Sorting in Exporting Market Selection: Evidence from China
Luhang Wang
Department of Economics, University of Toronto, ON., Canada

September, 2009

Abstract
This paper is motivated by concerns about what China has been exporting to the world
and whether quality upgrade has happened along with the tremendous export growth, especially following the entry into WTO in the December of 2001. The major difficulty in investigating quality upgrade is that product quality is rarely observed. This paper tries toinfer a firm’s ability of producing higher quality products from the markets to which the firmchooses to ship its products, utilizing the fact that consumers on different markets have different degrees of sensitivity towards quality relative to price. A second dimension of heterogeneity is introduced to the heterogeneous firms trade models initiated by Melitz (2003) ina specific way to illustrate the sorting of firms more capable of improving quality into morequality sensitive markets. The model also predicts that trade liberalization strengthens firms’
incentives to carry out quality-improving investment and enter more markets.

−1−1 Introduction
Chinese economy has been growing rapidly in recent decades and the increase in exports has
been a key contributor. Brandt and Rawski (2008) show that more than half of the GDP growthfrom 2003 to 2005 comes from export growth. The total export volume has tripled from 250 billion USD in the year 2000 to more than 760 billion USD in 2005, around half through processing
trade and half through ordinary trade. Besides the remarkable growth in volume, studies alsofind significant expansion of product lines and fast growing overlapping with OECD countries
in Chinese exports, Wang and Wei (2008), Rodik (2006) and Schott (2007). However, the quality
aspect of Chinese exports does not progress as fast, if improved at all. Hallak and Schott (2009)find China is one of the countries with the sharpest quality decline during the period of 1989 to2003.
Given quality’s importance to firms’ viability on international market and its implication forsustained economic growth in developing countries, this phenomenon deserves attention. The
study of Hallak and Schott is based on cross country aggregate data, so from their finding it is notclear whether Chinese firms in general are falling behind in appealing to oversea consumers or
it is a composition effect as there have been enormous start-up exporters and new exporters areprobably not as good as the existing ones in in quality products1 . The second scenario is more
optimistic than the first one if export experience helps firm climb up quality ladder. Micro level
data is necessary for getting better understanding of the aggregate declining quality. However,as with the aggregate data, quality is rarely well measured at micro level. Good quality measurehas been developed for very special industries2 but not economy wide. Adopting unit value as aquality proxy is problematic since unit value also contains cost information which means lower
unit value does not necessarily imply inferior quality, it could be that the firm charging lower
price is more efficient in costs. This has been confirmed in many studies, including Hallak andSchott (2009).
This paper proposes an alternative way of inferring firms’ product quality by utilizing the as1 New exporters are generally smaller than the existing exporters and sell their products cheaper.
2 One example is French Wine. See Crozet, Head and Mayer (2009)−2−

sociation between firms and the specific markets they choose to ship their products to. As longas both firms and markets are heterogeneous and there are fixed costs for firms to export to amarket, firms and markets should not match up in a random way. The specific sorting mechanism depends on how we believe firms and markets are differentiated. Firm heterogeneity hasreceived a lot of attention in the literature since Melitz (2003). In Melitz model, firms are differentiated by marginal cost and the sorting results in a strict hierarchy market structure with themost productive firms start exporting earlier and export to the largest number of markets andless productive firms export to a subset of markets later when trading costs become lower. The
introduction of additional dimensions of heterogeneity is usually motivated by the lack of such
hierarchy structure in the data, as in Lileeva and Trefler (2007) and Bernard, Redding and Schott(2009). Kuegler and Verhoogen(2008) and Johnson(2008) literally have firms differentiated along
two dimensions but have one parameter govern both. Hallak and Sivadasan(2007) differentiate
firms by productivity (ability to produce at lower marginal cost) and caliber (ability to upgrade
quality with lower fixed cost) and show that one dimension heterogeneity is enough as long as afixed cost to export is the only market threshold to overcome since under this scenario these twoabilities are substitutable from a firm’s perspective. However, when a minimum quality requirement is introduced as a second threshold, the two abilities have different roles.
For empirical consideration, with only one entry or not observation, it is hard to identify two
thresholds in Hallak and Sivadasan (2007). This paper sets up a model which substitutes theirminimum quality requirement with consumers’ intensity of preference for quality which can be
estimated when micro trade data is available. The model shows that when markets are differentiated along dimensions of relative quality-sensitivity and fixed entry costs, high calibre firms
will self-select into more quality-sensitive markets while low calibre but marginal cost efficient
exporters can make profits from less quality-sensitive markets with higher entry costs. In addition to generating a non-hierarchy sorting pattern, this model also gives predictions on firms’
investment and market entry behaviour following trade liberalization, no matter in the form oflowering variable trading costs or fixed trading costs. The increase of quality premium after tradeliberalization provides firms more incentive to invest in quality upgrading and simultaneously−3−enter more markets. This is related to the literature on the relationship between exporting andproductivity growth, as reviewed in Lileeva and Trefler (2007).
Utilizing exporting data of Chinese firms (2000 - 2005) from China’s Customs and annual
survey data(1999 - 2005), this paper also provides some tentative tests on the predictions of themodel. As a tentative study, this paper only looks at a small group of products that are coded
51 and 522 in Broad Economic Categorization system. These are motor cars and non-industrial
transport equipments. The first step is to estimate the market specific preference with the customs transaction data. The demand estimation shows significant difference in consumers preference across markets, with the estimates of 19 out of 45 market specific price coefficients being
significantly different from the one of a restricted model. The second step is to check whether
the interaction of the market attributes with firms’ quality related attributes is significantly correlated with a firm’s presence on a market, the firm’s decision on whether to enter a market,
and whether the correlation with the entry decisions has changed after becoming a WTO member. Since both firm attributes and market attributes are needed to test sorting, we can only thematched sample of the two data sets. Based on the belief that a firm’s usage of capital is positively correlated with its products’ quality, I interact the estimated market attributes with firms’
real capital stocks then I find positive correlation between the interaction term and the statusof the firm and market pair being active. Regarding the entry decision, I include both the thechange in a firm’s real capital stock in two consecutive years and its interaction with the difference in the attributes of the potential market and the firms’ existing markets. I find positive
correlation between entry and both the level of investment and the constructed interaction term.
Furthermore, the correlations become strong after the entry into WTO.
The remaining part is organized as follwing: section 2 sets up the model and derives predictions regarding sorting and entry; section 3 shows the empirical work which is composed of data
description, demand estimation and correlation tests. Section 4 concludes.

