Revisiting Complementarity Between Japanese FDI
and the Import of Intermediate Goods: Agglomeration
Effects and Parent-firm Heterogeneity

伊藤匡・松浦寿幸・楊志海
Tadashi Ito, Toshiyuki Matsuura, and Chih-Hai Yang2020 年 2 月 25 日
KEO Discussion Paper No.152

Revisiting Complementarity between Japanese FDI and the Import ofIntermediate Goods: Agglomeration Effects and Parent-firm Heterogeneity♣
Tadashi Ito
Gakushuin University
tadashi.ito@gakushuin.ac.jp
Toshiyuki Matsuura
Keio University
matsuura@sanken.keio.ac.jp
Chih-Hai Yang
National Central University
chyang@mgt.ncu.edu.tw

Abstract
This study revisits the nexus between FDI and international trade in the dynamic perspective,
focusing on the intermediate goods imports of Japanese affiliates in China. To examine theeffect of the formation of the agglomeration and the recent FDI made by small firms, both ofwhich has been overlooked in the literature, we construct a unique parent-affiliate-tradetransaction matched panel dataset and estimate a discrete-time hazard model over the 2000–
2006 period. We found while firms in agglomerated regions shorten the duration of trade,
Japanese affiliates owned by smaller firms tend to import intermediate goods for a longer
duration. (97 words)
Keywords: FDI by SMEs, Trade duration, Intermediate goods, Agglomeration,
JEL classifications: F14, F21, F23

This study is based on a report of Yang’s visiting research project at the Institute of Developing Economies (IDE),
JETRO, in 2015. We thank Hisamitsu Saito, Ayako Obashi, Kozo Kiyota, Deborah Swenson, Shigeyuki Abe,
Fukunari Kimura, Toshihiro Okubo and other seminar participants at Asian Economic Panel, Western Economic
Association International at Santiago, the University of British Columbia and the Japanese Economic Association
for their helpful comments. Yang gratefully acknowledges financial support from IDE-JETRO. The study is also
conducted as part of an economic analysis of trade policy and trade agreements at IDE-JETRO. This paper utilizes
the data constructed under the auspices of JSPS KAKENHI Grant Numbers JP26380311 and JP26285058.
Matsuura and Yang appreciate the financial support from the Interchange Association, Japan.
♣

11. Introduction
When China symbolically joined the world trading system by becoming a member of theWorld Trade Organization (WTO) in 2001, Japanese firms dramatically increased their foreign
direct investment (FDI) flows to China, surpassing Japan’s largest trade and investment partner,
the United States (US) by 2005. Large enterprises as well as small and medium-sizedenterprises (SMEs) set up their plants in China. As Table 1 shows, the share of SMEs
(companies with less than 300 employees) has steadily increased over the past few decades.
In particular, the share of firms with less than 1,000 employees accounts for 47.7% of the firmsthat began investing after 2001. The relocation of SMEs, which employ the majority of theJapanese labor force, to foreign countries, including China, has revived concerns of thehollowing out of the local economy in Japan.
[Insert Table 1 approximately here]
If FDI has complementary relationship with the trade of intermediate goods from homecountry, FDI may not accelerate the hollowing out of domestic production. Thus, it is anempirical question whether the recent FDI driven by SMEs complement or substitute the tradeof the intermediate goods. This study provides some stylized facts to contribute toward better
understanding of the nexus between FDI and trade, and from a dynamic perspective, analyzes
the duration of intermediate goods trade when it is accompanied by an increase in FDIs byusing a unique parent–firm–affiliate, product-level dataset for the 2000–2006 period. Morespecifically, we investigate the effects of firm characteristics and agglomeration on the FDItrade complementarity.
Furthermore, exploring the dynamic pattern of MNEs’ import of intermediate goods may
offers policy implications for policy makers in developing countries. Decreases in imports ofintermediate goods and increases in local procurement are the two sides of a coin. A local
procurement by MNEs is considered as a source of technology spillover to local firms. From2the perspective of developing country policy makers who want to increase local procurement
by MNEs, this study provides implications as to which type of MNEs are invited and which
local factors positively affect local procurement.
This study is structured as follows. Section 2 briefly review the literature and section 3
describes the data sources. Section 4 presents the empirical specifications and demonstrates
several important stylized facts about the dynamics of intermediate goods trade. Section 5
explains our estimation results. Section 6 discusses these results. The final section summarizes
our concluding remarks.
2. Literature
Whether FDIs are substitutes or complements of international trade has long been debatedin the international trade literature. Brainard (1993, 1997) argued that FDIs and exports aresubstitutes because of the trade-off between proximity and concentration. When the production
function is characterized by increasing returns to scale, a firm can benefit from lower costs byconcentrating all of its production in one plant, but it incurs trade costs, such as transportation
costs or import tariffs, to serve foreign markets through exports. However, by setting up a plantin each market, a firm can save on trade costs, but it loses any cost reductions gained throughscale effects. Thus, this trade-off determines if a firm exports or invests to serve its foreign
demand.
Complementarity may arise in a vertical production relationship. When an assemblycompany relocates its plant to a foreign country, the company that has supplied parts andcomponents to the assembly firm now starts to export to the assembly firm’s foreign plant.
The same can be said for operations within the same firm. Specifically, when a firm
“unbundles” its production stages and relocates a stage to a foreign country, the parent-firm
exports inputs (typically, parts and components) to its affiliate plant abroad. The recent
development of global supply chains has brought this type of FDI to the fore. Head and Ries3

(2001) found a complementary relationship for the case of Japanese manufacturing firms.
Similar findings are obtained by Chen and Ku (2000) for Taiwan and Fung et al. (2010) for
East Asia. Hanson et al. (2005) examined trade in intermediate inputs for further processing
between parent firms and their foreign affiliates using firm-level data on US multinationals
and found that demand for imported inputs is higher when affiliates face lower trade costs,
lower wages for low-skilled labor, and lower corporate tax rates.
There are still several concerns that the complementary relationship between FDIs andexports may not last long. First, as mentioned above, the share of FDI by SMEs have been
growing. And many of SMEs are single-product firms. If it is difficult for SMEs to “unbundle”
their production stages, exports from the parent SMEs to their foreign affiliates may notincrease. Second, FDI flows to China tend to concentrate on the coastal areas, which mean thatthe multinational enterprise (MNE) affiliates in China have formed agglomerations in somecoastal regions. Agglomerations of foreign firms may attract domestic suppliers to seek
transactions with MNE affiliates. Therefore, once an agglomeration of suppliers develops, the
MNE affiliates may reduce imports of intermediate goods from the home country. Third, as
Belderbos et al. (2001) and Kiyota et al. (2008) demonstrated, MNE affiliates tend to increase
local procurement as they accumulate experience in the local market.1 These results imply
that imports from the home country may gradually be substituted with the passage of time. In
other words, the choice between imports from the home country or purchases from local
suppliers may also change over time. Thus, the complementary relationship should be
examined from a dynamic perspective.
Pioneering works on dynamic trade relationships can be traced to Besedes and Prusa (2006a,
2006b). Employing a survival analysis to a study of the duration of US imports, they found1 Kee and Tang (2016) also reports Chinse firms have gradually substituted imported material withdomestic material during the period from 2000 to 2007.
4

