A re-examination of Wagner's law for ten countries based on cointegration and error-correction modelling techniques
Bibliographic record
Abstract
Following Mann's (National Tax Journal, 33, 189–201, 1980) study, five different versions of Wagner's law are empirically examined using annual time-series data on ten countries over the period 1951 to 1996. Included are three of the emerging industrialized countries of Asia: South Korea, Taiwan, and Thailand, and seven industrialized countries: Australia, Canada, Japan, New Zealand, USA, the United Kingdom, and South Africa. The analysis is an advance over previous work in two respects. First, the stationarity properties of the data, the order of integration using the Augmented Dickey–Fuller (Journal of American Statistical Association, 74, 427–31, 1979, Econometrica, 49(4), 1057–72, 1981) test and the Kwiatkowski et al. (Journal of Econometrics, 1, 159–78, 1992) test are empirically investigated. Second, the hypothesis of a long-run relationship between income and government spending is tested using bivariate cointegrated systems and by employing the methodology of cointegration analysis as suggested by Johansen and Juselius (Oxford Bulletin of Economics and Statistics, 52, 169–210, 1990) and Johansen (Journal of Policy Modelling, 14, 313–34, 1992). Unidirectional Granger causality is found running from income to government spending for the newly industrialized countries of South Korea and Taiwan, and the industrialized countries of Japan, the United Kingdom, and the United States, supporting Wagner's hypothesis for those countries. For the five remaining countries in this study: Australia, Canada, New Zealand, South Africa, and Thailand, no causal relationship between income and government spending is found.
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 | 0.015 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".