Donor Advised Funds in Canada, Australia and the US: Differing Regulatory Regimes, Differing Streams of Policy Drift
Bibliographic record
Abstract
Abstract Donor Advised Funds (DAFs) are the fastest growing destination for charitable giving, and subject to vigorous debate over whether they should be more tightly regulated. Virtually all of the research on DAFs and the arguments for increased regulation emanate from the US. This article compares regulation in Canada and Australia with the US to demonstrate how different regimes lead to different uses of DAFs and different ‘market’ configurations. The conceptual framework presents three motivational scenarios for their use: as pseudo foundations, tax savings and protection of privacy. The differential effects of regulation on these donor scenarios explains why total DAF assets in Australia are proportionately much lower than its North American counterparts, mainly because its regime is not skewed as heavily toward the tax savings motivated donor. The findings raise serious questions as to whether DAFs have actually democratized philanthropy, as is so often claimed. In terms of policy change, all three countries have experienced policy drift, although for different reasons. However, COVID-19 pandemic may have created new windows of opportunity for regulatory reform.
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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 | 0.011 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.007 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".