OSIPP Discussion Paper: DP-2020-E-004

Heterogeneous Impact of Non-Tariff Measures through the Global
Value Chains: Empirical Evidence from China

May 10, 2020

Qizhong Yang *College of Economics, Aoyama Gakuin University

Tsunehiro Otsuki †Osaka School of International Public Policy (OSIPP), Osaka University【Keywords】Global value chain, Non-tariff measure【JEL Codes】F14, F23【 Abstract 】 This paper investigates the impact of non-tariff measure(NTM)’s stringency on Chinese firms’ positioning in the GVC, which ismeasured by two types of GVC positioning indices. We then estimate theimpact of NTMs on various firm performance by paying special attention to
how the impacts vary across firms with different positioning in the GVC. The
results show that NTMs imposed against and imposed by China could
significantly reduce firms’ linkages with foreign countries, thereby reducing
the firms’ importance within the GVC. We also find that stricter NTMs could
even hinder firms’ innovative activities and decrease exports and imports.
Further analysis indicates that these negative impacts of NTMs on firms areheterogeneous across firms depending on their original position in the GVC.
*†

Email: yangqz@aoyamagakuin.jp
Email: otsuki@osipp.osaka-u.ac.jp

Heterogeneous Impact of Non-Tariff Measures through the Global
Value Chains: Empirical Evidence from China
Qizhong YANG†
Tsunehiro OTSUKI††
Abstract
Over the last decades, the rising fragmentation of the production process across countries has
been an increasingly key trend in international trade. On the other hand, technical standards andregulations in international trade are considered to have a potential effect on the organization andstructure of the global value chains (GVC). By using firm-level customs data, manufacturing census
data, and China’s Input-Output Tables, this paper investigates the impact of non-tariff measure(NTM)’s stringency on Chinese firms’ positioning in the GVC, which is measured by two types of
GVC positioning indices, namely, output upstreamness and input downstreamness indices. We thenestimate the impact of NTMs on various firm performance by paying special attention to how the
impacts vary across firms with different positioning in the GVC. The empirical results show that
NTMs imposed against and imposed by China could significantly reduce firms’ linkages withforeign countries, thereby reducing the firms’ importance within the GVC. We also find that stricter
NTMs could even hinder firms’ innovative activities and decrease exports and imports. Further
analysis indicates that these negative impacts of NTMs on firms are heterogeneous across firms
depending on their original position in the GVC; firms with higher output upstreamness or input
downstreamness receive smaller effect than those with lower GVC positioning index.

JEL Classification: F14, F23, L11
Keywords: Global value chain, Non-tariff measure, Upstreamness, Downstreamness.

† College of Economics, Aoyama Gakuin University,

Address: 4-4-25 Shibuya, Shibuya-ku, Tokyo, 150-8366, JAPAN.
E-mail: yangqz@aoyamagakuin.jp
Tel: +81-3-3409-6567 ext.12815†† Osaka School of International Public Policy, Osaka University,
Address: 1-31 Machikaneyama, Toyonaka, Osaka 560-0043, JAPAN.
11. Introduction
The rising fragmentation of production in the global value chain (GVC) has been an increasingly
key trend in international trade and the division of labor in the GVC has been gradually replacing
the traditional mode of division of labor. With the revolutionary breakthrough and cross-integrationof information technology, new materials, new energy and other fields, the traditional world
manufacturing development pattern has changed. That is, world production is now structured into
global value chains. The regional decentralization of production led to the rise of intermediate
goods trade (especially in the field of manufacturing), which promoted the transformation of themode of production from “made in one country” to “made in the world”, and the mode of tradefrom “trade in goods” to “trade in tasks” (WTO & IDE-JETRO (2011)). Deeply involved in thedivision of global value chain, Chinese manufacturing industry has been rapidly developed.
According to the World Input-Output Database (WIOD), China’s manufacturing industry has been
growing at an average annual rate of 17.32% since 1995 the manufacturing output reached 175
billion US dollars in 2010, accounting for 14.10% of the world’s total manufacturing output.
Research on the positioning in the GVC has been made progress in recent years. In Antràs andChor (2013), which pioneered this field, the upstreamness index of an industry was measured by
the number of production stages, up to the final consumer of the industry. Similarly, Fally (2012)
composed the GVC measure based on the concept that “if the product of the industry is used in anindustry far from the final consumer in the value chain, the industry is located upstream”. Antràs
et al. (2012) proved that Fally (2012)’s composition was identical with that of Antràs and Chor(2013). By using the upstreamness index, one can analyze the spillover effects of economic shocks
in one country on the economy of another. For example, if a major industry in a country is locatedupstream in the GVC, production and exports in that country will be more susceptible to demand
shocks in countries located downstream of GVC. Therefore, in order to consider the impact of theseexternal economic shocks on the country’s economy, it is important to understand which country
is located downstream of the country in the GVC of the country. In addition, it is important tocompare the positioning of each country in the GVC with the benefits obtained from the trade inthat country in examining the competitiveness of the industry at the national level.
On the other hand, in the past decade, while import tariff increases have been constrained bymultilateral and regional trade agreements, non-tariff measures (NTMs) have spread in the world,
certainly in those developing countries such as China. The imposition of NTMs can raise thevariable costs of producing the exported goods: technical standards require upgrading or at least
adaptation of products or packaging, and varying standards across destinations reduce opportunities
for economies of scale. However, this additional cost may also become a fixed cost thereby
2discouraging intermediate suppliers from serving markets with stricter NTMs. Taking China as an
example, since Chinese firms have been playing a leading role in the world processing trade, theimposition of NTMs would have a significant effect on the trade pattern of Chinese firms: when
the NTMs imposed against China by a foreign country are stricter than those of China, it is moredifficult for the downstream firms in the foreign country to import intermediate inputs from Chinese
suppliers. Also, stricter NTMs imposed by China will hinder the upstream suppliers in foreigncountries export intermediate inputs to the Chinese buyers. The farther position in the GVC awayfrom the Chinese market (i.e. the more upstream the supplier is or the more downstream thedemander is from the point of view of Chinese firms), the more difficult to grasp the components
subject to NTM regulations because the longer distance in the GVC indicates the more markets
with different environment of NTMs are held between China and the traders, in other words, theharder for one to foresee the net effect of NTMs. What makes the question more complex is thateven facing the same environment of NTMs, the effect of NTMs on firms would vary acrossdepending on firms’ positioning in the GVC. For a specific firm, while it can choose to export andimport different types and different amount of goods to foreign countries so that each firm may
have a unique positioning in the GVC, it is the government who decide what type and how strict of
the NTMs to impose. Under this situation, firms facing the same type and the same stringency of
NTMs will receive different impact if they have different compositions and values of export orimport.
This paper aims to shed light on whether the stringency of NTMs would have a latent effect onfirms’ role in the GVC. By using firm-level customs data, manufacturing census data, and China’s
Input-Output Tables, this paper investigates the impact of NTM on Chinese firms’ positioning in
the GVC, which is measured by two types of GVC positioning indices, namely, output
upstreamness and input downstreamness indices. We then estimate the impact of NTMs on various
firm performance by paying particular attention to how the impacts vary across firms with different
positioning in the GVC. The empirical results show that NTMs imposed against and imposed by
China could significantly reduce firms’ linkages with foreign countries so that decrease firms’
importance within the GVC. We also find that stricter NTMs could even hinder firms’ innovative
activities and decrease exports and imports of Chinese firms. Further analysis indicates that thesenegative impacts of NTMs on firms are heterogeneous across firms depending on their original
position in the GVC; firms with higher output upstreamness or input downstreamness receive
smaller effect than those with lower GVC positioning index.

3

The remainder of this paper is organized as follows. Section 2 provides a brief review of the
background. Section 3 presents the estimation models and the data used. Section 4 provides theestimation results and discussion. Section 5 concludes.

