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Record W2214972767 · doi:10.1093/jeg/lbw006

International trade and local labor markets: Do foreign and domestic shocks affect regions differently?

2016· article· en· W2214972767 on OpenAlexaff
Mark D. Partridge, Dan S. Rickman, M. Rose Olfert, Ying Tan

Bibliographic record

VenueJournal of Economic Geography · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEconomicsWageInternational economicsInternational tradeLabour economics

Abstract

fetched live from OpenAlex

Despite the attention given to international trade in discussion of the economic struggles of many US regions, it is unclear whether international trade shocks impact local economies more, or differently, than shocks originating within the domestic economy. A challenge in making this discernment is separating trade shocks from common or domestic shocks. Therefore, using US county-level data for 1990–2010, this study carefully constructs shocks to local economies, isolating those arising from international imports and exports to assess whether trade shocks have different effects from domestic shocks. In confirmatory analysis, we also employ a novel combination of IV and matching strategies. We examine a variety of indicators including employment growth, population growth, employment rates, wage rates and poverty rates. The results suggest that international trade shocks have some different effects than overall domestic shocks, though likely less than commonly perceived. We also find that domestic shocks dominate international trade shocks in explaining variation in regional labor market outcomes.

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)0.004
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 score0.027
Threshold uncertainty score0.054

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.011
GPT teacher head0.209
Teacher spread0.198 · 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

Citations40
Published2016
Admission routes1
Has abstractyes

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