Which Job Skills Are Complementary To IT Adoption And Use? Sabrina Wulff Pabilonia, U.S. Bureau of Labor Statistics
Bibliographic record
Abstract
Abstract: Previous research has shown that the returns to computer use vary by occupation and the types of software applications used. We use data on nine different job skill requirements from the BLS ’ National Compensation Survey to examine which skills may be important in explaining the wage premium attributable to IT adoption and use in Canada. The data on job skills are linked to workers in the 1999–2004 Canadian Workplace and Employee Survey (WES), by detailed occupation and major industry. Using the panel in the WES and employing a first-differences model, we are able to control for time-invariant individual and establishmentlevel unobserved heterogeneity. We find that workers whose jobs are more autonomous and involve greater interpersonal skills receive a wage increase from using a computer in general. Furthermore, we find interpersonal skills are complementary to e-mail use. The ability to work autonomously is complementary to e-mail use and word processing.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".