Sexual Bias and Household Consumption : A Semiparametic Analysis of Engel curves in Rural China
Bibliographic record
Abstract
We analyze Engel curves for nuclear households in rural China. The sample includes more than 5000 nuclear families covering nineteen out of thirty Chinese provinces. We consider expenditures on food, also subdivided into several food subcategories such as cereals, or meat and fish, and other consumption categories such as alcohol and tobacco, medical, and educational goods. We use the semiparametric partially linear model. This allows for any functional form relationship between the budget shares and total expenditures, but assumes that the demographic variables enter the model in a linear way. We correct for potential endogeneity of total expenditures. Our results suggest that there are economies of scale in families' consumption expenditure patterns. We find some differences in consumption patterns which relate to differences in gender of children, which can be seen as evidence of sexual bias related to a commonly believed existing preference for boys.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".