What (if any) are the returns to computer use?
Bibliographic record
Abstract
Using North American data, we revisit the question first broached by Krueger (1993 Krueger, AB. 1993. How computers have changed the wage structure: evidence from microdata. Quarterly Journal of Economics, 108: 33–60. [Crossref], [Web of Science ®] , [Google Scholar]) and re-examined by DiNardo and Pischke (1997 DiNardo, JE and Pischke, J-S. 1997. The returns to computer use revisited: have pencils changed the wage structure too?. Quarterly Journal of Economics, 112: 291–303. [Crossref], [Web of Science ®] , [Google Scholar]) of whether there exists a real wage differential associated with computer use. Employing a mixed effects model with matched employer–employee data to correct for the fact that workers and workplaces that use computers are self-selected, we find that computer users enjoy an almost 4% wage premium over nonusers. Failure to correct for worker and workplace selection effect leads to a more than twofold overestimate of this premium.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".