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Trade and Competitiveness

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.

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.

Doing Business Trading Across Borders and Logistics Performance Index: similar yet different

Valentina Saltane's picture


People who look at the Doing Business report’s Trading Across Borders indicator and the Logistics Performance Index (LPI) often wonder why one country can perform well on one of the rankings but not so well on the other although they both measure trade and logistics. In fact, earlier this year, the Doing Business team organized a workshop at the World Bank Global Knowledge and Research Hub in Kuala Lumpur to clarify the differences between the two datasets.

Let’s start off with a few definitions:

The Doing Business report is a World Bank Group flagship publication, which covers 11 areas of business regulations. Trading Across Borders is one of these areas. It looks specifically at the logistical processes of exporting and importing. Data is updated annually and the latest edition covers 190 economies. Doing Business collects data from local experts and measures performance as reported by domestic entrepreneurs, while taking into consideration factual laws and regulations.

The Logistics Performance Index is a benchmarking tool which focuses on trade logistics. It is created to help countries identify the challenges and opportunities they face as they relate to customs, border management, transport infrastructure, and logistics services. Updated biennially, the latest data and report cover 160 economies. Data is collected from global freight forwarders and express carriers who provide feedback on the logistical “friendliness” of the countries they operate.

Growth and development: Why openness to trade is necessary but not sufficient

Selina Jackson's picture
Photo © Dominic Chavez/World Bank


We are experiencing a battle of ideas regarding the state of the global economy and prospects for growth. Larry Summers has been leading the group of economists proclaiming that the world entered an era of secular stagnation since the global financial crisis. On the other end, Standard Chartered Bank and other players have been arguing that we are experiencing an economic super cycle—defined as average growth of around 3.5 percent from 2000-2030—due to strong growth in emerging markets and fueled by a global demographic dividend.

There is not even agreement on the factors that drive global growth and development. While parts of the Americas and Asia just concluded the Trans Pacific Partnership (TPP) and recent World Trade Organization (WTO) agreements on trade facilitation and information technology products show progress is possible, the Transatlantic Trade and Investment Partnership (TTIP) negotiations between the U.S. and the EU remain highly controversial and the upcoming WTO Ministerial in Nairobi will likely underwhelm. 

However, if you look at the facts, the situation is very clear:

A good diagnosis for the city economy?

Dmitry Sivaev's picture



One walks into a doctor’s office knowing what hurts but with little knowledge of what should be done to fix it. Identifying proper treatment requires sophisticated tests, participation of experts and, often, second opinions.

Cities, arguably, are as complicated as human bodies. Our knowledge of diagnosing cities, however, is far less advanced than in human biology and medicine.  Most mayors know very clearly what they want for their cities – jobs, economic growth, high incomes and a good quality of life for the people. But it is very difficult to identify what prevents private-sector firms, the agents that create jobs and provide incomes, from growing and delivering these benefits to a city. And we have no X-ray machine to aid in the effort.
 
As a part of the World Bank Group's Competitive Cities project, we thought hard about ways to help cities identify the roots of their problems and design interventions to address them. We set out on a journey to put together methodologies and guidelines for cities that want to figure out what they can do to help firms thrive and create jobs.  We learned from our own experience of working with cities, and from other urban practitioners. We reviewed many methodological and appraisal materials, and we trial-tested our ideas.

So what have we achieved? We certainly didn’t invent an X-ray machine, but we have developed “Growth Pathways” – a methodology and a decision-support system to help guide cities and practitioners through diagnostic exercises.

Competitive Cities: Bucaramanga, Colombia – An Andean Achiever

Z. Joe Kulenovic's picture


Modern business facilities, tourist attractions, and an expanding skyline: Bucaramanga, Colombia. 

