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Record W2800467749 · doi:10.1080/00036846.2018.1468554

Market access and occupational upgrading: evidence from the 19th century American transportation network

2018· article· en· W2800467749 on OpenAlexaff
Jeff Chan

Bibliographic record

VenueApplied Economics · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFiscal Policy and Economic Growth
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsMarket accessAgricultureOccupational mobilityWork (physics)SortingEconomicsLabour economicsDemographic economicsBusinessGeography

Abstract

fetched live from OpenAlex

This article investigates the extent to which county-level market access affects workers’ occupational upgrading and industrial sorting by exploiting the substantial spatial variation and rapid expansion of the United States’ transportation network coverage from 1870 to 1880. First, I find that individuals who enjoyed greater market access in 1880 were more likely to work in higher-paying occupations. Importantly, this result holds across all sectors of employment, for younger and older workers, and for migrants and non-migrants, suggesting that any market size effects on occupational upgrading were not specific to any one group. I also provide results showing that workers were more likely to switch industries within agriculture, but are less likely to do so from manufacturing or services. Finally, I find some evidence of changes to sectoral reallocation, principally away from agriculture, being associated with higher market access. My findings suggest that the expansion of the transportation network in played an important role in determining the type of work Americans performed in the nineteenth century.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.837

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 studies0.0000.000
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 head0.045
GPT teacher head0.245
Teacher spread0.200 · 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 teacher head, 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
Published2018
Admission routes1
Has abstractyes

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