Has ICT polarized skill demand? Evidence from eleven countries over 25 years. Mimeo, London School of Economics
Bibliographic record
Abstract
The labor markets of the US and many other OECD countries have become more “polarized ” with demand for workers in the middle of the skill distribution falling relative to those at the top and (in recent years) also the bottom of the skill distribution. We test the hypothesis of Autor, Levy, and Murnane (2003) that this is partly due to information and communication technologies (ICT) complementing highly educated workers and substituting for routine tasks often performed by middle educated workers (with little effect on low educated workers performing manual non-routine tasks). We use a new dataset on the US, Japan, and nine Western European countries between 1980 and 2004 finding evidence consistent with ICT-based polarization. We show that industries that experienced faster growth in ICT also experienced increases in relative demand for high education and falls in relative demand for mid-level education. Trade openness is also associated with polarization, but this is not robust to controls for technology (like R&D). Measured technologies can account for up to a quarter of the growth in demand for the college educated in OECD countries the last quarter century.
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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 | 0.002 | 0.005 |
| 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 | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 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".