While there exists a large and well-established literature that empirically examines the strength of agglomeration economies for developed countries,<sup>22</sup> there have been comparatively very few papers that have done likewise for developing countries. The main exceptions are recent papers by Duranton (2016), Chauvin _et al_ . (2017), and Quintero and Roberts (2018). Duranton provides estimates of the strength of agglomeration economies for Colombia, while Chauvin _et al_ . do likewise for Brazil, China and India.<sup>23</sup> Quintero and Roberts, meanwhile, present estimates of the strength of agglomeration economies for 16 Latin American and Caribbean countries.<sup>24</sup> In all three cases, these papers estimate the strength of agglomeration economies using either individual or pooled cross-sections of data on workers drawn from either household or labor force surveys. They estimate the strength of agglomeration economies by regressing a worker’s nominal wage on a measure of either the size or density of the city in which the worker lives while controlling for observable characteristics of the worker, including, most notably, the worker’s level of education and workforce experience, as proxied by the worker’s age.

In what follows below, we follow a similar approach to Duranton (2016), Chauvin _et al._ (2017) and Quintero and Roberts (2018) in estimating the strength of agglomeration economies for Indonesia.<sup>25</sup> Hence, we draw on micro-data for Indonesian workers from the same survey – SAKERNAS – that we used to measure commuting flows, to estimate the size of the agglomeration wage premium in a simple cross-sectional regression framework.<sup>26</sup> Importantly, in doing so, we estimate the size of this premium based on each of the different approaches – Duranton’s algorithm, the AI, the two cluster algorithms, and the NTL approach – to defining metro areas.

# _4.1. Empirical framework_

4.1.1. Estimation strategy Similar to previous papers for other countries (Duranton, 2016; Chauvin _et al_ ., 2017; Quintero and Roberts, 2018), we identify the strength of Indonesia’s agglomeration wage premium, using the following basic regression:

> 22 See Rosenthal and Strange (2004) and Combes and Gobillon (2015) for excellent reviews of this literature.

> 23 Chauvin _et al_ . (2017) also present estimates of the strength of agglomeration economies for the United States for purposes of comparison.

> 24 See also Roberts (2018a).

> 25 We do not attempt to distinguish between the possible different underlying sources of agglomeration economies. These sources include the various matching, sharing and learning mechanisms and are discussed in detail by e.g., Duranton and Puga (2004).

> 26 Specifically, we use data from the August 2014 round of SAKERNAS. These data are representative for Indonesian districts. This cross-sectional framework leaves us unable to control for sorting of workers based on time-invariant unobservable characteristics as their ability (as distinct from their level of education) and motivation. In this sense, we fall short of the standards of what researchers have been able to achieve in terms of the identification of agglomeration effects for countries such as the United States (Glaeser and Mare, 2001), France (Combes _et al_ ., 2008), or Spain (De la Roca and Puga, 2017) where detailed panel data sets allow the researcher to control for time-invariant unobservable characteristics of workers.

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