2. Background
2.1 NTM Regulations
During the past decades, there has been an increasing number of trade disputes related to NTMs.
The rapid growth of NTMs has induced a large literature on their effects on trade and development,much of those has treated NTMs as pure trade barriers and advocated the removal for welfare
improvement. While early studies warned about the dangers of marginalization of the poor withincreasing standards, recent studies document mostly positive effects on household income,reduced risk and income variability, technology adoption and quality of produce (Beghin et al.
(2015)). In order to understand the comprehensive effects of NTMs, it is important to know how
they affect the organization and structure of value chains and in particular the endogeneity ofvertical coordination.
Previous studies have provided different measures to reflect the changing environment in termsof NTMs. These measures include: (1) the price-wedge-ad valorem equivalent method, whichmeasures the NTM regime by its impact on price; (2) inventory-based frequency and countmeasures, which simply counts the number or frequency of NTMs affecting a given market; (3)measures of stringency and heterogeneity across countries for SPS and standard-like NTMs
regimes, using maximum residue limits and other policies that can be aggregated in a meaningfulmanner, and (4) measures of transparency and harmonization. The existing studies have appliedthese measures to analyze how the NTMs could affect trade focusing on various aspects. For
example, Devadason et al. (2018) applied the same type of measures with this paper to examine thecoverage, frequency, and diversity of NTMs for the food sector in Malaysia, and then estimates
their impact on food imports from ASEAN.
Beghin et al. (2015) made a comprehensive review on the literatures on various effect of NTMs.
The existing evidence from the studies investigating the effect of NTMs on welfare, trade, industrialorganization and labor markets, is mixed in a sense that NTMs can promote or impede trade andeconomic growth, possibly reflecting their complex effects on industries and firms.

2.2 Global Value Chain
Research on the international competitiveness of manufacturing industry can be roughly divided
into two categories according to different trade accounting systems: first, analysis of traditional4

trade index. Balassa (1965) proposed to use the revealed comparative advantage index to measure
the international competitiveness of a country’s industry. Since this approach ignored the importfactor, later studies further proposed the trade competitiveness index, which measures theinternational competitiveness of a country’s industry by the proportion of the net trade of acountry’s industry to the total trade. The second category is the value-added trade index analysis.
Hummels et al. (2001) proposed that vertical specialization index (including forward and backward
vertical specialization index) can be used to measure the division of labor position of a country’s
industry in the GVC. Koopman et al. (2010) put forward the GVC Participation index and GVC
positioning index to measure the division of labor position of a country’s industry in the global
value chain 1, which has been developed by Wang et al. (2017) in which proposed a pair of GVC
participation indices that improves upon the measures in the existing literature. Antràs & Chors(2013) estimated the average position of a country’s industry in the GVC by constructing industry
upstreamness and downstreamness index on the basis of the US input and output table.
Recent studies have developed various theoretical frameworks, emphasizing the operation andinfluence of the rise of the GVC on the general equilibrium model of international trade. These
measures envision a world in which production in the GVC features some element of sequentiality,
among them is the term “snakes” given by Baldwin and Venables (2013) to refer to purely
sequential value chains, in which each production stage obtains its inputs from a unique upstream
stage. The other term of “spiders” they introduced described a flatter GVC in which each production
stage sources from several upstream suppliers simultaneously. The measures of GVC positioning
we employed in this paper are defined in a general way such that they can be computed for
production processes that have both “snake” and “spider” type features.
2.3 Chinese Manufacturing Firms
Since its accession to the WTO in 2001, China’s export growth has been remarkable. According
to the WTO’s statistics, the China’s share in world exports grew from 3.3% in 1997 to 8.7% in
2007, and the China’s share in total world trade in manufactured goods jumped from 4.7% in 2000
to 12% in 2007, making the country now the largest exporter in the world today. China’s
development has been inducing scholars to carry out a large number of empirical studies on
productivity, international trade and other aspects. Shi (2011) indicates that China's export growthis mainly driven by quantity growth, that is the growth in intensive margin, which accounts forabout 70% of overall export growth. Previous studies also found that export was beneficial for
1

Studies related to the GVC participation index who referred to Koopman et al. (2010) include, for example, Sun et al.
(2019). Sun et al. (2019) evaluated the carbon efficiency and the GVC position index and further analyzed the impact
of position of manufacturing in GVC on carbon efficiency using the panel data of 60 countries from 2000 to 2011.
5

firms to improve their productivity, suggesting that there was a “learning effect” of export. Brandt
et al. (2012) supported the growth of productivity, in which they found that the average annual
productivity growth of Chinese manufacturing firms for incumbents is 2.85% for a gross output
production function and 7.96% for a value-added production function over 1998-2007, which isamong the highest compared to other countries. Regarding the role in the GVC, China consistently
occupies the most upstream (resp. downstream) position along the global output supply (resp. input
demand) chain, mainly due to a large share of manufacturing industry’s output in its gross output(Miller and Temurshoev (2017)). While the value-added export scale of China's manufacturingindustry has achieved significant growth during the recent years, its export was dominated by lowtech manufacturing industry and it was not until recent years that has shifted to the medium- andhigh-tech manufacturing industry.
During the process of empirical analysis, some China’s databases have been frequently employed,
among those is the Annual Survey of Industrial Firms (ASIF) database. As a database collected by
the National Bureau of Statistics of China, the ASIF database has the advantages of large sample
size, numerous indicators and long time series. The ASIF database has been applied to topic related
to the spill-over effect of foreign direct investment (FDI). Xu and Sheng (2012) examined thespillover effects on Chinese domestic manufacturing firms and found evidence of positive
spillovers arise from forward linkages where domestic firms purchase high-quality intermediate
goods or equipment from foreign firms in the upstream sectors. Lu et al. (2017) then improved on
the strategy to identify the spillover effect of horizontal FDI.
Some studies further examined topics related to value-added exports by combining the ASIF
database with the database of the Chinese Customs Trade Statistics (CCTS). Kee and Tang (2016)provided micro-level evidence of China’s rising ratio of domestic value added in exports to grossexports. Upward et al. (2013) provided an assessment of the Chinese export boom from 2000 to2007. They tried to measure the share of domestic value-added in China’s manufacturing exports
by using a modification of a method proposed by Hummels et al. (2001) which takes into accountthe prevalence of processing firms, and further measured the skill-and technology-intensity of thefirms which produce those exports. Lu et al. (2018) calculated the foreign value-added ratio tomeasure the GVC participation of Chinese exporting firms from 2000 to 2006. They found that
productivity increased foreign value-added ratio, and that the rise in productivity led to an increasein foreign value-added ratio for both first-time and continuous exporters, while financial constraints
only significantly affect first-time exporters.

6

3. Data and Methodology
3.1 GVC Positioning Index
In this paper, we employ measures of GVC positioning introduced in Antràs and Chor (2018),
whose framework referred to Miller and Temurshoev (2017), Fally (2012) and Antràs and Chor(2013). We employed the WIOD database for the 2000-2014 period 2 3 to calculated the GVC
positioning index. The WIOD database is built on national accounts data that were developed
within the Seventh Framework Program of the European Commission. Using the input-output tablefrom the WIOD, one can devise measures of the extent to which a particular industry in China isrelatively upstream or downstream in the GVC. The following Figure 1 is prepared based on aschematic version of an input-output table from the WIOD. This figure covers the economy of thewhole world with J countries (indexed by i or j) and S industries (indexed by r or s). Typically, the
WIOD database covers 28 EU countries, 15 other major countries in the world and 56 industries
based on the NACE (Nomenclature of Economic Activities) Rev. 1 classification for each country.
In its top left J×S by J×S block, the table contains information on intermediate purchases
𝑟𝑟𝑟𝑟). The right of thisby industry s in country j from industry r in country i (denoted by 𝑍𝑍𝑖𝑖𝑖𝑖

block on the table contains an additional J × S by J block with information on the final-use

expenditure in each country j on goods originating from industry r in country i (denotedby 𝐹𝐹𝑖𝑖𝑖𝑖𝑟𝑟 ). The last row of Figure 1 shows that gross output in that industry and country(denoted by 𝑌𝑌𝑖𝑖𝑟𝑟 ) equals to the sum of all intermediate purchases made from source industries
r in countries i and 𝐹𝐹𝑖𝑖𝑖𝑖𝑟𝑟 . That is, we can define 𝑌𝑌𝑖𝑖𝑟𝑟 as:
𝐽𝐽

𝐽𝐽

𝑟𝑟𝑟𝑟
𝑟𝑟𝑟𝑟
+ ∑𝑆𝑆𝑠𝑠=1 𝐹𝐹𝑖𝑖𝑖𝑖𝑟𝑟 = ∑𝑆𝑆𝑠𝑠=1 ∑𝑗𝑗=1 𝑍𝑍𝑖𝑖𝑖𝑖+ 𝐹𝐹𝑖𝑖𝑟𝑟 . (1)𝑌𝑌𝑖𝑖𝑟𝑟 = ∑𝑆𝑆𝑠𝑠=1 ∑𝑗𝑗=1 𝑍𝑍𝑖𝑖𝑖𝑖

Here, we denote the total final use of output originating from industry r in country i by 𝐹𝐹𝑖𝑖𝑟𝑟 =∑𝐽𝐽𝑗𝑗=1 𝐹𝐹𝑖𝑖𝑖𝑖𝑟𝑟 .

