O¤shoring, Relationship-Speci…city, and Domestic Production
Networks
Taiji Furusawa
Hitotsubashi University

Keiko Ito
Senshu University

Tomohiko Inui
RIETI

Heiwai Tang
Johns Hopkins University

November 10, 2015

Abstract
An economy is an interlinked web of production units. This paper examines both theoretically and empirically how …rms’o¤shoring decisions lead to reorganization of domestic production networks. We build a buyer-seller model that features supplier heterogeneity in e¢ ciency
and distance, as well as intermediate inputs that vary in the degree of speci…city to the relationship with the buyer. Sourcing inputs from more regions requires additional up-front …xed costs,
but reduces variable costs due to the use of more e¢ cient suppliers. More productive buyers
source inputs from a larger range of domestic regions, especially for generic inputs. Inputs thatare more relationship-speci…c are more likely to be insourced and less likely to be outsourced
from distant regions or foreign countries. A drop in o¤shoring costs will induce the more productive buyers to replace some of the less e¢ cient domestic suppliers with foreign suppliers,
with generic input suppliers more likely to be dropped despite their higher productivity. The
resulting reduction in input costs will induce buyers to expand the geographic scope of domestic
outsourcing. Using unique and exhaustive data on the buyer-seller network in Japan, we …nd
evidence supporting the main predictions of the model.
Key Words: Production Networks; O¤shoring; Relationship Speci…city
JEL classi…cation: F10, F14, L14

This study is conducted as a part of the Project “Global Markets and Japan’s Industrial Growth” undertaken at
Research Institute of Economy, Trade and Industry (RIETI), and receives …nancial support from JSPS KAKENHI
Grant (No. 23243050, 26220503, and 15K03456). We would like to thank the Ministry of Economy, Trade andIndustry for providing the micro data of the Basic Survey of Japanese Business Structure and Activities, and RIETI
for allowing us to access TSR data used in this study. We would also like to thank Tran Lam Anh Duong, Hiroki
Ichimiya, Hayato Kato, and Kenji Suganuma for research assistance; as well as Andy Bernard, Masahisa Fujita,
Masayuki Morikawa, Andreas Moxnes, Yukiko Saito, Jonathan Vogel, Ryuhei Wakasugi and participants at APTS in
Canberra, Boston College, ETSG in Paris, George Washington, HKU, RIETI, University of Strathclyde, and World
Congress of Econometric Society in Montreal for comments.

1

1

Introduction

There is ample evidence about the micro and macro economic e¤ects of foreign outsourcing.1 Recent
research has turned to micro-level data to examine these e¤ects, with a particular focus on the labormarket.2 Despite the large and growing literature, the focus has been so far about the e¤ects on theindustry or the …rm that o¤shores. Little research has been done to assess how o¤shoring reshapes
the domestic buyer-supplier network, and thus the macroeconomy. After all, an economy is aninterlinked web of production units, each relying on inputs provided by its suppliers to produce
output, which will then be sold to other …rms further downstream.
This paper studies both theoretically and empirically how downstream …rms’ o¤shoring decisions are linked to the reorganization of their domestic production networks. To this end, we use
unique and exhaustive Japanese …rm-level data on buyer-supplier links for analysis. Our network
data cover over half of the …rms operating in Japan, in both manufacturing and non-manufacturingsectors. In addition to the rich data set, Japan provides an interesting case for a study on thee¤ects of o¤shoring for various reasons. First, the Japanese economy is well known for its reliance on long-term relationships along the production chains. Anecdotal evidence suggests that
relationship-speci…c investments, facilitated by long-term relationships, are reasons for Japan’s success in manufacturing.3 Second, existing empirical studies focus on the e¤ects of o¤shoring on the
manufacturing sector and largely overlook the e¤ects on the service sectors, which are signi…cantly
more important for developed nations and increasingly so for emerging markets. Finally, in addition
to the direct contribution of suppliers’productivity to buyers, how the choices of suppliers can a¤ectbuyers’productivity have not been fully understood. Such understanding is particularly importantin light of Japanese …rms’increasing participation in global value chains.4 A detailed analysis on the
reorganization of domestic production networks in Japan during a period of increasing o¤shoring
can shed light on all these issues.
To guide our empirical analysis, we extend the models of Antràs, Fort, and Tintelnot (2014)
and Bernard, Moxnes, and Saito (2015) to study the pattern of foreign outsourcing in a world
with two-sided heterogeneity in e¢ ciency across both buyers and sellers. Our extension considers
multiple types of inputs, which di¤er from each other in the degree of relationship speci…city to…nal-good production. Such extension permits a systematic analysis of how domestic production
1

See Feenstra (2008) for a comprehensive summary of the literature.
There is a large and growing literature about the e¤ects of o¤shoring on labor market outcomes (e.g. Ebenstein,
et al. 2014; Hummels et al., 2014, etc.). The e¤ects of o¤shoring on …rms’ productivity, the focus of this paper,
receives relatively little attention in the empirical literature (Olsen, 2006). A notable exception is Antràs, Fort, andTintelnot (2014), which theoretically and quantitatively examines how o¤shoring, due to a decline in o¤shoring costs,
raises …rm productivity.
3
See Liker and Choi (2004) for a thorough discussion on the feature of long-term relationships in the Japanese
production network, which is in sharp contrast to the one in the U.S.
4
For instance, a Nikkei Sangyo Shimbun article on August 31, 2011 reported that Kubota Corporation, a large
industry machinery manufacturing …rm in Japan, announced the plan to increase its overseas parts and components
procurement share form 25% in 2011 to 70 % in 2021. A new overseas procurement base will be built in India, in
addition to their existing bases in Thailand and China. As part of this o¤shoring plan, the company would need toreorganize the procurement relationships with the existing domestic suppliers.
2

2

networks evolve due to o¤shoring, along both the sectoral and geographic dimensions.
In the model, buyers need to incur …xed search and coordination costs to …rst …nd and thenmaintain relationships with suppliers from di¤erent domestic regions and foreign countries. Such…xed costs rise in the distance from the seller, more so for the more relationship-speci…c inputs.5
As a result, buyers are less likely to outsource relationship-speci…c inputs in the …rst place andare more likely to outsource them to nearby suppliers, even though they are less productive thansome suppliers in other input sectors located farther away. Since sourcing inputs from more regions
requires additional up-front …xed costs, the more productive …rms, who …nd incurring the extra…xed costs to search in additional regions pro…table, will end up having a larger geographic scopeof domestic outsourcing. As such, the pro…ts of the more productive …rms are higher not only
because of their higher e¢ ciency, but also because of lower variable costs due to the use of moree¢ cient domestic suppliers.
When o¤shoring costs, either …xed or variable, decline, …nal good producers that have been
outsourcing domestically may start outsourcing to foreign suppliers. This is true especially forbuyers that have relatively higher core productivity. A …rm that starts o¤shoring inputs will drop
the less productive suppliers of the same input in every domestic region. This restructuring of thedomestic production network will lead to an increase in the …rm’s pro…ts and measured productivity,
inducing the buyer to outsource more inputs and to further replace the less e¢ cient suppliers withthe more e¢ cient ones located farther away. Our model also predicts that the inputs that areo¤shored tend to be less relationship-speci…c. Hence, the domestic input suppliers that are directly
displaced by o¤shoring tend to be farther away and potentially more productive, compared to those
suppliers that continue to sell to the same buyer.
We then empirically examine the theoretical predictions using unique and exhaustive data thatcover close to 4 million buyer-supplier relationships of 800 thousands …rms in the Japanese domestic
production network for two years –2005 and 2010. The data set is the most comprehensive we areaware of for a study on domestic production chains.6 We …nd evidence largely supporting the main
theoretical predictions. Speci…cally, we …nd that the more productive …rms source inputs from more
suppliers, from more regions, and from the more distant regions, con…rming the recent theoreticaland empirical …ndings by Bernard, Moxnes and Saito (2014, 2015). Distant suppliers are moreproductive on average, while productive …rms are more likely to o¤shore input production. Aboveand beyond this geographic pattern of outsourcing, we also uncover evidence about the patternof outsourcing based on the relationship-speci…city of inputs. In particular, the negative distancee¤ects on domestic sourcing are magni…ed for the more relationship-speci…c inputs. Hence, …rms
5
The idea that monitoring and communication costs increase in distance and shape relationship-speci…c investments has been empirically veri…ed in the …nance literature, such as Lerner (1995) and Petersen and Rajan (2002).
6
The closest counterpart that we can think of is the paper by Atalay, et al. (2011), who analyze the buyer-seller
network in the U.S. using Compustat data that over only publicly listed …rms and their top 5 customers. Our data
set covers half of the registered …rms in Japan, and information about their top 24 buyers and top 24 sellers. We use
the information reported by both buyers and sellers to maximize the number of links (see Section 3 below). Recent
research by Bernard, Moxnes and Saito (2015) and Carvalho, Nirei, and Saito (2014) also use the same network data
to study di¤erent research questions.

