Document Type
Article
Publication Date
6-1-2024
Journal / Book Title
Healthcare Analytics
Abstract
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.
DOI
10.1016/j.health.2023.100287
MSU Digital Commons Citation
Wang, John; Pei, Zhi Kacie; Wang, Yawei; and Qin, Zhaoqiong, "An investigation of income inequality through autoregressive integrated moving average and regression analysis" (2024). Department of Information Management and Business Analytics Faculty Scholarship and Creative Works. 173.
https://digitalcommons.montclair.edu/infomgmt-busanalytics-facpubs/173
Rights
This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/bync-nd/4.0/).
Published Citation
Wang, J., Pei, Z. K., Wang, Y., & Qin, Z. (2024). An investigation of income inequality through autoregressive integrated moving average and regression analysis. Healthcare Analytics, 5, 100287.