this paper fills the gap by examining how HSR expansion may enable firms to choose domestic over imported materials, thereby driving up firm DVAR and moving China up the GVCs.

This paper first proposes a new methodology to estimate firm-level DVAR from the export revenue function, based solely on customs transaction data. With the rise of GVCs, where production stages span multiple countries, traditional trade statistics are inadequate to measure a country’s contribution, leading to the need to properly measure DVAR in exports. Previous studies, such as Johnson and Noguera (2012) and Koopman et al. (2014), relied on industry input-output (IO) tables to estimate aggregate DVAR. However, IO tables tend to over-represent large firms with higher import-to-sales ratios (Amiti and Koning, 2007), which causes aggregation biases and may underestimate DVAR.3 Kee and Tang (2016) taking a ground-up approach, address the aggregation bias by measuring firm DVAR directly from customs transaction data and constructing industry- and country-level DVAR as the weighted averages of firm DVARs. However, while only customs transaction data is necessary for measuring firm DVAR for the exporters that use all their imports for exports, such as the processing exporters in China, for exporters who also produce for domestic markets, their approach still requires matching customs data with industrial census data to infer the proportion of imports used for exports. The matching requirement severely limits the applicability of their approach in countries without industrial census data. This paper proposes a new method to estimate the proportion of imports used in exports directly from firms’ export revenue function. Relying solely on customs transaction data, this approach eliminates the need for additional industrial census data, making it more applicable for future research and policy work on GVCs for countries with such data limitations.

To estimate the proportion of imports used for exports in the firm-level export revenue function regressions, this paper builds on the classic approach to estimate production function introduced by Olley and Pakes (1996), who use firm investment as a proxy for firm productivity. In this paper, to control for unobserved firm productivity that influences both imports and exports in the export revenue regressions, firm’s capital goods imports —part of a firm’s investment— is used as a proxy. This data is readily available from customs transaction records, allowing for an effective way to account for the unobserved productivity. The results from the firm-level export revenue function regressions show that, as expected, while the estimated proportion of imports used in exports is significantly less than one for the non-processing exporters that also produce for the domestic market, it is not significantly different from one for the processing exporters. This outcome reflects both economic reality and Chinese government regulations, adding credibility to the estimates. Firm-level DVAR is constructed using these estimated import proportions, and then aggregated to the industry and country levels. The resulting aggregate DVAR for China shows an upward trend from 2000 to 2016, driven primarily by processing exporters. This pattern is consistent with earlier findings from Koopman et al. (2012) and Kee and Tang (2016), even with extended time coverage into the more recent years. Robustness checks using bootstrapping confirm that these DVAR estimates are statistically similar to those in

> 3See United Nations Statistical Division (1999).

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