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Why 2018 global growth will be strong, and why there is still cause for concern, in 10 charts

Carlos Arteta's picture
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Download the January 2018 Global Economic Prospects report.

Global growth accelerated to 3 percent in 2017, supported by a broad-based cyclical recovery encompassing more than half of the world’s economies, and is expected to edge up to 3.1 percent in 2018. Global trade regained significant momentum, supported by an upturn in investment.

As headwinds ease for commodity exporters, growth across emerging and developing economies is expected to pick up. However, risks to the outlook remain titled to the downside, such as the possibility of disorderly financial market adjustment or rising geopolitical tensions.

A major concern in the subdued pace of potential growth across emerging market and developing economies, which is expected to further decline in the next decade. Structural reforms will be essential to stem this decline, and counter the negative effects of any future crisis that could materialize.

The broad-based recovery should continue

Global growth accelerated markedly in 2017, supported by a broad-based recovery across advanced economies and emerging market and developing economies (EMDEs), and it is expected to edge up in 2018.

Energy and raw materials prices gained in December, beverages and fertilizer prices fell – Pink Sheet

John Baffes's picture
Energy commodity prices gained 2 percent in December—the sixth consecutive monthly gain—led by a 6 percent increase in coal prices, the World Bank’s Pink Sheet reported.

Agriculture prices declined marginally, as a 5 percent decline in beverages, led by cocoa (down 10 percent) outweighed a 2 percent increase in raw materials prices, led by cotton (up 6 percent) and natural rubber (up 5 percent). Fertilizer prices declined 5 percent, led by a 11 percent drop in urea.

Metals and mineral prices gained less than 1 percent. A large gain in iron ore (up 12 percent) was offset by declines in zinc and nickel. Precious metals prices declined 2 percent, led by a 1 percent decline in gold.

The pink sheet is a monthly report that monitors commodity price movements.
Energy and raw material price indexes increased in December while beverage and fertilizer prices declined sharply.


Going Deeper into TCdata360 Data Availability Leaders and Laggers

Reg Onglao's picture

Note: This is the second 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. The first blog post can be viewed here.

With open data comes missing data. In this blog series, we hope to explore data availability by looking at it from various perspectives within the context of the TCdata360 platform[1]: by country, dataset, topic, and indicator.

In our previous blog post, we took a look at the country-level data availability over time through an interactive motion bubble plot inspired by the famous Gapminder visualization. In this follow-up post, we’ll still look at data availability from a geographical lens – but now looking into country classifications and other details that aren’t evident in a bubble plot, as well as the data availability leaders and laggers over time.

Overall Data Availability Leaders and Laggers

First, let’s focus on comparing individual countries to get a better sense of country-level differences in data availability. We computed for each country’s overall data availability by taking the median data availability across all years (1955-2016). Looking at the top 20 and bottom 20 countries in terms of overall data availability generates a few interesting patterns.

Improved Trade Policies Can Expand Exports and Drive Growth in Nepal

Gonzalo Varela's picture
Pashminas – scarves and shawls in bright colors made from the wool of Himalayan goats – are one of Nepal’s most well-known exports. In 2016, the country exported over $25 million worth of these products, comprising 3.6% of the country’s total exports. Although the products are popular on international markets, exporters face many challenges in getting their products to customers.

Three reasons why maritime transport must act on climate change

Nancy Vandycke's picture

For years, the transport sector has been looking at solutions to reduce its carbon footprint. A wide range of stakeholders has taken part in the public debate on transport and climate change, yet one mode has remained largely absent from the conversation: maritime transport.

Tackling emissions from the shipping industry is just as critical as it is for other modes of transport. First, international maritime transport accounts for the lion’s share of global freight transport: ships carry around 80% of the volume of all world trade and 70% of its value. In addition, although shipping is considered the most energy-efficient mode of transport, it still uses huge amounts of so-called bunker fuels, a byproduct of crude oil refining that takes a heavy toll on the environment.

Several key global players are now calling on the maritime sector to challenge the status quo and limit its climate impact. From our perspective, we see at least three major reasons that can explain why emissions from maritime transport are becoming a global priority.

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.

Energy prices surged in November, beverages and fertilizer prices fell–Pink Sheet

John Baffes's picture
Also available in: Español | Français

Energy commodity prices surged 8 percent in November—the fifth consecutive monthly gain—led by a 9 percent increase in oil prices, the World Bank’s Pink Sheet reported.

Agriculture prices made marginal gains as a 1 percent decline in beverages was balanced by a 1 percent increase in food prices, notably natural rubber (down 12 percent) and cotton (off 2 percent). Fertilizer prices declined 3 percent, led by a 6 percent drop in Urea.

Metals and mineral prices were unchanged. Gains in nickel and iron ore were balanced by declines in lead and aluminum. Precious metals prices rose marginally.

The pink sheet is a monthly report that monitors commodity price movements.

Didn’t make it to our trade research conference? Here’s what you missed

Ana Fernandes's picture

What would bring together the China trade shock, road blocks in the West Bank, and the Belt and Road initiative? The 6th Annual IMF-World Bank-WTO Trade Research Conference, at which staff of the three institutions presented the results of twelve research projects. 
The Conference is over, but the website lives on, and here you can find preliminary versions of papers. To whet your appetite, here are three examples of research that use creative methodologies and raise provocative questions.

The innovation imperative: How Asia can leverage exponential technologies to improve lives and promote growth

Amira Karim's picture
Singapore: Global Innovation Forum

Robots will take over our jobs, disrupt our industries and erode our competitiveness.
Such were commonly expressed fears about advances in automation, artificial intelligence, and 3D printing – key representations of exponential technologies – during the inaugural Global Innovation Forum that took place in Singapore.
While robots continue to bear the brunt of public skepticism, participants at the Forum also expressed optimism about the emergence of innovations that could dramatically transform the quality of life for the poorest people in society, particularly in Asia, the region that was acknowledged by many participants as leading the pace of innovation around the globe.

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.