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Trade

Watch the Growth of Trade country-level data availability in TCdata360

Reg Onglao's picture

Note: This is the first blog of a series of blog posts on data availability within the context of TCdata360, wherein each post will focus on a different aspect of data availability.

With open data comes missing data. We know that all indicators are not created equal and some are better covered than others. Ditto for countries in which coverage can range from near universal such as the United States of America to very sparse indeed such as Saint Martin (French part).

TCdata360 is no exception. While our data spans across over 200 countries and 2000+ indicators, our data suffers from some of the same gaps as many other datasets do: uneven coverage and quality. With that basic fact in mind, we have set about exploring what our data gaps tell us — we have 'data-fied' our data gaps so to speak.

In the next few blogs we'll explore our data gaps to identify any patterns we can find within the context of the TCdata360 platform[1] — which countries and regions throw up surprises, which topics are better covered than others, which datasets and indicators grow more 'fashionable' when, and the like. In this first blog, we’ll look at data availability at the country level.

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.

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.

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

Interactive chord diagram to visualize trade

Siddhesh Kaushik's picture

What comes to mind when we think of trade? Quite possibly, exports, imports and trade balance. Is there a quick way to get this information without having to look at tables? Most of us would like to see how much a country imports and exports, which are the major trade partners, and what is the trade balance. We have introduced a d3.js based interactive Chord diagram to quickly visualize this information.

For example, here is a visual of Australia’s Exports and Imports for 2015. The chart shows top countries to which Australia exported or imported that year, and the remaining are bundled as “others”. Here is how you can interpret the diagram.

Each country has a different color. The length of the arc for Australia represents Australia’s total imports and the other parts of the arc show Australia’s exports to various countries. We can see the Import arc is slightly bigger than the Export arc and hence Australia has an overall negative trade balance.

From data blur to slow-mo clarity: big data in trade and competitiveness

Prasanna Lal Das's picture

Tolstoy's War and Peace was the big data of its time. A memorable moment from the epic novel occurs when Prince Andrei awakens following a severe injury on the battlefield. He fears the worst but, "above him there was nothing but the sky, the lofty heavens, not clear, yet immeasurably lofty, with gray clouds slowly drifting across them. 'How quiet, solemn, and serene, not at all as it was when I was running.'" Time appears to slow down and the Prince sees life more lucidly than ever before as he discovers the potential for happiness within him.

In many ways the scene captures what we demand of big data—not the bustle of zillions of data points as confusing as the fog of war, but sharp, clear insights that bring the right information into relief and help us connect strands previously unseen. The question of whether this idea is achievable is the starting point of a paper about big data on trade and competitiveness just published by the World Bank Group. In it, we asked—can big data help policy makers see the world in ways they haven't before? Are decisions that are informed by the vast amounts of data that envelop us better than decisions based on traditional tools? We didn't want a story trumpeting the miracles of big data; we wanted instead to see the reality of big data in action, in its messiness and its splendor.

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.

TCdata360: Filling Gaps in Open Trade and Competitiveness Data

Klaus Tilmes's picture
This blog originally appears on The Trade Post 

The World Bank Group just launched a new open data platform for trade and competitiveness – TCdata360. Try it today and share your visuals on Twitter with the hashtag #TCdata360.


Open data – statistics that are accessible to all at little or no cost – is a critical component of global development and the World Bank Group’s twin goals of ending poverty and boosting shared prosperity. How can we measure progress towards our objectives without a method of tracking how far we’ve come?

High quality and freely available data serves different stakeholders in different ways. For those of us working in global development, data helps us set baselines, identify what types of policies are effective, track progress and evaluate impact. For the private sector, open data helps companies operate more efficiently, identify areas where industries can improve, and pinpoint areas for new investment. Citizens benefit from open data by getting an understanding of what governments are doing to help them, and transparent data can help reinforce trust. The public sector utilizes data in many ways, including tracking progress against peers and pinpointing areas where countries might be excelling or lagging behind.

The World Bank Group offers a variety of open data sources for public use. The newest platform, TCdata360, focuses on trade and competitiveness and aggregates thousands of data points from dozens of vetted sources. This type of high quality data helps us get an unbiased, objective, and comprehensive view of how the world economy works and demonstrates how all the pieces of the global economy are integrated. Without it, there would be no evidence base on areas that we know to be critical for development, such as global value chains, foreign direct investment, or even starting new businesses.

TCdata360 has three distinct advantages over other data websites:
  • It’s comprehensive.  TCdata360 offers 2,000 indicators aggregated from across more than 20 data sources. These sources include other well-known World Bank Group data sets such as Doing Business, the Logistics Performance Index, and the World Development Indicators, as well as data from other reputable sources, including the IMF, World Economic Forum, United Nations, and WTO. It’s a one-stop shop for all things trade and competitiveness, one that does not exist anywhere else
     
  • It’s constantly updated. Because TCdata360 pulls data from other sources as soon as they are updated, TCdata360 is updated. This eliminates the need for searching around for the most current figures on trade and competitiveness – TCdata360 will always be current.
     
  • It’s easy to use. You do not need to be a trade expert or economist to use TCdata360. There are no complicated queries to manage or spreadsheets to navigate. The site is visual and is based on easy-to-interpret charts, graphs and maps, which are all downloadable and shareable. It’s simple, interactive and visual. For advanced users, it offers features like an API (application programming interface).
This chart from TCdata360 tracks the number of days required to start a business in Kenya
This chart from TCdata360 tracks the number of days required to start a business in Kenya

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