reduced-form fixed-effect regression specification for exporting firm _i_ in year _t_ is expressed as: where the dependent variable in this specification is the firm-level DVAR estimate from Section 3, and the key variable of interest is the Connectivity Index (CI), which captures the share of domestic suppliers linked to exporting firms through HSR, reflecting the influence of HSR on firms’ DVAR through their supplier network.15 Control variables include firm-specific measures such as importweighted input tariffs and exchange rate appreciation, along with city-specific metrics such as income levels, FDI, telecommunications infrastructure, internet connectivity, passenger and goods transport, internal city transportation, industrial composition, and population density.

**Potential Domestic Suppliers and Connectivity Index** To construct the CI, we first need to identify each exporting firm’s domestic suppliers. Since data on actual domestic suppliers is unavailable— and even if it were, using such data could introduce selection bias and endogeneity issues, potentially distorting the regression analysis—we avoid these problems by constructing the CI based on each firm’s _potential domestic suppliers_ instead. This approach mitigates bias while still capturing the influence of HSR on firm DVAR through supply chain connections.16

For each exporting firm, potential domestic suppliers are identified as firms that export products with the same HS 6-digit codes as the firm’s imported inputs. For instance, if customs transaction data show that an apparel firm exports men’s shirts and imports cotton fabric under HS code “520819,” all textile firms exporting cotton fabric with the same code are considered potential domestic suppliers. The term _“potential”_ indicates that no direct transaction is observed between the textile and apparel firms, but the fabric produced by the textile firm could theoretically be used as an input for the apparel firm. This approach assumes that products with the same HS 6-digit code are substitutes.

An advantage of this approach—matching upstream and downstream firms based on product codes—is that it eliminates the need for firm-level matching by names or reliance on industry IO tables, which often pose significant data limitations. This method also captures heterogeneity by allowing each firm to have its own set of relevant domestic suppliers, even within narrowly defined industries. Furthermore, since we focus on potential rather than actual suppliers, we avoid issues of endogeneity or selection bias, making the analysis more robust and flexible across various data environments. However, several caveats are worth noting. First, this strategy may overlook domestic 15In Section 6, we establish the theoretical foundation for the Connectivity Index, which captures the role of HSR in linking exporting firms to domestic suppliers.

> 16Alternatively, industry IO tables could be employed to link downstream firms with upstream suppliers, as in Javorcik (2004). However, this approach presents several challenges. First, IO tables, typically available at the regional or industry level, obscure firm-level heterogeneity, potentially leading to aggregation bias. Second, since IO tables are often based on data from larger firms, they may introduce selection bias and underestimate the production linkages among small and medium-sized enterprises. Finally, the infrequent updates of these tables limit their ability to capture annual variation.

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