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Record W4376136487 · doi:10.1177/00157325231159240

The Link Between Aid-for-Trade and Contingent Protection

2023· article· en· W4376136487 on OpenAlexaboutno aff
Neha Bhardwaj Upadhayay

Bibliographic record

VenueForeign Trade Review · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicInternational Development and Aid
Canadian institutionsnot available
Fundersnot available
KeywordsOrder (exchange)BusinessInternational tradeProduction (economics)Market accessInternational economicsEconomicsFinanceMicroeconomics

Abstract

fetched live from OpenAlex

Foreign aid, in theory, is expected to mitigate constraints that impede the economic development of recipient countries. At the same time that help is committed, donors are seemingly taking actions that are harmful to developing economies in obvious ways. An example is the tacit circumvention of the putative rules-based global trading system through contingent protection activities. In this article, it is postulated that, on one hand aid-for-trade (AfT) is expected to have positive impact on the exports of aid recipients by better integration into the global trading order, on the other hand, aid provider (donor) curtails access to its own markets by actuating contingent protection against the recipient (exporter). Using contingent protection cases data from 2003 to 2018 (a 15-year period) against 106 recipient countries of the United States of America’s AfT, this study finds a significant and positive impact of AfT on the surge in contingent protection activities. This effect is entirely driven by the aid for economic infrastructure and services, while the other main category of AfT- production sector, has no discernible effect on the rise in protection against the recipient. To examine the heterogeneity in donor decisions, this study is expanded to other traditional donors like Australia, Canada, the European Union (EU) and New Zealand. This article finds that Australia behaves similar to the USA; however, for Canada and the EU, the relationship between aid and market access is not statistically significant. JEL Codes: F1, F35, O19

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.971
Threshold uncertainty score0.452

Codex and Gemma teacher scores by category

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

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 head0.077
GPT teacher head0.340
Teacher spread0.263 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

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

Citations1
Published2023
Admission routes1
Has abstractyes

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