Male Sterilization and Persistence of Violence: Evidence from Emergency in India
Bibliographic record
Abstract
Can forced sterilization programs targeting men lead to male-perpetrated violence? This paper investigates the impact of a government-mandated male sterilization program introduced in India on the rise of violence. Launched in April 1976, the program predominantly targeted men and saw heterogeneous implementation across India over 10 months. Using various household surveys and newly digitized historical data sources, we study whether the program triggered unintended effects on violence, measured by crime rates. Using a difference-indifferences strategy by exploiting geographical variation in coercion intensity, we find that an increase in exposure to the program led to an increase in violent crime rates of 7% for the average district, which persisted over time. Violent crimes against women primarily drive the increase in crime rates, as rapes are increasing by 22% for the average district. We find that the program was ineffective in reducing fertility, so we hypothesize that a forced sterilization program targeting men may increase violence against women through two main channels: the program inducing trauma and impacting perceptions of masculinity. In line with those channels, we see that districts with high coercion intensity correlate with more harmful gender norms: higher levels and acceptance of Intimate Partner Violence, lower bargaining power of women and lower contraception adoption.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".