Has ICT Polarized Skill Demand? Evidence from Eleven Countries over 25 years.” NBER Working Paper No
Bibliographic record
Abstract
OECD labor markets have become more “polarized ” with employment in the middle of the skill distribution falling relative to 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 the analytical tasks primarily performed by highly educated workers and substituting for routine tasks generally performed by middle educated workers (with little effect on low educated workers performing manual non-routine tasks). Using industry level data on the US, Japan, and nine European countries 1980-2004 we find evidence consistent with ICT-based polarization. Industries with faster growth of ICT had greater increases in relative demand for high educated workers and bigger falls in relative demand for middle educated workers. Trade openness is also associated with polarization, but this is not robust to controls for technology (like R&D). Technologies can account for up to a quarter of the growth in demand for the college educated in the quarter century since 1980.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| 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) | 0.004 | 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".