absent from our equation, introducing an omitted variable problem. In an effort to solve this, we include control variables, as well as a set of job and firm characteristics in robustness tests.<sup>17</sup>

# **3 Data**

Our first stage (equation 4) analysis draws on successive rounds of household survey micro-data for 16 LAC countries that, apart from Brazil, are taken from the Socio-Economic Database for Latin America and the Caribbean (SEDLAC). This database has been jointly constructed by the Center for Distributive, Labor and Social Studies (CEDLAS) at the Universidad National de La Plata and the World Bank’s Poverty Group for the Latin American and the Caribbean region.<sup>18</sup> Previously, data availability has been an important obstacle to carrying out large scale work on agglomeration economies for developing countries. In particular, surveys are not uniform across LAC countries. Comparability is, therefore, an issue of great concern. One of the great advantages of SEDLAC is that it provides harmonized survey micro-data. Strong comparability of the data is ensured across time and countries by using similar definitions of variables in each country/year, and by applying consistent methods of processing the data (CEDLAS and The World Bank, 2014). SEDLAC provides as close to perfect comparability as can be hoped for given the continuing prevalence of differences in coverage and questionnaires of household surveys across LAC countries.

The version of SEDLAC we use covers different survey years for different countries - for example, from 1974 to 2014 for Argentina, from 1987 to 2013 for Chile, and from 2001 to 2014 for Colombia. To ensure consistency across the LAC countries on which our analysis focuses, we make use only of SEDLAC data from 2000 onwards. To allay potential concerns over a lack of representativeness of the survey data at the level at which we analyze it, we pool successive cross-sections of data from this period. SEDLAC covers 24 countries. However, eight countries had to be dropped either because: (i) changes in administrative units and their coding over time prevent SEDLAC from providing reliable geographic identifiers; or (ii) technical difficulties prevented the loading of the micro-data from SEDLAC. The 16 countries analyzed are: Argentina, Brazil, Bolivia, Chile, Colombia, Costa Rica, Dominican Republic, Ecuador, El Salvador, Guatemala, Honduras, Mexico, Nicaragua, Panama, Peru, and Uruguay.

Our analysis focuses on a broad sample of workers covering both formal and informal sectors irrespective of job characteristics. However, we choose to restrict our sample only to wage workers, excluding self-employed workers whose reported income levels may not be comparable across countries (Duranton, 2016). Likewise, we exclude workers who report zero-income (mostly family helpers in the sectors of agriculture and retail trade). Our final sample comprises 4,083,256 employed wage workers ages 14 to 65. A worker’s wage is taken to be the nominal hourly wage earned

> 17A possible third concern is that of reverse causality - i.e. that density or human capital in a city can be determined by its productivity premium. Using lagged right hand side variables in equation 6 could partially address this issue, unless there is strong path dependence, as seems likely, in which case both the premiums and the right hand side variables could be determined by a city’s historical productivity premium. Additionally, market access measures used exclude a city’s own market to mitigate reverse causation concerns.

> 18 https://data.worldbank.org/data-catalog/sedlac

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