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Record W4402377417 · doi:10.1080/02673037.2024.2400155

The state house prices make: the political elasticities of house prices and rents

2024· article· en· W4402377417 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

VenueHousing Studies · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHousing, Finance, and Neoliberalism
Canadian institutionsTrinity College
Fundersnot available
KeywordsEconomic rentHouse priceEconomicsHouse of RepresentativesLower housePoliticsState (computer science)Labour economicsMicroeconomicsEconometricsPolitical scienceMathematicsLaw

Abstract

fetched live from OpenAlex

ABSTARCTFiscal policy allocation is not purely determined by the labour-capital conflict, but increasingly around cross-class housing coalitions. Although rising house prices are conventionally understood as drivers of fiscal austerity, this view has been challenged. Alternatively, governments may use fiscal policies to support house price growth to meet the primary economic interests of homeowners and compensate non-homeowners through the welfare system. Using an econometric analysis of 19 advanced economies between 1980 and 2018, we show house prices have positive effects on taxation revenue as well as fiscal spending on public investment, welfare and education. A second multi-level analysis provides a political explanation of this observed outcome by demonstrating parties respond to rising house prices by proposing more welfare and public investment spending in their manifestos. Conterminously rising house prices and rents also lead to greater welfare spending, suggesting governments use fiscal policy to protect those excluded from homeownership from labour market risks.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.763
Threshold uncertainty score0.547

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
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 head0.044
GPT teacher head0.268
Teacher spread0.224 · 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