Import Competition and Innovation: Evidence from China∗
Matilde Bombardini†

Bingjing Li‡

Ruoying Wang§

Preliminary and Incomplete[Please click here for the latest version]
December 4, 2017

Abstract
Does foreign competition encourage innovation? This question has received renewed attention recently in two prominent papers by Autor, Dorn, Hanson, Pisano and Shu (2016) andBloom, Draca and Van Reenen (2016), which come to different conclusions. We contribute tothis area of research by exploring the link between innovation and import competition in China,
a country that during the period we study (2000-2007) saw both a rapid increase in patenting
and a lowering of import barriers due to accession to the WTO. Combining manufacturing firm
survey data with customs and patent data, we find that import competition encouraged innovation, but only for the most productive firms. Those top firms saw an increase in patenting
probability 3.6 - 4% for every percentage point drop in import tariffs. The result is quantitatively similar whether we use a sector-wide tariff on output or a weighted tariff at the firm levelas a measure of import competition. Consistent with the main finding, top firms also feature
increased R&D expenditures and an increase in market shares following import liberalization.
In addition, we find that in the face of more competition, top firms not only strengthen theircore technology, but also enlarge the scope of their patent portfolio. We rationalize the empiricalfindings with a model of step-by-step innovation à la Aghion et al. (2009) where import competition generally discourages innovation by reducing sector-wide profits, but encourages firms
close to the technological frontier to escape competition by increasing investments in R&D.
Keywords: China, Import competition, Innovation, Heterogeneous firms.
JEL Codes: F12, F14, O31.

∗
We would like to thank Daron Acemoglu, Philippe Aghion, Nancy Gallini, Viktoria Hnatkovska, Hiro Kasahara,
Kevin Lim, Jess Perla, Maria Tito, Daniel Trefler, and seminar participants at UBC and CIFAR for helpful comments.
All errors are our own.
†
University of British Columbia, CIFAR and NBER.
‡
National University of Singapore.
§
University of British Columbia.

1

1

Introduction

The link between innovation and prosperity is not only at the heart of a large literature on endogenous growth, but also the target of much attention by policymakers. A recent paper by Akcigit
et al. (2017) documents a long-run relationship between innovation and growth in the United States.
This broad consensus on the value of innovation stands in contrast with the disagreement on what
are its main drivers. We focus on a question that has proven particularly difficult to settle, thatis the link between competition, in particular import competition, and innovation. In fact, despite
recent evidence on the effect of trade on innovation in several developed countries (Aghion et al.,2017; Autor et al., 2016; Bloom et al., 2016), no clear consensus has emerged on whether importcompetition encourages or discourages innovation. This paper contributes to this discussion by
analyzing firm level evidence from China. We ask the following questions: what is the impact ofimport competition on Chinese firm’s capacity to innovate? And what firms are affected the most?
China serves as an interesting case to study the relationship between import competition andinnovation because it experienced both a rapid growth in patenting and intensified foreign competition after its accession to the WTO in December 2001. From 2001 to 2007, invention patents filed
in the State Intellectual Property Office (SIPO) of China grew at an average annual rate of 25 percent, comparing to 6 percent in the US patent office (USPTO). In 2007, the number of total patents
filed in SIPO has reached 53 percent that of the USPTO. During this post-WTO period, China
experienced large drop in import tariff barriers and other kinds of local protections mandated by
WTO, which significantly increased presence of foreign competition. The effectively applied tariff
dropped by 6.2 percentage points, from 0.166 to 0.104. Total import quadrupled from 243.5 billion
USD in 2001 to 956.1 billion USD in 2007. We exploit the variation in the import tariff decreases
across industry and over time to identify the shock to import competition on firms. It is worth
noting that, even though China’s first application to the GATT dates to 1986, the schedule of tariffchanges was not known until September 2001. The negotiation took 18 meetings between 1996
and 2001 and was characterized by Vice Minister LONG Yongtu, Head of the Chinese Delegationin his Statement on Sep 17, 2001: “The complexity and difficulty of this process are beyond theimagination of almost everybody.” It is unlikely that anybody knew the timing and extent of tariff
cuts and much less the impact of those tariff custs on imports.

2

More specifically, we draw on the Chinese Annual Survey of Manufacturing firms and match it to
the patent and customs data. The matched firms account for over two thirds of total patents filed by
Chinese enterprise assignees during 2001-2007. We estimate the elasticity of firm patent application
on two-period lagged industry output tariff through a Poisson count data model, controlling forindustry fixed effects and time trends. Output tariff is measured as the average import tariff faced
by products that the industry (firm) produces. The idea is that, when the import tariff decreases,
there are more competing foreign goods (final or intermediates) in the domestic market. Thus adecrease in the import tariff proxies for an increase in total import of the industry. Moreover, we
use the WTO accession tariff that is scheduled at the time of China’s WTO entry, instead of using
the actually applied tariff for each year. In this way, we try to minimize the concern that concurrent
tariff may be affected by industry lobbying that is correlated with productivity or innovation, ouroutcome variable. We find that for firms above the 75 percentile in terms of productivity, a one
percentage point decrease in output tariff could induce 3.6 - 4 percent increase in patenting. This
increase is due to both strengthening firms’ core technology, and enlarging the technology scope.
There are several challenges in our measurement and identification of the problem at hand.
First, we measure innovation with patent applications in SIPO. Of course, patent application is
not the only output of innovation. Many innovative activities such as improvement in management
or business model is not patentable, and firms may prefer to keep some new formula secrecy1 .
However, survey evidence shows that all outputs of innovation are positively correlated (Hall et al.,2014; Moser, 2013), and comparing to productivity, patenting is a more direct and precise wayof measuring technology progress at the firm level (Griliches, 1990; Nagaoka et al., 2010). Therefore, we use patent application count as our benchmark measure of innovation, and maintain theassumption that patent count is a sufficient statistic in measuring firm innovation.
Another concern is that the measured increase in patenting is totally driven by the change
in the propensity to patent. For example, studies have shown that stronger IP protection due
to entrance into the WTO would boost domestic patent filing, and the patent system may shiftfirms’ innovation effort from unpatentable to patentable products (Qian, 2007; Arora et al., 2015).
1

Hall et al. (2014) reviewed the literature on choice between formal IP and secrecy. They concluded that although
the choice is made strategically and is affected by various industry and market characteristics, empirical evidenceshows that secrecy and IP are usually complements. And the choices within formal IP — patent, trademark andcopyrights — are used as complements as well.

3

There definitely exist nation-wide trends in IP protection and propensity to patent in China. The
question is whether the propensity to patent due to IP protection has differentially affected sectorsthat experienced different degrees of import liberalization. Because we show, similarly to Brandt
et al. (2017), that tariff cuts are unrelated to observable pre-trends like productivity growth, we do
not find the conditions for our regressions to pick up this type of spurious correlation. Furthermore,
if patenting propensity differentially changes by aggregate sectors, our sector-time dummies will
absorb different trends in industry patenting propensity. Another concern is that some firms may
file multiple patents for the same technology to form a strategic portfolio. If firms become morestrategic due to competition, our estimate will be upward biased. We check for the specification
with patent dummy, instead of patent counts, as our dependent variable, and our results remain.
To reconcile our empirical findings with the theory, there are several existing frameworks that
we can take off the shelf. First, there is the “trapped factor” theory used to explain findings in
Bloom et al. (2016), who find that European firms increased investment in technology after Chinese
imports increased. The basic idea is that the increased low-tech import lowers the opportunity costof firms to switch technology, and frees up the labor that was initially trapped in low-tech tasks,
so that they can now engage in research and development. This story is unlikely to be suitableto our case as China is still a developing country and its labor force in manufacturing is not welleducated. The second possible theoretical framework is the monopolistic competition structure thatis widely used in the trade literature. In these models, especially model with Constant Elasticity
of Substitution (CES) demand system, the negative price effect tends to be very big, therefore,
without any market size effect, it is hard to generate pro-innovation incentives after competition
intensifies. The third strand of literature is the series of papers by Aghion and co-authors that
explore the “escape competition” effect. This family of models emphasizes the distinction between
pre- and post- innovation returns. While these models adopt a stylized framework with either
duopolies or monopolies,2 we explore how robust their insights are in a more general model ofmonopolistic competition.
Therefore, we combine the features of the monopolistic competition as in Melitz and Ottaviano (2008) and the step-by-step innovation as in Aghion et al. (2009) to generate a model withheterogeneous firms and escape competition effect at the top. On the one hand, from the monopo2

