We’re going on a trip. Let’s travel straight up in the air, until we reach a height of 36,000 km or so above the Earth’s surface. We find ourselves orbiting the planet, along with most of the world’s weather satellites. Now look down. What do you see?
If it’s daytime, you can enjoy our world’s natural features—including oceans, forests, and deserts—in all their color. Cities aren’t as easily discerned. But change our view in space from day to night, and the Earth below transforms into black marble studded with jewels of light. Most of the marble is dark and inscrutable, except for cities. The lights of Earth’s cities and towns, and the transportation networks that connect them, reveal a grand pattern of human settlement and activity across the planet.
Explore the interactive project page with maps of nighttime lights and economic inequality.
Let’s imagine we stay up here for a while, orbiting. As time passes, you notice something interesting. The Earth’s natural surface in daylight, moving on geologic time scales, doesn’t seem to change much over time—at least not to the naked eye. Earth at night is another story. The torches of urban centers expand to blaze brighter; rivers of light flow out into new suburbs; the “connective infrastructure” of new highways, railways, bridges, and roads twists into glowing webs across the black; and deep-water ports pop up on coastlines, drawing swarms of ships like fireflies. With each year, this network of human industry is both brightening the landscape and more closely connecting the features on it.
By analyzing high-resolution satellite imagery of nighttime light intensity in a given area, researchers are able to estimate economic output at the local level. This measure is especially useful in remote, poor, and conflict-prone areas that lack reliable census, survey, and administrative data. However, since economic progress is often uneven, it’s not just the average level of nighttime light within a geographic region that matters. The geographical dispersion of nighttime light within a subnational locality also tells us something about how widely or narrowly the benefits of economic growth and development are being shared. As some geographic regions explode into luminescence, others stay dim or even recede into darkness—visual representations of economic stagnation and loss—and a growing body of research demonstrates this kind of spatial inequality can have far-reaching consequences. It can increase political polarization, slow economic development, provoke social unrest, and elevate the risk of violent conflict and terrorism.
Many governments, aid agencies, and development banks claim that they are making efforts to promote “inclusive growth,” especially within economically disadvantaged regions. But there is a need for rigorous evaluation of which groups within low- and middle-income countries reap the biggest economic benefits from these development projects—and which groups get left behind.
Unlike some of its peers, China has said very little about whether it expects its development projects to reduce economic disparities within host countries. However, there are some reasons to think that Chinese development projects could be part of the solution. Beijing has demonstrated that it is both willing and able to address the unmet infrastructure financing needs of developing countries. These development projects—in particular, investments in highways, railways, roads, bridges, tunnels, and ports—could strengthen economic ties between rural and urban areas and thereby help to spread the benefits of economic growth to more remote and traditionally disadvantaged areas.
In a new AidData Working Paper released today, a team of economists and political scientists from William & Mary, Leibniz University Hannover, Heidelberg University, Helmut Schmidt University Hamburg, and Harvard University leverage a new geolocated dataset of Chinese Government-financed projects worldwide to evaluate how these investments alter the geographic distribution of economic activity within provinces and districts in low- and middle-income countries.
To consistently measure economic inequality from year to year in more than 32,000 subnational localities around the world, they calculate a Gini coefficient using annual data on the geographical dispersion of nighttime light. The Gini coefficient is a measure of inequality that varies from 0 to 1. A value of 1 represents perfect economic inequality (for example, a province in which a single 1 km x 1 km grid cell within that subnational jurisdiction harbors all economic activity) and 0 represents perfect economic equality (a province in which every 1 km x 1 km grid cell within that subnational jurisdiction has the same level of economic activity).
Read the study on Chinese infrastructure projects and the diffusion of economic activity in developing countries.
The findings from the study are encouraging: Chinese development projects—in particular, “connective infrastructure” projects like roads and bridges—are found to create a more equal distribution of economic activity within the provinces and districts where they were located. The study also measures the impact of Chinese development projects on economic inequality between provinces and districts, and here too the results provide grounds for optimism: Chinese Government-financed projects appear to reduce, rather than widen, economic disparities between regions.
This analysis was made possible by a far-reaching effort at AidData to assign precise geographic coordinates to AidData’s first global dataset of Chinese development projects, released last October. The result of thousands of hours of geocoding by dozens of research assistants is a first-of-its-kind dataset that pinpoints the locations of 3,485 Chinese development projects worth USD $273.6 billion that were implemented in 6,184 locations in 138 countries over a fifteen-year period (2000-2014).
Download the geocoded dataset of Chinese Government-financed projects worldwide.
Many of those 138 low- and middle-income countries suffer from high levels of spatial inequality, where most economic activity is concentrated in a few cities and little economic activity exists in the rural towns and villages. Therefore, a significant implication of the study is that, by helping households and companies in rural areas access goods, services, and jobs in more distant markets, China’s investments in connective infrastructure are spreading economic activity to rural areas that have historically suffered from benign neglect or active discrimination. By reducing spatial inequality, Chinese development projects are also helping to address a root cause of global instability.