Offshoring and Productivity: A Micro-data Analysis
Bibliographic record
Abstract
Offshoring has become increasingly important for businesses, especially manufacturing firms, to compete in increasingly competitive domestic and international markets. This paper empirically studies the association between offshoring, productivity and plant characteristics by focusing on the geographical dimension of plants ’ business activities. Using Statistics Canada’s Survey of Innovation 2005, which linked to Annual Surveys of Manufacturers, it demonstrates that material offshoring was highly associated with firms’ outward-oriented business activities including foreign operation, investing in foreign M&E, and exporting, after controlling for offshoring and operating locations advantages and industry-specific effects. For R&D offshoring, it is found that it was mainly associated with investment in foreign M&E. In addition, this paper shows that material offshoring is positively associated with productivity and that the association is significantly larger for material offshoring to non-U.S. countries than for material offshoring to the U.S. after controlling for the effects of being multinationals, the education level of workers, and plant size.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".