−4−

2 Model2.1 Demand Specification
Assume the utility of a representative consumer in country m is of the CES form,Z

Um = (

γ

i ∈Vm

σ−1

σ

[λ(im) x m(i ) ] σ d i ) σ−1(1)

As in a standard Dixit-Stiglitz utility specification, i denotes varieties; Vm is the set of varieties
available to consumers on market m; and σ is the elasticity of substitution among varieties. γmis a parameter added to the standard setting to represent consumers’ intensity of preference for
quality as in Hallak (2006). The variation in γ across markets captures the fact that consumers’appreciation of the same product improvement may not be uniform everywhere.
Given budget E m , each variety’s price p m(i ) and quality λ(i ) , consumers’ utility maximization
leads to the following demand function for variety i on market mγ (σ−1) −σ
1−σp m(i ) P m
Em

x m(i ) = λ(im)

(2)

R
1
p )
1−σ
where P m = ( i ∈Vm pem(id i ) 1−σ is the aggregate price index on market m, and pem(i ) = m(iγm is the)λ(i )quality adjusted price. Consumers’ expenditure on variety i , which is also the revenue of the firm
producing variety i from market m, is

R m(i ) = (pem(i ) 1−σ
)
Em
Pm

(3)

2.2 Cost Specification
The cost side specification is similar to Hallak and Sivadasan (2007). The firm corresponding
to variety i is endowed with two abilities: calibre ξi and productivity ϕi . Calibre ξi measures−5−

firm i ’s efficiency in improving quality and productivity ϕi measures its production efficiency asin Melitz (2003). For simplicity, assume there are only two quality levels: λ and 1, where λ > 1
represents higher quality. To be able to produce a variety of quality λ, firm i needs to incur an
investment of ξIi . This means firms with higher calibre can update the quality of their varieties atlower costs. If firm i chooses not to incur the investment, it can only produce a variety of quality
1. For the second dimension of ability, the marginal cost for firm i to produce a λ version of its
βcvariety is cλ
ϕi and the marginal cost of producing the low quality version is ϕi . This means moreproductive firms have lower marginal costs in producing its variety of either quality. A third costelement is the fixed costs of entering a market, denoted by F m for market m. Entry costs varyacross markets but are the same to all firms for any specific market.

2.3 Firm’s Optimization Problem
Given the above demand and cost structures, firm i ’s optimization problem involves choosing aquality level, markets to enter and the profits-maximizing price on each chosen market. Assume
quality is homogeneous within a firm, that is, a firm can choose only one quality level and ship
its variety of the same quality to all the markets it chooses3 , the optimization problem can be
resolved through backward induction as following:
1. Calculate firm i ’s operating profits-quality schedule for each market.
2. Find the set of markets where firm i ’s operating profits from selling the high quality versionof variety i exceed the entry costs. Then sum up the profits net of entry costs across allthese markets. Do the same assuming firm i is producing the low quality version of varietyi.
3. Compare the difference in net profits obtained in (2) against firm i ’s quality upgrade costsIξi .

If the premium profits from offering high quality variety are larger than the quality

upgrade costs, firm i would incur the investment to upgrade quality and export to markets
3 As shown later, this can be achieved by assuming the cost elasticity of quality never exceeds the intensity ofpreference for quality.

−6−

we find in (2) for high quality version of its variety; otherwise, firm i will not invest to
upgrade quality, just produce the low quality version of its variety and ship it to markets
we find in (2) for the low quality version of its variety.
Starting with the operating profits-quality schedule in the first step, with quality λ, the profit
maximizing price of variety i is

p m (ϕi , λ) = (σ cλβ)
σ − 1 ϕi

(4)

The resulting operating profits are

σ−1πm (ϕi , λ) = P m
Em (σ −σ 1c)( )1−σ λ(σ−1)(γm −β)σ−1
σ − 1 ϕi(5)With quality 1, the price and operating profits will bep m (ϕi , 1) = (σc)σ − 1 ϕiσ−1πm (ϕi , 1) = P m
Em (σ −σ 1c)( )1−σσ−1
σ − 1 ϕi(6)(7)

Comparing (??) with (??), we haveπm (ϕi , λ) = πm (ϕi , 1)λ(σ−1)(γm −β)(8)The two operating profits are equal only when γm = β. Moreover, πm (ϕi , 1) does not depend
on γm , while πm (ϕi , λ) increases with γm . The implication is that when consumers intensityof preference for quality γm dominates the cost elasticity of quality β, a firm can make more
operating profits from the high quality version of its variety and the premium increases with the−7−intensity of consumers’ preference for quality. We are going to assume γm ≥ β for any m to ruleout quality downgrading on some markets by firms that have invested in quality improvement
and so quality heterogeneity within firm.
Before moving on to the second step, we make some simplification assumptions on market
characteristics. Firstly, we assume markets are heterogeneous only along two dimensions: entry
costs F m and intensity of preference for quality γm . Other market characteristics such as total
expenditure E m and price index P m are assumed to be the same across markets and we are goingto ignore their subscript m. As a result, the preference for quality is the only market side factor
that differentiates operating profits across markets, so we can use γ instead of m as subscriptof operating profits functions. Secondly, we assume market entry costs to be independent ofquality preference. Suppose the joint distribution of entry costs and quality preference is f (F, γ)
and denote the marginal distributions by f F (F ) and f γ (γ) respectively, we then have f (F, γ) =f F (F ) f γ (γ).
Given the distribution of market characteristics and firm i ’s potential operating profits fromeach market, we can derive its overall profits net of entry costs from exporting a high quality
version of its variety, using hats to denote profits net of entry costs.

b i , λ) =
Π(ϕ

Z
F,γ

[πγ (ϕi , λ) − F ]1{πγ (ϕi ,λ)≥F } f (F, γ)d F d γ

Z Z Fϕ ,γ

=

i

γ 0

[πγ (ϕi , λ) − F ] f F (F )d F f γ (γ)d γ(9)1{πγ (ϕi ,λ)≥F } in the first line is an indicator function which equals 1 if operating profits πγ (ϕi , λ)exceed entry costs F and 0 otherwise. F ϕi ,γ , the upper limit of the integral over F in the second
line, gives the entry costs of the marginal market firm i enters in the subset of markets withquality preference γ, which is determined by

F ϕi ,γ = P σ−1 E (cσ −σ 1)( )1−σ λ(σ−1)(γ−β)σ−1
σ − 1 ϕi
−8−(10)

Similarly, conditional on 1, firm i ’s total profits from exporting will beb i , 1) =
Π(ϕ

=

Z

[πγ (ϕi , 1) − F ]1{πγ (ϕi ,λ)≥F )} f F (F )d F f γ (γ)d γF,γZ Fϕ ,1i
0[π(ϕi , 1) − F ] f F (F )d F

(11)

and the upper limit of the integral F ϕi ,1 is determined by

σ−1
F ϕi ,1 = P m
Em (σ −σ 1c)( )1−σσ−1
σ − 1 ϕi

(12)

The premium profits from exporting a high quality variety isb i ) = Π(ϕb i , λ) − Π(ϕb i , 1)∆Π(ϕZ Z Fϕ ,1i=[λ(σ−1)(γ−β) − 1]π(ϕi , 1) f F (F )d F f γ (γ)d γ
γ 0

Z Z Fϕ ,γ+i

γ F ϕi ,1[πγ (ϕi , λ) − F ] f F (F )d F f γ (γ)d γ
Z= P r ob(F < F ϕi ,1 )Z Z Fϕ ,γ+i

γ F ϕi ,1γ[λ(σ−1)(γ−β) − 1]π(ϕi , 1) f γ (γ)d γ[πγ (ϕi , λ) − F ] f F (F )d F f γ (γ)d γ(13)

