Competition and Gains from Trade: A Quantitative
Analysis of China Between 1995 and 2004
Wen-Tai Hsu

Yi Lu

Guiying Laura Wu y

December 6, 2015

Abstract
This paper provides a quantitative analysis of gains from trade for China overthe period of 1995–2004, which was when China’s openness drastically improved .
We decompose gains from trade in two ways. First, we disentangle pro-competitivee¤ects from a traditional Ricardian e¤ect. Second, we separate the e¤ect due to tari¤
reductions from that due to reductions in non-tari¤ trade costs. Our quantitative
analysis shows that the pro-competitive e¤ects account for 25:4% of the total welfare
gains from trade, whereas the allocative e¢ ciency alone accounts for 22:3%. We also
…nd that tari¤ reductions account for about 31:6% of reductions of overall trade costs,
whereas the associated relative contribution to overall gains is slightly larger at 39:6%.
In our multi-sector analysis, we …nd that when a sectoral markup is higher in 1995, there
tends to be a larger reduction in the respective sectoral trade cost between 1995 and2004, a tendency that is generally welfare improving. One methodological advantageof this paper’s quantitative framework is that its application is not constrained byindustrial or product classi…cations, and so it can be applied to countries of any size.

Hsu: School of Economics, Singapore Management University. 90 Stamford Road, Singapore 178903.
Email: wentaihsu@smu.edu.sg; Lu: Department of Economics, National Singapore of Singapore. 1 Arts
Link, Singapore, 117570. Email: ecsluyi@nus.edu.sg; Wu: Division of Economics, School of Humanities
and Social Sciences, Nanyang Techonological University. 14 Nanyang Drive, Singapore 637332. Email:guiying.wu@ntu.edu.sg.
yFor their helpful comments, we thank Costas Arkolakis, Issac Baley, Pao-Li Chang, Jonathan Eaton,
Chang-Tai Hsieh, Nicolas Jacquet, Sam Kortum, Hong Ma, Tom Sargent, Michael Zheng Song, Ping Wang,
Yong Wang and Daniel Yi Xu. We also thank Lianming Zhu and Yunong Li for their excellent research assistance. Hsu gratefully acknowledge the …nancial support provided by the Sing Lun Fellowship of Singapore
Management University.

1

Introduction

It is well understood that competition may a¤ect gains from trade via changes in the distribution of markups.1 For example, in the event of trade liberalization, allocative e¢ ciency
may improve if the dispersion of markups is reduced. This is because when markups are
the same across all goods, …rst-best allocative e¢ ciency is attained, as the condition thatthe price ratio equals the marginal cost ratio, for any pair of goods, holds. With markup
dispersion, …rms with low markups may produce/employ more than optimal whereas thosewith high markups may produce/employ less than optimal. Moreover, the average level ofmarkups also matter because welfare improves when consumers bene…t from lower markupsof the goods they consume and when producers gain from higher markups (hence higher
pro…ts) in foreign markets. Jointly, these e¤ects of level and dispersion of markups can betermed pro-competitive e¤ects of trade.
This paper aims to provide quantitative analyses of gains from trade for China overthe period of 1995–2004, which was when China drastically improved openness, partly due
to joining World Trade Organization (WTO) at the end of 2001.2 We will focus on thedecomposition of welfare gains by disentangling pro-competitive e¤ects from a traditional
Ricardian e¤ect to gauge its relative importance. The main e¤ect of entry to the WTO istari¤ reductions,3 but numerous other factors may have also improved China’s openness.4
Thus, we are also interested in quantitatively separating the e¤ect due to tari¤ reductions
from that due to reductions in non-tari¤ trade costs. As entry to the WTO also involvesome deregulations, the e¤ect of tari¤ reduction provides a lower bound of the e¤ect due tothe WTO.
Our point of departure is two-fold. First, Brandt, Van Biesebroeck, Wang and Zhang(2012) and Lu and Yu (2015) have both estimated …rm-level markups using Chinese manufacturing data and the approach by De Loecker and Warzynski (2012; henceforth DLW).
Lu and Yu (2015) show that the larger the tari¤ reduction due to the WTO entry in one
industry, the greater the reduction in the dispersion of markups in that industry. Brandt
et al. present similar results on levels of markups. Their results hint at the existence of1

For examples of theoretical analyses of how trade may a¤ect welfare through markups, see Devereux
and Lee (2001), Epifani and Gancia (2011), Holmes et al. (2014) and Arkolakis et al. (2015).
2
Between 1995 and 2004, the import share increased from 0:13 to 0:22, whereas the export share increased
from 0:15 to 0:25. The proportion of exporters among manufacturing …rms increased from 4:4% to 10:5%.
3
As a condition to the entry to WTO (and its earlier form, GATT), China was required to lower tari¤seven before entry. The tari¤s were reduced substantially between 1992 and 1997. Another round of tari¤
reductions took place after 2001 to carry out its promise to WTO members.
4
These factors include, for instance, developing infrastructure, including various seaports and airports
and their inland connections, and expanding the education system, which accumulated human capital whichfacilitates communications with the rest of the world.

1

pro-competitive e¤ects, but a formal welfare analysis is warranted.5
Second, Edmond, Midrigan and Xu (2015) have also provided a quantitative analysis ofpro-competitive e¤ects of trade using data from Taiwanese manufacturing …rms and Atkeson
and Burstein’s model (2008), which features heterogeneous-product Cournot competition.
Their model has a sensible feature that links markups with …rms’ market shares. The
Taiwanese data works well for their oligopoly environment because they can go down to very…ne product level to look at a few …rms to examine their market shares. However, it could
be di¢ cult to apply their framework to a large economy (such as the US, Japan or China)
where even in the …nest level of industry or product, there may be hundreds of …rms so that…rms’market shares are typically much smaller compared with a similar data set for a small
country. The problem here is that when …rms’market shares are “diluted” by country size
for a given industry or product category, so are pro-competitive e¤ects. This is not to saythat pro-competitive e¤ects do not exist in large countries; rather, it may be that there are
actually several markets in an industry or product category, but we simply do not know how
to separate them. In light of this problem, we propose an alternative framework that does
not tie markups with industrial/product classi…cations, and therefore could be applied todata from countries of any size.
We build our quantitative framework on the model by Bernard, Eaton, Jensen and Kortum (2003; henceforth BEJK). To help understand, we note three features of BEJK. First,
the productivity of …rms is heterogeneous and follows Frechét distribution, which can di¤er
across countries. Second, …rms compete in Bertrand fashion market by market with active…rms charging prices at the second lowest marginal costs. Third, although di¤erences inmarkups are driven by productivity di¤erences through limit pricing, it turns out that theresulting markup distribution is invariant to the trade cost. Later, Holmes, Hsu and Lee(2014) …nd that this invariance is due to the assumption that the productivity distribution
is fat-tailed (Frechét). If productivity draws are from a non-fat-tailed distribution, then the
distribution of markups may change with the trade cost, and pro-competitive e¤ects of trade
may be observed.
Following the above discussion, we examine the distribution of markups in China in 1995
and 2004, which are shown in Figure 1. The distributions are highly skewed to the right,
and it is clear that the distribution in 2004 is more condensed than that in 1995. Indeed,the (unweighted) mean markup decreases 1:43 to 1:37 and the standard deviation decreases
from 0:50 to 0:48.6 Under the BEJK structure, this suggests that one needs to deviate from5

For a survey of earlier evidence of the impact of foreign competition on markups, see Tybout (2003).
The harmonic means weighted by revenue are 1:259 and 1:229 for 1995 and 2004, respectively. The
above-mentioned pattern also exists when we break the sample into exporters and non-exporters. For detailsof markup estimations, see Section 3.
62

fat-tailed distributions to account for such changes.
We adapt the BEJK variant by Holmes et al. (2014) by adding the following parameterization: we assume that productivity draws are from log-normal distributions and thatthe number of …rms per product is a random realization from Poisson distribution. The
log-normal distribution has been widely used in empirical applications, and the Poisson parameters provide a parsimonious way to summarize the overall competitive pressure (or entrye¤ort) in the economy.7 As the …rms observed in the data are supposed to be those thatsurvive the Bertrand competition, it is the latent competitors that drive the markups, andhence markups are not tied to other active …rms in a given industrial/product category.
The main data sets we use are Chinese …rm-level data from the Economic Censuses in1995 and 2004. We choose these two years because they are the Economic Census years
before and after entry to the WTO. We prefer using the Economic Census rather than thecommonly used annual survey data that reports only …rms with revenues of at least 5 million
renminbi. Since we are concerned with potential resource misallocation in markup channels,
it is important to have data on the entire distribution, instead of using a truncated one.
Because the model is static and because we would like to remain agnostic about how
the underlying environment changes over time, we estimate all parameters in each data year
separately, as if we are taking snapshots of the Chinese economy in the respective years.
This is important because we can then gauge the e¤ect of “actual improvement in openness”
via the change in the estimated trade cost and conduct corresponding welfare analysis. As
we focus on competition, our empirical implementation relies heavily on markups. We …rst
estimate …rm-level markups following DLW and then use moments of markups to discipline
model parameters, along with the moments of trade ‡ows, active number of …rms, andfraction of exporters.
The model performs well as the macro variables reproduced by the estimated model aresimilar in magnitude to the data counterparts. Moreover, the pattern of changes in theparameters between 1995 and 2004 are strikingly consistent with well-known facts about the
Chinese economy during this period. The estimated trade friction drops signi…cantly from1995 to 2004, while the Poisson entry parameters also increase, re‡ecting the fact that not
only China becomes more open, but its markets also become more competitive. The mean
productivity in China relative to the rest of the world (ROW) also increases signi…cantly,
and this is consistent with the high growth rate of China during this period.
To gauge the gains from trade between 1995 and 2004, we conduct a counter-factual
7

For examples of applications of log-normal distribution, see Cabral and Mata (2003) and Head, Mayer,
and Thoenig (2014). Another non-fat-tailed distribution that is often used is bounded Pareto, e.g. Helpman,
Melitz and Rubinstein (2008) and Melitz and Redding (2015). Eaton, Kortum and Sotelo (2013) also model…nite number of …rms as a Poisson random variable, but for a very di¤erent purpose.

3

analysis based on 2004 estimates but revert the trade cost back to the level estimated using 1995 data. The gain from trade is about 9:4%, and the relative contribution of theoverall pro-competitive e¤ect is 25:4% of the total gains. The improvement of allocativee¢ ciency accounts for 22:3% of the total gains, whereas the markup level e¤ect accounts
for the remaining 3:1%. This sends two messages: (1) Signi…cant resource misallocationis re‡ected in the markup dispersion; (2) Although both producers’ and consumers’ mean
markups decreased with trade liberalization, the decrease in consumers’mean markup waslarger, causing a positive e¤ect due to levels. But, such an e¤ect is much smaller whencompared to resource misallocation. Another counter-factual is to compare with autarky,
and the relative contribution of pro-competitive e¤ects remains similar. We also conduct
a series of alternative estimations and counter-factual analyses to gauge the robustness ofour benchmark result. This includes a symmetric-country model, an alternative measure ofmarkups, and a counter-factual analysis based on 1995 estimates. Among these cases, therelative contribution of pro-competitive e¤ects ranges between 19:4% and 31:4%.
For the second decomposition, we …rst calculate average tari¤s facing China (including
both import and export tari¤s), weighted by trade volumes. The average tari¤ drops from
15:7% to 4:3% between 1995 and 2004. Using the estimated trade costs, we decompose theminto tari¤ and non-tari¤ trade costs. Despite entry to the WTO being such a major event,
our calculation shows that tari¤ reductions account for only 31:6% of reductions of overall
trade costs, whereas the associated relative contribution to overall gains is slightly larger at
35 40%. In other words, tari¤ reductions are a signi…cant contributing factor in enhancing
China’s openness, but are less important than the reduction in non-tari¤ trade frictions.
The framework in this paper can be easily extended to a multi-sector economy, and we dothis to account for various heterogeneity across sectors. The welfare results in the multi-sector
economy remain similar to the one-sector economy, with the relative contribution of the procompetitive e¤ects and tari¤ reductions around 20% and 35%, respectively. Exploiting thevariations in sectoral markups and trade costs, we attempt to answer the question of whether
China trade-liberalized the “right” sectors? The rationale is that the overall allocativee¢ ciency would be better improved if the government targets its trade liberalization morein the higher-markup sectors because this would reduce the dispersion of markups acrosssectors. We …nd that when a sectoral markup was higher in 1995, there was a tendency fora larger reduction in the estimated trade cost or import tari¤ between 1995 and 2004.
Besides related studies already discussed, our literature review starts with Arkolakis,
Costinot and Rodriguez-Clare (2012; henceforth ACR), who show that there is a class ofin‡uential trade models in which the welfare measure can be summarized by a simple statisticthat depends only on domestic expenditure share and trade elasticity. This class includes
4BEJK and features no pro-competitive e¤ects. By using Holmes et al. (2014), our welfare
formula extends the ACR formula in the sense that a productive e¢ ciency index closely
traces the ACR statistic, and that the pro-competitive e¤ects enter as two multiplicative
terms.8
Whereas Edmond et al. (2015) and this paper adopt oligopolistic approaches to study
pro-competitive e¤ects of trade, another approach is to couple monopolistic competition witha non-CES preference, and this includes Ottaviano, Tabuchi and Thisse (2002), Melitz andOttaviano (2008), Behrens and Murata (2012), Feenstra (2014), and Arkolakis, Costinot,
Donaldson, and Rodriguez-Clare (2015). In particular, Arkolakis et al. show that procompetitive e¤ects are “elusive”, and Feenstra shows that the pro-competitive e¤ects could
emerge when productivity draws are from a bounded distribution. Note that the economicsof the pro-competitive e¤ects are very di¤erent in a monopolistic competition model thanin the oligopoly model we consider. In monopolistic competition, a change in the trade cost
only a¤ects a domestic …rm through general equilibrium e¤ects that might shift or rotate
the …rm’s demand curve. In contrast, in a Bertrand environment, the pro-competitive forceof trade operates at the level of the particular good, not through general equilibrium.9
Our work is also related to Atkeson and Burstein (2008), de Blas and Russ (2012), andGoldberg, De Loecker, Khandelwal and Pavcnik (2015), who provide analyses of how tradea¤ects the distribution of markup. Our work is di¤erent from these papers in that our
focus is on quantitative welfare analysis. This paper also relates to the literature on thewelfare impact of China’s growth and trade integration, e.g. Song, Storesletten and Zilibotti(2011), di Giovanni, Levchenko and Zhang (2014) and Hsieh and Ossa (2015). The literature
discussion above focuses speci…cally on trade. We note our paper is also part of a broader
literature on how allocative e¢ ciency a¤ects aggregate productivity, including Restuccia andRogerson (2008), Hsieh and Klenow (2009), and Peters (2012).
The rest of the paper is organized as follows. Section 2 lays out the model; Section 3
explains the data and quanti…es the model; Section 4 presents the results on counter-factual
analyses; Section 5 extends the model to multiple sectors; and Section 6 concludes.
8

If markups were a constant, then the pro-competitive terms drop out, reducing the welfare measure
to the ACR statistic. It is worth noting that trade may sometimes a¤ect welfare without observed trade‡ows. For example, Salvo (2010) and Schmitz (2005) show that the threat of competition from imports canin‡uence domestic outcomes, even if in the end, the imports don’t come in.
9
Other recent studies on gains from trade via di¤erent angles from the ACR …nding include at least
Melitz and Redding (2015) on re-examining the selection e¤ect in gains from trade and an additional e¤ect
due to thinner tails (bounded Pareto); Caliendo and Parro (2015) on the roles of intermediate goods andsectoral linkages; and di Giovanni, Levchenko, and Zhang (2014) on the global welfare impact of China’s
trade integration and productivity growth. Our work di¤ers in that we focus on the pro-competitive e¤ects.

