The Economic Capacity Categorization of Cities dataset shows the projected change in population and categorization of 769 cities based on their economic capacity to meet urban service needs between 2015 and 2030. Cities are categorized into four groups based on their current economic strength, projected population, and economic growth. Gross domestic product (GDP) per capita is used as an indicator of a city’s income. The four categories used in the dataset are: struggling, emerging, thriving, and stabilizing cities. This categorization can help identify cities that will likely face the greatest challenges in providing urban services in the future. It will also assist in locating cities that have the opportunity to avoid locking in unsustainable patterns of urban development.
The dataset was created by the World Resources Institute (WRI) with data from Oxford Economics' Global Cities 2030 database in conjunction with the United Nations World Urbanization Prospects and the World Bank's World Development Indicators. Oxford Economics’ Global Cities 2030 service has been developed to explore market trends and opportunities among the world’s largest 750 cities. The service provides city (and corresponding national) annual historic estimates (back to 2000 where possible) and forecasts (to 2030) to support business decision making and strategy, research analysis, urban planning, and client consultation and engagements. This classification of cities can help to understand the challenges that cities will face in the years ahead. WRI believes that the pattern of urbanization will change in the future, and a new vision of how to build and manage cities is crucial in developing cities where everyone can live, move, and thrive.
The Economic Capacity Categorization of Cities dataset was created by reviewing patterns in income, projected population, and economic growth data. The cities included in the dataset were from Oxford Economics’ Global Cities 2030 database and have at least 400,000 inhabitants or were deemed “strategically” important, like country capitals. Researchers collected data on current GDP per capita as a means of understanding a city’s current economic strength. This data was combined with projected growth in GDP per capita between 2015 and 2030 relative to the projected growth in urban population over the same time period. WRI proposed that these are good measures of how well a city’s resource base can serve its population into the future.
These two measures were used to distribute cities into the following four categories:
Emerging Cities: These cities have a low GDP per capita today, and a high ratio of projected growth in GDP per capita to projected growth in population between 2015 and 2030, as compared to other cities. They are classified as emerging cities because, while their economic strength is low today, their projected economic growth is greater than their projected population growth, indicating projected increases in economic productivity. These cities are more likely to have the capacity to overcome current resource constraints and strengthen their position globally. Most of the cities in this category are in East Asia and the Pacific and South Asia, with some in Europe and Central Asia, and Latin America and the Caribbean as well.
Thriving Cities: These cities have a high GDP per capita today, and a high ratio of projected growth in GDP per capita to projected growth in population between 2015 and 2030, as compared to other cities. They are classified as thriving cities because, not only are they economically strong today, their economic growth is projected to outpace their urban population growth in coming years. These cities are growing and thriving. Cities from East Asia, Europe and Central Asia, North America, and Latin America and the Caribbean fall within this category.
Stabilizing Cities: These cities have a high GDP per capita today, and a low ratio of projected growth in GDP per capita to projected growth in population between 2015 and 2030, as compared to other cities. They are classified as stabilizing cities because they are economically strong today, but their economic growth is expected to be lower relative to their population growth when compared to emerging or thriving cities. In that sense, these cities are starting to stabilize and in some cases, their economies are starting to shrink. It is primarily cities from North America, Latin America, and the Middle East that fall within this category.
In addition, WRI calculated the percent of projected population change for the same cities from the years 2015 to 2030. This data was taken from the same data providers. For the full documentation, please click on the “Learn more” button.
Excerpts of this description page were taken from the source metadata. Resource Watch shows only a subset of the dataset. For access to the full dataset and additional information, click on the “Learn more” button.
New Categorization of Cities based on Current Income and Expected Population and Economic Growth (2015-2030)
There is no universally accepted definition of what constitutes an urban area. A city typically refers to a geographic area that conforms to a political, jurisdictional, or administrative boundary. Many contiguous urban areas or urban agglomerations, however, extend well beyond a city’s jurisdictional boundaries. Most countries define urban areas by a single population or density threshold. Many countries use a low threshold to identify urban areas. In this dataset, the urban agglomerations from Oxford Economics’ Global Cities 2030 database are used to define the extent of cities.
Much of the data used is derived from national censuses, and several countries have not conducted a census in more than a decade.
All projections are based on historical patterns; factors such as conflicts, pandemics, migration, climate change, economic recessions, and natural disasters, among others, can all influence future urbanization, but are not accounted for in the projections.
When cities are grouped into broad categories for the sake of comparison and generalizations, diversity is muted and nuance is lost. For example, many countries have tremendous diversity between primary cities and secondary cities, yet this is difficult to show with the city categorizations used in the dataset.
Beard, V.A., A. Mahendra, and M.I. Westphal. 2016. “Towards a More Equal City: Framing the Challenges and Opportunities.” Working Paper. Washington, DC: World Resources Institute, p. 9, based on data from Oxford Economics Global Cities 2030 database. Oxford, UK: Oxford Economics: https://www.oxfordeconomics.com/global-cities-service. Accessed through Resource Watch, (date). www.resourcewatch.org.
All analysis, reports, tables, base don WRI's analysis of the Oxford Economics data will properly reference and credit Oxford Economics as the data source. WRI will retain ownership of all copyright and intellectual property. Restrictions Apply.