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Record W1847978797 · doi:10.5430/ijfr.v6n3p64

Capital Flight and Foreign Direct Investment

2015· article· en· W1847978797 on OpenAlexvenueno aff
Yi-Hui Chiang, Thomas Lee

Bibliographic record

VenueInternational Journal of Financial Research · 2015
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsInflowForeign direct investmentTerm (time)Error correction modelEconometricsEconomicsCapital (architecture)Capital outflowMathematicsMacroeconomicsCointegrationCapital formationPhysicsHuman capitalGeographyMeteorologyFinancial capitalMarket economy

Abstract

fetched live from OpenAlex

To systematically study the relationship between foreign direct investment inflow (FDI inflow) and capital flight, this chapter uses yearly-data in 1984-2012 in China, sets up two propositions and applies our model to explore the micro-relation and macro-relation between FDI inflow and capital flight.At proposition (4-1), we can find that the estimated values of long-term foreign direct investment inflow (FDI inflow) and long-term capital flight are significant correlation. Then, under Augmented Dickey-Fuller test (ADF test) or Philips-Perron test (PP test), the residuals reject the null hypothesis with statistical significant at 5% level, which implies that the result is stable. Causal test also shows that the relation between FDI inflow and capital flight is Granger causal. Besides, results of error correction model (ECM) show that FDI inflow and capital flight, they are not only have the long-term positive relation, but also have short-term dynamic relationship. So we can find the proposition (4-1) is true.At proposition (4-2), at first, we can find that the estimated values of long-term FDI inflow and long-term export are obviously correlated with statistical significant at 1% level, the R square value reaches highly level at 0.9, and they imply the model is good to fit. Next, results of error correction model show that FDI inflow and export, they are not only have the long-term positive relation, but also have short-term dynamic relationship. So we can find the first part of proposition (4-2) is true, which means that if FDI inflow increases, export will grows, too.Finally, for the remainder of proposition (4-2), at first, we can find that the estimated values of long-term capital flight and long-term export are obviously correlated. Then, under ADF test or PP test, we can find the remainder of proposition (4-2) is true, which means that if export grows up, capital flight will increase, too. In short, FDI inflow increase, export will grows apparently, capital flight will increase obviously which will attract more FDI inflow.

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)0.000
metaresearch head score (Gemma)0.002
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 score0.029
Threshold uncertainty score0.057

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
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 head0.078
GPT teacher head0.339
Teacher spread0.262 · 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
Published2015
Admission routes1
Has abstractyes

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