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Record W2909775384 · doi:10.1111/jacf.12309

Financing Urban Revitalization: A Pro‐Growth Template

2018· article· en· W2909775384 on OpenAlex

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

VenueJournal of applied corporate finance · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsInstitute of Health Economics
Fundersnot available
KeywordsProperty taxProsperityRevenuePopulationRedevelopmentEconomicsFinanceBusinessEconomic growthPolitical scienceLaw

Abstract

fetched live from OpenAlex

The co‐authors recommend American cities adopt a particular property‐tax rate cutting strategy. They contrast relatively prosperous San Francisco with impoverished Baltimore. Both cities actually raised property taxes frequently between 1950 and 1975 with roughly the same results–falling population and rising crime. During the same period, many other cities also raised tax rates to make up for lower economic output, thereby encouraging more people and businesses to leave. The change in San Francisco's economic fortunes did not arise out of either a successful crime‐fighting program (it had worse crime than Baltimore in 1975) or through the rising prosperity of Silicon Valley forty miles to its south (still too small and far away to make a difference). Rather, the inflection point for San Francisco was in 1978 when a statewide referendum (“Proposition 13”) limited property taxes to 1% of assessed value. San Francisco's revenue declined by 18% the next year, 1979, but by 1982, its revenue was 66% higher than before Prop 13, despite the lower rates. Prop 13 improved cash flows to owners of real property in San Francisco and protected their property rights. Investors bought, built, and improved the city's residential and commercial capital stock, attracting new residents and creating new job opportunities Politicians are reluctant to try to adopt Prop 13‐like measures on their own, however, because the short‐term consequences for politicians are painful as several years are required for underlying economic activity to grow enough to offset rate cuts. The key is to build a financial bridge before crossing the river through four‐steps: 1. Announce a property tax rate cap that is immediately binding but which would take effect over several years in the future. Rational investors would immediately begin to invest and expand the city's tax base. 2. During the transition period, the city should limit its spending to a “maintenance of service” level, while allocating any added revenue to an escrow fund. 3. The city should supplement this reserve with the proceeds of sales of assets on its balance sheet via sale‐and‐leaseback contracts (SLBs). 4. If revenue falls in the short run, cash would be withdrawn from the escrow fund in order to continue to maintain levels of government services at accustomed levels.

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)0.001
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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score0.931

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.041
GPT teacher head0.203
Teacher spread0.162 · 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