that the median duration of exporting a product to the US is very short—typically two to four
years. They further examined the extent to which product differentiation affects the durationof the US import-trade relationship. Applying the Cox proportional hazard model to conduct
empirical estimations, they estimated the hazard rate to be at least 23% higher for
homogeneous goods than for differentiated products. Obashi (2010) examined whether theduration of intermediate goods trade is longer than that for the usual transactions of goods
within East Asia because of the relation-specific nature of the international fragmentation of
production. A series of survival analyses provided evidence that the stability of international
production networks is a particularly prominent feature of East Asia. In addition, East Asian
countries are more likely to engage in lasting trade relationships of intermediate goods witheach other than with outsiders. Shao et al. (2012) focused on China’s manufacturing exports
at the six-digit harmonized system (HS) code product from 1995 to 2007 to isolate the factors
affecting export duration. Their results show that export durations tend to be rather short-lived.
Crucially, the duration is longer for differentiated products and parts and components,
highlighting the stability of intermediate goods trade resulting from international production
fragmentation, as indicated by Obashi (2010).
Building on the previous literature about trade duration, we incorporate the FDI–tradenexus, which helps us better understand the hollowing-out effect of FDIs. More specifically,
we study what kind of firms are more likely to continue to import intermediate goods from thehome country and what kind of intermediate goods imported from the home country are lesslikely to be substituted for local procurement, which are questions that could not be addressedby the product-level trade data or firm-level data used in the previous studies2. Our parent–
affiliate–transaction matched panel dataset enables us to explore these issues in a dynamic
2 We are not able to examine whether local procurement dose really substitute the import from Japan in

our current framework because we do not have the information of products that are procured from local
market.
5

perspective, and furthermore, we are able to calculate the quantitative sizes of impacts on trade
duration and identify which factors contribute the most.

3. Data Sources
This study focuses on the stability of intermediate goods trade between Japanese affiliates
in China and their parent companies. To do this, we construct a unique parent-affiliatetransaction panel dataset, covering 929 Japanese affiliates in China from 2000 to 2006. Thissection describes how the dataset was constructed.
The first data source is China’s Annual Survey of Industrial Firms (CASIF), which is
conducted by the National Bureau of Statistics (NBS) of China and consists of all state-owned
enterprises (SOEs) and private firms with sales above RMB 5 million, the so-called “scaleabove” enterprises. firms with sales above RMB 5 million, the so-called “scale-above”
enterprises. It covered 162,885 firms in 2000 and 301,961 firms in 2006. This dataset provides
abundant information such as company name, location, type of ownership, sales, output,
capital, labor and intermediate input. In the dataset, each firm reports its ownership type; viz.,
SOEs, private-owned enterprises, and foreign-owned enterprises (FOEs) 3 Unfortunately,
nationality information for the FOEs is unavailable, preventing us from differentiating
Japanese affiliates from those of other countries.
The second data source is the Toyo Keizai’s Overseas Japanese Companies Data (Kaigai
Shinshutsu Kigyo Soran). Toyo Keizai’s survey on outward FDIs primarily covers foreignsubsidiaries with a 10% or higher Japanese ownership. Crucially, the Japanese foreign
affiliates in China covered in this survey have information about their Chinese names andaddresses, enabling us to link them to the CASIF database and thereby access the dataset ofJapanese affiliates.
3 FOEs include Hong Kong-, Macao-, and Taiwan-owned firms (HMTs) and other foreign-owned firms.

6

We link the aforementioned compiled dataset to two more data sources. One is the China
Customs data, which contains detailed information on each transaction, including the eightdigit HS code for trading products, product unit, quantity, unit value of each eight-digit HS
product, total value, import origin, export destination, and type of trade. As firm identification
numbers are different in the CASIF and Customs data, we use the affiliates’ Chinese names tomatch the two datasets for information on the Japanese affiliates’ imports of intermediate
goods.4 In order to focus on the imports of intermediate goods in this study, we exclude theimports of final goods, which are identified by the Broad Economic Category (BEC)
classification.5
To consider the parent-firm characteristics, we complement Toyo Keizai’s survey with theTokyo Shoko Research (TSR) database, since Toyo Keizai’s survey only has limited
information on parent firms (e.g., name of company and location). TSR Data is one of thelargest databases compiled by a private company and it records both listed and non-listedcompanies in Japan. It gives several facts about each firm, including the year of establishment,
the paid-up capital, and the current number of employees.

4. Empirical Specification and Data Overview
4.1 Empirical Specification
The semi-parametric Cox proportional hazards model has been widely used (see Besedes
and Prusa, 2006a, 2006b; Brenton et al., 2010; and Obashi, 2010) to investigate therelationship between the duration of intermediate goods imports and its determinants.
However, a recent study by Hess and Persson (2012) argued that the Cox model is

4 The information needed to match CASIF and Customs data are available only for the period from 2000

to 2006. Therefore, our analysis is restricted to this period.
5 We define transport equipment, except for parts and components (51 and 52), as well as durable, semidurable, and non-durable consumer goods (61, 62, and 63) as final goods.
7inappropriate for analyzing the duration of a trade relationship. For example, given that trade
duration refers to the number of years such a relationship continues, we observe that many
trade durations are of equal length. The many tided trade durations might cause asymptotic
bias in the estimated coefficients. Hess and Persson (2012) thus proposed using a discrete-timehazard model instead of the continuous-time Cox proportional hazard model.
The estimation procedure in a discrete-time hazard model is as follows: let Ti be acontinuous, non-negative random variable measuring the survival of intermediate goods
imported from the parent country by the affiliates of Japanese MNEs in China. In the discretetime framework, the probability that an affiliate terminates its imports of intermediate goods
in a given time interval k, conditional on its survival up to the beginning of the interval andexplanatory variables, or the hazard rate, ishik = P(Ti < tk +1 | Ti ≥ tk , X ik ) = F ( X ik′ β + γ k )(1)where X ik is a vector of time-varying covariates, while γ k is a function of time that allows
the hazard rate to vary across periods. As the underlying baseline hazard rate is unknown inpractice, we incoporate γ k into the model as a set of dummy variables to identify the duration(the period dummy variables). F(.) is an appropriate distribution function such that 0 ≤ hik ≤ 1
for all i, k. The subscript i denotes a separate time period of intermediate goods imports.
Introducing a binary variable, yik , equaling one if period i is observed to terminate the imports
of intermediate goods during the kth interval, Hess and Persson (2012) derived the loglikelihood for the observed data as follows:nki

ln L = ∑∑ [ yik ln(hik ) + (1 − yik ) ln(1 − hik )]
i =1 k =1

8(2)

