Economics of Healthy Aging in India: A Multidimensional Perspective

India is currently the second populous country globally, with >9% of its population accounting for the aged population. As per the latest estimates, >130 million people aged ≥60 years live in India (Bloom et al., 2015). According to United Nations (UN) projections, these numbers will likely double and reach 20% by 2050 (WHO, 2020a). The older population will constitute a large population in India, and it will be the second largest population, with >200 million elderly people in the coming decades (Rajan and Aliyar, 2008). Similarly, an increase in life expectancy, which has almost improved by 20 points since 1950, will likely add up a large chunk of the population in the coming ages. Figure 1 shows a dramatic shift in life expectancy at age 60 years from 1950 to 2040 in India.

Figure 1

The population aged ≥70 years has almost doubled since 1950 in India. The oldest-old population is expected to increase by 6% by 2050, given the rise in life expectancy at age ≥60 years (Agarwal et al., 2016). Apart from the rise in life expectancy, the decline in the total fertility rate (TFR) has almost achieved a replacement level in India (IIPS and MOFHW, 2016). This decline is mainly due to various socio-economic and health policy interventions. On the other hand, India's advancement in healthcare has significantly brought down the mortality rates, thus contributing significantly to the growth of the elderly population.

Population aging is on the rise in India. There is a shift in the dependent population from children to older adults, which has increased the AI. The AI currently stands at 38 for every 100 children, but this will reach 75 by the year 2040 (Subaiya and Bansod, 2011). Figure 2 mirrors the rapid rise of India's elderly population in the next few decades.

Figure 2

Demographic changes are attributed to increasing welfare and progress in human conditions. These changes are then reflected by factors like mortality, fertility, and life expectancy. But once the demographic transition occurs, these changes will imply a significant challenge on economic outcomes due to the greater concentration of the elderly population. Although there are some positive changes associated with this demographic shift, it also necessitates some important implications for countries and their path to economic development. Population aging involves these challenges through healthcare, economic burden, and social well-being. It also increases the vulnerability of the elderly population, especially women (Prakash, 1997; Gopal, 2006; Jones and Powell, 2006). Similarly, many other key challenges also arise due to the small portion of the elderly in the formal sector and their greater concentration in the unorganized sector, imbalanced with no social security or social provisions. Finally, poor accessibility and lack of awareness result in greater inequalities due to the underutilization of social and healthcare security measures at older ages. While the low level of labor force participation, lack of skills, and low literacy are the prevalent challenges that are faced by the elderly Indian population, the productivity of the elderly population is also a challenge, given the low levels of physical and human capital (Raju, 2011; Bhagat, 2015).

Aging is a natural process, but it multiplies the challenges of physical and mental health as the individual grows older. These challenges then create a circular impact, affecting other aspects of individuals’ health and well-being. Increasing age therefore not only effects the health of populations but also results in increasing burden through healthcare costs, work opportunities, and social security net. Therefore, understanding and examining these challenges are pivotal for policy-making in India, given the rapid rise of the aging population and its implication on human development.

Longitudinal Ageing Survey of India (LASI) is a nationally representative survey of over 72,000 older adults aged ≥45 years across all the states and union territories in India, except Sikkim, conducted in 2018–19. It is a longitudinal survey to be conducted after every 2 years for the next 25 years. LASI covers a broad range of issues involving health, economic, and social determinants of the aging population in India. We used the latest estimates from UN population projections to compute the AI. By contrast, the recent projections of the population census are also used to reveal the overall scenario of aging in India.

