metadata: authors: 0: author: Kee,Hiau Looi
1: author: Xie,Enze
2: author: Xu,Mingzhi
count: China,World
docna: 0: docna: Firm Linkages and Domestic Value Added in
            Exports : Moving up the Global Value Chains with High-Speed Railways
docty: Policy Research Working Paper
abstracts: cdata!: Since 2000, China has been upgrading its
            infrastructure, exemplified by the expansion of the
            high-speed railways (HSR), while simultaneously moving up
            the global value chains, evidenced by the rising domestic
            value-added ratio (DVAR) in exports. To analyze the impacts
            of the HSR on China&#x2019;s DVAR, this paper develops a
            new methodology to estimate firm-level DVAR using only
            customs transaction data, without relying on the industry
            input-output tables or matching firm-level industrial census
            data. This paper also proposes a novel way to capture
            firm-level input-output linkages by matching the custom
            product codes of importers and exporters. The results
            confirm that the HSR increases the DVAR through firm
            linkages by connecting downstream exporters with upstream
            domestic suppliers. A two-sector model shows that, by
            improving the probability of exporters connecting with
            low-communication cost domestic suppliers, the HSR decreases
            domestic material prices and increases the variety of
            accessible domestic materials, hence pushing up DVAR.
display_title: Firm Linkages and Domestic Value
            Added in Exports : Moving up the Global Value Chains with
            High-Speed Railways
listing_relative_url: /research/2024/11/34427478/firm-linkages-domestic-value-added-exports-moving-up-global-value-chains-high-speed-railways
topicv3: Infrastructure and Growth m1326244 1083,Global Value Chains(GVC) m1326472 767,Trade m1326924 698
publishtoextweb_dt: 2024-11-25T00:00:00Z
dois: 10.1596/1813-9450-10985
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 55
author_keys: 0: author_key: 559677
1: author_key: 2180081
2: author_key: NA
model_extractions: Policy Research Working Paper 10985 Firm Linkages and Domestic Value Added in Exports Moving up the Global Value Chains with High-Speed Railways _Hiau Looi Kee Enze Xie Mingzhi Xu_ Development Economics Development Research Group November 2024
Policy Research Working Paper 10985

# **Abstract**

Since 2000, China has been upgrading its infrastructure, exemplified by the expansion of the high-speed railways (HSR), while simultaneously moving up the global value chains, evidenced by the rising domestic value-added ratio (DVAR) in exports. To analyze the impacts of the HSR on China’s DVAR, this paper develops a new methodology to estimate firm-level DVAR using only customs transaction data, without relying on the industry input-output tables or matching firm-level industrial census data. This paper also proposes a novel way to capture firm-level input-output linkages by matching the custom product codes of importers and exporters. The results confirm that the HSR increases the DVAR through firm linkages by connecting downstream exporters with upstream domestic suppliers. A two-sector model shows that, by improving the probability of exporters connecting with low-communication cost domestic suppliers, the HSR decreases domestic material prices and increases the variety of accessible domestic materials, hence pushing up DVAR.

This paper is a product of the Development Research Group, Development Economics. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at hlkee@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
Firm Linkages and Domestic Value Added in Exports: Moving up the Global Value Chains with High-Speed Railways<sup>*</sup> Hiau Looi Kee Enze Xie World Bank Zhejiang University, World Bank Mingzhi Xu Peking University

**Keywords** : Domestic value-added in exports, firm input-output linkages, global value chains, high-speed railways, domestic suppliers

**JEL Classification Number** : F10, H54, F14, R40, D21

> *We thank Treb Allen, Rob Feenstra, Keith Head, Hans-Jörg Schmerer, He Wang, Shangjin Wei, Kei-mu Yi, and participants at the 3rd Banca d’Italia, European Central Bank and World Bank Conference on “Trade, Value Chains and Financial Linkages in the Global Economy,” Peking University and World Bank for valuable comments. Special thank to Kunfu Zhu for providing estimates for foreign content embodied in domestic materials and domestic content embodied in foreign materials. Hiau Looi thanks the Vatican police for letting her fulfill her promise and wishes. She dedicates this paper to her father, whose frequent trips to China taking HSR to remote places inspired her, and formed the genesis of this paper. This paper has been supported by the World Bank’s Multidonor Trust Fund. Mingzhi Xu is thankful for financial support from the Natural Science Foundation of China (Grant No.72322007). The authors declare that they have no relevant or material financial interests that relate to the research described in this paper. The results and opinions presented in this paper do not represent the views of our institutions, the Executive Directors, or the countries they represent. Hiau Looi Kee: Development Research Group, the World Bank, hlkee@worldbank.org. Enze Xie, corresponding author: School of Economics, Zhejiang University, and Development Research Group, the World Bank, enzexie@zju.edu.cn. Mingzhi (Jimmy) Xu: Institute of New Structural Economics, Peking University, mingzhixu@nsd.pku.edu.cn.
_“I am taking the HSR to Shanghai for a meeting because of punctuality and it allows me to come back to Beijing on the same day. HSR improves the connection between businesses, allowing for more face to face contacts, particularly for trips up to 5 hours.”_ _“I am traveling to Shanghai to meet my suppliers. I can introduce you to my suppliers if you need recommendations.”_ _“I will let you know the details regarding the design after I have seen it in person, Director Pan. Don’t worry, I will make sure the chips and electric board are good before agreeing to the contract.”_ — _Three business passengers on board the Beijing-Shanghai High-Speed Train G1, 06/19/2024_

# **1 Introduction**

Since the early 2000s, China has been experiencing a dramatic upgrading of domestic infrastructure, exemplified by the construction of high-speed railways (HSR). Against this backdrop of massive infrastructure modernization, China is also deepening its engagement in global value chains (GVCs), manifested by its rising domestic value-added ratio (DVAR) in exports, driven by firms substituting domestic for imported materials (Koopman et al., 2014).1 GVCs often require firm-specific investment and intense face-to-face communication (Antràs and Chor, 2022). By easing domestic travels and increasing the frequency and convenience of communication between exporting firms and domestic suppliers, could HSR provide the vehicle, pun intended, promoting the substitution of domestic for imported materials, thereby increasing firm DVAR and moving China up the GVCs?

This paper explores the role of HSR in facilitating China’s rising DVAR in exports through firmlevel input-output (IO) linkages between upstream domestic intermediate input suppliers and downstream output exporters. It is well accepted that supply chain management focusing on production network is vital for the survival and performance of firms (Bernard and Moxnes, 2018; Bernard et al., 2019). Factors that may improve domestic firm-to-firm linkages may, therefore, affect domestic input sourcing and the resulting DVAR. While the impacts of transportation infrastructure, such as airways, roads, and railways, on trade and development, have been well studied, the effect of modern transportation, such as HSR, on a firm’s sourcing decision to choose between domestic and imported materials —one of the defining characteristics of firms engage in GVCs —has been largely overlooked.2 By emphasizing the linkages between upstream and downstream firms along the production chain,

> 1China’s rising domestic content in export has also been documented in Koopman et al. (2012) and Johnson and Noguera (2012). Kee and Tang (2016) further shows that the rising DVAR is primarily driven by the processing exporters substituting domestic for imported materials when a greater variety of domestic materials became available at lower prices, driven by input tariff reduction and foreign direct investment (FDI) liberalization during the early 2000s.

> 2Li and Li (2013), Faber (2014), Donaldson and Hornbeck (2016), Baum-Snow et al. (2017), Fan et al. (2023), and Donaldson (2018) investigate the economic impact of highways and railroads in different settings, while Söderlund (2023) emphasizes the importance of airways in facilitating face-to-face business travel that promotes trade.

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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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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
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).