See for example Aghion et al. (2009), and also the IO literature surveyed in Gilbert (2006)4listic competition framework, there is a “price index effect” that will discourage innovation for all
firms. On the other hand, the opportunity of innovating and retaining the market from the foreigncompetitors generate incentives for top firms to innovate more.
Our paper is related to several strands of literature. First, it is related to the studies about
China’s gains from accessing the WTO. In addition to the usual gains from trade such as selection
through exports, or access to more imported varieties, evidence has shown that accessing the WTO
helped correct resource misallocation and accelerated the market reforms that is sometimes difficult
to implement within political constraints (Khandelwal et al., 2013; Lu and Yu, 2015). In a recent
and very related paper, Brandt et al. (2017) studied the effect of lowering import tariffs on industry
average productivity and mark-up. They find that when import competition intensifies, there isa decrease in output price level and mark-up, and an increase in aggregate productivity. Whileconfirming their findings, we go one step further to study the innovation channel of productivity
increase, among other potential channels such as purchasing new machines.
The second strand of literature looks at channels through which trade could impact firm’s
capacity to innovate. On the one hand, getting access to bigger markets due to export liberalization
has been found to induce firms to switch to skill intensive technology (Bustos, 2011), increase R&D
spending (Aw et al., 2011) and engage in more innovation (Aghion et al., 2017; Trefler and Yu,2017). On the other hand, import liberalization could enable access to better imported inputs,
which helps to enhance knowledge diffusion (MacGarvie, 2006), complement R&D spending (Bøler
et al., 2015), and induce quality upgrading (Fieler et al., 2016).3 We focus on the specific channelof import competition brought by trade liberalization, while controlling for other channels, i.e.
accessing the export market and better intermediate inputs.
We also contribute to the theory on trade and innovation in a heterogeneous firm framework.
Conclusions usually differ because of different assumptions or targeted data moments. An earlier
contribution is Atkeson and Burstein (2010). Under the assumption of zero spillover or diffusion,
they find theoretically that there is little dynamic gains from trade by allowing firms to innovate.
Perla et al. (2015) could generate dynamic gains from trade, while the prediction on the heterogeneous effect among firms is contrary to ours: only firms at the productivity cutoff would find it
optimal to innovate. Three recent work in progress that is closely related to our model are Aghion3

See also Teshima (2008)5

et al. (2017), Akcigit et al. (2017) and Lim et al. (2017). Aghion et al. (2017) also builds on theframework of Melitz and Ottaviano (2008), but focus on the effect of exporting. Consistent withus, they also find a negative effect of competition on the price index which would discourage innovation among firms away from the technology frontier. In Akcigit et al. (2017), importing happensin sectors the home country is very lagged behind, therefore, only firms at the middle-lower partof the productivity distribution would react to import competition. In contrast, we model foreign
entry to be more technological advanced, which is more consistent with the data in China. Lim
et al. (2017) build a dynamic model where firms have to move up a quality ladder and endogenously
choose to upgrade or not. Their framework could also generate an escape-competition effect in faceof trade liberalization.
Our paper is also related to the literature that explores the reason behind the rapid increasein patenting in China after 2000. The most studied causes are increased investment in R&D (Weiet al., 2017; Hu et al., 2005; Hu and Jefferson, 2009), improvement in Intellectual Property Right(IPR) protection (Ang et al., 2014), ownership reforms, government’s pro-patenting policies, andFDI. Fang et al. (2017) found that privatization of state-owned firms motivate more patenting,especially in prefectures with higher IPR protection. Shifts in demographic structure also affects
patenting behavior in China. Xie and Zhang (2015) find that rising wages have propelled laborintensive sectors to become more innovative, and firms in female-intensive industries have exhibited
more innovations than those in male-intensive industries. While we focus on the effect of importcompetition and market structure, we take into consideration these other forces through controlling
for region and ownership characteristics.
The remainder of the paper is organized as follows: Section 2 shows our theoretical frameworkand the escape-competition mechanism. Section 3 describes the data and summary statistics.
Section 4 shows our empirical framework, and section 5 discusses the empirical results. Section 6
concludes.

2

Theoretical framework

We build a model of monopolistic competition where domestic producers compete in the homemarket (China) with other domestic firms. Let us consider a model with a home country (China)

6and a foreign country (the rest of the world).

2.1

Market structure

There is a finite number, N , of varieties in a sector indexed by i, and a homogeneous good which
we also use as numeraire. To simplify notation we also assume that L = 1 since population size
will not play any role in our analysis. The consumer utility function follows the linear demand
literature as in Ottaviano et al. (2002) and Melitz and Ottaviano (2008),

U = q0 + α

N
X
i=1

N
N
X
1 X
1
qi di − γ(qi )2 di − η
qi
2
2
i=1

!2
(1)

i

where q0 and qi denote the individual consumption of, respectively, the numeraire good and differentiated good i. The parameters α and η index the relative importance of the differentiated goodover the numeraire. The parameter γ governs the degree of substitutability between the differentiated varieties. Following Melitz and Ottaviano (2008), the quantity demanded for each variety canbe expressed as a function of the average price and the price of the variety pi :

qi =

1
η 1
α
− pi +
p̄
η+γ γ
η+γγ

Differently from Melitz and Ottaviano (2008) we fix the number of varieties and do not allow for
free entry. We assume that for each variety there is one domestic firm and one foreign firm and thatthey compete in a Bertrand fashion. This is a similar setup to Bernard et al. (2003). The domestic
firm has unit cost ci while the foreign firm has cost c∗i , which includes an iceberg transport costd > 1 so that d units have to be shipped for one unit to arrive. The firm cost parameter ci follows
a distribution F (c) in the home country, while c∗i is distributed according to F ∗ (c) in the foreigncountry.
The presence of foreign firms intensifies competition in three ways. First, if a foreign firm ismore productive than the domestic firm, the domestic firm will be priced out of the market. Second,even if a foreign firm is not as productive as the domestic firm, it could force the domestic firm toengage in limit pricing instead of charging the unconstrained monopolistic markup. Third, there isa general effect of foreign entry on the average price in the market.

7

When not ambiguous, we drop the subscript i. The unconstrained profit-maximizing pricepi and markup mi (defined here as price minus marginal cost) are given by the following linear
functions of the average price p̄, and the firm cost ci :c2cm = ρ + µp̄ −2
p = ρ + µp̄ +

(2)

αγ
η
where we defined ρ = 2(η+γ), and µ = 2(η+γ). When unconstrained by its direct competitor, profits

for firm i take the following quadratic form:

π (c, p̄) =1(2ρ + 2µp̄ − c)24γ(3)Under the threat of foreign entry, the domestic firm cannot charge a price higher than the cost of
the foreign firm, therefore the markup is either the unconstrained one, m̄i or the difference between
the foreign firm’s cost and the firm’s own cost:m = min {m̄, c∗ − c} .

Although there is a potential number of N firms, some may decide not to operate if their costis too high. We denote like Melitz and Ottaviano (2008) by cD the highest cost compatible with
non-negative profits. Given the average price p̄, the cutoff cost cD , price, quantity and profit canbe rewritten as:

cD = 2ρ + 2µp̄m̄ =cD − c2

p = c+mq =π (c, m) =1
(cD − c − m)γ1m (cD − c − m)γ

We next describe the innovation decision of the firm.

8

2.2

Firm innovation decisions

We assume that the cost of innovation is quadratic in the probability of success, that is a firm pays
1 2
C (I) = 2φI to achieve a probabilityI of improving its productivity by a fixed step size ∆.4 The

firm’s expected return is then equal to:Π (I, c) = I · Eπ (∆c) + (1 − I) · Eπ (c)The first order derivative for the firm problem delivers an optimal level of investment that is proportional to the expected increase in profits due to the cost improvement:I (c) = φ (Eπ (∆c) − Eπ (c)).
Indicating by π (c, m) the profit for firms with cost c and markup m we can rewrite the investment
as follows:

I (c)
φ

Z ∆c+m̄(∆c)

∗

Z cD

∗

π (∆c, c − ∆c) dF (c ) +=∆c+m̄(∆c)∆c

Z c+m̄(c)−π (∆c, m̄) dF (c∗ )π (c, c∗ − c) dF (c∗ ) −Z cD

π (c, m̄) dF (c∗ )c+m̄(c)c

In order to write the profit for firm of productivity c we have to distinguish different cases.
i) Profits are equal to π (∆c, c∗ − ∆c) if the home firm successfully innovates and the foreign firm’s
cost is low enough to force the domestic firm to engage in limit pricingii) Profits are equal to π (∆c, m̄) if the home firm innovates and the foreign firm is not productive
enough to affect pricingiii) Profits are equal to π (c, c∗ − c) if the home firm does not innovate and the foreign firm is
productive enough to induce limit pricingiv) Profits are equal to π (c, m̄) if the home firm does not innovate, but can charge the unconstrained markup
4

The fixed step size is an assumption common to the literature, e.g. Aghion et al. (2001) and Aghion et al. (2009)
but can be micro founded easily by assuming that the firm draws from the cost distribution conditional on the cost
being below its current one. Under common productivity distributions like Pareto this in expectation leads to an
average step that is a constant.

