census has defined additional urban units, such as “census towns”, administrative units formally classified as villages but which satisfy various criteria common to urban settlements; and “urban agglomerations” (UA), typically an urban area that consists of a statutory town and its adjoining outgrowths. While the concepts of census town and UAs try to capture the urbanization process taking place outside the administrative boundaries of statutory towns, it is widely agreed that these understate the extent of urban growth. Nighttime lights lend strong support to this. Panel (a) of Figure 1 plots three versions of Chennai: the statutory town (dashed red boundary), the UA (dashed black boundary), and the natural city (the shaded area). Further, towns in the vicinity are also plotted. The figure serves to show that urban spread can well exceed the administrative boundaries of officially defined cities and encompass many settlements—towns and villages— which are otherwise treated as wholly sperate entities in official statistics.

China presents a different case. In the literature and policy documents, prefectures (nearly 300 in total) and four municipalities directly under the central government are considered administrative cities in China (e.g., Chauvin, Glaeser, and Tobio, 2017). However, there are vast suburban and rural areas in each prefecture. In the official City Statistical Yearbooks, data are reported for both the entire prefecture as well as a core area, called the “city proper”. The latter is often used in studies of Chinese cities (e.g., Anderson and Ge, 2005; Au and Henderson, 2006; Fang, Li and Song, 2017). Panel (b) of Figure 1 shows that Dalian prefecture, known for its seaport and role as an economic center in northern China, contains four natural cities with one located in the city proper and three outside. As the case of Dailan prefecture shows, a focus on the city proper can simultaneously lead to the inclusion of large amounts of rural areas as well as the exclusion of other urban areas inside the prefecture.<sup>13</sup> The natural city data contains both geocoded locations and boundaries of the natural cities, making them ready for matching with WBES firm-level data. Other publicly available city datasets of developing countries such as the one from World Urbanization Prospects only contain centroids of cities.

WBES firms are mapped to their natural cities using geographic information system (GIS) software. To maximize the number of firms that can be matched to our natural cities, 2016 natural city boundaries are used and firms falling outside a natural city but within a 2km band from the city boundary are assigned to that city.<sup>14</sup> Table A1 shows the results of the matching exercise by country. Overall, a total of 21,857 firms are matched to 489 natural cities, an average of 45 firms per city. The share of WBES firms matched to a natural city is 87%, with the matching rates within individual countries ranging from 40% (Mongolia) to 100% (Myanmar and the PRC).<sup>15</sup> The unmatched firms are likely to be those located in rural areas of very small

> 13 Appendix 1 presents further comparison of the two types of city data by an investigation of Zipf’s law for China and India.

> 14 Results do not change if firms falling outside the 2 km band from the analysis are excluded. These are available upon request.

> 15 The initial procedure resulted in a low matching rate for the PRC (59%), which is probably due to the country’s regulations on collecting and using geographic information, and the unique coordinate system used for geographic data in the PRC. To address the issue, prefecture information available in the WBES data for the PRC is used to assign each unmatched firm to the largest natural city in its prefecture.  This results in a 100% matching rate.

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