4
GVCs by strengthening IO linkages and fostering business interactions.

Second, this paper connects to emerging research on firm IO linkages and their effects on sourcing decisions. Most studies have relied on industry IO tables to measure these linkages. Amiti and Koning (2007), using Indonesia’s industry IO table, demonstrate that lowering upstream input tariffs leads downstream firms to increase imports and boost productivity. Similarly, Javorcik (2004), drawing on Lithuania’s IO data, find that downstream FDI generates significant positive spillovers for upstream domestic firms. More recent work, leverages granular data to trace firm-to-firm connections. Amiti and Weinstein (2011) use a unique dataset to link Japanese firms to their banks to study the effects of the health of banks providing trade finance and growth in a firm’s exports relative to its domestic sales. Kee (2015) identifies firm IO linkages between downstream exporters and local suppliers by matching names and addresses. The study reveals significant positive horizontal spillovers, known as sharedsupplier spillovers, when both FDI and domestic firms rely on common local input suppliers. AlfaroUreña et al. (2022) match tax IDs of firms in Costa Rica and show that domestic firms experience significant performance gains when they become suppliers to FDI firms. Alfaro et al. (2024) match shipment-level import data with the U.S. credit register to obtain information on importer-supplier relations at the firm level and credit relationships at the bank-firm level. Arnarson et al. (2024) analyze labor division along the supply chain for generating scale economies and productivity growth, by linking buyers and suppliers in Denmark and Sweden. Finally, a closely related study by Bernard et al. (2019) examines how HSR promotes domestic outsourcing among Japanese firms, using a detailed dataset of supplier-customer links across 800,000 firms. Complementing existing approaches, this paper introduces a straightforward method to capture firm-level IO linkages by connecting exporters to potential domestic suppliers. Moreover, the simple two-sector model provides an easy theoretical treatment of firm IO linkages through relative input prices.

This paper further expands the growing literature on the impacts of modern transportation infrastructure projects, specifically HSR, on economic development. Many recent works look specifically at the wide-ranging effects of HSR in China, on topics such as income growth (Lin, 2017; Qin, 2017), innovation (Gao and Zheng, 2020), profitability (Kuang et al., 2021), investment efficiency (Wu et al., 2022), bank loan price (Geng et al., 2023), trade credit (Ouyang et al., 2024), skill demand and inequality (Xu and Hu, 2024). Besides these firm-level analyses, Fang et al. (2023) show the entry of HSR leads to significant reductions in airlines travel delays, while Lin et al. (2023) find that connection to the HSR increases intercity investment. Beyond China, Charnoz et al. (2018) show that HSR enables firms in France to outsource more to their remote affiliates, while Bernard et al. (2019) shows that firms in Japan are more inclined to outsource production tasks to other firms connected via HSR. Gumpert et al. (2022) show that in Germany, HSR enhances the circulation of managers, which not only improves information transmission but also increases monitoring efficiency. Most of these studies look at the role of HSR in improving the exchange and acquisition of information, as well as in reducing logistics and communication costs. Complementing these findings, this paper provides 5
the first evidence with a theoretical underpinning on how IO linkages facilitated by HSR connecting exporting firms with their domestic suppliers, enable the substitution of domestic for imported materials, thus pushing up the firm and aggregate DVAR in exports.

The rest of the paper is structured as follows: Section 2 covers the background of HSR development in China. Section 3 introduces the new approach to estimate firm-level DVAR using only customs transaction data, and section 4 details the empirical specifications and variable measurements. Section 5 presents the effects of HSR expansion on firms’ DVAR, along with robustness checks. The model is outlined in section 6, and conclusions are drawn in section 7. The Appendix provides a detailed discussion on the datasets used and the construction of key variables. It also provides additional robustness checks on the city-level impact of HSR connections on firms’ DVAR.

# **2 Background of HSR Development in China**

In the late 20th century, China invested heavily in transportation infrastructure, such as railways and expressways, to promote economic development. However, the previous transportation infrastructure construction mainly focused on freight transport, while passenger travel remained comparatively underdeveloped, suffering from low-quality service and constrained travel speed.

To mitigate the overloaded transportation network at the time and provide significantly improved passenger travel services, the Medium and Long Term Railway Plan (MLTRP) was approved in 2004, initially proposing the construction plan of the HSR network. High-speed rail typically refers to rail systems designed for trains traveling at speeds exceeding 250 km/h.6 The MLTRP proposed four horizontal and four vertical form corridors, linking nearly all major cities in China. Accelerated by the fiscal stimulus aimed at helping the economy recover from the global financial crisis, the construction of the HSR speeded up since 2008. In 2016, the MLTRP was further revised to expand China’s highspeed rail network to eight horizontal and eight vertical corridors. After years of development, China now boasts the largest high-speed rail system worldwide (Lawrence et al., 2019). By the end of 2015, the network spanned 19,000 kilometers and connected 190 cities, solidifying its position as the world’s longest high-speed rail system. This rapid expansion, particularly between 2008 and 2015, provides a unique opportunity to analyze the causal effects of high-speed rail on social and economic outcomes.

**Comparative Analysis of HSR and Other Modes of Transportation** Compared to other modes of transportation, HSR has the following important and unique attributes: _(1) Passenger-dedicated:_ HSR is exclusively used for passenger transportation instead of freight. As a result, HSR expansion

> 6In comparison, the common speed limit of the national trunk highway is 120 km/h. Before the major expansion of the network in 2007, there was only one passenger-dedicated high-speed rail line between Qinhuangdao and Shenyang, introduced in 2003 with a top speed of 200 km/h. Technically speaking, the Qinhuangdao-Shenyang line was not used as an HSR as the carrying speed was below the minimum speed criteria, and it functioned as the pilot railway to test the feasibility of HSR operations.

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reduces communication costs between regions rather than trade costs. _(2) Mainly used for business purposes:_ Survey data indicates that nearly half of HSR travel is for business purposes, with leisure and commuting making up the remaining major segments (Lawrence et al., 2019; Lin, 2017).7 _(3) Designed for middle-distance journeys:_ As pointed out by Lawrence et al. (2019), HSR dominates travel between 150 km and 800 km and competes with air travel for distances up to 1,200 km.8 _(4) Frequent, convenient, punctual, safe and high-quality service:_ Compared to airlines, HSR is less impacted by weather, features simpler security checks, and involves shorter waiting times. Unlike airports, HSR stations are centrally located, offering more convenient access. Passengers can also use phones and the internet during travel, reducing the opportunity cost of time—an advantage particularly valuable for business travelers.9 _(5) Stable and relatively low price:_ HSR fares in China are relatively low compared to other countries, making it an attractive option for passengers across various income levels.

In short, the expansion of HSR has had a profound impact on business travel, reshaping the flow of information and business opportunities across regions in China. This development has facilitated better matching between production and consumption, as well as between different production hubs.10

**Network Effects of the HSR** Beyond these important attributes, the most defining feature of HSR is its _network effects_ . While direct HSR connections between cities are crucial, the value of indirect connections –whether through transfers across multiple HSR lines or integration with other transport modes –plays an even bigger role. For instance, the Zhengzhou-Xi’an line was initially an isolated route, serving only passengers between these two cities. However, after connecting to the BeijingGuangzhou HSR line in 2013, its passenger volume surged by 43 percent, and passenger-kilometers increased by 72 percent. By 2016, almost half of the passengers on the Zhengzhou-Xi’an line were traveling from stations beyond the immediate route (Lawrence et al., 2019).

Given the network characteristics of HSR, the effect of an HSR opening in a city on firms’ DVAR depends not only on direct connections but also on the extent of indirect links across multiple lines. It also hinges on how many firms in those connected prefectures have potential IO linkages with the initial firm. Figure A1a illustrates the number of prefecture pairs connected via HSR, both directly and indirectly, over the sample period. As shown in Figure A1b, HSR construction surged after the financial crisis, steadily increasing the number of prefecture pairs connected both directly and

> 7Ouyang et al. (2024) present recent evidence from a survey of corporate managers, revealing that site visits are crucial for both suppliers and customers in overcoming information asymmetry. These visits play a key role in building and maintaining supplier-customer relationships, especially for middle-to-long distance travel, ranging from 200 to 500 km. 8In 2013, the average travel distance on the Beijing-Shanghai and Beijing-Guangzhou lines, two of the most important HSR routes in China, was around 500 km. Amos et al. (2010) suggests that HSR holds a comparative advantage over airlines for trips under 800 km.

> 9Fang et al. (2023) highlights that delays are a persistent issue in China’s airline industry, making the punctuality and convenience of HSR a significant competitive threat to air travel.

> 10Business interviews conducted in cities newly connected to HSR, as summarized by Lawrence et al. (2019), reveal that while HSR has limited influence on the location choices of manufacturing firms, it significantly boosts business travel. Sales, procurement, and professional service staff, in particular, heavily rely on HSR for their travel needs.

