these fixed and recent boundaries is useful because it allows us to analyze changes in the same area aiming to answer the question of how city form has changed through time.

The Global Human Settlements population grid (GHS-POP) uses data from the Gridded Population of the World and reallocates these estimates using the GHS BUILT data set. An alternative would have been to use data from LandScan, which uses a proprietary algorithm to provide population estimates information on elevation, slope, and land cover, as well as locations of road and rail networks, hydrologic features and drainage systems, utility networks, airports, and populated urban places (Henderson et al 2020). While LandScan reports estimates of ambient population averaged throughout the day, GHS POP reports nighttime (residential) population estimates.

The entire data set consists of 13,135 cities with at least 50,000 inhabitants in 2015. The data set focuses on measures of city built-up and city population for four points in time: 1975, 1990, 2000, and 2015. It also includes other economic, geographic and environmental variables such as city GDP, elevation, precipitation and temperature, CO2 emissions, PM 2.5 emissions and concentration levels, for similar points in time. We construct a sample of cities that have recorded positive built-up and GDP since 1990. We drop the 1975 layer as it potentially underestimates built-up areas due to the coarser resolution of the Landsat2 Multispectral Scanner (60m compared to 30m). Using the 1975 data would introduce an upward bias in city growth estimates. To further reduce measurement errors and avoid outliers, we drop cities with population less than 10,000 inhabitants in 1990 and 2000, and those with a population/builtup coefficient larger than 500,000 in 1990. This gives us a final sample of 9,468 cities, which includes cities of all sizes (Table 1). More than half the cities in the sample have less than 200,000 inhabitants.

**Table 1: Distribution of Cities by City Size and Region**

|**Region**|**Less than**<br>**70k**|**70k -**<br>**100k**|**100k -**<br>**200k**|**200k -**<br>**500k**|**500k -**<br>**1m**|**1m- 10m**|**More**<br>**than 10m**|
|---|---|---|---|---|---|---|---|
|East Asia & Pacific|612|609|819|476|131|116|11|
|Europe & Central Asia|345|297|324|225|77|56|2|
|Latin America & Caribbean|267|228|255|158|60|51|3|
|Middle East & North Africa|221|137|199|113|43|40|2|
|North America|77|86|92|60|22|33|2|
|South Asia|312|362|588|390|122|84|7|
|Sub-Saharan Africa|300|290|439|217|55|52|1|

Source: GHS urban centers database One needs to keep in mind that our analysis uses cities as the unit of analysis and does not include urban agglomerations, which often include adjacent suburbs. However, to enable comparability, a density threshold of 1,500 people per km<sup>2</sup> is used as a cutoff. Figure 1 shows the boundaries for Casablanca, Cairo and Dakar with this approach. To check for robustness of this arbitrary cutoff, we selected the 50 fastest growing cities from 1990 to 2015 to assess the population variations across different sizes of urban areas. Multiple ring buffers (from negative 5km to positive 5km away from the edge of the urban boundary) were added to the urban boundary. Except for two outliers—Guangzhou, China, and Jakarta, Indonesia— 4