This is similar to a standard log-likelihood function for a binary panel regression model.
Assuming the functional form for the hazard rate to be a normal, logistic, or extreme-value
minimum distribution, it will lead to a probit, logit, or cloglog (complimentary log-log) model,
respectively. The cloglog model with period-specific intercepts represents the exact groupedduration analog of the Cox proportional hazard model (Hess and Persson, 2012).
4.2 Definition of variables
The vector X for the hazard function (1) consists of three dimensions: affiliate and productcharacteristics, regional characteristics (agglomeration), and parent-firm characteristics. For
the affiliate characteristics, affiliate size (Alabor) is measured by the logarithm of the numberof workers. A large affiliate generally needs a range of intermediate goods and some of them
have to be imported, suggesting a longer duration. Thus, a negative coefficient is expected
to be associated with this variable. The terms Awage and KL-ratio refer to the logarithm of
the total wage bill per worker and the capital-to-labor ratio, respectively. We also include theshare of export to total sales (Export). Most Japanese affiliates in China undertake industrial
processing for exports, suggesting that they need to import intermediate goods, whichtranslates into longer trade durations.
For product characteristics, we include five variables. The first is Im-value, which is thelogarithm of lagged import value. It is expected to be associated with a negative coefficientbecause import goods with larger value are more difficult to be replaced with local
procurement in the short term.6 Second, we include Diff, which takes the value one if the
products are categorized as differentiated goods, and zero otherwise. The definition ofdifferentiated goods is per Rauch’s (1999) commodity classification.7 Differentiated goods
6 In previous studies, such as Besedes and Prusa (2006a, 2006b) and Obashi (2010) use the initial value ofimport, namely the import value in the first year instead of lagged import value. We confirmed our results
do not change even if we replace lagged import value with the initial value of import.
7 Rauch (1999) classifies products traded on an organized exchange as “homogeneous goods” andproducts that are not sold on the exchanges, but whose benchmark prices exist, as “reference priced.” Wetreat all other products as differentiated goods.
9are expected to be more likely to survive, because differentiated goods display more complex
technologies. They are thus less easily substitutable by local supplies. Third, to control for
product characteristics in the global value chain, we use the upstreamness index (Upstream).
The concept of an upstreamness index was proposed by Antràs et al. (2012) and it measures
how far an industry is from final consumption. An industry whose output is used mainly as anintermediate input for other industries is considered to be relatively upstream.8 Since theseproducts are required by other relatively upstream industries, they embody a higher degree oftechnological content. Again, these products are less likely to be substituted for local
procurement, leading to a longer trade duration. Fourth, the home dummy (Home) equals one
if an affiliate shows a positive value for intermediate goods imported from Japan. As Japan is
one of the world’s leading countries in technology, it is difficult to replace its intermediate
goods with local supplies, meaning that trade with the home country should have a longer
duration than that with other countries. Lastly, we include “process trading” (Process), whichtakes the value one if the products are included under process trading. In the Chinese Custom
data, exports can be classified into processing exports and ordinary exports. Processing exports
means that the products exported are made using imported inputs to assemble the final goods.
One of the features of process trading is that it enjoys zero tariffs for the imported intermediate
goods. 9 The dummy variable, Process, takes the value one if the intermediate goods areimported under a process trading scheme, otherwise zero.
Variables capturing the regional characteristics are our key variables. We consider three
regional agglomeration variables. A widely defined agglomeration is the number of local firms
in the same industry in the same region (Agg-local-r). Under the condition of hightransportation costs, the products of local firms can replace the imported ones, leading to a
8 For a detailed definition of the upstreamness index, please see the supplemental appendix in Antràs et al.

(2012).
9 For details of process trading, see Yu (2014).

10negative influence on the duration of trade. Two more narrowly defined agglomeration
variables are Agg-foreign-r and Agg-Jaffiliates-r, denoting the number of foreign affiliates(including Japanese affiliates) in the same industry in the same region, and the number ofJapanese affiliates in the same industry in the same region.
As for variables for parent-firm characteristics, we include a size dummy of the parent firm
(Plarge) that equals one if the parent firm has 1,000 employees or more.10 And to examine
the MNEs’ own production networks, we also control for the number of foreign manufacturing
affiliates worldwide or in China that belong to the same parent firm (Pnetwork and PnetworkChina) or for the years of experience in managing production for each MNE (Exper and ExperChina). Table 2 summarizes the variable definitions and basic statistics.
[Insert Table 2 approximately here]
4.2 Data Overview
Before starting our econometric analysis, we provide a brief overview of the sample firms
and their imports of intermediate goods. Figure 1 shows the share of local procurement andimports in total procurement by Japanese MNEs affiliates in China from 1995 to 2007, using
the Survey of Oversea Business and Activities (Ministry of Economy, Trade, and Industry ofJapan). The imports from Japan used to be a major source of the procurement of Japaneseaffiliates in China. However, while the share of imports from Japan has gradually decreased
from 50% to 30%, that for local procurement has increased from 30% to 50%. At the sametime, the share of imports from countries other than Japan has also decreased from 20% to

10 The legal definition of SMEs in Japan is firms with 300 or more employees or whose paid-in capital ismore than 3,000 million yen. However, we define large firms as those firms with more than 1,000 employees
following the definition used in some labor statistics, such as the Census of Wage Structure by the Ministryof Health, Labor, and Welfare of Japan. The other reason is that the share of firms with fewer than 300
employees is quite limited. As shown in Table 1, it is slightly more than 20%. The estimation results are
qualitatively the same and quantitatively very similar, even if we use different thresholds for the parent firms’
number of employees; namely, 300 or 500.
11