In the same way, gross output in industry s in country j (denoted by 𝑌𝑌𝑗𝑗𝑠𝑠 ) also equals to the sumof (1) all intermediate purchases made from source industries r in countries i and (2) country j’svalue-added employed in the production of industry s itself (denoted by 𝑉𝑉𝑉𝑉𝑗𝑗𝑠𝑠 ). That is, 𝑌𝑌𝑗𝑗𝑠𝑠 canbe defined as:
2

The WIOD Input-output database is documented in Timmer et al. (2015) and is available at < http://www.wiod.org>.
In this paper, we utilize the World IO Tables released in 2016. For a detailed description of the database construction,
see Timmer et al. (2016).
3 A global IO table has also been developed by the Institute of Developing Economies-Japan External Trade
Organization (IDE-JETRO). Although the IDE-JETRO international IO table covers other Southeast Asian countries,
including Thailand, and is available from 1985, it is only available at five year intervals (2005 is the latest year
available). Its industry classification is less detailed (covering 24-26 industries) than that of the WIOD (which covers
56 industries) and is not harmonized throughout the period. Consequently, this paper utilizes the WIOD.
7

𝐽𝐽

𝑟𝑟𝑟𝑟
𝑌𝑌𝑗𝑗𝑠𝑠 = ∑𝑆𝑆𝑠𝑠=1 ∑𝑗𝑗=1 𝑍𝑍𝑖𝑖𝑖𝑖+ 𝑉𝑉𝑉𝑉𝑗𝑗𝑠𝑠 . (2)The WIOD database contains information on linkages in a full production network of the world,
where each country-industry could potentially be traversed in a large number of production chains.
Based on this setting, the measures of GVC positioning described below will seek to capture theaverage position of each country-industry in the production chains in which it is involved. Although
our further goal is to convert the industry-level measures into the firm-level using the micro datafrom China, the world’s input-output table is necessary for us to capture the linkages of China withthe world. Thus, we need to derive the GVC positioning measures of all countries and then extract
those of Chinese industires for further calculation.

Figure 1. The Structure of the WIOD’s Input-output Table

Source: Figure. 1 of Antràs and Chor (2018), which referred to the WIOD database.
3.1.1 Output Upstreamness Index
The GVC positioning measures are based on the idea that an industry that sells disproportionately
to final consumers would appear to be downstream in the GVC, while an industry that sells little to
final consumers is more likely to be upstream in the GVC. Invoking equation (1), we first define
𝑟𝑟𝑟𝑟
𝑟𝑟𝑟𝑟
𝑎𝑎𝑖𝑖𝑖𝑖= 𝑍𝑍𝑖𝑖𝑖𝑖/𝑌𝑌𝑗𝑗𝑠𝑠 as the dollar amount of industry r’s output from country i needed to produce one

dollar worth of industry s’s output in country j. With this notation, equation (1) can be rewritten as:
𝐽𝐽

𝑟𝑟𝑟𝑟 𝑠𝑠
𝑌𝑌𝑖𝑖𝑟𝑟 = ∑𝑆𝑆𝑠𝑠=1 ∑𝑗𝑗=1 𝑎𝑎𝑖𝑖𝑖𝑖
𝑌𝑌𝑗𝑗 + 𝐹𝐹𝑖𝑖𝑟𝑟 . (3)Iterating this identity, we can express industry r’s output in country i as an infinite sequence ofterms which reflect the use of this country-industry’s output at different positions in the GVC,
which can be expressed as the following equation:
𝐽𝐽

𝐽𝐽

𝐽𝐽

𝑟𝑟𝑟𝑟 𝑠𝑠
𝑟𝑟𝑟𝑟 𝑠𝑠𝑠𝑠 𝑡𝑡
𝑌𝑌𝑖𝑖𝑟𝑟 = 𝐹𝐹𝑖𝑖𝑟𝑟 + ∑𝑆𝑆𝑠𝑠=1 ∑𝑗𝑗=1 𝑎𝑎𝑖𝑖𝑖𝑖
𝐹𝐹𝑗𝑗 + ∑𝑆𝑆𝑠𝑠=1 ∑𝑗𝑗=1 ∑𝑆𝑆𝑡𝑡=1 ∑𝑘𝑘=1 𝑎𝑎𝑖𝑖𝑖𝑖
𝑎𝑎𝑗𝑗𝑗𝑗 𝐹𝐹𝑘𝑘 + ⋯. (4)Building on this identity, Antràs and Chor (2013) suggested to calculate the (weighted) average
position of a country-industry’s output in global value chains by multiplying each of the terms in8

(4) by its respective production-staging distance from final use plus one, and dividing by 𝑌𝑌𝑖𝑖𝑟𝑟 , orwe can derive the Output Upstreamness for industry r of country i (𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 ) index as:
𝐽𝐽

𝑟𝑟𝑟𝑟 𝑠𝑠∑𝑆𝑆
𝑠𝑠=1 ∑𝑗𝑗=1 𝑎𝑎𝑖𝑖𝑖𝑖 𝐹𝐹𝑗𝑗

𝐹𝐹𝑟𝑟

𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 = 1・ 𝑖𝑖𝑟𝑟 + 2・

𝑌𝑌𝑖𝑖𝑟𝑟

𝑌𝑌𝑖𝑖

+ 3・

𝐽𝐽

𝐽𝐽
𝑟𝑟𝑟𝑟 𝑠𝑠𝑠𝑠 𝑡𝑡
𝑆𝑆∑𝑆𝑆
𝑠𝑠=1 ∑𝑗𝑗=1 ∑𝑡𝑡=1 ∑𝑘𝑘=1 𝑎𝑎𝑖𝑖𝑖𝑖 𝑎𝑎𝑗𝑗𝑗𝑗𝐹𝐹𝑘𝑘

𝑌𝑌𝑖𝑖𝑟𝑟+ ⋯. (5)One can easily see that 𝑂𝑂𝑈𝑈𝑖𝑖𝑟𝑟 ≥ 1, and that larger values are associated with relatively higher

levels of upstreamness of the output originating from industry r in country i.

The OU index is identical to industries’ total forward linkages in terms of gross output. Industries
that have a high total forward linkage supply a significant part of their output as intermediate inputs
to other industries, and that is precisely what places industries in an upstream position in the output
supply chain with respect to many industries buying inputs from that industry.

3.1.2 Input Downstream Index
Based on the identity in 3.1.1, we now turn to the direction of the downstreamness measure fromthe perspective of a country-industry pair’s use of intermediate inputs and primary factors of
production. The downstreamness index is defined based on the idea that other things equal, it seems
plausible that production processes that embody a larger amount of intermediate inputs relative totheir use of primary factors of production will be relatively downstream in value chains. By
𝑟𝑟𝑟𝑟
𝑟𝑟𝑟𝑟
defining 𝑏𝑏𝑖𝑖𝑖𝑖= 𝑍𝑍𝑖𝑖𝑖𝑖/𝑌𝑌𝑖𝑖𝑟𝑟 as the share of industry r’s output in country i that is used in industry s incountry j, equation (2) can be rewritten as:

𝐽𝐽

𝑟𝑟𝑠𝑠 𝑟𝑟
𝑌𝑌𝑗𝑗𝑠𝑠 = ∑𝑆𝑆𝑟𝑟=1 ∑𝑖𝑖=1 𝑏𝑏𝑖𝑖𝑖𝑖
𝑌𝑌𝑖𝑖 + 𝑉𝑉𝑉𝑉𝑗𝑗𝑠𝑠 . (6)Iterating the identity in Eq. (4), we have:

𝐽𝐽

𝐽𝐽

𝐽𝐽

𝑟𝑟𝑟𝑟
𝑡𝑡𝑡𝑡 𝑟𝑟𝑟𝑟
𝑌𝑌𝑗𝑗𝑠𝑠 = 𝑉𝑉𝑉𝑉𝑗𝑗𝑠𝑠 + ∑𝑆𝑆𝑟𝑟=1 ∑𝑖𝑖=1 𝑏𝑏𝑖𝑖𝑖𝑖
𝑉𝑉𝑉𝑉𝑟𝑟𝑖𝑖 + ∑𝑆𝑆𝑟𝑟=1 ∑𝑖𝑖=1 ∑𝑆𝑆𝑡𝑡=1 ∑𝑘𝑘=1 𝑏𝑏𝑘𝑘𝑘𝑘
𝑏𝑏𝑖𝑖𝑖𝑖 𝑉𝑉𝑉𝑉𝑡𝑡𝑘𝑘 + ⋯. (7)With reference to Antràs and Chor (2013), we define the Input Downstreamness (𝐼𝐼𝐼𝐼𝑗𝑗𝑠𝑠 ) index forindustry s of country j, which captures a given country-industry pair from primary factors of
production as:
𝐼𝐼𝐼𝐼𝑗𝑗𝑠𝑠 = 1・

𝑉𝑉𝑉𝑉𝑠𝑠𝑗𝑗
𝑌𝑌𝑗𝑗𝑠𝑠

+ 2・

𝐽𝐽

𝑟𝑟𝑟𝑟
𝑟𝑟
∑𝑆𝑆
𝑟𝑟=1 ∑𝑖𝑖=1 𝑏𝑏𝑖𝑖𝑖𝑖 𝑉𝑉𝑉𝑉𝑖𝑖

𝑌𝑌𝑗𝑗𝑠𝑠

+ 3・

𝐽𝐽

𝐽𝐽
𝑡𝑡𝑡𝑡 𝑟𝑟𝑟𝑟
𝑡𝑡𝑆𝑆∑𝑆𝑆
𝑟𝑟=1 ∑𝑖𝑖=1 ∑𝑡𝑡=1 ∑𝑘𝑘=1 𝑏𝑏𝑘𝑘𝑘𝑘 𝑏𝑏𝑖𝑖𝑖𝑖 𝑉𝑉𝑉𝑉𝑘𝑘

𝑌𝑌𝑗𝑗𝑠𝑠+ ⋯. (8)

𝐼𝐼𝐼𝐼𝑗𝑗𝑠𝑠 measures are identical to the total backward linkage expressed in terms of gross

output. Contrary to 𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 , industries that have a high total backward linkage purchase asignificant part of their inputs in the form of intermediate inputs from other industries thatare located more upstream than them, and this kind of purchase places industries that havea high total backward linkage in a downstream position in the input demand chain withrespect to many other suppliers exporting inputs to these industries.
93.1.3

Convert Industry-level GVC Positioning Index to Firm-level Ones

After deriving the industry-level OU and ID indices, next we move on to converting these indices𝑋𝑋
into firm-level indices based on Chor et al. (2014). The upstreamness of firm f’s exports (𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓)𝑀𝑀) are computed as:
and its imports (𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓𝑋𝑋
𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓
= ∑𝑁𝑁
𝑟𝑟=1

𝑋𝑋𝑓𝑓𝑓𝑓𝑓𝑓
𝑋𝑋𝑓𝑓𝑓𝑓𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 ,𝑀𝑀
𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓
= ∑𝑁𝑁
𝑟𝑟=1

𝑀𝑀𝑓𝑓𝑓𝑓𝑓𝑓
𝑀𝑀𝑓𝑓𝑓𝑓𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 . (9)𝑁𝑁
Here, 𝑋𝑋𝑓𝑓𝑓𝑓 = ∑𝑁𝑁
𝑟𝑟=1 𝑋𝑋𝑓𝑓𝑓𝑓𝑓𝑓 and 𝑀𝑀𝑓𝑓𝑓𝑓 = ∑𝑟𝑟=1 𝑀𝑀𝑓𝑓𝑓𝑓𝑓𝑓 are respectively the total exports and imports

within industry r of firm f, respectively. In other words, Chor et al. (2014) took a weighted averageof the upstreamness across industries, using the export shares (respectively, import shares) of eachindustry to capture the importance of that industry in firm f’s export (respectively, import) mix. For
example, when one observes changes in say the export upstreamness of a particular firm, these stemfrom changes in the underlying composition of its exports as reflected by the set of export shares,i.e.

𝑋𝑋𝑓𝑓𝑓𝑓𝑓𝑓
𝑋𝑋𝑓𝑓𝑓𝑓.

𝑋𝑋
𝑀𝑀
Based on the same theory, the downstreamness of firm f’s exports (𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓) and its imports (𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓)

are defined as:

𝑋𝑋
𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓
= ∑𝑁𝑁
𝑠𝑠=1

𝑋𝑋𝑓𝑓𝑓𝑓𝑓𝑓
𝑋𝑋𝑓𝑓𝑓𝑓𝐼𝐼𝐼𝐼𝑗𝑗𝑠𝑠 ,𝑀𝑀
𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓
= ∑𝑁𝑁
𝑠𝑠=1

𝑀𝑀𝑓𝑓𝑓𝑓𝑓𝑓
𝑀𝑀𝑓𝑓𝑓𝑓𝐼𝐼𝐼𝐼𝑗𝑗𝑠𝑠 . (10)𝑁𝑁
Here, 𝑋𝑋𝑓𝑓𝑓𝑓 = ∑𝑁𝑁
𝑠𝑠=1 𝑋𝑋𝑓𝑓𝑓𝑓𝑓𝑓 and 𝑀𝑀𝑓𝑓𝑓𝑓 = ∑𝑠𝑠=1 𝑀𝑀𝑓𝑓𝑓𝑓𝑓𝑓 are respectively the total exports and imports

within industry s of firm f.

As discussed in Miller and Temurshoev (2017), OU and ID are expected to be strongly
positively correlated 4. The observed positive correlation indicates that an industry that isclose to (resp. far away from) final uses as final output users turns out to be, on average,
also close to (resp. far away from) final uses as suppliers of primary inputs.
Table 1 (corresponds to Table 1 of Miller and Temurshoev (2017)) gives the interpretationof the values of OU and ID measures. For example, an industry with larger OU should have(i) a larger share of intermediate output in its gross output; (ii) intermediates output supply
links that are stronger and more highly interconnected with industries that have the same two
characteristics. Eq. (5) fully captures the complexity and size of industry i’s entire output
supply network.

4

According to Miller and Temurshoev (2017), the corresponding correlations for each year range between (0.36,0.43), while the overall correlation coefficient for all pairwise observations is 0.40. As for this paper, thecorrelation coefficient for all pairwise observation is 0.28 in terms of exports and 0.17 in terms of imports.
10

Table 1. The Interpretations of OU and ID Index

Large

OU measure

ID measure(a) Large (small) share of intermediate(a) Large (small) share of intermediate

output (final demand) in gross output

input (value added) in gross input(b) Strong intermediate output supply
links with similar industries

links with similar industries(a) Small (large) share of intermediate
Small(b) Strong intermediate input demand

output (final demand) in gross output(a) Small (large) share of intermediate
input (value added) in gross input(b) Weak intermediate output supply
links with similar industries(b) Weak intermediate input demand
links with similar industries

3.2 Coverage Ratio / Frequency Index
To measure the regulatory intensity of NTM incidence, we employ the import coverage ratio 5(CR) and the frequency ratio (FI) for the products covered by NTMs with respect to China’s export
and import. The coverage ratio of each industry s in year t are then calculated as the export orimport shares of product items covered by NTMs in the product group category. The CR (and FI)
reflects the relative value (number of transactions) of affected export/import, varies between 0%(no coverage) and 100% (all products covered), and is expressed as follows:∑ 𝐷𝐷

𝑉𝑉
𝑉𝑉𝑚𝑚𝑚𝑚

𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 = � ∑𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚�, (11)where s denotes the industry, m denotes the product category of the HS6-digit level, 𝐷𝐷𝑚𝑚𝑚𝑚 is adummy variable for the product m with NTM in year t (1 if there is an NTM measure and 0
otherwise), and 𝑉𝑉𝑚𝑚𝑚𝑚 is China’s exports/imports of product item m in year t, and
∑ 𝐷𝐷

𝑀𝑀

𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠 = � ∑𝑚𝑚𝑚𝑚 𝑚𝑚𝑚𝑚�, (12)𝑀𝑀𝑚𝑚𝑚𝑚where 𝑀𝑀𝑚𝑚𝑚𝑚 is a dummy variable that is equal to 1 if there is an export/import of product m in year
t and 0 otherwise.

