When you ask people in Moldova how they feel about poverty, they start by talking about money - how jobs pay too little and everything costs too much. But if you dig a little deeper, they will tell you about other things too: the feeling of living in towns and villages that have been abandoned due to mass emigration, the lack of access to parks for their children to play in, the personal shame of not being able to find a job.
Poverty measurement is about generating a set of numbers - a person’s income, or the percentage of the population living below a certain standard. Statistics about Moldova will tell you that it has the lowest GDP per person and lowest life expectancy in Europe, and that officially 9.6 per cent of the population live in poverty. But what do these statistics really tell us about the challenges that poor people face?
For the past year we have been working with partners in Moldova to understand how poverty measurement is changing - making use of technology and innovation to become cheaper, more comprehensive and better at reflecting human experiences. This blog outlines some thoughts on the changes we are seeing.
Economists frequently debate the best ways to measure poverty, but one area of broad agreement is that other factors beyond income matter as well. How can these other factors be captured in a simple way that is useful for policymakers? One example is the Multidimensional Poverty Index (MPI), which brings together 10 indicators in three areas: health, education and living standards. Alternatively, the Commission on Global Poverty has argued for dashboards which display separate dimensions of poverty alongside income, rather than trying to create a single measure.
These statistical exercises are all based on the analysis of existing survey data. But what if we are measuring the wrong things? Some researchers have called for access to nature to be included in measures of poverty and wellbeing. Others believe that poor people themselves should decide which aspects of poverty we measure. The Individual Deprivation Measure and an MPI constructed in El Salvador have attempted to do this. Nesta’s own work with the UNDP in Moldova has sought to consult those living in poverty about what matters to them, as the basis of an exercise by the National Bureau of Statistics to rethink the dimensions it uses to measure poverty.
Alongside improving the quality of statistics, how can we make data on poverty more useful for policymakers? In a blog earlier this year, Geoff Mulgan argued that for statistics to be useful they need to be tied stories, feelings and detail. Beyond reading the latest 164 page human development report [PDF] on Moldova - what is the best way to capture the more subjective side of poverty?
The Commission on Global Poverty recommended that quick surveys be used to find out what poor people feel, as a way to help policymakers interpret statistical results. Examples of this include the World Bank’s Listening to Africa initiative - a pilot that used mobile phones to regularly collect information on living conditions and UNICEF’s U-Report, an SMS survey which it uses to carry out polls of young people on a variety of issues. We expect to see much more experimentation in this area in the future.
The collection of poverty statistics through household surveys is very expensive - the World Bank estimates that it will cost almost $1bn to help the 78 poorest countries implement surveys every three years between 2016 and 2030. The expense of household surveys means that the data deficit can’t be filled by better surveys alone. Beyond surveys, there are a number of innovations designed to make data collection cheaper, faster and more comprehensive. These experimental approaches include:
Data mining - These techniques are an attempt to analyse the ‘data exhaust’ that individuals leave behind when communicating online, moving around a city or making purchases with their credit cards. The UN Global Pulse Lab has experimented with using Twitter to track food prices in Indonesia.
Proxy indicators for poverty - If it is too expensive to go to every household and ask them questions about their living standards, perhaps we can measure other variables which can allow us to predict who might be living in poverty? Experiments in this area have used satellite and mobile phone data to do this.
Making better use of existing data - Sharing data that governments already hold on families and individuals across departments could help governments create a comprehensive picture of who is living in poverty. In the UK, the ONS is experimenting with the use of administrative data to estimate household income.
Traditionally, governments measure poverty then develop plans to eliminate it. But governments are only one part of the solution to poverty. The NGO Fundación Paraguaya developed an approach to eliminating poverty based on a self-assessment survey, bespoke action plans and access to microcredit. Poverty Stoplight, the tool that it developed uses a traffic light system which allows individuals to measure their living standards across 50 indicators.
In other cases, the answer may be to recognise the political dimensions of poverty. Communities that aren’t included in official survey exercises are taking it upon themselves to collect their own data. One example is Slum Dwellers International, which has created a global, community-generated database on informal settlements. This data can be a useful tool for communities and NGOs in their social and political struggles.
Poverty measurement is changing rapidly. Some areas that we think are worth exploring include:
Experiments that use technology to make data collection cheaper or more comprehensive (e.g. the collection of disaggregated data, rather than averages) - data mining, proxy indicators, data sharing.
Pilots that seek to incorporate what people feel about poverty - and their stories - into official statistics.
Tools and techniques that help civil society organisations and citizens themselves use data to take action to address their problems.
What innovations in poverty measurement have we missed?
Image courtesy of DECODE