How Household Income and Consumption are Affected by Lifestyle Diseases in an Emerging Economy

Abstract

This article is a commentary on the research paper titled ‘Impact of lifestyle diseases on income and household consumption: Evidence from an emerging economy’ published in the Health Marketing Quarterly Journal [1]. In this paper, the authors showcase the abnormal increase in lifestyle diseases such as diabetes, hypertension and heart diseases, in India, an emerging economy. Such emerging economies are characterized by low insurance coverage, with out-of-pocket expenses accounting for over 89% of medical expenses, as compared to the 18% world average (Koshy, 2017). The authors argue that this warrants an investigation into the effects of lifestyle diseases on household income and consumption in emerging economies. Household income may be affected, as earning members, if diagnosed with lifestyle diseases, may be unable to perform well in a regular work environment, as they did in the past. This may have an impact on the income earned or in some cases, result in unemployment as well. On the other hand, the medical expenses may increase due to increased spending on drugs and consultation. Further, the lifestyle diseases may result in individuals setting constraints on the type of food that may be consumed. It may also result in increased expenses on other categories such as travel and fitness [2-4]. To study the effects of lifestyle diseases on household income and consumption, the authors use the panel data from India Human Development Surveys, 2004 and 2011 [2,3]. Specifically, the authors create a subsample of control and treatment groups, from the panel data. The control group included households that did not report any morbidity issue in 2004 and 2011 [4], whereas the treatment group included households that did not report any health issue in 2004, but reported at least one of the three issues (diabetes, high blood pressure and heart disease) in 2011 (treatment group). As there were several differences between the two groups, the authors applied propensity score matching to create matched control and treatment groups based on several household characteristics in 2004. This enabled the authors to compute the ‘matched difference-in-difference’ between the control and treatment households.