Bivariate analysis was used to depict the overall aging scenario in India. At the same time, a multidimensional AI was computed to mirror the aging scenario across the states in India. There are different methods through which healthy aging is computed (Peel et al., 2004). These methods include the HAI, active aging index (AAI), and scale measures of the AI (Zaidi et al., 2013, 2017; Zaidi, 2015). Although these measures compute the healthy and active aging indicators, they hardly measure the economic aspects that are key to age-related challenges (De São José et al., 2017). Therefore, to measure the economic aspect of aging, we adopted a disintegrated method to formulate the different indices and make the results comparable at the national level. The advantage of this method over the other methods is it includes any dimension so that multiple factors can be included in the study (Kulkarni and Doke, 2015; Kwatra et al., 2020). We examine the health aspect of aging and the factors such as economic and social security outcomes, which affect aging outcomes (Neumayer, 2001). Thus, a set of indices are computed using the UN methodology, and then the indices are compared across Indian states. The description of the variables and their computation are given in the supplementary table.

We used a two-step procedure to compute the composite indices of economic, health, and social outcomes. First, the percentages are computed and converted into scores based on the following formula: Xi score=[(x−min(x))/(max(x)−min(x))] {X_{i\,}}{\rm{score}} = \left[ {\left( {x - \min \left( x \right)} \right)/\left( {\max \left( x \right) - \min \left( x \right)} \right)} \right]

Equal weights are assigned to all the indicators considered for the derivation of all the composited indices. Then, the scores are averaged to compute a composite index based on the following formula: CI=Average(Xi−j) where CI means the composite index and X is the number of indicates from i to j. {\rm{CI}} = {\rm{Average}}\left( {{X_{i - j}}} \right)\,{\rm{where}}\,{\rm{CI}}\,{\rm{means}}\,{\rm{the}}\,{\rm{composite}}\,{\rm{index}}\,{\rm{and}}\,X\,{\rm{is}}\,{\rm{the}}\,{\rm{number}}\,{\rm{of}}\,{\rm{indicates}}\,{\rm{from}}\,i\,{\rm{to}}\,j.

The composite index score ranges from 0 to 100, with 0 representing the lowest score and 100 representing the highest score for each state.

Aging and economic challenges go hand in hand. With unprecedented changes in the population age structure, population aging is evolving as a challenge in restructuring the labor market and its impact through the rise in healthcare costs and social security risks. These factors can slow the progress of economic growth, despite some positive externalities of aging, like increasing saving behavior, experienced workforce, and increase in unpaid working population associated with aging (Guest, 2006; Orlická, 2015).

Population aging is strongly associated with an increase in healthcare costs. It implies a significant impact on economic growth due to the increasing demand for budget spending on the elderly (Alemayehu and Warner, 2004). Aging further enhances a significant impact on fiscal and social financing, raising a country's economic burden (Colin and Brys, 2019; Rouzet et al., 2019). With >9% of the aging population, India will face unprecedented challenges both in terms of health and socio-economic outcomes.

Table 1 presents the data on labor market conditions of the elderly in India. Only 35% of the total elderly population currently working is >60 years, whereas 46.18% is at age ≥50 years. Around 51% of male individuals are currently working at age ≥60 years as compared to 22% of female individuals. It reflects the inequality in work among the older adults in India. Looking from the type of work, it is clear from the results that more than half of the elderly are involved in work related to agriculture, 21% of male individuals aged ≥50 years are involved in non-agriculture activities as compared to 10% women. Similarly, there is an asymmetry in wage and salary class, but the resulting difference between male and female individuals is very low. Looking at the nature of work, 46% population >50 years is working at their own farms or dwelling units.

The results provide a holistic view of the economic aspects of the population in India. Given the unprecedented socio-economic development over the past 50 years, economic progress has achieved an upward trend in India, encompassed by demographic dividend. But there will be a shift in the population age structure in the coming ages, resulting in the population aging and reduction in the likelihood of entering into the labor market (Petrou et al., 2001). Similarly, governed by changing demographics and technological advancement, India's future economic market needs challenging issues to address healthcare, social security, and other fiscal policies. Therefore, the unskilled workforce can be challenging and needs to be addressed through policy interventions (Fehr et al., 2010).

