DP2019-19

Infrastructure Bottlenecks andDual-Channel Exporters*
Xiao FENG
Yongjin WANG
Laixun ZHAO

Revised October 15, 2019

* The Discussion Papers are a series of research papers in their draft form, circulated to encourage
discussion and comment. Citation and use of such a paper should take account of its provisional character.
In some cases, a written consent of the author may be required.

Infrastructure Bottlenecks and Dual-Channel Exporters
By
Xiao Fenga Yongjin WangaaNankai University&b

Laixun Zhaob

Kobe University

This version: Oct. 15, 2019
Abstract: This paper investigates how infrastructure bottlenecks affect the firm’s
export mode. We find that under such bottlenecks, the most efficient firms become theso-called “dual-channel exporters”, which export only a fraction of their products
directly, with the remaining products exported via intermediaries. Using linked annual
survey of industrial production and transaction-level customs datasets in China, wedocument that a significant share of firms are dual-channel exporters, which are moreproductive than the direct and indirect exporters. Our quantitative model suggests thatthe elimination of exporting capacity constraint arising from infrastructure bottlenecks
leads to substantial aggregate productivity growth via improved resource reallocation.

Keywords: Infrastructure Bottlenecks; Dual-Channel Exporters; Productivity;
Capacity Constraint; Resource Misallocation
JEL Classification: F10, O18
Please address correspondence to:Feng: School of Economics, Nankai University, Tianjin 300071, China,
fengxiaonku@163.com;
Wang (Co-corresponding author): School of Economics, Nankai University, Tianjin 300071,
China, yjw@nankai.edu.cn;
Zhao (Co-Corresponding author): Research Institute for Economics & Business, Kobe
University, Kobe 657-8501, Japan, Zhao@rieb.kobe-u.ac.jp.

1. Introduction
Infrastructure bottlenecks, including the lack of large carriers, ports, railways andhighways, warehouses and quarantine testing facilities etc., are among the largest
inhibitors of exports in many developing countries, like Bangladesh, China and India.1
As such, firms in these countries face severe export capacity constraint. Also,intermediaries play a very important role for exports in developing economies (Olney,2015). However, the existing literature on infrastructure has focused exclusively on
trade flows and trade volume, and how infrastructure bottlenecks impact the mode ofexports is largely unknown. The issue is important because how firms export can impact
their profits and gains from trade at the national level via several channels. On the costside, exporting through intermediaries requires a commission (sometimes as high as 10%of profits) and often kickbacks to avoid additional red tape, 2 though some fixedexporting cost may be saved; on the benefit side, it also prevents a firm from reaping
the benefits of direct exporting, such as, building brand reputation, learning via
exporting and feedbacks (Bai et al., 2017).

1 For
Bangladesh, see https://www.export.gov/article?id=Bangladesh-Architectural-Construction-andEngineering-Services, and India, see https://www.dailypioneer.com/2017/columnists/removing-local-oldbottlenecks.html. For China, the lack of ocean carriers is one of the most severe constraints facing exporters. For
example, exporting automobiles requires roll-on ships, but there is not even one roll-on ship in China, and hence
foreign intermediaries are needed. See http://www.grand-freight.com/cn/news/view_114.html. In addition, there is alack of port infrastructure: https://www.dailypioneer.com/2017/columnists/removing-local-old-bottlenecks.html.
Furthermore, infrastructure bottlenecks also exist in the U.S., arguably the most developed country. Take crude
oil export as an example, only the LOOP (Louisiana Offshore Oil Port) can fully load VLCCs (very large crude
carriers) as things stand today, while the rest of America’s oil export terminals can only partially load a VLCC.
https://www.eia.gov/todayinenergy/detail.php?id=36232. Similarly, grain exports in the U.S. are also severely
limited by such bottlenecks, which are costing farmers, shippers and ultimately consumers millions of dollars a year.
https://www.seattletimes.com/nation-world/us-grain-exports-limited-by-infrastructure-bottlenecks/.
2 https://www.chinalawblog.com/2010/06/avoiding_kickbacks_in_china_just_say_no.html.
2

The present paper moves forward in this direction by examining the impacts ofinfrastructure bottlenecks on trade, resource misallocation, aggregate productivity andgains from trade. We find that infrastructure bottlenecks can have important
implications which are quite different from those of tariff and non-tariff barriers.
Specifically, we first develop a model of heterogeneous firms whose exporting
capacities are constrained due to infrastructure bottlenecks, and show theoretically thatexporters can be categorized into three types according to productivity: the least
productive ones become indirect exporters which export through intermediaries, themost productive ones export a fraction of their products by themselves and theremaining part through intermediaries, thus becoming “dual-channel exporters”, andthose with intermediate productivity are direct exporters which export on their own.
Intuitively, in the presence of infrastructure bottlenecks, such as inadequate port
capacity and unavailability of large carriers, each firm’s exporting quantity is severely
constrained. Further, the quality of “soft infrastructure”, including red tape associatedwith export license, export rebate approval, customs clearing, etc., can exacerbate thenegative impact of the aforementioned “hard infrastructure”. For instance, in China,
railway carriage and shipping space are controlled by a few State-owned enterprises(SOEs) and managed inefficiently,3 leading to bribes and corruption and resulting in“double bottlenecks” (i.e., in addition to hard infrastructure bottlenecks) for firms andbusinesses. Often, the most efficient firms (which usually export larger quantities) have

3 For Railway carriage, see: http://finance.sina.com.cn/chanjing/gsnews/20141214/141121072032.shtml. For
ocean Shipping, see : http://m.sohu.com/n/408865254/?_once_=000022_shareback_wechatfriends_bdbo3

to export through intermediaries above a certain limit, and thus they become the socalled “dual-channel exporters (DCX hereafter).”
Next, by linking the Chinese customs trade and annual survey of industrial firms’
datasets, we document that a significant share of Chinese exporters are DCX firms.
Further, consistent with our theoretical model, in our empirical analysis we find that the
DCX firms are larger and more productive than the direct and indirect exporters, andthat firms in regions with poorer port infrastructure are more likely to become DCX.
Lastly, for the counter-factual analysis, our quantitative exercises suggest thatremoving these capacity constraints can generate substantial gains in export volume
and productivity for developing countries like China, as resources are reallocated fromthe less productive firms to the most productive ones.
Our study is closely related to the literature on the role of infrastructure ininternational trade. Duranton et al. (2014) and Duranton (2015) estimate the effects ofinterstate highways on regional trade in the U.S. and Colombia, respectively. Martincus
and Blyde (2013) exploit a natural experiment involving destruction of key
infrastructure due to earthquakes in Chile, and find a significant negative impact of thediminished infrastructure on firm exports. While these studies focus on the impact on
trade flows, we are interested in how infrastructure bottlenecks shape the firm’s choiceof export mode and resource misallocation. In this sense, our study is related to Tombe
and Zhu (2018), who examine the impact of China’s internal labor migration on
resource allocation and productivity growth.

4This paper also complements the studies that emphasize the role of intermediaries
in facilitating trade (Bernard et al., 2010; Ahn et al., 2011; Bernard et al., 2015). Most
recently, Bernard et al. (2018) study carry-along trade (CAT): a significant share of theexports from the Belgian manufacturers are not produced by the firm. They use demandcomplementarities to rationalize their empirical findings. In contrast, the present paper
uncovers a novel export mode, i.e., the dual-channel exporters, which export someproducts directly and others through intermediaries.
Finally, the paper is related to the literature on misallocations in developing
countries (e.g., Hsieh and Klenow (2009) and Restuccia and Rogerson (2008)). In
particular, our paper is closely related to Khandelwal, Schott and Wei (2013), who
examine Chinese textile and clothing exports before and after the elimination of export
quotas, and find that export quotas are managed inefficiently in China in the sense thatthe most productive firms are prevented from entering the export market, substantiallyreducing aggregate productivity. Our results further suggest that infrastructure
bottlenecks constrain a firm’s ability to export directly, leading to resource
misallocation by shifting away resources from the most efficient exporters to the lessproductive ones and causing welfare losses.
The remaining part of the paper proceeds as follows. Section 2 sets up a theoreticalframework; Section 3 describes the data and classification of export modes; Section 4reports some stylized facts regarding export mode, and conducts empirical analysis and5robustness checks; Section 5 extends the theoretical model outlined in Section 2 andundertakes counterfactual exercises; and finally, Section 6 concludes.

