Determinants of Union Dissolution and Remarriage in India: Evidence from the National Family Health Survey
Bibliographic record
Abstract
Although rates of union dissolution due to divorce, desertion or separation are very low in India, the number of such women is quite large. Existing studies have established that union dissolution affects welfare of women substantially. Women facing dissolution may opt to remarry but that has its own challenges given the patriarchal nature of the Indian society. The analysis of determinants of union dissolution and remarriage in India is necessary to identify the groups at risk, and is also an underresearched area that needs to be supplemented by further studies. The present study uses the fifth round of the National Family Health Survey data undertaken in 2019-20 to analyze the incidence of union dissolution and remarriage among ever married women in India, its variation over socio-economic correlates and to identify its determinants. The econometric analysis is based on a sequential logit model. The study finds that the likelihood of dissolution is relatively higher among poor and less educated women, those belonging to the minority communities, and who are childless or there is an absence of sons amongst the children born. Remarriage, on the other hand, is not a socially driven phenomenon and depends largely on the personal choice of the woman. The results of this study imply that the adverse impact of dissolution will be magnified as dissolution is more likely among women who are already vulnerable. It calls for providing legal protection to women being abandoned and divorced, and introducing measures to ensure their socio-economic welfare.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".