Crime Protection Investment Spillovers: Theory and Evidence from the City of Buenos Aires
Bibliographic record
Abstract
This paper studies spillover effects from private crime protection technologies. Criminals and victims interact in a frictional market for offenses. Private protection diverts crime towards unprotected targets, but also discourages criminal activity. The relative strength of these two effects determines the sign of externalities among potential victims. Using originally collected data from Buenos Aires, evidence shows that (i) private protection investment is spatially concentrated, and (ii) neighbors’ investment in private protection has a causal positive and significant effect on own investment. To achieve identification, I exploit variation in the investment status of close neighbors as induced by their knowledge of crimes targeting friends, relatives, acquaintances or others, and occurred farther away. Neighbors’ investment in alarms and cameras significantly increases a household's propensity to invest in the same technology. Externalities raise the scope for government intervention, while geo-referenced information on private protection can inform policy with regard to the spatial distribution of policing effort and public investment in security.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".