The first term can be interpreted as the intensive margin of the premium in the sense that it represents the difference in exporting profits from the common markets of the two quality versions;the second term is corresponding to the extensive margin in the sense that firm i will only ship
to these markets if it is producing the high quality version of its variety.
For the investment decision in the third step, firm i will incur the quality upgrading investment if and only if the premium profits of a high quality variety can cover the costs of the investment, that is,−9−b i) ≥
∆Π(ϕ

I
ξi

(14)

2.4 Propositions 1 on Sorting
Sorting of high calibre firms into markets with stronger quality preference and low calibre but
more productive firms into less quality responsive markets.
Consider two firms k and j characterized by (ϕk , ξk ) and (ϕ j , ξ j ). Suppose• ϕk > ϕ j ;• ξk < ξ j ;b k ) < I , so firm k does not invest in quality upgrade and variety k is of quality 1;• ∆Π(ϕξkb j ) > I , so firm j invests in quality upgrade and variety j is of quality λ;• ∆Π(ϕξj• There is one market m 0 with entry costs F 0 and preference for quality γ0 where both firms
break even, that is, where F 0 = π(ϕk , 1) = πγ0 (ϕ j , λ).
Then on a market with γ < γ0 and F = F 0 we should be able to observe firm k but not firm j .4 On
the other hand, on a market with γ > γ0 and F 0 < F < πγ (ϕ j , λ), we should observe firm j but not
firm k.5 The sorting is illustrated in Figure 1.

4 Since firm k’s operating profits do not depend on γ, firm k breaks even on this market as well; firm j ’s operating

profits decreases when consumers’ quality preference is weaker, thus firm j ’s operating profits on this market will notcover its entry costs so firm j will not enter this market.
5 This is obviously from comparison of each firm’s operating profits against the entry costs− 10 −πF

Figure 1: Sorting in Export Market Selection

πγ (ϕ j , λ)πγ (ϕk , λ)01111111111
000000000
0
1000000000
111111111
0000000000 1
111111111
0
1000000000
111111111
0
1000000000
111111111
0
1000000000
111111111
0
1000000000
111111111
0
1000000000
111111111
0000000000 1
111111111
0000000000 1
111111111
0
1
0
1
0
1
0
1
0111
1
000
0
1
000
111
000
111
0
1
000
111
000
111
0
1
000
111
000
111
0
1
000
111
000
111
0
1
000
0111
1

k onlyNeitherπ(ϕ j , 1)m0

j onlyBothπ(ϕk , 1)γβFigure 1: Sorting in Exporting Market Selection: firm j is more productive than firm k thus πγ (ϕ j , λ)is positioned higher than πγ (ϕk , λ) and π(ϕ j , 1) higher than π(ϕk , 1). However, firm k has such a highcalibre ξk that the profits premium of the high quality variety represented by the area between πγ (ϕk , λ)and π(ϕk , 1) exceeds ξI . As a result, firm k enters all markets where the entry costs fall below πγ (ϕk , λ) forka given preference for quality γ; on the contrary, firm j ’s calibre is low so that the premium represented
by the area between πγ (ϕ j , λ) and π(ϕ j , 1) can not cover ξI and firm j enters the markets where entryjcosts fall below π(ϕ j , 1). The intersection of πγ (ϕk , λ) and π(ϕ j , 1) tells the entry costs and preference for
quality of market m 0 where both firms break even. Sorting means firm j but not firm k may enter somemarkets to the south-west of m 0 while firm k but not firm j may enter some markets to the north-east ofm0 .

− 11 −

2.5 Proposition 2 on Trade Liberalization and Quality Upgrade Investment
Trade liberalization provides incentive to invest in quality upgrade, no matter it takes theform of lowering variable iceberg trading costs or lowering fixed market entry costs.
As shown above, the incentive to invest in quality upgrading technology comes from the profits premium of exporting the high quality version of a variety. The premium is summarized in(??). This part shows that both the lowering of variable iceberg trading costs and fixed market
entry costs will drive up the premium thus provide more incentive for quality upgrading investment. We are going to use superscript 0 for variables before trade liberalization and superscript
1 for those after.

2.5.1 Lower Iceberg Trading Costs
The lowering of iceberg trading costs is equivalent to an improvement in production efficiency inthe sense that they increase operating profits in the same way. The increase of operating profits
will change the profits premium in (??) in several ways as shown below. For simplicity, suppose
trade liberalization does not affect fixed market entry costs but lowers the iceberg trading costs
everywhere.
Focusing on the subset of all markets with preference for quality γ• Firstly, since (λ(γ−β)(σ−1) −1)π1 (ϕi , 1) > (λ(γ−β)(σ−1) −1)π0 (ϕi , 1) for any given market, profits premium on common markets will increase.
• Secondly, F ϕi ,1 also increases with π(ϕi , 1) thus F ϕ1 i ,1 > F ϕ0 i ,1 . The fact that the two quality versions of the variety have more common markets also means that firm i can makepositive profits from exporting the low quality version of its variety to some markets where
before liberalization only high quality version can bring in positive profits, which in turnmeans a transformation of extensive margin to intensive margin. Since (λ(γ−β)(σ−1) −1)π1 (ϕi , 1) >(λ(γ−β)(σ−1) − 1)π0 (ϕi , 1) > π0γ (ϕi , λ) − F , the increase in the intensive margin must dominate the decrease in the extensive margin and the profits premium must increase for thesemarkets.
− 12 −

• Thirdly, the profits premium also increase on markets where firm i would enter only withthe high quality variety both before and after trade liberalization.
• Lastly, firm i may enter more markets with its high quality variety after liberalization.
In summary, we haveb 1 (ϕi ) − ∆Πb 0 (ϕi )∆ΠZ=
P r ob(F ≤ F ϕ0 i ,1 )[λ(γ−β)(σ−1) − 1][π1 (ϕi , 1) − π0 (ϕi , 1)] f γ (γ)d γ
γ

Z Z F1

+

ϕi ,1

γ

F ϕ0 ,1
i

Z

+

+

γ

{[λ(γ−β)(σ−1) − 1]π1 (ϕi , 1) − [π0 (ϕi , λ) − F ]} f F (F )d F f γ (γ)d γ

P r ob(F ϕ1 i ,1 < F ≤ F ϕ0 i ,γ )[π1γ (ϕi , λ) − π0γ (ϕi , λ)] f γ (γ)d γ

Z Z F1

+

+

ϕi ,γ

γ

F ϕ0 i ,γ[π1γ (ϕi , λ) − F ] f F (F )d F f γ (γ)d γ− 13 −

(15)

11
00

∆b
π1

1111
0000
0000
1111
0000
1111
0000
1111
0000001111110000001111110000001111110000000
11111110000000
11111110000000
11111110000000
11111110000000
11111110000000
1111111
000
111
000
111
000
111

Figure 2: Lowering Iceberg Trading Costs and High Quality Profits Premium

∆b
π

A

B

∆b
π0

C

D

0

F ϕ0 i ,1

F ϕ0 i ,γF ϕ1 i ,1

F ϕ1 i ,γF

Figure 2: Lowering Iceberg Trading Costs and High Quality Profits Premium: γ is fixed to draw this diagram. Region A represents the increase of premium from the common markets of the two quality versions both before and after trade liberalization; region B represents the increase of premium from markets
where extensive margin premium becomes intensive after liberalization; region C represents the increaseof premium from markets where only high quality variety is shipped to both before and after liberalization; region D represents the increase of premium from markets where it is only worthwhile to enter withhigh quality variety after liberalization.