Older people may not simply produce as much as they consume since there are associated costs involved due to increasing disease burden, loss of saving proportion due to covering of healthcare costs, increasing proportion of tax to GDP, and cost associated with public health spending. There is a risk of fall in labor supply due to an increase in the risk for informal caregiving with a greater proportion of the aging population. Similarly, engagement in other informal work of the elderly may enhance the value through volunteering and social capital, but it may result in serious implications on the structure of the labor market. There is also an impact of saving behavior on the economic outcomes, like a fall in capital investment (Marešová et al., 2015). Moreover, the increasing consumption pattern also enhances the risk for the elderly to get more engaged in work, which later has the potential health and social consequences like work discrimination, ageism, and marginalization at the workplace.

Older people may contribute through greater consumption, which can generate the economic boom. Healthy people at older ages can live long and healthy lives and be self-sufficient and pay more taxes to support healthcare costs and the social security system.

A healthy workforce at higher ages can be significant, but the trend has not been sufficient, despite increased life expectancy. Similarly, the workers’ higher ages fall in high-income groups only (Rice and Fineman, 2004). Furthermore, the education level and employment experience can reduce the aging burden through work and self-sufficiency in healthcare expenditure.

Economic consequences of aging are manifold. The shift in the age structure implies greater contributions from the elderly to avert the economic burden through greater participation in the formal and informal workforce, savings, and less tangible benefits (Van Der Gaag and De Beer, 2015). One of the challenges that aging implies is through the lack of social security coverage. Table 1 already shows the abysmal social security coverage of the elderly in India. More than 78% of the elderly population is uncovered in India, hinting toward the important challenge of population aging. Moreover, they are less likely covered by the other benefits since social security schemes do not cover the elderly population at large in informal sectors, reflecting their vulnerability. Thus, targeting the elderly for insurance coverage and other social security services can be critical for addressing the needs of the elderly, which can also help them in ensuring their well-being factors.

There is a scope for greater benefits of population aging in India, given the fall in replacement levels, as shown by the results. The shift in the market structure due to aging may also create new sectors of the economy. But there is a concern about the fall in wages that may decline the earnings of the young workforce (Weller, 2007; D’Addio et al., 2010).

Cognition health involves the mental functions of individuals and is key to work, happiness, and mental well-being (Erdtmann, 2015). Cognition is measured through multiple dimensions, and one such dimension is memory power (Dalgleish and Power, 2000). Cognition diminishes with age, affecting individuals’ health and well-being, apart from increasing the risk for disability (Murman, 2015). Table 3 shows the percentage of poor memory among the elderly. Based on the last word recall test, the elderly population remembers more than half of the words, which implies a good memory functioning (Skirbekk et al., 2012).

Table 3 shows the results of depression and depressive symptoms based on two different scales among the elderly. The results show that around 26% male individuals suffer from depressive symptoms as compared to 31% female individuals aged ≥50 years, whereas looking at the depression prevalence based on the CIDI-SF scale, around 9% female individuals suffer from depression as compared to 7% male individuals aged ≥50 years in India. Depression is a common symptom among the elderly and likely impacts the physical and economic well-being of older adults. Although older adults are less vulnerable to depressive symptoms than younger adults, the greater prevalence among female individuals likely reflects their vulnerability to psychological and mental health states of the elderly.

One of the important health factors at old age is functional health. It is a significant contributor to healthy aging and involves the risk of health and social well-being of poorer elderly adults. Functional health involves the capacity of an individual to interact with others or to perform the activities of daily living (ADL; Whittle and Goldenberg, 1996). Although the functional health limitations increase with age, better functional health at higher ages is a good indicator of healthy aging and can reduce the cost associated with care (WHO, 2001). It can further enhance the involvement of people in the labor market for a longer time and also reflect their physical health status (Chirikos, 1993). Table 4 shows the physical and functional health activities of aging, including physical impairments. Around 8% of the aging population suffers from an impairment, whereas the prevalence increases to 10% by age 60 years. Similarly, about 11% of the elderly aged ≥50 years face challenges in their ADL, while 41% face challenges in instrumental ADL.

