most informal workers reside in the east and the periphery of the city.

Third, I exploit the construction of a new subway line that connected remote locations with the center of Mexico City to provide causal evidence that transit infrastructure leads to a decrease in informality rates. Specifically, I estimate a series of difference-in-differences specifications that use variation in access to new transit. These specifications control for initial characteristics of census tracts and capture changes in informality after the transit shock in locations close to the new subway line. The key identification assumption is that the opening dates of these new commuting links were unrelated to other local demand- or supply-side shocks that affected locations near the new line. This assumption is supported by the decades-long planning horizon in which part of the line was included, and several unexpected and multi-year delays in the opening schedule. I further corroborate this assumption by documenting no apparent pre-trends among the most affected locations in the preceding periods.

The main finding is that informality rates decrease in locations close to the new subway stations. I find that the ratio of formal to informal residents increases by approximately 6%-7% in locations close to the new stations after the shock. Similarly, Workers’ informality rates decrease by 2 to 4 percentage points after the construction of the new line, and firms’ informality rates decrease by 1 to 3 percentage points. These estimates represent a 6.2% decrease in workers’ informality rates, using the average informality rate in the baseline year as a benchmark. I also construct bounds of the effect to control for the sorting of workers using retrospective questions. In particular, even under the extreme assumption that all workers who moved from the central areas to the treated locations were formal in the pre-period, the effect is still 4.5%.

To check the robustness of the difference-in-differences specification, I compare the new line with similar planned metro lines that were not completed over this period for unrelated reasons, using an expansion plan from 1980. Reassuringly, this robustness check yields similar estimates to the baseline specification. Another potential concern with identification is a change in the composition of households in areas close to the new stations. To examine this, I also estimate the difference-indifferences specification using household characteristics as dependent variables. I find that the transit shock did not lead to changes in the composition of households based on observable characteristics.<sup>6</sup> Also, to control for sorting, I also estimate bounds of the effect, assuming that all workers who moved to the locations that experience the shock were formal.

To calculate and decompose the welfare gains from transit improvements, I build a quantitative model with multiple sectors and wedges that captures the three empirical findings. The model allows me to quantify the aggregate effects of new infrastructure, while considering its additional impact on factor allocation. Following Baqaee and Farhi (2020), I provide a formula that decomposes the welfare effects of any trade/commuting costs shock into two different components: a “direct” effect term and an allocation term.<sup>7</sup> Intuitively, the sign of this additional impact will depend on whether

> 6Moreover, the quantitative model accounts for employment and location decisions within the city. Thus, I consider the change in household characteristics after the transit shock based on unobservables.

> 7This approach provides the main intuition for the allocation channel. Since it is a first-order approximation. To

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