Household and population projections at sub-national levels: an extended cohort-component approach
Bibliographic record
Abstract
This paper describes the core methodological ideas, the required input data, and estimation issues of the extended cohort-component approach to simultaneously project household composition and population distributions at sub-national levels.We assess the projection accuracy of this approach by calculating projections from 1990 to 2000 and comparing projected with the census-observed counts in 2000 for the 50 states and the District of Columbia and for sets of randomly selected 25 counties and 25 cities which are more or less evenly distributed across the United States.The comparisons show that most absolute percent errors of the main indices of household and population projections and the corresponding census observations are small -less than three percent -and almost all errors are less than ten percent.We then report illustrative household projections from 2000 to 2050 for the 50 states and the District of Columbia, and household/housing projections for the small town of Chapel Hill, North Carolina up to 2015 in order to demonstrate the practical capabilities of the new approach.Among many interesting numerical outcomes, the aging of American households over the next few decades across all states and the aging of the housing market in Chapel Hill are particularly striking trends in the projections.Key words: household projections, cohort-component approach, sub-national household projections, household projections at the state level, sub-state area household projections, aging of households, aging of housing markets.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".