Abstract
This paper introduces a new city-level panel dataset constructed using satellite nighttime light imagery and grid population data. The dataset contains over 1,500 cities covering 43 economies of Asia and the Pacific from 1992 to 2016. With the dataset, we perform a variety of analyses for Asia and the Pacific as a whole as well as five individual countries in the region. The exercise produces some novel evidence on several interrelated topics including urbanization status and patterns, relations between urbanization and economic growth, evolution of urban systems, primate cities, testing Zipf’s law and Gibrat’s law, the drivers of city growth, and emergence of city clusters.
Highlights
Since the 1980s, Asia and the Pacific has grown to become the most economically dynamic region in the world
We examine here whether the relationship between urbanization and economic development holds with our nighttime light (NTL) data, how other factors such as total population and land are correlated with urbanization, and how the relationship varies between urbanization in terms of urban habitants and urbanized land
Beyond the simple correlation between urbanization rates and income level, it may be interesting to explore how other country factors such as total population, land area, and economic structure are correlated with urbanization rates, and whether gross domestic product (GDP) per capita still plays a critical role when these factors are taken into account
Summary
Since the 1980s, Asia and the Pacific has grown to become the most economically dynamic region in the world. Thereafter, the data have been creatively applied to study important economic issues (e.g., national institutions and subnational development in Michalopoulos and Papaioannou 2014), there is some debate as to whether or not nighttime lights are an appropriate indicator of economic activity, especially in large urban areas (Mellander et al 2015). Instead of relying on nighttime lights to measure the economic activity of cities in Asia and the Pacific, we use the imagery to delineate the extent of urban agglomerations, which we call “natural cities” in order to distinguish them from officially defined cities. The primary units studied in this paper are what we call natural cities They are urban agglomerations that are not defined by administrative or political boundaries, but are instead identified based on satellite imagery that has captured the nighttime lights of human settlements since 1992. More technical details about the data development are outlined in Appendix 1
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