5

2

Model

2.1

Consumption and Production

There are two countries, which are indexed by i = 1; 2. In our empirical application, 1 means
China, and 2 means the ROW. As is standard in the literature of trade, we assume a single
factor of production, labor, that is inelastically supplied, and the labor force in each country
is denoted as Li . There is a continuum of goods with measure , and the utility function ofa representative consumer is
Q=

Z !

1

(q! )

1

for

d!

> 1,

0

where q! is the consumption of good !, is the elasticity of substitution, and !
is themeasure of goods that are actually produced. We will specify how ! is determined shortly.
The standard price index is
1
Z !
1
1
pj! d!
Pj.
0

Total revenue in country i is denoted as Ri , which also equals the total income. Welfare ofcountry i’s representative consumer is therefore Ri =Pi , which can also be interpreted as realGDP. The quantity demanded (qj! ) and expenditure (Ej! ) for the product ! in country j
are given byp

qj! = Qj

pj!
Pj

Ej! = Rj

pj!
Pj

;
1

;

1

j!
is country j’s spending share on the good !.
and j!
Pj
For each good !, there are n! number of potential …rms. Production technology isconstant returns to scale, and for a …rm k located at i, the quantity produced is given by

q!;ik = '!;ik `!;ik ;where '!;ik is the Hicks-neutral productivity of …rm k 2 f1; 2; :::; n!;i g, n!;i is the numberof entrants in country i for good !, and `!;ik is the amount of labor employed. Note thesubtle and important di¤erence between subscript j! and !; i. The former means that itis the purchase of ! by consumers at location j, and the latter is the sales or production

6

characteristics of the …rm located at i producing !.

2.2

Measure of Goods and Number of Firms

The number of …rms for each good ! 2 [0; ] in each country i is a random realization froma Poisson distribution with mean i . That is, the density function is given byfi (n) =

e

i

n
i

n!

:

The total number of …rms for good ! across the two countries is n! = n!;1 + n!;2 . There
are goods that have no …rms from either countries, and the total number of goods actually
produced is given by!=[1

f1 (0) f2 (0)] =1

e ( 1+ 2) :(1)

There is also a subset of goods produced by only one …rm in the world, and in this case,this …rm charges monopoly prices in both countries. For the rest, the number of …rms in theworld are at least two, and …rms engage in Bertrand competition. We do not model entry
explicitly. By this probabilistic formulation, we let i summarize the entry e¤ort in eachcountry. From (1), we see that the larger the mean numbers of …rms i , the larger the !.

2.3

Productivity, Trade Cost, Pricing and Markups

Let wages be denoted as wi . If the productivity of a …rm is 'i! , then its marginal cost is
wi ='i! before any delivery. Assume standard iceberg trade costs ij 1 (to deliver one unit
to j from i, it will need to ship ij units). Let ii = 1 for all i. Hence, for input !, thewdelivered marginal cost from country i’s …rm k to country j is therefore 'ij i . For each i!,!;ik
productivity '!;ik is drawn from log-normal distribution, i.e., ln '!;ik is distributed normallywith mean i and variance 2i . Let '!;i and '!;i be the …rst and second highest productivity
draws among the ni! draws.
For each !, the marginal cost to deliver to location 1, for the two lowest cost producers
at 1, and the two lowest cost producers at 2, are then
1j w1

'!;1

;

1j w1

'!;1

;

2j w2

'!;2

;

2j w2

'!;2

.

If the number of …rms is 1, 2, or 3, then we can simply set the missing element in the above
set to in…nity. Let aj! and aj! be the lowest and second lowest elements of this set. The

7

monopoly pricing for goods sold in country j is pj! = 1 aj! . In the equilibrium outcome ofBertrand competition, price will equal the minimum of the monopoly price and the marginal
cost aj! of the second lowest cost …rm to deliver to j, i.e.
pj! = min pj! ,aj! = mina ,a.
1 j! j!

The markup of good ! at j is therefore
mj! =

pj!
= min
aj!

aj!
1 aj!
,

.

Note that …rms’markups may di¤er from the markups for consumers. A non-exporter’s
markup is the same as the one facing consumers, but an exporter has one markup for eachmarket. Let the markup of an exporter producing ! be denoted as mf! . Then, due toconstant returns to scale,
mf! =

costs
revenue

1

=

E1!
E2!
m!;11 +
m 1
E1! + E2!
E1! + E2! !;2

1:

In other words, an exporter’s markup is a harmonic mean of the markups in each market,
weighted by relative revenue.
We can now de…ne producers’ aggregate markup, Misell . Let j (!) 2 f1; 2g denote thesource country for any particular good ! at destination j. Then, we haveMisellRRRi
f!: 1 (!)=ig 1! R1 d! + f!: 2 (!)=ig 2! R2 d!
R
=R
=
1
1
wi Li
m
R
d!
+
1
1!
1!
!:
(!)=i
f 1
g
f!: 2 (!)=ig m2! 2! R2 d!
! 1
Z
Z
R
R
1
2
m2!1 2! d!
;=
m1!1 1! d! +RRii
f!: 2 (!)=ig
f!: 1 (!)=ig(2)

which is the revenue-weighted harmonic mean of markups of all goods with source at locationi. Similarly, consumers’ aggregate markup Mibuy is the revenue-weighted harmonic meanacross goods with destination at i:
Mibuy =

Z !

1

mi!1 i! d!

:0Let the inverses of markups be called cost shares, as they are the shares of costs inrevenues. A harmonic mean of markups is the inverse of the weighted arithmetic mean of costshares. Harmonic means naturally appear here precisely because the weights are revenue.
8However, it is unclear how a harmonic variance could be de…ned. Since the (arithmetic)
variance of markup is positively related to the variance of cost shares, we choose to workwith cost shares in calculating moments for our empirical work.

2.4

Wages and General Equilibrium

Labor demand in country i from a non-exporter that produces input ! is`!;i =

1 Ri
qi!
=
'!;i
'!;i Pipi!
Pi:

For an exporter at i, its labor demand is
`!;1
`!;2

"
R1 p1!
1
q1! + q2!
=='!;1'!;1 P1 P1"
q1! + q2!
1
R1 p1!
=='!;2'!;2 P1
P1

R2
+
P2

p2!
P2

R2
+
P2

p2!
P2

#
#

:

Labor market clearing in country i isZ`!;i d! = Li ;

(3)

!2 i

where i is the set of ! produced at i.
To calculate the trade ‡ows, observe that the total exports from country i to country j
is
Z
Z
1
pj!
Rj;i =
Ej! d! = Rjd!:f!: j (!)=ig
f!: j (!)=ig Pj
where j (!) 2 f1; 2g denotes the source country for any particular good ! at destination j.
The balanced trade condition is therefore
R2;1 = R1;2 .

(4)

We choose country 1’s labor as numeraire, and hence w1 = 1, and w w2 is also the wage
ratio. Given fw; R1 ; R2 g, the realization of ni;! for each i and !, and the realization of '!;ikfor each …rm k 2 f1; 2; :::; ni;! g, pricing, markups, consumption decisions, labor demand,and trade ‡ows are all determined as described above. The two labor market clearing
conditions in (3) and the balanced trade condition (4) thus determine fw; R1 ; R2 g. For easiercomputation for our quantitative work, we use an algorithm of equilibrium computation
9

that reduces the above-mentioned system of equations to one equation in one unknown. We
describe such an algorithm in Appendix A1.

2.5

WelfareThis subsection shows how welfare is decomposed into di¤erent components. The welfare
decomposition is exactly that provided by Holmes et al. (2014). Here, we try to be brief andat the same time self-contained. Let Ai be the price index at i when all goods are priced atmarginal cost:Z!

aai! q~i!
d!,

Ai =

0

a
: ! 2 [0; !]g is the expenditure-minimizing consumption bundle that delivers
qi!
where q~ai = f~one unit of utility. Obviously, the product of producers’aggregate markup and labor income
entails total revenue (2), and we can write welfare at location i as
Ri
= wi LiPi
1= wi LiAi

WiT otal =wi Li

Misell1
Pi
Ai

MisellMibuy
W Prod W TOT

Mibuy
Pi
W A:

Without loss of generality we will focus on the welfare of country 1, and by choosing
numeraire, we can let w1 = 1. As the labor supply Li will be …xed in the analysis, the …rst
term in the welfare decomposition is a constant that we will henceforth ignore. The second
term 1=Ai is the productive e¢ ciency index W Prod , and this is what the welfare index would
be with constant markup. The index varies when there is technical change determining theunderlying levels of productivity. It also varies when trade costs decline, decreasing the costfor foreign …rms to deliver goods to the domestic country. Terms-of-trade e¤ects also show
up in W Prod because a lower wage from a source country will raise the index. It can beshown that this term traces the ACR statistics closely in terms of its elasticity with respect
to trade costs.
The third term is a “terms of trade” e¤ect on markups (W TOT ) that depends on theratio of producers’ aggregate markup to consumers’ aggregate markup. Alternatively, wecall it markup level e¤ect. This term is intuitive because a country’s welfare improves whenits …rms sell goods with higher markups while its consumers buy goods with lower markups.
This term drops out in two special cases: under symmetric countries where the two countries
are mirror images of each other; and under autarky, as there is no di¤erence between the10two aggregate markups.
The fourth term is the allocative e¢ ciency index W A
WiA

Ai

R!
a q~a d!
Mibuy
= R0! i! i!
Pi
ai! q~i! d!
0

1:

a
The inequality follows from the fact that under marginal cost pricing, q~!;iis the optimal
bundle, whereas q~i! is the optimal bundle under actual pricing. If markups are constant,then for any pair of goods, the ratio of actual prices equals the ratio of marginal cost. In
this case, the two bundles become the same and WiA = 1. Once there is any dispersion of
markups, welfare deteriorates because resource allocation is distorted. Goods with higher
markups are produced less than optimally (employment is also less than optimal), and thosewith low markups are produced more than optimally (employment is also more than optimal).
Note that as Holmes et al. focus on the symmetric country case, they do not explicitly
analyze the markup level e¤ect W TOT . As …tting to the Chinese economy, we allow asymmetries between countries in all aspects of the model (labor force, productivity distribution,
entry and wages).

3

Quantifying the Model

We use the following two steps to quantify the model. First, we estimate the markup
distribution and infer the elasticity of substitution from such distribution. Then, given, measures of fw; R1 ; R2 g, we use moments of markups, trade ‡ows, number of …rms andfraction of exporters to estimate the remaining parameters by Simulated Method of Moments(SMM). Note that, unlike Edmond et al. (2015) whose benchmark focuses on symmetriccountries, our empirical implementation focuses on asymmetric countries, as the large wage
gap between China and the ROW should not be ignored since it may have a large impact onparameter estimates, as well as potential large general equilibrium e¤ects in counter-factuals.

3.1

Data

Our …rm-level data set comes from the Economic Census data (1995 and 2004) from China’s
National Bureau of Statistics (NBS), which covers all manufacturing …rms, including stateowned enterprises (SOE). The sample sizes for 1995 and 2004 are 458; 327 and 1; 324; 752,
respectively.10 The bene…t of using this data set, instead of the commonly used …rm-level10

The original data sets have larger sample sizes, but they also include some (but not all) nonmanufacturing industries, as well as …rms without independent accounting and village …rms, which entail

11

survey data set, which only includes …rms with revenues of at least 5 million renminbi, isthat we do not have to deal with the issue of truncation. As we are concerned with potential
resource misallocation between …rms, it is important to have the entire distribution. We
estimate the models separately for the years 1995 and 2004.
We obtain world manufacturing GDP and GDP per capita from the World Bank’s World
Development Indicators (WDI). The aggregate Chinese trade data is obtained from the UN
COMTRADE. We also use tari¤ data for various purposes, including gauging the relative
importance of tari¤ reductions in the overall reduction in trade frictions. The tari¤ data is
obtained from World Integrated Trade Solution (WITS), which was developed by the WorldBank and incorporates trade data from various sources. For our quantitative analysis, we
calculate an economy-wide average tari¤, and for our multiple-sector analysis, we calculate
sectoral average tari¤s. We provide details about the data and the method we use to calculatethese average tari¤s in Appendix A2.

3.2

Estimation of Markups

Under constant returns to scale assumption, a natural way to estimate markups is by taking
the ratio of revenue to total costs, i.e., revenue productivity, or what we call raw markup.
However, it is important to recognize that, in general, raw markups may di¤er across …rms,
not only because of the real markup di¤erences, but also because of di¤erences in the technology with which they operate. To control for this potential source of heterogeneity, we
use modern IO methods to purge our markup estimates of the di¤erences in technology. In
particular, we estimate markups following DLW’s approach,11 who calculate markups as
m! =

X
!
;
X
!

X
where X
! is the input elasticity of output for input X, and ! is the share of expenditure on
input X in total revenue. To map our model into …rm-level data, we relax the assumptionsof a single factor of production and constant returns to scale. Following DLW, we assume
a translog production function.12 The estimation of …rm-level markup hinges on choosing
an input X that is free of any adjustment costs, and the estimation of its output elasticity

numerous missing values. The …nal sample is obtained from excluding these cases and adjusting for industrial
code consistency.
11
We also conduct estimation and counter-factual analysis under raw markups as a robustness check.
12
In our implementation of the DLW approach using Chinese …rm-level data under translog production
function, which allows variable returns to scale, it turns out that the returns to scale are quite close toconstant. See Table A1 in the appendix. Interestingly, Edmond et al. (2015) also found similar results using
Taiwanese …rm-level data.

