A Comparative Analysis of the Impact of Taxation on the SME Economy: The Case of UK and US – New York State in the Year 2000
Bibliographic record
Abstract
The purpose of this paper is to report on a comparative study of the impact on the SME economy (fewer than 250 employees) of the UK and US (New York State) tax regimes. This explorative study is part of the ongoing small business taxation research programme undertaken in association with NatWest Bank. The research involves (a) the computation of the tax position of a sample of UK-based small businesses (a self-employed person, a partnership, and a small limited company); (b) the application of the tax regime of New York State to the UK business cases studies; (c) the development of two computer simulation models that estimate the direct tax burden incurred by small businesses in the United Kingdom; and (d) the application of the tax regime of New York State to the UK models. This research forms the basis of a comparative discussion about the business tax regime in the United Kingdom and USA and throws some light on the on-going debate about the development of the tax regimes applicable to small businesses in OECD countries. The paper concludes with a summary of the key findings and policy implications and offers a brief discussion on progress towards tax harmonisation from the small business perspective.
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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".