− 14 −

2.5.2 Lower Fixed Market Entry Costs
Trade liberalization may take the form of lowering fixed market entry costs. For simplicity, assume trade liberalization only brings down entry costs and has no affect on variable iceberg
trading costs. With lower entry costs, firm i can bring in positive from more markets no matter
it is producing a low quality or a high quality variety which also means an increase along theintensive margin of quality premium. The new common markets used to contribute to quality
premium along the extensive margin but after trade liberalization their contribution is along theintensive margin. Since π(ϕi , 1) < F 0 and πγ (ϕi , λ) − π(ϕi , 1) > πγ (ϕi , λ) − F 0 , the contribution
along the intensive margin after trade liberalization must be larger than the contribution along
the extensive margin before trade liberalization. The premium on markets where firm i would
export its high quality variety to both before and after liberalization increase by the same magnitude as the entry costs decrease. The third source of increase in premium comes from exporting
the high quality variety to markets where it was not profitable before trade liberalization.
In summary, quality premium will increase with lower fixed market entry costs.

b 0 (ϕi )b 1 (ϕi ) − ∆Π∆ΠZ Z{[λ(γ−β)(σ−1) − 1]π1 (ϕi , 1) − [π0 (ϕi , λ) − F ]}d m f γ (γ)d γ=Tγ m∈M λ0

M c1

Z Z

+

γ

T
m∈M λ0 M λ1

0
1
− Fm
)d m f γ (γ)d γ
(F m

Z Z

+

+

γ m∈M n0T

M λ1[π1γ (ϕi , λ) − F ]d m f γ (γ)d γ(16)where M c0 denotes the set of common markets where both quality versions of variety i may earn
positive profits net of entry costs; M λ0 denotes the set of markets where only the high quality
version can achieve that; M n0 denotes the set of markets where operating profits of neither quality
version of i can cover entry costs; M λ1 denotes the set of markets where only the high quality
version can be profitable after liberalization.
Given that the lowering of either type of trading costs drives quality premium in the same
− 15 −

direction, we come to the conclusion that trade liberalization provides firms more incentive toinvest in quality upgrading technology.

3 Empirical Evidence for Sorting
The empirical part is going to test the following predictions from the model in the previous section.
Firstly, firms sort into different oversea markets, more specifically, firms that are more efficient in quality improving will invest more and export to more quality sensitive markets. The
empirical counterpart of the sorting theory is the contribution of the interactions of firms’ characteristics and market characteristics to the probability that we observe the firm-market pairin the data. Based on the belief that robots and machines are better in quality producing andcontrol than manual labour, a firm’s capital capital stock will be used as a proxy for quality production. Kraay, Soloaga and Tybout (2002) also finds positive correlation between firms’ capitalstocks and their quality measure in the study on chemicals manufacturers in Mexico, Columbia
and Morocco. The market quality preference will be estimated from the China’s customs transaction data. With these firm and market characteristics available, I can check the effect of theinteractions of these two on the probablity of the firm’s presence on the market. This argument
regarding the extensive margin of trade can also be extended to the intensive margin, that is, weshould see larger trade volume between firms using more capital and markets where consumers
are willing to pay more for quality.
Secondly, in order to make the exporting to more quality sensitive markets profitable, firms
may need to investment quality upgrading first. Investment may lower firms’ production costs or
improve products quality, but it is more likely to be the latter for firms in a less developed country
with cheaper labour as China. Thus beyond the positive relationship between investment andentry into a new market, we should also observe a positive contribution of the interaction ofquality preference and investment. Since trade liberalization lowers trading costs and makes
entering new markets more profitable, we should also observe a stronger correlation between− 16 −

investment and market entry after the entry of WTO.
This section is organized as following: 3.1 describes the data used for this study; 3.2 carries
out the demand estimation to get market specific quality preference adjusted price elasticity;
3.3 shows the evidence for sorting: firms using larger capital stock are more likely to be present
on markets with lower quality preference adjusted price elasticities; 3.4 shows the evidence ofcorrelation between investment and new market entry.

3.1 Data Description
The data are from two sources. Trading data (2000 2005) are from Customs of China. These aretransaction level records and include the following information: the identification of the trade
partner in China, 8-digit HS product code, the unit of measurement, the direction of trade (import or export), the forms of trade (the main categories are ordinary trade versus processing
trade), the quantity transacted, the money layout of the transaction, whether to be transferred ina third country, and the means of transportation.
There are two facts regarding the composition of Chinese exports that need special attention. Firstly, it is known that a lot of Chinese firms do not carry out international transactions
directly by themselves, but through specialized trading companies, as documented in Manova
and Zhang (2008). Since the purpose of this study is to show the correlation between producer
and market side characteristics, these transactions can not be included because of lack of producers’ identification. The share of trade through specialized trading companies has declined
from about one third to one fifth during the period of 2000 and 2005, but given that total tradevolume has been tripled from around 250 billion USD to more than 760 billion USD and the majority part of the exports through specialized trading companies is in the form of ordinary trade,
we should keep in mind that not all Chinese firms that are producing for international markets
are covered in this study.
The second fact is that within direct exports by manufacturing firms, ordinary trade accounts
for only 31 % of total export volume and its share increased to 36 % in 2005. The majority partof exports is through processing trade. In this case, unit values do not reflect much information− 17 −

on consumers’ preference as these transactions are very likely to happen only within a multinational. These transactions will be excluded as well for the current study.
In the end, we are looking at exports through ordinary trade by manufacturing firms. As
shown in Table 1, this part has experienced the fastest growth in total volume: more manufacturers are exporting and the average size becomes larger with the number effect stronger.
For the current draft, I also narrow down the scope of products by focusing on products thatfall into Broad Economic Categories 51 and 522. They are are passenger motor cars and transport
equipment for non-industrial use other than passenger motor cars. These two categories cover
62 8-digit HS product lines. The trading summaries of these products are provided in Table 2. The
growth pattern is quite similar to the whole picture of Chinese exports. I also narrow down thenumber of markets to investigate. From 2000 to 2005, Chinese manufacturers of these products
ship to more than 200 countries. I will focus on the top 45 markets which account for morethan 80% of the total exports through ordinary trade by manufacturers in this period. Table 3
gives a list of these countries. They are quite diversified in terms of market size and degree ofdevelopment.
The second source of data comes from the annual surveys on state-owned and above-scale(5 million RMB) manufacturing firms by China’s National Bureau of Statistics from 1998 to 2005.
This data set provides information on a wide range of firms’ economic activities. This data set
and additional variables constructed are documented in detail in Brandt, Van Biesebroeck andZhang (2009). The key variable taken from there and used in the current studies are firms’ employment and real capital stock.
All the records available for the selected products and markets in the Customs dataset will be
used to estimate the demand structure. But for the analysis of sorting, we need a matched sample
containing both firm and market characteristics. The two data sets are matched by available
information on firms’ identification. As shown in Table 4, we get better match results for later
years than for earlier years. In summary, we get 566 matched firms exporting to the largest 45
markets and these transactions account for three fifth of the total exports through ordinary tradeby manufacturers on the top 45 markets.
− 18 −

