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Global Economy

International Debt Statistics 2019: External debt stocks at end-2017 stood at over $7 trillion

Evis Rucaj's picture
Also available in: 中文 | Español | Français | العربية
The 2019 edition of International Debt Statistics (IDS) has just been published.
International Debt Statistics 2019 presents statistics and analysis on the external debt and financial flows (debt and equity) for the world's economies for 2017. This publication provides more than 200 time series indicators from 1970 to 2017 for most reporting countries. To access the report and related products you can:

 
This year's edition is released just 10 months after the 2017 reference period, making comprehensive debt statistics available faster than ever before. It presents comprehensive stock and flow data for individual countries and for regional and analytical groupings. 

In addition to the data published in multiple formats online, IDS includes a concise analysis of the global debt landscape, which will be expanded on in a series of Debt Bulletin over the next year.

Global Findex 2017 microdata available for download

Leora Klapper's picture
We're thrilled to release the 2017 Global Findex microdata, featuring individual survey responses from roughly 150,000 adults globally. Get it here, along with documentation including a variable list, questionnaire, and information on sampling procedures and data weighting.
 
Downloading the data is easy. At the microdata library, you'll see a screen that looks like this:
 

 

Nearly 1 in 2 in the world lives under $5.50 a day

Dean Mitchell Jolliffe's picture
Also available in: Français | Español | العربية

Today, less than 10 percent of the world population lives in extreme poverty. Based on information about basic needs collected from 15 low-income countries, the World Bank defines the extreme poor as those living on less than $1.90 a day. However, because more people in poverty live in middle-income, rather than low-income, countries today, higher poverty lines have been introduced. These lines are $3.20 and $5.50 a day, which are more typical of poverty thresholds for middle-income countries.

Introducing the online guide to the World Development Indicators: A new way to discover data on development

World Bank Data Team's picture
Also available in: العربية | Español | 中文 | Français

The World Development Indicators (WDI) is the World Bank’s premier compilation of international statistics on global development. Drawing from officially recognized sources and including national, regional, and global estimates, the WDI provides access to almost 1,600 indicators for 217 economies, with some time series extending back more than 50 years. The database helps users—analysts, policymakers, academics, and all those curious about the state of the world—to find information related to all aspects of development, both current and historical.

An annual World Development Indicators report was available in print or PDF format until last year. This year, we introduce the World Development Indicators website: a new discovery tool and storytelling platform for our data which takes users behind the scenes with information about data coverage, curation, and methodologies. The goal is to provide a useful, easily accessible guide to the database and make it easy for users to discover what type of indicators are available, how they’re collected, and how they can be visualized to analyze development trends.

So, what can you do on the new World Development Indicators website?

1. Explore available indicators by theme

The indicators in the WDI are organized according to six thematic areas: Poverty and Inequality, People, Environment, Economy, States and Markets, and Global Links. Each thematic page provides an overview of the type of data available, a list of featured indicators, and information about widely used methodologies and current data challenges.

Measuring India’s economy using PPPs shows it surpassed France 25 years ago

Edie Purdie's picture

The ICP blog series explores ideas and issues under the International Comparison Program umbrella – including innovations in price and data collection, discussions on purpose and methodology, as well the use of purchasing power parities in the growing world of development data. Authors from across the globe, whether ICP practitioners or researchers making use of ICP data, are encouraged to submit relevant blogs for consideration to [email protected].

Earlier this summer, new data published by the World Bank showed that the Gross Domestic Product (GDP) of India had recently surpassed that of France, and that it was on track to overtake the UK economy too. Many news outlets jumped upon this new ranking of India’s economy, now sixth from top. But most media articles did not mention that the World Bank’s other measure, which compares GDP across countries using purchasing power parities (PPPs), has placed India ahead of both France and the UK for the last 25 years.

Applications open for third round of funding for collaborative data innovation projects

World Bank Data Team's picture
Photo Credit: The Crowd and The Cloud


The Global Partnership for Sustainable Development Data and the World Bank Development Data Group are pleased to announce that applications are now open for a third round of support for innovative collaborations for data production, dissemination, and use. This follows two previous rounds of funding awarded in 2017 and earlier in 2018.

