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The World Region

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.

The Big Picture

TCdata360 classifies its data into four high-level topics: Innovation, Investment, Sectors, and Trade (plus Economy). We’ll be looking at the indicators and datasets under these 4 topics for this whole blog series.

Overall, data availability has increased over time at a compound annual growth rate (CAGR)[2] of 6.8%. CAGR can be thought of as the growth rate which allows the initial value to grow to the latest value, assuming that the growth rate is compounded throughout the entire time period.

To get a sense of the overall increase across countries (and to be able to compare them somewhat consistently), we computed for data availability over time by taking the percent of available indicators per country per year. This is what the pattern looks like when we take the median across all countries.

Is your country LGBTI inclusive? With better data, we’ll know

Clifton Cortez's picture

The World Bank is developing a global standard for measuring countries’ inclusion of LGBTI individuals.

They laughed in our faces … but then we showed them the data

By the early 1990s, Dr. Mary Ellsberg had spent years working with women’s health in Nicaragua. Armed with anecdotes of violence against women, she joined a local women’s organization to advance a bill criminalizing domestic violence.

When presented with the bill, lawmakers “pretty much laughed in our faces,” she explained in a 2015 TEDx talk. “They said no one would pay attention to this issue unless we got some ‘hard numbers’ to show that domestic violence was a problem.”

Dr. Ellsberg went back to school and wrote her doctoral dissertation on violence against women. Her study showed that 52% of Nicaraguan women had experienced physical or sexual abuse by an intimate partner. Subsequently, the Nicaraguan parliament unanimously passed the domestic violence bill.

Later, the World Health Organization used Dr. Ellsberg’s indicators to measure violence against women in countries across the world, which showed the global magnitude of the problem.

“One out of three women will experience physical or sexual abuse by her partner,” Dr. Ellsberg said. Because of the data, “violence against women is at the very top of the human rights agenda.”

Dr. Ellsberg knew that domestic violence was a problem, but it was data that prompted leaders to combat the issue.

Similarly, there are plenty of documented cases of discrimination and abuse against lesbian, gay, bisexual, transgender, and intersex (LGBTI) people. But what’s the magnitude of the discrimination?

Chart: 16 of the 17 Warmest Years on Record Occurred Since 2001

Tariq Khokhar's picture
Also available in: العربية | Español | Français

Sixteen of the 17 warmest years in the 136-year record have occurred since 2001. The year 2016 ranks as the warmest on record. Recent analysis finds that climate change could push more than 100 million more people into poverty by 2030. But good development—­rapid, inclusive, and climate informed—­can prevent most of the impacts of climate change on extreme poverty by 2030.

 

Chart: CO2 Emissions are Unprecedented

Tariq Khokhar's picture
Also available in: العربية | Español | Français

Global emissions of carbon dioxide, a major greenhouse gas and driver of climate change, increased from 22.4 billion metric tons in 1990 to 35.8 billion in 2013, a rise of 60 percent. The increase in emissions of CO2 and other greenhouse gases has contributed to a rise of about 0.8 degrees Celsius in the mean global temperature above pre-industrial times. Read more in the 2017 Atlas of Sustainable Development Goals
 

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.

Five years of investments in open data

Tim Herzog's picture
Also available in: 中文
 
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This year marks the fifth anniversary of the World Bank’s efforts to help countries launch their own open data initiatives, and harness the power of open data to benefit their citizens. A new report provides insights into how open data is benefitting countries, what strategies are working well, what could be improved.

The report provides the most comprehensive snapshot of Bank-funded open data activities to date. In the last five years, the Bank has provided technical assistance and funding for open data activities in over 50 countries, conservatively estimated at more than $50 million from a variety of sources. In many cases Bank funding has leveraged support from other partners or co-sponsorship by countries and other institutions. Within the Bank, the Trust Fund for Statistical Capacity Building (TFSCB) has been the most significant source of funding for open data. The TFSCB has financed over 20 projects in 16 countries, as well as 6 grants for regional and global activities.

Supporting over 45 countries with national and sector-specific open data

Support for open data has taken a variety of forms. To date, 45 Open Data Readiness Assessments (ODRAs) have been completed at national and sub-national levels, which have helped raise awareness and catalyze public and private efforts to advance open data within countries. There are now sector-specific ODRA tools for business, energy, and transport. The Bank has invested in a range of open data learning and knowledge products, including data literacy courses and the Open Data Toolkit, and collaborated with its global partners to support academic research, a series of regional conferences, and open data implementation. The report also found that these initial efforts have catalyzed longer-term project investments, i.e., IBRD loans and IDA credits, with open data implementation components in at least 14 countries.