2. Model Setup
2.1 Preference
Consumer preferences are represented by the following CES utility function:
U  [

s 1

w 

s

q(w) s d w ]s 1

where q(w) denotes the consumption for variety w , and s  1 is the elasticity of
substitution between any two varieties. The set  measures the mass of available
varieties. Utility maximization yields the demand function:q(w)  Bp(w)s , B  EP s 1

where P  [ 

(1)

1

w 

p(w)1sd w ]1s is the price index, and B measures the real market

size. The associated revenue for the producer of variety w is given as
R(w)  q(w)11/s B1/s  Bp(w)1s(2)

2.2 Production
The timing for the firms is as follows. In stage 1, a mass of firms Me enters, pay
a fixed entry cost F and then receive information about their innate ability j , whichis assumed to be Pareto distributed with a cumulative probability function of:G(j)  1  (j /b)k ; j  b, k  1 .

In stage 2, given its type, each firm decides whether or not to produce by paying
the fixed production cost fd . Then, if a firm decides to export, it must choose the modesof export: either to export directly by paying the fixed cost fD , or indirectly by paying
6the fixed cost fI  fD , because exporting directly typically entails huge sunk cost for
setting up its own logistic systems including warehousing, transportation, packaging,customs clearance, freight and logistics hubs. Both modes require an iceberg trade cost:t w for direct exporting and gtw for exporting through intermediaries, where g  1 .

Here as in Ahn et al. (2011), exporting through intermediaries entails a higher variable
cost as the intermediaries incur an additional per-unit cost to aggregate orders acrossclients and prepare the variety for the foreign market. Also, exporting throughintermediaries in imperfectly competitive markets could lead to “double
marginalization,” whose end result is similar to assuming a higher variable cost.
However, in the presence of infrastructure bottlenecks, a firm may be forced to useintermediaries to export even when it is capable of overcoming the fixed cost associatedwith direct exporting. This is more so for developing countries whose exporting
infrastructure in general and port-infrastructure in particular are inadequate whenconnecting with foreign countries. The presence of such infrastructure bottlenecks
implies not only a higher iceberg type cost, but also an exporting capacity constraintthat limits a country’s exporting quantity and leads to a convex trade cost. In this sense,
infrastructure bottlenecks play a similar role as exporting quotas.
The situation could get worse in the presence of poor soft infrastructure whenexporting opportunity is not based on efficiency but other considerations. First, as is
illustrated in Khandelwal, Schott and Wei (2013), exporting quotas in China aremanaged by inefficient institutions, e.g., the exporting license allocation is not basedon productivity. Second, the allocation of transport opportunities is also managed
7

inefficiently in China.4 As mentioned in the Introduction, because transportation is a
scarce resource and is monopolized either by a few SOEs or directly controlled by thegovernment, only a fraction of the connected firms (through “guanxi”) can transport
their products using the railway system and ports, leading to bribes and corruption andresulting in “double bottlenecks”.
Given such “double bottlenecks”, we assume that each firm is endowed with afixed transportation capacity q such that if it exports q  q , the above-capacity
quantity ( qx  q ) must be exported through intermediaries. Hence, q can also beinterpreted as the inverse of the infrastructure bottleneck: less bottleneck, a higher q .
Then the variable costs of exporting associated with each mode are (see also Table 1):
 w
 gt q x
 jw
VC x (j, q x )   tq x
 j
 w
 t q  g w t (q x  q )j jIndirect exporters
Direct exporters

(3)

DCX

where qx is the quantity of exports that reach the foreign consumers, t  1 is theiceberg trade cost of direct export such that for each unit arriving at the foreign country
t units must be shipped, and gt is the iceberg trade cost of indirect export.

Table 1. Export mode and trade costs
Export mode
Indirect

variable costDirectwt

Dual-channel

wfD

wt

for q  q ;wgt for q  q

wfD

4

wgt

Fixed costwfI

A typical example is railway transport which is monopolized by the Ministry of Railways and has been in astate of shortage for a long time, which creates conditions for corruption. For example, the rent of transporting 100
million tons of coal from Ordos to Tianjin port is 25.5 billion Yuan. The coal dealers go as far as using 20 billion
Yuan for bribes (above the regulated rail transport cost) to get the train and transport the coal to Tianjin. As a result,they keep only 5.5 billion as profits. See: http://business.sohu.com/20120611/n345255649.shtml.
8Note: g  1 and fI  fD .

Finally in stage 3, firms choose the quantity to maximize profits, and consumption
takes place afterwards. We solve this problem by backward induction.
2.3 Profits
If production is for domestic sales, the profit function can be written as
1

pH (j)  max B s q

1

1
w
s 
q  wfdjqProfit maximization enables us to rewrite:
pH (j) 1
s w 1 sB()
 wfd
s s 1j(4a)

As usual, there exists a productivity cutoff jH* satisfying pH (jH* )  0 , such that a firmwith productivity below which exits; equivalently,1
s w 1s
R(jH* )  wfd , R(jH* )  B()
ss  1 jH*

Next, for an indirect exporter, the profit is given by
1

pI (j)  max B s q

1

q

1
gw t
s 
q  wfIj

1s g w t 1 s
B[(
)
]
 wfI
s
s 1 j(4b)

Finally, for a direct exporter, the profit is1

pD (j)  max B s q
q

1

1
w
s 
tq  wfDj

1
s w 1 s
B[(
) t]
 wfD
s
s 1 j(4c)

2.4 Sorting of firms
2.4.1 Benchmark: no export capacity constraint
We start our analysis from the benchmark of no exporting capacity constraint. A
firm chooses whether to export directly or via intermediaries. Then, there exists two

9

productivity cutoffs jI* and jD* , such that firms with j  [jI*, jD* ] export via
intermediaries, while more efficient ones with j  [jD* , ) export directly, where
1
s
g
B[(
) w t ]1s  wfIss  1 jI*
1
s w t 1 s
B[()
] (1  g 1s )  w ( fD  fI )ss  1 jD*

(5)

Combing eqs. (4a)-(4c) and (5), we obtainj*f( I )s 1  (gt )s 1 I ,
*
fd
jH

fD
1
*
jD s 1
fI
( )

jI*
g s 1  1

f

We assume t s 1 I  1 and
fd

fD
fI

(6)

 g s 1 ; that is, the selling cost is higher in theforeign market than in the domestic market, and the fixed cost is higher for directexporting than indirect exporting. Then, it is straightforward that the most efficientfirms choose to export directly, firms of intermediate productivity export throughintermediaries, while firms with lower productivity sell domestically only.
2.4.2 Export capacity constraint
Next, on top of the benchmark, we examine how infrastructure bottlenecks distorta firm’s export mode. Due to the infrastructure bottlenecks, each firm can only export
directly its own product up to an upper bound q . Hence, the most efficient firms haveto export its above-capacity quantity through intermediaries, and become the “dualchannel exporters”.
The profit function of a typical DCX firm is given by1s 1

pDCX  max B s (q  q 2 ) s 
q2

w gt
wt
q 
q  fD , s.t. q2  0
j 2
j

10

(7)

Profit maximization gives:
pDCX 

(g  1)w t
1 w gt 1 s
B()
q  fD
s
rj
j

(7’)

The export capacity constraint leads to a “jump” in the firm’s marginal cost curve which
occurs at the constrained output q . Specifically, for a firm with productivity j themarginal cost equals

wtj

if q  q , and equals

gw tjif otherwise.

