Open Access Journal Article

Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study

by Diego Vallarino a,*
a
Independent Researcher, Madrid, Spain
*
Author to whom correspondence should be addressed.
REA  2024 3(1):23; https://doi.org/10.58567/rea03010001
Received: 2 January 2024 / Accepted: 6 February 2024 / Published Online: 19 February 2024
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Abstract

This research delves into the temporal dynamics of a nation's pursuit of a targeted GDP per capita level, employing five different survival machine learning models, remarkably Deep Learning algorithm (DeepSurv) and Survival Random Forest. This nuanced perspective moves beyond static evaluations, providing a comprehensive understanding of the developmental processes shaping economic trajectories over time. The economic implications underscore the intricate balance required between calculated risk-taking and strategic vulnerability mitigation. These findings guide policymakers in formulating resilient economic strategies for sustained development and growth amid the complexities inherent in contemporary economic landscapes.

Keywords: Temporal dynamics; Survival analysis; Machine learning models; Economic development; Calculated risk-taking; Vulnerability mitigation; Developmental processes; Resilient economic strategies;
Copyright: © 2024 by Vallarino. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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ACS Style
Vallarino, D. Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study. Review of Economic Assessment, 2024, 3, 23. https://doi.org/10.58567/rea03010001
AMA Style
Vallarino D. Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study. Review of Economic Assessment; 2024, 3(1):23. https://doi.org/10.58567/rea03010001
Chicago/Turabian Style
Vallarino, Diego 2024. "Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study" Review of Economic Assessment 3, no.1:23. https://doi.org/10.58567/rea03010001
APA style
Vallarino, D. (2024). Temporal Dynamics of Countries' Journey to Cluster-Specific GDP per Capita: A Comprehensive Survival Study. Review of Economic Assessment, 3(1), 23. https://doi.org/10.58567/rea03010001

