The Size, Growth, and Composition of Government: Analysis and Evidence for Canada and the United States
Bibliographic record
Abstract
The topic of measuring the growth and size of government, on which there is now a robust literature and policy debate, held little interest for economists in the 18th and 19th centuries and throughout much of the 20th century. Although it is a bit dangerous to date when perceptions of the importance of the topic began to shift, a good place to start is with Richard Bird's research for the Canadian Tax Foundation in 1970 on the growth of government spending in Canada. The purpose of this paper is to briefly review what Bird recognized is an evolutionary process, and then to examine the manner in which the growth and size of government can be measured in Canada and the United States. The trends in four key measures following the Second World War are defined and documented. The paper reveals two especially important features. The first is the increase in the role of the subnational government sector. The second is that, in both countries, the public sector is trending away from spending on (and taxing for) the public's physical infrastructure and toward transfers to individuals, particularly in the form of health and income security programs.
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) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.025 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 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".