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indirectly. However, after 2010, indirect connections grew at a much faster pace due to strong network effects. By the end of our sample period, 36% of prefecture pairs were linked indirectly, compared to just 2.6% connected directly. In summary, the substantial network effects from indirect HSR connections became increasingly prominent in the late 2010s. In the subsequent analysis, if two prefecture-level cities are linked either directly or through transfers on HSR lines, we consider them to have an established HSR connection.

# **3 Estimating DVAR Based on the Export Revenue Function**

This section introduces a novel methodology to estimate DVAR in exports, by directly estimating the share of imports used for exports from firm’s export revenue function. This method extends the applicability of DVAR estimation as in Kee and Tang (2016), but without requiring the matching of customs transactions to firm census data.

# **3.1 Conceptual Framework**

Let’s start with the export revenue function of an exporting firm, who use both domestic materials ( _M_<sup>_D_</sup> ) and imported materials ( _M_<sup>_W_</sup> ), combined with labor ( _L_ ) and capital ( _K_ ) to produce output for exports and domestic sales. For each year _t_ , the firm’s total export revenue _EXPit_ comprises the following components specific for export: export profits ( _πit_ ), cost of labor ( _wtLit_ ), cost of capital ( _rtKit_ ), cost of domestic materials � _p_<sup>_D_</sup> _t_<sup>_M D_</sup> _it_ �, and cost of imported materials � _p_<sup>_W_</sup> _t_<sup>_M W_</sup> _it_ �: From customs transaction data, we observe the total value of firms imports, _IMPit_ . Let’s define 0 _≤ κ ≤_ 1 equals the share of imports that is used for exports, such that _p_<sup>_W_</sup> _t_<sup>_M W_</sup> _it_<sup>=</sup><sup>_κIMPit_.Then</sup> (1) can be rewritten as Note that (2) is a structural relationship between the export value of a firm and the import value of a _κ_ . firm, which may allow for the estimation of the share of imports used for exports, As in Kee and Tang (2016), we define the domestic value added (DVA) in export as the sum of export-specific profits, costs of labor, capital, and domestic materials used for export: 8
and the domestic value added ratio (DVAR) in exports is defined as the share of DVA in exports: However, the domestic materials used in the production of exports may contain foreign contents, which should not be included in DVA. In contrast, the imported materials used in the production of exports that may contain domestic contents, which should be included in DVA. We define the foreign content embedded in domestic materials used for exports, and the domestic content embedded in imported materials used for exports, as _δ_<sup>_F_</sup> and _δ_<sup>_D_</sup> , respectively, expressed in terms of the shares of exports for both. Thus, the DVAR definition is subject to the following adjustment: Wang et al. (2013) provides estimates for foreign content embedded in total domestic production and domestic content embedded in total imports, both estimates can be used to infer _δ_<sup>_F_</sup> and _δ_<sup>_D_</sup> .11 There are two trade regimes in China, processing trade and ordinary trade (non-processing trade).12 Legally, the processing firms are required to use all their imports as imported intermediate inputs for exports, implying that _κ_ = 1, and thus: In contrast, for the ordinary or non-processing exporters, only a fraction of their imports, 0 _≤ κ ≤_ 1, will be used as inputs for the export production, given that these firms also produce for the domestic markets. To construct the DVAR for these firms, proper estimates for _κ_ are needed. Kee and Tang (2016) relies on industry census data for total material and total revenue at firm level to infer _κ_ for each firm. However, while customs transaction data is often accessible, matching it with less accessible industrial census data can be challenging, which limit the broader applicability of their method.13 To address this difficulty, we estimate _κ_ based on (2), allowing for a more direct calculation without requiring matched census data:

> 11Given that not all domestic materials are used in the production of exports, to obtain the part of foreign content embedded in domestic materials used for export, we multiply the estimates with _κ_ , which is the share of imports used for exports. The assumption is that the share of foreign content embedded in domestic materials used for exports and the share of imports used for exports are the same. Likewise for domestic content embodied in imports, given that not all imports are used for the production of exports, to obtain the domestic content embodied in imports that are used for exports, we multiply the estimates with _κ_ .

> 12Please refer to Branstetter and Feenstra (2002) for detailed discussions about the trade regimes of China. 13Please refer to Brandt et al. (2017) for a discussion on merging difficulties, particularly for the post-2007 period.

9
7 _κ_ Equation ( ) shows that it is possible to estimate , based on firm-level exports and imports data. In particular, if _DV Ait_ can be controlled nonparametrically by _Dit_ ( _·_ ), then (7) can be reformulated into a regression with a classical measurement error term, _εit_ : where the coefficient on imports, _κ_ , has a structural interpretation as the share of imported inputs used specifically for the export production.

With an unbiased estimate of _κ_ , we can adjust firm-level imports to determine _DV ARit_ for the ordinary exporters:

# **3.2 Econometric Specification**

The import coefficient has a structural interpretation as the share of imports used for exports, when regress firm’s exports on imports according to (8). This is analogous to the production function estimation, where regressing firm’s output on inputs, resulting in the structural interpretations of the input coefficients as input shares or elasticities. Similar to the production function estimation which is subjected to simultaneity bias, estimation of _κ_ according to (8) could be inconsistent due to factors that affect both exports and imports of firms. One such factors is firm’s productivity: productivity increases could simultaneously raise both imports and exports, leading to an upward bias in the least squares estimate. In contrast, another factor could be currency depreciation, which might reduce imports while boosting exports, causing a downward bias. The net effect of these opposing forces is empirical and cannot be determined without further investigation.

To control for the potential bias in the regression of (8), firm-level exchange rate data can be constructed according to Kee and Tang (2016) and introduced directly in the regression. However, is unobserved and econometric to address. One firm productivity requires specific techniques way is to use an instrumental variable (IV) correlated with imports but unrelated to exports. Given that firm-level IVs are often difficult to identify, here we adopt the control function approach from Olley and Pakes (1996) (OP) to control for the unobserved productivity. Intuitively, firms make decisions on inputs and investments based on their productivity. Part of the investment is imported capital goods, which can be observed from customs transaction data. Thus, firm-level imported capital goods could serve as a for the unobserved similar to OP used investment in proxy productivity, firm production function estimation to proxy for productivity.

10
**Behavioral Framework** We assume that a firm’s current profit depends on its own state variables, including firm productivity ( _φ_ ), exchange rates ( _E_ ), and capital stock ( _K_ ). Profits are also influenced by other common factors, such as factor prices. At the start of each period, an incumbent exporting firm observes the exchange rate of each source country, its productivity and capital stock, and makes two decisions simultaneously: (1) how much to use in variable inputs, including imported materials, _P_<sup>_W_</sup> _M_<sup>_W_</sup> , which is part of imports, _IMP_ , and (2) the optimal level of investment, which includes imported capital goods, _I_<sup>_W_</sup> . We abstract from modeling firm entry and exit decisions in export markets, as the customs transaction data cannot reliably distinguish true exits from missing values. Following OP, we assume that productivity ( _φ_ ) evolves according to a Markov process, leading to a Markov perfect Nash equilibrium in firms’ decision regarding capital goods imports. With this framework, we can use observed capital goods imports to control for the unobserved productivity, accounting for exchange rates, capital stock, and other common state variables. The imported capital stock of each firm, _Kit_<sup>_W_, is constructed using the perpetual inventory method, with a depreciation rate</sup> of _γ_ :

where (11) describes the law of motion for firm-level imported capital stock, which is determined by adding current-period imported capital good investment to the existing imported capital stock, after accounting for depreciation. In other words, at the start of each period, after observing productivity, exchange rates, and current imported capital stock, firms decide on their investment in imported capital goods. This investment augments the existing capital stock, net of depreciation, to establish the capital stock level for the following period.

**Estimation** The regression specification in (12), which is based on the export revenue identity (8), provides a structural interpretation of the coefficient on imports, _κ_ , which represents the share of imported inputs used in the production of exports:

where the dependent variable, _EXPit_ , represents the value of exports for firm _i_ in year _t_ , with the key variable of interest _IMPit_ , being the value of imports. We decompose the domestic value added of firm _i_ in year _t_ into the following terms: (1) _D_ ( _Eit, Kit_<sup>_W_),which is a function of firm’s exchange</sup> rate, _Eit_ , and imported capital stock, _Kit_<sup>_W_;(2)yearfixed-effects,</sup><sup>_αt_,whichcapturecommonstate</sup> variables across firms, such as wages and rental prices of capital; (3) firm fixed-effects, _αi_ , which account for firm-specific characteristics, such as foreign ownership and processing trade status; and 11
(4) firm productivity, _φit_ , which is unobserved by researchers but known to the firm when making import and investment decisions. The error term, _εit_ , captures measurement errors or unexpected productivity shocks.

