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Record W4388445264 · doi:10.1515/jgd-2023-0013

Trade Boomers: Evidence from the Commodities-for-Manufactures Boom in Brazil

2023· article· en· W4388445264 on OpenAlexaff
Ridwan Karim

Bibliographic record

VenueJournal of Globalization and Development · 2023
Typearticle
Languageen
FieldHealth Professions
TopicGlobal Health Care Issues
Canadian institutionsWilfrid Laurier University
Fundersnot available
KeywordsEconomicsBaby boomChinaFree tradeBoomInternational economicsDemographic economicsInternational tradePopulationGeography

Abstract

fetched live from OpenAlex

Abstract China’s growing prominence as a trade superpower has placed competitive pressure on manufacturing industries in Brazil, while simultaneously bolstering demand for its commodities. I investigate the effects of this so-called manufactures-for-commodities boom on Brazilian birth outcomes from 2000 to 2010. Exploiting exogenous variation in patterns of trade growth with China across different regions within Brazil, I find that both import and export growth led to higher birth weights for babies, and lower infant mortality rates. I also find that negative import shocks reduced fertility rates across all age groups for women, suggesting that selectivity in births induced by negative income shocks, combined with concentration of household resources on the children that are born led to better infant health outcomes. Additional evidence is consistent with income effects playing a role in explaining the results, while ruling out better provision of healthcare and changes to household composition as mechanisms. I also explore changes in trade-induced pollution levels and social assistance programs as a potential mechanism. The findings indicate that increased import and export growth can improve infant health, highlighting another potential benefit from trade liberalization.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.397
Threshold uncertainty score0.315

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.112
GPT teacher head0.453
Teacher spread0.341 · 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 designNot applicable
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

Citations1
Published2023
Admission routes1
Has abstractyes

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