2020 Report

2. Ageing India: The unsexy story

The Indian demographic is relatively young with 67 out of 100 persons in the working age population (15 to 59 years). Much of the focus of Indian policy makers is on job creation and economic growth while entrepreneurs and businesses largely cater to this new class of creators, spenders and consumers. India also has a low dependency ratio and this positively correlates to higher economic growth. The working age population is expected to increase till 2031, and stay stable until 2041. A higher working age population is suitable for high economic growth and allows us as a nation to reap (economic) benefit from the (demographic) dividend.

However, this is only half the story.

Kerala, a state with the highest HDI among Indian states and high life expectancy, has a population ageing faster than all other states. In fact, 15% of the population of Kerala is over 80 years, and this also happens to be the fastest growing group among the old. The fertility rates in Tamil Nadu, New Delhi, Andhra Pradesh, Telangana, Gujarat, Punjab and West Bengal are lower than replacement rates, and thus their window of opportunity from the demographic dividend will close by 2021. For a group of states that include Karnataka, Odisha, Himachal Pradesh, Maharashtra, Assam, J&K, Uttarakhand, Haryana, this opportunity window is expected to start closing in 2031. A recent article in a business paper highlights the demographic shifts and divide among different regions.

For example, by 2036, Tamil Nadu will be India’s oldest state, with a median age of over 40 while Bihar will have a median age of ~30 years.

Longevity, i.e., average life of general population, is expected to rise from 67.5 years in 2015 to 75.9 years in 2050, indicating an ‘ageing population bulge’ over the coming decades. The gender differences in choices is an aspect that needs particular attention as globally, women live longer than men, and are differently equipped to manage their lives. Looking into the Indian census 2011 household data, carried out once every decade by the government, one notices the changing family patterns among Indian households. In particular, the breakdown of the ‘Indian joint family system’, which has been a traditional support structure for older adults.

Today, the entrepreneurial ecosystem in India is largely focused on age-segmented target groups – Gen Z, millennials, etc. – and build solutions of convenience to fulfil their needs – finance, education, ecommerce, education, mobility, travel and hospitality, etc. While these solutions do not exclude any particular population group, the participation of older adults is quite low and restricted, mainly due to unique digital barriers faced by them. There isn’t adequate data available to understand the needs of older adults, their digital habits and changes in their consumption patterns. For example, it is very common to see family or trusted non-family members helping older adults navigate new technologies. Similarly, it is also not unlikely to see older adults preferring a bank branch to deposit a check over a digital transfer using a mobile app, and a lot of it has to do with trust over convenience.

As technology is becoming ubiquitous and old ways of transactions (human-to-human, human assisted) are moving digital, many are not fully equipped to negotiate the digital transitions safely and effectively. Nor are newer technologies taking into account the unique needs of this group during their design and build process. For example, it is difficult to find ethnographic studies of older adults as participants in the digital economy. While many older adults are successfully navigating the internet and the broader space using technologies and quite conversant with mobile apps, their use is limited to communication (WhatsApp), email, cab hailing, ecommerce and basic banking services. The social media chatter between two neighbors, a 24-year-old and her 68-year-old lady on how even the most popular apps still ignore the elderly population while designing them gives one a sense of the depth of such issues.

On a broader note, while there is data available around ageing in India, the data is static in nature and mostly part of government surveys conducted over longer time periods. In specific areas (like senior housing, geriatric services, insurance, etc.), there is industry data but again very specific and focused on the target group for products and services. This also highlights the need to gather and build on credible data and research to understand population sub-groups better.

Finally, but very importantly, the categorization of ‘senior citizens’ by chronological age severely limits our ability to understand the many sub-groups (by age, medical conditions, mobility, family support structure, financial security, living arrangements, etc.), and thus acts as a poor proxy to understand their quality of life. This is also likely to result in designing a one-size-fits-all approach to address the needs of this heterogeneous group and lose out on the opportunity to cater to uniquely addressable groups within them.

For example, recognizing the biological ageing process and re-imagining mid- and later-life transitions could help in building better pathways to higher quality of life. Here is one such model that looks at potential career transitions into second and third careers, taking into account longer lifespans, and moving away from the binary of work and retirement lives.