For many years, financial globalization has been promoted as a vehicle to raise living standards throughout the world, particularly in developing countries. However, a mounting body of empirical literature shows that in practice the effects of financial globalization have been overall mixed; financial globalization has only brought limited positive effects while it has also increased risks.
The World Region
This blog is part of a series using data from World Development Indicators to explore progress towards the Sustainable Development Goals and their associated targets. The new Atlas of Sustainable Development Goals 2017, published in April 2017, and the SDG Dashboard provide in-depth analyses of all 17 goals.
As Agriculture Economists who work on advancing the food and agriculture agenda, SDG 2 articulates much of our work in the Sustainable Development agenda and illustrates how food and agriculture are intertwined with poverty reduction. Goal 2 seeks to “End hunger, achieve food security and improve nutrition, and promote sustainable agriculture.”
Without making progress on Goal 2, we can’t achieve the Bank’s twin goals of ending poverty and boosting shared prosperity.
But what does Goal 2 mean, exactly? On the surface, it might seem to be a matter of producing more food in a sustainable way. But a deeper dive into this SDG reveals that it is not quite that simple.
Photo: totojang1977 / Shutterstock.com
In my last blog, I compared Public-Private Partnerships (PPPs) with marriage. I had explained that, though very different, the public and private can come together as they each possess characteristics beneficial to the other. Great in theory, but often difficult in practice.
Critics of PPPs abound and listing them here would be impractical. But whether they are auditors, civil society or within the World Bank Group, critics help us improve. We try to respond to our critics, including through blogs such as this one.
This blog is based on new child mortality estimates released today by the United Nations Inter-agency Group for Child Mortality Estimation (UN IGME)
There has been substantial progress in reducing child mortality in the past several decades. Between 1990 and 2016, the global under-five mortality rate dropped by 56 percent from 93 deaths per 1,000 live births to 41 deaths per 1,000 live births. Over the last sixteen years, the reduction in child mortality rates accelerated, compared to the previous decade. As a consequence, around 50 million more young children survived the first five years of life since 2000 who would have died had under-five mortality remained at the same level as in 2000.
But even in 2016, 15,000 children died every day (totaling 5.6 million a year). While a substantial reduction from the 35,000 deaths a day in 1990 (12.6 million a year), more needs to be done to meet target 3.2 of the Sustainable Development Goals, which aims for all countries fewer than 25 deaths of under-5s per 1,000 live births.
The last quarter century saw remarkable progress against extreme poverty, globally. Between 1990 and 2013, the percentage of the world’s population living at or under $1.90/day fell from 35.3% to 10.7% - that is, from more than one in three people to approximately one in ten, planet-wide. Even in the shorter period between 2002 and 2013, the reduction was from 25.8% to 10.7%, meaning that about 850 million people moved out of extreme poverty during that decade alone.
This post looks at the recently updated “Global Chinese Official Finance Dataset” from research group AidData. The post is also available here as an R Notebook which means you see the code behind the charts and analysis.
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.
Delivering pension or disability services may sound mundane, but if you have seen the recent award-winning movie, I, Daniel Blake, it is anything but. As the film poignantly demonstrates, treating citizens with respect and approaching them as humans rather than case numbers is not just good practice -- it can mean life or death. In the film, Mr. Blake, an elderly tradesman with a heart condition, attempts to apply for a disability pension. In the process, he navigates a Kafkaesque maze of dozens of office visits, automated phone calls, and dysfunctional online forms. All of this is confusing and often dehumanizing.
If a trade economist were abruptly woken up by somebody shouting, “preferential trade agreements” (PTAs), their first thought is likely to be “trade creation among participants and trade diversion away from those left out.” That is a measure of the influence of Jacob Viner’s classic book The Customs Union Issue on the profession, on the policy debate and on our attitude towards PTAs.
Brexit and the renegotiation of NAFTA have renewed interest in the impact of trade agreements and the consequences of undoing them. In a recent paper, Mattoo, Mulabdic and Ruta (2017) and column (VOXEU), we use new information on the content of PTAs to examine their trade effects.
Is poverty absolute or relative? When we think of (one-dimensional) income poverty, should we define the threshold that separates the poor from the non-poor as the cost of purchasing a fixed basket of goods and services that allows people to meet their basic needs? Or should we instead think of it as relative deprivation: as earning or consuming less than some given proportion of the country’s average living standard?