As in OP, given the assumption that productivity evolves according to a Markov process, _φit_ is therefore monotonically increasing in investment when investment is larger than zero, allowing us to invert (10) to construct a control function for _φit_ : which can be substituted into (12) to arrive at the following specification: with where _φ_ ( _Iit_<sup>_W, Eit, K_</sup> _it_<sup>_W_) can be proxied semiparametrically using a higher-order polynomial function in</sup> the regressions. Although we cannot separately identify _D_ ( _Eit, Kit_<sup>_W_) and</sup><sup>_φit_, least squares estimation</sup> of (14) will still yield a consistent estimate of _κ_ . In practice, we estimate (14) for both processing and non-processing firms using Chinese customs transaction data from 2000 to 2016. For processing firms, we expect the import coefficient to be close to one, while for non-processing firms, we anticipate the coefficient to be significantly less than one.

# **3.3 Results of DVAR Estimation**

Equation (14) serves as the foundation for our industry-level estimations, where we allow _κ_ to vary between processing and non-processing firms within the same industry. We test the hypothesis that _κ_ for processing firms is significantly higher than for non-processing firms and, moreover, that _κ_ for processing firms is not statistically different from one. Table 1 presents the pooled regression results based on (14). Column (1) shows the least squares estimation with only firm and year fixedeffects, yielding an estimated _κ_ of 0.756. In Columns (2) and (3), we separate ordinary exporters from processing exporters. The import share for ordinary exporters is estimated at 0.352, while for processing exporters it is significantly higher at 1.112. Columns (4) and (5) incorporate a thirdorder polynomial function of investment, capital, and exchange rates to better control for unobserved productivity. The import shares are lower, suggesting the polynomial function effectively accounts for productivity. Importantly, the import share for processing firms remains significantly higher than for ordinary firms, and is not statistically different from one, as expected.

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Table 1: Share of Import Used for Exports: Baseline Pool Regressions

|Industries|(1)<br>All|(2)<br>Ordinary|(3)<br>Processing|(4)<br>Ordinary|(5)<br>Processing|
|---|---|---|---|---|---|
|Dependent Variable|EXP|EXP|EXP|EXP|EXP|
|IMP|0.756***|0.352***|1.112***|0.327***|1.014***|
||(0.030)|(0.023)|(0.031)|(0.031)|(0.039)|
|_φ_(I, E, K)|No|No|No|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|
|Observations|419,030|185,493|226,042|68,250|36,691|
|Adjusted R<sup>2</sup>|0.726|0.720|0.779|0.737|0.801|

Notes: Robust standard errors clustered at the industry-year level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level.

Table 2 presents the regression results at the industry level, with the polynomial control function only added in the even numbered columns. Ordinary and processing firms are pooled together, and an interaction term between the processing firm dummy and imports allows us to estimate separate import shares for each group. The results indicate that processing firms across all industries have significantly higher import shares, consistently close to one. In contrast, the import shares for non-processing firms range from 0.114 to 0.673, with a simple average of around 0.321. Notably, we cannot reject the that use all their materials for whereas hypothesis processing firms imported exports, ordinary exporters only use part of their imports for export production. Based on these findings, we set the import shares of processing firms to one when constructing their DVAR. For ordinary firms, DVAR will be constructed using the industry-specific import shares estimated in this table.

Figures 1 and 2 present the DVARs at aggregate country- and industry-levels, for the processing and non-processing exports from 2000 to 2016. These aggregate DVARs are constructed as the export share weighted average of firm-level DVAR, according to Kee and Tang (2016). The confidence intervals of these aggregate DVARs, based on the associated bootstrap standard errors, are also shown in the figures. Over the sample period, China’s DVAR in exports has steadily increased, from 0.382 in 2000 to 0.746 in 2016. This is driven largely by the rise in the DVAR for processing exports, from 0.341 to 0.641, while the DVAR of ordinary exports has remained stable at around 0.853 over the sample period. Similar pattern is also observed at the industry level with processing exports display increasing DVARs in nearly all industries.

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Table 2: Share of Im ort Used for Ex orts: b Industr <u>p p y y</u>

||(1)|(2)|(3)|(4)|(5)|(6)|
|---|---|---|---|---|---|---|
|Industries|Chemicals,<br>and Tran|Machinery<br>sportation|Electroni<br>and Optical|cs, Metals<br>Equipment|Furn<br>and|iture<br>Wood|
|Dependent Variable|EXP|EXP|EXP|EXP|EXP|EXP|
|IMP|0.298***|0.199***|0.462***|0.373***|0.425***|0.403***|
||(0.019)|(0.029)|(0.026)|(0.032)|(0.046)|(0.070)|
|Processing_×_IMP|0.664***|0.768***|0.578***|0.691***|0.565***|0.718***|
||(0.027)|(0.045)|(0.038)|(0.062)|(0.071)|(0.123)|
|Observations|91116|37901|124516|48633|27827|3526|
|Adjusted R<sup>2</sup>|0.716|0.739|0.739|0.781|0.723|0.781|
||(7)|(8)|(9)|(10)|(11)|(12)|
|Industries|Miscel|laneous|Plastic,<br>and|Rubber<br>Toys|Tex<br>and A|tiles<br>pparels|
|Dependent Variable|EXP|EXP|EXP|EXP|EXP|EXP|
|IMP|0.272***|0.114**|0.188***|0.165***|0.773***|0.673***|
||(0.037)|(0.050)|(0.018)|(0.033)|(0.061)|(0.058)|
|Processing_×_IMP|0.929***|1.021***|0.892***|0.800***|0.542***|0.462***|
||(0.054)|(0.145)|(0.041)|(0.109)|(0.070)|(0.119)|
|Observations|49708|4820|43356|6279|77819|3178|
|Adjusted R<sup>2</sup>|0.76|0.78|0.8|0.812|0.741|0.773|
|_φ_(I,E,K)|No|Yes|No|Yes|No|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|Yes|Yes|Yes|Yes|

Notes: Robust standard errors clustered at the industry-year level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level.

14
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the results. For all the overlapping years, we cannot reject the null hypothesis, indicating that our current estimates are statistically indistinguishable from those in Kee and Tang (2016).

Table 3: Robustness Check: Comparisons with Kee and Tang (2016) via Bootrapping

|Year|2000|2001|2002|2003|2004|2005|2006|2007|
|---|---|---|---|---|---|---|---|---|
|(1) DVAR|0.922|0.916|0.906|0.896|0.899|0.914|0.907|0.907|
|(2) DVAR (Kee and Tang,2016)|0.924|0.915|0.918|0.914|0.900|0.893|0.904|0.900|
|(3) Diference (absolute value)|0.002|0.001|0.012|0.018|0.001|0.021|0.003|0.007|
|(4) Standard Deviation|0.020|0.024|0.026|0.024|0.023|0.019|0.019|0.022|
|(5) T-statistics|0.075|0.022|0.475|0.740|0.061|1.148|0.139|0.338|

Notes: Row (1) presents the aggregate country-level DVAR of the ordinary exports of the current approach; (2) is the aggregate country-level DVAR of the ordinary exports from Kee and Tang (2016); (3) shows the absolute differences between (1) and (2); (4) is the bootstrap standard deviation of (1), based on 1,000 repetitions; (5) divides (3) with (4) to obtain the t-statistics under the null hypothesis that (3) is not different from zero.

There could be a concern that the presence of zero’s in capital good imports could bias the results, as firms do not import capital goods every period. The zeros in capital goods imports may be meaningful and be due to omitted variables that are also correlated with and lead to may important imports inconsistent estimates of _κ_ . To address this, we employ a Heckman selection model. In the first stage, we use a fixed-effects conditional logit regression to explain the likelihood of positive capital goods imports. We then incorporate the inverse Mill’s ratio from this regression as a control variable in the export revenue function estimation. The first-stage model includes firm-level import-weighted and export-weighted exchange rates, firm-level tariffs, and a full set of year and firm fixed-effects.