Figure 1. Output choice of DCX firmsMR

MC (jC* )*MC (jDC)qq

Figure 1 illustrates how the firm decides its optimal export quantity in the presenceof the export capacity constraint, in which the horizontal axis represents the firm’s
output and the vertical axis represents the marginal revenue and marginal cost. There**exist two cutoff productivities jC* and jDC, with jC*  jDC, such that the optimal

outputs for both the firm with j  jC* and MC (j) and MC (j) gw t
jare just equal to q , where

11

wt
j

*
and the firm with j  jDC

B(

*
rjDC
)s  qw gt*
 gjC*and jDC(8)*] produce q and export directly. For
As a result, firms with productivity j  [jC* , jDC*, ) , the optimal output is larger than q even at the higher
those with j  [jDC

marginal cost, so they become DCX.
* jD* , satisfying
As is evident, direct exporters exist only if jDCj*t(1  g1s )gsq*( DC )s , q* 
q(s  1)(fD  fI )
jD*
jD*

(9)

We restrict parameters such that both the direct exporters and DCX exist. Then we have*, )
Proposition 1 (firm sorting): The most efficient firms with productivity j  (jDC*
) export directly, those withare the dual-channel exporters, those with j  (jD* , jDCj  (jI* , jD* ) export via intermediaries, and the least productive firms with productivityj  jH* exit the export market.
*
Further, from eq. (8), one sees that jDC
is increasing in the export capacity

constraint q . Hence we obtain:
Proposition 2 (share of DCX): The share of the dual-channel exporters is increasingin the infrastructure bottlenecks.
2.5 General equilibrium
Free entry requires the fixed entry cost to equal the expected value of entry,*(i) if jI*  jD*  jC*  jDC, there are three types of exporters: the indirectexporters, direct exporters and DCX, and the free entry condition is given by

12



jD*

jH
*
jDC

jI

jC*

F   * pH (j)dG(j)   * pI (j)dG(j)   * pD (j)dG (j)
 *

jC

jD



pD (q , j)dG(j)   * pDC (j)dG(j)

(10a)

jDC

*
*
(ii) if jI*  jC*  jD*  jDC
or jC*  jI*  jD*  jDC, again there are three typesof exporters with the direct exporters exporting the constrained quantity, and the freeentry condition is given by


jD*

jH
*
jDC

jI

F   * pH (j)dG (j)   * pI (j)dG(j)
 *

jD



pD (q , j)dG(j)   * pDC (j)dG (j)

(10b)

jDC

*
*
*
(iii) if jI*  jC*  jDC
 jD* , jC*  jI*  jDC
 jD* or jC*  jDC
 jI*  jD* ,then there are only two types of exporters: the indirect exporters and DCX, and the freeentry condition is given by


jD*



jH

jI

jD

F   * pH (j)dG(j)   * pI (j)dG(j)   * pDC (j)dG(j)(10c)

where the profit functions are specified in eqs. (4a), (4b), (4c) and (7’), respectively,1s 1

and pD (q , j)  B s q s wt
q  wfD .
j

3. Data and measurement of variables
3.1 Data sets
We exploit two main datasets to identify the firm’s export modes: the Annual
Survey of Industrial Firms (ASIF) complied by the National Bureau of Statistics (NBS)
of China and the transaction level trade data obtained from China’s General
Administration of Customs (CGAC).
3.1.1 The production dataset

13

The ASIF dataset contains production data of Chinese manufacturing firms from1998 to 2007. All SOEs and “above-scale” non-SOEs with annual sales exceeding
RMB 5 million are included in the dataset. Following Cai and Liu (2009), we clean thesample and omit outliers by using the following criteria. First, observations missing key
financial variables (such as total assets, net value of fixed assets, sales and gross valueof the firm’s output and productivity) are excluded. Second, we drop firms with fewer
than eight workers as they fall under a different legal regime, following Brandt et al.
(2012). Third, observations with exports exceeding total sales or with total asset lower
than net value of fixed assets are also dropped. To deal with changes in the Chinese
Industry Classification (CIC) codes in 2003, we merge some industries to obtain aconsistent classification over the entire sample period as in Brandt et al. (2012).
3.1.2 The transaction level trade data
The second dataset we use is the extremely disaggregated product-level tradetransaction data obtained from CGAC. It records a variety of information for eachtrading firm’s product list, including price, quantity and value at the HS 8-digit level.
The CGAC dataset also reports if a firm undertakes processing trade or ordinary tradeat the transaction level. We exclude processing-trade firms from our analysis, and focus
on only the observations of ordinary exporters, as processing firms in China do not needto search for buyers and also receive special treatment on tariffs and customs clearing.
Both the NBS and the CGAC datasets provide firm-level export information,
however, the latter only records information on the firm’s direct exports, and firms

14which export through intermediaries report positive export values in the NBS dataset
but are not recorded in the CGAC dataset. Combining the NBS and the CGAC datasets
together allows us to compute each firm’s share of direct export and that throughintermediaries. We therefore combine the NBS and the CGAC datasets to identify eachfirm’s export mode (see Section 3.1.3).
Following Yu (2015), we adopt two methods to match the two datasets. First, we
identify each firm’s Chinese name and year. That is, if a firm has an exact Chinese namein both datasets in a particular year, it should be the same firm. Second, to increase thenumber of qualified matching firms, we use another matching technique to serve as asupplement; namely, we rely on two other common variables to identify the firms:
postal code and the last seven digits of the firm’s phone number. As a result, we obtain
310931 matched firms which are about 44.52% of the exporters and account for 81.40%
of total export value in the firm-level production data, and they are 61.74% of China's
total exports during 2000–2006.
3.1.3 Classification of exporting modes (see Tables 1 and 2)
A firm can export either directly by itself or indirectly through trading
companies/intermediaries. We classify a firm as a dual-channel exporter (DCX) if itsatisfies the following criteria: first, it reports positive export values in both the NBS
and CGAC datasets; second, the value of exports is much larger in the NBS dataset thanin the CGAC dataset such that export_custom < export_NBS *(1-η); a firm is classified
as an indirect exporter if it reports a positive export in the NBS data but is not

15

documented in the CGAC dataset; a firm is tagged as a direct exporter if it is neither adual-channel exporter nor an indirect exporter. We have tried alternative values of η,and the results are similar, so we set η=0.1 in the main text.
One might be concerned that the discrepancy between the export values reported
in NBS and CGAC is due to measurement errors. To alleviate this concern, we infer
the intermediary’s export value share of the NBS dataset and compare it with that of
the CGAC dataset. More specifically, the intermediary’s export value share of the NBS
dataset equalsmm
X NBS  X CGACX NBS  X CGAC(
)  ( tc
)  28.44% ,umm
X CGAC  X CGACX CGAC  X CGAC

where X NBS and X CGAC indicate the total export value computed with the NBS andm
CGAC datasets, respectively; X CGAC
is the export value of the matched firms in the

CGAC dataset, measuring the direct exporter’s exporting value in the NBS dataset;tcum
X CGACand X CGAC
are the export values of the trading companies and the unmatchedmanufacturing firms in the CGAC dataset, respectively. Thus, the numerator and thedenominator measure respectively the export values of the intermediaries and directexports in the NBS dataset. This ratio is 28.44%, which is quite close to 26.2%--thenumber computed using the CGAC dataset.
Table 1. Classification of export modes
Exporting mode

Definition

Indirectexport_ NBS >0, export_custom =0

Directexport_NBS >0, export_custom >0, export_custom >export_NBS *(1-η)

DCX

export_NBS >0, export_custom >0, export_custom <export_NBS *(1-η)

Note: export_NBS and export_custom refer to the export value reported in the NBS and CGAC datasets,
respectively.
16

Table 2. Classification of firms
Firms in the NBS dataset

Firms in the CGAC dataset

unmatched

Indirect exporters

matchedDirect exporters

Direct exporters

DCX exporters

DCX exporters

matched

Unmatched manufactures

Unmatched,exporting mode unknown

Trade companies

intermediaries

3.2 TFP estimation
In our baseline regression, we estimate the firm level TFP as in Brandt et al. (2017),
who estimate the production function using the ACF approach proposed by Ackerberg
et al., (2015). To check the robustness of the results, we also try alternative measures of
TFP as proposed by Olley and Pakes (1996) and Levinsohn and Petrin (2003).