Chart: It's Never Been Faster to Start A Business

Tariq Khokhar's picture
Also available in: 中文 | Español | العربية

Over the last 15 years, the Doing Business project has recorded nearly 3,200 reforms in 186 economies around the world. The area that's seen the greatest number of reforms is starting a business. Today, the time taken to start a new small or medium business has less than halved to an average of 20 days worldwide, compared with 52 in 2003. Read more in Doing Business 2018

International Debt Statistics 2018 shows BRICs doubled bilateral lending commitments to low-income countries in 2016 to $84 billion

World Bank Data Team's picture
Also available in: العربية | Français | Español
The 2018 edition of International Debt Statistics (IDS) has just been published.

IDS 2018 presents statistics and analysis on the external debt and financial flows (debt and equity) of the world’s economies for 2016. It provides more than 200 time series indicators from 1970 to 2016 for most reporting countries. To access the report and related products you can:

This year’s edition is released less than 10 months after the 2016 reference period, making comprehensive debt statistics available faster than ever before. In addition to the data published in multiple formats online, IDS includes a concise analysis of the global debt landscape, which will be expanded on in a series of bulletins over the coming year.

Why monitor and analyze debt?

The core purpose of IDS is to measure the stocks and flows of debts in low- and middle-income countries that were borrowed from creditors outside the country. Broadly speaking, stocks of debt are the current liabilities that require payment of principal and/or interest to creditors outside the country. Flows of debt are new payments from, or repayments to, lenders.

These data are produced as part of the World Bank’s own work to monitor the creditworthiness of its clients and are widely used by others for analytical and operational purposes. Recurrent debt crises, including the global financial crisis of 2008, highlight the importance of measuring and monitoring external debt stocks and flows, and managing them sustainably. Here are three highlights from the analysis presented in IDS 2018:

Net financial inflows to low-and middle income countries grew, but IDA countries were left behind

In 2016, net financial flows into low- and middle-income countries grew to $773 billion - a more than three-fold increase over 2015 levels, but still lower than levels seen between 2012 and 2014.

However, this trend didn’t extend to the world’s poorest countries. Among the group of IDA-only countries, these flows fell 34% to $17.6 billion - their lowest level since 2011. This fall was driven by drops in inflows from bilateral and private creditors.

The Dirty Truth – Measuring Soil Health

Vini Vaid's picture
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The importance of soil health in agrarian societies is indisputable – soil health has a direct relationship with agricultural productivity and sustainability. Yet, its highly complex nature renders it much more challenging to measure than other agricultural inputs, such as fertilizers or pesticides. Household surveys, particularly those in low-income contexts where agriculture is the primary means of livelihood, have generally relied on subjective assessments of soil health – and for good reason. Subjective assessment is relatively inexpensive, and alternative methodological options have historically been prohibitively expensive. Recent advances in rapid low-cost technologies, namely spectral soil analysis, however, have increased the feasibility of integrating objective plot-level soil health measurement in household surveys.

This new Guidebook provides practical guidance for survey practitioners aiming to implement objective soil health measurement via spectral analysis in household and farm surveys, particularly in low-income smallholder farmer contexts. Two methodological experiments, in Ethiopia and Uganda, provide the foundation for this Guidebook. In each study, plot-level soil samples were collected following best-practice protocols and analyzed using wet chemistry and spectral analysis methods at ICRAF’s Soil-Plant Diagnostics Laboratory, in addition to a subjective module of soil health questions asked of the plot manager. The Guidebook offers (i) a comparison of subjective farmer assessments of soil health with laboratory testing, and (ii) step-by-step guidance on how to implement spectral soil analysis in a household- or farm-level survey, from questionnaire design to soil sample collection, labeling, and processing.

The Guidebook is the result of collaboration between the World Bank's Living Standards Measurement Study (LSMS) team, the World Agroforestry Centre, the Central Statistical Agency of Ethiopia, and the Uganda Bureau of Statistics.

For practical advice on household survey design, visit the LSMS Guidebooks page: http://go.worldbank.org/0ZOAP159L0

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