Wrong Origin or Wrong Neighborhood: Explaining Lower Labor Market Performance of French Individuals of African Origin
Bibliographic record
Abstract
In this study, I estimate models with cluster xed e ects, in order to disentangle the ef-fects of neighborhoods of residence from the ones of parents' nationalities on the probabilityfor a French individual to be employed and on his wages. I take advantage of the samplingstructure of the LFS to purge cluster e ects, using a conditional logit model for employmentand panel OLS for wages. The employment probability gap and the wage gap between Frenchworkers with French-born parents and French workers with parents of African origin may bedecomposed into three components: one due to a gap in observables, one due to a gap inneighborhood quality, and one unexplained component. It is found that the neighborhoodquality accounts for around one quarter of the employment di erential, while the unexplainedpart covers the rest. Wage di erentials are mainly explained by di erences in average in-dividual charecteristics. When cluster e ects are introduced in the model, di erences inneighborhood quality accounts for around one quarter of the wage gap.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".