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Record W4362553137 · doi:10.1596/39554

Personal Income Tax Piggybacking

2023· book· en· W4362553137 on OpenAlexaboutno aff
Muhammad Khudadad Chattha, Jürgen René Blum, Roy Kelly

Bibliographic record

VenueWorld Bank, Washington, DC eBooks · 2023
Typebook
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsnot available
Fundersnot available
KeywordsTaxable incomeBusinessLabour economicsEconomicsAccounting

Abstract

fetched live from OpenAlex

Personal income tax (PIT) piggybacking is a local surcharge levied by Subnational government (SNGs) on top of the taxable personal income or on the personal income tax liability already being levied by the central government. In contrast to tax sharing arrangements, piggybacking provides more SNG autonomy since the SNG is granted the power to set and levy the piggybacking rate, typically within certain bounds established by the central government, thereby strengthening the accountability between SNGs and their residents. Different versions of PIT piggybacking have been implemented largely in high-income countries, including Denmark, Norway, US, Canada, Spain, and Portugal. Croatia, a middle-income country, has adopted a PIT piggybacking system. While PIT piggybacking is an important source of SNG revenue in these countries, if Indonesia were to adopt a PIT piggyback, it would be one of the first few major middle-income countries to do so.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.207
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.007

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 head0.025
GPT teacher head0.224
Teacher spread0.199 · 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

Citations1
Published2023
Admission routes1
Has abstractyes

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