Re-examining The Time-varying Relationship Between Health and Income Distributions

Abstract

In this paper, we address the issue of the time-varying relationship between health and long-term income and show that income profile over time is more important than the permanent income of a specific period in explaining general health condition. A functional probit regression model is introduced to investigate how the income profiles of middle-aged people can affect the health condition of the last period of the given period using the Panel Study of Income Dynamics data. We also perform the probit estimation to check the robustness of the functional regression results. The empirical results of our paper clearly indicate a time-varying relationship between the entire distribution of income and health status at one point in time, which cannot be accurately explained by the current income or permanent income of the given period.

Keywords:

• Health-income gradient
• functional regression estimation
• binary probit regression

JEL Classifications:

• C32
• C50
• G12

Acknowledgments

We would like to thank two anonymous referees for helpful comments.

Disclosure Statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1 The cohort dummy can capture the year-effect of the income variable.

2 There is no clear rule for the determination of components, and scree plots represent one commonly used method for the empirical analysis of functional regression models.

3 5–8 sub-grouping actually yielded similar results; we only present 5-group results here to save space.

4 The probit estimation results are fairly robust to the classifications, and equi-spaced intervals generate similar point estimates values.

5 The probit estimation can only generate 5 different point estimates, and the functional shape here is just for presentation purposes.

Additional information

Funding

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea [grant number NRF- 2019S1A5A2A01041477].