10%. These facts suggest that MNE affiliates in China have gradually substituted their imports
for local procurement.
[Insert Figure 1 approximately here]
Table 3 shows the regional distribution of Japanese affiliates in China. Two things arenoteworthy. First, almost all Japanese affiliates are located in the coastal areas—the Bohai
economic zone, the Yangtze River Delta, and the Pearl River Delta. These areas represent 96%of the regional distribution of Japanese affiliates in China. Second, of these coastal areas, theYangtze River Delta area accounts for 49% of the Japanese affiliates. Moreover, 26% of theaffiliates in China are located in Shanghai. These facts suggest that the Japanese affiliates in
China form a dense agglomeration in these areas.
[Insert Table 3 approximately here]
Table 4 compares product and affiliate characteristics by parent-firm size. As for productcharacteristics, while Diff, and Im-value are higher for large firms; Home, Upstream andProcess are larger for SMEs, suggesting that SMEs in China import more “upstream”
intermediate goods from Japan and engage more actively in process trading. Looking ataffiliate characteristics, those affiliates owned by large firms are, on average, larger in terms ofthe number of employees, pay higher wages, and have a higher KL-ratio. In contrast, affiliates
owned by SMEs tend to be more active in terms of export activities.
[Insert Table 4 approximately here]
We also check the Kaplan-Meier survival rate by type of trade regimes, ordinary trade,and process trading and by parent firm size, which are presented in panel A and B of Figure
1. We confirmed the survival rate of the trade transition ties under process trading regime ishigher than those under ordinary trade. For parent firm size, the difference is not sosignificant, but it seems the survival rate for small firms is higher than that for larger firms.
[Insert Figure 2 approximately here]125. Empirical Results
5.1 Baseline Model
What determines the duration of imported intermediate goods for Japanese-owned
enterprises in China? Based on product-level data of the eight-digit HS code, the results ofthe estimations using the discrete-time hazard rate of a probit model are reported in Table 5.
Column (1) and column (2) show the benchmark models comprising product and affiliate
characteristics. While column (1) uses whole sample, we focus on import from home country
in column (2).
[Insert Table 5 approximately here]
As for the influence of product characteristics, some interesting findings are contained in
the various estimations. The coefficient for the logarithm of the import value in year t-1(Import-value) is statistically significant with a negative sign, suggesting that the larger the
trade value in year t-1. Crucially, we find a significantly negative coefficient of the variable
Home, indicating that, if intermediate goods are imported from Japan, the trade relationshipsurvives longer than in the case of imports from other countries. Japan is at the forefront oftechnology and possesses outstanding capability in producing many key parts and components.
Thus, intermediate goods imported from Japan are not easily substitutable through local
procurement, so the decline is slower in terms of the hazard rate.11
The coefficient for Diff is negative and significant, meaning that the traded goods’
characteristics matter to the trade duration. The differentiated products (Diff) dominate otherproduct types in terms of their survival rates, which last longer for the Japanese affiliates.

11 One may be interested whether the import of intermediate goods from home is substituted by local

procurement or by the import from third countries or not. Since the local procurement at product-level are
not available, we cannot examine this issue directly. However, looking at Figure 1, while the share of local
procurement is increasing, the ratio of import from other countries is decreasing, implying that the importfrom home country is more likely to be substituted by local procurement.
13Moreover, as the estimated coefficient for Upstream is significantly negative, this suggests that,
if an industry is located upstream across industries (i.e., selling a disproportionate share of its
output to the relatively upstream industries), its hazard rate is lower and has a longer trade
duration. The fragmentation of production across national boundaries is a distinctive featureof the Asian production network (Athukorala and Yamashita, 2006). Thus, production indeveloping countries entails the sourcing of inputs and components from multiple suppliers,
particularly from their home countries. That an industry’s output is required by other relatively
upstream industries implies a higher degree of technological content embodied in theirproducts, thereby making its replacement through local procurement difficult. The abovefindings are consistent with existing product-level studies such as Besedes and Prusa (2006a,
2006b) and Obashi (2010). Another interesting phenomenon is that, if imported goods are used
for process trading (Process), they experience a lower hazard rate; that is, they survive for alonger period. This result can be attributed to the specific export-promoting measures adopted
by the Chinese government that allow the firms undertaking process trade to enjoy zero tariffs
on their imported inputs. This induces exporters to continue importing intermediate goods forthe assembly of exports in order to remain eligible for zero import tariffs.
Turning to affiliate characteristics, which are not examined in the extant literature, thecoefficient for Export is negative, which is consistent with our conjecture: The survival rate ishigher for the intermediate goods imported by affiliates engaging in industrial processing forexports. The variables Alabor, Awage, and KL-ratio show significantly positive coefficients,suggesting that larger affiliates, which pay higher wages to employees and are more capitalintensive, tend to show a shorter duration of intermediate goods imports. Large and capitalintensive Japanese affiliates tend to establish local production networks and purchase inputs
from local (Chinese-owned or foreign-owned) suppliers, thereby experiencing a shorter
duration of intermediate goods imports.
14

In columns (2), we restrict our sample to imports from Japan. Basically, our results do notchange much. As indicated in Table 6, the sample size in columns (2) is 70% of that in columns(1). This may imply that our results are mainly driven by imports from Japan. Therefore, we
use the sample for imports from Japan in all of our remaining estimations.
As mentioned earlier, the agglomeration of suppliers may affect the procurement patternsof Japanese affiliates because they can purchase their intermediate goods locally if local
suppliers, either local firms or other foreign affiliates, can supply qualified commodities. How
does regional agglomeration affect the duration of the intermediate goods trade? Column from
(3) through (4) in Table 5 report the results by including the agglomeration variable andexcluding the province fixed effect.12
Using the number of local firms within an industry and region (Agg-local-r) as a measureof agglomeration, the estimates in column (4) show that the agglomeration variable has apositive and significant coefficient. Recognizing the importance of innovation, China has been
implementing various policies to encourage innovative activities since the late 1990s, aimed
at transforming the economy into a knowledge-based one and catching up with the developed
countries in technological capability.13 With this technological upgrading in China, local firms
can gradually produce qualified parts and provide them to the MNE affiliates, including
Japanese affiliates, in China. Thus, in a region with more local firms in terms of numbers or
sales, the Japanese affiliates exhibit a higher hazard rate to stop importing intermediate goods.
Correspondingly, the procurement of intermediate goods for Japanese affiliates can comefrom other foreign affiliates in China. Using the number of foreign firms to measure the degreeof agglomeration (Agg-foreign-r), the estimates in column (4) show that within a region with
12 We construct the agglomeration variable by region (Bohai, Yangtze River Delta, Perl River Delta,

Central region, West region, and North East region). However, some firms may transact with firms locating
within a province. As a robustness check, we used the agglomeration variable by province and confirmedthat the results do not change so much. The estimation results are provided in Appendix A1.
13 China’s R&D intensity (R&D expenditures-to-GDP ratio) has tripled from 0.57% in 1996 to 1.70% in
2009.
15

more foreign affiliates, the Japanese affiliates located there tend to have a shorter duration forimporting intermediate goods. Foreign affiliates overall have superior technologicalcapabilities and Japanese affiliates might acquire their needed intermediate goods from otheraffiliates, thereby reducing imports. Similarly, for Japanese affiliates located in a region withmany other Japanese-owned enterprises (Agg-Jaffiliates-r), their imports of intermediate
goods have a shorter duration, as shown in column (5). This finding is reasonable because they
may prefer acquiring intermediate inputs from other Japanese affiliates, if available.14