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References

  1. Assa, J., & Meddeb, R. (2021). Towards a multidimensional vulnerability index. United Nations Development Programme. Retrieved April, 14, 2023.
  2. Azodi, C. B., Tang, J., & Shiu, S. H. (2020). Opening the Black Box: Interpretable Machine Learning for Geneticists. Trends in Genetics 36, 442–455. https://doi.org/10.1016/j.tig.2020.03.005
  3. Balica, S. F., Dinh, Q., & Popescu, I. (2023). Chapter 4 - Vulnerability and exposure in developed and developing countries: large-scale assessments. Hydro-Meteorological Hazards, Risks, and Disasters (Second Edition) 5, 103–143. https://doi.org/10.1016/B978-0-12-819101-9.00013-3
  4. Barnwal, A., Cho, H., & Hocking, T. (2022). Survival Regression with Accelerated Failure Time Model in XGBoost. Journal of Computational and Graphical Statistics, 31(4), 1292–1302. https://doi.org/10.1080/10618600.2022.2067548
  5. Barrett, J. K., Siannis, F., & Farewell, V. T. (2011). A semi-competing risks model for data with interval-censoring and informative observation: An application to the MRC cognitive function and ageing study. Statistics in Medicine, 30(1), 1–10. https://doi.org/10.1002/sim.4071
  6. Basak, P., Linero, A., Sinha, D., & Lipsitz, S. (2022). Semiparametric analysis of clustered interval-censored survival data using soft Bayesian additive regression trees (SBART). Biometrics, 78(3), 880–893. https://doi.org/10.1111/biom.13478
  7. Cui, P., Shen, Z., Li, S., Yao, L., Li, Y., Chu, Z., & Gao, J. (2020). Causal Inference Meets Machine Learning. Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 3527–3528. https://doi.org/10.1145/3394486.3406460
  8. Cuperlovic-Culf, M. (2018). Machine Learning Methods for Analysis of Metabolic Data and Metabolic Pathway Modeling. Metabolites, 8(1). https://doi.org/10.3390/metabo8010004
  9. Dutta, I., & Mishra, A. (2023). Economics Measuring Vulnerability to Poverty: A Unified Framework Measuring Vulnerability to Poverty: A Unified Framework *.
  10. Finch, H. (2005). Comparison of Distance Measures in Cluster Analysis with Dichotomous Data. Journal of Data Science 3. http://dx.doi.org/10.6339/JDS.2005.03(1).192
  11. Gorfine, M., & Zucker, D. M. (2022). Shared Frailty Methods for Complex Survival Data: A Review of Recent Advances. https://doi.org/10.48550/arXiv.2205.05322
  12. Hair, J. F., & Fávero, L. P. (2019). Multilevel modeling for longitudinal data: concepts and applications. RAUSP Management Journal, 54(4), 459–489. https://doi.org/10.1108/RAUSP-04-2019-0059
  13. Haradal, S., Hayashi, H., & Uchida, S. (2018). Biosignal Data Augmentation Based on Generative Adversarial Networks. 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), 368–371. https://doi.org/10.1109/EMBC.2018.8512396
  14. Jiang, R. (2022). A novel parameter estimation method for the Weibull distribution on heavily censored data. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 236(2), 307–316. https://doi.org/10.1177/1748006X19887648
  15. Jin, P., Haider, H., Greiner, R., Wei, S., & Häubl, G. (2021). Using survival prediction techniques to learn consumer-specific reservation price distributions. PLoS ONE, 16(4 April). https://doi.org/10.1371/journal.pone.0249182
  16. Jin, Z., Shang, J. and Z. Q. and L. C. and X. W. and Q. B. (2020). RFRSF: Employee Turnover Prediction Based on Random Forests and Survival Analysis. Web Information Systems Engineering – WISE 2020, 503–515. https://doi.org/10.1007/978-3-030-62008-0_35
  17. Khan, F. M., & Zubek, V. B. (2008). Support Vector Regression for Censored Data (SVRc): A Novel Tool for Survival Analysis. 2008 Eighth IEEE International Conference on Data Mining, 863–868. https://doi.org/10.1109/ICDM.2008.50
  18. Le-Van, C., & Tran-Nam, B. (2023). Comparing the Harrod-Domar, Solow and Ramsey growth models and their implications for economic policies. Fulbright Review of Economics and Policy. https://doi.org/10.1108/frep-06-2023-0022
  19. Maharana, K., Mondal, S., & Nemade, B. (2022). A review: Data pre-processing and data augmentation techniques. Global Transitions Proceedings, 3(1), 91–99. https://doi.org/10.1016/j.gltp.2022.04.020
  20. Mumuni, A., & Mumuni, F. (2022). Data augmentation: A comprehensive survey of modern approaches. Array, 16, 100258. https://doi.org/10.1016/j.array.2022.100258
  21. Nolan, B., Roser, M., & Thewissen, S. (2019). GDP Per Capita Versus Median Household Income: What Gives Rise to the Divergence Over Time and how does this Vary Across OECD Countries? Review of Income and Wealth, 65(3), 465–494. https://doi.org/10.1111/roiw.12362
  22. Organization of American States (2023). Strategic Counsel for Organizational Development and Management for Results. Internal Report
  23. Raghunathan, T. E. (2004). What Do We Do with Missing Data? Some Options for Analysis of Incomplete Data. Annual Review of Public Health, 25(1), 99–117. https://doi.org/10.1146/annurev.publhealth.25.102802.124410
  24. Thenmozhi, M., Jeyaseelan, V., Jeyaseelan, L., Isaac, R., & Vedantam, R. (2019). Survival analysis in longitudinal studies for recurrent events: Applications and challenges. Clinical Epidemiology and Global Health, 7(2), 253–260. https://doi.org/10.1016/j.cegh.2019.01.013
  25. Vinzamuri, B., Li, Y., & Reddy, C. K. (2017). Pre-Processing Censored Survival Data using Inverse Covariance Matrix based Calibration. IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, 29(10), 2111-2124. https://doi.org/10.1109/TKDE.2017.2719028
  26. Wang, L., Li, Y., Zhou, J., Zhu, D., & Ye, J. (2017). Multi-task Survival Analysis. 2017 IEEE International Conference on Data Mining (ICDM), 485–494. https://doi.org/10.1109/ICDM.2017.58
  27. Wang, P., Li, Y., & Reddy, C. K. (2017). Machine Learning for Survival Analysis: A Survey. https://doi.org/10.48550/arXiv.1708.04649
  28. Yuan, H., Xie, F., Ong, M. E. H., Ning, Y., Chee, M. L., Saffari, S. E., Abdullah, H. R., Goldstein, B. A., Chakraborty, B., & Liu, N. (2022). AutoScore-Imbalance: An interpretable machine learning tool for development of clinical scores with rare events data. Journal of Biomedical Informatics, 129, 104072. https://doi.org/10.1016/j.jbi.2022.104072
  29. Yunita, R., Gunarto, T., Marselina, M., & Yuliawan, D. (2023). The Influence of GDP per Capita, Income Inequality, and Population on CO2 Emission (Environmental Kuznet Curve Analysis in Indonesia). International Journal of Social Science, Education, Communication and Economics (SINOMICS JOURNAL), 2(2), 217–230. https://doi.org/10.54443/sj.v2i2.130
  30. Zelenkov, Y. (2020). Bankruptcy prediction using survival analysis technique. Proceedings - 2020 IEEE 22nd Conference on Business Informatics, CBI 2020, 2, 141–149. https://doi.org/10.1109/CBI49978.2020.10071
  31. Zhao, Z. L., Yu, H. J., & Cheng, F. (2022). An Analysis of Factors Affecting Agricultural Tractors’ Reliability Using Random Survival Forests Based on Warranty Data. IEEE Access, 10, 50183–50194. https://doi.org/10.1109/ACCESS.2022.3172348
  32. Zhou, F., Fu, L., Li, Z., & Xu, J. (2022). The recurrence of financial distress: A survival analysis. International Journal of Forecasting, 38(3), 1100–1115. https://doi.org/10.1016/j.ijforecast.2021.12.005