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Record W2185462597 · doi:10.1111/twec.12413

Border Effects Before and After 9/11: Panel Data Evidence Across Industries

2016· article· en· W2185462597 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.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueWorld Economy · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of WaterlooCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsPanel dataEconomicsVariety (cybernetics)International tradeYield (engineering)Free trade agreementEconometricsInternational economicsDemographic economicsFree tradeComputer science

Abstract

fetched live from OpenAlex

Abstract The paper builds a unique industry‐level panel data set to estimate the border effects associated with US–Canada trade for each year from 1992 to 2005. We first establish the theoretical foundation of our empirical model as a multisector version of Anderson and van Wincoop. Estimates from data aggregated at the province/state level yield border effects that increase slightly in the early 1990s, then decline after the implementation of the North American Free Trade Agreement ( NAFTA ), but then increase significantly after 2001. Results based on three‐digit NAICS level data reveal higher border effects in the early 1990s and substantial heterogeneity across industries. The results are robust to a variety of specifications and models, and they suggest that the security measures adopted in the aftermath of the tragic events of 11 September 2001 had considerable adverse effects on US–Canada trade.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.637
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.002
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.092
GPT teacher head0.266
Teacher spread0.175 · 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