How Do Restrictions on High-Skilled Immigration Affect Offshoring? Evidence from the H-1B Program
Bibliographic record
Abstract
Highly skilled workers are not only a crucial and relatively scarce input into firms’ productive and innovative processes, but are also a critical resource determining competitive advantage. An increasingly high proportion of these workers in the United States were born abroad and permitted to work on skilled worker visas. How do multinational firms respond when artificial constraints, namely, policies restricting skilled immigration, are placed on their ability to hire scarce human capital? This paper combines visa microdata and comprehensive data on U.S. multinational firm activity to demonstrate that firms respond to restrictions on H-1B immigration by increasing foreign affiliate employment at the intensive and extensive margins, particularly in China, India, and Canada. The most impacted jobs were R&D-intensive ones, but there is some evidence that non-R&D employment was also affected. The paper highlights a means by which firms can circumvent constraining policies and mitigate country-level risk, and it also suggests that, for the average multinational company (MNC), this means is imperfect; for every visa rejection, they hire 0.4 employees abroad. The most globalized MNCs are the most likely to respond to these restrictions by offshoring, highlighting that firm capabilities—in the form of prior internationalization—shape the decision and ability to offshore in response to skilled immigration restrictions; indeed, these firms hire 0.9 employees abroad for every visa rejection. More broadly, the paper provides evidence of a push factor for internationalizing knowledge activity: artificial constraints on resources result in firms circumventing restrictive policies in ways that may not be anticipated by policy makers. This paper was accepted by Alfonso Gambardella, business strategy. Funding: This work was supported by the Mack Institute for Innovation Management. Supplemental Material: The online appendix and data are available at https://doi.org/10.1287/mnsc.2023.4715 .
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".