPULATIONS
Bibliographic record
Abstract
Using data from the Canadian Survey of Labour and Income Dynamics (1993 through 2001), the first essay examines how wage differences between working age people with and without disabilities changed over time. Using Oaxaca-Blinder and Juhn, Murphy, and Pierce techniques to measure the share of the wage gap that remains unexplained after controlling for differences in observed wage-determining characteristics, the essay finds a substantial and growing wage gap between persons with and without disabilities that varies significantly throughout the distribution. Low skilled/low wage disabled workers are less seriously impacted by wage discrimination than their higher skilled/higher wage counterparts. Using retrospective data from the 1990 Panel of the Survey of Income and Program Participation, the second essay exploits state-level variation in legislation prohibiting disability discrimination prior to the passage of the Americans with Disabilities Act of the 1990 to test the effect of such laws on the timing of Social Security Disability Insurance (DI) application following the onset of a health condition. Using hazard models, the essay finds that workers who lived in states that had traditional disability discrimination prohibitions or such prohibitions plus a reasonable accommodation requirement were significantly slower in applying for DI benefits than were workers in states with no such prohibitions. Increasing the likelihood of acceptance onto the program increases the speed of application. Using data from the United States Current Population Survey, the British Household Panel Study, the German Socio-Economic Panel and the Japanese Survey of Income Redistribution, the third essay uses kernel density estimation to show how the income distribution changed between the peak years of the 1990s business cycle in these four major OECD countries. The entire after-tax household size-adjusted income distribution moved to the right in the United States and Great Britain. Germany and Japan experienced a decline in the middle mass of their income distributions that spread mostly to the right. In the United States and Japan, younger persons fared better than older persons, while the opposite was the case in Great Britain and Germany. Income inequality fell in all four countries among the older population.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".