imported materials. The upstream material sector is perfectly competitive, with suppliers scattered across different locations potentially linked to downstream firms via HSR following a Bernoulli distribution. There are positive iceberg communication costs to connect a downstream firm with its domestic supplier unless the supplier is located in a city that is accessible to the exporting firm via HSR. The number of suppliers, which is also the number of varieties of domestic materials, is endogenously determined in the equilibrium. The HSR expansion boosts connections between exporters and domestic suppliers, leading to lower prices and broader access of domestic inputs. This enables the downstream exporting firms to substitute import with domestic materials, raising DVAR. The model’s predictions — that DVAR increases with greater domestic input variety and firm DVAR increases with the proportion of domestic suppliers that are connected to the exporters via HSR — are consistent with the empirical findings in Kee and Tang (2016) and in this study.

This paper contributes to several strands of literature. First, it advances research on GVCs and value-added trade by proposing a novel approach to estimating DVAR that streamlines the data required for calculation. Two primary approaches are commonly used to measure GVCs (Antràs and Chor, 2022): macro and micro methods. The macro approach, which relies on aggregate World Input-Output tables to assess a country’s GVC participation (Hummels et al., 2001; Antràs and Chor, 2012; Johnson and Noguera, 2012; Koopman et al., 2014; Johnson, 2014), has been widely applied. However, this approach overlooks the rich heterogeneity in firms’ input sourcing and imported input allocation decisions, leading to significant aggregation bias, particularly for countries heavily involved in processing trade (Johnson, 2018; Koopman et al., 2012; Bems and Kikkawa, 2021; Kee and Tang, 2016; Puzzello, 2012).4 Recent micro approaches to GVC measurement use detailed firm-level transactions. Tang et al. (2016) and Bems and Kikkawa (2021) infuse firm information in IO tables, while Kee and Tang (2016) directly estimate DVAR from the customs transaction data. However, these approaches rely on complex firm-to-firm data or matching customs records with census data when firms do not use all their imported inputs for exporting, therefore limiting their scope to specific countries or industries and raising concerns about broader applicability.5 This paper builds on Kee and Tang (2016) by incorporating firm-level heterogeneity through customs data, reducing aggregation bias and complementing IO-based macro approaches. With fewer data requirements, the export revenue function approach applies to a wider range of developed and developing countries, enhancing the external validity of micro-level GVC measures. It also enables bootstrapped standard errors for statistical testing on DVAR estimates. From a policy perspective, this paper emphasizes the critical role of domestic transportation infrastructure—beyond trade and FDI liberalization—in advancing China’s position in 4Moreover, the infrequent updates of IO tables, coupled with significant time lags and discrepancies in benchmark years across countries, pose challenges for researchers attempting to capture the dynamic shifts in global value chains. For a comprehensive review of the GVC literature, including the advantages and limitations of both macro and micro approaches, see Antràs and Chor (2022) and Johnson (2018).

5Even in countries where both accounting-type firm records and transaction-based customs information are available, combining these datasets can be challenging and often leads to matching errors. For a detailed discussion on matching efficiency, see Egger et al. (2021).

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