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Record W2074498945 · doi:10.5539/ibr.v4n3p45

The Financial Setting for FDI Inflows into The Czech Republic and Slovakia

2011· article· en· W2074498945 on OpenAlexvenueno aff
Edward M. Jankovic, Pan G. Yatrakis

Bibliographic record

VenueInternational Business Research · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal Financial Crisis and Policies
Canadian institutionsnot available
Fundersnot available
KeywordsCzechForeign direct investmentSlovakTransparency (behavior)Capital marketFinancial marketInvestment (military)DemocracyBusinessPolitical riskCapital (architecture)EconomicsMarket economyPoliticsEconomic systemFinanceMacroeconomicsPolitical science

Abstract

fetched live from OpenAlex

This study examines the relationship between foreign direct investment in the Czech Republic and Slovakia and such potentially explanatory factors as trade flows, measures of economic and financial stability, and country risk. The authors find that as the Czech and Slovak Republics progress toward market economies, some policy points to consider include: transparency of markets, economic systems, social and political organizations; an increase in commerce and investment, which makes reversals of reforms less likely and the condition of financial factors that contribute to increased investment.At this time, these transition economies possess many of the resources needed for development, such as educated labor forces, an entrepreneurial orientation among the citizens, and available land. Yet, another key resource, capital, is in short supply in the region. An important precondition to obtaining capital is the demonstration of economic and political stability (see Kyrkilis and Pantelidi 2006). By improving the transparency of their legal, banking, and capital markets sectors, the Czech and Slovak Republics can accelerate their progress toward free markets and democratic societies.Our research finds that relationships exist among country risk ratings, financial market variables, and expected returns in this region; such relationships can be useful in developing policies to improve capital markets and attract external capital. The importance of building upon other researchers in this field updates that research plus incorporates new aspects of issues now developing. This research topic is fluid and demands continuous review and testing.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.587

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.103
GPT teacher head0.334
Teacher spread0.232 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations7
Published2011
Admission routes1
Has abstractyes

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