The results demonstrate that the first-stage selection model effectively explains the presence of positive capital goods imports, with a statistically significant Chi-squared value of 3494.66. When _κ_ the inverse Mill’s ratios are included in the second-stage export revenue estimations, the estimated values increase slightly but remain statistically similar to the original estimates. Moreover, the inverse Mill’s ratios are generally significant. These findings suggest that selection bias due to zero capital goods imports is likely not an issue for this dataset. Full results are available upon request.

# **4 Empirical Specification**

This section presents empirical evidence on the impact of HSR on firm-level DVAR. Our main analysis focuses on the period from 2007 to 2016, when the full-scale HSR expansion began.14 The firm-level

> 14Only one pilot HSR line, the Qinhuangdao-Shenyang Line, was constructed between 2003 and 2007. As noted by Xu and Hu (2024), this line did not meet the speed criteria for an official HSR. By contrast, Lawrence et al. (2019) identified the Beijing-Tianjin line, which opened in 2008, as China’s first fully operational HSR line. Additionally, Faber (2014) noted that China’s national trunk highway system was completed ahead of schedule by the end of 2007. Focusing on the period between 2007 and 2016 allows us to exclude the confounding effects of national highway construction.

16
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.

17
suppliers that do not engage in exports and thus are absent from the customs transaction data. Second, it miss intermediate that are but not this may products produced domestically imported. Lastly, approach assumes that products classified under the same HS 6-digit code are substitutes, implying that imported goods can be replaced with their domestically produced counterparts, which may not always hold true in practice.

Based on the definition of potential domestic suppliers, the CI for a single imported product ( _j_ ) of each exporting firm ( _i_ ) in each year ( _t_ ) is calculated as follows: where _Njt_ is the total number of potential suppliers of product _j_ in year _t_ , which equals the total number of exporters of product _j_ in year _t_ . The indicator variable, _Mjit_ , takes on the value 1 if firm _i_ imports product _j_ in year _t_ , and 0 otherwise; Likewise, the indicator variable, _dnit_ , takes on the value 1 if the potential supplier _n_ is connected to firm _i_ via the HSR network in year _t_ . So the denominator is the total number of potential suppliers of a given imported product in a given year, and the numerator is the total number of potential suppliers that are connected via HSR to the exporting firm in a given year. In short, for each imported product _j_ , the variable, _CIjit_ , is the share of potential suppliers that are connected to firm _i_ via HSR. The variable _CIjit_ takes a value of 0 if firm _i_ does not import product _j_ in year _t_ or if none of the potential suppliers are connected to firm _i_ through the HSR network. Conversely, if all potential suppliers of product _j_ are connected via HSR, _CIjit_ equals 1. A higher value of _CIjit_ indicates a greater likelihood that firm _i_ can source material _j_ domestically through HSR-enabled connections.

For firms importing multiple products, the variable _CIit_ represents the weighted average share of potential suppliers connected to the firm via HSR. The weights correspond to the share of each product _j_ in firm _i_ ’s total imports for year _t_ , capturing the importance of HSR connections across the firm’s product portfolio.

where _sjit_ is the share of product _j_ in the total import of firm _i_ in year _t_ . If a larger share of products firm _i_ imported has potential local suppliers that are connected via HSR, the likelihood that firm _i_ may source locally will be higher and may result in a higher DVAR. In addition, we refine the CI by incorporating the distance between potential suppliers and the exporting firm. Specifically, if the distance between firm _i_ and potential supplier _n_ falls within a certain range, _dint_ is assigned a value of 1; otherwise, it takes the value of 0. This adjustment allows us to capture more granular variations in how distance influences the likelihood of sourcing materials locally.

Figure 3 presents the evolution of CI from 2007 to 2016. The black line is the unrestricted CI which represents the share of potential suppliers that are connected to exporting firms via HSR, both 18
<!-- Start of picture text --> — Distance Unrestricted — 150km-800km — 150km-1200km<br>— 150km-1600km — 150km-2300km<br>8<br>6<br>x<<br>@®<br>no}<br>£<br>i}<br>Ss 4<br>@O<br>c<br>Cc<br>fo}<br>o)<br>2<br>0<br>2007 2008 2009 2010 2011 2012 2013 2014 2015 2016<br>Year<br><!-- End of picture text -->
Table 4: The Impact of HSR on Firms’ DVAR: Baseline Results

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|
|CI|0.008***|0.008***|0.007**|0.025***|
||(0.002)|(0.003)|(0.003)|(0.006)|
|ln(FDI)||0.242|0.309||
|||(0.189)|(0.189)||
|FIT||0.782***|0.780***|0.779***|
|||(0.023)|(0.023)|(0.023)|
|∆ln(E)||-2.803***|-1.992***|-1.850***|
|||(0.652)|(0.653)|(0.666)|
|Control|No|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|Year FE|Yes|Yes|No|No|
|Industry-Year FE|No|No|Yes|No|
|City-Year FE|No|No|No|Yes|
|Observations|249,853|213,444|213,444|213,331|
|Adjusted R<sup>2</sup>|0.712|0.720|0.721|0.722|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The city-level control variables include GDP Per capita, total output of FDI firms, the number of fixed telephone users, the number of mobile phone users, the number of internet users, the total passenger ridership and freight ridership by transportation other than railway and the secondary industry share, service industry share, inner city public transportation, and population density. Firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation). All control variables are measured in logarithm except for the secondary and third industry share, firm tariffs, and exchange rate.

# **5.1 Heterogeneous Impact of HSR**

HSR’s impact on firm DVAR may vary depending on the flexibility firms have in substituting imported materials with domestic ones. This flexibility is influenced by factors such as firm ownership, trade regime, industry, the type of materials used, and the geographical proximity between firms and potential suppliers. To better understand which firms benefit the most from HSR expansion, we reconstruct the CI incorporating distance information, split the sample by ownership, trade regime, and industry, and interact CI with the characteristics of the firms’ imported products.

**Geographical Distance** The impact of HSR on DVAR likely varies with the distance between exporting firms and potential suppliers. Firms located closer together may not rely on HSR for connections, while those further apart might turn to air travel. We expect HSR to have the most significant effect over medium to long distances. Table 5 presents results using CI constructed based on the distance between the exporting firms and their potential suppliers. The findings confirm that HSR has the greatest impact on firm DVAR when suppliers are located within medium to long distances—up to 1600 km (e.g. from Beijing to Shanghai), and up to 2300 km (e.g., from Beijing to Shenzhen).

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Table 5: The Impact of HSR on Firms’ DVAR: By Distance

|Distance Range (km)|(1)<br>150-800|(2)<br>150-1200|(3)<br>150-1600|(4)<br>150-2300|
|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|
|CI|-0.014|-0.000|0.024***|0.028***|
||(0.009)|(0.008)|(0.006)|(0.006)|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Observations<br>|229,526|229,526|229,526|229,526|
|Adjusted R<sup>2</sup>|0.722|0.722|0.722|0.722|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

**Firm Ownership Type, Trade Regime, and Industry** Tables 6 and 7 report the regression results trade and based on our The by firm ownership, regime, industry affiliation, preferred specification. findings show that HSR has the largest impact on the DVAR of FDI firms, processing firms, and electronics firms. Given that China’s aggregate exports are dominated by processing exporters in the electronics sector with FDI, these results suggest that the overall increase in China’s DVAR in Figure 1 is largely due to these firms gain access to local suppliers through HSR expansion.

**Substitutability of Imported Inputs** The impact of HSR expansion on firms’ input sourcing decisions also hinges on the substitutability between domestic and imported inputs. To capture this, we interact CI with _Diffi,t_ 0—a firm-level, import-weighted differentiation index that reflects the degree of product differentiation in the firm’s import bundle. This interaction term examines the different impacts of HSR on firm DVAR, based on the complexity of the imported inputs of the exporters:

where the variable _Diffi,t_ 0 _≡_<sup>�</sup> _j_<sup>_sjit_</sup> 0<sup>_×Rauchj_measuresthedegreeofdifferentiationinthe</sup> products imported by firm _i_ at the initial period. Here, _sjit_ 0 represents the share of product _j_ (classified at the HS 6-digit level) in the total imports of firm _i_ during its first observed year, _t_ 0. The term _Rauchj_ refers to the product differentiation indicator from Rauch (1999), which assigns a value of 1 for differentiated products and 0 for homogeneous products.18 Firms importing a larger proportion of differentiated products will have a higher value of _Diffi,t_ 0.

