Evaluating A USDA Program That Gives SNAP Participants Financial Incentives To Buy Fresh Produce In Supermarkets
Bibliographic record
Abstract
Pricing incentives may reduce disparities in obesity among Supplemental Nutrition Assistance Program (SNAP) participants by increasing fruit and vegetable purchases. However, few studies have evaluated the feasibility and effectiveness of those incentives in supermarkets, as opposed to farmers markets. In 2015 and 2016, as part of a US Department of Agriculture (USDA) pilot program, a dollar-matching program in Michigan provided SNAP participants with a subsidy on fresh produce purchases. Using data on millions of individual transactions from thirty-two stores, we found that SNAP participants' spending on fresh produce was significantly higher at stores that implemented the subsidy than at control stores during both intervention periods (7.4 percent and 2.2 percent higher in 2015 and 2016, respectively). Our results highlight the effectiveness and feasibility of dollar-matching programs for fruit and vegetable purchases by SNAP participants who shop at supermarkets, and they support the USDA's expansion of existing programs to that setting in additional states.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".