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Record W3125893419

The Impact of Access to Rail Transportation on Agricultural Improvement: The American Midwest as a Test Case, 1850-1860

2010· preprint· en· W3125893419 on OpenAlexaboutno aff
Robert A. Margo, Jeremy Atack

Bibliographic record

VenueRePEc: Research Papers in Economics · 2010
Typepreprint
Languageen
FieldEnvironmental Science
TopicAmerican Environmental and Regional History
Canadian institutionsnot available
Fundersnot available
KeywordsAgricultural economicsRevenueCensusAgricultureQuarter (Canadian coin)ProductivityGeographyAgricultural landEconomicsBusinessEconomic growthArchaeologyFinance
DOInot available

Abstract

fetched live from OpenAlex

During the 1850s, land in U.S. farms increased by more than a third—100 million acres—and almost 50 million acres, an area almost equal to that of the states of Indiana and Ohio combined, were converted from their raw, natural state into productive farmland. The time and expense of transforming this land into a productive agricultural resource represented a significant fraction of domestic capital formation at the time and was an important contributor to American economic growth. Even more impressive, however, was the fact that almost half of these total net additions to cropland occurred in just seven Midwestern states which comprised somewhat less than one-eighth of the land area of the country at that time. Using a new GISbased transportation database linked to county-level census data, we estimate that at least a quarter (and possibly two-thirds or more) of this increase in cultivable land can be linked directly to the coming of the railroad to the Midwest. Farmers responded to the shrinking transportation wedge which raised agricultural revenue productivity by rapidly expanding the area under cultivation and these changes, in turn, drove rising farm and land values.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.880
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.018
GPT teacher head0.300
Teacher spread0.282 · 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.

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

Citations2
Published2010
Admission routes1
Has abstractyes

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