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Global Economy

Chart: Economic Development and the Composition of Wealth

Tariq Khokhar's picture
Also available in: Español

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, 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

Introducing Data360R — data to the power of R

Reg Onglao's picture

Last January 2017, the World Bank launched TCdata360 (, 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:

Financing Economic Growth in LDCs: A Tale of National Savings and Natural Resources

Simon Davies's picture

This blog is part of a series using data from World Development Indicators to explore progress towards the Sustainable Development Goals and their associated targets. The new Atlas of Sustainable Development Goals 2017, published in April 2017, and the SDG Dashboard provide in-depth analyses of all 17 goals.

Investing today is important for economic growth tomorrow: working hard today to build more and better schools, clinics, roads, bridges, parks, factories, offices, houses and other infrastructure will improve both economic output and living standards in the future. Investing sustainably is especially crucial for Least Developed Countries (LDCs) if they are to achieve the 7 percent growth target (8.1) set by the 2030 Agenda of the Sustainable Development Goals (SDGs).

Yet investing for the future means saving more and consuming less today. For every worker building roads and factories that will be used tomorrow, there is one fewer worker producing goods and goodies to be consumed today. For every dollar a family saves, that is one fewer bottle of coke or bag of rice to be consumed today.

Building up assets…

Between 2001 and 2015, LDCs invested an average of 22 percent of their Gross National Income (GNI), while the global average was 23 percent and the OECD average 21 percent. This translates to between a fifth and a quarter of today’s production being invested for the future, rather than being consumed now.

Much LDC investment is self-financed. Over the same period, domestic savings in LDCs averaged over 16 percent of GNI. This is lower than the global savings rate (of 25 percent of GNI) but this is to be expected as capital and investment flows in from wealthier countries. It gives LDCs the chance to increase their capital stock while keeping a reasonable degree of consumption.

The 2017 Atlas of Sustainable Development Goals: a new visual guide to data and development

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

The World Bank is pleased to release the 2017 Atlas of Sustainable Development Goals. With over 150 maps and data visualizations, the new publication charts the progress societies are making towards the 17 SDGs.

The Atlas is part of the World Development Indicators (WDI) family of products that offer high-quality, cross-country comparable statistics about development and people’s lives around the globe. You can:

The 17 Sustainable Development Goals and their associated 169 targets are ambitious. They will be challenging to implement, and challenging to measure. The Atlas offers the perspective of experts in the World Bank on each of the SDGs.

Trends, comparisons + country-level analysis for 17 SDGs

For example, the interactive treemap below illustrates how the number and distribution of people living in extreme poverty has changed between 1990 and 2013. The reduction in the number of poor in East Asia and Pacific is dramatic, and despite the decline in the Sub-Saharan Africa’s extreme poverty rate to 41 percent in 2013, the region’s population growth means that 389 million people lived on less than $1.90/day in 2013 - 113 million more than in 1990

Note: the light shaded areas in the treemap above represent the largest number of people living in extreme poverty in that country, in a single year, over the period 1990-2013.

Newly published data, methods and approaches for measuring development

Things to do with Trade and Competitiveness Data… thank you API

Alberto Sanchez Rodelgo's picture

Who are Spain's neighbors? Is Canada closer to Spain than Portugal? What about Estonia or Greece? The answer? Depends on the data you are looking at!

Earlier this week I crunched data based on a selected list of indicators from the new Open Trade and Competitiveness platform from the World Bank (TCdata360) and found some interesting trends[1]. In 2009 Spain was closer to economies like Estonia, Belgium, France and Canada while 6 years later in 2015, Spain's closest neighbors were Greece and Portugal. How and when did this shift happen?

Other trends I spotted using the same data? It seems the Sub-Saharan region ranks the lowest in Ease of Doing Business, that in 2007 Israel held the record for R&D expenditure as % of GDP, while in the same year Malta topped FDI net inflows as % GDP, and that the largest annual GDP growth in the last 20 years occurred in Equatorial Guinea in 1997.

Figure 1: Dots represent values for an economy at a given point in time for years 1996 to 2016 overlaying their box-plot distributions. Colors correspond to geographical regions.