When the World Bank’s Competitive Cities team set out to analyze what some of the world’s most successful cities have done to spur economic growth and job creation, the first one we visited was Bucaramanga, capital of Colombia’s Santander Department. Nestled in the country’s rugged Eastern Cordillera, landlocked and without railroad links, this metropolitan area of just over 1 million people has consistently had one of Latin America’s best-performing economies. Bucaramanga, with Colombia’s lowest unemployment rate and with per capita income at 170 percent of the national average, is on the threshold of attaining high-income status as defined by the World Bank.  

Bucaramanga and its surrounding region are rife with contrasts. On the one hand, it has a relatively less export-intensive economy and higher rates of informal business establishments and workers than Colombia as a whole. Indeed, informality has often been cited as a key constraint to firms’ ability to access support programs and to scale up. On the other, Santander’s rates of poverty and income inequality, and its gender gap in labor-force participation, are all better than the national average, and it has consistently led the country on a number of measures of economic growth, including aggregate output, job creation and consumption.   
 
But the numbers tell only part of the story. A qualitative transformation of Bucaramanga’s economy is under way. Once dominated by lower-value-added industries like clothing, footwear and poultry production, the city is now home to knowledge-intensive activities such as precision manufacturing, logistics, biomedical, R&D labs and business process outsourcing, as well as an ascendant tourism sector. Meanwhile, Santander’s oil industry, long a major employer in the region, has been a catalyst for developing and commercializing innovative technologies, rather than just drilling for, refining and shipping petroleum.

All these achievements are neither random nor accidental: They are the result of local stakeholders successfully working together to respond to the challenges of globalization and external competitive pressures.

Making cities competitive – What will it take?

Megha Mukim's picture



Cities are the future. They are where people live and work. They are where growth happens and where innovation takes place. But they are also poles of poverty and, much too often, centers of unemployment.

How can we unleash the potential of cities? How do we make them more competitive? These are urgent questions. Questions, as it turns out, with complex answers – that could potentially have huge returns for job creation and poverty reduction.

Cities vary enormously when it comes to their economic performance. While 72 percent of cities grow faster than their countries, these benefits do not happen uniformly across all cities. The top 10 percent of cities increase GDP almost three times more than the remaining 90 percent. They create jobs four to five times faster. Their residents enjoy higher incomes and productivity, and they are magnets for external investment.
 
We’re not just talking about the “household names”among global cities: Competitive cities are often secondary cities, many of them exhibiting success amidst adversity – some landlocked and in lagging regions within their countries. For instance, Saltillo (Mexico), Meknes (Morocco), Coimbatore (India), Gaziantep (Turkey), Bucaramanga (Colombia), and Onitsha (Nigeria) are a few examples of cities that have been competitive in the last decade.
 
So how do cities become competitive? We define competitive cities as those that successfully help firms and industries create jobs, raise productivity and increase the incomes of citizens. A team at the World Bank Group spent the last 18 months investigating, creating and updating our knowledge base for the benefit of WBG’s clients. In our forthcoming report, “Competitive Cities for Jobs and Growth,”* we find that the recipe includes several basic ingredients.

In the long term, cities moving up the income ladder will transform their economies, changing from “market towns” to “production centers” to “financial and creative centers,” increasing efficiencies and productivity at each stage. But economic data clearly shows there are large gains to be had even without full-scale economic transformation: Cities can move from $2,500 to $20,000 in per capita income while still remaining a “production center.”  In such cases, cities become more competitive at what they already do, finding niche products and markets in tradable goods and services. Competitive cities are those that manage to attract new firms and investors, while still nurturing established businesses and longtime residents. 
 
What sort of policies do competitive cities use? We find that leading cities focus their energies on leveraging both economy-wide and sector-specific policies. In practice, we see how successful cities create a favorable business climate and target individual sectors for pro-active economic development initiatives. They use a combination of policies focused on cross-cutting issues such as land, capital markets and infrastructure, while not losing focus on the needs of different industries and firms. The crucial factor is consultation, collaboration and partnerships with the private sector. In fact, success also involves building coalitions for growth with neighbors and other tiers of government.


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