Top Incomes in France: booming inequalities?
Bibliographic record
Abstract
I study the evolution of top incomes in France, using income tax tabulations, and confronting them with data from a large sample of households with exhaustive sampling at the upper end of the income distribution, issued by the French tax administration. Results exhibit a strong increase in market income inequalities measured by top income shares since the late 1990s. The surge in top wages at the top 1% and top 0.1% of the wage distribution is predominantly responsible for the explosion of top income shares, and puts an end to 30 years of stability of the French wage hierarchy. I show that top income and top wage mobility is low, stable, and comparable to that in Canada (where income concentration is 2 times higher) and cannot be responsible for this surge in top income and top wage shares. Neither can the decline of top marginal income tax rates fully explain this evolution through effects on reported incomes. However, in a context of strengthened European tax competition and increased high-skilled labor mobility, my results suggest that France, along with other European countries may be on its way to bridging part of its “top income gap” with English-speaking countries. Paris School of Economics. Contact:48 Bv Jourdan, 75014 Paris, Tel:+33(0)1 43 13 63 37. E-mail: camille.landais(at)ens.fr. Acknowledgements: I am grateful to Thomas Piketty, Emmanuel Saez, Tony Atkinson, Eric Maurin, Facundo Alvaredo, Pierre-Yves Cabannes, Clement Carbonnier, Gabrielle Fack, Julien Grenet, Antoine Bozio, Laurent Bach and participants of the Lunch Seminar at Paris School of Economics for their helpful comments. All errors, of course, are mine alone.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".