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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
Also available in: Español | العربية | Français
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.

Are South Asian countries sinking into a debt trap?

Bidisha Das's picture
 

This blog is part of a series based on International Debt Statistics 2018.

The 2018 edition of International Debt Statistics (IDS 2018) which presents statistics and analysis on financial flows (debt and equity) for 123 low-and middle-income countries has just been released. One of the key observations of IDS 2018 is that net financial flows in 2016 to all developing countries witnessed a more than threefold increase over their 2015 level. This was driven entirely by net debt flows, which increased by $542 billion in 2016. Consequently, total external debt outstanding of all developing countries went up to $6.9 trillion, an increase of 4.1 percent over 2015. Interestingly, South Asia seems to deviate from this norm of IDS 2018.

External debt outstanding of South Asia contracted in 2016

South Asia is the only region that has shown a contraction in the total external debt outstanding in 2016. The total external debt stock of South Asia contracted by almost 2 percent as net debt flows into the region turned negative ($-7.7) for the first time in a decade. More specifically, this is the result of net long-term external debt flows turning negative (-$12.5 billion) implying that principal repayments by South Asia, on long-term external debt far exceeded disbursements.

Interactive product export streamgraphs with data360r (now in CRAN!)

Reg Onglao's picture

Building beautiful, interactive charts is becoming easier nowadays in R, especially with open source packages such as plot.ly, ggplot2 and leaflet. But behind the scenes, there is an often untold, gruesome part of creating data visualizations -- downloading, cleaning, and processing data into the correct format.

Making data access and download easier is one of the reasons we developed data360r, recently available on CRAN and the newest addition to the TCdata360 Data Science Corner.

Data360r is a nifty R wrapper for the TCdata360 API, where R users ranging from beginners to experts can easily download trade and competitiveness data, metadata, and resources found in TCdata360 using single-line R functions.

In an earlier blog, we outlined some benefits of using data360r. In this blog, we’ll show you how to make an interactive streamgraph using the data360r and streamgraph packages in just a few lines of code! For more usecases and tips, go to https://tcdata360.worldbank.org/tools/data360r.

Introducing Data360R — data to the power of R

Reg Onglao's picture
 

Last January 2017, the World Bank launched TCdata360 (tcdata360.worldbank.org/), a new open data platform that features more than 2,000 trade and competitiveness indicators from 40+ data sources inside and outside the World Bank Group. Users of the website can compare countries, download raw data, create and share data visualizations on social media, get country snapshots and thematic reports, read data stories, connect through an application programming interface (API), and more.

New Partnership for Capacity Development in Household Surveys for Welfare Analysis

Vini Vaid's picture

In low- and middle-income countries, household surveys are often the primary source of socio-economic data used by decision makers to make informed decisions and monitor national development plans and the SDGs. However, household surveys continue to suffer from low quality and limited cross-country comparability, and many countries lack the necessary resources and know-how to develop and maintain sustainable household survey systems.
 
The World Bank’s Center for Development Data (C4D2) in Rome and the Bank of Italy— with financial support by the Italian Agency for Development Cooperation and commitments from other Italian and African institutions—have launched a new initiative to address these issues.

The Partnership for Capacity Development in Household Surveys for Welfare Analysis aims to improve the quality and sustainability of national surveys by strengthening capacity in regional training centers in the collection, analysis, and use of household surveys and other microdata, as well as in the integration of household surveys with other data sources.
 
On Monday, nine partners signed an MoU describing the intent of the Partnership, at the Bank of Italy in Rome. The signatories included Haishan Fu (Director, Development Data Group, World Bank), Valeria Sannucci (Deputy Governor, Bank of Italy), Pietro Sebastiani (Director General for Cooperation and Development, Ministry of Foreign Affairs and International Cooperation of the Italian Republic), Laura Frigenti (Director, Italian Agency for Development Cooperation), Giorgio Alleva (President, Italian National Institute of Statistics), Stefano Vella (Research Manager, Italian National Institute of Health), Oliver Chinganya (Director, African Centre for Statistics of the UN Economic Commission for Africa), Frank Mkumbo (Rector, Eastern Africa Statistical Training Center), and Hugues Kouadio (Director, École Nationale Supérieure de Statistique et d’Économie Appliquée).
 
The Partnership will offer a biannual Training Week on household surveys and thematic workshops on specialized topics to be held in Italy in training facilities made available by the Bank of Italy, as well as regular short courses and seminars held at regional statistical training facilities to maximize outreach and impact. The first of a series of Training-of-Trainers (ToT) courses will be held in Fall 2017.
 
For more information, please contact: [email protected].

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