Working Part-Time: Earnings Penalties Among People with Disabilities Across Occupational Groups in France
Bibliographic record
Abstract
Although part-time work can provide important accommodations and flexibility for people with disabilities, the rise of contingent labor has left many of these workers in lower-paid jobs that are often associated with manual labor or routine service work. Why do people with disabilities work part-time? What are the benefits and drawbacks of part-time work, and do these vary across occupations? Using data from the 2013–2019 French Labor Force Survey (N = 122,033), we find that people with disabilities are more likely to work part-time across occupations, citing health reasons or the lack of full-time work opportunities. These higher rates of part-time work contribute to disability-related earnings gaps, but even among part-time workers, people with disabilities earn less than those without disabilities and experience wage penalties that can vary across occupations. By focusing on the reasons for part-time work and its effects across occupational groups, our analysis highlights structural factors—beyond disability status—that shape employment outcomes and are not fully addressed by equal pay policies for part- and full-time workers.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".