What Shapes Automotive Investment Decisions in a Contemporary Global Economy?
Bibliographic record
Abstract
Amid considerable public debate, governments across North America have offered rich incentive packages to entice investment from multinational automotive corporations. This article explores the relative importance of government incentives in influencing automotive corporations' decisions to invest in Canada on the basis of a data analysis of investment incentives in Canada and the United States, a case study of Toyota's decision to invest in the Woodstock assembly plant, and a series of interviews with industry and government stakeholders. Our article concludes that although locational costs are a major determinant of investment decisions, the corporate decision to build a new assembly plant is a multimillion-dollar long-term investment with considerable risks. In assessing the risks and estimated costs of these investments, we found evidence of major variances between companies in what determined their decision. Although incentives were usually important, our research points to the influence on investment decisions of soft factors, including the structural relationship between the home office and branch plant; leadership of branch plant operations; and relationships between actors, in particular between corporate actors at headquarters and the branch plant, different levels of government, and government and corporate actors. Finally, we show that institutional fragmentation, including competition within and between different levels and branches of government, can erode policy effectiveness when governments attempt to attract foreign capital.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".