4. Evidence
In this section, we examine the data and establish the following stylized facts: (i)exporters in China are more likely to be located in coastal regions; (ii) a significant
share of the exporting firms are DCX; (iii) indirect exports account for a significant
share of the total exports, and the export volume of the intermediaries is more volatile
than that of the direct exporters; (iv) the average productivity of the DCX firms is higher
than the direct and indirect exporters; (v) a firm is more likely to become DCX rather
than a direct exporter in regions with poorer port infrastructure. Observe that all of thesefacts are consistent with our theoretical model.
4.1 Distribution of exporters and non-exporters across regions

17

Table 3 reports the share of firms located in the coastal regions and landlocked
regions for the exporters and non-exporters. Our theoretical model implies that theexporters would concentrate in the coastal regions where export capacity is lessconstrained. From Table 3, it is clear that the distribution of exporting firms is unevenly
skewed to the coastal regions, with the share of firms there being 95% for the exporters
and 84.23% for the non-exporters during our sample period of 2000-2006.
Table 3. Distribution of exporters across regions
Share

Firms

Landlocked

CoastalNon-exporters

15.77%

84.23%

Exporters

5%95%

Non-exporters

18.6%

81.4%

Exporters

5.66%

94.34%

Non-exporters

13.54%

86.46%

Exporters

4.5%

95.5%

2000-2006

2000-2003

2004-2006

4.2 Share of firms across export modes
To compare with the literature (e.g., Ahn et al., 2011), we split the firms in thecustoms data into two categories: the direct exporters and trade intermediaries, where
the set of intermediary firms is identified based on Chinese characters that have theEnglish-equivalent meaning of “importer”, “exporter”, and/or “trading” in the firm’sname. Note that the above identification methods could underestimate the number ofintermediaries, as the carry-along exporters are also tabbed as direct exporters.
18

Consistent with the literature, trade intermediaries account for 32.07 percent of the totalexports for ordinary exporters.
Figure 2 depicts the real exporting value of the continuous ordinary exporters ofdifferent exporting modes over 2000-2006. As is evident, the export value of theintermediaries is more volatility than that of the direct exporters, consistent with our“export capacity constraint” hypothesis.
Figure 2. Export value over exporting types
80000
60000
40000
20000
0
2000

2001

2002

2003

Direct

2004

2005

2006

Intermediary

4.3 Share of firms across export modes
We can further classify the exporting firms into three modes according to Tables 1
and 2, and document the share of exporters across modes.5 As is illustrated in Figure
3, the share of DCX and direct exporters in the custom dataset are 34.08% and 32.31%,
respectively. Note that the share of the intermediaries is the lower bound of the indirectexporters, as each intermediary exports for more than one manufacturing firm.
Figure 3. Share of firms across export modes

5 For the unmatched firms in the custom data, we assume the share of the DCX firms to be identical with thefigure in the matched sample.
19

35%

34.08%
34%

33.60%

33%
32.31%
32%

31%
Intermediaries

DirectDCX

4.2 Comparing firm productivity across export modes: baseline regression
In this subsection, we regress firm productivity on the export mode dummies,
where we choose the indirect exporters as the benchmark and include two dummies in
the regression equations, namely, Direct and DCX. As in the first column of Table 4,
the DCX firms are 1.6% and 0.9% more efficient than the indirect and direct exporters,
respectively. This finding squares well with our theoretical explanation.
Table 4. Firm productivity across export modes: baseline regression

Direct

DCX

(1)

(2)

(3)

0.005***

0.004***

0.005***(0.001)(0.001)(0.001)0.016***

0.014***

0.014***(0.001)(0.001)(0.001)

Y

Y

Y

Y

Firm controls
Year FE

Y

20

Industry FE+ Province FEYY

Industry FE*Province FEYN

258,155

258,155

258,126

R-squared

0.413

0.414

0.429

Note: (1) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1 in thebaseline regression.

4.3 Robustness checks
4.3.1 Redefining DCX
In Table 4, we have used   0.1 and defined firms as DCX if their export value
reported in the CGAC dataset is 10% less than that reported in the NBS dataset. To
alleviate the concern of measurement error, we now experiment with alternative valuesof and the results are shown in Table 5. Specifically, we let   0.25 and  0.4 respectively, and repeat the exercises in Table 4. We find a significantly
positive coefficient on the variable DCX, and the magnitude is very close to theestimates in Table 4, thus the finding that DCX firms are more productive than otherexporters is robust to measurement errors.
Table 5. Robustness check I: redefining DCX
(1)

(2)

(3)

(4)

η=0.25
Direct(6)

η=0.400.005*** 0.004*** 0.005*** 0.005*** 0.004*** 0.005***(0.001)DCX(5)(0.001)(0.001)(0.001)(0.001)(0.001)0.016*** 0.014*** 0.014*** 0.017*** 0.015*** 0.014***21

(0.001)
Firm controls(0.001)(0.001)Y

Y
YYear FE

Y

Y

Industry FE+ Province FEYY

Industry FE*Province FE(0.001)(0.001)(0.001)

Y

Y

Y

Y

Y

Y

Y

Y

Y

N

249,677

249,677

249,645239,863

239,863

239,831

R-squared

0.412

0.412

0.428

0.410

0.411

0.427

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

4.3.2 Alternative measures of TFP
Our second group of robustness checks considers alternative measurements of TFP.
First, when computing TFP, we use two different price indices as in Brandt et al. (2017).
Specifically, for the period 1998-2003, firms are required to report both the nominal
and real prices. We then compute the mean firm-level price changes at each 4-digit
industry. Our first price index excludes as outliers the observations for which the pricechange differs by more than 1/2 of the standard deviation from the mean (about 15–25%
of observations), while our second price index excludes outliers that see a price changethat is at least one standard deviation away from the mean change (dropping 8% ofobservations). In columns 1-2 of Table 6, we measure TFP using the ACF method withthe second price index. In columns 3-4 and 5-6, we report our estimates using the OlleyPakes and Levinsohn-Petrin methods, respectively, which are robust to alternative
measures of TFP.

22

Table 6. Robustness check II: alternative measures of TFP
(1)

(2)

(3)

tfp_acf2
Direct

DCX

(4)

(5)

tfp_op(6)
tfp_lp0.004*** 0.005***

0.002*

0.002*

0.006*** 0.006***(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)0.014*** 0.014*** 0.015*** 0.015*** 0.016*** 0.015***(0.001)(0.001)(0.001)(0.001)(0.001)(0.001)

Firm controls

Y

Y

Y

Y

Y

Y

Year FE

Y

Y

Y

Y

Y

Y

Industry FE+ Province FE

Y

Industry FE* Province FE

Y
Y

Y
Y

Y

N

258,139

258,109258,411258,385

258,582

258,553

R-squared

0.427

0.442

0.547

0.559

0.389

0.405

Note:(1) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1.