12

X! . As labor is largely not freely chosen in China (particularly state-owned enterprises) andcapital is often considered a dynamic input (which makes its output elasticity di¢ cult tointerpret), we choose intermediate materials as the input to estimate …rm markup (see also
DLW). The full details of the markup estimation are relegated to Appendix A3.
Table 1 gives summary statistics of the markup distribution,13 with breakdowns ineach year and between exporters and non-exporters. Observe that the (unweighted) mean
markups all decrease between 1995 and 2004 for all …rms, both exporters and non-exporters.
The (unweighted) standard deviation of markups decreases for non-exporters, but increases
slightly for exporters. Because there are more non-exporters than exporters and the decreasein non-exporters’standard deviation is larger than the increase in exporter’s standard deviation, the overall standard deviation decreases. Almost all of the percentiles decreased
between 1995 and 2004. This is consistent with the pattern seen in Figure 1 where the entire
distribution becomes more condensed.
However, we note that the pattern described in Table 1 only hints at the existence of procompetitive e¤ects. The reduction of dispersion of …rm markups does not necessarily meanthat the allocative e¢ ciency increases because allocative e¢ ciency depends on consumers’
markups rather than …rms’ markups. It does show that the markets facing Chinese …rms
become more competitive. Also, we cannot reach a conclusion yet about the markup levele¤ect, as we do not observe the consumers’aggregate markup directly. We need to quantify
the model and simulate both types of markups to conduct welfare analysis.

3.3

Elasticity of Substitution

As a preference parameter, we infer a common elasticity of substitution for both years.
Note that the model implies that m 2 1; 1 , and hence the monopoly markup is theupper bound of markup distribution. Recall the economics behind this. An active …rm ofa product charges the second lowest marginal cost when such cost is su¢ ciently low. Whenthe second marginal cost is high, the markup is bounded by the monopoly markup because
the …rm’s pro…t is still subject to the substitutability between products. The higher thesubstitutability ( ), the lower the monopoly markup the …rm will charge.
As we examine the e¤ects of markups, we infer using the upper bound of the markup
distribution. Considering the possibility of measurement errors and outliers, we equate=(
1) to the 99th percentile of estimated markup distribution (using the pooled sample
13

Following the literature, e.g., Goldberg, De Loecker, Khandelwal and Pavcnik (2015) and Lu and Yu(2015), we trim the estimated markup distribution in the top and bottom 2:5 percentiles to alleviate theconcern that the extreme outliers may drive the results. Our results are robust to alternative trims (e.g, thetop and bottom 1%; results are available upon request). We also drop estimated markups that are lower
than one, as our structural model does not generate such markups.

13

from 1995–2004). We obtain that = 1:40, which re‡ects that the 99th percentile is around
3:5.14Note that the inferred here is quite di¤erent from the literature, which typically estimates under monopolistic competition models that often feature constant markups. Undera constant-markup model and using the harmonic mean of …rm markups in 1995, 1:259, this
implies = 4:86. However, in the current model, this value of implies that m 2 [1; 1:259],
which will cut 50:6% o¤ the estimated markup distribution. Then, these large markups wheremost distortions come from are ignored. In fact, the pro-competitive e¤ects of trade become
negligible under m 2 [1; 1:259] because the associated allocative e¢ ciency is much closer tothe …rst-best case (constant markup) without the very skewed larger half of the markups.
Edmond et al. (2015) also found that the extent of pro-competitive e¤ects depends largelyon the extent to which markups can vary in the model. After all, estimations/calibrations
should be model speci…c, and = (1) in our model is the upper bound rather than the
average of markups.

3.4

Simulated Method of Moments

We estimate the remaining parameters using SMM for 1995 and 2004 separately.
To calculate w = w2 =w1 , we …rst obtain the GDP per capita of China and the ROW from
WDI.15 We then calculate wi by multiplying GDP per capita by the labor income shares for
the ROW and China, which are taken from Karabarbounis and Neiman (2014).16 For R1 andR2 , we …rst obtain the manufacturing GDPs of China and the ROW from WDI data. We
then use the input-output table for China (2002) and the US (1997–2005) to obtain GDP’sshare of total revenue. We then use such shares and the manufacturing GDPs to impute R1
and R2 as total revenue. Although our model does not distinguish value added and revenue,
we choose to interpret Ri as total revenue rather than GDP to be consistent with our export
and import moments, which are also in terms of revenue.
Given fw; R1 ; R2 g, , and all the remaining parameters, we can simulate various momentsin the model. For i = 1; 2, the remaining parameters are
14

Note that this estimate of is not sensitive to sample size. In our multi-sector exercise, s is separately
inferred for each sector s using the markup distribution of that sector. The unweighted mean of s is 1.44,
and 23 out of 29 s are within one standard deviation from the mean, (1:27; 1:61). See Section 5.1.
15
The ROW’s GDP per capita is the population-weighted average of GDP per capita across all countries
other than China.
16
The ROW’s labor share is the weighted average of labor share across all countries besides China, withthe weight being relative GDP.

14

:
:
i :
i :
i :

trade cost
total measure of goods
mean number of …rms per productmean parameter of log-normal productivity draw
standard deviation parameter of log-normal productivity draw

Note that for productivity, we normalize 2 = 0 (when ln ' is zero, ' = 1) because only
the relative magnitude of 1 to 2 matters. Choosing 2 amounts to choosing a unit. In
order to use SMM to estimate these seven parameters, we need at least seven moments.
We use the following 12 moments: the import and export shares; relative number of …rms;
fraction of exporters; weighted mean and standard deviation of cost shares for both exporters
and non-exporters; and the median and 95th percentile of cost shares for exporters and nonexporters.17 We use moments of exporters and non-exporters separately because the way
in which parameters of countries 1 and 2 (China and the ROW) enter these moments di¤er
between these two groups. The intuition is clear: Chinese exporters face direct competitionin the ROW’s markets and non-exporters face foreign competition on their home turf. As welack …rm-level data from the ROW, this approach is crucial for backing out the parametersof the ROW. For comparison, we also estimate a symmetric country version in which case
ROW’s parameters are the same as China’s.
Recall that the actual measure of goods is given by (1): ! = 1 e ( 1 + 2 ) , but thisis not directly observed. What is observable is the number of active Chinese …rms:
N1 =

1

e

Pr

1

w
1
<
:
'1!
'2!

Divide both sides by N , a large number that is chosen for normalization. The moment we
use is the relative number of Chinese …rms:
N1
=
N

1

e
N

1

Pr

w
1
<
;
'1!
'2!

(5)

The choice of N does not a¤ect the estimates, but we must choose the same N for both 1995
and 2004 in order to gauge the increase in . For this purpose, we choose N to be 2 million.
The estimation result is shown in Table 2. The model …ts the data moments reasonably well, and the small standard errors indicate that each parameter is relatively precisely
estimated. As we estimate the models for 1995 and 2004 separately, the changes of theparameters are strikingly consistent with well-known empirical patterns about the Chinese
17

The import share is the import penetration ratio, i.e. IM/(R1-EX+IM), and the export share is thetotal export divided by the same denominator. All the cost share moments are weighted by revenues.

15

economy during this period. From 1995 to 2004, the estimate of shows a dramatic decrease
from 2:31 to 1:66. The measure of goods more than triples from 0:26 to 0:85. This basicallyre‡ects the sharp increase in the number of …rms between the two Economic Censuses, from458,327 in 1995 to 1,324,752 in 2004, which is almost triple. The mean number of …rms per
product in China ( 1 ) increased from 2:44 to 2:61, about 7% increase, whereas in the ROW
it increased from 5:27 to 5:83, about 10:6% increase. Given that the ROW is larger thanChina, it may be reasonable that the ROW’s Poisson entry parameter had a larger increase.
China’s mean log productivity ( 1 ) relative to the ROW increased from 2:40 to 1:79.
These numbers are negative, meaning that China’s productivity is lower than that of theROW ( 2 is normalized to 0). Also, we see a slight decrease in the dispersion parameterof the productivity distribution in both countries ( 1 ; 2 ). Interestingly, the productivity
dispersion is larger in China than in the ROW, which is consistent with the …nding by Hsieh
and Klenow (2009).18
Based on the 2004 estimation, we calculate a Jacobian matrix in which each entry gives
a rate of change of a moment to a parameter, and this is shown in Table 3. The larger theabsolute value of a rate of change, the more sensitive this moment is to the parameter, andhence the more useful this moment is in identifying this parameter, at least at the local areaof the optimal estimates. With such Jacobian matrices, the asymptotic variance-covariancematrices of the optimal estimates can be calculated to produce the standard errors reported
in Table 2.
Trade cost a¤ects almost all moments signi…cantly, and it is natural to see that thetwo trade moments, the relative number of Chinese …rms and the fraction of exporters are
particularly strong for identifying this. Interestingly, when increases, the 95th percentiles
of markups for both exporters and non-exporters increase sharply. For non-exporters, this is
intuitive because a higher provides non-exporters more insulation from foreign competition,and the top non-exporters gain more from this. For exporters, a higher makes it harderfor them to compete in foreign markets, but recall that an exporter’s markup is a harmonicmean of the markups in both the domestic and foreign markets. It must be that the gains
in markups at home outweigh the losses in markups in foreign markets.
For 1 and 2 , the 95th percentiles of markups and the relative number of active …rms
are crucial in identifying these two parameters, with the trade moments playing some role18

2

The mean of a log-normal distribution is e + =2 . According to our estimates of 1 and 1 in these
two years, this translates to an annual productivity growth rate of 6:9%. This impressive growth rate is
actually similar to the 7:96% estimated by Brandt, Van Biesebroeck and Zhang (2012). Note that the 6:9%
growth rate here is relative to the ROW. If the ROW also grows in their productivity, the actual productivity
growth rate could be even higher. In fact, Brandt, Van Biesebroeck, Wang, and Zhang (2012) …nd a 12%
average TFP growth rate at industry level. The data used in both above-mentioned papers is the annual
manufacturing survey data from 1998 to 2007.

16

as well. The intuition is as follows. Fixing other parameters, when i increases, the numberof entrants per product in country i increases. Due to the non-fat-tailed nature of the
productivity distribution, the ratio between the top two draws is narrowed, but since thisratio is indeed the markup and since this is particularly pronounced for the top markups, the95th percentiles are particularly useful in identifying these two parameters. The fact that we
observe increases in i during this period may re‡ect that the 95th percentiles of markups
decrease during this period. Intuitively, the relative number of (active) Chinese …rms is also
useful for identifying 1 , as seen clearly in (5).19
For the measure of goods , it is obvious that the relative number of Chinese …rms is themost useful moment. An increase in mean productivity parameter 1 increases export share,
the number of Chinese …rms, and the fraction of exporters, but decreases the import share.
These are all intuitive. However, an increase in 1 sharply increases the 95th percentile
markup for non-exporters but sharply decreases the 95th percentile markup for exporters.
This is because top non-exporters are actually not the most productive …rms –their productivities are somewhere in the middle of the distribution and hence they gain in markup by
having higher productivity. In contrast, top exporters are the most productive …rms, andthey lose in markup when they become even more productive, due to the compression at theupper tail of the productivity distribution.
For 1 and 2 , …rst note that they are not only dispersion parameters, but their increases
will induce increases in means as well. So, the direction of changes due to a change in 1 issimilar to that of a change in 1 , but the intensities are quite di¤erent. For example, 1 has
much larger e¤ects on moments of markups, including both means and standard deviationsof the cost shares, than 1 . Moreover, the 95th percentile markup for exporters is extremely
sensitive to 1 because 1 a¤ects the top productivities much more than 1 . Also note theinteresting pattern: 1 and 2 almost always a¤ect moments in opposite ways. An increasein 2 increases both the mean and dispersion of the ROW’s productivity, and this increases
China’s import share, and decreases China’s export share, number of …rms and fraction ofexporters. It decreases Chinese non-exporters’ median and 95th percentile markups, but
increases those of Chinese exporters.
Finally, we discuss a point that is often mentioned in studies of the Chinese economy.
China underwent various reforms, including but not limited to trade reforms, in this decade.
One notable reform is that of SOEs during the late 90s, which is well known to make China’s
various industries more competitive. Although we do not model the source of distortion
19

Trade ‡ows are also useful, as an increase in 1 raises active …rms’productivities in China, increasing
the export share and reducing the import share. On the other hand, an increase in 2 raises active …rms’
productivities in the ROW, increasing the import share and reducing the export share in China.

17

explicitly in our model and rather treat markups (and their distribution) as a re‡ection of
distortion, the fact that we observe increases in both 1 and may be partly due to thesereforms. The compression in markup distribution (Table 1 and Figure 1) and the increasing
number of manufacturing …rms are also consistent with the above-mentioned reforms.

4

Gains from Trade

In this section, we conduct a battery of counter-factual analyses to examine the welfare gains
from trade.

4.1

Benchmark Result

For each year (1995 or 2004), given the estimated parameters and fw; R1 ; R2 g from data,
we can calculate the implied labor force L1 and L2 using labor market clearing conditions.
Then, under all estimated parameters and implied fL1 ; L2 g, we can also simulate a set offw; R1 ; R2 g. The bottom three rows in Table 2 show the simulated fw; R1 ; R2 g, which turnout to be quite close to the data counterpart,20 serving as additional validation of the model.
To examine gains from trade, we conduct two counter-factual analyses by …xing all parameter values at the 2004 level and changing only . In the …rst analysis, we simulate welfare
and its components when is changed to the 1995 level, and we calculate the percentagechanges of welfare and its components. In the second analysis, we take to an inhibitive
value so that the economy becomes autarky.
The results are shown in Table 4. The welfare gains from 1995’s openness to 2004’s levelare 9:43%, in which the pro-competitive e¤ects account for (2:10+0:29)=9:39 25:4%. Moreover, the allocative e¢ ciency W A accounts for 2:10=9:39 22:3% of these gains, whereas themarkup level e¤ect accounts for the remaining 3:1%. In fact, both aggregate markups M selland M buy decrease during this period, which is a natural result under trade liberalization,
but the percentage decrease in the consumers’ aggregate markup M buy is larger. Overall,although the markup level e¤ect is positive, it is relatively small, whereas the combinede¤ect can account for about a quarter of the total gains. The total gains from autarky to
the 2004 level are, of course, much larger, at 33:4%, but the decomposition is similar to the…rst analysis.
Next, we examine whether the result of “diminishing returns in openness”in Edmond etal. (2015) holds here. The following table summarizes the welfare gains reported in their20

Here, the largest discrepancy between data values and simulated value is the total revenue of the ROW
in 1995, which is about 10:5%. For all the other numbers, the discrepancies are all less than 5:2%.

18

study, as well as the breakdown in Ricardian gains and allocative e¢ ciency. There is an
obvious “diminishing returns” in allocative e¢ ciency, as the opening up from autarky to
10% import share improves welfare by 1:2%, whereas further opening up from 10% to 20%
improves welfare by only 0:3%. But such a diminishing-returns pattern does not show up
in the Ricardian component. As a result, the relative contribution of allocative e¢ ciency
diminishes rapidly from 1:2=3:1 38% to 0:3=2:8 10:7%.
Import share
0 to 10%
10% to 20%%

in Edmond et al.

