Paper short abstract:

This study tried to capture health in a multi-dimensional way, using the novel index of computing 'Years of Good Life' (YoGL). Here, healthy is defined as being out of poverty, out of physical limitation, without cognitive disability, and with positive life satisfaction simultaneously. Inequality in terms of gender, place of residence, as well as among states of India is computed in the study.

Paper long abstract:

With unprecedented rise in life expectancy, research on counting life years spent in different health condition (i.e., ‘good’ health and ‘less than good’ health) gained significant global importance. Though rising life expectancy itself, is an indicator of development, healthy life expectancy brings in the ‘quality of life’ dimension to it. The study investigates the heterogeneities in healthy life among older-adults in India, using a novel concept of counting ‘expected years of good life’. Whether the there is convergence between the states or states are diverging in terms of 'healthy longevity?

Methodology

Grounded in Desai and Sen's ‘capable longevity’ approach, the index of YoGL (Years of Good Life) is an amalgamation of objective and subjective dimensions of health. A year is counted as ‘good’ one if a person is simultaneously out of poverty, free from severe activity limitations and cognitive impairments, i.e., objective capabilities, and experiences positive life satisfaction, i.e., subjective health. The study used demographic methods to quantify the healthy life years within the life expectancy using Sullivan's method of life table construction. It is a prevalence based method where the age-specific proportion of population having a 'good life' is used to divide the life expectancy at a particular age into two sections - 'the healthy years' and 'unhealthy years'. The information regarding the proportion of 'good life' is calculated using the Longitudinal Ageing Study in India, 2017-18 (LASI wave-I) dataset. The survey provides sub-nationally representative data for the population aged 45 years and above in India, regarding different measures of health, well-being, health care utilisation, health expenditure, etc. Information regarding life expectancy are collected from Sample Registration System (SRS) Abridged Life Tables, 2015-19, Registrar General of India. To calculate the threshold of the four dimensions, different cutoff points are set and sensitivity analysis of those cut off are also done as a robustness check. Using household level consumption expenditure data the bottom 20th quintile of the population is identified as poor; for cognitive performance the data uses a a battery comprising different aspects of cognitions, such as, memory (immediate word recall and delayed word recall), orientation (date, time, place), retrieval fluency (verbal fluency), arithmetic, executive functioning and object naming etc. The cut of is set at bottom 10th quintile (score 19 out of 43) for cognitive impairment which is sensitive for both literate as well as illiterate population. People who does not have any difficulty in Daily life activities (ADL) such as, limitations in dressing, walking across the room, bathing, eating, getting out of bed and using toilet etc. are considered to be out of physical limitation. For positive life satisfaction, a likert scale comprising of 7 dimensions - 1) In most ways, my life is close to ideal; 2) the conditions of my life are excellent; 3) I am satisfied with my life; 4) so far, I have got the important things I want in life; and 5) if I could live my life again, I would change almost nothing, are avaiable in the dataset. Having a score between 5-10 is considered to have negative or low life satisfaction.

Analysis

As mentioned previously, the demographic construction of healthy longevity is done using life table construction method. Years of good life is separately calculated for both the genders, rural and urban population and for major states of India separately. To capture the relative contribution of different factors, regression based decomposition after Shorrocks (1982) & Fields (2003) is applied here. The control variables in the regression model are Education, percentage population working, female population, percentage population widowed, etc.

Conclusion

The results show, at age 50, YoGL for male is 13.9 years (55.6% of remaining life expectancy) and for females, 11.3 years (41.7% of remaining life expectancy). Supporting the ‘gender-health-mortality paradox’, results reveal that women’s advantage in life expectancy doesn’t translate into equal advantage in ‘good-years’ for all older age-groups. AMong the states, the inequality is diverging in terms of healthy longevity. Inter-state variability is higher in terms of YoGL, than in life expectancy; from 64% years counts as ‘good life’ in Punjab to 34% of remaining years as ‘good life’ in Odisha. Regression decomposition indicates functional limitations contributes 32%, cognitive impairment 20% and birth-cohorts contributes around 18% in explaining the regional heterogeneity. The take away policy formulation from this study would be to reduce the inequality in terms of health among regions of India.