4.3.3 Changing sample periods
In order to fulfill its WTO commitments, China revised the “Foreign Trade Law”
and implemented it on July 1, 2004. In this revision, foreign trade management rights
were changed from the approval system to the registration system, and the requirements
for business qualifications were deleted. Prior to 2004, not all firms were allowed tochoose their exporting modes, and in fact some firms had to export throughintermediaries. Thus, our results could be obscured by the fact that only some firms had

23

export rights prior to 2004. To alleviate this concern, here we divide our whole sample
into two sub-periods, 2000-2003 and 2004-2006, and report the results in Table 7.
As expected, the pattern that DCX firms are significantly more productive thanfirms of other modes only holds for the period after 2004. Moreover, the productivity
gap between DCX and other exporters is even larger during 2004-2006, respectively 9%-13% and 14%-17% more productive than the direct and indirect exporters.
Table 7. Robustness check III: before and after 2004
(1)

(2)

(3)

(4)

2000-2003
Direct

DCX

(5)

(6)

2004-2006

0.014*** 0.012*** 0.011***0.003**

0.002*

0.003**(0.002)(0.001)(0.001)(0.001)(0.002)(0.002)0.014*** 0.012*** 0.011*** 0.017*** 0.014*** 0.014***(0.001)

Firm controls(0.001)(0.001)Y

Y
YYear FE

Y

Y

Industry FE+ ProvinceYY(0.001)(0.001)(0.001)

Y

Y

Y

Y

Y

Y

Y

FE
Industry FE* Province FE

Y

Y

N

108,381

108,381

108,347

149,774

149,772

149,738

R-squared

0.359

0.360

0.379

0.485

0.486

0.502

Note:(1) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1.

24

4.4 Port infrastructure and export mode
As is predicted in the theoretical model laid out in section 2, infrastructure
bottlenecks are the key in generating the dual-channel exporters. In this subsection, we
test whether firms, especially the more productive ones, in regions with poorer/better
port infrastructure are more likely to become DCX rather than direct exporters.
Specifically, we regress DCX it on measures of port infrastructure, firm productivity
and other firm controls. We set DCX it  1 if a firm is a dual-channel exporter, and
DCX it  0 for direct exporters.
Note that in the regressions below, we deliberately exclude the indirect exportersfrom our sample for two reasons: first, as predicted by our theoretical model, only the(potential) direct exporters in the unconstrained model choose to become DCX; second,a better infrastructure has two effects on the firm’s export modes. On the one hand, it
increases the probability of being a direct exporter, on the other hand it reduces theprobability of being an indirect exporter. As a result, if we allow DCX it  0 to include
both the direct and indirect exporters, the estimating coefficient of DCX it on thequality of infrastructure bottlenecks would be insignificant as the aforementioned two
opposing effects cancel out each other.6
4.4.1 Customs and export mode: baseline results
In Table 8, we measure a city’s port infrastructure by the indicator variable

customit , which equals 1 if the city has a custom and 0 otherwise. We expect cities with6

We thank Qing Liu and Yi Lu for suggestions on this point.
25

customs to have a better port infrastructure because most of these cities are locatedalong the coast, and hence face less geographical trade barriers. We also expect firms
in these cities to be more connected with custom officials, suggesting a better soft
infrastructure for these firms. As is illustrated in Table 8, firms located in cities withcustoms are less likely to be DCX, and the more productive firms are more likely to
become DCX, consistent with our theoretical predictions.
Table 8. Customs and exporting mode: baseline regression
DCX

(1)

(2)

(3)

Custom-0.039**-0.029**-0.025*(0.015)(0.014)(0.014)0.098***

0.097***(0.020)(0.020)0.024***

0.028***(0.005)(0.005)0.063***

0.062***(0.003)(0.004)0.071***

0.065***(0.009)(0.009)Y

tfp_acf

age

lnl

soe

Year FE

Y

Y

Industry FE+ Province FEYY

Industry FE* Province FE

Y
26

Observations

102,138

93,715

92,815R-squared

0.093

0.121

0.193

Note:(1) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1.

4.4.2 Customs and export mode: redefining DCX
As mentioned previously, our estimates might be obscured by measurement errors,
for instance, statistical errors could result in a firm reporting less or more export volume
in China’s CGAC data. To alleviate this concern, we adopt more conservative
definitions of DCX, and define a firm as DCX if its export volume reported in the
CGAC dataset is either 25% or 40% lower than that reported in the NBS data. As isevident from Table 9, the facts that firms located in cities without customs and the moreproductive firms are more likely to become DCX still hold for these conservative
definitions of DCX.
Table 9. Robustness check I: redefining DCX
DCX

(1)

(2)

(3)

(4)

η=0.25
Custom(6)

η=0.40-0.042**-0.031*-0.026*-0.042**-0.030*-0.025*(0.017)(0.016)(0.015)(0.017)(0.016)(0.015)0.098***

0.098***

0.093***

0.091***(0.022)(0.022)(0.023)(0.024)

Y

Y

Y

Y

Y

Y

Y

Y

tfp_acf

Firm controls
Year FE

(5)

Y

27

Y

Industry FE+

Y

Y

Y

Y

Province FE
Industry FE*YYProvince FE
Observations

92,877

85,24084,28386,39379,24678,260R-squared

0.117

0.148

0.229

0.128

0.160

0.246

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1.

4.4.2 Customs and export mode: controlling for confounding factors
The previous results may also be obscured by heterogeneity in export intensity
across firms although our theoretical model predicts a constant export intensity underconstant elasticity of substitution. For instance, the most efficient firms could
coincidently be the firms with lower export intensity when they face higher trade costs.
To correct for this bias, in columns (1)-(2) of Table 10, we control for the firm’s export
intensity, and the baseline results still hold.
An alternative explanation for why the most productive firms choose to become
DCX could be that they produce multiple products; that is, the most productive firms
produce more non-core products which enable them to export their core products
directly, and non-core products through intermediaries. To test this competing
hypothesis, we drop the multi-product firms from the regressions in columns (3)-(4) of
Table 10, and the impacts of port infrastructure become even stronger in terms of both
statistical significance and economic magnitude.

28

A certain fraction of firms in China serves only the exporting market, and is known
as pure exporters, which could also confound our results. We therefore drop the pureexporters, and again find the results robust.
Table 10. Robustness check II: Controlling for confounding factors
DCX

(1)

(2)

Control for export intensity

Custom(3)(4)

Single product DCX

(5)

(6)

Drop pure exporters-0.025*-0.024*-0.035*** -0.033***-0.032***-0.032***(0.014)(0.013)(0.008)(0.008)(0.009)(0.009)0.124***

0.120***

0.054***

0.049***

0.045***

0.041***(0.020)(0.020)(0.014)(0.014)(0.012)(0.013)0.352***

0.362***

0.191***

0.195***

0.189***

0.193***(0.019)(0.020)(0.016)(0.019)(0.015)(0.017)

Firm controls

Y

Y

Y

Y

Y

Y

Year FE

Y

Y

Y

Y

Y

Y

Industry FE+

Y

tfp_acf

Export_intensity

YY

Province FE
Industry FE*

Y

Y

Y

Province FE
Observations

93,715

92,81557,36356,34949,178

48,164R-squared

0.168

0.236

0.297

0.383

0.284

0.375

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1.