3.2 Demand Estimate
Discrete choice model is widely used in the Industrial Organization literature to estimate thedemand system of differentiated goods. Anderson, de Palma and Thisse (1992) shows the equivalence between the demand system derived from the discrete choice model and the one derived
from the CES utility specification common in the International Trade literature. Kraay, Soloaga
and Tybout (2002), Khandelwal (2009) are two examples of using this framework for producer
specific quality estimation. Instead of trying to infer firms’ qualities from their market shares ona specific market, I will use across markets variation to identify market heterogeneities in pricerelative to quality sensitivities.
u i m f t is the indirect utility that consumer i on market m receives from consuming the variety
of firm f at time t . Price of variety f , p i m f t , is allowed to differ across markets, but qualityof f λ f is held constant. Consumers on different markets have different sensitivities to pricepand quality change, denote by αλm and αm respectively. Randomness is introduced by allowing
heterogeneous perceived quality by each individual consumer, that is, consumer i ’s perceived
quality of variety f is λ f + λi m f t , where λi m f t is drawn independently from a Type I extreme
value distribution which is common across varieties, markets and years. Thus we haveu i m f t = αλm ∗ (λ f + λi m f t ) − αm ∗ p i m f t
p(17)

Consumer i on market m chooses to purchase variety f and not other varieties or the outside
option available on market m if and only if u i m f t > u i m f 0 t and u i m f t > u m0t . Since utility isordinal, dividing the indirect utility by a positive number will not affect market shares implied by
the original u i m f t . We divide the indirect utility of every consumer on market m by αλm and getpubi m f t = λ f −αm
αλm∗ p i m f t + λi m f t(18)− 19 −

This looks the same as the standard indirect utility specification in discrete choice models, but
we have a slightly different interpretation of the price coefficient as the quality preference adjusted price elasticity. Let βm =p

αm
. If we have βm < β0m , we know consumers on market m are
αλm

more quality concerned than consumers on market m 0 .
Integrate over consumers on market m, we can derive the market share of variety f

sm f t =exp λ f −βm ∗p i m f t
P
exp ubm0t + f 0 ∈Vm t exp λ f 0 −βm ∗p i m f 0 t(19)

which implies

l n(s m f t ) = λ f − βm ∗ p i m f t + D mt

where D mt = exp ubm0t +P

f 0 ∈Vm t exp(20)

λ f 0 −βm ∗p i m f 0 t

captures the market factors, such as market sizeand outside option, that have the same impact on all the active firms.
Market share in [??] plus a random factor gives the econometric specification of the demand
estimate. I make a simplification assumption that the randomness of the aggregated demandsystem comes only from measurement errors. In other words, the quality of a variety is assumed
to be the same no matter to which market the firm is shipping its products thus it can be fully
captured by firm fixed effects. This resolves the problem related to the general unobservability ofquality. Suppose firms do change their varieties to cater to different tastes across markets6 , thenwe would have a λ f m term in ?? which is not observable as the market-firm specific part of thequality of a variety, it is correlated with the observed price. Then we have the endogeneity bais inestimating the price coefficient and an instrument for price is necessary for consistent estimateof the price coefficient. This is one of the caveats of the work done by now7 .
6 We still assume firms do not differentiate consumers on one specific market. Otherwise, even λi m f t will not be

pure random and the integration from individual choice towards market shares will be much more complicated andwe may need random utility models.
7 I have tried using local wage, a cost shifter, as a price instrument. But unfortunately, after controlling firms’
fixed effects, there is not much variation left as firms in general do not produce in many locations and even if they

− 20 −

The estimation is carried out by pooling observations from all markets together, allowing
market specific price coefficients and controlling firm fixed effects, market by year fixed effects
and product fixed effects at 8-digit HS level. Table 5 shows the results. The first column gives theestimate of adjusted price elasticity which is restricted to be same across markets. The second
and third columns show the market specific estimates and the difference is whether the firm
fixed effects are controlled. The fourth and fifth columns show the F-statistics testing whether
the market specific estimates differ from the restricted one and whether the inclusion of the firm
fixed effects have significantly changed the market specific estimates.
We get significantly negative estimates for price coefficients on all markets.When checking
the ranking of countries according to our estimates, one should keep in mind that β is a the ratio
of price sensitivity over quality sensitivity. That means we can only derive the ranking of quality
sensitivity by taking the reverse ranking of β directly when the price sensitivity is constant acrosscountries. This does not seem to be the case here. We have obtained high estimates of β fordeveloped countries like Japan, Korea the United States, and lower ones for poor countries likeTogo and Nigeria, which is in contrast to the general sense that consumers in richer countries
are more quality concern. One possible explanation is that consumers in Japan, Korea and theUnited States are also relatively more price sensitive in these selected products. Price sensitivity
is about how consumers react to price change for a given product and the more efficient the market, the larger the response, holding other factors constant. Another possible explanation is thatthe consumers of the imported products in poor countries are not representative of the country’s
population. For current practice, the scope of products is restricted to transport equipment andmotor cars which could be perceived as luxury goods in poor countries and only consumed by
the richer people, thus only their preference is reflected in the estimates. We can check this laterby expanding the scope of products to ordinary, everyday consumption goods.
The F-test suggests that the βs do differ across markets as 19 of the market specific estimates
are significantly different from the restricted estimate. The inclusion of firm fixed effects havesignificantly changed 28 market specific estimates suggesting that controlling quality is impordo, the multi-location firms are very likely to be in a zone where many firms cluster and the residual from within firm
demeaning does not display much variation either.

− 21 −

tant in estimating the demand system.

3.3 Test for Sorting
The model suggests firms more capable of producing quality self-select into more quality sensitive markets, while more marginal cost efficient firms self-select into more price sensitive markets. If this sorting pattern actually exists, we should be able to observe a possible correlation between the probability that a firm and market pair is active and the interaction of the firm’s quality producing ability and the market’s preference for quality. The ratio of price sensitivity overquality sensitivity βm provides a measure of a market’s preference for quality. The smaller themagnitude of βm the larger the consumers’ reaction to quality change relative to price change.
If we do believe the usage of capital does help to improve quality, then capital stock, conditionalon firms employment size, does provide a measure for firms’ ability of producing quality.
Using all the firms that can be matched between the Customs and Annual Survey data andthe 45 markets, we construct a balanced panel for each year and then pool all years together.
We use dummy variable D to represent whether a firm-market pair is active. Value 1 is assigned
to active pairs. Table 6 summaries the number of total firm-market pairs and active ones in theconstructed sample. Then we run a logit model of D on the interaction of the estimate of −βmfrom 4.2 and other controls. The results are shown in Table 6. As expected, the estimates of thecoefficient of the interaction term are significantly positive in all the specifications8 . I also checkthe relationship between the export volumes and the interaction term conditional on the pair isactive and find that even though the sign is positive, the correlation is only significant at 10 %level in some specifications.
8 It is a bit surprising to see these regression results suggest that conditional on employment scale, capital stock is
negatively correlated with the possibility of being an active pair. We have some concern regarding our constructionof real capital. The capital depreciation rate might have been overestimated for earlier years which may lead to anunderestimate of the capital stock of firms staying longer in the annual survey sample and these firms are more likely
to be long term exporters and compose more active pairs.