9v) Profits are equal to zero if the foreign is more productive than either ∆c (if the home firm
innovates) or c (if the home firm does not innovate)
Replacing profits with their expressions, we find the following expression for the value of investment:

I (c)
=
φ

1
γ

Z ∆c+m̄(∆c)
∆c

−

1
γ

1
(c − ∆c) (cD − c ) dF (c ) +4γ∗

Z c+m̄(c)∗∗(c∗ − c) (cD − c∗ ) dF (c∗ ) −

c

Z cD

(cD − c)2 dF (c∗ )∆c+m̄(∆c)
Z cD

1
4γ

(cD − c)2 dF (c∗ )c+m̄(c)The difference in profits due to innovation is depicted in figure 1. The figure is drawn against
possible values of the Foreign cost c∗ given Home cost c. The expected profit gain from innovation
can be derived by aggregating the red line over the distribution of c∗ . Given cost cutoff cD , theexpected profit gain from innovation is the highest when foreign firms enter more where the gainin profit is the most, that is, around the Home cost c.

2.3

The price index

So far we have expressed the innovation decision as a function of the average price p̄. To derive theaverage price we first express the expected price for a good that was initially produced by domestic
firm with cost c:P̄ ≡ E (p|c) = I (c) p1 (c) + (1 − I (c)) p0 (c)where p1 (c) and p0 (c) are defined as follows:
p1 (c)=

 Z cD −2(cD −∆c)1
cD + c∗Λ ∆c > cD
dF (c∗ )220
Z ∆c
Z m̄(∆c)+∆c
Z cD+∆cdF (c∗ ) +c∗ dF (c∗ ) +max{0,cD −2(cD −∆c)}=cD + ∆cdF (c∗ )2

 Z cD −2(cD −c)cD + c∗dF (c∗ )20
Z m̄(c)+cZ cD
cD + ccdF (c∗ ) +c∗ dF (c∗ ) +dF (c∗ )2
max{0,cD −2(cD −c)}
c
m̄+c



p0 (c)

∆c

¯
m+∆c

1
Λ c > cD2
Z c+where Λ () is an indicator function. Whether the firm successfully innovates or not, there are four
possible cases for the final price of a good. In the first case, the foreign cost is significantly lower
than the domestic one, so that the foreign firm can charge a monopoly price. In the second case,the foreign cost is lower than the domestic, but not sufficiently low, so the foreigner firm has to do
10

limit pricing and charge the domestic firm’s cost. In the third case, the foreign firm has slightly
higher cost than the domestic firm and the domestic firm has to do limit pricing. In the fourth case,the foreign firm is so unproductive that the domestic firm can still charge the monopoly price. The
four cases for given cD and the average price are illustrated in figure 2. For illustration purposes,
cD. This ensures the existence of case one. In
we make the assumption that ∆c > 21 cD , i.e. c > 2∆other words, if the domestic cost is low enough, foreign firms could never come in and charge themonopoly price.

2.4

Simulation

For our numerical simulation we assume that productivity in both countries follows a Fréchet
distribution, i.e. Z = 1c

Prob (Z < z) = e−T z−θso that the domestic and foreign costs c = z1 and c∗ = zd∗ distribute as Weibull. We adopt thefollowing parameters for the simulation.
Parameter

Value

Parameter

Value

θ

5

µ

0.1

T

4

ρ

1

T∗

10

∆

0.5

d0

1.6

d1

1.1

The steps for the simulation are as follows:
1. Simulate 1000 domestic and 1000 foreign firms according to the distribution parameters
θ, T, T ∗ and the iceberg cost d.
2. Solve for the optimal price and investment decision for an initial guess of the cutoff cost cD .
We set it to the maximum value of the Home cost distribution draws;
3. Construct the average price and the new cutoff production cost based on the pricing andinnovation decisions in step 2;11

4. Iterate on steps 2 and 3 until individual pricing decisions and the average price are mutuallyconsistent.
We compute this equilibrium under two levels of trade costs: an initial d0 and a lower d1 . Figure 3
shows how effective foreign costs decrease with a decrease in the transport cost.
Figure 4 shows how, when the iceberg cost decreases, the top firms increase innovation, while thebottom firms do not. This is the escape-competition effect as in Aghion et al. (2009): only the mostproductive firms have a sufficiently high probability of surviving the increase in foreign competition,
so for those firms the overall decline in profits due to entry of potentially more productive firms
firms is dwarfed by this incentive to innovate that would not have been present in the absence of
foreign competition. Having illustrated how this prediction can emerge even in a model that ismore general and rich than the stylized version in Aghion et al. (2009), we proceed to show how
this prediction is borne out in the context of import liberalization during China’s accession to the
WTO.

3

Data

3.1

International trade

Industry level
Our baseline measure of import competition uses the average import tariff China imposes on the
products of each four-digit industry. The import tariff information is obtained from China’s WTO
accession document, which specifies the tariff target for each year since 2001 for each six-digit HS
product. Figure 5 plots the actually applied tariff against the bounded tariff for 2001 and 2005. In
the early years, the actual tariff did not completely comply with the bounded tariff. In 2001, only
32% of the 5,085 six-digit HS products complied with the WTO bounded tariff. The compliance
rate increased to 97% in 2005. Figure 6 shows the average and inter-quartile range of the WTO
accession tariff and the actual tariff during 2001-2007. During this period, the average boundedtariff dropped from 0.1372 to 0.1002, and the average actual tariff dropped from 0.1588 to 0.0982.
Both tariffs exhibit variation across products and time.
We map the products into the China Industrial Classification (CIC) system at the four-digit

12

level (424 sectors), using the concordance developed in Brandt et al. (2017)5 , and take simple
average6 to arrive at the industry level output tariff. Then we take log plus one to arrive at ourmeasure of log output tariff,

Hs
X
1
.
tariffimport= ln 1 +pt
Hs

output
τst

(4)

p=1

For product p that is matched to industry s, tariffimportis the WTO specified import tariff for
pt
China in year t. Hs denotes the total number of six-digit HS products within industry s.
We use the accession tariff instead of the actually applied tariff, because the actual tariff may
be subject to the same contemporaneous forces that affect firms’ innovation incentives. While
the pre-determined accession tariff is exempt from such endogeneity concerns, we require it tobe also independent from any expectations in future innovation trends. We test such restriction
by regressing the change in the accession tariff from 2001 onward on the change in patenting or
firm productivity during 1998-2000. Table 3 shows that we cannot reject the hypothesis that theaccession tariff is not correlated with pre-trend in patenting or productivity growth.
Any effect that industry output tariff have on the industry could affect other industries throughthe input-output linkages. If the upstream industries experienced higher competitive pressure, thedownstream firms would likely to get cheaper and higher quality inputs. We capture such effectthrough the change in input tariff. Specifically, we define input tariff as
!
input
τst

= ln 1 +X

csk tariffoutput
kt

.

(5)

k

The input share of industry k good used in industry s, csk , is obtained from China’s 2002 inputoutput table. The sum of csk is smaller than 1, and the other shares include non-manufacturing
inputs, and labor and capital inputs. The effect of these other inputs are subsumed into the error
term of our specification and is assumed to be independent from our main explanation variable:
5

Their concordance is based on the HS-CIC concordance table constructed by the National Bureau of Statistics(NBS).
6
There is a possible bias with trade volume weighted averages: Trade volume is negatively correlated with tariff
levels. Taking weighted average will tend to give more weight to the most liberalized product lines and thus underestimate the change in effective protection and could cause an upward bias in the estimated effect of trade liberalization.
To add: result using weighted tariff in the appendix.

13

change in trade induced competition in the manufacturing sector. The input tariff captures any
competition effects transferred from import liberalization in input industries. This is what Fieler
et al. (2016) call the magnifying effect in their quantitative analysis.
For the importers, the input tariff calculated in equation (5) also captures the direct effectof getting access to cheaper or better foreign inputs. Therefore, we also control for an importer
dummy and the interaction between the input tariff and the dummy. A firm is defined as importer
after its initial appearance in the customs importer registry.
Another channel through which trade could affect innovation is the market size effect broughtby export liberalization as studied in Aghion et al. (2017) and Trefler and Yu (2017). We control
for such effect through export demand shock from other countries, which is defined as

demandEst=

X Xpcs,2000
Xs,2000p,clog Mpct(6)

where Mpct denotes country c’s import from the world other than China of product p at time t.
After taking log, we weight the country-product demand shocks by the export share of China during
year 2000. Xpcs,2000 denotes China’s export of product p to country c in industry s in year 2000.7
Finally, we focus on year 2001-2005 because this is the period that experienced highest tariffchange after the WTO accession for most products and industries.

