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Record W2529687912 · doi:10.1016/j.jinteco.2019.103276

Volatility in the small and in the large: The lack of diversification in international trade

2019· article· en· W2529687912 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJournal of International Economics · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversité du Québec à Montréal
FundersHorizon 2020European Research CouncilFonds de Recherche du Québec-Société et CultureAgence Nationale de la Recherche
KeywordsDiversification (marketing strategy)EconomicsVolatility (finance)PortfolioInternational economicsMonetary economicsTerms of tradeDestinationsInternational tradeFinancial economicsBusinessTourism

Abstract

fetched live from OpenAlex

How does international trade affect the risk exposure of firms and countries? Trade induces specialization, thus increasing economies' exposure to idiosyncratic supply shocks. But greater geographic diversification in trade destinations offers natural hedging properties against demand shocks. In this paper, we offer an integrated economic and econometric view of the impact of trade on firms and countries volatility. Exporters' volatility is shown to directly depend on the (lack of) diversification in their portfolio of clients. Indeed, most exporters, including the largest, have one or two main clients that dwarf the others. This structure of trade networks implies that individual exporters are strongly exposed to microeconomic demand shocks. The concentration of trade flows further implies that such risk does not wash out across firms, thus contributing to aggregate fluctuations.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.107
Threshold uncertainty score0.241

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.081
GPT teacher head0.244
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