29

4.4.3 Customs and export mode: alternative measures of infrastructure
In previous regressions, we have measured port infrastructure with a dummy
variable for if a city has a custom. This measure is imperfect because the port
infrastructure in cities with customs can also exhibit significant heterogeneity. In Table
11, we use two other variables to measure a city’s port infrastructure: one is the days ofexport customs clearing, and the other is the share of firm staff specialized in handling
governmental relationships. These two measures are computed using the Investment
Climate Survey 2005 conducted by the World Bank across 120 cities in China. In thequestionnaire, firm managers are required to answer the following two questions: “Howmany days did the export customs clearance take on average in 2004 for your company?”and “Does your company have specialized staff to handle governmental relationships(for example, a government relations office)? If yes, how many staff?” We have theabove two measures only in 120 cities, so we use them as robustness checks, and theresults are reported in Table 10. Firms located in cities with a longer time of customs
clearing or poorer institution quality are more likely to be DCX.
Table 11. Robust Check III: Alternative measures of infrastructure
DCX

(1)

(2)

(3)

Custom_clearance

0.013***

0.010**

0.010*(0.005)(0.004)(0.005)

Employment_gov

30

(4)

(5)

(6)

17.429***12.318*86.790***(5.212)(6.457)(24.235)

tfp_acf

Firm controls

0.086***

0.090***

0.083***

0.089***(0.021)(0.023)(0.021)(0.022)

Y

Y

Y

Y

Y

Y

Y

Y

Y

Y

Year FE

Y

Y

Industry FE+YYProvince FE
Industry FE*YYProvince FE
Observations

86,25579,31178,45078,516

72,19571,292R-squared

0.098

0.126

0.197

0.122

0.153

0.232

Note:(1) Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1.

4.4.4 Customs and exporting mode: interaction of infrastructure and firm
productivity
Here we test whether the most productive firms are more likely to become DCX
in cities with better infrastructure, by introducing an interaction term of firm
productivity and customs (Custom *tfp), and the results are reported in Table 12. The
regression coefficient on the interaction term Custom *tfp is negatively significant,suggesting that in cities with customs the most productive firms are less likely to
become DCX as infrastructure bottlenecks are less severe there.
Table 12.

Interaction of Infrastructure and Firm Productivity

DCX

(1)

(2)

(3)

Custom *tfp

-0.098***-0.102***-0.103***31

Custom

tfp_acf(0.031)(0.031)(0.032)

0.028

0.042

0.046*(0.028)(0.027)(0.026)0.149***

0.148***

0.146***(0.030)(0.029)(0.030)0.025***

0.028***(0.005)(0.005)0.062***

0.062***(0.003)(0.004)0.071***

0.065***(0.008)(0.009)
Y

age

lnl

Soe

Year FE

Y

Y

Industry FE+ Province FEYY

Industry FE* Province FE

Y

Observations

93,752

93,715

92,815R-squared

0.094

0.121

0.194

Note: Robust standard errors in parentheses, *** p<0.01, ** p<0.05, * p<0.1; (2) η=0.1.

5. Quantitative Analysis
This section estimates the quantitative effects of export capacity constraints onexport volume and productivity. To this end, we first extend the model laid out in32

Section 2 to two countries, the home country 1 and foreign country 2, which can bethought of China and the rest of the world (ROW) respectively.
5.1 Theoretical framework
In each country, there are Li units of labor, and we normalize the wage and labor
supply of country 1 to one. We adopt the standard assumption for country 2 in the sensethat there is neither exporting capacity constraint nor trade intermediaries as in Melitz(2003) (see Table 13 for details).
Table 13. Export mode and trade costs
Export mode
Country 1
IndirectVariable cost

Fixed costDirect

w1gt
w1t

w1f12Iw1t for q  q ;Dual-channel

w1gt for q  qCountry 2
Directw2t

w 1 f12D
w 1 f12D

w 2 f21

Note: g  1 and f12I  f12D .

Denote fii as the fixed cost of production for country i , fijI the fixed cost ofexporting through intermediaries and fijD the fixed cost of direct exporting fromcountry i to country

j . As before, we assume the iceberg trade cost of exporting

directly and indirectly to be wi gt and wi t , respectively. To ensure the existence of
indirect exporters for country 1, we focus on the case ofThen the productivity cutoffs can be written as

33

f12D
f12I

 g s 1 .

r (j* )w
1
w2 f22  22 22  B2 ( 2 )1s
*
s
s
rj22
r (j* )w t
1
w2 f21  21 21  B1 ( 2 )1s
*
s
s
rj21
r (j* )w1
w1 f11  11 11  B1( 1 )1s
*
s
s
rj11

r (j* )
w gt
1
w1 f12I  12 12I  B2 ( 1 )1s
*
s
s
rj12
I
w1 gt 1s s 11
w1 ( f12D  f12I )  B2 (
) (g
 1)
*
s
rj12
D
w gt sq  B2 ( 1
)
*
rj12
DC
*
j1*2DC  gj12
C

where Bi  wi Li Pi s1, i  1, 2 , and firms with

(11)

*
*
j  [j12
I , j12D ]export through****intermediaries, those with j  [j12
D , j12C ] export directly, those with j  [j12C , j12DC ]*export at the constrained output q , and those with j  [j12DC , ) become DCX.

Evidently, the share of firms exporting at the constrained quantity is increasing in therelative variable cost of indirect exporting, g .
From eqs. (11), we further obtain
1

s

f
w
*
*
j21
 j11
t( 21 )s 1 ( 2 )s 1
f11
w1

1
f12I s 
w1 ss1*1
 j22 gt(
) ( )
f22
w2
1
 f
 s 1 w s

f
*
*
12I 
 12D
j12
( 1 )s 1
D  j22 t 

1s
w2
(1

g
)
f

22 
s 1
1
wt
q
*
* s
j12
[
]s ( 1 )C  j22(s  1)f22
w2
s 1
1
w gt
q
*
* s
j12
[
]s ( 1 )DC  j22(s  1)f22
w2
*
j12
I

(12)

Free entry requires the fixed entry cost equal the expected value of entry. The freeentry condition for country 2 is standard, given by




j22

j21

w 2 F2   * p22 (j )dG 2 (j )   * p21 (j )dG 2 (j )
34

(13a)

For country 1,****(i) if j12
I  j12D  j12C  j12DC , there are three types of exporters, the indirectexporters, direct exporters and DCX, and the free entry condition is given by
*
j12
D



*
j12
C

w 1F1   * p11 (j )dG 1 (j )   *
j11

*
j12
DC

  *

j 12C

p12 I (j )dG 1 (j )   *

j12 I



p12 D (q , j )dG 1 (j )   *j12 D Cj 12 D

p12 D (j )dG 1 (j )p12 DC (j )dG 1 (j )

(13b)

*
*
*
*
*
*
*
*
(ii) if j12
I  j12C  j12D  j12DC or j12C  j12I  j12D  j12DC , again there arethree types of exporters with the direct exporters exporting the constrained quantity,
and the free entry condition is given by


*
j12
D

j11

j12 I

w1F1   * p11 (j)dG1 (j)   *
*
j12
DC

 *

j12D

(iii)

if

p12I (j)dG1 (j)

p12D (q , j)dG1 (j)   *j12DC

p12DC (j)dG1(j)

*
*
*
*
j12
I  j12C  j12DC  j12D

,

*
*
*
*
j12
C  j12I  j12DC  j12D

(13c)

or

*
*
*
*
j12
C  j12DC  j12I  j12D , then there are only two types of exporters, the indirectexporters and DCX, and the free entry condition is given by


*
j12
D

j11

j12I

w1F1   * p11(j)dG1(j)   *p12I (j)dG1(j)   * p12DC (j)dG1(j)j12D

where Gi (j)  1  (j / bi )k , k  s  1 and

35

(13d)

w
1
B2 ( 2 )1s  w2 f22srj
w2 t 1 s
1
p21 (j)  B1 ()
 w 2 f21s
rjw
1
p11 (j)  B1 ( 1 )1s  w1 f11
s
rj
w gt
1
p12I (j)  B2 ( 1 )1s  w1 f12Is
rj
w1t 1s1
p12D (j)  B2 ()
 w1 f12Dsrj
1 s 1wt
p12D (q , j)  B2 s q s  1 q  w1 f12D
j
1
s 1
w gt
wt
p12DC (j)  max B2 s (q  q ) s  1 q  1 q  w1 f12D , s.t. q  0
qjj
w1 gt 1sw1 (g  1)t1
 B2 ()
q  w1 f12Dsrjjp22 (j) Overall,

we

have

seven

productivity(14)

cutoffstosolve:

*
*
*
*
*
*
*
*
*
{j22
, j21
, j11
, j12
I , j12D , j12C , j12DC } . By eq. (11), we can express j21 as functions of j11
*
*
*
*
*
and w2 , and j12I
, j12D, j12Cand j12DC
as functions of j22
and w2 . Consequently,**
we obtain j11
and j22by solving the free entry conditions for the two countries given
*
*
*
*
*
w2 . Then j21, j12I, j12D, j12Cand j12DC
can be derived subsequently.

