(Baqaee and Farhi, 2019).<sup>11</sup> Finally, given the presence of complementarity across suppliers of the same product, we address previously unanswered questions on the importance of firms’ overall connectivity within a production network for the amplification of shocks. We leverage computational innovations in big data to compute the second-order effects of productivity shocks using the entire matrix of production linkages between firms and industries. This innovation helps quantify the non-linear effects of productivity shocks _directly_ using the network without relying on approximations using final sales.<sup>12</sup> Our paper is also related to research on trade collapses during adverse shocks (Behrens et al., 2013; Giovanni and Levchenko, 2009; Bricongne et al., 2012), and shock transmission through GVCs during the Covid-19 pandemic via disruptions to imports, exports, or aggregate production (Bonadio et al., 2021; Baqaee and Farhi, 2020; Cakmakli et al., 2021; Demir and Javorcik, 2020; Gerschel et al., 2020; Heise, 2020; Lafrogne-Joussier et al., 2022; Bas et al., 2023; Chakrabati et al., 2021; Khanna et al., 2022; Miranda-Pinto et al., 2022). In contrast, we analyze how domestic transactions were affected during Covid-19 lockdowns in a large developing country. Our policy motivation stems from the observation that policymakers worldwide are interested in quantifying the trade-off between strict lockdowns that affect GDP through complex buyer-seller networks and more lenient measures that increase production and trade. More importantly, even beyond the Covid-19 crisis, our estimates of how substitutable suppliers are within a product category will help policymakers quantify the economy-wide effects of any disruptive events (e.g., natural disasters or sanctions) on trade and production that are expected to be reasonably short lived.<sup>13</sup>

# 2 DATA AND CONTEXT

**Firm-to-firm trade.** Our primary data source is daily establishment-level transactions with distinct information on establishment locations.<sup>14</sup> This data is provided by the tax authority of a large Indian state with a diversified production structure, around 50% urbanization rates, and high levels of population density. To benchmark the size of this Indian state to other firm-to-firm trade datasets, the population of this state is roughly three times the population of Belgium, seven times the population of Costa Rica, and double the population of Chile.

The data contains daily transactions between all registered establishments in this state and

> 11Prior work has highlighted the aggregate implications of supplier churn (Baqaee et al., 2023; Khanna et al., 2022). We focus on the substitution among suppliers given a fixed set of suppliers as in Baqaee and Farhi (2019).

> 12As firm-to-firm data become common (Panigrahi, 2021; Demir et al., 2024; Dhyne et al., 2021; Alfaro-Urena et al., 2022; Chacha et al., 2022), our methods can be used to quantify shock propagation through complex networks. 13We may hesitate to use these elasticities for exercises on long-term structural transformations.

> 14While we use the term “firm" throughout the paper, our data is actually at the more granular establishment level, and we can identify the parent firms for each establishment as well.

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