Abstract
During the last few decades, income inequality in emerging Asian economies has been increased dramatically. It is widely recognized that income inequality has severely impacted population health. This study attempts to estimate the impact of income inequality on health outcomes in emerging Asian economies for a time horizon ranging from 1991 to 2019. Our empirical analysis shows that income inequality has a negative effect on life expectancy in the long run. We also find that positive changes in income inequality decrease life expectancy, but a negative change in income inequality increases life expectancy in the long run in emerging Asian economies. The symmetric and asymmetric results are robust to different measures of econometric methods. Thus, governments should pay more attention to the consequences of their economic policies on income inequality to improve health outcomes.
Highlights
To be healthy is the primary concern of any individual because, without health, a person cannot play an active role in any walk of life
This study explores the nonlinear hidden impacts of income inequality on health outcomes in emerging economies, though past studies have to ignore the nonlinear relationships
We found that income inequality has a statistically negative significant influence on health in the symmetric model in the long run
Summary
To be healthy is the primary concern of any individual because, without health, a person cannot play an active role in any walk of life. After the economic crisis of the year 2008, the awareness related to the consequences of income inequality increased in the USA and all across the globe These concerns were not baseless; instead, based on the fact that during the period 2009–2012, there was a 31.4% rise in income of the richest 1% of people against 0.4% of the bottom 99% people [18]. This study explores the nonlinear hidden impacts of income inequality on health outcomes in emerging economies, though past studies have to ignore the nonlinear relationships. The NARDL approach is more flexible to the cointegration dynamics between concern variables Such types of approaches are explored non-linear hidden impacts of income inequality and health outcomes. In this study, we have applied the non-linear panel ARDL-PMG model, and for that purpose, we have decomposed the variables of Gini into its positive and negative components by using the partial sum procedures as shown below:. The data set was constructed from the [28, 29]
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