Testing Motives for Charitable Giving: A Revealed-Preference Methodology with Experimental Evidence
Bibliographic record
Abstract
A large economics literature seeks to understand the reasons why individuals make charitable contributions. Fundamental features of most models of charitable giving are the inclusion of externalities induced by other agents and the Lancasterian characteristics approach to specifying utility functions. This paper develops a general, revealed-preference methodology for testing a variety of preference structures that allow for both externalities and characteristics. The tests are simple linear programs that are transparent, computationally efficient, and straightforward to implement. We show how the technique applies to standard models of privately provided public goods and novel models that account for other-regarding preferences based on relative consumption and donations among individuals. We also conduct an original experiment that enables testing and comparing many models on a single data set. Our experiment design allows us to focus on intrinsic motivations which are often hard to disentangle from other extrinsic or image effects in field data. The results provide the first revealed-preference evidence on the importance of social comparisons when individuals make charitable contributions. Models that include preferences for either relative consumption or donations yield significantly greater explanatory power than the standard model of impure altruism.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".