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Record W2111916083 · doi:10.1111/twec.12217

The Macroeconomic Effects of the Canada–US Free Trade Agreement on Canada: A Counterfactual Analysis

2014· article· en· W2111916083 on OpenAlexaboutno aff
Lin Zhang, Zaichao Du, Chêng Hsiao, Hua Yin

Bibliographic record

VenueWorld Economy · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
FundersMinistry of Education of the People's Republic of China
KeywordsCounterfactual thinkingEconomicsUnemploymentProductivityReal gross domestic productCounterfactual conditionalUnemployment rateFree trade agreementMacroeconomicsInternational economicsFree trade

Abstract

fetched live from OpenAlex

Abstract We evaluate the macroeconomic effects of the Canada–US Free Trade Agreement (FTA) on Canada's economy using a counterfactual analysis. We exploit the dependence of GDP growth (labour productivity and unemployment, respectively) among different economic entities and construct the counterfactuals using data from countries other than Canada. We find that in the adjustment period from 1989:Q1 to 1992:Q1, Canada's economy bore the short‐run adjustment costs of the FTA with a decline of the annual real GDP by 2.56 per cent and a decline of the labour productivity by 0.62 per cent. After the adjustment period, the FTA had a positive and permanent effect of 1.86 per cent on Canada's annual real GDP growth and raised the labour productivity from 1992 to 1994 by 2.39 per cent on average. Moreover, the FTA increased Canada's annual unemployment rate by 1.81 per cent in the period 1989–94.

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)0.014
metaresearch head score (Gemma)0.031
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: Empirical
Teacher disagreement score0.978
Threshold uncertainty score0.244

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.031
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0020.003
Science and technology studies0.0020.002
Scholarly communication0.0050.001
Open science0.0020.001
Research integrity0.0020.002
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 head0.009
GPT teacher head0.154
Teacher spread0.145 · 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

Citations10
Published2014
Admission routes1
Has abstractyes

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