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Record W2304648552 · doi:10.5539/ijef.v8n4p1

Integrated Model to Measure the Impact of Terrorism and Political Stability on FDI Inflows: Empirical Study of Pakistan

2016· article· en· W2304648552 on OpenAlexvenueno aff
Sundas Rauf, Rashid Mehmood, Aisha Rauf, Shafaqat Mehmood

Bibliographic record

VenueInternational Journal of Economics and Finance · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsnot available
Fundersnot available
KeywordsOpenness to experienceForeign direct investmentTerrorismEconomicsPolitical stabilityPoliticsOrdinary least squaresInvestment (military)International economicsMonetary economicsMacroeconomicsEconometricsPolitical science

Abstract

fetched live from OpenAlex

<p>To condense saving-investment gap, transformation of technology, creation of employment opportunities and more importantly, increasing economic development of host countries, Foreign Direct Investment (FDI) is proven to be a significant source of investment predominantly for developing countries. Numerous standing studies have scrutinized the economic impact of terrorism and political stability by referring to decrease in FDI. This study empirically enlightens the determinants of FDI for Pakistan over the period 1970 to 2013, by using annual secondary time series data. Adopting the optimistic approach, in this study, variables in the combination of terrorism, political stability, trade openness and GDP have been analyzed applying Ordinary Least Square (OLS) method. As expected, the projected results confirm that GDP, trade openness and political stability have positive and significant impact whilst terrorism has negative influence on FDI inflows in Pakistan. Because of the political stability along with stable GDP growth rate, inverse impact of terrorism has been found statistically insignificant.</p>

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.389
Threshold uncertainty score0.211

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.047
GPT teacher head0.308
Teacher spread0.260 · 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 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

Citations12
Published2016
Admission routes1
Has abstractyes

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