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Chart: 100 Million People Pushed into Poverty by Health Costs in 2010

Tariq Khokhar's picture



Universal health coverage (UHC) means that all people can obtain the health services they need without suffering financial hardship. A new report produced by the World Bank and the World Health Organization, finds that health expenditures are pushing about 100 million people per year into “extreme poverty,” those who live on $1.90 or less a day; and about 180 million per year into poverty using a $3.10 per day threshold.

You can access the report, data, interactive visualizations, and background papers at: http://data.worldbank.org/universal-health-coverage/

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.

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?

Malawi’s Fourth Integrated Household Survey 2016-2017 & Integrated Household Panel Survey 2016: Data and documentation now available

Heather Moylan's picture
Malawi IHS4 Enumerator administering household questionnaire
using World Bank Survey Solutions
Photo credit: Heather Moylan, World Bank

The Malawi National Statistical Office (NSO), in collaboration with the World Bank’s Living Standards Measurement Study (LSMS), disseminated the findings from the Fourth Integrated Household Survey 2016/17 (IHS4), and the Integrated Household Panel Survey 2016 (IHPS), on November 22, 2017 in Lilongwe, Malawi. Both surveys were implemented under the World Bank Living Standards Measurement Study-Integrated Surveys on Agriculture (LSMS-ISA) initiative, with funding from the United States Agency for International Development (USAID).

The IHS4 is the fourth cross-sectional survey in the IHS series, and was fielded from April 2016 to April 2017. The IHS4 2016/17 collected information from a sample of 12,447 households, representative at the national-, urban/rural-, regional- and district-levels.

In parallel, the third (2016) round of the Integrated Household Panel Survey (IHPS) ran concurrently with the IHS4 fieldwork. The IHPS 2016 targeted a national sample of 1,989 households that were interviewed as part of the IHPS 2013, and that could be traced back to half of the 204 panel enumeration areas that were originally sampled as part of the Third Integrated Household Survey (IHS3) 2010/11.

The panel sample expanded each wave through the tracking of split-off individuals and the new households that they formed. The IHPS 2016 maintained a 4 percent household-level attrition rate (the same as 2013), while the sample expanded to 2,508 households. The low attrition rate was not a trivial accomplishment given only 54 percent of the IHPS 2016 households were within one kilometer of their 2010 location.

Latest from the LSMS: New data from Malawi, measuring soil health & food consumption and expenditure in household surveys

Vini Vaid's picture

 

Message from Gero Carletto (Manager, LSMS)

A few weeks ago, I attended a meeting of the Committee for the Coordination of Statistical Activities (CCSA) in Muscat, Oman, where I joined a panel discussion on how global survey initiatives like the LSMS or Multiple Indicators Cluster Survey (MICS) can help us measure and monitor many of the SDG indicators. We also discussed how global initiatives like the UN Statistical Commission’s Inter-Secretariat Working Group on Household Surveys (ISWGHS) can help coordinate these efforts and position the household survey agenda within the global data landscape. Everyone seems to agree that monitoring more than 70 SDG indicators will require high-quality, more frequent, and internationally comparable household surveys. Yet, the narrative on household surveys continues to be lopsided. In my view, this is partly because strengthening traditional data sources like surveys and censuses is seen as outmoded and ineffective when compared with the more glittering promises offered by alternative data sources like Big Data.

At the risk of sounding like a luddite, I believe that it’s important for countries and donors alike to continue investing in household surveys to both validate and add value to new types of data. In many of the countries we work in, leapfrogging to the digital revolution without having gone through an analog evolution may be an ephemeral proposition. This in no way means that we should continue doing things the same way: during the past decade, household surveys have evolved dramatically, increasingly relying on technological innovation and new methods to make survey data cheaper, more accurate, and more policy relevant. Methodological and technological innovation remains at the core of the LSMS’s raison d’être and, together with our partners, we will continue pushing the frontier. Until more robust and fully validated alternatives materialize, household survey critics may want to recall the old saying, “Can’t live with ‘em, can’t live without ‘em!”

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.

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 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.

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