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Record W7010447810

Impact of China's Integration on Selected OECD Economies

2023· article· en· W7010447810 on OpenAlexaboutno aff

Bibliographic record

VenueAgEcon Search (University of Minnesota, USA) · 2023
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsMonopolistic competitionLiberalizationChinaComputable general equilibriumForeign direct investmentAccessionGoods and servicesFree tradeInvestment (military)
DOInot available

Abstract

fetched live from OpenAlex

The trade reforms that China has embraced as a result of its WTO accession are a continuation of a long standing trend that saw sustained reduction in non-tariff barriers and in levels and dispersion of tariffs. However, in the area of services, China’s commitments represent milestones. Plans include the opening of key services sectors to foreign participation, elimination of geographical limitations, forms of establishment, and scope of business activities among others. What are the implications of these reforms for China and OECD countries? The paper provides some estimates on the basis of a multi-country, multi-sector computable general equilibrium model of the world economy that features increasing returns to scale and large-group monopolistic competition. Importantly, the model includes a treatment of foreign direct investment on a bilateral basis which, given the importance of foreign presence in the Chinese economy, is essential for understanding the impacts of its liberalisation. Our results show that China itself clearly stands to gain substantially from its liberalisation. Implementation of the WTO commitments by China in goods and services sectors is estimated to increase its real income by almost 2%, while a scenario with full liberalisation is expected to yield a 3% increase in its real income. A major part of these gains comes from the improved efficiency with which China uses its resources. We find a limited impact on OECD economies as a result of China’s implementation of WTO commitments and complete liberalisation in the area of tariffs and services barriers. The structure of bilateral trade flows between China and individual OECD economies reflect divergent patterns of comparative advantages as well as differences in structure of trade barriers and geographical location. The most direct impact is expected through improved export performance of OECD countries that are already trading with or investing intensively in China but still face significant market access barriers. The observed trade patterns suggest that the impact through the market access channel is likely to be more important for Korea, Japan, Australia, and New Zealand, while the impact on other OECD economies is likely to be limited. The second channel through which China’s liberalisation may affect OECD economies is increased competitiveness of Chinese exporters who would experience declining costs of intermediate products and services as a result of liberalisation. The non-negligible market shares of China in OECD countries’ imports suggest that increased import competition is indeed an important outcome of China’s liberalisation. However, these competitiveness effects felt in both domestic OECD markets and third country markets are almost always outweighed by the market access effects (through better access to China’s market), resulting in the majority of cases in overall net gains for the OECD countries. Finally, FDI-related effects are important as they dominate the modest welfare gains of most OECD countries in the services liberalisation scenarios. While China experiences losses from its outward FDI, most OECD countries benefit from increased incomes from their investments in China. The scenarios in which China is assumed to fully remove its import duties and services barriers result in expansion of global gains by an additional one percentage point as compared to the WTO accession scenario. This suggests that China’s WTO commitments in the area of both goods tariffs and services barriers are already quite ambitious and deliver the bulk of the gains that can be had from such reforms. Still, most OECD countries enjoy additional gains in both absolute and per capita terms from the fuller liberalisation scenario. It is important to note that our results are conditional on production, consumption, trade and investment data reflecting the time of China’s WTO accession and may hence be only approximate given the pace of structural changes within the Chinese economy as well as the relationships between China and its OECD commercial partners. Our results are also broadly in line with the existing literature and, more fundamentally, with the underlying trade data. On a per capita basis the biggest gainers from implementation of WTO commitments by China are Korea, Japan, EU15, Canada and US. All the gaining OECD countries benefit from allocative efficiency, substantial favourable terms of trade effects and increased income from services FDI to China. It should be noted however that our analysis has not accounted for the dynamic effects of China’s openness and is therefore likely to provide lower bound estimates.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.207

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.062
GPT teacher head0.234
Teacher spread0.172 · 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 designObservational
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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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