Economic growth and ethnic violence: An empirical investigation of Hindu–Muslim riots in India
Why this work is in the frame
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Bibliographic record
Abstract
Abstract Most studies of Hindu–Muslim riots in India have tended to emphasize the effects of social, cultural, or political factors on the occurrence of ethnic violence. In this article, the authors focus on the relationship between economic conditions and riots. Specifically, this article examines the effect of economic growth on the outbreak of Hindu–Muslim riots in 15 Indian states between 1982 and 1995. Controlling for other factors, the authors find that just a 1% increase in the growth rate decreases the expected number of riots by over 5%. While short-term changes in growth influence the occurrence of riots, this study finds no evidence of a relationship between the levels of wealth in a state and the incidence of ethnic riots. Moreover, by including state fixed effects, the authors determine that the negative relationship found between economic growth and riots is driven primarily by the relationship between growth and riots within a state over time rather than across states. These results are robust to controlling for a number of other factors such as economic inequality, demographic variables, political competition, temporal lags, spillover effects from adjacent states, and year effects. Finally, to address potential concerns that economic growth could be a consequence rather than a cause of violence or that other unobserved factors could confound the relationship between economic growth and the occurrence of Hindu–Muslim riots, the authors also employ instrumental variables (IV) estimation, using percentage change in rainfall as an instrument for growth. The results with IV estimation are similar to the results with non-IV estimation in terms of sign and significance, indicating that the negative effect of economic growth on riots is not due to reverse causality or omitted variables bias.
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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 | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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