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Record W4386298123 · doi:10.18174/636509

Doorrekening effecten van btw-verhoging op sierteeltproducten in Nederland en EU : update situatie 2023

2023· report· nl· W4386298123 on OpenAlexaff
Michiel van Galen, Gerben Jukema, G.M. Splinter

Bibliographic record

Venuenot available
Typereport
Languagenl
FieldEconomics, Econometrics and Finance
TopicEconomic Analysis and Policy
Canadian institutionsImpact
Fundersnot available
KeywordsRevenueUnemploymentBusinessOrnamental plantAgricultural economicsTax revenueTariffEconomicsInternational economicsFinanceInternational tradePublic economicsEconomic growthHorticulture

Abstract

fetched live from OpenAlex

Ornamental horticultural products are taxed at a reduced VAT rate in the Netherlands (9% rather than 21%) as well as in 14 other EU countries. Increasing VAT would have a negative impact on turnover and employment in the supply chain. These calculations show that a VAT increase in the Netherlands would lead to a loss of around €200 million in turnover in the ornamentals sector at wholesale prices (-1.6%). If VAT were to be increased in other EU countries too, this would have a particularly significant impact on the Netherlands’ exports and thus on primary production. In this scenario, the drop in turnover for the ornamentals sector (at wholesale prices) would be €930 million (-7%). The expected impact on the government’s VAT revenue would be partly offset by fewer flowers and plants being bought, and in the shorter term by a decline in tax receipts and social insurance premiums from businesses and employees, along with an increase in unemployment benefits being paid out.

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)0.007
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.149
Threshold uncertainty score0.296

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0050.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0130.002

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 head0.067
GPT teacher head0.290
Teacher spread0.223 · 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 designObservational
Domainnot available
GenreEmpirical

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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