The Causal Effects of Education on Technology Adoption: Evidence from the Canadian Workplace and Employee Survey ∗
Bibliographic record
Abstract
Adoption of innovations by firms and workers is an important part of the process of technological change. Many prior studies find that highly educated workers tend to adopt new technologies faster than those with less education. Such a positive correlation between the level of education and the rate of technology adoption, however, does not necessarily reflect the true causal effect of education on technology adoption. Relying on data from the Workplace and Employee Survey (WES) (1999-2004), this study assesses the causal effects of education on technology adoption by using instrumental variables for schooling derived from Canadian compulsory school attendance laws. WES is an employer-employee linked panel data set, which provides rich information on firms and workers, including not only information on computer use, but also general information on technology adoption. We find that education increases the probability of using computer in the job. We also find that employees with more education possess longer work experiences in using computer, and are more likely to experience upgrade in computer-controlled or computer-assisted technology and experience upgrade in technological device than those with less education. Findings from this study not only shed light on the causal relationships between education and technology adoption, but also contribute to the growing literature on the private and social benefits of education.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".