Employment transitions and the cycle of income inequality in postindustrial societies
Bibliographic record
Abstract
This article presents the claim that the current transition from service to knowledge employment impacts income inequality in a manner that is comparable to the previous transition from agriculture to industry. This contention is tested by an employment transition index that calculates the difference of employment between the higher- and lower-paid sectors of the economy. The calculation of this index is consistent with Kuznets’s view that income inequality increases during the early stages of industrialization due to the presence of a small and higher-wage modern sector that encroaches on the total numbers employed in the large and lower-wage traditional sector. However, income inequality eventually declines with continued industrialization as more workers enter the modern sector of the economy. Results confirm the central argument of this study for a panel of 25 Organisation for Economic Co-operation and Development countries from 1980 to 2008. According to the findings, classical Kuznetsian variables such as sector-dualism are not significant and/or signed in unanticipated directions in their prediction of income inequality. Instead, the employment transition index returns robust negative associations with income inequality and consistently outperforms sector-dualism. Furthermore, knowledge employment returns positive and significant connections with the dependent variable net of the employment transition index. These results confirm that both between- and within-sector employment patterns are key determinants of income inequality.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".