# **4.2.2 Demographic and sector heterogeneity**

In addition to heterogeneity across countries, it is also fruitful to ask whether, following the example of Duranton (2016) for Colombia, the elasticities of the city premium with respect to population density, average human capital and market access are heterogeneous across different sub-groups of workers. To explore this question, we perform a series of (1st and 2nd stage) regressions using subgroups of workers for the following four dimensions that have been drawn from our broad sample (which consists of all employed wage workers aged 14 to 65):

1. Young (14-35 years old) versus Old (36-65 years old)

2. Male versus Female 3. Private versus Public Sector 4. Formal versus Informal<sup>38</sup> For age groups, we use 35 years as the dividing line between Young and Old because this is roughly the mean age of workers across all 16 countries. Meanwhile, given that our sample already excludes all self-employed workers and workers who report zero income, we define informal workers as those who work for firms that have 5 or fewer employees. Our regressions for Private -v- Public Sector and Formal -v- Informal workers exclude Brazil. This is because, for Brazil, no public-sector workers were left in our original broad sample after data-cleaning, while IPUMS does not provide data that allow us to distinguish between formal and informal workers in a manner akin to that for other countries, for which the data instead come from SEDLAC.

Table 7 summarizes the results of our regressions (full results are available upon request from the authors). The most striking differences in estimated effects come when comparing Private -vPublic Sector workers and Formal -v- Informal workers. In both cases, the estimated elasticities of the city premium with respect to average years of schooling and market access are higher for private sector and formal workers than they are for public sector and informal workers respectively. For human capital externalities, the difference in the impact is the largest when comparing Private and Public Sector subsamples. We expect wages to be more closely connected with productivity in less regulated Private labor markets than in the Public Sector (wages are a better measure of productivity in private than in the public sector)<sup>39</sup> which is consistent with the stronger association observed between human capital externalities and productivity premiums. Although they are

> 38SEDLAC provides two different indicators for whether a worker is to be considered informal (CEDLAS and The World Bank, 2014). The first, which is based on a _productive_ definition of informality, identifies a worker as informal if _(s)he belongs to any of the following categories: (i) unskilled self-employed, (ii) salaried worker in a small private firm, (iii) zero-income workers._ Meanwhile, the second, which is instead based on a _legalistic or social protection_ notion of informality, identifies a salaried worker as informal if _s(he) does not have the right to a pension linked to employment when retired._ We choose to rely on the indicator based on the productive definition of informality because this suffers from fewer missing observations. Because our sample already excludes both self-employed and zero-income workers, this amounts to equating informal employment with employment by very small private firms (5 employees or fewer).

> 39Lucifora and Meurs (2006) Christofides and Michael (2013) study wage gaps between private and public sectors in Europe, while Panizza (2001) studies them in Latin America. They all find a positive gap in favor of the public

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