GLOBALIZATION IN THE U.S. AUTO INDUSTRY: INTERNATIONAL MERGERS AND ACQUISITIONS AS DRIVERS OF INFORMATION SHARING
Bibliographic record
Abstract
There is growing interest in the role that corporate restructurings play in innovation. Although existing research has looked at the impacts of leveraged buyouts of companies on R&D spending and profits, few studies have looked at the effect that buyouts have on the output of R&D, and those studies focus on the impacts of domestic M&As. This paper contributes to the literature by empirically investigating the patenting behavior of “the Big 3” U.S. automobile companies and the effects of international knowledge spillovers from their foreign acquisitions. A log-log regression specification is used to isolate flows of knowledge for 1980-2000 between the country of the acquired subsidiary and the U.S. manufacturer. The Lexis-Nexis patent database is used to count international patent citations and “inventor country of origin” as measures of international information flows. Results indicate that there is a significant difference for the automobile industry in the information flows arising from M&As in two distinct locales: Germany, and the U.K. and Canada, with the latter pair giving greater knowledge spillovers for low levels of M&A, whereas the former gives greater spillovers as the volume of M&A activity increases.
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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 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".