3are less likely to source relationship-speci…c inputs from the more distant regions or from foreigncountries.
In addition to empirically portraying the pattern of domestic and foreign outsourcing basedon the model, we also …nd that …rms that start o¤shoring become more productive. The net
e¤ect of o¤shoring on the buyers’ domestic outsourcing is nuanced. This can be because on the
one hand, some existing domestic suppliers are displaced by foreign suppliers producing the same
input but on the other hand, o¤shoring enhances …rms’productivity, raising the sustainability ofexisting supplier-buyer relationships and the likelihood of forming new ones. We …nd a marginallysigni…cant e¤ect of o¤shoring on the buyers’likelihood of dropping domestic suppliers, and a much
more signi…cant e¤ect on the buyers’likelihood of adding new domestic suppliers.
Our paper is related to various strands of literature in network and international trade. First,
it relates to the burgeoning literature on the relation between network and trade, which is wellsummarized by a recent review article by Chaney (2014b). The literature goes back to the seminal paper by Rauch (1999), who shows that colonial ties and common languages between two
countries induce trade, especially for di¤erentiated products. In another seminal study, Rauch andTrindade (2002) show that the presence of ethnic Chinese immigrants in the destination countries
facilitates imports from China, particularly so for di¤erentiated products. The authors attributethese …ndings to the importance of network and search frictions in international trade. Recent
studies seek to develop micro-founded models to study the dynamics and patterns of international
trade networks. Bernard, Moxnes and Ulltveit-Moe (2014) develop an importer-exporter networkmodel and use Norwegian data to establish important and novel stylized facts about international
trade at the importer-exporter level. Such facts highlight the importance of the buyer margin of
trade adjustments in response to economic shocks and policy changes. Chaney (2014a) develops amodel to study the dynamics of exporters’penetration into foreign markets, through establishingnew contacts and expanding existing trade networks.
Second, our paper contributes to the growing literature on the domestic production networks.
Ober…eld (2013) develops a general-equilibrium theory of the structure of production based on buyers’optimal choices of suppliers to study how the endogenous network formation shapes the productivity and organization of production. Carvalho and Gabaix (2013) study how the input-output
linkages in an economy propagate and multiply underlying …rm-level and sector-level idiosyncratic
shocks to macro volatility. The recently available data on the domestic buyer-supplier links inJapan has engendered a series of interesting papers about the pattern and dynamics of production
networks. Using the same data set as ours, Bernard, Moxnes and Saito (2015) develop a networkmodel that features two-sided heterogeneity in e¢ ciency and distance. Exploiting an arguably exogenous extension of the high-speed train line (Shinkansen), the authors empirically examine themodel predictions that feature negative assortative matching between domestic buyers and sellers,and show that a reduction in search costs after the opening of a new train line induces the additionof new suppliers that are farther away and less productive. Their model also endogenizes a …rm’s
performance as a function of the diversity and quality of its suppliers. Carvalho, Nirei, and Saito4

(2014) quantitatively assess the propagation of shocks, such as earthquakes, through the domestic
production chains. They show that external shocks on downstream …rms a¤ect not only the directly
linked upstream …rms, but also …rms that are two or three degrees away from the a¤ected …rms.
Third, our paper is related to the literature that studies the non-e¢ ciency aspect of …rm performance. In particular, a recent paper by Holmes and Stevens (2015) shows that small …rms specializein the niche segment of a product market, while large …rms specialize in the generic segment totake advantage of the economies of scale of production. Our focus on the relationship-speci…city of
inputs is conceptually similar to the niche segment in their study. Both of us emphasize that thetraditional concept of e¢ ciency is only one dimension to determine the heterogeneous …rm performance in response to import competition or o¤shoring. We show that the speci…city of inputs tothe buyer has been largely overlooked and can play a signi…cant role in determining how …rms area¤ected by o¤shoring.7 These …ndings also shed light on our understanding of the tradability of jobs
(e.g., Jensen and Kletzer, 2005), which also hinges on the importance of face-to-face interactions
and thus communication costs that increase in distance.
The paper proceeds as follows. Section 2 presents a theoretical model. Section 3 discusses thedata used in the paper. Section 4 presents our empirical design and results. The …nal section
concludes.

2

A Model of Firms’Domestic and Foreign Outsourcing

2.1

Setup

We build an industry equilibrium model that features domestic and foreign outsourcing of inputs
to input suppliers from multiple regions, including a foreign country. In essence, our model is a
simple extension of Antràs, Fort and Tintelnot (2014) and Bernard, Moxnes and Saito (2015) into
the one with multiple types of inputs that di¤er from each other in relationship-speci…city to thebuyer’s industry. After characterizing the equilibrium input sourcing pattern, we will then examine
how the reduction of o¤shoring costs a¤ect …nal good producers’choices of outsourcing to domestic
suppliers.
For simplicity, we consider an industry with only domestic demand. There are N …nal good
producers that produce di¤erentiated products. An individual consumer’s utility function is

u=

"N
X

1

yi

i=1

where yi and

#

1

;

> 1 denote the consumption of …nal good variety i and the elasticity of substitution

7

Another dimension of …rm performance is product quality, which has been studied by a large and growing
literature, such as Khandelwal (2010), Baldwin and Harrigan (2011), and Hallak and Sivadasan (2013), among
others.

5

between di¤erent varieties, respectively. The aggregate demand for variety i therefore isyi =where P =hPN

1i=1 pi

i 1
1

pi E
;
P1

denotes the price index and E represents the exogenously-given total

expenditure on the …nal goods. Assuming that …rms are all small and take P as given, we have theusual mill pricing with markup equal to =(1).

Production of the …nal good requires K di¤erent types of inputs, which di¤er in the degree ofrelationship-speci…city to the production of a …nal good. As we will formulate shortly, we order
inputs in such a way as relationship-speci…city increases with index k 2 f1; 2;; Kg (i.e., inputsof type 1 are the most generic while inputs of type K are the most relationship-speci…c). For each
input type k, there is a continuum of di¤erentiated inputs with a mass of 1. Each input jk 2 [0; 1]

should be insourced or outsourced to an input producer located in one of M regions of the country
or in the foreign country, which we identify as region M + 1. We assume that in each region, there
is only one producer for each input jk of type k. Since there is a continuum of inputs, [0; 1] for eachtype of input, there is a mass K[0; 1] of input producers in each domestic region as well as in

the foreign country. From the viewpoint of a …nal good producer, it can source each di¤erentiatedinput jk of input type k to itself or to one of M + 1 …rms, located in M + 1 separate regions.
For each input jk of type k, each of the …nal good producers and M +1 input producers draws its
own productivity z from the Fréchet distribution, with a cumulative distribution function de…nedon (0; 1) by

Fk (z) = e Tk zk;where Tk > 0 is positively related with the likelihood of a high-productivity draw whilek > 1

governs the variance of the draw. An input producer with productivity z has a unit cost of
production wr ck =z, where wr is a region-speci…c cost parameter such as the wage rate and ck
is a cost parameter that is speci…c to the input type. We assume that wr = 1 for any domestic
region r = 1;

; M , and wM +1 = w < 1. The unit cost of production of input jk equals w0 ck =z

in the case of insourcing; it is likely that w0 > 1 since specialized input producers tend to enjoy
cost advantage. Note that insourcing is represented by r = 0 and that each …nal good producer
draw its productivity z independently for each individual input of type k.
Shipping an input entails an iceberg transport cost ofk (d) > 1, where d denotes the distance ofthe shipment, de…ned broadly to include coordination or communication costs. As such, the icebergtransport cost may depend on the type of inputs being shipped. Intuitively, we assume that theslope ofk (d),

0 (d), increases with the relationship-speci…city of inputs (i.e., k). In other words,kdistance matters more for shipping the more relationship-speci…c inputs. Moreover, we assume only
for simplicity that for every domestic region where a buyer is located, the foreign country is thefarthest away.
Production of a …nal good involves two stages. The …rst stage is to make K composite inputs,

6each produced from input varieties of jk 2 [0; 1]. Speci…cally, a …nal good producer i can produce

xik units of a composite input k with the following constant returns to scale (CES) production

function:
xik =

Z 1

xik (jk )

k

1

k

k

djk

k

1

:

0

where

k is the elasticity of substitution between di¤erent input varieties in the production of thecomposite input k.
The second stage is to assemble the composite inputs into …nal goods. Assembly technology of…nal good producer i can be expressed by the Cobb-Douglas function:yi = 'i

K
k=1

xik

k

;

k

where …nal good producers are di¤erent in their core productivity, 'i , for i = 1; 2;; N.

Given supplier heterogeneity and varying iceberg costs that depend on the sector characteristics
and the distance from the suppliers, individual …nal good producers choose for each input whether
they outsource input production and to whom they outsource the input if they outsource it atall. Borrowing the insights from Antràs, Fort, and Tintelnot (2014), we assume that a …nal good
producer needs to pay a …xed search cost of fk (d) to …nd a prospective supplier of inputs of typek in a region with the distance d; …nal good producers do not incur any …xed costs for insourcing.
This search cost naturally depends on the degree of relationship-speci…city of inputs such that the
search cost is higher for any given distance and is increasing faster in distance, the higher the
relationship-speci…city of the input is (i.e., both fk (d) and fk0 (d) increases with k). Because searchis costly, …rms may not buy input varieties from all regions, including the foreign country. With
ik de…ned as the set of regions from which …rm i sources its inputs of type k, this means thatis not just a subset but may be a proper subset of f1; 2;ik; M; M + 1g. Since insourcing entails

no …xed costs, however, …rm i always considers insourcing as an option when it chooses a supplier
for any input.
Finally, we assume for simplicity that …nal good producers have all the bargaining power against
input producers, such that the price of an input equals its unit cost.8

2.2

Firms’Optimal Sourcing Strategies

In this subsection, we …rst derive each …nal good producer’s pro…ts as a function of parameters that
describe the sourcing environment. We …rst take the sets of …rm i’s sourcing regions, fKik gk=1 , asgiven, before solving for the …nal good producers’ sourcing decisions. Finally, we summarize themain theoretical results about the …rms’sourcing strategies, which will be tested in the empiricalsection below.
For each variety jk 2 [0; 1] of input type k, …rm i searches the regions in

ik

for an input

producer that o¤ers the lowest price for the input jk . Since the assumptions of supplier productivity
8

Introducing explicit negotiation between buyer and seller would not change the results qualitatively.

7

distribution and the production structure are the same as Eaton and Kortum (2002), we can directly
apply their results here to show that the input-price probability distribution for every input variety
of type k ise ik p k ;Gik (p) = 1
where
ik = Tk (w0 ck )

k

X

+

r2

(1)

Tk (wr ck k (dir ))

(2)

k

ik

with dir being the distance between region r and …rm i. Note that our interpretation of transport
cost allows us to neglect it in the case of insourcing, as re‡ected in the …rst term of the right-handside of (2).9 The share of inputs insourced issik0 =Tk (w0 ck )

k

=

ik

P

w0 k +

l2

while the share of inputs sourced from region r 2 f1; 2;sikr =

w0 k
ik(wl l (dil ));k; M + 1g is given by(wr k (dir )) kP
w0 k + l2 ik (wl l (dil ))

:

(3)

k

Furthermore, the input price index for the inputs of type k is given by
pik =

1
k

k

ik

where
k =

where (x) =

;

(4)

1

k+1

1

k

k

;

k < 1 + k;k

R1 x 1 t
e dt is the gamma function.
0 t

Now, we are ready to derive the pro…t function for …nal good producer i. For a given set of
input price indices, the optimal levels of composite input k to produce yi units of …nal good equals
xik =

k yi

'i pikjK
j=1 pij :Consequently, the unit cost of a …nal good is given by
K

i =

1 X
1
pik xik =
yi
'i

K
k
k=1 pik =

k=1

9

1
'i

K
k
k=1 k

Bernard, Moxnes and Saito (2015) characterize insourcing in the same manner.

