et al., 2022), we assume informal firms sell no output to the formal sector and source a smaller proportion of their inputs from formal firms compared to small formal firms. We find that this further reduces spatial inequality in outlinks and the prominence of urban hubs relative to the formal network. Moreover, we continue to underestimate the impact of domestic shocks while overestimating the effects of trade shocks.

Our paper contributes to the literature on macroeconomic development, informality, firm networks, and spatial inequality.

First, we contribute to a growing body of research at the intersection of trade and macroeconomic development that integrates granular administrative data such as employer-employee records and data from credit registries, with broader data sources like population censuses to achieve a more accurate assessment of aggregate economic outcomes. To date, this literature has primarily focused on employment outcomes, sector shares (see e.g. Albert et al., 2021), and consumption (see e.g. Fan et al., 2023), where informal activity is somewhat more observable. However, informal activity along supply chains remains particularly elusive (B ̈ohme and Thiele, 2014; Atkin and Khandelwal, 2020).<sup>8</sup> Our results highlight the implications of the non-random selection of firms into administrative records. This is particularly important, given the growing reliance on such data in the literature (Donaldson, 2025).

Our approach to employ a structural model to bridge gaps in our understanding of informal firm dynamics also aligns with the recent literature in this field (see e.g. Ulyssea, 2018; DixCarneiro et al., 2024). Unlike related studies that focus on firm and worker-level dynamics, we do not model the endogenous response of firms and workers to simulated shocks. Crucially, however, our research design allows us to examine the role of informality for Kenya’s regionlevel input-output matrix. This is particularly relevant for research that seeks to complement predictions about aggregate national welfare with welfare estimates at the regional level to study geographic heterogeneity in the impact of international trade (Topalova, 2010; Arkolakis et al., 2023), infrastructure investments (Demir et al., 2024) or climate and weather shocks (Albert et al., 2021; Castro-Vincenzi et al., 2024).

Second, we contribute to the literature on spatial production networks (Bernard et al., 2019; Panigrahi, 2022; Miyauchi, 2024), shock propagation in firm networks (see e.g. Baqaee and Farhi, 2019; Carvalho et al., 2021; Arkolakis et al., 2023; Chacha et al., 2024), and urban primacy (as published in Jefferson (1989), Jefferson, 1939; Memon, 1976; Ades and Glaeser, 1995; Soo, 2005). We analyze the spatial distribution of formal firms in an economy with a large informal

> 8A related literature in public finance studies why informality arises along supply chains, and how tax policy can alter its incidence (De Paula and Scheinkman, 2010; Zhou, 2022; Gadenne et al., 2022; Almunia et al., 2023). Relative to this literature, we focus on reconstructing a more complete network that includes informal firms rather than studying the marginal firm’s decision to formalize.

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