5.2 Does the Duration in the Trade of Intermediate Goods Differ according to Parent-firm Sizeand Production Network?
In Table 6, we examine the impact of parent-firm characteristics. In column (1), we include
the variable for parent-firm size, Plarge. We find that its impact is positive and significant,suggesting that SMEs tend to experience longer trade durations than those for large firms.
Turning to the variables for MNE production networks or the experience of foreign
production, more striking results emerge, which are presented in columns (2)–(5) in Table 6.
The MNE production network variables, both Pnetwork and Pnetwork-China, are associatedwith significantly positive coefficients, as shown in columns (2) and (3). Large Japanese
MNEs, such as Toyota and Hitachi, have numerous foreign affiliates in China and other
Southeast Asian countries that specialize in producing parts or assembly work and support the
production needs of one another. Their affiliates in China thus substitute imported intermediate
goods by procuring products from other affiliates belonging to the same parent firm.
Alternatively, Japanese affiliates owned by SMEs generally find it difficult to build their own
production networks and continue to import needed intermediate goods from abroad, thereby
14 One may ask why not including these three agglomeration variables in one equation. Actually, we triedit. However, because of strong multicolinearity, we give it up and instead, we put these variables one-byone.
16

showing a longer duration.
[Insert Table 6 approximately here]
Another parent-firm characteristic that potentially affects the affiliates’ hazard rates ofintermediate goods imports is FDI experience. The variables Exper and Exper-China, shownin columns (4) and (5), denote years of experience of foreign production and years ofexperience of production in China by a parent firm, respectively. We find that for parent firms
with more FDI experience, their affiliates in China tend to exhibit a shorter period of importingintermediate goods; that is, there is a higher hazard rate. This is probably because MNEs learn
more about local or foreign suppliers as their experience of foreign production increases. Firms
that have more years of foreign production experience are more likely to substitute theirimports of intermediate goods for local procurement.
In sum, we find that the duration of the imports of intermediate goods by SMEs is longer
than that by large firms. Furthermore, firms with their own large production networks andgreater experience of foreign production induce decreases in the imports of intermediate goods.
As SMEs usually have only a few foreign affiliates and their experience of foreign production
is limited, their trade duration might be shorter than that of large firms. This fact implies that
trade in intermediate goods from Japan to China will continue to increase when considering
the fact that FDI by FDI has been growing.

5.3 Extensions
We conduct several robustness checks. First, we employ various techniques of discretetime survival models such as the cloglog model, the random-effect model probit, and therandome effect logit model. The results are presented in Appendix Table A2 and we confirm
that all estimates are very similar. Second, as additional affiliate characteristics, we distinguishwhether affiliates belong to final goods producing sectors (FGP) or intermediate goods
17

producing sectors (IGP). We define FGP using China’s input-output table in 2002. We refer tothe industry-level final demand use ratio (the final demand use value divided by the sum of
the final demand use value and the intermediary demand use value) and define FGP as thoseindustries whose final demand use ratio is above median and IGP as others. In Appendix Table
A3, we found the sum of the coefficients of focal variables and their interaction terms with
FGP dummy are smaller in absolute value except Process and Export, implying if the affiliates
belong to FGP, the trade transaction ties are less likely to be terminated. In other words, thesurvival rate of trade relationship for IGP tends to be shorter. This result may imply while FDI
in FGP specialize in labor intensive production process in China and continue to import theintermediate goods, affiliates in IGP are more like local sales and local procurement oriented.
Third, since we use product-level data, one may be interested in the effect of tariff.
Actually, as shown in Table 2, a majority of our observations are classified as “process trading,”
which is not under the effect of tariff. We believe the effect of tariff may be limited. One way
to inculpate this effect is to include product-year (period) fixed effect. As indicated inAppendix Table A4, we confirmed the robustness of the results including product-period fixed
effect.

6. Discussion
While agglomeration has a positive impact on the hazard rate of intermediate goods imports
in our estimation results, FDI by SMEs has a negative impact on the probability of thetermination of trade-transaction ties. One may be interested in which of the agglomeration orparent-firm characteristics are more influential in the duration of trade for intermediate goods.
To measure the quantitative size of the impact of the determinants in our survival model, we
calculate how much the predicted probability would change if the firm or regional
characteristics changed. Figure 3 illustrates the predicted probability and the impact of changes
18

in attributes, based on the results in column (2) in Table 6. Our baseline predicted probabilityof the termination of imports is calculated for those products that are differentiated (Diff=1),
traded by large firms (Plarge=1), and exported by affiliates (Export=1). For other products andparent-firm and regional attributes, we use average values in our sample.
[Insert Figure 3 approximately here]
As for agglomeration, a change of one standard deviation of the agglomeration variable
increases the predicted probability from 26.8% to 28.2% compared with the predictedprobability of the baseline. A change of one standard deviation of the agglomeration variable
is almost equivalent to the change in average value of the agglomeration variable from 2000
to 2006. Therefore, this change in predicted probability (28.2% − 26.8% = 1.4%) can beconsidered as the contribution of changes in agglomeration over the period. Next, looking at
the contribution of SMEs (plarge = 0), the difference in predicted probability between large
firms and the SMEs is only 0.5% (= 26.8% − 26.3%). However, recall that product and affiliate
characteristics differ according to parent-firm size, as discussed in Table 4. For example, theimported goods by SMEs have a higher value for Upstream and engage more in process
trading. Similarly, the affiliates owned by SMEs are smaller in terms of the number of workers
and have lower KL-ratios. Since these characteristics lower the probability of the terminationof trade-transaction ties, imported goods by the SMEs may have lower predicted probabilities.
We calculate the predicted probability for the products with average SME attributes andaverage large firm attributes. In this case, the difference between the SME and large firms is
1.7% (= 27.4% − 25.6%). Therefore, the contribution of FDI by SMEs slightly exceeds thatof agglomeration.

