R&D Tax Incentives as Enabling Institutions: Directing MNEs’ R&D Outsourcing Decisions
Bibliographic record
Abstract
MNEs’ R&D governance decisions are shaped by the host institutional environment, which entails constraining institutions and enabling institutions. The former restricts economic activities, and the latter orients MNEs toward certain possibilities over others. Prior internalization studies emphasize constraining institutions. They argue that because market failure increases transaction costs, MNEs pursue R&D internalization. This paper conceptualizes R&D tax incentives as the proxy for enabling institutions that actively and purposely guide MNEs’ R&D governance decisions toward R&D outsourcing. We hypothesize that R&D tax incentives direct MNEs to pursue R&D outsourcing by reducing the costs of R&D contracts, acting as information signals of legitimacy that facilitate MNEs’ access to local resources, and addressing market failure to compensate MNEs’ return on R&D. Specifically, the relationship is enhanced as the influence of the constraining institutions decreases. Thus, we hypothesize that the relationship is strengthened when 1) the host country’s intellectual property regulatory institution is stronger than that of the MNE’s home country, 2) a bilateral investment treaty exists between MNEs’ home and host countries, and 3) the MNE has prior outsourcing experience in the host country. We find empirical support for all our hypotheses using the Business Research Microdata of Statistics Canada.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 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".