Foreign Direct Investment and Border Security Issues-- A Multi-Country, Multi-Sector Computable General Equilibrium Framework
Bibliographic record
Abstract
The objectives of this research are to develop a multi-region multi-sector CGE model with FDI that will allow assessing the impact of security measures on the Canadian and U.S. economies. We assess the impact in terms of Canada-U.S. bilateral trade and foreign direct investment, but also in terms of Canada’s trade and FDI with the rest of the world. This study is conducted at the aggregate and sectoral level to illuminate the impact on different industries of the Canadian and U.S. economy. Finally we assess the impact of increased costs on Canada’s economic prosperity, as measured by GDP, and international competiveness as measured by its terms of trade. The methodology is a Computable General Equilibrium (CGE) model, the quantitative instrument that allows measures of the impact of policy change on economic variables, in terms of the entire economy but also in terms of specific industries. The model describes the structure of the Canadian and U.S. economies and the rest of the world. The model features production activities and consumption in each region as well as the flow of trade and investment among regions. It assumes free movement of labour and physical capital across economic sectors, and foreign direct investment across economic regions. The model distinguishes between the activities of domestic and foreign-owned firms at the microeconomic level, both in terms of demand and production characteristics following the methodology of Petri (1997), and Verikios and Zhang (2001), and as further developed in Mérette, Papadaki, Lan and Hernandez (2008) and in Mérette, Georges and Dissou (2008). Trade costs generated by border delays will take two forms. In the case of goods and services, we assume that border delays and compliance costs led to a sectoral drop in exports and imports as documented in Storer and Globerman (2009) and in Grady (2009). They also affect investment returns and hence distort investor decisions. In the case of firms producing goods, they have the choice of holding inventories rather than facing tariff equivalencies described above. Inventories affect transportation cost that may be absorbed by carriers. How the cost burden will be shared across sectors will determine the impact across production sectors, especially between transportation and non-transportation sectors. We expect the overall cost relatively smaller than previous literature but sectoral effect very significant.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".