Skilled Labor Uncertainty and Corporate Investment: Evidence from H-1B Visa Lottery Cycles
Bibliographic record
Abstract
We study how periodic uncertainty about skilled labor supply affects corporate investment using the H-1B visa program for skilled workers as an empirical setting. Exploiting cross-regional variation in H-1B labor flows based on historical immigrant enclaves, we find that firms in regions that attract more H-1B workers concentrate their investments in the quarter after uncertainty about visa access is resolved by the H-1B lottery. Consistent with a skilled labor uncertainty channel, we find that the investment spikes are confined to industries where invested capital cannot be easily redeployed, to firms that cannot easily find domestic substitutes for lost H-1B workers, and to firms that cannot easily arrange alternative employment visas for their foreign employees. This paper was accepted by Camelia Kuhnen, finance. Funding: S.-J. Xu gratefully acknowledges research support from the University of Alberta. Supplemental Material: The internet appendix and data files are available at https://doi.org/10.1287/mnsc.2023.03486 .
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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 | 0.016 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".