Factors Determining Offshore Location Choice for R&D Projects: A Comparative Study of Developed and Emerging Regions
Bibliographic record
Abstract
abstract This paper examines empirically the determinants of offshore location choice between country clusters. Based on a dataset of 1722 R&D projects by MNEs located in developed and emerging countries, we examine the impact of home and host country, industry, and firm level variables on choice of location. We draw on the extended OLI paradigm to develop our hypotheses. Using the EU15 as a base group, a multinomial logit model is estimated between the regions of USA&Canada, Eastern Europe&Russia, emerging countries of Asia, and India&China. At the regional level, findings show that the R&D wage difference and knowledge infrastructure difference between home and host countries, the science and engineering talent pool size, and political risk level of host countries are important determining factors. At the firm level, experience of overseas R&D projects and prior experience of research in the host country are found to be important location determinants. A distinguishing feature of the paper is that we examine regions in relation to a base region, and then further examine the impact of a marginal change in independent variables on the likelihood of the choice of a region for new offshore R&D projects.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".