Total Welfare Ricardian W A
3:1
1:9
1:2
2:8
2:5
0:3

Importance of W A
38:7%
10:7%

Panel B of Table 4 reports the result from a similar exercise. Note that Edmond et al.’s
pro-competitive e¤ect only includes allocative e¢ ciency but not the markup level e¤ect astheir formulation focuses on symmetric countries. To compare, we ignore the markup levele¤ect. A similar diminishing returns pattern in allocative e¢ ciency is obvious, dropping from
5:5% to 1:5%. But, unlike in Edmond et al., we also see sharp diminishing returns in ourcounter-factuals for total welfare and the Ricardian component. As a result, we do not see
a diminishing relative contribution in allocative e¢ ciency. Indeed, the relative contribution
stays around 24%, which is quite close to the results reported in Panel A.
Looking at both panels together, the relative contribution of pro-competitive e¤ects range
from 23:3% to 27:6%, and the relative contribution of allocative e¢ ciency ranges from 22:3%
to 24:6%. Despite the di¤erences in model structures, our estimates turn out to be in theballpark of Edmond et al.’s estimates, which range from 11% to 38%.
Recall the welfare formula in ACR, 1& ln ( = 0 ) where is the trade elasticity and and0
denote the share of aggregate spending on domestic goods before and after the change of=d ln . We
trade cost. Following the literature, we calculate trade elasticity by d ln 1
simulate trade elasticities local to our benchmark estimates for 1995 and 2004, and they are
2:49 and 1:28, respectively. Using the trade elasticity implied by our 2004 model, thegains from trade between 1995 and 2004 according to ACR’s formula are 11:6%, whereas thegains calculated with the trade elasticity implied by our 1995 model are 5:9%. The (total)
gains from trade in this period based on our 2004 and 1995 model are 9:4% and 7:8% (seeTable 7), respectively, and these numbers are in the ballpark of the ACR statistics.
The overall gains from trade are substantially larger in our model compared with typicalresults in the literature. As highlighted by ACR, trade elasticities are crucial in determining
the magnitude of welfare gains. Hence, this result is not surprising, as the trade elasticities here are smaller due to smaller values of . As discussed in Section 3.3, the elasticity
19

of substitution inversely determines the upper bound of markups, rather than the averagemarkup. Thus, the larger welfare gains in our quantitative analyses are fundamentally a direct consequence of accommodating the empirical markup dispersion in the BEJK oligopoly
environment, which di¤ers drastically from constant-markup models, whether they are perfectly or monopolistically competitive.

4.2

Symmetric Countries

For the purposes of comparison, we also estimate a symmetric-country case. The estimation
results are shown in Table 5 and the counter-factual results in Table 6. The changes intrade cost , measure of goods and number of …rms per product between 1995 and2004 are all in the same direction as in the benchmark case. Note that the estimated issimilar to a weighted average of estimated 1 and 2 , with the ROW weighted more heavily,
since the ROW is much larger than China. Also, observe that although the standard errors
here are somewhat smaller than those in the benchmark estimation, the …t of moments
becomes signi…cantly worse. This is because there are fewer parameters in the symmetriccountry estimation, re‡ecting that the symmetric-country estimation misses out the large
discrepancy in entry and productivity distribution seen in Table 2. It may also be partly
because symmetric-country model misses out the general equilibrium e¤ect in the adjustmentof relative wages, which change from 10:5 to 5:3 (See Table 2), meaning that Chinese wages
relative to the ROW almost doubled in this decade.
For counter-factual results, …rst note that the markup level e¤ect does not show up in
Table 6 because this term drops out under symmetric countries. Also, note that the overall
welfare gains become much smaller than the benchmark case (e.g. 2:7% versus 9:4%). Bothcomponents also become much smaller. As the distributions of the number of entrants
and productivity draws become the same between the two countries, the Ricardian gains are
reduced because active …rms’productivity di¤erences between two countries are now reduced.
Moreover, not only do the distribution of markups become similar, but the dispersion of
markups also becomes smaller. In fact, looking at the autarky, we see that the allocativee¢ ciency is much larger in the symmetric-country case than in the benchmark case (0:941
versus 0:898). As the allocative e¢ ciency is larger to start with, it is not surprising thatthe gains in allocative e¢ ciency are smaller (0:7% versus 2:1% and 2:5% versus 7:5%). The
same rationale explains why we see a pronounced diminishing-returns (dropping from 32:5%
to 13:3%) pattern in Panel B that is absent in the asymmetric-country case.
Under symmetric countries, the results in Edmond et al. rely on the cross-country productivity di¤erences across di¤erent sectors to generate pro-competitive e¤ects. However,20

our exercise indicates that asymmetries between countries could also be important sourcesof gains, both in Ricardian component and the pro-competitive e¤ects. Nevertheless, therelative contributions of the pro-competitive e¤ects are still somewhat close to those at thebenchmark case, albeit the variation is somewhat larger.

4.3

Robustness

We conduct three robustness checks. Recall that in the benchmark case, the counter-factual
analyses are based on 2004 estimates and change back to the 1995 level. In our …rst
robustness check, we conduct a counter-factual analysis based on 1995 estimates and change
to the 2004 level. In our second check, we use an alternative measure of markups toestimate the model and run counter-factuals. That is, by invoking the constant-returns-toscale assumption, we calculate raw markups by taking the ratio of revenue to total costs.
For our third check, recall that we used the 99th percentile of the markup distribution toinfer , but now we also report results based on the 97.5th percentile.
The results are reported in Table 7. The relative contribution of pro-competitive e¤ects
ranges from 19:4% to 24:0%, and that of allocative e¢ ciency ranges from 19:3% to 22:4%.
These indicate that the benchmark results are quite robust, as the importance of allocativee¢ ciency remains similar, and the markup level e¤ect remains small.
The only di¤erence between the …rst robustness check and the benchmark is that allparameters besides are …xed at the 1995 levels instead of at the 2004 levels. Both theoverall gains and the pro-competitive e¤ects are smaller in the …rst robustness check than in
the benchmark. As China had smaller productivity and smaller entry in 1995, this indicatesa complementary e¤ect between trade liberalization and other fundamentals in the sensethat there are more gains from trade when productivity and entry are higher.
Next, note that the inferred from raw markups is about 1:67, which implies a smaller
upper bound of markups than the benchmark case. So it is not surprising that the procompetitive e¤ects are slightly less important under raw markups. This also explains why
using the 97.5th percentile of the markup distribution to infer also induces smaller procompetitive e¤ects.

21

4.4

Gains from Tari¤ Reductions

Following the literature, the iceberg trade cost can be represented as = (1 + t)
where t denotes economy-wide average tari¤.21 Equivalently, we can write
ln = ln (1 + t) + ln

non-tari¤

non-tari¤,:

The average import and export tari¤s are 25:5% and 6:4% respectively in 1995, and theweighted average is 15:7%. (For details of the calculation, see Appendix A2). The corresponding numbers in 2004 are 6:3%, 3:2% and 4:3%. When China experienced sizable
decrease in the export tari¤ of about 50%, the drop in the import tari¤ was much larger.
The large drop in the average tari¤ from 15:7% to 4:3% is evidence of the power of the WTO
entry. Using our benchmark estimate of , 2:311 and 1:664 in 1995 and 2004, respectively,
the corresponding non-tari¤ are 1:997 and 1:595. This means that the decrease in tari¤saccounts for ln (1 + t) = ln = 31:6% of the decrease in overall trade friction. Although
this is not a small magnitude, it indicates that the decrease in non-tari¤ trade friction iseven more important.
Next, we conduct two counter-factual exercises to single out the e¤ect of tari¤s, and theresults are shown in Table 8. Panel A shows the result based on the 2004 estimates. That is,based on 2004 estimates, we keep non-tari¤ at the 2004 level, and consider the e¤ect of tari¤s.
Hence, changes from 1:845 in 1995 to 1:664 in 2004. Then, we compare this e¤ect withthe overall e¤ect when non-tari¤ is also allowed to change. Panel B reports the results basedon 1995 estimates, and hence changes from 2:311 in 1995 to 2:083 in 2004 when non-tari¤
is kept at the 1995 level.
When non-tari¤ is kept at the 2004 level, the reduction in tari¤s from 1995 to 2004 brings
3:7% total welfare gains, with the pro-competitive e¤ects accounting for about 20:3% of
these gains. But when non-tari¤ is allowed to change, the total gains increase to 9:4% andthe pro-competitive e¤ects contribute about 25:4%. The relative contribution of the tari¤
reduction is 39:6%. The di¤erence in the relative contribution of the pro-competitive e¤ects
is consistent with the …ndings of “diminishing returns” as reported in Panel B of Table 4
and discussed in Section 4.1.
The key di¤erence between Panels A and B in Table 8 is the magnitudes of non-tari¤. In
1995, non-tari¤ = 1:997 is relatively large, and a 31:6% decrease in overall trade friction due
to tari¤s accounts for 35:2% of the total welfare gains. In contrast, in 2004, non-tari¤ = 1:595
is relatively small, and a 31:6% decrease of overall trade friction due to tari¤s accounts
for 39:6% of the total welfare gains. In other words, the same proportional change in trade21

For example, see Caliendo and Parro (2015).

22

friction due to tari¤ reductions entails larger gains when the non-tari¤ trade friction is lower.

5

Multiple-Sector Economy

The framework in this paper can be easily extended to a multiple-sector economy, which
we do for three reasons. First, the model is more realistically matched to data, taking into
account the cross-sector heterogeneity in trade costs, as well as in productivity distribution,
entry e¤ort and preference parameters. Second, we conduct similar welfare analyses to gauge
the robustness of our previous results for this multiple-sector extension. Third, exploiting
the variations in sectoral markups and trade costs, we attempt to answer the question ofwhether China trade-liberalized the “right” sectors by examining whether there was larger
trade liberalization in sectors with higher initial markups in 1995.

5.1

Model and Estimation

Model Modi…cation There are S sectors, which are indexed by s = 1; 2; :::; S. The utility
function of a representative consumer isSss=1 (Qs ) ;U=P
where s 2 (0; 1), Ss=1 s = 1, and Qs is the consumption of the composite good of sectors given by a CES aggregator:
Qs =

Z !s

s

(qs;! )

1

s
s

1

s

; for

d!

s > 1,

0where s is the elasticity of substitution of sector s. The aggregate and sectoral price indices
are therefore
Pjs

Pj =

S
s=1

Pjs

Z !s

s

s
1

1 s
pjs!
d!

1

s

:

0

The Cobb-Douglas structure implies that Pjs Qjs =of good s! is given by
1 s
pjs!
Ejs! = s Rj
Pjs23

s Rj , and country j’s total expenditures Rj js! ;and the total revenue of all …rms at i in sector s isZ
Z
Rs;i =s R1 1s! d! +fs!: 1 (s!)=ig
fs!: 2 (s!)=igs R2 2s! d!

For each sector s, all the parameters in the one-sector economy now become sectorspeci…c. That is, for each sector s there is a s and a s , and for sector s and country i, there
is a set f is ; is ; is g. For each sector, pricing and markups follow the previous formulation.
Wages and General Equilibrium The labor demand for a non-exporter at i that produces good s! is
s
1
pis!
qis!
s Ri
=
:
`s!;i ='s!;i
's!;i Pis
Pis
The labor demand for an exporter at i = 1 or 2 is
`s!;1

"

q1s! + q2s!
1
=
=
's!;1
's!;1

`s!;2 =

1s q1s! + q2s!
=
's!;2
's!;2

"

s R1
P1s

p1s!
P1s

s s R1P1sss s R2
+
P2s
s

p1s!
P1s

s R2
+
P2s

p2s!
P2s
p2s!
P2s

s

#
s

;

#

:

Labor market clearing in country i is
S Z
X`s!;i d! = Li ;!2 s;is=1where s;i is the set of s! produced at i.
For trade ‡ows, observe that country j’s total import from country i is
Rj;i =

S Z
X
s=1

where
j and

fs!: j (s!)=ig

Ejs! d! = Rj j;ij (s!) 2 f1; 2g denotes the source country for any particular good s! at destination

j;i is the total spending share of j on i’s goods:

j;i =

S
X
s=1

s

Z

fs!: j (s!)=ig

js! d!:The balanced trade condition R2;1 = R1;2 holds in equilibrium.

24

(6)

Welfare The welfare of country i is decomposed in the same way as before
MisellMibuy

1
Ai

WiT otal = wi Li

Mibuy
;
Pi

Ai

where
Ai =
Mibuy =

S
s=1
S
X

Ais

s

,

s

s

Misbuy

S
s=1

Pi =
1

s=1

! 1

Pis

;

s

Ri
Misell =
=
wi Li

,

s

S
X
Rs;i

Ri
s=1

Missell

1

! 1

; (7)

and Ais , Pis , and Misbuy are de…ned in the same way as before, and Missell is
Missell =Z

f!: s1 (!)=ig1
m1s!

s R1 1s!

Rs;i

d! +

Z

f!: s2 (!)=ig1
m2s!

s R2 2s!

Rs;i

d!

! 1

:

(8)

The sectoral welfare cannot be further decomposed into the three components as in the
one-sector model. This breaks down because there is no simple analogue of Ri = wi Li Misell
P isat the sectoral level. Indeed, wi Li = s MRsell.
isQuantifying the Model To quantify the model, we focus on 29 2-digit manufacturingsectors in Chinese Industrial Classi…cations (CIC).22 We …rst calibrate f s gSs=1 . Recall that
P1s Q1s = s R1 . We use information about expenditure share in China’s 1997 and 2002
input-output table to calibrate st , where t = 1997; 2002. We then set s to be the average
between two years.23 We then follow the same procedure as in the one-sector economy
case to infer the elasticity of substitution s and estimate the remaining parameters by
SMM using sectoral …rm-level data. Note that one convenience in our framework is thatto implement SMM, moments are generated given wages w and total revenue R1 and R2 ,and each sector is actually estimated separately, which largely simpli…es the estimation andequilibrium computation for counter-factuals.
The parameter estimates are shown in Tables 9A and 9B. In both tables, we also reportthe (unweighted) mean, standard deviation, maximum and minimum of the estimates andpercentage changes across sectors. There are substantial variations across industries in their22
We include all 2-digit CIC manufacturing sectors except Sector 43 because we do not have the necessary
data to calculate markups for this industry.
23
Speci…cally, we …rst map the input-output code to 2-digit CIC sectors. Then, we calculate the expenditure share for each 2-digit CIC sector, where the expenditure is calculated by subtracting exports from total
use, which already includes imports.

25

moments. The model performs well in accommodating these variations with corresponding
variations in the estimates. The changes in the unweighted means of parameters between
1995 and 2004 are all consistent with the pattern observed in the one-sector case, except
for the parameter 1 (see Table 2). In particular, all estimated trade costs decrease except
for Tobacco Processing.24 Also observe that the mean s is 1:44, which is quite close to ourbenchmark in the one-sector economy, and s in most industries (23 out of 29) are within
one standard deviation from the mean, (1:27; 1:61).

