Identification of Time-Varying Transformation Models with Fixed Effects,\n with an Application to Unobserved Heterogeneity in Resource Shares
Bibliographic record
Abstract
We provide new results showing identification of a large class of fixed-T\npanel models, where the response variable is an unknown, weakly monotone,\ntime-varying transformation of a latent linear index of fixed effects,\nregressors, and an error term drawn from an unknown stationary distribution.\nOur results identify the transformation, the coefficient on regressors, and\nfeatures of the distribution of the fixed effects. We then develop a\nfull-commitment intertemporal collective household model, where the implied\nquantity demand equations are time-varying functions of a linear index. The\nfixed effects in this index equal logged resource shares, defined as the\nfractions of household expenditure enjoyed by each household member. Using\nBangladeshi data, we show that women's resource shares decline with household\nbudgets and that half of the variation in women's resource shares is due to\nunobserved household-level heterogeneity.\n
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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 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".