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Record W2581703587 · doi:10.24297/ijmit.v11i1.4936

IMPACT OF PAK-INDIA TRADE ON ECONOMY OF PAKISTAN BY USING COMPUTABLE GENERAL EQUILIBIUM MODEL (CGE)

2016· article· en· W2581703587 on OpenAlexaff
Muhammad Memon, Dr.Nadeem Bhatti, Faiz Muhammad Shaikh, Anwar Ali Shah G. Syed

Bibliographic record

VenueINTERNATIONAL JOURNAL OF MANAGEMENT & INFORMATION TECHNOLOGY · 2016
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsThe Scarborough Hospital
Fundersnot available
KeywordsComputable general equilibriumTariffAgricultureEconomicsBusinessDeveloping countryApplied general equilibriumAgricultural economicsInternational tradeGeneral equilibrium theoryEconomic growthMacroeconomicsGeography

Abstract

fetched live from OpenAlex

This research investigates the Impact of PAK-INDIA trade on Economy of Pakistan. Data were collected from GTAP-7 database and six sectors were included in the database, Textile, Pharmaceutical, Automobile parts and engineering, Agriculture, Financial and Insurance services and logistics. Data were analyzed by using GEM-software. Different simulation run on GTAP-7 database and various tariff rates applied. It was revealed that if India were removing the sensitive list item, in this scenario both countries would have positive impact on GDP, Export, Import and Employment of Pakistan. The results indicates that there in Agriculture, textile, Auto Pakistan’s is head on India in MFN status. In Pharmaceutical, Financial services and Logistics India has positive gain.  It was further revealed that if Pakistan is given MFN status to India, Pakistan’s import decreased and Export increased and overall positive impact on Economy. This research analyzes the potential economic costs and benefits of Pak-India trade in Textile, Pharmaceutical, Automobile parts and engineering, Agriculture, Financial and Insurance services and logistics.  The first scenario is when normal trading relation with India will be restored; it means that both countries will give the MFN (Most Favored Nations) status to each other. In the second scenario, the SAFTA will be operative and there will be free trade between India and Pakistan and both countries will remove all tariffs and custom duties from each others’ imports. The Global trade analysis GTAP model is used to analyze the possible impact of SAFTA on Pakistan in a multi country, multi sector applied General equilibrium frame work. After employing the simplified static analysis framework, the analysis based on simulations reveals that current demand for Pakistani Textile, Pharmaceutical, Automobile parts and engineering, Agriculture, Financial and Insurance services and logistics will expand after the FTA and consumer surplus will increase. The drop in the domestic prices of dates will increase the production of many downstream industries, which will have pleasant multiplier effects on the economy of Pakistan. The government may reduce MFN tariffs on industrial dates before implementing the FTA. A key rule of multilateral trade system is that the reduction in trade barriers should be applied on a most-favored nation basis (MFN) to all WTO members. The only exception to the MFN principle built into the GATT legal framework is the provision for reciprocal free trade within customs unions and free trade areas (GATT article XXIV). Following the analytical framework discussed by PO managerial (2001), we employ the simplified static analysis by using CGE model for policy implication, which reveals that Pakistan will gain benefit from Pak-India trade. Results based on this research reveal that on SAFTA, grounds, here will be net export benefits in Pakistan’s economy.

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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.316
Threshold uncertainty score0.411

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.026
GPT teacher head0.259
Teacher spread0.233 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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