typically include population size (Rozenfield _et al._ , 2011; Dijkstra and Poelman, 2014; Aubrecht _et al._ , 2016), other definitions may include other geospatial information such as land cover (Mertes _et al._ , 2015), night time lights (Balk _et al._ , 2006; Zhang and Seto, 2011; Brecht, Deichmann and Wang, 2013), travel time (Uchida and Nelson, 2010; Day _et al_ ., 2017) and/or commuting flows (Duranton, 2015; Nelson and Rae, 2016; OECD, 2012; Sanchez-Serra, 2016). Specifically, this paper reestimates the relationships depicted in Figure 1 using two alternative methods that have been proposed in the literature for the consistent definition of urban areas across countries which can be feasibly implemented at a global scale using readily available population and travel time data.<sup>9</sup> The first method – the Agglomeration Index (AI) – was originally proposed by Uchida and Nelson (2010) and delineates urban areas on the basis of population densities and travel times to large urban centers.<sup>10</sup> Meanwhile, the second method, which, for brevity, we shall refer to as the “cluster method”, has been developed by Dijkstra and Poelman (2014) and relies on the identification of dense spatially contiguous clusters of population.<sup>11</sup> Having re-estimated the relationships, the paper identifies whether LAC countries continue to exhibit significant differences of the sort portrayed in Figure 1. In doing so, we are able to assess the extent to which Latin America’s apparent “overurbanization” qualifies as one of Satterthwaite’s urban myths.

In addition to re-examining the key basic stylized facts of urbanization, development and structural transformation, this paper also makes two additional contributions to the literature. First, by implementing both the AI and cluster methods to defining urban areas, the paper provides the first systematic comparison of results between these two methods using a global data set. Secondly, because both of these methods rely on globally gridded population data sets for their implementation and there exist several alternative such data sets, the paper also provides the first systematic investigation as to how the choice of input data affects the results obtained from these methods. In particular, the paper analyzes how the use of three main gridded population data sets influence the results obtained from these methods. These data sets are the _LandScan 2012_ global gridded population data set that is produced by Oak Ridge National Laboratory, the _WorldPop_ family of gridded population data sets that are produced by the University of Southampton, UK, and the _GHSL Global Population Grid_ (GHS-Pop) that has recently been generated by the European Commission’s Joint Research Center (JRC).<sup>12</sup> 9 The requirement that the methods be implementable at a global scale automatically precludes methods that rely on commuting flow data. Applications of such methods have been restricted to either individual countries (e.g. Duranton, 2015; Nelson and Rae, 2016; Sanchez-Serra, 2016) or small samples of countries (OECD, 2012).

10 Uchida and Nelson built on earlier work by Chomitz, Buys and Thomas (2005) which identified population density and remoteness from large metropolitan areas as important dimensions along which to classify an area’s degree of rurality (and, therefore, implicitly, its degree of urbanization). Chomitz _et al._ found that many small settlements in LAC that are officially classified as urban are actually embedded in an agriculturally based countryside. See also the World Bank’s 2005 LAC Flagship report “Beyond the City: The Rural Contribution to Development” (de Ferranti _et al_ ., 2005), for which the paper by Chomitz _et al_ . was originally prepared as input. This report was one of the first, if not the first, official World Bank publication to raise the issue of the lack of comparability of official urban population shares across LAC countries.

> 11 Dijkstra and Poelman’s algorithm for identifying urban clusters is closely related to that which Rozenfield _et al._ (2011) apply to identify cities in the United States.

12 Other global gridded population data sets include CIESIN’s GRUMP (Global Rural-Urban Mapping Project) Population Count Grid <u>(http://sedac.ciesin.columbia.edu/data/set/grump-v1-population-count) and its Gridded</u> Population of the World version 4 (GPW v 4) (http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4) data sets. Various institutions and authors have also created gridded population data sets for individual countries or regions. These include a European population grid for 2006 produced by the Austrian Institute of Technology (http://www.efgs.info/data/) which covers EU28 and EFTA countries; and population grids for Haiti, Pakistan and 4