Discrimination towards Youth in Goods and Services Markets: Evidence from Field Experiments in France
Bibliographic record
Abstract
In this study, we carried out seven distinct and independent rounds of correspondence tests to detect discriminatory behavior in domains and markets in France that have not previously been subjected to much investigation in the literature. The study areas included: purchasing a used car; purchasing an auto insurance policy; applying for a car loan; purchasing supplemental health insurance; enrolling in an adult training program; purchasing an existing small business; and renting vacation accommodations. Access to these items and services are associated with either potential pathways to a middle-class job or hallmarks of a middle-class living standard. We seek to discern evidence of discriminatory behavior according to the criteria of age, gender, ethnic origin, and the reputation of the neighborhood of residence (advantaged or disadvantaged). We discern statistically significant patterns in our observed statistical outcome (callback rates) in all seven markets, which we interpret as possibly indicative of discriminatory behavior; however, the criteria, the magnitudes, and the signs differ from one market to another. One finding is that differential treatment based on ethnicity and the reputation of the neighborhood (i.e., neutral or disadvantaged) might not be as systematic and mutually reinforcing as they are frequently perceived to be in the domains of labor and housing markets.
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".