Much Ado About Nothing: American Jobs and the Rise of Service Outsourcing to China and India
Bibliographic record
Abstract
We examine the impact on U.S. labor markets of offshore outsourcing in services to China and India.We also consider the reverse flow or 'inshoring' which is the sale of services produced in the United States to unaffiliated buyers in China and India.Using March-to-March matched CPS data for 1996-2006 we examine the impacts on (1) occupation and industry switching, (2) weeks spent unemployed as a share of weeks in the labor force, and (3) earnings.We precisely estimate small positive effects of inshoring and smaller negative effects of offshore outsourcing.The net effect is positive.To illustrate how small the effects are, suppose that over the next nine years all of inshoring and offshore outsourcing grew at rates experienced during 1996-2005 in business, professional and technical services i.e., in segments where China and India have been particularly strong.Then workers in occupations that are exposed to inshoring and offshore outsourcing (1) would switch 4-digit occupations 2 percent less often, (2) would spend 0.1 percent less time unemployed, and (3) would earn 1.5 percent more.These are not annual changes -they are changes over nine years -and are thus best described as small positive effects.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".