A new way of measuring rural poverty yields unexpected results in Malawi
When Maxton Tsoka and his team of researchers set out to rural Malawi to test a new way of measuring poverty, they thought they had a pretty good idea of what clues they should look for. Beauty wasn’t one of them.
As it turns out, dwellers in Chambogho and in the other 15 villages that they visited repeatedly mentioned physical appearance as a sign of wealth.
"We initially didn’t think of physical appearance as one of the indicators, but it turns out that they use it to distinguish the status of people in their communities," said Mtisunge Matope, one of the five researchers on Maxton’s team from the Centre for Social Research at the University of Malawi tasked by FAO with conducting a field test of the project.
Another team member, Donald Chitekwe, was equally surprised to find how much importance the villagers attached to this variable. In all, 55 out of the 64 discussion groups mentioned it.
In hindsight, it makes sense.
"Those who are well to do often look better," Donald said. "That’s because they have money, so they are able to buy lotions that nourish their skin; they can buy better clothes; they eat good, healthy food that makes them look better."
While it would be difficult use a subjective indicator like physical appearance, the fact that it was highlighted by so many respondents speaks volumes about the importance of self-perception in relation to poverty: beyond the numbers there are humans and their emotions.
Capturing poverty in all its dimensions
In December of 2021, FAO published a report in collaboration with the Oxford Poverty and Human Development Initiative (OPHI) that introduced an innovative way of measuring poverty in rural areas, where the majority of the world’s less well-off live, but for which reliable and harmonized data is difficult to come by.
The idea is that a more precise identification of who the extreme poor are can help decision-makers shape more accurate policies to tackle rural poverty and hunger.
This so-called Rural Multidimensional Poverty Index (R-MPI) was built on the widely accepted notion that household income alone does not fully capture a person’s wellbeing. Various indicators, such as food security, living standards, education and health are just as important to human development.
In order to test the effectiveness of the R-MPI, Maxton and his team set out to visit 15 communities spread across eight districts in Malawi. This southern African country of 19 million people is one of the poorest in the world, with the majority of its rural-based citizens dependent on agriculture for their livelihoods.
The researchers interviewed hundreds of people including community leaders, farmers, herders, fishers, women heads of households, traders and estate workers.
The team started their research with 18 indicators and proceeded to check whether the ones they had picked — such as child mortality rates, school attendance, the availability of cooking fuel or exposure to climate change risks — applied in the real world.
Confirmations and surprises
Overall, the field research confirmed that such a multidimensional approach is more accurate in capturing poverty in all its aspects. In fact, the team found that as much as 14 percent of the rural poor identified by the R-MPI in Malawi had not been identified as poor by traditional monetary metrics.
At the same time, they were fascinated to find out that some of the official R-MPI indicators were hardly mentioned by the villagers, while others cited came as a surprise. For instance, apart from mentioning physical appearance, the interviewees gave far more importance to the amount of work that people did, rather than to other indicators.
"That’s because the richest don’t work and employ other people to do almost everything," Maxton said. "At the other end of the spectrum are people who are always working."
The relatively low importance that the villagers gave to education was equally surprising to the team.
"Education was not found to be useful in distinguishing between the rich and the poor," Maxton said. The explanation: "Sometimes the uneducated are richer than the educated. Because of this, people do not see education as an indicator differentiating the poor from the rich."
Similarly, having access to land was not an indicator in the original version of the R-MPI, but it was raised quite a lot by the villagers.
State of mind was another important indicator that emerged during the discussions. Being unhappy was identified with poverty, while the well-off were said to be happy and stress free. This state of mind was ascribed to having what they needed in life, including enough food and extra money to buy other items.
Moreover, the researchers were impressed at how accurately the people they interviewed were able to gauge the relative wealth of their neighbours. When it came to an indicator such as housing, respondents went beyond the basics — such as the construction materials — and provided a much more nuanced assessment, mentioning the home’s amenities, surroundings, type of windows, security of the doors, and so on.
Even more importantly, the research demonstrated the efficiency of the R-MPI in capturing their status.
"When we asked the participants to define poverty in their own words, what I found surprising is that most of the definitions that were given mentioned —without prompting— were the dimensions listed in the R-MPI, such as nutrition, living standards, livelihood and resources, as well as exposure to shocks and risks," said Patrick Msukwa, another member of Maxton’s team.
The development of the R-MPI is part of FAO’s ongoing efforts to help countries design and enact policies that address the condition of poor and small-scale farmers, enhancing their livelihoods and improving their resilience and ability to escape extreme poverty.