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Increasing The Economic Efficiency of Cotton-Textile Clusters: Ways to Optimize Costs
Corresponding Author(s) : Rustamova Mekhrigiyo Mukhtor qizi
American Journal of Economics and Business Management,
Vol. 8 No. 3 (2025): March
Abstract
This research investigates the economic efficiency of cotton-textile clusters and explores effective strategies for cost optimization and productivity enhancement. Cotton production is traditionally labor-intensive and resource -demanding, particularly during the harvesting season, which incurs significant costs. The study employs econometric modeling using Stata 18.0 and SPSS 25.0, analyzing data from 133 clusters to assess key economic factors influencing total expenses. Results indicate that land area, farm management efficiency, financial resources, and irrigation investments significantly impact cost structures. The findings highlight that mechanization, improved financial management, and optimized irrigation systems contribute to expense reduction and overall profitability. The study provides practical recommendations for enhancing the economic sustainability of cotton-textile clusters.
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- Y. Ruan, “Exploring Multiple Regression Models: Key Concepts and Applications,” Sci. Technol. Eng. Chem. Environ. Prot., vol. 1, no. 7, 2024, doi: 10.61173/yjpt3s59.
- W. H. Greene, “Econometric Analysis”, 8th ed., Pearson, 2020.
- S. Janković, “The Multivariate Statistical Analysis – Multiple Linear Regression”, Int. J. Biomed. Healthc., vol. 10, no. 4, pp. 173–175, 2022, doi: 10.5455/ijbh.2022.10.173-175.
- J. D. Angrist and J. S. Pischke, “Mastering 'Metrics: The Path from Cause to Effect”, Princeton Univ. Press, 2014.
- J. Jiang, “Multiple Linear Regression,” Appl. Med. Stat., vol. 345, pp. 345–367, 2021, doi: 10.1002/9781119716822.ch15.
- J. M. Wooldridge, “Introductory Econometrics: A Modern Approach”, 7th ed., Cengage Learning, 2019.
- V. Mignon, “The Multiple Regression Model”, Classroom Companion Econ., pp. 105–170, 2024, doi: 10.1007/978-3-031-52535-3_3.
- A. N. Rakhimov, “Econometric Analysis of Production by the German Method”, Eur. J. Interdiscip. Res. Dev., vol. 3, pp. 153–157, May 2022. [Online].
- Available: http://ejird.journalspark.org/index.php/ejird/article/view/47.
- K. M. Eshmurodov, Kh. M. Shaimov, I. Khujaev, and J. Khujaev, “Method of Lines for Solving Linear Equations of Mathematical Physics with the Third and First Types of Boundary Conditions”, J. Phys. Conf. Ser., vol. 2131, pp. 1–10, 2021.
- J. I. Khujaev, “Algorithm for Calculation of Three-Dimensional Temperature Polya Xlopka-Syrtsa”, Vestnik TashGTU, no. 3(87), pp. 36–39, 2014.
- S. A. Rakhimov, “Issues on Analyzing Production Processes by Using Practical Econometric Models”, Int. J. Trends Commer. Econ., vol. 11, no. 1, 2023. [Online]. Available: http://academicjournalonline.org/index.php/ijtce/issue/archive.
- A. N. Rakhimov, “In Economics Some Village Farm Products Working of Release Econometric Analysis”, Econ. Finance Innov. J., no. 2, pp. 23–29, Dec. 2022. [Online]. Available: www.sbtsue.efin.uz.
- I. Khujaev, J. Khujaev, M. Eshmurodov, and K. Shaimov, “Differential-Difference Method to Solve Problems of Hydrodynamics”, J. Phys. Conf. Ser., vol. 1333, pp. 1–8, 2019.
- A. C. Cameron and P. K. Trivedi, “Microeconometrics Using Stata”, Stata Press, 2010.
- B. H. Baltagi, “Econometric Analysis of Panel Data”, 6th ed., Wiley, 2022.
References
Y. Ruan, “Exploring Multiple Regression Models: Key Concepts and Applications,” Sci. Technol. Eng. Chem. Environ. Prot., vol. 1, no. 7, 2024, doi: 10.61173/yjpt3s59.
W. H. Greene, “Econometric Analysis”, 8th ed., Pearson, 2020.
S. Janković, “The Multivariate Statistical Analysis – Multiple Linear Regression”, Int. J. Biomed. Healthc., vol. 10, no. 4, pp. 173–175, 2022, doi: 10.5455/ijbh.2022.10.173-175.
J. D. Angrist and J. S. Pischke, “Mastering 'Metrics: The Path from Cause to Effect”, Princeton Univ. Press, 2014.
J. Jiang, “Multiple Linear Regression,” Appl. Med. Stat., vol. 345, pp. 345–367, 2021, doi: 10.1002/9781119716822.ch15.
J. M. Wooldridge, “Introductory Econometrics: A Modern Approach”, 7th ed., Cengage Learning, 2019.
V. Mignon, “The Multiple Regression Model”, Classroom Companion Econ., pp. 105–170, 2024, doi: 10.1007/978-3-031-52535-3_3.
A. N. Rakhimov, “Econometric Analysis of Production by the German Method”, Eur. J. Interdiscip. Res. Dev., vol. 3, pp. 153–157, May 2022. [Online].
Available: http://ejird.journalspark.org/index.php/ejird/article/view/47.
K. M. Eshmurodov, Kh. M. Shaimov, I. Khujaev, and J. Khujaev, “Method of Lines for Solving Linear Equations of Mathematical Physics with the Third and First Types of Boundary Conditions”, J. Phys. Conf. Ser., vol. 2131, pp. 1–10, 2021.
J. I. Khujaev, “Algorithm for Calculation of Three-Dimensional Temperature Polya Xlopka-Syrtsa”, Vestnik TashGTU, no. 3(87), pp. 36–39, 2014.
S. A. Rakhimov, “Issues on Analyzing Production Processes by Using Practical Econometric Models”, Int. J. Trends Commer. Econ., vol. 11, no. 1, 2023. [Online]. Available: http://academicjournalonline.org/index.php/ijtce/issue/archive.
A. N. Rakhimov, “In Economics Some Village Farm Products Working of Release Econometric Analysis”, Econ. Finance Innov. J., no. 2, pp. 23–29, Dec. 2022. [Online]. Available: www.sbtsue.efin.uz.
I. Khujaev, J. Khujaev, M. Eshmurodov, and K. Shaimov, “Differential-Difference Method to Solve Problems of Hydrodynamics”, J. Phys. Conf. Ser., vol. 1333, pp. 1–8, 2019.
A. C. Cameron and P. K. Trivedi, “Microeconometrics Using Stata”, Stata Press, 2010.
B. H. Baltagi, “Econometric Analysis of Panel Data”, 6th ed., Wiley, 2022.