Household Surveys as a Source of Data for Event History Analysis
Why this work is in the frame
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Bibliographic record
Abstract
The authors introduce a method that allows the use of data from rotating panel surveys, a design used in many household or labour force surveys, to realize statistical analyses similar to event history analysis. The method is illustrated with two examples, one on the dynamics of poverty — the effect of demographic and socioeconomic factors on the hazard of becoming poor in Argentina — and the other on family dynamics — the conversion of consensual unions into marriages. Both examples use data from the Argentinean Encuesta Permanente de Hogares, a national survey that is not designed to collect prospective or biographical data. The method allows for the use of time-varying independent variables and thus allows one to estimate the effect of an event on the hazard of another event, as in conventional event history analysis; several examples are provided.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it