One caveat with the estimation of equation 5.5 is that the change in CMA may capture other shocks in the economy that shifts the allocation of labor across sectors and locations. These shocks can change the decision of workers to operate in the formal or informal sector, thus, generating a correlation between the change in CMA and the error term. This generates a bias in the estimation of _κ_ . To deal with this problem, I estimate equation 5.5 by two-stage least squares using two instruments. The first instrument is the change in the CMA measures when the number of residents and workers is held fixed, and the second instrument is the treatment dummy variable. The idea is to capture changes in commuting costs and clean the estimation from other economic shocks. For example, the treatment dummy variable captures changes in residents, employment, and market access in the treated census tracts only because of line B and not other economic shocks in the city.

Table 5 reports the results for the labor supply elasticity across sectors. I obtained estimates of _κ_ between 1.1 and 2.4. These estimates are consistent with the model and the commuting elasticities. The first two columns show the results for the OLS and the other four columns for the IV using each instrument separately. In my preferred specifications, which are the ones in columns 4 and 6, I obtained a point estimate between 1.5 and 2.4. For the counterfactuals, I take an average between these two numbers. Comparing the estimates from the 2SLS and OLS, it suggests that there were other shocks in the economy that created a downward bias for _κ_ . For instance, these shocks reallocated workers from the informal to the formal sector generating a negative correlation between the change in the CMA measures and the error term.<sup>4950</sup>

# **5.4 Labor and Capital Wedges**

Labor and capital wedges are a crucial parameter for the quantitative analysis. I follow the popular approach from Hsieh and Klenow (2009) and use the inverse of the wage bill and capital share to calibrate the distortions.<sup>51</sup> From the profit-maximization condition, the inverse of the labor and commercial floorspace share paid by each firm is where _wislis_ is the wage bill, _qiszis_ is the commercial floorspace payments, and _pisyis_ are total sales or value-added. I can observe the left-hand side of this equation for each firm in the Economic Census, and use the average labor share _βs_ and markups in each industry to calibrate the wedges. To aggregate from the firm level to the census-tract-sector cell, I take the mean of the inverse of the

> I am controlling for this variable after taking the ratio between the formal and the informal sectors since this wage index cancels out. Then, the reallocation of workers does not bias the estimation of _κ_ since what matters is the share of formal to informal residents across census tracts even if there is migration.

> 49I didn’t instrument the change in CMA with the two instruments since both of them capture changes in commuting cost because of the transit shock.

> 50Relative to previous studies on estimating labor supply elasticities across sectors, such as Galle et al. (2017), Lagakos and Waugh (2013), and Berger et al. (2019), my estimates are similar.

> 51Other papers such as Busso et al. (2012) and Levy (2018) that have explored the role of resource misallocation in Mexico also use the same method.

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