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Record W2997190565 · doi:10.5430/rwe.v10n4p48

Foreign Capital Flows as Factors of Economic Growth in Bulgaria, Czech Republic, Hungary and Poland

2019· article· en· W2997190565 on OpenAlexvenueno aff
Tatiana Rodionova, Sergey Yakubovskіy, Andrii Kyfak

Bibliographic record

VenueResearch in World Economy · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicBusiness and Economic Development
Canadian institutionsnot available
Fundersnot available
KeywordsForeign direct investmentEconomicsCzechGross fixed capital formationCapital (architecture)Foreign capitalInvestment (military)Emerging marketsCapital formationCapital flowsInternational economicsMonetary economicsEconomyMarket economyMacroeconomicsFinancial capitalHuman capitalGeography

Abstract

fetched live from OpenAlex

While foreign investment is generally associated with economic growth, it can also pose significant risks to the economies of the recipient countries. An empirical study is carried out to test the causality between various forms of capital inflows and economic growth of four emerging market countries of Central and Eastern Europe: Bulgaria, the Czech Republic, Hungary and Poland. Using the vector autoregression framework it is found that prior to the crisis events in the world economy and euro area capital inflows, especially foreign direct investment, played significant role in boosting economic growth. However, afterwards there is no evidence of such impact and the reverse trend is observed: now economic growth is the factor driving capital inflows, again, mainly direct investments, to the countries. Also, as a result of the steady increase in value of the accumulated assets possessed by foreign investors in national economies negative effects of attracting foreign capital could be observed, which take the form of high volumes of repatriated profits, exceeding received investment and posing new threats for national economies.

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)0.001
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 score0.040
Threshold uncertainty score0.079

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0020.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.265
Teacher spread0.240 · 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".

Quick stats

Citations16
Published2019
Admission routes1
Has abstractyes

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