Does Spatial Variation in Heterogeneity Matter? Assessing the Adoption Patterns of Business Improvement Districts
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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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 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".