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A Universal Model for General Gross Domestic Product Across Global Economies

Received: 3 April 2024     Accepted: 9 May 2024     Published: 17 May 2024
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Abstract

In addressing the need for a robust economic measure, this paper presents a mathematical model to forecast Gross Domestic Product (GDP) across diverse global economies. Our model, constructed from a dataset spanning 39 years from 16 varied economies, deciphers GDP by dissecting its fundamental components of population and productivity. Through meticulous literature review and data analysis, the research develops four predictive models, using linear and exponential trends, to represent the immediate and projected rates of change in both population and productivity. The research reveals a nuanced dynamic between these elements, identifying productivity, especially in infrastructure, healthcare, telecommunications, and innovation, as a pivotal force in driving economic growth. The study not only underlines the significant influence of these sectors but also the critical role of developed economies in aiding less developed ones to counteract the widening poverty gap. A comprehensive sensitivity analysis within the paper evaluates the impact of these factors on GDP, equipping policymakers with essential insights into enhancing economic progress. By combining immediate and long-term growth metrics derived from twenty-four influential variables into a cohesive predictive model, this research illuminates the complex interplay of forces shaping GDP trajectories. It suggests that while boosting population can yield short-term economic gains, enduring prosperity hinges on amplifying productivity. Moreover, the study points to the potential socio-economic divides that necessitate proactive measures for equitable development. Although challenges such as data dependency and growth discrepancies are acknowledged, the model proposes more frequent data analyses for capturing economic fluctuations accurately. Conclusively, the paper bridges a critical gap in economic modeling literature and provides a pragmatic framework for crafting inclusive economic policies and development strategies, thus making a significant contribution to both theoretical and applied economic fields.

Published in International Journal of Economics, Finance and Management Sciences (Volume 12, Issue 3)
DOI 10.11648/j.ijefm.20241203.11
Page(s) 127-141
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Economic Growth, Gross Domestic Product, Mathematical Modeling, Prediction, Global Economy, Economics, Forecasting, Economic Policy

References
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  • APA Style

    Gao, B. (2024). A Universal Model for General Gross Domestic Product Across Global Economies. International Journal of Economics, Finance and Management Sciences, 12(3), 127-141. https://doi.org/10.11648/j.ijefm.20241203.11

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    ACS Style

    Gao, B. A Universal Model for General Gross Domestic Product Across Global Economies. Int. J. Econ. Finance Manag. Sci. 2024, 12(3), 127-141. doi: 10.11648/j.ijefm.20241203.11

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    AMA Style

    Gao B. A Universal Model for General Gross Domestic Product Across Global Economies. Int J Econ Finance Manag Sci. 2024;12(3):127-141. doi: 10.11648/j.ijefm.20241203.11

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  • @article{10.11648/j.ijefm.20241203.11,
      author = {Billy Gao},
      title = {A Universal Model for General Gross Domestic Product Across Global Economies
    },
      journal = {International Journal of Economics, Finance and Management Sciences},
      volume = {12},
      number = {3},
      pages = {127-141},
      doi = {10.11648/j.ijefm.20241203.11},
      url = {https://doi.org/10.11648/j.ijefm.20241203.11},
      eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijefm.20241203.11},
      abstract = {In addressing the need for a robust economic measure, this paper presents a mathematical model to forecast Gross Domestic Product (GDP) across diverse global economies. Our model, constructed from a dataset spanning 39 years from 16 varied economies, deciphers GDP by dissecting its fundamental components of population and productivity. Through meticulous literature review and data analysis, the research develops four predictive models, using linear and exponential trends, to represent the immediate and projected rates of change in both population and productivity. The research reveals a nuanced dynamic between these elements, identifying productivity, especially in infrastructure, healthcare, telecommunications, and innovation, as a pivotal force in driving economic growth. The study not only underlines the significant influence of these sectors but also the critical role of developed economies in aiding less developed ones to counteract the widening poverty gap. A comprehensive sensitivity analysis within the paper evaluates the impact of these factors on GDP, equipping policymakers with essential insights into enhancing economic progress. By combining immediate and long-term growth metrics derived from twenty-four influential variables into a cohesive predictive model, this research illuminates the complex interplay of forces shaping GDP trajectories. It suggests that while boosting population can yield short-term economic gains, enduring prosperity hinges on amplifying productivity. Moreover, the study points to the potential socio-economic divides that necessitate proactive measures for equitable development. Although challenges such as data dependency and growth discrepancies are acknowledged, the model proposes more frequent data analyses for capturing economic fluctuations accurately. Conclusively, the paper bridges a critical gap in economic modeling literature and provides a pragmatic framework for crafting inclusive economic policies and development strategies, thus making a significant contribution to both theoretical and applied economic fields.
    },
     year = {2024}
    }
    

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