Gross Domestic Philanthropy : An International Analysis of GDP, Tax and Giving
Bibliographic record
Abstract
This paper aims to update findings previously published by CAF in 2006 around international comparisons of charitable giving, and to provide an analysis of the relationship between GDP, tax and giving within a number of countries. The purpose of this paper is not to provide all the answers but merely to act as a document which will hopefully stimulate further discussion and understanding around this important issue. Throughout, it should be borne in mind that we have conducted the analysis amongst 24 countries. The key findings from this analysis of 24 countries are: The top four countries in terms of charitable giving by individuals as a percentage of GDP are the United States of America, New Zealand, Canada and the United Kingdom.Generosity is not restricted to the Western economies analysed, showing that giving can be a global phenomenon.Two of the BRICS countries (Russia and India) appear in the Top 10 of countries analysed, indicating the potential of transitional economies to be future leaders in providing charitable resources.There is no significant correlation between levels of taxation and government spending and the amount given to charity across all taxes looked at, with the exception of employer social security charges.There is a correlation between charitable giving and other aspects of giving such as volunteering time and helping a stranger backing up other data sources which have shown that those who volunteer their time are more likely to give monetarily to charity.
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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 | 0.001 |
| Bibliometrics | 0.007 | 0.017 |
| Science and technology studies | 0.000 | 0.000 |
| 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 | 0.001 |
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".