commuting flow data – with the economist’s “first best” approach based on the identification of a city’s functional area using data on origin – destination (O-D) commuting flows. At the same time, while international organizations have developed global maps of consistently defined cities, there has been no effort to explore how the use of these maps to define cities affects key empirical results that are crucial to a proper understanding of the working of urban economies, such as, for example, the estimated strength of agglomeration economies.

Given the above, the aims of this paper are two-fold. First, we compare the results of different algorithms and approaches for delineating metropolitan areas in terms of the basic descriptions they provide of the urban landscape (notably, the number of metro areas, the sub-national administrative units that these areas cover, and their population sizes). And, second, we assess whether the choice of approach to defining metro areas matters when it comes to estimating the strength of agglomeration economies.

More specifically, we compare four different approaches. The first of these requires _O-D commuting flow_ data and adheres to the economist’s preferred approach of defining metro areas as functional local labor markets. Specifically, for this first approach, we make use of an algorithm recently developed by Duranton (2015b). The other three approaches are “second best” approaches which instead rely on global data sets derived, wholly or in part, from satellite imagery. These three approaches are the _Agglomeration Index_ (AI), which was originally developed by Uchida and Nelson (2009) for the World Bank’s 2009 World Development Report on “Reshaping Economic Geography” (World Bank, 2008); a “ _Cluster Algorithm_ ” developed by Dijkstra and Poelman (2014) which associates cities with dense clusters of population; and, finally, the identification and delineation of metro areas based on the “thresholding of _Night-Time Lights_ data _”_ (NTL), similar to, for example, Ellis and Roberts (2016) and CAF (2017). These latter three approaches all have the obvious advantage that they can be applied to any country in the world using readily available data sets, including those countries for which O-D commuting flow data are not available.

In all our empirics, we focus on the case of a single large developing country – Indonesia. Unlike many developing countries, Indonesia has the advantage that its national labor force survey ( _Survei Tenaga Kerja Nasional_ , SAKERNAS) allows for the derivation of an O-D matrix of commuting flows between sub-national areas (districts), which is the crucial input for the application of the Duranton (2015b) algorithm in defining metro areas. Beyond the main focus of this paper, however, Indonesia’s urban landscape is interesting to study in and of itself. Indonesia has, in recent decades, been one of the world’s most rapidly urbanizing countries and, within the country, there is intense policy interest in the issue of how to delineate metropolitan areas (see World Bank, 2018).<sup>7</sup> By focusing on Indonesia, our paper also contributes to the, to date, limited credible empirical evidence on the strength of agglomeration economies in developing countries. This is a knowledge gap that economists such as Overman and Venables (2005), Duranton (2015a), and Glaeser and Henderson (2017) have made recent calls for the profession to fill.

> 7 The Government of Indonesia has itself attempted to identify metropolitan areas. This began in 2008 with Government Regulation 26/2008 (Peraturan Pemerintah/PP) that specified nine multi-district urban areas (“ _kawasan perkotaan yang bersifat lintas wilayah_ ”). A further four multi-district urban areas were added in 2017 through Government Regulation 13/2017.

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