Does Spatial Variation in Heterogeneity Matter? Assessing the Adoption Patterns of Business Improvement Districts
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Because they supplement the municipal provision of local public goods, Business Improvement Districts (BIDs) provide an opportunity to examine the space, scope, and determinants of the provision of local public goods. A BID is formed when a group of merchants or commercial property owners in a neighborhood vote in favor of package of self‐assessments and local public goods to be funded with those assessments. These districts solve a collective action problem in the provision of public goods because once a majority has voted in favor, participation is compulsory for all merchants or commercial property owners in the neighborhood. I use a unique dataset on adoption patterns of BIDs in California to test two main claims suggested by the theoretical literature: first, that businesses respond to individual heterogeneity that determines the quality of local public goods, and second, that the type of heterogeneity—overall or spatial—matters. In contrast to the literature on residents, this study finds at best a weak correlation between a city's adoption of a BID and heterogeneity. In addition, despite the theoretical preference for spatial over overall heterogeneity, BIDs are not more likely to be adopted by spatially heterogeneous cities.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it