8

k
k

ik

:

It immediately follows that …rm i’s pro…t can be expressed asi ('i ) = B

K X
X

1
i

k=1 r2

where
B=

1

1

1

P

1

E;

P =

fk (dir );

(5)

ik

"N
X
i=1

1
i

1

#11

:

The pro…t function (5) provides a lot of information about a …rm’s optimal sourcing. Adding a
new region r for sourcing inputs of type k comes with an additional …xed cost fk (dir ), but confersa bene…t of lowering the marginal cost of production, due to an expansion of the supplier set (i.e.,
an increase inKik ). The optimal choice of the set of sourcing regions, described by f ik gk=1 , is an

optimal decision based on balancing these costs and bene…ts.

To …nd a …rm’s optimal sourcing strategy, we derive from (5) the …rst-order approximation of achange ini ('i ) when …rm i adds region ri ('i )j

0 to its set of sourcing regions for inputs of type k 0 ,i ('i )j0
ik0 [fr g

ik0

Tk0 (wr0 ck0 k0 (dir0 ))= ~ i ('i )ik0 ( ik0 )where ~ i ('i )

B'i

1

K
k (1
k=1 k

)

k(

k0

fk0 (dir0 );

(6)

1)
k

ik

ik0 :

denotes …rm i’s operating pro…ts. Notice thatik0

depends on the set of sourcing regions that does not include region r0 in question.
The …rst result that we obtain is to con…rm the nesting property of sourcing observed by Antràs,Fort, and Tintelnot (2014). It follows immediately from (5) that ~ i ('i ) is supermodular in

ik and'i so that the marginal bene…t of expanding the regions of search increases with …rm i’s coreproductivity 'i . Thus, our counterpart of their nesting property is summarized by the following
proposition.
Proposition 1 A …rm with a higher core productivity outsources more input varieties and from
more regions. With the optimal set of sourcing regions,core productivity, we haveik ('i )

ik , expressed as a function of a …rm’s

ik ('i0 ) for 'i < 'i0 .

The model also delivers results about the relationship between input sourcing and distance.
It is evident from (3) that the share of domestic region r as a source of input outsourcing, sikr ,decreases with the distance from r, dir . This, of course, is not surprising in the presence of icebergtransport costs. Since the input price equals wr ck k (dir )=z while the input price distribution for
the inputs sourced from region r equals Gik (p) regardless of r, as shown by Eaton and Kortum(2002), inputs supplied from more distant regions tend to be produced by the more e¢ cient input
producers. We summarize these …ndings in the following proposition, similar to the key predictionsof Bernard, Moxnes and Saito (2015).
9

Figure 1: Relationship-speci…city and optimal sourcing

Proposition 2 Final good producers buy inputs from a larger mass of …rms in closer domestic
regions. Moreover, the more distant suppliers are on average more productive.
We also …nd that the relationship-speci…city a¤ects the sourcing pattern. Both marginal bene…tand marginal cost of expanding the sourcing regions vary with input types as (6) indicates. Figure 1
illustrates two marginal bene…t curves that correspond to two input types, k1 and k2 , with k1 < k2 .10
We see from (2) and (6) that if

ik0 is a singleton, the marginal bene…t equals

i ('i ) for any input

type k 0 . Since the slope ofdecreases faster with dirk (dir ) is higher for a larger k, however, Tk0 (wr0 ck0 k0 (dir0 )) k = ik0 ( ik0 )00 if k is higher. Thus, the marginal bene…t of adding a sourcing region r0

decreases with distance dir , and at a faster rate for the more relationship-speci…c inputs. Figure 1
also depicts two schedules of the …xed search costs, capturing the marginal costs of adding a regionin search, for both k1 and k2 . Since k2 is more relationship-speci…c, the curve for fk2 lies abovethat for fk1 and is also steeper. It is clear that in equilibrium, …rm i searches for a supplier over a
wider range of regions, the less relationship speci…c the type of input is. Thus, we have establishedanother, novel nesting property,i1

iK , for any …nal good producer i.

i2

Proposition 3 Firms source the less relationship-speci…c inputs from more regions.
To …nd the optimal sourcing pattern for every input type k, each …nal good producer i ranks
regions according to the value of Tk (wr ck k (dir )) and purchases its inputs from all regions withTk (wr ck k (dir )) below a certain threshold.
In addition, we see from (2) that …rm i does not only search a wider range of regions for lessrelationship-speci…c inputs, but also sources rather evenly to …rms across regions. Equivalently,
inputs with high relationship-speci…city tend to be sourced disproportionately more to …rms inregions that are closer to the …nal good producer, i.e., sikr decreases faster with dir , the larger k is.
10

Note that these curves are drawn as if regions are continuous rather than discrete.

10Since the closest to a …nal good producer is the …rm itself, the …nal good producers insource morevarieties of inputs, the more relationship-speci…c are the inputs.
Proposition 4 The more relationship-speci…c inputs tend to be insourced and also outsourced disproportionately more to …rms in nearby regions. The less relationship-speci…c inputs tend to besourced more evenly across a wider range of regions.

2.3

O¤shoring and Firms’Network of Input Transaction

In this section, we examine how a reduction in o¤shoring costs a¤ects …rms’o¤shoring and theirdomestic sourcing strategies.
Propositions 1 shows that the more e¢ cient …rms tend to outsource input production to moredomestic regions. Notice that despite the fact that the foreign country is farthest away from …rmi, it is not necessarily the last to be included in its set of sourcing region since wM +1 < wr for all
r = 1; 2;; M . Indeed, sincek (dir ) increases less signi…cantly with dir for generic inputs, theranking of the foreign country in the list is higher for lower k. We can naturally extend Proposition
3 to obtain the following proposition.
Proposition 5 While the more productive …rms are more likely to o¤ shore inputs, the more
relationship-speci…c inputs are less likely to be o¤ shored.
Recall that our model incorporates two types of o¤shoring costs: the …xed search and coordination costs in the foreign country, fk (diM +1 ), and the (marginal) variable o¤shoring costs,
wM +1 k (diM +1 ). A reduction of o¤shoring costs can take a reduction of either type of the costs.
Let us …rst consider the impact of a reduction of fk (diM +1 ) for all k. It follows from (2) and (6)that a reduction of fk (diM +1 ) will result in more o¤shoring. There are two types of new o¤shoring.
The …rst one is about the …rst-time o¤shoring by the relatively more e¢ cient …rms that had noto¤shored any input production before. The second one is about increasing the o¤shoring scopeto the less generic inputs by …rms that have already o¤shored some inputs. For both cases, weknow from Proposition 5 that the inputs that are newly o¤shored should be those that are the least
relationship-speci…c of all the inputs that had not been o¤shored.
How does a …rm that starts or expands o¤shoring change its domestic sourcing pattern? Suppose…rm i begins o¤shoring some of type-k inputs. It follows immediately from (3) that some suppliersof input k in each region, in particular the less e¢ cient ones, will be directly replaced by foreign…rms. In addition to this direct e¤ect, o¤shoring has an indirect productivity e¤ect on the …rm’s
sourcing strategy. We see from (2) and (4) that o¤shoring increases

ik and hence lowers the input

price index, pik . This entails an increase in the marginal pro…ts of increasing
which in turn lead to an expansion of

ij for all j 6= k,ij for some j. If that happens, some input producers fromdistant regions are added to the input supplier network of …rm i, while some less productive …rms
in every sourcing region will be dropped. Firms that start or expand o¤shoring also switch frominsourcing some inputs to outsourcing.
11

A reduction of the …xed search and coordination costs also a¤ect the …rms that do not change
their individual sourcing strategies. As some …rms expand the scope of o¤shoring, the …nal goodprice index falls due to the productivity e¤ect. This adversely a¤ects the …rms that do not change
their sourcing strategies. While they experience no direct e¤ect of o¤shoring, they will experience
negative indirect e¤ects due to increased competition from the newly o¤shoring …rms in the goods
market.
Proposition 6 A …rm that starts or expands o¤ shoring due to a reduction in the …xed o¤ shoring
cost drops the less productive suppliers who produce the same type of inputs across all domestic
regions. As its unit production cost decreases due to o¤ shoring, the …rm may also increase outsourcing and expand sourcing regions for other types of inputs. As such, distant suppliers withhigher productivity replace the less productive suppliers in every sourcing region for those input
types. In contrast, …rms that do not change o¤ shoring strategy may reduce the scope of searchby dropping distant suppliers of certain input types and add the less productive suppliers in every
remaining region.
Next, we consider a reduction in the variable o¤shoring costs: either wM +1 ork (diM +1 ) de-creases for all k. This is a type of reduction in o¤shoring costs investigated by Grossman andRossi-Hansberg (2008, 2012). Similar to the case discussed above, some …nal good producers will
expand the scope of o¤shoring as a result of declining variable o¤shoring costs. The direct andindirect e¤ects of this induced change on …rms are the same as in the previous case, as summarized
by Proposition 6. The adverse e¤ect of not engaging in o¤shoring is also identical to the previous
case.
However, the e¤ects are di¤erent for the …nal good producers that have already o¤shored some
inputs before the reduction in wM +1 ork (diM +1 ). Di¤erent from the previous case when there isan adverse competition e¤ect, existing o¤shorers bene…t from the reduction in variable o¤shoring
costs. According to (3), they will o¤shore more input varieties of the types of inputs that havealready been o¤shored, and drop less e¢ cient …rms in every domestic region. They may also reshape
the supplier-buyer network for other input types such that they drop the closer and less e¢ cient
suppliers, and add more distant and e¢ cient suppliers.
Proposition 7 O¤ shoring …rms do not bene…t from a reduction in the …xed search and coordination
costs for foreign suppliers unless it is optimal for them to expand the scope of o¤ shoring to include
new input types. In contrast, a reduction in the variable o¤ shoring costs always bene…ts such …rms
and induces them to expand the scope of o¤ shoring and drop the less productive …rms in all domestic
regions for the already-o¤ shored input types. They may also drop the closer, less e¢ cient suppliers
and add the more distant, e¢ cient suppliers for other types of inputs.

12

3

Data

We confront the theoretical predictions with a unique buyer-supplier data set for the Japanese…rms’buyer-seller network. We will …rst discuss the data source and a few key patterns of the dataset, before presenting the regression speci…cations and empirical results in Section 4.

