Addressing Non-ignorable Panel Attrition Using External Population Data: Analysis of Demographic Events From Survey Data
Abstract
Empirical analysis of variation in demographic events within the population is facilitated by using longitudinal survey data because of the richness of covariate measures in such data, but there is wave-on-wave dropout. When attrition is related to the event, it precludes consistent estimation of the impacts of covariates on the event and on event probabilities in the absence of additional assumptions. The paper introduces an adjustment procedure based on Bayes Theorem that directly addresses the problem of nonignorable dropout. It uses population information external to the survey sample to convert estimates of event probabilities and marginal effects of covariates on them that are conditional on retention in the longitudinal data to unconditional estimates of these quantities. In many plausible and verifiable circumstances, it produces estimates of the marginal effect of covariates closer to the true unconditional quantities than the conditional estimates obtained from estimation using the survey data alone.
Ingestion failed: Traceback (most recent call last): File "/srv/app/app/worker.py", line 85, in run_once process_job(db, job) File "/srv/app/app/worker.py", line 39, in process_job pdf_path, info = fetch_pdf_temp(candidate["url"]) ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ File "/srv/app/app/downloader.py", line 129, in fetch_pdf_temp raise ValueError(f"PDF source returned HTTP {response.status_code}.") ValueError: PDF source returned HTTP 403.
No local PDF is available.
GROBID Extracted text; discontinued.
This text is generated from TEI extraction for accessibility, search, and TTS. Formulas, tables, figures, page layout, and references may not perfectly match the original PDF.
No accessible text representation is available. The text extraction service has been discontinued for the time being. If you require this service, for accessibility or any other reason, please submit an issue/request on this page.
Metadata
Issues
No public issues have been filed for this DOI.
Submit an issue
Record history
| When | Event | Field | Old | New |
|---|---|---|---|---|
| 2026-06-18 19:37:53.011249+00:00 | identifier_assigned | DSEID | DSEID-001-9654716 |