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Record W3097965227 · doi:10.3386/w25120

Trade and Domestic Production Networks

2018· report· en· W3097965227 on OpenAlexaff
Felix Tintelnot, Ayumu Ken Kikkawa, Magne Mogstad, Emmanuël Dhyne

Bibliographic record

VenueNational Bureau of Economic Research · 2018
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsRevenueProduction (economics)BusinessInternational tradeInternational economicsEconomicsMonetary economicsFinanceMicroeconomics

Abstract

fetched live from OpenAlex

We use Belgian data with information on domestic firm-to-firm sales and foreign trade transactions to study how international trade affects firms' unit cost and the consumer's real wage. We show theoretically that the gains from trade depend on domestic firm-to-firm linkages. Furthermore, we develop a tractable model of endogenous network formation, allowing firm-to-firm connections to form or break in response to import price changes. Quantitatively, we find that for small import price changes, alternative models that assume a roundabout production structure, despite falsely implying that all firms are connected within one link, yield similar predictions for the change in the real wage to the model that fits the actual linkages between firms. For large changes in the price of foreign goods, both the existing network structure and the endogeneity of the connections between firms are found to be quantitatively important.

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)0.008
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.121
Threshold uncertainty score0.241

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.013
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.453
GPT teacher head0.452
Teacher spread0.001 · 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 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

Citations64
Published2018
Admission routes1
Has abstractyes

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