the normalized cells of our type-by-type matrix _π_ that captures the total number of links between all types. We normalise the rows of _π_ by dividing each entry in a row by the sum of that row. We abstract from any endogenous network adjustments (see e.g. Panigrahi, 2022; Eaton et al., 2022; Arkolakis et al., 2023). We assume that _υjt_ is an independent draw from a uniform distribution _U_ [ _−_ 10 _,_ 10] for every type _j_ in every time period _t_ . Each type starts with a randomly chosen output drawn from the distribution _U_ [0 _,_ 100] in _t_ = 0.

Using this set-up, we first simulate the output process without any shock. Then, we simulate the output process following a negative output shock to sector-region-size type _j_ ’s value added _υjt_ in the first time period.<sup>29</sup> We repeat this exercise for all types _j_ .

To study the relevance of unobserved informal firms, we conduct our simulation exercise twice. In the first scenario, we use the matrix _π_ derived from administrative records. In the second scenario, we use an alternative version of _π_ using our revised network that accounts for the presence of informal firms.<sup>30</sup> Our primary question is: how do domestic shocks impact each type when informal firms are considered versus when they are not? For each simulated shock, we compute: (i) the absolute reduction in output of each type using the original adjacency matrix (excluding informal firms) and (ii) the absolute reduction in output of each type using the new adjacency matrix (including informal firms), both averaged across all time periods. This yields a matrix of shock impacts where each row corresponds to a supplier who is shocked and each column corresponds to a buyer who faces the impact of the shock. We then aggregate across rows to compute the average impact of the shocks on buyers.

We find that the higher the incidence of informality in a sector and region, the more we underestimate the adverse impact of a domestic output shock. Figure 10 shows that our established employment-based measure of informality negatively correlates with the impact of the shock under the two scenarios. To align with our most granular measure of informality, we aggregate the response to shocks at the sector-region level.<sup>31</sup> As shown in Table C4, a one percentage point decrease in the formal sector share corresponds with a 4.9 percentage point larger output drop following a domestic shock.

Figure C5 presents the distribution of the ratio of the shock impact at the sector-county level (revised network/baseline network). This ratio exceeds one for 48% of the sector-county pairs and 42% of the sector-county-size types, indicating the baseline network on average underestimates the impact of domestic shocks for these types. Among the types for which we underestimate

> 29We compute the impact of the shock on each type’s output over 100 periods of time by comparing the two output processes. All of the outputs reported below are averages across the 100 time periods. 30 We ensure that the random component of output, _υjt_ , is identical across these two scenarios for each type _j_ in every time period _t_ to ensure it does not affect our results.

> 31Put differently, we average the impact across each buyer’s suppliers and then compute a weighted sum for each sector-region cell. The weights are determined by a type’s entry probabilities.

30