Firm Level
In addition to the industry level measure of trade shocks, we also construct firm level trade shocks.

outputτist= ln 1 +Xpdemand
Eist

input
τist

7

X
P pi,t−1 tariffimportpt
p0 Xp0 i,t−1!
;!
Mpci,t−1P= ln 1 +log Mpct ;p0 c0 Mp0 c0 i,t−1
p,c
!
X
Xpi0
P
= ln 1 +tariffimport.
pt
0 i,t−1
M
0
p
p
p
X

(7)

(8)

(9)

The definition of demand shock in equation (6) is consistent
with those in Bombardini et al. (2016) and AghionP
Xpcs,2000
p,c Xs,2000 Mpct . This measure gives similar results.

alternative
et al. (2017). An alternative measure is Est
= log

14

where Xpit−1 and Mpit−1 denotes firm i’s export and import in product p in the previous year,
respectively. To reduce missing values, for firms that import (export) with gap years, we use themost recent year that it had imported (exported) to calculate the weight. The firm level export
demand shock is constructed from the product-country level export demand faced by China, wherec denotes destination countries.

3.2

Patent and other Firm-Level Data

The firm level sample of our study comes from the Annual Industrial Survey conducted by the
National Bureau of Statistics (NBS) of China (Hereafter referred to as the NBS data), 1998-2007.
The survey covers all state-owned firms, and private firms with annual sales larger than 5 million
RMB. It has become a standard data set for studying firm level behavior in China’s manufacturingsector (Brandt et al., 2012; Hsieh and Song, 2015). In addition, we match the NBS data the customs
data and patent data to get firm level trade and patenting information.
We distinguish between processing and non-processing firms. A firm is defined as processing firm
if during all years with available customs data (2000-2007), over 90% of its total export is throughprocessing export. Among the firms defined as processing firms, 88% are foreign or HMT owned.
On the one hand, processing trade is not subject to any import tariffs, and since most processing
firms are oriented abroad, they are not likely to be subject to domestic competition either. On theother hand, fall in import tariff could affect firm’s choice between ordinary and processing trademode (Brandt and Morrow, 2016) , thus indirectly affect firms’ incentive to innovate. To avoid
such complication, we drop the processing firms from our NBS sample (account for 12% in termsof patents filed during 2003-2007).
We measure innovative activity using invention patent applied by firms. There are three categories of patents in the Chinese system: invention, utility and industrial design. The invention
patents are equivalent to the utility patents in the US, and is subject to the agreement on TradeRelated Aspects of Intellectual Property Rights (TRIPS), which requires, for example, a search in
the international patent database to determine the novelty of patents during examination. Eachapplication under the invention category needs to go through two rounds of examination for novelty
and non-obviousness, while the other two categories got granted immediately8 . It takes on aver8

The granted utility or design patents can be revoked if another party sue the patent holder in the court.

15

age two to four years from application to patent granting. The length of protection for invention
patents is twenty years, while that for the other two categories is only ten years. For these reasons,
we focus on the invention patent category as innovation outputs.
Patent data is obtained from the State Intellectual Property Office (SIPO) of China. It covers
all invention patents applied during 1985-2015. We identify whether a patent belongs to an NBS
firm by matching assignee name to the list of NBS firms. Since the NBS data is an unbalanced
panel, there are years during which an NBS firm does not have observation in the NBS data (mostlikely because it is not big enough), but it has observation in the patent data. Table 1 shows thetotal patent count and the matched patent count for year 2007. Of all invention patents, 72% wereapplied by firms, among which 39% belonged to firms located in China. The NBS firms cover 62%
of all patents applied by Chinese corporates. For comparison, Autor et al. (2016) found that theshare of US corporate patents applied by Compustat firms in the manufacturing sector is around
56% in 1999 and around 50% in 20079 . Our percentage is higher than theirs because their firm
data set only include publicly traded firms, whereas ours cover a larger universe of Chinese firms.
Out of the patents belonging to NBS firms, 81% falls in our sample of non-processing firms with
non-missing data. 5,904 firms in our final sample filed for at least one patent in 2007.
Autor et al. (2016) emphasized the importance of controlling for different industry trends. Table
2 shows the evolution of patent distribution and application per firm for 1999, 2003 and 2007. The
sample used is the primary sample without Huawei and ZTE. As with the US, we do see different
trends among sectors. The share of patent count in chemicals and petroleum declined from 39%
in 1999 to 23.8% in 2007; the share metal and metal products sector decreased from 12.9% in1999 to 7.5% in 2007. On the other hand, computers and electronics sector experienced increasein patenting share from 20.3% in 1999 to 34.6% in 2007. Machinery and equipment sector also
experienced an increase, from 9.2% in 1999 to 17.2% in 2007. In terms of application per firm, allsectors experienced an increase, with the most notable increase happening for the computers andelectronics sector. In 2003 and 2007, the top three patenting sectors are chemicals and petroleum,
computers and electronics, and machinery and equipment sector, and they together account for overa quarter of manufacturing patent application. Therefore, when controlling for sectoral trends, we
9

In Autor et al. (2016), the percentage of US corporate patents in Compustat is 72%. Out of these patents, theshare of manufacturing patents is 77.2% and 70% in 1999 and 2007, respectively. That is how we arrive at 56% and
50%.

16

control for these three sectors.

3.3

Tariff reforms and industry competition

Since our hypothesis is that the WTO accession tariff reduction affect innovation incentives throughchange in the competition environment, we now examine the industry market structure and its
relationship with tariff and productivity.
Following Aghion et al. (2015), we measure competition through variations of the Lerner Index,
which is usually defined as total profit net of financial cost, divided by total revenue or value added.
We test three versions of the Lerner Index definition for robustness:P
Lerner Ist =−is Profit
P ist

P

is Finance Feeist

is Value Addedist

P

Lerner IIst =Lerner IIIst =is Profitist
P
Value Addedist
P
Pis
ist −
is Finance Feeist
is ProfitP.
Revenueist
is

The indices are measured at the sector s - year t level, by aggregating up firm level values. We
regress them on the WTO accession tariffs,
output+ δs + δt + εst .
log Lernerst = β0 + β τ τst(10)

Table 4 shows the results. Taking column (1) as our baseline, a one percentage point drop in
tariff is related to 5.6% drop in the Lerner index, which is 5.6% more competition.

4

Estimation Framework

In the empirical analysis, we estimate the effect of tariff reduction on firm’s innovation capacity
measured by patent application. We assume firms apply for patent at Poisson rate λist , so that
Patist |λist ∼ Poisson (λist ), and E (Patist |λist ) = λist . The Poisson arrival rate is affected by firm
and industry characteristics, as well as changes in market structure.

17

We run the following baseline specification:
outputoutputλist = exp( β1 τs,t−2
× Topis,t−2 + β2 τs,t−2(11)+EXP CONTROList−2 + IMP CONTROList−2+Topis,t−2 + δs + δct )outputwhere Patist denotes patent applied at time t for firm i in sector s. τs,t−2
is the industry importtariff as defined in equation (4), EXP CONTROList−2 and IMP CONTROList−2 control for effects
brought by shocks on exports and imported inputs,
demanddemandEXP CONTROList−2 = α1 Dexporter+ α2 Es,t−2+ α3 Es,t−2
× Dexporter
ist−2
ist−2

(12)

input
input
× Dimporter.
+ γ3 τs,t−2
IMP CONTROList−2 = γ1 Dimporter+ γ2 τs,t−2
ist−2
ist−2

(13)

demand measures the market size effect brought by export tariff changes, as defined by equation
Es,t−2
input(6). τis,t−2
is the two period lagged input tariff measure which is defined by equation (5). We use

two-period lagged tariff shocks to take into account that it takes a while for innovative ideas tobe turned into patents. Further, we control for industry fixed effect δs at the four-digit level, andsector-year fixed effect δct to take into account the different sectoral trends. c is a categorical variable
on four sectors: chemicals and petroleum, computers and electronics, machinery and equipment
sector, and others .
Following Bustos (2011), we divide firms into four groups for any industry-year cell, according
to two period lagged TFP quartiles. The TFP estimation procedure follows Ackerberg et al. (2015),
De Loecker and Warzynski (2012) and Brandt et al. (2017). Topis,t−2 is then defined to be a dummy
which equals to 1 if the firm is above 75 percentile in terms of productivity among industry s firms
out measures the percentage change in patenting rate after thetwo periods before. Coefficient −βtopindustry output tariff decreases by 0.01.
For firms with product composition information, we could also construct competition, export,
and imported input shocks at the firm level, which are defined by equations (7)-(9). We run the18

following firm level specification:
outputoutputλist = exp( β1 τs,t−2
× Topis,t−2 + β2 τs,t−2+ Topis,t−2(14)importdemand+αEs,t−2+ γτs,t−2+ δs + δct )Since import tariff declined most during 2001-2005, we restrain our time period of analysis to
2001-2005 for the tariff shock. And since we assumed it takes one to two years for patents to comeout, our patent variables cover years 2003-2007.

