Discussion: Measuring poverty, well-being and progress - Innovative approaches and their implications for statistical capacity development


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Will the bottom billion always be with us?

By Patick Love (original post on OECD Insights blog here.)

I know a photographer who worked in the Egyptian oases at the time when the people living there became poor. Their wealth and number of possessions didn’t change, but with the arrival of television and other modern media, they suddenly learned that they were living in a backward, disadvantaged area. Until then, they’d believed that they had everything you needed for a good life – food, water, animals, plants, friends, feuds… But they didn’t have fridges, schools, and most of the other goods and services to be found in the city. At the same period, people who lived in a metropolitan slum would not have considered themselves rich just because they had a TV, electricity and most of the other things the Bedouin lacked.

Poverty then isn’t just a question of income. The newly-published 2013 UN Human Development Report looks at two measures: poverty defined in strictly monetary terms as living on less than $1.25 a day, and the Oxford Poverty and Human Development Initiative’s Multidimensional Poverty Index (MPI). The MPI has three dimensions and ten indicators, all equally weighted, which reflect some Millennium Development Goals and international standards of poverty. The three dimensions are health, education and living standards; while the indicators are nutrition, child mortality, years of schooling, school attendance, cooking fuel, sanitation, water, electricity, type of floor of the dwelling, and assets.

The data support the optimism of the UN report’s title “The Rise of the South: Human Progress in a Diverse World”. More than 40 countries in the developing world have done better than expected in human development terms in recent decades, with their progress accelerating markedly over the past ten years. Of 22 countries having data on MPI poverty over time, 18 reduced MPI significantly, and most of them reduced multidimensional poverty faster than income poverty.

On current trends, half the 22 countries would eradicate MPI within 20 years and 18 within 41 years, but it would take 95 years for all 22 to eradicate multidimensional poverty.

What about the “bottom billion”, the poorest of the poor? In this article in 2010, Brian Keeley discussed Andy Sumner’s argument that if we focus on the poorest countries, we’ll actually miss most of the world’s poor. The new figures suggest that where the bottom billion live depends on whether you look at national averages, the subnational  level or the intensity of poverty experienced by each poor person.

At national level, the bottom billion are concentrated in the 30 poorest countries. But the situation can vary significantly from one region to another within a given country. For instance in Tanzania, 32.4% of the people in the Kilimanjaro region were poor in 2010, but the figure rises to 87.4% in the Dodoma region just 250 miles (400 km) away. Looking at 265 subnational regions, the bottom billion are spread across 44 countries.

The bottom billion by individual poverty profiles more than doubles the number of countries to 100. This is calculated by starting with people who are deprived in all 10 indicators. This gives 17 million in all, with India and Ethiopia having 4 million each. You then add people who are deprived in 95% of the indicators and so on until you reach 1 billion.

Surprisingly, 9.5% of the bottom billion live in upper Middle Income Countries, and 41,000 of the poorest bottom billion live in five High Income Countries. Unsurprisingly, 51.6% reside in South Asia, 32.7% reside in Sub-Saharan Africa, and 12.3% reside in East Asia and Pacific. Nearly 40% of the bottom billion poor reside in India.

But to get back to the optimism. Bangladesh was the original international “basket case” (a term used by the Henry Kissinger-led State Department in 1971). The image persists, but in reality Bangladesh is one of the three top performers in reducing MPI, along with Nepal and Rwanda. The Economist argues that it got out of the basket thanks to four factors: it improved the status of women; the Green Revolution and remittances boosted incomes; the government maintained social spending; and non-government organisations managed to scale up their programmes to work nationwide.

You may have noticed that The Economist doesn’t cite economic growth. The Human Development Report says something similar: “Economic growth alone does not automatically translate into human development progress. Pro-poor policies and significant investments in people’s capabilities  – through a focus on education, nutrition and health, and employment skills – can expand access to decent work and provide for sustained progress. The 2013 Report identifies four specific focuses for sustaining development momentum: enhancing equity; enabling participation of citizens, including youth; confronting environmental pressures; and managing demographic change.

These themes will also be discussed at the OECD Global Forum on Development on 4-5 April. The Forum will be looking at how the global economic landscape has changed, and with it, the understanding of what development and poverty are all about. For example the session on the multidimensional nature of poverty will highlight the links between poverty reduction, natural resource management and growth as issues that are central to social protection and pro-poor growth.


Well-being and progress in societies are increasingly promoted as the core of the post-2015 development agenda.

For example, UN Resolution 65/309 calls for a “more holistic approach to development” based on the notion of sustainable happiness and well-being; it invites countries to develop measures that capture provisions for the pursuit of happiness and well-being in public and development policies.

Measuring well-being and progress in developing countries: Well-being figures prominently in recent OECD work on measuring progress “beyond GDP”. This approach looks at well-being as a complex and multi-dimensional phenomenon, encompassing a range of economic and non-economic factors that impact people’s lives, as reflected in the OECD’s Better Life Initiative. Launched in 2011, this well-being framework incorporates 11 dimensions and features achievements as well as inequalities, objective conditions as well as aspirations, and actual as well as future conditions. The framework is made operational through a set of indicators that benchmark countries’ performance and monitor progress.


  1. Measuring well-being and progress in developing countries: does it make sense? Can it be done?

  2. What are the relationships among complex concepts such as poverty, social cohesion and progress?

  3. What are the most promising examples of statistical methods developed to measure these complex concepts?


Statistical capacity development in an emerging post-2015 development agenda

Setting goals without statistical systems in place to track progress against them is useless at best, and counter-productive at worst. Development goals must reflect the realities and priorities of individual countries, but they also need to be measurable. This implies that statistical capacity development, which was widely neglected when the Millennium Developemnt Goals were first designed, should have crucial importance in any follow-up framework. Recent innovations in data production, dissemination and use suggest that there is a real possibility to “leapfrog” stages of statistical capacity development. “Big” and open data, as well as new forms of public-private engagement between data users and producers, offer unprecedented opportunities to overcome existing dichotomies and resource constraints in statistical production.


  1. MDG’s and statistical capacity development: what have we learned?

  2. How can statisticians take advantage of innovations in data production and dissemination to get information more quickly into the hands of users and policy makers, while maintaining quality and accountability standards?

  3. What country examples and good practices in statistical innovation are there, and how can they be replicated?



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