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Record W1993801478 · doi:10.3386/w16557

Firm Heterogeneity and Costly Trade: A New Estimation Strategy and Policy Experiments

2010· report· en· W1993801478 on OpenAlexafffund
Ivan Cherkashin, Svetlana Demidova, Hiau Looi Kee, Kala Krishna

Bibliographic record

VenueNational Bureau of Economic Research · 2010
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsMcMaster University
FundersMcMaster UniversityPrinceton University
KeywordsEstimationEconomicsBusinessEconometricsInternational tradeIndustrial organization

Abstract

fetched live from OpenAlex

This paper builds a tractable partial equilibrium model in the spirit of Melitz (2003), which incorporates two dimensions of heterogeneity: firms specific productivity shocks and firm-market specific demand shocks.The structural parameters of interest are estimated using only cross-sectional data, and counterfactual experiments regarding the effects of reducing costs, both fixed and marginal, or of trade preferences (with distortionary Rules of Origin) offered by an importing country are performed.Our counterfactuals make a case for "trade as aid" as such policies can create a ""win-win-win" scenario and are less subject to the usual worries regarding the efficacy of direct foreign aid.They also suggest that reducing fixed costs at various levels can be quite effective as export promotion devices, with the exports induced per dollar spent ranging from .4 to 25.

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)0.017
metaresearch head score (Gemma)0.051
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.017
Threshold uncertainty score0.092

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.051
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0020.002
Science and technology studies0.0010.001
Scholarly communication0.0030.004
Open science0.0020.002
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0060.000

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 head0.539
GPT teacher head0.503
Teacher spread0.036 · 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 designSimulation or modeling
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

Citations28
Published2010
Admission routes2
Has abstractyes

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