3.1

Data Sources

This paper uses two data sets for analysis. The …rst data set, compiled by the Tokyo Shoko Research,
Ltd. (TSR), contains basic …rm-level balance sheet information of over 800,000 …rms in Japan, for
two years - 2005 and 2010.11 It records information on …rms’ employment, sales, location, up to
three main industries (4-digit), establishment year, number of factories, among others.12 Crucial to
our analysis, the TSR data also provide information on between-…rm relationships, speci…cally thenames of a …rm’s main suppliers, buyers, and shareholders. On both the buyer and supplier sides
of a …rm, a maximum of 24 …rm names are collected.13 We use a two-way matching method toconstruct the domestic production network in Japan. Speci…cally, we use information reported by abuyer about their sellers, and information reported by a seller about their buyers, to maximize thenumber of buyer-supplier links. Since a buyer of a seller can be identi…ed on both ends, the numberof buyers (sellers) of a seller (buyer) can be way above 24. The top seller in our constructed network
data in Japan has over 11,000 buyers in 2010, while the top buyer has close to 8,000 suppliers. Mostof the …rms in the sample have substantially fewer buyers and sellers. This skewed distributionof the buyer-supplier links will be described below. To calculate the distance between each buyersupplier pair, we identify the longitude and latitude of each …rm based on detailed address available
in the TSR data, using the geocoding service from the Center for Spatial Information Science at
the University of Tokyo to compute the distance.
The second data set is from the Basic Survey on Japanese Business Structure and Activities(BSJBSA), collected annually by Japan’s Ministry of Economy, Trade and Industry (METI). The
survey data set covers all …rms with at least 50 employees or 30 million yen of paid-in capital in
the Japanese manufacturing, mining, wholesale and retail, and several other service sectors. Firms’
responses to the surveys are mandatory. The data set contains detailed information on …rms’
business activities, such as their main industry (3 digit), number of employees, sales, purchases,
exports, and imports (including a breakdown of the regional destination of exports and the origin ofpurchases and imports). This data set covers 22,939 and 24,892 …rms in 2005 and 2010, respectively.
We obtain sellers’information from the TSR data and buyers’information, in particular several
measures of o¤shoring, from the BSJBSA data. We then merge the two data sets using …rms’
names, addresses, and telephone numbers. The merged data contain over 800,000 buyer-supplierpairs. In this paper, we use the subsample that has manufacturing …rms in the downstream.
11

The surveys were conducted in 2006 and 2011, respectively. We use both TSR Company Information Databaseand TSR Company Linkage Database in this paper.
12
According to Carvalho, Nirei and Saito (2014), the TSR data cover more than half of all …rms in Japan.
13
In addition, the names of the three major banks a …rm borrows from are also recorded.

13

3.2

Data Summary

Before discussing our main empirical …ndings, let us describe several key features of our network
data. Table 1 reports the summary statistics of the number of buyer-supplier links. There are two
sets of numbers. The …rst set, solely from the Tokyo Shoko Research (TSR) data, shows that there
are about 3.6 million buyer-supplier links in Japan in 2005. The number of links increased to 4.5
million in 2010. The increase can be due to an increase in the number of buyers or sellers, or justan increase in the number of links per buyer. While assessing the reasons for the increase in thedensity of the domestic production network is beyond the scope of this paper, we …nd evidence thatthe mean number of sellers of a buyer in Japan has increased from 4.9 to 5.5 between 2005 and2010, while the median number of sellers per buyer has increased from 2 to 3. The large di¤erence
between the mean and the median number of sellers per buyer suggests that the distribution of
the number of buyer-supplier links is highly skewed. Figure 1, which plots the log number ofsellers per buyer against the fraction of buyers having at least that many sellers, shows a power-law
distribution, as highlighted by Bernard, Moxnes, and Saito (2015).
A potential reason for the increase in the number of buyer-supplier links can be measurement
error, due to …rms reporting more customers or suppliers in the 2010 survey compared to thatin 2005. To avoid such measurement errors from biasing our empirical results, we will use abalanced panel of suppliers and buyers that exist in both 2005 and 2010 in the regression analysis
below. Moreover, our model treats buyers and sellers as independent …rms. In reality, …rms
may optimally choose to vertically integrate with upstream suppliers to strike a balance between
providing incentives and control (see, for instance, Antràs and Chor, 2013). To deal with both data
issues, we use a restricted data sample by excluding observations for suppliers or buyers that only
exist in either 2005 or 2010, as well as all headquarter-subsidiary links. Panel B of Table 1 shows
the corresponding statistics for the restricted sample. The number of buyer-supplier links drops toless than half a million. Not surprisingly, the mean and the median number of sellers per buyer
both increase, implying that the …rms that are retained in our regression sample tend to be larger.
The TSR data only contain information on some basic balance-sheet variables and whether
two …rms have a buyer-supplier relationship. In addition to removing observations that may cause
estimation biases, we merge our network data to the BSJBSA, using a common …rm identi…er. We
can therefore obtain a sample that provides detailed balance-sheet data for our regression analyses.
In particular, we have information about a buyer …rm’s imports, material purchased, capital, andlabor. With all these information, we can then estimate a buyer’s total factor productivity (TFP).
Panel C in Table 1 shows the summary statistics of the number of links in the merged data set.
About 40% of the pairs in the restricted TSR sample (Panel B of Table 1) can be merged. Not
surprisingly, since BSJBSA covers mainly large …rms, the mean and the median numbers of sellers
per buyer based on the merged data are both higher. In the sample of downstream manufacturing…rms and their suppliers that we use in our regression analysis below, the mean and the median
numbers of sellers per buyer are about 22 and 10 for the year 2010, respectively. One could beconcerned about our focus on large …rms, but if the goal of the study is to assess the e¤ects of14

o¤shoring on the domestic production network, our large-…rm focus should be …ne as on average,
only large …rms would engage in direct o¤shoring due to the higher associated …xed costs compared
to domestic outsourcing. Figure 2 con…rms how our merged sample is skewed toward the larger
downstream …rms. The fraction of …rms that have at least n links is also larger due to the factthat …rms with a small number of links are underrepresented in our merged sample. That said,
the power-law distribution of the (log) number of sellers per buyer is preserved. The slope of the
distribution of the number of links based on the merged sample is very close to that based on theoriginal TSR sample.
Table A1 in the appendix shows the summary statistics of the numbers of buyer-supplier links bya buyer’s broad sector. According to the original TSR data, the top 3 sectors that have the highest
number of buyer-supplier links are manufacturing, wholesale and retail trade, and information
services. In our regression analysis below, we will focus on the e¤ects of o¤shoring by manufacturing…rms in the downstream on supplier dropping and adding across all sectors. Table A2 shows
the same set of summary statistics reported in Panels B and C of Table 1, but by a buyer’s
manufacturing sector.
Table 2 shows the basic characteristics of importers in the BSJBSA sample. The fraction of
manufacturing …rms that import is 29.7% in 2005, and increases to 30.8% in 2010. Importers’
average import intensity, measured in terms of the …rm’s imports divided by total intermediate
input purchases, also increases slightly from 16.3% to 19.2% during the same period. Asia is a very
important input source for Japanese importers. Among importers, the average share of imports
from Asia in total imports is over 80 percent in both 2005 and 2010.
Table 3 further shows the summary statistics of the main variables of interests used in the
regression analyses. Reading horizontally across columns, we report the summary statistics fordi¤erent subsamples used in our regressions, namely all manufacturing buyers; those that alreadyimported (o¤shored) in 2005, those that did not import in 2003-2005; those among non-importersthat started importing between 2005 and 2010; and those that remain non-importers throughout
2005-2010; and …nally, those that imported in both 2005 and 2010. Reading vertically across rows,
we reported the mean, median, minimum, and maximum for 1) the numbers of buyers (Panel A);2) the number of sellers per buyer (Panel B); 3) the number of sellers’prefectures per buyer (PanelC), all based on the 2005 data sample. We then report the corresponding numbers using the 2010
sample.
In the sample of all manufacturing …rms, the mean and median number of domestic suppliers
per buyer is 19.3 and 8, respectively. The mean and median number of prefectures that a buyer willsource from is 4.83 and 4 (out of a total of 47), respectively. By comparing these numbers acrossgroups of buyers and years, we discover a few important facts. First, the number of sellers per
buyer, in terms of both mean and median, increases from 2005 to 2010.14 Second, importers havemore domestic suppliers in both years on average. Third, among importers, those that imported
14

As explained above, some of these increases are possibly caused by measurement and reporting errors, which will
be addressed once we restrict our sample to include buyers and sellers that exist in both years of our sample.

15

in both 2005 and 2010 have more domestic suppliers than those that just started importing since
2005.
Before conducting formal regression analysis, we graphically illustrate whether the propositions
presented in the previous section tend to hold or not. First, according to Proposition 1, we expectthat the more productive buyers tend to source from more suppliers. In Figure 3, we examine
the relationship between the buyer-supplier distance and suppliers’characteristics. In Panel A of
Figure 3, we explore the relationship between the buyer-supplier distance and supplier productivity(Proposition 1). We …rst group buyer-supplier pairs into ten deciles, based on their distance. We
then compute the average labor productivity of suppliers, measured as sales per employee for eachdecile.15 We then partial out any industry-speci…c e¤ects by demeaning these productivity measuresfrom their corresponding industry averages. There is a U-shaped relationship between the buyersupplier distance and average seller productivity, contrary to a positive relationship predicted by
Proposition 1. However, excluding suppliers which are fairly close to buyers (less than 30 percentileof the distance distribution), we observe a positive relationship between the two, as we expected.
In Panel B, we use the same method to illustrate any potential relationship between the buyersupplier distance and the relationship-speci…city of inputs purchased from upstream suppliers, suggested by Proposition 3. In particular, we …rst group buyer-supplier pairs based on their distancedecile, then compute the average fraction of buyer-supplier links with inputs from the di¤erentiatedsectors. In order to identify suppliers which provide di¤erentiated inputs, we use the Rauch measures at the 4-digit industry level, which equals one for the di¤erentiated-good industries and zerootherwise (Rauch, 1999). Since the Rauch measure is not available for most of the service industries,
for each buyer, we calculate the fraction of di¤erentiated-good suppliers in the total number of manufacturing suppliers only. There is a weakly negative relationship between relationship-speci…cityand distance for 2010, as predicted by our model.
Finally in Panel C, we provide another illustration of a possible relationship between buyersupplier distance and the relationship-speci…city of inputs, using the share of trade that is handled
by intermediaries of each sector from Bernard, Jensen, Redding, and Schott (2010) (BJRS hereafter). We interpret a higher share of intermediation as an indication of a lower degree of input
relationship-speci…city. There appears to be a U-shaped relationship between distance and theintermediation index across deciles. Notice that the three graphs illustrated here provide only preliminary evidence about the potential relationship between the domestic sourcing patterns. In thenext section, we will conduct formal regression analyses, controlling for key confounding factors.

