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Record W3022641295 · doi:10.1093/qje/qjaa014

The Making of the Modern Metropolis: Evidence from London*

2020· preprint· en· W3022641295 on OpenAlexafffund
Stephan Heblich, Stephen J. Redding, Daniel Sturm

Bibliographic record

VenueThe Quarterly Journal of Economics · 2020
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Toronto
FundersEconomic and Social Research CouncilUniversity of TorontoLondon School of Economics and Political ScienceInstitute for New Economic ThinkingUniversity of BristolPrinceton University
KeywordsMetropolitan areaResidenceLand ValuesCounterfactual conditionalEconomic geographyLand valueScale (ratio)GeographyPopulationIndustrial RevolutionRegional scienceValue (mathematics)EconomicsAgricultural economicsLand useDemographic economicsEngineeringSociologyDemographyCivil engineeringArchaeologyCartographyComputer science

Abstract

fetched live from OpenAlex

Abstract Using newly constructed spatially disaggregated data for London from 1801 to 1921, we show that the invention of the steam railway led to the first large-scale separation of workplace and residence. We show that a class of quantitative urban models is remarkably successful in explaining this reorganization of economic activity. We structurally estimate one of the models in this class and find substantial agglomeration forces in both production and residence. In counterfactuals, we find that removing the whole railway network reduces the population and the value of land and buildings in London by up to 51.5% and 53.3% respectively, and decreases net commuting into the historical center of London by more than 300,000 workers.

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)0.000
metaresearch head score (Gemma)0.003
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.184
Threshold uncertainty score0.365

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.004
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.001

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 head0.075
GPT teacher head0.248
Teacher spread0.173 · 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

Citations7
Published2020
Admission routes2
Has abstractyes

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