that agglomeration forces are larger in the formal sector. The intuition for this result is that informal varieties are more substitutable than formal ones, and as a result, agglomeration externalities in the informal sector are lower.

Panel B in Table 4 describes the main results for this estimation. As in all gravity equations, trade flows decrease with commuting times. The estimate of _σs_ is consistent with the results from the previous literature. In particular, the elasticity of substitution in the informal sector is 6.94, and in the formal sector it is 5.39, suggesting that agglomeration externalities are 0.16 in the informal sector, and 0.22 in the formal sector.<sup>45</sup>

# **5.3 Labor Supply Elasticity across Sectors**

In this section, I estimate the main equation from the model to recover the labor supply elasticity across sectors, _κ_ . This parameter governs the reallocation of workers from the informal to the formal economy. I build market access measures following Tsivanidis (2019). According to the model, these measures represent the wage index for each sector. Hence, they capture whether workers obtained better access to formal jobs relative to informal jobs after the transit shock.

For this estimation, I calculate travel times across the different census tracts in Mexico City with and without Line B of the subway using the network analysis toolkit from Arcmap. I compute travel times for three different transportation modes: car, walking, and the public transit system. I calibrate speeds for different types of roads and the public system using random trips from Google Maps. Table C1 describes the values obtained for each category and each mode of the transportation system.<sup>46</sup> With the commuting times at hand, I define the commuter market access (CMA) for location <u>1</u> _n_ and sector _s_ as CMA _ns_ = _Wnsθs_<sup>.Thisisanindexoftheaccessibilityofjobsinlocation</sup><sup>_n_to</sup> employment in sector _s_ . Following Tsivanidis (2019) and Donaldson and Hornbeck (2016), I can solve the following system of equations to compute MA measures for both firms and workers specific to each sector and location: where _L_<sup> ̃</sup> _is_ represents the total amount of labor hired by location _i_ and sector _s_ ; _Lns_ corresponds to the total number of workers that reside in location _n_ and work in sector _s_ ; and FMA _is_ is a firm market access measure that captures whether firms in _i_ have good access to workers from sector _s_ .<sup>47</sup>

> 45These externalities are relatively large compared to the previous findings in the literature, where they are around 0.1. However, both numbers are still reasonable, especially in developing contexts. For example, Tsivanidis (2019) find that agglomeration externalities in Bogota are around 0.21, which is a larger value than those of previous findings.

> 46Section C1 in the appendix explains the procedure.

> 47Tsivanidis (2019) estimates these measures for Bogotá and shows that with data of commuting costs, and the number of residents and workers in each sector and location, the system of equation 5.4 has a unique solution. Another way to prove the existence and uniqueness of this system of equations is to apply the theorem from Allen et al. (2015).

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