An investigation of income inequality through autoregressive integrated moving average and regression analysis

Income inequality is a prominent contributor to health disparities in the U.S. As a leading capitalist nation, the U.S. registers the highest healthcare expenditure among developed countries yet grapples with widening income disparities. The chasm between the rich and the underprivileged has expande...

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Bibliographic Details
Main Authors: John Wang, Zhi Kacie Pei, Yawei Wang, Zhaoqiong Qin
Format: Article
Language:English
Published: Elsevier 2024-06-01
Series:Healthcare Analytics
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2772442523001545
Description
Summary:Income inequality is a prominent contributor to health disparities in the U.S. As a leading capitalist nation, the U.S. registers the highest healthcare expenditure among developed countries yet grapples with widening income disparities. The chasm between the rich and the underprivileged has expanded significantly in recent decades, profoundly impacting American society. This study explores the nuances of income inequality, its ramifications, and potential remedies, analyzed through the Gini Coefficient. Advanced forecasting models, including AutoRegressive Integrated Moving Average and Regression Analysis, are employed to anticipate future patterns. The research highlights the value of healthcare analytics in understanding the complexities of income inequality. The findings underscore the pressing need for effective policies to address this mounting challenge.
ISSN:2772-4425