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Record W2338240207 · doi:10.1142/9789813220720_0005

The Impact of BITs and DTTs on FDI Inflow and Outflow: Evidence from China

2017· book-chapter· en· W2338240207 on OpenAlex

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

VenueWORLD SCIENTIFIC eBooks · 2017
Typebook-chapter
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsCentre for International Governance InnovationWestern University
Fundersnot available
KeywordsOutflowInflowChinaForeign direct investmentEnvironmental scienceMaterials scienceEconomicsGeographyMeteorologyMacroeconomics

Abstract

fetched live from OpenAlex

This paper examines the impact of both China's bilateral investment treaties (BITs) and double tax treaties (DTTs) simultaneously on China's bilateral Foreign Direct Investment (FDI) inflows and outflows. Using China bilateral FDI flow data from 1985 to 2010, we find that the cumulative number of bilateral investment treaties (BITs) China signed has a positive (though not always statistically significant) but minor impact on both China's FDI inflows and outflows. The effect of a dummy BIT using dyadic data is always significant and positive for China's FDI inflows, while negative but not always significant for China's FDI outflows. We also find evidence that the cumulative number of double tax treaties (DTTs) tends to promote China's FDI inflows and outflows in most equations with weighted cumulative BITs. However, tax treaty dummies do not reveal any robust effect on FDI flow. Generally, BITs and DTTs are more inclined to affect China's FDI inflows than to affect China's FDI outflows.

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 categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.647
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0020.000
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 head0.046
GPT teacher head0.261
Teacher spread0.215 · 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