Gender Differences in Intra-Household Financial Decision-Making: An Application of Coarsened Exact Matching
Bibliographic record
Abstract
Most studies that explore collective models of intra-household decision-making use economic outcomes such as human capital, earnings, assets, and relative income shares as proxies of the relative distribution of bargaining power. These studies, however, fail to incorporate important measures of control over and management of the economic resources within households. In the current study, a direct measure of financial decision-making power within the household is used to directly assess the distribution of bargaining power. Coarsened exact matching, an identification strategy not yet applied in studies of this nature, is applied to couple-level observational data from South Africa’s longitudinal National Income Dynamics Study. The influence of gender differences in intra-household decision-making on resource allocations to per capita household expenditure is assessed. In the case of greater financial decision-making power in couples being assigned to wives rather than husbands, per capita household expenditure on education increases significantly. The empowerment of women with financial decision-making power therefore holds the promise of realizing the benefits of investments in human capital.
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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 | 0.016 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".