5.2

Gains from Trade

When examining the welfare analysis in the multi-sector economy, we focus on the two key
counter-factuals shown in Table 10. In this table, Panel A is similar to Panel A in Table
4 (the benchmark in the one-sector economy). Whereas we changed in the one-sector
economy, we now change f s g for all sectors s from the 2004 values to the 1995 values (or to
inhibitive values). The relative contribution of pro-competitive e¤ects here is around 20%,
which is slightly smaller than the numbers in Table 4. Similarly, allocative e¢ ciency accounts
for almost all of the pro-competitive e¤ects. Panel B shows the e¤ect of tari¤ reductions,non-tari¤
while …xing non-tari¤ trade costs
at 2004 levels. Note that the column of “%schange (from trade costs in 1995)”is copied from Panel A. The relative contribution of tari¤
reductions accounts 34:8% of the total welfare gains. This is slightly lower than the number(39:6%) in Table 8 and remains larger than the proportion of average tari¤ reductions in theoverall reduction in trade costs (31:6%, see Section 4.4). In sum, the results of welfare gains
are similar to those in the one-sector economy case.

5.3

Did China Trade-Liberalized the Right Sectors?

In this subsection, we try to answer the question of whether China trade-liberalized the rightsectors? We examine the relationship between trade liberalization and sectoral consumers’
buy
buy
aggregate markup (M1s
) under the 1995 model. That is, if a sector has a higher M1sin1995, do we also actually see a larger degree of trade liberalization between 1995 and 2004?
The rationale is as follows. Recall from (7) that aggregate markup M1buy is a harmonic meanbuyof sectoral markups (M1s). From both one-sector and multi-sector welfare analysis, weobserve that most pro-competitive gains from trade are due to allocative e¢ ciency. As theoverall allocative e¢ ciency depends on the dispersion of markups across sectors, if a sectorbuys has higher M1s
initially, then allocative e¢ ciency will improve more if the government
24This is mainly because the import and export shares decrease from 0:021 and 0:052 to 0:010 and 0:016
in this sector.

26

targets its trade liberalization more in these higher markup sectors.
buy
A quick examination is to rank the 29 sectors by their values of M1s
at 1995 and divide
buy
them into two groups – the …rst being 15 sectors with the smaller values of M1s
and thebuy
are then 1:21
second being those with the larger values. The weighted average of the M1s
and 1:36, respectively. The corresponding weighted average of the changes in trade costs s(i.e,
0:446 and 0:856, respectively. An alternative measures = s;2004
s;1995 ) areof trade liberalization is the changes in sectoral import tari¤s,25 which directly relate to the
WTO entry but do not account for other factors of trade liberalization. In this case, thecorresponding changes are 0:162 and 0:215, respectively. These simple statistics show atendency where the higher the initial level of sectoral markups, the larger the reduction intrade costs (or import tari¤s).
Columns 1 and 5 of Table 11 show similar results by regressing the changes in sectoralbuy
trade costs and in sectoral import tari¤s on sectoral markups M1sat 1995.26 Note thatthese descriptive results su¢ ce for our purpose, as we only want to examine whether China
on average trade-liberalized the right sectors, smoothing the dispersion of markups acrosssectors, even if this happened by chance. In other words, we do not try to establish causality. Nevertheless, we also examine conditional correlations by following Tre‡er (2004) inaccounting for factors that may a¤ect the changes in tari¤s. Columns 3 and 7 show theresults when we add controls for log of wage rates, employment, exports, and imports, all at
1995. The rationale of these controls is that they are highly correlated with various kindsof protectionism.27 As the share of SOEs is presumably a good indicator of protectionism
in China, we also add this as a control (see columns 2, 4, 6, and 8). The above-mentioned
tendency still remains.28
One often-mentioned merit of trade liberalization (or tari¤ reduction) is that it is an easierroute to reducing domestic protectionism compared with using domestic industrial policies.
Before joining the WTO, import tari¤s varied greatly in China, but the WTO conditions
generally require larger tari¤ reductions in those industries with higher initial tari¤s (see Lu
and Yu 2015). We do not know whether the Chinese government had benevolent motives
and wanted to enhance welfare; it could simply be a mechanical result of China wanting toenter the WTO. Anyway, our structural approach allows a welfare assessment in the context
of sectoral reallocation both in terms of improved overall allocative e¢ ciency (Table 10) and25

For details of how the sectoral import tari¤s are calculated, see Appendix A2.
As sector-level data is grouped data from either …rms or products, we weight the regressions by tradevolume and imports when the dependent variables are changes in trade cost and import tari¤s, respectively.
27
For a detailed explanation, see Tre‡er (2004), p. 878.
28
All the coe¢ cients on sectoral markup at 1995 are signi…cant except in column 4, which is marginallyinsigni…cant (with a p-value at 0.11). Nevertheless, the value of this coe¢ cient is similar to those in columns
1-3. Also, as the sample size is small (29), one should use caution when interpreting the signi…cance levels.
26

27

the results in this subsection.

6

Conclusion

Using Chinese trade data and …rm-level data at 1995 and 2004, this paper studies procompetitive e¤ects of trade quantitatively under an oligopoly model with …nite numbers of…rms for each product. The benchmark counter-factual shows that pro-competitive e¤ects
account for 25:4% of the total gains from trade from 1995 to 2004 and 23:3% from autarky
to 2004. Allocative e¢ ciency plays a much more important role than the markup level e¤ect.
These benchmark quantitative magnitudes of pro-competitive e¤ects are robust to a varietyof robustness checks, ranging from 19:4% to 25:4% of total gains from trade from 1995 to
2004 and from 19:6% to 31:4% of those gains from autarky to 2004 (Tables 4, 6 and 7).
When comparing with the symmetric-country case, we …nd that the gains from tradeand its components are substantially smaller in the symmetric-country case, indicating theimportant role played by the di¤erences in productivities and markups. As the distributionsof the number of entrants and productivity draws become the same between the two countries, the Ricardian gains are reduced because active …rms’productivity di¤erences between
two countries are now reduced. Moreover, not only do the distribution of markups become
similar, but the dispersion of markups also becomes smaller. Taking advantage of the tari¤data, we also …nd that tari¤ reductions account for about 32% of reductions in overall trade
costs, whereas the associated relative contribution to overall gains is slightly larger at 39:6%.
This provides the lower bound of the e¤ects attributable to the WTO entry.
The welfare results remain similar in the multi-sector economy, with the relative contribution of the pro-competitive e¤ects and tari¤ reductions around 20% and 35%, respectively.
Exploiting the variations in sectoral markups and trade costs, we …nd that China on average
trade-liberalized the “right” sectors in the sense that the dispersion of markups is reduced
because there tended to be larger trade liberalization in sectors with higher initial markups.
Even though we do not know exactly how this happened, to target trade liberalization in
sectors with higher markups is a useful take away. This is particularly so when it is di¢ cultto eliminate distortions in some industries via domestic measures.

28

Appendix
A1. Algorithm of Computing Equilibrium
We describe a procedure that reduces three equilibrium conditions in three unknowns fw; R1 ; R2 g
to one equation in one unknown w. This is useful for faster computation.
One-Sector Economy First, observe from the de…nition of the producers’ aggregate
markup for country 1:
M1sell

! 1
Z
Z
R1
R
2
=
=
m1!1 1! d! +m2!1 2! d!
w1 L1
R
1
f!: 1 (!)=1g
f!: 2 (!)=1g
!
1
Z
Z
1;2
1
d!
=
m1! 1! d! +;m2!1 2!
2;1
f!: 1 (!)=1g
f!: 2 (!)=1g

2= 1;2 , where j;i denote thein which the second line uses the balanced trade condition R
R1
2;1
total spending share of country j’s consumers on good from country i. Note that j;i =Rsell
f!: (!)=ig j! d! only depends on relative wage w, but not on R1 and R2 . Hence, M1
jbecomes a function of w only. For any given w, we can calculate M1sell (w). Then, given
w1 = 1 and L1 , we get R1 (w) = M1sell (w) L1 . For R2 , we use the balanced trade condition
again:
R2 (w) =1;2(w)

R1 (w) :2;1

In fact, Misell = wRi Li i is equivalent to the labor market clearing condition of country i. Next,
we calculate
M2sell (w) =Z

f!: 1 (!)=ig

m!;11 !;1

2;1(w) d! +Zf!: 2 (!)=ig

1;2

m!;21 !;2 d!

! 1:Finally, given L2 , we can use the market clearing condition of country 2 to solve for w:
M2sell (w) =R2 (w):
wL2

Given the solution of w, we obtain R1 and R2 via the Ri (w) formula above.
Multiple-Sector Economy The algorithm for calculating an equilibrium in a multiplesector economy is similar. From (7) and (8), we can derive the following formula of M1sell

29

and M2sell :M1sell =

" S
X

s

s=1

M2sell (w) =

" S
X
s=1

s

Z

f!: s1 (!)=1g
Z

f!: s1 (!)=2g

1m1s!
1s! d! +

1
m1s!
1s!

Z

1
m2s!
2s!

1;2

!# 1

d!
2;1
f!: s2 (!)=1g
!# 1
Z
2;1
1
(w) d! +m2s! 2s! d!
;
1;2
f!: s2 (!)=2g

in which j;i is the total spending share of j on i’s goods given in (6). Then, we still calculate
2 (w), and the last is used
R1 (w) = M1sell (w) L1 , R2 (w) = 1;2 (w) R1 (w), and M2sell (w) = RwL
2
2;1
to pin down equilibrium wage ratio w.

A2. Calculation of Average Tari¤s
There are two data sources for tari¤s in World Integrated Trade Solution (WITS): TRAINS
and WTO-IDB (WTO’s Integrated Data Base). We use TRAINS as it covers more countries
and more years. An observation of tari¤ is an average tari¤ at HS 6-digit product level, and itis speci…c to a pair of importing and exporting countries. We extract data from WITS when
China is involved as either an importing or exporting country and use “e¤ectively applied
rates”(AHS).29 The reported tari¤s are already averages of …ner HS lines. Nevertheless, thereported “Simple Average”at HS 6-digit level is essentially equal to the reported “Weighted
Average”.30 But, since there are more missing values in “Weighted Average”, we opt to use“Simple Average”. Note that WITS does not report China’s import tari¤s in 1995, and so
we take averages of the 1994 and 1996 tari¤s as proxies.
In calculating sectoral average tari¤s, we use the mapping of HS 6-digit to CIC 2-digit
manufacturing sectors using the concordance table from the National Bureau of Statisticsof China.31 We then use trade values (exports or imports in the corresponding product orindustry) from the previous year (1994 and 2003) as weights to calculate average tari¤s.32
29

Another duty type that is also reported is MFN (most favored nation). Although there may be sizabledi¤erences between the two, but the number of observations that they di¤er is small. For example, forexport tari¤s, these numbers are 6914 and 8950, or about 12% and 4% of the samples, for 1995 and 2004,
respectively.
30
For example, in the 1995 and 2004 export tari¤ data, the maximum absolute di¤erence between the
two averages is 0.01 percentage point, and they di¤er merely in 4 and 33 observations in 1995 and 2004,
respectively.
31
We thank Yifan Zhang for sharing this concordance table. Note that this table is based on HS2002.
Hence, whenever HS1992 and HS1996 are used in the tari¤ data, we map them to HS2002 using the concordance tables available from WITS. See http://wits.worldbank.org/product_concordance.html.
32
We slightly prefer using previous-year trade values as weight because if there is any change in tari¤s,
there may be some induced changes in trade values, making the average tari¤ using current-year trade values“less exogenous”. Note that these trade values actually come from UN COMTRADE, and so whenever what
is provided by the WITS is less complete, we use the ones from UN COMTRADE.

30

Similarly, to calculate an economy-wide tari¤, we again take a weighted average of tari¤sacross all sectors and between imports and exports.

A3. Estimation of Markups
In this subsection, we provide the details for calculating …rm markups using DLW’s method.
Speci…cally, we assume that …rm i at time t has the following production technology33
Qit = Fit (Lit ; Kit ; Mit ; ! it ) ;(9)where Lit , Kit , and Mit are the inputs of labor, capital and intermediate materials, respectively; ! it denotes …rm-speci…c productivity. The production function F (:) is assumed to becontinuous and twice-di¤erentiable with respect to all of its arguments.
Consider the following cost minimization problem …rm i faces at time t
minfLit ;Kit ;Mit g

wit Lit + rit Kit + pm
it Mits:t: Fit (Lit ; Kit ; Mit ; ! it )

(10)

Qit ;

where wit , rit , and pm
it denote the wage rate, rental price of capital and the price of intermediate inputs, respectively; and Qit is a given number of output.
The estimation of …rm-level markup hinges on choosing an input that is free of any adjustment costs, and the estimation of its output elasticity. As labor is largely not freely
chosen in China (particularly state-owned enterprises) and capital is often considered a dynamic input (which makes its output elasticity di¢ cult to interpret), we choose intermediatematerials as the input to estimate …rm markup (see also DLW). Speci…cally, the Lagrangianfunction associated with the optimization problem (10) can be written as
L (Lit ; Kit ; Mit ;

it ;

it )

= wit Lit + rit Kit + pm
it Mit+ it [Qit

Fit (Lit ; Kit ; Mit ; ! it )] :Hence, the …rst-order condition for intermediate materials is
@L
= pm
it
@Mit

it

33

@Fit
= 0:
@Mit

(11)

Note that the framework is robust to any arbitrary number of inputs. As we only observe three inputs(i.e., labor, capital and intermediate materials) in our data, here we focus on production technology involving
only these three inputs.

31

it
Rearranging equation (11) and multiplying both sides by M
yield
Qit

1 pm
it Mitit QitPit pmit Mit;=it Pit Qit@Fit Mit=@Mit Qit(12)

where Pit is the price of the …nal good.
@L
Note that it = @Q
= mcit represents the marginal cost of production at a given level ofit
Pit
output. De…ne …rm markup it as the ratio of price over marginal cost, i.e. it mc= Pitit .
itHence, equation (12) leads to the following estimation expression of …rm markup34
it =

m
m 1
;
it ( it )

(13)
pm M

it
@Fit Mititwhere mis the output elasticity of intermediate materials and mis theitit@Mit Qit
Pit Qitshare of the expenditure of intermediate materials in total revenue.
As the information about the expenditure on intermediate materials and total revenue
is available in the data, m
it can be readily calculated. However, the output elasticity ofmintermediate materials, it ; needs to be obtained through estimating the production function(9). There is a large literature on the estimation of the production function focusing on how
to control for unobserved productivity shocks (for a review, see Ackerberg, Benkard, Berryand Pakes 2007). The solutions range from the instrumental variable estimation to the GMM
estimation, and to the control function approach proposed by Olley and Pakes (1996). We
adopt the control function approach developed by Ackerberg, Caves and Frazier (2006),
which comprises a two-step estimation.
Similar to DLW, we assume a translog production function when estimating markups.
Speci…cally, the production function to be estimated is expressed asqit =l lit +

k kit ++ lk lit kit +m mit +

2ll lit +km kit mit ++ lkm lit kit mit + ! it + "it ;2
kk kit +2
mm mitlm lit mit(14)

where the lowercase letters represent the logarithm of the uppercase letters; ! it is …rm-speci…cproductivity; and "it is an i.i.d. error term. = ( l ; k ; m ; ll ; kk ; mm ; lk ; km ; lm ; lkm )34

Note that this expression holds under any form of market competition and demand function. Speci…cally,
DLW discuss some alternative market structures, which lead to a similar estimation expression for …rm
markup. These alternative market structures include Cournot competition, Bertrand competition, andmonopolistic competition.

