in the primary occupation.<sup>19</sup> We also perform estimation considering an alternative narrower sample of workers which is restricted to _prime age_ males - i.e. males aged 20-55 - working in the private sector (see table B1 in the appendix).<sup>20</sup> In general, we find very similar results for both our broad and narrow samples.

For Brazil, the coding of administrative units in SEDLAC did not allow us to identify specific locations and map them to variables from other sources used in the second stage. Thus, we take micro-data on workers from the population census sample for 2000 that is available in IPUMS International.<sup>21</sup> We perform our own harmonization of the IPUMS International data for Brazil with the SEDLAC data for the other 15 countries in our sample.

For our second stage (equation 5) analysis of the determinants of the city premium, we further carefully match the harmonized survey data from SEDLAC and IPUMS with data from a LAC Geo-spatial Database that was constructed for the World Bank by the University of Southampton’s GeoData Center (Branson et al., 2017).<sup>22</sup> This database was constructed with the specific purpose of aligning with the identifiers for sub-national areas we have in SEDLAC. This dataset includes information on a location’s population density (population per km<sup>2</sup> of admin unit), aggregate stock of human capital, market access, terrain ruggedness (elevation variation calculated as in Nunn and Puga (2012)), mean air temperature, and total precipitation.<sup>23</sup> Gridded Population of the World (GPW) v4 global 1 km population count data for 2015 was used to determine the population of locations, which correspond to administrative units, specified in table 1. UN adjusted measurements were used, which adjust raster cell values so that when summed to the national level they are consistent with UN population estimates. Robustness tests were carried out with other population data sources, such as WorldPop and Landscan-2012.<sup>24</sup> Similar results were obtained.

Average years of schooling by administrative unit were estimated using the household surveys

> 19Wages are measured at 2005 Purchasing Power Parity (PPP) exchange rates. In SEDLAC, rural wages are also increased by 15 percent to capture differences in rural-urban prices (CEDLAS and The World Bank, 2014, p 23). In our analysis, we undo this by multiplying the mean hourly wage of a rural worker by the factor 0.8695.

> 20Bacolod et al. (2009), Glaeser and Resseger (2010),Duranton (2016), and Chauvin et al. (2017) focus only on prime-age males.

> 21The second stage results for the pooled sample excluding Brazil are very similar to those including Brazil.

> 22This database was constructed as input into the World Bank’s LAC Flagship Report “Raising the Bar for Productive Cities in Latin America and the Caribbean” (Ferreyra and Roberts, 2018).

> 23 The market access variable attempts to capture the spillovers of agglomeration of other locations in the focal city. The construction of the measure is similar to a market potential measure, but it leaves the focus economy size put and focuses on the effect of proximity to other markets. This helps to distinguish market access from our measure of agglomeration, while also mitigating reverse causality concerns. Following Henderson and Wang (2007), we define market access for city _l_ as

> where _nk_ is the size of city _k_ measured by population, _dlk_ is the estimated travel time by automobile between the focal city _l_ and each of the _other_ cities, indexed by _k_ , and _N_ is the total number of cities in the country. Travel times incorporate average road congestion, current speed limits, and road type details between city centroids (Branson et al., 2017).

> 24GPW v4 can be found at http://sedac.ciesin.columbia.edu/data/collection/gpw-v4, World Pop at http://www.worldpop.org.uk/, and Landscan-2012 at http://web.ornl.gov/sci/landscan/.

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