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.
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:
View the “about the data” section for a full description of the concepts and definitions in IDS.
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.
China has provided foreign assistance to countries around the world since the 1950s. Since it’s not part of the DAC group of donors who report their activities in a standard manner, there isn’t an official dataset which breaks down where Chinese foreign assistance goes, and what it’s used for.
A team of researchers at AidData, in the College of William and Mary have just updated their “Chinese Global Official Finance” dataset. This is an unofficial compilation of over 4,000 Chinese-financed projects in 138 countries, from 2000 to 2014, based on a triangulation of public data from government systems, public records and media reports. The team have coded these projects with over 50 variables which help to group and characterize them.
Activity-level data on an increasingly important donor
This dataset is interesting for two reasons. First, China and other emerging donors are making an impact on the development finance landscape. As the Bank has reported in the past (see International Debt Statistics 2016), bilateral creditors are a more important source of finance than they were just five years ago. And the majority of these increases are coming from emerging donors with China playing a prominent role.
Second, this dataset’s activity-level data gives us a look at trends and allocations in Chinese bilateral finance which can inform further analysis and research. Organizations like the World Bank collect data on financial flows directly from government sources for our operational purposes, but we’re unable to make these detailed data publicly available. We compile these data into aggregate financial flow statistics presented from the “debtor perspective”, but they’re not disaggregated by individual counterparties or at an activity-level. So there can be value added from sources such as AidData’s China dataset.
A detailed view, but only part of the picture of all financial flows
However, this dataset has limitations. It only presents estimates of “official bilateral credits”. These are flows between two governments, and are just one part of the total financial flows coming from China. By contrast, the World Bank is able to integrate the granular data it collects from countries into the full set of financial flows to and from its borrowing countries. This situates official bilateral credit among the broader spectrum of providers of long-term financing (such as bondholders, financial intermediaries, and other private sector entities), sources of short-term debt (including movements in bank deposits), and equity investments (foreign direct and portfolio investments). This data integration leads to better quality statistics.
In short, AidData’s China dataset provides more detail on one type of financial flow, but is likely to be less reliable for a number of low-income countries. With these caveats in mind, I’ve done a quick exploration of the dataset to produce some summary statistics and give you an idea of what’s inside.
Looking at foreign assistance by type of flow
First, let’s see what the trends in different types of foreign assistance look like. AidData researchers code the projects they’ve identified into three types of “flow”:
Official Development Assistance (ODA), which contains a grant element of 25% or more and is primarily intended for development.
Other Official Flows (OOF), where the grant element is under 25% and the the financing more commercial in nature.
Vague Official Finance, where there isn’t enough information to assign it to either category.
Here are the total financial values of the projects in AidData’s dataset, grouped by flow type and year:
It looks like more Chinese finance is classed as OOF ($216bn in the period above) than ODA ($81bn), and that 2009 is a bit of an outlier. With this dataset, we next can figure out which countries are the top recipients of ODA and OOF, and also which sectors are most financed.
There’s a crisis in learning. The quality and quantity of education vary widely within and across countries. Hundreds of millions of children around the world are growing up without even the most basic life skills.
The 2018 World Development Report draws on fields ranging from economics to neuroscience to explore this issue, and suggests improvements countries can make. You can get the full report here and to give you a flavor of what’s inside, I’ve pulled out a few of the charts and ideas that I found most striking while reading through it.
The report sets out several arguments for the value of education. The clearest one for me? It’s a powerful tool for raising incomes. Each additional year of schooling raises an individual’s earnings by 8–10 percent, especially for women. This isn’t just because more able or better-connected people receive more education: “natural experiments” from a variety of countries - such as Honduras, Indonesia, Philippines, the U.S., and the U.K. - prove that schooling really does drive the increased earnings. More education is also linked with longer, healthier lives, and it has lasting benefits for individuals and society as a whole.
The World Bank Group surveys its stakeholders from country governments, development organizations, civil society, private sector, academia, and media in all client countries across the globe. Building a dialogue with national governments and non-state partners based of the data received directly from them is an effective way to engage stakeholders in discussions in any development area at any possible level.
Let's take the education sector as an example to see how Country Survey data might influence the engagement that the Bank Group has on this highly prioritized area of work.
When Country Surveys ask what respondents identify as the greatest development priority in their country, overall, education is perceived as a top priority (31%, N=263) in India.1 However, in a large country, stakeholder opinions across geographic locations may differ, and the Country Survey data can be 'sliced and diced' to provide insight into stakeholders' opinions based on their geography, gender, level of collaboration with the Bank Group, etc. In India the data analyzed at the state level shows significant differences in stakeholder perceptions of the importance of education. The survey results can be used as a basis for further in-depth analyses of client's needs in education in different states and, therefore, lead to more targeted engagement on the ground. In the case of the India Country Survey, the Ns at the geographical level may be too small to reach specific conclusions, but this example illustrates the possibility for targeted analysis.
In most regions of the world, over 70 percent of freshwater is used for agriculture. By 2050, feeding a planet of 9 billion people will require an estimated 50 percent increase in agricultural production and a 15 percent increase in water withdrawals.
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.
Most of the world's extreme poor live in Sub-Saharan Africa and South Asia. While over 1 in 10 people live in extreme poverty globally, in Sub-Saharan Africa, that figure is 4 in 10, representing 389 million people - that's more poor people than all other regions combined. Read more in the new report on Poverty and Shared Prosperity
This is a guest blog written by Jeroen Smits of the Global Data Lab, an initiative hosted by the Nijmegen Center for Economics (NiCE) at Radboud University in the Netherlands.
Disaggregation of indicators at the subnational level is one of the key elements to effectively monitor the Sustainable Development Goals (SDGs). At the same time, this is a great challenge, as in the case for many countries, only indicators at the national level are available.
This is particularly the case for poor countries, where administrative systems are less equipped and capable to generate reliable and representative information. Strengthening those systems is the preferred solution, but that takes time and does not produce the indicators for earlier years required for tracing developments over time.