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Relative versus absolute poverty headcount ratios: the full breakdown

Juan Feng's picture
Also available in: 中文 | العربية | Français | Español

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).

New Metadata Query Feature in DataBank

Paige Morency-Notario's picture

DataBank is a data retrieval, analysis, and visualization tool that allows users to create, save, and share custom charts, tables, and maps. We launched the tool two years ago and have been making improvements based on user feedback ever since. Last year we released a multilingual version of the tool, and today we're pleased to announce a new feature that allows users to query country, series, time, and footnote metadata.

What can DataBank do?

  • It enables users to easily create custom queries on data drawn from 52 databases
  • It lets users create and customize charts, tables, and maps
  • It makes it easy to select, save and share data and visualizations
  • It's available on both computers and mobile devices
  • DataBank and selected data are available in English, Spanish, French, Arabic, and Chinese
  • It now allows users to create custom metadata queries
  • Watch the tutorial and read the FAQs to learn more about the basics of DataBank 

ICP 2011: 7 Million Prices, 199 Economies, 8 Regions, and 15 Partners

Haishan Fu's picture
Also available in: 中文

On behalf of the International Comparison Program (ICP) Executive Board and the World Bank, I’d like to thank everyone who’s contributed to the success of the 2011 round. The results are now available in report form, as a data download, and through interactive applications.
 
The largest global statistical program 
The ICP is hosted by the World Bank, and estimates purchasing power parities, or PPPs, for use as currency converters to compare the size and price levels of economies around the world. In terms of geographic scope, implementation time frame and institutional partnerships, many people consider it to be the largest ever global statistical initiative. It’s conducted under the authority of the United Nations Statistical Commission, and the 2011 ICP round collected over 7 million prices from 199 economies in eight regions, with the help of 15 regional and international partners. It’s the most extensive effort to measure PPPs ever undertaken.

Advancing the Data Revolution through Country-Owned Data

Johannes Kiess's picture
During the World Bank’s Spring Meetings, we launched the Open Aid Map to publish and visualize the sub-national locations of donor-financed projects on an interactive, open source platform. This means we now have access to a common platform that allows us to see who is funding what and where within developing countries.

Data and Development

Mahmoud Mohieldin's picture

WASHINGTON, DC – Since the turn of the century, the international development community has rallied behind the Millennium Development Goals, which set specific targets in eight key areas, including poverty, child mortality, and disease, to be achieved by 2015. In formulating the post-2015 development agenda, measuring the MDGs’ successes – and identifying where progress has lagged – is critically important. And that demands more and better data.

To be sure, international institutions and many developing countries have invested significantly in improving data collection to track better their performance against MDG targets. In 2003, only four countries had two data points for 16 or more of the 22 principal MDG indicators; by last year, that figure had soared to 118 countries.

But development data remain a scarce resource in the developing world. Given their value in measuring – and propelling – social and economic progress, this shortage must be addressed urgently. A catalyst is needed to expand the production and use of development data. With this in mind, the high-level panel on the post-2015 development agenda is right to call for a global “data revolution.”

World Bank to publish Purchasing Power Parities in December 2013

Frederic A. Vogel's picture

Given the complex nature of the ICP and the fact that it has become the largest worldwide statistical operation, the program decided that the December release will be postponed until March 2014, in an effort to produce the utmost quality results. Read more ...

The preliminary results from the 2011 round of the International Comparison Program (ICP) will be released in December 2013 followed by a more in-depth report in March 2014. The first release will provide Purchasing Power Parities (PPPs), price level indexes, and real expenditures for gross domestic product (GDP) and major aggregates for over 190 countries. Major economic indicators on the global economy produced by the World Bank are based on PPPs which are used to provide internationally comparable price and volume measures for GDP and its expenditure components. The same PPPs are used to determine comparable poverty levels across countries based on the $1.25 per day poverty line.

Announcing the launch of the GPFI Basic Set of Financial Inclusion Indicators

Leora Klapper's picture


GPFI: 2011 Branch Penetration Map

The ascent of financial inclusion in the policy agendas of governments and international organizations has been swift, to say the least. Its rise has been accompanied by a torrent of financial inclusion data, from supply-side indicators of bank branch penetration, to demand-side measures of the usage of formal accounts, to wide-ranging data on finance at the firm level. Yet with all these different datasets floating around, it has often been difficult to arrive at a holistic understanding of the financial inclusion landscape in a particular country, or develop international standards of measurement and monitoring. With the release on April 21st of the G20’s Financial Inclusion Data Portal showcasing the ‘G20 Basic Set of Financial Inclusion Indicators’, we hope that that will change.

DataDive Q&A with Data Ambassadors: Identifying Fraud and Corruption with Technology

Itir Sonuparlak's picture

This post is part of the Q&A Series with the Data Ambassadors from DataDive2013. You can also read an interview with the fraud and corruption data ambassadorsa recap of Data Dive 2013, and watch the presentations from the weekend.

Photo credit: Itir Sonuparlak

During DataDive 2013, each project had an assigned data ambassador, a leader to guide and direct the research and efforts of the teams. In the days following the DataDive, we spoke with two of the data ambassadors from the fraud and corruption related projects to learn more about their experiences. Read their responses below and join the conversation in our comments section.

  • Taimur Sajid develops financial models to asses risk for a financial firm and acted as a data ambassador during the DataDive.
  • Marc Maxson is an Innovation Consultant with Global Giving and brought his Heuristic Auditing Tool to the DataDive.

 

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