International competition in corporate taxation: evidence from the OECD time series
Bibliographic record
Abstract
Despite numerous studies, controversy remains about the impact of economic globalization on corporate taxation. Theoretical models of tax competition generate different predictions about trends in the level of tax burdens and the degree of convergence in tax burdens across countries. In this paper we present a purely empirical analysis of the evolution of tax burdens across OECD countries since the 1950s. Issues of measurement and methodology have contributed to the inconclusive character of studies to date, so we begin with an assessment of alternative measures of the burden. Problems with some commonly used measures of the tax burden are considered and the most plausible measures identified. Descriptive analysis of these time series reveals no evidence of a competitive ‘race to the bottom’ in corporate taxation and little evidence of even a harmonization of the tax burden. Many inferential studies of corporate taxation base their conclusions on cross-sectional analysis; in contrast, we adopt an explicitly time-series method to what are essentially time-series questions. Cointegration methodology originally developed to study issues of convergence of living standards is applied, and fails to reveal evidence of convergence of tax burdens for the OECD and Europe as a whole. It does, however, indicate that there has been some harmonization within smaller groups of countries, mainly in northern Europe. Important questions remain about the effectiveness and impact of corporate taxation in an increasingly open and integrated global economy, but we find little evidence to support fears that the burden of taxation is being lifted from corporations. — Kenneth Stewart and Michael Webb
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".