These measures can provide a useful indication of the stringency and frequency of NTMs
imposed on Chinese firms’ export and imposed by China against the imports to China. However,they do not indicate the deterrent effect that NTMs may have on exporters’ pricing and exporting
decisions. As for the dataset, we employ the UNCTAD TRAINS database 6. This database covers
NTMs imposed/affecting specific country during 2000-2018. They are based on the classification
5 The coverage ratio and the frequency index only indicates the extent of NTM coverage on export and import. It does
not take specific effect of some NTM into consideration. That is, although the stringency and impact of NTMs mayvary, the coverage ratio and frequency index only consider the number of NTMs.
6 The database is available at < https://trains.unctad.org>.
11

of import measures by UNCTAD (2013), which includes 15 chapters, comprising technical andnontechnical measures. The UNCTAD TRAINS database contains information on the effectively
applied non-tariff measures at HS 2-digit, 4-digit, 6-digit, 8-digit and 10-digit product aggregations.
However, to be consistent with the CCTS database described later, we first unified the HS codes to
6-digit ones. To derive the industry-level NTM stringency indices, we then need to allocate the HS
6-digit NTM regulation information to industries that are based on NACE Rev.1 classification.
Finally, we have the industry-year specific databases covering the period 2000-2014 which include
all the industry-year combinations with non-missing information on input-output and NTM
imposition.

3.3 Data
3.3.1 Industry-level Input-output Data and NTM Data
As mentioned at the previous section, we employ the WIOD database for the industry-level
input-output table. The WIOD provides panel information on the global Input-output tables for the
27 EU countries, 13 other major countries and the rest of the world (ROW) 7. These tables areconstructed on the basis of the officially published input-output tables, in conjunction with thenational accounts and international trade statistics. Table A1 shows the codes and the descriptionsof all the contained industries.
As for NTM regulations, the UNCTAD TRAINS database includes 15 chapters, comprisingtechnical and nontechnical measures. One limitation is that UNCTAD database only provides
cross-sectional information on NTMs in force at the time of data collection (data on China iscollected in 2016), indicating that NTMs those issued and then revoked before 2016 are notcaptured. Fortunately, information on the effective date of each NTM is available, so even though
it might be a strong assumption given China has been going through substantial reform following
WTO accession, we can convert the cross-sectional data into a panel one that covers our sample
period by ignoring the revoked NTMs. Tables 2 and 3 respectively show the calculated coverage
ratio and frequency index of NTMs related to exports (i.e. NTMs imposed against China) andNTMs related to imports (i.e. NTMs imposed by China) during the sample period. We can confirm
that for all industries, the stringency of NTMs imposed against China is significantly higher thanthat of NTMs imposed by China. Further, the stringency of NTM regulations also vary acrossindustries. Another fact is that, industries with higher coverage ratio also have relatively higher
frequency index values with some exceptions such as industry number C29 (i.e. manufacture of7

The 13 countries include non-EU OECD member countries, including Japan and the US, and emerging economies
including China, Indonesia and Mexico. The detailed list for countries is given by Table A2.
12machinery and equipment), whose coverage ratio (=62.3%) is about twice higher than its frequency
index (=30.3%) than that of imports.

Table 2. Coverage Ratio and Frequency Index of NTMs Imposed against Each Chinese Industry

Source: Author’s calculations based on UNCTAD TRAINS and World Input-Output Database.

13

Table 3. Coverage Ratio and Frequency Index of NTMs Imposed by Each Chinese Industry

Source: Author’s calculations based on UNCTAD TRAINS and World Input-Output Database.

3.3.2

Panel Data on Industrial Firms

With the industry-level trade and NTM data, we next convert them into firm-level. This study
draws on two main sources of Chinese firm data. The first data used is from the Annual Survey ofIndustrial Firms (ASIF) which is conducted by the National Bureau of Statistics of China for the
2001-2007 period8. These surveys cover all of state-owned enterprises (SOEs) and non-SOEs withannual sales over 5 million Chinese yuan. As the first two columns of Table 4 show, from 2001 to
2007, the database includes more than 1.7 million observations, and the sample size of each year
increases from about 169,000 in 2001 to about 337,000 in 2007. While about 550,000 firms madethe list in the seven-year sample period, only 46,000 firms (accounting for about 8%) appeared on
the list for more than two consecutive years due to various reasons such as bankrupt, restructuring
and reorganization. The number of firms covered in the surveys varies from approximately 162,000
to 270,000. Though the exact number varies across years, the data set of each year has more than
100 variables, which providing the basic information for each surveyed firm, including the firm’s
identification number, location code, industry affiliation, ownership structure, and the financial and
8 To be exact, the ASIF database after 2008 is also available. Most literatures only use the data collected from 20002007, because there is a significant change in statistical methods and dimensions for data collected after 2008 and there
is also criticism of the data reliability. Refer to Hsieh and Klenow (2009) and Chen (2018) for details.
14operational information extracted from accounting statements, such as sales, employment, R&D
expenditure, fixed assets, and total wage bill.

3.3.3

Firm-level Data on Trade

The second data source is the database of the Chinese Customs Trade Statistics (CCTS) collectedby the General Administration of Customs of China. The CCTS database covers monthly recordsof all merchandise transactions passing through Chinese customs from 2001 to 2013, including
firm identification (name, address, ownership), HS product code, value of imports and exports,
quantity of goods, customs regimes, means of transportation, customs code, origin, and destination
country. We collapse the data to annual data to make it consistent with the firm-level ASIF data.
The product codes of traded goods are HS8-digit codes and we convert them to HS6-digit ones.
The export and import values are reported as free-on-board (FOB) values in US dollars. Table 4
gives the observed number of Chinese firms who had export and import records during 2001-2007.
From the middle two columns, we can see that the number of exporting firms kept increasing fromabout 67,000 in 2001 to about 177,000 in 2007 so that comes to an aggregated number of about
800,000 during the sample period. In terms of the importing firms, the number also consistently
increased from about 66,000 to about 120,000 during the seven years. Both the increased numberof Chinese exporting and importing firms implies that China has been enhancing the connection
with the GVC since 2001, in which China just joined the WTO.

3.3.4

Matching Industrial Firm Data with the Trade Data

We matched the ASIF and CCTS data by firm name, because firm names are relatively moreavailable and consistent over time compared with the other possible identifiers. The two data sets
do not completely match for the following reasons. First, the ASIF data includes a large number of
non-trade firms, which do not appear in the CCTS data. Second, firms who export via trading agentsare reported as exporters in the ASIF data, but their exports will be recorded under the name of the
trading agent in the CCTS data. Third, the ASIF data only includes larger firms in the
manufacturing industry, while the CCTS data records all trade transactions including those madeby small firms and firms outside the manufacturing industry. These inconsistencies are illustrated
in Fig. 2. Table 5 respectively gives the sample size of the exporting and importing firms in thematched data for each year. For both exporting and importing firms, the annual number ofobservations slowly increased from less than 20,000 in 2001 to more than 50,000 for export andmore than 32,000 for import in 2007. Totally, the number of exporting firms is slightly larger than15

that of importing firms, and the aggregated sample size of exporting firms is about 240,000 andthat of importing firms is about 180,000.

Figure 2. Matching A

SIF and CCTS Database

Table 6 further gives information about the proportion of firms appear in the ASIF and CCTS
databases which also appear in the matched ASIF-CCTS data. We can see that only less than 15%
of firms in the ASIF database appear in the matched data, mainly because about one-quarter offirms that export to foreign countries. Though not listed in the table, only half of the exporting firms
in the ASIF appear in the matched data. The most plausible explanation is that the remaining firms
classified as exports in the ASIF data were actually exported via trading agents, just like the Cat 3
of Fig. 2 demonstrates. As for the percentage of firms appear in the CCTS data which also appear
in the matched ASIF-CCTS data, the matched data contains less than 30% of all customs-registeredfirms. The proportion of firms in either the ASIF and the CCTS data which are in the matched data
has not significantly changed during the sample period, and has kept relatively a flat and low level.
One possible reason is that the cleaned CCTS data contains the population of all non-service trade,and therefore includes trade by small-scale firms whose annual sale are not high enough to be
included in the ASIF data. Another explanation is that the cleaned CCTS data includes trade by the
agricultural industry, which are excluded from the cleaned ASIF data.

