Dynamic interplay of kinship and net-fertility: a comprehensive analysis across demographic transitions in Mexico
Bibliographic record
Abstract
This study explores the relationship between grandparental co-residence and net fertility – measured as the number of children under five – in Mexico across three key phases of its demographic transition: 1930 (pre-transitional), 1970 (population growth), and 2015 (fertility decline). Using census microdata and Poisson and multinomial regression models, we assess how intergenerational household structures interact with family socioeconomic status and cultural context to influence fertility outcomes. A central innovation is the use of a reconstructed 10% sample of the 1930 census, complemented by an imputation strategy to infer kinship ties not recorded in the original data. This enabled one of the earliest large-scale analyses of family co-residence and reproduction in historical Mexico. Findings reveal that the effects of grandparental co-residence vary by context. In 1930, cohabitation with grandmothers – especially in rural indigenous households – was associated with lower fertility, while cohabitation with grandfathers in non-indigenous rural areas corresponded to higher fertility. In 1970, amid pronatalist policies and economic growth, these effects weakened overall but persisted modestly in rural contexts. By 2015, co-residence – particularly with both grandparents – was associated with higher fertility and lower variability in fertility (CV), suggesting a stabilizing role in reproductive behavior. In contrast, households without grandparents exhibited lower fertility and greater heterogeneity, appearing to lead the shift toward reduced fertility. These findings illustrate how extended family structures both reflect and shape reproductive adaptation across shifting demographic contexts. By integrating evolutionary concepts such as cooperative breeding and social learning biases, the study offers insight into how kin networks can either support or constrain fertility depending on historical, socioeconomic, and cultural conditions. In doing so, it also contributes methodologically by addressing the complexity of nested and interactive effects – an essential step for understanding fertility dynamics in culturally diverse populations undergoing demographic transformation.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".