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Record W2269729608 · doi:10.3386/w21935

How Exporters Grow

2016· preprint· en· W2269729608 on OpenAlexafffund
Doireann Fitzgerald, Stefanie Haller, Yaniv Yedid‐Levi

Bibliographic record

VenueNational Bureau of Economic Research · 2016
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of British Columbia
FundersSocial Sciences and Humanities Research Council of CanadaNational Science Foundation
KeywordsMarkup languagePrice elasticity of demandElasticity (physics)MicroeconomicsIndustrial organizationEconomicsCustomer baseBusinessEconometricsMarketingComputer science

Abstract

fetched live from OpenAlex

We show that in successful episodes of export market entry, there are statistically and economically significant post-entry dynamics of quantities, but no post-entry dynamics of markups.This suggests that shifts in demand play an important role in successful entry, but that firms do not use dynamic manipulation of markups as an instrument to shift demand.We structurally estimate two competing models of customer base accumulation to match these moments.In the first model, firms use marketing and advertising to acquire new customers and thereby shift demand and increase sales.In the second, they use temporarily low markups to do so.The marketing and advertising model fits the quantity and markup moments well, and implies that successful entry is associated with high selling expenses.The second model cannot simultaneously fit quantity and markup moments, even with a counterfactually high price elasticity of demand and trade elasticity.We conclude that successful market entry is more likely to be associated with high selling expenses than low markups.

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 score0.041
Threshold uncertainty score0.136

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 communication0.0050.006
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0410.008

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 head0.501
GPT teacher head0.427
Teacher spread0.074 · 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

Citations27
Published2016
Admission routes2
Has abstractyes

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