5

Main Results

5.1

Baseline Estimates

Table 5 shows the regression results for specification (11). From left to right, we gradually add inexport and input tariff controls. All columns control for the four-digit industry fixed effects andsector-year effects. For firms below 75 percentile of TFP, the effect of import competition is almost
zero, with big standard errors. Relative to them, the top firm are highly responsive to import tariff
drops. Taking column (4) as our baseline result, after a one percentage point drop in import tariff,
the top firms increase their patent application effort by 3.6 percentage points more, relative to theless productive firms.
Being an exporter increases average patent application per firm by 0.9. Decrease in export
tariffs in general discourages non-exporters to innovate, while it tends to encourage exporters. This
result is consistent with what is found for French firms in Aghion et al. (2017).
For the effect of accessing imported inputs, importers on average file for one more patent than
non-importers. While change in input tariff has no effect for non-importers, the encouragement
effect of innovation for importers is quite big. A one standard deviation decrease in input tariffincreases patenting rate by 3.7%.
Table 6 shows the results for firm level specification (14). The coefficients on the heterogeneous
effect of output competition remain stable across columns, and the magnitude is close to theindustry specification in Table 5. Controlling for firm fixed effects significantly decreased thenumber of observation, as the Poisson routine would drop the firms that never patent. In Table B3

19

in the appendix, we show regressions with firm fixed effect in an OLS specification, which would
preserve firms that never patent.
In all the specifications so far, we use patent application counts as the measure of innovation
outcome. The granting rate for the patent applications is around 60% during the sample period.
To better control for the quality of patent application, in Table B1, we run the same specification
as in column (4) of Table 5 using patent grants and citation weighted patent counts as our outcome
variables. The effect of a one percentage point drop in output tariff remains the same as inour benchmark. One may also be concerned that firms file for multiple patents under the same
technology to better protect itself in case of law suits. Therefore, giving each application thesame weight would possibly overstate the effort to innovate. Further more, if firms become morestrategic due to competition, our estimate would be upward biased. We check for the specification
with patent dummy, instead of patent counts, as our dependent variable. The coefficient magnitude
is not readily comparable, but the direction and significance of the effect remains. In column (4)and (5) of Table B1, we use alternative specifications that have been used in the literature, otherthan Poisson, and still the direction and significance of the estimated effect remains.
In Table 7 column (1)-(2), we show the long term regression by running the industry specificationon years 2003 and 2007 only. Both columns controlled for ownership, region, year and industry
dummies. Column (2) also includes the export and import controls. The long term effect of aone percentage point decrease in output tariff encourages top firms to increase patent rate by 4.6
percentage points.
The time period we look at is one where the patent rate in China picked up rapidly. One concern
is that the differential patenting behavior between top and other firms and across industries is causedby an unobservable factor that also determined the tariff measures. Therefore, in columns (3)-(4),we first added to columns (1)-(2) firms’ patent applications in the pre-exposure years as additional
control. Indeed, patent application is a rather persistent feature for firms. The coefficient in frontof past patent is highly significant and positive. Since patenting was a rather high-tech activity,
we should expect that firms that patent before the WTO accession would continue patenting. The
point estimate of the interaction term becomes smaller, with slightly higher standard errors, making
the estimate less precise. Second, we run a falsification test in columns (5)-(6) by regressing thepre-exposure patent application in 1998 and 2001, on the future tariff rates in 2001 and 2005.
20

The interaction term is weakly negative, and not significant at the 10 percent significance level.
Therefore, we do not find evidence that the pre-exposure patenting behavior is related to the WTO
accession tariffs.

5.2

Technology deepening v.s. technology scope

Next, we further investigate the dimensions of innovation that are induced by import competition.
Specifically, we decompose the total patent count into patents filed in the core technology of a firm,
versus the total number of technology classes the firm file patents into. Technology class is definedaccording to the six-digit International Patent Classification (IPC)10 . A technology class is definedas the core technology if a firm has accumulated the most patent applications in that class up to
the previous year. The technology scope is the sum of the number of classes a firm files patent ina specific year.
Table 8 shows the estimation result for the industry specification applying on core technology
and technology scope. Column (1) is repeating column (4) in Table 5 as benchmark. The results
suggest that the top firms react to increase in import competition by both increasing innovation in
the core technology as well as broadening its technology space. The point estimate for the effect on
patent scope (column 2) is smaller than the overall effect (column 1), whereas the point estimate
for the core patent (column 3) is larger than the overall effect. The result remains very similar
when we only look at firms that have applied for patents before (columns 4-6). Column (7) showsthe effect on the ratio of scope to core. The second row show that on average, firms react more byincreasing their patent in core technology, which is consistent with previous columns. There isn’t adifferential effect for the top firms in terms of the relative magnitude of core and scope innovation.

5.3

Effect on firm scale and productivity

In this section, we look at the effect of import liberalization on other firm outcome variables.
First, we are interested in whether surviving firms get bigger. Table 9 runs OLS regressions oflog domestic output on the industry output tariff and other import and export controls. There is
10

There are 4944 six digit IPC in 2007. For example, in 2007, Huawei filed patents in 144 technology classes.
According to our definition, its core patent class was H04L12, “Data switching networks”. Other technology classes
that it filed patent in are H04L29, “Arrangements, apparatus, circuits or systems”, and H04L1, “Arrangements for
detecting or preventing errors in the information received”, etc.

21

a weak increase in the domestic output for firms surviving the competition, 0.43 percent increase,
after a one percentage point drop in output tariff.
The effect on productivity is more pronounced. After a one percentage point drop in output
tariff, the top firms see increase in productivity by 0.17 percent. This is consistent with the estimateof 0.19 percent in Brandt et al. (2017). In column (2), we estimate the effect of import competitionon the R&D input. Consistent with the result for the patent application, the top firms react more
to import competition and put more effort into research and development in the face of more
liberalized import market. Column (3) and (4) shows that the elasticity of capital and labor on
output tariff is 1.12 and 0.36, respectively.

6

Conclusion

The China Miracle has been a manufacturing success. But after over forty years of rapid growthwith cheap labor, imitation, and institutional reforms, China’s manufacturing sector has arrived ata crossroad where further growth depends much on indigenous innovation. In this paper, we study
the impact of change in competition environment brought by foreign imports on Chinese firm’s
innovation capacity, measured by patent application. Using a newly combined data set that covers
the universe of medium to large manufacturing firms, and more than 60% of corporate innovators,
we find that the increase in import competition following China’s accession to the WTO during
2001-2005 induced more productive firms to innovate more. We explain this phenomenon using amodel with step-by-step innovation and monopolistic competition which could generate an escapecompetition effect for the top firms.
Our finding adds to the debate on the effect of international competition on innovation. Fora developing country like China, opening to international competition served as a stimulating
mechanism for the top firms to invest in research to improve products and processes. In the meantime, a less productive firm may find it not as attractive to innovate. Whether the aggregate effectis positive or negative depends on the extent of technology spillover and other policy environments.
We believe this is a fruitful future research path to pursue.

22

References
Ackerberg, D. A., K. Caves, and G. Frazer (2015). Identification properties of recent production
function estimators. Econometrica 83 (6), 2411–2451.
Aghion, P., A. Bergeaud, M. Lequien, and M. Melitz (2017). The impact of exports on innovation:
Theory and evidence. Technical report, working paper.
Aghion, P., R. Blundell, R. Griffith, P. Howitt, and S. Prantl (2009). The Effects of Entry on
Incumbent Innovation and Productivity. The Review of Economics and Statistics 91 (1), 20–32.
Aghion, P., J. Cai, M. Dewatripont, L. Du, A. Harrison, and P. Legros (2015). Industrial policy
and competition. American Economic Journal: Macroeconomics 7 (4), 1–32.
Aghion, P., C. Harris, P. Howitt, and J. Vickers (2001). Competition, imitation and growth withstep-by-step innovation. The Review of Economic Studies 68 (3), 467–492.
Akcigit, U., S. T. Ates, and G. Impullitti (2017). Innovation and trade policy in a globalized world.
Technical report.
Akcigit, U., J. Grigsby, and T. Nicholas (2017). The rise of american ingenuity: Innovation andinventors of the golden age. Technical report, National Bureau of Economic Research.
Ang, J. S., Y. Cheng, and C. Wu (2014). Does enforcement of intellectual property rights matter
in China? evidence from financing and investment choices in the high-tech industry. Review of
Economics and Statistics 96 (2), 332–348.
Arora, A., S. Belenzon, and A. Patacconi (2015). Killing the golden goose? the decline of sciencein corporate r&d. Technical report, National Bureau of Economic Research.
Atkeson, A. and A. T. Burstein (2010). Innovation, Firm Dynamics, and International Trade.
Journal of political economy 118 (3), 433–484.
Autor, D., D. Dorn, G. H. Hanson, G. Pisano, and P. Shu (2016). Foreign competition and domestic
innovation: Evidence from US patents. Technical report.

