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Record W3093853256 · doi:10.1002/ijfe.2271

Did reduction in corporate tax rate attract <scp>FDI</scp> in Pakistan?

2020· article· en· W3093853256 on OpenAlexaff
Mian Sajid Nazir, Qaisar Hafeez, Salah U‐Din

Bibliographic record

VenueInternational Journal of Finance & Economics · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Taxation and Avoidance
Canadian institutionsMount Royal UniversityHEC Montréal
Fundersnot available
KeywordsForeign direct investmentOpenness to experienceAutocracyEconomicsCorporate taxMonetary economicsInternational economicsExchange rateDemocracyError correction modelPoliticsBusinessMacroeconomicsValue-added taxTax avoidance

Abstract

fetched live from OpenAlex

Abstract Foreign direct investment (FDI) is a very important factor for economic growth because of its associated benefits such as advanced technology, employment, and economic development for the developing host countries. The factors of lower corporate tax rate, social peace, consistent energy supply, better infrastructure, skilled labor, political stability and business openness are very important for foreign investors. Therefore, this study investigates the impact of corporate tax reduction, terrorism, energy shortfall, availability of labor, infrastructure and degree of business openness on the FDI in Pakistan. Further, it compares democratic and autocratic regimes to evaluate their impact on attracting FDI in Pakistan. A vector error correction (VEC) model is used on the secondary data of the period 1989–2016. The results of the study show that corporate tax rate, terrorism, and energy shortfall have significant negative impact on FDI. The study results also confirm that autocratic period was more favorable to attract FDI providing openness to the economy when compared to democratic period. The findings of this study could be used by policy makers for future FDI policy of Pakistan.

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 score0.581

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 communication0.0000.002
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 head0.035
GPT teacher head0.248
Teacher spread0.213 · 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

Citations8
Published2020
Admission routes1
Has abstractyes

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