The Great Reversal in the Demand for Skill and Cognitive Tasks
Why this work is in the frame
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Bibliographic record
Abstract
What explains the current low rate of employment in the US? While there has been substantial debate over this question in recent years, we believe that considerable added insight can be derived by focusing on changes in the labor market at the turn of the century. In particular, we argue that in about the year 2000, the demand for skill (or, more specifically, for cognitive tasks often associated with high educational skill) underwent a reversal. Many researchers have documented a strong, ongoing increase in the demand for skills in the decades leading up to 2000. In this paper, we document a decline in that demand in the years since 2000, even as the supply of high education workers continues to grow. We go on to show that, in response to this demand reversal, high-skilled workers have moved down the occupational ladder and have begun to perform jobs traditionally performed by lower-skilled workers. This de-skilling process, in turn, results in high-skilled workers pushing low-skilled workers even further down the occupational ladder and, to some degree, out of the labor force all together. In order to understand these patterns, we offer a simple extension to the standard skill biased technical change model that views cognitive tasks as a stock rather than a flow. We show how such a model can explain the trends in the data that we present, and offers a novel interpretation of the current employment situation in the US.
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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 | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it