Closed doors everywhere? A meta-analysis of field experiments on ethnic discrimination in rental housing markets
Bibliographic record
Abstract
Discrimination is long seen as a meaningful factor for ethnic inequalities on rental housing markets. Yet empirically, the extent of discrimination is still debatable. For the first time, this article provides a quantitative meta-analysis of field experiments (in person audits and correspondence tests) that were run over the last four decades in the United States, Canada and Europe (N = 71). Special focus is given to a possible inflation of effect sizes by publication bias; to time trends; and to evidence for statistical discrimination. Taken together, nearly all experiments document the occurrence of ethnic discrimination. Effect sizes are inflated by publication bias, but there is still substantial evidence left once the bias is removed. The analysis reveals a consistent decline in the extent of discrimination over time, from moderate levels of discrimination in the 1970s and 1980s, up to only small but still statistically significant levels in the 1990s and 2000s. A significant part of the discriminatory behaviour can be attributed to missing information about the social status of applicants, which supports theories on statistical discrimination. It is discussed how future research could move our knowledge on the underlying mechanisms forward.
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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 | 0.082 | 0.176 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.024 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".