7. Concluding Remarks
This study revisits the nexus between FDI and international trade in the dynamic perspective,
19

focusing on the intermediate goods imports of Japanese affiliates in China. To examine
whether the formation of the local agglomeration in host country and the recent FDI made bysmall firms accelerate the substitution between the import of intermediate goods from homeand local procurement or not, we utilize a unique parent-affiliate-trade transaction matchedpanel dataset and estimate a discrete-time hazard model over the 2000–2006 period.
This study focus on the roles of products, affiliates, or parent-firm characteristics andagglomerations. Estimates obtained from the discrete-time hazard model show thatintermediate goods, such as differentiated goods and products that belong to upstream
industries, survive for a longer time, which is consistent with previous studies. Intermediate
goods imported from Japan have a stronger impact on lowering the hazard rate, suggesting
they contain sophisticated technologies and are thus less easily substitutable by local
procurement. Using various measures of agglomeration indicators, we consistently find apositive relationship between agglomeration and the hazard rate of intermediate goods imports.
As for parent-firm characteristics, we find that SMEs tend to engage in longer traderelationships. Moreover, the parent firms’ foreign production networks and greater foreign
production experience are found to reduce the duration. These findings also suggest that SMEs,
which usually do not have foreign production networks, tend to continue importingintermediate goods from their home country. Finally, we calculate the quantum of the impactof the determinants of our survival model. We find that the difference in predicted probability
between SMEs and large firms is slightly larger than the changes in predicted probability due
to increases in agglomeration.
Our paper also offers an important policy implication for policy makers wishing to MNEs’
local procurement since MNEs may replace the import of intermediate goods from home withlocal procurement. Our estimation result imply that the agglomeration of local firms
accelerates the decreases in MNEs’ import of intermediate goods from home country, which20may be replaced by the increases in local procurement. As we discussed, this effect may reflect
the technological upgrading of local firms in regions with larger agglomeration. Thus, it isimportant to have policies that promote the concentration of local firms with technologicalcapability.
Although this study provides interesting insights, there remain various issues to be studiedin future research. Frist, how does the termination of transaction ties affect the Japanese parent
firms’ performances? From policymakers’ viewpoint, the effect on domestic employment may
be an important agenda. Second, the fact that Japanese affiliates in China tend to importintermediate goods from home rather than procure form local market implies Japanese FDI in
China concentrate on downstream sector. However, it is not clear why FDI in upstream sector
is difficult. Exploring factors that hinder FDI in upstream sectors is also important policy issue.
The third issue is to examine the impact of the recent soaring wages in China on therelationship between FDI and trade of intermediate goods. Donaubauer and Dreger (2018)
demonstrate that rise in Chinese wage changes the distribution of FDI both within China and
ASEAN countries, implying that China lost the competitiveness as a destination for FDI inlabor intensive industries. Exploring the effect of wage increase on the survival rate of tradetransition ties may be an interesting exercise. Last, distinguishing the difference between
arm’s-length trade and intra-firm trade is also important issue. For example, recent studies,such as Ramondo et al. (2016), have shown that the MNE firms that engage in intra-firm trade
are heavily concentrated within the large MNE category. Exploring the survival rate of intrafirm trade may be an interesting exercise.

21

Reference
Antràs, P., Chor, D., Fally, T., and Hillberry, R. (2012), Measuring the Upstreamness of
Production and Trade Flows, American Economic Review Papers and proceedings,
102(3), 412-416.
Athukorala, P.C. and Yamashita, N. (2006), Production Fragmentation and Trade Integration:
East Asmaia in a Global Context, The North American Journal of Economics andFinance, 17(3), 233-256.
Belderbos, R., Capannelli, G., Fukao, K. (2001), Backward Vertical Linkages of Foreign
Manufacturing Affiliates: Evidence from Japanese Multinationals, World Development
29(1), 189-208.
Besedes, T. and Prusa, T. (2006a), Ins, Outs, and the Duration of Trade, Canadian Journal of
Economics, 39(1), 266-295.
Besedes, T. and Prusa, T. (2006b), Production Differentiation and Duration of US Import Trade,
Journal of International Economics, 70(2), 339-358.
Brainard, L., (1993), A Simple Theory of Multinational Corporations and Trade with a Tradeoff between Proximity and Compensation, NBER working paper, No.4269.
Brainard, L., (1997), An Empirical Assessment of the Proximity-Concentration Trade-off
Between Multinational Sales and Trade, American Economic Review, 87(4), 520-544
Chen, T-J., and Ku, Y-H., (2000), The effect of Foreign Direct Investment on Firm Growth:
The Case of Taiwan’s Manufacturers, Japan and the World Economy, 12(2), 153-172.
Donaubauer, J., and Dreger, C. (2018), The End of Cheap Labor: Are Foreign Investors
Leaving China?, Asian Economic Papers, 17(2), 94-107.
Fung, K-C., Iizaka, H., and Siu, A., (2010), United States, Japanese and Korean FDI and IntraEast Asian Trade, Asian Economic Paper, 9(3), 129-154.
Hanson, G.H., Mataloni, R.J., and Slaughter M.J. (2005), Vertical Production Networks in
22

Multinational Firms, Review of Economics and Statistics, 87(4): 664-678
Head, K. and Ries, J. (2001), Overseas Investment and Firm Exports, Review of International
Economics, 9, 108-122.
Hess, W., and Persson, M., (2012), The Duration of Trade Revisited: Continuous-time versus
Discrete-time Hazards, Empirical Economics, 43(3), 1083-1107.
Kee, H-L and Tang, H. (2016), Domestic Value Added in Exports: Theory and Firm Evidence
from China, American Economic Review, 106(6): 1402-36.
Kiyota, K., Matsuura, T., Urata, S., and Wei, Y. (2008), Reconsidering the Backward Vertical
Linkage of Foreign Affiliates: Evidence from Japanese Multinationals, World
Development, 36(8), 1398-1414.
Obashi, A.(2010), Stability of International Production Network: Is East Asia Special?
International Journal of Business and Development Studies, 2(1), 63-94.
Ramondo, N., Rappoport, V., Ruhl, K., (2016), Intra-firm Trade and Vertical Fragmentation in
US Multinational Corporations, Journal of International Economics, 98(1), 51-59.
Rauch, J., (1999), Networks versus Markets in International Trade, Journal of International
Economics, 48(1), 7-35.
Shao, J., K. Xu and B. Qiu (2012), Analysis of Chinese Manufacturing Export Duration, China
and World Economy, 20(4), 56-73.
Yu, Miaojie., (2014), Process Trade, Tariff Reductions and Firm Productivity: Evidence from
Chinese Firms, Economic Journal, 125, 943-988.

23

Table 1 Distribution of parent-firm size of Japanese affiliates by entry year as of 2006
# of employees-299
300-499500-999
1000-2999
3000Total

-1990
14.1%
6.7%
11.3%
23.2%
44.7%
100%

Entry year
1991-1995 1996-2000
19.0%
18.1%
8.3%
8.1%
12.0%
12.0%
22.3%
22.0%
38.5%
39.7%
100%
100%

Source: Authors’ calculations

24

200123.1%
10.1%
14.5%
23.7%
28.6%
100%

Table 2 Variable definitions and basic statistics
Variable
Product characteristics
Initial-value
Im-valueHome
Diff
Upstream
Process
Affiliate characteristics
ExportAlaborAwage
KL-ratio
Regional characteristics
Agg-local-r
Agg-foreign-r
Agg-Jaffiliates-r
Parent firm characteristics
PlargePnetwork
Pnetwork-China
Exper
Exper-China

N

Mean

Stan. Dev.

p25

p75

Definition

95437
95437
95437
95437
95437
95437

7.88
7.92
0.73
0.68
2.56
0.59

2.56
2.57
0.45
0.47
0.64
0.49

6.138
6.174
0
0
2.254
0

9.657
9.716
1
1
2.945
1

Initital traded value in each span
Lagged import value
Dummy variable for imported goods from home
Dummy variable for differentiated goods (Rauch's classification)
Constructed by Antras et al. (2012). For details, see the text.
Dummy variable for imported goods under process trading

95437
95437
95437
95437

0.94
6.58
3.09
4.78

0.24
1.30
0.55
1.10

1
5.649
2.741
4.076

1
7.527
3.413
5.528

Exported dummylogged numher of employees for affiliates
logged average wage for affiliates
Capital-Labor ratio

95437
95437
95437

7.49
5.86
7.22

0.74
0.77
1.11

6.957
5.472
6.633

7.92
6.409
8.045

# of local firms by industry and province# of foreign firms by industry and province# of Japanese firms by industry and province

95437
89318
89265
89318
86057

0.64
18.86
30.18
4.92
9.54

0.48
22.63
13.01
6.09
3.89

0
4
21
1
7

1
21
40
5
12

Dummy variable for parent firms with more than 1000 emp# of foreign manufacturing affiliates beloinging to the same parent firm
# of manufacturing affiliates in China beloinging to the same parent firm
experience year of foreign production at parent firm
experience year of production in China at parent firm

Note: Numbers in parentheses are standard deviations. The means and standard deviations are calculated by pooling the 2000–2006 data.

