Detailing Poverty Incidence through Fractals: Which of the Gross National Product or Multidimensional Poverty Index Explain Poverty Incidence Better?

Joy M Mirasol, Zeny L. Maureal, Noel Lacpao
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Abstract

This paper attempts to explain poverty incidence of the 97 countries using fractal analysis. Gross National Product (GNP) and Multidimensional Poverty Index (MPI) of each country were used as poverty indicators. Fractal dimensions were obtained, compared and analyzed. The three variables have fractal characteristics of ruggedness and self-similarity. Results revealed that the ruggedness of poverty incidence across the countries is due to the ruggedness of the MPI, that is, the deprivation to basic services such as health, education, and standard of living affects the quality of living. Thus, MPIs explain poverty incidence more precisely. With this finding, implications to policymakers to alleviate poverty can be addressed.Keywords:  Fractal analysis, Gross National Product, Multidimensional Poverty Index (MPI), poverty indicators, spectrum
通过分形分析贫困率:国民生产总值或多维贫困指数哪个能更好地解释贫困率?
本文试图用分形分析来解释这97个国家的贫困发生率。每个国家的国民生产总值(GNP)和多维贫困指数(MPI)作为贫困指标。得到了分形维数,并进行了比较和分析。这三个变量具有坚固性和自相似性的分形特征。结果显示,各国贫困发生率的高低是由于MPI的高低造成的,也就是说,对卫生、教育和生活水平等基本服务的剥夺影响了生活质量。因此,mpi更准确地解释了贫困发生率。有了这一发现,就可以解决对政策制定者减轻贫困的影响。关键词:分形分析;国民生产总值;多维贫困指数
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