Data and Code for: Detecting Mother-Father Differences in Spending on Children: A New Approach Using Willingness-to-Pay Elicitation
Bibliographic record
Abstract
This paper tests whether mothers and fathers differ in their spending on their daughters relative to their sons. We compare mothers' and fathers' willingness to pay (WTP) for specific goods for their children, diverging from the previous literature's approach of comparing the expenditure effects of mothers' versus fathers' income. Our method, which we apply in Uganda, allows us to estimate gender differences and explore mechanisms with greater precision. We find that fathers have a lower WTP for their daughters' human capital than their sons' human capital, whereas mothers do not. We then examine why spending patterns differ between mothers and fathers, e.g., altruism, personal returns to investing in children. We find evidence that altruism plays a role: fathers' WTP for goods that simply bring joy to their daughters is lower than their WTP for such goods for their sons, but mothers' is not.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 teacher head, 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".