Mapping China's Global Investments and Inequality
Geocoded Chinese Development Projects and the Diffusion of Economic Activity
AidData's Geocoded Global Chinese Official Finance, Version 1.0.1
This dataset geolocates Chinese official finance projects committed or implemented from 2000-2014 to capture 3,485 projects totaling $273.6 billion. The data includes both Chinese aid and non-concessional official financing.
The Geographic Dispersion of Nighttime Light Intensity
West Africa’s Shifting Nighttime Coast
How do patterns of nighttime light change over time on West Africa’s coast? Toggling the slider reveals differences between 2012 (left) and 2016 (right) that may be subtle from space but significant on the ground. Some cities have become brighter while others have grown dim; increased economic activity along roads manifests as glowing lines, like those that connect Ghana's coast to the interior; and clusters of fishing vessels swarm near different ports.
By analyzing high-resolution satellite images like these of nighttime light intensity, 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.
But since economic progress is often uneven, it’s not just the amount of nighttime light within a geographic region that matters. The geographical dispersion of nighttime light also tells us something about how widely or narrowly the benefits of economic growth and development are being shared.
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.
Photos by NASA’s Earth Observatory. Map by Soren Patterson for AidData.
Measuring Economic Inequality with Nighttime Lights
Spatial Inequality in West Africa
How equally or unequally is economic activity distributed within countries? Using annual data on the geographical dispersion of nighttime light, the Connective Financing report calculates a Gini coefficient to consistently measure economic inequality from year to year in more than 32,000 localities around the world.
Each locality receives a Gini score that varies from 0 to 1. A Gini of 1 represents perfect economic inequality: a province or district in which a single 1 km x 1 km square of land contains all the economic activity in that administrative area. A Gini of 0 represents the opposite: a province of perfect economic equality where every single 1 km x 1 km grid cell in the province has the same level of economic activity. Compare the 2012 Gini scores for ADM1 districts in West Africa (left) to the satellite image of those distric's nighttime lights in 2012 (right). Districts in yellow and green contain more equal dispersions of economic activity, while districts in orange and red are less equal in how economic activity is distributed.
By observing how economic inequality changes over time, and by combining these measures with new data on the locations of Chinese Government-financed projects worldwide, the Connective Financing report evaluates how Chinese investments influence the geographic distribution of economic activity within provinces and districts in low- and middle-income countries.
Photo by NASA’s Earth Observatory. Map by Soren Patterson for AidData.
About the Report
Using the most comprehensive dataset of Chinese development project locations ever assembled, a new AidData Working Paper shows that Chinese infrastructure investments narrow economic inequalities within poor countries. Published by a team of economists and political scientists from William & Mary, Leibniz University Hannover, Heidelberg University, Helmut Schmidt University Hamburg, and Harvard University, Connective Financing leverages 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.
About the Dataset
After releasing the first-ever global dataset of Chinese development projects in October of 2017, AidData embarked on a far-reaching effort to assign precise geographic coordinates to thousands of projects. The result of thousands of hours of geocoding by dozens of research assistants is a first-of-its-kind geocoded global dataset that pinpoints 3,485 Chinese development projects worth USD $273.6 billion implemented in 6,190 locations in 138 countries over a fifteen-year period (2000-2014). This dataset only includes projects that were both recommended for research in the original dataset and projects whose status was completed or implemented.