23

Aw, B. Y., M. J. Roberts, and D. Yi Xu (2011). R&D investment, exporting, and productivity
dynamics. The American Economic Review 101 (4), 1312–1344.
Bernard, A. B., J. Eaton, J. B. Jensen, and S. Kortum (2003). Plants and productivity in international trade. The American Economic Review 93 (4), 1268–1290.
Bloom, N., M. Draca, and J. Van Reenen (2016). Trade induced technical change? The impactof Chinese imports on innovation, it and productivity. The Review of Economic Studies 83 (1),
87–117.
Blundell, R. and J. L. Powell (2003). Endogeneity in nonparametric and semiparametric regression
models. Econometric society monographs 36, 312–357.
Bøler, E. A., A. Moxnes, and K. H. Ulltveit-Moe (2015). R&D, international sourcing, and thejoint impact on firm performance. The American Economic Review 105 (12), 3704–3739.
Bombardini, M., K. Head, M. D. Tito, and R. Wang (2016). How the breadth and depth of importrelationships affect the performance of Canadian manufacturers. Working paper.
Brandt, L. and P. M. Morrow (2016). Tariffs and the organization of trade in China. Technicalreport.
Brandt, L., J. Van Biesebroeck, L. Wang, and Y. Zhang (2017, September). Wto accession andperformance of Chinese manufacturing firms. American Economic Review 107 (9), 2784–2820.
Brandt, L., J. Van Biesebroeck, and Y. Zhang (2012). Creative accounting or creative destruction? Firm-level productivity growth in Chinese manufacturing. Journal of Development Economics 97 (2), 339–351.
Bustos, P. (2011). Trade liberalization, exports, and technology upgrading: Evidence on the impact
of MERCOSUR on Argentinian firms. The American economic review 101 (1), 304–340.
De Loecker, J. and F. Warzynski (2012, May). Markups and Firm-Level Export Status. American
Economic Review 102 (6), 2437–71.
Fang, L. H., J. Lerner, and C. Wu (2017). Intellectual property rights protection, ownership, andinnovation: Evidence from China. The Review of Financial Studies 30 (7), 2446–2477.
24Fieler, A. C., M. Eslava, and D. Y. Xu (2016). Trade, quality upgrading, and input linkages:
Theory and evidence from Colombia.
Gilbert, R. (2006). Looking for mr. schumpeter: Where are we in the competition–innovation
debate? Innovation policy and the economy 6, 159–215.
Griliches, Z. (1990). Patent statistics as economic indicators: A survey. Journal of Economic
Literature 28 (4), 1661–1707.
Hall, B., C. Helmers, M. Rogers, and V. Sena (2014). The choice between formal and informal
intellectual property: a review. Journal of Economic Literature 52 (2), 375–423.
Hsieh, C.-T. and Z. M. Song (2015). Grasp the large, let go of the small: the transformation of thestate sector in China. Working paper, National Bureau of Economic Research.
Hu, A. G. and G. H. Jefferson (2009). A great wall of patents: What is behind China’s recent
patent explosion? Journal of Development Economics 90 (1), 57–68.
Hu, A. G., G. H. Jefferson, and Q. Jinchang (2005). R&D and technology transfer: firm-level
evidence from Chinese industry. The Review of Economics and Statistics 87 (4), 780–786.
Khandelwal, A. K., P. K. Schott, and S.-J. Wei (2013). Trade liberalization and embedded institutional reform: evidence from Chinese exporters. The American Economic Review 103 (6),2169–2195.
Lim, K., D. Trefler, and M. Yu (2017). Trade and innovation: The role of scale and competition
effects. Technical report.
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.
MacGarvie, M. (2006). Do firms learn from international trade? The Review of Economics andStatistics 88 (1), 46–60.
Melitz, M. J. and G. I. Ottaviano (2008). Market size, trade, and productivity. The review of
economic studies 75 (1), 295–316.

25

Moser, P. (2013). Patents and innovation: evidence from economic history. The Journal of Economic
Perspectives 27 (1), 23–44.
Nagaoka, S., K. Motohashi, and A. Goto (2010). Patent statistics as an innovation indicator.
Handbook of the Economics of Innovation 2, 1083–1127.
Ottaviano, G., T. Tabuchi, and J.-F. Thisse (2002). Agglomeration and trade revisited. International Economic Review , 409–435.
Perla, J., C. Tonetti, and M. E. Waugh (2015). Equilibrium technology diffusion, trade, and growth.
Technical report, National Bureau of Economic Research.
Qian, Y. (2007). Do national patent laws stimulate domestic innovation in a global patenting
environment? a cross-country analysis of pharmaceutical patent protection, 1978–2002. The
Review of Economics and Statistics 89 (3), 436–453.
Teshima, K. (2008). Import competition and innovation at the plant level: evidence from mexico.
Trefler, D. and M. Yu (2017). Market size and innovation in China. Technical report, (Work inprogress, only abstract available).
Wei, S.-J., Z. Xie, and X. Zhang (2017). From “made in China” to “innovated in China”: Necessity,
prospect, and challenges. Journal of Economic Perspectives 31 (1), 49–70.
Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data. MIT press.
Xie, Z. and X. Zhang (2015). The patterns of patents in China. China Economic Journal 8 (2),
122–142.

26

Figures

Figure 1: Difference in expected profits due to innovation
Eπ (∆c) − Eπ (c)c + m̄(c)

c

∆c

cD

c∗

∆c + m̄(∆c)

Figure 2: p (c) as function of foreign cost c∗
p0 (c) , p1 (c)p0 (c)
p1 (c)c∆ccD
2c + m̄(c)cD − 2 (cD − c)

c
cD

∆c

cD − 2 (cD − ∆c)∆c + m̄(∆c)

27c∗Figure 3: The Cost Distribution
2.5
HomeForeign before
Foreign after

2

1.5

1

0.5

0
0

0.5

1

1.5

2

2.5

Figure 4: Change in Innovation

Change in Log Innovation

0.2

0

-0.2

-0.4
0

0.2

0.4

0.6

Production Cost

28

0.8

1

1.2

Figure 5: Actually applied tariff and bounded tariff
2005

actual import tariff.2.4

actual import tariff
.5
1

.6

1.5

2001

.4
bounded import tariff.6.8

45 degree line
0

.2

.4
bounded import tariff

10

Tariff rate (%)
15

20

25

Figure 6: Tariff trends

5

.2

0

0

45 degree line
0

1995

2000

Bounded2005

25-75 percentile

29

2010

Actual.6

Tables
Table 1: Patent sample construction# application# patenting firms

patent per firm

233,271
167,670
65,62140,05732,34829,212
13,799
7,279
5,904

5.74
4.76
5.50
5.48

SIPO data
All assignee
Firm assignee
Firm located in China
matched to NBS
and non-processing
NBS dataAll
Non-processing# firms
329,836
317,467

Notes: Statistics for year 2007.

Table 2: Patent distribution across sectors(1)

Application year(2)
(3)
Patent share
1999
2003
2007

(4)
(5)
(6)
No. patents per firm
1999 2003
2007

Chem., Petrol., Rubber
Computers, Electronics
Metal, Metal Products
Machinery, Equipment
Food, Tobacco
Clay, Stone, Glass
Transportation
Paper, Print
Textile, Apparel, Leather
Wood, Furniture
Other Manufacturing

39.0%
20.3%
12.9%
9.2%
6.6%
5.0%
3.3%
1.3%
1.2%
1.1%
0.1%

1.79
2.39
2.20
1.40
1.45
1.20
1.56
1.38
1.11
1.29
1.00

35.5%
30.6%
6.7%
11.8%
4.4%
3.2%
4.2%
1.6%
1.3%
0.4%
0.3%

23.8%
34.6%
7.5%
17.2%
4.3%
2.2%
6.0%
1.3%
2.2%
0.5%
0.2%

2.30
5.35
2.72
1.74
1.91
1.84
2.43
1.68
1.44
1.38
1.39

3.64
8.38
4.42
2.91
3.85
2.15
4.35
3.16
3.01
2.00
2.33

Notes: The sample used is the primary sample of non-processing NBS firms, dropping Huawei
and ZTE. Industries are ordered by column (1), ranking of patent share in 1999. Columns(1)-(3) show the share out of total patent count for each sector. Columns (4)-(6) shows theaverage number of patent application per patenting firm.