4

Regression Analyses and Results

In this section, we empirically examine the propositions presented in Section 2. For notational
clarity, let us denote buyer, seller, industry (3-digit), and region (one of 47 prefectures) by i, j, s,
and r respectively. Notice that when industry and region …xed e¤ects are included, we will be clear
15

Information required to calculate value added is not available for suppliers.

16

about whether they are for the buyer’s (i) or the seller’s (j) industry or region.

4.1

On Firms’Domestic Sourcing Patterns

Proposition 1 is about the …nal good producer’s (buyer’s) productivity and the geographic scopeof domestic outsourcing. To empirically examine this proposition, we estimate the following speci…cation:
ln(#pref ecture)i =+ln(T F P )i + F Esi + F Eri + "i ;(7)where ln(#pref ecture)i stands for the number of regions buyer i sources from; ln(T F P )i represents
buyer i’s productivity. Buyer’s industry and region …xed e¤ects are included (F Esi and F Eri ).
According to Proposition 1, there should be a positive relation between a buyer’s productivity
and the number of regions that it will outsource input production to. Thus,

is estimated to bepositive:
Table 4 reports the estimation results. Columns (1) through (4) show the results for estimatingeq. (7) using the 2005 cross-sectional data on the buyer-supplier network, while columns (5) through(8) use that from 2010. Standard errors are always clustered at the buyer’s industry level. In column(1), when we estimate a buyer’s total factor productivity (TFP) using the standard Olley-Pakes
method, we …nd a positive and signi…cant correlation between a buyer’s TFP and the number ofregions (prefectures) that it purchases inputs from, after controlling for buyer industry and region…xed e¤ects. Speci…cally, the coe¢ cient of 0.447 implies that a one standard deviation increase in abuyer’s ln(T F P ) (i.e., 0.41) is associated with about 20% increase (0.18 log points) in the number
prefectures the buyer will buy inputs from. This positive correlation between buyer productivity
and the geographic scope of outsourcing is observed when productivity is estimated using the index
method (column (2)), or sales-based or value-added based output per worker (columns (3) and (4),respectively). When we use the data sample from 2010 in columns (5) through (8), we continue
to …nd that more productive buyers sourced from more domestic regions, con…rming Proposition
1. Notice that the magnitude of the correlation is smaller when the 2010 sample is used. For the2010 sample, the coe¢ cient of 0.149 implies that a one standard deviation increase in ln(T F P )(i.e., 0.53) is only associated with about 8% increase in the number prefectures the buyer will buy
inputs from. The reason for the decline in the magnitude of the relation between …rm productivity
geographic scope of outsourcing could be related to the lower costs of o¤shoring, which we will
explore next.
Now let us turn to Proposition 2, which is about the distance between a buyer and a region andthe number of suppliers used in the region. To this end, we …rst estimate the following speci…cation
at the buyer-prefecture level:ln(#sellers)ir =+ln(dist)ir + fF Eg + "ir ;(8)where ln(#sellers)ir is the (log) number of sellers buyer i purchase inputs from in region r. ln(dist)ir17

is the log distance between buyer i and its seller’s (j) prefecture.16 One is added to distance forsame-prefecture outsourcing, which by de…nition has a zero distance. fF Eg includes various …xede¤ects, which can include buyers’prefecture …xed e¤ects to capture any unobserved characteristics
of a buyer’s location (e.g., infrastructure of agglomeration e¤ects), buyers’industry …xed e¤ects tocapture any sector-speci…c di¤erences (e.g., complexity of the …nal-good production), and sellers’
prefecture …xed e¤ects to capture any unobserved characteristics of the seller’s location. Since theunit of observation is at the buyer-(seller)prefecture level, we have enough degree of freedom toinclude buyer …xed e¤ects as well, which will control for all buyer-speci…c unobserved characteristics.
In that case, we examine the relationship between the seller-buyer distance and the scope of domestic
sourcing by exploring any cross-region variation within a buyer.
We estimate eq. (8) using our network data for 2005 and 2010.17 According to Proposition2,< 0. Table 5 presents the regression results. Based on the sample for the cross-section of

the buyer-supplier network from 2005, Column (1) shows a negative and statistically signi…cantcorrelation between ln(dist)ir and the number of sellers in region r, after controlling for buyers’
prefecture, buyers’industry, and sellers’prefecture …xed e¤ects. This …nding con…rms Proposition
2. In column (2), we control for buyer …xed e¤ects and seller’s prefecture …xed e¤ects instead.
The correlation remains negative and statistically signi…cant. Importantly, the magnitude of thecoe¢ cient increases from -0.09 in column (1) to -0.15 in column (2). What it implies is thatcross-buyer heterogeneity in …rm e¢ ciency and other attributes matter. When controlling for all
buyer-speci…c attributes, within-…rm variation in sourcing patterns across regions becomes evenmore pronounced. In columns (3) and (4), we use the network data from the 2010 cross-section.
We continue to …nd a strongly negative relationship between distance and the number of suppliers
sourced from di¤erent regions. The size of the coe¢ cients ofare of similar magnitude to thosefor the 2005 sample, regardless of the set of …xed e¤ects included.
Now let us examine Proposition 3, which predicts that the negative distance e¤ect on …rms’
outsourcing is stronger for relationship-speci…c inputs. To verify this prediction, we estimate thefollowing speci…cations at the buyer-prefecture-industry level:ln(#sellers)irs =+RSsj + F Ei + F Erj + F Esj + "irsln(dist)ir + ln(dist)ir(9)

where ln(#sellers)irs is the (log) buyer i’s number of sellers in region r and sector s. Like before,ln(dist)ir is the log distance between buyer i and sellers’ region r, and RSsj measures the degreeof relationship-speci…city of sector s inputs. According to Proposition 3, the number of sellers ofeach buyer in a sector-region should be lower from prefectures that are farther away, more so forthe more relation-speci…c (seller) sectors. Therefore,< 0 and< 0.

Table 6 reports the regression results. Column (1) shows that the distance between a buyer
and its sellers’prefectures is negatively correlated with the number of sellers from the prefecture.
Moreover, the coe¢ cient on the interaction between ln(dist)ir and the proxy for the RSsj , the1617

We use capital city of the prefecture to compute the distance.
For this regression, we use the sample of Panel B of Table 1.

18

intermediation index adopted from BJRS (2010), is positive and signi…cant. These results suggestthat while buyers will buyer from few sellers from a more distant region, such negative e¤ect isalleviated in sectors where inputs are less relationship-speci…c (i.e., those that tend to be handled
by intermediaries).
In column (2), we repeat the same regression by using the famous Rauch index to proxy for the
inputs’relationship-speci…city. We …nd con…rming results that distance has a negative e¤ect on thescope of outsourcing, more so for inputs that are more relationship-speci…c (a negative coe¢ cienton the interaction term). Qualitatively identical results are obtained when the 2010 cross-sectional
data are used in columns (3) and (4).
Proposition 2 also predicts that the more distant suppliers are on average more productive due
to …xed costs of o¤shoring that are increasing in distance. To verify this claim, we estimate thefollowing equation at the buyer-supplier level:ln (Sales=Emp)j =+ln(dist)ij + F Ei + F Erj + F Esj + "ir ;(10)where ln (Sales=Emp)j stands for seller j’s labor productivity and ln(dist)ij is the log distance
between buyer i and seller j, instead of the distance from the capital of the seller’s prefecture.
Buyer (F Ei ), sellers’ prefecture (F Erj ), and sellers’ industry (F Erj ) …xed e¤ects are included todeal with, for instance, any agglomeration e¤ects and economic geographic factors that a¤ect a…rm’s sourcing pattern. Notice that the same value of ln (Sales=Emp)j may repeat multiple times
in the regression sample, as a seller can be selling to di¤erent buyers.
According to Proposition 2,> 0. Table 7 reports the results of estimating eq. (10). Standard

errors are clustered at the buyer level. We …nd a negative and statistically signi…cant correlation
between the buyer-seller distance and seller labor productivity. This result is observed regardlessof using the network data from 2005 or 2010.

4.2

Determinants of Firm’s O¤shoring Decisions

Now let us turn to empirically examining the determinants of …rm’s o¤shoring. According to
Proposition 5, the more productive buyers are more likely to o¤shore. To empirically examine thisclaim, we estimate the following speci…cation using our two-year panel data on Japan’s production
networks (2005 and 2010):
imp_dit =where+ln(T F P )i;t 1 + F Esi + F Eri + "i ;(11)

imp_dit indicates the change in buyer i’s o¤shoring status between t 1 (2005) and t (2010),

which equals 1 if it didn’t import in all years over the 2003-2005 period, and start importing inboth 2010 and 2011, 0 otherwise. The decision to consider import starters that import in both 2010
and 2011 is to remove noisy one-time (occasional) importers (see Blum et al., 2013). We continue
to include both buyer industry (F Esi ) and region (F Eri ) …xed e¤ects.
Table 8 reports the estimation results. Similar to Table 4, we continue to use four di¤erent ways
19

to measure a buyer’s productivity: Olley-Pakes TFP, index TFP, sales-based and value-added-based
productivity, respectively. Standard errors are clustered at the buyer’s industry level. With the
exception of column (1) when the TFP estimated based on the Olley-Pakes method is used, we …ndthat a buyer’s productivity in 2005 is positively and signi…cantly correlated with the likelihood of
its o¤shoring between 2005 and 2010, con…rming Proposition 1.
Proposition 5 also predicts that the more relationship-speci…c inputs are less likely to be o¤shored. To this end, we estimate the following regression speci…cation:
imp_dis =+ RSs + F Ei + F Esj + "is ;(12)

where imp_dis is an o¤shoring dummy, which is equal to 1 if buyer i is currently importing
something in sector s, 0 if it does not but is sourcing something from another local supplier.18
Table 9 reports the estimation results. In column (1) when the (inverse) relationship-speci…cityof inputs is measured by the BJRS intermediation index, we …nd a positive correlation between
the (inverse) measure of relationship-speci…city of the input sector and the likelihood of the buyer’so¤shoring in the same sector. In column (2) when relationship-speci…city is measured by the
Rauch index, we …nd a negative correlation between the measure and the likelihood of the buyer’so¤shoring.
Finally, we examine Proposition 6, which is about how o¤shoring …rms change their domestic
sourcing patterns, which will result in an improvement in productivity. The resulting productivity
increase will lead to another round of supplier churning. In particular, distant suppliers with higher
productivity will be added.
To test these predictions, we estimate the following speci…cations:ln (sales)i =+imp_di + F Esi + F Eri + "i ;(13)

ln (#pref ecture)i =+

imp_di + F Esi + F Eri + "i ;(14)

