Is the Rise in Wage Inequality the Price to Pay for Innovation and Growth
Bibliographic record
Abstract
This article presents an analysis of the models that have been developed in the literature to account for the rise in wage inequality during the 1980s and the 1990s. These models build upon the assumption of an acceleration in the rate of technological progress associated with the diffusion of the new information and communication technologies. What is at stake in this literature is not to justify the existence of skill-biased technical per se, but rather to explain the main and recent dimensions of such a bias on relative wages. A first dimension lies in the extent of wage inequality, namely the aggravation of inequality since the 1980s in developed economies. The aim is to explain why such a bias has occurred in the 1980s, while technical change has diffused all along the twentieth century, precisely during a period where skilled labour supply increased. A second dimension of wage inequality concerns its fractal nature, that is the fact that it persists within education groups. Furthermore, within groups inequality features a strong transitory dimension, and differs in nature according to the level of education considered. Depending on the determinants - exogenous or endogenous - and the consequences on wage inequality - permanent or transitory - that such models explain through the recent acceleration in the rate of technological change, two categories of models can be distinguished. In the first one, the technological bias is analysed by focusing on the impact of technical change on the returns to human capital. Human capital embeds for example individual ability, the ability to absorb and use new technologies, assets, luck, specific technological competencies or general education. The second category of models highlight the mechanisms likely to explain the interaction between technological progress, supply and demand for skilled workers. These approaches however explain only a restrictive part of the evolution of wage inequality. In the long run on the one hand, and when considering dimensions such as work organization on the other hand, other categories of models are necessary.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".