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Record W2167908642 · doi:10.1111/caje.12319

A theory on the role of wholesalers in international trade based on economies of scope

2018· preprint· en· W2167908642 on OpenAlexvenueno aff
Anders Åkerman

Bibliographic record

VenueCanadian Journal of Economics/Revue canadienne d économique · 2018
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsFixed costBusinessProductivityIntermediaryDistribution (mathematics)CommerceIndustrial organizationScope (computer science)Economies of scopeInternational tradeEconomicsEconomies of scaleMarketing

Abstract

fetched live from OpenAlex

Abstract This paper offers a theoretical foundation for the existence of wholesalers and other intermediaries in international trade and analyzes their role in an economy with heterogeneous manufacturing firms and fixed costs of exporting. Wholesalers are assumed to possess a technology such that they can buy manufacturing goods domestically and sell in foreign markets and they can, unlike manufacturers, export more than one good. A wholesaler therefore faces an additional fixed cost, which increases in the number of goods it handles. The presence of wholesale firms leads to productivity sorting. The most productive firms export on their own by paying a fixed cost, but a range of firms with intermediate productivity levels export through international wholesalers. A higher fixed cost of exporting to a destination means that wholesalers handle: (i) a higher share of total export volumes to this destination and (ii) a higher share of the exported product scope (i.e., the number of exported products) to this destination. A higher fixed cost of exporting gives wholesalers a larger role, since these can spread the fixed cost across more than one good. The wholesale technology therefore exhibits economies of scope. An empirical analysis using Swedish firm‐level data supports the main assumption and predictions of the model.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.468
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.154
GPT teacher head0.181
Teacher spread0.027 · 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 designTheoretical or conceptual
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

Citations77
Published2018
Admission routes1
Has abstractyes

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