16

Table 4. Sample Size of the ASIF and CCTS Database

Table 5. Sample Size of the Matched ASIF-CCTS Dataset

Table 6. Percentage of Firms in the ASIF and CCTS Database Which Appear in the Matched
ASIF-CCTS Dataset

3.4 Estimation Strategies
This paper aims to investigate the following two questions: (1) whether NTM imposed against(by) China may affect Chinese firm’s GVC positioning which is represented by our GVC
17

positioning indices– OU and ID; (2) whether the effect of NTM imposed against (by) China mayvary across firms that are located at different positioning in the GVC. We examine these questions
separately in our analyses.
3.4.1 NTM Regulations and Firms’ GVC Positioning
We first investigate the relationship between NTM regulations and firms’ GVC positon with thewith the following regression specification.
𝑋𝑋
𝑀𝑀
𝑋𝑋
𝑀𝑀
�𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓, 𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓, 𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓, 𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓
� = 𝛼𝛼 + 𝛽𝛽𝑡𝑡 {𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 , 𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠 } + 𝛾𝛾𝑡𝑡 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑡𝑡 + 𝛿𝛿𝑡𝑡 𝑍𝑍𝑓𝑓𝑓𝑓 + 𝜀𝜀𝑓𝑓𝑓𝑓 . (13)The outcome variables of interest are the four indices of firms’ positioning in the GVC: the𝑋𝑋𝑀𝑀), the average upstreamness of firms’ imports (𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓),average upstreamness of firms’ exports (𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓𝑋𝑋), and the average downstreamness of firms’
the average downstreamness of firms’ export (𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓

𝑀𝑀) . These GVC positioning indices are calculated in firm-level. The explanatory
import ( 𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓

variables include the coverage ratio (𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 ), the frequency index ( 𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠 ) which is calculated inindustry-level. The other explanatory variables are the year dummy (𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑡𝑡 ) and firm characteristics,

𝑍𝑍𝑓𝑓𝑓𝑓 , which consist of firm f’s total stock, paid-up capital and ownership type. The error term, 𝜀𝜀𝑓𝑓𝑓𝑓 ,captures the correlated shocks within firm f over year t. During the estimation, all the continuous

dependent and independent variables are converted to the logarithmic scale, so that the estimatedcoefficients can be interpreted into the NTM regulation elasticities of the GVC positioning.
In our matched ASIF-CCTS trade statistics, the sample comprises more than 430,000 exporter𝑋𝑋𝑋𝑋
industry-year observations with data on 𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓and 𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓, along with more than 635,000 importer-𝑀𝑀𝑀𝑀, and 𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓. When we are to use information fromindustry-year observations with data on 𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓firms’ balance sheets for variables in 𝑍𝑍𝑓𝑓𝑓𝑓 , the sample will significantly decline for both exporters

and importers, which mainly because that the ASIF database is an unbalanced database. Importantly,

this variation in sample sizes across specifications does not appear to generate estimation bias.

3.4.2 Transmission of NTM through GVC on Firm Performance
We then estimate how the NTMs affect firms’ performance with the following specification.
𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑃𝑓𝑓𝑓𝑓 = 𝜑𝜑 + 𝜃𝜃𝑡𝑡 {𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 , 𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠 } + 𝛿𝛿𝑡𝑡 {𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 , 𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠 } ∗ {𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 , 𝐼𝐼𝐼𝐼𝑖𝑖𝑟𝑟 } + 𝜌𝜌𝑡𝑡 𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑡𝑡 + 𝜎𝜎𝑡𝑡 𝑍𝑍𝑓𝑓𝑓𝑓 + 𝜈𝜈𝑓𝑓𝑓𝑓 .(14)

For the dependent variable, we choose variables reflecting firm’s performance including total
sale (𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑆𝑓𝑓𝑓𝑓 ), R&D expenditure (𝑅𝑅&𝐷𝐷𝑓𝑓𝑓𝑓 ), sale of new product (𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑁𝑓𝑓𝑓𝑓 ), annual export amount(𝐸𝐸𝐸𝐸𝐸𝐸𝑓𝑓𝑓𝑓 ) and annual import amount (𝐼𝐼𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓 ) at year t. The explanatory variables include thecoverage ratio (𝐶𝐶𝐶𝐶𝑡𝑡 ), the frequency index (𝐹𝐹𝐹𝐹𝑡𝑡 ), the year dummy (𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑌𝑡𝑡 ) and firm characteristics,

𝑍𝑍𝑓𝑓𝑓𝑓 . The variable set 𝑍𝑍𝑓𝑓𝑓𝑓 includes firm f’s total stock, paid-up capital and ownership type, which18

is identical with those of Section 3.4.1. Similar with the first stage estimation, all the continuous
dependent and independent variables are converted to the logarithmic scale. To capture thepotential heterogeneity of the impact across different firms’ positioning in the GVC, we
additionally use the interaction term of the GVC index and the NTM regulation index. It should be
noted that, because the firm-level OU and ID indices are related to the stringency of NTM
regulations, 𝐶𝐶𝐶𝐶𝑠𝑠𝑠𝑠 , and 𝐹𝐹𝐹𝐹𝑠𝑠𝑠𝑠 , we cannot directly include them as explanatory variables due to theendogeneity problem. Thus, instead of the firm-level indices, here we use the industry-level ones,

𝑂𝑂𝑂𝑂𝑖𝑖𝑟𝑟 , 𝐼𝐼𝐼𝐼𝑖𝑖𝑟𝑟 , to generate the interaction term.
3.4.3

Descriptive Statistics

Table 7 shows the country-level descriptive statistics of the key variables in this paper -GVC
positioning measures and NTMs stringency indices. Unlike the firm-level data, the country-levelindices have a longer span of sample period which is from 2000 to 2014 for the GVC positioning
indices and from 2001 to 2018 for the NTM variables. For the GVC positioning indices, we cansee that the output upstreamness idex, OU, ranges between 2.6 and 3.0 during the sample period
and the mean is 2.8. The input downstreamness index, ID, has a similar value range to OU, and has
a slightly lower mean of 2.7 9. For NTMs regulations, the coverage ratio and frequency index of
NTMs imposed against China (i.e. CR_Exp and FI_Exp) are about three times higher than NTMs
imposed by China (i.e. CR_Imp and FI_Imp), indicating that Chinese firms tend to be easier topurchase intermediates from foreign countries than to provide their goods as a supplier in the GVC.
Tables 8 gives summary statistics of the variables used in the empirical analysis. As mentioned
above, the sample period for the firm-level estimation should be matched with that for the ASIF
database as the ASIF database only provides reliable statistics during the 2001-2007 period. Fromthe perspective of OU and ID indices, we can see that while the firm-level indices are notsignificantly changed from the country-level ones, the ID index has slightly increased from 2.7 toaround 3.2 while the firm-level indices are not significantly changed from the country-level ones.
This change indicates that Chinese firms had lost their total backward linkage to some extent
after 2008, which is somewhat puzzling because China has been rapidly developing and because
it has become one of the largest importers (and, of course, one of the largest exporters as well)during the past decade. One possible explanation would be that the frim-level ID lost its value
because it takes account of the variation of firms’ heterogeneous choice of export and importamount. As the firms-level ID index is calculated using the trade records of only the middle- and9 This result corresponds to Antràs and Chor (2018), in which employed the same database to obtain China’s output
uptreamness (equals to 2.819) and input downstreamness (equals to 2.900) at the year of 2011.
19

large-scale firms only, it would have reflected those firms’ flexible choice on export and importand ignored those small-scale firms’ trade strategies. As for NTMs regulations, both the coverage
ratio and frequency index have smaller mean values compared to the country-level ones, whichindicates that Chinese firms had been facing looser regulations when making international trade
before the year of 2007 than those from 2008 to 2018.