32

is the vector of production function coe¢ cients.
To proxy ! it , Levinsohn and Petrin (2003) assume thatmit = mt (kit ; ! it ; exit ) ;where exit denotes the exporter status (i.e. taking value 1 if exporters and 0 otherwise).
Given the monotonicity of mt (:), we can have! it = ht (mit ; kit ; exit ) :In the …rst stage, we estimate the following equation
qit =it + "it ;

where
it

=

l lit +

k kit ++ lk lit kit +m mit +

km kit mit +2ll lit +2
kk kit +lm lit mit +2
mm mitlkm lit kit mit + ht (mit ; kit ; exit ) ;and obtain the estimates of the expected output ( ^ it ) and the error term (^"it ).
Meanwhile, to recover all the production function coe¢ cients in the second stage, we
model that …rm productivity follows a …rst-order Markov movement, i.e.
! it = gt (! it 1 ) + it ;where it is an idiosyncratic shock.
From the …rst stage, the productivity for any given value of! it ( ) = ^ itl lit +

k kit ++ lk lit kit +m mit +

km kit mit +2ll lit +can be computed as2
kk kit +lm lit mit +2
mm mitlkm lit kit mit!

:

The idiosyncratic shock to productivity given , it ( ), can then be obtained through a
non-parametric regression of ! it ( ) on ! it 1 ( ).
To identify the coe¢ cients of the production function, Ackerberg, Caves and Frazier(2006) assume that capital is determined one period beforehand and hence is not correlatedwith it ( ). Meanwhile, wage rates and prices of intermediate materials are assumed to varyacross …rms and be serially correlated.
Therefore, the moment conditions used to estimate the coe¢ cients of the production

33

function are
E ( it ( ) Yit0 ) = 0;
where Yit = flit 1 ; lit2 1 ; mit 1 ; m2it 1 ; kit ; kit2 ; lit 1 mit 1 ; lit 1 kit ; mit 1 kit ; lit 1 mit 1 kit g.
We estimate the translog production function (14) separately for each 2-digit industry
using the Annual Survey of Manufacturing Firms conducted by the NBS from 1998 to 2005.
Speci…cally, we use the logarithm of sales de‡ated by 2-digit ex-factory price indices tomeasure qit , the logarithm of employment to measure lit , the logarithm of the net valueof …xed assets de‡ated by investment price indices to measure kit , and the logarithm ofintermediate materials35 de‡ated by input price indices to measure mit ; both price indices
are provided by Brandt, Van Biesebroeck and Zhang (2012).
Once ^ = ^ l ; ^ k ; ^ m ; ^ ll ; ^ kk ; ^ mm ; ^ lk ; ^ km ; ^ lm ; ^ lkm is obtained, we can readily calculate the …rm markup using equation (13), i.e.
m1
^ it = ^it ( m;it )mwhere ^it = ^ m + 2 ^ mm mit + ^ lm lit + ^ km kit + ^ lmk lit kit . Production estimates are reported
in Table A1.

35

We calculate the value of intermediate materials as total production costs-current year depreciation*production costs/(production costs+selling costs+administrative costs+…nancial costs)-total wagestotal welfare bene…ts.

34

References
Ackerberg, D., C. L. Benkard, S. Berry, and A. Pakes (2007), “Econometric Tools for Analyzing Market Outcomes" in Handbook of Econometrics, Volume 6A, ed. James J. Heckman
and Edward E. Leamer, 4171–276. Amsterdam and Boston: Elsevier, North-Holland.
Ackerberg, D., K. Caves, and G. Frazer (2006), “Structural Identi…cation of Production
Functions", working paper, UCLA.
Arkolakis, C., A. Costinot, and A. Rodriguez-Clare (2012), “New Trade Models, Same Old
Gains?,”American Economic Review, 102(1): 94–130.
Arkolakis, C., A. Costinot, D. Donaldson, and A. Rodriguez-Clare (2015), “The Elusive
Pro-Competitive E¤ects of Trade,”working paper, Yale University, July 2015.
Atkeson, A., and A. Burstein (2008), “Pricing to Market, Trade Costs, and International
Relative Prices,”American Economic Review, 98(5): 1998–2031.
Behrens, K., and Y. Murata, (2012) “Trade, Competition, and E¢ ciency,”Journal of International Economics, 87(1): 1–17.
Bernard, A. B., J. Eaton, J. B. Jensen, and S. Kortum (2003), “Plants and Productivity inInternational Trade,”American Economic Review, 93(4): 1268–1290.
Behrens, K., G. Mion, Y. Murata, and J. Südekum, (2014) “Trade, wages, and productivity,”
International Economic Review, 55(4): 1305–1348.
Brandt, L., J. Van Biesebroeck, L. Wang and Y. Zhang (2012), “WTO Accession and Performance of Chinese Manufacturing Firms,” working paper, Department of Economics,
University of Toronto.
Brandt, L., J. Van Biesebroeck, Y. Zhang (2012), "Creative Accounting or Creative Destruction: Firm Level Productivity Growth in Chinese Manufacturing", Journal of Development Economics, 97(2): 339–351.
Cabral, L., and J. Mata (2003), “On the Evolution of the Firm Size Distribution: Facts andTheory,”American Economic Review 93: 1075–1090.
Caliendo, L. and F. Parro (2015), “Estimates of the Trade and Welfare E¤ects of NAFTA,”
Review of Economic Studies, 82(1): 1–44.
de Blas, B., and K. Russ (2015), “Understanding Markups in the Open Economy under
Bertrand Competition,”American Economic Journal: Macroeconomics, 7(2): 157–80.
di Giovanni, J., A. A. Levchenko and J. Zhang (2014), “The Global Welfare Impact of China:
Trade Integration and Technological Change,” American Economic Journal: Macroeconomics, 6(3): 153–183.
De Loecker, J. and F. Warzynski (2012), “Markups and Firm-level Export Status,”American
Economic Review, 102(6): 2437–2471.
35Devereux, M. B and K. M. Lee (2001), “Dynamic Gains from International Trade with
Imperfect Competition and Market Power,” Review of Development Economics, 5(2):239–255.
Eaton, J. and S. Kortum (2002), “Technology, Geography, and Trade,”Econometrica, 70(5):1741–1779.
Eaton, J., S. Kortum, and S. Sotelo (2013), “International Trade: Linking Micro and Macro,”in Advances in Economics and Econometrics: 1st ed. Vol 2. Cambridge: Cambridge
University Press: 329–370.
Edmond, C., V. Midrigan, and D. Y. Xu (2015), “Competition, Markups, and the Gainsfrom International Trade,”American Economic Review, 105(10): 3183–3221.
Epifani, P., and G. Gancia (2011), “Trade, markup heterogeneity and misallocations,”Journal of International Economics, 83(1): 1–13.
Feenstra, R. C. (2014), “Restoring the Product Variety and Pro-competitive Gains from
Trade with Heterogeneous Firms and Bounded Productivity,”NBER Working Paper No.
19833, January 2014.
Goldberg, P. K., J. De Loecker, A. K. Khandelwal, and N. Pavcnik (2015), “Prices, Markups
and Trade Reform,”Econometrica, forthcoming.
Head, K., T. Mayer, and M. Thoenig (2014), “Welfare and Trade without Pareto,”American
Economic Review (Papers and Proceedings), 104(5): 310–316.
Helpman, E., M. Melitz, and Y. Rubinstein (2008), “Estimating Trade Flows: Trading
Partners and Trading Volumes.”Quarterly Journal of Economics 123: 441–487.
Holmes, T. J., W.-T. Hsu, and S. Lee (2014), “Allocative E¢ ciency, Mark-ups, and the
Welfare Gains from Trade," Journal of International Economics, 94(2): 195–206.
Hsieh, C.-T. and P. J. Klenow (2009), “Misallocation and Manufacturing TFP in China andIndia,”Quarterly Journal of Economics, 124(4): 1403–1448.
Hsieh, C.-T. and R. Ossa (2015), “A Global View of Productivity Growth in China,”working
paper, Booth School of Business, University of Chicago.
Karabarbounis, L., and B. Neiman (2014), “The Global Decline of the Labor Share,”Quarterly Journal of Economics, 129(1): 61–103.
Levinsohn, J. A., and A. K. Petrin (2003), “Estimating Production Functions Using Inputs
to Control for Unobservables,”Review of Economic Studies, 70(2): 317–342.
Lu, Y. and L. Yu (2015), “Trade Liberalization and Markup Dispersion: Evidence from
China’s WTO Accession,”American Economic Journal: Applied Economics, 7(4): 221–
253.
Melitz, M., and G. Ottaviano (2008), “Market Size, Trade, and Productivity,” Review of
Economic Studies, 75(1): 295–316.
36

Melitz, M. J., and S. J. Redding (2015), “New Trade Models, New Welfare Implications,”
American Economic Review, 105(3): 1105–46.
Neary, J. P. (2003), “Globalization and Market Structure,” Journal of the European Economic Association, 1: 245–271.
Olley, G. S., and A. Pakes (1996), “The Dynamics of Productivity in the Telecommunications
Equipment Industry,”Econometrica 64, 1263–1298
Ottaviano, G., T. Tabuchi, and J.-F. Thisse (2002), “Agglomeration and Trade Revisited,"
International Economic Review, 43(2): 409–435.
Peters, M. (2012) “Heterogenous Mark-Ups, Growth and Endogenous Misallocation,”working paper, Yale University.
Restuccia, D., and R. Rogerson (2008), “Policy Distortions and Aggregate Productivity with
Heterogeneous Plants,”Review of Economic Dynamics, 11: 707–720.
Salvo, A. (2010), “Inferring Market Power under the Threat of Entry: The Case of theBrazilian Cement Industry,”RAND Journal of Economics, 41(2): 326–350.
Schmitz, Jr. J. A. (2005), “What Determines Productivity? Lessons from the Dramatic
Recovery of the U.S. and Canadian Iron Ore Industries Following Their Early 1980s
Crisis,”Journal of Political Economy, 113(3): 582–625.
Song, Z., K. Storesletten, and F. Zilibotti (2011), “Growing Like China,”American Economic
Review, 101: 202–241.
Tre‡er, D. (2004), “The Long and Short of the Canada-U.S. Free Trade Agreement,”American Economic Review, 94(4): 870–895.
Tybout, J. R. (2003), “Plant- and …rm-level evidence on ‘New’Trade Theories,”in E. Kwan
Choi and James Harrigan (Eds.), Handbook of International Trade, Blackwell Pub.: 388–
415.

37

3
2
density
0

1

1995
2004

1

1.5

2
markup2.5

3

Figure 1: Markup Distributions (1995 versus 2004)

38

Table 1: Detailed Markup Distributions
All firms
Year
Mean
Std. dev.
p1
p5
p10
p25
p50
p75
p90
p95
p99

1995
1.428
0.495
1.005
1.022
1.044
1.114
1.262
1.538
2.015
2.464
3.528

Exporters
2004
1.372
0.479
1.004
1.019
1.036
1.091
1.207
1.437
1.893
2.379
3.509

1995
1.340
0.431
1.003
1.017
1.034
1.084
1.120
1.414
1.784
2.199
3.299

39

2004
1.318
0.438
1.004
1.017
1.032
1.077
1.168
1.362
1.747
2.183
3.364

Non-exporters
1995
2004
1.432
1.379
0.498
0.483
1.005
1.004
1.023
1.019
1.045
1.037
1.116
1.093
1.266
1.213
1.544
1.447
2.023
1.909
2.475
2.400
3.537
3.523

Table 2: SMM Results
Predeterminedw
Relative wages (the ROW to China)
R1
China's manufacturing sales ($b)
R2
ROW's manufacturing sales ($b)σ
Inferred from p99 markup
Moments for SMM
import shareexport sharerelative number of firms
fraction of exporters
mean cost share for exporters
std of cost share for exporters
p50 markup for exporters
p95 markup for exporters
mean cost share for non-exporters
std of cost share for non-exporters
p50 markup for non-exporters
p95 markup for non-exporters
Parameter values
τ, trade costγ, measure of goods
λ_1, Poisson parameter, China
λ_2, Poisson parameter, ROW
μ_1, mean of log productivity, China relative to ROW
η_1, std of log productivity, China
η_2, std of log productivity, ROW

1995

2004

10.5
918,291
9,397,500
1.40

5.3
2,343,328
14,737,500
1.40

Data
0.130
0.153
0.210
0.044
0.845
0.135
1.120
2.199
0.789
0.147
1.266
2.475

Model
0.144
0.190
0.219
0.024
0.798
0.142
1.212
2.457
0.712
0.187
1.391
2.775

Data
0.222
0.249
0.596
0.105
0.801
0.142
1.168
2.183
0.829
0.139
1.213
2.400

Model
0.262
0.277
0.616
0.062
0.804
0.124
1.203
1.839
0.775
0.152
1.264
2.056

Estimates
2.311
0.261
2.442
5.286
-2.401
0.444
0.349

s.e.
0.020
0.002
0.062
0.061
0.024
0.008
0.016

Estimates
1.664
0.849
2.607
5.828
-1.785
0.410
0.293

s.e.
0.005
0.005
0.040
0.063
0.009
0.001
0.011

Simulated macro moments under estimated parameters
Data
Model
w
10.5
10.3
R1
918,291
954,812R2
9,397,500 8,410,637

Data
Model
5.3
5.3
2,343,328 2,398,028
14,737,500 13,974,893

Notes: All units, if any, are in billions USD, current price. The import share is the import penetration ratio, i.e. IM/(R1EX+IM), and the export share is the total export divided by the same denominator. All the cost share moments are weighted
by firms' revenues. Recall that a firm's cost share is the inverse of its markup. p# denotes the #-th percentile.

40

Table 3: Jacobian Matrix
moments
import shareexport sharerelative number of firms
fraction of exporters
mean cost share for exporters
std of cost share for exporters
p50 markup for exporters
p95 markup for exporters
mean cost share for non-exporters
std of cost share for non-exporters
p50 markup for non-exporters
p95 markup for non-exportersτ
-0.514-0.9770.316-0.214-0.024
0.009
0.038
0.369
-0.109
0.070
0.176
1.019

γ
0.005
0.007
0.775
0.001
0.001
-0.0120.009-0.132-0.001-0.001
0.007-0.031

λ_1-0.076
0.087
0.110
0.007
0.013
-0.009-0.020-0.1850.019-0.006-0.037-0.111

λ_2
0.002-0.070-0.013-0.0120.006-0.012-0.005-0.1640.002-0.001-0.004-0.025μ_1-0.246
1.169
0.479
0.154
0.026
-0.022-0.029-0.594-0.066
0.031
0.141
0.583

η_1
-0.004
0.726
0.212
0.190
-0.102-0.1390.168-12.864-0.367
0.186
0.619
2.772

η_2
0.586
-0.345-0.922-0.026-0.062
0.074
0.034
1.008
0.092
-0.029-0.189-0.522

Notes: Each entry of this table gives the rate of change of a moment to a parameter. This is based on thebenchmark estimation of the 2004 model. The larger the absolute value of the rate of change, the moresensitive this moment is to the parameter, and the more useful this moment is in identifying this parameter.

