The gender gap in intergenerational mobility: Evidence of educational persistence in Brazil
Bibliographic record
Abstract
This paper employs mobility matrices, univariate regressions and multivariate econometric techniques based on the recently published nationally representative household survey (PNAD-2014) from Brazil to investigate the relevance of the gendered patterns in the intergenerational transmission of educational attainment between parents and their descendants. The empirical evidence from these three different approaches is absolutely unanimous: In Brazil there is a significant variation in degree of mobility across genders, with a higher mobility level for daughters than for sons. The reason for this gender gap in mobility lies in the chances of attaining the educational levels: regardless of the educational background of the parents, females have a lower chance of remaining without school certificate and a greater probability to achieve a tertiary education. The results of this paper point out also that the educational attainment of children is strongly associated with the education of their most educated parent, regardless of their gender and this correlation is higher for female than for male. Concerning the evolution of the persistence in education over time, the findings indicate for both sexes a significant increase in intergeneration mobility over the last decades. However this positive evolution is much more modest when the relative deviation in education across generations is excluded from the investigation. Finally, this study has demonstrated that parental occupation levels and individual characteristics (race, locality of residence and year of birth) also have a statistically significant effect on the prospects for mobility.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".