Impact des incitations à l’investissement du gouvernement fédéral canadien dans le secteur manufacturier, de 1965 à 1974
Bibliographic record
Abstract
The Canadian federal government calls upon substantial fiscal incentives between 1965 to 1974 to increase the flow of capital expenditures in the manufacturing sector, in order to reduce regional disparities, to alleviate an excessive unemployment rate and to insure a higher growth rate. The objective of our research is to evaluate the effectiveness of these incentives in inducing larger investment expenditures. We use econometric investment functions based on neoclassical and "hybrid" models of firm behavior, applied to Canadian yearly manufacturing time series from 1946 to 1974. The neoclassical and hybrid models agree that the incentives have a substantial impact during the years 65-69; and a marginal impact during the 69-74 years. The neoclassical model explains the marginal impact of incentives in the 69-74 period by a displacement through time of investment projects; there is an acceleration-deceleration effect attributed to the incentives. Investment expenditures of the 69-74 period are submitted to an upward pressure because of the 69-74 incentives, and to a downward pressure because of a deceleration effect associated with the 65-69 incentives. We conclude that the incentives are effective in the short run in stimulating investment expenditures (the mean lag of their impact is approximately eighteen months) but that an acceleration-deceleration effect shows up after three years.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".