Avg (ln(dist)i ) =+imp_di + F Esi + F Eri + "i;(15)where buyer’s sector and region …xed e¤ects are included (F Esi and F Eri ), since buyer …xed e¤ects
cannot be included.
In this regressions, we are looking for indirect e¤ects of o¤shoring, due to increased buyer’s
productivity, which induces the buyer to increase search e¤ort over a larger range of domestic
regions. Thus,> 0 in all three speci…cations. Notice that the indirect e¤ect is only realized after

the …rm has become more productive. So if we control for the change in sales,ln(sales)i from2005 and 2010, we should expect no signi…cant relationship.
Table 10 reports the estimation results. In columns (1)-(2), we …nd a positive and signi…cantcorrelation between the change in the …rm’s o¤shoring status and the …rm’s sales growth. In
18

To construct the importing dummy, we need to construct a new data set with a o¤shoring dummy equal to 1 if
there are positive imports in sector s by buyer i, 0 otherwise. Notice that we do not …ll in 0 for all possible supplying
industries. We only do that for the industries in which buyer i is currently sourcing something domestically.

20

columns (3)-(4), we …nd a positive and signi…cant correlation between the change in the …rm’so¤shoring status and the …rm’s measured TFP. However, in columns (5) through (8), we …nd nonet e¤ect on the number of prefectures that the new o¤shorer will source inputs from, or outsourcing
to more distant suppliers.

4.3

On the Relation between O¤shoring and Supplier Churning

The …nal part of the paper examines the proposition 7, which describes the supplier adding anddropping patterns by …rms that o¤shore. We …rst examine whether a buyer’s decision of o¤shoring
are associated with the likelihood of dropping its existing domestic suppliers. To this end, we
estimate the following speci…cation using our two-year panel data on Japan’s production networks(2005 and 2010):
Dropijt =+4 Impit +1 Sales_Grit +2 log (Sizeit 1 )(16)+Xij;t 1 + fF Eg + "ijt ;where i, j, and t stand for domestic buyer, domestic seller, and year, respectively. Dropijt isa dummy variable, which is equal to 1 if seller j was dropped by buyer i after 2005 and before
2010 (inclusive), 0 otherwise. The variable of interest, 4Impit , is a dummy indicating the …rm’s
switching from no o¤shoring (in 2003-2005) to o¤shoring (in both 2010 and 2011). In other words,

this speci…cation uses a sample of …rms that did not o¤shore in 2005, and aims to gauge the e¤ectof the extensive margin of o¤shoring on the …rms’original domestic suppliers.
To control for buyer-speci…c changes in input demand, we include buyer i’s sales growth between
2005 and 2010, 4Sales_Grit , and (log) …rm size in 2005, log (Sizeit 1 ), measured either by sales

or employment. On the seller’s side, we also control for its initial sales in 2005 and sales growth.
These controls capture situations like suppliers’unexplained drop in performance, in the absenceof any changes on the buyer’s side. If buyer i’s starting to o¤shore input production is associatedwith a higher likelihood of seller dropping, a positive estimate ofis expected. fF Eg includes

four …xed e¤ects: seller and buyer industry and region (prefecture) …xed e¤ects, respectively. The
buyer’s and seller’s industry …xed e¤ects are included to ensure that any increased incidence of
supplier dropping is independent of the trend of structural change in the Japanese economy. The
buyer’s and seller’s region …xed e¤ects control for any agglomeration e¤ects and any region-speci…cbusiness cycles.
To examine whether downstream …rms’ o¤shoring decisions are related to the probability ofadding suppliers, we run the same set of regressions but with the dependent variable replaced by
Addijt , a dummy variable equal to 1 if domestic supplier j was newly added by buyer i after 2005
and before 2010 (inclusive), 0 otherwise. While our theory is relatively silent about the patternof adding suppliers, such …ndings can enhance our understanding of how o¤shoring by buyers canchange the domestic production network.
We …rst examine how o¤shoring by a …rm a¤ects its probability of dropping existing suppliers.

21

Table 11 reports the results of estimating speci…cation (16), using the sample of downstream manufacturing …rms that purchase inputs from domestic suppliers but not foreign suppliers in 2005. All
regressions include sellers’ and buyers’ industry and region (prefecture) …xed e¤ects. In columns(1) and (2), we …nd a positive and marginally signi…cant (at the 10% level) relationship between
a buyer’s o¤shoring participation and the probability of dropping its domestic suppliers. These
results are obtained after controlling for the buyer-seller distance, the initial size (in terms of sales
or employment) of both the buyer and seller, and the growth rate of the buyer and seller’s sales(or employment), in addition to the four …xed e¤ects.
The last two columns in Table 11 reports the results of estimating speci…cation (16), using
Addijt as the dependent variable, which is equal to 1 if a new buyer-supplier relationship was formed
between 2005 and 2010 (inclusive). Column (3) shows a positive and signi…cant relation betweeno¤shoring participation and the buyer’s probability of adding new suppliers, after controlling forthe buyer’s and seller’s industry …xed e¤ect, buyer’s and seller’s region …xed e¤ects, and a hostof controls. This positive correlation is unlikely to be driven by the underlying demand or supply
shocks hitting the buyer as we already control for its sales growth. The robust …ndings about
the strong positive correlation between buyers’o¤shoring and supplier adding, conditional on sales
growth, provides indirect evidence that o¤shoring has a signi…cantly positive e¤ect on the buyer’s
productivity.
In summary, we …nd a marginally signi…cant correlation between buyers’o¤shoring and the likelihood of dropping domestic suppliers. By no means that these results imply causality. In researchin progress, we will repeat the same analysis using instruments for …rms’o¤shoring decisions.

5

Concluding Remarks

In this paper, we study both theoretically and empirically how downstream …rms’o¤shoring decisions lead to the reorganization of the domestic production network. We build a buyer-seller modelthat features supplier heterogeneity in e¢ ciency and distance, as well as intermediate inputs thatvary in the degree of speci…city to the relationship with the buyer. The model predicts that themore productive buyers will source inputs from a larger range of geographic regions, especially for
generic inputs. Inputs that are more relationship-speci…c are less likely to be sourced from distantregions or foreign countries. Starting from these equilibrium sourcing patterns, a decline in o¤shoring costs leads to dropping of the less productive suppliers that are closer to the buyers, which
are to be replaced by the more distant and productive suppliers due to the indirect productivitye¤ect of o¤shoring. Less relationship-speci…c suppliers are more likely to be dropped, despite theirhigher productivity.
Using unique and exhaustive data that cover close to 4 million buyer-seller links in Japan, we …nd
evidence largely supporting the main theoretical predictions. In particular, we …nd that the moreproductive …rms source inputs from more suppliers, from more regions, and from the more distantregions. We also …nd that the more productive …rms are also more likely to o¤shore input produc-22tion. As predicted by the model, distant suppliers are also more productive on average. Above andbeyond this geographic pattern of outsourcing, we also uncover evidence about the sectoral patternof outsourcing, along the dimension of relationship-speci…city of inputs. In particular, …rms are lesslikely to source relationship-speci…c inputs from the more distant regions or from foreign countries.
The negative distance e¤ects are also more pronounced for the more relationship-speci…c inputs.
Preliminary evidence shows that o¤shoring improves …rms’ productivity, which induces supplier
churning, especially on the margin of adding new suppliers.

23

6References
1. Antràs, P., T.C. Fort, and F. Tintelnot (2014), “The Margins of Global Sourcing: Theory
and Evidence from U.S. Firms”, Harvard University manuscript.
2. Antràs, P. and D. Chor (2013) “Organizing the global value chain”, Econometrica, 81 (6),2127-2204.
3. Atalay, E., A. Hortacsu, J. Roberts, and C. Syverson (2011) “Network Structure of Production.” Proceedings of the National Academy of Sciences, 108(13), 5199-5202.
4. Baldwin, R. and J. Harrigan (2011) “Zeros, Quality, and Space: Trade Theory and Trade
Evidence.” American Economic Journal: Microeconomics, 3 (May): 60-88.
5. Banerjee, A. and E. Du‡o (2000) "Reputation E¤ects and the Limits of Contracting: A Studyof the Indian Software Industry," Quarterly Journal of Economics, 115: 989-1017.
6. Bernard A.B., J.B. Jensen, S.J. Redding, P.K. Schott (2010) “Wholesalers and retailers in
US trade.” American Economic Review Papers and Preceedings, 100, 408-413.
7. Bernard, A., A. Moxnes and K.H. Ulltveit-Moe (2014) "Two-sided Heterogeneity and Trade,"
Dartmouth University Working Paper.
8. Bernard, A., A. Moxnes and Y. Saito (2014) "Geography and Firm Performance in theJapanese Production Network," RIETI Working Paper, 14-E-034.
9. Bernard, A., A. Moxnes and Y. Saito (2015) "Production Networks, Geography and Firm
Performance," Dartmouth University Working Paper.

10. Blum, B.S., S. Claro, and I. J. Horstmann (2013). "Occasional and perennial exporters."
Journal of International Economics, 90, no. 1: 65-74.
11. Carvalho, V., M. Nirei, and Y. Saito (2014) "Supply Chain Disruptions: Evidence from theGreat East Japan Earthquake", RIETI Discussion Paper Series.
12. Carvalho, V. and X. Gabaix (2013) "The Great Diversi…cation and Its Undoing," American
Economic Review, 2013, vol. 103(5), 1697-1727
13. Chaney, T. (2014a) "The Network Structure of International Trade", American Economic
Review, 104(11), 3600-3634.
14. Chaney, T. (2014b) "Networks in International Trade", Toulouse School of Economics Working Paper.
15. Eaton, J. and S. Kortum (2002) “Technology, Geography, and Trade”, Econometrica, 70,
1741-1779.

