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Urban Economics

2020· reference-entry· en· W4236745080 on OpenAlexaff
Kristian Behrens, Jacques‐François Thisse

Bibliographic record

Venuenot available
Typereference-entry
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsPopulationMetropolitan areaDistribution (mathematics)GeographyPosition (finance)Economic geographySpace (punctuation)Natural resourceInterdependenceEconomicsMathematicsComputer scienceEcologyDemographySociology

Abstract

fetched live from OpenAlex

Location matters. The most striking example is the highly uneven distribution of population and wealth across space. For example, cities occupy approximately 2 percent of the Earth’s land surface, but host more than half of the world’s population and produce about 80 percent of its economic output. The twenty most populous US metropolitan statistical areas (MSAs) account for almost 45 percent of the total US population and produce 52.2 percent of total US GDP on barely 15.2 percent of total US surface. Similar patterns hold for other countries, with even starker concentrations of population and economic activity in the growing megalopolises of developing countries. This entry discusses why population and economic activity are not more evenly spread across space. The first reason that comes to mind is that places are intrinsically heterogeneous along different dimensions such as topography, resource endowments, access to natural transportation routes, or climate. More importantly, what makes a location desirable to an agent is the unintended byproduct of the other agents’ location choices: everyone cares about her own position, but the actual choice is relative to those of the others. To put it differently, the desirability of a specific location depends on where the others are located. This simple fact is the essence of spatial equilibrium, a formal concept used by economists to analyze the spatial distribution of economic agents and activity. It describes a situation where each economic agent optimally chooses her own location—taking the locations of the others as given—and where these interdependent location choices are mutually compatible. The outcome is determined by the interplay between two sets of competing forces. First, everything else equal, agents want to be close together (“agglomeration forces”). This comes from the fact that moving people, goods, and ideas across space is costly, which pushes toward geographic concentration to reduce these costs and increase the benefits generated by clustering. Second, everything else equal, there are limits to geographic concentration at any point in space, which tends to push agents apart (“dispersion forces”). The main limits to agglomeration lie in competition for land, which is an immobile good in (more or less) limited supply, and different other negatives—congestion, noise, pollution—that increase with geographic concentration. Because only a limited number of agents can be spatially close to each other, most interactions occur across distant locations and are, therefore, costly. Each agent trades off the benefits generated by the agglomeration forces and the costs generated by the dispersion forces to choose his or her own location, which depends on where the others are located. The resulting spatial equilibrium is the outcome of these interdependent optimization processes carried out by economic agents who pursue their own interests.

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 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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.373
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.010

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 head0.049
GPT teacher head0.204
Teacher spread0.156 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreOther

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
Published2020
Admission routes1
Has abstractyes

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