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Relative versus absolute poverty headcount ratios: the full breakdown

Juan Feng's picture
Also available in: 中文

Most countries in the world measure their poverty using an absolute threshold, or in other words, a fixed standard of what households should be able to count on in order to meet their basic needs. A few countries, however, have chosen to measure their poverty using a relative threshold, that is, a cutoff point in relation to the overall distribution of income or consumption in a country.

Chart 1


The chart above shows the differences between relative and absolute poverty headcount ratios for countries that have measured both. You can select other countries from the drop down list, but for example, you can see that Romania switched from measuring poverty in absolute terms to measuring poverty in relative terms in 2006.  Absolute poverty headcount ratios steadily declined from 35.9% in 2000 to 13.8% in 2006. However, by relative measures, the national poverty headcount ratio in 2006 was 24.8%.  This does not mean that poverty bumped up in 2006. These two numbers are simply not comparable, but what exactly do they both mean?

Kenya’s re-based national accounts: myths, facts, and the consequences

Johan Mistiaen's picture

A month ago, the Kenya National Bureau of Statistics (KNBS) Kenya released a set of re-based and revised National Accounts Statistics (NAS), the culmination of an exercise that started in 2010.  Press coverage, reactions from investors and the public have been generally favorable, but some confusion still looms regarding some of the facts and consequences.  We wrote this blog post to debunk some of the myths.

NAS, including Gross Domestic Product (GDP), are typically measured by reference to the economic structure in a “base” year.  Statisticians sample businesses in different industries to collect data that measures how fast they are growing.  The weight they give to each sector depends on its importance to the economy in the base year.  As time passes and the structure of the economy changes, these figures become less and less accurate.

Re-basing is a process of using more recently collected data to replace an old base year with a new one to reflect the structural changes in the economy.  Re-basing also provides an opportunity to add new or more comprehensive data, incorporate new or better statistical methods, and apply advancements in classification and compilation standards. The current gold standard is the 2008 System of National Accounts (SNA).

Open Data On the Ground: Jamaica’s Crimebot

Samuel Lee's picture
Some areas of Jamaica, particularly major cities such as Kingston and Montego Bay, experience high levels of crime and violence. If you were to type, “What is Jamaica’s biggest problem” in a Google search, you’ll see that the first five results are about crime.

Africa’s urban population growth: trends and projections

Leila Rafei's picture
Also available in: 中文 | العربية | Español | Français
On the periphery of Lagos, Nigeria, lies Makoko, a burgeoning slum community perched on a lagoon. Residents live in makeshift homes on stilts made of collected wood and tarp, and get around primarily by canoe.  Once a small fishing village, Makoko now draws migrants from neighboring countries, who flock to Nigeria for low-paying, unskilled jobs.

Making data work for everyone

Ana Revenga's picture
When you think about ending poverty and promoting shared prosperity, what comes to mind?  For many people, it is building schools and roads, developing effective safety net programs, improving health facilities, making more and better jobs available, and so forth… for good reason.

But how do we know where to build these roads and schools? How do we find out who needs health facilities, and what kinds of skills exist in a particular country in order to design better employment programs? How do we know where and which kinds of deprivation exist, in order to design safety net programs that actually work?

The answer? Data. Good data. And lots of it. The World Bank Group’s 2014 Policy Research Report: A Measured Approach to Ending Poverty and Boosting Shared Prosperity, takes a carefully considered view of the progress and challenges of measuring and monitoring the twin goals, and pays special attention to data.  
Most Recent Household Consumption Surveys Available, Africa
Source: World Bank Africa Region, Statistics Practice Team

Have your say: what do you want from a development data revolution?

Haishan Fu's picture

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If you’ve been reading anything related to international development in the last year, you will have seen rich conversations around the the idea of a “data revolution”. What exactly would a data revolution look like? What would its aims be? Is it about data collection, use, analysis, all of the above, or something else entirely?

To answer these and other questions, the United Nations Secretary General recently formed an Independent Expert Advisory Group (IEAG) on the “Data revolution for development”. I’m part of this group: we’ve been tasked with making recommendations on how to achieve a data revolution. We have to do it quickly - and we want to get your inputs too!

Identifying poor-rich gaps in accessing maternal health care

Haruna Kashiwase's picture

The most recent data show significant strides in reducing maternal mortality at the national level over the past 20 years.  Improvements in access to maternal health care, especially in skilled birth assistance, have contributed to the reduction of maternal mortality. 

While these improvements are impressive, the national level data often mask inequalities in skilled birth assistance within countries. There may be gaps within a country, for example, where wealthy women might have better access than women from poor households. According to the World Health Organization, "The high number of maternal deaths in some areas of the world reflects inequities in access to health services, and highlights the gap between rich and poor."

Good Open Data. . . by design

Victoria L. Lemieux's picture

An unprecedented number of individuals and organizations are finding ways to explore, interpret and use Open Data. Public agencies are hosting Open Data events such as meetups, hackathons and data dives. The potential of these initiatives is great, including support for economic development (McKinsey, 2013), anti-corruption (European Public Sector Information Platform, 2014) and accountability (Open Government Partnership, 2012). But is Open Data's full potential being realized?

Data ambassadors wrapping up at
DataDive2013. Photo:
Carlos Teodoro Linares Carvalho.

A news item from Computer Weekly casts doubt. A recent report notes that, in the United Kingdom (UK), poor data quality is hindering the government's Open Data program. The report goes on to explain that – in an effort to make the public sector more transparent and accountable – UK public bodies have been publishing spending records every month since November 2010. The authors of the report, who conducted an analysis of 50 spending-related data releases by the Cabinet Office since May 2010, found that that the data was of such poor quality that using it would require advanced computer skills.

Far from being a one-off problem, research suggests that this issue is ubiquitous and endemic. Some estimates indicate that as much as 80 percent of the time and cost of an analytics project is attributable to the need to clean up "dirty data" (Dasu and Johnson, 2003).

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