MétaCan
Menu
Back to cohort
Record W4400710606 · doi:10.1787/153057a8-en

Real compensation per employee in selected OECD countries, Quarter 3 of 2022

2023· other· en· W4400710606 on OpenAlexaboutno aff

Bibliographic record

VenueTaxing wages · 2023
Typeother
Languageen
FieldSocial Sciences
TopicRegional Development and Policy
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Compensation (psychology)Compensation of employeesBusinessMathematicsOperations managementEconomicsGeographyPsychologySocial psychologyArchaeology

Abstract

fetched live from OpenAlex

This annual publication provides details of taxes paid on wages in OECD countries. This year’s edition focuses on the impact of recent inflation on labour taxation in the OECD and how countries adjust their tax systems in response. For the year 2022, the report also examines personal income taxes and social security contributions paid by employees, social security contributions and payroll taxes paid by employers, and cash benefits received by workers. It illustrates how these taxes and benefits are calculated in each member country and examines how they impact household incomes. The results also enable quantitative cross-country comparisons of labour cost levels and the overall tax and benefit position of single persons and families on different levels of earnings. The publication shows average and marginal effective tax rates on labour costs for eight different household types, which vary by income level and household composition (single persons, single parents, one or two earner couples with or without children). The average tax rates measure the part of gross wage earnings or labour costs taken in tax and social security contributions, both before and after cash benefits, and the marginal tax rates the part of a small increase of gross earnings or labour costs that is paid in these levies.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.050
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.308
Teacher spread0.286 · 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; a candidate call from one teacher head, not a consensus.

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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueTaxing wagesSame topicRegional Development and PolicyFrench-language works237,207