25

Table 3 Regional distribution of Japanese affiliates in China# of firms

1 Bohai economic zone
11 Beijing
12 Tenjing
13 Hebei
21 Liaoning
37 Shandong
Subtotal

Share

39
65
16
69
58
247

4%
7%
2%
7%
6%
27%

2 Yangtze River Delta
31 Shang Hai
32 Jiangsu
33 Zhejiang
Subtotal

238
159
56
453

26%
17%
6%
49%

3 Perl River Delta
35 Fujian
44 Guangdong
Subtotal

25
161
186

3%
17%
20%

4 Central region
5 West region
6 North East region
Total

10
22
11
929

1%
2%
1%
100%

Source: Authors’ calculations

26

Table 4 Product and affiliate characteristics by parent-firm size

Source: Authors’ calculations

27

Table 5 Survival of intermediate goods imports for Japanese affiliates in China
Estimation
methodImport origin
Im_valueHome
Diff
Upstream
Process
ExportAlaborAwage
KL-ratio
Agg-local-r

(1)

(3)

(4)

(5)

all countryJapan-0.0402***(0.0006)-0.0625***(0.0036)-0.0178***(0.0034)-0.0698***(0.0025)-0.0596***(0.0033)-0.0008(0.0069)
0.0093***(0.0015)0.0125***(0.0032)
0.0264***(0.0018)-0.0411*** -0.0405*** -0.0404*** -0.0404***(0.0007) (0.0007)(0.0007)(0.0007)

Agg-foreign-r
Agg-Jaffiliates-r
Observations
Period FE
Industry FE
Province FE
log LL
Pseudo R2(2)-0.0164*** -0.0169*** -0.0168*** -0.0169***(0.0040) (0.0040)(0.0040)(0.0040)-0.0781*** -0.0779*** -0.0778*** -0.0778***(0.0029) (0.0029)(0.0029)(0.0029)-0.0479*** -0.0463*** -0.0453*** -0.0456***(0.0038) (0.0037)(0.0037)(0.0037)0.0009-0.0067-0.0084-0.0070(0.0076) (0.0075)(0.0075)(0.0075)
0.0093*** 0.0082*** 0.0076*** 0.0070***(0.0018) (0.0017)(0.0017)(0.0017)0.0075** 0.0111*** 0.0120*** 0.0139***(0.0038) (0.0037)(0.0037)(0.0036)
0.0265*** 0.0248*** 0.0240*** 0.0238***(0.0021) (0.0020)(0.0020)(0.0020)0.0268***(0.0034)
0.0234***(0.0034)
0.0166***(0.0030)

95,437

69,19869,202

69,202

69,202

Yes
Yes-57830
0.109

Yes
Yes
-41886
0.113

Yes
No
-42010
0.110

Yes
No
-42017
0.110

Yes
No
-42026
0.110

Yes

Yes

Yes

Yes

Yes

Note: Marginal effects at mean-level are presented in this table. Figures in parentheses
are standard errors. *** represents statistical significance at the 1% level.

28

Table 6 Impact of the parent-firm characteristics
(1)

(2)

(3)

(4)

(5)

-0.0410*** -0.0407*** -0.0406*** -0.0406*** -0.0408***(0.0007)(0.0007)(0.0007)(0.0007)(0.0007)
Diff-0.0170*** -0.0170*** -0.0171*** -0.0170*** -0.0163***(0.0040)(0.0042)(0.0042)(0.0042)(0.0043)
Upstream-0.0784*** -0.0775*** -0.0773*** -0.0775*** -0.0771***(0.0029)(0.0030)(0.0030)(0.0030)(0.0031)
Process-0.0396*** -0.0393*** -0.0390*** -0.0392*** -0.0379***(0.0038)(0.0039)(0.0039)(0.0039)(0.0040)
Export-0.0726*** -0.0709*** -0.0721*** -0.0717*** -0.0777***(0.0054)(0.0056)(0.0056)(0.0056)(0.0058)
Alabor
0.0090*** 0.0111*** 0.0103*** 0.0114*** 0.0081***(0.0018)(0.0019)(0.0019)(0.0019)(0.0020)
Awage
0.0021-0.0008-0.0003-0.0006-0.0045(0.0038)(0.0039)(0.0039)(0.0040)(0.0041)
KL-ratio0.0207*** 0.0221*** 0.0223*** 0.0225*** 0.0229***(0.0021)(0.0021)(0.0021)(0.0021)(0.0022)
Agg-local-r
0.0354*** 0.0362*** 0.0360*** 0.0357*** 0.0358***(0.0034)(0.0036)(0.0036)(0.0036)(0.0037)
Plarge0.0103**0.00310.00400.00650.0135***(0.0042)(0.0048)(0.0051)(0.0047)(0.0046)
Pnetwork
0.0004***(0.0001)
Pnetwork-China
0.0006***(0.0002)
Exper
0.0013***(0.0004)
Exper-China
0.0024***(0.0005)
Im-value

Observations
Period FE
Industry FE
log LL
Pseudo R2

69,202
Yes
Yes-419150.112

64,205
Yes
Yes-38901
0.113

64,160Yes
Yes-38876
0.113

64,205
Yes
Yes-389040.113

61,909Yes
Yes-375490.113

Note: Marginal effects at the mean level are presented in this table. Figures in parentheses
are standard errors. *** and ** represent statistical significance at the 1% and 5% levels,
respectively.