− 22 −

3.4 Investment and Entry
The second prediction of the model is related to firms’ simultaneous decisions on investment
and entering new markets when trade liberalization lowers trading costs. Table 9 summarizes
the firms’ entry and investment behaviour by year. We can see the incidence of positive net investment, the number of new exporters and the number of new active firm-market pairs have all
increased in this period. The next question is whether the investment decision and entry decision are associated as the model suggests, in other words, whether the firms incur more investment in this period are also those who explore more markets, especially, more quality sensitivemarkets.
Several new variables are constructed. The first one is a dummy E N indicating whether apotential exporter actually enters a market. In the pooled panel we constructed above, E N takes
value 1 when a firm shows up in a market for the first time in any year other than the initial year
2000 and takes value 0 if the firm is active in exporting but never exports to the market. Exporting
experience also matters for exploring new markets. I construct three variables related to pastexporting experience for each potential entry,say firm f ’s decision on whether to enter market
m. The first one exp1 captures the difference between βm and the βs of the markets where firmf has exporting experience. I take the difference between βm and the volume weighted averageof the past βs. The second one exp2 is a count of market year cells corresponding to which firmf has been active in exporting. The third one exp3 is firm f ’s accumulated export volume in our
sample9Investment here is measured as the log of the difference between real capital stocks in two
consecutive years. I include the interaction of investment and the first experience measure since
our sorting story suggests that if a firm has only been exporting to markets where consumers
are more price sensitive, it may need to improve its ability of producing quality to appeal to the
9 So by including the experience variables, we are actually only looking at the existing exporters’ new markets

entry decision. However existing exporters entering new markets is not the main source of China’s export growthduring this period. New exporters have a much more important role in explaining export growth. As our key interest
here is to identify the sorting pattern rather than the source of growth, it is fine to work with this sample. To study
the entry decision of new exporters, a non-exporter comparison group need to be constructed which is left for futurework.

− 23 −

more quality sensitive consumers on the market it decides to enter. I also include the interactionof the WTO dummy, which takes value 1 since year 2002, and the investment measure, since as
the model predicts a stronger association between investment and market entry when trade liberalization lowers trading costs. The WTO effect may also interact with the sorting effect, so I also
tried the specification including the interaction of the three terms, the WTO dummy, investment
measure and our first experience measure and run regressions separately for subsamples before
and after the entry into WTO. The results are shown in Table 10.
When investment is included by itself or with its interaction with exp1, we get significantly
positive coefficients, but the significance disappears once the interactions with WTO are included. When we run regression for the before and after WTO subsamples separately, we getsignificantly positive estimates for the after WTO sample and insigfinicant or negative estimates
for the before sample. This is supportive to the model. The experience variables are significant
in all specifications. I also drop the experience variables to include new exporters in the sample
and run the regression for before and after WTO sample separately and it turns out after the entry into WTO, there is a significantly positive association between investment and new markets
entry.

4 Conclusion
This paper sets up a sorting model with the implication that the set of markets to which a firmchooses to ship its products could be a reflection of its quality level. I find positive correlation
between the possibility that a firm is active in exporting to a market and the interaction of themarket side variable representing its relative preference for quality and the firm side variable correlated with its ability in producing high quality products. Firms’ entry into a potential market isalso positively correlated its investment and the interaction of the investment with the differencein the attributes of the potential market and the firms’ existing markets.
The next step is to carry out the demand estimation to all products to be able to provide a big
picture of where Chinese firms have been shipping their products to and how that distribution− 24 −

has been changing over time, more specifically, how the markets coverage by the experienced
exporters’ has changed and where the new ones are located. On the other hand, no causality
relationship has been identified so far. The entry into WTO provides a good experiment to study
the effect of trade liberalization on firms’ decisions on investment, market entry which may provide some insights on the relationship between exporting and productivity growth.

References[1] Anderson, Simon, Andre de Palma and Jacques-Francois Thisse, 1992. "Discrete Choice Theory of Product Differentiation," Cambridge: MIT Press.
[2] Bernard, Andrew B., Stephen J. Redding and Peter K. Schott, 2009. "Multi-Product Firms andTrade Liberalization," Unpublished working paper, January.
[3] Brandt, Loren, Johannes Van Biesebroeck and Yifan Zhang, 2009. "Creative Accounting or
Creative Destruction? Firm-level Productivity growth in Chinese Manufacturing," NBER
working paper 15152, July.
[4] Brandt, Loren and Thomas G, Rawski, 2008. China’s Great Economic Transformation," Cambridge University Press.
[5] Brooks, Eileen L., 2006. "Why Don’t Firms Export More? Product Quality and Colombian
Plants," Journal of Development Economics 80, pages 160-178.
[6] Crozet, Matthieu, Keith Head and Thierry Mayer, 200. "Quality Sorting and Trade: FirmLevel Evidence for French Wine," 7295 C.E.P.R. Discussion Papers.
[7] Hallak, Juan Carlos, 2006. "Product Quality and the Direction of Trade," Journal of International Economics, vol. 68(1), pages 238-265, January.
[8] Hallak, Juan Carlos and Jagadeesh Sivadasan, 2008. "Productivity, Quality and Exporting
Behavior Under Minimum Quality Requirements,Ť Unpublished working paper, July.

− 25 −

[9] Hallak, Juan Carlos and Peter K. Schott, 2009. Estimating Cross-Country Differences in Product Quality," Unpublished working paper, January.
[10] Johnson, Robert C., 2008. "Trade and Prices with Heterogeneous Firms," Unpublished working paper, January.
[11] Khandelwal, Amit, 2009. "The Long and Short (of ) Quality Ladders," NBER working paper
15178, July.
[12] Kraary, Aart, Isidro Soloaga and James Tybout, 2002. "Product Quality, Productive Efficiency,
and International Technology Diffusion: Evidence from Plant-Level Panel Data," WorldBank.
[13] Kugler, Maurice and Eric Verhoogen, 2008. "The Quality-Complementarity Hipothesis: Theory and Evidence from Colombia," NBER working paper 14418, October.
[14] Lileeva, Alla and Daniel Trefler, 2007. "Improved Access to Foreign Markets Raises Plant-Level
Productivity ... for Some Plants," Unpublished working paper, July.
[15] Manova, Kalina and Zhiwei Zhang, 2008. "China Exporters and Importers: Firms, Products,
and Trade Partners," Unpublished working paper, June.
[16] Melitz, Marc J., 2003. "The Impact of Trade on Intra-Industry Reallocations and Aggregate
Industry Productivity," Econometrica, vol. 71(6), pages 1695-1725, November.
[17] Rodrik, Dani, 2006. "WhatŠs so Special about ChinaŠs Exports?" NBER Working Paper
w11947.
[18] Schott Peter K., 2007. "The Relative Sophistication of Chinese Exports," Unpublished working paper, March.
[19] Zhi, Wang and Shangjin Wei, 2008. "What Accounts for the Rising Sophistication of China’s
Export," NBER working paper w13771, February.

