Moreover, in table 2 I estimate line B’s effect on the overall log number of individuals and disentangle the effect from the previous regression between formal and informal workers. In panel A, I report the results for the pool of workers, while panels B and C report the results for the number of formal and informal workers. The dependent variable in the first and third columns is the log number of workers. On the one hand, the point estimates suggest that the effect is very small on the number of individuals. For instance, it is only 1.7% in the case of the pool of workers, and 2.2% for low-skilled. On the other hand, the second and fourth columns show the estimates for the log number of formal workers. The results suggest that the locations affected by the shock experienced an increase in formal workers between 3% and 6%. This effect is larger than the estimate on the number of individuals. Finally, the third and sixth column reports the results for the log number of informal workers. In the case of municipality fixed effects, the number of informal workers decreased by around 3% in the locations that experienced the shock.

There are two main potential concerns of the interpretation of this effect. First, line B may be endogenous; I address this concern in section 3.2.3 by comparing line B with planned lines. Second, worker sorting maybe explaining the results. I address this concern in section 3.2.4.

Second, I use data from the Economic Censuses and test whether the shock also generated an indirect effect affecting the “treated” location in terms of jobs. I estimate the following specification to study whether areas close to the new subway lines experienced changes in workers’ informality rates: where _yi,t_ is one of the outcomes of interest of census tract _i_ at moment _t_ . I estimate equation 3.3 for the following outcomes: the share of informal workers and the share of informal firms; T _i_ is one of the four different treatment variables; _δi_ are census tract fixed effects, and _δs_ ( _i_ ) _,t_ are state-time or municipality-time specific trends, _γt · Xi_ are census-tract characteristics-time-specific trends that include the distance controls, _εi,t_ , is the error term of the regression. The coefficients of interest are the parameters _βτ_ , and the baseline year is 1994. Since the line was built in 2000, the placebo for parallel trends corresponds to 1999. I compute the standard errors with clusters at the census-tract level.

Figure 4 and table B4 in the Online Appendix report the point estimates for the main outcome, the share of informal workers. I find that workers’ informality rates decrease in locations near line B after the transit shock. I also find evidence of parallel trends since the point estimate is small and not significant in 1999. On average, informality rates decrease between 2.0 and 4.0 percentage points in locations that experienced the shock. The results are similar using the standard definition of informality or a stricter definition of informality that considers only informal and non-salaried workers that do not have an actual contract with the establishment. Moreover, these effects are robust to the use of the Euclidean distance, the walking distance using the network of roads, or 13