The Niti Aayog’s celebratory report claiming a reduction in India’s poverty from 24.9% to 15% comes at an opportune time from a geo-political standpoint. It represents a positive step towards understanding poverty beyond just monetary aspects. However, one cannot ignore the opportunity missed by the body: the opportunity to include data on the disabled population in the country. Numerous studies have revealed that households with disabled members face higher levels of multidimensional poverty compared to those without disabilities.
Admittedly, defining disability is as challenging as defining poverty, but their inclusion doesn’t necessarily have to be. People with disabilities are believed to be at a heightened risk of poverty. Sadly, the way poverty is measured often overlooks their unique challenges and needs, leaving them invisible in poverty indices.
India’s national Mutlidimensional Poverty Index (MPI) comprises three equally weighted dimensions: Health, Education, and Standard of living, which are represented by a total of 12 indicators. While using a multidimensional index to discuss poverty in India is a novel approach, the report’s limited health indicators, such as Nutrition, Child and Adolescent Mortality, and Maternal Health, fall short in considering the impact of disability on poverty. A family with a disabled person—irrespective of whether the disability is by birth or acquired later—faces recurring medical expenditure throughout their lifetime, influencing all 12 indicators in various ways. However, the term disability has been used only in a preventive context relating to maternal health and disabilities occurring at birth in the entire report. The omission of disabilities as a factor in health in families that have a disabled member neglects the long-term, recurring effect of health costs, impacting all the other indicators in the MPI.
For example, in the education dimension, a person with a locomotor disability might have low or no school attendance due to the lack of accessible schools. This impacts the ‘school attendance’ indicator. On the other hand, a child with autism may attend school every day and not be categorised as ‘deprived’ in the ‘years of schooling’ indicator, but this does not necessarily mean that the child has successfully achieved the required level of education. Disability’s impact on poverty can thus remain invisible in such cases. Including an indicator of sustainable livelihood would highlight the challenges faced by the disabled community, considering that accessibility and discrimination are most apparent in the livelihood sphere. Such cases, where the longitudinal impact on poverty is disguised, necessitate better-structured indicators.
The challenges of using a monetary approach to poverty from a disability perspective are the assumption that households share resources equitably, that the consumption needs of all members are the same, and that the resources available to the members are accessible to a person with a disability. A social exclusion or relative poverty measure seeks to understand poverty from a relative standpoint; though this seems apt, it would invariably underestimate poverty among the disabled population.
From a disability standpoint, it helps to understand poverty from a capability perspective. Measurement of poverty using the capability approach focuses on deprivations in certain basic capabilities. Using the capabilities approach in a multidimensional poverty index would inevitably include people with disabilities. Though the MPI and the capabilities approach both have their own demerits, it seems an amalgamation of both methods would give the needed intersectional data for apt policymaking. One of the criticisms of the Alkire & Foster methodology, which the Niti Ayog has also used, is the discretion in terms of deciding the levels of deprivation and weightage of each indicator. The capabilities approach does something that no other poverty measurement approach has been able to achieve: the conversion of capability into well-being. Sen and Nussbaum call it a ‘conversion handicap’, which would be multiplied when measuring poverty indicators for people with disabilities. Another aspect that MPI doesn’t cater to is the need to understand the quality of the indicators. Due to the lack of a benchmark for these indicators, the assessment that a person who has attended school for
10 years is not deprived is at the very least faulty.
The Sustainable Development Goals (SDGs) provide a powerful framework covering access to education and employment, availability of schools that are sensitive to students with disabilities, inclusion and empowerment of persons with disabilities, accessible transport, accessible public and green spaces, and building the capacity of countries to disaggregate data by disability, none of which is unfortunately taken into consideration while studying poverty in India.
While the SDGs seek to ‘leave no one behind”, Niti Ayog’s MPI is regrettably leaving behind people with disabilities. The question that arises now is how the MPI could have included disability in its study. One way would be to include disability as an indicator. But this wouldn’t necessarily fit, as disability alone doesn’t make someone poor; it’s the inaccessibility and resulting inequality in society that deprive them of opportunities, in turn making them poor. The issue with large-scale studies is that the collected data isn’t disaggregated enough to be made available for intersectional studies. The current MPI Report has disaggregated data from a geographical perspective; however, including a disability status in the next data collection cycle would assist policymakers in making targeted policy changes to address poverty as well as disability-related concerns.
What could also work is a participatory approach to selecting variables, which would include people with disabilities in the consultation process for developing poverty metrics.
(The writer is manager, Policy Advocacy and Coordination at the Association of People with Disabilities)