Measuring Poverty So That No-One Is Left Behind
The United Nations (UN) made a commitment to ‘leaving no one behind’ in the 2030 Agenda for Sustainable Development. To realise this promise, the UN has set a number of goals and targets, including ending poverty everywhere, in all its forms.
As a result, there has been a renewed interest in studying how we measure poverty. This is not only within the traditional monetary framework, where poverty is measured according to material resources such as minimum basic income, but also within non-monetary and multidimensional frameworks such as the presence and interconnectivity of:
- A lack of education or employment
- Inadequate housing; poor health and nutrition
- Low personal security
- Social isolation
The UN goals and targets will stimulate action over the next fifteen years in areas of critical importance for humanity and the planet. With this in mind, I have recently published two co-authored articles, developing innovative tools for measuring and analysing poverty, that move away from the popular ‘head count ratio’ and that look to prioritise progress among the poorest in society.
The problem with the head count ratio
Probably the best-known poverty metric, the head count ratio is the proportion of the population that fails to meet a subsistence threshold such as living without basic income or basic facilities (eg access to improved sanitation facilities or clean drinking water).
While easy to understand, the headcount ratio ignores the depth of deprivations, failing to capture improvement or deterioration in a population unless a threshold is crossed. Put simply, if the poor become poorer, the head count index doesn’t change, putting the most vulnerable at risk of being overlooked.
Sanitation deprivation in Bangladesh
To demonstrate how the headcount ratio may fail to capture the depth of deprivations, my paper with Dr Gaston Yalonetzky examines sanitation deprivation in Bangladesh. The paper looks at developing innovative tools for measuring and analysing poverty involving ordered categorical variables, where categories are measured by a known order or scale eg one to five.
Examples of these variables may include different poverty indicators, such as those used in the World Health Organisation’s and UNICEF’s WASH programme – the accessibility, availability and quality of drinking water, sanitation, hygiene, schools, and healthcare facilities.
For monetary variables, various tools (eg poverty gap measure) have been developed to capture depth of deprivations. However, unlike monetary variables, various arithmetic operations are not permitted with ordered categorical variables and so traditional monetary poverty measurement tools become ineffective.
Between 2007 and 2014, we documented an overall reduction in the headcount ratio of sanitation deprivation in Bangladesh, but various crucial differences across deprivation categories (depth) go unnoticed by the head count ratio.
A comparison between two of the country’s provinces—Dhaka and Rajshahi—provides striking insights. In 2007, the headcount ratio was very similar in both provinces. By 2014, the head count ratio fell more quickly in Rajshahi giving the impression that Rajshahi was performing better. However, Rajshahi was not as effective in reducing the proportion of people in the two poorest sanitation categories.
Dr Yalonetzky and I have developed a methodology so that changes in poorer deprivation categories are not ignored. Normally, the data for an ordinal variable consist of the proportion of the population in each of the ordered categories.
Simply, these proportions in different categories can be used to construct poverty measures so that they are weighted sums of population proportions in deprivation categories. The values of these weights depend on the order of the corresponding categories. Our measures can be easily used in the targeting and evaluation of poverty alleviation programmes.
"In its initial phase, the programme successfully brought behavioural changes in several indicators, but we surprisingly find that behavioural changes did not occur among the poorest who initially did not comply with multiple conditions simultaneously."
Anti-poverty programmes in the Philippines
Conventionally the evaluation of anti-poverty programmes estimate impact on individual outcomes. In a second research paper with Melba Tutor, we explore how these programmes should evaluate multiple outcomes simultaneously to more holistically view the breadth and depth of deprivation and to more effectively address current and future poverty.
A ‘counting approach’ is one way to identify the poor in multidimensional poverty measurement, which entails the intuitive procedure of counting the number of dimensions in which people suffer deprivation.
In our paper we examined The Pantawid Pamilyang Pilipino Program (4Ps) which provides cash grants to beneficiary families conditional on compliance with pre-specified human capital investments, aimed at inducing targeted behavioural changes. It is the flagship poverty reduction programme in the Philippines.
We show that the counting approach framework generates findings and insights that are missed by traditional single-outcome evaluation exercises.
In its initial phase, the programme successfully brought behavioural changes in several indicators, but we surprisingly find that behavioural changes did not occur among the poorest who initially did not comply with multiple conditions simultaneously.
Both articles show that fulfilling the UN commitment to ‘leaving no one behind’ and monitoring their sustainability in the future requires innovative methods that are distinct from those that are widely applied within the traditional monetary poverty measurement framework.
The need for further research in developing appropriate methodologies to facilitate the assessment and monitoring of poverty reduction while using non-monetary indicators and to ensure that the poorest are not left behind cannot be overemphasised. We need to make sure that all human beings can fulfil their potential in dignity and equality, and in a healthy environment.