SMALL AREA ESTIMATION: A BUSINESS SURVEY APPLICATION
Bibliographic record
Abstract
Statistics Canada’s Survey of Employment, Payrolls and Hours (SEPH) combines data from a monthly payroll survey with administrative data from the Canada Revenue Agency (CRA). Using this administrative data allows SEPH to produce estimates for domains with small sample sizes. SEPH is currently investigating regression composite estimation as a means to further improve the efficiency of its estimates. Despite these efforts, there are still domains of interest for which reliable estimates can not be produced. In this presentation we propose an estimator that combines small area estimation methods with the regression composite estimator and illustrate it using data from SEPH. RESUME L’Enquete sur l’emploi, la remuneration et les heures de travail (EERH) de Statistiques Canada combine des donnees issues d’une enquete mensuelle sur la remuneration et des donnees administratives de l’Agence du revenu du Canada (ARC). En utilisant ces donnees, l’EERH peut produire des estimations – relativement aux domaines – en prenant des echantillons de petite taille. L’EERH evalue presentement l’estimateur composite de regression afin de determiner s’il permettrait d’augmenter la precision de ses estimations. En depit de ces efforts, il reste des domaines d’interet pour lesquels on ne dispose d’aucun estimateur fiable. Cette presentation se penche sur un estimateur qui allie les methodes d’estimation de petits domaines a celles de l’estimateur composite de regression. Les donnees de l’EERH serviront d’etalon pour l’evaluation de cet estimateur. MOTS CLES : Modele transversal et de series chronologiques; MPLSBE Statistics Canada’s Survey of Employment, Payrolls and Hours (SEPH) is a monthly survey designed to produce estimates of levels and monthtomonth trends of payrolls, employment, paid hours and earnings. The target population is composed of all employees in Canada except for those in a few select industries (ex. agriculture, fishing and trapping, etc.). The survey makes extensive use of administrative data with the aid of a monthly survey. The administrative source is the Canada Revenue Agency’s (CRA) Payroll Deduction Accounts (PD7) file, which includes the number of employees and the gross monthly payroll for the approximately 1 million employers in Canada. The administrative data is combined with data from the monthly Business Payroll Survey (BPS) through the use of the Generalized Regression (GREG) estimator. Taking advantage of the administrative data has allowed SEPH to produce quality estimates at a moderately detailed industry by province level. However, data users are asking for levels of detail for which the SEPH sample is unable to support estimates of reliable quality. To address these demands, SEPH is investigating the use of small area estimation techniques which would produce quality estimates at domains where there are very few sampled units.
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".