Kee and Tang (2016). The results also hold when accounting for the presence of zeros in firm capital goods imports, further reinforcing the robustness of the estimates.

To illuminate the mechanism and trace the formation of IO linkages within domestic supply chains following HSR connections, this paper introduces a novel measure that captures firm-level IO relationships using customs transaction data. Previous studies, such as Javorcik (2004), Amiti and Weinstein (2011), Kee (2015), Bernard et al. (2019), and Alfaro-Ureña et al. (2022), rely on either industry IO tables or unique firm datasets with names or tax information to link firms and their suppliers. However, these approaches are often constrained by data limitations and applicability issues. Firm-level IO linkage in this paper is captured by identifying the _potential domestic suppliers_ for each exporting firm based on matching the product codes of the firms’ imports and exports in the customs transaction data. For each exporting firm, the potential domestic suppliers are those firms that export products sharing the same Harmonized System (HS) 6-digit codes with the imported materials of the exporting firm. Instead of using imported materials, exporting firms could have potentially sourced these materials that the same the domestically from firms export products, thereby capturing firmlevel IO linkages along the supply chains. Unlike traditional methods that rely on industry IO tables or detailed firm-level supplier data, this approach accounts for firm heterogeneity, recognizing that each exporting firm within the same industry may have a unique set of domestic suppliers. Moreover, it avoids selection bias since no actual transactions happen between the exporting firms and their potential suppliers. It is also considerably easier in terms of data requirements.

Leveraging the staggered rollout of HSR networks across Chinese cities from 2008 to 2016, this paper presents robust evidence that firm-level DVAR increases as the proportion of potential domestic suppliers connected via HSR grows. These effects are particularly strong when HSR links firms with domestic suppliers over medium to long distances, suggesting that HSR is the dominant mode of transportation for firm-to-firm interactions across these ranges within the supply chain. The impact is most significant for foreign-invested firms, processing exporters, and companies in the electronics industry, where DVAR rises substantially with HSR expansion. Meanwhile, firms importing more homogeneous products—easier to substitute with domestic alternatives—experience greater increases in DVAR. Finally, we observe significant DVAR growth among exporting firms located in cities and regions newly connected by HSR, underscoring the network’s role in enhancing domestic supply chain integration. Robustness checks address the potential endogeneity issue of the regressions and illustrate that the channel via which HSR may affect firm DVAR is through the substitution of domestic for imported materials.

To rationalize the relationship between HSR and firm DVAR, this paper presents a simple twosector model. The downstream final good sector is monopolistically competitive, consisting of ineach has a and a of with a finitely many firms, different productivity produces unique variety output translog function of domestic and imported materials (Diewert and Wales, 1988; Feenstra and Weinstein, 2017). In this setting, a firm’s DVAR rise with the fall of the relative price of domestic to 3