> 18Rauch (1999) classifies products at the 4-digit level of the Standard International Trade Classification (SITC, version 2). Homogeneous goods include those traded on organized exchanges and reference-priced goods, while the remaining are categorized as differentiated products. The correspondence between the HS 2007, HS 2012, and SITC 4 classifications is provided by the UN Trade Statistics (UN Trade Statistics Correspondence Tables).

21
Table 8 presents the results. Column (1) uses the conservative classification of differentiated products of Rauch (1999), while Column (2) employs the liberal classification. The negative and significant coefficients in both columns indicate that the positive effect of improved CI on firm DVAR is less for firms importing more differentiated products. In other words, firms that import less differentiated imports will benefit more as HSR expands which allow them to find compatible domestic materials.

Table 6: The Impact of HSR on Firms’ DVAR: By Firm Ownership Type and Trade Regime

|Subsample|(1)<br>SOE|(2)<br>FIE|(3)<br>DPE|(4)<br>Processing|
|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|
|CI|0.003|0.032***|0.008|0.027**|
||(0.031)|(0.008)|(0.011)|(0.011)|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Observations|5,981|155,451|67,232|85,005|
|Adjusted R<sup>2</sup>|0.554|0.729|0.697|0.682|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

Table 7: The Im act of HSR on Firms’ DVAR: B Industr <u>p y y</u>

|Industries|(1)<br>Chemicals,<br>Machinery<br>and Transportation|(2)<br>Electrical,<br>Metals<br>and Optical Equipment|(3)<br>Furniture<br>and<br>Wood Products|(4)<br>Miscellaneous|(5)<br>Plastic,<br>Rubber<br>and Toys|(6)<br>Textiles<br>and<br>Apparels|
|---|---|---|---|---|---|---|
|Dependent Variable|DVAR|DVAR|DVAR|DVAR|DVAR|DVAR|
|CI|0.007|0.031***|-0.020|0.056***|0.004|0.021|
||(0.012)|(0.012)|(0.016)|(0.015)|(0.020)|(0.023)|
|Control|Yes|Yes|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|Yes|Yes|
|Observations|60,055|68,081|15,492|27,384|21,584|33,152|
|Adjusted R<sup>2</sup>|0.663|0.712|0.704|0.745|0.769|0.663|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

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Table 8: The Impact of HSR on Firms’ DVAR: The Role of Product Differentiation

|Rauch Classifcation|(1)<br>Conservative|(2)<br>Liberal|
|---|---|---|
|Dependent Variable|DVAR|DVAR|
|CI_×_Diff_t_0|-0.016***<br>(0.003)|-0.018***<br>(0.003)|
|CI|0.034***|0.033***|
||(0.007)|(0.006)|
|Control|Yes|Yes|
|Firm FE|Yes|Yes|
|City-Year FE|Yes|Yes|
|Observations<br>|197,307|197,307|
|Adjusted R<sup>2</sup>|0.723|0.723|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

# **5.2 Extended Analysis: Impact of HSR on Exports and Imports**

Finally, to confirm that HSR expansion enables exporting firms to substitute imported materials with domestic alternatives without compromising their exports, Table 9 shifts the focus to export value, export variety, import value, and import variety. The results indicate that CI positively impacts exports and negatively impacts imports, reinforcing the idea that HSR gives exporting firms better access to domestic materials, which they use as substitutes for imported inputs. Table 10 examines the effects 6. of CI on export and import prices across firm types, similar to Table The findings show that export prices for FDI and processing exporters decline, while import prices remain unaffected by CI. This supports the hypothesis that HSR expansion allows these firms to replace imported materials with cheaper domestic inputs, leading to higher DVAR and lower export prices.

Table 9: The Impact of HSR on Firms’ Imports and Exports

|Dependent Variable|(1)<br>ln(Export Variety)|(2)<br>ln(Import Variety)|(3)<br>ln(Export)|(4)<br>ln(Import)|
|---|---|---|---|---|
|CI|0.159***|0.023|0.110**|-0.358***|
||(0.021)|(0.022)|(0.043)|(0.051)|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Observations|286,794|286,794|286,794|286,794|
|Adjusted R<sup>2</sup>|0.834|0.808|0.760|0.767|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

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Table 10: The Impact of HSR on Firms’ Trade Unit Values

||(1)|(2)|(3)|(4)|
|---|---|---|---|---|
|Panel A: Export|SOE|FIE|DPE|Processing|
|Dependent Variable|ln(Unit Value)|ln(Unit Value)|ln(Unit Value)|ln(Unit Value)|
|CI|-0.028|-0.132***|-0.031|-0.143***|
||(0.143)|(0.041)|(0.049)|(0.050)|
|Observations|54,378|1,116,530|409,525|597,549|
|Adjusted R2|0.789|0.729|0.754|0.762|
|Panel B: Import|SOE|FIE|DPE|Processing|
|Dependent Variable|ln(Unit Value)|ln(Unit Value)|ln(Unit Value)|ln(Unit Value)|
|CI|-0.077|0.018|0.033|-0.050|
||(0.162)|(0.037)|(0.066)|(0.050)|
|Observations|108,192|2,368,932|423,344|1,143,154|
|Adjusted R<sup>2</sup>|0.740|0.744|0.760|0.797|
|Control|Yes|Yes|Yes|Yes|
|Firm FE|Yes|Yes|Yes|Yes|
|City-Year FE|Yes|Yes|Yes|Yes|
|Product FE|Yes|Yes|Yes|Yes|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation).

# **5.3 Robustness Checks**

**Alternative Connectivity Indices: Endogeneity and the Channels of Influence** A potential concern is that the CI measure, constructed as per (16) and (17) based on current-year import product scopes and shares, may face endogeneity issues due to unobserved firm productivity. More productive firms may import more, leading to a larger set of potential suppliers connected by HSR, and at the same time, their DVARs may be lower due to greater reliance on imports. This could introduce a downward bias in the CI coefficients. However, since such a bias would work against our results, the fact that the estimated CI coefficients in Table 4 are positive and significant helps alleviate this concern.

To assess the severity of the endogeneity issue, we fix the import product scopes and shares and allow the CI to change only when more domestic suppliers are connected to exporters through HSR expansion. For this purpose, we construct two versions of CI: the initial-value CI (ICI) and the fullsample CI (FCI). ICI is based on the import product scopes and shares of each firm at the start of the sample period, while FCI is derived from the data across the entire sample period for each firm. Details on how these variables are constructed can be found in Appendix C.

Given that both ICI and FCI hold the import product scopes and shares fixed by design, they eliminate any endogenous changes resulting from firms adjusting their product mix or quantities, effectively addressing the potential endogeneity concern. However, by fixing firms’ import structure 24
at the initial period, ICI only allows the initial set of potential suppliers to influence firm DVAR, effectively disregarding the possibility of firms substituting imported materials with domestic ones as HSR expands. This approach shuts down the very channel through which HSR is expected to impact firm DVAR. In other words, while ICI captures the effects of HSR expansion overtime via the initial set of suppliers, it does not account for firms adding or dropping imported materials in response to new domestic suppliers becoming accessible through HSR. As a result, ICI may not show an effect on firm DVAR, despite addressing the endogeneity concern of the original CI.

On the other hand, by contruction, FCI uses the product scope of the full sample of each firm, thus all across this such that incorporating potential suppliers period, any contemporaneous adding or dropping of imported materials in direct response to HSR expansion will not be reflected in FCI to curb endogeneity bias. Nevertheless it considers the cumulative effects of past, present, and future import product mixes as well as the corresponding domestic suppliers. This broader approach allows FCI to capture the impact of HSR expansion on firm DVAR more comprehensively over time.19 In addition, by using the weighted average share of each imported product over the full sample period, FCI in as additions or removals captures non-contemporaneous changes product composition, any of products are already factored into the weighted averages. This construction allows FCI to reflect shifts in firms’ sourcing behavior over time. As a result, FCI is expected to have a positive impact on firm DVAR, potentially with a larger effect, as it addresses the potential downward bias caused by endogeneity related to unobserved firm productivity.

