Educational Assortative Mating in Sub-Saharan Africa: Compositional Changes and Implications for Household Wealth Inequality
Bibliographic record
Abstract
Sub-Saharan Africa (SSA) is undergoing rapid transformations in the realm of union formation in tandem with significant educational expansion and rising labor force participation rates. Concurrently, the region remains the least developed and most unequal along multiple dimensions of human and social development. In spite of this unique scenario, never has the social stratification literature examined patterns and implications of educational assortative mating for inequality in SSA. Using 126 Demographic and Health Surveys from 39 SSA countries between 1986 and 2016, this study is the first to document changing patterns of educational assortative mating by marriage cohort, subregion, and household location of residence and relate them to prevailing sociological theories on mating and development. Results show that net of shifts in educational distributions, mating has increased over marriage cohorts in all subregions except for Southern Africa, with increases driven mostly by rural areas. Trends in rural areas align with the status attainment hypothesis, whereas trends in urban areas are consistent with the inverted U-curve framework and the increasing applicability of the general openness hypothesis. The inequality analysis conducted through a combination of variance decomposition and counterfactual approaches reveals that mating accounts for a nonnegligible share (3% to 12%) of the cohort-specific inequality in household wealth, yet changes in mating over time hardly move time trends in wealth inequality, which is in line with findings from high-income societies.
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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 | 0.003 |
| 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 | 0.001 |
| 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".