24

16. Ebenstein, A., Harrison, A., McMillan, M., & Phillips, S. (2014). “Estimating the impact oftrade and o¤shoring on American workers using the current population surveys.” Review of
Economics and Statistics, 96(4), 581-595.
17. Feenstra R. (2008) “O¤shoring in the Global Economy”, Ohlin Lectures.
18. Grossman, G.M. and E. Rossi-Hansberg (2008), “Trading Tasks: A Simple Theory of O¤shoring”, American Economic Review, 98, 1978-1997.
19. Grossman, G.M. and E. Rossi-Hansberg (2012), “Task Trade Between Similar Countries”,
Econometrica, 80, 593-629.
20. Hallak, J. and J. Sivadasan (2013) “Product and Process Productivity: Implications for Quality Choice and Conditional Exporter Premia”, Journal of International Economics, 91(1), pp.
53-67.
21. Holmes, T. and John Stevens (2015) An Alternative Theory of the Plant Size Distribution with
Geography and Intra- and International Trade, forthcoming Journal of Political Economy.
22. Hummels, D., R. Jorgensen, J.R. Munch, and C. Xiang (2014) “The Wage E¤ects of O¤shoring: Evidence from Danish Matched Worker-…rm Data.” American Economic Review,
104(6): 1597-1629.
23. Jensen, J. Bradford, and Lori G. Kletzer (2005) “Tradable Services: Understanding the Scopeand Impact of Services O¤shoring.” Brookings Trade Forum 2005: 75-116.
24. Khandelwal, A. (2010) “The Long and Short (of) Quality Ladders.” Review of Economic
Studies, 77 (4), 1450-1476.
25. Lerner, J. (1995) “Venture Capitalists and the Oversight of Private Firms.” Journal of Finance, 50, 1: 301-318.
26. Liker, J.K., and T.Y Choi (2004) "Building Deep Supplier Relationships.” Harvard Business
Review 82(12).
27. Ober…eld, E. (2013) “Business Networks, Production Chains, and Productivity: A Theory of
Input-Output Architecture.” Princeton University, mimeo.
28. Olsen, K. B. (2006) “Productivity impacts of o¤shoring and outsourcing.” OECD, mimeo.
29. Petersen, M.A., and R.G. Rajan (2002) “Does distance still matter? The information revolution in small business lending.” Journal of Finance, 57 (6), 2533–2570.
30. Rauch, J. (1999) "Networks versus markets in international trade." Journal of International
Economics, 48, 7–35.

25

31. Rauch, J. and Trindade, V. (2002) “Ethnic Chinese networks in international trade.”Review
of Economics and Statistics, 84 (1), 116–130.

26

Table 1: Summary Statistics of the Network Data and the Merged Sample
A. Full Sample of the Network Data from Tokyo Shoko Research (TSR)

2005
2010

Nb ObsMean nb of sellers

Median nb ofsellers

3,586,090
4,463,168

4.89
5.47

2
3

B. Restricted TSR Sample (Only buyers and sellers that exist in both 2005 and 2010;
headquarter-subsidiary pairs excluded)

2005
2010

Nb ObsMean nb of sellers

Median nb ofsellers

361,777
458,984

7.06
8.07

3
4

C. Restricted Sample Merged with the Basic Survey

2005
2010

Nb Obs% of pair in
TSR merged

Mean nb of sellers

Median nb ofsellers

149,645
187,676

41.36
40.89

17.88
21.86

8
10

Samples decribed in Panel B and C include buyers and sellers that have at least 10 employees,
respectively.

Figure 2: Distribution of Buyers with Different
Nb of SuppliersTable 2: Characteristics of Downstream Firms (Buyers) in the
Basic Survey
All industries
2005
2010
No. of firms in the BSJBSA
22,93924,892
Nb. of importers
5,344
5,659
Nb. of importers from Asia
4,315
4,786
Fraction of firms that import
0.233
0.227
Fraction of firms that import from Asia
0.188
0.192
Average importer's import intensity(imports/ total purchases)
Aveage firms' shares of imports from Asia
(imports from Asia / total imports)
Manufacturing industries
Nb. of firms in the BSJBSA
Nb. of importers
Nb. of importers from Asia
Fraction of firms that import
Fraction of firms that import from Asia
Average importer's import intensity(imports/ total purchases)
Aveage firms' shares of imports from Asia
(imports from Asia / total imports)
Sample: BSJBSA (2005, 2010)

0.183

0.212

0.795

0.821

11,021
3,270
2,747
0.297
0.249

11,361
3,494
3,082
0.308
0.271

0.163

0.192

0.824

0.846

Table 3: Summary Statistics (Number of Buyers and Sellers)
All mfg.
Existing
Nonbuyers in Importers in importers in
Sample:2005
2005
2003-2005
Panel A: Number of buyers (2005)
8,404
2,117
5,611

Import starters
between 20052010

Nonimporters
2005-2010

Continuous
importers
2005-2010

341

4,179

1,436

Panel B: Number of sellers per buyer (2005)
Mean
19.33
34.78
13.40
Median
8
11
7
Min.
1
1
1
Max.
3,552
3,004
3,552

20.67
9
1
1,056

13.53
7
1
3,552

38.34
12
1
3,004

Panel C: Number of sellers' prefectures per buyer (2005)
Mean
4.84
6.79
4.01
Median
4
5
3
Min.
1
1
1
Max.
47
47
46

5.25
4
1
38

3.99
3
1
46

7.00
5
1
47

Import starters
between 20052010

Nonimporters
2005-2010

Continuous
importers
2005-2010

346

4,320

1,444

Panel E: Number of sellers per buyer (2010)
Mean
24.04
41.68
17.55
Median
10
15
10
Min.
1
1
1
Max.
3,629
2,795
3,629

27.92
12
1
1,353

16.85
9
1
3,629

47.09
16
1
2,795

Panel F: Number of sellers' prefectures per buyer (2010)
Mean
5.75
7.89
4.91
Median
4
6
4
Min.
1
1
1
Max.
46
46
46

6.53
5
1
40

4.75
4
1
46

8.30
6
1
46

All mfg.
Existing
Nonbuyers in Importers in importers in
Sample:2010
2010
2008-2010
Panel D: Number of buyers (2010)
8,605
2,021
4,674

Note: Sellers whose employment size is less than 10 persons are excluded. Sellers who have a capital relationship (parents,
affiliates, or mutually owned) with their buyers are excluded. Only manufacturing buyers are included.

Figure 3. The Relationship between Distance and Supplier
Characteristics(A) Average seller labor productivity of sellers bybuyer-seller distance
0.3
0.25
0.2
0.15
0.1
0.05
0
-0.05 0.1

0.3

0.5

0.7

0.9

Percentile of ln(distance)
note: industry fixed effects are partialed out.

(B) Average fraction of sellers in differentiatedindustries by buyer-seller distance

1
0.95
0.9
0.85
0.8
0.75
0.1

0.3

0.5

0.7

0.9

Percentile of ln(distance)(C) Average intemediation index by buyer-seller
distance

0.15

0.145

0.14

0.135
0.1

0.3

0.5

0.7

Percentile of ln(distance)

Note: The 2010 network sample is used.

0.9

Table 4: Buyer's Productivity and the Number of Domestic Sourcing Prefectures
Dep Var: ln(# sellers' prefectures)buyer(1)
Sample

(2)

OP

Index

0.447***(0.056)

0.532***(0.062)

0.394***(0.021)

Buyer's Ind FE
Buyer's Prefecture FE

yes
yes

yes
yes

R_sq
Nb of Obs

.14
8159

.144
7631

Measure of Buyer's TFPln(TFP)buyer

(3)

(4)

(5)

(6)

2005

(7)

(8)

2010

Sales/Emp VA/Emp

OP

Index

Sales/Emp VA/Emp

0.322***(0.017)0.149***(0.033)0.356***(0.047)0.365***(0.022)0.283***(0.020)

yes
yes

yes
yes

yes
yes

yes
yes

yes
yes

yes
yes

.23
8186

.183
8167

.148
7059

.151
7022

.246
7089

.198
7047

Note: The regression sample includes manufacturing buyers only and domestic suppliers that are either manufacturing or non-manufacturing. The unit ofobservation is at the buyer level. All regressions include buyer's industry and buyer's prefecture fixed effects. Columns (1) through (4) use the 2005 crosssection sample, while columns (5) through (8) use the one from 2010. Standard errors, clustered at the buyer's industry level, are reported in parentheses. ***,**, * indicate significance at the 1%, 5%, and 10% levels, respectively.

Table 5: Relationship between Distance and the Number of Sellers Across Domestic
Regions
Dependent Variable: ln(# sellers)buyer, seller's pref
(1)
(2)
(3)
(4)
Sample2005
2010
ln(dist)buyer,seller's pref

-0.0904***(0.001)

Buyers' Industry FE
Buyers' Prefecture FE
Sellers' Prefecture FE
Buyer's FE

yes
yes
yes

R_sq
Nb of Obs

.164
121476-0.150***(0.001)

yes
yes
.555
121476-0.0999***(0.001)
yes
yes
yes

.181
149588-0.157***(0.001)

yes
yes
.548
149588

Note: The regression sample includes manufacturing buyers only and domestic suppliers that are either manufacturing
or non-manufacturing. The unit of observation is at the buyer-(seller's)prefecture level. All regressions include sellers'
prefecture fixed effects. Standard errors, clustered at the buyer level, are reported in parentheses. ***, **, * indicate
significance at the 1%, 5%, and 10% levels, respectively.

Table 6: Distance, Nb of Sellers, and Relationship-Specificity of Inputs
Dependent Variable: ln(# sellers)buyer, seller's pref, sector(1)(2)
Sample2005
Measure of RS
BJRS
Rauch
ln(dist)buyer,seller's pref
-0.0285*** -0.0187***(0.002)(0.001)ln(dist)buyer,seller's pref x RSseller's ind

Buyer's FE
Sellers' Prefecture FE
Sellers' Industry FE
R_sq
Nb of Obs

(3)

(4)
2010

BJRS
Rauch
-0.0305*** -0.0207***(0.002)(0.001)

0.0424***(0.006)-0.005***(0.002)0.0641***(0.006)-0.00146(0.001)

yes
yes
yes

yes
yes
yes

yes
yes
yes

yes
yes
yes

.275
102874.275
103115.24164373.257
133610Note: The regression sample includes manufacturing buyers only and domestic suppliers that are either
manufacturing or non-manufacturing. The unit of observation is at the buyer-(seller's)prefecture-sector level.
All regressions include sellers' industry, sellers' prefecture, and buyer fixed effects. Standard errors, clustered at
the buyer level, are reported in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels,
respectively.

