Trends in racial and ethnic discrimination in hiring in six Western countries
Bibliographic record
Abstract
We examine trends in racial and ethnic discrimination in hiring in six European and North American countries: Canada, France, Germany, Great Britain, the Netherlands, and the United States. Our sample includes all available discrimination estimates from 90 field experimental studies of hiring discrimination, encompassing more than 170,000 applications for jobs. The years covered vary by country, ranging from 1969 to 2017 for Great Britain to 1994 to 2017 for Germany. We examine trends in discrimination against four racial-ethnic origin groups: African/Black, Asian, Latin American/Hispanic, and Middle Eastern or North African. The results indicate that levels of discrimination in callbacks have remained either unchanged or slightly increased overall for most countries and origin categories. There are three notable exceptions. First, hiring discrimination against ethnic groups with origins in the Middle East and North Africa increased during the 2000s relative to the 1990s. Second, we find that discrimination in France declined, although from very high to "merely" high levels. Third, we find evidence that discrimination in the Netherlands has increased over time. Controls for study characteristics do not change these trends. Contrary to the idea that discrimination will tend to decline in Western countries, we find that discrimination has not fallen over the last few decades in five of the six Western countries we examine.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".