Table 7. Descriptive Statistics of Country-level Coverage Ratio, Frequency Index and OU, ID
Index

Source: Author’s calculations based on UNCTAD TRAINS and World Input-Output Database.
Table 8. Descriptive Statistics of Firm-level Coverage Ratio, Frequency Index and OU, ID Index
Regarding Export Records

Source: Author’s calculations based on UNCTAD TRAINS and World Input-Output Database.
Table 9. Descriptive Statistics of Firm-level Coverage Ratio, Frequency Index and OU, ID Index
Regarding Import Records

Source: Author’s calculations based on UNCTAD TRAINS and World Input-Output Database.
Tables 10 and 11 describes the dependent variables in the estimation of the effect of NTMs onfirm’s performances. All the variables are in units of one thousand Chinese yuan. Generally
speaking, Chinese firms tend to spend about 0.001% of their annual sales on R&D activities andthe outcome of R&D, the sale of new products, accounts for about 20% of their annual sales.

20

Table 10. Descriptive Statistics of Firm Performances Regarding Export Records

Source: Author’s calculations based on ASIF and CCTS databases.
Table 11. Descriptive Statistics of Firm Performances Regarding Import Records

Source: Author’s calculations based on ASIF and CCTS databases.
4. Empirical Result
4.1 Effect of NTM on GVC Positioning
We now present our estimation results. Table 12 shows the effect of the two measures of NTM
regulations, 𝐶𝐶𝐶𝐶𝑡𝑡 and 𝐹𝐹𝐹𝐹𝑡𝑡 , on firms’ positioning in the GVC. Columns 1-4 of the table correspond𝑋𝑋
to the effect of 𝐶𝐶𝐶𝐶𝑡𝑡 on firms’ GVC positioning in terms of export, 𝑂𝑂𝑈𝑈𝑓𝑓𝑓𝑓, while the first two𝑋𝑋) based on the ordinary least squares (OLS) andcolumns are for the output upstreamness (𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓𝑋𝑋)
fixed-effects model estimation and the latter two columns are for the input downstreamness (𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓

using the OLS and fixed-effects model, respectively. The results suggest a significant and negative
effect of NTM regulations on both firms’ output upstreamness and input downstreamness, where

the stringency of NTM regulations are measured by the coverage ratio. These results are robust
against the choice of estimation models, and indicates that exported goods by Chinese firms havea smaller share of intermediate input and output in gross output. The results also indicate weaker
intermediate output supply or input demand links with similar industries when facing stricter NTM
regulations. Columns 5-8 shows the effect of 𝐹𝐹𝐹𝐹𝑡𝑡 on firms’ GVC positioning in terms of import.
𝑋𝑋
Columns 5-6 are for the output upstreamness (𝑂𝑂𝑂𝑂𝑓𝑓𝑓𝑓) based on the OLS and fixed effects estimation

𝑋𝑋) using the OLS and fixed effects model.
and Columns 7-8 are for the input downstreamness (𝐼𝐼𝐼𝐼𝑓𝑓𝑓𝑓

The results show that the signs and the magnitudes of the coefficients remain unchanged even when

the NTM regulations are measured by the frequency index, which confirms the negative effect of
NTM regulations on GVC positioning.

21

Table 13 shows the effect of NTM regulations on GVC positioning in terms of Chinese firms’
imports. The results are quite similar to the export case. Again, we can find a negative effect of
NTM stringency on both firms’ output upstreamness and input downstreamness. This means thatimported goods by Chinese firms have smaller share of intermediate input and output in gross
output when facing stricter regulations of NTM. Recall that OU and ID respectively indicates firm’s
total forward and backward linkages in the GVC. This result indicates that higher pressure of NTM
regulations results in a decrease of total forward and backward linkages in terms of gross output.
According to Miller and Temurshoev (2017), total forward linkage measures are often used asindicators of firm’s importance or keyness in the supply chain. A Firm with high total forward
linkage is interpreted as a more appropriate target for economic stimulation purposes because it
will bring more benefit to the entire economy than a firm with lower total forward linkage. In the
meanwhile, a firm with high total backward linkage is interpreted as a more suitable target for an
economic stimulation, because it purchases a significant part of its inputs in the form ofintermediate inputs from other firms so that will lead other firms to also expand their outputs inorder to meet that firm’s increased intermediate demands. As long as the estimation results indicate,
we can say that the negative effect of NTM regulations on firms’ output upstreamness and input
downstreamness would hinder Chinese firms from purchasing intermediate or providing output toother firms so that further decreases their importance and competitiveness in the GVC.
In summary, it can be said that the NTM regulations will act as an obstacle in the way of Chinese
firms to get accessed with the world’s production chains. The analysis uncovered the fact that facing
stricter NTM regulations, firms will choose to purchase the intermediate from firms located closer
to the midstream and also provide their outputs to firms located closer to the midstream of the GVC.
This conclusion can also be translated as the strict NTM regulations will drive firms to decrease
their linkages with the world’s production chains.

22

Table 12. Estimated Effect of NTMs Imposed against China on Firms’ Positioning in the GVC

Table 13. Estimated Effect of NTMs Imposed by China on Firms’ Positioning in the GVC

4.2 Effect of NTM on Firm Performance
The findings of the negative impact of NTM on firms’ GVC positioning in the previous
subsection motivate us to further examine the relationship between NTM regulations and various
firm performances in consideration of the difference in the GVC positioning. As mentioned above,
as we regard the firms’ GVC positioning as an endogenous variable, instead of the firm-level GVC
positioning measure, we use the industry-level ones to capture the heterogeneous effect of NTM
regulations. We first examine how NTM regulations affect the firms’ import and export amount.
Table 14 presents the estimated effect of NTMs imposed against China on firms’ export. It shows
strong evidence that stricter NTM regulations results in less export activities of firms. These results,
regardless of which measure of NTM regulations we employ, suggest that manufacturers locatedoutside of China typically are more willing to outsource processing activities to China when the23

NTM regulations imposed against China are not very strict. When we include the interaction termof NTM regulation and GVC positioning index, we find that the negative effect of NTM regulations
on exports vary depending on the GVC positioning. The positive coefficient of the interaction term,along with the smaller magnitude than that of NTM regulation index, indicates that the larger thefirms’ output upstreamness and input downstreamness are, the less negative is the effect of the
NTM regulations.
Table 15 shows the effect of NTM regulations on firms’ imports. Similarly, the results again
indicate that foreign firms typically outsource processing activities to China when China imposes
looser NTM regulations. The opposite sign of the interaction terms with the NTM index also tell
the same story that the more firms located near both ends of the GVC, the less negative is the effectof NTM regulations impose on firms’ processing activities.
We then examine how NTM regulations affect firms’ R&D activities. Table 16 shows the resultsof how NTM regulations imposed against China can affect Chinese firms’ innovative activities.
Columns 1-6 correspond to the relationship between NTM and firms’ R&D expenditures andColumns 7-12 shows the effect on firms’ sale of new products. Here, one can regard the sale of
new products as kind of the outcome of the R&D activities. We find strong evidence that NTM
regulations are negatively related with firms’ innovative activities. For both R&D expenditures andsales of new products, one percent increase in NTM regulations against China will bring a decreaseby 0.1-0.2 percent, regardless of the measure of NTM regulation. This implies that Chinese firms
facing stricter NTMs may be compelled to cut the budget for innovative activities in order tocomply with the NTM regulations. As for the total sales, as Columns 13-18 indicate, the NTM
regulations imposed against China also hinder Chinese firms’ production activities. The interaction
terms between NTM regulations and GVC positioning further show us that the negative effect varyacross firms regarding their positioning in the GVC. The results coincide with the previous case,that is, the positive and smaller magnitudes of the coefficients indicate that firms with higher output
upstreamness and input downstreamness seem to be less affected by NTM regulations imposed
against China and (Columns 1-6). For the rest columns of Table 16, we can see how NTM
regulations affect firms’ annual sales. Again, we get the similar conclusion that the more firms arelocated at the edges of GVC, the smaller negative effect they receive from NTM regulations.
In contrast to Table 16, Table 17 shows how NTM regulations imposed by China can affectChinese firms. Surprisingly, the results indicate that stricter NTM regulations imposed by China
may stimulate Chinse firms to arrange more R&D activities (Columns 7-12). However, when werecall that NTMs imposed against China are found to hinder firms’ R&D activities, this result canbe reasonable. Unlike the previous case, the positive impact on R&D expenditures as well as the24sales of new products indicates the NTM plays a roll of protectionism policy as we now use themeasures of NTMs that are imposed by China (i.e. against countries whoever want to export toChina).
Finally, let us take a look at how NTM regulations will affect firms’ total sales. Columns 13-18
of Tables 16 and 17 show that the NTM regulations imposed against China as well as NTMs
imposed by China may decrease the firms’ total sales, due possibly to the above-mentioned
negative effects on firms’ export and import activities. Without exception, the negative effects arefound to vary across firms according to which position the firm is located in the GVC. Thus, thefindings from the analyses so far suggest that NTM regulations tend to suppress firms’ performancefrom various aspects and the effect is quite heterogeneous with firms’ positioning in the GVC.

