sector and demonstrate that ignoring informality can lead to overestimating spatial inequality in firm-to-firm trade and the extent of urban primacy. This oversight may cause researchers to underestimate the economic connectedness and vulnerability of smaller regions. Our finding that formal sector activity is disproportionately concentrated in urban hubs is also consistent with spatial patterns in other contexts that exhibit a similar formal-core, informal-periphery structure (Z ́arate, 2022). In complementary work, Bacilieri et al. (2023) examine how varying reporting thresholds for firm-to-firm transactions affect the comparability of aggregate network statistics. While they demonstrate that missing data can bias network statistics, our focus differs in two key ways. First, transaction reporting thresholds are not a concern in our setting; we instead address other sources of network incompleteness in tax data. Second, while they analyze aggregate statistics, we examine distributional implications at the regional level – a margin where we find informality plays a substantial role.

Finally, we contribute to the growing literature on estimating network statistics and reconstructing networks with missing data (e.g., Chandrasekhar (2016); Mungo et al. (2023)).<sup>9</sup> We contribute to this literature by proposing the use of a structural approach. The structural route seeks to tackle two challenges: first, nodes are missing in a non-random manner. Second, we do not observe network characteristics for missing nodes, but linking preferences of missing firms can systematically differ from those of observed firms. To address these challenges, we combine multiple data sources and estimate a network formation model that recovers both the prevalence of missing firms and their linking behavior, accounting for sectoral and geographic preferences. In comparison, Mungo et al. (2023), for example, use a link prediction algorithm to predict the existence of links among missing nodes using data on the characteristics and linking behavior of non-missing nodes. This approach is challenging to apply in the context of informality, as informal firms are missing in a non-random manner and information on their linkages are not available at a sufficient granularity to recover a network with sectoral and spatial heterogeneity.<sup>10</sup> The paper is organized as follows: Section 2 describes our data. Section 3 examines how representative the trade patterns captured in the administrative data are of overall economic activity and discusses the role of the informal sector. We present and estimate a network formation model in Section 4. Section 5 describes the patterns we observe in our revised network that now incorporates informal firms, while Section 6 analyzes the sensitivity of these results to alternative assumptions.

> 9See L ̈u and Zhou (2011) for a survey of algorithms used for link prediction, a technique commonly applied in network reconstruction.

> 10Rare instances where researchers have used survey data to collect information on firm-to-firm links of informal firms focus on specific sub-sectors. Moreover, explicitly prompting for the tax status of trade partners adds an additional challenge due to the sensitive nature of this information.

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