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Record W2759033152 · doi:10.1177/1091142117730634

Does Foundation Giving Stimulate or Suppress Private Giving? Evidence from a Panel of Canadian Charities

2017· article· en· W2759033152 on OpenAlexaffabout
Iryna Khovrenkov

Bibliographic record

VenuePublic Finance Review · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicNonprofit Sector and Volunteering
Canadian institutionsUniversity of Regina
Fundersnot available
KeywordsFoundation (evidence)Liberian dollarPublic goodCrowding outWelfareEmpirical evidencePublic economicsCrowdsPrivate information retrievalEconomicsEmpirical researchPublic relationsPolitical scienceLawFinanceMicroeconomics

Abstract

fetched live from OpenAlex

As non-governmental providers of public goods, charities are funded by governments and also by individuals and foundations. How do foundation grants to charities affect private donations to these organizations? The standard economic theory on voluntary contributions to the public good hypothesizes that foundation giving will crowd out private donations. An alternative giving dynamic may arise whereby foundations act as complements to private donations because they can provide a signal of charity quality to individuals and thereby influence their decisions to give. This article offers a rigorous empirical analysis of the relationship between foundation and private donations by utilizing a unique data set on Canadian social welfare and community charities matched with their foundation donors. Empirical findings confirm that an additional dollar of foundation grants to charities crowds in private giving by three dollars on average, suggesting that private donors may look to foundation grants for information on charities to make informed giving decisions.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.739
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.002
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.181
GPT teacher head0.366
Teacher spread0.185 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations6
Published2017
Admission routes2
Has abstractyes

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