41

Table 4: Counter-factual Analysis

τ, trade cost
WelfareTotal Welfare
W_Prod
W_A
W_TOT

Panel A: Counter-factual from 2004 estimates
Under 2004
estimates
τ at 1995% change
Autarky
1.664
2.311
1,000,000
1.90E+21
1.04E+15
0.965
1.003

1.73E+21
9.78E+14
0.945
1.000

Relative contribution
Pro-competitive effects
Importance of W_A

9.4%
6.8%
2.1%
0.3%

1.42E+21
8.44E+14
0.897
1.000

25.4%
22.3%

% change

33.4%
23.8%
7.5%
0.3%
23.3%
22.4%

Panel B: Counter-factual from autarky

τ, trade cost

Autarky
1,000,000

WelfareTotal Welfare
W_Prod
W_A
W_TOT

1.42E+21
8.44E+14
0.897
1.000

10% import% change
share from autarky
2.252
1.74E+21
9.81E+14
0.946
1.000

Relative contribution
Pro-competitive effects
Importance of W_A

22.6%
16.2%
5.5%
0.0%
24.2%
24.2%

20% import
share
1.810
1.85E+21
1.02E+15
0.960
1.002

% changefrom 10%import share

6.0%
4.3%
1.5%
0.2%
27.6%
24.6%

Notes: In Panel A, all the analysis is done under 2004 estimates, and only the trade cost (τ) changes.
The reported percentage changes in this panel are under the changes from the corresponding τ to2004's τ. Panel B reports results when τ is changed from an inhibitive level (autarky) to the level thatentails 10%, and then from 10% to 20%, with other parameters fixed at the 2004 estimates.

42

Table 5: SMM Results (Symmetric Countries)
Predeterminedw
Relative wages (the ROW to China)
R1
China's manufacturing sales ($b)
R2
ROW's manufacturing sales ($b)σ
Inferred from p99 markup
Moments
import shareexport sharerelative number of firms
fraction of exporters
mean cost share for exporters
std of cost share for exporters
p50 markup for exporters
p95 markup for exporters
mean cost share for non-exporters
std of cost share for non-exporters
p50 markup for non-exporters
p95 markup for non-exporters
Parameter values
τ, trade costγ, measure of goodsλ, Poisson parameter, China
η, std. of log productivity

1995

2004

1.0
9182919182911.40

1.023433282343328
1.40

Data
0.130
0.153
0.210
0.044
0.845
0.135
1.120
2.199
0.789
0.147
1.266
2.475

Model
0.053
0.049
0.213
0.064
0.731
0.158
1.370
2.564
0.759
0.170
1.289
2.399

Data
0.222
0.249
0.596
0.105
0.801
0.142
1.168
2.183
0.829
0.139
1.213
2.400

Model
0.117
0.114
0.611
0.140
0.747
0.142
1.334
2.052
0.793
0.148
1.230
1.995

Estimate
2.329
0.228
3.635
0.399

s.e.
0.008
0.002
0.010
0.003

Estimate
1.738
0.699
4.219
0.407

s.e.
0.003
0.003
0.080
0.005

Notes: All the units, if any, are in billions USD, current price. For the detailed definition of moments, see Table 2.

43

Table 6: Counter-factual Analysis (Symmetric Countries)

τ, trade cost
WelfareTotal Welfare
W_ProdW_A

Panel A: Counter-factual from 2004 estimates
Under 2004
estimates
τ at 1995% change
Autarky
1.738
2.329
1,000,000
2.30E+19
1.31E+13
0.964

2.24E+19
1.28E+13
0.958

Relative contribution
Importance of W_A

2.7%
2.1%
0.6%

2.13E+19
1.24E+130.940623.7%% change

8.1%
5.4%
2.5%
31.4%

Panel B: Counter-factual from autarky

τ, trade cost

Autarky
1,000,000

WelfareTotal Welfare
W_ProdW_A

2.13E+19
1.24E+13
0.941

10% import% change
share from autarky
1.815
2.29E+19
1.30E+13
0.964

Relative contribution
Importance of W_A

7.5%
5.0%
2.4%
32.5%

20% import
share
1.465
2.37E+19
1.34E+13
0.9681% changefrom 10%import share

3.6%
3.1%
0.5%
13.3%

Notes: Under symmetric countries, W_TOT = 1. In Panel A, all the analysis is done under 2004
estimates, and only the trade cost (τ) changes. The reported percentage changes in this panel areunder the changes from the corresponding τ to 2004's τ. Panel B reports results when τ is changed
from an inhibitive level (autarky) to the level that entails 10%, and then from 10% to 20%, with otherparameters fixed at the 2004 estimates.

44

Table 7: Robustness Check of Counter-factual Analyses
Robustness Check 1: Based on 1995 Estimatesτ, trade cost

Under 1995
estimates
2.311

WelfareTotal Welfare
W_Prod
W_A
W_TOT

1.85E+19
2.78E+13
0.943
1.002

% change to ττ at 2004at 2004
1.664% changefrom autarky
Autarky to τ at 1995
1,000,000

1.99E+19
2.95E+13
0.957
1.003

1.43E+19
2.29E+13
0.889
1.000

Relative contribution
Pro-competitive effects
Importance of W_A

7.7%
6.0%
1.5%
0.1%

20.8%
19.3%

28.7%
21.2%
6.1%
0.1%

21.6%
21.1%

Robustness Check 2: Under Raw Markups
τ at 1995
2.080% change

Autarky
1,000,000% changeτ, trade cost

Under 2004
estimates
1.654

WelfareTotal Welfare
W_Prod
W_A
W_TOT

2.90E+13
1.57E+07
0.963
0.997

2.74E+13
1.50E+07
0.951
0.997

5.8%
4.5%
1.2%
0.0%

2.43E+13
1.37E+07
0.925
1.000

19.2%
14.9%
4.0%
-0.3%

Relative contribution
Pro-competitive effects
Importance of W_A

21.1%
21.1%

19.6%
21.1%

Robustness Check 3: Using the 97.5th percentile to Infer Sigma
τ at 1995
2.161% change

Autarky
1,000,000% changeτ, trade cost

Under 2004
estimates
1.685

WelfareTotal Welfare
W_Prod
W_A
W_TOT

4.49E+15
2.47E+09
0.963
1.004

4.22E+15
2.35E+09
0.951
1.004

6.4%
5.1%
1.2%
0.0%

3.69E+15
2.13E+09
0.918
1.000

21.8%
15.7%
4.9%
0.4%

Relative contribution
Pro-competitive effects
Importance of W_A

19.4%
19.4%

24.0%
22.4%

Notes: In the first robustness check, the analysis is based on the 1995 estimate and we change τ to the2004 level. In the next two robustness checks, analyses are done based on 2004 estimates, as in thebenchmark case.

45

Table 8: The Effect of Tariffs
Panel A: Counter-factual based on 2004 estimatesτ, trade cost

Under 2004
estimates
1.664

Tariff at 1995,
non-tariff τ at
2004
1.845

WelfareTotal Welfare
W_Prod
W_A
W_TOT

1.90E+21
1.04E+15
0.965
1.003

1.83E+21
1.01E+15
0.959
1.002

% change

3.7%
2.9%
0.6%
0.1%

Relative contribution
Pro-competitive effects
Importance of W_A

Tariff at 1995,
non-tariff τ at
1995
2.311

1.73E+21
9.78E+14
0.945
1.000

20.3%
16.8%

% change

Relative
contribution oftariff reduction

9.4%
6.8%
2.1%
0.3%

39.6%
42.8%
29.9%
44.8%

25.4%
22.3%

Panel B: Counter-factual based on 1995 estimatesτ, trade cost

Under 1995
estimates
2.311

Tariff at 2004,
non-tariff τ at
1995
2.083

WelfareTotal Welfare
W_Prod
W_A
W_TOT

1.85E+19
2.78E+13
0.943
1.002

1.90E+19
2.84E+13
0.949
1.001

% change

2.7%
2.1%
0.7%
0.0%

Relative contribution
Pro-competitive effects
Importance of W_A

22.8%
24.3%

Tariff at 2004,
non-tariff τ at
2004
1.664

1.99E+19
2.95E+13
0.957
1.003

% change

7.7%
6.0%
1.5%
0.1%

Relative
contribution oftariff reduction

35.2%
34.7%
44.3%

20.8%
19.3%

Notes: In Panel A, all the analysis is done under 2004 estimates, and only the trade cost (τ) changes. The reported
percentage changes in this panel are under the changes from the corresponding τ to 2004's τ. Panel B reports results
under 1995 estimates.

46

Table 9A: Estimation Result in Multi-Sector Model (Part A)
cic2d
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
39
40
41
42
Mean
Standard
deviation
Max
Min

Industry definition
Food processing
Food manufacturing
Beverage manufacturing
Tobacco processing
Textile industry
Garments & other fiber products
Leather, furs, down & related products
Timber processing, bamboo, cane, palm
fiber & straw products
Furniture manufacturing
Papermaking & paper products
Printing industry
Cultural, educational & sports goods
Petroleum processing & coking
Raw chemical materials & chemical
products
Medical & pharmaceutical products
Chemical fiber
Rubber products
Plastic products
Nonmetal mineral products
Smelting & pressing of ferrous metals
Smelting & pressing of nonferrous metals
Metal products
Ordinary machinery
Special purpose equipment
Transport equipment
Electric equipment & machinery
Electronic & telecommunications
equipment
Instruments, meters, cultural & officeequipment
Other manufacturing

Predetermined
σ
α
1.51
0.049
1.33
0.017
1.21
0.014
1.22
0.014
1.49
0.059
1.37
0.023
1.39
0.016

1995
0.018
0.009
0.009
0.0003
0.013
0.009
0.006

γ
2004 % change
0.044
139.1
0.018
109.2
0.015
72.4
0.0002
-29.8
0.049
269.5
0.028
204.7
0.014
136.9

1995
2.47
4.62
4.80
4.49
1.88
3.52
1.96

τ
2004 % change
2.347
-4.9
2.49
-46.1
3.33
-30.7
4.81
6.9
1.69
-10.4
2.91
-17.2
1.61
-18.0

1995
25.6
17.5
25.3
37.9
19.7
10.8
9.9

Tariff2004 % change
16.6
-35.2
9.9
-43.6
7.7
-69.8
9.8
-74.3
7.6
-61.4
9.2
-15.0
5.5
-44.3

1995
1.97
3.93
3.83
3.26
1.57
3.18
1.79

1.40

0.011

0.009

0.026

198.8

2.10

1.64

-21.9

7.8

2.6

-67.1

1.95

1.60

-17.9

1.26
1.48
1.29
1.35
1.45

0.008
0.020
0.009
0.007
0.050

0.005
0.008
0.010
0.002
0.001

0.015
0.025
0.027
0.009
0.006

215.2
200.5
157.5
254.9
346.7

2.47
2.59
2.68
2.11
1.96

1.92
2.16
2.28
1.72
1.54

-22.2
-16.3
-15.1
-18.7
-21.6

8.3
23.7
5.3
4.1
8.6

1.0
4.0
0.9
1.5
5.0

-88.0
-83.0
-83.5
-64.3
-42.2

2.28
2.09
2.55
2.03
1.80

1.90
2.08
2.26
1.70
1.46

-16.6
-0.5
-11.4
-16.6
-18.9

1.50

0.072

0.015

0.073

382.9

2.51

1.74

-30.5

14.6

7.2

-51.0

2.19

1.62

-25.7

1.33
2.01
1.50
1.54
1.35
1.78
1.67
1.43
1.50
1.35
1.36
1.51

0.017
0.010
0.010
0.027
0.050
0.092
0.031
0.032
0.052
0.030
0.076
0.061

0.004
0.001
0.003
0.011
0.035
0.005
0.002
0.014
0.017
0.011
0.014
0.012

0.007
0.003
0.009
0.045
0.094
0.013
0.009
0.048
0.084
0.038
0.036
0.037

71.9
337.5
237.4
320.7
172.8
159.1
294.3
249.6
393.0
241.5
161.0
197.9

4.43
3.16
2.09
1.76
4.62
2.46
2.12
1.98
3.07
2.41
2.62
1.71

2.76
2.23
1.84
1.72
2.39
2.17
1.83
1.76
1.64
1.61
2.18
1.53

-37.7
-29.4
-11.8
-2.4
-48.3
-11.6
-13.9
-10.8
-46.6
-33.2
-16.8
-10.5

6.9
22.0
20.2
13.9
12.8
10.9
7.7
13.2
17.5
16.6
43.5
11.3

3.8
4.9
11.0
5.4
5.9
4.9
3.9
4.0
5.1
5.3
12.7
3.0

-44.9
-77.7
-45.6
-61.0
-54.0
-55.2
-49.4
-69.9
-71.0
-68.2
-70.8
-73.0

4.15
2.59
1.74
1.55
4.10
2.22
1.97
1.75
2.61
2.07
1.83
1.54

2.66
2.12
1.66
1.63
2.26
2.07
1.76
1.70
1.56
1.53
1.93
1.48

-35.8
-17.9
-4.5
5.4
-44.9
-6.6
-10.7
-2.8
-40.3
-26.0
5.9
-3.4

1.34

0.121

0.005

0.017

271.3

2.19

1.51

-31.1

13.5

1.3

-90.5

1.93

1.49

-22.8

1.35

0.013

0.004

0.012

194.4

1.85

1.52

-18.2

15.7

4.3

-72.6

1.60

1.45

-9.2

1.32

0.009

0.006

0.016

173.0

2.30

1.68

-26.9

8.8

2.8

-67.8

2.12

1.64

-22.7

1.44

0.034

0.01

0.03

211.522.72

2.09-21.23

15.64

5.74

-61.87

2.35

1.97-13.77

0.17

0.029

0.01

0.02

95.89

0.96

0.70

13.23

9.12

3.66

17.25

0.80

0.62

16.52

2.01
1.21

0.121
0.007

0.03
0.00

0.09
0.00

392.98-29.81

4.80
1.71

4.81
1.51

6.95
-48.28

43.46
4.08

16.57
0.87

-14.99-90.54

4.15
1.54

4.38
1.45

34.39
-44.9047

Non-tariff τ
2004 % change
2.01
2.5
2.27
-42.3
3.09
-19.3
4.38
34.4
1.57
-0.3
2.67
-16.0
1.53
-14.6

Table 9B: Estimation Result in Multi-Sector Model (Part B)
cic2d
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
39
40
41
42
Mean
Standard
deviation
Max
Min

