G-multipliers in Canada: How large? And Why?
Bibliographic record
Abstract
We estimate the effects of government spending on GDP in Canada using the sign restrictions approach with quarterly data that spans from 1961 to 2019. The variables that enter our vector autoregressive model are carefully chosen to reflect the distinct characteristics of the economy, in particular, its linkages with US business cycles. We find large multipliers that are above 2 on impact and in the long-run. They are not specific to the state of the economy. Moreover, neither net exports and real exchange rates nor terms-of-trade respond significantly to the government spending shock. Hence, we explore two channels that involve specific closed-economy characteristics of Canada to explain the size of the multipliers. First, the production of public goods in Canada features a much larger labour share than the production of private goods. Second, we argue that the level of public capital relative to its GDP is suboptimal. Based on a general equilibrium model, we show and explain how these two characteristics matter for the multipliers.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".