− 26 −

Table 1: China's Total Export Decompostion, 2000-2005
Export Value: Billions US Dollar
2000
51.93
24.57
90.56
59.66
16.56
5.96
67%
21%
31%

2001
59.69
25.85
99.21
59.58
15.93
5.54
70%
22%
32%

2002
78.45
30.06
126.99
67.21
17.40
5.52
72%
24%
33%

2003
111.2735.67
181.61
85.26
18.55
5.93
75%
25%
34%

2004
161.9048.55
252.73103.49
20.06
6.92
78%
27%
35%

2005
221.5164.82
323.89123.62
19.07
8.71
80%
29%
36%

Growth
327%
164%
258%
107%
15%
46%
13%
8%
5%

2000
41075
7606
25150
7748
1431
2059
73831
56%

2001
45427
8402
25345
8500
1479
2037
79174
57%

2002
54693
9309
26505
9559
1502
2024
90507
60%

2003
67887
10015
28625
12565
1593
2008
106527
64%

2004
87854
11353
31636
16651
1653
1996
13084367%2005
111875
12662
34198
19703
1783
2181
15873570%

Growth
172%
66%
36%
154%
25%
6%
115%
15%

Average Volumn per Firm: Millions US Dollar
2000
Ordinary_Trade
1.26
MFG
PT_Pure_Assembly
3.23
PT_Import_Assembly
3.60
Ordinary_Trade
7.70
STC
PT_Pure_Assembly
11.57
PT_Import_Assembly
2.90
Average of All MFG
2.26
Ave. OT&MFG/Ave.OT
56%

2001
1.31
3.08
3.91
7.01
10.77
2.72
2.33
56%

2002
1.43
3.23
4.79
7.03
11.59
2.73
2.60
55%

2003
1.64
3.56
6.34
6.79
11.65
2.96
3.08
53%

2004
1.84
4.28
7.99
6.22
12.14
3.47
3.54
52%

2005
1.98
5.12
9.47
6.27
10.70
3.99
3.84
52%

Growth
57%
59%
163%
-19%
-8%
38%
70%
-4%

MFG

STC

Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Share of MFG
Share of OT by MFG
Share of OT in MFG

Number of Firms

MFG

STC

Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Total of MFG
Share of OT in MFG

Table 2: China Exports of BEC51 and BEC522 Decompostion, 2000 - 2005
Export Value: Millions US Dollar
2000
471.547.26
545.87560.71222.31
61.35
55%
25%
46%

2001
480.506.55
506.82646.32152.53
53.16
54%
26%
48%

2002
508.318.73
733.57686.56
92.04
51.23
60%
24%
41%

2003
1035.8124.30
825.281168.52
45.12
75.34
59%
33%
55%

2004
1590.3824.17
1072.111380.63
34.82
38.56
65%
38%
59%

2005
2252.2635.13
1421.25
1509.50
44.50
56.47
70%
42%
61%

Growth
378%
384%
160%
169%
-80%
-8%
15%
17%
15%

2000
451
12
79
679
4
25
542
83%

2001
516
16
94
734
7
24
626
82%

2002
644
16
103
876
9
31
763
84%

2003
816
22
119
1120
6
24
957
85%

2004
1062
20
142
1159
4
24
1224
87%

2005
1514
27
171
1302
6
28
1712
88%

Growth
236%
125%
116%
92%
50%
12%
216%
5%

Average Volumn per Firm: Millions US Dollar
2000
Ordinary_Trade
1.05
MFG
PT_Pure_Assembly
0.61
PT_Import_Assembly
6.91
Ordinary_Trade
0.83
STC
PT_Pure_Assembly
55.58
PT_Import_Assembly
2.45
Average of All MFG
1.89
Ave. OT&MFG/Ave.OT
55%

2001
0.93
0.41
5.39
0.88
21.79
2.21
1.59
59%

2002
0.79
0.55
7.12
0.78
10.23
1.65
1.64
48%

2003
1.27
1.10
6.94
1.04
7.52
3.14
1.97
64%

2004
1.50
1.21
7.55
1.19
8.71
1.61
2.19
68%

2005
1.49
1.30
8.31
1.16
7.42
2.02
2.17
69%

Growth
42%
115%
20%
40%
-87%
-18%
15%
13%

MFG

STC

Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Share of MFG
Share of OT by MFG
Share of OT in MFG

Number of Firms

MFG

STC

Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Ordinary_Trade
PT_Pure_Assembly
PT_Import_Assembly
Total of MFG
Share of OT in MFG

Table 3: Country List

Country

United StatesJapan
VietnamIndonesia
Nigeria
United Arab Emirates
Korea Rep.
United Kingdom
Germany
Myanmar
MexicoHong Kong
TurkeyTogo
IranPhilippines
Spain
RussiaSaudi Arabia
Australia
Mali
Italy
Malaysia
Colombia
BrazilArgentina
South Africa
FranceSyria
CanadaUkraineEcuador
Netherlands
Pakistan
GuatemalaPanamaBangladesh
GreeceChile
Belgium
LebanonVenezuela
Peru
Algeria
Egypt

Volume of Exports
through Ordinary
Trade (Million USD)
1198.09703.15413.78297.76255.35141.77141.09136.65130.80127.01
126.51117.65110.80110.71
99.22
96.43
88.76
86.31
81.79
77.92
62.59
60.04
59.12
51.39
49.39
48.85
47.55
46.69
44.70
44.30
43.13
42.26
41.50
41.03
39.20
37.08
36.42
33.51
32.31
31.85
31.57
31.15
30.98
29.82
29.18

Table 4: Export Volume by Matched and Unmatched Exporters
2000
2001
2002
2003
Volume(Millions
USD)

2004

2005

Unmatched
Exporter

127

192

207

346

480

685

MatchedExporters

112

150

200

500

840

1124

Table 5: Demand Estimation
Dep. lnq

All
Algeria
Argentina
Australia
Bangladesh
BelgiumBrazilCanadaChile
Colombia
Ecuador
Egypt
FranceGermany
GreeceGuatemalaHong Kong
Indonesia
Iran
Italy
Japan
Korea Rep.
LebanonRegression

Test

Market Specific Market Specific Same - ββ, Without Firmβ, With Firm FE
Across Markets
FE

F test F test (p(p value) value)-1.045***(0.0296)-0.732***(0.0937)-0.684***(0.151)-0.966***(0.0455)-0.847***(0.0910)-1.231***(0.0847)-1.004***(0.141)-1.021***(0.0650)-1.137***(0.0619)-0.885***(0.106)-0.817***(0.0664)-0.832***(0.0768)-0.951***(0.0651)-0.901***(0.0421)-0.905***(0.0719)-0.673***(0.0762)-0.805***(0.0332)-1.043***(0.0661)-1.215***(0.0717)-1.003***(0.0543)-1.126***(0.0371)-1.185***(0.0485)-0.911***(0.0833)-0.759***(0.0957)-0.677***(0.150)-1.042***(0.0500)-0.914***(0.0971)-1.235***(0.0848)-1.094***(0.137)-1.126***(0.0689)-1.191***(0.0647)-0.875***(0.106)-0.822***(0.0682)-0.838***(0.0825)-1.004***(0.0684)-0.984***(0.0470)-0.899***(0.0721)-0.732***(0.0766)-0.944***(0.0452)-1.019***(0.0728)-1.226***(0.0745)-0.988***(0.0577)-1.290***(0.0473)-1.346***(0.0552)-0.910***(0.0819)0.00280.68580.01380.4207

