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Debt data: how debt inflows differ among developing countries

Molly Fahey Watts's picture
Also available in: العربية | Español | Français

The World Bank Group’s International Debt Statistics (IDS) 2015 was released today. The Bank’s flagship debt data publication features 2013 data on external debt stocks and flows, as well as other major financial indicators on the 124 developing countries that report to the World Bank’s Group’s Debt Reporting System.

The major news from this year’s IDS is that net external debt flows to developing countries rose 28% in 2013, driven by a sharp 50% increase in short-term debt inflows. Additionally, foreign direct investment in emerging economies proved to be steady and resilient, bringing net capital flows (debt and equity) to $1.2 trillion.

For more detailed analysis and trends on debt statistics, take a look at IDS's debt portal featuring online tables. Here are a few highlights I thought I'd share.

Data Group launches newly revamped Statistical Capacity Indicator website

Annette Kinitz's picture

When a country’s statistical capacity improves and policy makers use accurate statistics to inform their decisions, this results in better development policy design and outcomes. In this regard, the Statistical Capacity Indicator (SCI) serves as an essential monitoring and tracking tool, as well as helps National Statistics Offices (NSOs) worldwide to address a country’s gaps in their capabilities to collect, produce, and use data.
 
The Statistical Capacity Indicator’s Global Reach
Since 2004, the SCI continues to assess the capacity of a developing country’s ability to adhere to international statistical standards and methods, whether or not its activities are in line with internationally recommend periodicity, and whether the data are available in a timely fashion.

To this end, there are 25 indicators that annually monitor and “grade” a country’s statistical capacity progress and thus form the basis for the overall SCI score calculation.
 
While NSOs are the main users of the SCI score, the World Bank Group, international development agencies, and donor countries also refer to the SCI score for their own evaluation and monitoring purposes.

New surveys reveal dynamism, challenges of open data-driven businesses in developing countries

Alla Morrison's picture

Open data for economic growth continues to create buzz in all circles.  We wrote about it ourselves on this blog site earlier in the year.  You can barely utter the phrase without somebody mentioning the McKinsey report and the $3 trillion open data market.  The Economist gave the subject credibility with its talk about a 'new goldmine.' Omidyar published a report a few months ago that made $13 trillion the new $3 trillion.  The wonderful folks at New York University's GovLab launched the OpenData500 to much fanfare.  The World Bank Group got into the act with this study.  The Shakespeare report was among the first to bring attention to open data's many possibilities. Furthermore, governments worldwide now routinely seem to insert economic growth in their policy recommendations about open data – and the list is long and growing.

Map

Geographic distribution of companies we surveyed. Here is the complete list.
 
We hope to publish a detailed report shortly but here meanwhile are a few of the regional findings in greater detail.

Open India: new interactive app features state-level sectoral data

Vilas Mandlekar's picture
Also available in: Français | Español
What is the World Bank Group (WBG) doing to help address India's development challenges? And how is the Bank doing in implementing its programs in India's low-income states?  These are some of the questions that are addressed via Open India (openindia.worldbankgroup.org), a new web-based app that lays out the WBG's Country Partnership Strategy (CPS), operational projects, and knowledge products in India.

What makes the Open India site unique?
This web app takes a new and different approach in presenting the WBG's partnership strategy and current projects, by doing so in a transparent, interactive, and easy-to-use web platform. It features data visualizations that connect the main engagement areas  ̶   Economic Integration, Spatial Transformation, and Social Inclusion  ̶   with the underlying challenges that are being addressed through the WBG's operations and knowledge products in India.  An essential component of the new Open India web app is sectoral data that quantifies India's development challenges. For example, the range of India's infrastructure and transportation gaps is presented as a data visualization below.
 

Source: Open India
 

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).

Open Data On the Ground: Jamaica’s Crimebot

Samuel Lee's picture
Also available in: Español | Français | العربية
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

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