The Price Is Not Right: Grocery Taxation, Race, and Food Security During COVID-19
Bibliographic record
Abstract
Using recent data from a national sample of 2.8 million households surveyed by the Household Pulse Survey (HPS) (U.S. Census Bureau 2022), this research examines the relationships across food insecurity, grocery taxes, and race during the COVID-19 pandemic. This research asks: How does a respondent’s household food insecurity differ in states that impose grocery taxes compared to states without grocery taxes? How does a respondent’s food insecurity differ by their race? How do these trends reflect government COVID-19 relief policy during this time? This analysis provides a baseline reporting of factors associated with food insecurity during the Covid-19 pandemic using the most recent HPS data. Overall, 35% of respondents report being food insecure at some point during the pandemic. Using survey-weighted logistic regressions and controlling for background demographic and socio-economic variables, this research finds that food insecurity is higher for respondents living in states with grocery taxes compared to those in states without grocery taxes. As the pandemic progresses, a substantial gap in food insecurity is observed; respondents in states with grocery taxes reported higher food insecurity compared to respondents in states without grocery taxes, net of other effects. This research also finds that food insecurity is a more salient issue for Black respondents; Black households in states with grocery taxes have a 43% predicted probability of reporting current food insecurity compared to 34% for white respondents in states without grocery taxes, net of the other covariates. Grocery taxes and being Black negatively affect food security during the Covid-19 pandemic.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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; both teacher heads agree on what is shown here.
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".