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Record W4403434960 · doi:10.1093/wber/lhae033

A Measurement of Aggregate Trade Restrictions and Their Economic Effects

2024· article· en· W4403434960 on OpenAlex
Julia Estefania‐Flores, Davide Furceri, Swarnali Ahmed Hannan, Jonathan D. Ostry, Andrew K. Rose

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.

Bibliographic record

VenueThe World Bank Economic Review · 2024
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsUniversity of TorontoGlobal Affairs Canada
FundersHong Kong Institute for Monetary ResearchKeio UniversityInternational Myeloma Foundation
KeywordsEconomicsAggregate (composite)International economicsEconometricsMacroeconomicsMonetary economics

Abstract

fetched live from OpenAlex

Abstract This study develops a new Measure of Aggregate Trade Restrictions (MATR) using data from the IMF's Annual Report on Exchange Arrangements and Exchange Restrictions. MATR is a measure of direct and indirect official government policy related to the international flow of goods and services. MATR is simple, plausible, quantitative, easily updated, based on relevant measures of trade policy and other international restrictions affecting trade (e.g., payment restrictions), and covers an unbalanced sample of up to 157 countries from 1949 to 2019. MATR is strongly correlated with, and—importantly—more comprehensive, in terms of country and time coverage, than existing measures of de jure openness; it is also granular. As such, MATR empowers empirical analysis to increase coverage in research related to trade restrictions and other trade-related openness policies. MATR is used in the study to show that direct and indirect restrictions to trade are associated with significant contractions in output.

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)0.001
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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.916
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.055
GPT teacher head0.235
Teacher spread0.179 · 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