Bibliographic record
Abstract
Abstract Governments perform national, labor-intensive censuses on a regular schedule. Censuses represent many of the largest peacetime expansions and contractions in federal hiring. The predetermined occurrence and scale of the census offers an economic experiment in the effects of temporary government hiring. This paper describes the construction of a data series on census hiring in the United States since 1950 and also collects available data on census employment in England and Wales, Canada, Korea, and Japan. Regressing total employment changes on census hiring yields coefficients extremely close to 1, indicating that there is no spillover from census hiring to the rest of the economy. Using census hiring and occurrence as instruments for government hiring in the US, Canada, and Korea, I estimate the effect of federal hiring on overall employment. Different samples yield varying jobs multipliers, with point estimates varying from -0.01 to 1.48. Including Korean and Canadian data yields lower multipliers, while including pre-1990 US data yields higher multipliers. In no specification can I reject the hypothesis that the job multiplier equals 1. In all specifications, standard errors are large enough that I can reject neither Keynesian nor crowd-out effects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.021 | 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".