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Record W2972501382 · doi:10.1111/aepr.12282

Comment on “Trade Wars and the WTO: Causes, Consequences and Change”

2019· article· en· W2972501382 on OpenAlexaboutno aff
Junji Nakagawa

Bibliographic record

VenueAsian Economic Policy Review · 2019
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsChinaInternational tradeTrade warNegotiationIntellectual propertyMultilateral trade negotiationsMarket accessEconomicsInternational economicsTrade barrierInvestment (military)Transatlantic Trade and Investment PartnershipFree tradePolitical scienceLawAgriculturePolitics

Abstract

fetched live from OpenAlex

Hoekman (2020) is right when he points out that the rise of trade war between the USA and China is partially caused by the ineffectiveness of the working practices of the World Trade Organization (WTO), including rule making and resolving trade tensions. If we refer to “the draft framework,”11 The draft framework for the USA–China trade negotiation. May 2018. Accessed 4 August 2019, Available from URL; https://xqdoc.imedao.com/16329fa0c8b2da913fc9058b.pdf presented by the USA in May 2018 as a list of US demands for its negotiation with China, some of them are related to the WTO. Section 2 on intellectual property rights and Section 5 on the reduction of agricultural tariffs are based on the WTO. The other demands are, however, not based on the WTO. Sections 1 and 7 are related to the reduction of US trade deficits with China, and Sections 4 and 6 are related to the improvement of US investment/service market access in China. Section 3 deals with US imposition of restrictions on Chinese investments in sensitive US technology sectors. This means that the USA–China trade war takes place mainly outside of the WTO, and the chance is very small that the reform of the WTO in its rule making and dispute settlement functions will settle the USA–China trade war. This does not mean that the WTO need not be reformed, and Hoekman (2020) provides for well-thought options for reforming the WTO, particularly its rule making functions. He focuses on two characteristics of the rule making mechanism of the WTO, namely, consensus-based decision making and special and differential treatment (SDT) of developing countries. Consensus is a heritage of the GATT, where major issues of trade negotiation used to be settled by the four developed countries, or Quad (USA, EC, Japan, and Canada), before they were brought to the whole members for adoption. In contrast, under the WTO, with more members at different levels of economic development, it has been very difficult for a small number of influential members to reach agreement before consensus decision making. As a result, consensus permits WTO members to veto any initiatives. SDT is another challenge of the WTO in its rule making function. Under the SDT, as Hoekman (2020) precisely points out, any developing country may self-designate itself as “developing country,” and offer less than developed countries in trade negotiation, and exempt itself from full implementation of rules. In light of these two characteristics of the rule making of the WTO, Hoekman (2020) proposes us a unique means of partially overcoming them, that is, open plurilateral agreements (OPAs). He is realistic in suggesting an agreement between the four major proponents, China, EU, Japan, and USA as a first step. This might be a new Quad under the WTO, as they are the four major trading countries in the world. If these four members could reach agreement on such new issues as digital trade or industrial subsidies, such result might be brought to the consensus decision making. Hoekman (2020) is more discreet and perhaps more realistic. Rather than expecting consensus decision making on such new issues, he argues for rule making by partial members of the WTO. There are precedents of partial agreements under the WTO, that is critical mass agreements (CMAs) and plurilateral agreements (PAs). CMAs are agreements among partial members of the WTO comprising a critical mass of trade in specific goods or services, and their benefits are applied to all WTO members on an most-favored nation basis. PAs are closed, and its disciplines/benefits are applied to only their members. Information Technology Agreement and the Fourth protocol to the General Agreement on Trade in Services on basic telecommunication are examples of CMA, and Government Procurement Agreement is an example of the PA. OPA, which Hoekman (2020) proposes as a third option, is in fact a CMA, because their benefits are applied to all WTO members. The chances of ongoing negotiations on E-commerce or investment facilitation to become OPA are, however, unclear. Both negotiations have their roots at the joint statements of about 70 members at the 11th WTO Ministerial Conference of December 2017. Negotiations started in January 2019, with over 70 members participating. However, negotiating members have not decided on the final form of the agreements. If they agree to make them as OPA (CMA), they will need to apply them to all WTO members. At this stage of negotiation, we cannot expect whether they will be so generous in admitting such free-riding. If they are not so generous, the final outcome will be either PAs or preferential trade agreements (PTAs). If the former is the case, consensus requirement will be an almost insurmountable hurdle. If the latter is the case, they will be another PTA, with scarce discipline by the WTO. Hoekman (2020) argues for utilization of OPAs (CMAs) as a means of improving the rule making function of the WTO. Given the stalemate of the Doha Development Agenda and proliferation of PTAs including the Trans-Pacific Partnership (TPP) and the Comprehensive and Progressive Agreement for the Trans-Pacific Partnership (CPTPP), this may be the only means available for reviving the WTO as a forum for rule making. Whether his argument will come true will depend on the political will of the WTO members.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.931
Threshold uncertainty score0.999

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 head0.086
GPT teacher head0.268
Teacher spread0.181 · 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.

Study designTheoretical or conceptual
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
Published2019
Admission routes1
Has abstractyes

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