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Record W2268526373 · doi:10.3386/w12249

China's FDI and Non-FDI Economies and the Sustainability of Future High Chinese Growth

2006· report· en· W2268526373 on OpenAlexaff
John Whalley, Xian Xin

Bibliographic record

VenueNational Bureau of Economic Research · 2006
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsWestern University
Fundersnot available
KeywordsForeign direct investmentChinaSustainabilityBusinessInternational tradeInternational economicsEconomicsEconomic geographyEconomyGeographyMacroeconomicsBiology

Abstract

fetched live from OpenAlex

This paper presents assesses of the contribution of inward FDI to China's recent rapid economic growth using a two stage growth accounting approach. Recent econometric literature focuses on testing whether Chinese growth depends on inward FDI rather than measuring the contribution. Foreign Invested Enterprises (FIEs), often (but not exclusively) are joint ventures between foreign companies and Chinese enterprises, and can be thought of as forming a distinctive subpart of the Chinese economy. These enterprises account for over 50% of China's exports and 60% of China's imports. Their share in Chinese GDP has been over 20% in the last two years, but they employ only 3% of the workforce, since their average labor productivity exceeds that of Non-FIEs by around 9:1. Their production is more heavily for export rather than the domestic market because FIEs provide access to both distribution systems abroad and product design for export markets. Our decomposition results indicate that China's FIEs may have contributed over 40% of China's economic growth in 2003 and 2004, and without this inward FDI, China's overall GDP growth rate could have been around 3.4 percentage points lower. We suggest that the sustainability of both China' export and overall economic growth may be questionable if inward FDI plateaus in the future.

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)0.008
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.430
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.001
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 head0.105
GPT teacher head0.382
Teacher spread0.277 · 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
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".

Quick stats

Citations110
Published2006
Admission routes1
Has abstractyes

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