Table 11 presents the results when ICI and FCI are substituted for CI in our preferred regression specification, as shown in Column (4) of Table 4, which includes firm fixed-effects and city-year fixedeffects. As anticipated, Column (1) reveals that ICI has no significant effect on firm DVAR, confirming that for HSR to influence firm DVAR, firms must adjust their imports, both in terms of scope and value. In contrast, Column (2) shows that FCI has a positive and significant effect on firm DVAR. This indicates that when the entire set of available products and their associated domestic suppliers over the full sample period are considered, the impact of HSR on firm DVAR becomes more pronounced. While the estimated coefficient is larger, it is not statistically different from the previous estimate, suggesting that the endogeneity concern is not severe. Moreover, when FCI is applied in place of CI for the full analysis, the results remain consistent with those obtained using CI. In the Appendix, we also present more robustness checks based on city-level regressions leveraging the roll-out of HSR across different cities and years to further validate the positive impact of HR on firm DVAR.

> 19For instance, a firm exporting electronics after 2010 could be considered a potential supplier for firms importing electronics in 2008, as the exporting firm may have been serving the domestic market at that time. Likewise, a firm exporting electronics in 2008 could be a potential supplier for firms importing electronic components after 2010, since the exporting firm may have been serving or shifted exclusively to the domestic market post-2008.

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Table 11: Robustness Check: Alternative Connectivity Indices

|Dependent Variable|(1)<br>DVAR|(2)<br>DVAR|
|---|---|---|
|ICI|0.001<br>(0.002)||
|FCI||0.039***<br>(0.012)|
|Control|Yes|Yes|
|Firm FE|Yes|Yes|
|City-Year FE|Yes|Yes|
|Observations|213,331|213,313|
|Adjusted R<sup>2</sup>|0.722|0.722|

Notes: Robust standard errors clustered at the firm level are in parentheses, ***, **, * denote significance at 1%, 5%, 10% level, respectively. The firm-level control variables include input tariffs and import-weighted exchange rate variation (appreciation). In column (1), we further control for the share of products imported by firms in the initial year that accounts for their total imports in subsequent years.

# **6 Theoretical Analysis**

In this section, we introduce a simple two-sector model to explain the previous empirical findings, illustrating how HSR expansion may enhance the DVAR of exporting firms. The core mechanism is driven by the anecdotal evidence highlighted earlier, where HSR enables employees to travel between cities for business meetings with domestic suppliers. In theoretical terms, this implies that HSR expansion increases the likelihood of exporting firms connecting with domestic suppliers at lower communication costs. This, in turn, reduces input prices and expands the variety of domestic materials available, allowing firms to substitute imported materials with domestic alternatives, ultimately raising their DVAR.

The economy consists of two sectors. The upstream materials sector, _M_ , supplies inputs for the downstream final goods sector, _Y_ . Sector _Y_ operates in a monopolistically competitive market, where an infinite number of firms, each with a unique productivity level _φ_ , produce differentiated varieties _ω_ . The materials sector, _M_ , is perfectly competitive, featuring an infinite number of identical firms, each producing a distinct variety of material, _η_ . The economy is endowed with a large labor supply, _L_ , and includes a numeraire sector employing _L_ 0, which allows us to normalize the wage rate to one.

# **6.1 Final Goods Sector**

We begin with sector _Y_ , the final goods sector, following the standard framework of a heterogeneous firm model with constant elasticity of substitution (CES) preferences across the differentiated final goods varieties: 26
with the elasticity of substitution between varieties equals _σ_ = 1/(1 _− ρ_ ). The aggregate price index for _Y_ , _pY_ , is itself a CES function of the individual price of the varieties, Given the CES preference structure, the demand curve for each variety _ω_ is downward sloping and takes the following form: There are infinitely many firms with heterogeneous productivity, _φ_ , in this sector. In a symmetric _ω_ equilibrium, each firm chooses to produce a unique variety, , with the following constant returns to scale Cobb-Douglas production function of the composite material, _M_ , and labor, _Ly_ : where the composite material, _M_ , is made up of both domestic materials, _M_<sup>_D_</sup> , and imported materials, _M_<sup>_W_</sup> . The elasticities of output with respect to materials and labor are denoted by _θ_ and 1 _− θ_ , respectively. To maximize profits, each firm with productivity _φ_ adjusts its input usage such that the marginal revenue products of _M_ and _L_ are equal to their respective marginal costs, which determines the downward sloping input demand curves: where _pM_ is the price of the composite material and the wage rate is normalized to 1. Under profit maximization, the firm’s marginal cost and price for a given productivity level, _φ_ , are determined by the following relationships: where _A ≡_ ( _θ_ )<sup>_θ_</sup> (1 _− θ_ )<sup>1</sup><sup>_−θ_</sup> is a scalar. Equation (26) is the horizontal supply curve of variety _ω_ . Combined with (20), the downward-sloping demand curve of _ω_ , gives us the equilibrium output and 27
_ω_ : revenue for variety Substituting (28) into (23) gives us the downward-sloping material input demand curve: Without loss of generality, and following Kee and Tang (2016), we assume the unit material cost function for the composite materials, _pM_ � _pM_<sup>_W, pD_</sup> _M_ �, is a translog function that is symmetric and homogeneous of degree one, given the prices of imported materials, _p_<sup>_W_</sup> _M_<sup>, and domestic materials,</sup><sup>_pD_</sup> _M_<sup>.</sup> This functional form a second-order to as discussed in provides approximation any price aggregate, Diewert and Wales (1988) and Feenstra and Weinstein (2017), allowing for flexibility in modeling material substitution between imported and domestic inputs: The assumptions of symmetry and homogeneity of degree one impose the following restrictions on the translog parameters: With the translog price function, we can demonstrate that _∂_ ln _pM_ / _∂_ ln _p_<sup>_W_</sup> _M_<sup>equalstheshareofim-</sup> ported materials in total material costs, and this share is a log-linear function of the relative price of 28
domestic to imported materials: Similarly, _∂_ ln _pM_ / _∂_ ln _p_<sup>_D_</sup> _M_<sup>equalstheshareofdomesticmaterialintotalmaterialcost,andisa</sup> log-linear function of the relative price of domestic to imported materials: Thus, if _p_<sup>_D_</sup> _M_<sup>drops, the unit price of composite materials,</sup><sup>_pM_, will also drop in equilibrium, while</sup> the share of domestic materials will increase, given _aWD >_ 0. From (35), we can drive the demand for domestic materials: In summary, with the constant returns to scale production function in (22), the composite material constitutes a fixed share _θ_ of total revenue, as expressed in (23). Within this fixed share, the allocation between domestic and imported materials follows a translog specification, leading to variable shares that adjust based on the relative prices of domestic and imported materials, as shown in (34) and (35). Ultimately, the shares of domestic and imported materials in total revenue are not constant but vary according to the relative price changes.

The analysis so far has focused on firms in sector _Y_ that serve only the domestic market (with the superscript of _D_ omitted for simplicity). Now let’s extend this to consider the export market for each variety _ω_ of _Y_ , assuming the products sold domestically and for export are identical. The key 29
difference between serving the domestic market and the export market lies in the variable trade costs, such as transportation costs and foreign tariffs, that firms incur when exporting. With a multiplicative iceberg ad valorem variable trade cost, _t >_ 1, the marginal cost of exporting variety _ω_ for a firm with productivity _φ_ is: Thus, following the constant markup rule from profit maximization, the equilibrium price, export _ω_ supply, and revenue for variety in the export market are given as follows: By taking the ratio of domestic to export revenue, dividing (28) by (44), and adding the superscript _D_ to denote domestic variables, we see that the proportion of export revenue to total revenue for variety _ω_ is constant and depends solely on the iceberg trade cost: Equation (47) shows that as the variable trade cost increases, the share of export in the total revenue _ω_ of variety drops.