Combing the labor market clearing condition with the free entry condition, we get
the number of entrants for each country:Mie s  1 Lik s Fi(15)

By normalizing country 1’s wage to one, we can further solve country 2’s wage via thetrade balance condition:w 2L2 = L1

M 2e f21

M 2e f21  M 1e

 w 2L2

M 2e

M 2e  M 1e f12

where

36

or w2 =

L1f21

L2f21  L1

 w2

L2

L2  L1f12

(16)



f21 

j r21(j)dG2(j)
*
21

(17a)



 * r11(j)dG1(j)j11

For f12 ,****if j12I  j12D  j12C  j12DC ,

(i)

f12

*
j12
 j12* DC  * r12I (j)dG1 (j)   * r12D (j)dG1 (j) j12I

j12D


*
j

12
DCX


*
r
(
j
)
dG
(
j
)

r
(
j
)
dG
(
j
)
 j*

12D
12C
1
1
j12* DC 12DC

12C



(17b)



j r22 (j)dG2 (j)
*
22

*
*
*
*
*
*
*
*
(ii) if j12
I  j12C  j12D  j12DC or j12C  j12I  j12D  j12DC ,

f12 

j*

 j12* D*  * r12I (j)dG1 (j)   *12DC r12D (j12)dG1 (j)   * r12DC (j)dG1 (j) C j12I

j12 D
j12 DC * r22 (j)dG2 (j)

(17c)

j22

(iii)

if

*
*
*
*
j12
I  j12C  j12DC  j12D

,

*
*
*
*
j12
C  j12I  j12DC  j12D

or

*
*
*
*
j12
C  j12DC  j12I  j12D ,

f12 


 j12* D  * r12I (j)dG1(j)   * r12DCX (j)dG1 (j)  j12I

j12D

(17d)



 * r22 (j)dG2 (j)
j22

with

 w2 1s
r22 (j)  B2   rj ， w2 t 1s
r21(j)  B1  rj , w1 1s
r11(j)  B1   rj , w gt 1s
 w1t 1s
 w1gt 1s



 .
r12I (j)  B2  1 
r
(
j
)

B
r
(
j
)

B

, 12D
and 12DC2
2
 rj  rj  rj 
*
*
*
*
*
*
*
w2 .
, j21
, j11
, j12
Clearly, f12 and f21 are only functions of {j22I , j12D , j12C , j12DC } and5.2 Quantification
5.2. 1 Parameters

37

This subsection quantifies the impacts of export capacity constraints on aggregate
productivity, export and gains from trade, using the model laid out in the previous
subsection. We normalize the labor of country one to 1, and foreign labor to 4 to matchthe labor size of China and ROW. Given that ROW affects China only through theaggregate variables, we assume Fi and fii to be identical across countries, and sets  3 , consistent with the estimates in Bernard et al. (2003).

The fixed cost of domestic production can be recovered from the zero-profit cutoff
condition: fii 1
l , where we use the 5% lowest firm labor to proxy l min .
s  1 minTo

identify the entry cost, we use the equilibrium free entry conditionFi 

1[1  G (jii* )]{[l (j )]  l min }s 1

In the data, we use the one period survival rate (0.6495) to measure [1  G (jii* )] ,and the difference between the mean and lowest 5% labor to measure [l (j)]  lmin .
For the productivity distribution, we assume the shape parameter to be k  4 , and thelower bound of the productivity distribution to be b1  b2  0.009 .
The fixed cost of indirect exporting can be calculated by combining the share ofkthe exporting firms
indirect exporters

B2

B1

 B2
f  s 1
 (gt )1s 11 
and the export-domestic sales ratio of theB
f12I 
 1(gt )1s , from which we obtain f12I  0.7316 .

A key parameter is g , the relative iceberg trade cost of indirect to direct exporting,which contains two components: one is the “explicit” commission rate which ranges

38

over 0.5%-5% in the observed contract (Bai et al., 2017);7 the other part is the “implicit”
cost associated with bribery and kickbacks. Although we do not observe bothcomponents, the latter one seems to be more important in practice. Because of the strict
control of railway and ocean shipping, transportation has been in a state of chronic
shortage, which creates environments for corruption. For instance, in the notorious
corruption case of Zhijun Liu who is the former Minister of Railways of China, the coaldealers would spend 20 of 25.5 billion yuan as bribes to get “guanxi” (see footnote 4).
In another bribery case of Shanghai Wansheng International Freight Agency co., LTD,the total kickbacks amount to 0.4242 million yuan associated with sales of 4.1081
million yuan (including taxes).8
In the former case, the firm spends more than 78% of its gross profits to get thetrain; while in the latter case the firm spends more than 10% of its total revenue to breakthrough the infrastructure bottlenecks. Thus, in our counterfactual exercises, we try
alternative values of g ranging from 1.01 to 1.1.
Having obtained f12I and g , we can compute the fixed cost of direct exportingkfkfrom the share of indirect exporters which equals 1   g s 1  1 s 1 ( 12D  1)1s , giving
f12I

f12D (see Table 14).

Table 14. Model parameters
Parameter
L1

7
8

Definition

Value

Identification

Labor force in country 1

1normalization

https://zhidao.baidu.com/question/309184569441265564.html
http://www.cicn.com.cn/zggsb/2015-06/10/cms72533article.shtml
39

Labor force in ROW

4

Elasticity of substitution

3

Fixed cost of production

0.6931Lowest 5% of Labor

Fixed cost of indirect exporting

0.7316

fraction of exporters

Fixed cost of direct exporting

0.74970.9202

Fraction of indirect exporters

Entry cost

0.5385Fraction of firm producing

Commission rate of theintermediary

0.01-0.1

b1 , b2

Lower bound of the productivity
draw

0.009

k

Pareto shape parameter

4

L2
s

f11 , f22
f12I
f12 D , f21
F1 , F 2
g  1

Relative labor size of ROW to
China

Lower bound of TFP

We calibrate t and q to match the export intensity of the indirect exporters andthe share of DCX firms. As is illustrated in Table 15 the discrepancy between the modeland data is reasonably small.
Table 15. Data and model moments
Data

Model

Indirect intensity

0.3934

0.3934

Share of DCX in exporters

0.34080.34085.2. 2 Counterfactuals
Table 16 reports the loss of productivity and gains from trade due to exporting
capacity constraint. In panel A, we assume wage is fully adjustable, while in panel B
we assume it is fixed. The latter scenario can be thought of as the short-run effects orthe case with a homogeneous good sector to pin down the wage.
As is evident, when g  1.1 , eliminating the exporting capacity constraint leads
to 2.00%-2.44% gains in aggregate productivity and 7.94%-10.74% in total exports,
respectively.
40

