Earnings dynamics and inequality amoung men in Luxembourg, 1988-2004: Evidence from administrative data
Bibliographic record
Abstract
Starting with the late 1980s and intensifying after early 1990s, Luxembourg evolved from an industrial economy to an economy dominated by the tertiary sector, which relies heavily on the cross-border workforce. This paper explored the implications of these labour market structural changes for the structure of earnings inequality and earnings mobility. Using an extraordinary longitudinal dataset drawn from administrative records on professional career, I decomposed Luxembourg�۪s growth in earnings inequality into persistent and transitory components and explored the extent to which changes in cross-sectional earnings inequality between 1988 and 2004 reflect changes in the transitory or permanent components of earnings. Thanks to the richness of the Luxembourgish data set, I am able to estimate a much richer model that nests the various specifications used in the US, Canadian and European literature up to date, thus rejecting several restrictions commonly imposed in the literature. I find that the growth in earnings inequality reflects an increase in long-term inequality and a decrease in earnings instability, and is accompanied by a decrease in earnings mobility. Thus in 2004 compared with 1988, low wage men in Luxembourg are worst off both in terms of their relative wage and in terms of their opportunity of improving their relative income position in a lifetime perspective. JEL Classification: C23, D31, J31, J60 Keywords: panel data, wage distribution, inequality, mobility
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.401 | 0.366 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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; both teacher heads agree on what is shown here.
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".