29

Figure 1 The share of local procurement and imports by Japanese MNE affiliates in
China
70%
Local procurement
Import from Japan
Import from other countries

60%
50%
40%
30%
20%
10%
0%

1995

1996

1997

1998

1999

2000

2001

2002

2003

2004

2005

2006

2007

Source: Authors’ own calculation based on the Survey of Oversea Business andActivities (Ministry of Economy, Trade, and Industry of Japan).
Figure 2 Kaplan-Meier Survival rate
11Ordinary trade
Process trade

0.8
0.6

0.6

0.4

0.4

0.2

0.2

0

1

2

3

4

5

Small firm
Large firm

0.8

6

7

0

Source: Authors’ calculations

30

1

2

3

4

5

6

7

Figure 3 Predicted probability that the transaction tie by an average firm is terminated.
Baseline

26.8%

Im-value

16.4%

Homogeneous goods (Diff=0)

28.5%

Upstream

21.9%

Ordinary trade (Process=0)

31.3%

non-Exporter(Export=0)

27.4%

Alabor

27.7%

Awage

27.0%

KL-ratio

29.2%

Agg-local-r

28.2%

SMEs (plarge=0)

26.3%

Pnetwork

28.0%

Average attributes of large firms

27.4%

Average attributes of SMEs

25.6%

0%

5%

10%

Source: Authors’ calculations

31

15%

20%

25%

30%

35%

Appendix Table A1 Agglomeration variable at province-level
Import Origin
Im-valueHome
Diff
Upstream
Process
ExportAlaborAwage
KL-ratio
Agg-local-p
Agg-foreign-p
Agg-Jaffiliates-p
Observations
Period FE
Industry FE
Province FE
log LL
Pseudo R2

(1)

(2)
(3)
Japan
-0.0407*** -0.0406*** -0.0405***(0.0007) (0.0007) (0.0007)-0.0167*** -0.0167*** -0.0169***(0.0040) (0.0040) (0.0040)-0.0781*** -0.0779*** -0.0781***(0.0029) (0.0029) (0.0029)-0.0469*** -0.0471*** -0.0457***(0.0038) (0.0038) (0.0038)-0.0056-0.0076-0.0035(0.0075) (0.0075) (0.0075)
0.0069*** 0.0073*** 0.0068***(0.0017) (0.0017) (0.0017)
0.0141*** 0.0130*** 0.0157***(0.0036) (0.0036) (0.0036)
0.0252*** 0.0247*** 0.0239***(0.0020) (0.0020) (0.0020)
0.0114***(0.0022)0.0160***(0.0023)
0.0023(0.0022)
69,202

69,202

69,202

Yes
No-42028
0.110

Yes
No
-42017
0.110

Yes
No
-42041
0.110

Yes

Yes

Yes

Note: Marginal effects at the mean level are presented in this table. Figures in parentheses
are standard errors. *** and ** represent statistical significance at the 1% and 5% levels,
respectively.

32

Appendix Table A2 Different Estimation Methods
Estimation
methodIm_value
Diff
Upstream
Process
ExportAlaborAwage
KL-ratio𝑙𝑛𝜎Observations
Period FE
Industry FE
Province FE
log LL
Pseudo R2

(1)

(2)

(3)
(4)
Random Random
Probitcloglog
effecteffectProbitLogit-0.0411***-0.0401***-0.1261***-0.2095***(0.0007) (0.0007) (0.0026) (0.0045)-0.0164***-0.0118***-0.0497***-0.0845***(0.0040) (0.0040) (0.0125) (0.0208)-0.0781***-0.0770***-0.2442***-0.4055***(0.0029) (0.0028) (0.0097) (0.0163)-0.0479***-0.0480***-0.1501***-0.2468***(0.0038) (0.0037) (0.0120) (0.0198)
0.00090.0001-0.0029 -0.0042(0.0076) (0.0072) (0.0232) (0.0385)
0.0093*** 0.0081*** 0.0269*** 0.0457***(0.0018) (0.0017) (0.0055) (0.0090)
0.0075** 0.0067* 0.0213* 0.0369*(0.0038) (0.0037) (0.0115) (0.0191)
0.0265*** 0.0252*** 0.0819*** 0.1347***(0.0021) (0.0020) (0.0065) (0.0108)-2.0330***-1.0523***(0.1192) (0.1251)
69,19869,19869,22869,228
Yes

Yes

Yes
Yes
-41886
0.113

Yes
Yes
-41877

Yes

Yes
Yes
-41870

Yes

Yes
Yes
-41867

Note: Marginal effects at the mean level are presented in this table. Figures in parentheses
are standard errors. *** and ** represent statistical significance at the 1% and 5% levels,
respectively.

33

Appendix A3 Interaction terms with Final Good Producing Sector Dummy
VARIABLES
Im_valueHome
Diff
Upstream
Process
ExportAlaborAwage
KL-ratio
FGP*Im_value
FGP*Home
FGP*Diff
FGP*Upstream
FGP*Process
FGP*Export
FGP*Alabor
FGP*Awage
FGP*KL-ratio
Observations
log LL
Pseudo P
Industry FE
Province FE

(1)

(2)

-0.0420***(0.0008)-0.0768***(0.0049)-0.0155***(0.0047)-0.0824***(0.0035)-0.0214***(0.0045)-0.0549***(0.0062)0.0155***(0.0020)0.0358***(0.0046)
0.0304***(0.0025)0.0042***(0.0012)0.0220***(0.0069)-0.0061(0.0068)
0.0258***(0.0051)-0.0622***(0.0066)-0.0269***(0.0093)-0.0116***(0.0029)-0.0382***(0.0063)-0.0139***(0.0034)-0.0436***(0.0010)-0.0096(0.0081)0.0260***(0.0059)-0.0565***(0.0075)-0.0449***(0.0106)-0.0121***(0.0033)-0.0508***(0.0073)-0.0141***(0.0040)

95,437-57756
0.110
Yes
Yes

69,202-41882
0.113
Yes
Yes

-0.0138**(0.0055)-0.0898***(0.0039)-0.0129**(0.0050)-0.0400***(0.0071)0.0146***(0.0023)
0.0368***(0.0052)
0.0286***(0.0028)0.0061***(0.0015)

Note: Marginal effects at the mean level are presented in this table. Figures in parentheses
are standard errors. *** and ** represent statistical significance at the 1% and 5% levels,
respectively.
34

Appendix A4 Including Product-Period Fixed Effect(1)
Baseline
Initial-value
Home
Diff
Upstream
Process
ExportAlaborAwage
KL-ratio
Observations
Period FE
Industry FE
Product-Period F
Province FE
log LL
Pseudo R2-0.0366***(0.0006)-0.0614***(0.0036)-0.0160***(0.0034)-0.0689***(0.0025)-0.0623***(0.0033)0.0010(0.0069)
0.0091***(0.0015)0.0125***(0.0032)
0.0250***(0.0018)
95,437
Yes

Yes
No
Yes
-582120.103(2)Productperiod FF-0.0417***(0.0007)-0.0988***(0.0038)-0.2151(0.3195)-0.1314(0.1714)-0.0660***(0.0035)-0.0042(0.0071)-0.0024*(0.0014)0.0126***(0.0033)
0.0187***(0.0018)
89,428
No
No
Yes
Yes
-520630.145

Note: Marginal effects at the mean level are presented in this table. Figures in parentheses
are standard errors. *** and ** represent statistical significance at the 1% and 5% levels,
respectively.

35