Ingroup Bias in Official Behavior: A National Field Experiment in China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Do ingroup biases distort the behavior of public officials? Recent studies detect large ethnic biases in elite political behavior, but their case selection leaves open the possibility that bias obtains under relatively narrow historical and institutional conditions. We clarify these scope conditions by studying ingroup bias in the radically different political, historical, and ethnic environment of contemporary China. In a national field experiment, local officials were 33% less likely to provide assistance to citizens with ethnic Muslim names than to ethnically-unmarked peers. We find evidence consistent with the ingroup bias interpretation of this finding and detect little role for strategic incentives mediating this effect. This result demonstrates that neither legacies of institutionalized racism nor electoral politics are necessary to produce large ingroup biases in official behavior. It also suggests that ethnically motivated distortions to governance are more prevalent than previously documented.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it