This initiative is supported by the World Bank’s Trust Fund for Statistical Capacity Building (TFSCB) with financing from the United Kingdom’s Department for International Development (DFID), the Government of Korea and the Department of Foreign Affairs and Trade of Ireland.

Scaling local data and synergies with official statistics

The themes for this year’s call for proposals are scaling local data for impact, which aims to target innovations that have an established proof of concept which benefits local decision-making, and fostering synergies between the communities of non-official data and official statistics, which looks for collaborations that take advantage of the relative strengths and responsibilities of official (i.e. governmental) and non-official (e.g.,private sector, civil society, social enterprises and academia) actors in the data ecosystem.

The 2018 Atlas of Sustainable Development Goals: an all-new visual guide to data and development

World Bank Data Team's picture
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Download PDF (30Mb) / View Online

“The World Bank is one of the world’s largest producers of development data and research. But our responsibility does not stop with making these global public goods available; we need to make them understandable to a general audience.

When both the public and policy makers share an evidence-based view of the world, real advances in social and economic development, such as achieving the Sustainable Development Goals (SDGs), become possible.” - Shanta Devarajan

We’re pleased to release the 2018 Atlas of Sustainable Development Goals. With over 180 maps and charts, the new publication shows the progress societies are making towards the 17 SDGs.

It’s filled with annotated data visualizations, which can be reproducibly built from source code and data. You can view the SDG Atlas online, download the PDF publication (30Mb), and access the data and source code behind the figures.

This Atlas would not be possible without the efforts of statisticians and data scientists working in national and international agencies around the world. It is produced in collaboration with the professionals across the World Bank’s data and research groups, and our sectoral global practices.
 

Trends and analysis for the 17 SDGs

Chart: Economic Development and the Composition of Wealth

Tariq Khokhar's picture
Also available in: Español | العربية | Français

The composition of wealth fundamentally changes with economic development. Natural capital—energy, minerals, land and forests—is the largest component of wealth in low-income countries. Its value goes up, but its share of total wealth decreases as economies develop. By contrast, the share of human capital, estimated as the present value of future incomes for the labor force, increases as economies develop. Overall, human capital accounts for two-thirds of the wealth of nations. Read more in The Changing Wealth of Nations

 

Chart: Global Wealth Grew 66% Between 1995 and 2014

Tariq Khokhar's picture
Also available in: 中文 | العربية | Français | Español

Global wealth grew by 66% between 1995 and 2014 to a total of over 1,140 Trillion dollars. The share of the world’s wealth held by middle-income countries is growing — it increased from 19% to 28% between 1995 and 2014, while the share of high-income OECD countries fell from 75% to 65%. Read more in The Changing Wealth of Nations

 

Tracing the roots of TCdata360 datasets: an interactive network graph

Reg Onglao's picture

When doing data analysis, it's common for indicators to take the spotlight whereas datasets usually take the backseat as an attribution footnote or as a metadata popup.

However, we often forget how intertwined dataset sources are and how this affects data analysis. For instance, we can never assume that indicators from different datasets are mutually exclusive – it's possible for them to be the same indicator or to have an influence on the other as a component weight in an index, if the other dataset were used as a source for the other.

In this blog, we're interested to see if this applies to TCdata360 by taking a deeper look at its "dataset genealogy" and answer questions such as – Is it safe to do cross-dataset analysis using TCdata360 datasets? Are there interesting patterns in the relationships between TCdata360 datasets?

Quick introduction to network graphs

We call a dataset which serves as a data source for another dataset as "source", and a dataset which pulls indicator data from another as "target". Collectively, all of these are called "nodes".

To see the relationships between TCdata360 datasets, we mapped these in a directed network graph wherein each dataset is a node. By directed, we mean that source nodes are connected to their target nodes through an arrow, since direction is important to identify source from target nodes. For the purposes of this blog, we restricted the network graph to contain datasets within TCdata360 only; thus, all data sources and targets external to TCdata360 will not be included in our analysis.

Here's how the network graph looks like.

Each dataset is represented by a circle (aka "node") and is grouped and color-coded by data owner or institution. The direction from any source to target node is clearer in the interactive version, wherein there's a small arrow on the connecting line which shows the direction from target to source.

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