Short-term earnings mobility in the Canadian and German context: the role of cognitive skills
Bibliographic record
Abstract
Abstract It is well-established that human capital contributes to unequal levels of earnings mobility. Individuals with higher levels of human capital, typically measured through education, earn more on average and are privy to greater levels of upward change over time. Nevertheless, other factors may have an incremental effect over education, namely cognitive ability and the skill demands of employment. To deepen insight into whether these aspects contribute to earnings mobility over a four-year period, the present study examines positional change in Canada and Germany—two contexts typified as examples of liberal and coordinated market economies. A series of descriptive indices and relative change models assess how different measures of human capital are associated with earnings mobility. The results indicate that, while individuals with higher cognitive skills experience greater earnings stability and upward mobility in both countries, there is only an incremental effect of skills on mobility in Germany once we account for educational credentials. The results also provide evidence on the role of skill demands for earnings mobility; in both countries, advanced skills at work are associated with greater short-term mobility, even while controlling for cognitive ability and other factors. Together the results showcase how longitudinal data containing detailed measures of human capital allow for deeper insight into what facilitates earnings mobility.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".