Improving data on ageing to leave no one behind
Getting equipped with the tools and ideology necessary to understand how ageing is linked to exclusion.
At the end of August, I participated in a meeting in Winchester, UK, with colleagues from national statistical offices, UN agencies, NGOs and academia, to discuss the need for better disaggregation by age and ageing-related statistics. The UK’s Office of National Statistics hosted the technical group to lay the groundwork for the creation of the Titchfield City Group on Ageing and Age-disaggregated Data, which will provide expert recommendations to the UN Statistical Commission. Other city groups have significantly improved the collection of data, such as the Washington Group’s work on disability statistics.
In order to leave no one behind – a core aim of Agenda 2030’s Sustainable Development Goals – we need to understand all the dimensions through which people are excluded. To gain an accurate picture and understand exclusion over a person’s life cycle, it’s vital that we disaggregate by age the data available on such issues as nutrition, poverty or disability. There are many challenges to collecting data on ageing, but three major issues emerge that impact the quality of policies on ageing in developing countries.
Major surveys collect little data on people aged over 49
USAID’s Demographic and Health Surveys (DHS) and UNICEF’s Multiple Indicator Cluster Survey, two of the biggest tools for generating global statistics, focus primarily on children and women under the age of 49. This bias means that collectively we lack data on issues relevant to, although not limited to, older people. For example, although there is little data on domestic violence among people over 49, this doesn’t mean it doesn’t occur. Nor is there much global data on HIV/AIDS rates in older populations, yet case studies have shown that in some countries the highest prevalence of HIV/AIDS is among men over 49. For instance, Namibia’s 2013 DHS expanded the age range of the questionnaires to include men and women age 15–65. They found that 11% of men aged 15–49 had HIV, compared to 16% of men 50–64. New tools are needed to capture issues relevant to older persons better and to highlight issues missed due to the age caps in the major surveys.
Stereotypes about ageing lead to inadequate analysis
Another problem is that many demographic analyses lump everyone aged above 60 into one group, despite the diversity that exists among them. Beyond the biological differences between a healthy 60 year old and a healthy 95 year old, the 60-plus population includes some of the world’s most powerful and privileged people, as well as some of the most marginalised. A clear illustration of the challenges presented by age-related stereotypes is the oft-used dependency ratio. This analysis classifies everyone over 65 as dependent and assumes that they do not work for their living. This simply is not accurate for many over-65s, globally.
The definition of a ‘household’ can matter
The World Bank’s measure of extreme poverty defines poverty at the household level. This can make it difficult to measure the wellbeing of older persons for several reasons. For example, if a person experiences an ageing-related disability, they will be less likely to live in a household independently. If they must join another household to survive, their consumption patterns may be significantly different from the rest of the household, but they would still be assigned the household level of poverty even if they were significantly better or worse off. The measure of poverty does not account for the changing needs of household members over the course of life. Additionally, most surveys don’t include people living in institutions or staying in hospitals, excluding many at the very end of their lives.
At Development Initiatives, we work on data to ensure that no one is left behind. Our P20 Initiative looks at the data on people in the poorest 20% of the global population – the P20. In our recent P20 Initiative baseline report we call for disaggregation by wealth quintile, gender, geography, age and disability. Through our analysis, we have found that the quality of the data available on people gets worse in higher age groups, particularly among the P20. One of the major reasons for this relates to civil registration and vital statistics systems: people in the P20 are far less likely to have their births and deaths registered. The Titchfield City Group provides a unique opportunity to ensure that we are equipped with tools and the ideology necessary to understand how age is linked to exclusion.
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