30

Table 3: WTO accession tariff and initial period growth rates(1)
outputoutputτ2002− τ2001(2)
outputoutputτ2003− τ2001(3)
outputoutputτ2004− τ2001(4)
outputoutputτ2005− τ2001

Panel A: Initial TFP growth
tfp2000 -tfp1998
0.001(0.005)
R2
0.330
Obs
424

0.001
(0.009)0.344
424-0.001(0.013)0.373
424-0.003(0.015)0.373
424

Panel B: Initial patent growth
patent2000 -patent1998-0.000(0.000)
R2
0.333
Obs
424

-0.000(0.000)0.349
424-0.000(0.000)0.376
424-0.000(0.000)0.375
424

Notes: Standard errors are clustered at the industry-year level. *** p<0.01, ** p<0.05, * p<0.1

Table 4: WTO accession tariff and Lerner Index

output
τst

R2
Obs

(1)
Lerner Ist(2)
Lerner IIst

(3)
Lerner IIIst

5.624***(1.111)2.990***(0.761)

5.149***(1.132)

0.458
1848

0.465
2016

0.509
1848

Notes: Time period, 2001-2005. Industry fixed effects
and year fixed effects controlled. *** p<0.01, ** p<0.05,* p<0.1

31

Table 5: Effect of import competition on patenting, industry level
result(1)(2)
Dep. var: Patent application counts
Output competition
outputτs,t−2× Topis,t−2-3.525**(1.412)
outputτs,t−2-0.506(1.643)Topis,t−2
1.210***(0.142)
Export control
Dexporter
ist−2
demandEs,t−2
demand × Dexporter
Es,t−2
ist−2-3.675**(1.428)-0.161(1.655)
1.234***(0.143)input
τis,t−2
inputτis,t−2
× Dimporter
ist−2

800,292(4)-3.418**(1.474)0.966(1.896)
1.177***(0.147)-3.577**(1.466)1.275(1.883)
1.209***(0.146)

1.278***(0.333)0.063***(0.021)
0.026(0.025)

Import control
Dimporter
ist−2

obs

(3)

800,292

0.909***(0.329)0.066***(0.021)
0.023(0.025)
1.799***(0.188)-0.336(11.268)-11.314***(3.848)0.984***(0.168)-4.936(10.972)-7.385**(3.639)800,292

800,292

Notes: The Top dummy equals to 1 if the firm is above 75 percentile in industry
s at time t − 2. All columns control for four-digit CIC industry fixed effects,
as well as four-sector by year fixed effects. The four sectors are: chemicals andpetroleum, computers and electronics, machinery and equipment sector, and others. Standard errors are clustered at the industry-year level. *** p<0.01, **p<0.05, * p<0.1

32

Table 6: Effect of import competition on patenting, firm level result(1)Dep. var: Patent application counts
Output competition
outputfirm τs,t−2
× Topis,t−2
outputfirm τs,t−2Topis,t−2-3.735***(1.086)-1.763(2.094)0.368***(0.118)

Export control
exportfirm τis,t−2

(2)

(3)

(4)

-3.662***(1.101)-1.619(2.098)0.369***(0.112)-4.069***(1.082)-2.301(2.597)0.423***(0.127)-3.992***(1.105)-1.974(2.566)
0.423***(0.119)-0.007(0.051)

Import control
input
firm τis,t−2

obs

11,483

11,111

0.004(0.056)
2.159(6.976)2.066(6.417)

8,461

8,210

Notes: Poisson specification. Firm fixed effects and time fixed effects are controlled.
See Table B3 for an OLS specification. The Top dummy equals to 1 if the firm isabove 75 percentile in industry s at time t − 2. Standard errors are clustered at theindustry-year level. *** p<0.01, ** p<0.05, * p<0.1

33

Table 7: Long term effects and falsification test

Dep. var(1)
Past output competition
τsoutput × Topis 2001,2005 -4.587***(1.534)
outputτs-0.0292001,2005(2.523)Topis 2001,2005
1.335***(0.152)Pre-exposure trends
Pat1998, 2001

I. 2003-2007
Pat2003, 2007(2)
(3)
-4.685***(1.576)0.646(2.623)
1.338***(0.156)(4)-3.005*(1.546)-0.333(2.323)1.225***(0.143)-2.982*(1.622)0.733(2.709)1.197***(0.145)0.128***(0.015)0.117***(0.013)Future output competition
τsoutput × Topis 2001,2005
τsoutput 2001,2005
Topis 2001,2005

obs

337,029

337,029

141,190

141,190

II. 1998-2001 (pre-exposure)
Pat1998, 2001
(5)
(6)

-1.768(2.189)0.099(2.724)0.897***(0.193)-1.815(2.193)0.920(3.400)0.859***(0.190)119,146

119,146

Notes: The specifications are Poisson with two years stacked. All columns control for ownership and region dummies,
four-digit CIC industry fixed effects, as well as four-sector by year fixed effects. The even columns include the export
and import controls in addition. Standard errors are clustered at the industry-year-top level. *** p<0.01, ** p<0.05, *
p<0.1

34

Table 8: Technology core vs. scope(1)
SampleDep. var

application

(2)
All
scope

(3)

(4)

(5)
Patented before
application
scope

(6)

(7)

core

-2.397**(0.974)
1.188(1.337)0.933***(0.090)-3.968**(1.832)
1.334(1.564)1.091***(0.192)-3.659***(1.591)
1.247(2.333)1.020***(0.152)-2.323**(1.092)2.281(1.650)0.768***(0.088)-4.833**(2.063)0.705(1.917)
0.953***(0.217)-0.472(0.821)3.654**(1.454)0.205***(0.071)

scope
core

core

Output competition
outputτs,t−2× Topis,t−2-3.577**(1.466)
outputτs,t−21.275(1.883)Topis,t−2
1.209***(0.146)
Export control
Dexporter
0.909***
ist−2(0.329)demandEs,t−2
0.066***(0.021)demand × Dexporter
Es,t−20.023
ist−2(0.025)
Import control
Dimporter
0.984***
ist−2(0.168)inputτis,t−2-4.936(10.972)
input
τis,t−2
× Dimporter-7.385**ist−2(3.639)1.582***(0.252)0.079***(0.019)-0.040**(0.020)0.401(0.409)0.082***(0.022)0.028(0.033)-0.022(0.455)0.059*(0.030)0.068**(0.034)0.517*(0.308)0.052**(0.022)0.012(0.023)-1.163**(0.502)
0.056*(0.033)0.124***(0.039)0.287(0.196)0.018(0.018)-0.003(0.016)

0.968***(0.163)-9.983(6.907)-4.063(3.685)0.631***(0.202)-6.497(7.036)1.744(3.905)0.469**(0.205)-2.990(12.473)-6.094(5.047)0.174(0.176)-13.008(8.584)-0.314(4.247)-0.043(0.195)-4.033(9.981)7.254*(4.330)0.172(0.172)-5.160(8.317)-4.132(4.427)obs

800,005

800,005

23,931

23,917

23,917

12,721

800,292

Notes: All columns control for ownership, region, four-digit CIC industry fixed effects, as well as four-sector by year fixed
effects. Standard errors are clustered at the industry-year level. *** p<0.01, ** p<0.05, * p<0.1

35

Table 9: Effects on domestic output(1)
Dep. var: log domestic output
Output competition
outputτs,t−2× Topis,t−2-0.482**(0.244)
outputτs,t−2-0.008(0.401)Topis,t−20.656***(0.033)
Export control
Dexporter
ist−2
demandEs,t−2
demand × Dexporter
Es,t−2
ist−2

(2)

(3)

(4)

-0.468*(0.242)-0.039(0.397)0.656***(0.032)-0.424*(0.239)-0.373(0.438)0.650***(0.032)-0.426*(0.239)-0.386(0.439)0.649***(0.032)0.546***(0.118)0.008(0.005)-0.034***(0.010)

Import control
Dimporter
ist−2
input
τis,t−2
inputτis,t−2
× Dimporter
ist−2

R2
obs

0.170
737,768

0.171
737,768

0.394***(0.119)0.008*(0.005)-0.037***(0.010)0.711***(0.075)4.634***(1.657)-7.408***(1.700)0.178
737,768

0.745***(0.083)4.736***(1.644)-7.447***(1.764)0.178
737,768

Notes: All columns control for four-digit CIC industry fixed effects, aggregate
sector by year fixed effects. Standard errors are clustered at the industry-yearlevel. *** p<0.01, ** p<0.05, * p<0.1

36

Table 10: Effect on TFP, R&D, capital and labor

Dep var
Output competition
outputτs,t−2× Topis,t−2
outputτs,t−2Topis,t−2Export control
Dexporter
ist−2exportτs,t−2exportτs,t−2× Dexporter
ist−2

Import control
Dimporter
ist−2
input
τis,t−2
inputτis,t−2
× Dimporter
ist−2

R2
obs

(1)
TFP
OLS

(2)
ln (R&D)
OLS(3)ln (capital)OLS(4)ln (labor)
OLS-0.167***(0.036)-0.447*(0.236)0.221***(0.005)-1.333***(0.226)0.276(0.374)0.302***(0.027)-1.122***(0.264)0.522(0.364)-0.003(0.036)-0.365**(0.152)-0.462*(0.279)0.051**(0.021)

0.043***(0.015)0.002(0.002)-0.003***(0.001)0.140(0.158)-0.000(0.006)0.022*(0.013)0.339***(0.075)-0.006(0.004)0.009(0.006)

