Wage dispersion, technology adoption and labor market polarization
Bibliographic record
Abstract
The last decades have seen labor markets in developed economies become increasingly polarized. In a recent contribution, Shim and Yang (2018) show that labor market polarization in the U.S. has been more pronounced in high-wage industries that in low-wage industries. This thesis seeks to investigate whether the observed relationship between wage differentials across industries and labor market polarization also holds for a different economy. Using Canadian decennial census data and WORLD KLEMS Growth and Productivity Accounts, our empirical results reveal remarkable similarity to those of Shim and Yang (2018) and confirm that wage differentials and labor market polarization are systematically linked. We introduce a two-sector neoclassical growth model in discrete time to scrutinize the relationship between inter-industry wage differentials and labor market polarization. Assuming a rigid wage structure, the model shows that firms in high-wage industries seek to cut overall production costs by substituting workers performing ’routine’ tasks with information- and communication technologies (ICT). As technological improvements have led to a rapid price decline for ICT, firms in high-wage industries have more economic incentives to dynamically substitute routine workers with ICT. Firms that pay a relatively high wage premium to workers, decrease routine employment more stronger than low-wage firms, which, in turn, has led to heterogeneous degrees of job polarization across industries.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.006 | 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".