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Record W4391328847 · doi:10.3386/w32083

The Effects of Immigration on Agricultural Development: Brazil in the Age of Mass Migration

2024· report· en· W4391328847 on OpenAlexaff
David Escamilla‐Guerrero, Andrea Papadia, Ariell Zimran

Bibliographic record

VenueNational Bureau of Economic Research · 2024
Typereport
Languageen
FieldSocial Sciences
TopicMigration, Racism, and Human Rights
Canadian institutionsYork University
FundersUniversity of St AndrewsVanderbilt University
KeywordsImmigrationMass migrationDemographic economicsEconomic geographyGeographyDevelopment economicsPolitical scienceDemographyEconomicsSociology

Abstract

fetched live from OpenAlex

We study the effects of immigration in Brazil during the Age of Mass Migration, focusing on the country's agricultural sector in 1920.This context combines the widely recognized value of historical perspective in studies of the effects of immigration with Brazil's unique position among major immigrant destinations of the period as a low-income country with a large agricultural sector and weak institutions to shed light on the effect of immigration in countries at an early stage of development.Instrumenting for a municipality's immigrant share using the interaction of aggregate immigrant inflows and the expansion of Brazil's railway network, we find that a greater immigrant share in a municipality led to an increase in farm values and that the bulk of the effect was the product of more intense cultivation of land.Finally, we find that it is unlikely that immigration's effect on agriculture slowed Brazil's structural transformation.

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)0.001
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 score0.092
Threshold uncertainty score0.183

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.146
GPT teacher head0.479
Teacher spread0.333 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

Explore more

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