Who Benefits From Government Tax-Subsidies for Corporate Charitable Food Donations?
Bibliographic record
Abstract
Leveraging government tax incentives to prompt corporate charitable giving has gained considerable popularity over the past decade. This paper sheds light on the broader consequences of the U.S. government's tax-subsidy policy for charitable food donations, which is determined based on the fair market value (FMV) of the donated products. We incorporate the tax-subsidy into a monopolist food retailer's after-tax profit function. Market demand is both price- and quality-dependent, and the shelf-life of the goods is determined by their initial quality and deterioration rate. The retailer makes joint quantity and pricing decisions over two periods, procuring goods at the start of the selling season and (possibly) donating at the end of period 1. We characterize the retailer's optimal policy and specify conditions under which she donates some, all, or none of her leftover inventory. We explore the impact of government tax-subsidies on the retailer's actions, consumer surplus, quantity of donations, and total welfare. We show that tax-subsidies may motivate retailers to intentionally create supply scarcity (by donating more) to increase FMV (determined by the second-period price), thereby enhancing tax deductions. While tax-subsidies encourage donations, they can also unintentionally harm consumers by reducing supply and raising prices. Interestingly, we show that a higher subsidy does not necessarily lead to more donations; the retailer may choose to donate fewer units to achieve the same tax deduction while increasing sales revenue. We investigate conditions under which tax-subsidies can simultaneously increase donations, consumer surplus, and retail profit. We show that this outcome is possible only when retailers donate low-quality goods in modest quantities. Our findings reveal how FMV-dependent tax-subsidies can backfire, reducing both consumer surplus and total welfare while benefiting the retailer. Governments must carefully weigh the benefits of donations against potential harm caused to consumers.
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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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".