Table 16: Counterfactuals

g

t

q

Δ％gains from trade

Δ％tfp

Δ％Export

Panel A: flexible wage
1.01

1.93330.0273-5.90%-1.08%-2.26%

1.02

1.91710.0273-6.76%-1.20%-2.76%

1.03

1.90140.0272-7.60%-1.30%-3.30%

1.04

1.88590.0272-8.50%-1.42%-3.88%

1.05

1.87080.0271-9.34%-1.52%-4.51%

1.06

1.85610.0271-10.17%-1.62%-5.11%

1.07

1.84160.0271-11.00%-1.72%-5.76%

1.08

1.82750.0270-11.86%-1.82%-6.47%

1.09

1.81360.0270-12.69%-1.91%-7.19%

1.1

1.80000.0269-13.51%-2.00%-7.94%

Panel B: fixed wage
1.01

1.92970.0273-5.90%-1.13%-2.57%

1.02

1.91040.0274-6.76%-1.28%-3.36%

1.03

1.89120.0274-7.60%-1.43%-4.20%

1.04

1.87240.0274-8.50%-1.59%-5.08%

1.05

1.85380.0274-9.34%-1.74%-5.96%

1.06

1.83590.0274-10.17%-1.88%-6.85%

1.07

1.818

0.0274-11.00%-2.02%-7.79%

1.08

1.80070.0274-11.86%-2.17%-8.74%

1.09

1.78340.0274-12.69%-2.31%-9.73%

1.1

1.76670.0274-13.51%-2.44%-10.74%

Note: Δ％ represents percentage change of the variable in interest under exporting capacity constraints.

6. Conclusions
How firms export matters not only for their own profits but also for a country’s
aggregate resource allocation, productivity and gains from trade. In this paper, we haveexamined how infrastructure bottlenecks distort the firm’s choice of export modethrough an augmented heterogeneous firm model. Specifically, our theoretical model
predicts that when a firm’s exporting capacity is constrained by infrastructure
bottlenecks, the most productive firms have to export through intermediaries andbecome the so-called dual-channel exporters. With linked annual survey of industrial
production and transaction level customs datasets in China, we document that a
41

significant share of firms are DCX. Further, DCX firms are more productive comparedwith direct and indirect exporters, and in line with our theoretical model, thisphenomenon is more salient in the landlocked provinces of China.
The contribution of the present paper is twofold: first, we have uncovered a novel
export mode, namely the dual-channel exporters, and thus complement the works
emphasizing the role of intermediaries in facilitating international trade; second, we
demonstrate both theoretically and empirically that the presence of dual-channel
exporters can be attributed, at least partly, to the exporting capacity constrained due topoor exporting infrastructures. Our quantitative exercises suggest infrastructure
bottlenecks can substantially reduce total exports and the aggregate productivity byshifting resources from the most productive dual-channel exporters to the least
productive firms.

Acknowledgement: We are grateful to Wei Jin, Qing Liu, Zhiyuan Li, Yi Lu, Guobing Shen,
Yongqin Wang, Hongsong Zhang, Xiaodong Zhu as well as seminar participants at Nankai,
Fudan, and the institute of the China and World Economy. Wang acknowledges the financial
support of the National Natural Science Foundation of China Grant No. 71573141, and Zhao
is grateful to the support of JSPS grant No. 19H01484.

Reference
Ackerberg, Daniel A., Kevin Caves, and Garth Frazer. 2015. Identification Properties ofRecent Production Function Estimators. Econometrica 83 (6), 2411-51.
Ahn, J., Amit K. Khandelwal, and Shangjin Wei, 2011, The Role of Intermediaries in
Facilitating Trade, Journal of International Economics 84(1), 73-85.
42

Bai, X., Krishna, K. and Ma, H., 2017, How You Export Matters: Export Mode, Learning
and Productivity in China, Journal of International Economics 104(C), 122-137.
Bernard, A., J. Bradford Jensen, and Peter K. Schott, 2010, Wholesalers and Retailers in
US Trade, American Economic Review y 100 (2), 408-13.
Bernard, A., Marco Grazzi, and Chiara Tomasi, 2015, Intermediaries in International Trade:
Products and Destinations, Review of Economics and Statistics 97(4), 916-920.
Bernard, A. B., Blanchard, E. J., Van Beveren, I., & Vandenbussche, H., 2018, Carryalong Trade. Review of Economic Studies 86(2), 526-563.
Blyde, J., and Danielken M., 2015, Logistic Infrastructure and the International Locationof Fragmented Production. Journal of International Economics 95(2), 319-332.
Brandt L, Van Biesebroeck J, Wang L, 2017, WTO Accession and Performance ofChinese Manufacturing Firms, American Economic Review 107(9), 2784-2820.
Cai, H. and Liu Q., 2009, Does Competition Encourage Unethical Behavior? The Case of
Corporate Profit Hiding in China, Economic Journal 119, 764-95.
Duranton, G., P. M. Morrow and M. A. Turner, 2014, Roads and Trade: Evidence from the
U.S. Review of Economic Studies 81(2), 681-724.
Duranton, G. 2015, Roads and Trade in Colombia, Economics of Transition 4(1), 16-36.
Hornok, Cecília, and Miklós Koren, 2015a, Administrative Barriers to Trade. Journal ofInternational Economics 96, 110-122.
Hornok, Cecília, and Miklós Koren, 2015b, Per-shipment Costs and the Lumpiness ofInternational Trade. Review of Economics and Statistics 97(2), 525-530.
Hsieh, Chang-Tai, and Peter J. Klenow, 2009, Misallocation India. Quarterly Journal of
Economics 124 (4), 1403-1448.
Levinsohn, J. and Petrin A. 2003, Estimating Production Functions Using Inputs toControl for Unobservable, Review of Economic Studies 70(2), 317-41.
Martincus, C.V., J. Carballo, and A. Graziano. 2015, Customs, Journal of International
Economics 96(1), 119-137.
Melitz, M.J. 2003, The Impact of Trade on Intra-Industry Reallocations and Aggregate
Industry Productivity. Econometrica 71(6), 1695-1725.
Olley, S. and Pakes A. 1996, The Dynamics of Productivity in the Telecommunications
Equipment Industry, Econometrica 64(6), 1263-97.
Olney William W., 2016, Impact of Corruption on Firm-Level Export Decisions,
Economic Inquiry 54(2), 1105-1127.
Restuccia, Diego, and Richard Rogerson, 2008, Policy Distortions Heterogeneous
Establishments. Review of Economic Dynamics 11(4), 707-720.

43

Appendix: Estimation
Outer loop: Compute the optimal t and qto minimizeM  m 12  m 2 2 , m1  iI  iI , m2  sDCX  sDCX ,B2

 gt 

and iI are export-domestic sales ratio of the indirectexporters, and s DC X

 j*
 k
 (s  1) f12 I j 12 I  s


12
D
C

 and sDCX are the share of
 
 


*
gt q


 j 12 I 

where iI 

B1

1 s

k

the dual channel exporters computed from the model and data, respectively.
**, j 22, w 2 } by solving the free entry
Inner loop: Given t and q , we first compute {j11and labor market clearing condition, then we compute B i  w i Li Pi s  1 , i  1, 2 , theprice index, the social welfarewiPiand the weighted productivity,**
 j12* I r11 (j )

j12
j12
r12 I (j )r12 D (j )
D
C


j
dG
(
j
)

j
dG
(
j
)

j
dG
(
j
)
1
1
1
j12* I
 j12* D  j11*

1
R
R
R


j 
*
*
* 
j12 DCX
r12D (j12C )r12 DCX (j )1  G (j11 ) jdG 1 (j )   *jdG 1 (j )  j12* C
jRR
12 DCX

And the export volume:j*j* j12* D*  * r12I (j)dG1 (j)   *12C r12D (j)dG1 (j)   *12DCX r12D (j12)dG1 (j) 
C


j12 D
j12C
X1  M 1e  j12 I



r
(
j
)
dG
(
j
)
 j*
12DCX
1


12 DCX

where M1e s  1 L1.
k s F144