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Record W2303417067 · doi:10.1257/aer.20140372

A Balls-and-Bins Model of Trade: Comment

2016· article· en· W2303417067 on OpenAlexaff
Bernardo S. Blum, Sebastián Claro, Ignatius J. Horstmann

Bibliographic record

VenueAmerican Economic Review · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProduct (mathematics)EconomicsBenchmark (surveying)EconometricsMarket sizeStatistical modelMicroeconomicsMathematicsCommerceStatistics

Abstract

fetched live from OpenAlex

We show that the Armenter and Koren model's firm-product-country results rely on the assumption that export shipment size is independent of firm size, and this assumption is contradicted by the data. When actual shipment sizes are used in the balls-and-bins model, it cannot reproduce the data on single product/single country exporters. Beyond just showing that the shipment size assumption matters to balls-and-bins outcomes, our results highlight the important fact that shipment size is an economic decision, co-determined with other export choices. For this reason, we argue that a balls-and-bins model cannot be a purely statistical benchmark model. (JEL F11, F14, O13, O19, Q37)

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: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.773

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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)0.0000.001

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 head0.078
GPT teacher head0.242
Teacher spread0.164 · 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

Citations4
Published2016
Admission routes1
Has abstractyes

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