Table 7: Distance and Seller's Productivity
Dependent Var: ln(Sales/ Emp)buyer, seller
2005
2010
Sampleln(dist)buyer,seller

Sellers' Industry FE
Sellers' Prefecture FE
Buyer's FE
R_sq
Nb of Obs

0.0504***(0.001)

0.0517***(0.001)

yes
yes
yes

yes
yes
yes

.682
341494.67433077

Note: The regression sample includes manufacturing buyers only and domestic
suppliers that are either manufacturing or non-manufacturing. The unit ofobservation is at the buyer-seller level. All regressions include sellers'industry, sellers' prefecture, and buyer fixed effects. Standard errors, clusteredat the buyer level, are reported in parentheses. ***, **, * indicate significanceat the 1%, 5%, and 10% levels, respectively.

Table 8: Buyer's Productivity and the Likelihood of Offshoring
Dep Var: Dummy for Buyer's Starting to Offshore between 2005 and 2010
(1)
(2)
(3)
(4)
Sample
2010
OP
Index
Sales/Emp
VA/EmpMeasure of Buyer's TFPln(TFP)buyer,2005

0.00856(0.021)

0.0567***(0.021)0.0343***(0.008)0.0255***(0.009)Buyer's Ind FE
Buyer's Prefecture FE

yes
yes

yes
yes

yes
yes

yes
yes

R_sq
Nb of Obs

.0794
4506

.0873
4172

.0854
4522

.0822
4509

Note: The regression sample includes manufacturing buyers only and domestic suppliers that areeither manufacturing or non-manufacturing. The unit of observation is at the buyer level. All
regressions include buyer's industry and buyer's prefecture fixed effects. Standard errors, clusteredat the buyer's industry level, are reported in parentheses. ***, **, * indicate significance at the 1%,5%, and 10% levels, respectively.

Table 9: Relationship-specificity of Inputs and Likelihood of Offshoring
Dep Var: import dummybuyer, sector
BJRS Intermediation Index
Measure of RS

Rauch Index

RSseller's ind

0.264***(0.018)-0.0550***(0.008)

Buyer's FE

yes

yes

R_sq
Nb of Obs

.43
75786

.441
75786

Note: The regression sample includes manufacturing buyers only and domestic suppliers that are either
manufacturing or non-manufacturing. The unit of observation is at the buyer-(seller's)sector level. Import data
are only available at the 2-digit level. All regressions include buyer fixed effects. Standard errors, clustered at
the buyer level, are reported in parentheses. ***, **, * indicate significance at the 1%, 5%, and 10% levels,
respectively.

Dep Var

Table 10: Buyer's Offshoring and Changes in the Pattern of Domestic Outsourcing
Δln(sales)buyer, 2005-2010 Δln(TFP)buyer, 2005-2010 Δln(# pref)buyer, 2005-10 ΔAvg ln(dist)buyer, 2005(1)
(2)
(3)
(4)
(5)
(6)

Offshore Dummy

0.0636***(0.022)0.0685*** 0.0354*(0.019)(0.022)0.0328*(0.018)0.0286(0.024)Δln(sales)buyer, 2005-2010

0.0202(0.024)-0.0120(0.034)0.117***(0.016)-0.0108(0.034)-0.0173(0.039)Buyer's Ind FE
Buyer's Prefecture FE

yes

yes
yes

yes

yes
yes

yes
yes

yes
yes

yes
yes

yes
yes

R_sq
Nb of Obs

.0992
4522

.112
4522

0.168
4506

0.179
4506

0.098
4386

0.12
4386

0.077
4386

0.077
4386

Note: The regression sample includes manufacturing buyers only and domestic suppliers that are either manufacturing or
non-manufacturing. The unit of observation is at the buyer level. All regressions include buyer's industry and buyer's
prefecture fixed effects. Standard errors, clustered at the buyer's industry level, are reported in parentheses. ***, **, *indicate significance at the 1%, 5%, and 10% levels, respectively.

Table 11: Offshoring and Supplier Churning
Dep Var:Drop Dummy

Add Dummy

(1)

(2)

(3)

(4)

d(Imp Dummy)buyer

0.0131(0.008)0.0150*(0.009)0.0284**(0.013)0.0229**(0.012)ln(sales)buyer,t-1

0.000567(0.002)-0.0125***(0.002)dln(sales)buyer,t-0.0277***(0.010)0.0801***(0.010)ln(sales)seller,t-1

0.0124***(0.001)0.00907***(0.002)dln(sales)seller,t-0.0279***(0.005)0.0580***(0.005)ln(distance)buyer-seller

0.00801***(0.001)0.00818***(0.001)0.0187***(0.002)0.0193***(0.002)ln(emp)buyer,t-1

0.000329(0.002)-0.0168***(0.002)dln(emp)buyer,t-0.0338***(0.011)

0.0978***(0.013)ln(emp)seller,t

0.0155***(0.002)0.0105***(0.002)dln(emp)seller,t-0.0327***(0.005)
yes
yes
yes
yes
.0459
53869

0.0538***(0.005)
yes
yes
yes
yes
.0647
62060

Buyers' Industry FE
Buyers' Prefecture FE
Sellers' Industry FE
Sellers' Prefecture FE
R_sq
Nb of Obs

yes
yes
yes
yes
.0462
53869

yes
yes
yes
yes
.0661
62060

The sample includes only manufacturing firms that did not import in 2003-2005. The unit of observation is at
the buyer-seller level. Standard errors (clustered at the buyer level) in parentheses. ***, **, * indicate
significance at the 1%, 5%, and 10% levels, respectively.

Appendix
Table A1: Summary Statistics of the Original TSR Data and the Merged Sample

Agriculture and forestry
Fishing
MiningConstruction
Manufacturing
Electricity, gas, and water supply
Information services
Transportation
Wholesale and retail trade
Finance and insutrance
Housing and real estate
Research
Hotels and accomodation
Living service
Education
Medical services
Miscellaneous services
Services, not elsewhere classified
Public servies
Not available

Full TSR sample (A in Table 1)
2005
2010
Nb ObsNb of Sellers
Nb ObsNb of Sellers
mean
medianmean
median8,888
2.77
2
13,476
2.85
2
2,668
3.68
2
2,708
3.48
2
5,762
5.21
3
6,176
5.72
3
1,013,087
5.27
3
1,242,916
5.46
3
842,0347.24
31,002,775
7.57
3
13,34932.48
414,54827.87
456,1815.10
291,8226.03
2106,0344.65
3152,774
5.53
3959,7205.11
31,159,663
5.33
3
29,6757.48
230,4926.12
250,687
3.86
2
117,443
4.83
249,5213.61
291,4594.46
237,1033.86
253,1224.10
248,8244.24
260,287
4.41
2
9,068
3.87
2
18,5305.59
219,6603.07
245,0963.88
325,9676.20
334,2527.31
395,9503.61
2117,521
3.70
2
8.50
5.5
6
3.00
3
34
211,8782.00
1
208,102
3.41
2

Table A2: Summary Statistics of the Original TSR Data and the Merged Sample
Restricted TSR sample (B inTable 1)

2005
Food products and beverages
TextilesLumber and wood products
Pulp, paper and paper products
Printing
Chemical products
Petroleum and coal products
Plastic products
Rubber products
Ceramic, stone and clay products
Iron and steel
Non-ferrous metals
Fabricated metal products
Machinery
Electrical machinery and appliances
Computer and electronic equipment
Electronic parts and devices
Transportation equipment
Miscellaneous mfg. industries
2010
Food products and beverages
TextilesLumber and wood products
Pulp, paper and paper products
Printing
Chemical products
Petroleum and coal products
Plastic products
Rubber products
Ceramic, stone and clay products
Iron and steel
Non-ferrous metals
Fabricated metal products
Machinery
Electrical machinery and appliances
Computer and electronic equipment
Electronic parts and devices
Transportation equipment
Miscellaneous mfg. industries

Nb ObsNb of Sellers
mean
median41,204
12,038
10,464
12,235
13,90522,501
1,440
15,326
4,994
15,997
12,418
7,755
30,66661,551
33,674
12,785
11,12431,735
9,965

5.84
4.49
4.84
6.58
4.88
10.39
9.86
5.62
7.80
5.69
9.82
8.93
4.70
7.33
11.71
18.75
7.67
14.56
5.27
mean
6.65
5.00
5.49
7.71
5.49
12.23
11.12
6.53
8.76
6.54
11.16
10.23
5.48
8.68
12.62
19.82
9.02
16.99
5.85

50,37815,435
12,985
15,824
18,241
28,583
1,813
19,9196,255
20,265
15,482
9,972
40,307
81,401
40,385
14,546
14,37540,361
12,457

4
3
3
4
3
5
5
3
3
3
4
4
3
4
4
5
4
4
3
median
4
3
3
4
3
6
5
4
4
4
5
4
4
4
5
5
4
5
3

Sample Merged the Basic Survey (C inTable 1)
% pairmergedNb ObsNb of Sellers
mean median
14,916
2,357
2,700
4,740
5,276
10,402
1,105
5,013
1,231
3,729
5,926
3,436
7,085
27,234
10,091
12,964
5,952
21,913
3,575

36.20
19.58
25.80
38.74
37.94
46.23
76.74
32.71
24.65
23.31
47.72
44.31
23.10
44.25
29.97
101.40
53.51
69.05
35.88

17,874
2,999
3,336
5,808
6,857
13,331
1,332
6,709
1,671
5,187
7,472
4,397
8,973
34,76912,844
13,775
7,637
28,106
4,599

35.48
19.43
25.69
36.70
37.59
46.64
73.47
33.68
26.71
25.60
48.26
44.09
22.26
42.71
31.80
94.70
53.13
69.64
36.92

14.43
7.06
15.00
18.23
13.99
18.00
32.50
11.24
12.43
12.03
21.24
17.53
10.75
20.62
19.11
44.70
14.10
27.81
15.15
mean
16.86
8.69
18.33
22.17
17.06
22.91
39.18
14.55
16.38
16.52
25.94
21.99
13.37
25.93
23.79
45.61
17.28
34.61
18.70

8
5.5
8
8
6
9
9.5
7
8
7
9
7
7
8
7
9
7
10
7
median
10
7
10
9.5
8
11
14
8
8
9
11
10
9
11
10
11
9
12
8.5