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Record W3197054028 · doi:10.52337/pjia.v4i2.195

THE ROLE OF AUTOMOBILE SECTOR IN GLOBAL BUSINESS CASE OF PAKISTAN

2021· article· en· W3197054028 on OpenAlexaboutno aff

Bibliographic record

VenuePakistan Journal of International Affairs · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsnot available
Fundersnot available
KeywordsAutomotive industryBusinessGovernment (linguistics)Index (typography)ChinaRevealed comparative advantagePrivate sectorComparative advantageIndustrial organizationEconomicsInternational tradeEconomic growthEngineering

Abstract

fetched live from OpenAlex

The global automobile sector is among the driving forces of worldwide economies. Similarly in Pakistan, the automobile sector is the one of greatest industries. Although the automobile is one of the leading private sectors in Pakistan, the industry is largely protected from the external race. Over the eras, the automotive manufacturing consumes made a known run-of-the-mill presentation in rapports of continual and continuous progress. The vehicle manufacturing of Pakistan ensures not to partake a noteworthy part in the overall additional worth of the industrial segment. This study is finding out the effects of high duties, production, technology, and government policies on automobile growth in Pakistan. It will contribute to other important factors that affect the growth of the Automobile Industry worldwide and specially in Pakistan. Many studies use the Balassa index to analyse comparative global trade benefits, which have been shown (particularly in agriculture), but the selected automotive industry has novel study possibilities. This research focuses on the competitiveness of the car industry, a crucial sector because of its high added value, competitive market, growing technical demands and a high level of employment. The objective of our article is to analyse the comparative benefits indicated by Markov's transition probability and the caplan-meier survival function of the global car trade, and the duration and stability of the Balassa indices. Data sources for 1997-2016 are worldwide HS6 car shipments. The article has arrived at several findings. Initially, analysing the global vehicle trade, it was found that the USA, China, Germany and Japan were the greatest vehicle manufacturers, but in the time examined, the main exporters were Germany, Japan and Canada, collectively accounting for 40% of all goods shipped, with 71% of the top 10 nations. Second, the most traded/exported automotive product, as we analysed it, was a worldwide vehicle with just dazzling internal ignition (1500-300cm 3) (870323), representing more than 40% of the whole 1997-2016 export of vehicles. Third, the Balassa calculations reveal that in every period evaluated by the most prominent automotive exporters in the world, Spain and Japan had the largest comparative advantages. This is a descriptive correlational study and the primary purpose is to examine variables and relationships. This study is conducted in the education sector in Karachi, Pakistan with four variables to investigate the causal relationship among different variables. This study constructed a conceptual framework to illustrate a causal relationship by defining the relevant variables. It indicates the independent variable (the cause) and the dependent variable (the effect). There is a positive relationship between production and automobile growth. The effects of Duties are 0.294 on automobile growth and the effect of government policy is 0.177 on automobile growth and the effect of production is 0.152 on automobile growth and the effect of technology is 0.150.

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)0.001
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 score0.306
Threshold uncertainty score0.391

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.018
GPT teacher head0.248
Teacher spread0.230 · 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

Citations71
Published2021
Admission routes1
Has abstractyes

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