Population Synthesis: Comparing the Major Techniques Using a Small, Complete Population of Firms
Bibliographic record
Abstract
Recently, disaggregate modeling efforts that rely on microdata have received wide attention by scholars and practitioners. Synthetic population techniques have been devised and are used as a viable alternative to the collection of microdata that normally are inaccessible because of confidentiality concerns or incomplete because of high acquisition costs. The two most widely discussed synthetic techniques are the synthetic reconstruction method (IPFSR), which makes use of iterative proportional fitting (IPF) techniques, and the combinatorial optimization (CO) method. Both methods are described in this article and then evaluated in terms of their ability to recreate a known population of firms, using limited data extracted from the parent population of the firms. Testing a synthetic population against a known population is seldom done, because obtaining an entire population usually is too difficult. The case presented here uses a small, complete population of firms for the City of Hamilton, Ontario, for the year 1990; firm attributes compiled are number of employees, 3‐digit standard industrial classification, and geographic location. Results are summarized for experiments based upon various combinations of sample size and tabulation detail designed to maximize the accuracy of resulting synthetic populations while holding input data costs to a minimum. The output from both methods indicates that increases in sample size and tabulation detail result in higher quality synthetic populations, although the quality of the generated population is more sensitive to increases in tabular detail. Finally, most tests conducted with the created synthetic populations suggest that the CO method is superior to the IPFSR method.
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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 | 0.010 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".