The Micro-Empirics of Collective Action: The Case of Business Improvement Districts
Bibliographic record
Abstract
This paper carries out a micro-level analysis of collective goods provision by focusing on the formation of Business Improvement Districts (BIDs). The paper's theoretical and empirical analysis is unusually complete in that it considers the entire process of collective action, including participation in initial organization, voting, and ultimate impact on property values. BID benefits are shown to be highly uneven, and BID formation is not a Pareto improvement. Furthermore, large “anchor participants” benefit disproportionately, and are crucial for the viability of the institution, consistent with Olson (1965). These results, while demonstrated in a particular setting, apply to collective action more generally. Whenever a market failure leaves room for a collective response, the presence of anchor participants encourages collective action, and the action – even though in a sense voluntary – has uneven benefits.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| 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".