In addition, Figure A5 in the Online Appendix depicts the main result from these regressions. I plot the coefficients for the most restricted definition of workers’ informality. The main finding is that there is a negative relationship between informality rates and transit improvements when locations that experienced the shock are compared with census tracts close to lines that were planned in the 1980s. For instance, informality rates for workers decrease on average between 4.0 and 11.0 percentage points, which is a more significant effect than the one found previously. This effect corresponds to a decrease of approximately 15%, using the mean of the control group before the shock. In most of the specifications, I also find parallel trends, suggesting that after the shock treated locations experienced changes in informality.

# **3.2.4 Households’ Composition and Lower Bound Effects**

A potential concern regarding the identification strategy from the previous section is that locations close to the new subway line might experience a change in the composition of households due to worker sorting.<sup>25</sup> For example, high-skilled workers that would prefer to work in the formal sector might migrate to these census tracts and, as a result, there would be a decrease in informality rates that could explain my findings. Ideally, I would deal with this issue by using a multi-year panel of workers before and after the shock. Unfortunately, no such panel is available.

I deal with this concern by comparing household characteristics before and after the shock. The goal is to show that at least in terms of observable covariates, there was no change in households’ composition. For that purpose, I run the same specification in equation 3.2 on household characteristics, such as the high-skilled share of workers on the left-hand side.

Table 3 reports the results, including all the set of controls. On average, I find that household characteristics in locations close to line B were not affected by the shock relative to other areas in Mexico City. For example, the point estimates for the share of high-skilled workers, the number of kids, or the household size are not significant. On the other hand, the coefficients that are significant are very small. For example, the student share’s point estimates imply that the locations affected by the shock experienced a slight increase of 0.4 percentage points. Overall, this finding suggests that at least in terms of observable characteristics, there is no change in households’ composition due to the transit shock that can bias my estimates.<sup>26</sup> Furthermore, I also estimate lower bounds of the effect using retrospective questions. In particular, INEGI asks the state in which the person was living before in the census. According to

> 25In the model, I am allowing for changes in terms of unobserved characteristics since it allows for migration within the city. However, the model only assumes one type of worker and, therefore, I also analyze changes in households’ composition in terms of unobserved characteristics.

> 26This result corroborates the findings of other papers in the Mexican context. For example, Gonzalez-Navarro and Quintana-Domeque (2016) exploit a random allocation of street asphalting in peripheral neighborhoods in Veracruz. The authors follow individuals for two years and find a negligible reallocation of households across locations in the city. Similarly, Hernández-Cortés et al. (2021) find negligible reallocation effects exploiting subways and BRT expansions in Mexico City. Moreover, it is related to other papers that have shown that there are high migration costs in developing countries.

15