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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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.065
Threshold uncertainty score0.130

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.006
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.012

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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreDataset

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

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