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International Borrowing, Specialization, and Unemployment in a Small Open Economy

2004· article· en· W1744353978 on OpenAlexaff
Patrick N. Osakwe, Shouyong Shi

Bibliographic record

VenueReview of International Economics · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsUnemploymentSmall open economyEconomicsSubsidyOpen economyShock (circulatory)WageExternal sectorLabour economicsTerms of tradeFull employmentInternational economicsMonetary economicsInternational tradeMonetary policyMacroeconomicsMarket economyExchange rate

Abstract

fetched live from OpenAlex

Abstract The authors show that an increase in international borrowing increases specialization and unemployment in a small open economy that is subject to terms‐of‐trade risks. The economy has a production advantage in the export sector. However, the size of the export sector is limited by the available funds. To insure workers against income fluctuations arising from terms‐of‐trade risks, firms in the export sector offer workers a stable wage rate with the possibility of unemployment. An increase in international borrowing increases specialization in the export sector, which leads to higher unemployment when the terms‐of‐trade shock is bad. A state‐contingent price subsidy can reduce unemployment without inefficiently reducing specialization. The results are robust to the introduction of risk‐averse firms.

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)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.034
GPT teacher head0.273
Teacher spread0.239 · 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

Citations0
Published2004
Admission routes1
Has abstractyes

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