Cost-Benefit Analysis for a Quinquennial Census: The 2016 Population Census of South Africa
Bibliographic record
Abstract
Abstract The question of whether to carry out a quinquennial Census is faced by national statistical offices in increasingly many countries, including Canada, Nigeria, Ireland, Australia, and South Africa. We describe uses and limitations of cost-benefit analysis in this decision problem in the case of the 2016 Census of South Africa. The government of South Africa needed to decide whether to conduct a 2016 Census or to rely on increasingly inaccurate postcensal estimates accounting for births, deaths, and migration since the previous (2011) Census. The cost-benefit analysis compared predicted costs of the 2016 Census to the benefits of improved allocation of intergovernmental revenue, which was considered by the government to be a critical use of the 2016 Census, although not the only important benefit. Without the 2016 Census, allocations would be based on population estimates. Accuracy of the postcensal estimates was estimated from the performance of past estimates, and the hypothetical expected reduction in errors in allocation due to the 2016 Census was estimated. A loss function was introduced to quantify the improvement in allocation. With this evidence, the government was able to decide not to conduct the 2016 Census, but instead to improve data and capacity for producing post-censal 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 | 0.012 | 0.058 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".