0.94630.00000.17630.23150.02510.00000.72030.03010.24190.00000.02430.00000.11090.46480.00110.71580.01220.62070.54450.0026

0.18950.0001

0.04240.15980.00000.39460.02460.00120.71560.00240.01530.00000.32320.00100.00000.00000.00000.00000.09780.1713

Malaysia-1.016***-1.022***(0.0617)(0.0653)
Mali-0.563***-0.595***(0.156)(0.155)
Mexico-0.931***-0.914***(0.0803)(0.0797)
Myanmar-0.973***-1.345***(0.102)(0.173)
Netherlands-0.934***-0.962***(0.0579)(0.0607)
Nigeria-0.611***-0.649***(0.0813)(0.0823)
Pakistan-0.916***-0.900***(0.0752)(0.0778)
Panama-1.035***-1.111***(0.0697)(0.0713)Peru-0.853***-0.874***(0.0847)(0.0862)
Philippines-0.825***-0.843***(0.0606)(0.0643)
Russia-1.118***-1.140***(0.0588)(0.0650)
Saudi Arabia-1.032***-1.078***(0.0586)(0.0625)
South Africa-0.887***-0.949***(0.0535)(0.0563)
Spain-1.120***-1.093***(0.0657)(0.0671)
Syria-0.749***-0.765***(0.106)(0.111)Togo-0.323*-0.423**(0.194)(0.186)
Turkey-0.859***-0.869***(0.0908)(0.0919)
Ukraine-1.023***-1.032***(0.0745)(0.0771)
United Arab Emirates-1.095***-1.130***(0.0468)(0.0515)
United Kingdom-0.959***-0.967***(0.0505)(0.0535)
United States-1.081***-1.183***(0.0338)(0.0405)
Venezuela-1.087***-1.067***(0.0705)(0.0721)
Vietnam-0.798***-1.106***(0.116)(0.144)Market*Year fixed effects, 8-d HS product fixed
Other controls
effects
Observations
26328
26328
26328
R-squared
0.544
0.418
0.550
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.72510.00060.0036

0.18940.10100.14260.08300.00160.16900.00680.00000.07190.06220.18750.35420.00000.04680.37580.00170.47770.14400.00000.60410.00000.08810.00700.47270.00000.01150.76980.00080.0446

0.05520.43630.84560.00240.09950.00000.14510.00150.00070.00000.76530.0002

0.67670.0315NN(p<0.05) (p<0.05)
19
28

Table 6: Constructed Firm-Market Panel# of Distinct Firms
Total pairs (# of distinct firms by 45)
Active pairs

2000
76
3420
272

2001
114
5130
451

2002
142
6390
707

2003
213
9585
1186

Table 7: Logit Regression of Firm's Presence on Firm and Market Characteristics
Dep. D 1 if active; 0 if not
(1)
(2)
(3)
(4)
- β * log(real capital)

0.178***(0.0128)0.157***(0.0393)

0.339***(0.0195)-2.032***(0.128)0.348***(0.0190)-2.038***(0.130)- β * log(employment)log(employment)
Constant

Other controls
Observations
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.173***
0.117***(0.0126)(0.0409)
0.000117*** 0.000118***(1.37e-05) (1.37e-05)
0.439***
0.446***(0.0223)(0.0217)-2.577***-2.629***(0.141)(0.145)K/Llog(real cap.)
K/Llog(real cap.)
Market and Year Fixed Effects
52605
52605
52605
52605

Table 8: Export Volume and Firm and Market Characteristics, Conditional on Being Acti
Dep. log(export volume)
(1)
(2)
(3)
(4)
- β * log(real capital)
log(real capital)
log(employment)
Constant

Other controls
Observations
R-squaredStandard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.144(0.0966)
0.111(0.103)0.261***(0.0404)
8.733***(0.293)0.0446*(0.0268)

0.271***(0.0415)
8.668***(0.287)0.143(0.0968)
0.111(0.104)0.262***(0.0447)
8.722***(0.318)0.0443*(0.0269)

0.274***(0.0462)8.650***(0.312)

C/L & - β *log(emp)
Market and Year Fixed Effects
6961
6961
6961
6961
0.089
0.088
0.089
0.088
K/L

- β * log(emp)Table 9: Summaries of Exporters Investment and Entry Behaviour# of Exporters with Net Investment Summaries (10 thousand RMB)
# of
# of New # of Active
Year
Positive Net Inv.
Exporters Exporters
Pairs
Mean
Median
p25
p75
2000
76
31
186506
4888
1251
30057
272
2001
114
34
30254
3425
1045
13715
67
451
2002
142
50
38525
4994
675
13499
63
707
2003
213
97
39419
3488
923
8837
110
1186
2004
299
112
35015
3680
1094
14106
157
1973
2005
326
158
44878
3127
594
10649
93
2390

# of New
Active Pairs
332
483
788
1328
1141

Table 10: Logit Regression of Market Entry on Firm Investment and Firm - Market
Characteristics
Dep. EN
(1)
(2)
(3)
(4)
(5)
1 pair becomes active
0 pair stays inactivelog(net Inv)

0.00252***(0.000565)

exp1*log(net Inv.)

0.00163***(0.000607)
0.00803***(0.00205)

WTO*log(net inv.)-0.00107(0.00237)

0.00382(0.00245)-0.00181(0.00238)
0.00798***(0.00206)
0.00366(0.00245)

exp1*WTO*log(net inv.)
exp2
exp3
Constant

0.0257***(0.00245)
0.0740***(0.0119)-2.424***(0.239)Other controls
Observations
22125
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1
Dep. EN
1 pair becomes active
0 pair stays inactivelog(net Inv)
exp1*log(net Inv.)

0.0249***0.0258***0.0249***(0.00247)(0.00246)(0.00247)
0.0773***0.0733***0.0766***(0.0120)(0.0119)(0.0120)-2.482***-2.395***-2.455***(0.239)(0.240)(0.240)Market and Year Fixed Effects
22125
22125
22125

(6)

(7)

(8)

(9)

0.00249(0.00280)-0.0216**(0.00959)

0.00146**(0.000631)
0.00942***(0.00215)

0.00230(0.00150)

0.00288***(0.000454)

WTO*log(net inv.)
exp1*WTO*log(net inv.)
exp2
exp3
Constant

0.146***(0.0313)0.0288(0.0479)-1.673*(0.919)Other controls
Observations
1311
Standard errors in parentheses*** p<0.01, ** p<0.05, * p<0.1

0.0236***(0.00251)
0.0742***(0.0125)-2.597***-2.611***-1.957***(0.248)(0.563)(0.153)Market and Year Fixed Effects
20461
3604
32431

-0.000943(0.00247)-0.00142(0.00617)
0.00273(0.00254)
0.0102(0.00639)
0.0250***(0.00247)
0.0766***(0.0120)-2.641***(0.269)22125