Are Foreign and Domestic Information Technology Professionals Complements or Substitutes?
Bibliographic record
Abstract
The globalization of work raises important questions related to the employment of workers across geographies and how the complementarity or substitution of workers across country borders influences firm profitability. In particular, tension often exists regarding the substitution or complementarity of workers located outside the U.S. or within the U.S. for American firms. We investigate this question in the context of information technology (IT) professionals and assess how domestic and foreign IT professionals contribute to firm profit by utilizing a rare firm-level dataset with information on the locational composition of IT professionals within and outside the U.S. Exploiting a labor market supply-side exogenous shock induced by the American Competitiveness in the Twenty-First Century Act (AC21), which increased the availability of H-1B visas in the U.S. in 2001, we find that foreign IT professionals located offshore and American IT professionals located onshore complement each other in generating profits. Our model and empirical findings are important both for informing firm choices and for shaping and creating public policies that so far appear to have been informed more by emotion than by data and science.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".