Now consider the domestic value added ( _DV A_ ) of each exporting variety _ω_ in sector _Y_ , which is the total revenue of the firm minus the cost of imported material: 30
where (48) is the definition of DVA. The DVA divided by firms’ total export revenue, _p_<sup>_W_</sup> ( _ω_ ) _y_<sup>_W_</sup> ( _ω_ ), is defined as the domestic value added ratio (DVAR) in (49). The right-hand side of (49) is obtained by substituting _θ_ for _pM M_ / _r_ ( _ω_ ). Substituting (34), the share of imported material in total material, into (49) to get (50). Finally, (51) shows that DVAR is negatively correlated with the price of domestic materials. Any factors that reduce the price of domestic materials increase the DVAR of final goods exporters. We will show in the later section how the expansion of HSR may lower the price of domestic materials through raising the probability of exporting firms connecting with domestic suppliers with low communication cost, which thus will lead to an increase in DVAR of exporters.20

# **6.2 Domestic Materials Sector**

For each final goods variety _ω_ , there is a perfectly competitive domestic material sector, sector _M_<sup>_D_</sup> , _ω_ with the demand governed by the production of each , which is assumed to require all the available discrete domestic material varieties, indexed by _η_ = 1 _, ..., L_ ( _ω_ ), where _L_ ( _ω_ ) _< ∞_ is the total number _ω_ : of accessible varieties for firm Equation (52) illustrates that the different varieties of domestic materials, _m_<sup>_D_</sup> _η_<sup>, are perfect substitutes,</sup> with all _L_ ( _ω_ ) accessible varieties included in the composite domestic materials for firm _ω_ . We assume each variety _η_ is produced by a domestic firm using one unit of labor per unit of output, with total output fixed at one unit per firm. Since labor supply is perfectly elastic and the wage rate is set at one, perfect competition ensures that the factory-gate price for each variety, _pη_ , is also equal to one.

In addition, domestic suppliers are assumed to be randomly distributed across cities surrounding each final goods firm, _ω_ . Some of these cities are connected to firm _ω_ via HSR, while others are not. Let the indicator variable _dη_ ( _ω_ ; _HSR_ ) = 1 if the domestic supplier _η_ of firm _ω_ is located in a city connected to _ω_ via HSR, and _dη_ ( _ω_ ; _HSR_ ) = 0 otherwise. This indicator, _dη_ ( _ω_ ; _HSR_ ), corresponds to _dnit_ from (16) in Section 4, which equals 1 if the potential supplier _n_ is connected to firm _i_ via HSR in year _t_ . Assume _dη_ ( _ω_ ; _HSR_ ) follows a Bernoulli distribution, with probability _CI_ ( _ω_ ; _HSR_ ) _∈_ [0 _,_ 1], indicating that _η_ and _ω_ are connected through the HSR network: 20Additionally, Xu and Hu (2024) demonstrates that HSR improves matching efficiency among firms, effectively tightening the product network and acting as a reduction in the cost of domestic material inputs at the aggregate level. As a simplified approach, Bernard et al. (2019) model HSR as a reduction in domestic transportation costs for inputs.

31
Again note that _CI_ ( _ω_ ; _HSR_ ) is the theoretical counterpart to _CIjit_ of (16) in Section 4, which is the share of potential suppliers of material _j_ that are connected to firm _i_ via HSR in year _t_ . As the HSR expands, more supplier _η_ will be connect to firm _ω_ via HSR, and thus will turn _dη_ ( _ω_ ; _HSR_ ) from 0 to 1, leading to increasing _CI_ ( _ω_ ; _HSR_ ): The Bernoulli distribution of _dη_ ( _ω_ ; _HSR_ ) implies that for each final goods firm _ω_ , the sum of connected suppliers follows a Binomial distribution: where (56) demonstrates that the sum of connected suppliers follows a Binomial distribution, with _L_ ( _ω_ ) representing the total accessible varieties and _CI_ ( _ω_ ; _HSR_ ) denoting the probability of an HSR connection between a domestic supplier and the exporting firm. Equation (57) indicates that for each _ω_ firm , the proportion of connected domestic suppliers out of the total accessible suppliers is given by _CI_ ( _ω_ ; _HSR_ ). As a result, the expected total number of connected domestic suppliers rises with the probability of an HSR connection, which is also captured by _CI_ ( _ω_ ; _HSR_ ).

For each final goods firm _ω_ to incorporate domestic material variety _η_ in the production of _y_ ( _ω_ ), firm _ω_ needs to conduct face-to-face meetings with supplier _η_ . We assume that such meetings incur a positive and multiplicative ad valorem iceberg communication costs _τ >_ 1, if supplier _η_ and firm _ω_ are not connected by HSR. In contrast, if they are connected via HSR, then the communication cost is reduced to zero, or _τ_ = 1. For simplicity, we assume that any communication costs, when present, will be borne by firm _ω_ . Thus, the price of domestic material _η_ facing the final goods firm _ω_ depends on both factory-gate price _pη_ and face-to-face communication costs for firm _ω_ to travel to the domestic supplier _η_ .

Thus, the Bernoulli distribution of _dη_ ( _ω_ ; _HSR_ ) further implies that _p_<sup>_D_</sup> _η_<sup>(</sup><sup>_ω_) has the following distri-</sup> bution: 32
In the perfectly competitive equilibrium, firm _ω_ will incorporate all the accessible varieties of domestic materials into the composite domestic material, _M_<sup>_D_</sup> , as specified by (52). The price of the composite domestic material for firm _ω_ is the expected price of the different varieties of domestic material: implying that for each firm _ω_ , as the probability of connecting with domestic suppliers via HSR increases, pushing up the number of connected suppliers, the average price of domestic material drops.

For each firm _ω_ , the equilibrium accessible variety _L_ ( _ω_ ), which is also the equilibrium composite domestic material _M_<sup>_D_</sup> ( _ω_ ), is determined by equating the domestic material demand from the final goods sector, as in (36), with the supply in (60), and then substituting the equilibrium domestic material price from (61): Thus, any factor that increases the probability of connecting domestic material suppliers to final goods _ω_ firm expands the set of accessible domestic suppliers and the total variety of domestic materials in equilibrium. The equilibrium also satisfies the labor market clearing condition, meaning that the total labor demand in the final goods sector, the total labor demand in the domestic materials sector, and _L_ : the labor demand in the numeraire sector must together equal the labor endowment

# **6.3 Impact of HSR Expansion**

Now consider an exogenous expansion of the HSR network. This expansion connects more cities to the HSR, effectively turning more _dη_ ( _ω_ ; _HSR_ ) from 0 to 1, which increases the likelihood that domestic material suppliers _η_ are connected to final goods firm _ω_ , thereby raising _CI_ ( _ω_ ; _HSR_ ). As shown in (62) and (64), this results in a decrease in the composite domestic material price for firm _ω_ and an 33
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This paper proposes a novel approach to estimating firm-level DVAR, based on an export revenue function, which relies solely on customs transaction data and does not require matching with industrial census data, significantly expanding its applicability. The comparison with previous estimates of Kee and Tang (2016) using both customs data and industrial census data confirms the validity of our new approach. Our estimations based on China’s customs transaction provide micro-level evidence of China’s rising DVAR during 2000 and 2016 and the increase are mainly driven by processing exporters.

HSR is the passenger-dedicated transportation infrastructure and mainly used for business purpose, which promotes the face-to-face communication between domestic buyers and sellers and reinforces domestic economic interactions and integration. To capture this IO linkages, this paper proposes a novel approach to measure firm-level IO linkages that only requires customs transaction data, by matching import product codes of exporting firms with export product codes of other domestic firms, implying that the latter can be viewed as the potential suppliers of the former. Empirical results based on this new measure of firm-level IO linkages, reveal that the rising DVAR can be attributed to HSR expansion, which enhances access to domestic materials by linking exporting firms with potential domestic suppliers. The effects of HSR connections are particularly significant for processing firms, foreign-invested firms, and firms in the electronics industry. Additionally, firms that import more homogeneous products and those connected to domestic suppliers over medium to long distances experience greater impacts from HSR.

In addition, this paper presents a two-sector theoretical model illustrating how HSR expansion affects firm DVAR by increasing the likelihood that exporting firms access domestic suppliers at lower communication costs. As the HSR network more domestic become accessible to grows, suppliers exporting firms, facilitating greater access to domestic materials. This improved access encourages the substitution of domestic materials for imported ones, thereby increasing firm-level DVAR. The findings of this paper indicate that, in addition to trade and FDI liberalization—previously recognized as key drivers of China’s rising DVAR—enhancing domestic transportation infrastructure can also play a critical role in enabling countries to capture more DVA and move up the GVC.

While it is beyond the scope of the current paper, our approach of estimating DVAR and capture firm-level IO linkages are general enough to applied to other countries, due to less data requirements. Regarding the last remark, we have started a new project on measuring the DVAR for a wide range of countries, based on the matched importer-exporter customs transaction data from the World Bank’s Exporter Dynamic Database and also applied it to Indonesia to analyze the impact of export ban on Nickel on downstream industry value-added creation.

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