Bibliographic record
Abstract
While a burgeoning literature has extolled the conceptual virtues of directly measuring the underlying job tasks that define work activities, in practice task-based approaches have been hampered by well-known data limitations. We study wage determination using data collected specifically to address these limitations. Most fundamentally, we construct the first longitudinal dataset containing job-level task information for individual workers. New quantitative task measures detail the amount of time spent performing People, Information, and Objects tasks at different skill levels. These measures have clear interpretations, suggest natural proxies for on-the-job human capital accumulation, and provide methodological guidance for future data collection initiatives. A model of comparative advantage highlights the benefits of our data and guides specification and interpretation of empirical models. We provide several new findings about the effect of current and past tasks on wages. First, current job tasks are quantitatively important, with high skilled tasks being paid double the rate of low skilled tasks. Second, there is no evidence of learning-by-doing (i.e., effects of past tasks) for low skilled tasks, but strong evidence for high skilled tasks. Current and past high skilled information tasks are particularly valuable, although high skilled interpersonal tasks also play a significant role. Shifting 10 percent of work time from low skilled people tasks to high skilled information tasks increases a worker’s yearly wage by 22% after ten years. Additional human capital accumulation accounts for 70% of this increase, and the direct effect of performing different tasks accounts for the remainder.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.015 | 0.002 |
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".