Co-authors: William Young, Lead Water Resources Management Specialist, the World Bank
The Ganges Basin in South Asia is home to some of the world’s poorest and most vulnerable communities. Annual floods during monsoon season cause widespread human suffering and economic losses. This year, torrential rains and catastrophic floods affected more than 45 million people, including 16 million children. By 2030, with ongoing climate change and socioeconomic development, floods may cost the region as much as $215 billion annually.
The idea that economic growth needs good governance and good governance needs economic growth takes us to a perennial chicken-and-egg debate: Which comes first in development—good governance OR economic growth? For decades, positions have been sharply divided between those who advocate “fix governance first” and others who say “stimulate growth first.”
China: More Mobility with Fewer Cars through a GEF Grant
Since our days in school, we have often been told to first define our terms before doing anything else. China is a country that does not shy away from acronyms, and “TOD,” or transit-oriented development—a concept that merges land use and transport planning—is one such acronym that has become wildly popular within the field of urban development.
So, recently, when government officials from seven Chinese cities and the Ministry of Housing and Urban-Rural Development gathered to launch the China Sustainable Cities Integrated Approach Pilot Project on the topic of TOD, it was clear that they all had the same definition of this three-letter acronym.
The macro-story on China is well known, but always bears repetition. Emerging from the carnage of the Mao era,China in 1980 had a GDP of $193 per capita, lower than Bangladesh, Chad or Malawi. It’s now the world’s second largest economy, with a thirty fold increase in GDP per capita, based on a textbook-defying combination of one party Communist state and capitalism – in the words of one tongue in cheek official ‘no capitalist state can match our devotion to the capitalist sector.’
Success on this scale inevitably finds many intellectual fathers claiming paternity – China is variously portrayed as a victory for a strong state; free markets; experimentation and for central planning.How China Escaped the Poverty Trapblows the conventional explanations away, drilling down into what actually happened, reconstructing the history of different cities and provinces through years of diligent research.
This book is a triumph, opening a window onto the political economy of China’s astonishing rise that takes as its starting point systems and complexity. Its lessons apply far beyond China’s borders. The author,Yuen Yuen Ang(originally from Singapore) is Associate Professor of Political Science at the University of Michigan.
Ang starts with a classic developmental chicken and egg problem – which comes first, good institutions or economic prosperity? Different camps within academia and the aid business urge developing countries either to ‘first, get the institutions right’ or ‘first, get growth going’ – and then the rest will follow.
As one of over 20 million people who work and live in Beijing, China, I used to find commuting to work in rush-hour traffic rather painful. However, things have changed dramatically since last year. Now I can bypass the traffic by riding a shared bike to the closest metro station and make better use of public transit. Similar change is happening to my family and friends.
The unprecedented booming of dockless shared bikes in China presents a promising solution to the “last-mile problem” that has perplexed city planners for years: providing easier access to the mass transit system while ensuring good ridership. Thanks to the GPS tracking device installed on thousands of dockless shared bikes, city planners in China are now equipped with new and better information to analyze the demand for—and the performance of—public transit systems. For the first time, city managers can clearly map out the attractiveness and accessibility of metro stations by analyzing individual-level biking trips.
Revisiting the scope of TOD. A commonly accepted textbook definition of the core area of TOD is an 800-meter radius around the metro station or other types of public transit hubs. This definition is based on the distance that can be reached by a 10-minute walk. However, the actual catchment of a metro station can reach a 2-3 km radius when biking prevails, as in Demark and Netherland. Our analysis illustrates that a big chunk of biking trips around metro stations even go beyond the 3km radius (see bright blue traces in Figure 1 below). This indicates that the spatial scope of planning and design around the metro stations should be contextualized. Accordingly, the price premium associated with adjacency to public transit service is more likely to be shared by a broader range of nearby real estate properties than expected.
“At 14:28:04 on May 12, 2008, an 8.0 earthquake struck suddenly, shaking the earth, with mountains and rivers shifted, devastated, and parted forever….” This was how China’s official report read, when describing the catastrophic consequences of the Sichuan earthquake, which left 5,335 students dead or missing.
Just two years ago, in Nepal, on April 25, 2015, due to a Mw 7.8 earthquake, 6,700 school buildings collapsed or were affected beyond repair. Fortunately, it occurred on Saturday—a holiday in Nepal—otherwise the human toll could have been as high as that of the Sichuan disaster, or even worse. Similarly, in other parts of the world—Pakistan, Bangladesh, Philippines, Haiti, Ecuador, and most recently Mexico—schools suffered from the impact of natural hazards.
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.
Global economic growth is accelerating. After registering the slowest pace since the 2007-2009 financial crisis in 2016, global growth is expected to rise to a 2.7 percent pace this year and 2.9 percent over 2018-19.
While much has been said about better economic news from the major advanced economies, the seven largest emerging market economies—call them the Emerging Market Seven, or EM7 – have been the main drivers of this anticipated pickup.
The contribution of the seven largest emerging market economies to global output has climbed substantially over the last quarter century.
The EM7 -- Brazil, China, India, Indonesia, Mexico, Russia and Turkey – accounted for 24 percent of global economic output over 2010-2016, up from 14 percent in 1990s. Although this is a smaller share than the Group of Seven major industrialized economies, the G7’s portion of global economic output has narrowed to 48 percent from 60 percent over the same time frame.