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Record W4313318932 · doi:10.1257/app.20200662

The Economic Incidence of Wildfire Suppression in the United States

2022· article· en· W4313318932 on OpenAlex
Patrick Baylis, Judson Boomhower

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAmerican Economic Journal Applied Economics · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSubsidyHarmEconomicsValue (mathematics)HazardNatural resource economicsDemographic economicsGeographyPublic economicsPolitical scienceEcologyStatisticsBiology

Abstract

fetched live from OpenAlex

This study measures the degree to which public expenditures on wild-fire protection subsidize development in harm’s way. We use administrative data on firefighting expenditures to measure the causal effect of nearby homes on the amount spent to extinguish wildfires. We use these estimates in an actuarial calculation yielding geographically differentiated expected implicit subsidies for homes across the western United States. The expected net present value of this subsidy can exceed 20 percent of home value, increases with fire hazard, and decreases surprisingly steeply with development density. We discuss potential behavioral responses by individuals and local governments using a simple economic model. (JEL D91, Q23, Q54, R52, R58)

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.536
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.004
GPT teacher head0.196
Teacher spread0.192 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it