methodologies for the consistent measurement of urbanization across countries.<sup>17</sup> In this paper, we focus on two of these methodologies – the Agglomeration Index (AI) that was originally developed by Uchida and Nelson (2010), and Dijkstra and Poelman’s (2014) cluster method. The AI methodology has already featured in several Flagship World Bank publications (World Bank, 2008; World Bank and IMF, 2013; and Ellis and Roberts, 2015). By contrast, the cluster method, which was originally developed for application to the European Union, is more recent and its use at the global level is just beginning to be explored. The two methodologies differ both in terms of their underlying conceptual approaches to defining urban areas and their data requirements. The decision to focus on these two methodologies is based on the fact that they can be feasibly implemented to produce estimates of urban population shares for a large global cross-section of countries. This is a basic requirement for the (re-)examination of the basic stylized facts of urbanization, development and structural transformation, and the comparison of LAC with non-LAC countries. Below we provide a more detailed overview of the two methodologies.

# 2.2.1. The Agglomeration Index

From a labor market perspective, individual urban areas should ideally be conceptualized and defined as functional urban areas (FUAs), where a functional urban area captures the spatial extent over which a city or town’s labor market may be considered integrated. Approaches to delineating FUAs typically begin with an urban core, defined by size and/or density criteria, and then identify a ‘commuting shed’, i.e. a surrounding area from which workers commute to the core.<sup>18</sup> A good example of an algorithm to identify FUAs is that used in the construction of the OECD’s Metropolitan Areas database.<sup>19</sup> Hence, this algorithm defines FUAs by first identifying densely inhabited urban cores using gridded population data, and then adding non-contiguous interconnected urban cores, as well as other surrounding municipalities, based on commuting patterns (OECD, 2012). More recently, Duranton (2015) has proposed an algorithm to identify FUAs which dispenses with the need to pre-define an urban core, and instead relies solely on a minimum commuting threshold. Duranton tests this algorithm on data for Colombia, obtaining good results.

Unfortunately, the commuting data necessary to implement either the OECD’s or Duranton’s algorithm for identifying FUAs are not available at a global scale. In this context, the AI can be seen as a methodology which adopts a similar labor market perspective to defining urban areas, but which instead relies on estimated travel times in combination with data on population density to allow for a consistent method of measuring urbanization levels that can be feasibly implemented for a broad cross-section of countries. More specifically, the AI identifies as “urban, agglomerated, or dense” those areas that meet a certain population density threshold and which are within a given travel time radius of a “sizeable” settlement, where “sizeable” settlements are identified on the basis

> 17 The proposal of new methodologies has also been aided by the increasing availability of high-resolution global gridded population data sets and data sets on built-up area derived from satellite imagery.

> 18 The use of commuting data to identify metropolitan areas in the United States dates back to the 1960s (Berry, Goheen and Goldstein, 1969). This approach is also similar to the delineation of Labour Market Areas in Europe, or Travel to Work Areas in the UK, with the main difference being that these other exercises aim to exhaustively divide up an entire country or region into distinct areas based on commuting patterns, rather than identifying urban areas specifically (UK Office for National Statistics, 2015; Coombes, Casado-Diaz, Martinez-Bernabeu and Carausu, 2012).

> 19 http://www.oecd.org/gov/regional-policy/regionalstatisticsandindicators.htm.

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