# **1 Introduction**

Limited opportunities for export-led growth and concerns over the unequal allocation of gains from trade have led policymakers and researchers to shift attention towards domestic supply chains and market integration to enhance economic development (Topalova, 2010; Atkin and Donaldson, 2015; Bustos et al., 2020; Grant and Startz, 2022; Goldberg and Reed, 2023). Progress in understanding the structure of domestic supply chains has been facilitated by the increasing availability of granular transaction-level firm network data, which are often sourced from tax records (see e.g. Panigrahi, 2022; Ad ̃ao et al., 2022; Alfaro-Ure ̃na et al., 2022; Boken et al., 2023).<sup>1</sup> These developments reflect a broader trend in the literature on development and structural transformation, where non-traditional novel micro-data such as credit registries, smartphone data, and matched employer-employee data enable new insights into classic questions (Bustos et al., 2020; Blanchard et al., 2021; Dix-Carneiro et al., 2024; Kreindler and Miyauchi, 2023). However, a potential drawback is that the data-generating process underlying such administrative records is often skewed toward particular segments of the economy, e.g. taxpayers in the case of firm-to-firm transaction data. This can leave us with a potentially incomplete view of economic activity due to the size and significance of the informal sector in many low and middle-income economies.

In this paper, we ask how observing only a selected segment of the economy, in our case, formal firms, might bias the inferences drawn from data on firm-to-firm trade networks constructed using tax records. Accounting for this bias is important because the structure of these networks can shape the aggregate and distributional impacts of infrastructure investments, industrial policies, and financial shocks (see e.g. Acemoglu et al., 2012; Liu, 2019; Demir et al., 2022; Balboni et al., 2023; Castro-Vincenzi et al., 2024; Demir et al., 2024; Huremovic et al., 2024; Donaldson, 2025) and inform tax policy and the design of social protection programs.<sup>2</sup> Formalsector data may not be representative of the overall spatial distribution of trade, especially if formal firms are concentrated in specific sectors and regions and exhibit distinct linking patterns. Given the scarcity of granular data on the informal sector, we lack empirical evidence on the nature and degree of the bias caused by neglecting informality.

We address this question by combining transaction-level administrative tax records of over 76,000 formal firms and 5.8 million firm-to-firm relationships in Kenya, with micro and aggregate data on informal sector activity obtained from labor force surveys and national accounts.<sup>3</sup> We first

> 1Transaction-level survey data (see e.g. Startz, 2021) or administrative industry-specific data (see e.g. Hansman et al., 2020), which often cover a wider range of firm and buyer-supplier characteristics, are a popular, albeit costly, complement to data relying on tax records.

> 2An advantage of transaction-level firm-to-firm trade data over traditional sources like input-output tables is the possibility to track spatial heterogeneity in economic activity, rather than being limited to national aggregates.

> 3The labor force survey module was integrated in the 2019 population census. The public micro data covers

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