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Record W3126139363

Private Investment, Public Aid and Endogenous Divergence in the Evolution of Urban Neighborhoods

2005· article· en· W3126139363 on OpenAlexaff
Lanny Arvan, David Nickerson

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing Market and Economics
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsInvestment (military)Government (linguistics)Distribution (mathematics)EconomicsEx-anteDivergence (linguistics)Public economicsPublic investmentBusinessMicroeconomicsPublic fundPolitical science
DOInot available

Abstract

fetched live from OpenAlex

This paper offers a novel explanation for urban blight and endogenous divergence in the overall quality and wealth of neighborhoods and simultaneously derives the salient features of actual urban renewal and other aid programs from optimizing government behavior based on collective public preferences. These features appear when the objective of such public aid programs is to restore the ex ante distribution of wealth or property values within a blighted neighborhood, while equilibria exhibiting deficient levels of private investment and blight itself can arise when residents accurately anticipate the potential provision of public aid to an affected neighborhood and ex ante investment in private insurance diminishes neighborhood eligibility for such aid. Examples of antipodal equilibria in which urban renewal programs entirely crowd out local private investment or in which neighborhood residents invest in efficent levels of private mitigation illustrate these results, which stand in direct contrast to both traditional explanations of urban blight and to the new social-interaction models of neighborhood divergence.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.013

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
Scholarly communication0.0010.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.022
GPT teacher head0.197
Teacher spread0.175 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2005
Admission routes1
Has abstractyes

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