0.242***(0.046)-0.005*(0.003)0.019***(0.004)-0.007*(0.004)-0.822(0.808)0.180**(0.080)0.639***(0.101)-0.121(2.096)-4.153(2.537)0.957***(0.048)8.180***(1.270)-4.012***(0.960)0.449***(0.024)4.130***(1.481)-1.105**(0.473)0.5678025980.137
5631440.228
7984140.196802598Notes: The Top dummy equals to 1 if the firm is above 75 percentile in industry s at timet − 2. All columns control for four-digit CIC industry fixed effects, as well as four-sector
by year fixed effects. The four sectors are: chemicals and petroleum, computers andelectronics, machinery and equipment sector, and others. Standard errors are clusteredat the industry-year level. *** p<0.01, ** p<0.05, * p<0.137Appendix
A

Control function approach

In Table 5 of the main text, we showed a reduced form relationship between firm patenting andimport competition measured by import tariffs. In this section, we take a further look at themechanism. We test whether more patenting is caused by increase in industry imports. To accountfor the endogeneity of trade volumes, we use the control function approach that is widely used in theliterature when dealing with Poisson count data regression (Aghion et al., 2009; Wooldridge, 2010;
Blundell and Powell, 2003). Table A1 shows the results. The odd columns shows the OLS regression,
adding export or import controls, one at a time. The even columns shows the corresponding second
stage result of the control function approach.
Table A1: Effect of import competition on patenting, industry level result(1)Dep. var: Patent application counts
Specification
OLS
Output competition
outputln Ms,t−2
× Topis,t−2
outputln Ms,t−2

Topis,t−20.102**(0.040)0.023(0.025)0.768***(0.088)

(2)

(3)

(4)

(5)

(6)

CF

OLS

CF

OLS

CF

0.121***(0.045)0.476(0.322)0.750***(0.090)0.107***(0.039)0.035(0.028)0.787***(0.087)0.126***(0.045)0.443(0.318)0.770***(0.089)0.104**(0.042)0.018(0.025)0.740***(0.088)0.112**(0.047)0.219(0.370)0.749***(0.090)1.586***(0.102)-0.184***(0.046)0.206***(0.041)1.588***(0.102)0.034(0.066)0.217***(0.044)1.756***(0.250)-4.439(11.949)-11.713**(5.161)

1.731***(0.251)
0.392(14.453)-11.607**(5.202)

Export control
Dexporter
ist−2
exp
ln Xs,t−2
exportln Xs,t−2
× Dexporter
ist−2

Import control
Dimporter
ist−2
input
ln Mis,t−2
input
ln Mis,t−2
× Dimporter
ist−2First Stage
Endogenous var.

outputln Ms,t−2

exportln Xs,t−2

input
ln Ms,t−2

Instruments

outputτs,t−2-5.544***(1.641)

exportτs,t−20.406***(0.021)input
τs,t−2-12.975***(2.299)obs

802,410802,410802,410802,410802,410

792,963

Notes: The Top dummy equals to 1 if the firm is above 75 percentile in industry s at time t − 2. All
columns control for four-digit CIC industry fixed effects, as well as four-sector by year fixed effects. The
four sectors are: chemicals and petroleum, computers and electronics, machinery and equipment sector, andothers. Standard errors are clustered at the industry-year level. *** p<0.01, ** p<0.05, * p<0.1

38

B

Robustness checks

B.1

Alternative measures

Table B1 shows the estimation result for different dependent variable measures and econometricspecifications. The first two columns use patent grants and citation weighted application as dependent variables. The estimated elasticity for the heterogeneous effect is comparable to column (4) of
Table 5. To minimize the concern for strategic patent portfolio construction (the incentive to filefor more patent is only to complete a patent portfolio rather than reacting to competition), we use
patent dummy instead of patent count in column (3). Columns (4) and (5) perform OLS regressions
directly on patent counts and log patents. The heterogeneous effect of import competition for top
firms remain significant.

39

Table B1: Alternative measures of innovation

Dep var(1)
Grantedapplication
Poisson(2)
Citationweighted
Poisson(3)
Patent
dummy
OLS

(4)
Patent
count
OLS

(5)
ln (P at + 1)
OLS

-2.836**(1.303)-0.454(2.094)
1.048***(0.122)-0.057***(0.008)-0.019(0.015)0.012***(0.001)-0.917***(0.229)-0.002(0.167)0.165***(0.032)-0.096***(0.014)-0.020(0.021)0.019***(0.002)

Output competition
outputτs,t−2× Topis,t−2-2.750*(1.483)
outputτs,t−22.732(2.370)Topis,t−2
1.126***(0.142)
Export control
Dexporter
0.928***
ist−2(0.159)demandEs,t−2
1.477**(0.626)demand × Dexporter
Es,t−2-0.227
ist−2(1.954)
Import control
Dimporter
1.599***
ist−2(0.284)input
τis,t−2-0.114(19.535)
inputτis,t−2
× Dimporter-16.220**
ist−2(6.997)0.875***(0.141)1.001(0.639)-1.143(1.679)0.042***(0.003)0.015**(0.007)-0.275***(0.025)0.293***(0.047)0.238***(0.071)-2.063***(0.373)0.055***(0.004)0.029***(0.009)-0.374***(0.035)1.415***(0.240)0.120(13.923)-11.554**(5.686)0.049***(0.004)0.240***(0.046)-0.556***(0.077)0.273***(0.047)0.569(0.459)-3.599***(0.911)

0.063***(0.005)0.257***(0.062)-0.739***(0.105)

R2
obs

770,3160.038
770,9780.008
770,9780.037
770,978

762,640

Notes: The Top dummy equals to 1 if the firm is above 75 percentile in industry s at time t − 2. All
columns control for four-digit CIC industry fixed effects, as well as four-sector by year fixed effects.
The four sectors are: chemicals and petroleum, computers and electronics, machinery and equipment
sector, and others. Standard errors are clustered at the industry-year level. *** p<0.01, ** p<0.05,* p<0.1

40

B.2

Dropping 2-digit sectors one at a time
Table B2: Drop 2-digit sectors, one at a time(1)
Dep. var: Patent application counts
Sector Dropped
outputfirm τs,t−2
× Topis,t−2
outputfirm τs,t−2Topis,t−2
obs
Sector Dropped
outputfirm τs,t−2
× Topis,t−2
outputfirm τs,t−2Topis,t−2
obs
Sector Dropped
output× Topis,t−2
firm τs,t−2
outputfirm τs,t−2Topis,t−2
obs

(2)

(3)

(4)

(5)

Food
-2.417*(1.415)-1.095(2.071)
1.099***(0.133)738657

Drinks-2.653*(1.416)-0.723(2.218)1.104***(0.133)790877

Tobacco-2.943**(1.412)0.377(2.231)
1.117***(0.132)803282

Textile-2.919**(1.387)1.183(2.025)
1.112***(0.131)681173

Furniture-2.890**(1.362)-0.465(1.965)
1.116***(0.131)778623Paper-2.852**(1.377)0.117(2.062)
1.108***(0.133)752471

Chemical-3.217**(1.400)-1.629(2.051)1.212***(0.150)665091Stone-2.874**(1.383)-0.466(1.999)1.120***(0.131)732278Metal-2.656*(1.428)-0.102(1.939)1.038***(0.140)724141

Machinery-3.151**(1.334)-0.272(2.077)1.226***(0.134)692330Transportation-2.083(1.585)-1.360(2.112)
1.097***(0.142)764506

Computers-4.202***(1.181)-0.687(1.656)
0.985***(0.128)731702Other-2.884**(1.371)-0.372(1.960)1.114***(0.131)791453

Notes: The Top dummy equals to 1 if the firm is above 75 percentile in industry s at time t − 2. All columns include
the export and import controls, the four-digit CIC industry fixed effects, as well as four-sector by year fixed effects.
The four sectors are: chemicals and petroleum, computers and electronics, machinery and equipment sector, andothers. Standard errors are clustered at the industry-year level. *** p<0.01, ** p<0.05, * p<0.1.

41

B.3

Firm Level regression
Table B3: Effect of import competition on patenting, linear firm level
result(1)
Dep. var: Patent application counts
Output competition
outputfirm τs,t−2
× Topis,t−2
outputfirm τs,t−2Topis,t−2-2.737**(1.196)
1.095*(0.617)0.365**(0.153)

Export control
exportfirm τis,t−2

(2)

(3)

(4)

-2.775**(1.255)1.220*(0.653)0.374**(0.160)-4.770**(2.122)
1.496(1.175)0.615**(0.251)-4.841**(2.212)1.748(1.244)
0.632**(0.263)0.004(0.017)

Import control
input
firmτis,t−2
R2
obs

0.580
134,534

0.581
128,104

0.014(0.032)2.360(1.965)2.397(2.070)0.596
71,2470.597
68,779Notes: OLS regression instead of Poisson. The Top dummy equals to 1 if the firm
is above 75 percentile in industry s at time t−2. All columns control for firm fixed
effects and year fixed effects. Standard errors are clustered at the industry-yearlevel. *** p<0.01, ** p<0.05, * p<0.142