4.3 Robustness Checks
To check the heterogeneous impact of NTM regulations on firms’ performance, we conduct arobustness check by dividing the sample into 20 groups according to firms’ OU and ID index andrun Eq. (14) using these subsamples. Tables 18 and 19 show the results of robustness check for theheterogeneous effect of NTMs. Here, we suppress the results of the subgroups whose observationis less than 100. For each subgroup with more than 100 observations, the coefficients and thesignificance levels are reported. Despite some exceptions, we can generally observe that firms withlower output upstreamness and input downstreamness in the GVC tend to receive the greater
negative effect of NTM regulations.

5. Conclusion
The rising fragmentation of production in the global value chain has been a key trend ininternational trade thus, it is critical for researchers to elucidate how changes in the economic
environment are likely to affect the specialization of countries within the GVC. By combining firmlevel customs data, manufacturing census data, and China’s Input-Output Tables, this paper
investigates the impact of NTM’s stringency on Chinese firms’ positioning in the GVC, which ismeasured by two types of GVC positioning indices, namely, output upstreamness and input
downstreamness indices. We then estimate the impact of NTMs on various firm performance bypaying special attention to how the impacts vary across firms with different positioning in the GVC.
The empirical results show that NTMs imposed against and imposed by China could significantly
reduce firms’ linkages with foreign countries, thereby reducing the firms’ importance within the
GVC. We also find that stricter NTMs could even hinder firms’ innovative activities and decrease
exports and imports. Further analysis indicates that these negative impacts of NTMs on firms are25

heterogeneous across firms depending on their original position in the GVC; firms with higher
output upstreamness or input downstreamness have smaller effect than those with lower GVC
positioning indices.
Table 14. Estimated Effect of NTMs Imposed against China on Firms’ Export

26

Table 15. Estimated Effect of NTMs Imposed against China on Firms’ Import

27

28

29

Table 18. Robustness Check for the Heterogeneous Effect of NTMs Imposed against China

Table 19. Robustness Check for the Heterogeneous Effect of NTMs Imposed by China

30

References
Antràs, P., Chor, D. (2013), “Organizing the Global Value Chain”, Econometrica, 81(6): 21272204.
Antràs, P., Chor, D. (2018), “On the Measurement of Upstreamness and Downstreamness in Global
Value Chains”, NBER Working Paper No. 24185, National Bureau of Economic Research.
Antràs, P., Chor, D., Fally, T., Hillberry, R. (2012), “Measuring the Upstreamness of Production
and Trade Flows”, American Economic Review: Papers & Proceedings, 102(3): 412-416.
Balassa, Bela. (1965). “Trade Liberalization and ‘Revealed’ Comparative Advantage”, Manchester
School of Economic and Social Studies, 33: 99-123.
Baldwin, R., Venables, A. (2013), “Spiders and Snakes: Offshoring and Agglomeration in the
Global Economy”, Journal of International Economics, 90(2): 245-254.
Beghin, J C., Maertens, M., Swinnen, J. (2015), “Nontariff Measures and Standards in Trade andGlobal Value Chains”, Annual Review of Resource Economics, 7: 425-450.
Brandt, L., Van Biesebroeck, J., Zhang, Y. F. (2012), “Creative Accounting or Creative
Destruction? Firm-level Productivity Growth in Chinese Manufacturing”, Journal of
Development Economics, 97: 339-351.
Chen, L. (2018), “Re-exploring the Usage of China’s Industrial Enterprise Database”, Economic
Review, 214: 140-153.
Chor, D., Manova, K., Yu, Z. (2014), “The Global Production Line Position of Chinese Firms”,
Industrial Upgrading and Urbanization Conference, Stockholm 28, 29.
Devadason, E. S., Chandran, VGR., Kalirajan, K. (2018), “Harmonization of Food Trade Standards
and Regulations in ASEAN: The Case of Malaysia’s Food Imports”, Agricultural Economics,
49: 97-109.
Fally, T. (2012), “Production Staging: Measurement and Facts,” mimeo UC Berkeley.
Grundke, R., Moser, C. (2019), “Hidden Protectionism? Evidence from Non-tariff barriers to Tradein the United States”, Journal of International Economics, 117: 143-157.
Hsieh, C. T., Klenow, P. J. (2009), “Misallocation and Manufacturing TFP in China and India”,
The Quarterly Journal of Economics, 124(4): 1403-1448.
Hummels, D., Ishii, J., Yi, K.M. (2001), “The Nature and Growth of Vertical Specialization in
World Trade”, Journal of International Economics, 54(1): 75-96.
Lu, Y., Tao, Z. G., Zhu, L. M. (2017), “Identifying FDI Spillovers”, Journal of International
Economics, 107: 75-90.
Lu, Y., Shi, H. M., Luo, W., Liu, B. (2018), “Productivity, Financial Constraints, and Firms’ Global
Value Chain Participation: Evidence from China”, Economic Modelling 73, 184-194.
31

Kee, H. L., Tang, H. W. (2016), “Domestic Value Added in Exports: Theory and Firm Evidence
from China”, American Economic Review, 106(6): 1402-1436.
Koopman, R., Powers, W., Wang, Z., Wei, S.J. (2010), “Give Credit Where Credit Is Due: Tracing
Value Added in Global Production Chains”, NBER Working Paper No.16426.
Miller R. E., Temurshoev, U. (2017), “Output Upstreamness and Input Downstreamness ofIndustries/Countries in World Production”, International Regional Science Review, 40(5):443-475.
Shi, B. Z. (2011), “Extensive Margin, Quantity and Price in China's Export Growth”, China
Economic Review, 22(2): 233-243.
Sun, C. W., Li, Z., Ma, T. M., He, R. Y. (2019), “Carbon Efficiency and International Specialization
Position: Evidence from Global Value Chain Position Index of Manufacture”, Energy Policy,
128: 235-242.
Timmer, M. P., Dietzenbacher, E., Los, B., Stehrer, R., de Vries, G. J. (2015), “An Illustrated User
Guide to the World Input–Output Database: The Case of Global Automotive
Production”, Review of International Economics, 23: 575-605.
Timmer, M. P., Los, B., Stehrer, R., de Vries, G. J. (2016), “An Anatomy of the Global Trade
Slowdown based on the WIOD 2016 Release”, GGDC research memorandum number 162,
University of Groningen.
Wang, Z., Wei, S. J., Yu, X. D., Zhu, K. F. (2017), “Measures of Participation in Global Value
Chains and Global Business Cycles,” NBER Working Paper 23222.
UNCTAD (United Nations Conference on Trade and Development). (2013), “World Investment
Report 2013—Global Value Chains: Investment and Trade for Development”, New York andGeneva: UN.
Upward, R., Wang, Z., Zheng J. H. (2013), “Weighing China’s Export Basket: The Domestic
Content and Technology Intensity of Chinese Exports”, Journal of Comparative Economics,
41: 527-543.
WTO & IDE-JETRO (2011), “Trade Patterns and Global Value Chains in East Asia: from Tradein Goods to Trade in Tasks”, Geneva, June.
Xu,X. P., Sheng,Y. (2012), “Productivity Spillovers from Foreign Direct Investment: Firm- Level
Evidence from China”, World Development,40:62-74.

32

Table A1. Manufacturing Industry Classification of World Input-Output Tables

Source: World Input-Output Database.

33

Table A2. List of Countries of the World Input-Output Tables

Source: World Input-Output Database.

34