Adjustment of unemployment estimates based on small area estimation in Korea
Bibliographic record
Abstract
The Korean Economically Active Population Survey (EAPS) has been conducted in order to produce unemployment statistics for Metropolitan Cities and Provincial levels, which are large areas. Large areas have been designated as planned domains, and local self-government areas (LSGA’s) as unplanned domains in the EAPS. In this study, we suggest small area estimation methods to adjust for the unemployment statistics of LSGA’s within large areas estimated directly from current EAPS data. We suggest synthetic and composite estimators under the Korean EAPS system, and for the model-based estimator we put forward the Hierarchical Bayes (HB) estimator from the general multi-level model. The HB estimator we use here has been introduced by You and Rao (2000). The mean square errors of the synthetic and composite estimates are derived by the Jackknife method from the EAPS data, and are used as a measure of accuracy for the small area estimates. Gibbs sampling is used to obtain the HB estimates and their posterior variances, and we use these posterior variances as a measure of precision for small area estimates. The total unemployment figures of the 10 LSGA’s within the ChoongBuk Province produced by the December 2000 EAPS data have been estimated using the small area estimation methods suggested in this study. The reliability of small area estimates is evaluated by the relative standard errors or the relative root mean square errors of these estimates. We suggest here that under the current Korean EAPS system, the composite estimates are more reliable than other small area estimates.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".