Replication Data for: Poverty and Prejudice: Evidence from Myanmar
Bibliographic record
Abstract
Does poverty drive prejudice? We study this question in Myanmar, a deeply divided society where anti-Muslim sentiment surged during a partial democratic transition in the mid-2010s. Drawing on theories of economic competition and scapegoating, we test whether material hardship predicts exclusionary attitudes using new data from a nationally representative survey of 22,000 adults belonging to the majority Buddhist group. We find a large and consistent association: both poorer individuals and poorer townships are more likely to express Islamophobia. This relationship persists when leveraging a plausibly exogenous income shock caused by severe flooding. Poverty is more predictive of anti-Muslim sentiment than key alternative explanations for intergroup animus. It also correlates with hostility toward other minorities (Hindus and Indians), indicating that poverty is tied to a more general tendency to denigrate outgroups. Our findings shed light on the economic foundations of polarized social preferences and may help identify communities at heightened risk of ethnoreligious conflict.
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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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.066 | 0.031 |
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".