Opportunity zones: do tax benefits go to the most distressed communities?
Bibliographic record
Abstract
Purpose The exact criteria used by state governors for choosing opportunity zones (OZs) are not publicly available. This paper aims to examine whether state governors selected the most distressed communities, or those with the highest proportions of minorities, as OZs. Design/methodology/approach This paper compares the distressed communities chosen as OZs in states throughout the country to an equal number of those eligible distressed communities but not selected. Moreover, this paper uses regression analysis to determine whether the poverty rate, median family income, population, percentage of population that is minority and the percentage of population that is African American are significant explanatory factors in the choice of OZs. Findings After describing the tax incentives for investing in OZs, this paper documents that governors did not select many of the most distressed communities, or those with high proportions of minorities, in their individual states. Originality/value This paper describes in some detail the way in which investors may generate tax benefits by investing in eligible property or businesses in OZs. It also examines the extent to which the degree of poverty and the percentage of the population that is minority (and African American) were key factors in the selection of OZs. It arises an issue that the chosen communities are not necessarily those most in need of more investment or those heavily populated by minorities, particularly African Americans.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".