Industry definition
Food processing
Food manufacturing
Beverage manufacturing
Tobacco processing
Textile industry
Garments & other fiber products
Leather, furs, down & related products
Timber processing, bamboo, cane, palm
fiber & straw products
Furniture manufacturing
Papermaking & paper products
Printing industry
Cultural, educational & sports goods
Petroleum processing & coking
Raw chemical materials & chemical
products
Medical & pharmaceutical products
Chemical fiber
Rubber products
Plastic products
Nonmetal mineral products
Smelting & pressing of ferrous metals

1995
2.78
3.03
3.05
2.75
3.04
3.03
2.85

λ_1
2004 % change
3.05
9.9
3.11
2.5
3.24
6.2
3.18
15.8
3.25
6.9
3.22
6.2
3.21
12.7

1995
5.40
4.25
5.82
6.00
5.73
5.27
5.13

λ_2
2004 % change
6.9
26.8
4.6
8.7
4.5
-23.1
5.6
-6.3
6.3
10.2
6.6
25.5
4.6
-10.4

1995
-2.37
-1.51
-1.20
-2.38
-2.36
-2.15
-2.19

μ_1
2004 % change
-1.67
29.8
-1.50
0.7
-1.23
-2.7
-1.33
44.0
-1.78
24.6
-0.93
56.7
-1.75
20.1

1995
0.43
0.15
0.11
0.34
0.35
0.55
0.33

η_1
2004 % change
0.46
8.2
0.34
136.2
0.22
95.7
0.44
28.6
0.38
9.2
0.42
-23.9
0.37
11.1

1995
0.38
0.33
0.09
0.27
0.35
0.11
0.44

η_2
2004 % change
0.37
-4.8
0.34
0.2
0.16
82.1
0.20
-23.6
0.17
-50.8
0.42
269.7
0.26
-40.3

2.85

2.98

4.7

5.26

5.4

2.7

-2.40

-1.74

27.5

0.41

0.32

-20.0

0.40

0.25

-38.7

2.51
3.02
2.97
2.44
2.70

2.87
2.76
2.61
3.02
2.87

14.4
-8.4
-12.2
23.9
6.3

5.53
5.59
6.41
5.06
5.37

5.0
6.1
5.6
4.6
6.1

-9.6
9.1
-12.7
-8.6
13.8

-2.03
-2.33
-2.37
-2.14
-2.25

-1.66
-1.79
-1.78
-1.70
-1.84

18.1
23.3
24.9
20.5
18.3

0.24
0.29
0.35
0.38
0.21

0.37
0.41
0.42
0.42
0.32

56.4
43.3
18.7
9.0
51.8

0.26
0.52
0.09
0.28
0.36

0.08
0.44
0.18
0.15
0.32

-70.6
-16.4
106.3
-46.4
-12.3

2.63

1.97

-25.1

4.84

6.7

39.0

-2.52

-1.92

23.7

0.43

0.51

17.1

0.52

0.36

-31.1

2.99
2.44
3.36
2.73
3.03
2.87

2.66
2.89
2.61
3.13
3.03
3.16

-11.1
18.2
-22.3
14.6
0.1
10.1

6.23
5.47
5.57
5.31
4.95
6.50

5.0
4.5
4.6
5.8
5.6
4.4

-19.4
-17.6
-18.2
9.1
12.5
-32.5

-2.09
-2.59
-2.27
-2.39
-1.59
-2.40

-1.61
-1.75
-1.72
-1.67
-1.39
-1.60

23.1
32.5
24.0
30.2
12.7
33.3

0.54
0.46
0.30
0.31
0.25
0.06

0.50
0.26
0.37
0.34
0.28
0.10

-8.3
-43.3
22.5
11.0
11.0
59.4

0.17
0.51
0.15
0.21
0.20
0.34

0.39
0.44
0.15
0.26
0.34
0.40

130.7
-14.5
5.1
22.6
65.3
20.0

Smelting & pressing of nonferrous metals

2.29

2.54

11.2

5.83

5.8

-0.6

-2.46

-1.77

28.1

0.36

0.40

9.5

0.23

0.33

41.7

Metal products
Ordinary machinery
Special purpose equipment
Transport equipment
Electric equipment & machinery
Electronic & telecommunications
equipment
Instruments, meters, cultural & officeequipment
Other manufacturing

3.03
2.24
2.12
2.74
2.44

2.83
2.50
2.49
2.53
2.95

-6.7
11.6
17.3
-7.6
20.9

5.54
4.64
5.17
6.12
5.93

5.8
7.3
6.9
5.9
5.9

5.2
56.7
33.3
-3.9
0.0

-2.42
-2.44
-2.63
-2.38
-2.43

-1.82
-1.79
-1.80
-1.72
-1.77

24.6
26.8
31.4
27.5
27.0

0.37
0.40
0.34
0.38
0.31

0.39
0.42
0.40
0.42
0.40

5.1
3.8
19.4
8.6
28.5

0.24
0.52
0.52
0.29
0.16

0.09
0.31
0.42
0.30
0.34

-60.4
-39.9
-19.5
6.2
114.4

2.41

2.47

2.5

5.81

5.9

1.8

-2.36

-1.77

25.1

0.51

0.53

3.5

0.52

0.44

-16.0

2.38

2.18

-8.7

4.95

5.8

16.2

-2.41

-1.75

27.5

0.45

0.46

1.9

0.51

0.44

-13.4

2.73

3.20

17.4

5.36

5.8

8.7

-2.25

-1.74

22.7

0.52

0.43

-17.6

0.34

0.16

-52.4

2.74

2.84

4.52

5.48

5.64

4.02

-2.25

-1.67

25.03

0.35

0.38

19.19

0.32

0.29

10.80

0.30

0.34

12.66

0.52

0.81

19.75

0.32

0.21

10.82

0.12

0.09

35.61

0.14

0.11

72.80

3.36
2.12

3.25
1.97

23.89
-25.11

6.50
4.25

7.27
4.39

56.75
-32.53

-1.20
-2.63

-0.93
-1.92

56.74
-2.72

0.55
0.06

0.53
0.10

136.22-43.25

0.52
0.09

0.44
0.08

269.74-70.5948

Table 10: Counter-factual Analysis in Multiple-Sector Economy
Panel A: Counter-factual from 2004 estimates

WelfareTotal Welfare
W_Prod
W_A
W_TOT

Under 2004
estimates

Trade costs({τ_s}) at 1995

% change

Autarky% change

1.78E+16
1.82E+09
0.95850.9992

1.66E+16
1.72E+09
0.94500.9990

7.24%
5.71%
1.43%
0.02%

1.38E+16
1.50E+09
0.905
1.000

28.2%
21.1%
5.9%
-0.1%

Relative contribution
Pro-competitive effects
Importance of W_A

20.0%
19.7%

20.6%
20.9%

Panel B: The Effect of Tariff, based on 2004 estimates

WelfareTotal Welfare
W_Prod
W_A
W_TOT

Under 2004
estimates

Tariffs at 1995,
non-tariff trade
costs at 2004

% change

1.78E+16
1.82E+09
0.95850.9992

1.73E+16
1.78E+09
0.95430.9994

2.52%
2.09%
0.44%
-0.02%

Relative contribution
Pro-competitive effects
Importance of W_A

Relative% change contribution of(from trade
tariffcosts in 1995)
reductions

7.24%
5.71%
1.43%
0.02%

34.8%
36.7%
30.8%
-100.0%

16.7%
17.5%

Notes: Similar to Table 4, all the analyses in Panel A are done under 2004 estimates, and only the trade
costs change. The reported percentage changes in this panel are under the changes from thecorresponding {τ_s} to 2004's. Panel B reports results on changing tariffs only and calculates therelative contribution of tariff reductions on welfare.

49

Table 11: Did China Trade-Liberalize the Right Sectors?
Dependent variable
Sectoral markup at
1995

Changes in trade costs between 1995 and 2004
(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

-2.109**-1.774**-1.980*-1.856-0.343**-0.378**-0.581*-0.596*(0.799)(0.833)(1.063)(1.117)(0.154)(0.164)(0.296)(0.303)

SOE share

0.242-0.967-0.0290.042(0.157)(0.728)(0.032)(0.171)

Log wage at 1995
Log employment at
1995
Log export at 1995

Log import at 1995

R2

Changes in import tariffs between 1995 and 2004

0.169

0.186

-0.072-0.056-0.065-0.064(0.209)(0.218)(0.066)(0.068)-0.153-0.352-0.021-0.011(0.125)(0.218)(0.029)(0.056)

0.166**

0.181**-0.045***-0.047**(0.077)(0.087)(0.016)(0.017)-0.0350.060

0.047**

0.044*(0.063)(0.116)(0.020)(0.024)

0.386

0.449

0.363

0.366

0.108

0.114

Notes : The regression is weighted by sectoral trade volume and sectoral imports when the dependent variable is the change

in trade cost and import tariff, respectively. Note that the sample size is small (29), and hence one should use caution wheninterpreting the significance levels.
* Significant at the 10 percent level.
** Significant at the 5 percent level*** Significant at the 1 percent level.

50

Table A1: Production Function Estimates

Industry
Food processing
Food manufacturing
Beverage manufacturing
Tobacco processing
Textile industry
Garments and other fiber products
Leather, furs, down and related products
Timber processing, bamboo, cane, palm fiber andstraw products
Furniture manufacturing
Papermaking and paper products
Printing industry
Cultural, educational and sports goods
Petroleum processing and coking
Raw chemical materials and chemical products
Medical and pharmaceutical products
Chemical fiber
Rubber products
Plastic products
Nonmetal mineral products
Smelting and pressing of ferrous metals
Smelting and pressing of nonferrous metals
Metal products
Ordinary machinery
Special purpose equipment
Transport equipment
Electric equipment and machinery
Electronic and telecommunications equipment
Instruments, meters, cultural and office equipment
Other manufacturing

Panel A: Output Elasticity With Respect to …
Labor
CapitalMaterials
MedianIQRMedianIQRMedianIQR
0.09[0.07,0.13]
0.03[0.01,0.05]
0.86[0.81,0.90]
0.14[0.11,0.18]
0.05[0.02,0.08]
0.82[0.76,0.87]
0.19[0.14,0.25]
0.02[-0.01,0.05]
0.78[0.71,0.84]
0.17[0.03,0.33]
0.24[0.10,0.35]
0.73[0.64,0.82]
0.16[0.11,0.22]
0.04[0.03,0.05]
0.84[0.77,0.89]
0.23[0.15,0.35]
0.05[0.04,0.07]
0.75[0.64,0.84]
0.20[0.12,0.28]
0.01[0.00,0.02]
0.81[0.73,0.88]

Panel B: Returns to Scale
DoubleTripleMedianIQRMedianIQR
0.99[0.98,1.00]
0.99[0.98,1.00]
1.02[1.00,1.04]
1.03[1.00,1.04]
1.01[0.97,1.04]
1.01[0.98,1.04]
1.14[1.05,1.23]
1.14[1.04,1.22]
1.03[0.99,1.06]
1.02[0.99,1.05]
1.02[1.00,1.05]
1.02[1.00,1.05]
1.01[1.00,1.03]
1.01[1.00,1.03]

Obs.
104,518
48,29541,894731
113,001
72,381
34,6550.15[0.10,0.21]

0.03[0.03,0.04]

0.83[0.76,0.88]

1.01[0.99,1.02]

1.00[0.99,1.02]

57,283

0.38
0.26
0.24
0.23
0.10
0.22
0.25
0.05
0.23
0.14
0.15
0.10
0.12
0.17
0.20
0.24
0.16
0.15
0.23
0.20
0.21

[0.33,0.44][0.23,0.29][0.21,0.26][0.15,0.34][0.07,0.14][0.18,0.25][0.18,0.32][0.01,0.09][0.19,0.27][0.09,0.19][0.09,0.22][0.07,0.14][0.08,0.16][0.13,0.23][0.16,0.26][0.22,0.28][0.11,0.22][0.11,0.21][0.17,0.30][0.13,0.29][0.14,0.29]

-0.02
0.05
0.11
0.06
0.06
0.04
0.19
0.16
0.06
0.06
0.05
0.03
0.03
0.09
0.08
0.08
0.07
0.06
0.10
0.09
0.06

[-0.03,0.00][0.04,0.06][0.08,0.15][0.05,0.08][0.05,0.07][0.03,0.05][0.13,0.26][0.15,0.18][0.06,0.07][0.05,0.07][0.04,0.06][0.03,0.04][0.03,0.04][0.08,0.11][0.06,0.09][0.06,0.10][0.06,0.09][0.05,0.07][0.09,0.11][0.07,0.10][0.04,0.07]

0.99
0.85
0.86
0.79
0.83
0.72
0.65
0.73
0.79
0.83
0.80
0.85
0.84
0.71
0.80
0.79
0.76
0.79
0.73
0.72
0.78

[0.90,1.07][0.80,0.89][0.77,0.94][0.70,0.86][0.78,0.87][0.67,0.76][0.55,0.74][0.69,0.76][0.73,0.83][0.77,0.88][0.72,0.86][0.80,0.90][0.79,0.88][0.66,0.76][0.73,0.85][0.73,0.85][0.69,0.82][0.73,0.84][0.65,0.80][0.63,0.79][0.70,0.84]

1.37
1.15
1.24
1.07
0.99
0.97
1.08
0.94
1.08
1.01
0.98
0.98
0.99
0.97
1.07
1.13
0.99
1.00
1.06
1.00
1.02

[1.30,1.44][1.13,1.19][1.17,1.29][1.04,1.11][0.98,1.00][0.96,0.97][1.04,1.12][0.92,0.95][1.06,1.09][1.00,1.03][0.97,1.01][0.97,0.99][0.99,1.00][0.96,1.00][1.06,1.09][1.09,1.16][0.99,1.00][0.99,1.01][1.05,1.08][0.97,1.04][1.00,1.06]

1.38
1.16
1.25
1.06
0.99
0.96
1.08
0.94
1.07
1.01
0.98
0.98
0.99
0.97
1.07
1.13
0.99
1.00
1.06
1.00
1.02

[1.32,1.46][1.13,1.20][1.18,1.30][1.04,1.10][0.98,1.00][0.96,0.97][1.04,1.11][0.92,0.95][1.06,1.09][1.00,1.03][0.97,1.00][0.97,0.99][0.99,1.00][0.95,0.99][1.06,1.08][1.10,1.16][0.98,1.00][0.99,1.01][1.05,1.08][0.96,1.03][1.00,1.05]

34,12655,60657,993
20,987
10,430108,197
17,595
4,925
20,66492,509226,792
29,102
20,671
117,081
148,586
77,15775,94363,631
48,71625,49439,978Notes: IQR means inter-quartile range. In Panel B, we calculate the r in k^rY=F(kK,kL,kM), where Y,K,L,M are output, capital, labor, and material, respectively. The calculation is local
to the data values and our estimate. The columns under "double" and "triple" are the results when k is chosen to be 2 and 3, respectively.

51