The impact of United States final demand on Canadian production : an input-output study
Bibliographic record
Abstract
In this thesis, the impact of United States final demand on Canadian demand and production is investigated using an interregional input-output model. First, the simple Leontief input-output model is considered. It is a disaggregated model of the production sector of an economy that allows a set of industry outputs to be expressed as a function of a corresponding set of industry final demands. It improves on other output determination models by admitting that industry outputs are interdependent. However, it requires the assumption of fixed production coefficients. Next, the extension of the model to incorporate interregional trade is considered. Several models are described that determine the industry outputs of each of a group of regions as functions of the industry final demands in all regions. A model is selected that differs from all of these, not in its essential algebraic structure, but in the method by which it is applied. In the simplified form in which it is used in this study, it requires that Canada's merchandise exports to the United States be reclassified according to the industry schemes of the Canadian and American input-output tables. The main advantage of the model over the other interregional models considered is that it allows the input-output tables of the individual regions to be used in their original form. Using the model, two questions are investigated. First, how do equal expenditures on the various components of United States final demand - Consumption, Fixed investment, Federal Government purchases, and State and local government purchases -compare in their impact on Canadian demand and output ? Second, in the period 1956 to I960, did variation in the level and pattern of United States final demand tend to aggravate fluctuations in Canadian demand, output, and net exports ? Several results are obtained. With reference to the first question, Investment expenditure is found to have considerably greater impact on Canadian demand and production than any of the other components of United States demand. The wide disparity in impact is largely explained by the concentration of Canadian exports to the United States on a few commodities. Concerning the second question, it is concluded that variations in both the level and pattern of United States final demand helped to generate fluctuations in the growth of Canadian demand and output. By contrast, the fluctuation of United States final demand tended to damp fluctuations in Canadian net exports.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".