Functional health is a challenge in terms of elderly productivity and healthy aging in India. Therefore, increasing functional health must be a priority since better functional health is also associated with greater wages and more working hours (WHO, 2020b). Furthermore, persons with good functional health can perform better traits than those with any limitations (David et al., 2001). Similarly, low physical impairment is also an important indicator of reducing disability and risk for well-being through greater participation in the labor market (Ferreira-Agreli et al., 2017). Functional inabilities can further stress aging as it is a lifecycle process. Functional abilities among the elderly enable well-being at an older age (Michel et al., 2016). Similarly, they result in the independence of the care functional limitations usually reflecting the onset of diseases among the elderly (Seals et al., 2016).

Despite the sharp increase in life expectancy, the challenge of life satisfaction is foremost at upper ages. Therefore, better life satisfaction and living with dignity are the key elements at older ages (Griffin and McKenna, 1999). The elderly, like other population groups, deserve to live with peace since better life satisfaction is an important element of healthy aging (Gwozdz and Sousa-Poza, 2010). A U-shaped relationship is found in the literature on aging and life satisfaction, with a significant rise in the aging population. The relation changes with age, given the significant drop in health and rise of psychological and economic dependency (Beja, 2018). Table 5 shows the life satisfaction among the elderly in India.

Low life satisfaction is increasing with age, but it is higher among female than male individuals. Female individuals have low life satisfaction, but it is lower than among male individuals than the overall life satisfaction among the elderly. Our results from Table 5 reflect levels of low life satisfaction in India, which clearly increases with age. More than 33% elderly have reported low level of life ageing 60 and above in India. Table 5 also shows the social activity of older adults in India at ages 50 years and 60 years. Earlier studies have found a strong correlation of well-being with social interactions (Kemperman et al., 2019). Adults having fewer interactions are likely to face challenges of psychological health, whereas poor well-being is strongly associated with poor social networks, particularly at older ages. Examining the social activities of older adults, we found that more that more than half of the elderly population are not socially active in India (52.1%) at age 50 years and above, but the proportion is slightly lower at 50.21% at age ≥60 years. The results clearly show that female individuals are more socially inactive (56.79%) in India than their male counterparts (52.90%). This decrease is mainly due to the life cycle events that increase with aging, while there is also the role of other socio-economic factors which include loss of family members, friends, retirement, and low neighborhood networks among the elderly (Wrzus et al., 2013).

Aging is a complex and multidimensional phenomenon, given its socio-economic and health implications (David et al., 2001). Population aging implies multiple economic, social, hygienic, and behavioral challenges on the one hand. On the other hand, economic dimensions mean the challenges of costs and social security concerns (Holzmann, 1988; Gopal, 2006; Chomik and Piggott, 2015). Therefore, evaluating the aging dimension is key before facing the challenges of population aging. Figure 4 shows India's composite AI, and the results indicate that economically weaker states are at higher risk of aging challenges, given their poorer score on the index. By contrast, states such as Tamil Nadu, Kerala, and West Bengal are better off on the aging composite index, given the higher score.

Figure 4

Figure 5 shows the AI for age ≥50 years. It shows similar results, given the context of aging challenge in India and across the states in India. Aging challenges need to be taken care of while evaluating the long-term health and social security challenges. Policy-makers must address aging issues, given their long- and short-term challenges and their implication on aging and health in India.

Figure 5

States performing poor on the AI must be targeted specifically to address future challenges of aging and their economic impact. Poor states like Bihar, Uttar Pradesh, and Madhya Pradesh need strong attention from policy-makers to address the needs of the elderly and avert the potential future challenges of population aging across these states. Similarly, there is also a need for an inclusive approach to address the social security coverage, which is abysmal throughout the country.