Sibling Death Clustering in India: State Dependence<i>Versus</i>Unobserved Heterogeneity
Bibliographic record
Abstract
Summary Data from a range of environments indicate that the incidence of death is not randomly distributed across families but, rather, that there is a clustering of death among siblings. A natural explanation of this would be that there are (observed or unobserved) differences across families, e.g. in genetic frailty, education or living standards. Another hypothesis that is of considerable interest for both theory and policy is that there is a causal process whereby the death of a child influences the risk of death of the succeeding child in the family. Drawing language from the literature on the economics of unemployment, the causal effect is referred to here as state dependence (or scarring). The paper investigates the extent of state dependence in India, distinguishing this from family level risk factors that are common to siblings. It offers some methodological innovations on previous research. Estimates are obtained for each of three Indian states, which exhibit dramatic differences in socio-economic and demographic variables. The results suggest a significant degree of state dependence in each of the three regions. Eliminating scarring, it is estimated, would reduce the incidence of infant mortality (among children who are born after the first child) by 9.8% in the state of Uttar Pradesh, 6.0% in West Bengal and 5.9% in Kerala.
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How this classification was reachedexpand
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".