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Record W7065782099

Earnings dynamics and inequality amoung men in Luxembourg, 1988-2004: Evidence from administrative data

2009· other· en· W7065782099 on OpenAlexaboutno aff

Bibliographic record

VenueUNU Collections (United Nations University) · 2009
Typeother
Languageen
Field
Topic
Canadian institutionsnot available
Fundersnot available
KeywordsNucleofectionHyporeflexiaDemotionFusible alloyTubulopathyTSG101
DOInot available

Abstract

fetched live from OpenAlex

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

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Bibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.274
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.4010.366
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.055
GPT teacher head0.288
Teacher spread0.234 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2009
Admission routes1
Has abstractyes

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